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# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import datetime
import json
from enum import Enum
from typing import Dict, List, Optional, Union
from sqlalchemy import TEXT, Boolean, Column, Index
from sqlalchemy.exc import IntegrityError
from sqlalchemy.orm import declarative_base
from promptflow._sdk._constants import (
RUN_INFO_CREATED_ON_INDEX_NAME,
RUN_INFO_TABLENAME,
FlowRunProperties,
ListViewType,
)
from promptflow._sdk._errors import RunExistsError, RunNotFoundError
from .retry import sqlite_retry
from .session import mgmt_db_session
Base = declarative_base()
class RunInfo(Base):
__tablename__ = RUN_INFO_TABLENAME
name = Column(TEXT, primary_key=True)
type = Column(TEXT) # deprecated field
created_on = Column(TEXT, nullable=False) # ISO8601("YYYY-MM-DD HH:MM:SS.SSS"), string
status = Column(TEXT, nullable=False)
display_name = Column(TEXT) # can be edited by users
description = Column(TEXT) # updated by users
tags = Column(TEXT) # updated by users, json(list of json) string
# properties: flow path, output path..., json string
# as we can parse and get all information from parsing the YAML in memory,
# we don't need to store any extra information in the database at all;
# however, if there is any hot fields, we can store them here additionally.
properties = Column(TEXT)
archived = Column(Boolean, default=False)
# NOTE: please always add columns to the tail, so that we can easily handle schema changes;
# also don't forget to update `__pf_schema_version__` when you change the schema
# NOTE: keep in mind that we need to well handle runs with legacy schema;
# normally new fields will be `None`, remember to handle them properly
start_time = Column(TEXT) # ISO8601("YYYY-MM-DD HH:MM:SS.SSS"), string
end_time = Column(TEXT) # ISO8601("YYYY-MM-DD HH:MM:SS.SSS"), string
data = Column(TEXT) # local path of original run data, string
run_source = Column(TEXT) # run source, string
__table_args__ = (Index(RUN_INFO_CREATED_ON_INDEX_NAME, "created_on"),)
# schema version, increase the version number when you change the schema
__pf_schema_version__ = "3"
@sqlite_retry
def dump(self) -> None:
with mgmt_db_session() as session:
try:
session.add(self)
session.commit()
except IntegrityError as e:
# catch "sqlite3.IntegrityError: UNIQUE constraint failed: run_info.name" to raise RunExistsError
# otherwise raise the original error
if "UNIQUE constraint failed" not in str(e):
raise
raise RunExistsError(f"Run name {self.name!r} already exists.")
@sqlite_retry
def archive(self) -> None:
if self.archived is True:
return
self.archived = True
with mgmt_db_session() as session:
session.query(RunInfo).filter(RunInfo.name == self.name).update({"archived": self.archived})
session.commit()
@sqlite_retry
def restore(self) -> None:
if self.archived is False:
return
self.archived = False
with mgmt_db_session() as session:
session.query(RunInfo).filter(RunInfo.name == self.name).update({"archived": self.archived})
session.commit()
@sqlite_retry
def update(
self,
*,
status: Optional[str] = None,
display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
start_time: Optional[Union[str, datetime.datetime]] = None,
end_time: Optional[Union[str, datetime.datetime]] = None,
system_metrics: Optional[Dict[str, int]] = None,
) -> None:
update_dict = {}
if status is not None:
self.status = status
update_dict["status"] = self.status
if display_name is not None:
self.display_name = display_name
update_dict["display_name"] = self.display_name
if description is not None:
self.description = description
update_dict["description"] = self.description
if tags is not None:
self.tags = json.dumps(tags)
update_dict["tags"] = self.tags
if start_time is not None:
self.start_time = start_time if isinstance(start_time, str) else start_time.isoformat()
update_dict["start_time"] = self.start_time
if end_time is not None:
self.end_time = end_time if isinstance(end_time, str) else end_time.isoformat()
update_dict["end_time"] = self.end_time
with mgmt_db_session() as session:
# if not update system metrics, we can directly update the row;
# otherwise, we need to get properties first, update the dict and finally update the row
if system_metrics is None:
session.query(RunInfo).filter(RunInfo.name == self.name).update(update_dict)
else:
# with high concurrency on same row, we may lose the earlier commit
# we regard it acceptable as it should be an edge case to update properties
# on same row with high concurrency;
# if it's a concern, we can move those properties to an extra column
run_info = session.query(RunInfo).filter(RunInfo.name == self.name).first()
props = json.loads(run_info.properties)
props[FlowRunProperties.SYSTEM_METRICS] = system_metrics.copy()
update_dict["properties"] = json.dumps(props)
session.query(RunInfo).filter(RunInfo.name == self.name).update(update_dict)
session.commit()
@staticmethod
@sqlite_retry
def get(name: str) -> "RunInfo":
with mgmt_db_session() as session:
run_info = session.query(RunInfo).filter(RunInfo.name == name).first()
if run_info is None:
raise RunNotFoundError(f"Run name {name!r} cannot be found.")
return run_info
@staticmethod
@sqlite_retry
def list(max_results: Optional[int], list_view_type: ListViewType) -> List["RunInfo"]:
with mgmt_db_session() as session:
basic_statement = session.query(RunInfo)
# filter by archived
list_view_type = list_view_type.value if isinstance(list_view_type, Enum) else list_view_type
if list_view_type == ListViewType.ACTIVE_ONLY.value:
basic_statement = basic_statement.filter(RunInfo.archived == False) # noqa: E712
elif list_view_type == ListViewType.ARCHIVED_ONLY.value:
basic_statement = basic_statement.filter(RunInfo.archived == True) # noqa: E712
basic_statement = basic_statement.order_by(RunInfo.created_on.desc())
if isinstance(max_results, int):
return [run_info for run_info in basic_statement.limit(max_results)]
else:
return [run_info for run_info in basic_statement.all()]
@staticmethod
@sqlite_retry
def delete(name: str) -> None:
with mgmt_db_session() as session:
run_info = session.query(RunInfo).filter(RunInfo.name == name).first()
if run_info is not None:
session.delete(run_info)
session.commit()
else:
raise RunNotFoundError(f"Run name {name!r} cannot be found.")
| promptflow/src/promptflow/promptflow/_sdk/_orm/run_info.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_orm/run_info.py",
"repo_id": "promptflow",
"token_count": 3138
} | 40 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import time
from promptflow._sdk._serving.monitor.data_collector import FlowDataCollector
from promptflow._sdk._serving.monitor.streaming_monitor import StreamingMonitor
from promptflow._sdk._serving.monitor.metrics import MetricsRecorder, ResponseType
from promptflow._sdk._serving.utils import streaming_response_required, get_cost_up_to_now
from promptflow._sdk._serving.flow_result import FlowResult
from promptflow._utils.exception_utils import ErrorResponse
from flask import request, g
class FlowMonitor:
"""FlowMonitor is used to collect metrics & data for promptflow serving."""
def __init__(self,
logger,
default_flow_name,
data_collector: FlowDataCollector,
metrics_recorder: MetricsRecorder):
self.data_collector = data_collector
self.metrics_recorder = metrics_recorder
self.logger = logger
self.flow_name = default_flow_name
def setup_streaming_monitor_if_needed(self, response_creator, data, output):
g.streaming = response_creator.has_stream_field and response_creator.text_stream_specified_explicitly
# set streaming callback functions if the response is streaming
if g.streaming:
streaming_monitor = StreamingMonitor(
self.logger,
flow_id=g.get("flow_id", self.flow_name),
start_time=g.start_time,
inputs=data,
outputs=output,
req_id=g.get("req_id", None),
streaming_field_name=response_creator.stream_field_name,
metric_recorder=self.metrics_recorder,
data_collector=self.data_collector,
)
response_creator._on_stream_start = streaming_monitor.on_stream_start
response_creator._on_stream_end = streaming_monitor.on_stream_end
response_creator._on_stream_event = streaming_monitor.on_stream_event
self.logger.info(f"Finish stream callback setup for flow with streaming={g.streaming}.")
else:
self.logger.info("Flow does not enable streaming response.")
def handle_error(self, ex: Exception, resp_code: int):
if self.metrics_recorder:
flow_id = g.get("flow_id", self.flow_name)
err_code = ErrorResponse.from_exception(ex).innermost_error_code
streaming = g.get("streaming", False)
self.metrics_recorder.record_flow_request(flow_id, resp_code, err_code, streaming)
def start_monitoring(self):
g.start_time = time.time()
g.streaming = streaming_response_required()
g.req_id = request.headers.get("x-request-id", None)
self.logger.info(f"Start monitoring new request, request_id: {g.req_id}")
def finish_monitoring(self, resp_status_code):
data = g.get("data", None)
flow_result: FlowResult = g.get("flow_result", None)
req_id = g.get("req_id", None)
flow_id = g.get("flow_id", self.flow_name)
# collect non-streaming flow request/response data
if self.data_collector and data and flow_result and flow_result.output and not g.streaming:
self.data_collector.collect_flow_data(data, flow_result.output, req_id)
if self.metrics_recorder:
if flow_result:
self.metrics_recorder.record_tracing_metrics(flow_result.run_info, flow_result.node_run_infos)
err_code = g.get("err_code", "None")
self.metrics_recorder.record_flow_request(flow_id, resp_status_code, err_code, g.streaming)
# streaming metrics will be recorded in the streaming callback func
if not g.streaming:
latency = get_cost_up_to_now(g.start_time)
self.metrics_recorder.record_flow_latency(
flow_id, resp_status_code, g.streaming, ResponseType.Default.value, latency
)
self.logger.info(f"Finish monitoring request, request_id: {req_id}.")
| promptflow/src/promptflow/promptflow/_sdk/_serving/monitor/flow_monitor.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_serving/monitor/flow_monitor.py",
"repo_id": "promptflow",
"token_count": 1717
} | 41 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import logging
from promptflow._sdk._configuration import Configuration
PROMPTFLOW_LOGGER_NAMESPACE = "promptflow._sdk._telemetry"
class TelemetryMixin(object):
def __init__(self, **kwargs):
# Need to call init for potential parent, otherwise it won't be initialized.
super().__init__(**kwargs)
def _get_telemetry_values(self, *args, **kwargs): # pylint: disable=unused-argument
"""Return the telemetry values of object.
:return: The telemetry values
:rtype: Dict
"""
return {}
class WorkspaceTelemetryMixin(TelemetryMixin):
def __init__(self, subscription_id, resource_group_name, workspace_name, **kwargs):
# add telemetry to avoid conflict with subclass properties
self._telemetry_subscription_id = subscription_id
self._telemetry_resource_group_name = resource_group_name
self._telemetry_workspace_name = workspace_name
super().__init__(**kwargs)
def _get_telemetry_values(self, *args, **kwargs): # pylint: disable=unused-argument
"""Return the telemetry values of run operations.
:return: The telemetry values
:rtype: Dict
"""
return {
"subscription_id": self._telemetry_subscription_id,
"resource_group_name": self._telemetry_resource_group_name,
"workspace_name": self._telemetry_workspace_name,
}
def is_telemetry_enabled():
"""Check if telemetry is enabled. Telemetry is enabled by default.
User can disable it by:
1. running `pf config set telemetry.enabled=false` command.
"""
config = Configuration.get_instance()
telemetry_consent = config.get_telemetry_consent()
if telemetry_consent is not None:
return str(telemetry_consent).lower() == "true"
return True
def get_telemetry_logger():
from promptflow._sdk._telemetry.logging_handler import PromptFlowSDKLogHandler, get_appinsights_log_handler
current_logger = logging.getLogger(PROMPTFLOW_LOGGER_NAMESPACE)
# avoid telemetry log appearing in higher level loggers
current_logger.propagate = False
current_logger.setLevel(logging.INFO)
# check if current logger already has an appinsights handler to avoid logger handler duplication
for log_handler in current_logger.handlers:
if isinstance(log_handler, PromptFlowSDKLogHandler):
# update existing handler's config
log_handler._is_telemetry_enabled = is_telemetry_enabled()
return current_logger
# otherwise, remove the existing handler and create a new one
for log_handler in current_logger.handlers:
current_logger.removeHandler(log_handler)
handler = get_appinsights_log_handler()
current_logger.addHandler(handler)
return current_logger
| promptflow/src/promptflow/promptflow/_sdk/_telemetry/telemetry.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_telemetry/telemetry.py",
"repo_id": "promptflow",
"token_count": 1051
} | 42 |
#! /bin/bash
echo "start promptflow serving"
cd /flow
dotnet Promptflow.dll --port "8080" --yaml_path "flow.dag.yaml" --assembly_folder "." --connection_folder_path "../connections" --log_path "" --serving | promptflow/src/promptflow/promptflow/_sdk/data/docker_csharp/runit/promptflow-serve/run.jinja2/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/data/docker_csharp/runit/promptflow-serve/run.jinja2",
"repo_id": "promptflow",
"token_count": 71
} | 43 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import datetime
import functools
import json
import uuid
from os import PathLike
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
from dateutil import parser as date_parser
from promptflow._sdk._configuration import Configuration
from promptflow._sdk._constants import (
BASE_PATH_CONTEXT_KEY,
DEFAULT_ENCODING,
DEFAULT_VARIANT,
FLOW_DIRECTORY_MACRO_IN_CONFIG,
FLOW_RESOURCE_ID_PREFIX,
PARAMS_OVERRIDE_KEY,
PROMPT_FLOW_DIR_NAME,
REGISTRY_URI_PREFIX,
REMOTE_URI_PREFIX,
RUN_MACRO,
TIMESTAMP_MACRO,
VARIANT_ID_MACRO,
AzureRunTypes,
DownloadedRun,
FlowRunProperties,
RestRunTypes,
RunDataKeys,
RunInfoSources,
RunStatus,
RunTypes,
)
from promptflow._sdk._errors import InvalidRunError, InvalidRunStatusError
from promptflow._sdk._orm import RunInfo as ORMRun
from promptflow._sdk._utils import (
_sanitize_python_variable_name,
is_remote_uri,
parse_remote_flow_pattern,
parse_variant,
)
from promptflow._sdk.entities._yaml_translatable import YAMLTranslatableMixin
from promptflow._sdk.schemas._run import RunSchema
from promptflow._utils.flow_utils import get_flow_lineage_id
from promptflow._utils.logger_utils import get_cli_sdk_logger
from promptflow.exceptions import UserErrorException
AZURE_RUN_TYPE_2_RUN_TYPE = {
AzureRunTypes.BATCH: RunTypes.BATCH,
AzureRunTypes.EVALUATION: RunTypes.EVALUATION,
AzureRunTypes.PAIRWISE_EVALUATE: RunTypes.PAIRWISE_EVALUATE,
}
REST_RUN_TYPE_2_RUN_TYPE = {
RestRunTypes.BATCH: RunTypes.BATCH,
RestRunTypes.EVALUATION: RunTypes.EVALUATION,
RestRunTypes.PAIRWISE_EVALUATE: RunTypes.PAIRWISE_EVALUATE,
}
logger = get_cli_sdk_logger()
class Run(YAMLTranslatableMixin):
"""Flow run entity.
:param flow: Path of the flow directory.
:type flow: Path
:param name: Name of the run.
:type name: str
:param data: Input data for the run. Local path or remote uri(starts with azureml: or public URL) are supported. Note: remote uri is only supported for cloud run. # noqa: E501
:type data: Optional[str]
:param variant: Variant of the run.
:type variant: Optional[str]
:param run: Parent run or run ID.
:type run: Optional[Union[Run, str]]
:param column_mapping: Column mapping for the run. Optional since it's not stored in the database.
:type column_mapping: Optional[dict]
:param display_name: Display name of the run.
:type display_name: Optional[str]
:param description: Description of the run.
:type description: Optional[str]
:param tags: Tags of the run.
:type tags: Optional[List[Dict[str, str]]]
:param created_on: Date and time the run was created.
:type created_on: Optional[datetime.datetime]
:param start_time: Date and time the run started.
:type start_time: Optional[datetime.datetime]
:param end_time: Date and time the run ended.
:type end_time: Optional[datetime.datetime]
:param status: Status of the run.
:type status: Optional[str]
:param environment_variables: Environment variables for the run.
:type environment_variables: Optional[Dict[str, str]]
:param connections: Connections for the run.
:type connections: Optional[Dict[str, Dict]]
:param properties: Properties of the run.
:type properties: Optional[Dict[str, Any]]
:param kwargs: Additional keyword arguments.
:type kwargs: Optional[dict]
"""
def __init__(
self,
flow: Optional[Union[Path, str]] = None,
name: Optional[str] = None,
# input fields are optional since it's not stored in DB
data: Optional[str] = None,
variant: Optional[str] = None,
run: Optional[Union["Run", str]] = None,
column_mapping: Optional[dict] = None,
display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[List[Dict[str, str]]] = None,
*,
created_on: Optional[datetime.datetime] = None,
start_time: Optional[datetime.datetime] = None,
end_time: Optional[datetime.datetime] = None,
status: Optional[str] = None,
environment_variables: Optional[Dict[str, str]] = None,
connections: Optional[Dict[str, Dict]] = None,
properties: Optional[Dict[str, Any]] = None,
source: Optional[Union[Path, str]] = None,
**kwargs,
):
# TODO: remove when RUN CRUD don't depend on this
self.type = kwargs.get("type", RunTypes.BATCH)
self.data = data
self.column_mapping = column_mapping
self.display_name = display_name
self.description = description
self.tags = tags
self.variant = variant
self.run = run
self._created_on = created_on or datetime.datetime.now()
self._status = status or RunStatus.NOT_STARTED
self.environment_variables = environment_variables or {}
self.connections = connections or {}
self._properties = properties or {}
self.source = source
self._is_archived = kwargs.get("is_archived", False)
self._run_source = kwargs.get("run_source", RunInfoSources.LOCAL)
self._start_time = start_time
self._end_time = end_time
self._duration = kwargs.get("duration", None)
self._portal_url = kwargs.get(RunDataKeys.PORTAL_URL, None)
self._creation_context = kwargs.get("creation_context", None)
# init here to make sure those fields initialized in all branches.
self.flow = flow
self._use_remote_flow = is_remote_uri(flow)
self._experiment_name = None
self._lineage_id = None
if self._use_remote_flow:
self._flow_name = parse_remote_flow_pattern(flow)
self._lineage_id = self._flow_name
# default run name: flow directory name + timestamp
self.name = name or self._generate_run_name()
experiment_name = kwargs.get("experiment_name", None)
if self._run_source == RunInfoSources.LOCAL and not self._use_remote_flow:
self.flow = Path(flow).resolve().absolute()
flow_dir = self._get_flow_dir()
# sanitize flow_dir to avoid invalid experiment name
self._experiment_name = _sanitize_python_variable_name(flow_dir.name)
self._lineage_id = get_flow_lineage_id(flow_dir=flow_dir)
self._output_path = Path(
kwargs.get("output_path", self._generate_output_path(config=kwargs.get("config", None)))
)
self._flow_name = flow_dir.name
elif self._run_source == RunInfoSources.INDEX_SERVICE:
self._metrics = kwargs.get("metrics", {})
self._experiment_name = experiment_name
elif self._run_source == RunInfoSources.RUN_HISTORY:
self._error = kwargs.get("error", None)
self._output = kwargs.get("output", None)
elif self._run_source == RunInfoSources.EXISTING_RUN:
# when the run is created from an existing run folder, the output path is also the source path
self._output_path = Path(source)
self._runtime = kwargs.get("runtime", None)
self._resources = kwargs.get("resources", None)
self._outputs = kwargs.get("outputs", None)
self._command = kwargs.get("command", None)
@property
def created_on(self) -> str:
return self._created_on.isoformat()
@property
def status(self) -> str:
return self._status
@property
def properties(self) -> Dict[str, str]:
result = {}
if self._run_source == RunInfoSources.LOCAL:
# show posix path to avoid windows path escaping
result = {
FlowRunProperties.FLOW_PATH: Path(self.flow).as_posix() if not self._use_remote_flow else self.flow,
FlowRunProperties.OUTPUT_PATH: self._output_path.as_posix(),
}
if self.run:
run_name = self.run.name if isinstance(self.run, Run) else self.run
result[FlowRunProperties.RUN] = run_name
if self.variant:
result[FlowRunProperties.NODE_VARIANT] = self.variant
if self._command:
result[FlowRunProperties.COMMAND] = self._command
if self._outputs:
result[FlowRunProperties.OUTPUTS] = self._outputs
elif self._run_source == RunInfoSources.EXISTING_RUN:
result = {
FlowRunProperties.OUTPUT_PATH: Path(self.source).resolve().as_posix(),
}
return {
**result,
**self._properties,
}
@classmethod
def _from_orm_object(cls, obj: ORMRun) -> "Run":
properties_json = json.loads(str(obj.properties))
flow = properties_json.get(FlowRunProperties.FLOW_PATH, None)
# there can be two sources for orm run object:
# 1. LOCAL: Created when run is created from local flow
# 2. EXISTING_RUN: Created when run is created from existing run folder
source = None
if getattr(obj, "run_source", None) == RunInfoSources.EXISTING_RUN:
source = properties_json[FlowRunProperties.OUTPUT_PATH]
return Run(
name=str(obj.name),
flow=Path(flow) if flow else None,
source=Path(source) if source else None,
output_path=properties_json[FlowRunProperties.OUTPUT_PATH],
run=properties_json.get(FlowRunProperties.RUN, None),
variant=properties_json.get(FlowRunProperties.NODE_VARIANT, None),
display_name=obj.display_name,
description=str(obj.description) if obj.description else None,
tags=json.loads(str(obj.tags)) if obj.tags else None,
# keyword arguments
created_on=datetime.datetime.fromisoformat(str(obj.created_on)),
start_time=datetime.datetime.fromisoformat(str(obj.start_time)) if obj.start_time else None,
end_time=datetime.datetime.fromisoformat(str(obj.end_time)) if obj.end_time else None,
status=str(obj.status),
data=Path(obj.data).resolve().absolute().as_posix() if obj.data else None,
properties={FlowRunProperties.SYSTEM_METRICS: properties_json.get(FlowRunProperties.SYSTEM_METRICS, {})},
# compatible with old runs, their run_source is empty, treat them as local
run_source=obj.run_source or RunInfoSources.LOCAL,
# experiment command node only fields
command=properties_json.get(FlowRunProperties.COMMAND, None),
outputs=properties_json.get(FlowRunProperties.OUTPUTS, None),
)
@classmethod
def _from_index_service_entity(cls, run_entity: dict) -> "Run":
"""Convert run entity from index service to run object."""
# TODO(2887134): support cloud eager Run CRUD
start_time = run_entity["properties"].get("startTime", None)
end_time = run_entity["properties"].get("endTime", None)
duration = run_entity["properties"].get("duration", None)
return Run(
name=run_entity["properties"]["runId"],
flow=Path(f"azureml://flows/{run_entity['properties']['experimentName']}"),
type=AZURE_RUN_TYPE_2_RUN_TYPE[run_entity["properties"]["runType"]],
created_on=date_parser.parse(run_entity["properties"]["creationContext"]["createdTime"]),
status=run_entity["annotations"]["status"],
display_name=run_entity["annotations"]["displayName"],
description=run_entity["annotations"]["description"],
tags=run_entity["annotations"]["tags"],
properties=run_entity["properties"]["userProperties"],
is_archived=run_entity["annotations"]["archived"],
run_source=RunInfoSources.INDEX_SERVICE,
metrics=run_entity["annotations"]["metrics"],
start_time=date_parser.parse(start_time) if start_time else None,
end_time=date_parser.parse(end_time) if end_time else None,
duration=duration,
creation_context=run_entity["properties"]["creationContext"],
experiment_name=run_entity["properties"]["experimentName"],
)
@classmethod
def _from_run_history_entity(cls, run_entity: dict) -> "Run":
"""Convert run entity from run history service to run object."""
# TODO(2887134): support cloud eager Run CRUD
flow_name = run_entity["properties"].get("azureml.promptflow.flow_name", None)
start_time = run_entity.get("startTimeUtc", None)
end_time = run_entity.get("endTimeUtc", None)
duration = run_entity.get("duration", None)
return Run(
name=run_entity["runId"],
flow=Path(f"azureml://flows/{flow_name}"),
type=AZURE_RUN_TYPE_2_RUN_TYPE[run_entity["runType"]],
created_on=date_parser.parse(run_entity["createdUtc"]),
start_time=date_parser.parse(start_time) if start_time else None,
end_time=date_parser.parse(end_time) if end_time else None,
duration=duration,
status=run_entity["status"],
display_name=run_entity["displayName"],
description=run_entity["description"],
tags=run_entity["tags"],
properties=run_entity["properties"],
is_archived=run_entity.get("archived", False), # TODO: Get archived status, depends on run history team
error=run_entity.get("error", None),
run_source=RunInfoSources.RUN_HISTORY,
portal_url=run_entity[RunDataKeys.PORTAL_URL],
creation_context=run_entity["createdBy"],
data=run_entity[RunDataKeys.DATA],
run=run_entity[RunDataKeys.RUN],
output=run_entity[RunDataKeys.OUTPUT],
)
@classmethod
def _from_mt_service_entity(cls, run_entity) -> "Run":
"""Convert run object from MT service to run object."""
flow_run_id = run_entity.flow_run_resource_id.split("/")[-1]
return cls(
name=flow_run_id,
flow=Path(f"azureml://flows/{run_entity.flow_name}"),
display_name=run_entity.flow_run_display_name,
description="",
tags=[],
created_on=date_parser.parse(run_entity.created_on),
status="",
run_source=RunInfoSources.MT_SERVICE,
)
def _to_orm_object(self) -> ORMRun:
"""Convert current run entity to ORM object."""
display_name = self._format_display_name()
return ORMRun(
name=self.name,
created_on=self.created_on,
status=self.status,
start_time=self._start_time.isoformat() if self._start_time else None,
end_time=self._end_time.isoformat() if self._end_time else None,
display_name=display_name,
description=self.description,
tags=json.dumps(self.tags) if self.tags else None,
properties=json.dumps(self.properties),
data=Path(self.data).resolve().absolute().as_posix() if self.data else None,
run_source=self._run_source,
)
def _dump(self) -> None:
"""Dump current run entity to local DB."""
self._to_orm_object().dump()
def _to_dict(
self,
*,
exclude_additional_info: bool = False,
exclude_debug_info: bool = False,
exclude_properties: bool = False,
):
from promptflow._sdk.operations._local_storage_operations import LocalStorageOperations
properties = self.properties
result = {
"name": self.name,
"created_on": self.created_on,
"status": self.status,
"display_name": self.display_name,
"description": self.description,
"tags": self.tags,
"properties": properties,
}
if self._run_source == RunInfoSources.LOCAL:
result["flow_name"] = self._flow_name
local_storage = LocalStorageOperations(run=self)
result[RunDataKeys.DATA] = (
local_storage._data_path.resolve().absolute().as_posix()
if local_storage._data_path is not None
else None
)
result[RunDataKeys.OUTPUT] = local_storage.outputs_folder.as_posix()
if self.run:
run_name = self.run.name if isinstance(self.run, Run) else self.run
result[RunDataKeys.RUN] = properties.pop(FlowRunProperties.RUN, run_name)
# add exception part if any
exception_dict = local_storage.load_exception()
if exception_dict:
if exclude_additional_info:
exception_dict.pop("additionalInfo", None)
if exclude_debug_info:
exception_dict.pop("debugInfo", None)
result["error"] = exception_dict
elif self._run_source == RunInfoSources.INDEX_SERVICE:
result["creation_context"] = self._creation_context
result["flow_name"] = self._experiment_name
result["is_archived"] = self._is_archived
result["start_time"] = self._start_time.isoformat() if self._start_time else None
result["end_time"] = self._end_time.isoformat() if self._end_time else None
result["duration"] = self._duration
elif self._run_source == RunInfoSources.RUN_HISTORY:
result["creation_context"] = self._creation_context
result["start_time"] = self._start_time.isoformat() if self._start_time else None
result["end_time"] = self._end_time.isoformat() if self._end_time else None
result["duration"] = self._duration
result[RunDataKeys.PORTAL_URL] = self._portal_url
result[RunDataKeys.DATA] = self.data
result[RunDataKeys.OUTPUT] = self._output
if self.run:
result[RunDataKeys.RUN] = self.run
if self._error:
result["error"] = self._error
if exclude_additional_info:
result["error"]["error"].pop("additionalInfo", None)
if exclude_debug_info:
result["error"]["error"].pop("debugInfo", None)
# hide properties when needed (e.g. list remote runs)
if exclude_properties is True:
result.pop("properties", None)
return result
@classmethod
def _load(
cls,
data: Optional[Dict] = None,
yaml_path: Optional[Union[PathLike, str]] = None,
params_override: Optional[list] = None,
**kwargs,
):
from marshmallow import INCLUDE
data = data or {}
params_override = params_override or []
context = {
BASE_PATH_CONTEXT_KEY: Path(yaml_path).parent if yaml_path else Path("./"),
PARAMS_OVERRIDE_KEY: params_override,
}
run = cls._load_from_dict(
data=data,
context=context,
additional_message="Failed to load flow run",
unknown=INCLUDE,
**kwargs,
)
if yaml_path:
run._source_path = yaml_path
return run
def _generate_run_name(self) -> str:
"""Generate a run name with flow_name_variant_timestamp format."""
try:
flow_name = self._get_flow_dir().name if not self._use_remote_flow else self._flow_name
variant = self.variant
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
variant = parse_variant(variant)[1] if variant else DEFAULT_VARIANT
run_name_prefix = f"{flow_name}_{variant}"
# TODO(2562996): limit run name to avoid it become too long
run_name = f"{run_name_prefix}_{timestamp}"
return _sanitize_python_variable_name(run_name)
except Exception:
return str(uuid.uuid4())
def _get_default_display_name(self) -> str:
display_name = self.display_name or self.name
return display_name
def _format_display_name(self) -> str:
"""
Format display name. Replace macros in display name with actual values.
The following macros are supported: ${variant_id}, ${run}, ${timestamp}
For example,
if the display name is "run-${variant_id}-${timestamp}"
it will be formatted to "run-variant_1-20210901123456"
"""
display_name = self._get_default_display_name()
time_stamp = datetime.datetime.now().strftime("%Y%m%d%H%M")
if self.run:
display_name = display_name.replace(RUN_MACRO, self._validate_and_return_run_name(self.run))
display_name = display_name.replace(TIMESTAMP_MACRO, time_stamp)
variant = self.variant
variant = parse_variant(variant)[1] if variant else DEFAULT_VARIANT
display_name = display_name.replace(VARIANT_ID_MACRO, variant)
return display_name
def _get_flow_dir(self) -> Path:
if not self._use_remote_flow:
flow = Path(self.flow)
if flow.is_dir():
return flow
return flow.parent
raise UserErrorException("Cannot get flow directory for remote flow.")
@classmethod
def _get_schema_cls(self):
return RunSchema
def _to_rest_object(self):
from azure.ai.ml._utils._storage_utils import AzureMLDatastorePathUri
from promptflow.azure._restclient.flow.models import (
BatchDataInput,
RunDisplayNameGenerationType,
SessionSetupModeEnum,
SubmitBulkRunRequest,
)
if self.run is not None:
if isinstance(self.run, Run):
variant = self.run.name
elif isinstance(self.run, str):
variant = self.run
else:
raise UserErrorException(f"Invalid run type: {type(self.run)}")
else:
variant = None
if not variant and not self.data:
raise UserErrorException("Either run or data should be provided")
# parse inputs mapping
inputs_mapping = {}
if self.column_mapping and not isinstance(self.column_mapping, dict):
raise UserErrorException(f"column_mapping should be a dictionary, got {type(self.column_mapping)} instead.")
if self.column_mapping:
for k, v in self.column_mapping.items():
if isinstance(v, (int, float, str, bool)):
inputs_mapping[k] = v
else:
try:
val = json.dumps(v)
except Exception as e:
raise UserErrorException(
f"Invalid input mapping value: {v}, "
f"only primitive or json serializable value is supported, got {type(v)}",
error=e,
)
inputs_mapping[k] = val
# parse resources
if self._resources is not None:
if not isinstance(self._resources, dict):
raise TypeError(f"resources should be a dict, got {type(self._resources)} for {self._resources}")
vm_size = self._resources.get("instance_type", None)
max_idle_time_minutes = self._resources.get("idle_time_before_shutdown_minutes", None)
# change to seconds
max_idle_time_seconds = max_idle_time_minutes * 60 if max_idle_time_minutes else None
else:
vm_size = None
max_idle_time_seconds = None
# use functools.partial to avoid too many arguments that have the same values
common_submit_bulk_run_request = functools.partial(
SubmitBulkRunRequest,
run_id=self.name,
# will use user provided display name since PFS will have special logic to update it.
run_display_name=self._get_default_display_name(),
description=self.description,
tags=self.tags,
node_variant=self.variant,
variant_run_id=variant,
batch_data_input=BatchDataInput(
data_uri=self.data,
),
inputs_mapping=inputs_mapping,
run_experiment_name=self._experiment_name,
environment_variables=self.environment_variables,
connections=self.connections,
flow_lineage_id=self._lineage_id,
run_display_name_generation_type=RunDisplayNameGenerationType.USER_PROVIDED_MACRO,
vm_size=vm_size,
max_idle_time_seconds=max_idle_time_seconds,
session_setup_mode=SessionSetupModeEnum.SYSTEM_WAIT,
)
if str(self.flow).startswith(REMOTE_URI_PREFIX):
if not self._use_remote_flow:
# in normal case, we will upload local flow to datastore and resolve the self.flow to be remote uri
# upload via _check_and_upload_path
# submit with params FlowDefinitionDataStoreName and FlowDefinitionBlobPath
path_uri = AzureMLDatastorePathUri(str(self.flow))
return common_submit_bulk_run_request(
flow_definition_data_store_name=path_uri.datastore,
flow_definition_blob_path=path_uri.path,
)
else:
# if the flow is a remote flow in the beginning, we will submit with params FlowDefinitionResourceID
# submit with params flow_definition_resource_id which will be resolved in pfazure run create operation
# the flow resource id looks like: "azureml://locations/<region>/workspaces/<ws-name>/flows/<flow-name>"
if not isinstance(self.flow, str) or (
not self.flow.startswith(FLOW_RESOURCE_ID_PREFIX) and not self.flow.startswith(REGISTRY_URI_PREFIX)
):
raise UserErrorException(
f"Invalid flow value when transforming to rest object: {self.flow!r}. "
f"Expecting a flow definition resource id starts with '{FLOW_RESOURCE_ID_PREFIX}' "
f"or a flow registry uri starts with '{REGISTRY_URI_PREFIX}'"
)
return common_submit_bulk_run_request(
flow_definition_resource_id=self.flow,
)
else:
# upload via CodeOperations.create_or_update
# submit with param FlowDefinitionDataUri
return common_submit_bulk_run_request(
flow_definition_data_uri=str(self.flow),
)
def _check_run_status_is_completed(self) -> None:
if self.status != RunStatus.COMPLETED:
error_message = f"Run {self.name!r} is not completed, the status is {self.status!r}."
if self.status != RunStatus.FAILED:
error_message += " Please wait for its completion, or select other completed run(s)."
raise InvalidRunStatusError(error_message)
@staticmethod
def _validate_and_return_run_name(run: Union[str, "Run"]) -> str:
"""Check if run name is valid."""
if isinstance(run, Run):
return run.name
elif isinstance(run, str):
return run
raise InvalidRunError(f"Invalid run {run!r}, expected 'str' or 'Run' object but got {type(run)!r}.")
def _validate_for_run_create_operation(self):
"""Validate run object for create operation."""
# check flow value
if Path(self.flow).is_dir():
# local flow
pass
elif isinstance(self.flow, str) and self.flow.startswith(REMOTE_URI_PREFIX):
# remote flow
pass
else:
raise UserErrorException(
f"Invalid flow value: {self.flow!r}. Expecting a local flow folder path or a remote flow pattern "
f"like '{REMOTE_URI_PREFIX}<flow-name>'"
)
if is_remote_uri(self.data):
# Pass through ARM id or remote url, the error will happen in runtime if format is not correct currently.
pass
else:
if self.data and not Path(self.data).exists():
raise UserErrorException(f"data path {self.data} does not exist")
if not self.run and not self.data:
raise UserErrorException("at least one of data or run must be provided")
def _generate_output_path(self, config: Optional[Configuration]) -> Path:
config = config or Configuration.get_instance()
path = config.get_run_output_path()
if path is None:
path = Path.home() / PROMPT_FLOW_DIR_NAME / ".runs"
else:
try:
flow_posix_path = self.flow.resolve().as_posix()
path = Path(path.replace(FLOW_DIRECTORY_MACRO_IN_CONFIG, self.flow.resolve().as_posix())).resolve()
# in case user manually modifies ~/.promptflow/pf.yaml
# fall back to default run output path
if path.as_posix() == flow_posix_path:
raise Exception(f"{FLOW_DIRECTORY_MACRO_IN_CONFIG!r} is not a valid value.")
path.mkdir(parents=True, exist_ok=True)
except Exception: # pylint: disable=broad-except
path = Path.home() / PROMPT_FLOW_DIR_NAME / ".runs"
warning_message = (
"Got unexpected error when parsing specified output path: "
f"{config.get_run_output_path()!r}; "
f"will use default output path: {path!r} instead."
)
logger.warning(warning_message)
return (path / str(self.name)).resolve()
@classmethod
def _load_from_source(cls, source: Union[str, Path], params_override: Optional[Dict] = None, **kwargs) -> "Run":
"""Load run from run record source folder."""
source = Path(source)
params_override = params_override or {}
run_metadata_file = source / DownloadedRun.RUN_METADATA_FILE_NAME
if not run_metadata_file.exists():
raise UserErrorException(
f"Invalid run source: {source!r}. Expecting a valid run source folder with {run_metadata_file!r}. "
f"Please make sure the run source is downloaded by 'pfazure run download' command."
)
# extract run info from source folder
with open(source / DownloadedRun.RUN_METADATA_FILE_NAME, encoding=DEFAULT_ENCODING) as f:
run_info = json.load(f)
return cls(
name=run_info["name"],
source=source,
run_source=RunInfoSources.EXISTING_RUN,
status=run_info["status"], # currently only support completed run
display_name=params_override.get("display_name", run_info.get("display_name", source.name)),
description=params_override.get("description", run_info.get("description", "")),
tags=params_override.get("tags", run_info.get("tags", {})),
created_on=datetime.datetime.fromisoformat(run_info["created_on"]),
start_time=datetime.datetime.fromisoformat(run_info["start_time"]),
end_time=datetime.datetime.fromisoformat(run_info["end_time"]),
**kwargs,
)
| promptflow/src/promptflow/promptflow/_sdk/entities/_run.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/entities/_run.py",
"repo_id": "promptflow",
"token_count": 14107
} | 44 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
# pylint: disable=unused-argument,no-self-use
import copy
from pathlib import Path
from typing import Optional
from marshmallow import RAISE, fields, post_load, pre_load
from marshmallow.decorators import post_dump
from marshmallow.schema import Schema, SchemaMeta
from pydash import objects
from promptflow._sdk._constants import BASE_PATH_CONTEXT_KEY, FILE_PREFIX, PARAMS_OVERRIDE_KEY
from promptflow._utils.logger_utils import LoggerFactory
from promptflow._utils.yaml_utils import load_yaml
module_logger = LoggerFactory.get_logger(__name__)
class PatchedMeta:
ordered = True
unknown = RAISE
class PatchedBaseSchema(Schema):
class Meta:
unknown = RAISE
ordered = True
@post_dump
def remove_none(self, data, **kwargs):
"""Prevents from dumping attributes that are None, thus making the dump more compact."""
return dict((key, value) for key, value in data.items() if value is not None)
class PatchedSchemaMeta(SchemaMeta):
"""Currently there is an open issue in marshmallow, that the "unknown" property is not inherited.
We use a metaclass to inject a Meta class into all our Schema classes.
"""
def __new__(cls, name, bases, dct):
meta = dct.get("Meta")
if meta is None:
dct["Meta"] = PatchedMeta
else:
if not hasattr(meta, "unknown"):
dct["Meta"].unknown = RAISE
if not hasattr(meta, "ordered"):
dct["Meta"].ordered = True
if PatchedBaseSchema not in bases:
bases = bases + (PatchedBaseSchema,)
return super().__new__(cls, name, bases, dct)
class PathAwareSchema(PatchedBaseSchema, metaclass=PatchedSchemaMeta):
schema_ignored = fields.Str(data_key="$schema", dump_only=True)
def __init__(self, *args, **kwargs):
# this will make context of all PathAwareSchema child class point to one object
self.context = kwargs.get("context", None)
if self.context is None or self.context.get(BASE_PATH_CONTEXT_KEY, None) is None:
raise Exception("Base path for reading files is required when building PathAwareSchema")
# set old base path, note it's an Path object and point to the same object with
# self.context.get(BASE_PATH_CONTEXT_KEY)
self.old_base_path = self.context.get(BASE_PATH_CONTEXT_KEY)
super().__init__(*args, **kwargs)
@pre_load
def add_param_overrides(self, data, **kwargs):
# Removing params override from context so that overriding is done once on the yaml
# child schema should not override the params.
params_override = self.context.pop(PARAMS_OVERRIDE_KEY, None)
if params_override is not None:
for override in params_override:
for param, val in override.items():
# Check that none of the intermediary levels are string references (azureml/file)
param_tokens = param.split(".")
test_layer = data
for layer in param_tokens:
if test_layer is None:
continue
if isinstance(test_layer, str):
raise Exception(
f"Cannot use '--set' on properties defined by reference strings: --set {param}"
)
test_layer = test_layer.get(layer, None)
objects.set_(data, param, val)
return data
@pre_load
def trim_dump_only(self, data, **kwargs):
"""Marshmallow raises if dump_only fields are present in the schema. This is not desirable for our use case,
where read-only properties can be present in the yaml, and should simply be ignored, while we should raise in.
the case an unknown field is present - to prevent typos.
"""
if isinstance(data, str) or data is None:
return data
for key, value in self.fields.items(): # pylint: disable=no-member
if value.dump_only:
schema_key = value.data_key or key
if data.get(schema_key, None) is not None:
data.pop(schema_key)
return data
class YamlFileSchema(PathAwareSchema):
"""Base class that allows derived classes to be built from paths to separate yaml files in place of inline yaml
definitions.
This will be transparent to any parent schema containing a nested schema of the derived class, it will not need a
union type for the schema, a YamlFile string will be resolved by the pre_load method into a dictionary. On loading
the child yaml, update the base path to use for loading sub-child files.
"""
def __init__(self, *args, **kwargs):
self._previous_base_path = None
super().__init__(*args, **kwargs)
@classmethod
def _resolve_path(cls, data, base_path) -> Optional[Path]:
if isinstance(data, str) and data.startswith(FILE_PREFIX):
# Use directly if absolute path
path = Path(data[len(FILE_PREFIX) :])
if not path.is_absolute():
path = Path(base_path) / path
path.resolve()
return path
return None
@pre_load
def load_from_file(self, data, **kwargs):
path = self._resolve_path(data, Path(self.context[BASE_PATH_CONTEXT_KEY]))
if path is not None:
self._previous_base_path = Path(self.context[BASE_PATH_CONTEXT_KEY])
# Push update
# deepcopy self.context[BASE_PATH_CONTEXT_KEY] to update old base path
self.old_base_path = copy.deepcopy(self.context[BASE_PATH_CONTEXT_KEY])
self.context[BASE_PATH_CONTEXT_KEY] = path.parent
data = load_yaml(path)
return data
return data
# Schemas are read depth-first, so push/pop to update current path
@post_load
def reset_base_path_post_load(self, data, **kwargs):
if self._previous_base_path is not None:
# pop state
self.context[BASE_PATH_CONTEXT_KEY] = self._previous_base_path
return data
class CreateBySchema(metaclass=PatchedSchemaMeta):
user_object_id = fields.Str(dump_only=True, attribute="userObjectId")
user_tenant_id = fields.Str(dump_only=True, attribute="userTenantId")
user_name = fields.Str(dump_only=True, attribute="userName")
| promptflow/src/promptflow/promptflow/_sdk/schemas/_base.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/schemas/_base.py",
"repo_id": "promptflow",
"token_count": 2701
} | 45 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import hashlib
import os
from os import PathLike
from pathlib import Path
from typing import Union
from promptflow._sdk._constants import DAG_FILE_NAME, DEFAULT_ENCODING
from promptflow._utils.logger_utils import LoggerFactory
from promptflow._utils.yaml_utils import dump_yaml, load_yaml
logger = LoggerFactory.get_logger(name=__name__)
def get_flow_lineage_id(flow_dir: Union[str, PathLike]):
"""
Get the lineage id for flow. The flow lineage id will be same for same flow in same GIT repo or device.
If the flow locates in GIT repo:
use Repo name + relative path to flow_dir as session id
Otherwise:
use device id + absolute path to flow_dir as session id
:param flow_dir: flow directory
"""
flow_dir = Path(flow_dir).resolve()
if not flow_dir.is_dir():
flow_dir = flow_dir.parent
try:
from git import Repo
repo = Repo(flow_dir, search_parent_directories=True)
lineage_id = f"{os.path.basename(repo.working_dir)}/{flow_dir.relative_to(repo.working_dir).as_posix()}"
logger.debug("Got lineage id %s from git repo.", lineage_id)
except Exception:
# failed to get repo, use device id + absolute path to flow_dir as session id
import uuid
device_id = uuid.getnode()
lineage_id = f"{device_id}/{flow_dir.absolute().as_posix()}"
logger.debug("Got lineage id %s from local since failed to get git info.", lineage_id)
# hash the value to avoid it gets too long, and it's not user visible.
lineage_id = hashlib.sha256(lineage_id.encode()).hexdigest()
return lineage_id
def resolve_flow_path(flow_path: Path):
"""Resolve given flow path to dag file path."""
if flow_path.is_dir():
flow_path = flow_path / DAG_FILE_NAME
return flow_path
def load_flow_dag(flow_path: Path):
"""Load flow dag from given flow path."""
flow_path = resolve_flow_path(flow_path)
if not flow_path.exists():
raise FileNotFoundError(f"Flow file {flow_path} not found")
with open(flow_path, "r", encoding=DEFAULT_ENCODING) as f:
flow_dag = load_yaml(f)
return flow_path, flow_dag
def dump_flow_dag(flow_dag: dict, flow_path: Path):
"""Dump flow dag to given flow path."""
flow_path = resolve_flow_path(flow_path)
with open(flow_path, "w", encoding=DEFAULT_ENCODING) as f:
dump_yaml(flow_dag, f)
return flow_path
| promptflow/src/promptflow/promptflow/_utils/flow_utils.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_utils/flow_utils.py",
"repo_id": "promptflow",
"token_count": 949
} | 46 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
class FlowType:
STANDARD = "standard"
CHAT = "chat"
EVALUATION = "evaluate"
class FlowJobType:
STANDARD = "azureml.promptflow.FlowRun"
EVALUATION = "azureml.promptflow.EvaluationRun"
# Use this storage since it's the storage used by notebook
DEFAULT_STORAGE = "workspaceworkingdirectory"
PROMPTFLOW_FILE_SHARE_DIR = "promptflow"
CLOUD_RUNS_PAGE_SIZE = 25 # align with UX
SESSION_CREATION_TIMEOUT_SECONDS = 10 * 60 # 10 minutes
SESSION_CREATION_TIMEOUT_ENV_VAR = "PROMPTFLOW_SESSION_CREATION_TIMEOUT_SECONDS"
ENVIRONMENT = "environment"
PYTHON_REQUIREMENTS_TXT = "python_requirements_txt"
ADDITIONAL_INCLUDES = "additional_includes"
BASE_IMAGE = "image"
AUTOMATIC_RUNTIME_NAME = "automatic"
AUTOMATIC_RUNTIME = "automatic runtime"
| promptflow/src/promptflow/promptflow/azure/_constants/_flow.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_constants/_flow.py",
"repo_id": "promptflow",
"token_count": 322
} | 47 |
# coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from typing import Any, Optional
from azure.core.configuration import Configuration
from azure.core.pipeline import policies
VERSION = "unknown"
class AzureMachineLearningDesignerServiceClientConfiguration(Configuration):
"""Configuration for AzureMachineLearningDesignerServiceClient.
Note that all parameters used to create this instance are saved as instance
attributes.
:param api_version: Api Version. The default value is "1.0.0".
:type api_version: str
"""
def __init__(
self,
api_version: Optional[str] = "1.0.0",
**kwargs: Any
) -> None:
super(AzureMachineLearningDesignerServiceClientConfiguration, self).__init__(**kwargs)
self.api_version = api_version
kwargs.setdefault('sdk_moniker', 'azuremachinelearningdesignerserviceclient/{}'.format(VERSION))
self._configure(**kwargs)
def _configure(
self,
**kwargs: Any
) -> None:
self.user_agent_policy = kwargs.get('user_agent_policy') or policies.UserAgentPolicy(**kwargs)
self.headers_policy = kwargs.get('headers_policy') or policies.HeadersPolicy(**kwargs)
self.proxy_policy = kwargs.get('proxy_policy') or policies.ProxyPolicy(**kwargs)
self.logging_policy = kwargs.get('logging_policy') or policies.NetworkTraceLoggingPolicy(**kwargs)
self.http_logging_policy = kwargs.get('http_logging_policy') or policies.HttpLoggingPolicy(**kwargs)
self.retry_policy = kwargs.get('retry_policy') or policies.AsyncRetryPolicy(**kwargs)
self.custom_hook_policy = kwargs.get('custom_hook_policy') or policies.CustomHookPolicy(**kwargs)
self.redirect_policy = kwargs.get('redirect_policy') or policies.AsyncRedirectPolicy(**kwargs)
self.authentication_policy = kwargs.get('authentication_policy')
| promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/_configuration.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/_configuration.py",
"repo_id": "promptflow",
"token_count": 741
} | 48 |
# coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from azure.core.exceptions import HttpResponseError
import msrest.serialization
class ACIAdvanceSettings(msrest.serialization.Model):
"""ACIAdvanceSettings.
:ivar container_resource_requirements:
:vartype container_resource_requirements: ~flow.models.ContainerResourceRequirements
:ivar app_insights_enabled:
:vartype app_insights_enabled: bool
:ivar ssl_enabled:
:vartype ssl_enabled: bool
:ivar ssl_certificate:
:vartype ssl_certificate: str
:ivar ssl_key:
:vartype ssl_key: str
:ivar c_name:
:vartype c_name: str
:ivar dns_name_label:
:vartype dns_name_label: str
"""
_attribute_map = {
'container_resource_requirements': {'key': 'containerResourceRequirements', 'type': 'ContainerResourceRequirements'},
'app_insights_enabled': {'key': 'appInsightsEnabled', 'type': 'bool'},
'ssl_enabled': {'key': 'sslEnabled', 'type': 'bool'},
'ssl_certificate': {'key': 'sslCertificate', 'type': 'str'},
'ssl_key': {'key': 'sslKey', 'type': 'str'},
'c_name': {'key': 'cName', 'type': 'str'},
'dns_name_label': {'key': 'dnsNameLabel', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword container_resource_requirements:
:paramtype container_resource_requirements: ~flow.models.ContainerResourceRequirements
:keyword app_insights_enabled:
:paramtype app_insights_enabled: bool
:keyword ssl_enabled:
:paramtype ssl_enabled: bool
:keyword ssl_certificate:
:paramtype ssl_certificate: str
:keyword ssl_key:
:paramtype ssl_key: str
:keyword c_name:
:paramtype c_name: str
:keyword dns_name_label:
:paramtype dns_name_label: str
"""
super(ACIAdvanceSettings, self).__init__(**kwargs)
self.container_resource_requirements = kwargs.get('container_resource_requirements', None)
self.app_insights_enabled = kwargs.get('app_insights_enabled', None)
self.ssl_enabled = kwargs.get('ssl_enabled', None)
self.ssl_certificate = kwargs.get('ssl_certificate', None)
self.ssl_key = kwargs.get('ssl_key', None)
self.c_name = kwargs.get('c_name', None)
self.dns_name_label = kwargs.get('dns_name_label', None)
class Activate(msrest.serialization.Model):
"""Activate.
:ivar when:
:vartype when: str
:ivar is_property: Anything.
:vartype is_property: any
"""
_attribute_map = {
'when': {'key': 'when', 'type': 'str'},
'is_property': {'key': 'is', 'type': 'object'},
}
def __init__(
self,
**kwargs
):
"""
:keyword when:
:paramtype when: str
:keyword is_property: Anything.
:paramtype is_property: any
"""
super(Activate, self).__init__(**kwargs)
self.when = kwargs.get('when', None)
self.is_property = kwargs.get('is_property', None)
class AdditionalErrorInfo(msrest.serialization.Model):
"""AdditionalErrorInfo.
:ivar type:
:vartype type: str
:ivar info: Anything.
:vartype info: any
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'info': {'key': 'info', 'type': 'object'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type:
:paramtype type: str
:keyword info: Anything.
:paramtype info: any
"""
super(AdditionalErrorInfo, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.info = kwargs.get('info', None)
class AdhocTriggerScheduledCommandJobRequest(msrest.serialization.Model):
"""AdhocTriggerScheduledCommandJobRequest.
:ivar job_name:
:vartype job_name: str
:ivar job_display_name:
:vartype job_display_name: str
:ivar trigger_time_string:
:vartype trigger_time_string: str
"""
_attribute_map = {
'job_name': {'key': 'jobName', 'type': 'str'},
'job_display_name': {'key': 'jobDisplayName', 'type': 'str'},
'trigger_time_string': {'key': 'triggerTimeString', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword job_name:
:paramtype job_name: str
:keyword job_display_name:
:paramtype job_display_name: str
:keyword trigger_time_string:
:paramtype trigger_time_string: str
"""
super(AdhocTriggerScheduledCommandJobRequest, self).__init__(**kwargs)
self.job_name = kwargs.get('job_name', None)
self.job_display_name = kwargs.get('job_display_name', None)
self.trigger_time_string = kwargs.get('trigger_time_string', None)
class AdhocTriggerScheduledSparkJobRequest(msrest.serialization.Model):
"""AdhocTriggerScheduledSparkJobRequest.
:ivar job_name:
:vartype job_name: str
:ivar job_display_name:
:vartype job_display_name: str
:ivar trigger_time_string:
:vartype trigger_time_string: str
"""
_attribute_map = {
'job_name': {'key': 'jobName', 'type': 'str'},
'job_display_name': {'key': 'jobDisplayName', 'type': 'str'},
'trigger_time_string': {'key': 'triggerTimeString', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword job_name:
:paramtype job_name: str
:keyword job_display_name:
:paramtype job_display_name: str
:keyword trigger_time_string:
:paramtype trigger_time_string: str
"""
super(AdhocTriggerScheduledSparkJobRequest, self).__init__(**kwargs)
self.job_name = kwargs.get('job_name', None)
self.job_display_name = kwargs.get('job_display_name', None)
self.trigger_time_string = kwargs.get('trigger_time_string', None)
class AetherAmlDataset(msrest.serialization.Model):
"""AetherAmlDataset.
:ivar registered_data_set_reference:
:vartype registered_data_set_reference: ~flow.models.AetherRegisteredDataSetReference
:ivar saved_data_set_reference:
:vartype saved_data_set_reference: ~flow.models.AetherSavedDataSetReference
:ivar additional_transformations:
:vartype additional_transformations: str
"""
_attribute_map = {
'registered_data_set_reference': {'key': 'registeredDataSetReference', 'type': 'AetherRegisteredDataSetReference'},
'saved_data_set_reference': {'key': 'savedDataSetReference', 'type': 'AetherSavedDataSetReference'},
'additional_transformations': {'key': 'additionalTransformations', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword registered_data_set_reference:
:paramtype registered_data_set_reference: ~flow.models.AetherRegisteredDataSetReference
:keyword saved_data_set_reference:
:paramtype saved_data_set_reference: ~flow.models.AetherSavedDataSetReference
:keyword additional_transformations:
:paramtype additional_transformations: str
"""
super(AetherAmlDataset, self).__init__(**kwargs)
self.registered_data_set_reference = kwargs.get('registered_data_set_reference', None)
self.saved_data_set_reference = kwargs.get('saved_data_set_reference', None)
self.additional_transformations = kwargs.get('additional_transformations', None)
class AetherAmlSparkCloudSetting(msrest.serialization.Model):
"""AetherAmlSparkCloudSetting.
:ivar entry:
:vartype entry: ~flow.models.AetherEntrySetting
:ivar files:
:vartype files: list[str]
:ivar archives:
:vartype archives: list[str]
:ivar jars:
:vartype jars: list[str]
:ivar py_files:
:vartype py_files: list[str]
:ivar driver_memory:
:vartype driver_memory: str
:ivar driver_cores:
:vartype driver_cores: int
:ivar executor_memory:
:vartype executor_memory: str
:ivar executor_cores:
:vartype executor_cores: int
:ivar number_executors:
:vartype number_executors: int
:ivar environment_asset_id:
:vartype environment_asset_id: str
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
:ivar inline_environment_definition_string:
:vartype inline_environment_definition_string: str
:ivar conf: Dictionary of :code:`<string>`.
:vartype conf: dict[str, str]
:ivar compute:
:vartype compute: str
:ivar resources:
:vartype resources: ~flow.models.AetherResourcesSetting
:ivar identity:
:vartype identity: ~flow.models.AetherIdentitySetting
"""
_attribute_map = {
'entry': {'key': 'entry', 'type': 'AetherEntrySetting'},
'files': {'key': 'files', 'type': '[str]'},
'archives': {'key': 'archives', 'type': '[str]'},
'jars': {'key': 'jars', 'type': '[str]'},
'py_files': {'key': 'pyFiles', 'type': '[str]'},
'driver_memory': {'key': 'driverMemory', 'type': 'str'},
'driver_cores': {'key': 'driverCores', 'type': 'int'},
'executor_memory': {'key': 'executorMemory', 'type': 'str'},
'executor_cores': {'key': 'executorCores', 'type': 'int'},
'number_executors': {'key': 'numberExecutors', 'type': 'int'},
'environment_asset_id': {'key': 'environmentAssetId', 'type': 'str'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'inline_environment_definition_string': {'key': 'inlineEnvironmentDefinitionString', 'type': 'str'},
'conf': {'key': 'conf', 'type': '{str}'},
'compute': {'key': 'compute', 'type': 'str'},
'resources': {'key': 'resources', 'type': 'AetherResourcesSetting'},
'identity': {'key': 'identity', 'type': 'AetherIdentitySetting'},
}
def __init__(
self,
**kwargs
):
"""
:keyword entry:
:paramtype entry: ~flow.models.AetherEntrySetting
:keyword files:
:paramtype files: list[str]
:keyword archives:
:paramtype archives: list[str]
:keyword jars:
:paramtype jars: list[str]
:keyword py_files:
:paramtype py_files: list[str]
:keyword driver_memory:
:paramtype driver_memory: str
:keyword driver_cores:
:paramtype driver_cores: int
:keyword executor_memory:
:paramtype executor_memory: str
:keyword executor_cores:
:paramtype executor_cores: int
:keyword number_executors:
:paramtype number_executors: int
:keyword environment_asset_id:
:paramtype environment_asset_id: str
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
:keyword inline_environment_definition_string:
:paramtype inline_environment_definition_string: str
:keyword conf: Dictionary of :code:`<string>`.
:paramtype conf: dict[str, str]
:keyword compute:
:paramtype compute: str
:keyword resources:
:paramtype resources: ~flow.models.AetherResourcesSetting
:keyword identity:
:paramtype identity: ~flow.models.AetherIdentitySetting
"""
super(AetherAmlSparkCloudSetting, self).__init__(**kwargs)
self.entry = kwargs.get('entry', None)
self.files = kwargs.get('files', None)
self.archives = kwargs.get('archives', None)
self.jars = kwargs.get('jars', None)
self.py_files = kwargs.get('py_files', None)
self.driver_memory = kwargs.get('driver_memory', None)
self.driver_cores = kwargs.get('driver_cores', None)
self.executor_memory = kwargs.get('executor_memory', None)
self.executor_cores = kwargs.get('executor_cores', None)
self.number_executors = kwargs.get('number_executors', None)
self.environment_asset_id = kwargs.get('environment_asset_id', None)
self.environment_variables = kwargs.get('environment_variables', None)
self.inline_environment_definition_string = kwargs.get('inline_environment_definition_string', None)
self.conf = kwargs.get('conf', None)
self.compute = kwargs.get('compute', None)
self.resources = kwargs.get('resources', None)
self.identity = kwargs.get('identity', None)
class AetherAPCloudConfiguration(msrest.serialization.Model):
"""AetherAPCloudConfiguration.
:ivar referenced_ap_module_guid:
:vartype referenced_ap_module_guid: str
:ivar user_alias:
:vartype user_alias: str
:ivar aether_module_type:
:vartype aether_module_type: str
"""
_attribute_map = {
'referenced_ap_module_guid': {'key': 'referencedAPModuleGuid', 'type': 'str'},
'user_alias': {'key': 'userAlias', 'type': 'str'},
'aether_module_type': {'key': 'aetherModuleType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword referenced_ap_module_guid:
:paramtype referenced_ap_module_guid: str
:keyword user_alias:
:paramtype user_alias: str
:keyword aether_module_type:
:paramtype aether_module_type: str
"""
super(AetherAPCloudConfiguration, self).__init__(**kwargs)
self.referenced_ap_module_guid = kwargs.get('referenced_ap_module_guid', None)
self.user_alias = kwargs.get('user_alias', None)
self.aether_module_type = kwargs.get('aether_module_type', None)
class AetherArgumentAssignment(msrest.serialization.Model):
"""AetherArgumentAssignment.
:ivar value_type: Possible values include: "Literal", "Parameter", "Input", "Output",
"NestedList", "StringInterpolationList".
:vartype value_type: str or ~flow.models.AetherArgumentValueType
:ivar value:
:vartype value: str
:ivar nested_argument_list:
:vartype nested_argument_list: list[~flow.models.AetherArgumentAssignment]
:ivar string_interpolation_argument_list:
:vartype string_interpolation_argument_list: list[~flow.models.AetherArgumentAssignment]
"""
_attribute_map = {
'value_type': {'key': 'valueType', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
'nested_argument_list': {'key': 'nestedArgumentList', 'type': '[AetherArgumentAssignment]'},
'string_interpolation_argument_list': {'key': 'stringInterpolationArgumentList', 'type': '[AetherArgumentAssignment]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword value_type: Possible values include: "Literal", "Parameter", "Input", "Output",
"NestedList", "StringInterpolationList".
:paramtype value_type: str or ~flow.models.AetherArgumentValueType
:keyword value:
:paramtype value: str
:keyword nested_argument_list:
:paramtype nested_argument_list: list[~flow.models.AetherArgumentAssignment]
:keyword string_interpolation_argument_list:
:paramtype string_interpolation_argument_list: list[~flow.models.AetherArgumentAssignment]
"""
super(AetherArgumentAssignment, self).__init__(**kwargs)
self.value_type = kwargs.get('value_type', None)
self.value = kwargs.get('value', None)
self.nested_argument_list = kwargs.get('nested_argument_list', None)
self.string_interpolation_argument_list = kwargs.get('string_interpolation_argument_list', None)
class AetherAssetDefinition(msrest.serialization.Model):
"""AetherAssetDefinition.
:ivar path:
:vartype path: str
:ivar type: Possible values include: "UriFile", "UriFolder", "MLTable", "CustomModel",
"MLFlowModel", "TritonModel", "OpenAIModel".
:vartype type: str or ~flow.models.AetherAssetType
:ivar asset_id:
:vartype asset_id: str
:ivar initial_asset_id:
:vartype initial_asset_id: str
:ivar serialized_asset_id:
:vartype serialized_asset_id: str
"""
_attribute_map = {
'path': {'key': 'path', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'asset_id': {'key': 'assetId', 'type': 'str'},
'initial_asset_id': {'key': 'initialAssetId', 'type': 'str'},
'serialized_asset_id': {'key': 'serializedAssetId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword path:
:paramtype path: str
:keyword type: Possible values include: "UriFile", "UriFolder", "MLTable", "CustomModel",
"MLFlowModel", "TritonModel", "OpenAIModel".
:paramtype type: str or ~flow.models.AetherAssetType
:keyword asset_id:
:paramtype asset_id: str
:keyword initial_asset_id:
:paramtype initial_asset_id: str
:keyword serialized_asset_id:
:paramtype serialized_asset_id: str
"""
super(AetherAssetDefinition, self).__init__(**kwargs)
self.path = kwargs.get('path', None)
self.type = kwargs.get('type', None)
self.asset_id = kwargs.get('asset_id', None)
self.initial_asset_id = kwargs.get('initial_asset_id', None)
self.serialized_asset_id = kwargs.get('serialized_asset_id', None)
class AetherAssetOutputSettings(msrest.serialization.Model):
"""AetherAssetOutputSettings.
:ivar path:
:vartype path: str
:ivar path_parameter_assignment:
:vartype path_parameter_assignment: ~flow.models.AetherParameterAssignment
:ivar type: Possible values include: "UriFile", "UriFolder", "MLTable", "CustomModel",
"MLFlowModel", "TritonModel", "OpenAIModel".
:vartype type: str or ~flow.models.AetherAssetType
:ivar options: This is a dictionary.
:vartype options: dict[str, str]
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AetherDataStoreMode
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'path': {'key': 'path', 'type': 'str'},
'path_parameter_assignment': {'key': 'PathParameterAssignment', 'type': 'AetherParameterAssignment'},
'type': {'key': 'type', 'type': 'str'},
'options': {'key': 'options', 'type': '{str}'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword path:
:paramtype path: str
:keyword path_parameter_assignment:
:paramtype path_parameter_assignment: ~flow.models.AetherParameterAssignment
:keyword type: Possible values include: "UriFile", "UriFolder", "MLTable", "CustomModel",
"MLFlowModel", "TritonModel", "OpenAIModel".
:paramtype type: str or ~flow.models.AetherAssetType
:keyword options: This is a dictionary.
:paramtype options: dict[str, str]
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AetherDataStoreMode
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
"""
super(AetherAssetOutputSettings, self).__init__(**kwargs)
self.path = kwargs.get('path', None)
self.path_parameter_assignment = kwargs.get('path_parameter_assignment', None)
self.type = kwargs.get('type', None)
self.options = kwargs.get('options', None)
self.data_store_mode = kwargs.get('data_store_mode', None)
self.name = kwargs.get('name', None)
self.version = kwargs.get('version', None)
class AetherAutoFeaturizeConfiguration(msrest.serialization.Model):
"""AetherAutoFeaturizeConfiguration.
:ivar featurization_config:
:vartype featurization_config: ~flow.models.AetherFeaturizationSettings
"""
_attribute_map = {
'featurization_config': {'key': 'featurizationConfig', 'type': 'AetherFeaturizationSettings'},
}
def __init__(
self,
**kwargs
):
"""
:keyword featurization_config:
:paramtype featurization_config: ~flow.models.AetherFeaturizationSettings
"""
super(AetherAutoFeaturizeConfiguration, self).__init__(**kwargs)
self.featurization_config = kwargs.get('featurization_config', None)
class AetherAutoMLComponentConfiguration(msrest.serialization.Model):
"""AetherAutoMLComponentConfiguration.
:ivar auto_train_config:
:vartype auto_train_config: ~flow.models.AetherAutoTrainConfiguration
:ivar auto_featurize_config:
:vartype auto_featurize_config: ~flow.models.AetherAutoFeaturizeConfiguration
"""
_attribute_map = {
'auto_train_config': {'key': 'autoTrainConfig', 'type': 'AetherAutoTrainConfiguration'},
'auto_featurize_config': {'key': 'autoFeaturizeConfig', 'type': 'AetherAutoFeaturizeConfiguration'},
}
def __init__(
self,
**kwargs
):
"""
:keyword auto_train_config:
:paramtype auto_train_config: ~flow.models.AetherAutoTrainConfiguration
:keyword auto_featurize_config:
:paramtype auto_featurize_config: ~flow.models.AetherAutoFeaturizeConfiguration
"""
super(AetherAutoMLComponentConfiguration, self).__init__(**kwargs)
self.auto_train_config = kwargs.get('auto_train_config', None)
self.auto_featurize_config = kwargs.get('auto_featurize_config', None)
class AetherAutoTrainConfiguration(msrest.serialization.Model):
"""AetherAutoTrainConfiguration.
:ivar general_settings:
:vartype general_settings: ~flow.models.AetherGeneralSettings
:ivar limit_settings:
:vartype limit_settings: ~flow.models.AetherLimitSettings
:ivar data_settings:
:vartype data_settings: ~flow.models.AetherDataSettings
:ivar forecasting_settings:
:vartype forecasting_settings: ~flow.models.AetherForecastingSettings
:ivar training_settings:
:vartype training_settings: ~flow.models.AetherTrainingSettings
:ivar sweep_settings:
:vartype sweep_settings: ~flow.models.AetherSweepSettings
:ivar image_model_settings: Dictionary of :code:`<any>`.
:vartype image_model_settings: dict[str, any]
:ivar properties: Dictionary of :code:`<string>`.
:vartype properties: dict[str, str]
:ivar compute_configuration:
:vartype compute_configuration: ~flow.models.AetherComputeConfiguration
:ivar resource_configurtion:
:vartype resource_configurtion: ~flow.models.AetherResourceConfiguration
:ivar environment_id:
:vartype environment_id: str
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
"""
_attribute_map = {
'general_settings': {'key': 'generalSettings', 'type': 'AetherGeneralSettings'},
'limit_settings': {'key': 'limitSettings', 'type': 'AetherLimitSettings'},
'data_settings': {'key': 'dataSettings', 'type': 'AetherDataSettings'},
'forecasting_settings': {'key': 'forecastingSettings', 'type': 'AetherForecastingSettings'},
'training_settings': {'key': 'trainingSettings', 'type': 'AetherTrainingSettings'},
'sweep_settings': {'key': 'sweepSettings', 'type': 'AetherSweepSettings'},
'image_model_settings': {'key': 'imageModelSettings', 'type': '{object}'},
'properties': {'key': 'properties', 'type': '{str}'},
'compute_configuration': {'key': 'computeConfiguration', 'type': 'AetherComputeConfiguration'},
'resource_configurtion': {'key': 'resourceConfigurtion', 'type': 'AetherResourceConfiguration'},
'environment_id': {'key': 'environmentId', 'type': 'str'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword general_settings:
:paramtype general_settings: ~flow.models.AetherGeneralSettings
:keyword limit_settings:
:paramtype limit_settings: ~flow.models.AetherLimitSettings
:keyword data_settings:
:paramtype data_settings: ~flow.models.AetherDataSettings
:keyword forecasting_settings:
:paramtype forecasting_settings: ~flow.models.AetherForecastingSettings
:keyword training_settings:
:paramtype training_settings: ~flow.models.AetherTrainingSettings
:keyword sweep_settings:
:paramtype sweep_settings: ~flow.models.AetherSweepSettings
:keyword image_model_settings: Dictionary of :code:`<any>`.
:paramtype image_model_settings: dict[str, any]
:keyword properties: Dictionary of :code:`<string>`.
:paramtype properties: dict[str, str]
:keyword compute_configuration:
:paramtype compute_configuration: ~flow.models.AetherComputeConfiguration
:keyword resource_configurtion:
:paramtype resource_configurtion: ~flow.models.AetherResourceConfiguration
:keyword environment_id:
:paramtype environment_id: str
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
"""
super(AetherAutoTrainConfiguration, self).__init__(**kwargs)
self.general_settings = kwargs.get('general_settings', None)
self.limit_settings = kwargs.get('limit_settings', None)
self.data_settings = kwargs.get('data_settings', None)
self.forecasting_settings = kwargs.get('forecasting_settings', None)
self.training_settings = kwargs.get('training_settings', None)
self.sweep_settings = kwargs.get('sweep_settings', None)
self.image_model_settings = kwargs.get('image_model_settings', None)
self.properties = kwargs.get('properties', None)
self.compute_configuration = kwargs.get('compute_configuration', None)
self.resource_configurtion = kwargs.get('resource_configurtion', None)
self.environment_id = kwargs.get('environment_id', None)
self.environment_variables = kwargs.get('environment_variables', None)
class AetherAzureBlobReference(msrest.serialization.Model):
"""AetherAzureBlobReference.
:ivar container:
:vartype container: str
:ivar sas_token:
:vartype sas_token: str
:ivar uri:
:vartype uri: str
:ivar account:
:vartype account: str
:ivar relative_path:
:vartype relative_path: str
:ivar path_type: Possible values include: "Unknown", "File", "Folder".
:vartype path_type: str or ~flow.models.AetherFileBasedPathType
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'container': {'key': 'container', 'type': 'str'},
'sas_token': {'key': 'sasToken', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'account': {'key': 'account', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'path_type': {'key': 'pathType', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword container:
:paramtype container: str
:keyword sas_token:
:paramtype sas_token: str
:keyword uri:
:paramtype uri: str
:keyword account:
:paramtype account: str
:keyword relative_path:
:paramtype relative_path: str
:keyword path_type: Possible values include: "Unknown", "File", "Folder".
:paramtype path_type: str or ~flow.models.AetherFileBasedPathType
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AetherAzureBlobReference, self).__init__(**kwargs)
self.container = kwargs.get('container', None)
self.sas_token = kwargs.get('sas_token', None)
self.uri = kwargs.get('uri', None)
self.account = kwargs.get('account', None)
self.relative_path = kwargs.get('relative_path', None)
self.path_type = kwargs.get('path_type', None)
self.aml_data_store_name = kwargs.get('aml_data_store_name', None)
class AetherAzureDatabaseReference(msrest.serialization.Model):
"""AetherAzureDatabaseReference.
:ivar server_uri:
:vartype server_uri: str
:ivar database_name:
:vartype database_name: str
:ivar table_name:
:vartype table_name: str
:ivar sql_query:
:vartype sql_query: str
:ivar stored_procedure_name:
:vartype stored_procedure_name: str
:ivar stored_procedure_parameters:
:vartype stored_procedure_parameters: list[~flow.models.AetherStoredProcedureParameter]
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'server_uri': {'key': 'serverUri', 'type': 'str'},
'database_name': {'key': 'databaseName', 'type': 'str'},
'table_name': {'key': 'tableName', 'type': 'str'},
'sql_query': {'key': 'sqlQuery', 'type': 'str'},
'stored_procedure_name': {'key': 'storedProcedureName', 'type': 'str'},
'stored_procedure_parameters': {'key': 'storedProcedureParameters', 'type': '[AetherStoredProcedureParameter]'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword server_uri:
:paramtype server_uri: str
:keyword database_name:
:paramtype database_name: str
:keyword table_name:
:paramtype table_name: str
:keyword sql_query:
:paramtype sql_query: str
:keyword stored_procedure_name:
:paramtype stored_procedure_name: str
:keyword stored_procedure_parameters:
:paramtype stored_procedure_parameters: list[~flow.models.AetherStoredProcedureParameter]
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AetherAzureDatabaseReference, self).__init__(**kwargs)
self.server_uri = kwargs.get('server_uri', None)
self.database_name = kwargs.get('database_name', None)
self.table_name = kwargs.get('table_name', None)
self.sql_query = kwargs.get('sql_query', None)
self.stored_procedure_name = kwargs.get('stored_procedure_name', None)
self.stored_procedure_parameters = kwargs.get('stored_procedure_parameters', None)
self.aml_data_store_name = kwargs.get('aml_data_store_name', None)
class AetherAzureDataLakeGen2Reference(msrest.serialization.Model):
"""AetherAzureDataLakeGen2Reference.
:ivar file_system_name:
:vartype file_system_name: str
:ivar uri:
:vartype uri: str
:ivar account:
:vartype account: str
:ivar relative_path:
:vartype relative_path: str
:ivar path_type: Possible values include: "Unknown", "File", "Folder".
:vartype path_type: str or ~flow.models.AetherFileBasedPathType
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'file_system_name': {'key': 'fileSystemName', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'account': {'key': 'account', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'path_type': {'key': 'pathType', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword file_system_name:
:paramtype file_system_name: str
:keyword uri:
:paramtype uri: str
:keyword account:
:paramtype account: str
:keyword relative_path:
:paramtype relative_path: str
:keyword path_type: Possible values include: "Unknown", "File", "Folder".
:paramtype path_type: str or ~flow.models.AetherFileBasedPathType
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AetherAzureDataLakeGen2Reference, self).__init__(**kwargs)
self.file_system_name = kwargs.get('file_system_name', None)
self.uri = kwargs.get('uri', None)
self.account = kwargs.get('account', None)
self.relative_path = kwargs.get('relative_path', None)
self.path_type = kwargs.get('path_type', None)
self.aml_data_store_name = kwargs.get('aml_data_store_name', None)
class AetherAzureDataLakeReference(msrest.serialization.Model):
"""AetherAzureDataLakeReference.
:ivar tenant:
:vartype tenant: str
:ivar subscription:
:vartype subscription: str
:ivar resource_group:
:vartype resource_group: str
:ivar data_lake_uri:
:vartype data_lake_uri: str
:ivar uri:
:vartype uri: str
:ivar account:
:vartype account: str
:ivar relative_path:
:vartype relative_path: str
:ivar path_type: Possible values include: "Unknown", "File", "Folder".
:vartype path_type: str or ~flow.models.AetherFileBasedPathType
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'tenant': {'key': 'tenant', 'type': 'str'},
'subscription': {'key': 'subscription', 'type': 'str'},
'resource_group': {'key': 'resourceGroup', 'type': 'str'},
'data_lake_uri': {'key': 'dataLakeUri', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'account': {'key': 'account', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'path_type': {'key': 'pathType', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword tenant:
:paramtype tenant: str
:keyword subscription:
:paramtype subscription: str
:keyword resource_group:
:paramtype resource_group: str
:keyword data_lake_uri:
:paramtype data_lake_uri: str
:keyword uri:
:paramtype uri: str
:keyword account:
:paramtype account: str
:keyword relative_path:
:paramtype relative_path: str
:keyword path_type: Possible values include: "Unknown", "File", "Folder".
:paramtype path_type: str or ~flow.models.AetherFileBasedPathType
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AetherAzureDataLakeReference, self).__init__(**kwargs)
self.tenant = kwargs.get('tenant', None)
self.subscription = kwargs.get('subscription', None)
self.resource_group = kwargs.get('resource_group', None)
self.data_lake_uri = kwargs.get('data_lake_uri', None)
self.uri = kwargs.get('uri', None)
self.account = kwargs.get('account', None)
self.relative_path = kwargs.get('relative_path', None)
self.path_type = kwargs.get('path_type', None)
self.aml_data_store_name = kwargs.get('aml_data_store_name', None)
class AetherAzureFilesReference(msrest.serialization.Model):
"""AetherAzureFilesReference.
:ivar share:
:vartype share: str
:ivar uri:
:vartype uri: str
:ivar account:
:vartype account: str
:ivar relative_path:
:vartype relative_path: str
:ivar path_type: Possible values include: "Unknown", "File", "Folder".
:vartype path_type: str or ~flow.models.AetherFileBasedPathType
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'share': {'key': 'share', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'account': {'key': 'account', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'path_type': {'key': 'pathType', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword share:
:paramtype share: str
:keyword uri:
:paramtype uri: str
:keyword account:
:paramtype account: str
:keyword relative_path:
:paramtype relative_path: str
:keyword path_type: Possible values include: "Unknown", "File", "Folder".
:paramtype path_type: str or ~flow.models.AetherFileBasedPathType
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AetherAzureFilesReference, self).__init__(**kwargs)
self.share = kwargs.get('share', None)
self.uri = kwargs.get('uri', None)
self.account = kwargs.get('account', None)
self.relative_path = kwargs.get('relative_path', None)
self.path_type = kwargs.get('path_type', None)
self.aml_data_store_name = kwargs.get('aml_data_store_name', None)
class AetherBatchAiComputeInfo(msrest.serialization.Model):
"""AetherBatchAiComputeInfo.
:ivar batch_ai_subscription_id:
:vartype batch_ai_subscription_id: str
:ivar batch_ai_resource_group:
:vartype batch_ai_resource_group: str
:ivar batch_ai_workspace_name:
:vartype batch_ai_workspace_name: str
:ivar cluster_name:
:vartype cluster_name: str
:ivar native_shared_directory:
:vartype native_shared_directory: str
"""
_attribute_map = {
'batch_ai_subscription_id': {'key': 'batchAiSubscriptionId', 'type': 'str'},
'batch_ai_resource_group': {'key': 'batchAiResourceGroup', 'type': 'str'},
'batch_ai_workspace_name': {'key': 'batchAiWorkspaceName', 'type': 'str'},
'cluster_name': {'key': 'clusterName', 'type': 'str'},
'native_shared_directory': {'key': 'nativeSharedDirectory', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword batch_ai_subscription_id:
:paramtype batch_ai_subscription_id: str
:keyword batch_ai_resource_group:
:paramtype batch_ai_resource_group: str
:keyword batch_ai_workspace_name:
:paramtype batch_ai_workspace_name: str
:keyword cluster_name:
:paramtype cluster_name: str
:keyword native_shared_directory:
:paramtype native_shared_directory: str
"""
super(AetherBatchAiComputeInfo, self).__init__(**kwargs)
self.batch_ai_subscription_id = kwargs.get('batch_ai_subscription_id', None)
self.batch_ai_resource_group = kwargs.get('batch_ai_resource_group', None)
self.batch_ai_workspace_name = kwargs.get('batch_ai_workspace_name', None)
self.cluster_name = kwargs.get('cluster_name', None)
self.native_shared_directory = kwargs.get('native_shared_directory', None)
class AetherBuildArtifactInfo(msrest.serialization.Model):
"""AetherBuildArtifactInfo.
:ivar type: Possible values include: "CloudBuild", "Vso", "VsoGit".
:vartype type: str or ~flow.models.AetherBuildSourceType
:ivar cloud_build_drop_path_info:
:vartype cloud_build_drop_path_info: ~flow.models.AetherCloudBuildDropPathInfo
:ivar vso_build_artifact_info:
:vartype vso_build_artifact_info: ~flow.models.AetherVsoBuildArtifactInfo
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'cloud_build_drop_path_info': {'key': 'cloudBuildDropPathInfo', 'type': 'AetherCloudBuildDropPathInfo'},
'vso_build_artifact_info': {'key': 'vsoBuildArtifactInfo', 'type': 'AetherVsoBuildArtifactInfo'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type: Possible values include: "CloudBuild", "Vso", "VsoGit".
:paramtype type: str or ~flow.models.AetherBuildSourceType
:keyword cloud_build_drop_path_info:
:paramtype cloud_build_drop_path_info: ~flow.models.AetherCloudBuildDropPathInfo
:keyword vso_build_artifact_info:
:paramtype vso_build_artifact_info: ~flow.models.AetherVsoBuildArtifactInfo
"""
super(AetherBuildArtifactInfo, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.cloud_build_drop_path_info = kwargs.get('cloud_build_drop_path_info', None)
self.vso_build_artifact_info = kwargs.get('vso_build_artifact_info', None)
class AetherCloudBuildDropPathInfo(msrest.serialization.Model):
"""AetherCloudBuildDropPathInfo.
:ivar build_info:
:vartype build_info: ~flow.models.AetherCloudBuildInfo
:ivar root:
:vartype root: str
"""
_attribute_map = {
'build_info': {'key': 'buildInfo', 'type': 'AetherCloudBuildInfo'},
'root': {'key': 'root', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword build_info:
:paramtype build_info: ~flow.models.AetherCloudBuildInfo
:keyword root:
:paramtype root: str
"""
super(AetherCloudBuildDropPathInfo, self).__init__(**kwargs)
self.build_info = kwargs.get('build_info', None)
self.root = kwargs.get('root', None)
class AetherCloudBuildInfo(msrest.serialization.Model):
"""AetherCloudBuildInfo.
:ivar queue_info:
:vartype queue_info: ~flow.models.AetherCloudBuildQueueInfo
:ivar build_id:
:vartype build_id: str
:ivar drop_url:
:vartype drop_url: str
"""
_attribute_map = {
'queue_info': {'key': 'queueInfo', 'type': 'AetherCloudBuildQueueInfo'},
'build_id': {'key': 'buildId', 'type': 'str'},
'drop_url': {'key': 'dropUrl', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword queue_info:
:paramtype queue_info: ~flow.models.AetherCloudBuildQueueInfo
:keyword build_id:
:paramtype build_id: str
:keyword drop_url:
:paramtype drop_url: str
"""
super(AetherCloudBuildInfo, self).__init__(**kwargs)
self.queue_info = kwargs.get('queue_info', None)
self.build_id = kwargs.get('build_id', None)
self.drop_url = kwargs.get('drop_url', None)
class AetherCloudBuildQueueInfo(msrest.serialization.Model):
"""AetherCloudBuildQueueInfo.
:ivar build_queue:
:vartype build_queue: str
:ivar build_role:
:vartype build_role: str
"""
_attribute_map = {
'build_queue': {'key': 'buildQueue', 'type': 'str'},
'build_role': {'key': 'buildRole', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword build_queue:
:paramtype build_queue: str
:keyword build_role:
:paramtype build_role: str
"""
super(AetherCloudBuildQueueInfo, self).__init__(**kwargs)
self.build_queue = kwargs.get('build_queue', None)
self.build_role = kwargs.get('build_role', None)
class AetherCloudPrioritySetting(msrest.serialization.Model):
"""AetherCloudPrioritySetting.
:ivar scope_priority:
:vartype scope_priority: ~flow.models.AetherPriorityConfiguration
:ivar aml_compute_priority:
:vartype aml_compute_priority: ~flow.models.AetherPriorityConfiguration
:ivar itp_priority:
:vartype itp_priority: ~flow.models.AetherPriorityConfiguration
:ivar singularity_priority:
:vartype singularity_priority: ~flow.models.AetherPriorityConfiguration
"""
_attribute_map = {
'scope_priority': {'key': 'scopePriority', 'type': 'AetherPriorityConfiguration'},
'aml_compute_priority': {'key': 'AmlComputePriority', 'type': 'AetherPriorityConfiguration'},
'itp_priority': {'key': 'ItpPriority', 'type': 'AetherPriorityConfiguration'},
'singularity_priority': {'key': 'SingularityPriority', 'type': 'AetherPriorityConfiguration'},
}
def __init__(
self,
**kwargs
):
"""
:keyword scope_priority:
:paramtype scope_priority: ~flow.models.AetherPriorityConfiguration
:keyword aml_compute_priority:
:paramtype aml_compute_priority: ~flow.models.AetherPriorityConfiguration
:keyword itp_priority:
:paramtype itp_priority: ~flow.models.AetherPriorityConfiguration
:keyword singularity_priority:
:paramtype singularity_priority: ~flow.models.AetherPriorityConfiguration
"""
super(AetherCloudPrioritySetting, self).__init__(**kwargs)
self.scope_priority = kwargs.get('scope_priority', None)
self.aml_compute_priority = kwargs.get('aml_compute_priority', None)
self.itp_priority = kwargs.get('itp_priority', None)
self.singularity_priority = kwargs.get('singularity_priority', None)
class AetherCloudSettings(msrest.serialization.Model):
"""AetherCloudSettings.
:ivar linked_settings:
:vartype linked_settings: list[~flow.models.AetherParameterAssignment]
:ivar priority_config:
:vartype priority_config: ~flow.models.AetherPriorityConfiguration
:ivar hdi_run_config:
:vartype hdi_run_config: ~flow.models.AetherHdiRunConfiguration
:ivar sub_graph_config:
:vartype sub_graph_config: ~flow.models.AetherSubGraphConfiguration
:ivar auto_ml_component_config:
:vartype auto_ml_component_config: ~flow.models.AetherAutoMLComponentConfiguration
:ivar ap_cloud_config:
:vartype ap_cloud_config: ~flow.models.AetherAPCloudConfiguration
:ivar scope_cloud_config:
:vartype scope_cloud_config: ~flow.models.AetherScopeCloudConfiguration
:ivar es_cloud_config:
:vartype es_cloud_config: ~flow.models.AetherEsCloudConfiguration
:ivar data_transfer_cloud_config:
:vartype data_transfer_cloud_config: ~flow.models.AetherDataTransferCloudConfiguration
:ivar aml_spark_cloud_setting:
:vartype aml_spark_cloud_setting: ~flow.models.AetherAmlSparkCloudSetting
:ivar data_transfer_v2_cloud_setting:
:vartype data_transfer_v2_cloud_setting: ~flow.models.AetherDataTransferV2CloudSetting
"""
_attribute_map = {
'linked_settings': {'key': 'linkedSettings', 'type': '[AetherParameterAssignment]'},
'priority_config': {'key': 'priorityConfig', 'type': 'AetherPriorityConfiguration'},
'hdi_run_config': {'key': 'hdiRunConfig', 'type': 'AetherHdiRunConfiguration'},
'sub_graph_config': {'key': 'subGraphConfig', 'type': 'AetherSubGraphConfiguration'},
'auto_ml_component_config': {'key': 'autoMLComponentConfig', 'type': 'AetherAutoMLComponentConfiguration'},
'ap_cloud_config': {'key': 'apCloudConfig', 'type': 'AetherAPCloudConfiguration'},
'scope_cloud_config': {'key': 'scopeCloudConfig', 'type': 'AetherScopeCloudConfiguration'},
'es_cloud_config': {'key': 'esCloudConfig', 'type': 'AetherEsCloudConfiguration'},
'data_transfer_cloud_config': {'key': 'dataTransferCloudConfig', 'type': 'AetherDataTransferCloudConfiguration'},
'aml_spark_cloud_setting': {'key': 'amlSparkCloudSetting', 'type': 'AetherAmlSparkCloudSetting'},
'data_transfer_v2_cloud_setting': {'key': 'dataTransferV2CloudSetting', 'type': 'AetherDataTransferV2CloudSetting'},
}
def __init__(
self,
**kwargs
):
"""
:keyword linked_settings:
:paramtype linked_settings: list[~flow.models.AetherParameterAssignment]
:keyword priority_config:
:paramtype priority_config: ~flow.models.AetherPriorityConfiguration
:keyword hdi_run_config:
:paramtype hdi_run_config: ~flow.models.AetherHdiRunConfiguration
:keyword sub_graph_config:
:paramtype sub_graph_config: ~flow.models.AetherSubGraphConfiguration
:keyword auto_ml_component_config:
:paramtype auto_ml_component_config: ~flow.models.AetherAutoMLComponentConfiguration
:keyword ap_cloud_config:
:paramtype ap_cloud_config: ~flow.models.AetherAPCloudConfiguration
:keyword scope_cloud_config:
:paramtype scope_cloud_config: ~flow.models.AetherScopeCloudConfiguration
:keyword es_cloud_config:
:paramtype es_cloud_config: ~flow.models.AetherEsCloudConfiguration
:keyword data_transfer_cloud_config:
:paramtype data_transfer_cloud_config: ~flow.models.AetherDataTransferCloudConfiguration
:keyword aml_spark_cloud_setting:
:paramtype aml_spark_cloud_setting: ~flow.models.AetherAmlSparkCloudSetting
:keyword data_transfer_v2_cloud_setting:
:paramtype data_transfer_v2_cloud_setting: ~flow.models.AetherDataTransferV2CloudSetting
"""
super(AetherCloudSettings, self).__init__(**kwargs)
self.linked_settings = kwargs.get('linked_settings', None)
self.priority_config = kwargs.get('priority_config', None)
self.hdi_run_config = kwargs.get('hdi_run_config', None)
self.sub_graph_config = kwargs.get('sub_graph_config', None)
self.auto_ml_component_config = kwargs.get('auto_ml_component_config', None)
self.ap_cloud_config = kwargs.get('ap_cloud_config', None)
self.scope_cloud_config = kwargs.get('scope_cloud_config', None)
self.es_cloud_config = kwargs.get('es_cloud_config', None)
self.data_transfer_cloud_config = kwargs.get('data_transfer_cloud_config', None)
self.aml_spark_cloud_setting = kwargs.get('aml_spark_cloud_setting', None)
self.data_transfer_v2_cloud_setting = kwargs.get('data_transfer_v2_cloud_setting', None)
class AetherColumnTransformer(msrest.serialization.Model):
"""AetherColumnTransformer.
:ivar fields:
:vartype fields: list[str]
:ivar parameters: Anything.
:vartype parameters: any
"""
_attribute_map = {
'fields': {'key': 'fields', 'type': '[str]'},
'parameters': {'key': 'parameters', 'type': 'object'},
}
def __init__(
self,
**kwargs
):
"""
:keyword fields:
:paramtype fields: list[str]
:keyword parameters: Anything.
:paramtype parameters: any
"""
super(AetherColumnTransformer, self).__init__(**kwargs)
self.fields = kwargs.get('fields', None)
self.parameters = kwargs.get('parameters', None)
class AetherComputeConfiguration(msrest.serialization.Model):
"""AetherComputeConfiguration.
:ivar target:
:vartype target: str
:ivar instance_count:
:vartype instance_count: int
:ivar is_local:
:vartype is_local: bool
:ivar location:
:vartype location: str
:ivar is_clusterless:
:vartype is_clusterless: bool
:ivar instance_type:
:vartype instance_type: str
:ivar properties: Dictionary of :code:`<any>`.
:vartype properties: dict[str, any]
:ivar is_preemptable:
:vartype is_preemptable: bool
"""
_attribute_map = {
'target': {'key': 'target', 'type': 'str'},
'instance_count': {'key': 'instanceCount', 'type': 'int'},
'is_local': {'key': 'isLocal', 'type': 'bool'},
'location': {'key': 'location', 'type': 'str'},
'is_clusterless': {'key': 'isClusterless', 'type': 'bool'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{object}'},
'is_preemptable': {'key': 'isPreemptable', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword target:
:paramtype target: str
:keyword instance_count:
:paramtype instance_count: int
:keyword is_local:
:paramtype is_local: bool
:keyword location:
:paramtype location: str
:keyword is_clusterless:
:paramtype is_clusterless: bool
:keyword instance_type:
:paramtype instance_type: str
:keyword properties: Dictionary of :code:`<any>`.
:paramtype properties: dict[str, any]
:keyword is_preemptable:
:paramtype is_preemptable: bool
"""
super(AetherComputeConfiguration, self).__init__(**kwargs)
self.target = kwargs.get('target', None)
self.instance_count = kwargs.get('instance_count', None)
self.is_local = kwargs.get('is_local', None)
self.location = kwargs.get('location', None)
self.is_clusterless = kwargs.get('is_clusterless', None)
self.instance_type = kwargs.get('instance_type', None)
self.properties = kwargs.get('properties', None)
self.is_preemptable = kwargs.get('is_preemptable', None)
class AetherComputeSetting(msrest.serialization.Model):
"""AetherComputeSetting.
:ivar name:
:vartype name: str
:ivar compute_type: Possible values include: "BatchAi", "MLC", "HdiCluster", "RemoteDocker",
"Databricks", "Aisc".
:vartype compute_type: str or ~flow.models.AetherComputeType
:ivar batch_ai_compute_info:
:vartype batch_ai_compute_info: ~flow.models.AetherBatchAiComputeInfo
:ivar remote_docker_compute_info:
:vartype remote_docker_compute_info: ~flow.models.AetherRemoteDockerComputeInfo
:ivar hdi_cluster_compute_info:
:vartype hdi_cluster_compute_info: ~flow.models.AetherHdiClusterComputeInfo
:ivar mlc_compute_info:
:vartype mlc_compute_info: ~flow.models.AetherMlcComputeInfo
:ivar databricks_compute_info:
:vartype databricks_compute_info: ~flow.models.AetherDatabricksComputeInfo
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'batch_ai_compute_info': {'key': 'batchAiComputeInfo', 'type': 'AetherBatchAiComputeInfo'},
'remote_docker_compute_info': {'key': 'remoteDockerComputeInfo', 'type': 'AetherRemoteDockerComputeInfo'},
'hdi_cluster_compute_info': {'key': 'hdiClusterComputeInfo', 'type': 'AetherHdiClusterComputeInfo'},
'mlc_compute_info': {'key': 'mlcComputeInfo', 'type': 'AetherMlcComputeInfo'},
'databricks_compute_info': {'key': 'databricksComputeInfo', 'type': 'AetherDatabricksComputeInfo'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword compute_type: Possible values include: "BatchAi", "MLC", "HdiCluster", "RemoteDocker",
"Databricks", "Aisc".
:paramtype compute_type: str or ~flow.models.AetherComputeType
:keyword batch_ai_compute_info:
:paramtype batch_ai_compute_info: ~flow.models.AetherBatchAiComputeInfo
:keyword remote_docker_compute_info:
:paramtype remote_docker_compute_info: ~flow.models.AetherRemoteDockerComputeInfo
:keyword hdi_cluster_compute_info:
:paramtype hdi_cluster_compute_info: ~flow.models.AetherHdiClusterComputeInfo
:keyword mlc_compute_info:
:paramtype mlc_compute_info: ~flow.models.AetherMlcComputeInfo
:keyword databricks_compute_info:
:paramtype databricks_compute_info: ~flow.models.AetherDatabricksComputeInfo
"""
super(AetherComputeSetting, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.compute_type = kwargs.get('compute_type', None)
self.batch_ai_compute_info = kwargs.get('batch_ai_compute_info', None)
self.remote_docker_compute_info = kwargs.get('remote_docker_compute_info', None)
self.hdi_cluster_compute_info = kwargs.get('hdi_cluster_compute_info', None)
self.mlc_compute_info = kwargs.get('mlc_compute_info', None)
self.databricks_compute_info = kwargs.get('databricks_compute_info', None)
class AetherControlInput(msrest.serialization.Model):
"""AetherControlInput.
:ivar name:
:vartype name: str
:ivar default_value: Possible values include: "None", "False", "True", "Skipped".
:vartype default_value: str or ~flow.models.AetherControlInputValue
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'default_value': {'key': 'defaultValue', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword default_value: Possible values include: "None", "False", "True", "Skipped".
:paramtype default_value: str or ~flow.models.AetherControlInputValue
"""
super(AetherControlInput, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.default_value = kwargs.get('default_value', None)
class AetherControlOutput(msrest.serialization.Model):
"""AetherControlOutput.
:ivar name:
:vartype name: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
"""
super(AetherControlOutput, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
class AetherCopyDataTask(msrest.serialization.Model):
"""AetherCopyDataTask.
:ivar data_copy_mode: Possible values include: "MergeWithOverwrite", "FailIfConflict".
:vartype data_copy_mode: str or ~flow.models.AetherDataCopyMode
"""
_attribute_map = {
'data_copy_mode': {'key': 'DataCopyMode', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_copy_mode: Possible values include: "MergeWithOverwrite", "FailIfConflict".
:paramtype data_copy_mode: str or ~flow.models.AetherDataCopyMode
"""
super(AetherCopyDataTask, self).__init__(**kwargs)
self.data_copy_mode = kwargs.get('data_copy_mode', None)
class AetherCosmosReference(msrest.serialization.Model):
"""AetherCosmosReference.
:ivar cluster:
:vartype cluster: str
:ivar vc:
:vartype vc: str
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'cluster': {'key': 'cluster', 'type': 'str'},
'vc': {'key': 'vc', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword cluster:
:paramtype cluster: str
:keyword vc:
:paramtype vc: str
:keyword relative_path:
:paramtype relative_path: str
"""
super(AetherCosmosReference, self).__init__(**kwargs)
self.cluster = kwargs.get('cluster', None)
self.vc = kwargs.get('vc', None)
self.relative_path = kwargs.get('relative_path', None)
class AetherCreatedBy(msrest.serialization.Model):
"""AetherCreatedBy.
:ivar user_object_id:
:vartype user_object_id: str
:ivar user_tenant_id:
:vartype user_tenant_id: str
:ivar user_name:
:vartype user_name: str
:ivar puid:
:vartype puid: str
:ivar iss:
:vartype iss: str
:ivar idp:
:vartype idp: str
:ivar altsec_id:
:vartype altsec_id: str
:ivar source_ip:
:vartype source_ip: str
:ivar skip_registry_private_link_check:
:vartype skip_registry_private_link_check: bool
"""
_attribute_map = {
'user_object_id': {'key': 'userObjectId', 'type': 'str'},
'user_tenant_id': {'key': 'userTenantId', 'type': 'str'},
'user_name': {'key': 'userName', 'type': 'str'},
'puid': {'key': 'puid', 'type': 'str'},
'iss': {'key': 'iss', 'type': 'str'},
'idp': {'key': 'idp', 'type': 'str'},
'altsec_id': {'key': 'altsecId', 'type': 'str'},
'source_ip': {'key': 'sourceIp', 'type': 'str'},
'skip_registry_private_link_check': {'key': 'skipRegistryPrivateLinkCheck', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword user_object_id:
:paramtype user_object_id: str
:keyword user_tenant_id:
:paramtype user_tenant_id: str
:keyword user_name:
:paramtype user_name: str
:keyword puid:
:paramtype puid: str
:keyword iss:
:paramtype iss: str
:keyword idp:
:paramtype idp: str
:keyword altsec_id:
:paramtype altsec_id: str
:keyword source_ip:
:paramtype source_ip: str
:keyword skip_registry_private_link_check:
:paramtype skip_registry_private_link_check: bool
"""
super(AetherCreatedBy, self).__init__(**kwargs)
self.user_object_id = kwargs.get('user_object_id', None)
self.user_tenant_id = kwargs.get('user_tenant_id', None)
self.user_name = kwargs.get('user_name', None)
self.puid = kwargs.get('puid', None)
self.iss = kwargs.get('iss', None)
self.idp = kwargs.get('idp', None)
self.altsec_id = kwargs.get('altsec_id', None)
self.source_ip = kwargs.get('source_ip', None)
self.skip_registry_private_link_check = kwargs.get('skip_registry_private_link_check', None)
class AetherCustomReference(msrest.serialization.Model):
"""AetherCustomReference.
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
:keyword relative_path:
:paramtype relative_path: str
"""
super(AetherCustomReference, self).__init__(**kwargs)
self.aml_data_store_name = kwargs.get('aml_data_store_name', None)
self.relative_path = kwargs.get('relative_path', None)
class AetherDatabaseSink(msrest.serialization.Model):
"""AetherDatabaseSink.
:ivar connection:
:vartype connection: str
:ivar table:
:vartype table: str
"""
_attribute_map = {
'connection': {'key': 'connection', 'type': 'str'},
'table': {'key': 'table', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword connection:
:paramtype connection: str
:keyword table:
:paramtype table: str
"""
super(AetherDatabaseSink, self).__init__(**kwargs)
self.connection = kwargs.get('connection', None)
self.table = kwargs.get('table', None)
class AetherDatabaseSource(msrest.serialization.Model):
"""AetherDatabaseSource.
:ivar connection:
:vartype connection: str
:ivar query:
:vartype query: str
:ivar stored_procedure_name:
:vartype stored_procedure_name: str
:ivar stored_procedure_parameters:
:vartype stored_procedure_parameters: list[~flow.models.AetherStoredProcedureParameter]
"""
_attribute_map = {
'connection': {'key': 'connection', 'type': 'str'},
'query': {'key': 'query', 'type': 'str'},
'stored_procedure_name': {'key': 'storedProcedureName', 'type': 'str'},
'stored_procedure_parameters': {'key': 'storedProcedureParameters', 'type': '[AetherStoredProcedureParameter]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword connection:
:paramtype connection: str
:keyword query:
:paramtype query: str
:keyword stored_procedure_name:
:paramtype stored_procedure_name: str
:keyword stored_procedure_parameters:
:paramtype stored_procedure_parameters: list[~flow.models.AetherStoredProcedureParameter]
"""
super(AetherDatabaseSource, self).__init__(**kwargs)
self.connection = kwargs.get('connection', None)
self.query = kwargs.get('query', None)
self.stored_procedure_name = kwargs.get('stored_procedure_name', None)
self.stored_procedure_parameters = kwargs.get('stored_procedure_parameters', None)
class AetherDatabricksComputeInfo(msrest.serialization.Model):
"""AetherDatabricksComputeInfo.
:ivar existing_cluster_id:
:vartype existing_cluster_id: str
"""
_attribute_map = {
'existing_cluster_id': {'key': 'existingClusterId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword existing_cluster_id:
:paramtype existing_cluster_id: str
"""
super(AetherDatabricksComputeInfo, self).__init__(**kwargs)
self.existing_cluster_id = kwargs.get('existing_cluster_id', None)
class AetherDataLocation(msrest.serialization.Model):
"""AetherDataLocation.
:ivar storage_type: Possible values include: "Cosmos", "AzureBlob", "Artifact", "Snapshot",
"SavedAmlDataset", "Asset".
:vartype storage_type: str or ~flow.models.AetherDataLocationStorageType
:ivar storage_id:
:vartype storage_id: str
:ivar uri:
:vartype uri: str
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_reference:
:vartype data_reference: ~flow.models.AetherDataReference
:ivar aml_dataset:
:vartype aml_dataset: ~flow.models.AetherAmlDataset
:ivar asset_definition:
:vartype asset_definition: ~flow.models.AetherAssetDefinition
:ivar is_compliant:
:vartype is_compliant: bool
:ivar reuse_calculation_fields:
:vartype reuse_calculation_fields: ~flow.models.AetherDataLocationReuseCalculationFields
"""
_attribute_map = {
'storage_type': {'key': 'storageType', 'type': 'str'},
'storage_id': {'key': 'storageId', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_reference': {'key': 'dataReference', 'type': 'AetherDataReference'},
'aml_dataset': {'key': 'amlDataset', 'type': 'AetherAmlDataset'},
'asset_definition': {'key': 'assetDefinition', 'type': 'AetherAssetDefinition'},
'is_compliant': {'key': 'isCompliant', 'type': 'bool'},
'reuse_calculation_fields': {'key': 'reuseCalculationFields', 'type': 'AetherDataLocationReuseCalculationFields'},
}
def __init__(
self,
**kwargs
):
"""
:keyword storage_type: Possible values include: "Cosmos", "AzureBlob", "Artifact", "Snapshot",
"SavedAmlDataset", "Asset".
:paramtype storage_type: str or ~flow.models.AetherDataLocationStorageType
:keyword storage_id:
:paramtype storage_id: str
:keyword uri:
:paramtype uri: str
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_reference:
:paramtype data_reference: ~flow.models.AetherDataReference
:keyword aml_dataset:
:paramtype aml_dataset: ~flow.models.AetherAmlDataset
:keyword asset_definition:
:paramtype asset_definition: ~flow.models.AetherAssetDefinition
:keyword is_compliant:
:paramtype is_compliant: bool
:keyword reuse_calculation_fields:
:paramtype reuse_calculation_fields: ~flow.models.AetherDataLocationReuseCalculationFields
"""
super(AetherDataLocation, self).__init__(**kwargs)
self.storage_type = kwargs.get('storage_type', None)
self.storage_id = kwargs.get('storage_id', None)
self.uri = kwargs.get('uri', None)
self.data_store_name = kwargs.get('data_store_name', None)
self.data_reference = kwargs.get('data_reference', None)
self.aml_dataset = kwargs.get('aml_dataset', None)
self.asset_definition = kwargs.get('asset_definition', None)
self.is_compliant = kwargs.get('is_compliant', None)
self.reuse_calculation_fields = kwargs.get('reuse_calculation_fields', None)
class AetherDataLocationReuseCalculationFields(msrest.serialization.Model):
"""AetherDataLocationReuseCalculationFields.
:ivar data_store_name:
:vartype data_store_name: str
:ivar relative_path:
:vartype relative_path: str
:ivar data_experiment_id:
:vartype data_experiment_id: str
"""
_attribute_map = {
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'data_experiment_id': {'key': 'dataExperimentId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_store_name:
:paramtype data_store_name: str
:keyword relative_path:
:paramtype relative_path: str
:keyword data_experiment_id:
:paramtype data_experiment_id: str
"""
super(AetherDataLocationReuseCalculationFields, self).__init__(**kwargs)
self.data_store_name = kwargs.get('data_store_name', None)
self.relative_path = kwargs.get('relative_path', None)
self.data_experiment_id = kwargs.get('data_experiment_id', None)
class AetherDataPath(msrest.serialization.Model):
"""AetherDataPath.
:ivar data_store_name:
:vartype data_store_name: str
:ivar relative_path:
:vartype relative_path: str
:ivar sql_data_path:
:vartype sql_data_path: ~flow.models.AetherSqlDataPath
"""
_attribute_map = {
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'sql_data_path': {'key': 'sqlDataPath', 'type': 'AetherSqlDataPath'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_store_name:
:paramtype data_store_name: str
:keyword relative_path:
:paramtype relative_path: str
:keyword sql_data_path:
:paramtype sql_data_path: ~flow.models.AetherSqlDataPath
"""
super(AetherDataPath, self).__init__(**kwargs)
self.data_store_name = kwargs.get('data_store_name', None)
self.relative_path = kwargs.get('relative_path', None)
self.sql_data_path = kwargs.get('sql_data_path', None)
class AetherDataReference(msrest.serialization.Model):
"""AetherDataReference.
:ivar type: Possible values include: "None", "AzureBlob", "AzureDataLake", "AzureFiles",
"Cosmos", "PhillyHdfs", "AzureSqlDatabase", "AzurePostgresDatabase", "AzureDataLakeGen2",
"DBFS", "AzureMySqlDatabase", "Custom", "Hdfs".
:vartype type: str or ~flow.models.AetherDataReferenceType
:ivar azure_blob_reference:
:vartype azure_blob_reference: ~flow.models.AetherAzureBlobReference
:ivar azure_data_lake_reference:
:vartype azure_data_lake_reference: ~flow.models.AetherAzureDataLakeReference
:ivar azure_files_reference:
:vartype azure_files_reference: ~flow.models.AetherAzureFilesReference
:ivar cosmos_reference:
:vartype cosmos_reference: ~flow.models.AetherCosmosReference
:ivar philly_hdfs_reference:
:vartype philly_hdfs_reference: ~flow.models.AetherPhillyHdfsReference
:ivar azure_sql_database_reference:
:vartype azure_sql_database_reference: ~flow.models.AetherAzureDatabaseReference
:ivar azure_postgres_database_reference:
:vartype azure_postgres_database_reference: ~flow.models.AetherAzureDatabaseReference
:ivar azure_data_lake_gen2_reference:
:vartype azure_data_lake_gen2_reference: ~flow.models.AetherAzureDataLakeGen2Reference
:ivar dbfs_reference:
:vartype dbfs_reference: ~flow.models.AetherDBFSReference
:ivar azure_my_sql_database_reference:
:vartype azure_my_sql_database_reference: ~flow.models.AetherAzureDatabaseReference
:ivar custom_reference:
:vartype custom_reference: ~flow.models.AetherCustomReference
:ivar hdfs_reference:
:vartype hdfs_reference: ~flow.models.AetherHdfsReference
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'azure_blob_reference': {'key': 'azureBlobReference', 'type': 'AetherAzureBlobReference'},
'azure_data_lake_reference': {'key': 'azureDataLakeReference', 'type': 'AetherAzureDataLakeReference'},
'azure_files_reference': {'key': 'azureFilesReference', 'type': 'AetherAzureFilesReference'},
'cosmos_reference': {'key': 'cosmosReference', 'type': 'AetherCosmosReference'},
'philly_hdfs_reference': {'key': 'phillyHdfsReference', 'type': 'AetherPhillyHdfsReference'},
'azure_sql_database_reference': {'key': 'azureSqlDatabaseReference', 'type': 'AetherAzureDatabaseReference'},
'azure_postgres_database_reference': {'key': 'azurePostgresDatabaseReference', 'type': 'AetherAzureDatabaseReference'},
'azure_data_lake_gen2_reference': {'key': 'azureDataLakeGen2Reference', 'type': 'AetherAzureDataLakeGen2Reference'},
'dbfs_reference': {'key': 'dbfsReference', 'type': 'AetherDBFSReference'},
'azure_my_sql_database_reference': {'key': 'azureMySqlDatabaseReference', 'type': 'AetherAzureDatabaseReference'},
'custom_reference': {'key': 'customReference', 'type': 'AetherCustomReference'},
'hdfs_reference': {'key': 'hdfsReference', 'type': 'AetherHdfsReference'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type: Possible values include: "None", "AzureBlob", "AzureDataLake", "AzureFiles",
"Cosmos", "PhillyHdfs", "AzureSqlDatabase", "AzurePostgresDatabase", "AzureDataLakeGen2",
"DBFS", "AzureMySqlDatabase", "Custom", "Hdfs".
:paramtype type: str or ~flow.models.AetherDataReferenceType
:keyword azure_blob_reference:
:paramtype azure_blob_reference: ~flow.models.AetherAzureBlobReference
:keyword azure_data_lake_reference:
:paramtype azure_data_lake_reference: ~flow.models.AetherAzureDataLakeReference
:keyword azure_files_reference:
:paramtype azure_files_reference: ~flow.models.AetherAzureFilesReference
:keyword cosmos_reference:
:paramtype cosmos_reference: ~flow.models.AetherCosmosReference
:keyword philly_hdfs_reference:
:paramtype philly_hdfs_reference: ~flow.models.AetherPhillyHdfsReference
:keyword azure_sql_database_reference:
:paramtype azure_sql_database_reference: ~flow.models.AetherAzureDatabaseReference
:keyword azure_postgres_database_reference:
:paramtype azure_postgres_database_reference: ~flow.models.AetherAzureDatabaseReference
:keyword azure_data_lake_gen2_reference:
:paramtype azure_data_lake_gen2_reference: ~flow.models.AetherAzureDataLakeGen2Reference
:keyword dbfs_reference:
:paramtype dbfs_reference: ~flow.models.AetherDBFSReference
:keyword azure_my_sql_database_reference:
:paramtype azure_my_sql_database_reference: ~flow.models.AetherAzureDatabaseReference
:keyword custom_reference:
:paramtype custom_reference: ~flow.models.AetherCustomReference
:keyword hdfs_reference:
:paramtype hdfs_reference: ~flow.models.AetherHdfsReference
"""
super(AetherDataReference, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.azure_blob_reference = kwargs.get('azure_blob_reference', None)
self.azure_data_lake_reference = kwargs.get('azure_data_lake_reference', None)
self.azure_files_reference = kwargs.get('azure_files_reference', None)
self.cosmos_reference = kwargs.get('cosmos_reference', None)
self.philly_hdfs_reference = kwargs.get('philly_hdfs_reference', None)
self.azure_sql_database_reference = kwargs.get('azure_sql_database_reference', None)
self.azure_postgres_database_reference = kwargs.get('azure_postgres_database_reference', None)
self.azure_data_lake_gen2_reference = kwargs.get('azure_data_lake_gen2_reference', None)
self.dbfs_reference = kwargs.get('dbfs_reference', None)
self.azure_my_sql_database_reference = kwargs.get('azure_my_sql_database_reference', None)
self.custom_reference = kwargs.get('custom_reference', None)
self.hdfs_reference = kwargs.get('hdfs_reference', None)
class AetherDataSetDefinition(msrest.serialization.Model):
"""AetherDataSetDefinition.
:ivar data_type_short_name:
:vartype data_type_short_name: str
:ivar parameter_name:
:vartype parameter_name: str
:ivar value:
:vartype value: ~flow.models.AetherDataSetDefinitionValue
"""
_attribute_map = {
'data_type_short_name': {'key': 'dataTypeShortName', 'type': 'str'},
'parameter_name': {'key': 'parameterName', 'type': 'str'},
'value': {'key': 'value', 'type': 'AetherDataSetDefinitionValue'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_type_short_name:
:paramtype data_type_short_name: str
:keyword parameter_name:
:paramtype parameter_name: str
:keyword value:
:paramtype value: ~flow.models.AetherDataSetDefinitionValue
"""
super(AetherDataSetDefinition, self).__init__(**kwargs)
self.data_type_short_name = kwargs.get('data_type_short_name', None)
self.parameter_name = kwargs.get('parameter_name', None)
self.value = kwargs.get('value', None)
class AetherDataSetDefinitionValue(msrest.serialization.Model):
"""AetherDataSetDefinitionValue.
:ivar literal_value:
:vartype literal_value: ~flow.models.AetherDataPath
:ivar data_set_reference:
:vartype data_set_reference: ~flow.models.AetherRegisteredDataSetReference
:ivar saved_data_set_reference:
:vartype saved_data_set_reference: ~flow.models.AetherSavedDataSetReference
:ivar asset_definition:
:vartype asset_definition: ~flow.models.AetherAssetDefinition
"""
_attribute_map = {
'literal_value': {'key': 'literalValue', 'type': 'AetherDataPath'},
'data_set_reference': {'key': 'dataSetReference', 'type': 'AetherRegisteredDataSetReference'},
'saved_data_set_reference': {'key': 'savedDataSetReference', 'type': 'AetherSavedDataSetReference'},
'asset_definition': {'key': 'assetDefinition', 'type': 'AetherAssetDefinition'},
}
def __init__(
self,
**kwargs
):
"""
:keyword literal_value:
:paramtype literal_value: ~flow.models.AetherDataPath
:keyword data_set_reference:
:paramtype data_set_reference: ~flow.models.AetherRegisteredDataSetReference
:keyword saved_data_set_reference:
:paramtype saved_data_set_reference: ~flow.models.AetherSavedDataSetReference
:keyword asset_definition:
:paramtype asset_definition: ~flow.models.AetherAssetDefinition
"""
super(AetherDataSetDefinitionValue, self).__init__(**kwargs)
self.literal_value = kwargs.get('literal_value', None)
self.data_set_reference = kwargs.get('data_set_reference', None)
self.saved_data_set_reference = kwargs.get('saved_data_set_reference', None)
self.asset_definition = kwargs.get('asset_definition', None)
class AetherDatasetOutput(msrest.serialization.Model):
"""AetherDatasetOutput.
:ivar dataset_type: Possible values include: "File", "Tabular".
:vartype dataset_type: str or ~flow.models.AetherDatasetType
:ivar dataset_registration:
:vartype dataset_registration: ~flow.models.AetherDatasetRegistration
:ivar dataset_output_options:
:vartype dataset_output_options: ~flow.models.AetherDatasetOutputOptions
"""
_attribute_map = {
'dataset_type': {'key': 'datasetType', 'type': 'str'},
'dataset_registration': {'key': 'datasetRegistration', 'type': 'AetherDatasetRegistration'},
'dataset_output_options': {'key': 'datasetOutputOptions', 'type': 'AetherDatasetOutputOptions'},
}
def __init__(
self,
**kwargs
):
"""
:keyword dataset_type: Possible values include: "File", "Tabular".
:paramtype dataset_type: str or ~flow.models.AetherDatasetType
:keyword dataset_registration:
:paramtype dataset_registration: ~flow.models.AetherDatasetRegistration
:keyword dataset_output_options:
:paramtype dataset_output_options: ~flow.models.AetherDatasetOutputOptions
"""
super(AetherDatasetOutput, self).__init__(**kwargs)
self.dataset_type = kwargs.get('dataset_type', None)
self.dataset_registration = kwargs.get('dataset_registration', None)
self.dataset_output_options = kwargs.get('dataset_output_options', None)
class AetherDatasetOutputOptions(msrest.serialization.Model):
"""AetherDatasetOutputOptions.
:ivar source_globs:
:vartype source_globs: ~flow.models.AetherGlobsOptions
:ivar path_on_datastore:
:vartype path_on_datastore: str
:ivar path_on_datastore_parameter_assignment:
:vartype path_on_datastore_parameter_assignment: ~flow.models.AetherParameterAssignment
"""
_attribute_map = {
'source_globs': {'key': 'sourceGlobs', 'type': 'AetherGlobsOptions'},
'path_on_datastore': {'key': 'pathOnDatastore', 'type': 'str'},
'path_on_datastore_parameter_assignment': {'key': 'PathOnDatastoreParameterAssignment', 'type': 'AetherParameterAssignment'},
}
def __init__(
self,
**kwargs
):
"""
:keyword source_globs:
:paramtype source_globs: ~flow.models.AetherGlobsOptions
:keyword path_on_datastore:
:paramtype path_on_datastore: str
:keyword path_on_datastore_parameter_assignment:
:paramtype path_on_datastore_parameter_assignment: ~flow.models.AetherParameterAssignment
"""
super(AetherDatasetOutputOptions, self).__init__(**kwargs)
self.source_globs = kwargs.get('source_globs', None)
self.path_on_datastore = kwargs.get('path_on_datastore', None)
self.path_on_datastore_parameter_assignment = kwargs.get('path_on_datastore_parameter_assignment', None)
class AetherDatasetRegistration(msrest.serialization.Model):
"""AetherDatasetRegistration.
:ivar name:
:vartype name: str
:ivar create_new_version:
:vartype create_new_version: bool
:ivar description:
:vartype description: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar additional_transformations:
:vartype additional_transformations: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'create_new_version': {'key': 'createNewVersion', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'additional_transformations': {'key': 'additionalTransformations', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword create_new_version:
:paramtype create_new_version: bool
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword additional_transformations:
:paramtype additional_transformations: str
"""
super(AetherDatasetRegistration, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.create_new_version = kwargs.get('create_new_version', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.additional_transformations = kwargs.get('additional_transformations', None)
class AetherDataSettings(msrest.serialization.Model):
"""AetherDataSettings.
:ivar target_column_name:
:vartype target_column_name: str
:ivar weight_column_name:
:vartype weight_column_name: str
:ivar positive_label:
:vartype positive_label: str
:ivar validation_data:
:vartype validation_data: ~flow.models.AetherValidationDataSettings
:ivar test_data:
:vartype test_data: ~flow.models.AetherTestDataSettings
"""
_attribute_map = {
'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
'weight_column_name': {'key': 'weightColumnName', 'type': 'str'},
'positive_label': {'key': 'positiveLabel', 'type': 'str'},
'validation_data': {'key': 'validationData', 'type': 'AetherValidationDataSettings'},
'test_data': {'key': 'testData', 'type': 'AetherTestDataSettings'},
}
def __init__(
self,
**kwargs
):
"""
:keyword target_column_name:
:paramtype target_column_name: str
:keyword weight_column_name:
:paramtype weight_column_name: str
:keyword positive_label:
:paramtype positive_label: str
:keyword validation_data:
:paramtype validation_data: ~flow.models.AetherValidationDataSettings
:keyword test_data:
:paramtype test_data: ~flow.models.AetherTestDataSettings
"""
super(AetherDataSettings, self).__init__(**kwargs)
self.target_column_name = kwargs.get('target_column_name', None)
self.weight_column_name = kwargs.get('weight_column_name', None)
self.positive_label = kwargs.get('positive_label', None)
self.validation_data = kwargs.get('validation_data', None)
self.test_data = kwargs.get('test_data', None)
class AetherDatastoreSetting(msrest.serialization.Model):
"""AetherDatastoreSetting.
:ivar data_store_name:
:vartype data_store_name: str
"""
_attribute_map = {
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_store_name:
:paramtype data_store_name: str
"""
super(AetherDatastoreSetting, self).__init__(**kwargs)
self.data_store_name = kwargs.get('data_store_name', None)
class AetherDataTransferCloudConfiguration(msrest.serialization.Model):
"""AetherDataTransferCloudConfiguration.
:ivar allow_overwrite:
:vartype allow_overwrite: bool
"""
_attribute_map = {
'allow_overwrite': {'key': 'AllowOverwrite', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword allow_overwrite:
:paramtype allow_overwrite: bool
"""
super(AetherDataTransferCloudConfiguration, self).__init__(**kwargs)
self.allow_overwrite = kwargs.get('allow_overwrite', None)
class AetherDataTransferSink(msrest.serialization.Model):
"""AetherDataTransferSink.
:ivar type: Possible values include: "DataBase", "FileSystem".
:vartype type: str or ~flow.models.AetherDataTransferStorageType
:ivar file_system:
:vartype file_system: ~flow.models.AetherFileSystem
:ivar database_sink:
:vartype database_sink: ~flow.models.AetherDatabaseSink
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'file_system': {'key': 'fileSystem', 'type': 'AetherFileSystem'},
'database_sink': {'key': 'databaseSink', 'type': 'AetherDatabaseSink'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type: Possible values include: "DataBase", "FileSystem".
:paramtype type: str or ~flow.models.AetherDataTransferStorageType
:keyword file_system:
:paramtype file_system: ~flow.models.AetherFileSystem
:keyword database_sink:
:paramtype database_sink: ~flow.models.AetherDatabaseSink
"""
super(AetherDataTransferSink, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.file_system = kwargs.get('file_system', None)
self.database_sink = kwargs.get('database_sink', None)
class AetherDataTransferSource(msrest.serialization.Model):
"""AetherDataTransferSource.
:ivar type: Possible values include: "DataBase", "FileSystem".
:vartype type: str or ~flow.models.AetherDataTransferStorageType
:ivar file_system:
:vartype file_system: ~flow.models.AetherFileSystem
:ivar database_source:
:vartype database_source: ~flow.models.AetherDatabaseSource
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'file_system': {'key': 'fileSystem', 'type': 'AetherFileSystem'},
'database_source': {'key': 'databaseSource', 'type': 'AetherDatabaseSource'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type: Possible values include: "DataBase", "FileSystem".
:paramtype type: str or ~flow.models.AetherDataTransferStorageType
:keyword file_system:
:paramtype file_system: ~flow.models.AetherFileSystem
:keyword database_source:
:paramtype database_source: ~flow.models.AetherDatabaseSource
"""
super(AetherDataTransferSource, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.file_system = kwargs.get('file_system', None)
self.database_source = kwargs.get('database_source', None)
class AetherDataTransferV2CloudSetting(msrest.serialization.Model):
"""AetherDataTransferV2CloudSetting.
:ivar task_type: Possible values include: "ImportData", "ExportData", "CopyData".
:vartype task_type: str or ~flow.models.AetherDataTransferTaskType
:ivar compute_name:
:vartype compute_name: str
:ivar copy_data_task:
:vartype copy_data_task: ~flow.models.AetherCopyDataTask
:ivar import_data_task:
:vartype import_data_task: ~flow.models.AetherImportDataTask
:ivar export_data_task:
:vartype export_data_task: ~flow.models.AetherExportDataTask
:ivar data_transfer_sources: This is a dictionary.
:vartype data_transfer_sources: dict[str, ~flow.models.AetherDataTransferSource]
:ivar data_transfer_sinks: This is a dictionary.
:vartype data_transfer_sinks: dict[str, ~flow.models.AetherDataTransferSink]
:ivar data_copy_mode: Possible values include: "MergeWithOverwrite", "FailIfConflict".
:vartype data_copy_mode: str or ~flow.models.AetherDataCopyMode
"""
_attribute_map = {
'task_type': {'key': 'taskType', 'type': 'str'},
'compute_name': {'key': 'ComputeName', 'type': 'str'},
'copy_data_task': {'key': 'CopyDataTask', 'type': 'AetherCopyDataTask'},
'import_data_task': {'key': 'ImportDataTask', 'type': 'AetherImportDataTask'},
'export_data_task': {'key': 'ExportDataTask', 'type': 'AetherExportDataTask'},
'data_transfer_sources': {'key': 'DataTransferSources', 'type': '{AetherDataTransferSource}'},
'data_transfer_sinks': {'key': 'DataTransferSinks', 'type': '{AetherDataTransferSink}'},
'data_copy_mode': {'key': 'DataCopyMode', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword task_type: Possible values include: "ImportData", "ExportData", "CopyData".
:paramtype task_type: str or ~flow.models.AetherDataTransferTaskType
:keyword compute_name:
:paramtype compute_name: str
:keyword copy_data_task:
:paramtype copy_data_task: ~flow.models.AetherCopyDataTask
:keyword import_data_task:
:paramtype import_data_task: ~flow.models.AetherImportDataTask
:keyword export_data_task:
:paramtype export_data_task: ~flow.models.AetherExportDataTask
:keyword data_transfer_sources: This is a dictionary.
:paramtype data_transfer_sources: dict[str, ~flow.models.AetherDataTransferSource]
:keyword data_transfer_sinks: This is a dictionary.
:paramtype data_transfer_sinks: dict[str, ~flow.models.AetherDataTransferSink]
:keyword data_copy_mode: Possible values include: "MergeWithOverwrite", "FailIfConflict".
:paramtype data_copy_mode: str or ~flow.models.AetherDataCopyMode
"""
super(AetherDataTransferV2CloudSetting, self).__init__(**kwargs)
self.task_type = kwargs.get('task_type', None)
self.compute_name = kwargs.get('compute_name', None)
self.copy_data_task = kwargs.get('copy_data_task', None)
self.import_data_task = kwargs.get('import_data_task', None)
self.export_data_task = kwargs.get('export_data_task', None)
self.data_transfer_sources = kwargs.get('data_transfer_sources', None)
self.data_transfer_sinks = kwargs.get('data_transfer_sinks', None)
self.data_copy_mode = kwargs.get('data_copy_mode', None)
class AetherDBFSReference(msrest.serialization.Model):
"""AetherDBFSReference.
:ivar relative_path:
:vartype relative_path: str
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'relative_path': {'key': 'relativePath', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword relative_path:
:paramtype relative_path: str
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AetherDBFSReference, self).__init__(**kwargs)
self.relative_path = kwargs.get('relative_path', None)
self.aml_data_store_name = kwargs.get('aml_data_store_name', None)
class AetherDockerSettingConfiguration(msrest.serialization.Model):
"""AetherDockerSettingConfiguration.
:ivar use_docker:
:vartype use_docker: bool
:ivar shared_volumes:
:vartype shared_volumes: bool
:ivar shm_size:
:vartype shm_size: str
:ivar arguments:
:vartype arguments: list[str]
"""
_attribute_map = {
'use_docker': {'key': 'useDocker', 'type': 'bool'},
'shared_volumes': {'key': 'sharedVolumes', 'type': 'bool'},
'shm_size': {'key': 'shmSize', 'type': 'str'},
'arguments': {'key': 'arguments', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword use_docker:
:paramtype use_docker: bool
:keyword shared_volumes:
:paramtype shared_volumes: bool
:keyword shm_size:
:paramtype shm_size: str
:keyword arguments:
:paramtype arguments: list[str]
"""
super(AetherDockerSettingConfiguration, self).__init__(**kwargs)
self.use_docker = kwargs.get('use_docker', None)
self.shared_volumes = kwargs.get('shared_volumes', None)
self.shm_size = kwargs.get('shm_size', None)
self.arguments = kwargs.get('arguments', None)
class AetherDoWhileControlFlowInfo(msrest.serialization.Model):
"""AetherDoWhileControlFlowInfo.
:ivar output_port_name_to_input_port_names_mapping: Dictionary of
<components·1f2aigm·schemas·aetherdowhilecontrolflowinfo·properties·outputportnametoinputportnamesmapping·additionalproperties>.
:vartype output_port_name_to_input_port_names_mapping: dict[str, list[str]]
:ivar condition_output_port_name:
:vartype condition_output_port_name: str
:ivar run_settings:
:vartype run_settings: ~flow.models.AetherDoWhileControlFlowRunSettings
"""
_attribute_map = {
'output_port_name_to_input_port_names_mapping': {'key': 'outputPortNameToInputPortNamesMapping', 'type': '{[str]}'},
'condition_output_port_name': {'key': 'conditionOutputPortName', 'type': 'str'},
'run_settings': {'key': 'runSettings', 'type': 'AetherDoWhileControlFlowRunSettings'},
}
def __init__(
self,
**kwargs
):
"""
:keyword output_port_name_to_input_port_names_mapping: Dictionary of
<components·1f2aigm·schemas·aetherdowhilecontrolflowinfo·properties·outputportnametoinputportnamesmapping·additionalproperties>.
:paramtype output_port_name_to_input_port_names_mapping: dict[str, list[str]]
:keyword condition_output_port_name:
:paramtype condition_output_port_name: str
:keyword run_settings:
:paramtype run_settings: ~flow.models.AetherDoWhileControlFlowRunSettings
"""
super(AetherDoWhileControlFlowInfo, self).__init__(**kwargs)
self.output_port_name_to_input_port_names_mapping = kwargs.get('output_port_name_to_input_port_names_mapping', None)
self.condition_output_port_name = kwargs.get('condition_output_port_name', None)
self.run_settings = kwargs.get('run_settings', None)
class AetherDoWhileControlFlowRunSettings(msrest.serialization.Model):
"""AetherDoWhileControlFlowRunSettings.
:ivar max_loop_iteration_count:
:vartype max_loop_iteration_count: ~flow.models.AetherParameterAssignment
"""
_attribute_map = {
'max_loop_iteration_count': {'key': 'maxLoopIterationCount', 'type': 'AetherParameterAssignment'},
}
def __init__(
self,
**kwargs
):
"""
:keyword max_loop_iteration_count:
:paramtype max_loop_iteration_count: ~flow.models.AetherParameterAssignment
"""
super(AetherDoWhileControlFlowRunSettings, self).__init__(**kwargs)
self.max_loop_iteration_count = kwargs.get('max_loop_iteration_count', None)
class AetherEntityInterfaceDocumentation(msrest.serialization.Model):
"""AetherEntityInterfaceDocumentation.
:ivar inputs_documentation: Dictionary of :code:`<string>`.
:vartype inputs_documentation: dict[str, str]
:ivar outputs_documentation: Dictionary of :code:`<string>`.
:vartype outputs_documentation: dict[str, str]
:ivar parameters_documentation: Dictionary of :code:`<string>`.
:vartype parameters_documentation: dict[str, str]
"""
_attribute_map = {
'inputs_documentation': {'key': 'inputsDocumentation', 'type': '{str}'},
'outputs_documentation': {'key': 'outputsDocumentation', 'type': '{str}'},
'parameters_documentation': {'key': 'parametersDocumentation', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword inputs_documentation: Dictionary of :code:`<string>`.
:paramtype inputs_documentation: dict[str, str]
:keyword outputs_documentation: Dictionary of :code:`<string>`.
:paramtype outputs_documentation: dict[str, str]
:keyword parameters_documentation: Dictionary of :code:`<string>`.
:paramtype parameters_documentation: dict[str, str]
"""
super(AetherEntityInterfaceDocumentation, self).__init__(**kwargs)
self.inputs_documentation = kwargs.get('inputs_documentation', None)
self.outputs_documentation = kwargs.get('outputs_documentation', None)
self.parameters_documentation = kwargs.get('parameters_documentation', None)
class AetherEntrySetting(msrest.serialization.Model):
"""AetherEntrySetting.
:ivar file:
:vartype file: str
:ivar class_name:
:vartype class_name: str
"""
_attribute_map = {
'file': {'key': 'file', 'type': 'str'},
'class_name': {'key': 'className', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword file:
:paramtype file: str
:keyword class_name:
:paramtype class_name: str
"""
super(AetherEntrySetting, self).__init__(**kwargs)
self.file = kwargs.get('file', None)
self.class_name = kwargs.get('class_name', None)
class AetherEnvironmentConfiguration(msrest.serialization.Model):
"""AetherEnvironmentConfiguration.
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
:ivar use_environment_definition:
:vartype use_environment_definition: bool
:ivar environment_definition_string:
:vartype environment_definition_string: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'use_environment_definition': {'key': 'useEnvironmentDefinition', 'type': 'bool'},
'environment_definition_string': {'key': 'environmentDefinitionString', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
:keyword use_environment_definition:
:paramtype use_environment_definition: bool
:keyword environment_definition_string:
:paramtype environment_definition_string: str
"""
super(AetherEnvironmentConfiguration, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.version = kwargs.get('version', None)
self.use_environment_definition = kwargs.get('use_environment_definition', None)
self.environment_definition_string = kwargs.get('environment_definition_string', None)
class AetherEsCloudConfiguration(msrest.serialization.Model):
"""AetherEsCloudConfiguration.
:ivar enable_output_to_file_based_on_data_type_id:
:vartype enable_output_to_file_based_on_data_type_id: bool
:ivar aml_compute_priority_internal:
:vartype aml_compute_priority_internal: ~flow.models.AetherPriorityConfiguration
:ivar itp_priority_internal:
:vartype itp_priority_internal: ~flow.models.AetherPriorityConfiguration
:ivar singularity_priority_internal:
:vartype singularity_priority_internal: ~flow.models.AetherPriorityConfiguration
:ivar environment:
:vartype environment: ~flow.models.AetherEnvironmentConfiguration
:ivar hyper_drive_configuration:
:vartype hyper_drive_configuration: ~flow.models.AetherHyperDriveConfiguration
:ivar k8_s_config:
:vartype k8_s_config: ~flow.models.AetherK8SConfiguration
:ivar resource_config:
:vartype resource_config: ~flow.models.AetherResourceConfiguration
:ivar torch_distributed_config:
:vartype torch_distributed_config: ~flow.models.AetherTorchDistributedConfiguration
:ivar target_selector_config:
:vartype target_selector_config: ~flow.models.AetherTargetSelectorConfiguration
:ivar docker_config:
:vartype docker_config: ~flow.models.AetherDockerSettingConfiguration
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
:ivar max_run_duration_seconds:
:vartype max_run_duration_seconds: int
:ivar identity:
:vartype identity: ~flow.models.AetherIdentitySetting
:ivar application_endpoints: Dictionary of :code:`<ApplicationEndpointConfiguration>`.
:vartype application_endpoints: dict[str, ~flow.models.ApplicationEndpointConfiguration]
:ivar run_config:
:vartype run_config: str
"""
_attribute_map = {
'enable_output_to_file_based_on_data_type_id': {'key': 'enableOutputToFileBasedOnDataTypeId', 'type': 'bool'},
'aml_compute_priority_internal': {'key': 'amlComputePriorityInternal', 'type': 'AetherPriorityConfiguration'},
'itp_priority_internal': {'key': 'itpPriorityInternal', 'type': 'AetherPriorityConfiguration'},
'singularity_priority_internal': {'key': 'singularityPriorityInternal', 'type': 'AetherPriorityConfiguration'},
'environment': {'key': 'environment', 'type': 'AetherEnvironmentConfiguration'},
'hyper_drive_configuration': {'key': 'hyperDriveConfiguration', 'type': 'AetherHyperDriveConfiguration'},
'k8_s_config': {'key': 'k8sConfig', 'type': 'AetherK8SConfiguration'},
'resource_config': {'key': 'resourceConfig', 'type': 'AetherResourceConfiguration'},
'torch_distributed_config': {'key': 'torchDistributedConfig', 'type': 'AetherTorchDistributedConfiguration'},
'target_selector_config': {'key': 'targetSelectorConfig', 'type': 'AetherTargetSelectorConfiguration'},
'docker_config': {'key': 'dockerConfig', 'type': 'AetherDockerSettingConfiguration'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'max_run_duration_seconds': {'key': 'maxRunDurationSeconds', 'type': 'int'},
'identity': {'key': 'identity', 'type': 'AetherIdentitySetting'},
'application_endpoints': {'key': 'applicationEndpoints', 'type': '{ApplicationEndpointConfiguration}'},
'run_config': {'key': 'runConfig', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword enable_output_to_file_based_on_data_type_id:
:paramtype enable_output_to_file_based_on_data_type_id: bool
:keyword aml_compute_priority_internal:
:paramtype aml_compute_priority_internal: ~flow.models.AetherPriorityConfiguration
:keyword itp_priority_internal:
:paramtype itp_priority_internal: ~flow.models.AetherPriorityConfiguration
:keyword singularity_priority_internal:
:paramtype singularity_priority_internal: ~flow.models.AetherPriorityConfiguration
:keyword environment:
:paramtype environment: ~flow.models.AetherEnvironmentConfiguration
:keyword hyper_drive_configuration:
:paramtype hyper_drive_configuration: ~flow.models.AetherHyperDriveConfiguration
:keyword k8_s_config:
:paramtype k8_s_config: ~flow.models.AetherK8SConfiguration
:keyword resource_config:
:paramtype resource_config: ~flow.models.AetherResourceConfiguration
:keyword torch_distributed_config:
:paramtype torch_distributed_config: ~flow.models.AetherTorchDistributedConfiguration
:keyword target_selector_config:
:paramtype target_selector_config: ~flow.models.AetherTargetSelectorConfiguration
:keyword docker_config:
:paramtype docker_config: ~flow.models.AetherDockerSettingConfiguration
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
:keyword max_run_duration_seconds:
:paramtype max_run_duration_seconds: int
:keyword identity:
:paramtype identity: ~flow.models.AetherIdentitySetting
:keyword application_endpoints: Dictionary of :code:`<ApplicationEndpointConfiguration>`.
:paramtype application_endpoints: dict[str, ~flow.models.ApplicationEndpointConfiguration]
:keyword run_config:
:paramtype run_config: str
"""
super(AetherEsCloudConfiguration, self).__init__(**kwargs)
self.enable_output_to_file_based_on_data_type_id = kwargs.get('enable_output_to_file_based_on_data_type_id', None)
self.aml_compute_priority_internal = kwargs.get('aml_compute_priority_internal', None)
self.itp_priority_internal = kwargs.get('itp_priority_internal', None)
self.singularity_priority_internal = kwargs.get('singularity_priority_internal', None)
self.environment = kwargs.get('environment', None)
self.hyper_drive_configuration = kwargs.get('hyper_drive_configuration', None)
self.k8_s_config = kwargs.get('k8_s_config', None)
self.resource_config = kwargs.get('resource_config', None)
self.torch_distributed_config = kwargs.get('torch_distributed_config', None)
self.target_selector_config = kwargs.get('target_selector_config', None)
self.docker_config = kwargs.get('docker_config', None)
self.environment_variables = kwargs.get('environment_variables', None)
self.max_run_duration_seconds = kwargs.get('max_run_duration_seconds', None)
self.identity = kwargs.get('identity', None)
self.application_endpoints = kwargs.get('application_endpoints', None)
self.run_config = kwargs.get('run_config', None)
class AetherExportDataTask(msrest.serialization.Model):
"""AetherExportDataTask.
:ivar data_transfer_sink:
:vartype data_transfer_sink: ~flow.models.AetherDataTransferSink
"""
_attribute_map = {
'data_transfer_sink': {'key': 'DataTransferSink', 'type': 'AetherDataTransferSink'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_transfer_sink:
:paramtype data_transfer_sink: ~flow.models.AetherDataTransferSink
"""
super(AetherExportDataTask, self).__init__(**kwargs)
self.data_transfer_sink = kwargs.get('data_transfer_sink', None)
class AetherFeaturizationSettings(msrest.serialization.Model):
"""AetherFeaturizationSettings.
:ivar mode: Possible values include: "Auto", "Custom", "Off".
:vartype mode: str or ~flow.models.AetherFeaturizationMode
:ivar blocked_transformers:
:vartype blocked_transformers: list[str]
:ivar column_purposes: Dictionary of :code:`<string>`.
:vartype column_purposes: dict[str, str]
:ivar drop_columns:
:vartype drop_columns: list[str]
:ivar transformer_params: Dictionary of
<components·1y90i4m·schemas·aetherfeaturizationsettings·properties·transformerparams·additionalproperties>.
:vartype transformer_params: dict[str, list[~flow.models.AetherColumnTransformer]]
:ivar dataset_language:
:vartype dataset_language: str
:ivar enable_dnn_featurization:
:vartype enable_dnn_featurization: bool
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'blocked_transformers': {'key': 'blockedTransformers', 'type': '[str]'},
'column_purposes': {'key': 'columnPurposes', 'type': '{str}'},
'drop_columns': {'key': 'dropColumns', 'type': '[str]'},
'transformer_params': {'key': 'transformerParams', 'type': '{[AetherColumnTransformer]}'},
'dataset_language': {'key': 'datasetLanguage', 'type': 'str'},
'enable_dnn_featurization': {'key': 'enableDnnFeaturization', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom", "Off".
:paramtype mode: str or ~flow.models.AetherFeaturizationMode
:keyword blocked_transformers:
:paramtype blocked_transformers: list[str]
:keyword column_purposes: Dictionary of :code:`<string>`.
:paramtype column_purposes: dict[str, str]
:keyword drop_columns:
:paramtype drop_columns: list[str]
:keyword transformer_params: Dictionary of
<components·1y90i4m·schemas·aetherfeaturizationsettings·properties·transformerparams·additionalproperties>.
:paramtype transformer_params: dict[str, list[~flow.models.AetherColumnTransformer]]
:keyword dataset_language:
:paramtype dataset_language: str
:keyword enable_dnn_featurization:
:paramtype enable_dnn_featurization: bool
"""
super(AetherFeaturizationSettings, self).__init__(**kwargs)
self.mode = kwargs.get('mode', None)
self.blocked_transformers = kwargs.get('blocked_transformers', None)
self.column_purposes = kwargs.get('column_purposes', None)
self.drop_columns = kwargs.get('drop_columns', None)
self.transformer_params = kwargs.get('transformer_params', None)
self.dataset_language = kwargs.get('dataset_language', None)
self.enable_dnn_featurization = kwargs.get('enable_dnn_featurization', None)
class AetherFileSystem(msrest.serialization.Model):
"""AetherFileSystem.
:ivar connection:
:vartype connection: str
:ivar path:
:vartype path: str
"""
_attribute_map = {
'connection': {'key': 'connection', 'type': 'str'},
'path': {'key': 'path', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword connection:
:paramtype connection: str
:keyword path:
:paramtype path: str
"""
super(AetherFileSystem, self).__init__(**kwargs)
self.connection = kwargs.get('connection', None)
self.path = kwargs.get('path', None)
class AetherForecastHorizon(msrest.serialization.Model):
"""AetherForecastHorizon.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.AetherForecastHorizonMode
:ivar value:
:vartype value: int
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'value': {'key': 'value', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.AetherForecastHorizonMode
:keyword value:
:paramtype value: int
"""
super(AetherForecastHorizon, self).__init__(**kwargs)
self.mode = kwargs.get('mode', None)
self.value = kwargs.get('value', None)
class AetherForecastingSettings(msrest.serialization.Model):
"""AetherForecastingSettings.
:ivar country_or_region_for_holidays:
:vartype country_or_region_for_holidays: str
:ivar time_column_name:
:vartype time_column_name: str
:ivar target_lags:
:vartype target_lags: ~flow.models.AetherTargetLags
:ivar target_rolling_window_size:
:vartype target_rolling_window_size: ~flow.models.AetherTargetRollingWindowSize
:ivar forecast_horizon:
:vartype forecast_horizon: ~flow.models.AetherForecastHorizon
:ivar time_series_id_column_names:
:vartype time_series_id_column_names: list[str]
:ivar frequency:
:vartype frequency: str
:ivar feature_lags:
:vartype feature_lags: str
:ivar seasonality:
:vartype seasonality: ~flow.models.AetherSeasonality
:ivar short_series_handling_config: Possible values include: "Auto", "Pad", "Drop".
:vartype short_series_handling_config: str or
~flow.models.AetherShortSeriesHandlingConfiguration
:ivar use_stl: Possible values include: "Season", "SeasonTrend".
:vartype use_stl: str or ~flow.models.AetherUseStl
:ivar target_aggregate_function: Possible values include: "Sum", "Max", "Min", "Mean".
:vartype target_aggregate_function: str or ~flow.models.AetherTargetAggregationFunction
:ivar cv_step_size:
:vartype cv_step_size: int
:ivar features_unknown_at_forecast_time:
:vartype features_unknown_at_forecast_time: list[str]
"""
_attribute_map = {
'country_or_region_for_holidays': {'key': 'countryOrRegionForHolidays', 'type': 'str'},
'time_column_name': {'key': 'timeColumnName', 'type': 'str'},
'target_lags': {'key': 'targetLags', 'type': 'AetherTargetLags'},
'target_rolling_window_size': {'key': 'targetRollingWindowSize', 'type': 'AetherTargetRollingWindowSize'},
'forecast_horizon': {'key': 'forecastHorizon', 'type': 'AetherForecastHorizon'},
'time_series_id_column_names': {'key': 'timeSeriesIdColumnNames', 'type': '[str]'},
'frequency': {'key': 'frequency', 'type': 'str'},
'feature_lags': {'key': 'featureLags', 'type': 'str'},
'seasonality': {'key': 'seasonality', 'type': 'AetherSeasonality'},
'short_series_handling_config': {'key': 'shortSeriesHandlingConfig', 'type': 'str'},
'use_stl': {'key': 'useStl', 'type': 'str'},
'target_aggregate_function': {'key': 'targetAggregateFunction', 'type': 'str'},
'cv_step_size': {'key': 'cvStepSize', 'type': 'int'},
'features_unknown_at_forecast_time': {'key': 'featuresUnknownAtForecastTime', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword country_or_region_for_holidays:
:paramtype country_or_region_for_holidays: str
:keyword time_column_name:
:paramtype time_column_name: str
:keyword target_lags:
:paramtype target_lags: ~flow.models.AetherTargetLags
:keyword target_rolling_window_size:
:paramtype target_rolling_window_size: ~flow.models.AetherTargetRollingWindowSize
:keyword forecast_horizon:
:paramtype forecast_horizon: ~flow.models.AetherForecastHorizon
:keyword time_series_id_column_names:
:paramtype time_series_id_column_names: list[str]
:keyword frequency:
:paramtype frequency: str
:keyword feature_lags:
:paramtype feature_lags: str
:keyword seasonality:
:paramtype seasonality: ~flow.models.AetherSeasonality
:keyword short_series_handling_config: Possible values include: "Auto", "Pad", "Drop".
:paramtype short_series_handling_config: str or
~flow.models.AetherShortSeriesHandlingConfiguration
:keyword use_stl: Possible values include: "Season", "SeasonTrend".
:paramtype use_stl: str or ~flow.models.AetherUseStl
:keyword target_aggregate_function: Possible values include: "Sum", "Max", "Min", "Mean".
:paramtype target_aggregate_function: str or ~flow.models.AetherTargetAggregationFunction
:keyword cv_step_size:
:paramtype cv_step_size: int
:keyword features_unknown_at_forecast_time:
:paramtype features_unknown_at_forecast_time: list[str]
"""
super(AetherForecastingSettings, self).__init__(**kwargs)
self.country_or_region_for_holidays = kwargs.get('country_or_region_for_holidays', None)
self.time_column_name = kwargs.get('time_column_name', None)
self.target_lags = kwargs.get('target_lags', None)
self.target_rolling_window_size = kwargs.get('target_rolling_window_size', None)
self.forecast_horizon = kwargs.get('forecast_horizon', None)
self.time_series_id_column_names = kwargs.get('time_series_id_column_names', None)
self.frequency = kwargs.get('frequency', None)
self.feature_lags = kwargs.get('feature_lags', None)
self.seasonality = kwargs.get('seasonality', None)
self.short_series_handling_config = kwargs.get('short_series_handling_config', None)
self.use_stl = kwargs.get('use_stl', None)
self.target_aggregate_function = kwargs.get('target_aggregate_function', None)
self.cv_step_size = kwargs.get('cv_step_size', None)
self.features_unknown_at_forecast_time = kwargs.get('features_unknown_at_forecast_time', None)
class AetherGeneralSettings(msrest.serialization.Model):
"""AetherGeneralSettings.
:ivar primary_metric: Possible values include: "AUCWeighted", "Accuracy", "NormMacroRecall",
"AveragePrecisionScoreWeighted", "PrecisionScoreWeighted", "SpearmanCorrelation",
"NormalizedRootMeanSquaredError", "R2Score", "NormalizedMeanAbsoluteError",
"NormalizedRootMeanSquaredLogError", "MeanAveragePrecision", "Iou".
:vartype primary_metric: str or ~flow.models.AetherPrimaryMetrics
:ivar task_type: Possible values include: "Classification", "Regression", "Forecasting",
"ImageClassification", "ImageClassificationMultilabel", "ImageObjectDetection",
"ImageInstanceSegmentation", "TextClassification", "TextMultiLabeling", "TextNER",
"TextClassificationMultilabel".
:vartype task_type: str or ~flow.models.AetherTaskType
:ivar log_verbosity: Possible values include: "NotSet", "Debug", "Info", "Warning", "Error",
"Critical".
:vartype log_verbosity: str or ~flow.models.AetherLogVerbosity
"""
_attribute_map = {
'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
'task_type': {'key': 'taskType', 'type': 'str'},
'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword primary_metric: Possible values include: "AUCWeighted", "Accuracy", "NormMacroRecall",
"AveragePrecisionScoreWeighted", "PrecisionScoreWeighted", "SpearmanCorrelation",
"NormalizedRootMeanSquaredError", "R2Score", "NormalizedMeanAbsoluteError",
"NormalizedRootMeanSquaredLogError", "MeanAveragePrecision", "Iou".
:paramtype primary_metric: str or ~flow.models.AetherPrimaryMetrics
:keyword task_type: Possible values include: "Classification", "Regression", "Forecasting",
"ImageClassification", "ImageClassificationMultilabel", "ImageObjectDetection",
"ImageInstanceSegmentation", "TextClassification", "TextMultiLabeling", "TextNER",
"TextClassificationMultilabel".
:paramtype task_type: str or ~flow.models.AetherTaskType
:keyword log_verbosity: Possible values include: "NotSet", "Debug", "Info", "Warning", "Error",
"Critical".
:paramtype log_verbosity: str or ~flow.models.AetherLogVerbosity
"""
super(AetherGeneralSettings, self).__init__(**kwargs)
self.primary_metric = kwargs.get('primary_metric', None)
self.task_type = kwargs.get('task_type', None)
self.log_verbosity = kwargs.get('log_verbosity', None)
class AetherGlobsOptions(msrest.serialization.Model):
"""AetherGlobsOptions.
:ivar glob_patterns:
:vartype glob_patterns: list[str]
"""
_attribute_map = {
'glob_patterns': {'key': 'globPatterns', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword glob_patterns:
:paramtype glob_patterns: list[str]
"""
super(AetherGlobsOptions, self).__init__(**kwargs)
self.glob_patterns = kwargs.get('glob_patterns', None)
class AetherGraphControlNode(msrest.serialization.Model):
"""AetherGraphControlNode.
:ivar id:
:vartype id: str
:ivar control_type: The only acceptable values to pass in are None and "IfElse". The default
value is None.
:vartype control_type: str
:ivar control_parameter:
:vartype control_parameter: ~flow.models.AetherParameterAssignment
:ivar run_attribution:
:vartype run_attribution: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'control_type': {'key': 'controlType', 'type': 'str'},
'control_parameter': {'key': 'controlParameter', 'type': 'AetherParameterAssignment'},
'run_attribution': {'key': 'runAttribution', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword control_type: The only acceptable values to pass in are None and "IfElse". The
default value is None.
:paramtype control_type: str
:keyword control_parameter:
:paramtype control_parameter: ~flow.models.AetherParameterAssignment
:keyword run_attribution:
:paramtype run_attribution: str
"""
super(AetherGraphControlNode, self).__init__(**kwargs)
self.id = kwargs.get('id', None)
self.control_type = kwargs.get('control_type', None)
self.control_parameter = kwargs.get('control_parameter', None)
self.run_attribution = kwargs.get('run_attribution', None)
class AetherGraphControlReferenceNode(msrest.serialization.Model):
"""AetherGraphControlReferenceNode.
:ivar id:
:vartype id: str
:ivar name:
:vartype name: str
:ivar comment:
:vartype comment: str
:ivar control_flow_type: Possible values include: "None", "DoWhile", "ParallelFor".
:vartype control_flow_type: str or ~flow.models.AetherControlFlowType
:ivar reference_node_id:
:vartype reference_node_id: str
:ivar do_while_control_flow_info:
:vartype do_while_control_flow_info: ~flow.models.AetherDoWhileControlFlowInfo
:ivar parallel_for_control_flow_info:
:vartype parallel_for_control_flow_info: ~flow.models.AetherParallelForControlFlowInfo
:ivar run_attribution:
:vartype run_attribution: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'comment': {'key': 'comment', 'type': 'str'},
'control_flow_type': {'key': 'controlFlowType', 'type': 'str'},
'reference_node_id': {'key': 'referenceNodeId', 'type': 'str'},
'do_while_control_flow_info': {'key': 'doWhileControlFlowInfo', 'type': 'AetherDoWhileControlFlowInfo'},
'parallel_for_control_flow_info': {'key': 'parallelForControlFlowInfo', 'type': 'AetherParallelForControlFlowInfo'},
'run_attribution': {'key': 'runAttribution', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword name:
:paramtype name: str
:keyword comment:
:paramtype comment: str
:keyword control_flow_type: Possible values include: "None", "DoWhile", "ParallelFor".
:paramtype control_flow_type: str or ~flow.models.AetherControlFlowType
:keyword reference_node_id:
:paramtype reference_node_id: str
:keyword do_while_control_flow_info:
:paramtype do_while_control_flow_info: ~flow.models.AetherDoWhileControlFlowInfo
:keyword parallel_for_control_flow_info:
:paramtype parallel_for_control_flow_info: ~flow.models.AetherParallelForControlFlowInfo
:keyword run_attribution:
:paramtype run_attribution: str
"""
super(AetherGraphControlReferenceNode, self).__init__(**kwargs)
self.id = kwargs.get('id', None)
self.name = kwargs.get('name', None)
self.comment = kwargs.get('comment', None)
self.control_flow_type = kwargs.get('control_flow_type', None)
self.reference_node_id = kwargs.get('reference_node_id', None)
self.do_while_control_flow_info = kwargs.get('do_while_control_flow_info', None)
self.parallel_for_control_flow_info = kwargs.get('parallel_for_control_flow_info', None)
self.run_attribution = kwargs.get('run_attribution', None)
class AetherGraphDatasetNode(msrest.serialization.Model):
"""AetherGraphDatasetNode.
:ivar id:
:vartype id: str
:ivar dataset_id:
:vartype dataset_id: str
:ivar data_path_parameter_name:
:vartype data_path_parameter_name: str
:ivar data_set_definition:
:vartype data_set_definition: ~flow.models.AetherDataSetDefinition
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'dataset_id': {'key': 'datasetId', 'type': 'str'},
'data_path_parameter_name': {'key': 'dataPathParameterName', 'type': 'str'},
'data_set_definition': {'key': 'dataSetDefinition', 'type': 'AetherDataSetDefinition'},
}
def __init__(
self,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword dataset_id:
:paramtype dataset_id: str
:keyword data_path_parameter_name:
:paramtype data_path_parameter_name: str
:keyword data_set_definition:
:paramtype data_set_definition: ~flow.models.AetherDataSetDefinition
"""
super(AetherGraphDatasetNode, self).__init__(**kwargs)
self.id = kwargs.get('id', None)
self.dataset_id = kwargs.get('dataset_id', None)
self.data_path_parameter_name = kwargs.get('data_path_parameter_name', None)
self.data_set_definition = kwargs.get('data_set_definition', None)
class AetherGraphEdge(msrest.serialization.Model):
"""AetherGraphEdge.
:ivar source_output_port:
:vartype source_output_port: ~flow.models.AetherPortInfo
:ivar destination_input_port:
:vartype destination_input_port: ~flow.models.AetherPortInfo
"""
_attribute_map = {
'source_output_port': {'key': 'sourceOutputPort', 'type': 'AetherPortInfo'},
'destination_input_port': {'key': 'destinationInputPort', 'type': 'AetherPortInfo'},
}
def __init__(
self,
**kwargs
):
"""
:keyword source_output_port:
:paramtype source_output_port: ~flow.models.AetherPortInfo
:keyword destination_input_port:
:paramtype destination_input_port: ~flow.models.AetherPortInfo
"""
super(AetherGraphEdge, self).__init__(**kwargs)
self.source_output_port = kwargs.get('source_output_port', None)
self.destination_input_port = kwargs.get('destination_input_port', None)
class AetherGraphEntity(msrest.serialization.Model):
"""AetherGraphEntity.
:ivar module_nodes:
:vartype module_nodes: list[~flow.models.AetherGraphModuleNode]
:ivar dataset_nodes:
:vartype dataset_nodes: list[~flow.models.AetherGraphDatasetNode]
:ivar sub_graph_nodes:
:vartype sub_graph_nodes: list[~flow.models.AetherGraphReferenceNode]
:ivar control_reference_nodes:
:vartype control_reference_nodes: list[~flow.models.AetherGraphControlReferenceNode]
:ivar control_nodes:
:vartype control_nodes: list[~flow.models.AetherGraphControlNode]
:ivar edges:
:vartype edges: list[~flow.models.AetherGraphEdge]
:ivar default_compute:
:vartype default_compute: ~flow.models.AetherComputeSetting
:ivar default_datastore:
:vartype default_datastore: ~flow.models.AetherDatastoreSetting
:ivar default_cloud_priority:
:vartype default_cloud_priority: ~flow.models.AetherCloudPrioritySetting
:ivar parent_sub_graph_module_ids:
:vartype parent_sub_graph_module_ids: list[str]
:ivar id:
:vartype id: str
:ivar workspace_id:
:vartype workspace_id: str
:ivar etag:
:vartype etag: str
:ivar tags: A set of tags.
:vartype tags: list[str]
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.AetherEntityStatus
"""
_attribute_map = {
'module_nodes': {'key': 'moduleNodes', 'type': '[AetherGraphModuleNode]'},
'dataset_nodes': {'key': 'datasetNodes', 'type': '[AetherGraphDatasetNode]'},
'sub_graph_nodes': {'key': 'subGraphNodes', 'type': '[AetherGraphReferenceNode]'},
'control_reference_nodes': {'key': 'controlReferenceNodes', 'type': '[AetherGraphControlReferenceNode]'},
'control_nodes': {'key': 'controlNodes', 'type': '[AetherGraphControlNode]'},
'edges': {'key': 'edges', 'type': '[AetherGraphEdge]'},
'default_compute': {'key': 'defaultCompute', 'type': 'AetherComputeSetting'},
'default_datastore': {'key': 'defaultDatastore', 'type': 'AetherDatastoreSetting'},
'default_cloud_priority': {'key': 'defaultCloudPriority', 'type': 'AetherCloudPrioritySetting'},
'parent_sub_graph_module_ids': {'key': 'parentSubGraphModuleIds', 'type': '[str]'},
'id': {'key': 'id', 'type': 'str'},
'workspace_id': {'key': 'workspaceId', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'tags': {'key': 'tags', 'type': '[str]'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword module_nodes:
:paramtype module_nodes: list[~flow.models.AetherGraphModuleNode]
:keyword dataset_nodes:
:paramtype dataset_nodes: list[~flow.models.AetherGraphDatasetNode]
:keyword sub_graph_nodes:
:paramtype sub_graph_nodes: list[~flow.models.AetherGraphReferenceNode]
:keyword control_reference_nodes:
:paramtype control_reference_nodes: list[~flow.models.AetherGraphControlReferenceNode]
:keyword control_nodes:
:paramtype control_nodes: list[~flow.models.AetherGraphControlNode]
:keyword edges:
:paramtype edges: list[~flow.models.AetherGraphEdge]
:keyword default_compute:
:paramtype default_compute: ~flow.models.AetherComputeSetting
:keyword default_datastore:
:paramtype default_datastore: ~flow.models.AetherDatastoreSetting
:keyword default_cloud_priority:
:paramtype default_cloud_priority: ~flow.models.AetherCloudPrioritySetting
:keyword parent_sub_graph_module_ids:
:paramtype parent_sub_graph_module_ids: list[str]
:keyword id:
:paramtype id: str
:keyword workspace_id:
:paramtype workspace_id: str
:keyword etag:
:paramtype etag: str
:keyword tags: A set of tags.
:paramtype tags: list[str]
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.AetherEntityStatus
"""
super(AetherGraphEntity, self).__init__(**kwargs)
self.module_nodes = kwargs.get('module_nodes', None)
self.dataset_nodes = kwargs.get('dataset_nodes', None)
self.sub_graph_nodes = kwargs.get('sub_graph_nodes', None)
self.control_reference_nodes = kwargs.get('control_reference_nodes', None)
self.control_nodes = kwargs.get('control_nodes', None)
self.edges = kwargs.get('edges', None)
self.default_compute = kwargs.get('default_compute', None)
self.default_datastore = kwargs.get('default_datastore', None)
self.default_cloud_priority = kwargs.get('default_cloud_priority', None)
self.parent_sub_graph_module_ids = kwargs.get('parent_sub_graph_module_ids', None)
self.id = kwargs.get('id', None)
self.workspace_id = kwargs.get('workspace_id', None)
self.etag = kwargs.get('etag', None)
self.tags = kwargs.get('tags', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
self.entity_status = kwargs.get('entity_status', None)
class AetherGraphModuleNode(msrest.serialization.Model):
"""AetherGraphModuleNode.
:ivar cloud_priority:
:vartype cloud_priority: int
:ivar default_data_retention_hint:
:vartype default_data_retention_hint: int
:ivar compliance_cluster:
:vartype compliance_cluster: str
:ivar euclid_workspace_id:
:vartype euclid_workspace_id: str
:ivar attached_modules:
:vartype attached_modules: list[str]
:ivar acceptable_machine_clusters:
:vartype acceptable_machine_clusters: list[str]
:ivar custom_data_location_id:
:vartype custom_data_location_id: str
:ivar alert_timeout_duration:
:vartype alert_timeout_duration: str
:ivar runconfig:
:vartype runconfig: str
:ivar id:
:vartype id: str
:ivar module_id:
:vartype module_id: str
:ivar comment:
:vartype comment: str
:ivar name:
:vartype name: str
:ivar module_parameters:
:vartype module_parameters: list[~flow.models.AetherParameterAssignment]
:ivar module_metadata_parameters:
:vartype module_metadata_parameters: list[~flow.models.AetherParameterAssignment]
:ivar module_output_settings:
:vartype module_output_settings: list[~flow.models.AetherOutputSetting]
:ivar module_input_settings:
:vartype module_input_settings: list[~flow.models.AetherInputSetting]
:ivar use_graph_default_compute:
:vartype use_graph_default_compute: bool
:ivar use_graph_default_datastore:
:vartype use_graph_default_datastore: bool
:ivar regenerate_output:
:vartype regenerate_output: bool
:ivar control_inputs:
:vartype control_inputs: list[~flow.models.AetherControlInput]
:ivar cloud_settings:
:vartype cloud_settings: ~flow.models.AetherCloudSettings
:ivar execution_phase: Possible values include: "Execution", "Initialization", "Finalization".
:vartype execution_phase: str or ~flow.models.AetherExecutionPhase
:ivar run_attribution:
:vartype run_attribution: str
"""
_attribute_map = {
'cloud_priority': {'key': 'cloudPriority', 'type': 'int'},
'default_data_retention_hint': {'key': 'defaultDataRetentionHint', 'type': 'int'},
'compliance_cluster': {'key': 'complianceCluster', 'type': 'str'},
'euclid_workspace_id': {'key': 'euclidWorkspaceId', 'type': 'str'},
'attached_modules': {'key': 'attachedModules', 'type': '[str]'},
'acceptable_machine_clusters': {'key': 'acceptableMachineClusters', 'type': '[str]'},
'custom_data_location_id': {'key': 'customDataLocationId', 'type': 'str'},
'alert_timeout_duration': {'key': 'alertTimeoutDuration', 'type': 'str'},
'runconfig': {'key': 'runconfig', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'module_id': {'key': 'moduleId', 'type': 'str'},
'comment': {'key': 'comment', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'module_parameters': {'key': 'moduleParameters', 'type': '[AetherParameterAssignment]'},
'module_metadata_parameters': {'key': 'moduleMetadataParameters', 'type': '[AetherParameterAssignment]'},
'module_output_settings': {'key': 'moduleOutputSettings', 'type': '[AetherOutputSetting]'},
'module_input_settings': {'key': 'moduleInputSettings', 'type': '[AetherInputSetting]'},
'use_graph_default_compute': {'key': 'useGraphDefaultCompute', 'type': 'bool'},
'use_graph_default_datastore': {'key': 'useGraphDefaultDatastore', 'type': 'bool'},
'regenerate_output': {'key': 'regenerateOutput', 'type': 'bool'},
'control_inputs': {'key': 'controlInputs', 'type': '[AetherControlInput]'},
'cloud_settings': {'key': 'cloudSettings', 'type': 'AetherCloudSettings'},
'execution_phase': {'key': 'executionPhase', 'type': 'str'},
'run_attribution': {'key': 'runAttribution', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword cloud_priority:
:paramtype cloud_priority: int
:keyword default_data_retention_hint:
:paramtype default_data_retention_hint: int
:keyword compliance_cluster:
:paramtype compliance_cluster: str
:keyword euclid_workspace_id:
:paramtype euclid_workspace_id: str
:keyword attached_modules:
:paramtype attached_modules: list[str]
:keyword acceptable_machine_clusters:
:paramtype acceptable_machine_clusters: list[str]
:keyword custom_data_location_id:
:paramtype custom_data_location_id: str
:keyword alert_timeout_duration:
:paramtype alert_timeout_duration: str
:keyword runconfig:
:paramtype runconfig: str
:keyword id:
:paramtype id: str
:keyword module_id:
:paramtype module_id: str
:keyword comment:
:paramtype comment: str
:keyword name:
:paramtype name: str
:keyword module_parameters:
:paramtype module_parameters: list[~flow.models.AetherParameterAssignment]
:keyword module_metadata_parameters:
:paramtype module_metadata_parameters: list[~flow.models.AetherParameterAssignment]
:keyword module_output_settings:
:paramtype module_output_settings: list[~flow.models.AetherOutputSetting]
:keyword module_input_settings:
:paramtype module_input_settings: list[~flow.models.AetherInputSetting]
:keyword use_graph_default_compute:
:paramtype use_graph_default_compute: bool
:keyword use_graph_default_datastore:
:paramtype use_graph_default_datastore: bool
:keyword regenerate_output:
:paramtype regenerate_output: bool
:keyword control_inputs:
:paramtype control_inputs: list[~flow.models.AetherControlInput]
:keyword cloud_settings:
:paramtype cloud_settings: ~flow.models.AetherCloudSettings
:keyword execution_phase: Possible values include: "Execution", "Initialization",
"Finalization".
:paramtype execution_phase: str or ~flow.models.AetherExecutionPhase
:keyword run_attribution:
:paramtype run_attribution: str
"""
super(AetherGraphModuleNode, self).__init__(**kwargs)
self.cloud_priority = kwargs.get('cloud_priority', None)
self.default_data_retention_hint = kwargs.get('default_data_retention_hint', None)
self.compliance_cluster = kwargs.get('compliance_cluster', None)
self.euclid_workspace_id = kwargs.get('euclid_workspace_id', None)
self.attached_modules = kwargs.get('attached_modules', None)
self.acceptable_machine_clusters = kwargs.get('acceptable_machine_clusters', None)
self.custom_data_location_id = kwargs.get('custom_data_location_id', None)
self.alert_timeout_duration = kwargs.get('alert_timeout_duration', None)
self.runconfig = kwargs.get('runconfig', None)
self.id = kwargs.get('id', None)
self.module_id = kwargs.get('module_id', None)
self.comment = kwargs.get('comment', None)
self.name = kwargs.get('name', None)
self.module_parameters = kwargs.get('module_parameters', None)
self.module_metadata_parameters = kwargs.get('module_metadata_parameters', None)
self.module_output_settings = kwargs.get('module_output_settings', None)
self.module_input_settings = kwargs.get('module_input_settings', None)
self.use_graph_default_compute = kwargs.get('use_graph_default_compute', None)
self.use_graph_default_datastore = kwargs.get('use_graph_default_datastore', None)
self.regenerate_output = kwargs.get('regenerate_output', None)
self.control_inputs = kwargs.get('control_inputs', None)
self.cloud_settings = kwargs.get('cloud_settings', None)
self.execution_phase = kwargs.get('execution_phase', None)
self.run_attribution = kwargs.get('run_attribution', None)
class AetherGraphReferenceNode(msrest.serialization.Model):
"""AetherGraphReferenceNode.
:ivar graph_id:
:vartype graph_id: str
:ivar default_compute:
:vartype default_compute: ~flow.models.AetherComputeSetting
:ivar default_datastore:
:vartype default_datastore: ~flow.models.AetherDatastoreSetting
:ivar id:
:vartype id: str
:ivar module_id:
:vartype module_id: str
:ivar comment:
:vartype comment: str
:ivar name:
:vartype name: str
:ivar module_parameters:
:vartype module_parameters: list[~flow.models.AetherParameterAssignment]
:ivar module_metadata_parameters:
:vartype module_metadata_parameters: list[~flow.models.AetherParameterAssignment]
:ivar module_output_settings:
:vartype module_output_settings: list[~flow.models.AetherOutputSetting]
:ivar module_input_settings:
:vartype module_input_settings: list[~flow.models.AetherInputSetting]
:ivar use_graph_default_compute:
:vartype use_graph_default_compute: bool
:ivar use_graph_default_datastore:
:vartype use_graph_default_datastore: bool
:ivar regenerate_output:
:vartype regenerate_output: bool
:ivar control_inputs:
:vartype control_inputs: list[~flow.models.AetherControlInput]
:ivar cloud_settings:
:vartype cloud_settings: ~flow.models.AetherCloudSettings
:ivar execution_phase: Possible values include: "Execution", "Initialization", "Finalization".
:vartype execution_phase: str or ~flow.models.AetherExecutionPhase
:ivar run_attribution:
:vartype run_attribution: str
"""
_attribute_map = {
'graph_id': {'key': 'graphId', 'type': 'str'},
'default_compute': {'key': 'defaultCompute', 'type': 'AetherComputeSetting'},
'default_datastore': {'key': 'defaultDatastore', 'type': 'AetherDatastoreSetting'},
'id': {'key': 'id', 'type': 'str'},
'module_id': {'key': 'moduleId', 'type': 'str'},
'comment': {'key': 'comment', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'module_parameters': {'key': 'moduleParameters', 'type': '[AetherParameterAssignment]'},
'module_metadata_parameters': {'key': 'moduleMetadataParameters', 'type': '[AetherParameterAssignment]'},
'module_output_settings': {'key': 'moduleOutputSettings', 'type': '[AetherOutputSetting]'},
'module_input_settings': {'key': 'moduleInputSettings', 'type': '[AetherInputSetting]'},
'use_graph_default_compute': {'key': 'useGraphDefaultCompute', 'type': 'bool'},
'use_graph_default_datastore': {'key': 'useGraphDefaultDatastore', 'type': 'bool'},
'regenerate_output': {'key': 'regenerateOutput', 'type': 'bool'},
'control_inputs': {'key': 'controlInputs', 'type': '[AetherControlInput]'},
'cloud_settings': {'key': 'cloudSettings', 'type': 'AetherCloudSettings'},
'execution_phase': {'key': 'executionPhase', 'type': 'str'},
'run_attribution': {'key': 'runAttribution', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword graph_id:
:paramtype graph_id: str
:keyword default_compute:
:paramtype default_compute: ~flow.models.AetherComputeSetting
:keyword default_datastore:
:paramtype default_datastore: ~flow.models.AetherDatastoreSetting
:keyword id:
:paramtype id: str
:keyword module_id:
:paramtype module_id: str
:keyword comment:
:paramtype comment: str
:keyword name:
:paramtype name: str
:keyword module_parameters:
:paramtype module_parameters: list[~flow.models.AetherParameterAssignment]
:keyword module_metadata_parameters:
:paramtype module_metadata_parameters: list[~flow.models.AetherParameterAssignment]
:keyword module_output_settings:
:paramtype module_output_settings: list[~flow.models.AetherOutputSetting]
:keyword module_input_settings:
:paramtype module_input_settings: list[~flow.models.AetherInputSetting]
:keyword use_graph_default_compute:
:paramtype use_graph_default_compute: bool
:keyword use_graph_default_datastore:
:paramtype use_graph_default_datastore: bool
:keyword regenerate_output:
:paramtype regenerate_output: bool
:keyword control_inputs:
:paramtype control_inputs: list[~flow.models.AetherControlInput]
:keyword cloud_settings:
:paramtype cloud_settings: ~flow.models.AetherCloudSettings
:keyword execution_phase: Possible values include: "Execution", "Initialization",
"Finalization".
:paramtype execution_phase: str or ~flow.models.AetherExecutionPhase
:keyword run_attribution:
:paramtype run_attribution: str
"""
super(AetherGraphReferenceNode, self).__init__(**kwargs)
self.graph_id = kwargs.get('graph_id', None)
self.default_compute = kwargs.get('default_compute', None)
self.default_datastore = kwargs.get('default_datastore', None)
self.id = kwargs.get('id', None)
self.module_id = kwargs.get('module_id', None)
self.comment = kwargs.get('comment', None)
self.name = kwargs.get('name', None)
self.module_parameters = kwargs.get('module_parameters', None)
self.module_metadata_parameters = kwargs.get('module_metadata_parameters', None)
self.module_output_settings = kwargs.get('module_output_settings', None)
self.module_input_settings = kwargs.get('module_input_settings', None)
self.use_graph_default_compute = kwargs.get('use_graph_default_compute', None)
self.use_graph_default_datastore = kwargs.get('use_graph_default_datastore', None)
self.regenerate_output = kwargs.get('regenerate_output', None)
self.control_inputs = kwargs.get('control_inputs', None)
self.cloud_settings = kwargs.get('cloud_settings', None)
self.execution_phase = kwargs.get('execution_phase', None)
self.run_attribution = kwargs.get('run_attribution', None)
class AetherHdfsReference(msrest.serialization.Model):
"""AetherHdfsReference.
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
:keyword relative_path:
:paramtype relative_path: str
"""
super(AetherHdfsReference, self).__init__(**kwargs)
self.aml_data_store_name = kwargs.get('aml_data_store_name', None)
self.relative_path = kwargs.get('relative_path', None)
class AetherHdiClusterComputeInfo(msrest.serialization.Model):
"""AetherHdiClusterComputeInfo.
:ivar address:
:vartype address: str
:ivar username:
:vartype username: str
:ivar password:
:vartype password: str
:ivar private_key:
:vartype private_key: str
"""
_attribute_map = {
'address': {'key': 'address', 'type': 'str'},
'username': {'key': 'username', 'type': 'str'},
'password': {'key': 'password', 'type': 'str'},
'private_key': {'key': 'privateKey', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword address:
:paramtype address: str
:keyword username:
:paramtype username: str
:keyword password:
:paramtype password: str
:keyword private_key:
:paramtype private_key: str
"""
super(AetherHdiClusterComputeInfo, self).__init__(**kwargs)
self.address = kwargs.get('address', None)
self.username = kwargs.get('username', None)
self.password = kwargs.get('password', None)
self.private_key = kwargs.get('private_key', None)
class AetherHdiRunConfiguration(msrest.serialization.Model):
"""AetherHdiRunConfiguration.
:ivar file:
:vartype file: str
:ivar class_name:
:vartype class_name: str
:ivar files:
:vartype files: list[str]
:ivar archives:
:vartype archives: list[str]
:ivar jars:
:vartype jars: list[str]
:ivar py_files:
:vartype py_files: list[str]
:ivar compute_name:
:vartype compute_name: str
:ivar queue:
:vartype queue: str
:ivar driver_memory:
:vartype driver_memory: str
:ivar driver_cores:
:vartype driver_cores: int
:ivar executor_memory:
:vartype executor_memory: str
:ivar executor_cores:
:vartype executor_cores: int
:ivar number_executors:
:vartype number_executors: int
:ivar conf: Dictionary of :code:`<string>`.
:vartype conf: dict[str, str]
:ivar name:
:vartype name: str
"""
_attribute_map = {
'file': {'key': 'file', 'type': 'str'},
'class_name': {'key': 'className', 'type': 'str'},
'files': {'key': 'files', 'type': '[str]'},
'archives': {'key': 'archives', 'type': '[str]'},
'jars': {'key': 'jars', 'type': '[str]'},
'py_files': {'key': 'pyFiles', 'type': '[str]'},
'compute_name': {'key': 'computeName', 'type': 'str'},
'queue': {'key': 'queue', 'type': 'str'},
'driver_memory': {'key': 'driverMemory', 'type': 'str'},
'driver_cores': {'key': 'driverCores', 'type': 'int'},
'executor_memory': {'key': 'executorMemory', 'type': 'str'},
'executor_cores': {'key': 'executorCores', 'type': 'int'},
'number_executors': {'key': 'numberExecutors', 'type': 'int'},
'conf': {'key': 'conf', 'type': '{str}'},
'name': {'key': 'name', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword file:
:paramtype file: str
:keyword class_name:
:paramtype class_name: str
:keyword files:
:paramtype files: list[str]
:keyword archives:
:paramtype archives: list[str]
:keyword jars:
:paramtype jars: list[str]
:keyword py_files:
:paramtype py_files: list[str]
:keyword compute_name:
:paramtype compute_name: str
:keyword queue:
:paramtype queue: str
:keyword driver_memory:
:paramtype driver_memory: str
:keyword driver_cores:
:paramtype driver_cores: int
:keyword executor_memory:
:paramtype executor_memory: str
:keyword executor_cores:
:paramtype executor_cores: int
:keyword number_executors:
:paramtype number_executors: int
:keyword conf: Dictionary of :code:`<string>`.
:paramtype conf: dict[str, str]
:keyword name:
:paramtype name: str
"""
super(AetherHdiRunConfiguration, self).__init__(**kwargs)
self.file = kwargs.get('file', None)
self.class_name = kwargs.get('class_name', None)
self.files = kwargs.get('files', None)
self.archives = kwargs.get('archives', None)
self.jars = kwargs.get('jars', None)
self.py_files = kwargs.get('py_files', None)
self.compute_name = kwargs.get('compute_name', None)
self.queue = kwargs.get('queue', None)
self.driver_memory = kwargs.get('driver_memory', None)
self.driver_cores = kwargs.get('driver_cores', None)
self.executor_memory = kwargs.get('executor_memory', None)
self.executor_cores = kwargs.get('executor_cores', None)
self.number_executors = kwargs.get('number_executors', None)
self.conf = kwargs.get('conf', None)
self.name = kwargs.get('name', None)
class AetherHyperDriveConfiguration(msrest.serialization.Model):
"""AetherHyperDriveConfiguration.
:ivar hyper_drive_run_config:
:vartype hyper_drive_run_config: str
:ivar primary_metric_goal:
:vartype primary_metric_goal: str
:ivar primary_metric_name:
:vartype primary_metric_name: str
:ivar arguments:
:vartype arguments: list[~flow.models.AetherArgumentAssignment]
"""
_attribute_map = {
'hyper_drive_run_config': {'key': 'hyperDriveRunConfig', 'type': 'str'},
'primary_metric_goal': {'key': 'primaryMetricGoal', 'type': 'str'},
'primary_metric_name': {'key': 'primaryMetricName', 'type': 'str'},
'arguments': {'key': 'arguments', 'type': '[AetherArgumentAssignment]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword hyper_drive_run_config:
:paramtype hyper_drive_run_config: str
:keyword primary_metric_goal:
:paramtype primary_metric_goal: str
:keyword primary_metric_name:
:paramtype primary_metric_name: str
:keyword arguments:
:paramtype arguments: list[~flow.models.AetherArgumentAssignment]
"""
super(AetherHyperDriveConfiguration, self).__init__(**kwargs)
self.hyper_drive_run_config = kwargs.get('hyper_drive_run_config', None)
self.primary_metric_goal = kwargs.get('primary_metric_goal', None)
self.primary_metric_name = kwargs.get('primary_metric_name', None)
self.arguments = kwargs.get('arguments', None)
class AetherIdentitySetting(msrest.serialization.Model):
"""AetherIdentitySetting.
:ivar type: Possible values include: "UserIdentity", "Managed", "AMLToken".
:vartype type: str or ~flow.models.AetherIdentityType
:ivar client_id:
:vartype client_id: str
:ivar object_id:
:vartype object_id: str
:ivar msi_resource_id:
:vartype msi_resource_id: str
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'client_id': {'key': 'clientId', 'type': 'str'},
'object_id': {'key': 'objectId', 'type': 'str'},
'msi_resource_id': {'key': 'msiResourceId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type: Possible values include: "UserIdentity", "Managed", "AMLToken".
:paramtype type: str or ~flow.models.AetherIdentityType
:keyword client_id:
:paramtype client_id: str
:keyword object_id:
:paramtype object_id: str
:keyword msi_resource_id:
:paramtype msi_resource_id: str
"""
super(AetherIdentitySetting, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.client_id = kwargs.get('client_id', None)
self.object_id = kwargs.get('object_id', None)
self.msi_resource_id = kwargs.get('msi_resource_id', None)
class AetherImportDataTask(msrest.serialization.Model):
"""AetherImportDataTask.
:ivar data_transfer_source:
:vartype data_transfer_source: ~flow.models.AetherDataTransferSource
"""
_attribute_map = {
'data_transfer_source': {'key': 'DataTransferSource', 'type': 'AetherDataTransferSource'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_transfer_source:
:paramtype data_transfer_source: ~flow.models.AetherDataTransferSource
"""
super(AetherImportDataTask, self).__init__(**kwargs)
self.data_transfer_source = kwargs.get('data_transfer_source', None)
class AetherInputSetting(msrest.serialization.Model):
"""AetherInputSetting.
:ivar name:
:vartype name: str
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AetherDataStoreMode
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar options: This is a dictionary.
:vartype options: dict[str, str]
:ivar additional_transformations:
:vartype additional_transformations: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'options': {'key': 'options', 'type': '{str}'},
'additional_transformations': {'key': 'additionalTransformations', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AetherDataStoreMode
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword options: This is a dictionary.
:paramtype options: dict[str, str]
:keyword additional_transformations:
:paramtype additional_transformations: str
"""
super(AetherInputSetting, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.data_store_mode = kwargs.get('data_store_mode', None)
self.path_on_compute = kwargs.get('path_on_compute', None)
self.options = kwargs.get('options', None)
self.additional_transformations = kwargs.get('additional_transformations', None)
class AetherInteractiveConfig(msrest.serialization.Model):
"""AetherInteractiveConfig.
:ivar is_ssh_enabled:
:vartype is_ssh_enabled: bool
:ivar ssh_public_key:
:vartype ssh_public_key: str
:ivar is_i_python_enabled:
:vartype is_i_python_enabled: bool
:ivar is_tensor_board_enabled:
:vartype is_tensor_board_enabled: bool
:ivar interactive_port:
:vartype interactive_port: int
"""
_attribute_map = {
'is_ssh_enabled': {'key': 'isSSHEnabled', 'type': 'bool'},
'ssh_public_key': {'key': 'sshPublicKey', 'type': 'str'},
'is_i_python_enabled': {'key': 'isIPythonEnabled', 'type': 'bool'},
'is_tensor_board_enabled': {'key': 'isTensorBoardEnabled', 'type': 'bool'},
'interactive_port': {'key': 'interactivePort', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword is_ssh_enabled:
:paramtype is_ssh_enabled: bool
:keyword ssh_public_key:
:paramtype ssh_public_key: str
:keyword is_i_python_enabled:
:paramtype is_i_python_enabled: bool
:keyword is_tensor_board_enabled:
:paramtype is_tensor_board_enabled: bool
:keyword interactive_port:
:paramtype interactive_port: int
"""
super(AetherInteractiveConfig, self).__init__(**kwargs)
self.is_ssh_enabled = kwargs.get('is_ssh_enabled', None)
self.ssh_public_key = kwargs.get('ssh_public_key', None)
self.is_i_python_enabled = kwargs.get('is_i_python_enabled', None)
self.is_tensor_board_enabled = kwargs.get('is_tensor_board_enabled', None)
self.interactive_port = kwargs.get('interactive_port', None)
class AetherK8SConfiguration(msrest.serialization.Model):
"""AetherK8SConfiguration.
:ivar max_retry_count:
:vartype max_retry_count: int
:ivar resource_configuration:
:vartype resource_configuration: ~flow.models.AetherResourceConfig
:ivar priority_configuration:
:vartype priority_configuration: ~flow.models.AetherPriorityConfig
:ivar interactive_configuration:
:vartype interactive_configuration: ~flow.models.AetherInteractiveConfig
"""
_attribute_map = {
'max_retry_count': {'key': 'maxRetryCount', 'type': 'int'},
'resource_configuration': {'key': 'resourceConfiguration', 'type': 'AetherResourceConfig'},
'priority_configuration': {'key': 'priorityConfiguration', 'type': 'AetherPriorityConfig'},
'interactive_configuration': {'key': 'interactiveConfiguration', 'type': 'AetherInteractiveConfig'},
}
def __init__(
self,
**kwargs
):
"""
:keyword max_retry_count:
:paramtype max_retry_count: int
:keyword resource_configuration:
:paramtype resource_configuration: ~flow.models.AetherResourceConfig
:keyword priority_configuration:
:paramtype priority_configuration: ~flow.models.AetherPriorityConfig
:keyword interactive_configuration:
:paramtype interactive_configuration: ~flow.models.AetherInteractiveConfig
"""
super(AetherK8SConfiguration, self).__init__(**kwargs)
self.max_retry_count = kwargs.get('max_retry_count', None)
self.resource_configuration = kwargs.get('resource_configuration', None)
self.priority_configuration = kwargs.get('priority_configuration', None)
self.interactive_configuration = kwargs.get('interactive_configuration', None)
class AetherLegacyDataPath(msrest.serialization.Model):
"""AetherLegacyDataPath.
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AetherDataStoreMode
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AetherDataStoreMode
:keyword relative_path:
:paramtype relative_path: str
"""
super(AetherLegacyDataPath, self).__init__(**kwargs)
self.data_store_name = kwargs.get('data_store_name', None)
self.data_store_mode = kwargs.get('data_store_mode', None)
self.relative_path = kwargs.get('relative_path', None)
class AetherLimitSettings(msrest.serialization.Model):
"""AetherLimitSettings.
:ivar max_trials:
:vartype max_trials: int
:ivar timeout:
:vartype timeout: str
:ivar trial_timeout:
:vartype trial_timeout: str
:ivar max_concurrent_trials:
:vartype max_concurrent_trials: int
:ivar max_cores_per_trial:
:vartype max_cores_per_trial: int
:ivar exit_score:
:vartype exit_score: float
:ivar enable_early_termination:
:vartype enable_early_termination: bool
:ivar max_nodes:
:vartype max_nodes: int
"""
_attribute_map = {
'max_trials': {'key': 'maxTrials', 'type': 'int'},
'timeout': {'key': 'timeout', 'type': 'str'},
'trial_timeout': {'key': 'trialTimeout', 'type': 'str'},
'max_concurrent_trials': {'key': 'maxConcurrentTrials', 'type': 'int'},
'max_cores_per_trial': {'key': 'maxCoresPerTrial', 'type': 'int'},
'exit_score': {'key': 'exitScore', 'type': 'float'},
'enable_early_termination': {'key': 'enableEarlyTermination', 'type': 'bool'},
'max_nodes': {'key': 'maxNodes', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword max_trials:
:paramtype max_trials: int
:keyword timeout:
:paramtype timeout: str
:keyword trial_timeout:
:paramtype trial_timeout: str
:keyword max_concurrent_trials:
:paramtype max_concurrent_trials: int
:keyword max_cores_per_trial:
:paramtype max_cores_per_trial: int
:keyword exit_score:
:paramtype exit_score: float
:keyword enable_early_termination:
:paramtype enable_early_termination: bool
:keyword max_nodes:
:paramtype max_nodes: int
"""
super(AetherLimitSettings, self).__init__(**kwargs)
self.max_trials = kwargs.get('max_trials', None)
self.timeout = kwargs.get('timeout', None)
self.trial_timeout = kwargs.get('trial_timeout', None)
self.max_concurrent_trials = kwargs.get('max_concurrent_trials', None)
self.max_cores_per_trial = kwargs.get('max_cores_per_trial', None)
self.exit_score = kwargs.get('exit_score', None)
self.enable_early_termination = kwargs.get('enable_early_termination', None)
self.max_nodes = kwargs.get('max_nodes', None)
class AetherMlcComputeInfo(msrest.serialization.Model):
"""AetherMlcComputeInfo.
:ivar mlc_compute_type:
:vartype mlc_compute_type: str
"""
_attribute_map = {
'mlc_compute_type': {'key': 'mlcComputeType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword mlc_compute_type:
:paramtype mlc_compute_type: str
"""
super(AetherMlcComputeInfo, self).__init__(**kwargs)
self.mlc_compute_type = kwargs.get('mlc_compute_type', None)
class AetherModuleEntity(msrest.serialization.Model):
"""AetherModuleEntity.
:ivar last_updated_by:
:vartype last_updated_by: ~flow.models.AetherCreatedBy
:ivar display_name:
:vartype display_name: str
:ivar module_execution_type:
:vartype module_execution_type: str
:ivar module_type: Possible values include: "None", "BatchInferencing".
:vartype module_type: str or ~flow.models.AetherModuleType
:ivar module_type_version:
:vartype module_type_version: str
:ivar resource_requirements:
:vartype resource_requirements: ~flow.models.AetherResourceModel
:ivar machine_cluster:
:vartype machine_cluster: list[str]
:ivar default_compliance_cluster:
:vartype default_compliance_cluster: str
:ivar repository_type: Possible values include: "None", "Other", "Git", "SourceDepot",
"Cosmos".
:vartype repository_type: str or ~flow.models.AetherRepositoryType
:ivar relative_path_to_source_code:
:vartype relative_path_to_source_code: str
:ivar commit_id:
:vartype commit_id: str
:ivar code_review_link:
:vartype code_review_link: str
:ivar unit_tests_available:
:vartype unit_tests_available: bool
:ivar is_compressed:
:vartype is_compressed: bool
:ivar execution_environment: Possible values include: "ExeWorkerMachine",
"DockerContainerWithoutNetwork", "DockerContainerWithNetwork", "HyperVWithoutNetwork",
"HyperVWithNetwork".
:vartype execution_environment: str or ~flow.models.AetherExecutionEnvironment
:ivar is_output_markup_enabled:
:vartype is_output_markup_enabled: bool
:ivar docker_image_id:
:vartype docker_image_id: str
:ivar docker_image_reference:
:vartype docker_image_reference: str
:ivar docker_image_security_groups:
:vartype docker_image_security_groups: str
:ivar extended_properties:
:vartype extended_properties: ~flow.models.AetherModuleExtendedProperties
:ivar deployment_source: Possible values include: "Client", "AutoDeployment", "Vsts".
:vartype deployment_source: str or ~flow.models.AetherModuleDeploymentSource
:ivar deployment_source_metadata:
:vartype deployment_source_metadata: str
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hash_v2:
:vartype identifier_hash_v2: str
:ivar kv_tags: This is a dictionary.
:vartype kv_tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar created_by:
:vartype created_by: ~flow.models.AetherCreatedBy
:ivar runconfig:
:vartype runconfig: str
:ivar cloud_settings:
:vartype cloud_settings: ~flow.models.AetherCloudSettings
:ivar category:
:vartype category: str
:ivar step_type:
:vartype step_type: str
:ivar stage:
:vartype stage: str
:ivar upload_state: Possible values include: "Uploading", "Completed", "Canceled", "Failed".
:vartype upload_state: str or ~flow.models.AetherUploadState
:ivar source_code_location:
:vartype source_code_location: str
:ivar size_in_bytes:
:vartype size_in_bytes: long
:ivar download_location:
:vartype download_location: str
:ivar data_location:
:vartype data_location: ~flow.models.AetherDataLocation
:ivar scripting_runtime_id:
:vartype scripting_runtime_id: str
:ivar interface_documentation:
:vartype interface_documentation: ~flow.models.AetherEntityInterfaceDocumentation
:ivar is_eyes_on:
:vartype is_eyes_on: bool
:ivar compliance_cluster:
:vartype compliance_cluster: str
:ivar is_deterministic:
:vartype is_deterministic: bool
:ivar information_url:
:vartype information_url: str
:ivar is_experiment_id_in_parameters:
:vartype is_experiment_id_in_parameters: bool
:ivar interface_string:
:vartype interface_string: str
:ivar default_parameters: This is a dictionary.
:vartype default_parameters: dict[str, str]
:ivar structured_interface:
:vartype structured_interface: ~flow.models.AetherStructuredInterface
:ivar family_id:
:vartype family_id: str
:ivar name:
:vartype name: str
:ivar hash:
:vartype hash: str
:ivar description:
:vartype description: str
:ivar version:
:vartype version: str
:ivar sequence_number_in_family:
:vartype sequence_number_in_family: int
:ivar owner:
:vartype owner: str
:ivar azure_tenant_id:
:vartype azure_tenant_id: str
:ivar azure_user_id:
:vartype azure_user_id: str
:ivar collaborators:
:vartype collaborators: list[str]
:ivar id:
:vartype id: str
:ivar workspace_id:
:vartype workspace_id: str
:ivar etag:
:vartype etag: str
:ivar tags: A set of tags.
:vartype tags: list[str]
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.AetherEntityStatus
"""
_attribute_map = {
'last_updated_by': {'key': 'lastUpdatedBy', 'type': 'AetherCreatedBy'},
'display_name': {'key': 'displayName', 'type': 'str'},
'module_execution_type': {'key': 'moduleExecutionType', 'type': 'str'},
'module_type': {'key': 'moduleType', 'type': 'str'},
'module_type_version': {'key': 'moduleTypeVersion', 'type': 'str'},
'resource_requirements': {'key': 'resourceRequirements', 'type': 'AetherResourceModel'},
'machine_cluster': {'key': 'machineCluster', 'type': '[str]'},
'default_compliance_cluster': {'key': 'defaultComplianceCluster', 'type': 'str'},
'repository_type': {'key': 'repositoryType', 'type': 'str'},
'relative_path_to_source_code': {'key': 'relativePathToSourceCode', 'type': 'str'},
'commit_id': {'key': 'commitId', 'type': 'str'},
'code_review_link': {'key': 'codeReviewLink', 'type': 'str'},
'unit_tests_available': {'key': 'unitTestsAvailable', 'type': 'bool'},
'is_compressed': {'key': 'isCompressed', 'type': 'bool'},
'execution_environment': {'key': 'executionEnvironment', 'type': 'str'},
'is_output_markup_enabled': {'key': 'isOutputMarkupEnabled', 'type': 'bool'},
'docker_image_id': {'key': 'dockerImageId', 'type': 'str'},
'docker_image_reference': {'key': 'dockerImageReference', 'type': 'str'},
'docker_image_security_groups': {'key': 'dockerImageSecurityGroups', 'type': 'str'},
'extended_properties': {'key': 'extendedProperties', 'type': 'AetherModuleExtendedProperties'},
'deployment_source': {'key': 'deploymentSource', 'type': 'str'},
'deployment_source_metadata': {'key': 'deploymentSourceMetadata', 'type': 'str'},
'identifier_hash': {'key': 'identifierHash', 'type': 'str'},
'identifier_hash_v2': {'key': 'identifierHashV2', 'type': 'str'},
'kv_tags': {'key': 'kvTags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'created_by': {'key': 'createdBy', 'type': 'AetherCreatedBy'},
'runconfig': {'key': 'runconfig', 'type': 'str'},
'cloud_settings': {'key': 'cloudSettings', 'type': 'AetherCloudSettings'},
'category': {'key': 'category', 'type': 'str'},
'step_type': {'key': 'stepType', 'type': 'str'},
'stage': {'key': 'stage', 'type': 'str'},
'upload_state': {'key': 'uploadState', 'type': 'str'},
'source_code_location': {'key': 'sourceCodeLocation', 'type': 'str'},
'size_in_bytes': {'key': 'sizeInBytes', 'type': 'long'},
'download_location': {'key': 'downloadLocation', 'type': 'str'},
'data_location': {'key': 'dataLocation', 'type': 'AetherDataLocation'},
'scripting_runtime_id': {'key': 'scriptingRuntimeId', 'type': 'str'},
'interface_documentation': {'key': 'interfaceDocumentation', 'type': 'AetherEntityInterfaceDocumentation'},
'is_eyes_on': {'key': 'isEyesOn', 'type': 'bool'},
'compliance_cluster': {'key': 'complianceCluster', 'type': 'str'},
'is_deterministic': {'key': 'isDeterministic', 'type': 'bool'},
'information_url': {'key': 'informationUrl', 'type': 'str'},
'is_experiment_id_in_parameters': {'key': 'isExperimentIdInParameters', 'type': 'bool'},
'interface_string': {'key': 'interfaceString', 'type': 'str'},
'default_parameters': {'key': 'defaultParameters', 'type': '{str}'},
'structured_interface': {'key': 'structuredInterface', 'type': 'AetherStructuredInterface'},
'family_id': {'key': 'familyId', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'hash': {'key': 'hash', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'sequence_number_in_family': {'key': 'sequenceNumberInFamily', 'type': 'int'},
'owner': {'key': 'owner', 'type': 'str'},
'azure_tenant_id': {'key': 'azureTenantId', 'type': 'str'},
'azure_user_id': {'key': 'azureUserId', 'type': 'str'},
'collaborators': {'key': 'collaborators', 'type': '[str]'},
'id': {'key': 'id', 'type': 'str'},
'workspace_id': {'key': 'workspaceId', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'tags': {'key': 'tags', 'type': '[str]'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword last_updated_by:
:paramtype last_updated_by: ~flow.models.AetherCreatedBy
:keyword display_name:
:paramtype display_name: str
:keyword module_execution_type:
:paramtype module_execution_type: str
:keyword module_type: Possible values include: "None", "BatchInferencing".
:paramtype module_type: str or ~flow.models.AetherModuleType
:keyword module_type_version:
:paramtype module_type_version: str
:keyword resource_requirements:
:paramtype resource_requirements: ~flow.models.AetherResourceModel
:keyword machine_cluster:
:paramtype machine_cluster: list[str]
:keyword default_compliance_cluster:
:paramtype default_compliance_cluster: str
:keyword repository_type: Possible values include: "None", "Other", "Git", "SourceDepot",
"Cosmos".
:paramtype repository_type: str or ~flow.models.AetherRepositoryType
:keyword relative_path_to_source_code:
:paramtype relative_path_to_source_code: str
:keyword commit_id:
:paramtype commit_id: str
:keyword code_review_link:
:paramtype code_review_link: str
:keyword unit_tests_available:
:paramtype unit_tests_available: bool
:keyword is_compressed:
:paramtype is_compressed: bool
:keyword execution_environment: Possible values include: "ExeWorkerMachine",
"DockerContainerWithoutNetwork", "DockerContainerWithNetwork", "HyperVWithoutNetwork",
"HyperVWithNetwork".
:paramtype execution_environment: str or ~flow.models.AetherExecutionEnvironment
:keyword is_output_markup_enabled:
:paramtype is_output_markup_enabled: bool
:keyword docker_image_id:
:paramtype docker_image_id: str
:keyword docker_image_reference:
:paramtype docker_image_reference: str
:keyword docker_image_security_groups:
:paramtype docker_image_security_groups: str
:keyword extended_properties:
:paramtype extended_properties: ~flow.models.AetherModuleExtendedProperties
:keyword deployment_source: Possible values include: "Client", "AutoDeployment", "Vsts".
:paramtype deployment_source: str or ~flow.models.AetherModuleDeploymentSource
:keyword deployment_source_metadata:
:paramtype deployment_source_metadata: str
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hash_v2:
:paramtype identifier_hash_v2: str
:keyword kv_tags: This is a dictionary.
:paramtype kv_tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword created_by:
:paramtype created_by: ~flow.models.AetherCreatedBy
:keyword runconfig:
:paramtype runconfig: str
:keyword cloud_settings:
:paramtype cloud_settings: ~flow.models.AetherCloudSettings
:keyword category:
:paramtype category: str
:keyword step_type:
:paramtype step_type: str
:keyword stage:
:paramtype stage: str
:keyword upload_state: Possible values include: "Uploading", "Completed", "Canceled", "Failed".
:paramtype upload_state: str or ~flow.models.AetherUploadState
:keyword source_code_location:
:paramtype source_code_location: str
:keyword size_in_bytes:
:paramtype size_in_bytes: long
:keyword download_location:
:paramtype download_location: str
:keyword data_location:
:paramtype data_location: ~flow.models.AetherDataLocation
:keyword scripting_runtime_id:
:paramtype scripting_runtime_id: str
:keyword interface_documentation:
:paramtype interface_documentation: ~flow.models.AetherEntityInterfaceDocumentation
:keyword is_eyes_on:
:paramtype is_eyes_on: bool
:keyword compliance_cluster:
:paramtype compliance_cluster: str
:keyword is_deterministic:
:paramtype is_deterministic: bool
:keyword information_url:
:paramtype information_url: str
:keyword is_experiment_id_in_parameters:
:paramtype is_experiment_id_in_parameters: bool
:keyword interface_string:
:paramtype interface_string: str
:keyword default_parameters: This is a dictionary.
:paramtype default_parameters: dict[str, str]
:keyword structured_interface:
:paramtype structured_interface: ~flow.models.AetherStructuredInterface
:keyword family_id:
:paramtype family_id: str
:keyword name:
:paramtype name: str
:keyword hash:
:paramtype hash: str
:keyword description:
:paramtype description: str
:keyword version:
:paramtype version: str
:keyword sequence_number_in_family:
:paramtype sequence_number_in_family: int
:keyword owner:
:paramtype owner: str
:keyword azure_tenant_id:
:paramtype azure_tenant_id: str
:keyword azure_user_id:
:paramtype azure_user_id: str
:keyword collaborators:
:paramtype collaborators: list[str]
:keyword id:
:paramtype id: str
:keyword workspace_id:
:paramtype workspace_id: str
:keyword etag:
:paramtype etag: str
:keyword tags: A set of tags.
:paramtype tags: list[str]
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.AetherEntityStatus
"""
super(AetherModuleEntity, self).__init__(**kwargs)
self.last_updated_by = kwargs.get('last_updated_by', None)
self.display_name = kwargs.get('display_name', None)
self.module_execution_type = kwargs.get('module_execution_type', None)
self.module_type = kwargs.get('module_type', None)
self.module_type_version = kwargs.get('module_type_version', None)
self.resource_requirements = kwargs.get('resource_requirements', None)
self.machine_cluster = kwargs.get('machine_cluster', None)
self.default_compliance_cluster = kwargs.get('default_compliance_cluster', None)
self.repository_type = kwargs.get('repository_type', None)
self.relative_path_to_source_code = kwargs.get('relative_path_to_source_code', None)
self.commit_id = kwargs.get('commit_id', None)
self.code_review_link = kwargs.get('code_review_link', None)
self.unit_tests_available = kwargs.get('unit_tests_available', None)
self.is_compressed = kwargs.get('is_compressed', None)
self.execution_environment = kwargs.get('execution_environment', None)
self.is_output_markup_enabled = kwargs.get('is_output_markup_enabled', None)
self.docker_image_id = kwargs.get('docker_image_id', None)
self.docker_image_reference = kwargs.get('docker_image_reference', None)
self.docker_image_security_groups = kwargs.get('docker_image_security_groups', None)
self.extended_properties = kwargs.get('extended_properties', None)
self.deployment_source = kwargs.get('deployment_source', None)
self.deployment_source_metadata = kwargs.get('deployment_source_metadata', None)
self.identifier_hash = kwargs.get('identifier_hash', None)
self.identifier_hash_v2 = kwargs.get('identifier_hash_v2', None)
self.kv_tags = kwargs.get('kv_tags', None)
self.properties = kwargs.get('properties', None)
self.created_by = kwargs.get('created_by', None)
self.runconfig = kwargs.get('runconfig', None)
self.cloud_settings = kwargs.get('cloud_settings', None)
self.category = kwargs.get('category', None)
self.step_type = kwargs.get('step_type', None)
self.stage = kwargs.get('stage', None)
self.upload_state = kwargs.get('upload_state', None)
self.source_code_location = kwargs.get('source_code_location', None)
self.size_in_bytes = kwargs.get('size_in_bytes', None)
self.download_location = kwargs.get('download_location', None)
self.data_location = kwargs.get('data_location', None)
self.scripting_runtime_id = kwargs.get('scripting_runtime_id', None)
self.interface_documentation = kwargs.get('interface_documentation', None)
self.is_eyes_on = kwargs.get('is_eyes_on', None)
self.compliance_cluster = kwargs.get('compliance_cluster', None)
self.is_deterministic = kwargs.get('is_deterministic', None)
self.information_url = kwargs.get('information_url', None)
self.is_experiment_id_in_parameters = kwargs.get('is_experiment_id_in_parameters', None)
self.interface_string = kwargs.get('interface_string', None)
self.default_parameters = kwargs.get('default_parameters', None)
self.structured_interface = kwargs.get('structured_interface', None)
self.family_id = kwargs.get('family_id', None)
self.name = kwargs.get('name', None)
self.hash = kwargs.get('hash', None)
self.description = kwargs.get('description', None)
self.version = kwargs.get('version', None)
self.sequence_number_in_family = kwargs.get('sequence_number_in_family', None)
self.owner = kwargs.get('owner', None)
self.azure_tenant_id = kwargs.get('azure_tenant_id', None)
self.azure_user_id = kwargs.get('azure_user_id', None)
self.collaborators = kwargs.get('collaborators', None)
self.id = kwargs.get('id', None)
self.workspace_id = kwargs.get('workspace_id', None)
self.etag = kwargs.get('etag', None)
self.tags = kwargs.get('tags', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
self.entity_status = kwargs.get('entity_status', None)
class AetherModuleExtendedProperties(msrest.serialization.Model):
"""AetherModuleExtendedProperties.
:ivar auto_deployed_artifact:
:vartype auto_deployed_artifact: ~flow.models.AetherBuildArtifactInfo
:ivar script_needs_approval:
:vartype script_needs_approval: bool
"""
_attribute_map = {
'auto_deployed_artifact': {'key': 'autoDeployedArtifact', 'type': 'AetherBuildArtifactInfo'},
'script_needs_approval': {'key': 'scriptNeedsApproval', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword auto_deployed_artifact:
:paramtype auto_deployed_artifact: ~flow.models.AetherBuildArtifactInfo
:keyword script_needs_approval:
:paramtype script_needs_approval: bool
"""
super(AetherModuleExtendedProperties, self).__init__(**kwargs)
self.auto_deployed_artifact = kwargs.get('auto_deployed_artifact', None)
self.script_needs_approval = kwargs.get('script_needs_approval', None)
class AetherNCrossValidations(msrest.serialization.Model):
"""AetherNCrossValidations.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.AetherNCrossValidationMode
:ivar value:
:vartype value: int
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'value': {'key': 'value', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.AetherNCrossValidationMode
:keyword value:
:paramtype value: int
"""
super(AetherNCrossValidations, self).__init__(**kwargs)
self.mode = kwargs.get('mode', None)
self.value = kwargs.get('value', None)
class AetherOutputSetting(msrest.serialization.Model):
"""AetherOutputSetting.
:ivar name:
:vartype name: str
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_store_name_parameter_assignment:
:vartype data_store_name_parameter_assignment: ~flow.models.AetherParameterAssignment
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AetherDataStoreMode
:ivar data_store_mode_parameter_assignment:
:vartype data_store_mode_parameter_assignment: ~flow.models.AetherParameterAssignment
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar path_on_compute_parameter_assignment:
:vartype path_on_compute_parameter_assignment: ~flow.models.AetherParameterAssignment
:ivar overwrite:
:vartype overwrite: bool
:ivar data_reference_name:
:vartype data_reference_name: str
:ivar web_service_port:
:vartype web_service_port: str
:ivar dataset_registration:
:vartype dataset_registration: ~flow.models.AetherDatasetRegistration
:ivar dataset_output_options:
:vartype dataset_output_options: ~flow.models.AetherDatasetOutputOptions
:ivar asset_output_settings:
:vartype asset_output_settings: ~flow.models.AetherAssetOutputSettings
:ivar parameter_name:
:vartype parameter_name: str
:ivar asset_output_settings_parameter_name:
:vartype asset_output_settings_parameter_name: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_store_name_parameter_assignment': {'key': 'DataStoreNameParameterAssignment', 'type': 'AetherParameterAssignment'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'data_store_mode_parameter_assignment': {'key': 'DataStoreModeParameterAssignment', 'type': 'AetherParameterAssignment'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'path_on_compute_parameter_assignment': {'key': 'PathOnComputeParameterAssignment', 'type': 'AetherParameterAssignment'},
'overwrite': {'key': 'overwrite', 'type': 'bool'},
'data_reference_name': {'key': 'dataReferenceName', 'type': 'str'},
'web_service_port': {'key': 'webServicePort', 'type': 'str'},
'dataset_registration': {'key': 'datasetRegistration', 'type': 'AetherDatasetRegistration'},
'dataset_output_options': {'key': 'datasetOutputOptions', 'type': 'AetherDatasetOutputOptions'},
'asset_output_settings': {'key': 'AssetOutputSettings', 'type': 'AetherAssetOutputSettings'},
'parameter_name': {'key': 'parameterName', 'type': 'str'},
'asset_output_settings_parameter_name': {'key': 'AssetOutputSettingsParameterName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_store_name_parameter_assignment:
:paramtype data_store_name_parameter_assignment: ~flow.models.AetherParameterAssignment
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AetherDataStoreMode
:keyword data_store_mode_parameter_assignment:
:paramtype data_store_mode_parameter_assignment: ~flow.models.AetherParameterAssignment
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword path_on_compute_parameter_assignment:
:paramtype path_on_compute_parameter_assignment: ~flow.models.AetherParameterAssignment
:keyword overwrite:
:paramtype overwrite: bool
:keyword data_reference_name:
:paramtype data_reference_name: str
:keyword web_service_port:
:paramtype web_service_port: str
:keyword dataset_registration:
:paramtype dataset_registration: ~flow.models.AetherDatasetRegistration
:keyword dataset_output_options:
:paramtype dataset_output_options: ~flow.models.AetherDatasetOutputOptions
:keyword asset_output_settings:
:paramtype asset_output_settings: ~flow.models.AetherAssetOutputSettings
:keyword parameter_name:
:paramtype parameter_name: str
:keyword asset_output_settings_parameter_name:
:paramtype asset_output_settings_parameter_name: str
"""
super(AetherOutputSetting, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.data_store_name = kwargs.get('data_store_name', None)
self.data_store_name_parameter_assignment = kwargs.get('data_store_name_parameter_assignment', None)
self.data_store_mode = kwargs.get('data_store_mode', None)
self.data_store_mode_parameter_assignment = kwargs.get('data_store_mode_parameter_assignment', None)
self.path_on_compute = kwargs.get('path_on_compute', None)
self.path_on_compute_parameter_assignment = kwargs.get('path_on_compute_parameter_assignment', None)
self.overwrite = kwargs.get('overwrite', None)
self.data_reference_name = kwargs.get('data_reference_name', None)
self.web_service_port = kwargs.get('web_service_port', None)
self.dataset_registration = kwargs.get('dataset_registration', None)
self.dataset_output_options = kwargs.get('dataset_output_options', None)
self.asset_output_settings = kwargs.get('asset_output_settings', None)
self.parameter_name = kwargs.get('parameter_name', None)
self.asset_output_settings_parameter_name = kwargs.get('asset_output_settings_parameter_name', None)
class AetherParallelForControlFlowInfo(msrest.serialization.Model):
"""AetherParallelForControlFlowInfo.
:ivar parallel_for_items_input:
:vartype parallel_for_items_input: ~flow.models.AetherParameterAssignment
"""
_attribute_map = {
'parallel_for_items_input': {'key': 'parallelForItemsInput', 'type': 'AetherParameterAssignment'},
}
def __init__(
self,
**kwargs
):
"""
:keyword parallel_for_items_input:
:paramtype parallel_for_items_input: ~flow.models.AetherParameterAssignment
"""
super(AetherParallelForControlFlowInfo, self).__init__(**kwargs)
self.parallel_for_items_input = kwargs.get('parallel_for_items_input', None)
class AetherParameterAssignment(msrest.serialization.Model):
"""AetherParameterAssignment.
:ivar value_type: Possible values include: "Literal", "GraphParameterName", "Concatenate",
"Input", "DataPath", "DataSetDefinition".
:vartype value_type: str or ~flow.models.AetherParameterValueType
:ivar assignments_to_concatenate:
:vartype assignments_to_concatenate: list[~flow.models.AetherParameterAssignment]
:ivar data_path_assignment:
:vartype data_path_assignment: ~flow.models.AetherLegacyDataPath
:ivar data_set_definition_value_assignment:
:vartype data_set_definition_value_assignment: ~flow.models.AetherDataSetDefinitionValue
:ivar name:
:vartype name: str
:ivar value:
:vartype value: str
"""
_attribute_map = {
'value_type': {'key': 'valueType', 'type': 'str'},
'assignments_to_concatenate': {'key': 'assignmentsToConcatenate', 'type': '[AetherParameterAssignment]'},
'data_path_assignment': {'key': 'dataPathAssignment', 'type': 'AetherLegacyDataPath'},
'data_set_definition_value_assignment': {'key': 'dataSetDefinitionValueAssignment', 'type': 'AetherDataSetDefinitionValue'},
'name': {'key': 'name', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword value_type: Possible values include: "Literal", "GraphParameterName", "Concatenate",
"Input", "DataPath", "DataSetDefinition".
:paramtype value_type: str or ~flow.models.AetherParameterValueType
:keyword assignments_to_concatenate:
:paramtype assignments_to_concatenate: list[~flow.models.AetherParameterAssignment]
:keyword data_path_assignment:
:paramtype data_path_assignment: ~flow.models.AetherLegacyDataPath
:keyword data_set_definition_value_assignment:
:paramtype data_set_definition_value_assignment: ~flow.models.AetherDataSetDefinitionValue
:keyword name:
:paramtype name: str
:keyword value:
:paramtype value: str
"""
super(AetherParameterAssignment, self).__init__(**kwargs)
self.value_type = kwargs.get('value_type', None)
self.assignments_to_concatenate = kwargs.get('assignments_to_concatenate', None)
self.data_path_assignment = kwargs.get('data_path_assignment', None)
self.data_set_definition_value_assignment = kwargs.get('data_set_definition_value_assignment', None)
self.name = kwargs.get('name', None)
self.value = kwargs.get('value', None)
class AetherPhillyHdfsReference(msrest.serialization.Model):
"""AetherPhillyHdfsReference.
:ivar cluster:
:vartype cluster: str
:ivar vc:
:vartype vc: str
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'cluster': {'key': 'cluster', 'type': 'str'},
'vc': {'key': 'vc', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword cluster:
:paramtype cluster: str
:keyword vc:
:paramtype vc: str
:keyword relative_path:
:paramtype relative_path: str
"""
super(AetherPhillyHdfsReference, self).__init__(**kwargs)
self.cluster = kwargs.get('cluster', None)
self.vc = kwargs.get('vc', None)
self.relative_path = kwargs.get('relative_path', None)
class AetherPortInfo(msrest.serialization.Model):
"""AetherPortInfo.
:ivar node_id:
:vartype node_id: str
:ivar port_name:
:vartype port_name: str
:ivar graph_port_name:
:vartype graph_port_name: str
:ivar is_parameter:
:vartype is_parameter: bool
:ivar web_service_port:
:vartype web_service_port: str
"""
_attribute_map = {
'node_id': {'key': 'nodeId', 'type': 'str'},
'port_name': {'key': 'portName', 'type': 'str'},
'graph_port_name': {'key': 'graphPortName', 'type': 'str'},
'is_parameter': {'key': 'isParameter', 'type': 'bool'},
'web_service_port': {'key': 'webServicePort', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword node_id:
:paramtype node_id: str
:keyword port_name:
:paramtype port_name: str
:keyword graph_port_name:
:paramtype graph_port_name: str
:keyword is_parameter:
:paramtype is_parameter: bool
:keyword web_service_port:
:paramtype web_service_port: str
"""
super(AetherPortInfo, self).__init__(**kwargs)
self.node_id = kwargs.get('node_id', None)
self.port_name = kwargs.get('port_name', None)
self.graph_port_name = kwargs.get('graph_port_name', None)
self.is_parameter = kwargs.get('is_parameter', None)
self.web_service_port = kwargs.get('web_service_port', None)
class AetherPriorityConfig(msrest.serialization.Model):
"""AetherPriorityConfig.
:ivar job_priority:
:vartype job_priority: int
:ivar is_preemptible:
:vartype is_preemptible: bool
:ivar node_count_set:
:vartype node_count_set: list[int]
:ivar scale_interval:
:vartype scale_interval: int
"""
_attribute_map = {
'job_priority': {'key': 'jobPriority', 'type': 'int'},
'is_preemptible': {'key': 'isPreemptible', 'type': 'bool'},
'node_count_set': {'key': 'nodeCountSet', 'type': '[int]'},
'scale_interval': {'key': 'scaleInterval', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword job_priority:
:paramtype job_priority: int
:keyword is_preemptible:
:paramtype is_preemptible: bool
:keyword node_count_set:
:paramtype node_count_set: list[int]
:keyword scale_interval:
:paramtype scale_interval: int
"""
super(AetherPriorityConfig, self).__init__(**kwargs)
self.job_priority = kwargs.get('job_priority', None)
self.is_preemptible = kwargs.get('is_preemptible', None)
self.node_count_set = kwargs.get('node_count_set', None)
self.scale_interval = kwargs.get('scale_interval', None)
class AetherPriorityConfiguration(msrest.serialization.Model):
"""AetherPriorityConfiguration.
:ivar cloud_priority:
:vartype cloud_priority: int
:ivar string_type_priority:
:vartype string_type_priority: str
"""
_attribute_map = {
'cloud_priority': {'key': 'cloudPriority', 'type': 'int'},
'string_type_priority': {'key': 'stringTypePriority', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword cloud_priority:
:paramtype cloud_priority: int
:keyword string_type_priority:
:paramtype string_type_priority: str
"""
super(AetherPriorityConfiguration, self).__init__(**kwargs)
self.cloud_priority = kwargs.get('cloud_priority', None)
self.string_type_priority = kwargs.get('string_type_priority', None)
class AetherRegisteredDataSetReference(msrest.serialization.Model):
"""AetherRegisteredDataSetReference.
:ivar id:
:vartype id: str
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
"""
super(AetherRegisteredDataSetReference, self).__init__(**kwargs)
self.id = kwargs.get('id', None)
self.name = kwargs.get('name', None)
self.version = kwargs.get('version', None)
class AetherRemoteDockerComputeInfo(msrest.serialization.Model):
"""AetherRemoteDockerComputeInfo.
:ivar address:
:vartype address: str
:ivar username:
:vartype username: str
:ivar password:
:vartype password: str
:ivar private_key:
:vartype private_key: str
"""
_attribute_map = {
'address': {'key': 'address', 'type': 'str'},
'username': {'key': 'username', 'type': 'str'},
'password': {'key': 'password', 'type': 'str'},
'private_key': {'key': 'privateKey', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword address:
:paramtype address: str
:keyword username:
:paramtype username: str
:keyword password:
:paramtype password: str
:keyword private_key:
:paramtype private_key: str
"""
super(AetherRemoteDockerComputeInfo, self).__init__(**kwargs)
self.address = kwargs.get('address', None)
self.username = kwargs.get('username', None)
self.password = kwargs.get('password', None)
self.private_key = kwargs.get('private_key', None)
class AetherResourceAssignment(msrest.serialization.Model):
"""AetherResourceAssignment.
:ivar attributes: Dictionary of :code:`<AetherResourceAttributeAssignment>`.
:vartype attributes: dict[str, ~flow.models.AetherResourceAttributeAssignment]
"""
_attribute_map = {
'attributes': {'key': 'attributes', 'type': '{AetherResourceAttributeAssignment}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword attributes: Dictionary of :code:`<AetherResourceAttributeAssignment>`.
:paramtype attributes: dict[str, ~flow.models.AetherResourceAttributeAssignment]
"""
super(AetherResourceAssignment, self).__init__(**kwargs)
self.attributes = kwargs.get('attributes', None)
class AetherResourceAttributeAssignment(msrest.serialization.Model):
"""AetherResourceAttributeAssignment.
:ivar attribute:
:vartype attribute: ~flow.models.AetherResourceAttributeDefinition
:ivar operator: Possible values include: "Equal", "Contain", "GreaterOrEqual".
:vartype operator: str or ~flow.models.AetherResourceOperator
:ivar value:
:vartype value: str
"""
_attribute_map = {
'attribute': {'key': 'attribute', 'type': 'AetherResourceAttributeDefinition'},
'operator': {'key': 'operator', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword attribute:
:paramtype attribute: ~flow.models.AetherResourceAttributeDefinition
:keyword operator: Possible values include: "Equal", "Contain", "GreaterOrEqual".
:paramtype operator: str or ~flow.models.AetherResourceOperator
:keyword value:
:paramtype value: str
"""
super(AetherResourceAttributeAssignment, self).__init__(**kwargs)
self.attribute = kwargs.get('attribute', None)
self.operator = kwargs.get('operator', None)
self.value = kwargs.get('value', None)
class AetherResourceAttributeDefinition(msrest.serialization.Model):
"""AetherResourceAttributeDefinition.
:ivar name:
:vartype name: str
:ivar type: Possible values include: "String", "Double".
:vartype type: str or ~flow.models.AetherResourceValueType
:ivar units:
:vartype units: str
:ivar allowed_operators:
:vartype allowed_operators: list[str or ~flow.models.AetherResourceOperator]
"""
_validation = {
'allowed_operators': {'unique': True},
}
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'units': {'key': 'units', 'type': 'str'},
'allowed_operators': {'key': 'allowedOperators', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type: Possible values include: "String", "Double".
:paramtype type: str or ~flow.models.AetherResourceValueType
:keyword units:
:paramtype units: str
:keyword allowed_operators:
:paramtype allowed_operators: list[str or ~flow.models.AetherResourceOperator]
"""
super(AetherResourceAttributeDefinition, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.type = kwargs.get('type', None)
self.units = kwargs.get('units', None)
self.allowed_operators = kwargs.get('allowed_operators', None)
class AetherResourceConfig(msrest.serialization.Model):
"""AetherResourceConfig.
:ivar gpu_count:
:vartype gpu_count: int
:ivar cpu_count:
:vartype cpu_count: int
:ivar memory_request_in_gb:
:vartype memory_request_in_gb: int
"""
_attribute_map = {
'gpu_count': {'key': 'gpuCount', 'type': 'int'},
'cpu_count': {'key': 'cpuCount', 'type': 'int'},
'memory_request_in_gb': {'key': 'memoryRequestInGB', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword gpu_count:
:paramtype gpu_count: int
:keyword cpu_count:
:paramtype cpu_count: int
:keyword memory_request_in_gb:
:paramtype memory_request_in_gb: int
"""
super(AetherResourceConfig, self).__init__(**kwargs)
self.gpu_count = kwargs.get('gpu_count', None)
self.cpu_count = kwargs.get('cpu_count', None)
self.memory_request_in_gb = kwargs.get('memory_request_in_gb', None)
class AetherResourceConfiguration(msrest.serialization.Model):
"""AetherResourceConfiguration.
:ivar instance_count:
:vartype instance_count: int
:ivar instance_type:
:vartype instance_type: str
:ivar properties: Dictionary of :code:`<any>`.
:vartype properties: dict[str, any]
:ivar locations:
:vartype locations: list[str]
:ivar instance_priority:
:vartype instance_priority: str
:ivar quota_enforcement_resource_id:
:vartype quota_enforcement_resource_id: str
"""
_attribute_map = {
'instance_count': {'key': 'instanceCount', 'type': 'int'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{object}'},
'locations': {'key': 'locations', 'type': '[str]'},
'instance_priority': {'key': 'instancePriority', 'type': 'str'},
'quota_enforcement_resource_id': {'key': 'quotaEnforcementResourceId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword instance_count:
:paramtype instance_count: int
:keyword instance_type:
:paramtype instance_type: str
:keyword properties: Dictionary of :code:`<any>`.
:paramtype properties: dict[str, any]
:keyword locations:
:paramtype locations: list[str]
:keyword instance_priority:
:paramtype instance_priority: str
:keyword quota_enforcement_resource_id:
:paramtype quota_enforcement_resource_id: str
"""
super(AetherResourceConfiguration, self).__init__(**kwargs)
self.instance_count = kwargs.get('instance_count', None)
self.instance_type = kwargs.get('instance_type', None)
self.properties = kwargs.get('properties', None)
self.locations = kwargs.get('locations', None)
self.instance_priority = kwargs.get('instance_priority', None)
self.quota_enforcement_resource_id = kwargs.get('quota_enforcement_resource_id', None)
class AetherResourceModel(msrest.serialization.Model):
"""AetherResourceModel.
:ivar resources:
:vartype resources: list[~flow.models.AetherResourceAssignment]
"""
_attribute_map = {
'resources': {'key': 'resources', 'type': '[AetherResourceAssignment]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword resources:
:paramtype resources: list[~flow.models.AetherResourceAssignment]
"""
super(AetherResourceModel, self).__init__(**kwargs)
self.resources = kwargs.get('resources', None)
class AetherResourcesSetting(msrest.serialization.Model):
"""AetherResourcesSetting.
:ivar instance_size:
:vartype instance_size: str
:ivar spark_version:
:vartype spark_version: str
"""
_attribute_map = {
'instance_size': {'key': 'instanceSize', 'type': 'str'},
'spark_version': {'key': 'sparkVersion', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword instance_size:
:paramtype instance_size: str
:keyword spark_version:
:paramtype spark_version: str
"""
super(AetherResourcesSetting, self).__init__(**kwargs)
self.instance_size = kwargs.get('instance_size', None)
self.spark_version = kwargs.get('spark_version', None)
class AetherSavedDataSetReference(msrest.serialization.Model):
"""AetherSavedDataSetReference.
:ivar id:
:vartype id: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword id:
:paramtype id: str
"""
super(AetherSavedDataSetReference, self).__init__(**kwargs)
self.id = kwargs.get('id', None)
class AetherScopeCloudConfiguration(msrest.serialization.Model):
"""AetherScopeCloudConfiguration.
:ivar input_path_suffixes: This is a dictionary.
:vartype input_path_suffixes: dict[str, ~flow.models.AetherArgumentAssignment]
:ivar output_path_suffixes: This is a dictionary.
:vartype output_path_suffixes: dict[str, ~flow.models.AetherArgumentAssignment]
:ivar user_alias:
:vartype user_alias: str
:ivar tokens:
:vartype tokens: int
:ivar auto_token:
:vartype auto_token: int
:ivar vcp:
:vartype vcp: float
"""
_attribute_map = {
'input_path_suffixes': {'key': 'inputPathSuffixes', 'type': '{AetherArgumentAssignment}'},
'output_path_suffixes': {'key': 'outputPathSuffixes', 'type': '{AetherArgumentAssignment}'},
'user_alias': {'key': 'userAlias', 'type': 'str'},
'tokens': {'key': 'tokens', 'type': 'int'},
'auto_token': {'key': 'autoToken', 'type': 'int'},
'vcp': {'key': 'vcp', 'type': 'float'},
}
def __init__(
self,
**kwargs
):
"""
:keyword input_path_suffixes: This is a dictionary.
:paramtype input_path_suffixes: dict[str, ~flow.models.AetherArgumentAssignment]
:keyword output_path_suffixes: This is a dictionary.
:paramtype output_path_suffixes: dict[str, ~flow.models.AetherArgumentAssignment]
:keyword user_alias:
:paramtype user_alias: str
:keyword tokens:
:paramtype tokens: int
:keyword auto_token:
:paramtype auto_token: int
:keyword vcp:
:paramtype vcp: float
"""
super(AetherScopeCloudConfiguration, self).__init__(**kwargs)
self.input_path_suffixes = kwargs.get('input_path_suffixes', None)
self.output_path_suffixes = kwargs.get('output_path_suffixes', None)
self.user_alias = kwargs.get('user_alias', None)
self.tokens = kwargs.get('tokens', None)
self.auto_token = kwargs.get('auto_token', None)
self.vcp = kwargs.get('vcp', None)
class AetherSeasonality(msrest.serialization.Model):
"""AetherSeasonality.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.AetherSeasonalityMode
:ivar value:
:vartype value: int
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'value': {'key': 'value', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.AetherSeasonalityMode
:keyword value:
:paramtype value: int
"""
super(AetherSeasonality, self).__init__(**kwargs)
self.mode = kwargs.get('mode', None)
self.value = kwargs.get('value', None)
class AetherSqlDataPath(msrest.serialization.Model):
"""AetherSqlDataPath.
:ivar sql_table_name:
:vartype sql_table_name: str
:ivar sql_query:
:vartype sql_query: str
:ivar sql_stored_procedure_name:
:vartype sql_stored_procedure_name: str
:ivar sql_stored_procedure_params:
:vartype sql_stored_procedure_params: list[~flow.models.AetherStoredProcedureParameter]
"""
_attribute_map = {
'sql_table_name': {'key': 'sqlTableName', 'type': 'str'},
'sql_query': {'key': 'sqlQuery', 'type': 'str'},
'sql_stored_procedure_name': {'key': 'sqlStoredProcedureName', 'type': 'str'},
'sql_stored_procedure_params': {'key': 'sqlStoredProcedureParams', 'type': '[AetherStoredProcedureParameter]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword sql_table_name:
:paramtype sql_table_name: str
:keyword sql_query:
:paramtype sql_query: str
:keyword sql_stored_procedure_name:
:paramtype sql_stored_procedure_name: str
:keyword sql_stored_procedure_params:
:paramtype sql_stored_procedure_params: list[~flow.models.AetherStoredProcedureParameter]
"""
super(AetherSqlDataPath, self).__init__(**kwargs)
self.sql_table_name = kwargs.get('sql_table_name', None)
self.sql_query = kwargs.get('sql_query', None)
self.sql_stored_procedure_name = kwargs.get('sql_stored_procedure_name', None)
self.sql_stored_procedure_params = kwargs.get('sql_stored_procedure_params', None)
class AetherStackEnsembleSettings(msrest.serialization.Model):
"""AetherStackEnsembleSettings.
:ivar stack_meta_learner_type: Possible values include: "None", "LogisticRegression",
"LogisticRegressionCV", "LightGBMClassifier", "ElasticNet", "ElasticNetCV",
"LightGBMRegressor", "LinearRegression".
:vartype stack_meta_learner_type: str or ~flow.models.AetherStackMetaLearnerType
:ivar stack_meta_learner_train_percentage:
:vartype stack_meta_learner_train_percentage: float
:ivar stack_meta_learner_k_wargs: Anything.
:vartype stack_meta_learner_k_wargs: any
"""
_attribute_map = {
'stack_meta_learner_type': {'key': 'stackMetaLearnerType', 'type': 'str'},
'stack_meta_learner_train_percentage': {'key': 'stackMetaLearnerTrainPercentage', 'type': 'float'},
'stack_meta_learner_k_wargs': {'key': 'stackMetaLearnerKWargs', 'type': 'object'},
}
def __init__(
self,
**kwargs
):
"""
:keyword stack_meta_learner_type: Possible values include: "None", "LogisticRegression",
"LogisticRegressionCV", "LightGBMClassifier", "ElasticNet", "ElasticNetCV",
"LightGBMRegressor", "LinearRegression".
:paramtype stack_meta_learner_type: str or ~flow.models.AetherStackMetaLearnerType
:keyword stack_meta_learner_train_percentage:
:paramtype stack_meta_learner_train_percentage: float
:keyword stack_meta_learner_k_wargs: Anything.
:paramtype stack_meta_learner_k_wargs: any
"""
super(AetherStackEnsembleSettings, self).__init__(**kwargs)
self.stack_meta_learner_type = kwargs.get('stack_meta_learner_type', None)
self.stack_meta_learner_train_percentage = kwargs.get('stack_meta_learner_train_percentage', None)
self.stack_meta_learner_k_wargs = kwargs.get('stack_meta_learner_k_wargs', None)
class AetherStoredProcedureParameter(msrest.serialization.Model):
"""AetherStoredProcedureParameter.
:ivar name:
:vartype name: str
:ivar value:
:vartype value: str
:ivar type: Possible values include: "String", "Int", "Decimal", "Guid", "Boolean", "Date".
:vartype type: str or ~flow.models.AetherStoredProcedureParameterType
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword value:
:paramtype value: str
:keyword type: Possible values include: "String", "Int", "Decimal", "Guid", "Boolean", "Date".
:paramtype type: str or ~flow.models.AetherStoredProcedureParameterType
"""
super(AetherStoredProcedureParameter, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.value = kwargs.get('value', None)
self.type = kwargs.get('type', None)
class AetherStructuredInterface(msrest.serialization.Model):
"""AetherStructuredInterface.
:ivar command_line_pattern:
:vartype command_line_pattern: str
:ivar inputs:
:vartype inputs: list[~flow.models.AetherStructuredInterfaceInput]
:ivar outputs:
:vartype outputs: list[~flow.models.AetherStructuredInterfaceOutput]
:ivar control_outputs:
:vartype control_outputs: list[~flow.models.AetherControlOutput]
:ivar parameters:
:vartype parameters: list[~flow.models.AetherStructuredInterfaceParameter]
:ivar metadata_parameters:
:vartype metadata_parameters: list[~flow.models.AetherStructuredInterfaceParameter]
:ivar arguments:
:vartype arguments: list[~flow.models.AetherArgumentAssignment]
"""
_attribute_map = {
'command_line_pattern': {'key': 'commandLinePattern', 'type': 'str'},
'inputs': {'key': 'inputs', 'type': '[AetherStructuredInterfaceInput]'},
'outputs': {'key': 'outputs', 'type': '[AetherStructuredInterfaceOutput]'},
'control_outputs': {'key': 'controlOutputs', 'type': '[AetherControlOutput]'},
'parameters': {'key': 'parameters', 'type': '[AetherStructuredInterfaceParameter]'},
'metadata_parameters': {'key': 'metadataParameters', 'type': '[AetherStructuredInterfaceParameter]'},
'arguments': {'key': 'arguments', 'type': '[AetherArgumentAssignment]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword command_line_pattern:
:paramtype command_line_pattern: str
:keyword inputs:
:paramtype inputs: list[~flow.models.AetherStructuredInterfaceInput]
:keyword outputs:
:paramtype outputs: list[~flow.models.AetherStructuredInterfaceOutput]
:keyword control_outputs:
:paramtype control_outputs: list[~flow.models.AetherControlOutput]
:keyword parameters:
:paramtype parameters: list[~flow.models.AetherStructuredInterfaceParameter]
:keyword metadata_parameters:
:paramtype metadata_parameters: list[~flow.models.AetherStructuredInterfaceParameter]
:keyword arguments:
:paramtype arguments: list[~flow.models.AetherArgumentAssignment]
"""
super(AetherStructuredInterface, self).__init__(**kwargs)
self.command_line_pattern = kwargs.get('command_line_pattern', None)
self.inputs = kwargs.get('inputs', None)
self.outputs = kwargs.get('outputs', None)
self.control_outputs = kwargs.get('control_outputs', None)
self.parameters = kwargs.get('parameters', None)
self.metadata_parameters = kwargs.get('metadata_parameters', None)
self.arguments = kwargs.get('arguments', None)
class AetherStructuredInterfaceInput(msrest.serialization.Model):
"""AetherStructuredInterfaceInput.
:ivar name:
:vartype name: str
:ivar label:
:vartype label: str
:ivar data_type_ids_list:
:vartype data_type_ids_list: list[str]
:ivar is_optional:
:vartype is_optional: bool
:ivar description:
:vartype description: str
:ivar skip_processing:
:vartype skip_processing: bool
:ivar is_resource:
:vartype is_resource: bool
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AetherDataStoreMode
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar overwrite:
:vartype overwrite: bool
:ivar data_reference_name:
:vartype data_reference_name: str
:ivar dataset_types:
:vartype dataset_types: list[str or ~flow.models.AetherDatasetType]
:ivar additional_transformations:
:vartype additional_transformations: str
"""
_validation = {
'dataset_types': {'unique': True},
}
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'data_type_ids_list': {'key': 'dataTypeIdsList', 'type': '[str]'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'skip_processing': {'key': 'skipProcessing', 'type': 'bool'},
'is_resource': {'key': 'isResource', 'type': 'bool'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'overwrite': {'key': 'overwrite', 'type': 'bool'},
'data_reference_name': {'key': 'dataReferenceName', 'type': 'str'},
'dataset_types': {'key': 'datasetTypes', 'type': '[str]'},
'additional_transformations': {'key': 'additionalTransformations', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword label:
:paramtype label: str
:keyword data_type_ids_list:
:paramtype data_type_ids_list: list[str]
:keyword is_optional:
:paramtype is_optional: bool
:keyword description:
:paramtype description: str
:keyword skip_processing:
:paramtype skip_processing: bool
:keyword is_resource:
:paramtype is_resource: bool
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AetherDataStoreMode
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword overwrite:
:paramtype overwrite: bool
:keyword data_reference_name:
:paramtype data_reference_name: str
:keyword dataset_types:
:paramtype dataset_types: list[str or ~flow.models.AetherDatasetType]
:keyword additional_transformations:
:paramtype additional_transformations: str
"""
super(AetherStructuredInterfaceInput, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.label = kwargs.get('label', None)
self.data_type_ids_list = kwargs.get('data_type_ids_list', None)
self.is_optional = kwargs.get('is_optional', None)
self.description = kwargs.get('description', None)
self.skip_processing = kwargs.get('skip_processing', None)
self.is_resource = kwargs.get('is_resource', None)
self.data_store_mode = kwargs.get('data_store_mode', None)
self.path_on_compute = kwargs.get('path_on_compute', None)
self.overwrite = kwargs.get('overwrite', None)
self.data_reference_name = kwargs.get('data_reference_name', None)
self.dataset_types = kwargs.get('dataset_types', None)
self.additional_transformations = kwargs.get('additional_transformations', None)
class AetherStructuredInterfaceOutput(msrest.serialization.Model):
"""AetherStructuredInterfaceOutput.
:ivar name:
:vartype name: str
:ivar label:
:vartype label: str
:ivar data_type_id:
:vartype data_type_id: str
:ivar pass_through_data_type_input_name:
:vartype pass_through_data_type_input_name: str
:ivar description:
:vartype description: str
:ivar skip_processing:
:vartype skip_processing: bool
:ivar is_artifact:
:vartype is_artifact: bool
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AetherDataStoreMode
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar overwrite:
:vartype overwrite: bool
:ivar data_reference_name:
:vartype data_reference_name: str
:ivar training_output:
:vartype training_output: ~flow.models.AetherTrainingOutput
:ivar dataset_output:
:vartype dataset_output: ~flow.models.AetherDatasetOutput
:ivar asset_output_settings:
:vartype asset_output_settings: ~flow.models.AetherAssetOutputSettings
:ivar early_available:
:vartype early_available: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'data_type_id': {'key': 'dataTypeId', 'type': 'str'},
'pass_through_data_type_input_name': {'key': 'passThroughDataTypeInputName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'skip_processing': {'key': 'skipProcessing', 'type': 'bool'},
'is_artifact': {'key': 'isArtifact', 'type': 'bool'},
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'overwrite': {'key': 'overwrite', 'type': 'bool'},
'data_reference_name': {'key': 'dataReferenceName', 'type': 'str'},
'training_output': {'key': 'trainingOutput', 'type': 'AetherTrainingOutput'},
'dataset_output': {'key': 'datasetOutput', 'type': 'AetherDatasetOutput'},
'asset_output_settings': {'key': 'AssetOutputSettings', 'type': 'AetherAssetOutputSettings'},
'early_available': {'key': 'earlyAvailable', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword label:
:paramtype label: str
:keyword data_type_id:
:paramtype data_type_id: str
:keyword pass_through_data_type_input_name:
:paramtype pass_through_data_type_input_name: str
:keyword description:
:paramtype description: str
:keyword skip_processing:
:paramtype skip_processing: bool
:keyword is_artifact:
:paramtype is_artifact: bool
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AetherDataStoreMode
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword overwrite:
:paramtype overwrite: bool
:keyword data_reference_name:
:paramtype data_reference_name: str
:keyword training_output:
:paramtype training_output: ~flow.models.AetherTrainingOutput
:keyword dataset_output:
:paramtype dataset_output: ~flow.models.AetherDatasetOutput
:keyword asset_output_settings:
:paramtype asset_output_settings: ~flow.models.AetherAssetOutputSettings
:keyword early_available:
:paramtype early_available: bool
"""
super(AetherStructuredInterfaceOutput, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.label = kwargs.get('label', None)
self.data_type_id = kwargs.get('data_type_id', None)
self.pass_through_data_type_input_name = kwargs.get('pass_through_data_type_input_name', None)
self.description = kwargs.get('description', None)
self.skip_processing = kwargs.get('skip_processing', None)
self.is_artifact = kwargs.get('is_artifact', None)
self.data_store_name = kwargs.get('data_store_name', None)
self.data_store_mode = kwargs.get('data_store_mode', None)
self.path_on_compute = kwargs.get('path_on_compute', None)
self.overwrite = kwargs.get('overwrite', None)
self.data_reference_name = kwargs.get('data_reference_name', None)
self.training_output = kwargs.get('training_output', None)
self.dataset_output = kwargs.get('dataset_output', None)
self.asset_output_settings = kwargs.get('asset_output_settings', None)
self.early_available = kwargs.get('early_available', None)
class AetherStructuredInterfaceParameter(msrest.serialization.Model):
"""AetherStructuredInterfaceParameter.
:ivar name:
:vartype name: str
:ivar label:
:vartype label: str
:ivar parameter_type: Possible values include: "Int", "Double", "Bool", "String", "Undefined".
:vartype parameter_type: str or ~flow.models.AetherParameterType
:ivar is_optional:
:vartype is_optional: bool
:ivar default_value:
:vartype default_value: str
:ivar lower_bound:
:vartype lower_bound: str
:ivar upper_bound:
:vartype upper_bound: str
:ivar enum_values:
:vartype enum_values: list[str]
:ivar enum_values_to_argument_strings: This is a dictionary.
:vartype enum_values_to_argument_strings: dict[str, str]
:ivar description:
:vartype description: str
:ivar set_environment_variable:
:vartype set_environment_variable: bool
:ivar environment_variable_override:
:vartype environment_variable_override: str
:ivar enabled_by_parameter_name:
:vartype enabled_by_parameter_name: str
:ivar enabled_by_parameter_values:
:vartype enabled_by_parameter_values: list[str]
:ivar ui_hint:
:vartype ui_hint: ~flow.models.AetherUIParameterHint
:ivar group_names:
:vartype group_names: list[str]
:ivar argument_name:
:vartype argument_name: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'parameter_type': {'key': 'parameterType', 'type': 'str'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
'default_value': {'key': 'defaultValue', 'type': 'str'},
'lower_bound': {'key': 'lowerBound', 'type': 'str'},
'upper_bound': {'key': 'upperBound', 'type': 'str'},
'enum_values': {'key': 'enumValues', 'type': '[str]'},
'enum_values_to_argument_strings': {'key': 'enumValuesToArgumentStrings', 'type': '{str}'},
'description': {'key': 'description', 'type': 'str'},
'set_environment_variable': {'key': 'setEnvironmentVariable', 'type': 'bool'},
'environment_variable_override': {'key': 'environmentVariableOverride', 'type': 'str'},
'enabled_by_parameter_name': {'key': 'enabledByParameterName', 'type': 'str'},
'enabled_by_parameter_values': {'key': 'enabledByParameterValues', 'type': '[str]'},
'ui_hint': {'key': 'uiHint', 'type': 'AetherUIParameterHint'},
'group_names': {'key': 'groupNames', 'type': '[str]'},
'argument_name': {'key': 'argumentName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword label:
:paramtype label: str
:keyword parameter_type: Possible values include: "Int", "Double", "Bool", "String",
"Undefined".
:paramtype parameter_type: str or ~flow.models.AetherParameterType
:keyword is_optional:
:paramtype is_optional: bool
:keyword default_value:
:paramtype default_value: str
:keyword lower_bound:
:paramtype lower_bound: str
:keyword upper_bound:
:paramtype upper_bound: str
:keyword enum_values:
:paramtype enum_values: list[str]
:keyword enum_values_to_argument_strings: This is a dictionary.
:paramtype enum_values_to_argument_strings: dict[str, str]
:keyword description:
:paramtype description: str
:keyword set_environment_variable:
:paramtype set_environment_variable: bool
:keyword environment_variable_override:
:paramtype environment_variable_override: str
:keyword enabled_by_parameter_name:
:paramtype enabled_by_parameter_name: str
:keyword enabled_by_parameter_values:
:paramtype enabled_by_parameter_values: list[str]
:keyword ui_hint:
:paramtype ui_hint: ~flow.models.AetherUIParameterHint
:keyword group_names:
:paramtype group_names: list[str]
:keyword argument_name:
:paramtype argument_name: str
"""
super(AetherStructuredInterfaceParameter, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.label = kwargs.get('label', None)
self.parameter_type = kwargs.get('parameter_type', None)
self.is_optional = kwargs.get('is_optional', None)
self.default_value = kwargs.get('default_value', None)
self.lower_bound = kwargs.get('lower_bound', None)
self.upper_bound = kwargs.get('upper_bound', None)
self.enum_values = kwargs.get('enum_values', None)
self.enum_values_to_argument_strings = kwargs.get('enum_values_to_argument_strings', None)
self.description = kwargs.get('description', None)
self.set_environment_variable = kwargs.get('set_environment_variable', None)
self.environment_variable_override = kwargs.get('environment_variable_override', None)
self.enabled_by_parameter_name = kwargs.get('enabled_by_parameter_name', None)
self.enabled_by_parameter_values = kwargs.get('enabled_by_parameter_values', None)
self.ui_hint = kwargs.get('ui_hint', None)
self.group_names = kwargs.get('group_names', None)
self.argument_name = kwargs.get('argument_name', None)
class AetherSubGraphConfiguration(msrest.serialization.Model):
"""AetherSubGraphConfiguration.
:ivar graph_id:
:vartype graph_id: str
:ivar graph_draft_id:
:vartype graph_draft_id: str
:ivar default_compute_internal:
:vartype default_compute_internal: ~flow.models.AetherComputeSetting
:ivar default_datastore_internal:
:vartype default_datastore_internal: ~flow.models.AetherDatastoreSetting
:ivar default_cloud_priority:
:vartype default_cloud_priority: ~flow.models.AetherCloudPrioritySetting
:ivar user_alias:
:vartype user_alias: str
:ivar is_dynamic:
:vartype is_dynamic: bool
"""
_attribute_map = {
'graph_id': {'key': 'graphId', 'type': 'str'},
'graph_draft_id': {'key': 'graphDraftId', 'type': 'str'},
'default_compute_internal': {'key': 'defaultComputeInternal', 'type': 'AetherComputeSetting'},
'default_datastore_internal': {'key': 'defaultDatastoreInternal', 'type': 'AetherDatastoreSetting'},
'default_cloud_priority': {'key': 'DefaultCloudPriority', 'type': 'AetherCloudPrioritySetting'},
'user_alias': {'key': 'UserAlias', 'type': 'str'},
'is_dynamic': {'key': 'IsDynamic', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword graph_id:
:paramtype graph_id: str
:keyword graph_draft_id:
:paramtype graph_draft_id: str
:keyword default_compute_internal:
:paramtype default_compute_internal: ~flow.models.AetherComputeSetting
:keyword default_datastore_internal:
:paramtype default_datastore_internal: ~flow.models.AetherDatastoreSetting
:keyword default_cloud_priority:
:paramtype default_cloud_priority: ~flow.models.AetherCloudPrioritySetting
:keyword user_alias:
:paramtype user_alias: str
:keyword is_dynamic:
:paramtype is_dynamic: bool
"""
super(AetherSubGraphConfiguration, self).__init__(**kwargs)
self.graph_id = kwargs.get('graph_id', None)
self.graph_draft_id = kwargs.get('graph_draft_id', None)
self.default_compute_internal = kwargs.get('default_compute_internal', None)
self.default_datastore_internal = kwargs.get('default_datastore_internal', None)
self.default_cloud_priority = kwargs.get('default_cloud_priority', None)
self.user_alias = kwargs.get('user_alias', None)
self.is_dynamic = kwargs.get('is_dynamic', False)
class AetherSweepEarlyTerminationPolicy(msrest.serialization.Model):
"""AetherSweepEarlyTerminationPolicy.
:ivar policy_type: Possible values include: "Bandit", "MedianStopping", "TruncationSelection".
:vartype policy_type: str or ~flow.models.AetherEarlyTerminationPolicyType
:ivar evaluation_interval:
:vartype evaluation_interval: int
:ivar delay_evaluation:
:vartype delay_evaluation: int
:ivar slack_factor:
:vartype slack_factor: float
:ivar slack_amount:
:vartype slack_amount: float
:ivar truncation_percentage:
:vartype truncation_percentage: int
"""
_attribute_map = {
'policy_type': {'key': 'policyType', 'type': 'str'},
'evaluation_interval': {'key': 'evaluationInterval', 'type': 'int'},
'delay_evaluation': {'key': 'delayEvaluation', 'type': 'int'},
'slack_factor': {'key': 'slackFactor', 'type': 'float'},
'slack_amount': {'key': 'slackAmount', 'type': 'float'},
'truncation_percentage': {'key': 'truncationPercentage', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword policy_type: Possible values include: "Bandit", "MedianStopping",
"TruncationSelection".
:paramtype policy_type: str or ~flow.models.AetherEarlyTerminationPolicyType
:keyword evaluation_interval:
:paramtype evaluation_interval: int
:keyword delay_evaluation:
:paramtype delay_evaluation: int
:keyword slack_factor:
:paramtype slack_factor: float
:keyword slack_amount:
:paramtype slack_amount: float
:keyword truncation_percentage:
:paramtype truncation_percentage: int
"""
super(AetherSweepEarlyTerminationPolicy, self).__init__(**kwargs)
self.policy_type = kwargs.get('policy_type', None)
self.evaluation_interval = kwargs.get('evaluation_interval', None)
self.delay_evaluation = kwargs.get('delay_evaluation', None)
self.slack_factor = kwargs.get('slack_factor', None)
self.slack_amount = kwargs.get('slack_amount', None)
self.truncation_percentage = kwargs.get('truncation_percentage', None)
class AetherSweepSettings(msrest.serialization.Model):
"""AetherSweepSettings.
:ivar limits:
:vartype limits: ~flow.models.AetherSweepSettingsLimits
:ivar search_space:
:vartype search_space: list[dict[str, str]]
:ivar sampling_algorithm: Possible values include: "Random", "Grid", "Bayesian".
:vartype sampling_algorithm: str or ~flow.models.AetherSamplingAlgorithmType
:ivar early_termination:
:vartype early_termination: ~flow.models.AetherSweepEarlyTerminationPolicy
"""
_attribute_map = {
'limits': {'key': 'limits', 'type': 'AetherSweepSettingsLimits'},
'search_space': {'key': 'searchSpace', 'type': '[{str}]'},
'sampling_algorithm': {'key': 'samplingAlgorithm', 'type': 'str'},
'early_termination': {'key': 'earlyTermination', 'type': 'AetherSweepEarlyTerminationPolicy'},
}
def __init__(
self,
**kwargs
):
"""
:keyword limits:
:paramtype limits: ~flow.models.AetherSweepSettingsLimits
:keyword search_space:
:paramtype search_space: list[dict[str, str]]
:keyword sampling_algorithm: Possible values include: "Random", "Grid", "Bayesian".
:paramtype sampling_algorithm: str or ~flow.models.AetherSamplingAlgorithmType
:keyword early_termination:
:paramtype early_termination: ~flow.models.AetherSweepEarlyTerminationPolicy
"""
super(AetherSweepSettings, self).__init__(**kwargs)
self.limits = kwargs.get('limits', None)
self.search_space = kwargs.get('search_space', None)
self.sampling_algorithm = kwargs.get('sampling_algorithm', None)
self.early_termination = kwargs.get('early_termination', None)
class AetherSweepSettingsLimits(msrest.serialization.Model):
"""AetherSweepSettingsLimits.
:ivar max_total_trials:
:vartype max_total_trials: int
:ivar max_concurrent_trials:
:vartype max_concurrent_trials: int
"""
_attribute_map = {
'max_total_trials': {'key': 'maxTotalTrials', 'type': 'int'},
'max_concurrent_trials': {'key': 'maxConcurrentTrials', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword max_total_trials:
:paramtype max_total_trials: int
:keyword max_concurrent_trials:
:paramtype max_concurrent_trials: int
"""
super(AetherSweepSettingsLimits, self).__init__(**kwargs)
self.max_total_trials = kwargs.get('max_total_trials', None)
self.max_concurrent_trials = kwargs.get('max_concurrent_trials', None)
class AetherTargetLags(msrest.serialization.Model):
"""AetherTargetLags.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.AetherTargetLagsMode
:ivar values:
:vartype values: list[int]
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'values': {'key': 'values', 'type': '[int]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.AetherTargetLagsMode
:keyword values:
:paramtype values: list[int]
"""
super(AetherTargetLags, self).__init__(**kwargs)
self.mode = kwargs.get('mode', None)
self.values = kwargs.get('values', None)
class AetherTargetRollingWindowSize(msrest.serialization.Model):
"""AetherTargetRollingWindowSize.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.AetherTargetRollingWindowSizeMode
:ivar value:
:vartype value: int
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'value': {'key': 'value', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.AetherTargetRollingWindowSizeMode
:keyword value:
:paramtype value: int
"""
super(AetherTargetRollingWindowSize, self).__init__(**kwargs)
self.mode = kwargs.get('mode', None)
self.value = kwargs.get('value', None)
class AetherTargetSelectorConfiguration(msrest.serialization.Model):
"""AetherTargetSelectorConfiguration.
:ivar low_priority_vm_tolerant:
:vartype low_priority_vm_tolerant: bool
:ivar cluster_block_list:
:vartype cluster_block_list: list[str]
:ivar compute_type:
:vartype compute_type: str
:ivar instance_type:
:vartype instance_type: list[str]
:ivar instance_types:
:vartype instance_types: list[str]
:ivar my_resource_only:
:vartype my_resource_only: bool
:ivar plan_id:
:vartype plan_id: str
:ivar plan_region_id:
:vartype plan_region_id: str
:ivar region:
:vartype region: list[str]
:ivar regions:
:vartype regions: list[str]
:ivar vc_block_list:
:vartype vc_block_list: list[str]
"""
_attribute_map = {
'low_priority_vm_tolerant': {'key': 'lowPriorityVMTolerant', 'type': 'bool'},
'cluster_block_list': {'key': 'clusterBlockList', 'type': '[str]'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'instance_type': {'key': 'instanceType', 'type': '[str]'},
'instance_types': {'key': 'instanceTypes', 'type': '[str]'},
'my_resource_only': {'key': 'myResourceOnly', 'type': 'bool'},
'plan_id': {'key': 'planId', 'type': 'str'},
'plan_region_id': {'key': 'planRegionId', 'type': 'str'},
'region': {'key': 'region', 'type': '[str]'},
'regions': {'key': 'regions', 'type': '[str]'},
'vc_block_list': {'key': 'vcBlockList', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword low_priority_vm_tolerant:
:paramtype low_priority_vm_tolerant: bool
:keyword cluster_block_list:
:paramtype cluster_block_list: list[str]
:keyword compute_type:
:paramtype compute_type: str
:keyword instance_type:
:paramtype instance_type: list[str]
:keyword instance_types:
:paramtype instance_types: list[str]
:keyword my_resource_only:
:paramtype my_resource_only: bool
:keyword plan_id:
:paramtype plan_id: str
:keyword plan_region_id:
:paramtype plan_region_id: str
:keyword region:
:paramtype region: list[str]
:keyword regions:
:paramtype regions: list[str]
:keyword vc_block_list:
:paramtype vc_block_list: list[str]
"""
super(AetherTargetSelectorConfiguration, self).__init__(**kwargs)
self.low_priority_vm_tolerant = kwargs.get('low_priority_vm_tolerant', None)
self.cluster_block_list = kwargs.get('cluster_block_list', None)
self.compute_type = kwargs.get('compute_type', None)
self.instance_type = kwargs.get('instance_type', None)
self.instance_types = kwargs.get('instance_types', None)
self.my_resource_only = kwargs.get('my_resource_only', None)
self.plan_id = kwargs.get('plan_id', None)
self.plan_region_id = kwargs.get('plan_region_id', None)
self.region = kwargs.get('region', None)
self.regions = kwargs.get('regions', None)
self.vc_block_list = kwargs.get('vc_block_list', None)
class AetherTestDataSettings(msrest.serialization.Model):
"""AetherTestDataSettings.
:ivar test_data_size:
:vartype test_data_size: float
"""
_attribute_map = {
'test_data_size': {'key': 'testDataSize', 'type': 'float'},
}
def __init__(
self,
**kwargs
):
"""
:keyword test_data_size:
:paramtype test_data_size: float
"""
super(AetherTestDataSettings, self).__init__(**kwargs)
self.test_data_size = kwargs.get('test_data_size', None)
class AetherTorchDistributedConfiguration(msrest.serialization.Model):
"""AetherTorchDistributedConfiguration.
:ivar process_count_per_node:
:vartype process_count_per_node: int
"""
_attribute_map = {
'process_count_per_node': {'key': 'processCountPerNode', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword process_count_per_node:
:paramtype process_count_per_node: int
"""
super(AetherTorchDistributedConfiguration, self).__init__(**kwargs)
self.process_count_per_node = kwargs.get('process_count_per_node', None)
class AetherTrainingOutput(msrest.serialization.Model):
"""AetherTrainingOutput.
:ivar training_output_type: Possible values include: "Metrics", "Model".
:vartype training_output_type: str or ~flow.models.AetherTrainingOutputType
:ivar iteration:
:vartype iteration: int
:ivar metric:
:vartype metric: str
:ivar model_file:
:vartype model_file: str
"""
_attribute_map = {
'training_output_type': {'key': 'trainingOutputType', 'type': 'str'},
'iteration': {'key': 'iteration', 'type': 'int'},
'metric': {'key': 'metric', 'type': 'str'},
'model_file': {'key': 'modelFile', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword training_output_type: Possible values include: "Metrics", "Model".
:paramtype training_output_type: str or ~flow.models.AetherTrainingOutputType
:keyword iteration:
:paramtype iteration: int
:keyword metric:
:paramtype metric: str
:keyword model_file:
:paramtype model_file: str
"""
super(AetherTrainingOutput, self).__init__(**kwargs)
self.training_output_type = kwargs.get('training_output_type', None)
self.iteration = kwargs.get('iteration', None)
self.metric = kwargs.get('metric', None)
self.model_file = kwargs.get('model_file', None)
class AetherTrainingSettings(msrest.serialization.Model):
"""AetherTrainingSettings.
:ivar block_list_models:
:vartype block_list_models: list[str]
:ivar allow_list_models:
:vartype allow_list_models: list[str]
:ivar enable_dnn_training:
:vartype enable_dnn_training: bool
:ivar enable_onnx_compatible_models:
:vartype enable_onnx_compatible_models: bool
:ivar stack_ensemble_settings:
:vartype stack_ensemble_settings: ~flow.models.AetherStackEnsembleSettings
:ivar enable_stack_ensemble:
:vartype enable_stack_ensemble: bool
:ivar enable_vote_ensemble:
:vartype enable_vote_ensemble: bool
:ivar ensemble_model_download_timeout:
:vartype ensemble_model_download_timeout: str
:ivar enable_model_explainability:
:vartype enable_model_explainability: bool
:ivar training_mode: Possible values include: "Distributed", "NonDistributed", "Auto".
:vartype training_mode: str or ~flow.models.AetherTabularTrainingMode
"""
_attribute_map = {
'block_list_models': {'key': 'blockListModels', 'type': '[str]'},
'allow_list_models': {'key': 'allowListModels', 'type': '[str]'},
'enable_dnn_training': {'key': 'enableDnnTraining', 'type': 'bool'},
'enable_onnx_compatible_models': {'key': 'enableOnnxCompatibleModels', 'type': 'bool'},
'stack_ensemble_settings': {'key': 'stackEnsembleSettings', 'type': 'AetherStackEnsembleSettings'},
'enable_stack_ensemble': {'key': 'enableStackEnsemble', 'type': 'bool'},
'enable_vote_ensemble': {'key': 'enableVoteEnsemble', 'type': 'bool'},
'ensemble_model_download_timeout': {'key': 'ensembleModelDownloadTimeout', 'type': 'str'},
'enable_model_explainability': {'key': 'enableModelExplainability', 'type': 'bool'},
'training_mode': {'key': 'trainingMode', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword block_list_models:
:paramtype block_list_models: list[str]
:keyword allow_list_models:
:paramtype allow_list_models: list[str]
:keyword enable_dnn_training:
:paramtype enable_dnn_training: bool
:keyword enable_onnx_compatible_models:
:paramtype enable_onnx_compatible_models: bool
:keyword stack_ensemble_settings:
:paramtype stack_ensemble_settings: ~flow.models.AetherStackEnsembleSettings
:keyword enable_stack_ensemble:
:paramtype enable_stack_ensemble: bool
:keyword enable_vote_ensemble:
:paramtype enable_vote_ensemble: bool
:keyword ensemble_model_download_timeout:
:paramtype ensemble_model_download_timeout: str
:keyword enable_model_explainability:
:paramtype enable_model_explainability: bool
:keyword training_mode: Possible values include: "Distributed", "NonDistributed", "Auto".
:paramtype training_mode: str or ~flow.models.AetherTabularTrainingMode
"""
super(AetherTrainingSettings, self).__init__(**kwargs)
self.block_list_models = kwargs.get('block_list_models', None)
self.allow_list_models = kwargs.get('allow_list_models', None)
self.enable_dnn_training = kwargs.get('enable_dnn_training', None)
self.enable_onnx_compatible_models = kwargs.get('enable_onnx_compatible_models', None)
self.stack_ensemble_settings = kwargs.get('stack_ensemble_settings', None)
self.enable_stack_ensemble = kwargs.get('enable_stack_ensemble', None)
self.enable_vote_ensemble = kwargs.get('enable_vote_ensemble', None)
self.ensemble_model_download_timeout = kwargs.get('ensemble_model_download_timeout', None)
self.enable_model_explainability = kwargs.get('enable_model_explainability', None)
self.training_mode = kwargs.get('training_mode', None)
class AetherUIAzureOpenAIDeploymentNameSelector(msrest.serialization.Model):
"""AetherUIAzureOpenAIDeploymentNameSelector.
:ivar capabilities:
:vartype capabilities: ~flow.models.AetherUIAzureOpenAIModelCapabilities
"""
_attribute_map = {
'capabilities': {'key': 'Capabilities', 'type': 'AetherUIAzureOpenAIModelCapabilities'},
}
def __init__(
self,
**kwargs
):
"""
:keyword capabilities:
:paramtype capabilities: ~flow.models.AetherUIAzureOpenAIModelCapabilities
"""
super(AetherUIAzureOpenAIDeploymentNameSelector, self).__init__(**kwargs)
self.capabilities = kwargs.get('capabilities', None)
class AetherUIAzureOpenAIModelCapabilities(msrest.serialization.Model):
"""AetherUIAzureOpenAIModelCapabilities.
:ivar completion:
:vartype completion: bool
:ivar chat_completion:
:vartype chat_completion: bool
:ivar embeddings:
:vartype embeddings: bool
"""
_attribute_map = {
'completion': {'key': 'Completion', 'type': 'bool'},
'chat_completion': {'key': 'ChatCompletion', 'type': 'bool'},
'embeddings': {'key': 'Embeddings', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword completion:
:paramtype completion: bool
:keyword chat_completion:
:paramtype chat_completion: bool
:keyword embeddings:
:paramtype embeddings: bool
"""
super(AetherUIAzureOpenAIModelCapabilities, self).__init__(**kwargs)
self.completion = kwargs.get('completion', None)
self.chat_completion = kwargs.get('chat_completion', None)
self.embeddings = kwargs.get('embeddings', None)
class AetherUIColumnPicker(msrest.serialization.Model):
"""AetherUIColumnPicker.
:ivar column_picker_for:
:vartype column_picker_for: str
:ivar column_selection_categories:
:vartype column_selection_categories: list[str]
:ivar single_column_selection:
:vartype single_column_selection: bool
"""
_attribute_map = {
'column_picker_for': {'key': 'columnPickerFor', 'type': 'str'},
'column_selection_categories': {'key': 'columnSelectionCategories', 'type': '[str]'},
'single_column_selection': {'key': 'singleColumnSelection', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword column_picker_for:
:paramtype column_picker_for: str
:keyword column_selection_categories:
:paramtype column_selection_categories: list[str]
:keyword single_column_selection:
:paramtype single_column_selection: bool
"""
super(AetherUIColumnPicker, self).__init__(**kwargs)
self.column_picker_for = kwargs.get('column_picker_for', None)
self.column_selection_categories = kwargs.get('column_selection_categories', None)
self.single_column_selection = kwargs.get('single_column_selection', None)
class AetherUIJsonEditor(msrest.serialization.Model):
"""AetherUIJsonEditor.
:ivar json_schema:
:vartype json_schema: str
"""
_attribute_map = {
'json_schema': {'key': 'jsonSchema', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword json_schema:
:paramtype json_schema: str
"""
super(AetherUIJsonEditor, self).__init__(**kwargs)
self.json_schema = kwargs.get('json_schema', None)
class AetherUIParameterHint(msrest.serialization.Model):
"""AetherUIParameterHint.
:ivar ui_widget_type: Possible values include: "Default", "Mode", "ColumnPicker", "Credential",
"Script", "ComputeSelection", "JsonEditor", "SearchSpaceParameter", "SectionToggle",
"YamlEditor", "EnableRuntimeSweep", "DataStoreSelection", "InstanceTypeSelection",
"ConnectionSelection", "PromptFlowConnectionSelection", "AzureOpenAIDeploymentNameSelection".
:vartype ui_widget_type: str or ~flow.models.AetherUIWidgetTypeEnum
:ivar column_picker:
:vartype column_picker: ~flow.models.AetherUIColumnPicker
:ivar ui_script_language: Possible values include: "None", "Python", "R", "Json", "Sql".
:vartype ui_script_language: str or ~flow.models.AetherUIScriptLanguageEnum
:ivar json_editor:
:vartype json_editor: ~flow.models.AetherUIJsonEditor
:ivar prompt_flow_connection_selector:
:vartype prompt_flow_connection_selector: ~flow.models.AetherUIPromptFlowConnectionSelector
:ivar azure_open_ai_deployment_name_selector:
:vartype azure_open_ai_deployment_name_selector:
~flow.models.AetherUIAzureOpenAIDeploymentNameSelector
:ivar ux_ignore:
:vartype ux_ignore: bool
:ivar anonymous:
:vartype anonymous: bool
"""
_attribute_map = {
'ui_widget_type': {'key': 'uiWidgetType', 'type': 'str'},
'column_picker': {'key': 'columnPicker', 'type': 'AetherUIColumnPicker'},
'ui_script_language': {'key': 'uiScriptLanguage', 'type': 'str'},
'json_editor': {'key': 'jsonEditor', 'type': 'AetherUIJsonEditor'},
'prompt_flow_connection_selector': {'key': 'PromptFlowConnectionSelector', 'type': 'AetherUIPromptFlowConnectionSelector'},
'azure_open_ai_deployment_name_selector': {'key': 'AzureOpenAIDeploymentNameSelector', 'type': 'AetherUIAzureOpenAIDeploymentNameSelector'},
'ux_ignore': {'key': 'UxIgnore', 'type': 'bool'},
'anonymous': {'key': 'Anonymous', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword ui_widget_type: Possible values include: "Default", "Mode", "ColumnPicker",
"Credential", "Script", "ComputeSelection", "JsonEditor", "SearchSpaceParameter",
"SectionToggle", "YamlEditor", "EnableRuntimeSweep", "DataStoreSelection",
"InstanceTypeSelection", "ConnectionSelection", "PromptFlowConnectionSelection",
"AzureOpenAIDeploymentNameSelection".
:paramtype ui_widget_type: str or ~flow.models.AetherUIWidgetTypeEnum
:keyword column_picker:
:paramtype column_picker: ~flow.models.AetherUIColumnPicker
:keyword ui_script_language: Possible values include: "None", "Python", "R", "Json", "Sql".
:paramtype ui_script_language: str or ~flow.models.AetherUIScriptLanguageEnum
:keyword json_editor:
:paramtype json_editor: ~flow.models.AetherUIJsonEditor
:keyword prompt_flow_connection_selector:
:paramtype prompt_flow_connection_selector: ~flow.models.AetherUIPromptFlowConnectionSelector
:keyword azure_open_ai_deployment_name_selector:
:paramtype azure_open_ai_deployment_name_selector:
~flow.models.AetherUIAzureOpenAIDeploymentNameSelector
:keyword ux_ignore:
:paramtype ux_ignore: bool
:keyword anonymous:
:paramtype anonymous: bool
"""
super(AetherUIParameterHint, self).__init__(**kwargs)
self.ui_widget_type = kwargs.get('ui_widget_type', None)
self.column_picker = kwargs.get('column_picker', None)
self.ui_script_language = kwargs.get('ui_script_language', None)
self.json_editor = kwargs.get('json_editor', None)
self.prompt_flow_connection_selector = kwargs.get('prompt_flow_connection_selector', None)
self.azure_open_ai_deployment_name_selector = kwargs.get('azure_open_ai_deployment_name_selector', None)
self.ux_ignore = kwargs.get('ux_ignore', None)
self.anonymous = kwargs.get('anonymous', None)
class AetherUIPromptFlowConnectionSelector(msrest.serialization.Model):
"""AetherUIPromptFlowConnectionSelector.
:ivar prompt_flow_connection_type:
:vartype prompt_flow_connection_type: str
"""
_attribute_map = {
'prompt_flow_connection_type': {'key': 'PromptFlowConnectionType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword prompt_flow_connection_type:
:paramtype prompt_flow_connection_type: str
"""
super(AetherUIPromptFlowConnectionSelector, self).__init__(**kwargs)
self.prompt_flow_connection_type = kwargs.get('prompt_flow_connection_type', None)
class AetherValidationDataSettings(msrest.serialization.Model):
"""AetherValidationDataSettings.
:ivar n_cross_validations:
:vartype n_cross_validations: ~flow.models.AetherNCrossValidations
:ivar validation_data_size:
:vartype validation_data_size: float
:ivar cv_split_column_names:
:vartype cv_split_column_names: list[str]
:ivar validation_type:
:vartype validation_type: str
"""
_attribute_map = {
'n_cross_validations': {'key': 'nCrossValidations', 'type': 'AetherNCrossValidations'},
'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
'cv_split_column_names': {'key': 'cvSplitColumnNames', 'type': '[str]'},
'validation_type': {'key': 'validationType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword n_cross_validations:
:paramtype n_cross_validations: ~flow.models.AetherNCrossValidations
:keyword validation_data_size:
:paramtype validation_data_size: float
:keyword cv_split_column_names:
:paramtype cv_split_column_names: list[str]
:keyword validation_type:
:paramtype validation_type: str
"""
super(AetherValidationDataSettings, self).__init__(**kwargs)
self.n_cross_validations = kwargs.get('n_cross_validations', None)
self.validation_data_size = kwargs.get('validation_data_size', None)
self.cv_split_column_names = kwargs.get('cv_split_column_names', None)
self.validation_type = kwargs.get('validation_type', None)
class AetherVsoBuildArtifactInfo(msrest.serialization.Model):
"""AetherVsoBuildArtifactInfo.
:ivar build_info:
:vartype build_info: ~flow.models.AetherVsoBuildInfo
:ivar download_url:
:vartype download_url: str
"""
_attribute_map = {
'build_info': {'key': 'buildInfo', 'type': 'AetherVsoBuildInfo'},
'download_url': {'key': 'downloadUrl', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword build_info:
:paramtype build_info: ~flow.models.AetherVsoBuildInfo
:keyword download_url:
:paramtype download_url: str
"""
super(AetherVsoBuildArtifactInfo, self).__init__(**kwargs)
self.build_info = kwargs.get('build_info', None)
self.download_url = kwargs.get('download_url', None)
class AetherVsoBuildDefinitionInfo(msrest.serialization.Model):
"""AetherVsoBuildDefinitionInfo.
:ivar account_name:
:vartype account_name: str
:ivar project_id:
:vartype project_id: str
:ivar build_definition_id:
:vartype build_definition_id: int
"""
_attribute_map = {
'account_name': {'key': 'accountName', 'type': 'str'},
'project_id': {'key': 'projectId', 'type': 'str'},
'build_definition_id': {'key': 'buildDefinitionId', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword account_name:
:paramtype account_name: str
:keyword project_id:
:paramtype project_id: str
:keyword build_definition_id:
:paramtype build_definition_id: int
"""
super(AetherVsoBuildDefinitionInfo, self).__init__(**kwargs)
self.account_name = kwargs.get('account_name', None)
self.project_id = kwargs.get('project_id', None)
self.build_definition_id = kwargs.get('build_definition_id', None)
class AetherVsoBuildInfo(msrest.serialization.Model):
"""AetherVsoBuildInfo.
:ivar definition_info:
:vartype definition_info: ~flow.models.AetherVsoBuildDefinitionInfo
:ivar build_id:
:vartype build_id: int
"""
_attribute_map = {
'definition_info': {'key': 'definitionInfo', 'type': 'AetherVsoBuildDefinitionInfo'},
'build_id': {'key': 'buildId', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword definition_info:
:paramtype definition_info: ~flow.models.AetherVsoBuildDefinitionInfo
:keyword build_id:
:paramtype build_id: int
"""
super(AetherVsoBuildInfo, self).__init__(**kwargs)
self.definition_info = kwargs.get('definition_info', None)
self.build_id = kwargs.get('build_id', None)
class AEVAComputeConfiguration(msrest.serialization.Model):
"""AEVAComputeConfiguration.
:ivar target:
:vartype target: str
:ivar instance_count:
:vartype instance_count: int
:ivar is_local:
:vartype is_local: bool
:ivar location:
:vartype location: str
:ivar is_clusterless:
:vartype is_clusterless: bool
:ivar instance_type:
:vartype instance_type: str
:ivar properties: Dictionary of :code:`<any>`.
:vartype properties: dict[str, any]
:ivar is_preemptable:
:vartype is_preemptable: bool
"""
_attribute_map = {
'target': {'key': 'target', 'type': 'str'},
'instance_count': {'key': 'instanceCount', 'type': 'int'},
'is_local': {'key': 'isLocal', 'type': 'bool'},
'location': {'key': 'location', 'type': 'str'},
'is_clusterless': {'key': 'isClusterless', 'type': 'bool'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{object}'},
'is_preemptable': {'key': 'isPreemptable', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword target:
:paramtype target: str
:keyword instance_count:
:paramtype instance_count: int
:keyword is_local:
:paramtype is_local: bool
:keyword location:
:paramtype location: str
:keyword is_clusterless:
:paramtype is_clusterless: bool
:keyword instance_type:
:paramtype instance_type: str
:keyword properties: Dictionary of :code:`<any>`.
:paramtype properties: dict[str, any]
:keyword is_preemptable:
:paramtype is_preemptable: bool
"""
super(AEVAComputeConfiguration, self).__init__(**kwargs)
self.target = kwargs.get('target', None)
self.instance_count = kwargs.get('instance_count', None)
self.is_local = kwargs.get('is_local', None)
self.location = kwargs.get('location', None)
self.is_clusterless = kwargs.get('is_clusterless', None)
self.instance_type = kwargs.get('instance_type', None)
self.properties = kwargs.get('properties', None)
self.is_preemptable = kwargs.get('is_preemptable', None)
class AEVAResourceConfiguration(msrest.serialization.Model):
"""AEVAResourceConfiguration.
:ivar instance_count:
:vartype instance_count: int
:ivar instance_type:
:vartype instance_type: str
:ivar properties: Dictionary of :code:`<any>`.
:vartype properties: dict[str, any]
:ivar locations:
:vartype locations: list[str]
:ivar instance_priority:
:vartype instance_priority: str
:ivar quota_enforcement_resource_id:
:vartype quota_enforcement_resource_id: str
"""
_attribute_map = {
'instance_count': {'key': 'instanceCount', 'type': 'int'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{object}'},
'locations': {'key': 'locations', 'type': '[str]'},
'instance_priority': {'key': 'instancePriority', 'type': 'str'},
'quota_enforcement_resource_id': {'key': 'quotaEnforcementResourceId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword instance_count:
:paramtype instance_count: int
:keyword instance_type:
:paramtype instance_type: str
:keyword properties: Dictionary of :code:`<any>`.
:paramtype properties: dict[str, any]
:keyword locations:
:paramtype locations: list[str]
:keyword instance_priority:
:paramtype instance_priority: str
:keyword quota_enforcement_resource_id:
:paramtype quota_enforcement_resource_id: str
"""
super(AEVAResourceConfiguration, self).__init__(**kwargs)
self.instance_count = kwargs.get('instance_count', None)
self.instance_type = kwargs.get('instance_type', None)
self.properties = kwargs.get('properties', None)
self.locations = kwargs.get('locations', None)
self.instance_priority = kwargs.get('instance_priority', None)
self.quota_enforcement_resource_id = kwargs.get('quota_enforcement_resource_id', None)
class AISuperComputerConfiguration(msrest.serialization.Model):
"""AISuperComputerConfiguration.
:ivar instance_type:
:vartype instance_type: str
:ivar instance_types:
:vartype instance_types: list[str]
:ivar image_version:
:vartype image_version: str
:ivar location:
:vartype location: str
:ivar locations:
:vartype locations: list[str]
:ivar ai_super_computer_storage_data: Dictionary of
:code:`<AISuperComputerStorageReferenceConfiguration>`.
:vartype ai_super_computer_storage_data: dict[str,
~flow.models.AISuperComputerStorageReferenceConfiguration]
:ivar interactive:
:vartype interactive: bool
:ivar scale_policy:
:vartype scale_policy: ~flow.models.AISuperComputerScalePolicy
:ivar virtual_cluster_arm_id:
:vartype virtual_cluster_arm_id: str
:ivar tensorboard_log_directory:
:vartype tensorboard_log_directory: str
:ivar ssh_public_key:
:vartype ssh_public_key: str
:ivar ssh_public_keys:
:vartype ssh_public_keys: list[str]
:ivar enable_azml_int:
:vartype enable_azml_int: bool
:ivar priority:
:vartype priority: str
:ivar sla_tier:
:vartype sla_tier: str
:ivar suspend_on_idle_time_hours:
:vartype suspend_on_idle_time_hours: long
:ivar user_alias:
:vartype user_alias: str
:ivar quota_enforcement_resource_id:
:vartype quota_enforcement_resource_id: str
:ivar model_compute_specification_id:
:vartype model_compute_specification_id: str
:ivar group_policy_name:
:vartype group_policy_name: str
"""
_attribute_map = {
'instance_type': {'key': 'instanceType', 'type': 'str'},
'instance_types': {'key': 'instanceTypes', 'type': '[str]'},
'image_version': {'key': 'imageVersion', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
'locations': {'key': 'locations', 'type': '[str]'},
'ai_super_computer_storage_data': {'key': 'aiSuperComputerStorageData', 'type': '{AISuperComputerStorageReferenceConfiguration}'},
'interactive': {'key': 'interactive', 'type': 'bool'},
'scale_policy': {'key': 'scalePolicy', 'type': 'AISuperComputerScalePolicy'},
'virtual_cluster_arm_id': {'key': 'virtualClusterArmId', 'type': 'str'},
'tensorboard_log_directory': {'key': 'tensorboardLogDirectory', 'type': 'str'},
'ssh_public_key': {'key': 'sshPublicKey', 'type': 'str'},
'ssh_public_keys': {'key': 'sshPublicKeys', 'type': '[str]'},
'enable_azml_int': {'key': 'enableAzmlInt', 'type': 'bool'},
'priority': {'key': 'priority', 'type': 'str'},
'sla_tier': {'key': 'slaTier', 'type': 'str'},
'suspend_on_idle_time_hours': {'key': 'suspendOnIdleTimeHours', 'type': 'long'},
'user_alias': {'key': 'userAlias', 'type': 'str'},
'quota_enforcement_resource_id': {'key': 'quotaEnforcementResourceId', 'type': 'str'},
'model_compute_specification_id': {'key': 'modelComputeSpecificationId', 'type': 'str'},
'group_policy_name': {'key': 'groupPolicyName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword instance_type:
:paramtype instance_type: str
:keyword instance_types:
:paramtype instance_types: list[str]
:keyword image_version:
:paramtype image_version: str
:keyword location:
:paramtype location: str
:keyword locations:
:paramtype locations: list[str]
:keyword ai_super_computer_storage_data: Dictionary of
:code:`<AISuperComputerStorageReferenceConfiguration>`.
:paramtype ai_super_computer_storage_data: dict[str,
~flow.models.AISuperComputerStorageReferenceConfiguration]
:keyword interactive:
:paramtype interactive: bool
:keyword scale_policy:
:paramtype scale_policy: ~flow.models.AISuperComputerScalePolicy
:keyword virtual_cluster_arm_id:
:paramtype virtual_cluster_arm_id: str
:keyword tensorboard_log_directory:
:paramtype tensorboard_log_directory: str
:keyword ssh_public_key:
:paramtype ssh_public_key: str
:keyword ssh_public_keys:
:paramtype ssh_public_keys: list[str]
:keyword enable_azml_int:
:paramtype enable_azml_int: bool
:keyword priority:
:paramtype priority: str
:keyword sla_tier:
:paramtype sla_tier: str
:keyword suspend_on_idle_time_hours:
:paramtype suspend_on_idle_time_hours: long
:keyword user_alias:
:paramtype user_alias: str
:keyword quota_enforcement_resource_id:
:paramtype quota_enforcement_resource_id: str
:keyword model_compute_specification_id:
:paramtype model_compute_specification_id: str
:keyword group_policy_name:
:paramtype group_policy_name: str
"""
super(AISuperComputerConfiguration, self).__init__(**kwargs)
self.instance_type = kwargs.get('instance_type', None)
self.instance_types = kwargs.get('instance_types', None)
self.image_version = kwargs.get('image_version', None)
self.location = kwargs.get('location', None)
self.locations = kwargs.get('locations', None)
self.ai_super_computer_storage_data = kwargs.get('ai_super_computer_storage_data', None)
self.interactive = kwargs.get('interactive', None)
self.scale_policy = kwargs.get('scale_policy', None)
self.virtual_cluster_arm_id = kwargs.get('virtual_cluster_arm_id', None)
self.tensorboard_log_directory = kwargs.get('tensorboard_log_directory', None)
self.ssh_public_key = kwargs.get('ssh_public_key', None)
self.ssh_public_keys = kwargs.get('ssh_public_keys', None)
self.enable_azml_int = kwargs.get('enable_azml_int', None)
self.priority = kwargs.get('priority', None)
self.sla_tier = kwargs.get('sla_tier', None)
self.suspend_on_idle_time_hours = kwargs.get('suspend_on_idle_time_hours', None)
self.user_alias = kwargs.get('user_alias', None)
self.quota_enforcement_resource_id = kwargs.get('quota_enforcement_resource_id', None)
self.model_compute_specification_id = kwargs.get('model_compute_specification_id', None)
self.group_policy_name = kwargs.get('group_policy_name', None)
class AISuperComputerScalePolicy(msrest.serialization.Model):
"""AISuperComputerScalePolicy.
:ivar auto_scale_instance_type_count_set:
:vartype auto_scale_instance_type_count_set: list[int]
:ivar auto_scale_interval_in_sec:
:vartype auto_scale_interval_in_sec: int
:ivar max_instance_type_count:
:vartype max_instance_type_count: int
:ivar min_instance_type_count:
:vartype min_instance_type_count: int
"""
_attribute_map = {
'auto_scale_instance_type_count_set': {'key': 'autoScaleInstanceTypeCountSet', 'type': '[int]'},
'auto_scale_interval_in_sec': {'key': 'autoScaleIntervalInSec', 'type': 'int'},
'max_instance_type_count': {'key': 'maxInstanceTypeCount', 'type': 'int'},
'min_instance_type_count': {'key': 'minInstanceTypeCount', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword auto_scale_instance_type_count_set:
:paramtype auto_scale_instance_type_count_set: list[int]
:keyword auto_scale_interval_in_sec:
:paramtype auto_scale_interval_in_sec: int
:keyword max_instance_type_count:
:paramtype max_instance_type_count: int
:keyword min_instance_type_count:
:paramtype min_instance_type_count: int
"""
super(AISuperComputerScalePolicy, self).__init__(**kwargs)
self.auto_scale_instance_type_count_set = kwargs.get('auto_scale_instance_type_count_set', None)
self.auto_scale_interval_in_sec = kwargs.get('auto_scale_interval_in_sec', None)
self.max_instance_type_count = kwargs.get('max_instance_type_count', None)
self.min_instance_type_count = kwargs.get('min_instance_type_count', None)
class AISuperComputerStorageReferenceConfiguration(msrest.serialization.Model):
"""AISuperComputerStorageReferenceConfiguration.
:ivar container_name:
:vartype container_name: str
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'container_name': {'key': 'containerName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword container_name:
:paramtype container_name: str
:keyword relative_path:
:paramtype relative_path: str
"""
super(AISuperComputerStorageReferenceConfiguration, self).__init__(**kwargs)
self.container_name = kwargs.get('container_name', None)
self.relative_path = kwargs.get('relative_path', None)
class AKSAdvanceSettings(msrest.serialization.Model):
"""AKSAdvanceSettings.
:ivar auto_scaler:
:vartype auto_scaler: ~flow.models.AutoScaler
:ivar container_resource_requirements:
:vartype container_resource_requirements: ~flow.models.ContainerResourceRequirements
:ivar app_insights_enabled:
:vartype app_insights_enabled: bool
:ivar scoring_timeout_ms:
:vartype scoring_timeout_ms: int
:ivar num_replicas:
:vartype num_replicas: int
"""
_attribute_map = {
'auto_scaler': {'key': 'autoScaler', 'type': 'AutoScaler'},
'container_resource_requirements': {'key': 'containerResourceRequirements', 'type': 'ContainerResourceRequirements'},
'app_insights_enabled': {'key': 'appInsightsEnabled', 'type': 'bool'},
'scoring_timeout_ms': {'key': 'scoringTimeoutMs', 'type': 'int'},
'num_replicas': {'key': 'numReplicas', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword auto_scaler:
:paramtype auto_scaler: ~flow.models.AutoScaler
:keyword container_resource_requirements:
:paramtype container_resource_requirements: ~flow.models.ContainerResourceRequirements
:keyword app_insights_enabled:
:paramtype app_insights_enabled: bool
:keyword scoring_timeout_ms:
:paramtype scoring_timeout_ms: int
:keyword num_replicas:
:paramtype num_replicas: int
"""
super(AKSAdvanceSettings, self).__init__(**kwargs)
self.auto_scaler = kwargs.get('auto_scaler', None)
self.container_resource_requirements = kwargs.get('container_resource_requirements', None)
self.app_insights_enabled = kwargs.get('app_insights_enabled', None)
self.scoring_timeout_ms = kwargs.get('scoring_timeout_ms', None)
self.num_replicas = kwargs.get('num_replicas', None)
class AKSReplicaStatus(msrest.serialization.Model):
"""AKSReplicaStatus.
:ivar desired_replicas:
:vartype desired_replicas: int
:ivar updated_replicas:
:vartype updated_replicas: int
:ivar available_replicas:
:vartype available_replicas: int
:ivar error:
:vartype error: ~flow.models.ModelManagementErrorResponse
"""
_attribute_map = {
'desired_replicas': {'key': 'desiredReplicas', 'type': 'int'},
'updated_replicas': {'key': 'updatedReplicas', 'type': 'int'},
'available_replicas': {'key': 'availableReplicas', 'type': 'int'},
'error': {'key': 'error', 'type': 'ModelManagementErrorResponse'},
}
def __init__(
self,
**kwargs
):
"""
:keyword desired_replicas:
:paramtype desired_replicas: int
:keyword updated_replicas:
:paramtype updated_replicas: int
:keyword available_replicas:
:paramtype available_replicas: int
:keyword error:
:paramtype error: ~flow.models.ModelManagementErrorResponse
"""
super(AKSReplicaStatus, self).__init__(**kwargs)
self.desired_replicas = kwargs.get('desired_replicas', None)
self.updated_replicas = kwargs.get('updated_replicas', None)
self.available_replicas = kwargs.get('available_replicas', None)
self.error = kwargs.get('error', None)
class AMLComputeConfiguration(msrest.serialization.Model):
"""AMLComputeConfiguration.
:ivar name:
:vartype name: str
:ivar vm_size:
:vartype vm_size: str
:ivar vm_priority: Possible values include: "Dedicated", "Lowpriority".
:vartype vm_priority: str or ~flow.models.VmPriority
:ivar retain_cluster:
:vartype retain_cluster: bool
:ivar cluster_max_node_count:
:vartype cluster_max_node_count: int
:ivar os_type:
:vartype os_type: str
:ivar virtual_machine_image:
:vartype virtual_machine_image: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'vm_priority': {'key': 'vmPriority', 'type': 'str'},
'retain_cluster': {'key': 'retainCluster', 'type': 'bool'},
'cluster_max_node_count': {'key': 'clusterMaxNodeCount', 'type': 'int'},
'os_type': {'key': 'osType', 'type': 'str'},
'virtual_machine_image': {'key': 'virtualMachineImage', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword vm_size:
:paramtype vm_size: str
:keyword vm_priority: Possible values include: "Dedicated", "Lowpriority".
:paramtype vm_priority: str or ~flow.models.VmPriority
:keyword retain_cluster:
:paramtype retain_cluster: bool
:keyword cluster_max_node_count:
:paramtype cluster_max_node_count: int
:keyword os_type:
:paramtype os_type: str
:keyword virtual_machine_image:
:paramtype virtual_machine_image: str
"""
super(AMLComputeConfiguration, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.vm_size = kwargs.get('vm_size', None)
self.vm_priority = kwargs.get('vm_priority', None)
self.retain_cluster = kwargs.get('retain_cluster', None)
self.cluster_max_node_count = kwargs.get('cluster_max_node_count', None)
self.os_type = kwargs.get('os_type', None)
self.virtual_machine_image = kwargs.get('virtual_machine_image', None)
class AmlDataset(msrest.serialization.Model):
"""AmlDataset.
:ivar registered_data_set_reference:
:vartype registered_data_set_reference: ~flow.models.RegisteredDataSetReference
:ivar saved_data_set_reference:
:vartype saved_data_set_reference: ~flow.models.SavedDataSetReference
:ivar additional_transformations:
:vartype additional_transformations: str
"""
_attribute_map = {
'registered_data_set_reference': {'key': 'registeredDataSetReference', 'type': 'RegisteredDataSetReference'},
'saved_data_set_reference': {'key': 'savedDataSetReference', 'type': 'SavedDataSetReference'},
'additional_transformations': {'key': 'additionalTransformations', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword registered_data_set_reference:
:paramtype registered_data_set_reference: ~flow.models.RegisteredDataSetReference
:keyword saved_data_set_reference:
:paramtype saved_data_set_reference: ~flow.models.SavedDataSetReference
:keyword additional_transformations:
:paramtype additional_transformations: str
"""
super(AmlDataset, self).__init__(**kwargs)
self.registered_data_set_reference = kwargs.get('registered_data_set_reference', None)
self.saved_data_set_reference = kwargs.get('saved_data_set_reference', None)
self.additional_transformations = kwargs.get('additional_transformations', None)
class AmlK8SConfiguration(msrest.serialization.Model):
"""AmlK8SConfiguration.
:ivar resource_configuration:
:vartype resource_configuration: ~flow.models.ResourceConfiguration
:ivar priority_configuration:
:vartype priority_configuration: ~flow.models.AmlK8SPriorityConfiguration
:ivar interactive_configuration:
:vartype interactive_configuration: ~flow.models.InteractiveConfiguration
"""
_attribute_map = {
'resource_configuration': {'key': 'resourceConfiguration', 'type': 'ResourceConfiguration'},
'priority_configuration': {'key': 'priorityConfiguration', 'type': 'AmlK8SPriorityConfiguration'},
'interactive_configuration': {'key': 'interactiveConfiguration', 'type': 'InteractiveConfiguration'},
}
def __init__(
self,
**kwargs
):
"""
:keyword resource_configuration:
:paramtype resource_configuration: ~flow.models.ResourceConfiguration
:keyword priority_configuration:
:paramtype priority_configuration: ~flow.models.AmlK8SPriorityConfiguration
:keyword interactive_configuration:
:paramtype interactive_configuration: ~flow.models.InteractiveConfiguration
"""
super(AmlK8SConfiguration, self).__init__(**kwargs)
self.resource_configuration = kwargs.get('resource_configuration', None)
self.priority_configuration = kwargs.get('priority_configuration', None)
self.interactive_configuration = kwargs.get('interactive_configuration', None)
class AmlK8SPriorityConfiguration(msrest.serialization.Model):
"""AmlK8SPriorityConfiguration.
:ivar job_priority:
:vartype job_priority: int
:ivar is_preemptible:
:vartype is_preemptible: bool
:ivar node_count_set:
:vartype node_count_set: list[int]
:ivar scale_interval:
:vartype scale_interval: int
"""
_attribute_map = {
'job_priority': {'key': 'jobPriority', 'type': 'int'},
'is_preemptible': {'key': 'isPreemptible', 'type': 'bool'},
'node_count_set': {'key': 'nodeCountSet', 'type': '[int]'},
'scale_interval': {'key': 'scaleInterval', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword job_priority:
:paramtype job_priority: int
:keyword is_preemptible:
:paramtype is_preemptible: bool
:keyword node_count_set:
:paramtype node_count_set: list[int]
:keyword scale_interval:
:paramtype scale_interval: int
"""
super(AmlK8SPriorityConfiguration, self).__init__(**kwargs)
self.job_priority = kwargs.get('job_priority', None)
self.is_preemptible = kwargs.get('is_preemptible', None)
self.node_count_set = kwargs.get('node_count_set', None)
self.scale_interval = kwargs.get('scale_interval', None)
class AmlSparkCloudSetting(msrest.serialization.Model):
"""AmlSparkCloudSetting.
:ivar entry:
:vartype entry: ~flow.models.EntrySetting
:ivar files:
:vartype files: list[str]
:ivar archives:
:vartype archives: list[str]
:ivar jars:
:vartype jars: list[str]
:ivar py_files:
:vartype py_files: list[str]
:ivar driver_memory:
:vartype driver_memory: str
:ivar driver_cores:
:vartype driver_cores: int
:ivar executor_memory:
:vartype executor_memory: str
:ivar executor_cores:
:vartype executor_cores: int
:ivar number_executors:
:vartype number_executors: int
:ivar environment_asset_id:
:vartype environment_asset_id: str
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
:ivar inline_environment_definition_string:
:vartype inline_environment_definition_string: str
:ivar conf: Dictionary of :code:`<string>`.
:vartype conf: dict[str, str]
:ivar compute:
:vartype compute: str
:ivar resources:
:vartype resources: ~flow.models.ResourcesSetting
:ivar identity:
:vartype identity: ~flow.models.IdentitySetting
"""
_attribute_map = {
'entry': {'key': 'entry', 'type': 'EntrySetting'},
'files': {'key': 'files', 'type': '[str]'},
'archives': {'key': 'archives', 'type': '[str]'},
'jars': {'key': 'jars', 'type': '[str]'},
'py_files': {'key': 'pyFiles', 'type': '[str]'},
'driver_memory': {'key': 'driverMemory', 'type': 'str'},
'driver_cores': {'key': 'driverCores', 'type': 'int'},
'executor_memory': {'key': 'executorMemory', 'type': 'str'},
'executor_cores': {'key': 'executorCores', 'type': 'int'},
'number_executors': {'key': 'numberExecutors', 'type': 'int'},
'environment_asset_id': {'key': 'environmentAssetId', 'type': 'str'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'inline_environment_definition_string': {'key': 'inlineEnvironmentDefinitionString', 'type': 'str'},
'conf': {'key': 'conf', 'type': '{str}'},
'compute': {'key': 'compute', 'type': 'str'},
'resources': {'key': 'resources', 'type': 'ResourcesSetting'},
'identity': {'key': 'identity', 'type': 'IdentitySetting'},
}
def __init__(
self,
**kwargs
):
"""
:keyword entry:
:paramtype entry: ~flow.models.EntrySetting
:keyword files:
:paramtype files: list[str]
:keyword archives:
:paramtype archives: list[str]
:keyword jars:
:paramtype jars: list[str]
:keyword py_files:
:paramtype py_files: list[str]
:keyword driver_memory:
:paramtype driver_memory: str
:keyword driver_cores:
:paramtype driver_cores: int
:keyword executor_memory:
:paramtype executor_memory: str
:keyword executor_cores:
:paramtype executor_cores: int
:keyword number_executors:
:paramtype number_executors: int
:keyword environment_asset_id:
:paramtype environment_asset_id: str
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
:keyword inline_environment_definition_string:
:paramtype inline_environment_definition_string: str
:keyword conf: Dictionary of :code:`<string>`.
:paramtype conf: dict[str, str]
:keyword compute:
:paramtype compute: str
:keyword resources:
:paramtype resources: ~flow.models.ResourcesSetting
:keyword identity:
:paramtype identity: ~flow.models.IdentitySetting
"""
super(AmlSparkCloudSetting, self).__init__(**kwargs)
self.entry = kwargs.get('entry', None)
self.files = kwargs.get('files', None)
self.archives = kwargs.get('archives', None)
self.jars = kwargs.get('jars', None)
self.py_files = kwargs.get('py_files', None)
self.driver_memory = kwargs.get('driver_memory', None)
self.driver_cores = kwargs.get('driver_cores', None)
self.executor_memory = kwargs.get('executor_memory', None)
self.executor_cores = kwargs.get('executor_cores', None)
self.number_executors = kwargs.get('number_executors', None)
self.environment_asset_id = kwargs.get('environment_asset_id', None)
self.environment_variables = kwargs.get('environment_variables', None)
self.inline_environment_definition_string = kwargs.get('inline_environment_definition_string', None)
self.conf = kwargs.get('conf', None)
self.compute = kwargs.get('compute', None)
self.resources = kwargs.get('resources', None)
self.identity = kwargs.get('identity', None)
class APCloudConfiguration(msrest.serialization.Model):
"""APCloudConfiguration.
:ivar referenced_ap_module_guid:
:vartype referenced_ap_module_guid: str
:ivar user_alias:
:vartype user_alias: str
:ivar aether_module_type:
:vartype aether_module_type: str
"""
_attribute_map = {
'referenced_ap_module_guid': {'key': 'referencedAPModuleGuid', 'type': 'str'},
'user_alias': {'key': 'userAlias', 'type': 'str'},
'aether_module_type': {'key': 'aetherModuleType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword referenced_ap_module_guid:
:paramtype referenced_ap_module_guid: str
:keyword user_alias:
:paramtype user_alias: str
:keyword aether_module_type:
:paramtype aether_module_type: str
"""
super(APCloudConfiguration, self).__init__(**kwargs)
self.referenced_ap_module_guid = kwargs.get('referenced_ap_module_guid', None)
self.user_alias = kwargs.get('user_alias', None)
self.aether_module_type = kwargs.get('aether_module_type', None)
class ApiAndParameters(msrest.serialization.Model):
"""ApiAndParameters.
:ivar api:
:vartype api: str
:ivar parameters: This is a dictionary.
:vartype parameters: dict[str, ~flow.models.FlowToolSettingParameter]
:ivar default_prompt:
:vartype default_prompt: str
"""
_attribute_map = {
'api': {'key': 'api', 'type': 'str'},
'parameters': {'key': 'parameters', 'type': '{FlowToolSettingParameter}'},
'default_prompt': {'key': 'default_prompt', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword api:
:paramtype api: str
:keyword parameters: This is a dictionary.
:paramtype parameters: dict[str, ~flow.models.FlowToolSettingParameter]
:keyword default_prompt:
:paramtype default_prompt: str
"""
super(ApiAndParameters, self).__init__(**kwargs)
self.api = kwargs.get('api', None)
self.parameters = kwargs.get('parameters', None)
self.default_prompt = kwargs.get('default_prompt', None)
class ApplicationEndpointConfiguration(msrest.serialization.Model):
"""ApplicationEndpointConfiguration.
:ivar type: Possible values include: "Jupyter", "JupyterLab", "SSH", "TensorBoard", "VSCode",
"Theia", "Grafana", "Custom", "RayDashboard".
:vartype type: str or ~flow.models.ApplicationEndpointType
:ivar port:
:vartype port: int
:ivar properties: Dictionary of :code:`<string>`.
:vartype properties: dict[str, str]
:ivar nodes:
:vartype nodes: ~flow.models.Nodes
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'port': {'key': 'port', 'type': 'int'},
'properties': {'key': 'properties', 'type': '{str}'},
'nodes': {'key': 'nodes', 'type': 'Nodes'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type: Possible values include: "Jupyter", "JupyterLab", "SSH", "TensorBoard",
"VSCode", "Theia", "Grafana", "Custom", "RayDashboard".
:paramtype type: str or ~flow.models.ApplicationEndpointType
:keyword port:
:paramtype port: int
:keyword properties: Dictionary of :code:`<string>`.
:paramtype properties: dict[str, str]
:keyword nodes:
:paramtype nodes: ~flow.models.Nodes
"""
super(ApplicationEndpointConfiguration, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.port = kwargs.get('port', None)
self.properties = kwargs.get('properties', None)
self.nodes = kwargs.get('nodes', None)
class ArgumentAssignment(msrest.serialization.Model):
"""ArgumentAssignment.
:ivar value_type: Possible values include: "Literal", "Parameter", "Input", "Output",
"NestedList", "StringInterpolationList".
:vartype value_type: str or ~flow.models.ArgumentValueType
:ivar value:
:vartype value: str
:ivar nested_argument_list:
:vartype nested_argument_list: list[~flow.models.ArgumentAssignment]
:ivar string_interpolation_argument_list:
:vartype string_interpolation_argument_list: list[~flow.models.ArgumentAssignment]
"""
_attribute_map = {
'value_type': {'key': 'valueType', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
'nested_argument_list': {'key': 'nestedArgumentList', 'type': '[ArgumentAssignment]'},
'string_interpolation_argument_list': {'key': 'stringInterpolationArgumentList', 'type': '[ArgumentAssignment]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword value_type: Possible values include: "Literal", "Parameter", "Input", "Output",
"NestedList", "StringInterpolationList".
:paramtype value_type: str or ~flow.models.ArgumentValueType
:keyword value:
:paramtype value: str
:keyword nested_argument_list:
:paramtype nested_argument_list: list[~flow.models.ArgumentAssignment]
:keyword string_interpolation_argument_list:
:paramtype string_interpolation_argument_list: list[~flow.models.ArgumentAssignment]
"""
super(ArgumentAssignment, self).__init__(**kwargs)
self.value_type = kwargs.get('value_type', None)
self.value = kwargs.get('value', None)
self.nested_argument_list = kwargs.get('nested_argument_list', None)
self.string_interpolation_argument_list = kwargs.get('string_interpolation_argument_list', None)
class Asset(msrest.serialization.Model):
"""Asset.
:ivar asset_id:
:vartype asset_id: str
:ivar type:
:vartype type: str
"""
_attribute_map = {
'asset_id': {'key': 'assetId', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword asset_id:
:paramtype asset_id: str
:keyword type:
:paramtype type: str
"""
super(Asset, self).__init__(**kwargs)
self.asset_id = kwargs.get('asset_id', None)
self.type = kwargs.get('type', None)
class AssetDefinition(msrest.serialization.Model):
"""AssetDefinition.
:ivar path:
:vartype path: str
:ivar type: Possible values include: "UriFile", "UriFolder", "MLTable", "CustomModel",
"MLFlowModel", "TritonModel", "OpenAIModel".
:vartype type: str or ~flow.models.AEVAAssetType
:ivar asset_id:
:vartype asset_id: str
:ivar serialized_asset_id:
:vartype serialized_asset_id: str
"""
_attribute_map = {
'path': {'key': 'path', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'asset_id': {'key': 'assetId', 'type': 'str'},
'serialized_asset_id': {'key': 'serializedAssetId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword path:
:paramtype path: str
:keyword type: Possible values include: "UriFile", "UriFolder", "MLTable", "CustomModel",
"MLFlowModel", "TritonModel", "OpenAIModel".
:paramtype type: str or ~flow.models.AEVAAssetType
:keyword asset_id:
:paramtype asset_id: str
:keyword serialized_asset_id:
:paramtype serialized_asset_id: str
"""
super(AssetDefinition, self).__init__(**kwargs)
self.path = kwargs.get('path', None)
self.type = kwargs.get('type', None)
self.asset_id = kwargs.get('asset_id', None)
self.serialized_asset_id = kwargs.get('serialized_asset_id', None)
class AssetNameAndVersionIdentifier(msrest.serialization.Model):
"""AssetNameAndVersionIdentifier.
:ivar asset_name:
:vartype asset_name: str
:ivar version:
:vartype version: str
:ivar feed_name:
:vartype feed_name: str
"""
_attribute_map = {
'asset_name': {'key': 'assetName', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'feed_name': {'key': 'feedName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword asset_name:
:paramtype asset_name: str
:keyword version:
:paramtype version: str
:keyword feed_name:
:paramtype feed_name: str
"""
super(AssetNameAndVersionIdentifier, self).__init__(**kwargs)
self.asset_name = kwargs.get('asset_name', None)
self.version = kwargs.get('version', None)
self.feed_name = kwargs.get('feed_name', None)
class AssetOutputSettings(msrest.serialization.Model):
"""AssetOutputSettings.
:ivar path:
:vartype path: str
:ivar path_parameter_assignment:
:vartype path_parameter_assignment: ~flow.models.ParameterAssignment
:ivar type: Possible values include: "UriFile", "UriFolder", "MLTable", "CustomModel",
"MLFlowModel", "TritonModel", "OpenAIModel".
:vartype type: str or ~flow.models.AEVAAssetType
:ivar options: This is a dictionary.
:vartype options: dict[str, str]
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AEVADataStoreMode
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'path': {'key': 'path', 'type': 'str'},
'path_parameter_assignment': {'key': 'PathParameterAssignment', 'type': 'ParameterAssignment'},
'type': {'key': 'type', 'type': 'str'},
'options': {'key': 'options', 'type': '{str}'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword path:
:paramtype path: str
:keyword path_parameter_assignment:
:paramtype path_parameter_assignment: ~flow.models.ParameterAssignment
:keyword type: Possible values include: "UriFile", "UriFolder", "MLTable", "CustomModel",
"MLFlowModel", "TritonModel", "OpenAIModel".
:paramtype type: str or ~flow.models.AEVAAssetType
:keyword options: This is a dictionary.
:paramtype options: dict[str, str]
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AEVADataStoreMode
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
"""
super(AssetOutputSettings, self).__init__(**kwargs)
self.path = kwargs.get('path', None)
self.path_parameter_assignment = kwargs.get('path_parameter_assignment', None)
self.type = kwargs.get('type', None)
self.options = kwargs.get('options', None)
self.data_store_mode = kwargs.get('data_store_mode', None)
self.name = kwargs.get('name', None)
self.version = kwargs.get('version', None)
class AssetOutputSettingsParameter(msrest.serialization.Model):
"""AssetOutputSettingsParameter.
:ivar name:
:vartype name: str
:ivar documentation:
:vartype documentation: str
:ivar default_value:
:vartype default_value: ~flow.models.AssetOutputSettings
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'documentation': {'key': 'documentation', 'type': 'str'},
'default_value': {'key': 'defaultValue', 'type': 'AssetOutputSettings'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword documentation:
:paramtype documentation: str
:keyword default_value:
:paramtype default_value: ~flow.models.AssetOutputSettings
"""
super(AssetOutputSettingsParameter, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.documentation = kwargs.get('documentation', None)
self.default_value = kwargs.get('default_value', None)
class AssetPublishResult(msrest.serialization.Model):
"""AssetPublishResult.
:ivar feed_name:
:vartype feed_name: str
:ivar asset_name:
:vartype asset_name: str
:ivar asset_version:
:vartype asset_version: str
:ivar step_name:
:vartype step_name: str
:ivar status:
:vartype status: str
:ivar error_message:
:vartype error_message: str
:ivar created_time:
:vartype created_time: ~datetime.datetime
:ivar last_updated_time:
:vartype last_updated_time: ~datetime.datetime
:ivar regional_publish_results: Dictionary of :code:`<AssetPublishSingleRegionResult>`.
:vartype regional_publish_results: dict[str, ~flow.models.AssetPublishSingleRegionResult]
"""
_attribute_map = {
'feed_name': {'key': 'feedName', 'type': 'str'},
'asset_name': {'key': 'assetName', 'type': 'str'},
'asset_version': {'key': 'assetVersion', 'type': 'str'},
'step_name': {'key': 'stepName', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'error_message': {'key': 'errorMessage', 'type': 'str'},
'created_time': {'key': 'createdTime', 'type': 'iso-8601'},
'last_updated_time': {'key': 'lastUpdatedTime', 'type': 'iso-8601'},
'regional_publish_results': {'key': 'regionalPublishResults', 'type': '{AssetPublishSingleRegionResult}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword feed_name:
:paramtype feed_name: str
:keyword asset_name:
:paramtype asset_name: str
:keyword asset_version:
:paramtype asset_version: str
:keyword step_name:
:paramtype step_name: str
:keyword status:
:paramtype status: str
:keyword error_message:
:paramtype error_message: str
:keyword created_time:
:paramtype created_time: ~datetime.datetime
:keyword last_updated_time:
:paramtype last_updated_time: ~datetime.datetime
:keyword regional_publish_results: Dictionary of :code:`<AssetPublishSingleRegionResult>`.
:paramtype regional_publish_results: dict[str, ~flow.models.AssetPublishSingleRegionResult]
"""
super(AssetPublishResult, self).__init__(**kwargs)
self.feed_name = kwargs.get('feed_name', None)
self.asset_name = kwargs.get('asset_name', None)
self.asset_version = kwargs.get('asset_version', None)
self.step_name = kwargs.get('step_name', None)
self.status = kwargs.get('status', None)
self.error_message = kwargs.get('error_message', None)
self.created_time = kwargs.get('created_time', None)
self.last_updated_time = kwargs.get('last_updated_time', None)
self.regional_publish_results = kwargs.get('regional_publish_results', None)
class AssetPublishSingleRegionResult(msrest.serialization.Model):
"""AssetPublishSingleRegionResult.
:ivar step_name:
:vartype step_name: str
:ivar status:
:vartype status: str
:ivar error_message:
:vartype error_message: str
:ivar last_updated_time:
:vartype last_updated_time: ~datetime.datetime
:ivar total_steps:
:vartype total_steps: int
:ivar finished_steps:
:vartype finished_steps: int
:ivar remaining_steps:
:vartype remaining_steps: int
"""
_attribute_map = {
'step_name': {'key': 'stepName', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'error_message': {'key': 'errorMessage', 'type': 'str'},
'last_updated_time': {'key': 'lastUpdatedTime', 'type': 'iso-8601'},
'total_steps': {'key': 'totalSteps', 'type': 'int'},
'finished_steps': {'key': 'finishedSteps', 'type': 'int'},
'remaining_steps': {'key': 'remainingSteps', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword step_name:
:paramtype step_name: str
:keyword status:
:paramtype status: str
:keyword error_message:
:paramtype error_message: str
:keyword last_updated_time:
:paramtype last_updated_time: ~datetime.datetime
:keyword total_steps:
:paramtype total_steps: int
:keyword finished_steps:
:paramtype finished_steps: int
:keyword remaining_steps:
:paramtype remaining_steps: int
"""
super(AssetPublishSingleRegionResult, self).__init__(**kwargs)
self.step_name = kwargs.get('step_name', None)
self.status = kwargs.get('status', None)
self.error_message = kwargs.get('error_message', None)
self.last_updated_time = kwargs.get('last_updated_time', None)
self.total_steps = kwargs.get('total_steps', None)
self.finished_steps = kwargs.get('finished_steps', None)
self.remaining_steps = kwargs.get('remaining_steps', None)
class AssetTypeMetaInfo(msrest.serialization.Model):
"""AssetTypeMetaInfo.
:ivar consumption_mode: Possible values include: "Reference", "Copy", "CopyAndAutoUpgrade".
:vartype consumption_mode: str or ~flow.models.ConsumeMode
"""
_attribute_map = {
'consumption_mode': {'key': 'consumptionMode', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword consumption_mode: Possible values include: "Reference", "Copy", "CopyAndAutoUpgrade".
:paramtype consumption_mode: str or ~flow.models.ConsumeMode
"""
super(AssetTypeMetaInfo, self).__init__(**kwargs)
self.consumption_mode = kwargs.get('consumption_mode', None)
class AssetVersionPublishRequest(msrest.serialization.Model):
"""AssetVersionPublishRequest.
:ivar asset_type: Possible values include: "Component", "Model", "Environment", "Dataset",
"DataStore", "SampleGraph", "FlowTool", "FlowToolSetting", "FlowConnection", "FlowSample",
"FlowRuntimeSpec".
:vartype asset_type: str or ~flow.models.AssetType
:ivar asset_source_type: Possible values include: "Unknown", "Local", "GithubFile",
"GithubFolder", "DevopsArtifactsZip".
:vartype asset_source_type: str or ~flow.models.AssetSourceType
:ivar yaml_file:
:vartype yaml_file: str
:ivar source_zip_url:
:vartype source_zip_url: str
:ivar source_zip_file:
:vartype source_zip_file: IO
:ivar feed_name:
:vartype feed_name: str
:ivar set_as_default_version:
:vartype set_as_default_version: bool
:ivar referenced_assets:
:vartype referenced_assets: list[~flow.models.AssetNameAndVersionIdentifier]
:ivar flow_file:
:vartype flow_file: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'asset_type': {'key': 'assetType', 'type': 'str'},
'asset_source_type': {'key': 'assetSourceType', 'type': 'str'},
'yaml_file': {'key': 'yamlFile', 'type': 'str'},
'source_zip_url': {'key': 'sourceZipUrl', 'type': 'str'},
'source_zip_file': {'key': 'sourceZipFile', 'type': 'IO'},
'feed_name': {'key': 'feedName', 'type': 'str'},
'set_as_default_version': {'key': 'setAsDefaultVersion', 'type': 'bool'},
'referenced_assets': {'key': 'referencedAssets', 'type': '[AssetNameAndVersionIdentifier]'},
'flow_file': {'key': 'flowFile', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword asset_type: Possible values include: "Component", "Model", "Environment", "Dataset",
"DataStore", "SampleGraph", "FlowTool", "FlowToolSetting", "FlowConnection", "FlowSample",
"FlowRuntimeSpec".
:paramtype asset_type: str or ~flow.models.AssetType
:keyword asset_source_type: Possible values include: "Unknown", "Local", "GithubFile",
"GithubFolder", "DevopsArtifactsZip".
:paramtype asset_source_type: str or ~flow.models.AssetSourceType
:keyword yaml_file:
:paramtype yaml_file: str
:keyword source_zip_url:
:paramtype source_zip_url: str
:keyword source_zip_file:
:paramtype source_zip_file: IO
:keyword feed_name:
:paramtype feed_name: str
:keyword set_as_default_version:
:paramtype set_as_default_version: bool
:keyword referenced_assets:
:paramtype referenced_assets: list[~flow.models.AssetNameAndVersionIdentifier]
:keyword flow_file:
:paramtype flow_file: str
:keyword version:
:paramtype version: str
"""
super(AssetVersionPublishRequest, self).__init__(**kwargs)
self.asset_type = kwargs.get('asset_type', None)
self.asset_source_type = kwargs.get('asset_source_type', None)
self.yaml_file = kwargs.get('yaml_file', None)
self.source_zip_url = kwargs.get('source_zip_url', None)
self.source_zip_file = kwargs.get('source_zip_file', None)
self.feed_name = kwargs.get('feed_name', None)
self.set_as_default_version = kwargs.get('set_as_default_version', None)
self.referenced_assets = kwargs.get('referenced_assets', None)
self.flow_file = kwargs.get('flow_file', None)
self.version = kwargs.get('version', None)
class AssignedUser(msrest.serialization.Model):
"""AssignedUser.
:ivar object_id:
:vartype object_id: str
:ivar tenant_id:
:vartype tenant_id: str
"""
_attribute_map = {
'object_id': {'key': 'objectId', 'type': 'str'},
'tenant_id': {'key': 'tenantId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword object_id:
:paramtype object_id: str
:keyword tenant_id:
:paramtype tenant_id: str
"""
super(AssignedUser, self).__init__(**kwargs)
self.object_id = kwargs.get('object_id', None)
self.tenant_id = kwargs.get('tenant_id', None)
class AuthKeys(msrest.serialization.Model):
"""AuthKeys.
:ivar primary_key:
:vartype primary_key: str
:ivar secondary_key:
:vartype secondary_key: str
"""
_attribute_map = {
'primary_key': {'key': 'primaryKey', 'type': 'str'},
'secondary_key': {'key': 'secondaryKey', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword primary_key:
:paramtype primary_key: str
:keyword secondary_key:
:paramtype secondary_key: str
"""
super(AuthKeys, self).__init__(**kwargs)
self.primary_key = kwargs.get('primary_key', None)
self.secondary_key = kwargs.get('secondary_key', None)
class AutoClusterComputeSpecification(msrest.serialization.Model):
"""AutoClusterComputeSpecification.
:ivar instance_size:
:vartype instance_size: str
:ivar instance_priority:
:vartype instance_priority: str
:ivar os_type:
:vartype os_type: str
:ivar location:
:vartype location: str
:ivar runtime_version:
:vartype runtime_version: str
:ivar quota_enforcement_resource_id:
:vartype quota_enforcement_resource_id: str
:ivar model_compute_specification_id:
:vartype model_compute_specification_id: str
"""
_attribute_map = {
'instance_size': {'key': 'instanceSize', 'type': 'str'},
'instance_priority': {'key': 'instancePriority', 'type': 'str'},
'os_type': {'key': 'osType', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
'runtime_version': {'key': 'runtimeVersion', 'type': 'str'},
'quota_enforcement_resource_id': {'key': 'quotaEnforcementResourceId', 'type': 'str'},
'model_compute_specification_id': {'key': 'modelComputeSpecificationId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword instance_size:
:paramtype instance_size: str
:keyword instance_priority:
:paramtype instance_priority: str
:keyword os_type:
:paramtype os_type: str
:keyword location:
:paramtype location: str
:keyword runtime_version:
:paramtype runtime_version: str
:keyword quota_enforcement_resource_id:
:paramtype quota_enforcement_resource_id: str
:keyword model_compute_specification_id:
:paramtype model_compute_specification_id: str
"""
super(AutoClusterComputeSpecification, self).__init__(**kwargs)
self.instance_size = kwargs.get('instance_size', None)
self.instance_priority = kwargs.get('instance_priority', None)
self.os_type = kwargs.get('os_type', None)
self.location = kwargs.get('location', None)
self.runtime_version = kwargs.get('runtime_version', None)
self.quota_enforcement_resource_id = kwargs.get('quota_enforcement_resource_id', None)
self.model_compute_specification_id = kwargs.get('model_compute_specification_id', None)
class AutoDeleteSetting(msrest.serialization.Model):
"""AutoDeleteSetting.
:ivar condition: Possible values include: "CreatedGreaterThan", "LastAccessedGreaterThan".
:vartype condition: str or ~flow.models.AutoDeleteCondition
:ivar value:
:vartype value: str
"""
_attribute_map = {
'condition': {'key': 'condition', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword condition: Possible values include: "CreatedGreaterThan", "LastAccessedGreaterThan".
:paramtype condition: str or ~flow.models.AutoDeleteCondition
:keyword value:
:paramtype value: str
"""
super(AutoDeleteSetting, self).__init__(**kwargs)
self.condition = kwargs.get('condition', None)
self.value = kwargs.get('value', None)
class AutoFeaturizeConfiguration(msrest.serialization.Model):
"""AutoFeaturizeConfiguration.
:ivar featurization_config:
:vartype featurization_config: ~flow.models.FeaturizationSettings
"""
_attribute_map = {
'featurization_config': {'key': 'featurizationConfig', 'type': 'FeaturizationSettings'},
}
def __init__(
self,
**kwargs
):
"""
:keyword featurization_config:
:paramtype featurization_config: ~flow.models.FeaturizationSettings
"""
super(AutoFeaturizeConfiguration, self).__init__(**kwargs)
self.featurization_config = kwargs.get('featurization_config', None)
class AutologgerSettings(msrest.serialization.Model):
"""AutologgerSettings.
:ivar ml_flow_autologger: Possible values include: "Enabled", "Disabled".
:vartype ml_flow_autologger: str or ~flow.models.MLFlowAutologgerState
"""
_attribute_map = {
'ml_flow_autologger': {'key': 'mlFlowAutologger', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword ml_flow_autologger: Possible values include: "Enabled", "Disabled".
:paramtype ml_flow_autologger: str or ~flow.models.MLFlowAutologgerState
"""
super(AutologgerSettings, self).__init__(**kwargs)
self.ml_flow_autologger = kwargs.get('ml_flow_autologger', None)
class AutoMLComponentConfiguration(msrest.serialization.Model):
"""AutoMLComponentConfiguration.
:ivar auto_train_config:
:vartype auto_train_config: ~flow.models.AutoTrainConfiguration
:ivar auto_featurize_config:
:vartype auto_featurize_config: ~flow.models.AutoFeaturizeConfiguration
"""
_attribute_map = {
'auto_train_config': {'key': 'autoTrainConfig', 'type': 'AutoTrainConfiguration'},
'auto_featurize_config': {'key': 'autoFeaturizeConfig', 'type': 'AutoFeaturizeConfiguration'},
}
def __init__(
self,
**kwargs
):
"""
:keyword auto_train_config:
:paramtype auto_train_config: ~flow.models.AutoTrainConfiguration
:keyword auto_featurize_config:
:paramtype auto_featurize_config: ~flow.models.AutoFeaturizeConfiguration
"""
super(AutoMLComponentConfiguration, self).__init__(**kwargs)
self.auto_train_config = kwargs.get('auto_train_config', None)
self.auto_featurize_config = kwargs.get('auto_featurize_config', None)
class AutoScaler(msrest.serialization.Model):
"""AutoScaler.
:ivar autoscale_enabled:
:vartype autoscale_enabled: bool
:ivar min_replicas:
:vartype min_replicas: int
:ivar max_replicas:
:vartype max_replicas: int
:ivar target_utilization:
:vartype target_utilization: int
:ivar refresh_period_in_seconds:
:vartype refresh_period_in_seconds: int
"""
_attribute_map = {
'autoscale_enabled': {'key': 'autoscaleEnabled', 'type': 'bool'},
'min_replicas': {'key': 'minReplicas', 'type': 'int'},
'max_replicas': {'key': 'maxReplicas', 'type': 'int'},
'target_utilization': {'key': 'targetUtilization', 'type': 'int'},
'refresh_period_in_seconds': {'key': 'refreshPeriodInSeconds', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword autoscale_enabled:
:paramtype autoscale_enabled: bool
:keyword min_replicas:
:paramtype min_replicas: int
:keyword max_replicas:
:paramtype max_replicas: int
:keyword target_utilization:
:paramtype target_utilization: int
:keyword refresh_period_in_seconds:
:paramtype refresh_period_in_seconds: int
"""
super(AutoScaler, self).__init__(**kwargs)
self.autoscale_enabled = kwargs.get('autoscale_enabled', None)
self.min_replicas = kwargs.get('min_replicas', None)
self.max_replicas = kwargs.get('max_replicas', None)
self.target_utilization = kwargs.get('target_utilization', None)
self.refresh_period_in_seconds = kwargs.get('refresh_period_in_seconds', None)
class AutoTrainConfiguration(msrest.serialization.Model):
"""AutoTrainConfiguration.
:ivar general_settings:
:vartype general_settings: ~flow.models.GeneralSettings
:ivar limit_settings:
:vartype limit_settings: ~flow.models.LimitSettings
:ivar data_settings:
:vartype data_settings: ~flow.models.DataSettings
:ivar forecasting_settings:
:vartype forecasting_settings: ~flow.models.ForecastingSettings
:ivar training_settings:
:vartype training_settings: ~flow.models.TrainingSettings
:ivar sweep_settings:
:vartype sweep_settings: ~flow.models.SweepSettings
:ivar image_model_settings: Dictionary of :code:`<any>`.
:vartype image_model_settings: dict[str, any]
:ivar properties: Dictionary of :code:`<string>`.
:vartype properties: dict[str, str]
:ivar compute_configuration:
:vartype compute_configuration: ~flow.models.AEVAComputeConfiguration
:ivar resource_configurtion:
:vartype resource_configurtion: ~flow.models.AEVAResourceConfiguration
:ivar environment_id:
:vartype environment_id: str
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
"""
_attribute_map = {
'general_settings': {'key': 'generalSettings', 'type': 'GeneralSettings'},
'limit_settings': {'key': 'limitSettings', 'type': 'LimitSettings'},
'data_settings': {'key': 'dataSettings', 'type': 'DataSettings'},
'forecasting_settings': {'key': 'forecastingSettings', 'type': 'ForecastingSettings'},
'training_settings': {'key': 'trainingSettings', 'type': 'TrainingSettings'},
'sweep_settings': {'key': 'sweepSettings', 'type': 'SweepSettings'},
'image_model_settings': {'key': 'imageModelSettings', 'type': '{object}'},
'properties': {'key': 'properties', 'type': '{str}'},
'compute_configuration': {'key': 'computeConfiguration', 'type': 'AEVAComputeConfiguration'},
'resource_configurtion': {'key': 'resourceConfigurtion', 'type': 'AEVAResourceConfiguration'},
'environment_id': {'key': 'environmentId', 'type': 'str'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword general_settings:
:paramtype general_settings: ~flow.models.GeneralSettings
:keyword limit_settings:
:paramtype limit_settings: ~flow.models.LimitSettings
:keyword data_settings:
:paramtype data_settings: ~flow.models.DataSettings
:keyword forecasting_settings:
:paramtype forecasting_settings: ~flow.models.ForecastingSettings
:keyword training_settings:
:paramtype training_settings: ~flow.models.TrainingSettings
:keyword sweep_settings:
:paramtype sweep_settings: ~flow.models.SweepSettings
:keyword image_model_settings: Dictionary of :code:`<any>`.
:paramtype image_model_settings: dict[str, any]
:keyword properties: Dictionary of :code:`<string>`.
:paramtype properties: dict[str, str]
:keyword compute_configuration:
:paramtype compute_configuration: ~flow.models.AEVAComputeConfiguration
:keyword resource_configurtion:
:paramtype resource_configurtion: ~flow.models.AEVAResourceConfiguration
:keyword environment_id:
:paramtype environment_id: str
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
"""
super(AutoTrainConfiguration, self).__init__(**kwargs)
self.general_settings = kwargs.get('general_settings', None)
self.limit_settings = kwargs.get('limit_settings', None)
self.data_settings = kwargs.get('data_settings', None)
self.forecasting_settings = kwargs.get('forecasting_settings', None)
self.training_settings = kwargs.get('training_settings', None)
self.sweep_settings = kwargs.get('sweep_settings', None)
self.image_model_settings = kwargs.get('image_model_settings', None)
self.properties = kwargs.get('properties', None)
self.compute_configuration = kwargs.get('compute_configuration', None)
self.resource_configurtion = kwargs.get('resource_configurtion', None)
self.environment_id = kwargs.get('environment_id', None)
self.environment_variables = kwargs.get('environment_variables', None)
class AvailabilityResponse(msrest.serialization.Model):
"""AvailabilityResponse.
:ivar is_available:
:vartype is_available: bool
:ivar error: The error response.
:vartype error: ~flow.models.ErrorResponse
"""
_attribute_map = {
'is_available': {'key': 'isAvailable', 'type': 'bool'},
'error': {'key': 'error', 'type': 'ErrorResponse'},
}
def __init__(
self,
**kwargs
):
"""
:keyword is_available:
:paramtype is_available: bool
:keyword error: The error response.
:paramtype error: ~flow.models.ErrorResponse
"""
super(AvailabilityResponse, self).__init__(**kwargs)
self.is_available = kwargs.get('is_available', None)
self.error = kwargs.get('error', None)
class AzureBlobReference(msrest.serialization.Model):
"""AzureBlobReference.
:ivar container:
:vartype container: str
:ivar sas_token:
:vartype sas_token: str
:ivar uri:
:vartype uri: str
:ivar account:
:vartype account: str
:ivar relative_path:
:vartype relative_path: str
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'container': {'key': 'container', 'type': 'str'},
'sas_token': {'key': 'sasToken', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'account': {'key': 'account', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword container:
:paramtype container: str
:keyword sas_token:
:paramtype sas_token: str
:keyword uri:
:paramtype uri: str
:keyword account:
:paramtype account: str
:keyword relative_path:
:paramtype relative_path: str
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AzureBlobReference, self).__init__(**kwargs)
self.container = kwargs.get('container', None)
self.sas_token = kwargs.get('sas_token', None)
self.uri = kwargs.get('uri', None)
self.account = kwargs.get('account', None)
self.relative_path = kwargs.get('relative_path', None)
self.aml_data_store_name = kwargs.get('aml_data_store_name', None)
class AzureDatabaseReference(msrest.serialization.Model):
"""AzureDatabaseReference.
:ivar table_name:
:vartype table_name: str
:ivar sql_query:
:vartype sql_query: str
:ivar stored_procedure_name:
:vartype stored_procedure_name: str
:ivar stored_procedure_parameters:
:vartype stored_procedure_parameters: list[~flow.models.StoredProcedureParameter]
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'table_name': {'key': 'tableName', 'type': 'str'},
'sql_query': {'key': 'sqlQuery', 'type': 'str'},
'stored_procedure_name': {'key': 'storedProcedureName', 'type': 'str'},
'stored_procedure_parameters': {'key': 'storedProcedureParameters', 'type': '[StoredProcedureParameter]'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword table_name:
:paramtype table_name: str
:keyword sql_query:
:paramtype sql_query: str
:keyword stored_procedure_name:
:paramtype stored_procedure_name: str
:keyword stored_procedure_parameters:
:paramtype stored_procedure_parameters: list[~flow.models.StoredProcedureParameter]
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AzureDatabaseReference, self).__init__(**kwargs)
self.table_name = kwargs.get('table_name', None)
self.sql_query = kwargs.get('sql_query', None)
self.stored_procedure_name = kwargs.get('stored_procedure_name', None)
self.stored_procedure_parameters = kwargs.get('stored_procedure_parameters', None)
self.aml_data_store_name = kwargs.get('aml_data_store_name', None)
class AzureDataLakeGen2Reference(msrest.serialization.Model):
"""AzureDataLakeGen2Reference.
:ivar file_system_name:
:vartype file_system_name: str
:ivar uri:
:vartype uri: str
:ivar account:
:vartype account: str
:ivar relative_path:
:vartype relative_path: str
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'file_system_name': {'key': 'fileSystemName', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'account': {'key': 'account', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword file_system_name:
:paramtype file_system_name: str
:keyword uri:
:paramtype uri: str
:keyword account:
:paramtype account: str
:keyword relative_path:
:paramtype relative_path: str
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AzureDataLakeGen2Reference, self).__init__(**kwargs)
self.file_system_name = kwargs.get('file_system_name', None)
self.uri = kwargs.get('uri', None)
self.account = kwargs.get('account', None)
self.relative_path = kwargs.get('relative_path', None)
self.aml_data_store_name = kwargs.get('aml_data_store_name', None)
class AzureDataLakeReference(msrest.serialization.Model):
"""AzureDataLakeReference.
:ivar tenant:
:vartype tenant: str
:ivar subscription:
:vartype subscription: str
:ivar resource_group:
:vartype resource_group: str
:ivar uri:
:vartype uri: str
:ivar account:
:vartype account: str
:ivar relative_path:
:vartype relative_path: str
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'tenant': {'key': 'tenant', 'type': 'str'},
'subscription': {'key': 'subscription', 'type': 'str'},
'resource_group': {'key': 'resourceGroup', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'account': {'key': 'account', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword tenant:
:paramtype tenant: str
:keyword subscription:
:paramtype subscription: str
:keyword resource_group:
:paramtype resource_group: str
:keyword uri:
:paramtype uri: str
:keyword account:
:paramtype account: str
:keyword relative_path:
:paramtype relative_path: str
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AzureDataLakeReference, self).__init__(**kwargs)
self.tenant = kwargs.get('tenant', None)
self.subscription = kwargs.get('subscription', None)
self.resource_group = kwargs.get('resource_group', None)
self.uri = kwargs.get('uri', None)
self.account = kwargs.get('account', None)
self.relative_path = kwargs.get('relative_path', None)
self.aml_data_store_name = kwargs.get('aml_data_store_name', None)
class AzureFilesReference(msrest.serialization.Model):
"""AzureFilesReference.
:ivar share:
:vartype share: str
:ivar uri:
:vartype uri: str
:ivar account:
:vartype account: str
:ivar relative_path:
:vartype relative_path: str
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'share': {'key': 'share', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'account': {'key': 'account', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword share:
:paramtype share: str
:keyword uri:
:paramtype uri: str
:keyword account:
:paramtype account: str
:keyword relative_path:
:paramtype relative_path: str
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AzureFilesReference, self).__init__(**kwargs)
self.share = kwargs.get('share', None)
self.uri = kwargs.get('uri', None)
self.account = kwargs.get('account', None)
self.relative_path = kwargs.get('relative_path', None)
self.aml_data_store_name = kwargs.get('aml_data_store_name', None)
class AzureMLModuleVersionDescriptor(msrest.serialization.Model):
"""AzureMLModuleVersionDescriptor.
:ivar module_version_id:
:vartype module_version_id: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'module_version_id': {'key': 'moduleVersionId', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword module_version_id:
:paramtype module_version_id: str
:keyword version:
:paramtype version: str
"""
super(AzureMLModuleVersionDescriptor, self).__init__(**kwargs)
self.module_version_id = kwargs.get('module_version_id', None)
self.version = kwargs.get('version', None)
class AzureOpenAIDeploymentDto(msrest.serialization.Model):
"""AzureOpenAIDeploymentDto.
:ivar name:
:vartype name: str
:ivar model_name:
:vartype model_name: str
:ivar capabilities:
:vartype capabilities: ~flow.models.AzureOpenAIModelCapabilities
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'model_name': {'key': 'modelName', 'type': 'str'},
'capabilities': {'key': 'capabilities', 'type': 'AzureOpenAIModelCapabilities'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword model_name:
:paramtype model_name: str
:keyword capabilities:
:paramtype capabilities: ~flow.models.AzureOpenAIModelCapabilities
"""
super(AzureOpenAIDeploymentDto, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.model_name = kwargs.get('model_name', None)
self.capabilities = kwargs.get('capabilities', None)
class AzureOpenAIModelCapabilities(msrest.serialization.Model):
"""AzureOpenAIModelCapabilities.
:ivar completion:
:vartype completion: bool
:ivar chat_completion:
:vartype chat_completion: bool
:ivar embeddings:
:vartype embeddings: bool
"""
_attribute_map = {
'completion': {'key': 'completion', 'type': 'bool'},
'chat_completion': {'key': 'chat_completion', 'type': 'bool'},
'embeddings': {'key': 'embeddings', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword completion:
:paramtype completion: bool
:keyword chat_completion:
:paramtype chat_completion: bool
:keyword embeddings:
:paramtype embeddings: bool
"""
super(AzureOpenAIModelCapabilities, self).__init__(**kwargs)
self.completion = kwargs.get('completion', None)
self.chat_completion = kwargs.get('chat_completion', None)
self.embeddings = kwargs.get('embeddings', None)
class BatchAiComputeInfo(msrest.serialization.Model):
"""BatchAiComputeInfo.
:ivar batch_ai_subscription_id:
:vartype batch_ai_subscription_id: str
:ivar batch_ai_resource_group:
:vartype batch_ai_resource_group: str
:ivar batch_ai_workspace_name:
:vartype batch_ai_workspace_name: str
:ivar cluster_name:
:vartype cluster_name: str
:ivar native_shared_directory:
:vartype native_shared_directory: str
"""
_attribute_map = {
'batch_ai_subscription_id': {'key': 'batchAiSubscriptionId', 'type': 'str'},
'batch_ai_resource_group': {'key': 'batchAiResourceGroup', 'type': 'str'},
'batch_ai_workspace_name': {'key': 'batchAiWorkspaceName', 'type': 'str'},
'cluster_name': {'key': 'clusterName', 'type': 'str'},
'native_shared_directory': {'key': 'nativeSharedDirectory', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword batch_ai_subscription_id:
:paramtype batch_ai_subscription_id: str
:keyword batch_ai_resource_group:
:paramtype batch_ai_resource_group: str
:keyword batch_ai_workspace_name:
:paramtype batch_ai_workspace_name: str
:keyword cluster_name:
:paramtype cluster_name: str
:keyword native_shared_directory:
:paramtype native_shared_directory: str
"""
super(BatchAiComputeInfo, self).__init__(**kwargs)
self.batch_ai_subscription_id = kwargs.get('batch_ai_subscription_id', None)
self.batch_ai_resource_group = kwargs.get('batch_ai_resource_group', None)
self.batch_ai_workspace_name = kwargs.get('batch_ai_workspace_name', None)
self.cluster_name = kwargs.get('cluster_name', None)
self.native_shared_directory = kwargs.get('native_shared_directory', None)
class BatchDataInput(msrest.serialization.Model):
"""BatchDataInput.
:ivar data_uri:
:vartype data_uri: str
:ivar type:
:vartype type: str
"""
_attribute_map = {
'data_uri': {'key': 'dataUri', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_uri:
:paramtype data_uri: str
:keyword type:
:paramtype type: str
"""
super(BatchDataInput, self).__init__(**kwargs)
self.data_uri = kwargs.get('data_uri', None)
self.type = kwargs.get('type', None)
class BatchExportComponentSpecResponse(msrest.serialization.Model):
"""BatchExportComponentSpecResponse.
:ivar component_spec_meta_infos:
:vartype component_spec_meta_infos: list[~flow.models.ComponentSpecMetaInfo]
:ivar errors:
:vartype errors: list[~flow.models.ErrorResponse]
"""
_attribute_map = {
'component_spec_meta_infos': {'key': 'componentSpecMetaInfos', 'type': '[ComponentSpecMetaInfo]'},
'errors': {'key': 'errors', 'type': '[ErrorResponse]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword component_spec_meta_infos:
:paramtype component_spec_meta_infos: list[~flow.models.ComponentSpecMetaInfo]
:keyword errors:
:paramtype errors: list[~flow.models.ErrorResponse]
"""
super(BatchExportComponentSpecResponse, self).__init__(**kwargs)
self.component_spec_meta_infos = kwargs.get('component_spec_meta_infos', None)
self.errors = kwargs.get('errors', None)
class BatchExportRawComponentResponse(msrest.serialization.Model):
"""BatchExportRawComponentResponse.
:ivar raw_component_dtos:
:vartype raw_component_dtos: list[~flow.models.RawComponentDto]
:ivar errors:
:vartype errors: list[~flow.models.ErrorResponse]
"""
_attribute_map = {
'raw_component_dtos': {'key': 'rawComponentDtos', 'type': '[RawComponentDto]'},
'errors': {'key': 'errors', 'type': '[ErrorResponse]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword raw_component_dtos:
:paramtype raw_component_dtos: list[~flow.models.RawComponentDto]
:keyword errors:
:paramtype errors: list[~flow.models.ErrorResponse]
"""
super(BatchExportRawComponentResponse, self).__init__(**kwargs)
self.raw_component_dtos = kwargs.get('raw_component_dtos', None)
self.errors = kwargs.get('errors', None)
class BatchGetComponentHashesRequest(msrest.serialization.Model):
"""BatchGetComponentHashesRequest.
:ivar module_hash_version: Possible values include: "IdentifierHash", "IdentifierHashV2".
:vartype module_hash_version: str or ~flow.models.AetherModuleHashVersion
:ivar module_entities: Dictionary of :code:`<AetherModuleEntity>`.
:vartype module_entities: dict[str, ~flow.models.AetherModuleEntity]
"""
_attribute_map = {
'module_hash_version': {'key': 'moduleHashVersion', 'type': 'str'},
'module_entities': {'key': 'moduleEntities', 'type': '{AetherModuleEntity}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword module_hash_version: Possible values include: "IdentifierHash", "IdentifierHashV2".
:paramtype module_hash_version: str or ~flow.models.AetherModuleHashVersion
:keyword module_entities: Dictionary of :code:`<AetherModuleEntity>`.
:paramtype module_entities: dict[str, ~flow.models.AetherModuleEntity]
"""
super(BatchGetComponentHashesRequest, self).__init__(**kwargs)
self.module_hash_version = kwargs.get('module_hash_version', None)
self.module_entities = kwargs.get('module_entities', None)
class BatchGetComponentRequest(msrest.serialization.Model):
"""BatchGetComponentRequest.
:ivar version_ids:
:vartype version_ids: list[str]
:ivar name_and_versions:
:vartype name_and_versions: list[~flow.models.ComponentNameMetaInfo]
"""
_attribute_map = {
'version_ids': {'key': 'versionIds', 'type': '[str]'},
'name_and_versions': {'key': 'nameAndVersions', 'type': '[ComponentNameMetaInfo]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword version_ids:
:paramtype version_ids: list[str]
:keyword name_and_versions:
:paramtype name_and_versions: list[~flow.models.ComponentNameMetaInfo]
"""
super(BatchGetComponentRequest, self).__init__(**kwargs)
self.version_ids = kwargs.get('version_ids', None)
self.name_and_versions = kwargs.get('name_and_versions', None)
class Binding(msrest.serialization.Model):
"""Binding.
:ivar binding_type: The only acceptable values to pass in are None and "Basic". The default
value is None.
:vartype binding_type: str
"""
_attribute_map = {
'binding_type': {'key': 'bindingType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword binding_type: The only acceptable values to pass in are None and "Basic". The default
value is None.
:paramtype binding_type: str
"""
super(Binding, self).__init__(**kwargs)
self.binding_type = kwargs.get('binding_type', None)
class BulkTestDto(msrest.serialization.Model):
"""BulkTestDto.
:ivar bulk_test_id:
:vartype bulk_test_id: str
:ivar display_name:
:vartype display_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar runtime:
:vartype runtime: str
:ivar created_by:
:vartype created_by: ~flow.models.SchemaContractsCreatedBy
:ivar created_on:
:vartype created_on: ~datetime.datetime
:ivar evaluation_count:
:vartype evaluation_count: int
:ivar variant_count:
:vartype variant_count: int
:ivar flow_submit_run_settings:
:vartype flow_submit_run_settings: ~flow.models.FlowSubmitRunSettings
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, ~flow.models.FlowInputDefinition]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, ~flow.models.FlowOutputDefinition]
:ivar batch_inputs:
:vartype batch_inputs: list[dict[str, any]]
:ivar batch_data_input:
:vartype batch_data_input: ~flow.models.BatchDataInput
"""
_attribute_map = {
'bulk_test_id': {'key': 'bulkTestId', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'runtime': {'key': 'runtime', 'type': 'str'},
'created_by': {'key': 'createdBy', 'type': 'SchemaContractsCreatedBy'},
'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
'evaluation_count': {'key': 'evaluationCount', 'type': 'int'},
'variant_count': {'key': 'variantCount', 'type': 'int'},
'flow_submit_run_settings': {'key': 'flowSubmitRunSettings', 'type': 'FlowSubmitRunSettings'},
'inputs': {'key': 'inputs', 'type': '{FlowInputDefinition}'},
'outputs': {'key': 'outputs', 'type': '{FlowOutputDefinition}'},
'batch_inputs': {'key': 'batch_inputs', 'type': '[{object}]'},
'batch_data_input': {'key': 'batchDataInput', 'type': 'BatchDataInput'},
}
def __init__(
self,
**kwargs
):
"""
:keyword bulk_test_id:
:paramtype bulk_test_id: str
:keyword display_name:
:paramtype display_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword runtime:
:paramtype runtime: str
:keyword created_by:
:paramtype created_by: ~flow.models.SchemaContractsCreatedBy
:keyword created_on:
:paramtype created_on: ~datetime.datetime
:keyword evaluation_count:
:paramtype evaluation_count: int
:keyword variant_count:
:paramtype variant_count: int
:keyword flow_submit_run_settings:
:paramtype flow_submit_run_settings: ~flow.models.FlowSubmitRunSettings
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, ~flow.models.FlowInputDefinition]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, ~flow.models.FlowOutputDefinition]
:keyword batch_inputs:
:paramtype batch_inputs: list[dict[str, any]]
:keyword batch_data_input:
:paramtype batch_data_input: ~flow.models.BatchDataInput
"""
super(BulkTestDto, self).__init__(**kwargs)
self.bulk_test_id = kwargs.get('bulk_test_id', None)
self.display_name = kwargs.get('display_name', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.runtime = kwargs.get('runtime', None)
self.created_by = kwargs.get('created_by', None)
self.created_on = kwargs.get('created_on', None)
self.evaluation_count = kwargs.get('evaluation_count', None)
self.variant_count = kwargs.get('variant_count', None)
self.flow_submit_run_settings = kwargs.get('flow_submit_run_settings', None)
self.inputs = kwargs.get('inputs', None)
self.outputs = kwargs.get('outputs', None)
self.batch_inputs = kwargs.get('batch_inputs', None)
self.batch_data_input = kwargs.get('batch_data_input', None)
class CloudError(msrest.serialization.Model):
"""CloudError.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar code:
:vartype code: str
:ivar message:
:vartype message: str
:ivar target:
:vartype target: str
:ivar details:
:vartype details: list[~flow.models.CloudError]
:ivar additional_info:
:vartype additional_info: list[~flow.models.AdditionalErrorInfo]
"""
_validation = {
'details': {'readonly': True},
'additional_info': {'readonly': True},
}
_attribute_map = {
'code': {'key': 'code', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
'details': {'key': 'details', 'type': '[CloudError]'},
'additional_info': {'key': 'additionalInfo', 'type': '[AdditionalErrorInfo]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword code:
:paramtype code: str
:keyword message:
:paramtype message: str
:keyword target:
:paramtype target: str
"""
super(CloudError, self).__init__(**kwargs)
self.code = kwargs.get('code', None)
self.message = kwargs.get('message', None)
self.target = kwargs.get('target', None)
self.details = None
self.additional_info = None
class CloudPrioritySetting(msrest.serialization.Model):
"""CloudPrioritySetting.
:ivar scope_priority:
:vartype scope_priority: ~flow.models.PriorityConfiguration
:ivar aml_compute_priority:
:vartype aml_compute_priority: ~flow.models.PriorityConfiguration
:ivar itp_priority:
:vartype itp_priority: ~flow.models.PriorityConfiguration
:ivar singularity_priority:
:vartype singularity_priority: ~flow.models.PriorityConfiguration
"""
_attribute_map = {
'scope_priority': {'key': 'scopePriority', 'type': 'PriorityConfiguration'},
'aml_compute_priority': {'key': 'AmlComputePriority', 'type': 'PriorityConfiguration'},
'itp_priority': {'key': 'ItpPriority', 'type': 'PriorityConfiguration'},
'singularity_priority': {'key': 'SingularityPriority', 'type': 'PriorityConfiguration'},
}
def __init__(
self,
**kwargs
):
"""
:keyword scope_priority:
:paramtype scope_priority: ~flow.models.PriorityConfiguration
:keyword aml_compute_priority:
:paramtype aml_compute_priority: ~flow.models.PriorityConfiguration
:keyword itp_priority:
:paramtype itp_priority: ~flow.models.PriorityConfiguration
:keyword singularity_priority:
:paramtype singularity_priority: ~flow.models.PriorityConfiguration
"""
super(CloudPrioritySetting, self).__init__(**kwargs)
self.scope_priority = kwargs.get('scope_priority', None)
self.aml_compute_priority = kwargs.get('aml_compute_priority', None)
self.itp_priority = kwargs.get('itp_priority', None)
self.singularity_priority = kwargs.get('singularity_priority', None)
class CloudSettings(msrest.serialization.Model):
"""CloudSettings.
:ivar linked_settings:
:vartype linked_settings: list[~flow.models.ParameterAssignment]
:ivar priority_config:
:vartype priority_config: ~flow.models.PriorityConfiguration
:ivar hdi_run_config:
:vartype hdi_run_config: ~flow.models.HdiRunConfiguration
:ivar sub_graph_config:
:vartype sub_graph_config: ~flow.models.SubGraphConfiguration
:ivar auto_ml_component_config:
:vartype auto_ml_component_config: ~flow.models.AutoMLComponentConfiguration
:ivar ap_cloud_config:
:vartype ap_cloud_config: ~flow.models.APCloudConfiguration
:ivar scope_cloud_config:
:vartype scope_cloud_config: ~flow.models.ScopeCloudConfiguration
:ivar es_cloud_config:
:vartype es_cloud_config: ~flow.models.EsCloudConfiguration
:ivar data_transfer_cloud_config:
:vartype data_transfer_cloud_config: ~flow.models.DataTransferCloudConfiguration
:ivar aml_spark_cloud_setting:
:vartype aml_spark_cloud_setting: ~flow.models.AmlSparkCloudSetting
:ivar data_transfer_v2_cloud_setting:
:vartype data_transfer_v2_cloud_setting: ~flow.models.DataTransferV2CloudSetting
"""
_attribute_map = {
'linked_settings': {'key': 'linkedSettings', 'type': '[ParameterAssignment]'},
'priority_config': {'key': 'priorityConfig', 'type': 'PriorityConfiguration'},
'hdi_run_config': {'key': 'hdiRunConfig', 'type': 'HdiRunConfiguration'},
'sub_graph_config': {'key': 'subGraphConfig', 'type': 'SubGraphConfiguration'},
'auto_ml_component_config': {'key': 'autoMLComponentConfig', 'type': 'AutoMLComponentConfiguration'},
'ap_cloud_config': {'key': 'apCloudConfig', 'type': 'APCloudConfiguration'},
'scope_cloud_config': {'key': 'scopeCloudConfig', 'type': 'ScopeCloudConfiguration'},
'es_cloud_config': {'key': 'esCloudConfig', 'type': 'EsCloudConfiguration'},
'data_transfer_cloud_config': {'key': 'dataTransferCloudConfig', 'type': 'DataTransferCloudConfiguration'},
'aml_spark_cloud_setting': {'key': 'amlSparkCloudSetting', 'type': 'AmlSparkCloudSetting'},
'data_transfer_v2_cloud_setting': {'key': 'dataTransferV2CloudSetting', 'type': 'DataTransferV2CloudSetting'},
}
def __init__(
self,
**kwargs
):
"""
:keyword linked_settings:
:paramtype linked_settings: list[~flow.models.ParameterAssignment]
:keyword priority_config:
:paramtype priority_config: ~flow.models.PriorityConfiguration
:keyword hdi_run_config:
:paramtype hdi_run_config: ~flow.models.HdiRunConfiguration
:keyword sub_graph_config:
:paramtype sub_graph_config: ~flow.models.SubGraphConfiguration
:keyword auto_ml_component_config:
:paramtype auto_ml_component_config: ~flow.models.AutoMLComponentConfiguration
:keyword ap_cloud_config:
:paramtype ap_cloud_config: ~flow.models.APCloudConfiguration
:keyword scope_cloud_config:
:paramtype scope_cloud_config: ~flow.models.ScopeCloudConfiguration
:keyword es_cloud_config:
:paramtype es_cloud_config: ~flow.models.EsCloudConfiguration
:keyword data_transfer_cloud_config:
:paramtype data_transfer_cloud_config: ~flow.models.DataTransferCloudConfiguration
:keyword aml_spark_cloud_setting:
:paramtype aml_spark_cloud_setting: ~flow.models.AmlSparkCloudSetting
:keyword data_transfer_v2_cloud_setting:
:paramtype data_transfer_v2_cloud_setting: ~flow.models.DataTransferV2CloudSetting
"""
super(CloudSettings, self).__init__(**kwargs)
self.linked_settings = kwargs.get('linked_settings', None)
self.priority_config = kwargs.get('priority_config', None)
self.hdi_run_config = kwargs.get('hdi_run_config', None)
self.sub_graph_config = kwargs.get('sub_graph_config', None)
self.auto_ml_component_config = kwargs.get('auto_ml_component_config', None)
self.ap_cloud_config = kwargs.get('ap_cloud_config', None)
self.scope_cloud_config = kwargs.get('scope_cloud_config', None)
self.es_cloud_config = kwargs.get('es_cloud_config', None)
self.data_transfer_cloud_config = kwargs.get('data_transfer_cloud_config', None)
self.aml_spark_cloud_setting = kwargs.get('aml_spark_cloud_setting', None)
self.data_transfer_v2_cloud_setting = kwargs.get('data_transfer_v2_cloud_setting', None)
class ColumnTransformer(msrest.serialization.Model):
"""ColumnTransformer.
:ivar fields:
:vartype fields: list[str]
:ivar parameters: Anything.
:vartype parameters: any
"""
_attribute_map = {
'fields': {'key': 'fields', 'type': '[str]'},
'parameters': {'key': 'parameters', 'type': 'object'},
}
def __init__(
self,
**kwargs
):
"""
:keyword fields:
:paramtype fields: list[str]
:keyword parameters: Anything.
:paramtype parameters: any
"""
super(ColumnTransformer, self).__init__(**kwargs)
self.fields = kwargs.get('fields', None)
self.parameters = kwargs.get('parameters', None)
class CommandJob(msrest.serialization.Model):
"""CommandJob.
:ivar job_type: Possible values include: "Command", "Sweep", "Labeling", "Pipeline", "Data",
"AutoML", "Spark", "Base".
:vartype job_type: str or ~flow.models.JobType
:ivar code_id:
:vartype code_id: str
:ivar command:
:vartype command: str
:ivar environment_id:
:vartype environment_id: str
:ivar input_data_bindings: Dictionary of :code:`<InputDataBinding>`.
:vartype input_data_bindings: dict[str, ~flow.models.InputDataBinding]
:ivar output_data_bindings: Dictionary of :code:`<OutputDataBinding>`.
:vartype output_data_bindings: dict[str, ~flow.models.OutputDataBinding]
:ivar distribution:
:vartype distribution: ~flow.models.DistributionConfiguration
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
:ivar parameters: Dictionary of :code:`<any>`.
:vartype parameters: dict[str, any]
:ivar autologger_settings:
:vartype autologger_settings: ~flow.models.MfeInternalAutologgerSettings
:ivar limits:
:vartype limits: ~flow.models.CommandJobLimits
:ivar provisioning_state: Possible values include: "Succeeded", "Failed", "Canceled",
"InProgress".
:vartype provisioning_state: str or ~flow.models.JobProvisioningState
:ivar parent_job_name:
:vartype parent_job_name: str
:ivar display_name:
:vartype display_name: str
:ivar experiment_name:
:vartype experiment_name: str
:ivar status: Possible values include: "NotStarted", "Starting", "Provisioning", "Preparing",
"Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed", "Canceled",
"NotResponding", "Paused", "Unknown", "Scheduled".
:vartype status: str or ~flow.models.JobStatus
:ivar interaction_endpoints: Dictionary of :code:`<JobEndpoint>`.
:vartype interaction_endpoints: dict[str, ~flow.models.JobEndpoint]
:ivar identity:
:vartype identity: ~flow.models.MfeInternalIdentityConfiguration
:ivar compute:
:vartype compute: ~flow.models.ComputeConfiguration
:ivar priority:
:vartype priority: int
:ivar output:
:vartype output: ~flow.models.JobOutputArtifacts
:ivar is_archived:
:vartype is_archived: bool
:ivar schedule:
:vartype schedule: ~flow.models.ScheduleBase
:ivar component_id:
:vartype component_id: str
:ivar notification_setting:
:vartype notification_setting: ~flow.models.NotificationSetting
:ivar secrets_configuration: Dictionary of :code:`<MfeInternalSecretConfiguration>`.
:vartype secrets_configuration: dict[str, ~flow.models.MfeInternalSecretConfiguration]
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
"""
_validation = {
'command': {'min_length': 1},
}
_attribute_map = {
'job_type': {'key': 'jobType', 'type': 'str'},
'code_id': {'key': 'codeId', 'type': 'str'},
'command': {'key': 'command', 'type': 'str'},
'environment_id': {'key': 'environmentId', 'type': 'str'},
'input_data_bindings': {'key': 'inputDataBindings', 'type': '{InputDataBinding}'},
'output_data_bindings': {'key': 'outputDataBindings', 'type': '{OutputDataBinding}'},
'distribution': {'key': 'distribution', 'type': 'DistributionConfiguration'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'parameters': {'key': 'parameters', 'type': '{object}'},
'autologger_settings': {'key': 'autologgerSettings', 'type': 'MfeInternalAutologgerSettings'},
'limits': {'key': 'limits', 'type': 'CommandJobLimits'},
'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
'parent_job_name': {'key': 'parentJobName', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'interaction_endpoints': {'key': 'interactionEndpoints', 'type': '{JobEndpoint}'},
'identity': {'key': 'identity', 'type': 'MfeInternalIdentityConfiguration'},
'compute': {'key': 'compute', 'type': 'ComputeConfiguration'},
'priority': {'key': 'priority', 'type': 'int'},
'output': {'key': 'output', 'type': 'JobOutputArtifacts'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
'schedule': {'key': 'schedule', 'type': 'ScheduleBase'},
'component_id': {'key': 'componentId', 'type': 'str'},
'notification_setting': {'key': 'notificationSetting', 'type': 'NotificationSetting'},
'secrets_configuration': {'key': 'secretsConfiguration', 'type': '{MfeInternalSecretConfiguration}'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword job_type: Possible values include: "Command", "Sweep", "Labeling", "Pipeline", "Data",
"AutoML", "Spark", "Base".
:paramtype job_type: str or ~flow.models.JobType
:keyword code_id:
:paramtype code_id: str
:keyword command:
:paramtype command: str
:keyword environment_id:
:paramtype environment_id: str
:keyword input_data_bindings: Dictionary of :code:`<InputDataBinding>`.
:paramtype input_data_bindings: dict[str, ~flow.models.InputDataBinding]
:keyword output_data_bindings: Dictionary of :code:`<OutputDataBinding>`.
:paramtype output_data_bindings: dict[str, ~flow.models.OutputDataBinding]
:keyword distribution:
:paramtype distribution: ~flow.models.DistributionConfiguration
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
:keyword parameters: Dictionary of :code:`<any>`.
:paramtype parameters: dict[str, any]
:keyword autologger_settings:
:paramtype autologger_settings: ~flow.models.MfeInternalAutologgerSettings
:keyword limits:
:paramtype limits: ~flow.models.CommandJobLimits
:keyword provisioning_state: Possible values include: "Succeeded", "Failed", "Canceled",
"InProgress".
:paramtype provisioning_state: str or ~flow.models.JobProvisioningState
:keyword parent_job_name:
:paramtype parent_job_name: str
:keyword display_name:
:paramtype display_name: str
:keyword experiment_name:
:paramtype experiment_name: str
:keyword status: Possible values include: "NotStarted", "Starting", "Provisioning",
"Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed",
"Canceled", "NotResponding", "Paused", "Unknown", "Scheduled".
:paramtype status: str or ~flow.models.JobStatus
:keyword interaction_endpoints: Dictionary of :code:`<JobEndpoint>`.
:paramtype interaction_endpoints: dict[str, ~flow.models.JobEndpoint]
:keyword identity:
:paramtype identity: ~flow.models.MfeInternalIdentityConfiguration
:keyword compute:
:paramtype compute: ~flow.models.ComputeConfiguration
:keyword priority:
:paramtype priority: int
:keyword output:
:paramtype output: ~flow.models.JobOutputArtifacts
:keyword is_archived:
:paramtype is_archived: bool
:keyword schedule:
:paramtype schedule: ~flow.models.ScheduleBase
:keyword component_id:
:paramtype component_id: str
:keyword notification_setting:
:paramtype notification_setting: ~flow.models.NotificationSetting
:keyword secrets_configuration: Dictionary of :code:`<MfeInternalSecretConfiguration>`.
:paramtype secrets_configuration: dict[str, ~flow.models.MfeInternalSecretConfiguration]
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
"""
super(CommandJob, self).__init__(**kwargs)
self.job_type = kwargs.get('job_type', None)
self.code_id = kwargs.get('code_id', None)
self.command = kwargs.get('command', None)
self.environment_id = kwargs.get('environment_id', None)
self.input_data_bindings = kwargs.get('input_data_bindings', None)
self.output_data_bindings = kwargs.get('output_data_bindings', None)
self.distribution = kwargs.get('distribution', None)
self.environment_variables = kwargs.get('environment_variables', None)
self.parameters = kwargs.get('parameters', None)
self.autologger_settings = kwargs.get('autologger_settings', None)
self.limits = kwargs.get('limits', None)
self.provisioning_state = kwargs.get('provisioning_state', None)
self.parent_job_name = kwargs.get('parent_job_name', None)
self.display_name = kwargs.get('display_name', None)
self.experiment_name = kwargs.get('experiment_name', None)
self.status = kwargs.get('status', None)
self.interaction_endpoints = kwargs.get('interaction_endpoints', None)
self.identity = kwargs.get('identity', None)
self.compute = kwargs.get('compute', None)
self.priority = kwargs.get('priority', None)
self.output = kwargs.get('output', None)
self.is_archived = kwargs.get('is_archived', None)
self.schedule = kwargs.get('schedule', None)
self.component_id = kwargs.get('component_id', None)
self.notification_setting = kwargs.get('notification_setting', None)
self.secrets_configuration = kwargs.get('secrets_configuration', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
class CommandJobLimits(msrest.serialization.Model):
"""CommandJobLimits.
:ivar job_limits_type: Possible values include: "Command", "Sweep".
:vartype job_limits_type: str or ~flow.models.JobLimitsType
:ivar timeout:
:vartype timeout: str
"""
_attribute_map = {
'job_limits_type': {'key': 'jobLimitsType', 'type': 'str'},
'timeout': {'key': 'timeout', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword job_limits_type: Possible values include: "Command", "Sweep".
:paramtype job_limits_type: str or ~flow.models.JobLimitsType
:keyword timeout:
:paramtype timeout: str
"""
super(CommandJobLimits, self).__init__(**kwargs)
self.job_limits_type = kwargs.get('job_limits_type', None)
self.timeout = kwargs.get('timeout', None)
class CommandReturnCodeConfig(msrest.serialization.Model):
"""CommandReturnCodeConfig.
:ivar return_code: Possible values include: "Zero", "ZeroOrGreater".
:vartype return_code: str or ~flow.models.SuccessfulCommandReturnCode
:ivar successful_return_codes:
:vartype successful_return_codes: list[int]
"""
_attribute_map = {
'return_code': {'key': 'returnCode', 'type': 'str'},
'successful_return_codes': {'key': 'successfulReturnCodes', 'type': '[int]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword return_code: Possible values include: "Zero", "ZeroOrGreater".
:paramtype return_code: str or ~flow.models.SuccessfulCommandReturnCode
:keyword successful_return_codes:
:paramtype successful_return_codes: list[int]
"""
super(CommandReturnCodeConfig, self).__init__(**kwargs)
self.return_code = kwargs.get('return_code', None)
self.successful_return_codes = kwargs.get('successful_return_codes', None)
class ComponentConfiguration(msrest.serialization.Model):
"""ComponentConfiguration.
:ivar component_identifier:
:vartype component_identifier: str
"""
_attribute_map = {
'component_identifier': {'key': 'componentIdentifier', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword component_identifier:
:paramtype component_identifier: str
"""
super(ComponentConfiguration, self).__init__(**kwargs)
self.component_identifier = kwargs.get('component_identifier', None)
class ComponentInput(msrest.serialization.Model):
"""ComponentInput.
:ivar name:
:vartype name: str
:ivar optional:
:vartype optional: bool
:ivar description:
:vartype description: str
:ivar type:
:vartype type: str
:ivar default:
:vartype default: str
:ivar enum:
:vartype enum: list[str]
:ivar min:
:vartype min: str
:ivar max:
:vartype max: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'optional': {'key': 'optional', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'default': {'key': 'default', 'type': 'str'},
'enum': {'key': 'enum', 'type': '[str]'},
'min': {'key': 'min', 'type': 'str'},
'max': {'key': 'max', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword optional:
:paramtype optional: bool
:keyword description:
:paramtype description: str
:keyword type:
:paramtype type: str
:keyword default:
:paramtype default: str
:keyword enum:
:paramtype enum: list[str]
:keyword min:
:paramtype min: str
:keyword max:
:paramtype max: str
"""
super(ComponentInput, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.optional = kwargs.get('optional', None)
self.description = kwargs.get('description', None)
self.type = kwargs.get('type', None)
self.default = kwargs.get('default', None)
self.enum = kwargs.get('enum', None)
self.min = kwargs.get('min', None)
self.max = kwargs.get('max', None)
class ComponentJob(msrest.serialization.Model):
"""ComponentJob.
:ivar compute:
:vartype compute: ~flow.models.ComputeConfiguration
:ivar component_id:
:vartype component_id: str
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, ~flow.models.ComponentJobInput]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, ~flow.models.ComponentJobOutput]
"""
_attribute_map = {
'compute': {'key': 'compute', 'type': 'ComputeConfiguration'},
'component_id': {'key': 'componentId', 'type': 'str'},
'inputs': {'key': 'inputs', 'type': '{ComponentJobInput}'},
'outputs': {'key': 'outputs', 'type': '{ComponentJobOutput}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword compute:
:paramtype compute: ~flow.models.ComputeConfiguration
:keyword component_id:
:paramtype component_id: str
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, ~flow.models.ComponentJobInput]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, ~flow.models.ComponentJobOutput]
"""
super(ComponentJob, self).__init__(**kwargs)
self.compute = kwargs.get('compute', None)
self.component_id = kwargs.get('component_id', None)
self.inputs = kwargs.get('inputs', None)
self.outputs = kwargs.get('outputs', None)
class ComponentJobInput(msrest.serialization.Model):
"""ComponentJobInput.
:ivar data:
:vartype data: ~flow.models.InputData
:ivar input_binding:
:vartype input_binding: str
"""
_attribute_map = {
'data': {'key': 'data', 'type': 'InputData'},
'input_binding': {'key': 'inputBinding', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data:
:paramtype data: ~flow.models.InputData
:keyword input_binding:
:paramtype input_binding: str
"""
super(ComponentJobInput, self).__init__(**kwargs)
self.data = kwargs.get('data', None)
self.input_binding = kwargs.get('input_binding', None)
class ComponentJobOutput(msrest.serialization.Model):
"""ComponentJobOutput.
:ivar data:
:vartype data: ~flow.models.MfeInternalOutputData
:ivar output_binding:
:vartype output_binding: str
"""
_attribute_map = {
'data': {'key': 'data', 'type': 'MfeInternalOutputData'},
'output_binding': {'key': 'outputBinding', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data:
:paramtype data: ~flow.models.MfeInternalOutputData
:keyword output_binding:
:paramtype output_binding: str
"""
super(ComponentJobOutput, self).__init__(**kwargs)
self.data = kwargs.get('data', None)
self.output_binding = kwargs.get('output_binding', None)
class ComponentNameAndDefaultVersion(msrest.serialization.Model):
"""ComponentNameAndDefaultVersion.
:ivar component_name:
:vartype component_name: str
:ivar version:
:vartype version: str
:ivar feed_name:
:vartype feed_name: str
:ivar registry_name:
:vartype registry_name: str
"""
_attribute_map = {
'component_name': {'key': 'componentName', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'feed_name': {'key': 'feedName', 'type': 'str'},
'registry_name': {'key': 'registryName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword component_name:
:paramtype component_name: str
:keyword version:
:paramtype version: str
:keyword feed_name:
:paramtype feed_name: str
:keyword registry_name:
:paramtype registry_name: str
"""
super(ComponentNameAndDefaultVersion, self).__init__(**kwargs)
self.component_name = kwargs.get('component_name', None)
self.version = kwargs.get('version', None)
self.feed_name = kwargs.get('feed_name', None)
self.registry_name = kwargs.get('registry_name', None)
class ComponentNameMetaInfo(msrest.serialization.Model):
"""ComponentNameMetaInfo.
:ivar feed_name:
:vartype feed_name: str
:ivar component_name:
:vartype component_name: str
:ivar component_version:
:vartype component_version: str
:ivar registry_name:
:vartype registry_name: str
"""
_attribute_map = {
'feed_name': {'key': 'feedName', 'type': 'str'},
'component_name': {'key': 'componentName', 'type': 'str'},
'component_version': {'key': 'componentVersion', 'type': 'str'},
'registry_name': {'key': 'registryName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword feed_name:
:paramtype feed_name: str
:keyword component_name:
:paramtype component_name: str
:keyword component_version:
:paramtype component_version: str
:keyword registry_name:
:paramtype registry_name: str
"""
super(ComponentNameMetaInfo, self).__init__(**kwargs)
self.feed_name = kwargs.get('feed_name', None)
self.component_name = kwargs.get('component_name', None)
self.component_version = kwargs.get('component_version', None)
self.registry_name = kwargs.get('registry_name', None)
class ComponentOutput(msrest.serialization.Model):
"""ComponentOutput.
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar type:
:vartype type: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword type:
:paramtype type: str
"""
super(ComponentOutput, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.description = kwargs.get('description', None)
self.type = kwargs.get('type', None)
class ComponentPreflightResult(msrest.serialization.Model):
"""ComponentPreflightResult.
:ivar error_details:
:vartype error_details: list[~flow.models.RootError]
"""
_attribute_map = {
'error_details': {'key': 'errorDetails', 'type': '[RootError]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword error_details:
:paramtype error_details: list[~flow.models.RootError]
"""
super(ComponentPreflightResult, self).__init__(**kwargs)
self.error_details = kwargs.get('error_details', None)
class ComponentSpecMetaInfo(msrest.serialization.Model):
"""ComponentSpecMetaInfo.
:ivar component_spec: Anything.
:vartype component_spec: any
:ivar component_version:
:vartype component_version: str
:ivar is_anonymous:
:vartype is_anonymous: bool
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar component_name:
:vartype component_name: str
:ivar description:
:vartype description: str
:ivar is_archived:
:vartype is_archived: bool
"""
_attribute_map = {
'component_spec': {'key': 'componentSpec', 'type': 'object'},
'component_version': {'key': 'componentVersion', 'type': 'str'},
'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
'properties': {'key': 'properties', 'type': '{str}'},
'tags': {'key': 'tags', 'type': '{str}'},
'component_name': {'key': 'componentName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword component_spec: Anything.
:paramtype component_spec: any
:keyword component_version:
:paramtype component_version: str
:keyword is_anonymous:
:paramtype is_anonymous: bool
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword component_name:
:paramtype component_name: str
:keyword description:
:paramtype description: str
:keyword is_archived:
:paramtype is_archived: bool
"""
super(ComponentSpecMetaInfo, self).__init__(**kwargs)
self.component_spec = kwargs.get('component_spec', None)
self.component_version = kwargs.get('component_version', None)
self.is_anonymous = kwargs.get('is_anonymous', None)
self.properties = kwargs.get('properties', None)
self.tags = kwargs.get('tags', None)
self.component_name = kwargs.get('component_name', None)
self.description = kwargs.get('description', None)
self.is_archived = kwargs.get('is_archived', None)
class ComponentUpdateRequest(msrest.serialization.Model):
"""ComponentUpdateRequest.
:ivar original_module_entity:
:vartype original_module_entity: ~flow.models.ModuleEntity
:ivar update_module_entity:
:vartype update_module_entity: ~flow.models.ModuleEntity
:ivar module_name:
:vartype module_name: str
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar overwrite_with_original_name_and_version:
:vartype overwrite_with_original_name_and_version: bool
:ivar snapshot_id:
:vartype snapshot_id: str
"""
_attribute_map = {
'original_module_entity': {'key': 'originalModuleEntity', 'type': 'ModuleEntity'},
'update_module_entity': {'key': 'updateModuleEntity', 'type': 'ModuleEntity'},
'module_name': {'key': 'moduleName', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'overwrite_with_original_name_and_version': {'key': 'overwriteWithOriginalNameAndVersion', 'type': 'bool'},
'snapshot_id': {'key': 'snapshotId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword original_module_entity:
:paramtype original_module_entity: ~flow.models.ModuleEntity
:keyword update_module_entity:
:paramtype update_module_entity: ~flow.models.ModuleEntity
:keyword module_name:
:paramtype module_name: str
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword overwrite_with_original_name_and_version:
:paramtype overwrite_with_original_name_and_version: bool
:keyword snapshot_id:
:paramtype snapshot_id: str
"""
super(ComponentUpdateRequest, self).__init__(**kwargs)
self.original_module_entity = kwargs.get('original_module_entity', None)
self.update_module_entity = kwargs.get('update_module_entity', None)
self.module_name = kwargs.get('module_name', None)
self.properties = kwargs.get('properties', None)
self.overwrite_with_original_name_and_version = kwargs.get('overwrite_with_original_name_and_version', None)
self.snapshot_id = kwargs.get('snapshot_id', None)
class ComponentValidationRequest(msrest.serialization.Model):
"""ComponentValidationRequest.
:ivar component_identifier:
:vartype component_identifier: str
:ivar compute_identity:
:vartype compute_identity: ~flow.models.ComputeIdentityDto
:ivar execution_context_dto:
:vartype execution_context_dto: ~flow.models.ExecutionContextDto
:ivar environment_definition:
:vartype environment_definition: ~flow.models.EnvironmentDefinitionDto
:ivar data_port_dtos:
:vartype data_port_dtos: list[~flow.models.DataPortDto]
"""
_attribute_map = {
'component_identifier': {'key': 'componentIdentifier', 'type': 'str'},
'compute_identity': {'key': 'computeIdentity', 'type': 'ComputeIdentityDto'},
'execution_context_dto': {'key': 'executionContextDto', 'type': 'ExecutionContextDto'},
'environment_definition': {'key': 'environmentDefinition', 'type': 'EnvironmentDefinitionDto'},
'data_port_dtos': {'key': 'dataPortDtos', 'type': '[DataPortDto]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword component_identifier:
:paramtype component_identifier: str
:keyword compute_identity:
:paramtype compute_identity: ~flow.models.ComputeIdentityDto
:keyword execution_context_dto:
:paramtype execution_context_dto: ~flow.models.ExecutionContextDto
:keyword environment_definition:
:paramtype environment_definition: ~flow.models.EnvironmentDefinitionDto
:keyword data_port_dtos:
:paramtype data_port_dtos: list[~flow.models.DataPortDto]
"""
super(ComponentValidationRequest, self).__init__(**kwargs)
self.component_identifier = kwargs.get('component_identifier', None)
self.compute_identity = kwargs.get('compute_identity', None)
self.execution_context_dto = kwargs.get('execution_context_dto', None)
self.environment_definition = kwargs.get('environment_definition', None)
self.data_port_dtos = kwargs.get('data_port_dtos', None)
class ComponentValidationResponse(msrest.serialization.Model):
"""ComponentValidationResponse.
:ivar status: Possible values include: "Succeeded", "Failed".
:vartype status: str or ~flow.models.ValidationStatus
:ivar error: The error response.
:vartype error: ~flow.models.ErrorResponse
"""
_attribute_map = {
'status': {'key': 'status', 'type': 'str'},
'error': {'key': 'error', 'type': 'ErrorResponse'},
}
def __init__(
self,
**kwargs
):
"""
:keyword status: Possible values include: "Succeeded", "Failed".
:paramtype status: str or ~flow.models.ValidationStatus
:keyword error: The error response.
:paramtype error: ~flow.models.ErrorResponse
"""
super(ComponentValidationResponse, self).__init__(**kwargs)
self.status = kwargs.get('status', None)
self.error = kwargs.get('error', None)
class Compute(msrest.serialization.Model):
"""Compute.
:ivar target:
:vartype target: str
:ivar target_type:
:vartype target_type: str
:ivar vm_size:
:vartype vm_size: str
:ivar instance_type:
:vartype instance_type: str
:ivar instance_count:
:vartype instance_count: int
:ivar gpu_count:
:vartype gpu_count: int
:ivar priority:
:vartype priority: str
:ivar region:
:vartype region: str
:ivar arm_id:
:vartype arm_id: str
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
"""
_attribute_map = {
'target': {'key': 'target', 'type': 'str'},
'target_type': {'key': 'targetType', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'instance_count': {'key': 'instanceCount', 'type': 'int'},
'gpu_count': {'key': 'gpuCount', 'type': 'int'},
'priority': {'key': 'priority', 'type': 'str'},
'region': {'key': 'region', 'type': 'str'},
'arm_id': {'key': 'armId', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword target:
:paramtype target: str
:keyword target_type:
:paramtype target_type: str
:keyword vm_size:
:paramtype vm_size: str
:keyword instance_type:
:paramtype instance_type: str
:keyword instance_count:
:paramtype instance_count: int
:keyword gpu_count:
:paramtype gpu_count: int
:keyword priority:
:paramtype priority: str
:keyword region:
:paramtype region: str
:keyword arm_id:
:paramtype arm_id: str
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
"""
super(Compute, self).__init__(**kwargs)
self.target = kwargs.get('target', None)
self.target_type = kwargs.get('target_type', None)
self.vm_size = kwargs.get('vm_size', None)
self.instance_type = kwargs.get('instance_type', None)
self.instance_count = kwargs.get('instance_count', None)
self.gpu_count = kwargs.get('gpu_count', None)
self.priority = kwargs.get('priority', None)
self.region = kwargs.get('region', None)
self.arm_id = kwargs.get('arm_id', None)
self.properties = kwargs.get('properties', None)
class ComputeConfiguration(msrest.serialization.Model):
"""ComputeConfiguration.
:ivar target:
:vartype target: str
:ivar instance_count:
:vartype instance_count: int
:ivar max_instance_count:
:vartype max_instance_count: int
:ivar is_local:
:vartype is_local: bool
:ivar location:
:vartype location: str
:ivar is_clusterless:
:vartype is_clusterless: bool
:ivar instance_type:
:vartype instance_type: str
:ivar instance_priority:
:vartype instance_priority: str
:ivar job_priority:
:vartype job_priority: int
:ivar shm_size:
:vartype shm_size: str
:ivar docker_args:
:vartype docker_args: str
:ivar locations:
:vartype locations: list[str]
:ivar properties: Dictionary of :code:`<any>`.
:vartype properties: dict[str, any]
"""
_attribute_map = {
'target': {'key': 'target', 'type': 'str'},
'instance_count': {'key': 'instanceCount', 'type': 'int'},
'max_instance_count': {'key': 'maxInstanceCount', 'type': 'int'},
'is_local': {'key': 'isLocal', 'type': 'bool'},
'location': {'key': 'location', 'type': 'str'},
'is_clusterless': {'key': 'isClusterless', 'type': 'bool'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'instance_priority': {'key': 'instancePriority', 'type': 'str'},
'job_priority': {'key': 'jobPriority', 'type': 'int'},
'shm_size': {'key': 'shmSize', 'type': 'str'},
'docker_args': {'key': 'dockerArgs', 'type': 'str'},
'locations': {'key': 'locations', 'type': '[str]'},
'properties': {'key': 'properties', 'type': '{object}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword target:
:paramtype target: str
:keyword instance_count:
:paramtype instance_count: int
:keyword max_instance_count:
:paramtype max_instance_count: int
:keyword is_local:
:paramtype is_local: bool
:keyword location:
:paramtype location: str
:keyword is_clusterless:
:paramtype is_clusterless: bool
:keyword instance_type:
:paramtype instance_type: str
:keyword instance_priority:
:paramtype instance_priority: str
:keyword job_priority:
:paramtype job_priority: int
:keyword shm_size:
:paramtype shm_size: str
:keyword docker_args:
:paramtype docker_args: str
:keyword locations:
:paramtype locations: list[str]
:keyword properties: Dictionary of :code:`<any>`.
:paramtype properties: dict[str, any]
"""
super(ComputeConfiguration, self).__init__(**kwargs)
self.target = kwargs.get('target', None)
self.instance_count = kwargs.get('instance_count', None)
self.max_instance_count = kwargs.get('max_instance_count', None)
self.is_local = kwargs.get('is_local', None)
self.location = kwargs.get('location', None)
self.is_clusterless = kwargs.get('is_clusterless', None)
self.instance_type = kwargs.get('instance_type', None)
self.instance_priority = kwargs.get('instance_priority', None)
self.job_priority = kwargs.get('job_priority', None)
self.shm_size = kwargs.get('shm_size', None)
self.docker_args = kwargs.get('docker_args', None)
self.locations = kwargs.get('locations', None)
self.properties = kwargs.get('properties', None)
class ComputeContract(msrest.serialization.Model):
"""ComputeContract.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar id:
:vartype id: str
:ivar name:
:vartype name: str
:ivar type:
:vartype type: str
:ivar location:
:vartype location: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar identity:
:vartype identity: ~flow.models.ComputeIdentityContract
:ivar properties:
:vartype properties: ~flow.models.ComputeProperties
"""
_validation = {
'type': {'readonly': True},
}
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'identity': {'key': 'identity', 'type': 'ComputeIdentityContract'},
'properties': {'key': 'properties', 'type': 'ComputeProperties'},
}
def __init__(
self,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword name:
:paramtype name: str
:keyword location:
:paramtype location: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword identity:
:paramtype identity: ~flow.models.ComputeIdentityContract
:keyword properties:
:paramtype properties: ~flow.models.ComputeProperties
"""
super(ComputeContract, self).__init__(**kwargs)
self.id = kwargs.get('id', None)
self.name = kwargs.get('name', None)
self.type = None
self.location = kwargs.get('location', None)
self.tags = kwargs.get('tags', None)
self.identity = kwargs.get('identity', None)
self.properties = kwargs.get('properties', None)
class ComputeIdentityContract(msrest.serialization.Model):
"""ComputeIdentityContract.
:ivar type:
:vartype type: str
:ivar system_identity_url:
:vartype system_identity_url: str
:ivar principal_id:
:vartype principal_id: str
:ivar tenant_id:
:vartype tenant_id: str
:ivar client_id:
:vartype client_id: str
:ivar client_secret_url:
:vartype client_secret_url: str
:ivar user_assigned_identities: This is a dictionary.
:vartype user_assigned_identities: dict[str, ~flow.models.ComputeRPUserAssignedIdentity]
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'system_identity_url': {'key': 'systemIdentityUrl', 'type': 'str'},
'principal_id': {'key': 'principalId', 'type': 'str'},
'tenant_id': {'key': 'tenantId', 'type': 'str'},
'client_id': {'key': 'clientId', 'type': 'str'},
'client_secret_url': {'key': 'clientSecretUrl', 'type': 'str'},
'user_assigned_identities': {'key': 'userAssignedIdentities', 'type': '{ComputeRPUserAssignedIdentity}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type:
:paramtype type: str
:keyword system_identity_url:
:paramtype system_identity_url: str
:keyword principal_id:
:paramtype principal_id: str
:keyword tenant_id:
:paramtype tenant_id: str
:keyword client_id:
:paramtype client_id: str
:keyword client_secret_url:
:paramtype client_secret_url: str
:keyword user_assigned_identities: This is a dictionary.
:paramtype user_assigned_identities: dict[str, ~flow.models.ComputeRPUserAssignedIdentity]
"""
super(ComputeIdentityContract, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.system_identity_url = kwargs.get('system_identity_url', None)
self.principal_id = kwargs.get('principal_id', None)
self.tenant_id = kwargs.get('tenant_id', None)
self.client_id = kwargs.get('client_id', None)
self.client_secret_url = kwargs.get('client_secret_url', None)
self.user_assigned_identities = kwargs.get('user_assigned_identities', None)
class ComputeIdentityDto(msrest.serialization.Model):
"""ComputeIdentityDto.
:ivar compute_name:
:vartype compute_name: str
:ivar compute_target_type: Possible values include: "Local", "Remote", "HdiCluster",
"ContainerInstance", "AmlCompute", "ComputeInstance", "Cmk8s", "SynapseSpark", "Kubernetes",
"Aisc", "GlobalJobDispatcher", "Databricks", "MockedCompute".
:vartype compute_target_type: str or ~flow.models.ComputeTargetType
:ivar intellectual_property_publisher:
:vartype intellectual_property_publisher: str
"""
_attribute_map = {
'compute_name': {'key': 'computeName', 'type': 'str'},
'compute_target_type': {'key': 'computeTargetType', 'type': 'str'},
'intellectual_property_publisher': {'key': 'intellectualPropertyPublisher', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword compute_name:
:paramtype compute_name: str
:keyword compute_target_type: Possible values include: "Local", "Remote", "HdiCluster",
"ContainerInstance", "AmlCompute", "ComputeInstance", "Cmk8s", "SynapseSpark", "Kubernetes",
"Aisc", "GlobalJobDispatcher", "Databricks", "MockedCompute".
:paramtype compute_target_type: str or ~flow.models.ComputeTargetType
:keyword intellectual_property_publisher:
:paramtype intellectual_property_publisher: str
"""
super(ComputeIdentityDto, self).__init__(**kwargs)
self.compute_name = kwargs.get('compute_name', None)
self.compute_target_type = kwargs.get('compute_target_type', None)
self.intellectual_property_publisher = kwargs.get('intellectual_property_publisher', None)
class ComputeInfo(msrest.serialization.Model):
"""ComputeInfo.
:ivar name:
:vartype name: str
:ivar compute_type: Possible values include: "ACI", "AKS", "AMLCOMPUTE", "IOT", "AKSENDPOINT",
"MIRSINGLEMODEL", "MIRAMLCOMPUTE", "MIRGA", "AMLARC", "BATCHAMLCOMPUTE", "UNKNOWN".
:vartype compute_type: str or ~flow.models.ComputeEnvironmentType
:ivar is_ssl_enabled:
:vartype is_ssl_enabled: bool
:ivar is_gpu_type:
:vartype is_gpu_type: bool
:ivar cluster_purpose:
:vartype cluster_purpose: str
:ivar public_ip_address:
:vartype public_ip_address: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'is_ssl_enabled': {'key': 'isSslEnabled', 'type': 'bool'},
'is_gpu_type': {'key': 'isGpuType', 'type': 'bool'},
'cluster_purpose': {'key': 'clusterPurpose', 'type': 'str'},
'public_ip_address': {'key': 'publicIpAddress', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword compute_type: Possible values include: "ACI", "AKS", "AMLCOMPUTE", "IOT",
"AKSENDPOINT", "MIRSINGLEMODEL", "MIRAMLCOMPUTE", "MIRGA", "AMLARC", "BATCHAMLCOMPUTE",
"UNKNOWN".
:paramtype compute_type: str or ~flow.models.ComputeEnvironmentType
:keyword is_ssl_enabled:
:paramtype is_ssl_enabled: bool
:keyword is_gpu_type:
:paramtype is_gpu_type: bool
:keyword cluster_purpose:
:paramtype cluster_purpose: str
:keyword public_ip_address:
:paramtype public_ip_address: str
"""
super(ComputeInfo, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.compute_type = kwargs.get('compute_type', None)
self.is_ssl_enabled = kwargs.get('is_ssl_enabled', None)
self.is_gpu_type = kwargs.get('is_gpu_type', None)
self.cluster_purpose = kwargs.get('cluster_purpose', None)
self.public_ip_address = kwargs.get('public_ip_address', None)
class ComputeProperties(msrest.serialization.Model):
"""ComputeProperties.
All required parameters must be populated in order to send to Azure.
:ivar created_on:
:vartype created_on: ~datetime.datetime
:ivar modified_on:
:vartype modified_on: ~datetime.datetime
:ivar disable_local_auth:
:vartype disable_local_auth: bool
:ivar description:
:vartype description: str
:ivar resource_id:
:vartype resource_id: str
:ivar compute_type: Required.
:vartype compute_type: str
:ivar compute_location:
:vartype compute_location: str
:ivar provisioning_state: Possible values include: "Unknown", "Updating", "Creating",
"Deleting", "Accepted", "Succeeded", "Failed", "Canceled".
:vartype provisioning_state: str or ~flow.models.ProvisioningState
:ivar provisioning_errors:
:vartype provisioning_errors: list[~flow.models.ODataErrorResponse]
:ivar provisioning_warnings: This is a dictionary.
:vartype provisioning_warnings: dict[str, str]
:ivar is_attached_compute:
:vartype is_attached_compute: bool
:ivar properties: Any object.
:vartype properties: any
:ivar status:
:vartype status: ~flow.models.ComputeStatus
:ivar warnings:
:vartype warnings: list[~flow.models.ComputeWarning]
"""
_validation = {
'compute_type': {'required': True, 'min_length': 1},
}
_attribute_map = {
'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'resource_id': {'key': 'resourceId', 'type': 'str'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'compute_location': {'key': 'computeLocation', 'type': 'str'},
'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ODataErrorResponse]'},
'provisioning_warnings': {'key': 'provisioningWarnings', 'type': '{str}'},
'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
'properties': {'key': 'properties', 'type': 'object'},
'status': {'key': 'status', 'type': 'ComputeStatus'},
'warnings': {'key': 'warnings', 'type': '[ComputeWarning]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword created_on:
:paramtype created_on: ~datetime.datetime
:keyword modified_on:
:paramtype modified_on: ~datetime.datetime
:keyword disable_local_auth:
:paramtype disable_local_auth: bool
:keyword description:
:paramtype description: str
:keyword resource_id:
:paramtype resource_id: str
:keyword compute_type: Required.
:paramtype compute_type: str
:keyword compute_location:
:paramtype compute_location: str
:keyword provisioning_state: Possible values include: "Unknown", "Updating", "Creating",
"Deleting", "Accepted", "Succeeded", "Failed", "Canceled".
:paramtype provisioning_state: str or ~flow.models.ProvisioningState
:keyword provisioning_errors:
:paramtype provisioning_errors: list[~flow.models.ODataErrorResponse]
:keyword provisioning_warnings: This is a dictionary.
:paramtype provisioning_warnings: dict[str, str]
:keyword is_attached_compute:
:paramtype is_attached_compute: bool
:keyword properties: Any object.
:paramtype properties: any
:keyword status:
:paramtype status: ~flow.models.ComputeStatus
:keyword warnings:
:paramtype warnings: list[~flow.models.ComputeWarning]
"""
super(ComputeProperties, self).__init__(**kwargs)
self.created_on = kwargs.get('created_on', None)
self.modified_on = kwargs.get('modified_on', None)
self.disable_local_auth = kwargs.get('disable_local_auth', None)
self.description = kwargs.get('description', None)
self.resource_id = kwargs.get('resource_id', None)
self.compute_type = kwargs['compute_type']
self.compute_location = kwargs.get('compute_location', None)
self.provisioning_state = kwargs.get('provisioning_state', None)
self.provisioning_errors = kwargs.get('provisioning_errors', None)
self.provisioning_warnings = kwargs.get('provisioning_warnings', None)
self.is_attached_compute = kwargs.get('is_attached_compute', None)
self.properties = kwargs.get('properties', None)
self.status = kwargs.get('status', None)
self.warnings = kwargs.get('warnings', None)
class ComputeRequest(msrest.serialization.Model):
"""ComputeRequest.
:ivar node_count:
:vartype node_count: int
:ivar gpu_count:
:vartype gpu_count: int
"""
_attribute_map = {
'node_count': {'key': 'nodeCount', 'type': 'int'},
'gpu_count': {'key': 'gpuCount', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword node_count:
:paramtype node_count: int
:keyword gpu_count:
:paramtype gpu_count: int
"""
super(ComputeRequest, self).__init__(**kwargs)
self.node_count = kwargs.get('node_count', None)
self.gpu_count = kwargs.get('gpu_count', None)
class ComputeRPUserAssignedIdentity(msrest.serialization.Model):
"""ComputeRPUserAssignedIdentity.
:ivar principal_id:
:vartype principal_id: str
:ivar tenant_id:
:vartype tenant_id: str
:ivar client_id:
:vartype client_id: str
:ivar client_secret_url:
:vartype client_secret_url: str
:ivar resource_id:
:vartype resource_id: str
"""
_attribute_map = {
'principal_id': {'key': 'principalId', 'type': 'str'},
'tenant_id': {'key': 'tenantId', 'type': 'str'},
'client_id': {'key': 'clientId', 'type': 'str'},
'client_secret_url': {'key': 'clientSecretUrl', 'type': 'str'},
'resource_id': {'key': 'resourceId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword principal_id:
:paramtype principal_id: str
:keyword tenant_id:
:paramtype tenant_id: str
:keyword client_id:
:paramtype client_id: str
:keyword client_secret_url:
:paramtype client_secret_url: str
:keyword resource_id:
:paramtype resource_id: str
"""
super(ComputeRPUserAssignedIdentity, self).__init__(**kwargs)
self.principal_id = kwargs.get('principal_id', None)
self.tenant_id = kwargs.get('tenant_id', None)
self.client_id = kwargs.get('client_id', None)
self.client_secret_url = kwargs.get('client_secret_url', None)
self.resource_id = kwargs.get('resource_id', None)
class ComputeSetting(msrest.serialization.Model):
"""ComputeSetting.
:ivar name:
:vartype name: str
:ivar compute_type: Possible values include: "BatchAi", "MLC", "HdiCluster", "RemoteDocker",
"Databricks", "Aisc".
:vartype compute_type: str or ~flow.models.ComputeType
:ivar batch_ai_compute_info:
:vartype batch_ai_compute_info: ~flow.models.BatchAiComputeInfo
:ivar remote_docker_compute_info:
:vartype remote_docker_compute_info: ~flow.models.RemoteDockerComputeInfo
:ivar hdi_cluster_compute_info:
:vartype hdi_cluster_compute_info: ~flow.models.HdiClusterComputeInfo
:ivar mlc_compute_info:
:vartype mlc_compute_info: ~flow.models.MlcComputeInfo
:ivar databricks_compute_info:
:vartype databricks_compute_info: ~flow.models.DatabricksComputeInfo
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'batch_ai_compute_info': {'key': 'batchAiComputeInfo', 'type': 'BatchAiComputeInfo'},
'remote_docker_compute_info': {'key': 'remoteDockerComputeInfo', 'type': 'RemoteDockerComputeInfo'},
'hdi_cluster_compute_info': {'key': 'hdiClusterComputeInfo', 'type': 'HdiClusterComputeInfo'},
'mlc_compute_info': {'key': 'mlcComputeInfo', 'type': 'MlcComputeInfo'},
'databricks_compute_info': {'key': 'databricksComputeInfo', 'type': 'DatabricksComputeInfo'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword compute_type: Possible values include: "BatchAi", "MLC", "HdiCluster", "RemoteDocker",
"Databricks", "Aisc".
:paramtype compute_type: str or ~flow.models.ComputeType
:keyword batch_ai_compute_info:
:paramtype batch_ai_compute_info: ~flow.models.BatchAiComputeInfo
:keyword remote_docker_compute_info:
:paramtype remote_docker_compute_info: ~flow.models.RemoteDockerComputeInfo
:keyword hdi_cluster_compute_info:
:paramtype hdi_cluster_compute_info: ~flow.models.HdiClusterComputeInfo
:keyword mlc_compute_info:
:paramtype mlc_compute_info: ~flow.models.MlcComputeInfo
:keyword databricks_compute_info:
:paramtype databricks_compute_info: ~flow.models.DatabricksComputeInfo
"""
super(ComputeSetting, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.compute_type = kwargs.get('compute_type', None)
self.batch_ai_compute_info = kwargs.get('batch_ai_compute_info', None)
self.remote_docker_compute_info = kwargs.get('remote_docker_compute_info', None)
self.hdi_cluster_compute_info = kwargs.get('hdi_cluster_compute_info', None)
self.mlc_compute_info = kwargs.get('mlc_compute_info', None)
self.databricks_compute_info = kwargs.get('databricks_compute_info', None)
class ComputeStatus(msrest.serialization.Model):
"""ComputeStatus.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar is_status_available:
:vartype is_status_available: bool
:ivar detailed_status: Anything.
:vartype detailed_status: any
:ivar error: Represents OData v4 error object.
:vartype error: ~flow.models.ODataError
"""
_validation = {
'is_status_available': {'readonly': True},
}
_attribute_map = {
'is_status_available': {'key': 'isStatusAvailable', 'type': 'bool'},
'detailed_status': {'key': 'detailedStatus', 'type': 'object'},
'error': {'key': 'error', 'type': 'ODataError'},
}
def __init__(
self,
**kwargs
):
"""
:keyword detailed_status: Anything.
:paramtype detailed_status: any
:keyword error: Represents OData v4 error object.
:paramtype error: ~flow.models.ODataError
"""
super(ComputeStatus, self).__init__(**kwargs)
self.is_status_available = None
self.detailed_status = kwargs.get('detailed_status', None)
self.error = kwargs.get('error', None)
class ComputeStatusDetail(msrest.serialization.Model):
"""ComputeStatusDetail.
:ivar provisioning_state:
:vartype provisioning_state: str
:ivar provisioning_error_message:
:vartype provisioning_error_message: str
"""
_attribute_map = {
'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
'provisioning_error_message': {'key': 'provisioningErrorMessage', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword provisioning_state:
:paramtype provisioning_state: str
:keyword provisioning_error_message:
:paramtype provisioning_error_message: str
"""
super(ComputeStatusDetail, self).__init__(**kwargs)
self.provisioning_state = kwargs.get('provisioning_state', None)
self.provisioning_error_message = kwargs.get('provisioning_error_message', None)
class ComputeWarning(msrest.serialization.Model):
"""ComputeWarning.
:ivar title:
:vartype title: str
:ivar message:
:vartype message: str
:ivar code:
:vartype code: str
:ivar severity: Possible values include: "Critical", "Error", "Warning", "Info".
:vartype severity: str or ~flow.models.SeverityLevel
"""
_attribute_map = {
'title': {'key': 'title', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
'code': {'key': 'code', 'type': 'str'},
'severity': {'key': 'severity', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword title:
:paramtype title: str
:keyword message:
:paramtype message: str
:keyword code:
:paramtype code: str
:keyword severity: Possible values include: "Critical", "Error", "Warning", "Info".
:paramtype severity: str or ~flow.models.SeverityLevel
"""
super(ComputeWarning, self).__init__(**kwargs)
self.title = kwargs.get('title', None)
self.message = kwargs.get('message', None)
self.code = kwargs.get('code', None)
self.severity = kwargs.get('severity', None)
class ConnectionConfigSpec(msrest.serialization.Model):
"""ConnectionConfigSpec.
:ivar name:
:vartype name: str
:ivar display_name:
:vartype display_name: str
:ivar config_value_type: Possible values include: "String", "Secret".
:vartype config_value_type: str or ~flow.models.ConfigValueType
:ivar description:
:vartype description: str
:ivar default_value:
:vartype default_value: str
:ivar enum_values:
:vartype enum_values: list[str]
:ivar is_optional:
:vartype is_optional: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'config_value_type': {'key': 'configValueType', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'default_value': {'key': 'defaultValue', 'type': 'str'},
'enum_values': {'key': 'enumValues', 'type': '[str]'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword display_name:
:paramtype display_name: str
:keyword config_value_type: Possible values include: "String", "Secret".
:paramtype config_value_type: str or ~flow.models.ConfigValueType
:keyword description:
:paramtype description: str
:keyword default_value:
:paramtype default_value: str
:keyword enum_values:
:paramtype enum_values: list[str]
:keyword is_optional:
:paramtype is_optional: bool
"""
super(ConnectionConfigSpec, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.display_name = kwargs.get('display_name', None)
self.config_value_type = kwargs.get('config_value_type', None)
self.description = kwargs.get('description', None)
self.default_value = kwargs.get('default_value', None)
self.enum_values = kwargs.get('enum_values', None)
self.is_optional = kwargs.get('is_optional', None)
class ConnectionDto(msrest.serialization.Model):
"""ConnectionDto.
:ivar connection_name:
:vartype connection_name: str
:ivar connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:vartype connection_type: str or ~flow.models.ConnectionType
:ivar configs: This is a dictionary.
:vartype configs: dict[str, str]
:ivar custom_configs: This is a dictionary.
:vartype custom_configs: dict[str, ~flow.models.CustomConnectionConfig]
:ivar expiry_time:
:vartype expiry_time: ~datetime.datetime
:ivar owner:
:vartype owner: ~flow.models.SchemaContractsCreatedBy
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'connection_name': {'key': 'connectionName', 'type': 'str'},
'connection_type': {'key': 'connectionType', 'type': 'str'},
'configs': {'key': 'configs', 'type': '{str}'},
'custom_configs': {'key': 'customConfigs', 'type': '{CustomConnectionConfig}'},
'expiry_time': {'key': 'expiryTime', 'type': 'iso-8601'},
'owner': {'key': 'owner', 'type': 'SchemaContractsCreatedBy'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword connection_name:
:paramtype connection_name: str
:keyword connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:paramtype connection_type: str or ~flow.models.ConnectionType
:keyword configs: This is a dictionary.
:paramtype configs: dict[str, str]
:keyword custom_configs: This is a dictionary.
:paramtype custom_configs: dict[str, ~flow.models.CustomConnectionConfig]
:keyword expiry_time:
:paramtype expiry_time: ~datetime.datetime
:keyword owner:
:paramtype owner: ~flow.models.SchemaContractsCreatedBy
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(ConnectionDto, self).__init__(**kwargs)
self.connection_name = kwargs.get('connection_name', None)
self.connection_type = kwargs.get('connection_type', None)
self.configs = kwargs.get('configs', None)
self.custom_configs = kwargs.get('custom_configs', None)
self.expiry_time = kwargs.get('expiry_time', None)
self.owner = kwargs.get('owner', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
class ConnectionEntity(msrest.serialization.Model):
"""ConnectionEntity.
:ivar connection_id:
:vartype connection_id: str
:ivar connection_name:
:vartype connection_name: str
:ivar connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:vartype connection_type: str or ~flow.models.ConnectionType
:ivar connection_scope: Possible values include: "User", "WorkspaceShared".
:vartype connection_scope: str or ~flow.models.ConnectionScope
:ivar configs: This is a dictionary.
:vartype configs: dict[str, str]
:ivar custom_configs: This is a dictionary.
:vartype custom_configs: dict[str, ~flow.models.CustomConnectionConfig]
:ivar expiry_time:
:vartype expiry_time: ~datetime.datetime
:ivar secret_name:
:vartype secret_name: str
:ivar owner:
:vartype owner: ~flow.models.SchemaContractsCreatedBy
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'connection_id': {'key': 'connectionId', 'type': 'str'},
'connection_name': {'key': 'connectionName', 'type': 'str'},
'connection_type': {'key': 'connectionType', 'type': 'str'},
'connection_scope': {'key': 'connectionScope', 'type': 'str'},
'configs': {'key': 'configs', 'type': '{str}'},
'custom_configs': {'key': 'customConfigs', 'type': '{CustomConnectionConfig}'},
'expiry_time': {'key': 'expiryTime', 'type': 'iso-8601'},
'secret_name': {'key': 'secretName', 'type': 'str'},
'owner': {'key': 'owner', 'type': 'SchemaContractsCreatedBy'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword connection_id:
:paramtype connection_id: str
:keyword connection_name:
:paramtype connection_name: str
:keyword connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:paramtype connection_type: str or ~flow.models.ConnectionType
:keyword connection_scope: Possible values include: "User", "WorkspaceShared".
:paramtype connection_scope: str or ~flow.models.ConnectionScope
:keyword configs: This is a dictionary.
:paramtype configs: dict[str, str]
:keyword custom_configs: This is a dictionary.
:paramtype custom_configs: dict[str, ~flow.models.CustomConnectionConfig]
:keyword expiry_time:
:paramtype expiry_time: ~datetime.datetime
:keyword secret_name:
:paramtype secret_name: str
:keyword owner:
:paramtype owner: ~flow.models.SchemaContractsCreatedBy
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(ConnectionEntity, self).__init__(**kwargs)
self.connection_id = kwargs.get('connection_id', None)
self.connection_name = kwargs.get('connection_name', None)
self.connection_type = kwargs.get('connection_type', None)
self.connection_scope = kwargs.get('connection_scope', None)
self.configs = kwargs.get('configs', None)
self.custom_configs = kwargs.get('custom_configs', None)
self.expiry_time = kwargs.get('expiry_time', None)
self.secret_name = kwargs.get('secret_name', None)
self.owner = kwargs.get('owner', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
class ConnectionOverrideSetting(msrest.serialization.Model):
"""ConnectionOverrideSetting.
:ivar connection_source_type: Possible values include: "Node", "NodeInput".
:vartype connection_source_type: str or ~flow.models.ConnectionSourceType
:ivar node_name:
:vartype node_name: str
:ivar node_input_name:
:vartype node_input_name: str
:ivar node_deployment_name_input:
:vartype node_deployment_name_input: str
:ivar node_model_input:
:vartype node_model_input: str
:ivar connection_name:
:vartype connection_name: str
:ivar deployment_name:
:vartype deployment_name: str
:ivar model:
:vartype model: str
:ivar connection_types:
:vartype connection_types: list[str or ~flow.models.ConnectionType]
:ivar capabilities:
:vartype capabilities: ~flow.models.AzureOpenAIModelCapabilities
:ivar model_enum:
:vartype model_enum: list[str]
"""
_attribute_map = {
'connection_source_type': {'key': 'connectionSourceType', 'type': 'str'},
'node_name': {'key': 'nodeName', 'type': 'str'},
'node_input_name': {'key': 'nodeInputName', 'type': 'str'},
'node_deployment_name_input': {'key': 'nodeDeploymentNameInput', 'type': 'str'},
'node_model_input': {'key': 'nodeModelInput', 'type': 'str'},
'connection_name': {'key': 'connectionName', 'type': 'str'},
'deployment_name': {'key': 'deploymentName', 'type': 'str'},
'model': {'key': 'model', 'type': 'str'},
'connection_types': {'key': 'connectionTypes', 'type': '[str]'},
'capabilities': {'key': 'capabilities', 'type': 'AzureOpenAIModelCapabilities'},
'model_enum': {'key': 'modelEnum', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword connection_source_type: Possible values include: "Node", "NodeInput".
:paramtype connection_source_type: str or ~flow.models.ConnectionSourceType
:keyword node_name:
:paramtype node_name: str
:keyword node_input_name:
:paramtype node_input_name: str
:keyword node_deployment_name_input:
:paramtype node_deployment_name_input: str
:keyword node_model_input:
:paramtype node_model_input: str
:keyword connection_name:
:paramtype connection_name: str
:keyword deployment_name:
:paramtype deployment_name: str
:keyword model:
:paramtype model: str
:keyword connection_types:
:paramtype connection_types: list[str or ~flow.models.ConnectionType]
:keyword capabilities:
:paramtype capabilities: ~flow.models.AzureOpenAIModelCapabilities
:keyword model_enum:
:paramtype model_enum: list[str]
"""
super(ConnectionOverrideSetting, self).__init__(**kwargs)
self.connection_source_type = kwargs.get('connection_source_type', None)
self.node_name = kwargs.get('node_name', None)
self.node_input_name = kwargs.get('node_input_name', None)
self.node_deployment_name_input = kwargs.get('node_deployment_name_input', None)
self.node_model_input = kwargs.get('node_model_input', None)
self.connection_name = kwargs.get('connection_name', None)
self.deployment_name = kwargs.get('deployment_name', None)
self.model = kwargs.get('model', None)
self.connection_types = kwargs.get('connection_types', None)
self.capabilities = kwargs.get('capabilities', None)
self.model_enum = kwargs.get('model_enum', None)
class ConnectionSpec(msrest.serialization.Model):
"""ConnectionSpec.
:ivar connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:vartype connection_type: str or ~flow.models.ConnectionType
:ivar config_specs:
:vartype config_specs: list[~flow.models.ConnectionConfigSpec]
"""
_attribute_map = {
'connection_type': {'key': 'connectionType', 'type': 'str'},
'config_specs': {'key': 'configSpecs', 'type': '[ConnectionConfigSpec]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:paramtype connection_type: str or ~flow.models.ConnectionType
:keyword config_specs:
:paramtype config_specs: list[~flow.models.ConnectionConfigSpec]
"""
super(ConnectionSpec, self).__init__(**kwargs)
self.connection_type = kwargs.get('connection_type', None)
self.config_specs = kwargs.get('config_specs', None)
class ContainerInstanceConfiguration(msrest.serialization.Model):
"""ContainerInstanceConfiguration.
:ivar region:
:vartype region: str
:ivar cpu_cores:
:vartype cpu_cores: float
:ivar memory_gb:
:vartype memory_gb: float
"""
_attribute_map = {
'region': {'key': 'region', 'type': 'str'},
'cpu_cores': {'key': 'cpuCores', 'type': 'float'},
'memory_gb': {'key': 'memoryGb', 'type': 'float'},
}
def __init__(
self,
**kwargs
):
"""
:keyword region:
:paramtype region: str
:keyword cpu_cores:
:paramtype cpu_cores: float
:keyword memory_gb:
:paramtype memory_gb: float
"""
super(ContainerInstanceConfiguration, self).__init__(**kwargs)
self.region = kwargs.get('region', None)
self.cpu_cores = kwargs.get('cpu_cores', None)
self.memory_gb = kwargs.get('memory_gb', None)
class ContainerRegistry(msrest.serialization.Model):
"""ContainerRegistry.
:ivar address:
:vartype address: str
:ivar username:
:vartype username: str
:ivar password:
:vartype password: str
:ivar credential_type:
:vartype credential_type: str
:ivar registry_identity:
:vartype registry_identity: ~flow.models.RegistryIdentity
"""
_attribute_map = {
'address': {'key': 'address', 'type': 'str'},
'username': {'key': 'username', 'type': 'str'},
'password': {'key': 'password', 'type': 'str'},
'credential_type': {'key': 'credentialType', 'type': 'str'},
'registry_identity': {'key': 'registryIdentity', 'type': 'RegistryIdentity'},
}
def __init__(
self,
**kwargs
):
"""
:keyword address:
:paramtype address: str
:keyword username:
:paramtype username: str
:keyword password:
:paramtype password: str
:keyword credential_type:
:paramtype credential_type: str
:keyword registry_identity:
:paramtype registry_identity: ~flow.models.RegistryIdentity
"""
super(ContainerRegistry, self).__init__(**kwargs)
self.address = kwargs.get('address', None)
self.username = kwargs.get('username', None)
self.password = kwargs.get('password', None)
self.credential_type = kwargs.get('credential_type', None)
self.registry_identity = kwargs.get('registry_identity', None)
class ContainerResourceRequirements(msrest.serialization.Model):
"""ContainerResourceRequirements.
:ivar cpu:
:vartype cpu: float
:ivar cpu_limit:
:vartype cpu_limit: float
:ivar memory_in_gb:
:vartype memory_in_gb: float
:ivar memory_in_gb_limit:
:vartype memory_in_gb_limit: float
:ivar gpu_enabled:
:vartype gpu_enabled: bool
:ivar gpu:
:vartype gpu: int
:ivar fpga:
:vartype fpga: int
"""
_attribute_map = {
'cpu': {'key': 'cpu', 'type': 'float'},
'cpu_limit': {'key': 'cpuLimit', 'type': 'float'},
'memory_in_gb': {'key': 'memoryInGB', 'type': 'float'},
'memory_in_gb_limit': {'key': 'memoryInGBLimit', 'type': 'float'},
'gpu_enabled': {'key': 'gpuEnabled', 'type': 'bool'},
'gpu': {'key': 'gpu', 'type': 'int'},
'fpga': {'key': 'fpga', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword cpu:
:paramtype cpu: float
:keyword cpu_limit:
:paramtype cpu_limit: float
:keyword memory_in_gb:
:paramtype memory_in_gb: float
:keyword memory_in_gb_limit:
:paramtype memory_in_gb_limit: float
:keyword gpu_enabled:
:paramtype gpu_enabled: bool
:keyword gpu:
:paramtype gpu: int
:keyword fpga:
:paramtype fpga: int
"""
super(ContainerResourceRequirements, self).__init__(**kwargs)
self.cpu = kwargs.get('cpu', None)
self.cpu_limit = kwargs.get('cpu_limit', None)
self.memory_in_gb = kwargs.get('memory_in_gb', None)
self.memory_in_gb_limit = kwargs.get('memory_in_gb_limit', None)
self.gpu_enabled = kwargs.get('gpu_enabled', None)
self.gpu = kwargs.get('gpu', None)
self.fpga = kwargs.get('fpga', None)
class ControlInput(msrest.serialization.Model):
"""ControlInput.
:ivar name:
:vartype name: str
:ivar default_value: Possible values include: "None", "False", "True", "Skipped".
:vartype default_value: str or ~flow.models.ControlInputValue
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'default_value': {'key': 'defaultValue', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword default_value: Possible values include: "None", "False", "True", "Skipped".
:paramtype default_value: str or ~flow.models.ControlInputValue
"""
super(ControlInput, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.default_value = kwargs.get('default_value', None)
class ControlOutput(msrest.serialization.Model):
"""ControlOutput.
:ivar name:
:vartype name: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
"""
super(ControlOutput, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
class CopyDataTask(msrest.serialization.Model):
"""CopyDataTask.
:ivar data_copy_mode: Possible values include: "MergeWithOverwrite", "FailIfConflict".
:vartype data_copy_mode: str or ~flow.models.DataCopyMode
"""
_attribute_map = {
'data_copy_mode': {'key': 'DataCopyMode', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_copy_mode: Possible values include: "MergeWithOverwrite", "FailIfConflict".
:paramtype data_copy_mode: str or ~flow.models.DataCopyMode
"""
super(CopyDataTask, self).__init__(**kwargs)
self.data_copy_mode = kwargs.get('data_copy_mode', None)
class CreatedBy(msrest.serialization.Model):
"""CreatedBy.
:ivar user_object_id:
:vartype user_object_id: str
:ivar user_tenant_id:
:vartype user_tenant_id: str
:ivar user_name:
:vartype user_name: str
"""
_attribute_map = {
'user_object_id': {'key': 'userObjectId', 'type': 'str'},
'user_tenant_id': {'key': 'userTenantId', 'type': 'str'},
'user_name': {'key': 'userName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword user_object_id:
:paramtype user_object_id: str
:keyword user_tenant_id:
:paramtype user_tenant_id: str
:keyword user_name:
:paramtype user_name: str
"""
super(CreatedBy, self).__init__(**kwargs)
self.user_object_id = kwargs.get('user_object_id', None)
self.user_tenant_id = kwargs.get('user_tenant_id', None)
self.user_name = kwargs.get('user_name', None)
class CreatedFromDto(msrest.serialization.Model):
"""CreatedFromDto.
:ivar type: The only acceptable values to pass in are None and "Notebook". The default value
is None.
:vartype type: str
:ivar location_type: The only acceptable values to pass in are None and "ArtifactId". The
default value is None.
:vartype location_type: str
:ivar location:
:vartype location: str
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'location_type': {'key': 'locationType', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type: The only acceptable values to pass in are None and "Notebook". The default
value is None.
:paramtype type: str
:keyword location_type: The only acceptable values to pass in are None and "ArtifactId". The
default value is None.
:paramtype location_type: str
:keyword location:
:paramtype location: str
"""
super(CreatedFromDto, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.location_type = kwargs.get('location_type', None)
self.location = kwargs.get('location', None)
class CreateFlowFromSampleRequest(msrest.serialization.Model):
"""CreateFlowFromSampleRequest.
:ivar flow_name:
:vartype flow_name: str
:ivar sample_resource_id:
:vartype sample_resource_id: str
:ivar flow_definition_file_path:
:vartype flow_definition_file_path: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar is_archived:
:vartype is_archived: bool
"""
_attribute_map = {
'flow_name': {'key': 'flowName', 'type': 'str'},
'sample_resource_id': {'key': 'sampleResourceId', 'type': 'str'},
'flow_definition_file_path': {'key': 'flowDefinitionFilePath', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword flow_name:
:paramtype flow_name: str
:keyword sample_resource_id:
:paramtype sample_resource_id: str
:keyword flow_definition_file_path:
:paramtype flow_definition_file_path: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword is_archived:
:paramtype is_archived: bool
"""
super(CreateFlowFromSampleRequest, self).__init__(**kwargs)
self.flow_name = kwargs.get('flow_name', None)
self.sample_resource_id = kwargs.get('sample_resource_id', None)
self.flow_definition_file_path = kwargs.get('flow_definition_file_path', None)
self.tags = kwargs.get('tags', None)
self.is_archived = kwargs.get('is_archived', None)
class CreateFlowRequest(msrest.serialization.Model):
"""CreateFlowRequest.
:ivar flow_name:
:vartype flow_name: str
:ivar description:
:vartype description: str
:ivar details:
:vartype details: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar flow:
:vartype flow: ~flow.models.Flow
:ivar flow_definition_file_path:
:vartype flow_definition_file_path: str
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar flow_run_settings:
:vartype flow_run_settings: ~flow.models.FlowRunSettings
:ivar is_archived:
:vartype is_archived: bool
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar identity:
:vartype identity: str
"""
_attribute_map = {
'flow_name': {'key': 'flowName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'details': {'key': 'details', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'flow': {'key': 'flow', 'type': 'Flow'},
'flow_definition_file_path': {'key': 'flowDefinitionFilePath', 'type': 'str'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'flow_run_settings': {'key': 'flowRunSettings', 'type': 'FlowRunSettings'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'identity': {'key': 'identity', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword flow_name:
:paramtype flow_name: str
:keyword description:
:paramtype description: str
:keyword details:
:paramtype details: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword flow:
:paramtype flow: ~flow.models.Flow
:keyword flow_definition_file_path:
:paramtype flow_definition_file_path: str
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword flow_run_settings:
:paramtype flow_run_settings: ~flow.models.FlowRunSettings
:keyword is_archived:
:paramtype is_archived: bool
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword identity:
:paramtype identity: str
"""
super(CreateFlowRequest, self).__init__(**kwargs)
self.flow_name = kwargs.get('flow_name', None)
self.description = kwargs.get('description', None)
self.details = kwargs.get('details', None)
self.tags = kwargs.get('tags', None)
self.flow = kwargs.get('flow', None)
self.flow_definition_file_path = kwargs.get('flow_definition_file_path', None)
self.flow_type = kwargs.get('flow_type', None)
self.flow_run_settings = kwargs.get('flow_run_settings', None)
self.is_archived = kwargs.get('is_archived', None)
self.vm_size = kwargs.get('vm_size', None)
self.max_idle_time_seconds = kwargs.get('max_idle_time_seconds', None)
self.identity = kwargs.get('identity', None)
class CreateFlowRuntimeRequest(msrest.serialization.Model):
"""CreateFlowRuntimeRequest.
:ivar runtime_type: Possible values include: "ManagedOnlineEndpoint", "ComputeInstance",
"TrainingSession".
:vartype runtime_type: str or ~flow.models.RuntimeType
:ivar identity:
:vartype identity: ~flow.models.ManagedServiceIdentity
:ivar instance_type:
:vartype instance_type: str
:ivar from_existing_endpoint:
:vartype from_existing_endpoint: bool
:ivar from_existing_deployment:
:vartype from_existing_deployment: bool
:ivar endpoint_name:
:vartype endpoint_name: str
:ivar deployment_name:
:vartype deployment_name: str
:ivar compute_instance_name:
:vartype compute_instance_name: str
:ivar from_existing_custom_app:
:vartype from_existing_custom_app: bool
:ivar custom_app_name:
:vartype custom_app_name: str
:ivar runtime_description:
:vartype runtime_description: str
:ivar environment:
:vartype environment: str
:ivar instance_count:
:vartype instance_count: int
"""
_attribute_map = {
'runtime_type': {'key': 'runtimeType', 'type': 'str'},
'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'from_existing_endpoint': {'key': 'fromExistingEndpoint', 'type': 'bool'},
'from_existing_deployment': {'key': 'fromExistingDeployment', 'type': 'bool'},
'endpoint_name': {'key': 'endpointName', 'type': 'str'},
'deployment_name': {'key': 'deploymentName', 'type': 'str'},
'compute_instance_name': {'key': 'computeInstanceName', 'type': 'str'},
'from_existing_custom_app': {'key': 'fromExistingCustomApp', 'type': 'bool'},
'custom_app_name': {'key': 'customAppName', 'type': 'str'},
'runtime_description': {'key': 'runtimeDescription', 'type': 'str'},
'environment': {'key': 'environment', 'type': 'str'},
'instance_count': {'key': 'instanceCount', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword runtime_type: Possible values include: "ManagedOnlineEndpoint", "ComputeInstance",
"TrainingSession".
:paramtype runtime_type: str or ~flow.models.RuntimeType
:keyword identity:
:paramtype identity: ~flow.models.ManagedServiceIdentity
:keyword instance_type:
:paramtype instance_type: str
:keyword from_existing_endpoint:
:paramtype from_existing_endpoint: bool
:keyword from_existing_deployment:
:paramtype from_existing_deployment: bool
:keyword endpoint_name:
:paramtype endpoint_name: str
:keyword deployment_name:
:paramtype deployment_name: str
:keyword compute_instance_name:
:paramtype compute_instance_name: str
:keyword from_existing_custom_app:
:paramtype from_existing_custom_app: bool
:keyword custom_app_name:
:paramtype custom_app_name: str
:keyword runtime_description:
:paramtype runtime_description: str
:keyword environment:
:paramtype environment: str
:keyword instance_count:
:paramtype instance_count: int
"""
super(CreateFlowRuntimeRequest, self).__init__(**kwargs)
self.runtime_type = kwargs.get('runtime_type', None)
self.identity = kwargs.get('identity', None)
self.instance_type = kwargs.get('instance_type', None)
self.from_existing_endpoint = kwargs.get('from_existing_endpoint', None)
self.from_existing_deployment = kwargs.get('from_existing_deployment', None)
self.endpoint_name = kwargs.get('endpoint_name', None)
self.deployment_name = kwargs.get('deployment_name', None)
self.compute_instance_name = kwargs.get('compute_instance_name', None)
self.from_existing_custom_app = kwargs.get('from_existing_custom_app', None)
self.custom_app_name = kwargs.get('custom_app_name', None)
self.runtime_description = kwargs.get('runtime_description', None)
self.environment = kwargs.get('environment', None)
self.instance_count = kwargs.get('instance_count', None)
class CreateFlowSessionRequest(msrest.serialization.Model):
"""CreateFlowSessionRequest.
:ivar python_pip_requirements:
:vartype python_pip_requirements: list[str]
:ivar base_image:
:vartype base_image: str
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar action: Possible values include: "Install", "Reset", "Update", "Delete".
:vartype action: str or ~flow.models.SetupFlowSessionAction
:ivar identity:
:vartype identity: str
"""
_attribute_map = {
'python_pip_requirements': {'key': 'pythonPipRequirements', 'type': '[str]'},
'base_image': {'key': 'baseImage', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'action': {'key': 'action', 'type': 'str'},
'identity': {'key': 'identity', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword python_pip_requirements:
:paramtype python_pip_requirements: list[str]
:keyword base_image:
:paramtype base_image: str
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword action: Possible values include: "Install", "Reset", "Update", "Delete".
:paramtype action: str or ~flow.models.SetupFlowSessionAction
:keyword identity:
:paramtype identity: str
"""
super(CreateFlowSessionRequest, self).__init__(**kwargs)
self.python_pip_requirements = kwargs.get('python_pip_requirements', None)
self.base_image = kwargs.get('base_image', None)
self.vm_size = kwargs.get('vm_size', None)
self.max_idle_time_seconds = kwargs.get('max_idle_time_seconds', None)
self.action = kwargs.get('action', None)
self.identity = kwargs.get('identity', None)
class CreateInferencePipelineRequest(msrest.serialization.Model):
"""CreateInferencePipelineRequest.
:ivar module_node_id:
:vartype module_node_id: str
:ivar port_name:
:vartype port_name: str
:ivar training_pipeline_draft_name:
:vartype training_pipeline_draft_name: str
:ivar training_pipeline_run_display_name:
:vartype training_pipeline_run_display_name: str
:ivar name:
:vartype name: str
:ivar pipeline_type: Possible values include: "TrainingPipeline", "RealTimeInferencePipeline",
"BatchInferencePipeline", "Unknown".
:vartype pipeline_type: str or ~flow.models.PipelineType
:ivar pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:vartype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:ivar graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:vartype graph_components_mode: str or ~flow.models.GraphComponentsMode
:ivar sub_pipelines_info:
:vartype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:ivar flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:vartype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:ivar pipeline_parameters: This is a dictionary.
:vartype pipeline_parameters: dict[str, str]
:ivar data_path_assignments: This is a dictionary.
:vartype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:ivar data_set_definition_value_assignments: This is a dictionary.
:vartype data_set_definition_value_assignments: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar asset_output_settings_assignments: This is a dictionary.
:vartype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:ivar graph:
:vartype graph: ~flow.models.GraphDraftEntity
:ivar pipeline_run_settings:
:vartype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar module_node_run_settings:
:vartype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:ivar module_node_ui_input_settings:
:vartype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar continue_run_on_step_failure:
:vartype continue_run_on_step_failure: bool
:ivar description:
:vartype description: str
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar enforce_rerun:
:vartype enforce_rerun: bool
:ivar dataset_access_modes: Possible values include: "Default", "DatasetInDpv2", "AssetInDpv2",
"DatasetInDesignerUI", "DatasetInDpv2WithDatasetInDesignerUI", "Dataset",
"AssetInDpv2WithDatasetInDesignerUI", "DatasetAndAssetInDpv2WithDatasetInDesignerUI",
"AssetInDesignerUI", "AssetInDpv2WithAssetInDesignerUI", "Asset".
:vartype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
_attribute_map = {
'module_node_id': {'key': 'moduleNodeId', 'type': 'str'},
'port_name': {'key': 'portName', 'type': 'str'},
'training_pipeline_draft_name': {'key': 'trainingPipelineDraftName', 'type': 'str'},
'training_pipeline_run_display_name': {'key': 'trainingPipelineRunDisplayName', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'pipeline_type': {'key': 'pipelineType', 'type': 'str'},
'pipeline_draft_mode': {'key': 'pipelineDraftMode', 'type': 'str'},
'graph_components_mode': {'key': 'graphComponentsMode', 'type': 'str'},
'sub_pipelines_info': {'key': 'subPipelinesInfo', 'type': 'SubPipelinesInfo'},
'flattened_sub_graphs': {'key': 'flattenedSubGraphs', 'type': '{PipelineSubDraft}'},
'pipeline_parameters': {'key': 'pipelineParameters', 'type': '{str}'},
'data_path_assignments': {'key': 'dataPathAssignments', 'type': '{LegacyDataPath}'},
'data_set_definition_value_assignments': {'key': 'dataSetDefinitionValueAssignments', 'type': '{DataSetDefinitionValue}'},
'asset_output_settings_assignments': {'key': 'assetOutputSettingsAssignments', 'type': '{AssetOutputSettings}'},
'graph': {'key': 'graph', 'type': 'GraphDraftEntity'},
'pipeline_run_settings': {'key': 'pipelineRunSettings', 'type': '[RunSettingParameterAssignment]'},
'module_node_run_settings': {'key': 'moduleNodeRunSettings', 'type': '[GraphModuleNodeRunSetting]'},
'module_node_ui_input_settings': {'key': 'moduleNodeUIInputSettings', 'type': '[GraphModuleNodeUIInputSetting]'},
'tags': {'key': 'tags', 'type': '{str}'},
'continue_run_on_step_failure': {'key': 'continueRunOnStepFailure', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'enforce_rerun': {'key': 'enforceRerun', 'type': 'bool'},
'dataset_access_modes': {'key': 'datasetAccessModes', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword module_node_id:
:paramtype module_node_id: str
:keyword port_name:
:paramtype port_name: str
:keyword training_pipeline_draft_name:
:paramtype training_pipeline_draft_name: str
:keyword training_pipeline_run_display_name:
:paramtype training_pipeline_run_display_name: str
:keyword name:
:paramtype name: str
:keyword pipeline_type: Possible values include: "TrainingPipeline",
"RealTimeInferencePipeline", "BatchInferencePipeline", "Unknown".
:paramtype pipeline_type: str or ~flow.models.PipelineType
:keyword pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:paramtype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:keyword graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:paramtype graph_components_mode: str or ~flow.models.GraphComponentsMode
:keyword sub_pipelines_info:
:paramtype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:keyword flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:paramtype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:keyword pipeline_parameters: This is a dictionary.
:paramtype pipeline_parameters: dict[str, str]
:keyword data_path_assignments: This is a dictionary.
:paramtype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:keyword data_set_definition_value_assignments: This is a dictionary.
:paramtype data_set_definition_value_assignments: dict[str,
~flow.models.DataSetDefinitionValue]
:keyword asset_output_settings_assignments: This is a dictionary.
:paramtype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:keyword graph:
:paramtype graph: ~flow.models.GraphDraftEntity
:keyword pipeline_run_settings:
:paramtype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword module_node_run_settings:
:paramtype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:keyword module_node_ui_input_settings:
:paramtype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword continue_run_on_step_failure:
:paramtype continue_run_on_step_failure: bool
:keyword description:
:paramtype description: str
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword enforce_rerun:
:paramtype enforce_rerun: bool
:keyword dataset_access_modes: Possible values include: "Default", "DatasetInDpv2",
"AssetInDpv2", "DatasetInDesignerUI", "DatasetInDpv2WithDatasetInDesignerUI", "Dataset",
"AssetInDpv2WithDatasetInDesignerUI", "DatasetAndAssetInDpv2WithDatasetInDesignerUI",
"AssetInDesignerUI", "AssetInDpv2WithAssetInDesignerUI", "Asset".
:paramtype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
super(CreateInferencePipelineRequest, self).__init__(**kwargs)
self.module_node_id = kwargs.get('module_node_id', None)
self.port_name = kwargs.get('port_name', None)
self.training_pipeline_draft_name = kwargs.get('training_pipeline_draft_name', None)
self.training_pipeline_run_display_name = kwargs.get('training_pipeline_run_display_name', None)
self.name = kwargs.get('name', None)
self.pipeline_type = kwargs.get('pipeline_type', None)
self.pipeline_draft_mode = kwargs.get('pipeline_draft_mode', None)
self.graph_components_mode = kwargs.get('graph_components_mode', None)
self.sub_pipelines_info = kwargs.get('sub_pipelines_info', None)
self.flattened_sub_graphs = kwargs.get('flattened_sub_graphs', None)
self.pipeline_parameters = kwargs.get('pipeline_parameters', None)
self.data_path_assignments = kwargs.get('data_path_assignments', None)
self.data_set_definition_value_assignments = kwargs.get('data_set_definition_value_assignments', None)
self.asset_output_settings_assignments = kwargs.get('asset_output_settings_assignments', None)
self.graph = kwargs.get('graph', None)
self.pipeline_run_settings = kwargs.get('pipeline_run_settings', None)
self.module_node_run_settings = kwargs.get('module_node_run_settings', None)
self.module_node_ui_input_settings = kwargs.get('module_node_ui_input_settings', None)
self.tags = kwargs.get('tags', None)
self.continue_run_on_step_failure = kwargs.get('continue_run_on_step_failure', None)
self.description = kwargs.get('description', None)
self.properties = kwargs.get('properties', None)
self.enforce_rerun = kwargs.get('enforce_rerun', None)
self.dataset_access_modes = kwargs.get('dataset_access_modes', None)
class CreateOrUpdateConnectionRequest(msrest.serialization.Model):
"""CreateOrUpdateConnectionRequest.
:ivar connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:vartype connection_type: str or ~flow.models.ConnectionType
:ivar connection_scope: Possible values include: "User", "WorkspaceShared".
:vartype connection_scope: str or ~flow.models.ConnectionScope
:ivar configs: This is a dictionary.
:vartype configs: dict[str, str]
:ivar custom_configs: This is a dictionary.
:vartype custom_configs: dict[str, ~flow.models.CustomConnectionConfig]
:ivar expiry_time:
:vartype expiry_time: ~datetime.datetime
"""
_attribute_map = {
'connection_type': {'key': 'connectionType', 'type': 'str'},
'connection_scope': {'key': 'connectionScope', 'type': 'str'},
'configs': {'key': 'configs', 'type': '{str}'},
'custom_configs': {'key': 'customConfigs', 'type': '{CustomConnectionConfig}'},
'expiry_time': {'key': 'expiryTime', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:paramtype connection_type: str or ~flow.models.ConnectionType
:keyword connection_scope: Possible values include: "User", "WorkspaceShared".
:paramtype connection_scope: str or ~flow.models.ConnectionScope
:keyword configs: This is a dictionary.
:paramtype configs: dict[str, str]
:keyword custom_configs: This is a dictionary.
:paramtype custom_configs: dict[str, ~flow.models.CustomConnectionConfig]
:keyword expiry_time:
:paramtype expiry_time: ~datetime.datetime
"""
super(CreateOrUpdateConnectionRequest, self).__init__(**kwargs)
self.connection_type = kwargs.get('connection_type', None)
self.connection_scope = kwargs.get('connection_scope', None)
self.configs = kwargs.get('configs', None)
self.custom_configs = kwargs.get('custom_configs', None)
self.expiry_time = kwargs.get('expiry_time', None)
class CreateOrUpdateConnectionRequestDto(msrest.serialization.Model):
"""CreateOrUpdateConnectionRequestDto.
:ivar connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:vartype connection_type: str or ~flow.models.ConnectionType
:ivar configs: This is a dictionary.
:vartype configs: dict[str, str]
:ivar custom_configs: This is a dictionary.
:vartype custom_configs: dict[str, ~flow.models.CustomConnectionConfig]
:ivar expiry_time:
:vartype expiry_time: ~datetime.datetime
"""
_attribute_map = {
'connection_type': {'key': 'connectionType', 'type': 'str'},
'configs': {'key': 'configs', 'type': '{str}'},
'custom_configs': {'key': 'customConfigs', 'type': '{CustomConnectionConfig}'},
'expiry_time': {'key': 'expiryTime', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:paramtype connection_type: str or ~flow.models.ConnectionType
:keyword configs: This is a dictionary.
:paramtype configs: dict[str, str]
:keyword custom_configs: This is a dictionary.
:paramtype custom_configs: dict[str, ~flow.models.CustomConnectionConfig]
:keyword expiry_time:
:paramtype expiry_time: ~datetime.datetime
"""
super(CreateOrUpdateConnectionRequestDto, self).__init__(**kwargs)
self.connection_type = kwargs.get('connection_type', None)
self.configs = kwargs.get('configs', None)
self.custom_configs = kwargs.get('custom_configs', None)
self.expiry_time = kwargs.get('expiry_time', None)
class CreatePipelineDraftRequest(msrest.serialization.Model):
"""CreatePipelineDraftRequest.
:ivar name:
:vartype name: str
:ivar pipeline_type: Possible values include: "TrainingPipeline", "RealTimeInferencePipeline",
"BatchInferencePipeline", "Unknown".
:vartype pipeline_type: str or ~flow.models.PipelineType
:ivar pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:vartype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:ivar graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:vartype graph_components_mode: str or ~flow.models.GraphComponentsMode
:ivar sub_pipelines_info:
:vartype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:ivar flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:vartype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:ivar pipeline_parameters: This is a dictionary.
:vartype pipeline_parameters: dict[str, str]
:ivar data_path_assignments: This is a dictionary.
:vartype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:ivar data_set_definition_value_assignments: This is a dictionary.
:vartype data_set_definition_value_assignments: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar asset_output_settings_assignments: This is a dictionary.
:vartype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:ivar graph:
:vartype graph: ~flow.models.GraphDraftEntity
:ivar pipeline_run_settings:
:vartype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar module_node_run_settings:
:vartype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:ivar module_node_ui_input_settings:
:vartype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar continue_run_on_step_failure:
:vartype continue_run_on_step_failure: bool
:ivar description:
:vartype description: str
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar enforce_rerun:
:vartype enforce_rerun: bool
:ivar dataset_access_modes: Possible values include: "Default", "DatasetInDpv2", "AssetInDpv2",
"DatasetInDesignerUI", "DatasetInDpv2WithDatasetInDesignerUI", "Dataset",
"AssetInDpv2WithDatasetInDesignerUI", "DatasetAndAssetInDpv2WithDatasetInDesignerUI",
"AssetInDesignerUI", "AssetInDpv2WithAssetInDesignerUI", "Asset".
:vartype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'pipeline_type': {'key': 'pipelineType', 'type': 'str'},
'pipeline_draft_mode': {'key': 'pipelineDraftMode', 'type': 'str'},
'graph_components_mode': {'key': 'graphComponentsMode', 'type': 'str'},
'sub_pipelines_info': {'key': 'subPipelinesInfo', 'type': 'SubPipelinesInfo'},
'flattened_sub_graphs': {'key': 'flattenedSubGraphs', 'type': '{PipelineSubDraft}'},
'pipeline_parameters': {'key': 'pipelineParameters', 'type': '{str}'},
'data_path_assignments': {'key': 'dataPathAssignments', 'type': '{LegacyDataPath}'},
'data_set_definition_value_assignments': {'key': 'dataSetDefinitionValueAssignments', 'type': '{DataSetDefinitionValue}'},
'asset_output_settings_assignments': {'key': 'assetOutputSettingsAssignments', 'type': '{AssetOutputSettings}'},
'graph': {'key': 'graph', 'type': 'GraphDraftEntity'},
'pipeline_run_settings': {'key': 'pipelineRunSettings', 'type': '[RunSettingParameterAssignment]'},
'module_node_run_settings': {'key': 'moduleNodeRunSettings', 'type': '[GraphModuleNodeRunSetting]'},
'module_node_ui_input_settings': {'key': 'moduleNodeUIInputSettings', 'type': '[GraphModuleNodeUIInputSetting]'},
'tags': {'key': 'tags', 'type': '{str}'},
'continue_run_on_step_failure': {'key': 'continueRunOnStepFailure', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'enforce_rerun': {'key': 'enforceRerun', 'type': 'bool'},
'dataset_access_modes': {'key': 'datasetAccessModes', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword pipeline_type: Possible values include: "TrainingPipeline",
"RealTimeInferencePipeline", "BatchInferencePipeline", "Unknown".
:paramtype pipeline_type: str or ~flow.models.PipelineType
:keyword pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:paramtype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:keyword graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:paramtype graph_components_mode: str or ~flow.models.GraphComponentsMode
:keyword sub_pipelines_info:
:paramtype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:keyword flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:paramtype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:keyword pipeline_parameters: This is a dictionary.
:paramtype pipeline_parameters: dict[str, str]
:keyword data_path_assignments: This is a dictionary.
:paramtype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:keyword data_set_definition_value_assignments: This is a dictionary.
:paramtype data_set_definition_value_assignments: dict[str,
~flow.models.DataSetDefinitionValue]
:keyword asset_output_settings_assignments: This is a dictionary.
:paramtype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:keyword graph:
:paramtype graph: ~flow.models.GraphDraftEntity
:keyword pipeline_run_settings:
:paramtype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword module_node_run_settings:
:paramtype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:keyword module_node_ui_input_settings:
:paramtype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword continue_run_on_step_failure:
:paramtype continue_run_on_step_failure: bool
:keyword description:
:paramtype description: str
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword enforce_rerun:
:paramtype enforce_rerun: bool
:keyword dataset_access_modes: Possible values include: "Default", "DatasetInDpv2",
"AssetInDpv2", "DatasetInDesignerUI", "DatasetInDpv2WithDatasetInDesignerUI", "Dataset",
"AssetInDpv2WithDatasetInDesignerUI", "DatasetAndAssetInDpv2WithDatasetInDesignerUI",
"AssetInDesignerUI", "AssetInDpv2WithAssetInDesignerUI", "Asset".
:paramtype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
super(CreatePipelineDraftRequest, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.pipeline_type = kwargs.get('pipeline_type', None)
self.pipeline_draft_mode = kwargs.get('pipeline_draft_mode', None)
self.graph_components_mode = kwargs.get('graph_components_mode', None)
self.sub_pipelines_info = kwargs.get('sub_pipelines_info', None)
self.flattened_sub_graphs = kwargs.get('flattened_sub_graphs', None)
self.pipeline_parameters = kwargs.get('pipeline_parameters', None)
self.data_path_assignments = kwargs.get('data_path_assignments', None)
self.data_set_definition_value_assignments = kwargs.get('data_set_definition_value_assignments', None)
self.asset_output_settings_assignments = kwargs.get('asset_output_settings_assignments', None)
self.graph = kwargs.get('graph', None)
self.pipeline_run_settings = kwargs.get('pipeline_run_settings', None)
self.module_node_run_settings = kwargs.get('module_node_run_settings', None)
self.module_node_ui_input_settings = kwargs.get('module_node_ui_input_settings', None)
self.tags = kwargs.get('tags', None)
self.continue_run_on_step_failure = kwargs.get('continue_run_on_step_failure', None)
self.description = kwargs.get('description', None)
self.properties = kwargs.get('properties', None)
self.enforce_rerun = kwargs.get('enforce_rerun', None)
self.dataset_access_modes = kwargs.get('dataset_access_modes', None)
class CreatePipelineJobScheduleDto(msrest.serialization.Model):
"""CreatePipelineJobScheduleDto.
:ivar name:
:vartype name: str
:ivar pipeline_job_name:
:vartype pipeline_job_name: str
:ivar pipeline_job_runtime_settings:
:vartype pipeline_job_runtime_settings: ~flow.models.PipelineJobRuntimeBasicSettings
:ivar display_name:
:vartype display_name: str
:ivar trigger_type: Possible values include: "Recurrence", "Cron".
:vartype trigger_type: str or ~flow.models.TriggerType
:ivar recurrence:
:vartype recurrence: ~flow.models.Recurrence
:ivar cron:
:vartype cron: ~flow.models.Cron
:ivar status: Possible values include: "Enabled", "Disabled".
:vartype status: str or ~flow.models.ScheduleStatus
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'pipeline_job_name': {'key': 'pipelineJobName', 'type': 'str'},
'pipeline_job_runtime_settings': {'key': 'pipelineJobRuntimeSettings', 'type': 'PipelineJobRuntimeBasicSettings'},
'display_name': {'key': 'displayName', 'type': 'str'},
'trigger_type': {'key': 'triggerType', 'type': 'str'},
'recurrence': {'key': 'recurrence', 'type': 'Recurrence'},
'cron': {'key': 'cron', 'type': 'Cron'},
'status': {'key': 'status', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword pipeline_job_name:
:paramtype pipeline_job_name: str
:keyword pipeline_job_runtime_settings:
:paramtype pipeline_job_runtime_settings: ~flow.models.PipelineJobRuntimeBasicSettings
:keyword display_name:
:paramtype display_name: str
:keyword trigger_type: Possible values include: "Recurrence", "Cron".
:paramtype trigger_type: str or ~flow.models.TriggerType
:keyword recurrence:
:paramtype recurrence: ~flow.models.Recurrence
:keyword cron:
:paramtype cron: ~flow.models.Cron
:keyword status: Possible values include: "Enabled", "Disabled".
:paramtype status: str or ~flow.models.ScheduleStatus
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
"""
super(CreatePipelineJobScheduleDto, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.pipeline_job_name = kwargs.get('pipeline_job_name', None)
self.pipeline_job_runtime_settings = kwargs.get('pipeline_job_runtime_settings', None)
self.display_name = kwargs.get('display_name', None)
self.trigger_type = kwargs.get('trigger_type', None)
self.recurrence = kwargs.get('recurrence', None)
self.cron = kwargs.get('cron', None)
self.status = kwargs.get('status', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
class CreatePublishedPipelineRequest(msrest.serialization.Model):
"""CreatePublishedPipelineRequest.
:ivar use_pipeline_endpoint:
:vartype use_pipeline_endpoint: bool
:ivar pipeline_name:
:vartype pipeline_name: str
:ivar pipeline_description:
:vartype pipeline_description: str
:ivar use_existing_pipeline_endpoint:
:vartype use_existing_pipeline_endpoint: bool
:ivar pipeline_endpoint_name:
:vartype pipeline_endpoint_name: str
:ivar pipeline_endpoint_description:
:vartype pipeline_endpoint_description: str
:ivar set_as_default_pipeline_for_endpoint:
:vartype set_as_default_pipeline_for_endpoint: bool
:ivar step_tags: This is a dictionary.
:vartype step_tags: dict[str, str]
:ivar experiment_name:
:vartype experiment_name: str
:ivar pipeline_parameters: This is a dictionary.
:vartype pipeline_parameters: dict[str, str]
:ivar data_path_assignments: This is a dictionary.
:vartype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:ivar data_set_definition_value_assignments: This is a dictionary.
:vartype data_set_definition_value_assignments: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar asset_output_settings_assignments: This is a dictionary.
:vartype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:ivar enable_notification:
:vartype enable_notification: bool
:ivar sub_pipelines_info:
:vartype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:ivar display_name:
:vartype display_name: str
:ivar run_id:
:vartype run_id: str
:ivar parent_run_id:
:vartype parent_run_id: str
:ivar graph:
:vartype graph: ~flow.models.GraphDraftEntity
:ivar pipeline_run_settings:
:vartype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar module_node_run_settings:
:vartype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:ivar module_node_ui_input_settings:
:vartype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar continue_run_on_step_failure:
:vartype continue_run_on_step_failure: bool
:ivar description:
:vartype description: str
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar enforce_rerun:
:vartype enforce_rerun: bool
:ivar dataset_access_modes: Possible values include: "Default", "DatasetInDpv2", "AssetInDpv2",
"DatasetInDesignerUI", "DatasetInDpv2WithDatasetInDesignerUI", "Dataset",
"AssetInDpv2WithDatasetInDesignerUI", "DatasetAndAssetInDpv2WithDatasetInDesignerUI",
"AssetInDesignerUI", "AssetInDpv2WithAssetInDesignerUI", "Asset".
:vartype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
_attribute_map = {
'use_pipeline_endpoint': {'key': 'usePipelineEndpoint', 'type': 'bool'},
'pipeline_name': {'key': 'pipelineName', 'type': 'str'},
'pipeline_description': {'key': 'pipelineDescription', 'type': 'str'},
'use_existing_pipeline_endpoint': {'key': 'useExistingPipelineEndpoint', 'type': 'bool'},
'pipeline_endpoint_name': {'key': 'pipelineEndpointName', 'type': 'str'},
'pipeline_endpoint_description': {'key': 'pipelineEndpointDescription', 'type': 'str'},
'set_as_default_pipeline_for_endpoint': {'key': 'setAsDefaultPipelineForEndpoint', 'type': 'bool'},
'step_tags': {'key': 'stepTags', 'type': '{str}'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'pipeline_parameters': {'key': 'pipelineParameters', 'type': '{str}'},
'data_path_assignments': {'key': 'dataPathAssignments', 'type': '{LegacyDataPath}'},
'data_set_definition_value_assignments': {'key': 'dataSetDefinitionValueAssignments', 'type': '{DataSetDefinitionValue}'},
'asset_output_settings_assignments': {'key': 'assetOutputSettingsAssignments', 'type': '{AssetOutputSettings}'},
'enable_notification': {'key': 'enableNotification', 'type': 'bool'},
'sub_pipelines_info': {'key': 'subPipelinesInfo', 'type': 'SubPipelinesInfo'},
'display_name': {'key': 'displayName', 'type': 'str'},
'run_id': {'key': 'runId', 'type': 'str'},
'parent_run_id': {'key': 'parentRunId', 'type': 'str'},
'graph': {'key': 'graph', 'type': 'GraphDraftEntity'},
'pipeline_run_settings': {'key': 'pipelineRunSettings', 'type': '[RunSettingParameterAssignment]'},
'module_node_run_settings': {'key': 'moduleNodeRunSettings', 'type': '[GraphModuleNodeRunSetting]'},
'module_node_ui_input_settings': {'key': 'moduleNodeUIInputSettings', 'type': '[GraphModuleNodeUIInputSetting]'},
'tags': {'key': 'tags', 'type': '{str}'},
'continue_run_on_step_failure': {'key': 'continueRunOnStepFailure', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'enforce_rerun': {'key': 'enforceRerun', 'type': 'bool'},
'dataset_access_modes': {'key': 'datasetAccessModes', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword use_pipeline_endpoint:
:paramtype use_pipeline_endpoint: bool
:keyword pipeline_name:
:paramtype pipeline_name: str
:keyword pipeline_description:
:paramtype pipeline_description: str
:keyword use_existing_pipeline_endpoint:
:paramtype use_existing_pipeline_endpoint: bool
:keyword pipeline_endpoint_name:
:paramtype pipeline_endpoint_name: str
:keyword pipeline_endpoint_description:
:paramtype pipeline_endpoint_description: str
:keyword set_as_default_pipeline_for_endpoint:
:paramtype set_as_default_pipeline_for_endpoint: bool
:keyword step_tags: This is a dictionary.
:paramtype step_tags: dict[str, str]
:keyword experiment_name:
:paramtype experiment_name: str
:keyword pipeline_parameters: This is a dictionary.
:paramtype pipeline_parameters: dict[str, str]
:keyword data_path_assignments: This is a dictionary.
:paramtype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:keyword data_set_definition_value_assignments: This is a dictionary.
:paramtype data_set_definition_value_assignments: dict[str,
~flow.models.DataSetDefinitionValue]
:keyword asset_output_settings_assignments: This is a dictionary.
:paramtype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:keyword enable_notification:
:paramtype enable_notification: bool
:keyword sub_pipelines_info:
:paramtype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:keyword display_name:
:paramtype display_name: str
:keyword run_id:
:paramtype run_id: str
:keyword parent_run_id:
:paramtype parent_run_id: str
:keyword graph:
:paramtype graph: ~flow.models.GraphDraftEntity
:keyword pipeline_run_settings:
:paramtype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword module_node_run_settings:
:paramtype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:keyword module_node_ui_input_settings:
:paramtype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword continue_run_on_step_failure:
:paramtype continue_run_on_step_failure: bool
:keyword description:
:paramtype description: str
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword enforce_rerun:
:paramtype enforce_rerun: bool
:keyword dataset_access_modes: Possible values include: "Default", "DatasetInDpv2",
"AssetInDpv2", "DatasetInDesignerUI", "DatasetInDpv2WithDatasetInDesignerUI", "Dataset",
"AssetInDpv2WithDatasetInDesignerUI", "DatasetAndAssetInDpv2WithDatasetInDesignerUI",
"AssetInDesignerUI", "AssetInDpv2WithAssetInDesignerUI", "Asset".
:paramtype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
super(CreatePublishedPipelineRequest, self).__init__(**kwargs)
self.use_pipeline_endpoint = kwargs.get('use_pipeline_endpoint', None)
self.pipeline_name = kwargs.get('pipeline_name', None)
self.pipeline_description = kwargs.get('pipeline_description', None)
self.use_existing_pipeline_endpoint = kwargs.get('use_existing_pipeline_endpoint', None)
self.pipeline_endpoint_name = kwargs.get('pipeline_endpoint_name', None)
self.pipeline_endpoint_description = kwargs.get('pipeline_endpoint_description', None)
self.set_as_default_pipeline_for_endpoint = kwargs.get('set_as_default_pipeline_for_endpoint', None)
self.step_tags = kwargs.get('step_tags', None)
self.experiment_name = kwargs.get('experiment_name', None)
self.pipeline_parameters = kwargs.get('pipeline_parameters', None)
self.data_path_assignments = kwargs.get('data_path_assignments', None)
self.data_set_definition_value_assignments = kwargs.get('data_set_definition_value_assignments', None)
self.asset_output_settings_assignments = kwargs.get('asset_output_settings_assignments', None)
self.enable_notification = kwargs.get('enable_notification', None)
self.sub_pipelines_info = kwargs.get('sub_pipelines_info', None)
self.display_name = kwargs.get('display_name', None)
self.run_id = kwargs.get('run_id', None)
self.parent_run_id = kwargs.get('parent_run_id', None)
self.graph = kwargs.get('graph', None)
self.pipeline_run_settings = kwargs.get('pipeline_run_settings', None)
self.module_node_run_settings = kwargs.get('module_node_run_settings', None)
self.module_node_ui_input_settings = kwargs.get('module_node_ui_input_settings', None)
self.tags = kwargs.get('tags', None)
self.continue_run_on_step_failure = kwargs.get('continue_run_on_step_failure', None)
self.description = kwargs.get('description', None)
self.properties = kwargs.get('properties', None)
self.enforce_rerun = kwargs.get('enforce_rerun', None)
self.dataset_access_modes = kwargs.get('dataset_access_modes', None)
class CreateRealTimeEndpointRequest(msrest.serialization.Model):
"""CreateRealTimeEndpointRequest.
:ivar name:
:vartype name: str
:ivar compute_info:
:vartype compute_info: ~flow.models.ComputeInfo
:ivar description:
:vartype description: str
:ivar linked_pipeline_draft_id:
:vartype linked_pipeline_draft_id: str
:ivar linked_pipeline_run_id:
:vartype linked_pipeline_run_id: str
:ivar aks_advance_settings:
:vartype aks_advance_settings: ~flow.models.AKSAdvanceSettings
:ivar aci_advance_settings:
:vartype aci_advance_settings: ~flow.models.ACIAdvanceSettings
:ivar linked_training_pipeline_run_id:
:vartype linked_training_pipeline_run_id: str
:ivar linked_experiment_name:
:vartype linked_experiment_name: str
:ivar graph_nodes_run_id_mapping: This is a dictionary.
:vartype graph_nodes_run_id_mapping: dict[str, str]
:ivar workflow:
:vartype workflow: ~flow.models.PipelineGraph
:ivar inputs:
:vartype inputs: list[~flow.models.InputOutputPortMetadata]
:ivar outputs:
:vartype outputs: list[~flow.models.InputOutputPortMetadata]
:ivar example_request:
:vartype example_request: ~flow.models.ExampleRequest
:ivar user_storage_connection_string:
:vartype user_storage_connection_string: str
:ivar user_storage_endpoint_uri:
:vartype user_storage_endpoint_uri: str
:ivar user_storage_workspace_sai_token:
:vartype user_storage_workspace_sai_token: str
:ivar user_storage_container_name:
:vartype user_storage_container_name: str
:ivar pipeline_run_id:
:vartype pipeline_run_id: str
:ivar root_pipeline_run_id:
:vartype root_pipeline_run_id: str
:ivar experiment_name:
:vartype experiment_name: str
:ivar experiment_id:
:vartype experiment_id: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'compute_info': {'key': 'computeInfo', 'type': 'ComputeInfo'},
'description': {'key': 'description', 'type': 'str'},
'linked_pipeline_draft_id': {'key': 'linkedPipelineDraftId', 'type': 'str'},
'linked_pipeline_run_id': {'key': 'linkedPipelineRunId', 'type': 'str'},
'aks_advance_settings': {'key': 'aksAdvanceSettings', 'type': 'AKSAdvanceSettings'},
'aci_advance_settings': {'key': 'aciAdvanceSettings', 'type': 'ACIAdvanceSettings'},
'linked_training_pipeline_run_id': {'key': 'linkedTrainingPipelineRunId', 'type': 'str'},
'linked_experiment_name': {'key': 'linkedExperimentName', 'type': 'str'},
'graph_nodes_run_id_mapping': {'key': 'graphNodesRunIdMapping', 'type': '{str}'},
'workflow': {'key': 'workflow', 'type': 'PipelineGraph'},
'inputs': {'key': 'inputs', 'type': '[InputOutputPortMetadata]'},
'outputs': {'key': 'outputs', 'type': '[InputOutputPortMetadata]'},
'example_request': {'key': 'exampleRequest', 'type': 'ExampleRequest'},
'user_storage_connection_string': {'key': 'userStorageConnectionString', 'type': 'str'},
'user_storage_endpoint_uri': {'key': 'userStorageEndpointUri', 'type': 'str'},
'user_storage_workspace_sai_token': {'key': 'userStorageWorkspaceSaiToken', 'type': 'str'},
'user_storage_container_name': {'key': 'userStorageContainerName', 'type': 'str'},
'pipeline_run_id': {'key': 'pipelineRunId', 'type': 'str'},
'root_pipeline_run_id': {'key': 'rootPipelineRunId', 'type': 'str'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword compute_info:
:paramtype compute_info: ~flow.models.ComputeInfo
:keyword description:
:paramtype description: str
:keyword linked_pipeline_draft_id:
:paramtype linked_pipeline_draft_id: str
:keyword linked_pipeline_run_id:
:paramtype linked_pipeline_run_id: str
:keyword aks_advance_settings:
:paramtype aks_advance_settings: ~flow.models.AKSAdvanceSettings
:keyword aci_advance_settings:
:paramtype aci_advance_settings: ~flow.models.ACIAdvanceSettings
:keyword linked_training_pipeline_run_id:
:paramtype linked_training_pipeline_run_id: str
:keyword linked_experiment_name:
:paramtype linked_experiment_name: str
:keyword graph_nodes_run_id_mapping: This is a dictionary.
:paramtype graph_nodes_run_id_mapping: dict[str, str]
:keyword workflow:
:paramtype workflow: ~flow.models.PipelineGraph
:keyword inputs:
:paramtype inputs: list[~flow.models.InputOutputPortMetadata]
:keyword outputs:
:paramtype outputs: list[~flow.models.InputOutputPortMetadata]
:keyword example_request:
:paramtype example_request: ~flow.models.ExampleRequest
:keyword user_storage_connection_string:
:paramtype user_storage_connection_string: str
:keyword user_storage_endpoint_uri:
:paramtype user_storage_endpoint_uri: str
:keyword user_storage_workspace_sai_token:
:paramtype user_storage_workspace_sai_token: str
:keyword user_storage_container_name:
:paramtype user_storage_container_name: str
:keyword pipeline_run_id:
:paramtype pipeline_run_id: str
:keyword root_pipeline_run_id:
:paramtype root_pipeline_run_id: str
:keyword experiment_name:
:paramtype experiment_name: str
:keyword experiment_id:
:paramtype experiment_id: str
"""
super(CreateRealTimeEndpointRequest, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.compute_info = kwargs.get('compute_info', None)
self.description = kwargs.get('description', None)
self.linked_pipeline_draft_id = kwargs.get('linked_pipeline_draft_id', None)
self.linked_pipeline_run_id = kwargs.get('linked_pipeline_run_id', None)
self.aks_advance_settings = kwargs.get('aks_advance_settings', None)
self.aci_advance_settings = kwargs.get('aci_advance_settings', None)
self.linked_training_pipeline_run_id = kwargs.get('linked_training_pipeline_run_id', None)
self.linked_experiment_name = kwargs.get('linked_experiment_name', None)
self.graph_nodes_run_id_mapping = kwargs.get('graph_nodes_run_id_mapping', None)
self.workflow = kwargs.get('workflow', None)
self.inputs = kwargs.get('inputs', None)
self.outputs = kwargs.get('outputs', None)
self.example_request = kwargs.get('example_request', None)
self.user_storage_connection_string = kwargs.get('user_storage_connection_string', None)
self.user_storage_endpoint_uri = kwargs.get('user_storage_endpoint_uri', None)
self.user_storage_workspace_sai_token = kwargs.get('user_storage_workspace_sai_token', None)
self.user_storage_container_name = kwargs.get('user_storage_container_name', None)
self.pipeline_run_id = kwargs.get('pipeline_run_id', None)
self.root_pipeline_run_id = kwargs.get('root_pipeline_run_id', None)
self.experiment_name = kwargs.get('experiment_name', None)
self.experiment_id = kwargs.get('experiment_id', None)
class CreationContext(msrest.serialization.Model):
"""CreationContext.
:ivar created_time:
:vartype created_time: ~datetime.datetime
:ivar created_by:
:vartype created_by: ~flow.models.SchemaContractsCreatedBy
:ivar creation_source:
:vartype creation_source: str
"""
_attribute_map = {
'created_time': {'key': 'createdTime', 'type': 'iso-8601'},
'created_by': {'key': 'createdBy', 'type': 'SchemaContractsCreatedBy'},
'creation_source': {'key': 'creationSource', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword created_time:
:paramtype created_time: ~datetime.datetime
:keyword created_by:
:paramtype created_by: ~flow.models.SchemaContractsCreatedBy
:keyword creation_source:
:paramtype creation_source: str
"""
super(CreationContext, self).__init__(**kwargs)
self.created_time = kwargs.get('created_time', None)
self.created_by = kwargs.get('created_by', None)
self.creation_source = kwargs.get('creation_source', None)
class Cron(msrest.serialization.Model):
"""Cron.
:ivar expression:
:vartype expression: str
:ivar end_time:
:vartype end_time: str
:ivar start_time:
:vartype start_time: str
:ivar time_zone:
:vartype time_zone: str
"""
_attribute_map = {
'expression': {'key': 'expression', 'type': 'str'},
'end_time': {'key': 'endTime', 'type': 'str'},
'start_time': {'key': 'startTime', 'type': 'str'},
'time_zone': {'key': 'timeZone', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword expression:
:paramtype expression: str
:keyword end_time:
:paramtype end_time: str
:keyword start_time:
:paramtype start_time: str
:keyword time_zone:
:paramtype time_zone: str
"""
super(Cron, self).__init__(**kwargs)
self.expression = kwargs.get('expression', None)
self.end_time = kwargs.get('end_time', None)
self.start_time = kwargs.get('start_time', None)
self.time_zone = kwargs.get('time_zone', None)
class CustomConnectionConfig(msrest.serialization.Model):
"""CustomConnectionConfig.
:ivar config_value_type: Possible values include: "String", "Secret".
:vartype config_value_type: str or ~flow.models.ConfigValueType
:ivar value:
:vartype value: str
"""
_attribute_map = {
'config_value_type': {'key': 'configValueType', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword config_value_type: Possible values include: "String", "Secret".
:paramtype config_value_type: str or ~flow.models.ConfigValueType
:keyword value:
:paramtype value: str
"""
super(CustomConnectionConfig, self).__init__(**kwargs)
self.config_value_type = kwargs.get('config_value_type', None)
self.value = kwargs.get('value', None)
class CustomReference(msrest.serialization.Model):
"""CustomReference.
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
:keyword relative_path:
:paramtype relative_path: str
"""
super(CustomReference, self).__init__(**kwargs)
self.aml_data_store_name = kwargs.get('aml_data_store_name', None)
self.relative_path = kwargs.get('relative_path', None)
class Data(msrest.serialization.Model):
"""Data.
:ivar data_location:
:vartype data_location: ~flow.models.ExecutionDataLocation
:ivar mechanism: Possible values include: "Direct", "Mount", "Download", "Hdfs".
:vartype mechanism: str or ~flow.models.DeliveryMechanism
:ivar environment_variable_name:
:vartype environment_variable_name: str
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar overwrite:
:vartype overwrite: bool
:ivar options: Dictionary of :code:`<string>`.
:vartype options: dict[str, str]
"""
_attribute_map = {
'data_location': {'key': 'dataLocation', 'type': 'ExecutionDataLocation'},
'mechanism': {'key': 'mechanism', 'type': 'str'},
'environment_variable_name': {'key': 'environmentVariableName', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'overwrite': {'key': 'overwrite', 'type': 'bool'},
'options': {'key': 'options', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_location:
:paramtype data_location: ~flow.models.ExecutionDataLocation
:keyword mechanism: Possible values include: "Direct", "Mount", "Download", "Hdfs".
:paramtype mechanism: str or ~flow.models.DeliveryMechanism
:keyword environment_variable_name:
:paramtype environment_variable_name: str
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword overwrite:
:paramtype overwrite: bool
:keyword options: Dictionary of :code:`<string>`.
:paramtype options: dict[str, str]
"""
super(Data, self).__init__(**kwargs)
self.data_location = kwargs.get('data_location', None)
self.mechanism = kwargs.get('mechanism', None)
self.environment_variable_name = kwargs.get('environment_variable_name', None)
self.path_on_compute = kwargs.get('path_on_compute', None)
self.overwrite = kwargs.get('overwrite', None)
self.options = kwargs.get('options', None)
class DatabaseSink(msrest.serialization.Model):
"""DatabaseSink.
:ivar connection:
:vartype connection: str
:ivar table:
:vartype table: str
"""
_attribute_map = {
'connection': {'key': 'connection', 'type': 'str'},
'table': {'key': 'table', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword connection:
:paramtype connection: str
:keyword table:
:paramtype table: str
"""
super(DatabaseSink, self).__init__(**kwargs)
self.connection = kwargs.get('connection', None)
self.table = kwargs.get('table', None)
class DatabaseSource(msrest.serialization.Model):
"""DatabaseSource.
:ivar connection:
:vartype connection: str
:ivar query:
:vartype query: str
:ivar stored_procedure_name:
:vartype stored_procedure_name: str
:ivar stored_procedure_parameters:
:vartype stored_procedure_parameters: list[~flow.models.StoredProcedureParameter]
"""
_attribute_map = {
'connection': {'key': 'connection', 'type': 'str'},
'query': {'key': 'query', 'type': 'str'},
'stored_procedure_name': {'key': 'storedProcedureName', 'type': 'str'},
'stored_procedure_parameters': {'key': 'storedProcedureParameters', 'type': '[StoredProcedureParameter]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword connection:
:paramtype connection: str
:keyword query:
:paramtype query: str
:keyword stored_procedure_name:
:paramtype stored_procedure_name: str
:keyword stored_procedure_parameters:
:paramtype stored_procedure_parameters: list[~flow.models.StoredProcedureParameter]
"""
super(DatabaseSource, self).__init__(**kwargs)
self.connection = kwargs.get('connection', None)
self.query = kwargs.get('query', None)
self.stored_procedure_name = kwargs.get('stored_procedure_name', None)
self.stored_procedure_parameters = kwargs.get('stored_procedure_parameters', None)
class DatabricksComputeInfo(msrest.serialization.Model):
"""DatabricksComputeInfo.
:ivar existing_cluster_id:
:vartype existing_cluster_id: str
"""
_attribute_map = {
'existing_cluster_id': {'key': 'existingClusterId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword existing_cluster_id:
:paramtype existing_cluster_id: str
"""
super(DatabricksComputeInfo, self).__init__(**kwargs)
self.existing_cluster_id = kwargs.get('existing_cluster_id', None)
class DatabricksConfiguration(msrest.serialization.Model):
"""DatabricksConfiguration.
:ivar workers:
:vartype workers: int
:ivar minimum_worker_count:
:vartype minimum_worker_count: int
:ivar max_mum_worker_count:
:vartype max_mum_worker_count: int
:ivar spark_version:
:vartype spark_version: str
:ivar node_type_id:
:vartype node_type_id: str
:ivar spark_conf: Dictionary of :code:`<string>`.
:vartype spark_conf: dict[str, str]
:ivar spark_env_vars: Dictionary of :code:`<string>`.
:vartype spark_env_vars: dict[str, str]
:ivar cluster_log_conf_dbfs_path:
:vartype cluster_log_conf_dbfs_path: str
:ivar dbfs_init_scripts:
:vartype dbfs_init_scripts: list[~flow.models.InitScriptInfoDto]
:ivar instance_pool_id:
:vartype instance_pool_id: str
:ivar timeout_seconds:
:vartype timeout_seconds: int
:ivar notebook_task:
:vartype notebook_task: ~flow.models.NoteBookTaskDto
:ivar spark_python_task:
:vartype spark_python_task: ~flow.models.SparkPythonTaskDto
:ivar spark_jar_task:
:vartype spark_jar_task: ~flow.models.SparkJarTaskDto
:ivar spark_submit_task:
:vartype spark_submit_task: ~flow.models.SparkSubmitTaskDto
:ivar jar_libraries:
:vartype jar_libraries: list[str]
:ivar egg_libraries:
:vartype egg_libraries: list[str]
:ivar whl_libraries:
:vartype whl_libraries: list[str]
:ivar pypi_libraries:
:vartype pypi_libraries: list[~flow.models.PythonPyPiOrRCranLibraryDto]
:ivar r_cran_libraries:
:vartype r_cran_libraries: list[~flow.models.PythonPyPiOrRCranLibraryDto]
:ivar maven_libraries:
:vartype maven_libraries: list[~flow.models.MavenLibraryDto]
:ivar libraries:
:vartype libraries: list[any]
:ivar linked_adb_workspace_metadata:
:vartype linked_adb_workspace_metadata: ~flow.models.LinkedADBWorkspaceMetadata
:ivar databrick_resource_id:
:vartype databrick_resource_id: str
:ivar auto_scale:
:vartype auto_scale: bool
"""
_attribute_map = {
'workers': {'key': 'workers', 'type': 'int'},
'minimum_worker_count': {'key': 'minimumWorkerCount', 'type': 'int'},
'max_mum_worker_count': {'key': 'maxMumWorkerCount', 'type': 'int'},
'spark_version': {'key': 'sparkVersion', 'type': 'str'},
'node_type_id': {'key': 'nodeTypeId', 'type': 'str'},
'spark_conf': {'key': 'sparkConf', 'type': '{str}'},
'spark_env_vars': {'key': 'sparkEnvVars', 'type': '{str}'},
'cluster_log_conf_dbfs_path': {'key': 'clusterLogConfDbfsPath', 'type': 'str'},
'dbfs_init_scripts': {'key': 'dbfsInitScripts', 'type': '[InitScriptInfoDto]'},
'instance_pool_id': {'key': 'instancePoolId', 'type': 'str'},
'timeout_seconds': {'key': 'timeoutSeconds', 'type': 'int'},
'notebook_task': {'key': 'notebookTask', 'type': 'NoteBookTaskDto'},
'spark_python_task': {'key': 'sparkPythonTask', 'type': 'SparkPythonTaskDto'},
'spark_jar_task': {'key': 'sparkJarTask', 'type': 'SparkJarTaskDto'},
'spark_submit_task': {'key': 'sparkSubmitTask', 'type': 'SparkSubmitTaskDto'},
'jar_libraries': {'key': 'jarLibraries', 'type': '[str]'},
'egg_libraries': {'key': 'eggLibraries', 'type': '[str]'},
'whl_libraries': {'key': 'whlLibraries', 'type': '[str]'},
'pypi_libraries': {'key': 'pypiLibraries', 'type': '[PythonPyPiOrRCranLibraryDto]'},
'r_cran_libraries': {'key': 'rCranLibraries', 'type': '[PythonPyPiOrRCranLibraryDto]'},
'maven_libraries': {'key': 'mavenLibraries', 'type': '[MavenLibraryDto]'},
'libraries': {'key': 'libraries', 'type': '[object]'},
'linked_adb_workspace_metadata': {'key': 'linkedADBWorkspaceMetadata', 'type': 'LinkedADBWorkspaceMetadata'},
'databrick_resource_id': {'key': 'databrickResourceId', 'type': 'str'},
'auto_scale': {'key': 'autoScale', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword workers:
:paramtype workers: int
:keyword minimum_worker_count:
:paramtype minimum_worker_count: int
:keyword max_mum_worker_count:
:paramtype max_mum_worker_count: int
:keyword spark_version:
:paramtype spark_version: str
:keyword node_type_id:
:paramtype node_type_id: str
:keyword spark_conf: Dictionary of :code:`<string>`.
:paramtype spark_conf: dict[str, str]
:keyword spark_env_vars: Dictionary of :code:`<string>`.
:paramtype spark_env_vars: dict[str, str]
:keyword cluster_log_conf_dbfs_path:
:paramtype cluster_log_conf_dbfs_path: str
:keyword dbfs_init_scripts:
:paramtype dbfs_init_scripts: list[~flow.models.InitScriptInfoDto]
:keyword instance_pool_id:
:paramtype instance_pool_id: str
:keyword timeout_seconds:
:paramtype timeout_seconds: int
:keyword notebook_task:
:paramtype notebook_task: ~flow.models.NoteBookTaskDto
:keyword spark_python_task:
:paramtype spark_python_task: ~flow.models.SparkPythonTaskDto
:keyword spark_jar_task:
:paramtype spark_jar_task: ~flow.models.SparkJarTaskDto
:keyword spark_submit_task:
:paramtype spark_submit_task: ~flow.models.SparkSubmitTaskDto
:keyword jar_libraries:
:paramtype jar_libraries: list[str]
:keyword egg_libraries:
:paramtype egg_libraries: list[str]
:keyword whl_libraries:
:paramtype whl_libraries: list[str]
:keyword pypi_libraries:
:paramtype pypi_libraries: list[~flow.models.PythonPyPiOrRCranLibraryDto]
:keyword r_cran_libraries:
:paramtype r_cran_libraries: list[~flow.models.PythonPyPiOrRCranLibraryDto]
:keyword maven_libraries:
:paramtype maven_libraries: list[~flow.models.MavenLibraryDto]
:keyword libraries:
:paramtype libraries: list[any]
:keyword linked_adb_workspace_metadata:
:paramtype linked_adb_workspace_metadata: ~flow.models.LinkedADBWorkspaceMetadata
:keyword databrick_resource_id:
:paramtype databrick_resource_id: str
:keyword auto_scale:
:paramtype auto_scale: bool
"""
super(DatabricksConfiguration, self).__init__(**kwargs)
self.workers = kwargs.get('workers', None)
self.minimum_worker_count = kwargs.get('minimum_worker_count', None)
self.max_mum_worker_count = kwargs.get('max_mum_worker_count', None)
self.spark_version = kwargs.get('spark_version', None)
self.node_type_id = kwargs.get('node_type_id', None)
self.spark_conf = kwargs.get('spark_conf', None)
self.spark_env_vars = kwargs.get('spark_env_vars', None)
self.cluster_log_conf_dbfs_path = kwargs.get('cluster_log_conf_dbfs_path', None)
self.dbfs_init_scripts = kwargs.get('dbfs_init_scripts', None)
self.instance_pool_id = kwargs.get('instance_pool_id', None)
self.timeout_seconds = kwargs.get('timeout_seconds', None)
self.notebook_task = kwargs.get('notebook_task', None)
self.spark_python_task = kwargs.get('spark_python_task', None)
self.spark_jar_task = kwargs.get('spark_jar_task', None)
self.spark_submit_task = kwargs.get('spark_submit_task', None)
self.jar_libraries = kwargs.get('jar_libraries', None)
self.egg_libraries = kwargs.get('egg_libraries', None)
self.whl_libraries = kwargs.get('whl_libraries', None)
self.pypi_libraries = kwargs.get('pypi_libraries', None)
self.r_cran_libraries = kwargs.get('r_cran_libraries', None)
self.maven_libraries = kwargs.get('maven_libraries', None)
self.libraries = kwargs.get('libraries', None)
self.linked_adb_workspace_metadata = kwargs.get('linked_adb_workspace_metadata', None)
self.databrick_resource_id = kwargs.get('databrick_resource_id', None)
self.auto_scale = kwargs.get('auto_scale', None)
class DatacacheConfiguration(msrest.serialization.Model):
"""DatacacheConfiguration.
:ivar datacache_id:
:vartype datacache_id: str
:ivar datacache_store:
:vartype datacache_store: str
:ivar dataset_id:
:vartype dataset_id: str
:ivar mode: The only acceptable values to pass in are None and "Mount". The default value is
None.
:vartype mode: str
:ivar replica:
:vartype replica: int
:ivar failure_fallback:
:vartype failure_fallback: bool
:ivar path_on_compute:
:vartype path_on_compute: str
"""
_attribute_map = {
'datacache_id': {'key': 'datacacheId', 'type': 'str'},
'datacache_store': {'key': 'datacacheStore', 'type': 'str'},
'dataset_id': {'key': 'datasetId', 'type': 'str'},
'mode': {'key': 'mode', 'type': 'str'},
'replica': {'key': 'replica', 'type': 'int'},
'failure_fallback': {'key': 'failureFallback', 'type': 'bool'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword datacache_id:
:paramtype datacache_id: str
:keyword datacache_store:
:paramtype datacache_store: str
:keyword dataset_id:
:paramtype dataset_id: str
:keyword mode: The only acceptable values to pass in are None and "Mount". The default value
is None.
:paramtype mode: str
:keyword replica:
:paramtype replica: int
:keyword failure_fallback:
:paramtype failure_fallback: bool
:keyword path_on_compute:
:paramtype path_on_compute: str
"""
super(DatacacheConfiguration, self).__init__(**kwargs)
self.datacache_id = kwargs.get('datacache_id', None)
self.datacache_store = kwargs.get('datacache_store', None)
self.dataset_id = kwargs.get('dataset_id', None)
self.mode = kwargs.get('mode', None)
self.replica = kwargs.get('replica', None)
self.failure_fallback = kwargs.get('failure_fallback', None)
self.path_on_compute = kwargs.get('path_on_compute', None)
class DataInfo(msrest.serialization.Model):
"""DataInfo.
:ivar feed_name:
:vartype feed_name: str
:ivar id:
:vartype id: str
:ivar data_source_type: Possible values include: "None", "PipelineDataSource", "AmlDataset",
"GlobalDataset", "FeedModel", "FeedDataset", "AmlDataVersion", "AMLModelVersion".
:vartype data_source_type: str or ~flow.models.DataSourceType
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar data_type_id:
:vartype data_type_id: str
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
:ivar relative_path:
:vartype relative_path: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar modified_date:
:vartype modified_date: ~datetime.datetime
:ivar registered_by:
:vartype registered_by: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar created_by_studio:
:vartype created_by_studio: bool
:ivar data_reference_type: Possible values include: "None", "AzureBlob", "AzureDataLake",
"AzureFiles", "AzureSqlDatabase", "AzurePostgresDatabase", "AzureDataLakeGen2", "DBFS",
"AzureMySqlDatabase", "Custom", "Hdfs".
:vartype data_reference_type: str or ~flow.models.DataReferenceType
:ivar dataset_type:
:vartype dataset_type: str
:ivar saved_dataset_id:
:vartype saved_dataset_id: str
:ivar dataset_version_id:
:vartype dataset_version_id: str
:ivar is_visible:
:vartype is_visible: bool
:ivar is_registered:
:vartype is_registered: bool
:ivar properties: This is a dictionary.
:vartype properties: dict[str, any]
:ivar connection_string:
:vartype connection_string: str
:ivar container_name:
:vartype container_name: str
:ivar data_storage_endpoint_uri:
:vartype data_storage_endpoint_uri: str
:ivar workspace_sai_token:
:vartype workspace_sai_token: str
:ivar aml_dataset_data_flow:
:vartype aml_dataset_data_flow: str
:ivar system_data:
:vartype system_data: ~flow.models.SystemData
:ivar arm_id:
:vartype arm_id: str
:ivar asset_id:
:vartype asset_id: str
:ivar asset_uri:
:vartype asset_uri: str
:ivar asset_type:
:vartype asset_type: str
:ivar is_data_v2:
:vartype is_data_v2: bool
:ivar asset_scope_type: Possible values include: "Workspace", "Global", "All", "Feed".
:vartype asset_scope_type: str or ~flow.models.AssetScopeTypes
:ivar pipeline_run_id:
:vartype pipeline_run_id: str
:ivar module_node_id:
:vartype module_node_id: str
:ivar output_port_name:
:vartype output_port_name: str
"""
_attribute_map = {
'feed_name': {'key': 'feedName', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'data_source_type': {'key': 'dataSourceType', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'data_type_id': {'key': 'dataTypeId', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'modified_date': {'key': 'modifiedDate', 'type': 'iso-8601'},
'registered_by': {'key': 'registeredBy', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'created_by_studio': {'key': 'createdByStudio', 'type': 'bool'},
'data_reference_type': {'key': 'dataReferenceType', 'type': 'str'},
'dataset_type': {'key': 'datasetType', 'type': 'str'},
'saved_dataset_id': {'key': 'savedDatasetId', 'type': 'str'},
'dataset_version_id': {'key': 'datasetVersionId', 'type': 'str'},
'is_visible': {'key': 'isVisible', 'type': 'bool'},
'is_registered': {'key': 'isRegistered', 'type': 'bool'},
'properties': {'key': 'properties', 'type': '{object}'},
'connection_string': {'key': 'connectionString', 'type': 'str'},
'container_name': {'key': 'containerName', 'type': 'str'},
'data_storage_endpoint_uri': {'key': 'dataStorageEndpointUri', 'type': 'str'},
'workspace_sai_token': {'key': 'workspaceSaiToken', 'type': 'str'},
'aml_dataset_data_flow': {'key': 'amlDatasetDataFlow', 'type': 'str'},
'system_data': {'key': 'systemData', 'type': 'SystemData'},
'arm_id': {'key': 'armId', 'type': 'str'},
'asset_id': {'key': 'assetId', 'type': 'str'},
'asset_uri': {'key': 'assetUri', 'type': 'str'},
'asset_type': {'key': 'assetType', 'type': 'str'},
'is_data_v2': {'key': 'isDataV2', 'type': 'bool'},
'asset_scope_type': {'key': 'assetScopeType', 'type': 'str'},
'pipeline_run_id': {'key': 'pipelineRunId', 'type': 'str'},
'module_node_id': {'key': 'moduleNodeId', 'type': 'str'},
'output_port_name': {'key': 'outputPortName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword feed_name:
:paramtype feed_name: str
:keyword id:
:paramtype id: str
:keyword data_source_type: Possible values include: "None", "PipelineDataSource", "AmlDataset",
"GlobalDataset", "FeedModel", "FeedDataset", "AmlDataVersion", "AMLModelVersion".
:paramtype data_source_type: str or ~flow.models.DataSourceType
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword data_type_id:
:paramtype data_type_id: str
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
:keyword relative_path:
:paramtype relative_path: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword modified_date:
:paramtype modified_date: ~datetime.datetime
:keyword registered_by:
:paramtype registered_by: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword created_by_studio:
:paramtype created_by_studio: bool
:keyword data_reference_type: Possible values include: "None", "AzureBlob", "AzureDataLake",
"AzureFiles", "AzureSqlDatabase", "AzurePostgresDatabase", "AzureDataLakeGen2", "DBFS",
"AzureMySqlDatabase", "Custom", "Hdfs".
:paramtype data_reference_type: str or ~flow.models.DataReferenceType
:keyword dataset_type:
:paramtype dataset_type: str
:keyword saved_dataset_id:
:paramtype saved_dataset_id: str
:keyword dataset_version_id:
:paramtype dataset_version_id: str
:keyword is_visible:
:paramtype is_visible: bool
:keyword is_registered:
:paramtype is_registered: bool
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, any]
:keyword connection_string:
:paramtype connection_string: str
:keyword container_name:
:paramtype container_name: str
:keyword data_storage_endpoint_uri:
:paramtype data_storage_endpoint_uri: str
:keyword workspace_sai_token:
:paramtype workspace_sai_token: str
:keyword aml_dataset_data_flow:
:paramtype aml_dataset_data_flow: str
:keyword system_data:
:paramtype system_data: ~flow.models.SystemData
:keyword arm_id:
:paramtype arm_id: str
:keyword asset_id:
:paramtype asset_id: str
:keyword asset_uri:
:paramtype asset_uri: str
:keyword asset_type:
:paramtype asset_type: str
:keyword is_data_v2:
:paramtype is_data_v2: bool
:keyword asset_scope_type: Possible values include: "Workspace", "Global", "All", "Feed".
:paramtype asset_scope_type: str or ~flow.models.AssetScopeTypes
:keyword pipeline_run_id:
:paramtype pipeline_run_id: str
:keyword module_node_id:
:paramtype module_node_id: str
:keyword output_port_name:
:paramtype output_port_name: str
"""
super(DataInfo, self).__init__(**kwargs)
self.feed_name = kwargs.get('feed_name', None)
self.id = kwargs.get('id', None)
self.data_source_type = kwargs.get('data_source_type', None)
self.name = kwargs.get('name', None)
self.description = kwargs.get('description', None)
self.data_type_id = kwargs.get('data_type_id', None)
self.aml_data_store_name = kwargs.get('aml_data_store_name', None)
self.relative_path = kwargs.get('relative_path', None)
self.created_date = kwargs.get('created_date', None)
self.modified_date = kwargs.get('modified_date', None)
self.registered_by = kwargs.get('registered_by', None)
self.tags = kwargs.get('tags', None)
self.created_by_studio = kwargs.get('created_by_studio', None)
self.data_reference_type = kwargs.get('data_reference_type', None)
self.dataset_type = kwargs.get('dataset_type', None)
self.saved_dataset_id = kwargs.get('saved_dataset_id', None)
self.dataset_version_id = kwargs.get('dataset_version_id', None)
self.is_visible = kwargs.get('is_visible', None)
self.is_registered = kwargs.get('is_registered', None)
self.properties = kwargs.get('properties', None)
self.connection_string = kwargs.get('connection_string', None)
self.container_name = kwargs.get('container_name', None)
self.data_storage_endpoint_uri = kwargs.get('data_storage_endpoint_uri', None)
self.workspace_sai_token = kwargs.get('workspace_sai_token', None)
self.aml_dataset_data_flow = kwargs.get('aml_dataset_data_flow', None)
self.system_data = kwargs.get('system_data', None)
self.arm_id = kwargs.get('arm_id', None)
self.asset_id = kwargs.get('asset_id', None)
self.asset_uri = kwargs.get('asset_uri', None)
self.asset_type = kwargs.get('asset_type', None)
self.is_data_v2 = kwargs.get('is_data_v2', None)
self.asset_scope_type = kwargs.get('asset_scope_type', None)
self.pipeline_run_id = kwargs.get('pipeline_run_id', None)
self.module_node_id = kwargs.get('module_node_id', None)
self.output_port_name = kwargs.get('output_port_name', None)
class DataLocation(msrest.serialization.Model):
"""DataLocation.
:ivar storage_type: Possible values include: "None", "AzureBlob", "Artifact", "Snapshot",
"SavedAmlDataset", "Asset".
:vartype storage_type: str or ~flow.models.DataLocationStorageType
:ivar storage_id:
:vartype storage_id: str
:ivar uri:
:vartype uri: str
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_reference:
:vartype data_reference: ~flow.models.DataReference
:ivar aml_dataset:
:vartype aml_dataset: ~flow.models.AmlDataset
:ivar asset_definition:
:vartype asset_definition: ~flow.models.AssetDefinition
"""
_attribute_map = {
'storage_type': {'key': 'storageType', 'type': 'str'},
'storage_id': {'key': 'storageId', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_reference': {'key': 'dataReference', 'type': 'DataReference'},
'aml_dataset': {'key': 'amlDataset', 'type': 'AmlDataset'},
'asset_definition': {'key': 'assetDefinition', 'type': 'AssetDefinition'},
}
def __init__(
self,
**kwargs
):
"""
:keyword storage_type: Possible values include: "None", "AzureBlob", "Artifact", "Snapshot",
"SavedAmlDataset", "Asset".
:paramtype storage_type: str or ~flow.models.DataLocationStorageType
:keyword storage_id:
:paramtype storage_id: str
:keyword uri:
:paramtype uri: str
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_reference:
:paramtype data_reference: ~flow.models.DataReference
:keyword aml_dataset:
:paramtype aml_dataset: ~flow.models.AmlDataset
:keyword asset_definition:
:paramtype asset_definition: ~flow.models.AssetDefinition
"""
super(DataLocation, self).__init__(**kwargs)
self.storage_type = kwargs.get('storage_type', None)
self.storage_id = kwargs.get('storage_id', None)
self.uri = kwargs.get('uri', None)
self.data_store_name = kwargs.get('data_store_name', None)
self.data_reference = kwargs.get('data_reference', None)
self.aml_dataset = kwargs.get('aml_dataset', None)
self.asset_definition = kwargs.get('asset_definition', None)
class DataPath(msrest.serialization.Model):
"""DataPath.
:ivar data_store_name:
:vartype data_store_name: str
:ivar relative_path:
:vartype relative_path: str
:ivar sql_data_path:
:vartype sql_data_path: ~flow.models.SqlDataPath
"""
_attribute_map = {
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'sql_data_path': {'key': 'sqlDataPath', 'type': 'SqlDataPath'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_store_name:
:paramtype data_store_name: str
:keyword relative_path:
:paramtype relative_path: str
:keyword sql_data_path:
:paramtype sql_data_path: ~flow.models.SqlDataPath
"""
super(DataPath, self).__init__(**kwargs)
self.data_store_name = kwargs.get('data_store_name', None)
self.relative_path = kwargs.get('relative_path', None)
self.sql_data_path = kwargs.get('sql_data_path', None)
class DataPathParameter(msrest.serialization.Model):
"""DataPathParameter.
:ivar name:
:vartype name: str
:ivar documentation:
:vartype documentation: str
:ivar default_value:
:vartype default_value: ~flow.models.LegacyDataPath
:ivar is_optional:
:vartype is_optional: bool
:ivar data_type_id:
:vartype data_type_id: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'documentation': {'key': 'documentation', 'type': 'str'},
'default_value': {'key': 'defaultValue', 'type': 'LegacyDataPath'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
'data_type_id': {'key': 'dataTypeId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword documentation:
:paramtype documentation: str
:keyword default_value:
:paramtype default_value: ~flow.models.LegacyDataPath
:keyword is_optional:
:paramtype is_optional: bool
:keyword data_type_id:
:paramtype data_type_id: str
"""
super(DataPathParameter, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.documentation = kwargs.get('documentation', None)
self.default_value = kwargs.get('default_value', None)
self.is_optional = kwargs.get('is_optional', None)
self.data_type_id = kwargs.get('data_type_id', None)
class DataPortDto(msrest.serialization.Model):
"""DataPortDto.
:ivar data_port_type: Possible values include: "Input", "Output".
:vartype data_port_type: str or ~flow.models.DataPortType
:ivar data_port_name:
:vartype data_port_name: str
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_store_intellectual_property_access_mode: Possible values include: "ReadOnly",
"ReadWrite".
:vartype data_store_intellectual_property_access_mode: str or
~flow.models.IntellectualPropertyAccessMode
:ivar data_store_intellectual_property_publisher:
:vartype data_store_intellectual_property_publisher: str
"""
_attribute_map = {
'data_port_type': {'key': 'dataPortType', 'type': 'str'},
'data_port_name': {'key': 'dataPortName', 'type': 'str'},
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_store_intellectual_property_access_mode': {'key': 'dataStoreIntellectualPropertyAccessMode', 'type': 'str'},
'data_store_intellectual_property_publisher': {'key': 'dataStoreIntellectualPropertyPublisher', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_port_type: Possible values include: "Input", "Output".
:paramtype data_port_type: str or ~flow.models.DataPortType
:keyword data_port_name:
:paramtype data_port_name: str
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_store_intellectual_property_access_mode: Possible values include: "ReadOnly",
"ReadWrite".
:paramtype data_store_intellectual_property_access_mode: str or
~flow.models.IntellectualPropertyAccessMode
:keyword data_store_intellectual_property_publisher:
:paramtype data_store_intellectual_property_publisher: str
"""
super(DataPortDto, self).__init__(**kwargs)
self.data_port_type = kwargs.get('data_port_type', None)
self.data_port_name = kwargs.get('data_port_name', None)
self.data_store_name = kwargs.get('data_store_name', None)
self.data_store_intellectual_property_access_mode = kwargs.get('data_store_intellectual_property_access_mode', None)
self.data_store_intellectual_property_publisher = kwargs.get('data_store_intellectual_property_publisher', None)
class DataReference(msrest.serialization.Model):
"""DataReference.
:ivar type: Possible values include: "None", "AzureBlob", "AzureDataLake", "AzureFiles",
"AzureSqlDatabase", "AzurePostgresDatabase", "AzureDataLakeGen2", "DBFS", "AzureMySqlDatabase",
"Custom", "Hdfs".
:vartype type: str or ~flow.models.DataReferenceType
:ivar azure_blob_reference:
:vartype azure_blob_reference: ~flow.models.AzureBlobReference
:ivar azure_data_lake_reference:
:vartype azure_data_lake_reference: ~flow.models.AzureDataLakeReference
:ivar azure_files_reference:
:vartype azure_files_reference: ~flow.models.AzureFilesReference
:ivar azure_sql_database_reference:
:vartype azure_sql_database_reference: ~flow.models.AzureDatabaseReference
:ivar azure_postgres_database_reference:
:vartype azure_postgres_database_reference: ~flow.models.AzureDatabaseReference
:ivar azure_data_lake_gen2_reference:
:vartype azure_data_lake_gen2_reference: ~flow.models.AzureDataLakeGen2Reference
:ivar dbfs_reference:
:vartype dbfs_reference: ~flow.models.DBFSReference
:ivar azure_my_sql_database_reference:
:vartype azure_my_sql_database_reference: ~flow.models.AzureDatabaseReference
:ivar custom_reference:
:vartype custom_reference: ~flow.models.CustomReference
:ivar hdfs_reference:
:vartype hdfs_reference: ~flow.models.HdfsReference
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'azure_blob_reference': {'key': 'azureBlobReference', 'type': 'AzureBlobReference'},
'azure_data_lake_reference': {'key': 'azureDataLakeReference', 'type': 'AzureDataLakeReference'},
'azure_files_reference': {'key': 'azureFilesReference', 'type': 'AzureFilesReference'},
'azure_sql_database_reference': {'key': 'azureSqlDatabaseReference', 'type': 'AzureDatabaseReference'},
'azure_postgres_database_reference': {'key': 'azurePostgresDatabaseReference', 'type': 'AzureDatabaseReference'},
'azure_data_lake_gen2_reference': {'key': 'azureDataLakeGen2Reference', 'type': 'AzureDataLakeGen2Reference'},
'dbfs_reference': {'key': 'dbfsReference', 'type': 'DBFSReference'},
'azure_my_sql_database_reference': {'key': 'azureMySqlDatabaseReference', 'type': 'AzureDatabaseReference'},
'custom_reference': {'key': 'customReference', 'type': 'CustomReference'},
'hdfs_reference': {'key': 'hdfsReference', 'type': 'HdfsReference'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type: Possible values include: "None", "AzureBlob", "AzureDataLake", "AzureFiles",
"AzureSqlDatabase", "AzurePostgresDatabase", "AzureDataLakeGen2", "DBFS", "AzureMySqlDatabase",
"Custom", "Hdfs".
:paramtype type: str or ~flow.models.DataReferenceType
:keyword azure_blob_reference:
:paramtype azure_blob_reference: ~flow.models.AzureBlobReference
:keyword azure_data_lake_reference:
:paramtype azure_data_lake_reference: ~flow.models.AzureDataLakeReference
:keyword azure_files_reference:
:paramtype azure_files_reference: ~flow.models.AzureFilesReference
:keyword azure_sql_database_reference:
:paramtype azure_sql_database_reference: ~flow.models.AzureDatabaseReference
:keyword azure_postgres_database_reference:
:paramtype azure_postgres_database_reference: ~flow.models.AzureDatabaseReference
:keyword azure_data_lake_gen2_reference:
:paramtype azure_data_lake_gen2_reference: ~flow.models.AzureDataLakeGen2Reference
:keyword dbfs_reference:
:paramtype dbfs_reference: ~flow.models.DBFSReference
:keyword azure_my_sql_database_reference:
:paramtype azure_my_sql_database_reference: ~flow.models.AzureDatabaseReference
:keyword custom_reference:
:paramtype custom_reference: ~flow.models.CustomReference
:keyword hdfs_reference:
:paramtype hdfs_reference: ~flow.models.HdfsReference
"""
super(DataReference, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.azure_blob_reference = kwargs.get('azure_blob_reference', None)
self.azure_data_lake_reference = kwargs.get('azure_data_lake_reference', None)
self.azure_files_reference = kwargs.get('azure_files_reference', None)
self.azure_sql_database_reference = kwargs.get('azure_sql_database_reference', None)
self.azure_postgres_database_reference = kwargs.get('azure_postgres_database_reference', None)
self.azure_data_lake_gen2_reference = kwargs.get('azure_data_lake_gen2_reference', None)
self.dbfs_reference = kwargs.get('dbfs_reference', None)
self.azure_my_sql_database_reference = kwargs.get('azure_my_sql_database_reference', None)
self.custom_reference = kwargs.get('custom_reference', None)
self.hdfs_reference = kwargs.get('hdfs_reference', None)
class DataReferenceConfiguration(msrest.serialization.Model):
"""DataReferenceConfiguration.
:ivar data_store_name:
:vartype data_store_name: str
:ivar mode: Possible values include: "Mount", "Download", "Upload".
:vartype mode: str or ~flow.models.DataStoreMode
:ivar path_on_data_store:
:vartype path_on_data_store: str
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar overwrite:
:vartype overwrite: bool
"""
_attribute_map = {
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'mode': {'key': 'mode', 'type': 'str'},
'path_on_data_store': {'key': 'pathOnDataStore', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'overwrite': {'key': 'overwrite', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_store_name:
:paramtype data_store_name: str
:keyword mode: Possible values include: "Mount", "Download", "Upload".
:paramtype mode: str or ~flow.models.DataStoreMode
:keyword path_on_data_store:
:paramtype path_on_data_store: str
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword overwrite:
:paramtype overwrite: bool
"""
super(DataReferenceConfiguration, self).__init__(**kwargs)
self.data_store_name = kwargs.get('data_store_name', None)
self.mode = kwargs.get('mode', None)
self.path_on_data_store = kwargs.get('path_on_data_store', None)
self.path_on_compute = kwargs.get('path_on_compute', None)
self.overwrite = kwargs.get('overwrite', None)
class DataSetDefinition(msrest.serialization.Model):
"""DataSetDefinition.
:ivar data_type_short_name:
:vartype data_type_short_name: str
:ivar parameter_name:
:vartype parameter_name: str
:ivar value:
:vartype value: ~flow.models.DataSetDefinitionValue
"""
_attribute_map = {
'data_type_short_name': {'key': 'dataTypeShortName', 'type': 'str'},
'parameter_name': {'key': 'parameterName', 'type': 'str'},
'value': {'key': 'value', 'type': 'DataSetDefinitionValue'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_type_short_name:
:paramtype data_type_short_name: str
:keyword parameter_name:
:paramtype parameter_name: str
:keyword value:
:paramtype value: ~flow.models.DataSetDefinitionValue
"""
super(DataSetDefinition, self).__init__(**kwargs)
self.data_type_short_name = kwargs.get('data_type_short_name', None)
self.parameter_name = kwargs.get('parameter_name', None)
self.value = kwargs.get('value', None)
class DataSetDefinitionValue(msrest.serialization.Model):
"""DataSetDefinitionValue.
:ivar literal_value:
:vartype literal_value: ~flow.models.DataPath
:ivar data_set_reference:
:vartype data_set_reference: ~flow.models.RegisteredDataSetReference
:ivar saved_data_set_reference:
:vartype saved_data_set_reference: ~flow.models.SavedDataSetReference
:ivar asset_definition:
:vartype asset_definition: ~flow.models.AssetDefinition
"""
_attribute_map = {
'literal_value': {'key': 'literalValue', 'type': 'DataPath'},
'data_set_reference': {'key': 'dataSetReference', 'type': 'RegisteredDataSetReference'},
'saved_data_set_reference': {'key': 'savedDataSetReference', 'type': 'SavedDataSetReference'},
'asset_definition': {'key': 'assetDefinition', 'type': 'AssetDefinition'},
}
def __init__(
self,
**kwargs
):
"""
:keyword literal_value:
:paramtype literal_value: ~flow.models.DataPath
:keyword data_set_reference:
:paramtype data_set_reference: ~flow.models.RegisteredDataSetReference
:keyword saved_data_set_reference:
:paramtype saved_data_set_reference: ~flow.models.SavedDataSetReference
:keyword asset_definition:
:paramtype asset_definition: ~flow.models.AssetDefinition
"""
super(DataSetDefinitionValue, self).__init__(**kwargs)
self.literal_value = kwargs.get('literal_value', None)
self.data_set_reference = kwargs.get('data_set_reference', None)
self.saved_data_set_reference = kwargs.get('saved_data_set_reference', None)
self.asset_definition = kwargs.get('asset_definition', None)
class DatasetIdentifier(msrest.serialization.Model):
"""DatasetIdentifier.
:ivar saved_id:
:vartype saved_id: str
:ivar registered_id:
:vartype registered_id: str
:ivar registered_version:
:vartype registered_version: str
"""
_attribute_map = {
'saved_id': {'key': 'savedId', 'type': 'str'},
'registered_id': {'key': 'registeredId', 'type': 'str'},
'registered_version': {'key': 'registeredVersion', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword saved_id:
:paramtype saved_id: str
:keyword registered_id:
:paramtype registered_id: str
:keyword registered_version:
:paramtype registered_version: str
"""
super(DatasetIdentifier, self).__init__(**kwargs)
self.saved_id = kwargs.get('saved_id', None)
self.registered_id = kwargs.get('registered_id', None)
self.registered_version = kwargs.get('registered_version', None)
class DatasetInputDetails(msrest.serialization.Model):
"""DatasetInputDetails.
:ivar input_name:
:vartype input_name: str
:ivar mechanism: Possible values include: "Direct", "Mount", "Download", "Hdfs".
:vartype mechanism: str or ~flow.models.DatasetDeliveryMechanism
:ivar path_on_compute:
:vartype path_on_compute: str
"""
_attribute_map = {
'input_name': {'key': 'inputName', 'type': 'str'},
'mechanism': {'key': 'mechanism', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword input_name:
:paramtype input_name: str
:keyword mechanism: Possible values include: "Direct", "Mount", "Download", "Hdfs".
:paramtype mechanism: str or ~flow.models.DatasetDeliveryMechanism
:keyword path_on_compute:
:paramtype path_on_compute: str
"""
super(DatasetInputDetails, self).__init__(**kwargs)
self.input_name = kwargs.get('input_name', None)
self.mechanism = kwargs.get('mechanism', None)
self.path_on_compute = kwargs.get('path_on_compute', None)
class DatasetLineage(msrest.serialization.Model):
"""DatasetLineage.
:ivar identifier:
:vartype identifier: ~flow.models.DatasetIdentifier
:ivar consumption_type: Possible values include: "RunInput", "Reference".
:vartype consumption_type: str or ~flow.models.DatasetConsumptionType
:ivar input_details:
:vartype input_details: ~flow.models.DatasetInputDetails
"""
_attribute_map = {
'identifier': {'key': 'identifier', 'type': 'DatasetIdentifier'},
'consumption_type': {'key': 'consumptionType', 'type': 'str'},
'input_details': {'key': 'inputDetails', 'type': 'DatasetInputDetails'},
}
def __init__(
self,
**kwargs
):
"""
:keyword identifier:
:paramtype identifier: ~flow.models.DatasetIdentifier
:keyword consumption_type: Possible values include: "RunInput", "Reference".
:paramtype consumption_type: str or ~flow.models.DatasetConsumptionType
:keyword input_details:
:paramtype input_details: ~flow.models.DatasetInputDetails
"""
super(DatasetLineage, self).__init__(**kwargs)
self.identifier = kwargs.get('identifier', None)
self.consumption_type = kwargs.get('consumption_type', None)
self.input_details = kwargs.get('input_details', None)
class DatasetOutput(msrest.serialization.Model):
"""DatasetOutput.
:ivar dataset_type: Possible values include: "File", "Tabular".
:vartype dataset_type: str or ~flow.models.DatasetType
:ivar dataset_registration:
:vartype dataset_registration: ~flow.models.DatasetRegistration
:ivar dataset_output_options:
:vartype dataset_output_options: ~flow.models.DatasetOutputOptions
"""
_attribute_map = {
'dataset_type': {'key': 'datasetType', 'type': 'str'},
'dataset_registration': {'key': 'datasetRegistration', 'type': 'DatasetRegistration'},
'dataset_output_options': {'key': 'datasetOutputOptions', 'type': 'DatasetOutputOptions'},
}
def __init__(
self,
**kwargs
):
"""
:keyword dataset_type: Possible values include: "File", "Tabular".
:paramtype dataset_type: str or ~flow.models.DatasetType
:keyword dataset_registration:
:paramtype dataset_registration: ~flow.models.DatasetRegistration
:keyword dataset_output_options:
:paramtype dataset_output_options: ~flow.models.DatasetOutputOptions
"""
super(DatasetOutput, self).__init__(**kwargs)
self.dataset_type = kwargs.get('dataset_type', None)
self.dataset_registration = kwargs.get('dataset_registration', None)
self.dataset_output_options = kwargs.get('dataset_output_options', None)
class DatasetOutputDetails(msrest.serialization.Model):
"""DatasetOutputDetails.
:ivar output_name:
:vartype output_name: str
"""
_attribute_map = {
'output_name': {'key': 'outputName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword output_name:
:paramtype output_name: str
"""
super(DatasetOutputDetails, self).__init__(**kwargs)
self.output_name = kwargs.get('output_name', None)
class DatasetOutputOptions(msrest.serialization.Model):
"""DatasetOutputOptions.
:ivar source_globs:
:vartype source_globs: ~flow.models.GlobsOptions
:ivar path_on_datastore:
:vartype path_on_datastore: str
:ivar path_on_datastore_parameter_assignment:
:vartype path_on_datastore_parameter_assignment: ~flow.models.ParameterAssignment
"""
_attribute_map = {
'source_globs': {'key': 'sourceGlobs', 'type': 'GlobsOptions'},
'path_on_datastore': {'key': 'pathOnDatastore', 'type': 'str'},
'path_on_datastore_parameter_assignment': {'key': 'PathOnDatastoreParameterAssignment', 'type': 'ParameterAssignment'},
}
def __init__(
self,
**kwargs
):
"""
:keyword source_globs:
:paramtype source_globs: ~flow.models.GlobsOptions
:keyword path_on_datastore:
:paramtype path_on_datastore: str
:keyword path_on_datastore_parameter_assignment:
:paramtype path_on_datastore_parameter_assignment: ~flow.models.ParameterAssignment
"""
super(DatasetOutputOptions, self).__init__(**kwargs)
self.source_globs = kwargs.get('source_globs', None)
self.path_on_datastore = kwargs.get('path_on_datastore', None)
self.path_on_datastore_parameter_assignment = kwargs.get('path_on_datastore_parameter_assignment', None)
class DataSetPathParameter(msrest.serialization.Model):
"""DataSetPathParameter.
:ivar name:
:vartype name: str
:ivar documentation:
:vartype documentation: str
:ivar default_value:
:vartype default_value: ~flow.models.DataSetDefinitionValue
:ivar is_optional:
:vartype is_optional: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'documentation': {'key': 'documentation', 'type': 'str'},
'default_value': {'key': 'defaultValue', 'type': 'DataSetDefinitionValue'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword documentation:
:paramtype documentation: str
:keyword default_value:
:paramtype default_value: ~flow.models.DataSetDefinitionValue
:keyword is_optional:
:paramtype is_optional: bool
"""
super(DataSetPathParameter, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.documentation = kwargs.get('documentation', None)
self.default_value = kwargs.get('default_value', None)
self.is_optional = kwargs.get('is_optional', None)
class DatasetRegistration(msrest.serialization.Model):
"""DatasetRegistration.
:ivar name:
:vartype name: str
:ivar create_new_version:
:vartype create_new_version: bool
:ivar description:
:vartype description: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar additional_transformations:
:vartype additional_transformations: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'create_new_version': {'key': 'createNewVersion', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'additional_transformations': {'key': 'additionalTransformations', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword create_new_version:
:paramtype create_new_version: bool
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword additional_transformations:
:paramtype additional_transformations: str
"""
super(DatasetRegistration, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.create_new_version = kwargs.get('create_new_version', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.additional_transformations = kwargs.get('additional_transformations', None)
class DatasetRegistrationOptions(msrest.serialization.Model):
"""DatasetRegistrationOptions.
:ivar additional_transformation:
:vartype additional_transformation: str
"""
_attribute_map = {
'additional_transformation': {'key': 'additionalTransformation', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword additional_transformation:
:paramtype additional_transformation: str
"""
super(DatasetRegistrationOptions, self).__init__(**kwargs)
self.additional_transformation = kwargs.get('additional_transformation', None)
class DataSettings(msrest.serialization.Model):
"""DataSettings.
:ivar target_column_name:
:vartype target_column_name: str
:ivar weight_column_name:
:vartype weight_column_name: str
:ivar positive_label:
:vartype positive_label: str
:ivar validation_data:
:vartype validation_data: ~flow.models.ValidationDataSettings
:ivar test_data:
:vartype test_data: ~flow.models.TestDataSettings
"""
_attribute_map = {
'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
'weight_column_name': {'key': 'weightColumnName', 'type': 'str'},
'positive_label': {'key': 'positiveLabel', 'type': 'str'},
'validation_data': {'key': 'validationData', 'type': 'ValidationDataSettings'},
'test_data': {'key': 'testData', 'type': 'TestDataSettings'},
}
def __init__(
self,
**kwargs
):
"""
:keyword target_column_name:
:paramtype target_column_name: str
:keyword weight_column_name:
:paramtype weight_column_name: str
:keyword positive_label:
:paramtype positive_label: str
:keyword validation_data:
:paramtype validation_data: ~flow.models.ValidationDataSettings
:keyword test_data:
:paramtype test_data: ~flow.models.TestDataSettings
"""
super(DataSettings, self).__init__(**kwargs)
self.target_column_name = kwargs.get('target_column_name', None)
self.weight_column_name = kwargs.get('weight_column_name', None)
self.positive_label = kwargs.get('positive_label', None)
self.validation_data = kwargs.get('validation_data', None)
self.test_data = kwargs.get('test_data', None)
class DatastoreSetting(msrest.serialization.Model):
"""DatastoreSetting.
:ivar data_store_name:
:vartype data_store_name: str
"""
_attribute_map = {
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_store_name:
:paramtype data_store_name: str
"""
super(DatastoreSetting, self).__init__(**kwargs)
self.data_store_name = kwargs.get('data_store_name', None)
class DataTransferCloudConfiguration(msrest.serialization.Model):
"""DataTransferCloudConfiguration.
:ivar allow_overwrite:
:vartype allow_overwrite: bool
"""
_attribute_map = {
'allow_overwrite': {'key': 'AllowOverwrite', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword allow_overwrite:
:paramtype allow_overwrite: bool
"""
super(DataTransferCloudConfiguration, self).__init__(**kwargs)
self.allow_overwrite = kwargs.get('allow_overwrite', None)
class DataTransferSink(msrest.serialization.Model):
"""DataTransferSink.
:ivar type: Possible values include: "DataBase", "FileSystem".
:vartype type: str or ~flow.models.DataTransferStorageType
:ivar file_system:
:vartype file_system: ~flow.models.FileSystem
:ivar database_sink:
:vartype database_sink: ~flow.models.DatabaseSink
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'file_system': {'key': 'fileSystem', 'type': 'FileSystem'},
'database_sink': {'key': 'databaseSink', 'type': 'DatabaseSink'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type: Possible values include: "DataBase", "FileSystem".
:paramtype type: str or ~flow.models.DataTransferStorageType
:keyword file_system:
:paramtype file_system: ~flow.models.FileSystem
:keyword database_sink:
:paramtype database_sink: ~flow.models.DatabaseSink
"""
super(DataTransferSink, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.file_system = kwargs.get('file_system', None)
self.database_sink = kwargs.get('database_sink', None)
class DataTransferSource(msrest.serialization.Model):
"""DataTransferSource.
:ivar type: Possible values include: "DataBase", "FileSystem".
:vartype type: str or ~flow.models.DataTransferStorageType
:ivar file_system:
:vartype file_system: ~flow.models.FileSystem
:ivar database_source:
:vartype database_source: ~flow.models.DatabaseSource
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'file_system': {'key': 'fileSystem', 'type': 'FileSystem'},
'database_source': {'key': 'databaseSource', 'type': 'DatabaseSource'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type: Possible values include: "DataBase", "FileSystem".
:paramtype type: str or ~flow.models.DataTransferStorageType
:keyword file_system:
:paramtype file_system: ~flow.models.FileSystem
:keyword database_source:
:paramtype database_source: ~flow.models.DatabaseSource
"""
super(DataTransferSource, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.file_system = kwargs.get('file_system', None)
self.database_source = kwargs.get('database_source', None)
class DataTransferV2CloudSetting(msrest.serialization.Model):
"""DataTransferV2CloudSetting.
:ivar task_type: Possible values include: "ImportData", "ExportData", "CopyData".
:vartype task_type: str or ~flow.models.DataTransferTaskType
:ivar compute_name:
:vartype compute_name: str
:ivar copy_data_task:
:vartype copy_data_task: ~flow.models.CopyDataTask
:ivar import_data_task:
:vartype import_data_task: ~flow.models.ImportDataTask
:ivar export_data_task:
:vartype export_data_task: ~flow.models.ExportDataTask
:ivar data_transfer_sources: This is a dictionary.
:vartype data_transfer_sources: dict[str, ~flow.models.DataTransferSource]
:ivar data_transfer_sinks: This is a dictionary.
:vartype data_transfer_sinks: dict[str, ~flow.models.DataTransferSink]
:ivar data_copy_mode: Possible values include: "MergeWithOverwrite", "FailIfConflict".
:vartype data_copy_mode: str or ~flow.models.DataCopyMode
"""
_attribute_map = {
'task_type': {'key': 'taskType', 'type': 'str'},
'compute_name': {'key': 'ComputeName', 'type': 'str'},
'copy_data_task': {'key': 'CopyDataTask', 'type': 'CopyDataTask'},
'import_data_task': {'key': 'ImportDataTask', 'type': 'ImportDataTask'},
'export_data_task': {'key': 'ExportDataTask', 'type': 'ExportDataTask'},
'data_transfer_sources': {'key': 'DataTransferSources', 'type': '{DataTransferSource}'},
'data_transfer_sinks': {'key': 'DataTransferSinks', 'type': '{DataTransferSink}'},
'data_copy_mode': {'key': 'DataCopyMode', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword task_type: Possible values include: "ImportData", "ExportData", "CopyData".
:paramtype task_type: str or ~flow.models.DataTransferTaskType
:keyword compute_name:
:paramtype compute_name: str
:keyword copy_data_task:
:paramtype copy_data_task: ~flow.models.CopyDataTask
:keyword import_data_task:
:paramtype import_data_task: ~flow.models.ImportDataTask
:keyword export_data_task:
:paramtype export_data_task: ~flow.models.ExportDataTask
:keyword data_transfer_sources: This is a dictionary.
:paramtype data_transfer_sources: dict[str, ~flow.models.DataTransferSource]
:keyword data_transfer_sinks: This is a dictionary.
:paramtype data_transfer_sinks: dict[str, ~flow.models.DataTransferSink]
:keyword data_copy_mode: Possible values include: "MergeWithOverwrite", "FailIfConflict".
:paramtype data_copy_mode: str or ~flow.models.DataCopyMode
"""
super(DataTransferV2CloudSetting, self).__init__(**kwargs)
self.task_type = kwargs.get('task_type', None)
self.compute_name = kwargs.get('compute_name', None)
self.copy_data_task = kwargs.get('copy_data_task', None)
self.import_data_task = kwargs.get('import_data_task', None)
self.export_data_task = kwargs.get('export_data_task', None)
self.data_transfer_sources = kwargs.get('data_transfer_sources', None)
self.data_transfer_sinks = kwargs.get('data_transfer_sinks', None)
self.data_copy_mode = kwargs.get('data_copy_mode', None)
class DataTypeCreationInfo(msrest.serialization.Model):
"""DataTypeCreationInfo.
:ivar id:
:vartype id: str
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar is_directory:
:vartype is_directory: bool
:ivar file_extension:
:vartype file_extension: str
:ivar parent_data_type_ids:
:vartype parent_data_type_ids: list[str]
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'is_directory': {'key': 'isDirectory', 'type': 'bool'},
'file_extension': {'key': 'fileExtension', 'type': 'str'},
'parent_data_type_ids': {'key': 'parentDataTypeIds', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword is_directory:
:paramtype is_directory: bool
:keyword file_extension:
:paramtype file_extension: str
:keyword parent_data_type_ids:
:paramtype parent_data_type_ids: list[str]
"""
super(DataTypeCreationInfo, self).__init__(**kwargs)
self.id = kwargs.get('id', None)
self.name = kwargs.get('name', None)
self.description = kwargs.get('description', None)
self.is_directory = kwargs.get('is_directory', None)
self.file_extension = kwargs.get('file_extension', None)
self.parent_data_type_ids = kwargs.get('parent_data_type_ids', None)
class DBFSReference(msrest.serialization.Model):
"""DBFSReference.
:ivar relative_path:
:vartype relative_path: str
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'relative_path': {'key': 'relativePath', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword relative_path:
:paramtype relative_path: str
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(DBFSReference, self).__init__(**kwargs)
self.relative_path = kwargs.get('relative_path', None)
self.aml_data_store_name = kwargs.get('aml_data_store_name', None)
class DbfsStorageInfoDto(msrest.serialization.Model):
"""DbfsStorageInfoDto.
:ivar destination:
:vartype destination: str
"""
_attribute_map = {
'destination': {'key': 'destination', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword destination:
:paramtype destination: str
"""
super(DbfsStorageInfoDto, self).__init__(**kwargs)
self.destination = kwargs.get('destination', None)
class DebugInfoResponse(msrest.serialization.Model):
"""Internal debugging information not intended for external clients.
:ivar type: The type.
:vartype type: str
:ivar message: The message.
:vartype message: str
:ivar stack_trace: The stack trace.
:vartype stack_trace: str
:ivar inner_exception: Internal debugging information not intended for external clients.
:vartype inner_exception: ~flow.models.DebugInfoResponse
:ivar data: This is a dictionary.
:vartype data: dict[str, any]
:ivar error_response: The error response.
:vartype error_response: ~flow.models.ErrorResponse
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
'stack_trace': {'key': 'stackTrace', 'type': 'str'},
'inner_exception': {'key': 'innerException', 'type': 'DebugInfoResponse'},
'data': {'key': 'data', 'type': '{object}'},
'error_response': {'key': 'errorResponse', 'type': 'ErrorResponse'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type: The type.
:paramtype type: str
:keyword message: The message.
:paramtype message: str
:keyword stack_trace: The stack trace.
:paramtype stack_trace: str
:keyword inner_exception: Internal debugging information not intended for external clients.
:paramtype inner_exception: ~flow.models.DebugInfoResponse
:keyword data: This is a dictionary.
:paramtype data: dict[str, any]
:keyword error_response: The error response.
:paramtype error_response: ~flow.models.ErrorResponse
"""
super(DebugInfoResponse, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.message = kwargs.get('message', None)
self.stack_trace = kwargs.get('stack_trace', None)
self.inner_exception = kwargs.get('inner_exception', None)
self.data = kwargs.get('data', None)
self.error_response = kwargs.get('error_response', None)
class DeployFlowRequest(msrest.serialization.Model):
"""DeployFlowRequest.
:ivar source_resource_id:
:vartype source_resource_id: str
:ivar source_flow_run_id:
:vartype source_flow_run_id: str
:ivar source_flow_id:
:vartype source_flow_id: str
:ivar flow:
:vartype flow: ~flow.models.Flow
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar flow_submit_run_settings:
:vartype flow_submit_run_settings: ~flow.models.FlowSubmitRunSettings
:ivar output_names_included_in_endpoint_response:
:vartype output_names_included_in_endpoint_response: list[str]
:ivar endpoint_name:
:vartype endpoint_name: str
:ivar endpoint_description:
:vartype endpoint_description: str
:ivar auth_mode: Possible values include: "AMLToken", "Key", "AADToken".
:vartype auth_mode: str or ~flow.models.EndpointAuthMode
:ivar identity:
:vartype identity: ~flow.models.ManagedServiceIdentity
:ivar endpoint_tags: This is a dictionary.
:vartype endpoint_tags: dict[str, str]
:ivar connection_overrides:
:vartype connection_overrides: list[~flow.models.ConnectionOverrideSetting]
:ivar use_workspace_connection:
:vartype use_workspace_connection: bool
:ivar deployment_name:
:vartype deployment_name: str
:ivar environment:
:vartype environment: str
:ivar environment_variables: This is a dictionary.
:vartype environment_variables: dict[str, str]
:ivar deployment_tags: This is a dictionary.
:vartype deployment_tags: dict[str, str]
:ivar app_insights_enabled:
:vartype app_insights_enabled: bool
:ivar enable_model_data_collector:
:vartype enable_model_data_collector: bool
:ivar skip_update_traffic_to_full:
:vartype skip_update_traffic_to_full: bool
:ivar enable_streaming_response:
:vartype enable_streaming_response: bool
:ivar use_flow_snapshot_to_deploy:
:vartype use_flow_snapshot_to_deploy: bool
:ivar instance_type:
:vartype instance_type: str
:ivar instance_count:
:vartype instance_count: int
:ivar auto_grant_connection_permission:
:vartype auto_grant_connection_permission: bool
"""
_attribute_map = {
'source_resource_id': {'key': 'sourceResourceId', 'type': 'str'},
'source_flow_run_id': {'key': 'sourceFlowRunId', 'type': 'str'},
'source_flow_id': {'key': 'sourceFlowId', 'type': 'str'},
'flow': {'key': 'flow', 'type': 'Flow'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'flow_submit_run_settings': {'key': 'flowSubmitRunSettings', 'type': 'FlowSubmitRunSettings'},
'output_names_included_in_endpoint_response': {'key': 'outputNamesIncludedInEndpointResponse', 'type': '[str]'},
'endpoint_name': {'key': 'endpointName', 'type': 'str'},
'endpoint_description': {'key': 'endpointDescription', 'type': 'str'},
'auth_mode': {'key': 'authMode', 'type': 'str'},
'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
'endpoint_tags': {'key': 'endpointTags', 'type': '{str}'},
'connection_overrides': {'key': 'connectionOverrides', 'type': '[ConnectionOverrideSetting]'},
'use_workspace_connection': {'key': 'useWorkspaceConnection', 'type': 'bool'},
'deployment_name': {'key': 'deploymentName', 'type': 'str'},
'environment': {'key': 'environment', 'type': 'str'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'deployment_tags': {'key': 'deploymentTags', 'type': '{str}'},
'app_insights_enabled': {'key': 'appInsightsEnabled', 'type': 'bool'},
'enable_model_data_collector': {'key': 'enableModelDataCollector', 'type': 'bool'},
'skip_update_traffic_to_full': {'key': 'skipUpdateTrafficToFull', 'type': 'bool'},
'enable_streaming_response': {'key': 'enableStreamingResponse', 'type': 'bool'},
'use_flow_snapshot_to_deploy': {'key': 'useFlowSnapshotToDeploy', 'type': 'bool'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'instance_count': {'key': 'instanceCount', 'type': 'int'},
'auto_grant_connection_permission': {'key': 'autoGrantConnectionPermission', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword source_resource_id:
:paramtype source_resource_id: str
:keyword source_flow_run_id:
:paramtype source_flow_run_id: str
:keyword source_flow_id:
:paramtype source_flow_id: str
:keyword flow:
:paramtype flow: ~flow.models.Flow
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword flow_submit_run_settings:
:paramtype flow_submit_run_settings: ~flow.models.FlowSubmitRunSettings
:keyword output_names_included_in_endpoint_response:
:paramtype output_names_included_in_endpoint_response: list[str]
:keyword endpoint_name:
:paramtype endpoint_name: str
:keyword endpoint_description:
:paramtype endpoint_description: str
:keyword auth_mode: Possible values include: "AMLToken", "Key", "AADToken".
:paramtype auth_mode: str or ~flow.models.EndpointAuthMode
:keyword identity:
:paramtype identity: ~flow.models.ManagedServiceIdentity
:keyword endpoint_tags: This is a dictionary.
:paramtype endpoint_tags: dict[str, str]
:keyword connection_overrides:
:paramtype connection_overrides: list[~flow.models.ConnectionOverrideSetting]
:keyword use_workspace_connection:
:paramtype use_workspace_connection: bool
:keyword deployment_name:
:paramtype deployment_name: str
:keyword environment:
:paramtype environment: str
:keyword environment_variables: This is a dictionary.
:paramtype environment_variables: dict[str, str]
:keyword deployment_tags: This is a dictionary.
:paramtype deployment_tags: dict[str, str]
:keyword app_insights_enabled:
:paramtype app_insights_enabled: bool
:keyword enable_model_data_collector:
:paramtype enable_model_data_collector: bool
:keyword skip_update_traffic_to_full:
:paramtype skip_update_traffic_to_full: bool
:keyword enable_streaming_response:
:paramtype enable_streaming_response: bool
:keyword use_flow_snapshot_to_deploy:
:paramtype use_flow_snapshot_to_deploy: bool
:keyword instance_type:
:paramtype instance_type: str
:keyword instance_count:
:paramtype instance_count: int
:keyword auto_grant_connection_permission:
:paramtype auto_grant_connection_permission: bool
"""
super(DeployFlowRequest, self).__init__(**kwargs)
self.source_resource_id = kwargs.get('source_resource_id', None)
self.source_flow_run_id = kwargs.get('source_flow_run_id', None)
self.source_flow_id = kwargs.get('source_flow_id', None)
self.flow = kwargs.get('flow', None)
self.flow_type = kwargs.get('flow_type', None)
self.flow_submit_run_settings = kwargs.get('flow_submit_run_settings', None)
self.output_names_included_in_endpoint_response = kwargs.get('output_names_included_in_endpoint_response', None)
self.endpoint_name = kwargs.get('endpoint_name', None)
self.endpoint_description = kwargs.get('endpoint_description', None)
self.auth_mode = kwargs.get('auth_mode', None)
self.identity = kwargs.get('identity', None)
self.endpoint_tags = kwargs.get('endpoint_tags', None)
self.connection_overrides = kwargs.get('connection_overrides', None)
self.use_workspace_connection = kwargs.get('use_workspace_connection', None)
self.deployment_name = kwargs.get('deployment_name', None)
self.environment = kwargs.get('environment', None)
self.environment_variables = kwargs.get('environment_variables', None)
self.deployment_tags = kwargs.get('deployment_tags', None)
self.app_insights_enabled = kwargs.get('app_insights_enabled', None)
self.enable_model_data_collector = kwargs.get('enable_model_data_collector', None)
self.skip_update_traffic_to_full = kwargs.get('skip_update_traffic_to_full', None)
self.enable_streaming_response = kwargs.get('enable_streaming_response', None)
self.use_flow_snapshot_to_deploy = kwargs.get('use_flow_snapshot_to_deploy', None)
self.instance_type = kwargs.get('instance_type', None)
self.instance_count = kwargs.get('instance_count', None)
self.auto_grant_connection_permission = kwargs.get('auto_grant_connection_permission', None)
class DeploymentInfo(msrest.serialization.Model):
"""DeploymentInfo.
:ivar operation_id:
:vartype operation_id: str
:ivar service_id:
:vartype service_id: str
:ivar service_name:
:vartype service_name: str
:ivar status_detail:
:vartype status_detail: str
"""
_attribute_map = {
'operation_id': {'key': 'operationId', 'type': 'str'},
'service_id': {'key': 'serviceId', 'type': 'str'},
'service_name': {'key': 'serviceName', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword operation_id:
:paramtype operation_id: str
:keyword service_id:
:paramtype service_id: str
:keyword service_name:
:paramtype service_name: str
:keyword status_detail:
:paramtype status_detail: str
"""
super(DeploymentInfo, self).__init__(**kwargs)
self.operation_id = kwargs.get('operation_id', None)
self.service_id = kwargs.get('service_id', None)
self.service_name = kwargs.get('service_name', None)
self.status_detail = kwargs.get('status_detail', None)
class DistributionConfiguration(msrest.serialization.Model):
"""DistributionConfiguration.
:ivar distribution_type: Possible values include: "PyTorch", "TensorFlow", "Mpi", "Ray".
:vartype distribution_type: str or ~flow.models.DistributionType
"""
_attribute_map = {
'distribution_type': {'key': 'distributionType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword distribution_type: Possible values include: "PyTorch", "TensorFlow", "Mpi", "Ray".
:paramtype distribution_type: str or ~flow.models.DistributionType
"""
super(DistributionConfiguration, self).__init__(**kwargs)
self.distribution_type = kwargs.get('distribution_type', None)
class DistributionParameter(msrest.serialization.Model):
"""DistributionParameter.
:ivar name:
:vartype name: str
:ivar label:
:vartype label: str
:ivar description:
:vartype description: str
:ivar input_type: Possible values include: "Text", "Number".
:vartype input_type: str or ~flow.models.DistributionParameterEnum
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'input_type': {'key': 'inputType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword label:
:paramtype label: str
:keyword description:
:paramtype description: str
:keyword input_type: Possible values include: "Text", "Number".
:paramtype input_type: str or ~flow.models.DistributionParameterEnum
"""
super(DistributionParameter, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.label = kwargs.get('label', None)
self.description = kwargs.get('description', None)
self.input_type = kwargs.get('input_type', None)
class DockerBuildContext(msrest.serialization.Model):
"""DockerBuildContext.
:ivar location_type: Possible values include: "Git", "StorageAccount".
:vartype location_type: str or ~flow.models.BuildContextLocationType
:ivar location:
:vartype location: str
:ivar dockerfile_path:
:vartype dockerfile_path: str
"""
_attribute_map = {
'location_type': {'key': 'locationType', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
'dockerfile_path': {'key': 'dockerfilePath', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword location_type: Possible values include: "Git", "StorageAccount".
:paramtype location_type: str or ~flow.models.BuildContextLocationType
:keyword location:
:paramtype location: str
:keyword dockerfile_path:
:paramtype dockerfile_path: str
"""
super(DockerBuildContext, self).__init__(**kwargs)
self.location_type = kwargs.get('location_type', None)
self.location = kwargs.get('location', None)
self.dockerfile_path = kwargs.get('dockerfile_path', "Dockerfile")
class DockerConfiguration(msrest.serialization.Model):
"""DockerConfiguration.
:ivar use_docker:
:vartype use_docker: bool
:ivar shared_volumes:
:vartype shared_volumes: bool
:ivar arguments:
:vartype arguments: list[str]
"""
_attribute_map = {
'use_docker': {'key': 'useDocker', 'type': 'bool'},
'shared_volumes': {'key': 'sharedVolumes', 'type': 'bool'},
'arguments': {'key': 'arguments', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword use_docker:
:paramtype use_docker: bool
:keyword shared_volumes:
:paramtype shared_volumes: bool
:keyword arguments:
:paramtype arguments: list[str]
"""
super(DockerConfiguration, self).__init__(**kwargs)
self.use_docker = kwargs.get('use_docker', None)
self.shared_volumes = kwargs.get('shared_volumes', None)
self.arguments = kwargs.get('arguments', None)
class DockerImagePlatform(msrest.serialization.Model):
"""DockerImagePlatform.
:ivar os:
:vartype os: str
:ivar architecture:
:vartype architecture: str
"""
_attribute_map = {
'os': {'key': 'os', 'type': 'str'},
'architecture': {'key': 'architecture', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword os:
:paramtype os: str
:keyword architecture:
:paramtype architecture: str
"""
super(DockerImagePlatform, self).__init__(**kwargs)
self.os = kwargs.get('os', None)
self.architecture = kwargs.get('architecture', None)
class DockerSection(msrest.serialization.Model):
"""DockerSection.
:ivar base_image:
:vartype base_image: str
:ivar platform:
:vartype platform: ~flow.models.DockerImagePlatform
:ivar base_dockerfile:
:vartype base_dockerfile: str
:ivar build_context:
:vartype build_context: ~flow.models.DockerBuildContext
:ivar base_image_registry:
:vartype base_image_registry: ~flow.models.ContainerRegistry
"""
_attribute_map = {
'base_image': {'key': 'baseImage', 'type': 'str'},
'platform': {'key': 'platform', 'type': 'DockerImagePlatform'},
'base_dockerfile': {'key': 'baseDockerfile', 'type': 'str'},
'build_context': {'key': 'buildContext', 'type': 'DockerBuildContext'},
'base_image_registry': {'key': 'baseImageRegistry', 'type': 'ContainerRegistry'},
}
def __init__(
self,
**kwargs
):
"""
:keyword base_image:
:paramtype base_image: str
:keyword platform:
:paramtype platform: ~flow.models.DockerImagePlatform
:keyword base_dockerfile:
:paramtype base_dockerfile: str
:keyword build_context:
:paramtype build_context: ~flow.models.DockerBuildContext
:keyword base_image_registry:
:paramtype base_image_registry: ~flow.models.ContainerRegistry
"""
super(DockerSection, self).__init__(**kwargs)
self.base_image = kwargs.get('base_image', None)
self.platform = kwargs.get('platform', None)
self.base_dockerfile = kwargs.get('base_dockerfile', None)
self.build_context = kwargs.get('build_context', None)
self.base_image_registry = kwargs.get('base_image_registry', None)
class DockerSettingConfiguration(msrest.serialization.Model):
"""DockerSettingConfiguration.
:ivar use_docker:
:vartype use_docker: bool
:ivar shared_volumes:
:vartype shared_volumes: bool
:ivar shm_size:
:vartype shm_size: str
:ivar arguments:
:vartype arguments: list[str]
"""
_attribute_map = {
'use_docker': {'key': 'useDocker', 'type': 'bool'},
'shared_volumes': {'key': 'sharedVolumes', 'type': 'bool'},
'shm_size': {'key': 'shmSize', 'type': 'str'},
'arguments': {'key': 'arguments', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword use_docker:
:paramtype use_docker: bool
:keyword shared_volumes:
:paramtype shared_volumes: bool
:keyword shm_size:
:paramtype shm_size: str
:keyword arguments:
:paramtype arguments: list[str]
"""
super(DockerSettingConfiguration, self).__init__(**kwargs)
self.use_docker = kwargs.get('use_docker', None)
self.shared_volumes = kwargs.get('shared_volumes', None)
self.shm_size = kwargs.get('shm_size', None)
self.arguments = kwargs.get('arguments', None)
class DoWhileControlFlowInfo(msrest.serialization.Model):
"""DoWhileControlFlowInfo.
:ivar output_port_name_to_input_port_names_mapping: Dictionary of
<components·1sqg750·schemas·dowhilecontrolflowinfo·properties·outputportnametoinputportnamesmapping·additionalproperties>.
:vartype output_port_name_to_input_port_names_mapping: dict[str, list[str]]
:ivar condition_output_port_name:
:vartype condition_output_port_name: str
:ivar run_settings:
:vartype run_settings: ~flow.models.DoWhileControlFlowRunSettings
"""
_attribute_map = {
'output_port_name_to_input_port_names_mapping': {'key': 'outputPortNameToInputPortNamesMapping', 'type': '{[str]}'},
'condition_output_port_name': {'key': 'conditionOutputPortName', 'type': 'str'},
'run_settings': {'key': 'runSettings', 'type': 'DoWhileControlFlowRunSettings'},
}
def __init__(
self,
**kwargs
):
"""
:keyword output_port_name_to_input_port_names_mapping: Dictionary of
<components·1sqg750·schemas·dowhilecontrolflowinfo·properties·outputportnametoinputportnamesmapping·additionalproperties>.
:paramtype output_port_name_to_input_port_names_mapping: dict[str, list[str]]
:keyword condition_output_port_name:
:paramtype condition_output_port_name: str
:keyword run_settings:
:paramtype run_settings: ~flow.models.DoWhileControlFlowRunSettings
"""
super(DoWhileControlFlowInfo, self).__init__(**kwargs)
self.output_port_name_to_input_port_names_mapping = kwargs.get('output_port_name_to_input_port_names_mapping', None)
self.condition_output_port_name = kwargs.get('condition_output_port_name', None)
self.run_settings = kwargs.get('run_settings', None)
class DoWhileControlFlowRunSettings(msrest.serialization.Model):
"""DoWhileControlFlowRunSettings.
:ivar max_loop_iteration_count:
:vartype max_loop_iteration_count: ~flow.models.ParameterAssignment
"""
_attribute_map = {
'max_loop_iteration_count': {'key': 'maxLoopIterationCount', 'type': 'ParameterAssignment'},
}
def __init__(
self,
**kwargs
):
"""
:keyword max_loop_iteration_count:
:paramtype max_loop_iteration_count: ~flow.models.ParameterAssignment
"""
super(DoWhileControlFlowRunSettings, self).__init__(**kwargs)
self.max_loop_iteration_count = kwargs.get('max_loop_iteration_count', None)
class DownloadResourceInfo(msrest.serialization.Model):
"""DownloadResourceInfo.
:ivar download_url:
:vartype download_url: str
:ivar size:
:vartype size: long
"""
_attribute_map = {
'download_url': {'key': 'downloadUrl', 'type': 'str'},
'size': {'key': 'size', 'type': 'long'},
}
def __init__(
self,
**kwargs
):
"""
:keyword download_url:
:paramtype download_url: str
:keyword size:
:paramtype size: long
"""
super(DownloadResourceInfo, self).__init__(**kwargs)
self.download_url = kwargs.get('download_url', None)
self.size = kwargs.get('size', None)
class EndpointSetting(msrest.serialization.Model):
"""EndpointSetting.
:ivar type:
:vartype type: str
:ivar port:
:vartype port: int
:ivar ssl_thumbprint:
:vartype ssl_thumbprint: str
:ivar endpoint:
:vartype endpoint: str
:ivar proxy_endpoint:
:vartype proxy_endpoint: str
:ivar status:
:vartype status: str
:ivar error_message:
:vartype error_message: str
:ivar enabled:
:vartype enabled: bool
:ivar properties: Dictionary of :code:`<string>`.
:vartype properties: dict[str, str]
:ivar nodes:
:vartype nodes: str
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'port': {'key': 'port', 'type': 'int'},
'ssl_thumbprint': {'key': 'sslThumbprint', 'type': 'str'},
'endpoint': {'key': 'endpoint', 'type': 'str'},
'proxy_endpoint': {'key': 'proxyEndpoint', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'error_message': {'key': 'errorMessage', 'type': 'str'},
'enabled': {'key': 'enabled', 'type': 'bool'},
'properties': {'key': 'properties', 'type': '{str}'},
'nodes': {'key': 'nodes', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type:
:paramtype type: str
:keyword port:
:paramtype port: int
:keyword ssl_thumbprint:
:paramtype ssl_thumbprint: str
:keyword endpoint:
:paramtype endpoint: str
:keyword proxy_endpoint:
:paramtype proxy_endpoint: str
:keyword status:
:paramtype status: str
:keyword error_message:
:paramtype error_message: str
:keyword enabled:
:paramtype enabled: bool
:keyword properties: Dictionary of :code:`<string>`.
:paramtype properties: dict[str, str]
:keyword nodes:
:paramtype nodes: str
"""
super(EndpointSetting, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.port = kwargs.get('port', None)
self.ssl_thumbprint = kwargs.get('ssl_thumbprint', None)
self.endpoint = kwargs.get('endpoint', None)
self.proxy_endpoint = kwargs.get('proxy_endpoint', None)
self.status = kwargs.get('status', None)
self.error_message = kwargs.get('error_message', None)
self.enabled = kwargs.get('enabled', None)
self.properties = kwargs.get('properties', None)
self.nodes = kwargs.get('nodes', None)
class EntityInterface(msrest.serialization.Model):
"""EntityInterface.
:ivar parameters:
:vartype parameters: list[~flow.models.Parameter]
:ivar ports:
:vartype ports: ~flow.models.NodePortInterface
:ivar metadata_parameters:
:vartype metadata_parameters: list[~flow.models.Parameter]
:ivar data_path_parameters:
:vartype data_path_parameters: list[~flow.models.DataPathParameter]
:ivar data_path_parameter_list:
:vartype data_path_parameter_list: list[~flow.models.DataSetPathParameter]
:ivar asset_output_settings_parameter_list:
:vartype asset_output_settings_parameter_list: list[~flow.models.AssetOutputSettingsParameter]
"""
_attribute_map = {
'parameters': {'key': 'parameters', 'type': '[Parameter]'},
'ports': {'key': 'ports', 'type': 'NodePortInterface'},
'metadata_parameters': {'key': 'metadataParameters', 'type': '[Parameter]'},
'data_path_parameters': {'key': 'dataPathParameters', 'type': '[DataPathParameter]'},
'data_path_parameter_list': {'key': 'dataPathParameterList', 'type': '[DataSetPathParameter]'},
'asset_output_settings_parameter_list': {'key': 'AssetOutputSettingsParameterList', 'type': '[AssetOutputSettingsParameter]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword parameters:
:paramtype parameters: list[~flow.models.Parameter]
:keyword ports:
:paramtype ports: ~flow.models.NodePortInterface
:keyword metadata_parameters:
:paramtype metadata_parameters: list[~flow.models.Parameter]
:keyword data_path_parameters:
:paramtype data_path_parameters: list[~flow.models.DataPathParameter]
:keyword data_path_parameter_list:
:paramtype data_path_parameter_list: list[~flow.models.DataSetPathParameter]
:keyword asset_output_settings_parameter_list:
:paramtype asset_output_settings_parameter_list:
list[~flow.models.AssetOutputSettingsParameter]
"""
super(EntityInterface, self).__init__(**kwargs)
self.parameters = kwargs.get('parameters', None)
self.ports = kwargs.get('ports', None)
self.metadata_parameters = kwargs.get('metadata_parameters', None)
self.data_path_parameters = kwargs.get('data_path_parameters', None)
self.data_path_parameter_list = kwargs.get('data_path_parameter_list', None)
self.asset_output_settings_parameter_list = kwargs.get('asset_output_settings_parameter_list', None)
class EntrySetting(msrest.serialization.Model):
"""EntrySetting.
:ivar file:
:vartype file: str
:ivar class_name:
:vartype class_name: str
"""
_attribute_map = {
'file': {'key': 'file', 'type': 'str'},
'class_name': {'key': 'className', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword file:
:paramtype file: str
:keyword class_name:
:paramtype class_name: str
"""
super(EntrySetting, self).__init__(**kwargs)
self.file = kwargs.get('file', None)
self.class_name = kwargs.get('class_name', None)
class EnumParameterRule(msrest.serialization.Model):
"""EnumParameterRule.
:ivar valid_values:
:vartype valid_values: list[str]
"""
_attribute_map = {
'valid_values': {'key': 'validValues', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword valid_values:
:paramtype valid_values: list[str]
"""
super(EnumParameterRule, self).__init__(**kwargs)
self.valid_values = kwargs.get('valid_values', None)
class EnvironmentConfiguration(msrest.serialization.Model):
"""EnvironmentConfiguration.
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
:ivar use_environment_definition:
:vartype use_environment_definition: bool
:ivar environment_definition_string:
:vartype environment_definition_string: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'use_environment_definition': {'key': 'useEnvironmentDefinition', 'type': 'bool'},
'environment_definition_string': {'key': 'environmentDefinitionString', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
:keyword use_environment_definition:
:paramtype use_environment_definition: bool
:keyword environment_definition_string:
:paramtype environment_definition_string: str
"""
super(EnvironmentConfiguration, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.version = kwargs.get('version', None)
self.use_environment_definition = kwargs.get('use_environment_definition', None)
self.environment_definition_string = kwargs.get('environment_definition_string', None)
class EnvironmentDefinition(msrest.serialization.Model):
"""EnvironmentDefinition.
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
:ivar asset_id:
:vartype asset_id: str
:ivar auto_rebuild:
:vartype auto_rebuild: bool
:ivar python:
:vartype python: ~flow.models.PythonSection
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
:ivar docker:
:vartype docker: ~flow.models.DockerSection
:ivar spark:
:vartype spark: ~flow.models.SparkSection
:ivar r:
:vartype r: ~flow.models.RSection
:ivar inferencing_stack_version:
:vartype inferencing_stack_version: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'asset_id': {'key': 'assetId', 'type': 'str'},
'auto_rebuild': {'key': 'autoRebuild', 'type': 'bool'},
'python': {'key': 'python', 'type': 'PythonSection'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'docker': {'key': 'docker', 'type': 'DockerSection'},
'spark': {'key': 'spark', 'type': 'SparkSection'},
'r': {'key': 'r', 'type': 'RSection'},
'inferencing_stack_version': {'key': 'inferencingStackVersion', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
:keyword asset_id:
:paramtype asset_id: str
:keyword auto_rebuild:
:paramtype auto_rebuild: bool
:keyword python:
:paramtype python: ~flow.models.PythonSection
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
:keyword docker:
:paramtype docker: ~flow.models.DockerSection
:keyword spark:
:paramtype spark: ~flow.models.SparkSection
:keyword r:
:paramtype r: ~flow.models.RSection
:keyword inferencing_stack_version:
:paramtype inferencing_stack_version: str
"""
super(EnvironmentDefinition, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.version = kwargs.get('version', None)
self.asset_id = kwargs.get('asset_id', None)
self.auto_rebuild = kwargs.get('auto_rebuild', None)
self.python = kwargs.get('python', None)
self.environment_variables = kwargs.get('environment_variables', None)
self.docker = kwargs.get('docker', None)
self.spark = kwargs.get('spark', None)
self.r = kwargs.get('r', None)
self.inferencing_stack_version = kwargs.get('inferencing_stack_version', None)
class EnvironmentDefinitionDto(msrest.serialization.Model):
"""EnvironmentDefinitionDto.
:ivar environment_name:
:vartype environment_name: str
:ivar environment_version:
:vartype environment_version: str
:ivar intellectual_property_publisher:
:vartype intellectual_property_publisher: str
"""
_attribute_map = {
'environment_name': {'key': 'environmentName', 'type': 'str'},
'environment_version': {'key': 'environmentVersion', 'type': 'str'},
'intellectual_property_publisher': {'key': 'intellectualPropertyPublisher', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword environment_name:
:paramtype environment_name: str
:keyword environment_version:
:paramtype environment_version: str
:keyword intellectual_property_publisher:
:paramtype intellectual_property_publisher: str
"""
super(EnvironmentDefinitionDto, self).__init__(**kwargs)
self.environment_name = kwargs.get('environment_name', None)
self.environment_version = kwargs.get('environment_version', None)
self.intellectual_property_publisher = kwargs.get('intellectual_property_publisher', None)
class EPRPipelineRunErrorClassificationRequest(msrest.serialization.Model):
"""EPRPipelineRunErrorClassificationRequest.
:ivar root_run_id:
:vartype root_run_id: str
:ivar run_id:
:vartype run_id: str
:ivar task_result:
:vartype task_result: str
:ivar failure_type:
:vartype failure_type: str
:ivar failure_name:
:vartype failure_name: str
:ivar responsible_team:
:vartype responsible_team: str
"""
_attribute_map = {
'root_run_id': {'key': 'rootRunId', 'type': 'str'},
'run_id': {'key': 'runId', 'type': 'str'},
'task_result': {'key': 'taskResult', 'type': 'str'},
'failure_type': {'key': 'failureType', 'type': 'str'},
'failure_name': {'key': 'failureName', 'type': 'str'},
'responsible_team': {'key': 'responsibleTeam', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword root_run_id:
:paramtype root_run_id: str
:keyword run_id:
:paramtype run_id: str
:keyword task_result:
:paramtype task_result: str
:keyword failure_type:
:paramtype failure_type: str
:keyword failure_name:
:paramtype failure_name: str
:keyword responsible_team:
:paramtype responsible_team: str
"""
super(EPRPipelineRunErrorClassificationRequest, self).__init__(**kwargs)
self.root_run_id = kwargs.get('root_run_id', None)
self.run_id = kwargs.get('run_id', None)
self.task_result = kwargs.get('task_result', None)
self.failure_type = kwargs.get('failure_type', None)
self.failure_name = kwargs.get('failure_name', None)
self.responsible_team = kwargs.get('responsible_team', None)
class ErrorAdditionalInfo(msrest.serialization.Model):
"""The resource management error additional info.
:ivar type: The additional info type.
:vartype type: str
:ivar info: The additional info.
:vartype info: any
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'info': {'key': 'info', 'type': 'object'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type: The additional info type.
:paramtype type: str
:keyword info: The additional info.
:paramtype info: any
"""
super(ErrorAdditionalInfo, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.info = kwargs.get('info', None)
class ErrorResponse(msrest.serialization.Model):
"""The error response.
:ivar error: The root error.
:vartype error: ~flow.models.RootError
:ivar correlation: Dictionary containing correlation details for the error.
:vartype correlation: dict[str, str]
:ivar environment: The hosting environment.
:vartype environment: str
:ivar location: The Azure region.
:vartype location: str
:ivar time: The time in UTC.
:vartype time: ~datetime.datetime
:ivar component_name: Component name where error originated/encountered.
:vartype component_name: str
"""
_attribute_map = {
'error': {'key': 'error', 'type': 'RootError'},
'correlation': {'key': 'correlation', 'type': '{str}'},
'environment': {'key': 'environment', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
'time': {'key': 'time', 'type': 'iso-8601'},
'component_name': {'key': 'componentName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword error: The root error.
:paramtype error: ~flow.models.RootError
:keyword correlation: Dictionary containing correlation details for the error.
:paramtype correlation: dict[str, str]
:keyword environment: The hosting environment.
:paramtype environment: str
:keyword location: The Azure region.
:paramtype location: str
:keyword time: The time in UTC.
:paramtype time: ~datetime.datetime
:keyword component_name: Component name where error originated/encountered.
:paramtype component_name: str
"""
super(ErrorResponse, self).__init__(**kwargs)
self.error = kwargs.get('error', None)
self.correlation = kwargs.get('correlation', None)
self.environment = kwargs.get('environment', None)
self.location = kwargs.get('location', None)
self.time = kwargs.get('time', None)
self.component_name = kwargs.get('component_name', None)
class EsCloudConfiguration(msrest.serialization.Model):
"""EsCloudConfiguration.
:ivar enable_output_to_file_based_on_data_type_id:
:vartype enable_output_to_file_based_on_data_type_id: bool
:ivar environment:
:vartype environment: ~flow.models.EnvironmentConfiguration
:ivar hyper_drive_configuration:
:vartype hyper_drive_configuration: ~flow.models.HyperDriveConfiguration
:ivar k8_s_config:
:vartype k8_s_config: ~flow.models.K8SConfiguration
:ivar resource_config:
:vartype resource_config: ~flow.models.AEVAResourceConfiguration
:ivar torch_distributed_config:
:vartype torch_distributed_config: ~flow.models.TorchDistributedConfiguration
:ivar target_selector_config:
:vartype target_selector_config: ~flow.models.TargetSelectorConfiguration
:ivar docker_config:
:vartype docker_config: ~flow.models.DockerSettingConfiguration
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
:ivar max_run_duration_seconds:
:vartype max_run_duration_seconds: int
:ivar identity:
:vartype identity: ~flow.models.IdentitySetting
:ivar application_endpoints: Dictionary of :code:`<ApplicationEndpointConfiguration>`.
:vartype application_endpoints: dict[str, ~flow.models.ApplicationEndpointConfiguration]
:ivar run_config:
:vartype run_config: str
"""
_attribute_map = {
'enable_output_to_file_based_on_data_type_id': {'key': 'enableOutputToFileBasedOnDataTypeId', 'type': 'bool'},
'environment': {'key': 'environment', 'type': 'EnvironmentConfiguration'},
'hyper_drive_configuration': {'key': 'hyperDriveConfiguration', 'type': 'HyperDriveConfiguration'},
'k8_s_config': {'key': 'k8sConfig', 'type': 'K8SConfiguration'},
'resource_config': {'key': 'resourceConfig', 'type': 'AEVAResourceConfiguration'},
'torch_distributed_config': {'key': 'torchDistributedConfig', 'type': 'TorchDistributedConfiguration'},
'target_selector_config': {'key': 'targetSelectorConfig', 'type': 'TargetSelectorConfiguration'},
'docker_config': {'key': 'dockerConfig', 'type': 'DockerSettingConfiguration'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'max_run_duration_seconds': {'key': 'maxRunDurationSeconds', 'type': 'int'},
'identity': {'key': 'identity', 'type': 'IdentitySetting'},
'application_endpoints': {'key': 'applicationEndpoints', 'type': '{ApplicationEndpointConfiguration}'},
'run_config': {'key': 'runConfig', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword enable_output_to_file_based_on_data_type_id:
:paramtype enable_output_to_file_based_on_data_type_id: bool
:keyword environment:
:paramtype environment: ~flow.models.EnvironmentConfiguration
:keyword hyper_drive_configuration:
:paramtype hyper_drive_configuration: ~flow.models.HyperDriveConfiguration
:keyword k8_s_config:
:paramtype k8_s_config: ~flow.models.K8SConfiguration
:keyword resource_config:
:paramtype resource_config: ~flow.models.AEVAResourceConfiguration
:keyword torch_distributed_config:
:paramtype torch_distributed_config: ~flow.models.TorchDistributedConfiguration
:keyword target_selector_config:
:paramtype target_selector_config: ~flow.models.TargetSelectorConfiguration
:keyword docker_config:
:paramtype docker_config: ~flow.models.DockerSettingConfiguration
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
:keyword max_run_duration_seconds:
:paramtype max_run_duration_seconds: int
:keyword identity:
:paramtype identity: ~flow.models.IdentitySetting
:keyword application_endpoints: Dictionary of :code:`<ApplicationEndpointConfiguration>`.
:paramtype application_endpoints: dict[str, ~flow.models.ApplicationEndpointConfiguration]
:keyword run_config:
:paramtype run_config: str
"""
super(EsCloudConfiguration, self).__init__(**kwargs)
self.enable_output_to_file_based_on_data_type_id = kwargs.get('enable_output_to_file_based_on_data_type_id', None)
self.environment = kwargs.get('environment', None)
self.hyper_drive_configuration = kwargs.get('hyper_drive_configuration', None)
self.k8_s_config = kwargs.get('k8_s_config', None)
self.resource_config = kwargs.get('resource_config', None)
self.torch_distributed_config = kwargs.get('torch_distributed_config', None)
self.target_selector_config = kwargs.get('target_selector_config', None)
self.docker_config = kwargs.get('docker_config', None)
self.environment_variables = kwargs.get('environment_variables', None)
self.max_run_duration_seconds = kwargs.get('max_run_duration_seconds', None)
self.identity = kwargs.get('identity', None)
self.application_endpoints = kwargs.get('application_endpoints', None)
self.run_config = kwargs.get('run_config', None)
class EvaluationFlowRunSettings(msrest.serialization.Model):
"""EvaluationFlowRunSettings.
:ivar flow_run_id:
:vartype flow_run_id: str
:ivar flow_run_display_name:
:vartype flow_run_display_name: str
:ivar batch_data_input:
:vartype batch_data_input: ~flow.models.BatchDataInput
:ivar inputs_mapping: This is a dictionary.
:vartype inputs_mapping: dict[str, str]
:ivar data_inputs: This is a dictionary.
:vartype data_inputs: dict[str, str]
:ivar connection_overrides:
:vartype connection_overrides: list[~flow.models.ConnectionOverrideSetting]
:ivar runtime_name:
:vartype runtime_name: str
:ivar aml_compute_name:
:vartype aml_compute_name: str
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
"""
_attribute_map = {
'flow_run_id': {'key': 'flowRunId', 'type': 'str'},
'flow_run_display_name': {'key': 'flowRunDisplayName', 'type': 'str'},
'batch_data_input': {'key': 'batchDataInput', 'type': 'BatchDataInput'},
'inputs_mapping': {'key': 'inputsMapping', 'type': '{str}'},
'data_inputs': {'key': 'dataInputs', 'type': '{str}'},
'connection_overrides': {'key': 'connectionOverrides', 'type': '[ConnectionOverrideSetting]'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'aml_compute_name': {'key': 'amlComputeName', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword flow_run_id:
:paramtype flow_run_id: str
:keyword flow_run_display_name:
:paramtype flow_run_display_name: str
:keyword batch_data_input:
:paramtype batch_data_input: ~flow.models.BatchDataInput
:keyword inputs_mapping: This is a dictionary.
:paramtype inputs_mapping: dict[str, str]
:keyword data_inputs: This is a dictionary.
:paramtype data_inputs: dict[str, str]
:keyword connection_overrides:
:paramtype connection_overrides: list[~flow.models.ConnectionOverrideSetting]
:keyword runtime_name:
:paramtype runtime_name: str
:keyword aml_compute_name:
:paramtype aml_compute_name: str
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
"""
super(EvaluationFlowRunSettings, self).__init__(**kwargs)
self.flow_run_id = kwargs.get('flow_run_id', None)
self.flow_run_display_name = kwargs.get('flow_run_display_name', None)
self.batch_data_input = kwargs.get('batch_data_input', None)
self.inputs_mapping = kwargs.get('inputs_mapping', None)
self.data_inputs = kwargs.get('data_inputs', None)
self.connection_overrides = kwargs.get('connection_overrides', None)
self.runtime_name = kwargs.get('runtime_name', None)
self.aml_compute_name = kwargs.get('aml_compute_name', None)
self.properties = kwargs.get('properties', None)
class ExampleRequest(msrest.serialization.Model):
"""ExampleRequest.
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, list[list[any]]]
:ivar global_parameters: This is a dictionary.
:vartype global_parameters: dict[str, any]
"""
_attribute_map = {
'inputs': {'key': 'inputs', 'type': '{[[object]]}'},
'global_parameters': {'key': 'globalParameters', 'type': '{object}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, list[list[any]]]
:keyword global_parameters: This is a dictionary.
:paramtype global_parameters: dict[str, any]
"""
super(ExampleRequest, self).__init__(**kwargs)
self.inputs = kwargs.get('inputs', None)
self.global_parameters = kwargs.get('global_parameters', None)
class ExecutionContextDto(msrest.serialization.Model):
"""ExecutionContextDto.
:ivar executable:
:vartype executable: str
:ivar user_code:
:vartype user_code: str
:ivar arguments:
:vartype arguments: str
"""
_attribute_map = {
'executable': {'key': 'executable', 'type': 'str'},
'user_code': {'key': 'userCode', 'type': 'str'},
'arguments': {'key': 'arguments', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword executable:
:paramtype executable: str
:keyword user_code:
:paramtype user_code: str
:keyword arguments:
:paramtype arguments: str
"""
super(ExecutionContextDto, self).__init__(**kwargs)
self.executable = kwargs.get('executable', None)
self.user_code = kwargs.get('user_code', None)
self.arguments = kwargs.get('arguments', None)
class ExecutionDataLocation(msrest.serialization.Model):
"""ExecutionDataLocation.
:ivar dataset:
:vartype dataset: ~flow.models.RunDatasetReference
:ivar data_path:
:vartype data_path: ~flow.models.ExecutionDataPath
:ivar uri:
:vartype uri: ~flow.models.UriReference
:ivar type:
:vartype type: str
"""
_attribute_map = {
'dataset': {'key': 'dataset', 'type': 'RunDatasetReference'},
'data_path': {'key': 'dataPath', 'type': 'ExecutionDataPath'},
'uri': {'key': 'uri', 'type': 'UriReference'},
'type': {'key': 'type', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword dataset:
:paramtype dataset: ~flow.models.RunDatasetReference
:keyword data_path:
:paramtype data_path: ~flow.models.ExecutionDataPath
:keyword uri:
:paramtype uri: ~flow.models.UriReference
:keyword type:
:paramtype type: str
"""
super(ExecutionDataLocation, self).__init__(**kwargs)
self.dataset = kwargs.get('dataset', None)
self.data_path = kwargs.get('data_path', None)
self.uri = kwargs.get('uri', None)
self.type = kwargs.get('type', None)
class ExecutionDataPath(msrest.serialization.Model):
"""ExecutionDataPath.
:ivar datastore_name:
:vartype datastore_name: str
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'datastore_name': {'key': 'datastoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword datastore_name:
:paramtype datastore_name: str
:keyword relative_path:
:paramtype relative_path: str
"""
super(ExecutionDataPath, self).__init__(**kwargs)
self.datastore_name = kwargs.get('datastore_name', None)
self.relative_path = kwargs.get('relative_path', None)
class ExecutionGlobsOptions(msrest.serialization.Model):
"""ExecutionGlobsOptions.
:ivar glob_patterns:
:vartype glob_patterns: list[str]
"""
_attribute_map = {
'glob_patterns': {'key': 'globPatterns', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword glob_patterns:
:paramtype glob_patterns: list[str]
"""
super(ExecutionGlobsOptions, self).__init__(**kwargs)
self.glob_patterns = kwargs.get('glob_patterns', None)
class ExperimentComputeMetaInfo(msrest.serialization.Model):
"""ExperimentComputeMetaInfo.
:ivar current_node_count:
:vartype current_node_count: int
:ivar target_node_count:
:vartype target_node_count: int
:ivar max_node_count:
:vartype max_node_count: int
:ivar min_node_count:
:vartype min_node_count: int
:ivar idle_node_count:
:vartype idle_node_count: int
:ivar running_node_count:
:vartype running_node_count: int
:ivar preparing_node_count:
:vartype preparing_node_count: int
:ivar unusable_node_count:
:vartype unusable_node_count: int
:ivar leaving_node_count:
:vartype leaving_node_count: int
:ivar preempted_node_count:
:vartype preempted_node_count: int
:ivar vm_size:
:vartype vm_size: str
:ivar location:
:vartype location: str
:ivar provisioning_state:
:vartype provisioning_state: str
:ivar state:
:vartype state: str
:ivar os_type:
:vartype os_type: str
:ivar id:
:vartype id: str
:ivar name:
:vartype name: str
:ivar created_by_studio:
:vartype created_by_studio: bool
:ivar is_gpu_type:
:vartype is_gpu_type: bool
:ivar resource_id:
:vartype resource_id: str
:ivar compute_type:
:vartype compute_type: str
"""
_attribute_map = {
'current_node_count': {'key': 'currentNodeCount', 'type': 'int'},
'target_node_count': {'key': 'targetNodeCount', 'type': 'int'},
'max_node_count': {'key': 'maxNodeCount', 'type': 'int'},
'min_node_count': {'key': 'minNodeCount', 'type': 'int'},
'idle_node_count': {'key': 'idleNodeCount', 'type': 'int'},
'running_node_count': {'key': 'runningNodeCount', 'type': 'int'},
'preparing_node_count': {'key': 'preparingNodeCount', 'type': 'int'},
'unusable_node_count': {'key': 'unusableNodeCount', 'type': 'int'},
'leaving_node_count': {'key': 'leavingNodeCount', 'type': 'int'},
'preempted_node_count': {'key': 'preemptedNodeCount', 'type': 'int'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
'state': {'key': 'state', 'type': 'str'},
'os_type': {'key': 'osType', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'created_by_studio': {'key': 'createdByStudio', 'type': 'bool'},
'is_gpu_type': {'key': 'isGpuType', 'type': 'bool'},
'resource_id': {'key': 'resourceId', 'type': 'str'},
'compute_type': {'key': 'computeType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword current_node_count:
:paramtype current_node_count: int
:keyword target_node_count:
:paramtype target_node_count: int
:keyword max_node_count:
:paramtype max_node_count: int
:keyword min_node_count:
:paramtype min_node_count: int
:keyword idle_node_count:
:paramtype idle_node_count: int
:keyword running_node_count:
:paramtype running_node_count: int
:keyword preparing_node_count:
:paramtype preparing_node_count: int
:keyword unusable_node_count:
:paramtype unusable_node_count: int
:keyword leaving_node_count:
:paramtype leaving_node_count: int
:keyword preempted_node_count:
:paramtype preempted_node_count: int
:keyword vm_size:
:paramtype vm_size: str
:keyword location:
:paramtype location: str
:keyword provisioning_state:
:paramtype provisioning_state: str
:keyword state:
:paramtype state: str
:keyword os_type:
:paramtype os_type: str
:keyword id:
:paramtype id: str
:keyword name:
:paramtype name: str
:keyword created_by_studio:
:paramtype created_by_studio: bool
:keyword is_gpu_type:
:paramtype is_gpu_type: bool
:keyword resource_id:
:paramtype resource_id: str
:keyword compute_type:
:paramtype compute_type: str
"""
super(ExperimentComputeMetaInfo, self).__init__(**kwargs)
self.current_node_count = kwargs.get('current_node_count', None)
self.target_node_count = kwargs.get('target_node_count', None)
self.max_node_count = kwargs.get('max_node_count', None)
self.min_node_count = kwargs.get('min_node_count', None)
self.idle_node_count = kwargs.get('idle_node_count', None)
self.running_node_count = kwargs.get('running_node_count', None)
self.preparing_node_count = kwargs.get('preparing_node_count', None)
self.unusable_node_count = kwargs.get('unusable_node_count', None)
self.leaving_node_count = kwargs.get('leaving_node_count', None)
self.preempted_node_count = kwargs.get('preempted_node_count', None)
self.vm_size = kwargs.get('vm_size', None)
self.location = kwargs.get('location', None)
self.provisioning_state = kwargs.get('provisioning_state', None)
self.state = kwargs.get('state', None)
self.os_type = kwargs.get('os_type', None)
self.id = kwargs.get('id', None)
self.name = kwargs.get('name', None)
self.created_by_studio = kwargs.get('created_by_studio', None)
self.is_gpu_type = kwargs.get('is_gpu_type', None)
self.resource_id = kwargs.get('resource_id', None)
self.compute_type = kwargs.get('compute_type', None)
class ExperimentInfo(msrest.serialization.Model):
"""ExperimentInfo.
:ivar experiment_name:
:vartype experiment_name: str
:ivar experiment_id:
:vartype experiment_id: str
"""
_attribute_map = {
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword experiment_name:
:paramtype experiment_name: str
:keyword experiment_id:
:paramtype experiment_id: str
"""
super(ExperimentInfo, self).__init__(**kwargs)
self.experiment_name = kwargs.get('experiment_name', None)
self.experiment_id = kwargs.get('experiment_id', None)
class ExportComponentMetaInfo(msrest.serialization.Model):
"""ExportComponentMetaInfo.
:ivar module_entity:
:vartype module_entity: ~flow.models.ModuleEntity
:ivar module_version:
:vartype module_version: str
:ivar is_anonymous:
:vartype is_anonymous: bool
"""
_attribute_map = {
'module_entity': {'key': 'moduleEntity', 'type': 'ModuleEntity'},
'module_version': {'key': 'moduleVersion', 'type': 'str'},
'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword module_entity:
:paramtype module_entity: ~flow.models.ModuleEntity
:keyword module_version:
:paramtype module_version: str
:keyword is_anonymous:
:paramtype is_anonymous: bool
"""
super(ExportComponentMetaInfo, self).__init__(**kwargs)
self.module_entity = kwargs.get('module_entity', None)
self.module_version = kwargs.get('module_version', None)
self.is_anonymous = kwargs.get('is_anonymous', None)
class ExportDataTask(msrest.serialization.Model):
"""ExportDataTask.
:ivar data_transfer_sink:
:vartype data_transfer_sink: ~flow.models.DataTransferSink
"""
_attribute_map = {
'data_transfer_sink': {'key': 'DataTransferSink', 'type': 'DataTransferSink'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_transfer_sink:
:paramtype data_transfer_sink: ~flow.models.DataTransferSink
"""
super(ExportDataTask, self).__init__(**kwargs)
self.data_transfer_sink = kwargs.get('data_transfer_sink', None)
class FeaturizationSettings(msrest.serialization.Model):
"""FeaturizationSettings.
:ivar mode: Possible values include: "Auto", "Custom", "Off".
:vartype mode: str or ~flow.models.FeaturizationMode
:ivar blocked_transformers:
:vartype blocked_transformers: list[str]
:ivar column_purposes: Dictionary of :code:`<string>`.
:vartype column_purposes: dict[str, str]
:ivar drop_columns:
:vartype drop_columns: list[str]
:ivar transformer_params: Dictionary of
<components·1gi3krm·schemas·featurizationsettings·properties·transformerparams·additionalproperties>.
:vartype transformer_params: dict[str, list[~flow.models.ColumnTransformer]]
:ivar dataset_language:
:vartype dataset_language: str
:ivar enable_dnn_featurization:
:vartype enable_dnn_featurization: bool
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'blocked_transformers': {'key': 'blockedTransformers', 'type': '[str]'},
'column_purposes': {'key': 'columnPurposes', 'type': '{str}'},
'drop_columns': {'key': 'dropColumns', 'type': '[str]'},
'transformer_params': {'key': 'transformerParams', 'type': '{[ColumnTransformer]}'},
'dataset_language': {'key': 'datasetLanguage', 'type': 'str'},
'enable_dnn_featurization': {'key': 'enableDnnFeaturization', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom", "Off".
:paramtype mode: str or ~flow.models.FeaturizationMode
:keyword blocked_transformers:
:paramtype blocked_transformers: list[str]
:keyword column_purposes: Dictionary of :code:`<string>`.
:paramtype column_purposes: dict[str, str]
:keyword drop_columns:
:paramtype drop_columns: list[str]
:keyword transformer_params: Dictionary of
<components·1gi3krm·schemas·featurizationsettings·properties·transformerparams·additionalproperties>.
:paramtype transformer_params: dict[str, list[~flow.models.ColumnTransformer]]
:keyword dataset_language:
:paramtype dataset_language: str
:keyword enable_dnn_featurization:
:paramtype enable_dnn_featurization: bool
"""
super(FeaturizationSettings, self).__init__(**kwargs)
self.mode = kwargs.get('mode', None)
self.blocked_transformers = kwargs.get('blocked_transformers', None)
self.column_purposes = kwargs.get('column_purposes', None)
self.drop_columns = kwargs.get('drop_columns', None)
self.transformer_params = kwargs.get('transformer_params', None)
self.dataset_language = kwargs.get('dataset_language', None)
self.enable_dnn_featurization = kwargs.get('enable_dnn_featurization', None)
class FeedDto(msrest.serialization.Model):
"""FeedDto.
:ivar name:
:vartype name: str
:ivar display_name:
:vartype display_name: str
:ivar description:
:vartype description: str
:ivar sharing_scopes:
:vartype sharing_scopes: list[~flow.models.SharingScope]
:ivar supported_asset_types:
:vartype supported_asset_types: ~flow.models.FeedDtoSupportedAssetTypes
:ivar regional_workspace_storage: This is a dictionary.
:vartype regional_workspace_storage: dict[str, list[str]]
:ivar intellectual_property_publisher:
:vartype intellectual_property_publisher: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'sharing_scopes': {'key': 'sharingScopes', 'type': '[SharingScope]'},
'supported_asset_types': {'key': 'supportedAssetTypes', 'type': 'FeedDtoSupportedAssetTypes'},
'regional_workspace_storage': {'key': 'regionalWorkspaceStorage', 'type': '{[str]}'},
'intellectual_property_publisher': {'key': 'intellectualPropertyPublisher', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword display_name:
:paramtype display_name: str
:keyword description:
:paramtype description: str
:keyword sharing_scopes:
:paramtype sharing_scopes: list[~flow.models.SharingScope]
:keyword supported_asset_types:
:paramtype supported_asset_types: ~flow.models.FeedDtoSupportedAssetTypes
:keyword regional_workspace_storage: This is a dictionary.
:paramtype regional_workspace_storage: dict[str, list[str]]
:keyword intellectual_property_publisher:
:paramtype intellectual_property_publisher: str
"""
super(FeedDto, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.display_name = kwargs.get('display_name', None)
self.description = kwargs.get('description', None)
self.sharing_scopes = kwargs.get('sharing_scopes', None)
self.supported_asset_types = kwargs.get('supported_asset_types', None)
self.regional_workspace_storage = kwargs.get('regional_workspace_storage', None)
self.intellectual_property_publisher = kwargs.get('intellectual_property_publisher', None)
class FeedDtoSupportedAssetTypes(msrest.serialization.Model):
"""FeedDtoSupportedAssetTypes.
:ivar component:
:vartype component: ~flow.models.AssetTypeMetaInfo
:ivar model:
:vartype model: ~flow.models.AssetTypeMetaInfo
:ivar environment:
:vartype environment: ~flow.models.AssetTypeMetaInfo
:ivar dataset:
:vartype dataset: ~flow.models.AssetTypeMetaInfo
:ivar data_store:
:vartype data_store: ~flow.models.AssetTypeMetaInfo
:ivar sample_graph:
:vartype sample_graph: ~flow.models.AssetTypeMetaInfo
:ivar flow_tool:
:vartype flow_tool: ~flow.models.AssetTypeMetaInfo
:ivar flow_tool_setting:
:vartype flow_tool_setting: ~flow.models.AssetTypeMetaInfo
:ivar flow_connection:
:vartype flow_connection: ~flow.models.AssetTypeMetaInfo
:ivar flow_sample:
:vartype flow_sample: ~flow.models.AssetTypeMetaInfo
:ivar flow_runtime_spec:
:vartype flow_runtime_spec: ~flow.models.AssetTypeMetaInfo
"""
_attribute_map = {
'component': {'key': 'Component', 'type': 'AssetTypeMetaInfo'},
'model': {'key': 'Model', 'type': 'AssetTypeMetaInfo'},
'environment': {'key': 'Environment', 'type': 'AssetTypeMetaInfo'},
'dataset': {'key': 'Dataset', 'type': 'AssetTypeMetaInfo'},
'data_store': {'key': 'DataStore', 'type': 'AssetTypeMetaInfo'},
'sample_graph': {'key': 'SampleGraph', 'type': 'AssetTypeMetaInfo'},
'flow_tool': {'key': 'FlowTool', 'type': 'AssetTypeMetaInfo'},
'flow_tool_setting': {'key': 'FlowToolSetting', 'type': 'AssetTypeMetaInfo'},
'flow_connection': {'key': 'FlowConnection', 'type': 'AssetTypeMetaInfo'},
'flow_sample': {'key': 'FlowSample', 'type': 'AssetTypeMetaInfo'},
'flow_runtime_spec': {'key': 'FlowRuntimeSpec', 'type': 'AssetTypeMetaInfo'},
}
def __init__(
self,
**kwargs
):
"""
:keyword component:
:paramtype component: ~flow.models.AssetTypeMetaInfo
:keyword model:
:paramtype model: ~flow.models.AssetTypeMetaInfo
:keyword environment:
:paramtype environment: ~flow.models.AssetTypeMetaInfo
:keyword dataset:
:paramtype dataset: ~flow.models.AssetTypeMetaInfo
:keyword data_store:
:paramtype data_store: ~flow.models.AssetTypeMetaInfo
:keyword sample_graph:
:paramtype sample_graph: ~flow.models.AssetTypeMetaInfo
:keyword flow_tool:
:paramtype flow_tool: ~flow.models.AssetTypeMetaInfo
:keyword flow_tool_setting:
:paramtype flow_tool_setting: ~flow.models.AssetTypeMetaInfo
:keyword flow_connection:
:paramtype flow_connection: ~flow.models.AssetTypeMetaInfo
:keyword flow_sample:
:paramtype flow_sample: ~flow.models.AssetTypeMetaInfo
:keyword flow_runtime_spec:
:paramtype flow_runtime_spec: ~flow.models.AssetTypeMetaInfo
"""
super(FeedDtoSupportedAssetTypes, self).__init__(**kwargs)
self.component = kwargs.get('component', None)
self.model = kwargs.get('model', None)
self.environment = kwargs.get('environment', None)
self.dataset = kwargs.get('dataset', None)
self.data_store = kwargs.get('data_store', None)
self.sample_graph = kwargs.get('sample_graph', None)
self.flow_tool = kwargs.get('flow_tool', None)
self.flow_tool_setting = kwargs.get('flow_tool_setting', None)
self.flow_connection = kwargs.get('flow_connection', None)
self.flow_sample = kwargs.get('flow_sample', None)
self.flow_runtime_spec = kwargs.get('flow_runtime_spec', None)
class FileSystem(msrest.serialization.Model):
"""FileSystem.
:ivar connection:
:vartype connection: str
:ivar path:
:vartype path: str
"""
_attribute_map = {
'connection': {'key': 'connection', 'type': 'str'},
'path': {'key': 'path', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword connection:
:paramtype connection: str
:keyword path:
:paramtype path: str
"""
super(FileSystem, self).__init__(**kwargs)
self.connection = kwargs.get('connection', None)
self.path = kwargs.get('path', None)
class Flow(msrest.serialization.Model):
"""Flow.
:ivar source_resource_id:
:vartype source_resource_id: str
:ivar flow_graph:
:vartype flow_graph: ~flow.models.FlowGraph
:ivar node_variants: This is a dictionary.
:vartype node_variants: dict[str, ~flow.models.NodeVariant]
:ivar flow_graph_layout:
:vartype flow_graph_layout: ~flow.models.FlowGraphLayout
:ivar bulk_test_data: This is a dictionary.
:vartype bulk_test_data: dict[str, str]
:ivar evaluation_flows: This is a dictionary.
:vartype evaluation_flows: dict[str, ~flow.models.FlowGraphReference]
"""
_attribute_map = {
'source_resource_id': {'key': 'sourceResourceId', 'type': 'str'},
'flow_graph': {'key': 'flowGraph', 'type': 'FlowGraph'},
'node_variants': {'key': 'nodeVariants', 'type': '{NodeVariant}'},
'flow_graph_layout': {'key': 'flowGraphLayout', 'type': 'FlowGraphLayout'},
'bulk_test_data': {'key': 'bulkTestData', 'type': '{str}'},
'evaluation_flows': {'key': 'evaluationFlows', 'type': '{FlowGraphReference}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword source_resource_id:
:paramtype source_resource_id: str
:keyword flow_graph:
:paramtype flow_graph: ~flow.models.FlowGraph
:keyword node_variants: This is a dictionary.
:paramtype node_variants: dict[str, ~flow.models.NodeVariant]
:keyword flow_graph_layout:
:paramtype flow_graph_layout: ~flow.models.FlowGraphLayout
:keyword bulk_test_data: This is a dictionary.
:paramtype bulk_test_data: dict[str, str]
:keyword evaluation_flows: This is a dictionary.
:paramtype evaluation_flows: dict[str, ~flow.models.FlowGraphReference]
"""
super(Flow, self).__init__(**kwargs)
self.source_resource_id = kwargs.get('source_resource_id', None)
self.flow_graph = kwargs.get('flow_graph', None)
self.node_variants = kwargs.get('node_variants', None)
self.flow_graph_layout = kwargs.get('flow_graph_layout', None)
self.bulk_test_data = kwargs.get('bulk_test_data', None)
self.evaluation_flows = kwargs.get('evaluation_flows', None)
class FlowAnnotations(msrest.serialization.Model):
"""FlowAnnotations.
:ivar flow_name:
:vartype flow_name: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
:ivar owner:
:vartype owner: ~flow.models.SchemaContractsCreatedBy
:ivar is_archived:
:vartype is_archived: bool
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar archived:
:vartype archived: bool
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
"""
_attribute_map = {
'flow_name': {'key': 'flowName', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
'owner': {'key': 'owner', 'type': 'SchemaContractsCreatedBy'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'archived': {'key': 'archived', 'type': 'bool'},
'tags': {'key': 'tags', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword flow_name:
:paramtype flow_name: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
:keyword owner:
:paramtype owner: ~flow.models.SchemaContractsCreatedBy
:keyword is_archived:
:paramtype is_archived: bool
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword archived:
:paramtype archived: bool
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
"""
super(FlowAnnotations, self).__init__(**kwargs)
self.flow_name = kwargs.get('flow_name', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
self.owner = kwargs.get('owner', None)
self.is_archived = kwargs.get('is_archived', None)
self.vm_size = kwargs.get('vm_size', None)
self.max_idle_time_seconds = kwargs.get('max_idle_time_seconds', None)
self.name = kwargs.get('name', None)
self.description = kwargs.get('description', None)
self.archived = kwargs.get('archived', None)
self.tags = kwargs.get('tags', None)
class FlowBaseDto(msrest.serialization.Model):
"""FlowBaseDto.
:ivar flow_id:
:vartype flow_id: str
:ivar flow_name:
:vartype flow_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar experiment_id:
:vartype experiment_id: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
:ivar owner:
:vartype owner: ~flow.models.SchemaContractsCreatedBy
:ivar flow_resource_id:
:vartype flow_resource_id: str
:ivar is_archived:
:vartype is_archived: bool
:ivar flow_definition_file_path:
:vartype flow_definition_file_path: str
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar identity:
:vartype identity: str
"""
_attribute_map = {
'flow_id': {'key': 'flowId', 'type': 'str'},
'flow_name': {'key': 'flowName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
'owner': {'key': 'owner', 'type': 'SchemaContractsCreatedBy'},
'flow_resource_id': {'key': 'flowResourceId', 'type': 'str'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
'flow_definition_file_path': {'key': 'flowDefinitionFilePath', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'identity': {'key': 'identity', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword flow_id:
:paramtype flow_id: str
:keyword flow_name:
:paramtype flow_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword experiment_id:
:paramtype experiment_id: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
:keyword owner:
:paramtype owner: ~flow.models.SchemaContractsCreatedBy
:keyword flow_resource_id:
:paramtype flow_resource_id: str
:keyword is_archived:
:paramtype is_archived: bool
:keyword flow_definition_file_path:
:paramtype flow_definition_file_path: str
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword identity:
:paramtype identity: str
"""
super(FlowBaseDto, self).__init__(**kwargs)
self.flow_id = kwargs.get('flow_id', None)
self.flow_name = kwargs.get('flow_name', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.flow_type = kwargs.get('flow_type', None)
self.experiment_id = kwargs.get('experiment_id', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
self.owner = kwargs.get('owner', None)
self.flow_resource_id = kwargs.get('flow_resource_id', None)
self.is_archived = kwargs.get('is_archived', None)
self.flow_definition_file_path = kwargs.get('flow_definition_file_path', None)
self.vm_size = kwargs.get('vm_size', None)
self.max_idle_time_seconds = kwargs.get('max_idle_time_seconds', None)
self.identity = kwargs.get('identity', None)
class FlowDto(msrest.serialization.Model):
"""FlowDto.
:ivar timestamp:
:vartype timestamp: ~datetime.datetime
:ivar e_tag: Any object.
:vartype e_tag: any
:ivar flow:
:vartype flow: ~flow.models.Flow
:ivar flow_run_settings:
:vartype flow_run_settings: ~flow.models.FlowRunSettings
:ivar flow_run_result:
:vartype flow_run_result: ~flow.models.FlowRunResult
:ivar flow_test_mode: Possible values include: "Sync", "Async".
:vartype flow_test_mode: str or ~flow.models.FlowTestMode
:ivar flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:vartype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:ivar studio_portal_endpoint:
:vartype studio_portal_endpoint: str
:ivar flow_id:
:vartype flow_id: str
:ivar flow_name:
:vartype flow_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar experiment_id:
:vartype experiment_id: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
:ivar owner:
:vartype owner: ~flow.models.SchemaContractsCreatedBy
:ivar flow_resource_id:
:vartype flow_resource_id: str
:ivar is_archived:
:vartype is_archived: bool
:ivar flow_definition_file_path:
:vartype flow_definition_file_path: str
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar identity:
:vartype identity: str
"""
_attribute_map = {
'timestamp': {'key': 'timestamp', 'type': 'iso-8601'},
'e_tag': {'key': 'eTag', 'type': 'object'},
'flow': {'key': 'flow', 'type': 'Flow'},
'flow_run_settings': {'key': 'flowRunSettings', 'type': 'FlowRunSettings'},
'flow_run_result': {'key': 'flowRunResult', 'type': 'FlowRunResult'},
'flow_test_mode': {'key': 'flowTestMode', 'type': 'str'},
'flow_test_infos': {'key': 'flowTestInfos', 'type': '{FlowTestInfo}'},
'studio_portal_endpoint': {'key': 'studioPortalEndpoint', 'type': 'str'},
'flow_id': {'key': 'flowId', 'type': 'str'},
'flow_name': {'key': 'flowName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
'owner': {'key': 'owner', 'type': 'SchemaContractsCreatedBy'},
'flow_resource_id': {'key': 'flowResourceId', 'type': 'str'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
'flow_definition_file_path': {'key': 'flowDefinitionFilePath', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'identity': {'key': 'identity', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword timestamp:
:paramtype timestamp: ~datetime.datetime
:keyword e_tag: Any object.
:paramtype e_tag: any
:keyword flow:
:paramtype flow: ~flow.models.Flow
:keyword flow_run_settings:
:paramtype flow_run_settings: ~flow.models.FlowRunSettings
:keyword flow_run_result:
:paramtype flow_run_result: ~flow.models.FlowRunResult
:keyword flow_test_mode: Possible values include: "Sync", "Async".
:paramtype flow_test_mode: str or ~flow.models.FlowTestMode
:keyword flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:paramtype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:keyword studio_portal_endpoint:
:paramtype studio_portal_endpoint: str
:keyword flow_id:
:paramtype flow_id: str
:keyword flow_name:
:paramtype flow_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword experiment_id:
:paramtype experiment_id: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
:keyword owner:
:paramtype owner: ~flow.models.SchemaContractsCreatedBy
:keyword flow_resource_id:
:paramtype flow_resource_id: str
:keyword is_archived:
:paramtype is_archived: bool
:keyword flow_definition_file_path:
:paramtype flow_definition_file_path: str
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword identity:
:paramtype identity: str
"""
super(FlowDto, self).__init__(**kwargs)
self.timestamp = kwargs.get('timestamp', None)
self.e_tag = kwargs.get('e_tag', None)
self.flow = kwargs.get('flow', None)
self.flow_run_settings = kwargs.get('flow_run_settings', None)
self.flow_run_result = kwargs.get('flow_run_result', None)
self.flow_test_mode = kwargs.get('flow_test_mode', None)
self.flow_test_infos = kwargs.get('flow_test_infos', None)
self.studio_portal_endpoint = kwargs.get('studio_portal_endpoint', None)
self.flow_id = kwargs.get('flow_id', None)
self.flow_name = kwargs.get('flow_name', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.flow_type = kwargs.get('flow_type', None)
self.experiment_id = kwargs.get('experiment_id', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
self.owner = kwargs.get('owner', None)
self.flow_resource_id = kwargs.get('flow_resource_id', None)
self.is_archived = kwargs.get('is_archived', None)
self.flow_definition_file_path = kwargs.get('flow_definition_file_path', None)
self.vm_size = kwargs.get('vm_size', None)
self.max_idle_time_seconds = kwargs.get('max_idle_time_seconds', None)
self.identity = kwargs.get('identity', None)
class FlowEnvironment(msrest.serialization.Model):
"""FlowEnvironment.
:ivar image:
:vartype image: str
:ivar python_requirements_txt:
:vartype python_requirements_txt: str
"""
_attribute_map = {
'image': {'key': 'image', 'type': 'str'},
'python_requirements_txt': {'key': 'python_requirements_txt', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword image:
:paramtype image: str
:keyword python_requirements_txt:
:paramtype python_requirements_txt: str
"""
super(FlowEnvironment, self).__init__(**kwargs)
self.image = kwargs.get('image', None)
self.python_requirements_txt = kwargs.get('python_requirements_txt', None)
class FlowFeature(msrest.serialization.Model):
"""FlowFeature.
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar state:
:vartype state: ~flow.models.FlowFeatureState
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'state': {'key': 'state', 'type': 'FlowFeatureState'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword state:
:paramtype state: ~flow.models.FlowFeatureState
"""
super(FlowFeature, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.description = kwargs.get('description', None)
self.state = kwargs.get('state', None)
class FlowFeatureState(msrest.serialization.Model):
"""FlowFeatureState.
:ivar runtime: Possible values include: "Ready", "E2ETest".
:vartype runtime: str or ~flow.models.FlowFeatureStateEnum
:ivar executor: Possible values include: "Ready", "E2ETest".
:vartype executor: str or ~flow.models.FlowFeatureStateEnum
:ivar pfs: Possible values include: "Ready", "E2ETest".
:vartype pfs: str or ~flow.models.FlowFeatureStateEnum
"""
_attribute_map = {
'runtime': {'key': 'Runtime', 'type': 'str'},
'executor': {'key': 'Executor', 'type': 'str'},
'pfs': {'key': 'PFS', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword runtime: Possible values include: "Ready", "E2ETest".
:paramtype runtime: str or ~flow.models.FlowFeatureStateEnum
:keyword executor: Possible values include: "Ready", "E2ETest".
:paramtype executor: str or ~flow.models.FlowFeatureStateEnum
:keyword pfs: Possible values include: "Ready", "E2ETest".
:paramtype pfs: str or ~flow.models.FlowFeatureStateEnum
"""
super(FlowFeatureState, self).__init__(**kwargs)
self.runtime = kwargs.get('runtime', None)
self.executor = kwargs.get('executor', None)
self.pfs = kwargs.get('pfs', None)
class FlowGraph(msrest.serialization.Model):
"""FlowGraph.
:ivar nodes:
:vartype nodes: list[~flow.models.Node]
:ivar tools:
:vartype tools: list[~flow.models.Tool]
:ivar codes: This is a dictionary.
:vartype codes: dict[str, str]
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, ~flow.models.FlowInputDefinition]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, ~flow.models.FlowOutputDefinition]
"""
_attribute_map = {
'nodes': {'key': 'nodes', 'type': '[Node]'},
'tools': {'key': 'tools', 'type': '[Tool]'},
'codes': {'key': 'codes', 'type': '{str}'},
'inputs': {'key': 'inputs', 'type': '{FlowInputDefinition}'},
'outputs': {'key': 'outputs', 'type': '{FlowOutputDefinition}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword nodes:
:paramtype nodes: list[~flow.models.Node]
:keyword tools:
:paramtype tools: list[~flow.models.Tool]
:keyword codes: This is a dictionary.
:paramtype codes: dict[str, str]
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, ~flow.models.FlowInputDefinition]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, ~flow.models.FlowOutputDefinition]
"""
super(FlowGraph, self).__init__(**kwargs)
self.nodes = kwargs.get('nodes', None)
self.tools = kwargs.get('tools', None)
self.codes = kwargs.get('codes', None)
self.inputs = kwargs.get('inputs', None)
self.outputs = kwargs.get('outputs', None)
class FlowGraphAnnotationNode(msrest.serialization.Model):
"""FlowGraphAnnotationNode.
:ivar id:
:vartype id: str
:ivar content:
:vartype content: str
:ivar mentioned_node_names:
:vartype mentioned_node_names: list[str]
:ivar structured_content:
:vartype structured_content: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'content': {'key': 'content', 'type': 'str'},
'mentioned_node_names': {'key': 'mentionedNodeNames', 'type': '[str]'},
'structured_content': {'key': 'structuredContent', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword content:
:paramtype content: str
:keyword mentioned_node_names:
:paramtype mentioned_node_names: list[str]
:keyword structured_content:
:paramtype structured_content: str
"""
super(FlowGraphAnnotationNode, self).__init__(**kwargs)
self.id = kwargs.get('id', None)
self.content = kwargs.get('content', None)
self.mentioned_node_names = kwargs.get('mentioned_node_names', None)
self.structured_content = kwargs.get('structured_content', None)
class FlowGraphLayout(msrest.serialization.Model):
"""FlowGraphLayout.
:ivar node_layouts: This is a dictionary.
:vartype node_layouts: dict[str, ~flow.models.FlowNodeLayout]
:ivar extended_data:
:vartype extended_data: str
:ivar annotation_nodes:
:vartype annotation_nodes: list[~flow.models.FlowGraphAnnotationNode]
:ivar orientation: Possible values include: "Horizontal", "Vertical".
:vartype orientation: str or ~flow.models.Orientation
"""
_attribute_map = {
'node_layouts': {'key': 'nodeLayouts', 'type': '{FlowNodeLayout}'},
'extended_data': {'key': 'extendedData', 'type': 'str'},
'annotation_nodes': {'key': 'annotationNodes', 'type': '[FlowGraphAnnotationNode]'},
'orientation': {'key': 'orientation', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword node_layouts: This is a dictionary.
:paramtype node_layouts: dict[str, ~flow.models.FlowNodeLayout]
:keyword extended_data:
:paramtype extended_data: str
:keyword annotation_nodes:
:paramtype annotation_nodes: list[~flow.models.FlowGraphAnnotationNode]
:keyword orientation: Possible values include: "Horizontal", "Vertical".
:paramtype orientation: str or ~flow.models.Orientation
"""
super(FlowGraphLayout, self).__init__(**kwargs)
self.node_layouts = kwargs.get('node_layouts', None)
self.extended_data = kwargs.get('extended_data', None)
self.annotation_nodes = kwargs.get('annotation_nodes', None)
self.orientation = kwargs.get('orientation', None)
class FlowGraphReference(msrest.serialization.Model):
"""FlowGraphReference.
:ivar flow_graph:
:vartype flow_graph: ~flow.models.FlowGraph
:ivar reference_resource_id:
:vartype reference_resource_id: str
"""
_attribute_map = {
'flow_graph': {'key': 'flowGraph', 'type': 'FlowGraph'},
'reference_resource_id': {'key': 'referenceResourceId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword flow_graph:
:paramtype flow_graph: ~flow.models.FlowGraph
:keyword reference_resource_id:
:paramtype reference_resource_id: str
"""
super(FlowGraphReference, self).__init__(**kwargs)
self.flow_graph = kwargs.get('flow_graph', None)
self.reference_resource_id = kwargs.get('reference_resource_id', None)
class FlowIndexEntity(msrest.serialization.Model):
"""FlowIndexEntity.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar schema_id:
:vartype schema_id: str
:ivar entity_id:
:vartype entity_id: str
:ivar kind: Possible values include: "Invalid", "LineageRoot", "Versioned", "Unversioned".
:vartype kind: str or ~flow.models.EntityKind
:ivar annotations:
:vartype annotations: ~flow.models.FlowAnnotations
:ivar properties:
:vartype properties: ~flow.models.FlowProperties
:ivar internal: Any object.
:vartype internal: any
:ivar update_sequence:
:vartype update_sequence: long
:ivar type:
:vartype type: str
:ivar version:
:vartype version: str
:ivar entity_container_id:
:vartype entity_container_id: str
:ivar entity_object_id:
:vartype entity_object_id: str
:ivar resource_type:
:vartype resource_type: str
:ivar relationships:
:vartype relationships: list[~flow.models.Relationship]
:ivar asset_id:
:vartype asset_id: str
"""
_validation = {
'version': {'readonly': True},
'entity_container_id': {'readonly': True},
'entity_object_id': {'readonly': True},
'resource_type': {'readonly': True},
}
_attribute_map = {
'schema_id': {'key': 'schemaId', 'type': 'str'},
'entity_id': {'key': 'entityId', 'type': 'str'},
'kind': {'key': 'kind', 'type': 'str'},
'annotations': {'key': 'annotations', 'type': 'FlowAnnotations'},
'properties': {'key': 'properties', 'type': 'FlowProperties'},
'internal': {'key': 'internal', 'type': 'object'},
'update_sequence': {'key': 'updateSequence', 'type': 'long'},
'type': {'key': 'type', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'entity_container_id': {'key': 'entityContainerId', 'type': 'str'},
'entity_object_id': {'key': 'entityObjectId', 'type': 'str'},
'resource_type': {'key': 'resourceType', 'type': 'str'},
'relationships': {'key': 'relationships', 'type': '[Relationship]'},
'asset_id': {'key': 'assetId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword schema_id:
:paramtype schema_id: str
:keyword entity_id:
:paramtype entity_id: str
:keyword kind: Possible values include: "Invalid", "LineageRoot", "Versioned", "Unversioned".
:paramtype kind: str or ~flow.models.EntityKind
:keyword annotations:
:paramtype annotations: ~flow.models.FlowAnnotations
:keyword properties:
:paramtype properties: ~flow.models.FlowProperties
:keyword internal: Any object.
:paramtype internal: any
:keyword update_sequence:
:paramtype update_sequence: long
:keyword type:
:paramtype type: str
:keyword relationships:
:paramtype relationships: list[~flow.models.Relationship]
:keyword asset_id:
:paramtype asset_id: str
"""
super(FlowIndexEntity, self).__init__(**kwargs)
self.schema_id = kwargs.get('schema_id', None)
self.entity_id = kwargs.get('entity_id', None)
self.kind = kwargs.get('kind', None)
self.annotations = kwargs.get('annotations', None)
self.properties = kwargs.get('properties', None)
self.internal = kwargs.get('internal', None)
self.update_sequence = kwargs.get('update_sequence', None)
self.type = kwargs.get('type', None)
self.version = None
self.entity_container_id = None
self.entity_object_id = None
self.resource_type = None
self.relationships = kwargs.get('relationships', None)
self.asset_id = kwargs.get('asset_id', None)
class FlowInputDefinition(msrest.serialization.Model):
"""FlowInputDefinition.
:ivar name:
:vartype name: str
:ivar type: Possible values include: "int", "double", "bool", "string", "secret",
"prompt_template", "object", "list", "BingConnection", "OpenAIConnection",
"AzureOpenAIConnection", "AzureContentModeratorConnection", "CustomConnection",
"AzureContentSafetyConnection", "SerpConnection", "CognitiveSearchConnection",
"SubstrateLLMConnection", "PineconeConnection", "QdrantConnection", "WeaviateConnection",
"function_list", "function_str", "FormRecognizerConnection", "file_path", "image".
:vartype type: str or ~flow.models.ValueType
:ivar default: Anything.
:vartype default: any
:ivar description:
:vartype description: str
:ivar is_chat_input:
:vartype is_chat_input: bool
:ivar is_chat_history:
:vartype is_chat_history: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'default': {'key': 'default', 'type': 'object'},
'description': {'key': 'description', 'type': 'str'},
'is_chat_input': {'key': 'is_chat_input', 'type': 'bool'},
'is_chat_history': {'key': 'is_chat_history', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type: Possible values include: "int", "double", "bool", "string", "secret",
"prompt_template", "object", "list", "BingConnection", "OpenAIConnection",
"AzureOpenAIConnection", "AzureContentModeratorConnection", "CustomConnection",
"AzureContentSafetyConnection", "SerpConnection", "CognitiveSearchConnection",
"SubstrateLLMConnection", "PineconeConnection", "QdrantConnection", "WeaviateConnection",
"function_list", "function_str", "FormRecognizerConnection", "file_path", "image".
:paramtype type: str or ~flow.models.ValueType
:keyword default: Anything.
:paramtype default: any
:keyword description:
:paramtype description: str
:keyword is_chat_input:
:paramtype is_chat_input: bool
:keyword is_chat_history:
:paramtype is_chat_history: bool
"""
super(FlowInputDefinition, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.type = kwargs.get('type', None)
self.default = kwargs.get('default', None)
self.description = kwargs.get('description', None)
self.is_chat_input = kwargs.get('is_chat_input', None)
self.is_chat_history = kwargs.get('is_chat_history', None)
class FlowNode(msrest.serialization.Model):
"""FlowNode.
:ivar name:
:vartype name: str
:ivar type: Possible values include: "llm", "python", "action", "prompt", "custom_llm",
"csharp".
:vartype type: str or ~flow.models.ToolType
:ivar source:
:vartype source: ~flow.models.NodeSource
:ivar inputs: Dictionary of :code:`<any>`.
:vartype inputs: dict[str, any]
:ivar use_variants:
:vartype use_variants: bool
:ivar activate:
:vartype activate: ~flow.models.Activate
:ivar comment:
:vartype comment: str
:ivar api:
:vartype api: str
:ivar provider:
:vartype provider: str
:ivar connection:
:vartype connection: str
:ivar module:
:vartype module: str
:ivar aggregation:
:vartype aggregation: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'source': {'key': 'source', 'type': 'NodeSource'},
'inputs': {'key': 'inputs', 'type': '{object}'},
'use_variants': {'key': 'use_variants', 'type': 'bool'},
'activate': {'key': 'activate', 'type': 'Activate'},
'comment': {'key': 'comment', 'type': 'str'},
'api': {'key': 'api', 'type': 'str'},
'provider': {'key': 'provider', 'type': 'str'},
'connection': {'key': 'connection', 'type': 'str'},
'module': {'key': 'module', 'type': 'str'},
'aggregation': {'key': 'aggregation', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type: Possible values include: "llm", "python", "action", "prompt", "custom_llm",
"csharp".
:paramtype type: str or ~flow.models.ToolType
:keyword source:
:paramtype source: ~flow.models.NodeSource
:keyword inputs: Dictionary of :code:`<any>`.
:paramtype inputs: dict[str, any]
:keyword use_variants:
:paramtype use_variants: bool
:keyword activate:
:paramtype activate: ~flow.models.Activate
:keyword comment:
:paramtype comment: str
:keyword api:
:paramtype api: str
:keyword provider:
:paramtype provider: str
:keyword connection:
:paramtype connection: str
:keyword module:
:paramtype module: str
:keyword aggregation:
:paramtype aggregation: bool
"""
super(FlowNode, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.type = kwargs.get('type', None)
self.source = kwargs.get('source', None)
self.inputs = kwargs.get('inputs', None)
self.use_variants = kwargs.get('use_variants', None)
self.activate = kwargs.get('activate', None)
self.comment = kwargs.get('comment', None)
self.api = kwargs.get('api', None)
self.provider = kwargs.get('provider', None)
self.connection = kwargs.get('connection', None)
self.module = kwargs.get('module', None)
self.aggregation = kwargs.get('aggregation', None)
class FlowNodeLayout(msrest.serialization.Model):
"""FlowNodeLayout.
:ivar x:
:vartype x: float
:ivar y:
:vartype y: float
:ivar width:
:vartype width: float
:ivar height:
:vartype height: float
:ivar index:
:vartype index: int
:ivar extended_data:
:vartype extended_data: str
"""
_attribute_map = {
'x': {'key': 'x', 'type': 'float'},
'y': {'key': 'y', 'type': 'float'},
'width': {'key': 'width', 'type': 'float'},
'height': {'key': 'height', 'type': 'float'},
'index': {'key': 'index', 'type': 'int'},
'extended_data': {'key': 'extendedData', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword x:
:paramtype x: float
:keyword y:
:paramtype y: float
:keyword width:
:paramtype width: float
:keyword height:
:paramtype height: float
:keyword index:
:paramtype index: int
:keyword extended_data:
:paramtype extended_data: str
"""
super(FlowNodeLayout, self).__init__(**kwargs)
self.x = kwargs.get('x', None)
self.y = kwargs.get('y', None)
self.width = kwargs.get('width', None)
self.height = kwargs.get('height', None)
self.index = kwargs.get('index', None)
self.extended_data = kwargs.get('extended_data', None)
class FlowNodeVariant(msrest.serialization.Model):
"""FlowNodeVariant.
:ivar default_variant_id:
:vartype default_variant_id: str
:ivar variants: This is a dictionary.
:vartype variants: dict[str, ~flow.models.FlowVariantNode]
"""
_attribute_map = {
'default_variant_id': {'key': 'default_variant_id', 'type': 'str'},
'variants': {'key': 'variants', 'type': '{FlowVariantNode}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword default_variant_id:
:paramtype default_variant_id: str
:keyword variants: This is a dictionary.
:paramtype variants: dict[str, ~flow.models.FlowVariantNode]
"""
super(FlowNodeVariant, self).__init__(**kwargs)
self.default_variant_id = kwargs.get('default_variant_id', None)
self.variants = kwargs.get('variants', None)
class FlowOutputDefinition(msrest.serialization.Model):
"""FlowOutputDefinition.
:ivar name:
:vartype name: str
:ivar type: Possible values include: "int", "double", "bool", "string", "secret",
"prompt_template", "object", "list", "BingConnection", "OpenAIConnection",
"AzureOpenAIConnection", "AzureContentModeratorConnection", "CustomConnection",
"AzureContentSafetyConnection", "SerpConnection", "CognitiveSearchConnection",
"SubstrateLLMConnection", "PineconeConnection", "QdrantConnection", "WeaviateConnection",
"function_list", "function_str", "FormRecognizerConnection", "file_path", "image".
:vartype type: str or ~flow.models.ValueType
:ivar description:
:vartype description: str
:ivar reference:
:vartype reference: str
:ivar evaluation_only:
:vartype evaluation_only: bool
:ivar is_chat_output:
:vartype is_chat_output: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'reference': {'key': 'reference', 'type': 'str'},
'evaluation_only': {'key': 'evaluation_only', 'type': 'bool'},
'is_chat_output': {'key': 'is_chat_output', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type: Possible values include: "int", "double", "bool", "string", "secret",
"prompt_template", "object", "list", "BingConnection", "OpenAIConnection",
"AzureOpenAIConnection", "AzureContentModeratorConnection", "CustomConnection",
"AzureContentSafetyConnection", "SerpConnection", "CognitiveSearchConnection",
"SubstrateLLMConnection", "PineconeConnection", "QdrantConnection", "WeaviateConnection",
"function_list", "function_str", "FormRecognizerConnection", "file_path", "image".
:paramtype type: str or ~flow.models.ValueType
:keyword description:
:paramtype description: str
:keyword reference:
:paramtype reference: str
:keyword evaluation_only:
:paramtype evaluation_only: bool
:keyword is_chat_output:
:paramtype is_chat_output: bool
"""
super(FlowOutputDefinition, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.type = kwargs.get('type', None)
self.description = kwargs.get('description', None)
self.reference = kwargs.get('reference', None)
self.evaluation_only = kwargs.get('evaluation_only', None)
self.is_chat_output = kwargs.get('is_chat_output', None)
class FlowProperties(msrest.serialization.Model):
"""FlowProperties.
:ivar flow_id:
:vartype flow_id: str
:ivar experiment_id:
:vartype experiment_id: str
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar flow_definition_file_path:
:vartype flow_definition_file_path: str
:ivar creation_context:
:vartype creation_context: ~flow.models.CreationContext
"""
_attribute_map = {
'flow_id': {'key': 'flowId', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'flow_definition_file_path': {'key': 'flowDefinitionFilePath', 'type': 'str'},
'creation_context': {'key': 'creationContext', 'type': 'CreationContext'},
}
def __init__(
self,
**kwargs
):
"""
:keyword flow_id:
:paramtype flow_id: str
:keyword experiment_id:
:paramtype experiment_id: str
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword flow_definition_file_path:
:paramtype flow_definition_file_path: str
:keyword creation_context:
:paramtype creation_context: ~flow.models.CreationContext
"""
super(FlowProperties, self).__init__(**kwargs)
self.flow_id = kwargs.get('flow_id', None)
self.experiment_id = kwargs.get('experiment_id', None)
self.flow_type = kwargs.get('flow_type', None)
self.flow_definition_file_path = kwargs.get('flow_definition_file_path', None)
self.creation_context = kwargs.get('creation_context', None)
class FlowRunBasePath(msrest.serialization.Model):
"""FlowRunBasePath.
:ivar output_datastore_name:
:vartype output_datastore_name: str
:ivar base_path:
:vartype base_path: str
"""
_attribute_map = {
'output_datastore_name': {'key': 'outputDatastoreName', 'type': 'str'},
'base_path': {'key': 'basePath', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword output_datastore_name:
:paramtype output_datastore_name: str
:keyword base_path:
:paramtype base_path: str
"""
super(FlowRunBasePath, self).__init__(**kwargs)
self.output_datastore_name = kwargs.get('output_datastore_name', None)
self.base_path = kwargs.get('base_path', None)
class FlowRunInfo(msrest.serialization.Model):
"""FlowRunInfo.
:ivar flow_graph:
:vartype flow_graph: ~flow.models.FlowGraph
:ivar flow_graph_layout:
:vartype flow_graph_layout: ~flow.models.FlowGraphLayout
:ivar flow_name:
:vartype flow_name: str
:ivar flow_run_resource_id:
:vartype flow_run_resource_id: str
:ivar flow_run_id:
:vartype flow_run_id: str
:ivar flow_run_display_name:
:vartype flow_run_display_name: str
:ivar batch_inputs:
:vartype batch_inputs: list[dict[str, any]]
:ivar batch_data_input:
:vartype batch_data_input: ~flow.models.BatchDataInput
:ivar flow_run_type: Possible values include: "FlowRun", "EvaluationRun",
"PairwiseEvaluationRun", "SingleNodeRun", "FromNodeRun".
:vartype flow_run_type: str or ~flow.models.FlowRunTypeEnum
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar runtime_name:
:vartype runtime_name: str
:ivar bulk_test_id:
:vartype bulk_test_id: str
:ivar created_by:
:vartype created_by: ~flow.models.SchemaContractsCreatedBy
:ivar created_on:
:vartype created_on: ~datetime.datetime
:ivar inputs_mapping: This is a dictionary.
:vartype inputs_mapping: dict[str, str]
:ivar output_datastore_name:
:vartype output_datastore_name: str
:ivar child_run_base_path:
:vartype child_run_base_path: str
:ivar working_directory:
:vartype working_directory: str
:ivar flow_dag_file_relative_path:
:vartype flow_dag_file_relative_path: str
:ivar flow_snapshot_id:
:vartype flow_snapshot_id: str
:ivar studio_portal_endpoint:
:vartype studio_portal_endpoint: str
"""
_attribute_map = {
'flow_graph': {'key': 'flowGraph', 'type': 'FlowGraph'},
'flow_graph_layout': {'key': 'flowGraphLayout', 'type': 'FlowGraphLayout'},
'flow_name': {'key': 'flowName', 'type': 'str'},
'flow_run_resource_id': {'key': 'flowRunResourceId', 'type': 'str'},
'flow_run_id': {'key': 'flowRunId', 'type': 'str'},
'flow_run_display_name': {'key': 'flowRunDisplayName', 'type': 'str'},
'batch_inputs': {'key': 'batchInputs', 'type': '[{object}]'},
'batch_data_input': {'key': 'batchDataInput', 'type': 'BatchDataInput'},
'flow_run_type': {'key': 'flowRunType', 'type': 'str'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'bulk_test_id': {'key': 'bulkTestId', 'type': 'str'},
'created_by': {'key': 'createdBy', 'type': 'SchemaContractsCreatedBy'},
'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
'inputs_mapping': {'key': 'inputsMapping', 'type': '{str}'},
'output_datastore_name': {'key': 'outputDatastoreName', 'type': 'str'},
'child_run_base_path': {'key': 'childRunBasePath', 'type': 'str'},
'working_directory': {'key': 'workingDirectory', 'type': 'str'},
'flow_dag_file_relative_path': {'key': 'flowDagFileRelativePath', 'type': 'str'},
'flow_snapshot_id': {'key': 'flowSnapshotId', 'type': 'str'},
'studio_portal_endpoint': {'key': 'studioPortalEndpoint', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword flow_graph:
:paramtype flow_graph: ~flow.models.FlowGraph
:keyword flow_graph_layout:
:paramtype flow_graph_layout: ~flow.models.FlowGraphLayout
:keyword flow_name:
:paramtype flow_name: str
:keyword flow_run_resource_id:
:paramtype flow_run_resource_id: str
:keyword flow_run_id:
:paramtype flow_run_id: str
:keyword flow_run_display_name:
:paramtype flow_run_display_name: str
:keyword batch_inputs:
:paramtype batch_inputs: list[dict[str, any]]
:keyword batch_data_input:
:paramtype batch_data_input: ~flow.models.BatchDataInput
:keyword flow_run_type: Possible values include: "FlowRun", "EvaluationRun",
"PairwiseEvaluationRun", "SingleNodeRun", "FromNodeRun".
:paramtype flow_run_type: str or ~flow.models.FlowRunTypeEnum
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword runtime_name:
:paramtype runtime_name: str
:keyword bulk_test_id:
:paramtype bulk_test_id: str
:keyword created_by:
:paramtype created_by: ~flow.models.SchemaContractsCreatedBy
:keyword created_on:
:paramtype created_on: ~datetime.datetime
:keyword inputs_mapping: This is a dictionary.
:paramtype inputs_mapping: dict[str, str]
:keyword output_datastore_name:
:paramtype output_datastore_name: str
:keyword child_run_base_path:
:paramtype child_run_base_path: str
:keyword working_directory:
:paramtype working_directory: str
:keyword flow_dag_file_relative_path:
:paramtype flow_dag_file_relative_path: str
:keyword flow_snapshot_id:
:paramtype flow_snapshot_id: str
:keyword studio_portal_endpoint:
:paramtype studio_portal_endpoint: str
"""
super(FlowRunInfo, self).__init__(**kwargs)
self.flow_graph = kwargs.get('flow_graph', None)
self.flow_graph_layout = kwargs.get('flow_graph_layout', None)
self.flow_name = kwargs.get('flow_name', None)
self.flow_run_resource_id = kwargs.get('flow_run_resource_id', None)
self.flow_run_id = kwargs.get('flow_run_id', None)
self.flow_run_display_name = kwargs.get('flow_run_display_name', None)
self.batch_inputs = kwargs.get('batch_inputs', None)
self.batch_data_input = kwargs.get('batch_data_input', None)
self.flow_run_type = kwargs.get('flow_run_type', None)
self.flow_type = kwargs.get('flow_type', None)
self.runtime_name = kwargs.get('runtime_name', None)
self.bulk_test_id = kwargs.get('bulk_test_id', None)
self.created_by = kwargs.get('created_by', None)
self.created_on = kwargs.get('created_on', None)
self.inputs_mapping = kwargs.get('inputs_mapping', None)
self.output_datastore_name = kwargs.get('output_datastore_name', None)
self.child_run_base_path = kwargs.get('child_run_base_path', None)
self.working_directory = kwargs.get('working_directory', None)
self.flow_dag_file_relative_path = kwargs.get('flow_dag_file_relative_path', None)
self.flow_snapshot_id = kwargs.get('flow_snapshot_id', None)
self.studio_portal_endpoint = kwargs.get('studio_portal_endpoint', None)
class FlowRunResult(msrest.serialization.Model):
"""FlowRunResult.
:ivar flow_runs:
:vartype flow_runs: list[any]
:ivar node_runs:
:vartype node_runs: list[any]
:ivar error_response: The error response.
:vartype error_response: ~flow.models.ErrorResponse
:ivar flow_name:
:vartype flow_name: str
:ivar flow_run_display_name:
:vartype flow_run_display_name: str
:ivar flow_run_id:
:vartype flow_run_id: str
:ivar flow_graph:
:vartype flow_graph: ~flow.models.FlowGraph
:ivar flow_graph_layout:
:vartype flow_graph_layout: ~flow.models.FlowGraphLayout
:ivar flow_run_resource_id:
:vartype flow_run_resource_id: str
:ivar bulk_test_id:
:vartype bulk_test_id: str
:ivar batch_inputs:
:vartype batch_inputs: list[dict[str, any]]
:ivar batch_data_input:
:vartype batch_data_input: ~flow.models.BatchDataInput
:ivar created_by:
:vartype created_by: ~flow.models.SchemaContractsCreatedBy
:ivar created_on:
:vartype created_on: ~datetime.datetime
:ivar flow_run_type: Possible values include: "FlowRun", "EvaluationRun",
"PairwiseEvaluationRun", "SingleNodeRun", "FromNodeRun".
:vartype flow_run_type: str or ~flow.models.FlowRunTypeEnum
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar runtime_name:
:vartype runtime_name: str
:ivar aml_compute_name:
:vartype aml_compute_name: str
:ivar flow_run_logs: Dictionary of :code:`<string>`.
:vartype flow_run_logs: dict[str, str]
:ivar flow_test_mode: Possible values include: "Sync", "Async".
:vartype flow_test_mode: str or ~flow.models.FlowTestMode
:ivar flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:vartype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:ivar working_directory:
:vartype working_directory: str
:ivar flow_dag_file_relative_path:
:vartype flow_dag_file_relative_path: str
:ivar flow_snapshot_id:
:vartype flow_snapshot_id: str
:ivar variant_run_to_evaluation_runs_id_mapping: Dictionary of
<components·1k1eaeg·schemas·flowrunresult·properties·variantruntoevaluationrunsidmapping·additionalproperties>.
:vartype variant_run_to_evaluation_runs_id_mapping: dict[str, list[str]]
"""
_attribute_map = {
'flow_runs': {'key': 'flow_runs', 'type': '[object]'},
'node_runs': {'key': 'node_runs', 'type': '[object]'},
'error_response': {'key': 'errorResponse', 'type': 'ErrorResponse'},
'flow_name': {'key': 'flowName', 'type': 'str'},
'flow_run_display_name': {'key': 'flowRunDisplayName', 'type': 'str'},
'flow_run_id': {'key': 'flowRunId', 'type': 'str'},
'flow_graph': {'key': 'flowGraph', 'type': 'FlowGraph'},
'flow_graph_layout': {'key': 'flowGraphLayout', 'type': 'FlowGraphLayout'},
'flow_run_resource_id': {'key': 'flowRunResourceId', 'type': 'str'},
'bulk_test_id': {'key': 'bulkTestId', 'type': 'str'},
'batch_inputs': {'key': 'batchInputs', 'type': '[{object}]'},
'batch_data_input': {'key': 'batchDataInput', 'type': 'BatchDataInput'},
'created_by': {'key': 'createdBy', 'type': 'SchemaContractsCreatedBy'},
'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
'flow_run_type': {'key': 'flowRunType', 'type': 'str'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'aml_compute_name': {'key': 'amlComputeName', 'type': 'str'},
'flow_run_logs': {'key': 'flowRunLogs', 'type': '{str}'},
'flow_test_mode': {'key': 'flowTestMode', 'type': 'str'},
'flow_test_infos': {'key': 'flowTestInfos', 'type': '{FlowTestInfo}'},
'working_directory': {'key': 'workingDirectory', 'type': 'str'},
'flow_dag_file_relative_path': {'key': 'flowDagFileRelativePath', 'type': 'str'},
'flow_snapshot_id': {'key': 'flowSnapshotId', 'type': 'str'},
'variant_run_to_evaluation_runs_id_mapping': {'key': 'variantRunToEvaluationRunsIdMapping', 'type': '{[str]}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword flow_runs:
:paramtype flow_runs: list[any]
:keyword node_runs:
:paramtype node_runs: list[any]
:keyword error_response: The error response.
:paramtype error_response: ~flow.models.ErrorResponse
:keyword flow_name:
:paramtype flow_name: str
:keyword flow_run_display_name:
:paramtype flow_run_display_name: str
:keyword flow_run_id:
:paramtype flow_run_id: str
:keyword flow_graph:
:paramtype flow_graph: ~flow.models.FlowGraph
:keyword flow_graph_layout:
:paramtype flow_graph_layout: ~flow.models.FlowGraphLayout
:keyword flow_run_resource_id:
:paramtype flow_run_resource_id: str
:keyword bulk_test_id:
:paramtype bulk_test_id: str
:keyword batch_inputs:
:paramtype batch_inputs: list[dict[str, any]]
:keyword batch_data_input:
:paramtype batch_data_input: ~flow.models.BatchDataInput
:keyword created_by:
:paramtype created_by: ~flow.models.SchemaContractsCreatedBy
:keyword created_on:
:paramtype created_on: ~datetime.datetime
:keyword flow_run_type: Possible values include: "FlowRun", "EvaluationRun",
"PairwiseEvaluationRun", "SingleNodeRun", "FromNodeRun".
:paramtype flow_run_type: str or ~flow.models.FlowRunTypeEnum
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword runtime_name:
:paramtype runtime_name: str
:keyword aml_compute_name:
:paramtype aml_compute_name: str
:keyword flow_run_logs: Dictionary of :code:`<string>`.
:paramtype flow_run_logs: dict[str, str]
:keyword flow_test_mode: Possible values include: "Sync", "Async".
:paramtype flow_test_mode: str or ~flow.models.FlowTestMode
:keyword flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:paramtype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:keyword working_directory:
:paramtype working_directory: str
:keyword flow_dag_file_relative_path:
:paramtype flow_dag_file_relative_path: str
:keyword flow_snapshot_id:
:paramtype flow_snapshot_id: str
:keyword variant_run_to_evaluation_runs_id_mapping: Dictionary of
<components·1k1eaeg·schemas·flowrunresult·properties·variantruntoevaluationrunsidmapping·additionalproperties>.
:paramtype variant_run_to_evaluation_runs_id_mapping: dict[str, list[str]]
"""
super(FlowRunResult, self).__init__(**kwargs)
self.flow_runs = kwargs.get('flow_runs', None)
self.node_runs = kwargs.get('node_runs', None)
self.error_response = kwargs.get('error_response', None)
self.flow_name = kwargs.get('flow_name', None)
self.flow_run_display_name = kwargs.get('flow_run_display_name', None)
self.flow_run_id = kwargs.get('flow_run_id', None)
self.flow_graph = kwargs.get('flow_graph', None)
self.flow_graph_layout = kwargs.get('flow_graph_layout', None)
self.flow_run_resource_id = kwargs.get('flow_run_resource_id', None)
self.bulk_test_id = kwargs.get('bulk_test_id', None)
self.batch_inputs = kwargs.get('batch_inputs', None)
self.batch_data_input = kwargs.get('batch_data_input', None)
self.created_by = kwargs.get('created_by', None)
self.created_on = kwargs.get('created_on', None)
self.flow_run_type = kwargs.get('flow_run_type', None)
self.flow_type = kwargs.get('flow_type', None)
self.runtime_name = kwargs.get('runtime_name', None)
self.aml_compute_name = kwargs.get('aml_compute_name', None)
self.flow_run_logs = kwargs.get('flow_run_logs', None)
self.flow_test_mode = kwargs.get('flow_test_mode', None)
self.flow_test_infos = kwargs.get('flow_test_infos', None)
self.working_directory = kwargs.get('working_directory', None)
self.flow_dag_file_relative_path = kwargs.get('flow_dag_file_relative_path', None)
self.flow_snapshot_id = kwargs.get('flow_snapshot_id', None)
self.variant_run_to_evaluation_runs_id_mapping = kwargs.get('variant_run_to_evaluation_runs_id_mapping', None)
class FlowRunSettings(msrest.serialization.Model):
"""FlowRunSettings.
:ivar flow_run_display_name:
:vartype flow_run_display_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar run_mode: Possible values include: "Flow", "SingleNode", "FromNode", "BulkTest", "Eval",
"PairwiseEval".
:vartype run_mode: str or ~flow.models.FlowRunMode
:ivar batch_inputs:
:vartype batch_inputs: list[dict[str, any]]
:ivar batch_data_input:
:vartype batch_data_input: ~flow.models.BatchDataInput
:ivar tuning_node_names:
:vartype tuning_node_names: list[str]
:ivar tuning_node_settings: This is a dictionary.
:vartype tuning_node_settings: dict[str, ~flow.models.TuningNodeSetting]
:ivar baseline_variant_id:
:vartype baseline_variant_id: str
:ivar default_variant_id:
:vartype default_variant_id: str
:ivar variants: This is a dictionary.
:vartype variants: dict[str, list[~flow.models.Node]]
:ivar variants_tools:
:vartype variants_tools: list[~flow.models.Tool]
:ivar variants_codes: This is a dictionary.
:vartype variants_codes: dict[str, str]
:ivar node_name:
:vartype node_name: str
:ivar bulk_test_id:
:vartype bulk_test_id: str
:ivar evaluation_flow_run_settings: This is a dictionary.
:vartype evaluation_flow_run_settings: dict[str, ~flow.models.EvaluationFlowRunSettings]
:ivar inputs_mapping: This is a dictionary.
:vartype inputs_mapping: dict[str, str]
:ivar data_inputs: This is a dictionary.
:vartype data_inputs: dict[str, str]
:ivar bulk_test_flow_id:
:vartype bulk_test_flow_id: str
:ivar bulk_test_flow_run_ids:
:vartype bulk_test_flow_run_ids: list[str]
:ivar aml_compute_name:
:vartype aml_compute_name: str
:ivar runtime_name:
:vartype runtime_name: str
:ivar flow_run_output_directory:
:vartype flow_run_output_directory: str
"""
_attribute_map = {
'flow_run_display_name': {'key': 'flowRunDisplayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'run_mode': {'key': 'runMode', 'type': 'str'},
'batch_inputs': {'key': 'batch_inputs', 'type': '[{object}]'},
'batch_data_input': {'key': 'batchDataInput', 'type': 'BatchDataInput'},
'tuning_node_names': {'key': 'tuningNodeNames', 'type': '[str]'},
'tuning_node_settings': {'key': 'tuningNodeSettings', 'type': '{TuningNodeSetting}'},
'baseline_variant_id': {'key': 'baselineVariantId', 'type': 'str'},
'default_variant_id': {'key': 'defaultVariantId', 'type': 'str'},
'variants': {'key': 'variants', 'type': '{[Node]}'},
'variants_tools': {'key': 'variantsTools', 'type': '[Tool]'},
'variants_codes': {'key': 'variantsCodes', 'type': '{str}'},
'node_name': {'key': 'nodeName', 'type': 'str'},
'bulk_test_id': {'key': 'bulkTestId', 'type': 'str'},
'evaluation_flow_run_settings': {'key': 'evaluationFlowRunSettings', 'type': '{EvaluationFlowRunSettings}'},
'inputs_mapping': {'key': 'inputsMapping', 'type': '{str}'},
'data_inputs': {'key': 'dataInputs', 'type': '{str}'},
'bulk_test_flow_id': {'key': 'bulkTestFlowId', 'type': 'str'},
'bulk_test_flow_run_ids': {'key': 'bulkTestFlowRunIds', 'type': '[str]'},
'aml_compute_name': {'key': 'amlComputeName', 'type': 'str'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'flow_run_output_directory': {'key': 'flowRunOutputDirectory', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword flow_run_display_name:
:paramtype flow_run_display_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword run_mode: Possible values include: "Flow", "SingleNode", "FromNode", "BulkTest",
"Eval", "PairwiseEval".
:paramtype run_mode: str or ~flow.models.FlowRunMode
:keyword batch_inputs:
:paramtype batch_inputs: list[dict[str, any]]
:keyword batch_data_input:
:paramtype batch_data_input: ~flow.models.BatchDataInput
:keyword tuning_node_names:
:paramtype tuning_node_names: list[str]
:keyword tuning_node_settings: This is a dictionary.
:paramtype tuning_node_settings: dict[str, ~flow.models.TuningNodeSetting]
:keyword baseline_variant_id:
:paramtype baseline_variant_id: str
:keyword default_variant_id:
:paramtype default_variant_id: str
:keyword variants: This is a dictionary.
:paramtype variants: dict[str, list[~flow.models.Node]]
:keyword variants_tools:
:paramtype variants_tools: list[~flow.models.Tool]
:keyword variants_codes: This is a dictionary.
:paramtype variants_codes: dict[str, str]
:keyword node_name:
:paramtype node_name: str
:keyword bulk_test_id:
:paramtype bulk_test_id: str
:keyword evaluation_flow_run_settings: This is a dictionary.
:paramtype evaluation_flow_run_settings: dict[str, ~flow.models.EvaluationFlowRunSettings]
:keyword inputs_mapping: This is a dictionary.
:paramtype inputs_mapping: dict[str, str]
:keyword data_inputs: This is a dictionary.
:paramtype data_inputs: dict[str, str]
:keyword bulk_test_flow_id:
:paramtype bulk_test_flow_id: str
:keyword bulk_test_flow_run_ids:
:paramtype bulk_test_flow_run_ids: list[str]
:keyword aml_compute_name:
:paramtype aml_compute_name: str
:keyword runtime_name:
:paramtype runtime_name: str
:keyword flow_run_output_directory:
:paramtype flow_run_output_directory: str
"""
super(FlowRunSettings, self).__init__(**kwargs)
self.flow_run_display_name = kwargs.get('flow_run_display_name', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
self.run_mode = kwargs.get('run_mode', None)
self.batch_inputs = kwargs.get('batch_inputs', None)
self.batch_data_input = kwargs.get('batch_data_input', None)
self.tuning_node_names = kwargs.get('tuning_node_names', None)
self.tuning_node_settings = kwargs.get('tuning_node_settings', None)
self.baseline_variant_id = kwargs.get('baseline_variant_id', None)
self.default_variant_id = kwargs.get('default_variant_id', None)
self.variants = kwargs.get('variants', None)
self.variants_tools = kwargs.get('variants_tools', None)
self.variants_codes = kwargs.get('variants_codes', None)
self.node_name = kwargs.get('node_name', None)
self.bulk_test_id = kwargs.get('bulk_test_id', None)
self.evaluation_flow_run_settings = kwargs.get('evaluation_flow_run_settings', None)
self.inputs_mapping = kwargs.get('inputs_mapping', None)
self.data_inputs = kwargs.get('data_inputs', None)
self.bulk_test_flow_id = kwargs.get('bulk_test_flow_id', None)
self.bulk_test_flow_run_ids = kwargs.get('bulk_test_flow_run_ids', None)
self.aml_compute_name = kwargs.get('aml_compute_name', None)
self.runtime_name = kwargs.get('runtime_name', None)
self.flow_run_output_directory = kwargs.get('flow_run_output_directory', None)
class FlowRuntimeCapability(msrest.serialization.Model):
"""FlowRuntimeCapability.
:ivar flow_features:
:vartype flow_features: list[~flow.models.FlowFeature]
"""
_attribute_map = {
'flow_features': {'key': 'flowFeatures', 'type': '[FlowFeature]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword flow_features:
:paramtype flow_features: list[~flow.models.FlowFeature]
"""
super(FlowRuntimeCapability, self).__init__(**kwargs)
self.flow_features = kwargs.get('flow_features', None)
class FlowRuntimeDto(msrest.serialization.Model):
"""FlowRuntimeDto.
:ivar runtime_name:
:vartype runtime_name: str
:ivar runtime_description:
:vartype runtime_description: str
:ivar runtime_type: Possible values include: "ManagedOnlineEndpoint", "ComputeInstance",
"TrainingSession".
:vartype runtime_type: str or ~flow.models.RuntimeType
:ivar environment:
:vartype environment: str
:ivar status: Possible values include: "Unavailable", "Failed", "NotExist", "Starting",
"Stopping".
:vartype status: str or ~flow.models.RuntimeStatusEnum
:ivar status_message:
:vartype status_message: str
:ivar error: The error response.
:vartype error: ~flow.models.ErrorResponse
:ivar from_existing_endpoint:
:vartype from_existing_endpoint: bool
:ivar endpoint_name:
:vartype endpoint_name: str
:ivar from_existing_deployment:
:vartype from_existing_deployment: bool
:ivar deployment_name:
:vartype deployment_name: str
:ivar identity:
:vartype identity: ~flow.models.ManagedServiceIdentity
:ivar instance_type:
:vartype instance_type: str
:ivar instance_count:
:vartype instance_count: int
:ivar compute_instance_name:
:vartype compute_instance_name: str
:ivar docker_image:
:vartype docker_image: str
:ivar published_port:
:vartype published_port: int
:ivar target_port:
:vartype target_port: int
:ivar from_existing_custom_app:
:vartype from_existing_custom_app: bool
:ivar custom_app_name:
:vartype custom_app_name: str
:ivar assigned_to:
:vartype assigned_to: ~flow.models.AssignedUser
:ivar endpoint_url:
:vartype endpoint_url: str
:ivar created_on:
:vartype created_on: ~datetime.datetime
:ivar modified_on:
:vartype modified_on: ~datetime.datetime
:ivar owner:
:vartype owner: ~flow.models.SchemaContractsCreatedBy
"""
_attribute_map = {
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'runtime_description': {'key': 'runtimeDescription', 'type': 'str'},
'runtime_type': {'key': 'runtimeType', 'type': 'str'},
'environment': {'key': 'environment', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'status_message': {'key': 'statusMessage', 'type': 'str'},
'error': {'key': 'error', 'type': 'ErrorResponse'},
'from_existing_endpoint': {'key': 'fromExistingEndpoint', 'type': 'bool'},
'endpoint_name': {'key': 'endpointName', 'type': 'str'},
'from_existing_deployment': {'key': 'fromExistingDeployment', 'type': 'bool'},
'deployment_name': {'key': 'deploymentName', 'type': 'str'},
'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'instance_count': {'key': 'instanceCount', 'type': 'int'},
'compute_instance_name': {'key': 'computeInstanceName', 'type': 'str'},
'docker_image': {'key': 'dockerImage', 'type': 'str'},
'published_port': {'key': 'publishedPort', 'type': 'int'},
'target_port': {'key': 'targetPort', 'type': 'int'},
'from_existing_custom_app': {'key': 'fromExistingCustomApp', 'type': 'bool'},
'custom_app_name': {'key': 'customAppName', 'type': 'str'},
'assigned_to': {'key': 'assignedTo', 'type': 'AssignedUser'},
'endpoint_url': {'key': 'endpointUrl', 'type': 'str'},
'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
'owner': {'key': 'owner', 'type': 'SchemaContractsCreatedBy'},
}
def __init__(
self,
**kwargs
):
"""
:keyword runtime_name:
:paramtype runtime_name: str
:keyword runtime_description:
:paramtype runtime_description: str
:keyword runtime_type: Possible values include: "ManagedOnlineEndpoint", "ComputeInstance",
"TrainingSession".
:paramtype runtime_type: str or ~flow.models.RuntimeType
:keyword environment:
:paramtype environment: str
:keyword status: Possible values include: "Unavailable", "Failed", "NotExist", "Starting",
"Stopping".
:paramtype status: str or ~flow.models.RuntimeStatusEnum
:keyword status_message:
:paramtype status_message: str
:keyword error: The error response.
:paramtype error: ~flow.models.ErrorResponse
:keyword from_existing_endpoint:
:paramtype from_existing_endpoint: bool
:keyword endpoint_name:
:paramtype endpoint_name: str
:keyword from_existing_deployment:
:paramtype from_existing_deployment: bool
:keyword deployment_name:
:paramtype deployment_name: str
:keyword identity:
:paramtype identity: ~flow.models.ManagedServiceIdentity
:keyword instance_type:
:paramtype instance_type: str
:keyword instance_count:
:paramtype instance_count: int
:keyword compute_instance_name:
:paramtype compute_instance_name: str
:keyword docker_image:
:paramtype docker_image: str
:keyword published_port:
:paramtype published_port: int
:keyword target_port:
:paramtype target_port: int
:keyword from_existing_custom_app:
:paramtype from_existing_custom_app: bool
:keyword custom_app_name:
:paramtype custom_app_name: str
:keyword assigned_to:
:paramtype assigned_to: ~flow.models.AssignedUser
:keyword endpoint_url:
:paramtype endpoint_url: str
:keyword created_on:
:paramtype created_on: ~datetime.datetime
:keyword modified_on:
:paramtype modified_on: ~datetime.datetime
:keyword owner:
:paramtype owner: ~flow.models.SchemaContractsCreatedBy
"""
super(FlowRuntimeDto, self).__init__(**kwargs)
self.runtime_name = kwargs.get('runtime_name', None)
self.runtime_description = kwargs.get('runtime_description', None)
self.runtime_type = kwargs.get('runtime_type', None)
self.environment = kwargs.get('environment', None)
self.status = kwargs.get('status', None)
self.status_message = kwargs.get('status_message', None)
self.error = kwargs.get('error', None)
self.from_existing_endpoint = kwargs.get('from_existing_endpoint', None)
self.endpoint_name = kwargs.get('endpoint_name', None)
self.from_existing_deployment = kwargs.get('from_existing_deployment', None)
self.deployment_name = kwargs.get('deployment_name', None)
self.identity = kwargs.get('identity', None)
self.instance_type = kwargs.get('instance_type', None)
self.instance_count = kwargs.get('instance_count', None)
self.compute_instance_name = kwargs.get('compute_instance_name', None)
self.docker_image = kwargs.get('docker_image', None)
self.published_port = kwargs.get('published_port', None)
self.target_port = kwargs.get('target_port', None)
self.from_existing_custom_app = kwargs.get('from_existing_custom_app', None)
self.custom_app_name = kwargs.get('custom_app_name', None)
self.assigned_to = kwargs.get('assigned_to', None)
self.endpoint_url = kwargs.get('endpoint_url', None)
self.created_on = kwargs.get('created_on', None)
self.modified_on = kwargs.get('modified_on', None)
self.owner = kwargs.get('owner', None)
class FlowSampleDto(msrest.serialization.Model):
"""FlowSampleDto.
:ivar sample_resource_id:
:vartype sample_resource_id: str
:ivar section: Possible values include: "Gallery", "Template".
:vartype section: str or ~flow.models.Section
:ivar index_number:
:vartype index_number: int
:ivar flow_name:
:vartype flow_name: str
:ivar description:
:vartype description: str
:ivar details:
:vartype details: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar flow:
:vartype flow: ~flow.models.Flow
:ivar flow_definition_file_path:
:vartype flow_definition_file_path: str
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar flow_run_settings:
:vartype flow_run_settings: ~flow.models.FlowRunSettings
:ivar is_archived:
:vartype is_archived: bool
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar identity:
:vartype identity: str
"""
_attribute_map = {
'sample_resource_id': {'key': 'sampleResourceId', 'type': 'str'},
'section': {'key': 'section', 'type': 'str'},
'index_number': {'key': 'indexNumber', 'type': 'int'},
'flow_name': {'key': 'flowName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'details': {'key': 'details', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'flow': {'key': 'flow', 'type': 'Flow'},
'flow_definition_file_path': {'key': 'flowDefinitionFilePath', 'type': 'str'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'flow_run_settings': {'key': 'flowRunSettings', 'type': 'FlowRunSettings'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'identity': {'key': 'identity', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword sample_resource_id:
:paramtype sample_resource_id: str
:keyword section: Possible values include: "Gallery", "Template".
:paramtype section: str or ~flow.models.Section
:keyword index_number:
:paramtype index_number: int
:keyword flow_name:
:paramtype flow_name: str
:keyword description:
:paramtype description: str
:keyword details:
:paramtype details: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword flow:
:paramtype flow: ~flow.models.Flow
:keyword flow_definition_file_path:
:paramtype flow_definition_file_path: str
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword flow_run_settings:
:paramtype flow_run_settings: ~flow.models.FlowRunSettings
:keyword is_archived:
:paramtype is_archived: bool
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword identity:
:paramtype identity: str
"""
super(FlowSampleDto, self).__init__(**kwargs)
self.sample_resource_id = kwargs.get('sample_resource_id', None)
self.section = kwargs.get('section', None)
self.index_number = kwargs.get('index_number', None)
self.flow_name = kwargs.get('flow_name', None)
self.description = kwargs.get('description', None)
self.details = kwargs.get('details', None)
self.tags = kwargs.get('tags', None)
self.flow = kwargs.get('flow', None)
self.flow_definition_file_path = kwargs.get('flow_definition_file_path', None)
self.flow_type = kwargs.get('flow_type', None)
self.flow_run_settings = kwargs.get('flow_run_settings', None)
self.is_archived = kwargs.get('is_archived', None)
self.vm_size = kwargs.get('vm_size', None)
self.max_idle_time_seconds = kwargs.get('max_idle_time_seconds', None)
self.identity = kwargs.get('identity', None)
class FlowSessionDto(msrest.serialization.Model):
"""FlowSessionDto.
:ivar session_id:
:vartype session_id: str
:ivar base_image:
:vartype base_image: str
:ivar packages:
:vartype packages: list[str]
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar flow_features:
:vartype flow_features: list[~flow.models.FlowFeature]
:ivar runtime_name:
:vartype runtime_name: str
:ivar runtime_description:
:vartype runtime_description: str
:ivar runtime_type: Possible values include: "ManagedOnlineEndpoint", "ComputeInstance",
"TrainingSession".
:vartype runtime_type: str or ~flow.models.RuntimeType
:ivar environment:
:vartype environment: str
:ivar status: Possible values include: "Unavailable", "Failed", "NotExist", "Starting",
"Stopping".
:vartype status: str or ~flow.models.RuntimeStatusEnum
:ivar status_message:
:vartype status_message: str
:ivar error: The error response.
:vartype error: ~flow.models.ErrorResponse
:ivar from_existing_endpoint:
:vartype from_existing_endpoint: bool
:ivar endpoint_name:
:vartype endpoint_name: str
:ivar from_existing_deployment:
:vartype from_existing_deployment: bool
:ivar deployment_name:
:vartype deployment_name: str
:ivar identity:
:vartype identity: ~flow.models.ManagedServiceIdentity
:ivar instance_type:
:vartype instance_type: str
:ivar instance_count:
:vartype instance_count: int
:ivar compute_instance_name:
:vartype compute_instance_name: str
:ivar docker_image:
:vartype docker_image: str
:ivar published_port:
:vartype published_port: int
:ivar target_port:
:vartype target_port: int
:ivar from_existing_custom_app:
:vartype from_existing_custom_app: bool
:ivar custom_app_name:
:vartype custom_app_name: str
:ivar assigned_to:
:vartype assigned_to: ~flow.models.AssignedUser
:ivar endpoint_url:
:vartype endpoint_url: str
:ivar created_on:
:vartype created_on: ~datetime.datetime
:ivar modified_on:
:vartype modified_on: ~datetime.datetime
:ivar owner:
:vartype owner: ~flow.models.SchemaContractsCreatedBy
"""
_attribute_map = {
'session_id': {'key': 'sessionId', 'type': 'str'},
'base_image': {'key': 'baseImage', 'type': 'str'},
'packages': {'key': 'packages', 'type': '[str]'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'flow_features': {'key': 'flowFeatures', 'type': '[FlowFeature]'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'runtime_description': {'key': 'runtimeDescription', 'type': 'str'},
'runtime_type': {'key': 'runtimeType', 'type': 'str'},
'environment': {'key': 'environment', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'status_message': {'key': 'statusMessage', 'type': 'str'},
'error': {'key': 'error', 'type': 'ErrorResponse'},
'from_existing_endpoint': {'key': 'fromExistingEndpoint', 'type': 'bool'},
'endpoint_name': {'key': 'endpointName', 'type': 'str'},
'from_existing_deployment': {'key': 'fromExistingDeployment', 'type': 'bool'},
'deployment_name': {'key': 'deploymentName', 'type': 'str'},
'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'instance_count': {'key': 'instanceCount', 'type': 'int'},
'compute_instance_name': {'key': 'computeInstanceName', 'type': 'str'},
'docker_image': {'key': 'dockerImage', 'type': 'str'},
'published_port': {'key': 'publishedPort', 'type': 'int'},
'target_port': {'key': 'targetPort', 'type': 'int'},
'from_existing_custom_app': {'key': 'fromExistingCustomApp', 'type': 'bool'},
'custom_app_name': {'key': 'customAppName', 'type': 'str'},
'assigned_to': {'key': 'assignedTo', 'type': 'AssignedUser'},
'endpoint_url': {'key': 'endpointUrl', 'type': 'str'},
'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
'owner': {'key': 'owner', 'type': 'SchemaContractsCreatedBy'},
}
def __init__(
self,
**kwargs
):
"""
:keyword session_id:
:paramtype session_id: str
:keyword base_image:
:paramtype base_image: str
:keyword packages:
:paramtype packages: list[str]
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword flow_features:
:paramtype flow_features: list[~flow.models.FlowFeature]
:keyword runtime_name:
:paramtype runtime_name: str
:keyword runtime_description:
:paramtype runtime_description: str
:keyword runtime_type: Possible values include: "ManagedOnlineEndpoint", "ComputeInstance",
"TrainingSession".
:paramtype runtime_type: str or ~flow.models.RuntimeType
:keyword environment:
:paramtype environment: str
:keyword status: Possible values include: "Unavailable", "Failed", "NotExist", "Starting",
"Stopping".
:paramtype status: str or ~flow.models.RuntimeStatusEnum
:keyword status_message:
:paramtype status_message: str
:keyword error: The error response.
:paramtype error: ~flow.models.ErrorResponse
:keyword from_existing_endpoint:
:paramtype from_existing_endpoint: bool
:keyword endpoint_name:
:paramtype endpoint_name: str
:keyword from_existing_deployment:
:paramtype from_existing_deployment: bool
:keyword deployment_name:
:paramtype deployment_name: str
:keyword identity:
:paramtype identity: ~flow.models.ManagedServiceIdentity
:keyword instance_type:
:paramtype instance_type: str
:keyword instance_count:
:paramtype instance_count: int
:keyword compute_instance_name:
:paramtype compute_instance_name: str
:keyword docker_image:
:paramtype docker_image: str
:keyword published_port:
:paramtype published_port: int
:keyword target_port:
:paramtype target_port: int
:keyword from_existing_custom_app:
:paramtype from_existing_custom_app: bool
:keyword custom_app_name:
:paramtype custom_app_name: str
:keyword assigned_to:
:paramtype assigned_to: ~flow.models.AssignedUser
:keyword endpoint_url:
:paramtype endpoint_url: str
:keyword created_on:
:paramtype created_on: ~datetime.datetime
:keyword modified_on:
:paramtype modified_on: ~datetime.datetime
:keyword owner:
:paramtype owner: ~flow.models.SchemaContractsCreatedBy
"""
super(FlowSessionDto, self).__init__(**kwargs)
self.session_id = kwargs.get('session_id', None)
self.base_image = kwargs.get('base_image', None)
self.packages = kwargs.get('packages', None)
self.vm_size = kwargs.get('vm_size', None)
self.max_idle_time_seconds = kwargs.get('max_idle_time_seconds', None)
self.flow_features = kwargs.get('flow_features', None)
self.runtime_name = kwargs.get('runtime_name', None)
self.runtime_description = kwargs.get('runtime_description', None)
self.runtime_type = kwargs.get('runtime_type', None)
self.environment = kwargs.get('environment', None)
self.status = kwargs.get('status', None)
self.status_message = kwargs.get('status_message', None)
self.error = kwargs.get('error', None)
self.from_existing_endpoint = kwargs.get('from_existing_endpoint', None)
self.endpoint_name = kwargs.get('endpoint_name', None)
self.from_existing_deployment = kwargs.get('from_existing_deployment', None)
self.deployment_name = kwargs.get('deployment_name', None)
self.identity = kwargs.get('identity', None)
self.instance_type = kwargs.get('instance_type', None)
self.instance_count = kwargs.get('instance_count', None)
self.compute_instance_name = kwargs.get('compute_instance_name', None)
self.docker_image = kwargs.get('docker_image', None)
self.published_port = kwargs.get('published_port', None)
self.target_port = kwargs.get('target_port', None)
self.from_existing_custom_app = kwargs.get('from_existing_custom_app', None)
self.custom_app_name = kwargs.get('custom_app_name', None)
self.assigned_to = kwargs.get('assigned_to', None)
self.endpoint_url = kwargs.get('endpoint_url', None)
self.created_on = kwargs.get('created_on', None)
self.modified_on = kwargs.get('modified_on', None)
self.owner = kwargs.get('owner', None)
class FlowSnapshot(msrest.serialization.Model):
"""FlowSnapshot.
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, ~flow.models.FlowInputDefinition]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, ~flow.models.FlowOutputDefinition]
:ivar nodes:
:vartype nodes: list[~flow.models.FlowNode]
:ivar node_variants: This is a dictionary.
:vartype node_variants: dict[str, ~flow.models.FlowNodeVariant]
:ivar environment:
:vartype environment: ~flow.models.FlowEnvironment
:ivar environment_variables: This is a dictionary.
:vartype environment_variables: dict[str, any]
:ivar language: Possible values include: "Python", "CSharp".
:vartype language: str or ~flow.models.FlowLanguage
"""
_attribute_map = {
'inputs': {'key': 'inputs', 'type': '{FlowInputDefinition}'},
'outputs': {'key': 'outputs', 'type': '{FlowOutputDefinition}'},
'nodes': {'key': 'nodes', 'type': '[FlowNode]'},
'node_variants': {'key': 'node_variants', 'type': '{FlowNodeVariant}'},
'environment': {'key': 'environment', 'type': 'FlowEnvironment'},
'environment_variables': {'key': 'environment_variables', 'type': '{object}'},
'language': {'key': 'language', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, ~flow.models.FlowInputDefinition]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, ~flow.models.FlowOutputDefinition]
:keyword nodes:
:paramtype nodes: list[~flow.models.FlowNode]
:keyword node_variants: This is a dictionary.
:paramtype node_variants: dict[str, ~flow.models.FlowNodeVariant]
:keyword environment:
:paramtype environment: ~flow.models.FlowEnvironment
:keyword environment_variables: This is a dictionary.
:paramtype environment_variables: dict[str, any]
:keyword language: Possible values include: "Python", "CSharp".
:paramtype language: str or ~flow.models.FlowLanguage
"""
super(FlowSnapshot, self).__init__(**kwargs)
self.inputs = kwargs.get('inputs', None)
self.outputs = kwargs.get('outputs', None)
self.nodes = kwargs.get('nodes', None)
self.node_variants = kwargs.get('node_variants', None)
self.environment = kwargs.get('environment', None)
self.environment_variables = kwargs.get('environment_variables', None)
self.language = kwargs.get('language', None)
class FlowSubmitRunSettings(msrest.serialization.Model):
"""FlowSubmitRunSettings.
:ivar node_inputs: This is a dictionary.
:vartype node_inputs: dict[str, any]
:ivar flow_run_display_name:
:vartype flow_run_display_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar run_mode: Possible values include: "Flow", "SingleNode", "FromNode", "BulkTest", "Eval",
"PairwiseEval".
:vartype run_mode: str or ~flow.models.FlowRunMode
:ivar batch_inputs:
:vartype batch_inputs: list[dict[str, any]]
:ivar batch_data_input:
:vartype batch_data_input: ~flow.models.BatchDataInput
:ivar tuning_node_names:
:vartype tuning_node_names: list[str]
:ivar tuning_node_settings: This is a dictionary.
:vartype tuning_node_settings: dict[str, ~flow.models.TuningNodeSetting]
:ivar baseline_variant_id:
:vartype baseline_variant_id: str
:ivar default_variant_id:
:vartype default_variant_id: str
:ivar variants: This is a dictionary.
:vartype variants: dict[str, list[~flow.models.Node]]
:ivar variants_tools:
:vartype variants_tools: list[~flow.models.Tool]
:ivar variants_codes: This is a dictionary.
:vartype variants_codes: dict[str, str]
:ivar node_name:
:vartype node_name: str
:ivar bulk_test_id:
:vartype bulk_test_id: str
:ivar evaluation_flow_run_settings: This is a dictionary.
:vartype evaluation_flow_run_settings: dict[str, ~flow.models.EvaluationFlowRunSettings]
:ivar inputs_mapping: This is a dictionary.
:vartype inputs_mapping: dict[str, str]
:ivar data_inputs: This is a dictionary.
:vartype data_inputs: dict[str, str]
:ivar bulk_test_flow_id:
:vartype bulk_test_flow_id: str
:ivar bulk_test_flow_run_ids:
:vartype bulk_test_flow_run_ids: list[str]
:ivar aml_compute_name:
:vartype aml_compute_name: str
:ivar runtime_name:
:vartype runtime_name: str
:ivar flow_run_output_directory:
:vartype flow_run_output_directory: str
"""
_attribute_map = {
'node_inputs': {'key': 'nodeInputs', 'type': '{object}'},
'flow_run_display_name': {'key': 'flowRunDisplayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'run_mode': {'key': 'runMode', 'type': 'str'},
'batch_inputs': {'key': 'batch_inputs', 'type': '[{object}]'},
'batch_data_input': {'key': 'batchDataInput', 'type': 'BatchDataInput'},
'tuning_node_names': {'key': 'tuningNodeNames', 'type': '[str]'},
'tuning_node_settings': {'key': 'tuningNodeSettings', 'type': '{TuningNodeSetting}'},
'baseline_variant_id': {'key': 'baselineVariantId', 'type': 'str'},
'default_variant_id': {'key': 'defaultVariantId', 'type': 'str'},
'variants': {'key': 'variants', 'type': '{[Node]}'},
'variants_tools': {'key': 'variantsTools', 'type': '[Tool]'},
'variants_codes': {'key': 'variantsCodes', 'type': '{str}'},
'node_name': {'key': 'nodeName', 'type': 'str'},
'bulk_test_id': {'key': 'bulkTestId', 'type': 'str'},
'evaluation_flow_run_settings': {'key': 'evaluationFlowRunSettings', 'type': '{EvaluationFlowRunSettings}'},
'inputs_mapping': {'key': 'inputsMapping', 'type': '{str}'},
'data_inputs': {'key': 'dataInputs', 'type': '{str}'},
'bulk_test_flow_id': {'key': 'bulkTestFlowId', 'type': 'str'},
'bulk_test_flow_run_ids': {'key': 'bulkTestFlowRunIds', 'type': '[str]'},
'aml_compute_name': {'key': 'amlComputeName', 'type': 'str'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'flow_run_output_directory': {'key': 'flowRunOutputDirectory', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword node_inputs: This is a dictionary.
:paramtype node_inputs: dict[str, any]
:keyword flow_run_display_name:
:paramtype flow_run_display_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword run_mode: Possible values include: "Flow", "SingleNode", "FromNode", "BulkTest",
"Eval", "PairwiseEval".
:paramtype run_mode: str or ~flow.models.FlowRunMode
:keyword batch_inputs:
:paramtype batch_inputs: list[dict[str, any]]
:keyword batch_data_input:
:paramtype batch_data_input: ~flow.models.BatchDataInput
:keyword tuning_node_names:
:paramtype tuning_node_names: list[str]
:keyword tuning_node_settings: This is a dictionary.
:paramtype tuning_node_settings: dict[str, ~flow.models.TuningNodeSetting]
:keyword baseline_variant_id:
:paramtype baseline_variant_id: str
:keyword default_variant_id:
:paramtype default_variant_id: str
:keyword variants: This is a dictionary.
:paramtype variants: dict[str, list[~flow.models.Node]]
:keyword variants_tools:
:paramtype variants_tools: list[~flow.models.Tool]
:keyword variants_codes: This is a dictionary.
:paramtype variants_codes: dict[str, str]
:keyword node_name:
:paramtype node_name: str
:keyword bulk_test_id:
:paramtype bulk_test_id: str
:keyword evaluation_flow_run_settings: This is a dictionary.
:paramtype evaluation_flow_run_settings: dict[str, ~flow.models.EvaluationFlowRunSettings]
:keyword inputs_mapping: This is a dictionary.
:paramtype inputs_mapping: dict[str, str]
:keyword data_inputs: This is a dictionary.
:paramtype data_inputs: dict[str, str]
:keyword bulk_test_flow_id:
:paramtype bulk_test_flow_id: str
:keyword bulk_test_flow_run_ids:
:paramtype bulk_test_flow_run_ids: list[str]
:keyword aml_compute_name:
:paramtype aml_compute_name: str
:keyword runtime_name:
:paramtype runtime_name: str
:keyword flow_run_output_directory:
:paramtype flow_run_output_directory: str
"""
super(FlowSubmitRunSettings, self).__init__(**kwargs)
self.node_inputs = kwargs.get('node_inputs', None)
self.flow_run_display_name = kwargs.get('flow_run_display_name', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
self.run_mode = kwargs.get('run_mode', None)
self.batch_inputs = kwargs.get('batch_inputs', None)
self.batch_data_input = kwargs.get('batch_data_input', None)
self.tuning_node_names = kwargs.get('tuning_node_names', None)
self.tuning_node_settings = kwargs.get('tuning_node_settings', None)
self.baseline_variant_id = kwargs.get('baseline_variant_id', None)
self.default_variant_id = kwargs.get('default_variant_id', None)
self.variants = kwargs.get('variants', None)
self.variants_tools = kwargs.get('variants_tools', None)
self.variants_codes = kwargs.get('variants_codes', None)
self.node_name = kwargs.get('node_name', None)
self.bulk_test_id = kwargs.get('bulk_test_id', None)
self.evaluation_flow_run_settings = kwargs.get('evaluation_flow_run_settings', None)
self.inputs_mapping = kwargs.get('inputs_mapping', None)
self.data_inputs = kwargs.get('data_inputs', None)
self.bulk_test_flow_id = kwargs.get('bulk_test_flow_id', None)
self.bulk_test_flow_run_ids = kwargs.get('bulk_test_flow_run_ids', None)
self.aml_compute_name = kwargs.get('aml_compute_name', None)
self.runtime_name = kwargs.get('runtime_name', None)
self.flow_run_output_directory = kwargs.get('flow_run_output_directory', None)
class FlowTestInfo(msrest.serialization.Model):
"""FlowTestInfo.
:ivar flow_run_id:
:vartype flow_run_id: str
:ivar flow_test_storage_setting:
:vartype flow_test_storage_setting: ~flow.models.FlowTestStorageSetting
"""
_attribute_map = {
'flow_run_id': {'key': 'flowRunId', 'type': 'str'},
'flow_test_storage_setting': {'key': 'flowTestStorageSetting', 'type': 'FlowTestStorageSetting'},
}
def __init__(
self,
**kwargs
):
"""
:keyword flow_run_id:
:paramtype flow_run_id: str
:keyword flow_test_storage_setting:
:paramtype flow_test_storage_setting: ~flow.models.FlowTestStorageSetting
"""
super(FlowTestInfo, self).__init__(**kwargs)
self.flow_run_id = kwargs.get('flow_run_id', None)
self.flow_test_storage_setting = kwargs.get('flow_test_storage_setting', None)
class FlowTestStorageSetting(msrest.serialization.Model):
"""FlowTestStorageSetting.
:ivar storage_account_name:
:vartype storage_account_name: str
:ivar blob_container_name:
:vartype blob_container_name: str
:ivar flow_artifacts_root_path:
:vartype flow_artifacts_root_path: str
:ivar output_datastore_name:
:vartype output_datastore_name: str
"""
_attribute_map = {
'storage_account_name': {'key': 'storageAccountName', 'type': 'str'},
'blob_container_name': {'key': 'blobContainerName', 'type': 'str'},
'flow_artifacts_root_path': {'key': 'flowArtifactsRootPath', 'type': 'str'},
'output_datastore_name': {'key': 'outputDatastoreName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword storage_account_name:
:paramtype storage_account_name: str
:keyword blob_container_name:
:paramtype blob_container_name: str
:keyword flow_artifacts_root_path:
:paramtype flow_artifacts_root_path: str
:keyword output_datastore_name:
:paramtype output_datastore_name: str
"""
super(FlowTestStorageSetting, self).__init__(**kwargs)
self.storage_account_name = kwargs.get('storage_account_name', None)
self.blob_container_name = kwargs.get('blob_container_name', None)
self.flow_artifacts_root_path = kwargs.get('flow_artifacts_root_path', None)
self.output_datastore_name = kwargs.get('output_datastore_name', None)
class FlowToolsDto(msrest.serialization.Model):
"""FlowToolsDto.
:ivar package: This is a dictionary.
:vartype package: dict[str, ~flow.models.Tool]
:ivar code: This is a dictionary.
:vartype code: dict[str, ~flow.models.Tool]
:ivar errors: This is a dictionary.
:vartype errors: dict[str, ~flow.models.ErrorResponse]
"""
_attribute_map = {
'package': {'key': 'package', 'type': '{Tool}'},
'code': {'key': 'code', 'type': '{Tool}'},
'errors': {'key': 'errors', 'type': '{ErrorResponse}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword package: This is a dictionary.
:paramtype package: dict[str, ~flow.models.Tool]
:keyword code: This is a dictionary.
:paramtype code: dict[str, ~flow.models.Tool]
:keyword errors: This is a dictionary.
:paramtype errors: dict[str, ~flow.models.ErrorResponse]
"""
super(FlowToolsDto, self).__init__(**kwargs)
self.package = kwargs.get('package', None)
self.code = kwargs.get('code', None)
self.errors = kwargs.get('errors', None)
class FlowToolSettingParameter(msrest.serialization.Model):
"""FlowToolSettingParameter.
:ivar type:
:vartype type: list[str or ~flow.models.ValueType]
:ivar default:
:vartype default: str
:ivar advanced:
:vartype advanced: bool
:ivar enum:
:vartype enum: list[any]
:ivar model_list:
:vartype model_list: list[str]
:ivar text_box_size:
:vartype text_box_size: int
:ivar capabilities:
:vartype capabilities: ~flow.models.AzureOpenAIModelCapabilities
:ivar allow_manual_entry:
:vartype allow_manual_entry: bool
"""
_attribute_map = {
'type': {'key': 'type', 'type': '[str]'},
'default': {'key': 'default', 'type': 'str'},
'advanced': {'key': 'advanced', 'type': 'bool'},
'enum': {'key': 'enum', 'type': '[object]'},
'model_list': {'key': 'model_list', 'type': '[str]'},
'text_box_size': {'key': 'text_box_size', 'type': 'int'},
'capabilities': {'key': 'capabilities', 'type': 'AzureOpenAIModelCapabilities'},
'allow_manual_entry': {'key': 'allow_manual_entry', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type:
:paramtype type: list[str or ~flow.models.ValueType]
:keyword default:
:paramtype default: str
:keyword advanced:
:paramtype advanced: bool
:keyword enum:
:paramtype enum: list[any]
:keyword model_list:
:paramtype model_list: list[str]
:keyword text_box_size:
:paramtype text_box_size: int
:keyword capabilities:
:paramtype capabilities: ~flow.models.AzureOpenAIModelCapabilities
:keyword allow_manual_entry:
:paramtype allow_manual_entry: bool
"""
super(FlowToolSettingParameter, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.default = kwargs.get('default', None)
self.advanced = kwargs.get('advanced', None)
self.enum = kwargs.get('enum', None)
self.model_list = kwargs.get('model_list', None)
self.text_box_size = kwargs.get('text_box_size', None)
self.capabilities = kwargs.get('capabilities', None)
self.allow_manual_entry = kwargs.get('allow_manual_entry', None)
class FlowVariantNode(msrest.serialization.Model):
"""FlowVariantNode.
:ivar node:
:vartype node: ~flow.models.FlowNode
:ivar description:
:vartype description: str
"""
_attribute_map = {
'node': {'key': 'node', 'type': 'FlowNode'},
'description': {'key': 'description', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword node:
:paramtype node: ~flow.models.FlowNode
:keyword description:
:paramtype description: str
"""
super(FlowVariantNode, self).__init__(**kwargs)
self.node = kwargs.get('node', None)
self.description = kwargs.get('description', None)
class ForecastHorizon(msrest.serialization.Model):
"""ForecastHorizon.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.ForecastHorizonMode
:ivar value:
:vartype value: int
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'value': {'key': 'value', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.ForecastHorizonMode
:keyword value:
:paramtype value: int
"""
super(ForecastHorizon, self).__init__(**kwargs)
self.mode = kwargs.get('mode', None)
self.value = kwargs.get('value', None)
class ForecastingSettings(msrest.serialization.Model):
"""ForecastingSettings.
:ivar country_or_region_for_holidays:
:vartype country_or_region_for_holidays: str
:ivar time_column_name:
:vartype time_column_name: str
:ivar target_lags:
:vartype target_lags: ~flow.models.TargetLags
:ivar target_rolling_window_size:
:vartype target_rolling_window_size: ~flow.models.TargetRollingWindowSize
:ivar forecast_horizon:
:vartype forecast_horizon: ~flow.models.ForecastHorizon
:ivar time_series_id_column_names:
:vartype time_series_id_column_names: list[str]
:ivar frequency:
:vartype frequency: str
:ivar feature_lags:
:vartype feature_lags: str
:ivar seasonality:
:vartype seasonality: ~flow.models.Seasonality
:ivar short_series_handling_config: Possible values include: "Auto", "Pad", "Drop".
:vartype short_series_handling_config: str or ~flow.models.ShortSeriesHandlingConfiguration
:ivar use_stl: Possible values include: "Season", "SeasonTrend".
:vartype use_stl: str or ~flow.models.UseStl
:ivar target_aggregate_function: Possible values include: "Sum", "Max", "Min", "Mean".
:vartype target_aggregate_function: str or ~flow.models.TargetAggregationFunction
:ivar cv_step_size:
:vartype cv_step_size: int
:ivar features_unknown_at_forecast_time:
:vartype features_unknown_at_forecast_time: list[str]
"""
_attribute_map = {
'country_or_region_for_holidays': {'key': 'countryOrRegionForHolidays', 'type': 'str'},
'time_column_name': {'key': 'timeColumnName', 'type': 'str'},
'target_lags': {'key': 'targetLags', 'type': 'TargetLags'},
'target_rolling_window_size': {'key': 'targetRollingWindowSize', 'type': 'TargetRollingWindowSize'},
'forecast_horizon': {'key': 'forecastHorizon', 'type': 'ForecastHorizon'},
'time_series_id_column_names': {'key': 'timeSeriesIdColumnNames', 'type': '[str]'},
'frequency': {'key': 'frequency', 'type': 'str'},
'feature_lags': {'key': 'featureLags', 'type': 'str'},
'seasonality': {'key': 'seasonality', 'type': 'Seasonality'},
'short_series_handling_config': {'key': 'shortSeriesHandlingConfig', 'type': 'str'},
'use_stl': {'key': 'useStl', 'type': 'str'},
'target_aggregate_function': {'key': 'targetAggregateFunction', 'type': 'str'},
'cv_step_size': {'key': 'cvStepSize', 'type': 'int'},
'features_unknown_at_forecast_time': {'key': 'featuresUnknownAtForecastTime', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword country_or_region_for_holidays:
:paramtype country_or_region_for_holidays: str
:keyword time_column_name:
:paramtype time_column_name: str
:keyword target_lags:
:paramtype target_lags: ~flow.models.TargetLags
:keyword target_rolling_window_size:
:paramtype target_rolling_window_size: ~flow.models.TargetRollingWindowSize
:keyword forecast_horizon:
:paramtype forecast_horizon: ~flow.models.ForecastHorizon
:keyword time_series_id_column_names:
:paramtype time_series_id_column_names: list[str]
:keyword frequency:
:paramtype frequency: str
:keyword feature_lags:
:paramtype feature_lags: str
:keyword seasonality:
:paramtype seasonality: ~flow.models.Seasonality
:keyword short_series_handling_config: Possible values include: "Auto", "Pad", "Drop".
:paramtype short_series_handling_config: str or ~flow.models.ShortSeriesHandlingConfiguration
:keyword use_stl: Possible values include: "Season", "SeasonTrend".
:paramtype use_stl: str or ~flow.models.UseStl
:keyword target_aggregate_function: Possible values include: "Sum", "Max", "Min", "Mean".
:paramtype target_aggregate_function: str or ~flow.models.TargetAggregationFunction
:keyword cv_step_size:
:paramtype cv_step_size: int
:keyword features_unknown_at_forecast_time:
:paramtype features_unknown_at_forecast_time: list[str]
"""
super(ForecastingSettings, self).__init__(**kwargs)
self.country_or_region_for_holidays = kwargs.get('country_or_region_for_holidays', None)
self.time_column_name = kwargs.get('time_column_name', None)
self.target_lags = kwargs.get('target_lags', None)
self.target_rolling_window_size = kwargs.get('target_rolling_window_size', None)
self.forecast_horizon = kwargs.get('forecast_horizon', None)
self.time_series_id_column_names = kwargs.get('time_series_id_column_names', None)
self.frequency = kwargs.get('frequency', None)
self.feature_lags = kwargs.get('feature_lags', None)
self.seasonality = kwargs.get('seasonality', None)
self.short_series_handling_config = kwargs.get('short_series_handling_config', None)
self.use_stl = kwargs.get('use_stl', None)
self.target_aggregate_function = kwargs.get('target_aggregate_function', None)
self.cv_step_size = kwargs.get('cv_step_size', None)
self.features_unknown_at_forecast_time = kwargs.get('features_unknown_at_forecast_time', None)
class GeneralSettings(msrest.serialization.Model):
"""GeneralSettings.
:ivar primary_metric: Possible values include: "AUCWeighted", "Accuracy", "NormMacroRecall",
"AveragePrecisionScoreWeighted", "PrecisionScoreWeighted", "SpearmanCorrelation",
"NormalizedRootMeanSquaredError", "R2Score", "NormalizedMeanAbsoluteError",
"NormalizedRootMeanSquaredLogError", "MeanAveragePrecision", "Iou".
:vartype primary_metric: str or ~flow.models.PrimaryMetrics
:ivar task_type: Possible values include: "Classification", "Regression", "Forecasting",
"ImageClassification", "ImageClassificationMultilabel", "ImageObjectDetection",
"ImageInstanceSegmentation", "TextClassification", "TextMultiLabeling", "TextNER",
"TextClassificationMultilabel".
:vartype task_type: str or ~flow.models.TaskType
:ivar log_verbosity: Possible values include: "NotSet", "Debug", "Info", "Warning", "Error",
"Critical".
:vartype log_verbosity: str or ~flow.models.LogVerbosity
"""
_attribute_map = {
'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
'task_type': {'key': 'taskType', 'type': 'str'},
'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword primary_metric: Possible values include: "AUCWeighted", "Accuracy", "NormMacroRecall",
"AveragePrecisionScoreWeighted", "PrecisionScoreWeighted", "SpearmanCorrelation",
"NormalizedRootMeanSquaredError", "R2Score", "NormalizedMeanAbsoluteError",
"NormalizedRootMeanSquaredLogError", "MeanAveragePrecision", "Iou".
:paramtype primary_metric: str or ~flow.models.PrimaryMetrics
:keyword task_type: Possible values include: "Classification", "Regression", "Forecasting",
"ImageClassification", "ImageClassificationMultilabel", "ImageObjectDetection",
"ImageInstanceSegmentation", "TextClassification", "TextMultiLabeling", "TextNER",
"TextClassificationMultilabel".
:paramtype task_type: str or ~flow.models.TaskType
:keyword log_verbosity: Possible values include: "NotSet", "Debug", "Info", "Warning", "Error",
"Critical".
:paramtype log_verbosity: str or ~flow.models.LogVerbosity
"""
super(GeneralSettings, self).__init__(**kwargs)
self.primary_metric = kwargs.get('primary_metric', None)
self.task_type = kwargs.get('task_type', None)
self.log_verbosity = kwargs.get('log_verbosity', None)
class GeneratePipelineComponentRequest(msrest.serialization.Model):
"""GeneratePipelineComponentRequest.
:ivar name:
:vartype name: str
:ivar display_name:
:vartype display_name: str
:ivar module_scope: Possible values include: "All", "Global", "Workspace", "Anonymous", "Step",
"Draft", "Feed", "Registry", "SystemAutoCreated".
:vartype module_scope: str or ~flow.models.ModuleScope
:ivar is_deterministic:
:vartype is_deterministic: bool
:ivar category:
:vartype category: str
:ivar version:
:vartype version: str
:ivar set_as_default_version:
:vartype set_as_default_version: bool
:ivar registry_name:
:vartype registry_name: str
:ivar graph:
:vartype graph: ~flow.models.GraphDraftEntity
:ivar pipeline_run_settings:
:vartype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar module_node_run_settings:
:vartype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:ivar module_node_ui_input_settings:
:vartype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar continue_run_on_step_failure:
:vartype continue_run_on_step_failure: bool
:ivar description:
:vartype description: str
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar enforce_rerun:
:vartype enforce_rerun: bool
:ivar dataset_access_modes: Possible values include: "Default", "DatasetInDpv2", "AssetInDpv2",
"DatasetInDesignerUI", "DatasetInDpv2WithDatasetInDesignerUI", "Dataset",
"AssetInDpv2WithDatasetInDesignerUI", "DatasetAndAssetInDpv2WithDatasetInDesignerUI",
"AssetInDesignerUI", "AssetInDpv2WithAssetInDesignerUI", "Asset".
:vartype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'module_scope': {'key': 'moduleScope', 'type': 'str'},
'is_deterministic': {'key': 'isDeterministic', 'type': 'bool'},
'category': {'key': 'category', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'set_as_default_version': {'key': 'setAsDefaultVersion', 'type': 'bool'},
'registry_name': {'key': 'registryName', 'type': 'str'},
'graph': {'key': 'graph', 'type': 'GraphDraftEntity'},
'pipeline_run_settings': {'key': 'pipelineRunSettings', 'type': '[RunSettingParameterAssignment]'},
'module_node_run_settings': {'key': 'moduleNodeRunSettings', 'type': '[GraphModuleNodeRunSetting]'},
'module_node_ui_input_settings': {'key': 'moduleNodeUIInputSettings', 'type': '[GraphModuleNodeUIInputSetting]'},
'tags': {'key': 'tags', 'type': '{str}'},
'continue_run_on_step_failure': {'key': 'continueRunOnStepFailure', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'enforce_rerun': {'key': 'enforceRerun', 'type': 'bool'},
'dataset_access_modes': {'key': 'datasetAccessModes', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword display_name:
:paramtype display_name: str
:keyword module_scope: Possible values include: "All", "Global", "Workspace", "Anonymous",
"Step", "Draft", "Feed", "Registry", "SystemAutoCreated".
:paramtype module_scope: str or ~flow.models.ModuleScope
:keyword is_deterministic:
:paramtype is_deterministic: bool
:keyword category:
:paramtype category: str
:keyword version:
:paramtype version: str
:keyword set_as_default_version:
:paramtype set_as_default_version: bool
:keyword registry_name:
:paramtype registry_name: str
:keyword graph:
:paramtype graph: ~flow.models.GraphDraftEntity
:keyword pipeline_run_settings:
:paramtype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword module_node_run_settings:
:paramtype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:keyword module_node_ui_input_settings:
:paramtype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword continue_run_on_step_failure:
:paramtype continue_run_on_step_failure: bool
:keyword description:
:paramtype description: str
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword enforce_rerun:
:paramtype enforce_rerun: bool
:keyword dataset_access_modes: Possible values include: "Default", "DatasetInDpv2",
"AssetInDpv2", "DatasetInDesignerUI", "DatasetInDpv2WithDatasetInDesignerUI", "Dataset",
"AssetInDpv2WithDatasetInDesignerUI", "DatasetAndAssetInDpv2WithDatasetInDesignerUI",
"AssetInDesignerUI", "AssetInDpv2WithAssetInDesignerUI", "Asset".
:paramtype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
super(GeneratePipelineComponentRequest, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.display_name = kwargs.get('display_name', None)
self.module_scope = kwargs.get('module_scope', None)
self.is_deterministic = kwargs.get('is_deterministic', None)
self.category = kwargs.get('category', None)
self.version = kwargs.get('version', None)
self.set_as_default_version = kwargs.get('set_as_default_version', None)
self.registry_name = kwargs.get('registry_name', None)
self.graph = kwargs.get('graph', None)
self.pipeline_run_settings = kwargs.get('pipeline_run_settings', None)
self.module_node_run_settings = kwargs.get('module_node_run_settings', None)
self.module_node_ui_input_settings = kwargs.get('module_node_ui_input_settings', None)
self.tags = kwargs.get('tags', None)
self.continue_run_on_step_failure = kwargs.get('continue_run_on_step_failure', None)
self.description = kwargs.get('description', None)
self.properties = kwargs.get('properties', None)
self.enforce_rerun = kwargs.get('enforce_rerun', None)
self.dataset_access_modes = kwargs.get('dataset_access_modes', None)
class GenerateToolMetaRequest(msrest.serialization.Model):
"""GenerateToolMetaRequest.
:ivar tools: This is a dictionary.
:vartype tools: dict[str, ~flow.models.ToolSourceMeta]
:ivar working_dir:
:vartype working_dir: str
"""
_attribute_map = {
'tools': {'key': 'tools', 'type': '{ToolSourceMeta}'},
'working_dir': {'key': 'working_dir', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword tools: This is a dictionary.
:paramtype tools: dict[str, ~flow.models.ToolSourceMeta]
:keyword working_dir:
:paramtype working_dir: str
"""
super(GenerateToolMetaRequest, self).__init__(**kwargs)
self.tools = kwargs.get('tools', None)
self.working_dir = kwargs.get('working_dir', None)
class GetDynamicListRequest(msrest.serialization.Model):
"""GetDynamicListRequest.
:ivar func_path:
:vartype func_path: str
:ivar func_kwargs: This is a dictionary.
:vartype func_kwargs: dict[str, any]
"""
_attribute_map = {
'func_path': {'key': 'func_path', 'type': 'str'},
'func_kwargs': {'key': 'func_kwargs', 'type': '{object}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword func_path:
:paramtype func_path: str
:keyword func_kwargs: This is a dictionary.
:paramtype func_kwargs: dict[str, any]
"""
super(GetDynamicListRequest, self).__init__(**kwargs)
self.func_path = kwargs.get('func_path', None)
self.func_kwargs = kwargs.get('func_kwargs', None)
class GetRunDataResultDto(msrest.serialization.Model):
"""GetRunDataResultDto.
:ivar run_metadata:
:vartype run_metadata: ~flow.models.RunDto
:ivar run_definition: Anything.
:vartype run_definition: any
:ivar job_specification: Anything.
:vartype job_specification: any
:ivar system_settings: Dictionary of :code:`<string>`.
:vartype system_settings: dict[str, str]
"""
_attribute_map = {
'run_metadata': {'key': 'runMetadata', 'type': 'RunDto'},
'run_definition': {'key': 'runDefinition', 'type': 'object'},
'job_specification': {'key': 'jobSpecification', 'type': 'object'},
'system_settings': {'key': 'systemSettings', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword run_metadata:
:paramtype run_metadata: ~flow.models.RunDto
:keyword run_definition: Anything.
:paramtype run_definition: any
:keyword job_specification: Anything.
:paramtype job_specification: any
:keyword system_settings: Dictionary of :code:`<string>`.
:paramtype system_settings: dict[str, str]
"""
super(GetRunDataResultDto, self).__init__(**kwargs)
self.run_metadata = kwargs.get('run_metadata', None)
self.run_definition = kwargs.get('run_definition', None)
self.job_specification = kwargs.get('job_specification', None)
self.system_settings = kwargs.get('system_settings', None)
class GetTrainingSessionDto(msrest.serialization.Model):
"""GetTrainingSessionDto.
:ivar properties:
:vartype properties: ~flow.models.SessionProperties
:ivar compute:
:vartype compute: ~flow.models.ComputeContract
"""
_attribute_map = {
'properties': {'key': 'properties', 'type': 'SessionProperties'},
'compute': {'key': 'compute', 'type': 'ComputeContract'},
}
def __init__(
self,
**kwargs
):
"""
:keyword properties:
:paramtype properties: ~flow.models.SessionProperties
:keyword compute:
:paramtype compute: ~flow.models.ComputeContract
"""
super(GetTrainingSessionDto, self).__init__(**kwargs)
self.properties = kwargs.get('properties', None)
self.compute = kwargs.get('compute', None)
class GlobalJobDispatcherConfiguration(msrest.serialization.Model):
"""GlobalJobDispatcherConfiguration.
:ivar vm_size:
:vartype vm_size: list[str]
:ivar compute_type: Possible values include: "AmlCompute", "AmlK8s".
:vartype compute_type: str or ~flow.models.GlobalJobDispatcherSupportedComputeType
:ivar region:
:vartype region: list[str]
:ivar my_resource_only:
:vartype my_resource_only: bool
:ivar redispatch_allowed:
:vartype redispatch_allowed: bool
:ivar low_priority_vm_tolerant:
:vartype low_priority_vm_tolerant: bool
:ivar vc_list:
:vartype vc_list: list[str]
:ivar plan_id:
:vartype plan_id: str
:ivar plan_region_id:
:vartype plan_region_id: str
:ivar vc_block_list:
:vartype vc_block_list: list[str]
:ivar cluster_block_list:
:vartype cluster_block_list: list[str]
"""
_attribute_map = {
'vm_size': {'key': 'vmSize', 'type': '[str]'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'region': {'key': 'region', 'type': '[str]'},
'my_resource_only': {'key': 'myResourceOnly', 'type': 'bool'},
'redispatch_allowed': {'key': 'redispatchAllowed', 'type': 'bool'},
'low_priority_vm_tolerant': {'key': 'lowPriorityVMTolerant', 'type': 'bool'},
'vc_list': {'key': 'vcList', 'type': '[str]'},
'plan_id': {'key': 'planId', 'type': 'str'},
'plan_region_id': {'key': 'planRegionId', 'type': 'str'},
'vc_block_list': {'key': 'vcBlockList', 'type': '[str]'},
'cluster_block_list': {'key': 'clusterBlockList', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword vm_size:
:paramtype vm_size: list[str]
:keyword compute_type: Possible values include: "AmlCompute", "AmlK8s".
:paramtype compute_type: str or ~flow.models.GlobalJobDispatcherSupportedComputeType
:keyword region:
:paramtype region: list[str]
:keyword my_resource_only:
:paramtype my_resource_only: bool
:keyword redispatch_allowed:
:paramtype redispatch_allowed: bool
:keyword low_priority_vm_tolerant:
:paramtype low_priority_vm_tolerant: bool
:keyword vc_list:
:paramtype vc_list: list[str]
:keyword plan_id:
:paramtype plan_id: str
:keyword plan_region_id:
:paramtype plan_region_id: str
:keyword vc_block_list:
:paramtype vc_block_list: list[str]
:keyword cluster_block_list:
:paramtype cluster_block_list: list[str]
"""
super(GlobalJobDispatcherConfiguration, self).__init__(**kwargs)
self.vm_size = kwargs.get('vm_size', None)
self.compute_type = kwargs.get('compute_type', None)
self.region = kwargs.get('region', None)
self.my_resource_only = kwargs.get('my_resource_only', None)
self.redispatch_allowed = kwargs.get('redispatch_allowed', None)
self.low_priority_vm_tolerant = kwargs.get('low_priority_vm_tolerant', None)
self.vc_list = kwargs.get('vc_list', None)
self.plan_id = kwargs.get('plan_id', None)
self.plan_region_id = kwargs.get('plan_region_id', None)
self.vc_block_list = kwargs.get('vc_block_list', None)
self.cluster_block_list = kwargs.get('cluster_block_list', None)
class GlobsOptions(msrest.serialization.Model):
"""GlobsOptions.
:ivar glob_patterns:
:vartype glob_patterns: list[str]
"""
_attribute_map = {
'glob_patterns': {'key': 'globPatterns', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword glob_patterns:
:paramtype glob_patterns: list[str]
"""
super(GlobsOptions, self).__init__(**kwargs)
self.glob_patterns = kwargs.get('glob_patterns', None)
class GraphAnnotationNode(msrest.serialization.Model):
"""GraphAnnotationNode.
:ivar id:
:vartype id: str
:ivar content:
:vartype content: str
:ivar mentioned_node_names:
:vartype mentioned_node_names: list[str]
:ivar structured_content:
:vartype structured_content: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'content': {'key': 'content', 'type': 'str'},
'mentioned_node_names': {'key': 'mentionedNodeNames', 'type': '[str]'},
'structured_content': {'key': 'structuredContent', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword content:
:paramtype content: str
:keyword mentioned_node_names:
:paramtype mentioned_node_names: list[str]
:keyword structured_content:
:paramtype structured_content: str
"""
super(GraphAnnotationNode, self).__init__(**kwargs)
self.id = kwargs.get('id', None)
self.content = kwargs.get('content', None)
self.mentioned_node_names = kwargs.get('mentioned_node_names', None)
self.structured_content = kwargs.get('structured_content', None)
class GraphControlNode(msrest.serialization.Model):
"""GraphControlNode.
:ivar id:
:vartype id: str
:ivar control_type: The only acceptable values to pass in are None and "IfElse". The default
value is None.
:vartype control_type: str
:ivar control_parameter:
:vartype control_parameter: ~flow.models.ParameterAssignment
:ivar run_attribution:
:vartype run_attribution: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'control_type': {'key': 'controlType', 'type': 'str'},
'control_parameter': {'key': 'controlParameter', 'type': 'ParameterAssignment'},
'run_attribution': {'key': 'runAttribution', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword control_type: The only acceptable values to pass in are None and "IfElse". The
default value is None.
:paramtype control_type: str
:keyword control_parameter:
:paramtype control_parameter: ~flow.models.ParameterAssignment
:keyword run_attribution:
:paramtype run_attribution: str
"""
super(GraphControlNode, self).__init__(**kwargs)
self.id = kwargs.get('id', None)
self.control_type = kwargs.get('control_type', None)
self.control_parameter = kwargs.get('control_parameter', None)
self.run_attribution = kwargs.get('run_attribution', None)
class GraphControlReferenceNode(msrest.serialization.Model):
"""GraphControlReferenceNode.
:ivar id:
:vartype id: str
:ivar name:
:vartype name: str
:ivar comment:
:vartype comment: str
:ivar control_flow_type: Possible values include: "None", "DoWhile", "ParallelFor".
:vartype control_flow_type: str or ~flow.models.ControlFlowType
:ivar reference_node_id:
:vartype reference_node_id: str
:ivar do_while_control_flow_info:
:vartype do_while_control_flow_info: ~flow.models.DoWhileControlFlowInfo
:ivar parallel_for_control_flow_info:
:vartype parallel_for_control_flow_info: ~flow.models.ParallelForControlFlowInfo
:ivar run_attribution:
:vartype run_attribution: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'comment': {'key': 'comment', 'type': 'str'},
'control_flow_type': {'key': 'controlFlowType', 'type': 'str'},
'reference_node_id': {'key': 'referenceNodeId', 'type': 'str'},
'do_while_control_flow_info': {'key': 'doWhileControlFlowInfo', 'type': 'DoWhileControlFlowInfo'},
'parallel_for_control_flow_info': {'key': 'parallelForControlFlowInfo', 'type': 'ParallelForControlFlowInfo'},
'run_attribution': {'key': 'runAttribution', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword name:
:paramtype name: str
:keyword comment:
:paramtype comment: str
:keyword control_flow_type: Possible values include: "None", "DoWhile", "ParallelFor".
:paramtype control_flow_type: str or ~flow.models.ControlFlowType
:keyword reference_node_id:
:paramtype reference_node_id: str
:keyword do_while_control_flow_info:
:paramtype do_while_control_flow_info: ~flow.models.DoWhileControlFlowInfo
:keyword parallel_for_control_flow_info:
:paramtype parallel_for_control_flow_info: ~flow.models.ParallelForControlFlowInfo
:keyword run_attribution:
:paramtype run_attribution: str
"""
super(GraphControlReferenceNode, self).__init__(**kwargs)
self.id = kwargs.get('id', None)
self.name = kwargs.get('name', None)
self.comment = kwargs.get('comment', None)
self.control_flow_type = kwargs.get('control_flow_type', None)
self.reference_node_id = kwargs.get('reference_node_id', None)
self.do_while_control_flow_info = kwargs.get('do_while_control_flow_info', None)
self.parallel_for_control_flow_info = kwargs.get('parallel_for_control_flow_info', None)
self.run_attribution = kwargs.get('run_attribution', None)
class GraphDatasetNode(msrest.serialization.Model):
"""GraphDatasetNode.
:ivar id:
:vartype id: str
:ivar dataset_id:
:vartype dataset_id: str
:ivar data_path_parameter_name:
:vartype data_path_parameter_name: str
:ivar data_set_definition:
:vartype data_set_definition: ~flow.models.DataSetDefinition
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'dataset_id': {'key': 'datasetId', 'type': 'str'},
'data_path_parameter_name': {'key': 'dataPathParameterName', 'type': 'str'},
'data_set_definition': {'key': 'dataSetDefinition', 'type': 'DataSetDefinition'},
}
def __init__(
self,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword dataset_id:
:paramtype dataset_id: str
:keyword data_path_parameter_name:
:paramtype data_path_parameter_name: str
:keyword data_set_definition:
:paramtype data_set_definition: ~flow.models.DataSetDefinition
"""
super(GraphDatasetNode, self).__init__(**kwargs)
self.id = kwargs.get('id', None)
self.dataset_id = kwargs.get('dataset_id', None)
self.data_path_parameter_name = kwargs.get('data_path_parameter_name', None)
self.data_set_definition = kwargs.get('data_set_definition', None)
class GraphDraftEntity(msrest.serialization.Model):
"""GraphDraftEntity.
:ivar module_nodes:
:vartype module_nodes: list[~flow.models.GraphModuleNode]
:ivar dataset_nodes:
:vartype dataset_nodes: list[~flow.models.GraphDatasetNode]
:ivar sub_graph_nodes:
:vartype sub_graph_nodes: list[~flow.models.GraphReferenceNode]
:ivar control_reference_nodes:
:vartype control_reference_nodes: list[~flow.models.GraphControlReferenceNode]
:ivar control_nodes:
:vartype control_nodes: list[~flow.models.GraphControlNode]
:ivar edges:
:vartype edges: list[~flow.models.GraphEdge]
:ivar entity_interface:
:vartype entity_interface: ~flow.models.EntityInterface
:ivar graph_layout:
:vartype graph_layout: ~flow.models.GraphLayout
:ivar created_by:
:vartype created_by: ~flow.models.CreatedBy
:ivar last_updated_by:
:vartype last_updated_by: ~flow.models.CreatedBy
:ivar default_compute:
:vartype default_compute: ~flow.models.ComputeSetting
:ivar default_datastore:
:vartype default_datastore: ~flow.models.DatastoreSetting
:ivar default_cloud_priority:
:vartype default_cloud_priority: ~flow.models.CloudPrioritySetting
:ivar extended_properties: This is a dictionary.
:vartype extended_properties: dict[str, str]
:ivar parent_sub_graph_module_ids:
:vartype parent_sub_graph_module_ids: list[str]
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'module_nodes': {'key': 'moduleNodes', 'type': '[GraphModuleNode]'},
'dataset_nodes': {'key': 'datasetNodes', 'type': '[GraphDatasetNode]'},
'sub_graph_nodes': {'key': 'subGraphNodes', 'type': '[GraphReferenceNode]'},
'control_reference_nodes': {'key': 'controlReferenceNodes', 'type': '[GraphControlReferenceNode]'},
'control_nodes': {'key': 'controlNodes', 'type': '[GraphControlNode]'},
'edges': {'key': 'edges', 'type': '[GraphEdge]'},
'entity_interface': {'key': 'entityInterface', 'type': 'EntityInterface'},
'graph_layout': {'key': 'graphLayout', 'type': 'GraphLayout'},
'created_by': {'key': 'createdBy', 'type': 'CreatedBy'},
'last_updated_by': {'key': 'lastUpdatedBy', 'type': 'CreatedBy'},
'default_compute': {'key': 'defaultCompute', 'type': 'ComputeSetting'},
'default_datastore': {'key': 'defaultDatastore', 'type': 'DatastoreSetting'},
'default_cloud_priority': {'key': 'defaultCloudPriority', 'type': 'CloudPrioritySetting'},
'extended_properties': {'key': 'extendedProperties', 'type': '{str}'},
'parent_sub_graph_module_ids': {'key': 'parentSubGraphModuleIds', 'type': '[str]'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword module_nodes:
:paramtype module_nodes: list[~flow.models.GraphModuleNode]
:keyword dataset_nodes:
:paramtype dataset_nodes: list[~flow.models.GraphDatasetNode]
:keyword sub_graph_nodes:
:paramtype sub_graph_nodes: list[~flow.models.GraphReferenceNode]
:keyword control_reference_nodes:
:paramtype control_reference_nodes: list[~flow.models.GraphControlReferenceNode]
:keyword control_nodes:
:paramtype control_nodes: list[~flow.models.GraphControlNode]
:keyword edges:
:paramtype edges: list[~flow.models.GraphEdge]
:keyword entity_interface:
:paramtype entity_interface: ~flow.models.EntityInterface
:keyword graph_layout:
:paramtype graph_layout: ~flow.models.GraphLayout
:keyword created_by:
:paramtype created_by: ~flow.models.CreatedBy
:keyword last_updated_by:
:paramtype last_updated_by: ~flow.models.CreatedBy
:keyword default_compute:
:paramtype default_compute: ~flow.models.ComputeSetting
:keyword default_datastore:
:paramtype default_datastore: ~flow.models.DatastoreSetting
:keyword default_cloud_priority:
:paramtype default_cloud_priority: ~flow.models.CloudPrioritySetting
:keyword extended_properties: This is a dictionary.
:paramtype extended_properties: dict[str, str]
:keyword parent_sub_graph_module_ids:
:paramtype parent_sub_graph_module_ids: list[str]
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(GraphDraftEntity, self).__init__(**kwargs)
self.module_nodes = kwargs.get('module_nodes', None)
self.dataset_nodes = kwargs.get('dataset_nodes', None)
self.sub_graph_nodes = kwargs.get('sub_graph_nodes', None)
self.control_reference_nodes = kwargs.get('control_reference_nodes', None)
self.control_nodes = kwargs.get('control_nodes', None)
self.edges = kwargs.get('edges', None)
self.entity_interface = kwargs.get('entity_interface', None)
self.graph_layout = kwargs.get('graph_layout', None)
self.created_by = kwargs.get('created_by', None)
self.last_updated_by = kwargs.get('last_updated_by', None)
self.default_compute = kwargs.get('default_compute', None)
self.default_datastore = kwargs.get('default_datastore', None)
self.default_cloud_priority = kwargs.get('default_cloud_priority', None)
self.extended_properties = kwargs.get('extended_properties', None)
self.parent_sub_graph_module_ids = kwargs.get('parent_sub_graph_module_ids', None)
self.id = kwargs.get('id', None)
self.etag = kwargs.get('etag', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
class GraphEdge(msrest.serialization.Model):
"""GraphEdge.
:ivar source_output_port:
:vartype source_output_port: ~flow.models.PortInfo
:ivar destination_input_port:
:vartype destination_input_port: ~flow.models.PortInfo
"""
_attribute_map = {
'source_output_port': {'key': 'sourceOutputPort', 'type': 'PortInfo'},
'destination_input_port': {'key': 'destinationInputPort', 'type': 'PortInfo'},
}
def __init__(
self,
**kwargs
):
"""
:keyword source_output_port:
:paramtype source_output_port: ~flow.models.PortInfo
:keyword destination_input_port:
:paramtype destination_input_port: ~flow.models.PortInfo
"""
super(GraphEdge, self).__init__(**kwargs)
self.source_output_port = kwargs.get('source_output_port', None)
self.destination_input_port = kwargs.get('destination_input_port', None)
class GraphLayout(msrest.serialization.Model):
"""GraphLayout.
:ivar node_layouts: This is a dictionary.
:vartype node_layouts: dict[str, ~flow.models.NodeLayout]
:ivar extended_data:
:vartype extended_data: str
:ivar annotation_nodes:
:vartype annotation_nodes: list[~flow.models.GraphAnnotationNode]
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'node_layouts': {'key': 'nodeLayouts', 'type': '{NodeLayout}'},
'extended_data': {'key': 'extendedData', 'type': 'str'},
'annotation_nodes': {'key': 'annotationNodes', 'type': '[GraphAnnotationNode]'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword node_layouts: This is a dictionary.
:paramtype node_layouts: dict[str, ~flow.models.NodeLayout]
:keyword extended_data:
:paramtype extended_data: str
:keyword annotation_nodes:
:paramtype annotation_nodes: list[~flow.models.GraphAnnotationNode]
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(GraphLayout, self).__init__(**kwargs)
self.node_layouts = kwargs.get('node_layouts', None)
self.extended_data = kwargs.get('extended_data', None)
self.annotation_nodes = kwargs.get('annotation_nodes', None)
self.id = kwargs.get('id', None)
self.etag = kwargs.get('etag', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
class GraphLayoutCreationInfo(msrest.serialization.Model):
"""GraphLayoutCreationInfo.
:ivar node_layouts: This is a dictionary.
:vartype node_layouts: dict[str, ~flow.models.NodeLayout]
:ivar extended_data:
:vartype extended_data: str
:ivar annotation_nodes:
:vartype annotation_nodes: list[~flow.models.GraphAnnotationNode]
"""
_attribute_map = {
'node_layouts': {'key': 'nodeLayouts', 'type': '{NodeLayout}'},
'extended_data': {'key': 'extendedData', 'type': 'str'},
'annotation_nodes': {'key': 'annotationNodes', 'type': '[GraphAnnotationNode]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword node_layouts: This is a dictionary.
:paramtype node_layouts: dict[str, ~flow.models.NodeLayout]
:keyword extended_data:
:paramtype extended_data: str
:keyword annotation_nodes:
:paramtype annotation_nodes: list[~flow.models.GraphAnnotationNode]
"""
super(GraphLayoutCreationInfo, self).__init__(**kwargs)
self.node_layouts = kwargs.get('node_layouts', None)
self.extended_data = kwargs.get('extended_data', None)
self.annotation_nodes = kwargs.get('annotation_nodes', None)
class GraphModuleNode(msrest.serialization.Model):
"""GraphModuleNode.
:ivar module_type: Possible values include: "None", "BatchInferencing".
:vartype module_type: str or ~flow.models.ModuleType
:ivar runconfig:
:vartype runconfig: str
:ivar id:
:vartype id: str
:ivar module_id:
:vartype module_id: str
:ivar comment:
:vartype comment: str
:ivar name:
:vartype name: str
:ivar module_parameters:
:vartype module_parameters: list[~flow.models.ParameterAssignment]
:ivar module_metadata_parameters:
:vartype module_metadata_parameters: list[~flow.models.ParameterAssignment]
:ivar module_output_settings:
:vartype module_output_settings: list[~flow.models.OutputSetting]
:ivar module_input_settings:
:vartype module_input_settings: list[~flow.models.InputSetting]
:ivar use_graph_default_compute:
:vartype use_graph_default_compute: bool
:ivar use_graph_default_datastore:
:vartype use_graph_default_datastore: bool
:ivar regenerate_output:
:vartype regenerate_output: bool
:ivar control_inputs:
:vartype control_inputs: list[~flow.models.ControlInput]
:ivar cloud_settings:
:vartype cloud_settings: ~flow.models.CloudSettings
:ivar execution_phase: Possible values include: "Execution", "Initialization", "Finalization".
:vartype execution_phase: str or ~flow.models.ExecutionPhase
:ivar run_attribution:
:vartype run_attribution: str
"""
_attribute_map = {
'module_type': {'key': 'moduleType', 'type': 'str'},
'runconfig': {'key': 'runconfig', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'module_id': {'key': 'moduleId', 'type': 'str'},
'comment': {'key': 'comment', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'module_parameters': {'key': 'moduleParameters', 'type': '[ParameterAssignment]'},
'module_metadata_parameters': {'key': 'moduleMetadataParameters', 'type': '[ParameterAssignment]'},
'module_output_settings': {'key': 'moduleOutputSettings', 'type': '[OutputSetting]'},
'module_input_settings': {'key': 'moduleInputSettings', 'type': '[InputSetting]'},
'use_graph_default_compute': {'key': 'useGraphDefaultCompute', 'type': 'bool'},
'use_graph_default_datastore': {'key': 'useGraphDefaultDatastore', 'type': 'bool'},
'regenerate_output': {'key': 'regenerateOutput', 'type': 'bool'},
'control_inputs': {'key': 'controlInputs', 'type': '[ControlInput]'},
'cloud_settings': {'key': 'cloudSettings', 'type': 'CloudSettings'},
'execution_phase': {'key': 'executionPhase', 'type': 'str'},
'run_attribution': {'key': 'runAttribution', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword module_type: Possible values include: "None", "BatchInferencing".
:paramtype module_type: str or ~flow.models.ModuleType
:keyword runconfig:
:paramtype runconfig: str
:keyword id:
:paramtype id: str
:keyword module_id:
:paramtype module_id: str
:keyword comment:
:paramtype comment: str
:keyword name:
:paramtype name: str
:keyword module_parameters:
:paramtype module_parameters: list[~flow.models.ParameterAssignment]
:keyword module_metadata_parameters:
:paramtype module_metadata_parameters: list[~flow.models.ParameterAssignment]
:keyword module_output_settings:
:paramtype module_output_settings: list[~flow.models.OutputSetting]
:keyword module_input_settings:
:paramtype module_input_settings: list[~flow.models.InputSetting]
:keyword use_graph_default_compute:
:paramtype use_graph_default_compute: bool
:keyword use_graph_default_datastore:
:paramtype use_graph_default_datastore: bool
:keyword regenerate_output:
:paramtype regenerate_output: bool
:keyword control_inputs:
:paramtype control_inputs: list[~flow.models.ControlInput]
:keyword cloud_settings:
:paramtype cloud_settings: ~flow.models.CloudSettings
:keyword execution_phase: Possible values include: "Execution", "Initialization",
"Finalization".
:paramtype execution_phase: str or ~flow.models.ExecutionPhase
:keyword run_attribution:
:paramtype run_attribution: str
"""
super(GraphModuleNode, self).__init__(**kwargs)
self.module_type = kwargs.get('module_type', None)
self.runconfig = kwargs.get('runconfig', None)
self.id = kwargs.get('id', None)
self.module_id = kwargs.get('module_id', None)
self.comment = kwargs.get('comment', None)
self.name = kwargs.get('name', None)
self.module_parameters = kwargs.get('module_parameters', None)
self.module_metadata_parameters = kwargs.get('module_metadata_parameters', None)
self.module_output_settings = kwargs.get('module_output_settings', None)
self.module_input_settings = kwargs.get('module_input_settings', None)
self.use_graph_default_compute = kwargs.get('use_graph_default_compute', None)
self.use_graph_default_datastore = kwargs.get('use_graph_default_datastore', None)
self.regenerate_output = kwargs.get('regenerate_output', None)
self.control_inputs = kwargs.get('control_inputs', None)
self.cloud_settings = kwargs.get('cloud_settings', None)
self.execution_phase = kwargs.get('execution_phase', None)
self.run_attribution = kwargs.get('run_attribution', None)
class GraphModuleNodeRunSetting(msrest.serialization.Model):
"""GraphModuleNodeRunSetting.
:ivar node_id:
:vartype node_id: str
:ivar module_id:
:vartype module_id: str
:ivar step_type:
:vartype step_type: str
:ivar run_settings:
:vartype run_settings: list[~flow.models.RunSettingParameterAssignment]
"""
_attribute_map = {
'node_id': {'key': 'nodeId', 'type': 'str'},
'module_id': {'key': 'moduleId', 'type': 'str'},
'step_type': {'key': 'stepType', 'type': 'str'},
'run_settings': {'key': 'runSettings', 'type': '[RunSettingParameterAssignment]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword node_id:
:paramtype node_id: str
:keyword module_id:
:paramtype module_id: str
:keyword step_type:
:paramtype step_type: str
:keyword run_settings:
:paramtype run_settings: list[~flow.models.RunSettingParameterAssignment]
"""
super(GraphModuleNodeRunSetting, self).__init__(**kwargs)
self.node_id = kwargs.get('node_id', None)
self.module_id = kwargs.get('module_id', None)
self.step_type = kwargs.get('step_type', None)
self.run_settings = kwargs.get('run_settings', None)
class GraphModuleNodeUIInputSetting(msrest.serialization.Model):
"""GraphModuleNodeUIInputSetting.
:ivar node_id:
:vartype node_id: str
:ivar module_id:
:vartype module_id: str
:ivar module_input_settings:
:vartype module_input_settings: list[~flow.models.UIInputSetting]
"""
_attribute_map = {
'node_id': {'key': 'nodeId', 'type': 'str'},
'module_id': {'key': 'moduleId', 'type': 'str'},
'module_input_settings': {'key': 'moduleInputSettings', 'type': '[UIInputSetting]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword node_id:
:paramtype node_id: str
:keyword module_id:
:paramtype module_id: str
:keyword module_input_settings:
:paramtype module_input_settings: list[~flow.models.UIInputSetting]
"""
super(GraphModuleNodeUIInputSetting, self).__init__(**kwargs)
self.node_id = kwargs.get('node_id', None)
self.module_id = kwargs.get('module_id', None)
self.module_input_settings = kwargs.get('module_input_settings', None)
class GraphNodeStatusInfo(msrest.serialization.Model):
"""GraphNodeStatusInfo.
:ivar status: Possible values include: "NotStarted", "Queued", "Running", "Failed", "Finished",
"Canceled", "PartiallyExecuted", "Bypassed".
:vartype status: str or ~flow.models.TaskStatusCode
:ivar run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:vartype run_status: str or ~flow.models.RunStatus
:ivar is_bypassed:
:vartype is_bypassed: bool
:ivar has_failed_child_run:
:vartype has_failed_child_run: bool
:ivar partially_executed:
:vartype partially_executed: bool
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar aether_start_time:
:vartype aether_start_time: ~datetime.datetime
:ivar aether_end_time:
:vartype aether_end_time: ~datetime.datetime
:ivar aether_creation_time:
:vartype aether_creation_time: ~datetime.datetime
:ivar run_history_start_time:
:vartype run_history_start_time: ~datetime.datetime
:ivar run_history_end_time:
:vartype run_history_end_time: ~datetime.datetime
:ivar run_history_creation_time:
:vartype run_history_creation_time: ~datetime.datetime
:ivar reuse_info:
:vartype reuse_info: ~flow.models.TaskReuseInfo
:ivar control_flow_info:
:vartype control_flow_info: ~flow.models.TaskControlFlowInfo
:ivar status_code: Possible values include: "NotStarted", "Queued", "Running", "Failed",
"Finished", "Canceled", "PartiallyExecuted", "Bypassed".
:vartype status_code: str or ~flow.models.TaskStatusCode
:ivar status_detail:
:vartype status_detail: str
:ivar creation_time:
:vartype creation_time: ~datetime.datetime
:ivar schedule_time:
:vartype schedule_time: ~datetime.datetime
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar request_id:
:vartype request_id: str
:ivar run_id:
:vartype run_id: str
:ivar data_container_id:
:vartype data_container_id: str
:ivar real_time_log_path:
:vartype real_time_log_path: str
:ivar has_warnings:
:vartype has_warnings: bool
:ivar composite_node_id:
:vartype composite_node_id: str
"""
_attribute_map = {
'status': {'key': 'status', 'type': 'str'},
'run_status': {'key': 'runStatus', 'type': 'str'},
'is_bypassed': {'key': 'isBypassed', 'type': 'bool'},
'has_failed_child_run': {'key': 'hasFailedChildRun', 'type': 'bool'},
'partially_executed': {'key': 'partiallyExecuted', 'type': 'bool'},
'properties': {'key': 'properties', 'type': '{str}'},
'aether_start_time': {'key': 'aetherStartTime', 'type': 'iso-8601'},
'aether_end_time': {'key': 'aetherEndTime', 'type': 'iso-8601'},
'aether_creation_time': {'key': 'aetherCreationTime', 'type': 'iso-8601'},
'run_history_start_time': {'key': 'runHistoryStartTime', 'type': 'iso-8601'},
'run_history_end_time': {'key': 'runHistoryEndTime', 'type': 'iso-8601'},
'run_history_creation_time': {'key': 'runHistoryCreationTime', 'type': 'iso-8601'},
'reuse_info': {'key': 'reuseInfo', 'type': 'TaskReuseInfo'},
'control_flow_info': {'key': 'controlFlowInfo', 'type': 'TaskControlFlowInfo'},
'status_code': {'key': 'statusCode', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
'creation_time': {'key': 'creationTime', 'type': 'iso-8601'},
'schedule_time': {'key': 'scheduleTime', 'type': 'iso-8601'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'request_id': {'key': 'requestId', 'type': 'str'},
'run_id': {'key': 'runId', 'type': 'str'},
'data_container_id': {'key': 'dataContainerId', 'type': 'str'},
'real_time_log_path': {'key': 'realTimeLogPath', 'type': 'str'},
'has_warnings': {'key': 'hasWarnings', 'type': 'bool'},
'composite_node_id': {'key': 'compositeNodeId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword status: Possible values include: "NotStarted", "Queued", "Running", "Failed",
"Finished", "Canceled", "PartiallyExecuted", "Bypassed".
:paramtype status: str or ~flow.models.TaskStatusCode
:keyword run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:paramtype run_status: str or ~flow.models.RunStatus
:keyword is_bypassed:
:paramtype is_bypassed: bool
:keyword has_failed_child_run:
:paramtype has_failed_child_run: bool
:keyword partially_executed:
:paramtype partially_executed: bool
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword aether_start_time:
:paramtype aether_start_time: ~datetime.datetime
:keyword aether_end_time:
:paramtype aether_end_time: ~datetime.datetime
:keyword aether_creation_time:
:paramtype aether_creation_time: ~datetime.datetime
:keyword run_history_start_time:
:paramtype run_history_start_time: ~datetime.datetime
:keyword run_history_end_time:
:paramtype run_history_end_time: ~datetime.datetime
:keyword run_history_creation_time:
:paramtype run_history_creation_time: ~datetime.datetime
:keyword reuse_info:
:paramtype reuse_info: ~flow.models.TaskReuseInfo
:keyword control_flow_info:
:paramtype control_flow_info: ~flow.models.TaskControlFlowInfo
:keyword status_code: Possible values include: "NotStarted", "Queued", "Running", "Failed",
"Finished", "Canceled", "PartiallyExecuted", "Bypassed".
:paramtype status_code: str or ~flow.models.TaskStatusCode
:keyword status_detail:
:paramtype status_detail: str
:keyword creation_time:
:paramtype creation_time: ~datetime.datetime
:keyword schedule_time:
:paramtype schedule_time: ~datetime.datetime
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
:keyword request_id:
:paramtype request_id: str
:keyword run_id:
:paramtype run_id: str
:keyword data_container_id:
:paramtype data_container_id: str
:keyword real_time_log_path:
:paramtype real_time_log_path: str
:keyword has_warnings:
:paramtype has_warnings: bool
:keyword composite_node_id:
:paramtype composite_node_id: str
"""
super(GraphNodeStatusInfo, self).__init__(**kwargs)
self.status = kwargs.get('status', None)
self.run_status = kwargs.get('run_status', None)
self.is_bypassed = kwargs.get('is_bypassed', None)
self.has_failed_child_run = kwargs.get('has_failed_child_run', None)
self.partially_executed = kwargs.get('partially_executed', None)
self.properties = kwargs.get('properties', None)
self.aether_start_time = kwargs.get('aether_start_time', None)
self.aether_end_time = kwargs.get('aether_end_time', None)
self.aether_creation_time = kwargs.get('aether_creation_time', None)
self.run_history_start_time = kwargs.get('run_history_start_time', None)
self.run_history_end_time = kwargs.get('run_history_end_time', None)
self.run_history_creation_time = kwargs.get('run_history_creation_time', None)
self.reuse_info = kwargs.get('reuse_info', None)
self.control_flow_info = kwargs.get('control_flow_info', None)
self.status_code = kwargs.get('status_code', None)
self.status_detail = kwargs.get('status_detail', None)
self.creation_time = kwargs.get('creation_time', None)
self.schedule_time = kwargs.get('schedule_time', None)
self.start_time = kwargs.get('start_time', None)
self.end_time = kwargs.get('end_time', None)
self.request_id = kwargs.get('request_id', None)
self.run_id = kwargs.get('run_id', None)
self.data_container_id = kwargs.get('data_container_id', None)
self.real_time_log_path = kwargs.get('real_time_log_path', None)
self.has_warnings = kwargs.get('has_warnings', None)
self.composite_node_id = kwargs.get('composite_node_id', None)
class GraphReferenceNode(msrest.serialization.Model):
"""GraphReferenceNode.
:ivar graph_id:
:vartype graph_id: str
:ivar default_compute:
:vartype default_compute: ~flow.models.ComputeSetting
:ivar default_datastore:
:vartype default_datastore: ~flow.models.DatastoreSetting
:ivar id:
:vartype id: str
:ivar module_id:
:vartype module_id: str
:ivar comment:
:vartype comment: str
:ivar name:
:vartype name: str
:ivar module_parameters:
:vartype module_parameters: list[~flow.models.ParameterAssignment]
:ivar module_metadata_parameters:
:vartype module_metadata_parameters: list[~flow.models.ParameterAssignment]
:ivar module_output_settings:
:vartype module_output_settings: list[~flow.models.OutputSetting]
:ivar module_input_settings:
:vartype module_input_settings: list[~flow.models.InputSetting]
:ivar use_graph_default_compute:
:vartype use_graph_default_compute: bool
:ivar use_graph_default_datastore:
:vartype use_graph_default_datastore: bool
:ivar regenerate_output:
:vartype regenerate_output: bool
:ivar control_inputs:
:vartype control_inputs: list[~flow.models.ControlInput]
:ivar cloud_settings:
:vartype cloud_settings: ~flow.models.CloudSettings
:ivar execution_phase: Possible values include: "Execution", "Initialization", "Finalization".
:vartype execution_phase: str or ~flow.models.ExecutionPhase
:ivar run_attribution:
:vartype run_attribution: str
"""
_attribute_map = {
'graph_id': {'key': 'graphId', 'type': 'str'},
'default_compute': {'key': 'defaultCompute', 'type': 'ComputeSetting'},
'default_datastore': {'key': 'defaultDatastore', 'type': 'DatastoreSetting'},
'id': {'key': 'id', 'type': 'str'},
'module_id': {'key': 'moduleId', 'type': 'str'},
'comment': {'key': 'comment', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'module_parameters': {'key': 'moduleParameters', 'type': '[ParameterAssignment]'},
'module_metadata_parameters': {'key': 'moduleMetadataParameters', 'type': '[ParameterAssignment]'},
'module_output_settings': {'key': 'moduleOutputSettings', 'type': '[OutputSetting]'},
'module_input_settings': {'key': 'moduleInputSettings', 'type': '[InputSetting]'},
'use_graph_default_compute': {'key': 'useGraphDefaultCompute', 'type': 'bool'},
'use_graph_default_datastore': {'key': 'useGraphDefaultDatastore', 'type': 'bool'},
'regenerate_output': {'key': 'regenerateOutput', 'type': 'bool'},
'control_inputs': {'key': 'controlInputs', 'type': '[ControlInput]'},
'cloud_settings': {'key': 'cloudSettings', 'type': 'CloudSettings'},
'execution_phase': {'key': 'executionPhase', 'type': 'str'},
'run_attribution': {'key': 'runAttribution', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword graph_id:
:paramtype graph_id: str
:keyword default_compute:
:paramtype default_compute: ~flow.models.ComputeSetting
:keyword default_datastore:
:paramtype default_datastore: ~flow.models.DatastoreSetting
:keyword id:
:paramtype id: str
:keyword module_id:
:paramtype module_id: str
:keyword comment:
:paramtype comment: str
:keyword name:
:paramtype name: str
:keyword module_parameters:
:paramtype module_parameters: list[~flow.models.ParameterAssignment]
:keyword module_metadata_parameters:
:paramtype module_metadata_parameters: list[~flow.models.ParameterAssignment]
:keyword module_output_settings:
:paramtype module_output_settings: list[~flow.models.OutputSetting]
:keyword module_input_settings:
:paramtype module_input_settings: list[~flow.models.InputSetting]
:keyword use_graph_default_compute:
:paramtype use_graph_default_compute: bool
:keyword use_graph_default_datastore:
:paramtype use_graph_default_datastore: bool
:keyword regenerate_output:
:paramtype regenerate_output: bool
:keyword control_inputs:
:paramtype control_inputs: list[~flow.models.ControlInput]
:keyword cloud_settings:
:paramtype cloud_settings: ~flow.models.CloudSettings
:keyword execution_phase: Possible values include: "Execution", "Initialization",
"Finalization".
:paramtype execution_phase: str or ~flow.models.ExecutionPhase
:keyword run_attribution:
:paramtype run_attribution: str
"""
super(GraphReferenceNode, self).__init__(**kwargs)
self.graph_id = kwargs.get('graph_id', None)
self.default_compute = kwargs.get('default_compute', None)
self.default_datastore = kwargs.get('default_datastore', None)
self.id = kwargs.get('id', None)
self.module_id = kwargs.get('module_id', None)
self.comment = kwargs.get('comment', None)
self.name = kwargs.get('name', None)
self.module_parameters = kwargs.get('module_parameters', None)
self.module_metadata_parameters = kwargs.get('module_metadata_parameters', None)
self.module_output_settings = kwargs.get('module_output_settings', None)
self.module_input_settings = kwargs.get('module_input_settings', None)
self.use_graph_default_compute = kwargs.get('use_graph_default_compute', None)
self.use_graph_default_datastore = kwargs.get('use_graph_default_datastore', None)
self.regenerate_output = kwargs.get('regenerate_output', None)
self.control_inputs = kwargs.get('control_inputs', None)
self.cloud_settings = kwargs.get('cloud_settings', None)
self.execution_phase = kwargs.get('execution_phase', None)
self.run_attribution = kwargs.get('run_attribution', None)
class HdfsReference(msrest.serialization.Model):
"""HdfsReference.
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
:keyword relative_path:
:paramtype relative_path: str
"""
super(HdfsReference, self).__init__(**kwargs)
self.aml_data_store_name = kwargs.get('aml_data_store_name', None)
self.relative_path = kwargs.get('relative_path', None)
class HdiClusterComputeInfo(msrest.serialization.Model):
"""HdiClusterComputeInfo.
:ivar address:
:vartype address: str
:ivar username:
:vartype username: str
:ivar password:
:vartype password: str
:ivar private_key:
:vartype private_key: str
"""
_attribute_map = {
'address': {'key': 'address', 'type': 'str'},
'username': {'key': 'username', 'type': 'str'},
'password': {'key': 'password', 'type': 'str'},
'private_key': {'key': 'privateKey', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword address:
:paramtype address: str
:keyword username:
:paramtype username: str
:keyword password:
:paramtype password: str
:keyword private_key:
:paramtype private_key: str
"""
super(HdiClusterComputeInfo, self).__init__(**kwargs)
self.address = kwargs.get('address', None)
self.username = kwargs.get('username', None)
self.password = kwargs.get('password', None)
self.private_key = kwargs.get('private_key', None)
class HdiConfiguration(msrest.serialization.Model):
"""HdiConfiguration.
:ivar yarn_deploy_mode: Possible values include: "None", "Client", "Cluster".
:vartype yarn_deploy_mode: str or ~flow.models.YarnDeployMode
"""
_attribute_map = {
'yarn_deploy_mode': {'key': 'yarnDeployMode', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword yarn_deploy_mode: Possible values include: "None", "Client", "Cluster".
:paramtype yarn_deploy_mode: str or ~flow.models.YarnDeployMode
"""
super(HdiConfiguration, self).__init__(**kwargs)
self.yarn_deploy_mode = kwargs.get('yarn_deploy_mode', None)
class HdiRunConfiguration(msrest.serialization.Model):
"""HdiRunConfiguration.
:ivar file:
:vartype file: str
:ivar class_name:
:vartype class_name: str
:ivar files:
:vartype files: list[str]
:ivar archives:
:vartype archives: list[str]
:ivar jars:
:vartype jars: list[str]
:ivar py_files:
:vartype py_files: list[str]
:ivar compute_name:
:vartype compute_name: str
:ivar queue:
:vartype queue: str
:ivar driver_memory:
:vartype driver_memory: str
:ivar driver_cores:
:vartype driver_cores: int
:ivar executor_memory:
:vartype executor_memory: str
:ivar executor_cores:
:vartype executor_cores: int
:ivar number_executors:
:vartype number_executors: int
:ivar conf: Dictionary of :code:`<string>`.
:vartype conf: dict[str, str]
:ivar name:
:vartype name: str
"""
_attribute_map = {
'file': {'key': 'file', 'type': 'str'},
'class_name': {'key': 'className', 'type': 'str'},
'files': {'key': 'files', 'type': '[str]'},
'archives': {'key': 'archives', 'type': '[str]'},
'jars': {'key': 'jars', 'type': '[str]'},
'py_files': {'key': 'pyFiles', 'type': '[str]'},
'compute_name': {'key': 'computeName', 'type': 'str'},
'queue': {'key': 'queue', 'type': 'str'},
'driver_memory': {'key': 'driverMemory', 'type': 'str'},
'driver_cores': {'key': 'driverCores', 'type': 'int'},
'executor_memory': {'key': 'executorMemory', 'type': 'str'},
'executor_cores': {'key': 'executorCores', 'type': 'int'},
'number_executors': {'key': 'numberExecutors', 'type': 'int'},
'conf': {'key': 'conf', 'type': '{str}'},
'name': {'key': 'name', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword file:
:paramtype file: str
:keyword class_name:
:paramtype class_name: str
:keyword files:
:paramtype files: list[str]
:keyword archives:
:paramtype archives: list[str]
:keyword jars:
:paramtype jars: list[str]
:keyword py_files:
:paramtype py_files: list[str]
:keyword compute_name:
:paramtype compute_name: str
:keyword queue:
:paramtype queue: str
:keyword driver_memory:
:paramtype driver_memory: str
:keyword driver_cores:
:paramtype driver_cores: int
:keyword executor_memory:
:paramtype executor_memory: str
:keyword executor_cores:
:paramtype executor_cores: int
:keyword number_executors:
:paramtype number_executors: int
:keyword conf: Dictionary of :code:`<string>`.
:paramtype conf: dict[str, str]
:keyword name:
:paramtype name: str
"""
super(HdiRunConfiguration, self).__init__(**kwargs)
self.file = kwargs.get('file', None)
self.class_name = kwargs.get('class_name', None)
self.files = kwargs.get('files', None)
self.archives = kwargs.get('archives', None)
self.jars = kwargs.get('jars', None)
self.py_files = kwargs.get('py_files', None)
self.compute_name = kwargs.get('compute_name', None)
self.queue = kwargs.get('queue', None)
self.driver_memory = kwargs.get('driver_memory', None)
self.driver_cores = kwargs.get('driver_cores', None)
self.executor_memory = kwargs.get('executor_memory', None)
self.executor_cores = kwargs.get('executor_cores', None)
self.number_executors = kwargs.get('number_executors', None)
self.conf = kwargs.get('conf', None)
self.name = kwargs.get('name', None)
class HistoryConfiguration(msrest.serialization.Model):
"""HistoryConfiguration.
:ivar output_collection:
:vartype output_collection: bool
:ivar directories_to_watch:
:vartype directories_to_watch: list[str]
:ivar enable_m_lflow_tracking:
:vartype enable_m_lflow_tracking: bool
"""
_attribute_map = {
'output_collection': {'key': 'outputCollection', 'type': 'bool'},
'directories_to_watch': {'key': 'directoriesToWatch', 'type': '[str]'},
'enable_m_lflow_tracking': {'key': 'enableMLflowTracking', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword output_collection:
:paramtype output_collection: bool
:keyword directories_to_watch:
:paramtype directories_to_watch: list[str]
:keyword enable_m_lflow_tracking:
:paramtype enable_m_lflow_tracking: bool
"""
super(HistoryConfiguration, self).__init__(**kwargs)
self.output_collection = kwargs.get('output_collection', True)
self.directories_to_watch = kwargs.get('directories_to_watch', ['logs'])
self.enable_m_lflow_tracking = kwargs.get('enable_m_lflow_tracking', True)
class HyperDriveConfiguration(msrest.serialization.Model):
"""HyperDriveConfiguration.
:ivar hyper_drive_run_config:
:vartype hyper_drive_run_config: str
:ivar primary_metric_goal:
:vartype primary_metric_goal: str
:ivar primary_metric_name:
:vartype primary_metric_name: str
:ivar arguments:
:vartype arguments: list[~flow.models.ArgumentAssignment]
"""
_attribute_map = {
'hyper_drive_run_config': {'key': 'hyperDriveRunConfig', 'type': 'str'},
'primary_metric_goal': {'key': 'primaryMetricGoal', 'type': 'str'},
'primary_metric_name': {'key': 'primaryMetricName', 'type': 'str'},
'arguments': {'key': 'arguments', 'type': '[ArgumentAssignment]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword hyper_drive_run_config:
:paramtype hyper_drive_run_config: str
:keyword primary_metric_goal:
:paramtype primary_metric_goal: str
:keyword primary_metric_name:
:paramtype primary_metric_name: str
:keyword arguments:
:paramtype arguments: list[~flow.models.ArgumentAssignment]
"""
super(HyperDriveConfiguration, self).__init__(**kwargs)
self.hyper_drive_run_config = kwargs.get('hyper_drive_run_config', None)
self.primary_metric_goal = kwargs.get('primary_metric_goal', None)
self.primary_metric_name = kwargs.get('primary_metric_name', None)
self.arguments = kwargs.get('arguments', None)
class ICheckableLongRunningOperationResponse(msrest.serialization.Model):
"""ICheckableLongRunningOperationResponse.
:ivar completion_result: Any object.
:vartype completion_result: any
:ivar location:
:vartype location: str
:ivar operation_result:
:vartype operation_result: str
"""
_attribute_map = {
'completion_result': {'key': 'completionResult', 'type': 'object'},
'location': {'key': 'location', 'type': 'str'},
'operation_result': {'key': 'operationResult', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword completion_result: Any object.
:paramtype completion_result: any
:keyword location:
:paramtype location: str
:keyword operation_result:
:paramtype operation_result: str
"""
super(ICheckableLongRunningOperationResponse, self).__init__(**kwargs)
self.completion_result = kwargs.get('completion_result', None)
self.location = kwargs.get('location', None)
self.operation_result = kwargs.get('operation_result', None)
class IdentityConfiguration(msrest.serialization.Model):
"""IdentityConfiguration.
:ivar type: Possible values include: "Managed", "ServicePrincipal", "AMLToken".
:vartype type: str or ~flow.models.IdentityType
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar secret:
:vartype secret: str
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'secret': {'key': 'secret', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type: Possible values include: "Managed", "ServicePrincipal", "AMLToken".
:paramtype type: str or ~flow.models.IdentityType
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword secret:
:paramtype secret: str
"""
super(IdentityConfiguration, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.properties = kwargs.get('properties', None)
self.secret = kwargs.get('secret', None)
class IdentitySetting(msrest.serialization.Model):
"""IdentitySetting.
:ivar type: Possible values include: "UserIdentity", "Managed", "AMLToken".
:vartype type: str or ~flow.models.AEVAIdentityType
:ivar client_id:
:vartype client_id: str
:ivar object_id:
:vartype object_id: str
:ivar msi_resource_id:
:vartype msi_resource_id: str
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'client_id': {'key': 'clientId', 'type': 'str'},
'object_id': {'key': 'objectId', 'type': 'str'},
'msi_resource_id': {'key': 'msiResourceId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type: Possible values include: "UserIdentity", "Managed", "AMLToken".
:paramtype type: str or ~flow.models.AEVAIdentityType
:keyword client_id:
:paramtype client_id: str
:keyword object_id:
:paramtype object_id: str
:keyword msi_resource_id:
:paramtype msi_resource_id: str
"""
super(IdentitySetting, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.client_id = kwargs.get('client_id', None)
self.object_id = kwargs.get('object_id', None)
self.msi_resource_id = kwargs.get('msi_resource_id', None)
class ImportDataTask(msrest.serialization.Model):
"""ImportDataTask.
:ivar data_transfer_source:
:vartype data_transfer_source: ~flow.models.DataTransferSource
"""
_attribute_map = {
'data_transfer_source': {'key': 'DataTransferSource', 'type': 'DataTransferSource'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_transfer_source:
:paramtype data_transfer_source: ~flow.models.DataTransferSource
"""
super(ImportDataTask, self).__init__(**kwargs)
self.data_transfer_source = kwargs.get('data_transfer_source', None)
class IndexedErrorResponse(msrest.serialization.Model):
"""IndexedErrorResponse.
:ivar code:
:vartype code: str
:ivar error_code_hierarchy:
:vartype error_code_hierarchy: str
:ivar message:
:vartype message: str
:ivar time:
:vartype time: ~datetime.datetime
:ivar component_name:
:vartype component_name: str
:ivar severity:
:vartype severity: int
:ivar details_uri:
:vartype details_uri: str
:ivar reference_code:
:vartype reference_code: str
"""
_attribute_map = {
'code': {'key': 'code', 'type': 'str'},
'error_code_hierarchy': {'key': 'errorCodeHierarchy', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
'time': {'key': 'time', 'type': 'iso-8601'},
'component_name': {'key': 'componentName', 'type': 'str'},
'severity': {'key': 'severity', 'type': 'int'},
'details_uri': {'key': 'detailsUri', 'type': 'str'},
'reference_code': {'key': 'referenceCode', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword code:
:paramtype code: str
:keyword error_code_hierarchy:
:paramtype error_code_hierarchy: str
:keyword message:
:paramtype message: str
:keyword time:
:paramtype time: ~datetime.datetime
:keyword component_name:
:paramtype component_name: str
:keyword severity:
:paramtype severity: int
:keyword details_uri:
:paramtype details_uri: str
:keyword reference_code:
:paramtype reference_code: str
"""
super(IndexedErrorResponse, self).__init__(**kwargs)
self.code = kwargs.get('code', None)
self.error_code_hierarchy = kwargs.get('error_code_hierarchy', None)
self.message = kwargs.get('message', None)
self.time = kwargs.get('time', None)
self.component_name = kwargs.get('component_name', None)
self.severity = kwargs.get('severity', None)
self.details_uri = kwargs.get('details_uri', None)
self.reference_code = kwargs.get('reference_code', None)
class InitScriptInfoDto(msrest.serialization.Model):
"""InitScriptInfoDto.
:ivar dbfs:
:vartype dbfs: ~flow.models.DbfsStorageInfoDto
"""
_attribute_map = {
'dbfs': {'key': 'dbfs', 'type': 'DbfsStorageInfoDto'},
}
def __init__(
self,
**kwargs
):
"""
:keyword dbfs:
:paramtype dbfs: ~flow.models.DbfsStorageInfoDto
"""
super(InitScriptInfoDto, self).__init__(**kwargs)
self.dbfs = kwargs.get('dbfs', None)
class InnerErrorDetails(msrest.serialization.Model):
"""InnerErrorDetails.
:ivar code:
:vartype code: str
:ivar message:
:vartype message: str
:ivar target:
:vartype target: str
"""
_attribute_map = {
'code': {'key': 'code', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword code:
:paramtype code: str
:keyword message:
:paramtype message: str
:keyword target:
:paramtype target: str
"""
super(InnerErrorDetails, self).__init__(**kwargs)
self.code = kwargs.get('code', None)
self.message = kwargs.get('message', None)
self.target = kwargs.get('target', None)
class InnerErrorResponse(msrest.serialization.Model):
"""A nested structure of errors.
:ivar code: The error code.
:vartype code: str
:ivar inner_error: A nested structure of errors.
:vartype inner_error: ~flow.models.InnerErrorResponse
"""
_attribute_map = {
'code': {'key': 'code', 'type': 'str'},
'inner_error': {'key': 'innerError', 'type': 'InnerErrorResponse'},
}
def __init__(
self,
**kwargs
):
"""
:keyword code: The error code.
:paramtype code: str
:keyword inner_error: A nested structure of errors.
:paramtype inner_error: ~flow.models.InnerErrorResponse
"""
super(InnerErrorResponse, self).__init__(**kwargs)
self.code = kwargs.get('code', None)
self.inner_error = kwargs.get('inner_error', None)
class InputAsset(msrest.serialization.Model):
"""InputAsset.
:ivar asset:
:vartype asset: ~flow.models.Asset
:ivar mechanism: Possible values include: "Direct", "Mount", "Download", "Hdfs".
:vartype mechanism: str or ~flow.models.DeliveryMechanism
:ivar environment_variable_name:
:vartype environment_variable_name: str
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar overwrite:
:vartype overwrite: bool
:ivar options: Dictionary of :code:`<string>`.
:vartype options: dict[str, str]
"""
_attribute_map = {
'asset': {'key': 'asset', 'type': 'Asset'},
'mechanism': {'key': 'mechanism', 'type': 'str'},
'environment_variable_name': {'key': 'environmentVariableName', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'overwrite': {'key': 'overwrite', 'type': 'bool'},
'options': {'key': 'options', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword asset:
:paramtype asset: ~flow.models.Asset
:keyword mechanism: Possible values include: "Direct", "Mount", "Download", "Hdfs".
:paramtype mechanism: str or ~flow.models.DeliveryMechanism
:keyword environment_variable_name:
:paramtype environment_variable_name: str
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword overwrite:
:paramtype overwrite: bool
:keyword options: Dictionary of :code:`<string>`.
:paramtype options: dict[str, str]
"""
super(InputAsset, self).__init__(**kwargs)
self.asset = kwargs.get('asset', None)
self.mechanism = kwargs.get('mechanism', None)
self.environment_variable_name = kwargs.get('environment_variable_name', None)
self.path_on_compute = kwargs.get('path_on_compute', None)
self.overwrite = kwargs.get('overwrite', None)
self.options = kwargs.get('options', None)
class InputData(msrest.serialization.Model):
"""InputData.
:ivar dataset_id:
:vartype dataset_id: str
:ivar mode: Possible values include: "Mount", "Download", "Upload", "ReadOnlyMount",
"ReadWriteMount", "Direct", "EvalMount", "EvalDownload".
:vartype mode: str or ~flow.models.DataBindingMode
:ivar value:
:vartype value: str
"""
_attribute_map = {
'dataset_id': {'key': 'datasetId', 'type': 'str'},
'mode': {'key': 'mode', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword dataset_id:
:paramtype dataset_id: str
:keyword mode: Possible values include: "Mount", "Download", "Upload", "ReadOnlyMount",
"ReadWriteMount", "Direct", "EvalMount", "EvalDownload".
:paramtype mode: str or ~flow.models.DataBindingMode
:keyword value:
:paramtype value: str
"""
super(InputData, self).__init__(**kwargs)
self.dataset_id = kwargs.get('dataset_id', None)
self.mode = kwargs.get('mode', None)
self.value = kwargs.get('value', None)
class InputDataBinding(msrest.serialization.Model):
"""InputDataBinding.
:ivar data_id:
:vartype data_id: str
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar mode: Possible values include: "Mount", "Download", "Upload", "ReadOnlyMount",
"ReadWriteMount", "Direct", "EvalMount", "EvalDownload".
:vartype mode: str or ~flow.models.DataBindingMode
:ivar description:
:vartype description: str
:ivar uri:
:vartype uri: ~flow.models.MfeInternalUriReference
:ivar value:
:vartype value: str
:ivar asset_uri:
:vartype asset_uri: str
:ivar job_input_type: Possible values include: "Dataset", "Uri", "Literal", "UriFile",
"UriFolder", "MLTable", "CustomModel", "MLFlowModel", "TritonModel".
:vartype job_input_type: str or ~flow.models.JobInputType
"""
_attribute_map = {
'data_id': {'key': 'dataId', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'mode': {'key': 'mode', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'MfeInternalUriReference'},
'value': {'key': 'value', 'type': 'str'},
'asset_uri': {'key': 'assetUri', 'type': 'str'},
'job_input_type': {'key': 'jobInputType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_id:
:paramtype data_id: str
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword mode: Possible values include: "Mount", "Download", "Upload", "ReadOnlyMount",
"ReadWriteMount", "Direct", "EvalMount", "EvalDownload".
:paramtype mode: str or ~flow.models.DataBindingMode
:keyword description:
:paramtype description: str
:keyword uri:
:paramtype uri: ~flow.models.MfeInternalUriReference
:keyword value:
:paramtype value: str
:keyword asset_uri:
:paramtype asset_uri: str
:keyword job_input_type: Possible values include: "Dataset", "Uri", "Literal", "UriFile",
"UriFolder", "MLTable", "CustomModel", "MLFlowModel", "TritonModel".
:paramtype job_input_type: str or ~flow.models.JobInputType
"""
super(InputDataBinding, self).__init__(**kwargs)
self.data_id = kwargs.get('data_id', None)
self.path_on_compute = kwargs.get('path_on_compute', None)
self.mode = kwargs.get('mode', None)
self.description = kwargs.get('description', None)
self.uri = kwargs.get('uri', None)
self.value = kwargs.get('value', None)
self.asset_uri = kwargs.get('asset_uri', None)
self.job_input_type = kwargs.get('job_input_type', None)
class InputDefinition(msrest.serialization.Model):
"""InputDefinition.
:ivar name:
:vartype name: str
:ivar type:
:vartype type: list[str or ~flow.models.ValueType]
:ivar default: Anything.
:vartype default: any
:ivar description:
:vartype description: str
:ivar enum:
:vartype enum: list[str]
:ivar enabled_by:
:vartype enabled_by: str
:ivar enabled_by_type:
:vartype enabled_by_type: list[str or ~flow.models.ValueType]
:ivar enabled_by_value:
:vartype enabled_by_value: list[any]
:ivar model_list:
:vartype model_list: list[str]
:ivar capabilities:
:vartype capabilities: ~flow.models.AzureOpenAIModelCapabilities
:ivar dynamic_list:
:vartype dynamic_list: ~flow.models.ToolInputDynamicList
:ivar allow_manual_entry:
:vartype allow_manual_entry: bool
:ivar is_multi_select:
:vartype is_multi_select: bool
:ivar generated_by:
:vartype generated_by: ~flow.models.ToolInputGeneratedBy
:ivar input_type: Possible values include: "default", "uionly_hidden".
:vartype input_type: str or ~flow.models.InputType
:ivar advanced:
:vartype advanced: bool
:ivar ui_hints: This is a dictionary.
:vartype ui_hints: dict[str, any]
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': '[str]'},
'default': {'key': 'default', 'type': 'object'},
'description': {'key': 'description', 'type': 'str'},
'enum': {'key': 'enum', 'type': '[str]'},
'enabled_by': {'key': 'enabled_by', 'type': 'str'},
'enabled_by_type': {'key': 'enabled_by_type', 'type': '[str]'},
'enabled_by_value': {'key': 'enabled_by_value', 'type': '[object]'},
'model_list': {'key': 'model_list', 'type': '[str]'},
'capabilities': {'key': 'capabilities', 'type': 'AzureOpenAIModelCapabilities'},
'dynamic_list': {'key': 'dynamic_list', 'type': 'ToolInputDynamicList'},
'allow_manual_entry': {'key': 'allow_manual_entry', 'type': 'bool'},
'is_multi_select': {'key': 'is_multi_select', 'type': 'bool'},
'generated_by': {'key': 'generated_by', 'type': 'ToolInputGeneratedBy'},
'input_type': {'key': 'input_type', 'type': 'str'},
'advanced': {'key': 'advanced', 'type': 'bool'},
'ui_hints': {'key': 'ui_hints', 'type': '{object}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type:
:paramtype type: list[str or ~flow.models.ValueType]
:keyword default: Anything.
:paramtype default: any
:keyword description:
:paramtype description: str
:keyword enum:
:paramtype enum: list[str]
:keyword enabled_by:
:paramtype enabled_by: str
:keyword enabled_by_type:
:paramtype enabled_by_type: list[str or ~flow.models.ValueType]
:keyword enabled_by_value:
:paramtype enabled_by_value: list[any]
:keyword model_list:
:paramtype model_list: list[str]
:keyword capabilities:
:paramtype capabilities: ~flow.models.AzureOpenAIModelCapabilities
:keyword dynamic_list:
:paramtype dynamic_list: ~flow.models.ToolInputDynamicList
:keyword allow_manual_entry:
:paramtype allow_manual_entry: bool
:keyword is_multi_select:
:paramtype is_multi_select: bool
:keyword generated_by:
:paramtype generated_by: ~flow.models.ToolInputGeneratedBy
:keyword input_type: Possible values include: "default", "uionly_hidden".
:paramtype input_type: str or ~flow.models.InputType
:keyword advanced:
:paramtype advanced: bool
:keyword ui_hints: This is a dictionary.
:paramtype ui_hints: dict[str, any]
"""
super(InputDefinition, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.type = kwargs.get('type', None)
self.default = kwargs.get('default', None)
self.description = kwargs.get('description', None)
self.enum = kwargs.get('enum', None)
self.enabled_by = kwargs.get('enabled_by', None)
self.enabled_by_type = kwargs.get('enabled_by_type', None)
self.enabled_by_value = kwargs.get('enabled_by_value', None)
self.model_list = kwargs.get('model_list', None)
self.capabilities = kwargs.get('capabilities', None)
self.dynamic_list = kwargs.get('dynamic_list', None)
self.allow_manual_entry = kwargs.get('allow_manual_entry', None)
self.is_multi_select = kwargs.get('is_multi_select', None)
self.generated_by = kwargs.get('generated_by', None)
self.input_type = kwargs.get('input_type', None)
self.advanced = kwargs.get('advanced', None)
self.ui_hints = kwargs.get('ui_hints', None)
class InputOutputPortMetadata(msrest.serialization.Model):
"""InputOutputPortMetadata.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar graph_module_node_id:
:vartype graph_module_node_id: str
:ivar port_name:
:vartype port_name: str
:ivar schema:
:vartype schema: str
:ivar name:
:vartype name: str
:ivar id:
:vartype id: str
"""
_validation = {
'id': {'readonly': True},
}
_attribute_map = {
'graph_module_node_id': {'key': 'graphModuleNodeId', 'type': 'str'},
'port_name': {'key': 'portName', 'type': 'str'},
'schema': {'key': 'schema', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword graph_module_node_id:
:paramtype graph_module_node_id: str
:keyword port_name:
:paramtype port_name: str
:keyword schema:
:paramtype schema: str
:keyword name:
:paramtype name: str
"""
super(InputOutputPortMetadata, self).__init__(**kwargs)
self.graph_module_node_id = kwargs.get('graph_module_node_id', None)
self.port_name = kwargs.get('port_name', None)
self.schema = kwargs.get('schema', None)
self.name = kwargs.get('name', None)
self.id = None
class InputSetting(msrest.serialization.Model):
"""InputSetting.
:ivar name:
:vartype name: str
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AEVADataStoreMode
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar options: This is a dictionary.
:vartype options: dict[str, str]
:ivar additional_transformations:
:vartype additional_transformations: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'options': {'key': 'options', 'type': '{str}'},
'additional_transformations': {'key': 'additionalTransformations', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AEVADataStoreMode
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword options: This is a dictionary.
:paramtype options: dict[str, str]
:keyword additional_transformations:
:paramtype additional_transformations: str
"""
super(InputSetting, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.data_store_mode = kwargs.get('data_store_mode', None)
self.path_on_compute = kwargs.get('path_on_compute', None)
self.options = kwargs.get('options', None)
self.additional_transformations = kwargs.get('additional_transformations', None)
class IntellectualPropertyPublisherInformation(msrest.serialization.Model):
"""IntellectualPropertyPublisherInformation.
:ivar intellectual_property_publisher:
:vartype intellectual_property_publisher: str
"""
_attribute_map = {
'intellectual_property_publisher': {'key': 'intellectualPropertyPublisher', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword intellectual_property_publisher:
:paramtype intellectual_property_publisher: str
"""
super(IntellectualPropertyPublisherInformation, self).__init__(**kwargs)
self.intellectual_property_publisher = kwargs.get('intellectual_property_publisher', None)
class InteractiveConfig(msrest.serialization.Model):
"""InteractiveConfig.
:ivar is_ssh_enabled:
:vartype is_ssh_enabled: bool
:ivar ssh_public_key:
:vartype ssh_public_key: str
:ivar is_i_python_enabled:
:vartype is_i_python_enabled: bool
:ivar is_tensor_board_enabled:
:vartype is_tensor_board_enabled: bool
:ivar interactive_port:
:vartype interactive_port: int
"""
_attribute_map = {
'is_ssh_enabled': {'key': 'isSSHEnabled', 'type': 'bool'},
'ssh_public_key': {'key': 'sshPublicKey', 'type': 'str'},
'is_i_python_enabled': {'key': 'isIPythonEnabled', 'type': 'bool'},
'is_tensor_board_enabled': {'key': 'isTensorBoardEnabled', 'type': 'bool'},
'interactive_port': {'key': 'interactivePort', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword is_ssh_enabled:
:paramtype is_ssh_enabled: bool
:keyword ssh_public_key:
:paramtype ssh_public_key: str
:keyword is_i_python_enabled:
:paramtype is_i_python_enabled: bool
:keyword is_tensor_board_enabled:
:paramtype is_tensor_board_enabled: bool
:keyword interactive_port:
:paramtype interactive_port: int
"""
super(InteractiveConfig, self).__init__(**kwargs)
self.is_ssh_enabled = kwargs.get('is_ssh_enabled', None)
self.ssh_public_key = kwargs.get('ssh_public_key', None)
self.is_i_python_enabled = kwargs.get('is_i_python_enabled', None)
self.is_tensor_board_enabled = kwargs.get('is_tensor_board_enabled', None)
self.interactive_port = kwargs.get('interactive_port', None)
class InteractiveConfiguration(msrest.serialization.Model):
"""InteractiveConfiguration.
:ivar is_ssh_enabled:
:vartype is_ssh_enabled: bool
:ivar ssh_public_key:
:vartype ssh_public_key: str
:ivar is_i_python_enabled:
:vartype is_i_python_enabled: bool
:ivar is_tensor_board_enabled:
:vartype is_tensor_board_enabled: bool
:ivar interactive_port:
:vartype interactive_port: int
"""
_attribute_map = {
'is_ssh_enabled': {'key': 'isSSHEnabled', 'type': 'bool'},
'ssh_public_key': {'key': 'sshPublicKey', 'type': 'str'},
'is_i_python_enabled': {'key': 'isIPythonEnabled', 'type': 'bool'},
'is_tensor_board_enabled': {'key': 'isTensorBoardEnabled', 'type': 'bool'},
'interactive_port': {'key': 'interactivePort', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword is_ssh_enabled:
:paramtype is_ssh_enabled: bool
:keyword ssh_public_key:
:paramtype ssh_public_key: str
:keyword is_i_python_enabled:
:paramtype is_i_python_enabled: bool
:keyword is_tensor_board_enabled:
:paramtype is_tensor_board_enabled: bool
:keyword interactive_port:
:paramtype interactive_port: int
"""
super(InteractiveConfiguration, self).__init__(**kwargs)
self.is_ssh_enabled = kwargs.get('is_ssh_enabled', None)
self.ssh_public_key = kwargs.get('ssh_public_key', None)
self.is_i_python_enabled = kwargs.get('is_i_python_enabled', None)
self.is_tensor_board_enabled = kwargs.get('is_tensor_board_enabled', None)
self.interactive_port = kwargs.get('interactive_port', None)
class JobCost(msrest.serialization.Model):
"""JobCost.
:ivar charged_cpu_core_seconds:
:vartype charged_cpu_core_seconds: float
:ivar charged_cpu_memory_megabyte_seconds:
:vartype charged_cpu_memory_megabyte_seconds: float
:ivar charged_gpu_seconds:
:vartype charged_gpu_seconds: float
:ivar charged_node_utilization_seconds:
:vartype charged_node_utilization_seconds: float
"""
_attribute_map = {
'charged_cpu_core_seconds': {'key': 'chargedCpuCoreSeconds', 'type': 'float'},
'charged_cpu_memory_megabyte_seconds': {'key': 'chargedCpuMemoryMegabyteSeconds', 'type': 'float'},
'charged_gpu_seconds': {'key': 'chargedGpuSeconds', 'type': 'float'},
'charged_node_utilization_seconds': {'key': 'chargedNodeUtilizationSeconds', 'type': 'float'},
}
def __init__(
self,
**kwargs
):
"""
:keyword charged_cpu_core_seconds:
:paramtype charged_cpu_core_seconds: float
:keyword charged_cpu_memory_megabyte_seconds:
:paramtype charged_cpu_memory_megabyte_seconds: float
:keyword charged_gpu_seconds:
:paramtype charged_gpu_seconds: float
:keyword charged_node_utilization_seconds:
:paramtype charged_node_utilization_seconds: float
"""
super(JobCost, self).__init__(**kwargs)
self.charged_cpu_core_seconds = kwargs.get('charged_cpu_core_seconds', None)
self.charged_cpu_memory_megabyte_seconds = kwargs.get('charged_cpu_memory_megabyte_seconds', None)
self.charged_gpu_seconds = kwargs.get('charged_gpu_seconds', None)
self.charged_node_utilization_seconds = kwargs.get('charged_node_utilization_seconds', None)
class JobEndpoint(msrest.serialization.Model):
"""JobEndpoint.
:ivar type:
:vartype type: str
:ivar port:
:vartype port: int
:ivar endpoint:
:vartype endpoint: str
:ivar status:
:vartype status: str
:ivar error_message:
:vartype error_message: str
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar nodes:
:vartype nodes: ~flow.models.MfeInternalNodes
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'port': {'key': 'port', 'type': 'int'},
'endpoint': {'key': 'endpoint', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'error_message': {'key': 'errorMessage', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'nodes': {'key': 'nodes', 'type': 'MfeInternalNodes'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type:
:paramtype type: str
:keyword port:
:paramtype port: int
:keyword endpoint:
:paramtype endpoint: str
:keyword status:
:paramtype status: str
:keyword error_message:
:paramtype error_message: str
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword nodes:
:paramtype nodes: ~flow.models.MfeInternalNodes
"""
super(JobEndpoint, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.port = kwargs.get('port', None)
self.endpoint = kwargs.get('endpoint', None)
self.status = kwargs.get('status', None)
self.error_message = kwargs.get('error_message', None)
self.properties = kwargs.get('properties', None)
self.nodes = kwargs.get('nodes', None)
class JobInput(msrest.serialization.Model):
"""JobInput.
All required parameters must be populated in order to send to Azure.
:ivar job_input_type: Required. Possible values include: "Dataset", "Uri", "Literal",
"UriFile", "UriFolder", "MLTable", "CustomModel", "MLFlowModel", "TritonModel".
:vartype job_input_type: str or ~flow.models.JobInputType
:ivar description:
:vartype description: str
"""
_validation = {
'job_input_type': {'required': True},
}
_attribute_map = {
'job_input_type': {'key': 'jobInputType', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword job_input_type: Required. Possible values include: "Dataset", "Uri", "Literal",
"UriFile", "UriFolder", "MLTable", "CustomModel", "MLFlowModel", "TritonModel".
:paramtype job_input_type: str or ~flow.models.JobInputType
:keyword description:
:paramtype description: str
"""
super(JobInput, self).__init__(**kwargs)
self.job_input_type = kwargs['job_input_type']
self.description = kwargs.get('description', None)
class JobOutput(msrest.serialization.Model):
"""JobOutput.
All required parameters must be populated in order to send to Azure.
:ivar job_output_type: Required. Possible values include: "Uri", "Dataset", "UriFile",
"UriFolder", "MLTable", "CustomModel", "MLFlowModel", "TritonModel".
:vartype job_output_type: str or ~flow.models.JobOutputType
:ivar description:
:vartype description: str
:ivar auto_delete_setting:
:vartype auto_delete_setting: ~flow.models.AutoDeleteSetting
"""
_validation = {
'job_output_type': {'required': True},
}
_attribute_map = {
'job_output_type': {'key': 'jobOutputType', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'auto_delete_setting': {'key': 'autoDeleteSetting', 'type': 'AutoDeleteSetting'},
}
def __init__(
self,
**kwargs
):
"""
:keyword job_output_type: Required. Possible values include: "Uri", "Dataset", "UriFile",
"UriFolder", "MLTable", "CustomModel", "MLFlowModel", "TritonModel".
:paramtype job_output_type: str or ~flow.models.JobOutputType
:keyword description:
:paramtype description: str
:keyword auto_delete_setting:
:paramtype auto_delete_setting: ~flow.models.AutoDeleteSetting
"""
super(JobOutput, self).__init__(**kwargs)
self.job_output_type = kwargs['job_output_type']
self.description = kwargs.get('description', None)
self.auto_delete_setting = kwargs.get('auto_delete_setting', None)
class JobOutputArtifacts(msrest.serialization.Model):
"""JobOutputArtifacts.
:ivar datastore_id:
:vartype datastore_id: str
:ivar path:
:vartype path: str
"""
_attribute_map = {
'datastore_id': {'key': 'datastoreId', 'type': 'str'},
'path': {'key': 'path', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword datastore_id:
:paramtype datastore_id: str
:keyword path:
:paramtype path: str
"""
super(JobOutputArtifacts, self).__init__(**kwargs)
self.datastore_id = kwargs.get('datastore_id', None)
self.path = kwargs.get('path', None)
class JobScheduleDto(msrest.serialization.Model):
"""JobScheduleDto.
:ivar job_type: Possible values include: "Command", "Sweep", "Labeling", "Pipeline", "Data",
"AutoML", "Spark", "Base".
:vartype job_type: str or ~flow.models.JobType
:ivar system_data:
:vartype system_data: ~flow.models.SystemData
:ivar name:
:vartype name: str
:ivar job_definition_id:
:vartype job_definition_id: str
:ivar display_name:
:vartype display_name: str
:ivar trigger_type: Possible values include: "Recurrence", "Cron".
:vartype trigger_type: str or ~flow.models.TriggerType
:ivar recurrence:
:vartype recurrence: ~flow.models.Recurrence
:ivar cron:
:vartype cron: ~flow.models.Cron
:ivar status: Possible values include: "Enabled", "Disabled".
:vartype status: str or ~flow.models.ScheduleStatus
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
"""
_attribute_map = {
'job_type': {'key': 'jobType', 'type': 'str'},
'system_data': {'key': 'systemData', 'type': 'SystemData'},
'name': {'key': 'name', 'type': 'str'},
'job_definition_id': {'key': 'jobDefinitionId', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'trigger_type': {'key': 'triggerType', 'type': 'str'},
'recurrence': {'key': 'recurrence', 'type': 'Recurrence'},
'cron': {'key': 'cron', 'type': 'Cron'},
'status': {'key': 'status', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword job_type: Possible values include: "Command", "Sweep", "Labeling", "Pipeline", "Data",
"AutoML", "Spark", "Base".
:paramtype job_type: str or ~flow.models.JobType
:keyword system_data:
:paramtype system_data: ~flow.models.SystemData
:keyword name:
:paramtype name: str
:keyword job_definition_id:
:paramtype job_definition_id: str
:keyword display_name:
:paramtype display_name: str
:keyword trigger_type: Possible values include: "Recurrence", "Cron".
:paramtype trigger_type: str or ~flow.models.TriggerType
:keyword recurrence:
:paramtype recurrence: ~flow.models.Recurrence
:keyword cron:
:paramtype cron: ~flow.models.Cron
:keyword status: Possible values include: "Enabled", "Disabled".
:paramtype status: str or ~flow.models.ScheduleStatus
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
"""
super(JobScheduleDto, self).__init__(**kwargs)
self.job_type = kwargs.get('job_type', None)
self.system_data = kwargs.get('system_data', None)
self.name = kwargs.get('name', None)
self.job_definition_id = kwargs.get('job_definition_id', None)
self.display_name = kwargs.get('display_name', None)
self.trigger_type = kwargs.get('trigger_type', None)
self.recurrence = kwargs.get('recurrence', None)
self.cron = kwargs.get('cron', None)
self.status = kwargs.get('status', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
class K8SConfiguration(msrest.serialization.Model):
"""K8SConfiguration.
:ivar max_retry_count:
:vartype max_retry_count: int
:ivar resource_configuration:
:vartype resource_configuration: ~flow.models.ResourceConfig
:ivar priority_configuration:
:vartype priority_configuration: ~flow.models.PriorityConfig
:ivar interactive_configuration:
:vartype interactive_configuration: ~flow.models.InteractiveConfig
"""
_attribute_map = {
'max_retry_count': {'key': 'maxRetryCount', 'type': 'int'},
'resource_configuration': {'key': 'resourceConfiguration', 'type': 'ResourceConfig'},
'priority_configuration': {'key': 'priorityConfiguration', 'type': 'PriorityConfig'},
'interactive_configuration': {'key': 'interactiveConfiguration', 'type': 'InteractiveConfig'},
}
def __init__(
self,
**kwargs
):
"""
:keyword max_retry_count:
:paramtype max_retry_count: int
:keyword resource_configuration:
:paramtype resource_configuration: ~flow.models.ResourceConfig
:keyword priority_configuration:
:paramtype priority_configuration: ~flow.models.PriorityConfig
:keyword interactive_configuration:
:paramtype interactive_configuration: ~flow.models.InteractiveConfig
"""
super(K8SConfiguration, self).__init__(**kwargs)
self.max_retry_count = kwargs.get('max_retry_count', None)
self.resource_configuration = kwargs.get('resource_configuration', None)
self.priority_configuration = kwargs.get('priority_configuration', None)
self.interactive_configuration = kwargs.get('interactive_configuration', None)
class KeyValuePairComponentNameMetaInfoErrorResponse(msrest.serialization.Model):
"""KeyValuePairComponentNameMetaInfoErrorResponse.
:ivar key:
:vartype key: ~flow.models.ComponentNameMetaInfo
:ivar value: The error response.
:vartype value: ~flow.models.ErrorResponse
"""
_attribute_map = {
'key': {'key': 'key', 'type': 'ComponentNameMetaInfo'},
'value': {'key': 'value', 'type': 'ErrorResponse'},
}
def __init__(
self,
**kwargs
):
"""
:keyword key:
:paramtype key: ~flow.models.ComponentNameMetaInfo
:keyword value: The error response.
:paramtype value: ~flow.models.ErrorResponse
"""
super(KeyValuePairComponentNameMetaInfoErrorResponse, self).__init__(**kwargs)
self.key = kwargs.get('key', None)
self.value = kwargs.get('value', None)
class KeyValuePairComponentNameMetaInfoModuleDto(msrest.serialization.Model):
"""KeyValuePairComponentNameMetaInfoModuleDto.
:ivar key:
:vartype key: ~flow.models.ComponentNameMetaInfo
:ivar value:
:vartype value: ~flow.models.ModuleDto
"""
_attribute_map = {
'key': {'key': 'key', 'type': 'ComponentNameMetaInfo'},
'value': {'key': 'value', 'type': 'ModuleDto'},
}
def __init__(
self,
**kwargs
):
"""
:keyword key:
:paramtype key: ~flow.models.ComponentNameMetaInfo
:keyword value:
:paramtype value: ~flow.models.ModuleDto
"""
super(KeyValuePairComponentNameMetaInfoModuleDto, self).__init__(**kwargs)
self.key = kwargs.get('key', None)
self.value = kwargs.get('value', None)
class KeyValuePairStringObject(msrest.serialization.Model):
"""KeyValuePairStringObject.
:ivar key:
:vartype key: str
:ivar value: Anything.
:vartype value: any
"""
_attribute_map = {
'key': {'key': 'key', 'type': 'str'},
'value': {'key': 'value', 'type': 'object'},
}
def __init__(
self,
**kwargs
):
"""
:keyword key:
:paramtype key: str
:keyword value: Anything.
:paramtype value: any
"""
super(KeyValuePairStringObject, self).__init__(**kwargs)
self.key = kwargs.get('key', None)
self.value = kwargs.get('value', None)
class KubernetesConfiguration(msrest.serialization.Model):
"""KubernetesConfiguration.
:ivar instance_type:
:vartype instance_type: str
"""
_attribute_map = {
'instance_type': {'key': 'instanceType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword instance_type:
:paramtype instance_type: str
"""
super(KubernetesConfiguration, self).__init__(**kwargs)
self.instance_type = kwargs.get('instance_type', None)
class Kwarg(msrest.serialization.Model):
"""Kwarg.
:ivar key:
:vartype key: str
:ivar value:
:vartype value: str
"""
_attribute_map = {
'key': {'key': 'key', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword key:
:paramtype key: str
:keyword value:
:paramtype value: str
"""
super(Kwarg, self).__init__(**kwargs)
self.key = kwargs.get('key', None)
self.value = kwargs.get('value', None)
class LegacyDataPath(msrest.serialization.Model):
"""LegacyDataPath.
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AEVADataStoreMode
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AEVADataStoreMode
:keyword relative_path:
:paramtype relative_path: str
"""
super(LegacyDataPath, self).__init__(**kwargs)
self.data_store_name = kwargs.get('data_store_name', None)
self.data_store_mode = kwargs.get('data_store_mode', None)
self.relative_path = kwargs.get('relative_path', None)
class LimitSettings(msrest.serialization.Model):
"""LimitSettings.
:ivar max_trials:
:vartype max_trials: int
:ivar timeout:
:vartype timeout: str
:ivar trial_timeout:
:vartype trial_timeout: str
:ivar max_concurrent_trials:
:vartype max_concurrent_trials: int
:ivar max_cores_per_trial:
:vartype max_cores_per_trial: int
:ivar exit_score:
:vartype exit_score: float
:ivar enable_early_termination:
:vartype enable_early_termination: bool
:ivar max_nodes:
:vartype max_nodes: int
"""
_attribute_map = {
'max_trials': {'key': 'maxTrials', 'type': 'int'},
'timeout': {'key': 'timeout', 'type': 'str'},
'trial_timeout': {'key': 'trialTimeout', 'type': 'str'},
'max_concurrent_trials': {'key': 'maxConcurrentTrials', 'type': 'int'},
'max_cores_per_trial': {'key': 'maxCoresPerTrial', 'type': 'int'},
'exit_score': {'key': 'exitScore', 'type': 'float'},
'enable_early_termination': {'key': 'enableEarlyTermination', 'type': 'bool'},
'max_nodes': {'key': 'maxNodes', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword max_trials:
:paramtype max_trials: int
:keyword timeout:
:paramtype timeout: str
:keyword trial_timeout:
:paramtype trial_timeout: str
:keyword max_concurrent_trials:
:paramtype max_concurrent_trials: int
:keyword max_cores_per_trial:
:paramtype max_cores_per_trial: int
:keyword exit_score:
:paramtype exit_score: float
:keyword enable_early_termination:
:paramtype enable_early_termination: bool
:keyword max_nodes:
:paramtype max_nodes: int
"""
super(LimitSettings, self).__init__(**kwargs)
self.max_trials = kwargs.get('max_trials', None)
self.timeout = kwargs.get('timeout', None)
self.trial_timeout = kwargs.get('trial_timeout', None)
self.max_concurrent_trials = kwargs.get('max_concurrent_trials', None)
self.max_cores_per_trial = kwargs.get('max_cores_per_trial', None)
self.exit_score = kwargs.get('exit_score', None)
self.enable_early_termination = kwargs.get('enable_early_termination', None)
self.max_nodes = kwargs.get('max_nodes', None)
class LinkedADBWorkspaceMetadata(msrest.serialization.Model):
"""LinkedADBWorkspaceMetadata.
:ivar workspace_id:
:vartype workspace_id: str
:ivar region:
:vartype region: str
"""
_attribute_map = {
'workspace_id': {'key': 'workspaceId', 'type': 'str'},
'region': {'key': 'region', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword workspace_id:
:paramtype workspace_id: str
:keyword region:
:paramtype region: str
"""
super(LinkedADBWorkspaceMetadata, self).__init__(**kwargs)
self.workspace_id = kwargs.get('workspace_id', None)
self.region = kwargs.get('region', None)
class LinkedPipelineInfo(msrest.serialization.Model):
"""LinkedPipelineInfo.
:ivar pipeline_type: Possible values include: "TrainingPipeline", "RealTimeInferencePipeline",
"BatchInferencePipeline", "Unknown".
:vartype pipeline_type: str or ~flow.models.PipelineType
:ivar module_node_id:
:vartype module_node_id: str
:ivar port_name:
:vartype port_name: str
:ivar linked_pipeline_draft_id:
:vartype linked_pipeline_draft_id: str
:ivar linked_pipeline_run_id:
:vartype linked_pipeline_run_id: str
:ivar is_direct_link:
:vartype is_direct_link: bool
"""
_attribute_map = {
'pipeline_type': {'key': 'pipelineType', 'type': 'str'},
'module_node_id': {'key': 'moduleNodeId', 'type': 'str'},
'port_name': {'key': 'portName', 'type': 'str'},
'linked_pipeline_draft_id': {'key': 'linkedPipelineDraftId', 'type': 'str'},
'linked_pipeline_run_id': {'key': 'linkedPipelineRunId', 'type': 'str'},
'is_direct_link': {'key': 'isDirectLink', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword pipeline_type: Possible values include: "TrainingPipeline",
"RealTimeInferencePipeline", "BatchInferencePipeline", "Unknown".
:paramtype pipeline_type: str or ~flow.models.PipelineType
:keyword module_node_id:
:paramtype module_node_id: str
:keyword port_name:
:paramtype port_name: str
:keyword linked_pipeline_draft_id:
:paramtype linked_pipeline_draft_id: str
:keyword linked_pipeline_run_id:
:paramtype linked_pipeline_run_id: str
:keyword is_direct_link:
:paramtype is_direct_link: bool
"""
super(LinkedPipelineInfo, self).__init__(**kwargs)
self.pipeline_type = kwargs.get('pipeline_type', None)
self.module_node_id = kwargs.get('module_node_id', None)
self.port_name = kwargs.get('port_name', None)
self.linked_pipeline_draft_id = kwargs.get('linked_pipeline_draft_id', None)
self.linked_pipeline_run_id = kwargs.get('linked_pipeline_run_id', None)
self.is_direct_link = kwargs.get('is_direct_link', None)
class LoadFlowAsComponentRequest(msrest.serialization.Model):
"""LoadFlowAsComponentRequest.
:ivar component_name:
:vartype component_name: str
:ivar component_version:
:vartype component_version: str
:ivar display_name:
:vartype display_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar is_deterministic:
:vartype is_deterministic: bool
:ivar flow_definition_file_path:
:vartype flow_definition_file_path: str
:ivar flow_definition_resource_id:
:vartype flow_definition_resource_id: str
:ivar flow_definition_data_store_name:
:vartype flow_definition_data_store_name: str
:ivar flow_definition_blob_path:
:vartype flow_definition_blob_path: str
:ivar flow_definition_data_uri:
:vartype flow_definition_data_uri: str
:ivar node_variant:
:vartype node_variant: str
:ivar inputs_mapping: This is a dictionary.
:vartype inputs_mapping: dict[str, str]
:ivar connections: This is a dictionary.
:vartype connections: dict[str, dict[str, str]]
:ivar environment_variables: This is a dictionary.
:vartype environment_variables: dict[str, str]
:ivar runtime_name:
:vartype runtime_name: str
:ivar session_id:
:vartype session_id: str
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
"""
_attribute_map = {
'component_name': {'key': 'componentName', 'type': 'str'},
'component_version': {'key': 'componentVersion', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'is_deterministic': {'key': 'isDeterministic', 'type': 'bool'},
'flow_definition_file_path': {'key': 'flowDefinitionFilePath', 'type': 'str'},
'flow_definition_resource_id': {'key': 'flowDefinitionResourceId', 'type': 'str'},
'flow_definition_data_store_name': {'key': 'flowDefinitionDataStoreName', 'type': 'str'},
'flow_definition_blob_path': {'key': 'flowDefinitionBlobPath', 'type': 'str'},
'flow_definition_data_uri': {'key': 'flowDefinitionDataUri', 'type': 'str'},
'node_variant': {'key': 'nodeVariant', 'type': 'str'},
'inputs_mapping': {'key': 'inputsMapping', 'type': '{str}'},
'connections': {'key': 'connections', 'type': '{{str}}'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'session_id': {'key': 'sessionId', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
}
def __init__(
self,
**kwargs
):
"""
:keyword component_name:
:paramtype component_name: str
:keyword component_version:
:paramtype component_version: str
:keyword display_name:
:paramtype display_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword is_deterministic:
:paramtype is_deterministic: bool
:keyword flow_definition_file_path:
:paramtype flow_definition_file_path: str
:keyword flow_definition_resource_id:
:paramtype flow_definition_resource_id: str
:keyword flow_definition_data_store_name:
:paramtype flow_definition_data_store_name: str
:keyword flow_definition_blob_path:
:paramtype flow_definition_blob_path: str
:keyword flow_definition_data_uri:
:paramtype flow_definition_data_uri: str
:keyword node_variant:
:paramtype node_variant: str
:keyword inputs_mapping: This is a dictionary.
:paramtype inputs_mapping: dict[str, str]
:keyword connections: This is a dictionary.
:paramtype connections: dict[str, dict[str, str]]
:keyword environment_variables: This is a dictionary.
:paramtype environment_variables: dict[str, str]
:keyword runtime_name:
:paramtype runtime_name: str
:keyword session_id:
:paramtype session_id: str
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
"""
super(LoadFlowAsComponentRequest, self).__init__(**kwargs)
self.component_name = kwargs.get('component_name', None)
self.component_version = kwargs.get('component_version', None)
self.display_name = kwargs.get('display_name', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
self.is_deterministic = kwargs.get('is_deterministic', None)
self.flow_definition_file_path = kwargs.get('flow_definition_file_path', None)
self.flow_definition_resource_id = kwargs.get('flow_definition_resource_id', None)
self.flow_definition_data_store_name = kwargs.get('flow_definition_data_store_name', None)
self.flow_definition_blob_path = kwargs.get('flow_definition_blob_path', None)
self.flow_definition_data_uri = kwargs.get('flow_definition_data_uri', None)
self.node_variant = kwargs.get('node_variant', None)
self.inputs_mapping = kwargs.get('inputs_mapping', None)
self.connections = kwargs.get('connections', None)
self.environment_variables = kwargs.get('environment_variables', None)
self.runtime_name = kwargs.get('runtime_name', None)
self.session_id = kwargs.get('session_id', None)
self.vm_size = kwargs.get('vm_size', None)
self.max_idle_time_seconds = kwargs.get('max_idle_time_seconds', None)
class LogRunTerminatedEventDto(msrest.serialization.Model):
"""LogRunTerminatedEventDto.
:ivar next_action_interval_in_seconds:
:vartype next_action_interval_in_seconds: int
:ivar action_type: Possible values include: "SendValidationRequest", "GetValidationStatus",
"SubmitBulkRun", "LogRunResult", "LogRunTerminatedEvent".
:vartype action_type: str or ~flow.models.ActionType
:ivar last_checked_time:
:vartype last_checked_time: ~datetime.datetime
"""
_attribute_map = {
'next_action_interval_in_seconds': {'key': 'nextActionIntervalInSeconds', 'type': 'int'},
'action_type': {'key': 'actionType', 'type': 'str'},
'last_checked_time': {'key': 'lastCheckedTime', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword next_action_interval_in_seconds:
:paramtype next_action_interval_in_seconds: int
:keyword action_type: Possible values include: "SendValidationRequest", "GetValidationStatus",
"SubmitBulkRun", "LogRunResult", "LogRunTerminatedEvent".
:paramtype action_type: str or ~flow.models.ActionType
:keyword last_checked_time:
:paramtype last_checked_time: ~datetime.datetime
"""
super(LogRunTerminatedEventDto, self).__init__(**kwargs)
self.next_action_interval_in_seconds = kwargs.get('next_action_interval_in_seconds', None)
self.action_type = kwargs.get('action_type', None)
self.last_checked_time = kwargs.get('last_checked_time', None)
class LongRunningOperationUriResponse(msrest.serialization.Model):
"""LongRunningOperationUriResponse.
:ivar location:
:vartype location: str
:ivar operation_result:
:vartype operation_result: str
"""
_attribute_map = {
'location': {'key': 'location', 'type': 'str'},
'operation_result': {'key': 'operationResult', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword location:
:paramtype location: str
:keyword operation_result:
:paramtype operation_result: str
"""
super(LongRunningOperationUriResponse, self).__init__(**kwargs)
self.location = kwargs.get('location', None)
self.operation_result = kwargs.get('operation_result', None)
class LongRunningUpdateRegistryComponentRequest(msrest.serialization.Model):
"""LongRunningUpdateRegistryComponentRequest.
:ivar display_name:
:vartype display_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar registry_name:
:vartype registry_name: str
:ivar component_name:
:vartype component_name: str
:ivar component_version:
:vartype component_version: str
:ivar update_type: Possible values include: "EnableModule", "DisableModule",
"UpdateDisplayName", "UpdateDescription", "UpdateTags".
:vartype update_type: str or ~flow.models.LongRunningUpdateType
"""
_attribute_map = {
'display_name': {'key': 'displayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'registry_name': {'key': 'registryName', 'type': 'str'},
'component_name': {'key': 'componentName', 'type': 'str'},
'component_version': {'key': 'componentVersion', 'type': 'str'},
'update_type': {'key': 'updateType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword display_name:
:paramtype display_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword registry_name:
:paramtype registry_name: str
:keyword component_name:
:paramtype component_name: str
:keyword component_version:
:paramtype component_version: str
:keyword update_type: Possible values include: "EnableModule", "DisableModule",
"UpdateDisplayName", "UpdateDescription", "UpdateTags".
:paramtype update_type: str or ~flow.models.LongRunningUpdateType
"""
super(LongRunningUpdateRegistryComponentRequest, self).__init__(**kwargs)
self.display_name = kwargs.get('display_name', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.registry_name = kwargs.get('registry_name', None)
self.component_name = kwargs.get('component_name', None)
self.component_version = kwargs.get('component_version', None)
self.update_type = kwargs.get('update_type', None)
class ManagedServiceIdentity(msrest.serialization.Model):
"""ManagedServiceIdentity.
All required parameters must be populated in order to send to Azure.
:ivar type: Required. Possible values include: "SystemAssigned", "UserAssigned",
"SystemAssignedUserAssigned", "None".
:vartype type: str or ~flow.models.ManagedServiceIdentityType
:ivar principal_id:
:vartype principal_id: str
:ivar tenant_id:
:vartype tenant_id: str
:ivar user_assigned_identities: Dictionary of :code:`<UserAssignedIdentity>`.
:vartype user_assigned_identities: dict[str, ~flow.models.UserAssignedIdentity]
"""
_validation = {
'type': {'required': True},
}
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'principal_id': {'key': 'principalId', 'type': 'str'},
'tenant_id': {'key': 'tenantId', 'type': 'str'},
'user_assigned_identities': {'key': 'userAssignedIdentities', 'type': '{UserAssignedIdentity}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type: Required. Possible values include: "SystemAssigned", "UserAssigned",
"SystemAssignedUserAssigned", "None".
:paramtype type: str or ~flow.models.ManagedServiceIdentityType
:keyword principal_id:
:paramtype principal_id: str
:keyword tenant_id:
:paramtype tenant_id: str
:keyword user_assigned_identities: Dictionary of :code:`<UserAssignedIdentity>`.
:paramtype user_assigned_identities: dict[str, ~flow.models.UserAssignedIdentity]
"""
super(ManagedServiceIdentity, self).__init__(**kwargs)
self.type = kwargs['type']
self.principal_id = kwargs.get('principal_id', None)
self.tenant_id = kwargs.get('tenant_id', None)
self.user_assigned_identities = kwargs.get('user_assigned_identities', None)
class MavenLibraryDto(msrest.serialization.Model):
"""MavenLibraryDto.
:ivar coordinates:
:vartype coordinates: str
:ivar repo:
:vartype repo: str
:ivar exclusions:
:vartype exclusions: list[str]
"""
_attribute_map = {
'coordinates': {'key': 'coordinates', 'type': 'str'},
'repo': {'key': 'repo', 'type': 'str'},
'exclusions': {'key': 'exclusions', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword coordinates:
:paramtype coordinates: str
:keyword repo:
:paramtype repo: str
:keyword exclusions:
:paramtype exclusions: list[str]
"""
super(MavenLibraryDto, self).__init__(**kwargs)
self.coordinates = kwargs.get('coordinates', None)
self.repo = kwargs.get('repo', None)
self.exclusions = kwargs.get('exclusions', None)
class MetricProperties(msrest.serialization.Model):
"""MetricProperties.
:ivar ux_metric_type:
:vartype ux_metric_type: str
"""
_attribute_map = {
'ux_metric_type': {'key': 'uxMetricType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword ux_metric_type:
:paramtype ux_metric_type: str
"""
super(MetricProperties, self).__init__(**kwargs)
self.ux_metric_type = kwargs.get('ux_metric_type', None)
class MetricSchemaDto(msrest.serialization.Model):
"""MetricSchemaDto.
:ivar num_properties:
:vartype num_properties: int
:ivar properties:
:vartype properties: list[~flow.models.MetricSchemaPropertyDto]
"""
_attribute_map = {
'num_properties': {'key': 'numProperties', 'type': 'int'},
'properties': {'key': 'properties', 'type': '[MetricSchemaPropertyDto]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword num_properties:
:paramtype num_properties: int
:keyword properties:
:paramtype properties: list[~flow.models.MetricSchemaPropertyDto]
"""
super(MetricSchemaDto, self).__init__(**kwargs)
self.num_properties = kwargs.get('num_properties', None)
self.properties = kwargs.get('properties', None)
class MetricSchemaPropertyDto(msrest.serialization.Model):
"""MetricSchemaPropertyDto.
:ivar property_id:
:vartype property_id: str
:ivar name:
:vartype name: str
:ivar type:
:vartype type: str
"""
_attribute_map = {
'property_id': {'key': 'propertyId', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword property_id:
:paramtype property_id: str
:keyword name:
:paramtype name: str
:keyword type:
:paramtype type: str
"""
super(MetricSchemaPropertyDto, self).__init__(**kwargs)
self.property_id = kwargs.get('property_id', None)
self.name = kwargs.get('name', None)
self.type = kwargs.get('type', None)
class MetricV2Dto(msrest.serialization.Model):
"""MetricV2Dto.
:ivar data_container_id:
:vartype data_container_id: str
:ivar name:
:vartype name: str
:ivar columns: This is a dictionary.
:vartype columns: dict[str, str or ~flow.models.MetricValueType]
:ivar properties:
:vartype properties: ~flow.models.MetricProperties
:ivar namespace:
:vartype namespace: str
:ivar standard_schema_id:
:vartype standard_schema_id: str
:ivar value:
:vartype value: list[~flow.models.MetricV2Value]
:ivar continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:vartype continuation_token: str
:ivar next_link: The link to the next page constructed using the continuationToken. If null,
there are no additional pages.
:vartype next_link: str
"""
_attribute_map = {
'data_container_id': {'key': 'dataContainerId', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'columns': {'key': 'columns', 'type': '{str}'},
'properties': {'key': 'properties', 'type': 'MetricProperties'},
'namespace': {'key': 'namespace', 'type': 'str'},
'standard_schema_id': {'key': 'standardSchemaId', 'type': 'str'},
'value': {'key': 'value', 'type': '[MetricV2Value]'},
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'next_link': {'key': 'nextLink', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_container_id:
:paramtype data_container_id: str
:keyword name:
:paramtype name: str
:keyword columns: This is a dictionary.
:paramtype columns: dict[str, str or ~flow.models.MetricValueType]
:keyword properties:
:paramtype properties: ~flow.models.MetricProperties
:keyword namespace:
:paramtype namespace: str
:keyword standard_schema_id:
:paramtype standard_schema_id: str
:keyword value:
:paramtype value: list[~flow.models.MetricV2Value]
:keyword continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:paramtype continuation_token: str
:keyword next_link: The link to the next page constructed using the continuationToken. If
null, there are no additional pages.
:paramtype next_link: str
"""
super(MetricV2Dto, self).__init__(**kwargs)
self.data_container_id = kwargs.get('data_container_id', None)
self.name = kwargs.get('name', None)
self.columns = kwargs.get('columns', None)
self.properties = kwargs.get('properties', None)
self.namespace = kwargs.get('namespace', None)
self.standard_schema_id = kwargs.get('standard_schema_id', None)
self.value = kwargs.get('value', None)
self.continuation_token = kwargs.get('continuation_token', None)
self.next_link = kwargs.get('next_link', None)
class MetricV2Value(msrest.serialization.Model):
"""MetricV2Value.
:ivar metric_id:
:vartype metric_id: str
:ivar created_utc:
:vartype created_utc: ~datetime.datetime
:ivar step:
:vartype step: long
:ivar data: Dictionary of :code:`<any>`.
:vartype data: dict[str, any]
:ivar sas_uri:
:vartype sas_uri: str
"""
_attribute_map = {
'metric_id': {'key': 'metricId', 'type': 'str'},
'created_utc': {'key': 'createdUtc', 'type': 'iso-8601'},
'step': {'key': 'step', 'type': 'long'},
'data': {'key': 'data', 'type': '{object}'},
'sas_uri': {'key': 'sasUri', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword metric_id:
:paramtype metric_id: str
:keyword created_utc:
:paramtype created_utc: ~datetime.datetime
:keyword step:
:paramtype step: long
:keyword data: Dictionary of :code:`<any>`.
:paramtype data: dict[str, any]
:keyword sas_uri:
:paramtype sas_uri: str
"""
super(MetricV2Value, self).__init__(**kwargs)
self.metric_id = kwargs.get('metric_id', None)
self.created_utc = kwargs.get('created_utc', None)
self.step = kwargs.get('step', None)
self.data = kwargs.get('data', None)
self.sas_uri = kwargs.get('sas_uri', None)
class MfeInternalAutologgerSettings(msrest.serialization.Model):
"""MfeInternalAutologgerSettings.
:ivar mlflow_autologger: Possible values include: "Enabled", "Disabled".
:vartype mlflow_autologger: str or ~flow.models.MfeInternalMLFlowAutologgerState
"""
_attribute_map = {
'mlflow_autologger': {'key': 'mlflowAutologger', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword mlflow_autologger: Possible values include: "Enabled", "Disabled".
:paramtype mlflow_autologger: str or ~flow.models.MfeInternalMLFlowAutologgerState
"""
super(MfeInternalAutologgerSettings, self).__init__(**kwargs)
self.mlflow_autologger = kwargs.get('mlflow_autologger', None)
class MfeInternalIdentityConfiguration(msrest.serialization.Model):
"""MfeInternalIdentityConfiguration.
:ivar identity_type: Possible values include: "Managed", "AMLToken", "UserIdentity".
:vartype identity_type: str or ~flow.models.MfeInternalIdentityType
"""
_attribute_map = {
'identity_type': {'key': 'identityType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword identity_type: Possible values include: "Managed", "AMLToken", "UserIdentity".
:paramtype identity_type: str or ~flow.models.MfeInternalIdentityType
"""
super(MfeInternalIdentityConfiguration, self).__init__(**kwargs)
self.identity_type = kwargs.get('identity_type', None)
class MfeInternalNodes(msrest.serialization.Model):
"""MfeInternalNodes.
:ivar nodes_value_type: The only acceptable values to pass in are None and "All". The default
value is None.
:vartype nodes_value_type: str
"""
_attribute_map = {
'nodes_value_type': {'key': 'nodesValueType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword nodes_value_type: The only acceptable values to pass in are None and "All". The
default value is None.
:paramtype nodes_value_type: str
"""
super(MfeInternalNodes, self).__init__(**kwargs)
self.nodes_value_type = kwargs.get('nodes_value_type', None)
class MfeInternalOutputData(msrest.serialization.Model):
"""MfeInternalOutputData.
:ivar dataset_name:
:vartype dataset_name: str
:ivar datastore:
:vartype datastore: str
:ivar datapath:
:vartype datapath: str
:ivar mode: Possible values include: "Mount", "Download", "Upload", "ReadOnlyMount",
"ReadWriteMount", "Direct", "EvalMount", "EvalDownload".
:vartype mode: str or ~flow.models.DataBindingMode
"""
_attribute_map = {
'dataset_name': {'key': 'datasetName', 'type': 'str'},
'datastore': {'key': 'datastore', 'type': 'str'},
'datapath': {'key': 'datapath', 'type': 'str'},
'mode': {'key': 'mode', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword dataset_name:
:paramtype dataset_name: str
:keyword datastore:
:paramtype datastore: str
:keyword datapath:
:paramtype datapath: str
:keyword mode: Possible values include: "Mount", "Download", "Upload", "ReadOnlyMount",
"ReadWriteMount", "Direct", "EvalMount", "EvalDownload".
:paramtype mode: str or ~flow.models.DataBindingMode
"""
super(MfeInternalOutputData, self).__init__(**kwargs)
self.dataset_name = kwargs.get('dataset_name', None)
self.datastore = kwargs.get('datastore', None)
self.datapath = kwargs.get('datapath', None)
self.mode = kwargs.get('mode', None)
class MfeInternalSecretConfiguration(msrest.serialization.Model):
"""MfeInternalSecretConfiguration.
:ivar workspace_secret_name:
:vartype workspace_secret_name: str
:ivar uri:
:vartype uri: str
"""
_attribute_map = {
'workspace_secret_name': {'key': 'workspaceSecretName', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword workspace_secret_name:
:paramtype workspace_secret_name: str
:keyword uri:
:paramtype uri: str
"""
super(MfeInternalSecretConfiguration, self).__init__(**kwargs)
self.workspace_secret_name = kwargs.get('workspace_secret_name', None)
self.uri = kwargs.get('uri', None)
class MfeInternalUriReference(msrest.serialization.Model):
"""MfeInternalUriReference.
:ivar file:
:vartype file: str
:ivar folder:
:vartype folder: str
"""
_attribute_map = {
'file': {'key': 'file', 'type': 'str'},
'folder': {'key': 'folder', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword file:
:paramtype file: str
:keyword folder:
:paramtype folder: str
"""
super(MfeInternalUriReference, self).__init__(**kwargs)
self.file = kwargs.get('file', None)
self.folder = kwargs.get('folder', None)
class MfeInternalV20211001ComponentJob(msrest.serialization.Model):
"""MfeInternalV20211001ComponentJob.
:ivar compute_id:
:vartype compute_id: str
:ivar component_id:
:vartype component_id: str
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, ~flow.models.JobInput]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, ~flow.models.JobOutput]
:ivar overrides: Anything.
:vartype overrides: any
"""
_attribute_map = {
'compute_id': {'key': 'computeId', 'type': 'str'},
'component_id': {'key': 'componentId', 'type': 'str'},
'inputs': {'key': 'inputs', 'type': '{JobInput}'},
'outputs': {'key': 'outputs', 'type': '{JobOutput}'},
'overrides': {'key': 'overrides', 'type': 'object'},
}
def __init__(
self,
**kwargs
):
"""
:keyword compute_id:
:paramtype compute_id: str
:keyword component_id:
:paramtype component_id: str
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, ~flow.models.JobInput]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, ~flow.models.JobOutput]
:keyword overrides: Anything.
:paramtype overrides: any
"""
super(MfeInternalV20211001ComponentJob, self).__init__(**kwargs)
self.compute_id = kwargs.get('compute_id', None)
self.component_id = kwargs.get('component_id', None)
self.inputs = kwargs.get('inputs', None)
self.outputs = kwargs.get('outputs', None)
self.overrides = kwargs.get('overrides', None)
class MinMaxParameterRule(msrest.serialization.Model):
"""MinMaxParameterRule.
:ivar min:
:vartype min: float
:ivar max:
:vartype max: float
"""
_attribute_map = {
'min': {'key': 'min', 'type': 'float'},
'max': {'key': 'max', 'type': 'float'},
}
def __init__(
self,
**kwargs
):
"""
:keyword min:
:paramtype min: float
:keyword max:
:paramtype max: float
"""
super(MinMaxParameterRule, self).__init__(**kwargs)
self.min = kwargs.get('min', None)
self.max = kwargs.get('max', None)
class MlcComputeInfo(msrest.serialization.Model):
"""MlcComputeInfo.
:ivar mlc_compute_type:
:vartype mlc_compute_type: str
"""
_attribute_map = {
'mlc_compute_type': {'key': 'mlcComputeType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword mlc_compute_type:
:paramtype mlc_compute_type: str
"""
super(MlcComputeInfo, self).__init__(**kwargs)
self.mlc_compute_type = kwargs.get('mlc_compute_type', None)
class ModelDto(msrest.serialization.Model):
"""ModelDto.
:ivar feed_name:
:vartype feed_name: str
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
:ivar relative_path:
:vartype relative_path: str
:ivar id:
:vartype id: str
:ivar version:
:vartype version: str
:ivar system_data:
:vartype system_data: ~flow.models.SystemData
:ivar arm_id:
:vartype arm_id: str
:ivar online_endpoint_yaml_str:
:vartype online_endpoint_yaml_str: str
"""
_attribute_map = {
'feed_name': {'key': 'feedName', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'system_data': {'key': 'systemData', 'type': 'SystemData'},
'arm_id': {'key': 'armId', 'type': 'str'},
'online_endpoint_yaml_str': {'key': 'onlineEndpointYamlStr', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword feed_name:
:paramtype feed_name: str
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
:keyword relative_path:
:paramtype relative_path: str
:keyword id:
:paramtype id: str
:keyword version:
:paramtype version: str
:keyword system_data:
:paramtype system_data: ~flow.models.SystemData
:keyword arm_id:
:paramtype arm_id: str
:keyword online_endpoint_yaml_str:
:paramtype online_endpoint_yaml_str: str
"""
super(ModelDto, self).__init__(**kwargs)
self.feed_name = kwargs.get('feed_name', None)
self.name = kwargs.get('name', None)
self.description = kwargs.get('description', None)
self.aml_data_store_name = kwargs.get('aml_data_store_name', None)
self.relative_path = kwargs.get('relative_path', None)
self.id = kwargs.get('id', None)
self.version = kwargs.get('version', None)
self.system_data = kwargs.get('system_data', None)
self.arm_id = kwargs.get('arm_id', None)
self.online_endpoint_yaml_str = kwargs.get('online_endpoint_yaml_str', None)
class ModelManagementErrorResponse(msrest.serialization.Model):
"""ModelManagementErrorResponse.
:ivar code:
:vartype code: str
:ivar status_code:
:vartype status_code: int
:ivar message:
:vartype message: str
:ivar target:
:vartype target: str
:ivar details:
:vartype details: list[~flow.models.InnerErrorDetails]
:ivar correlation: Dictionary of :code:`<string>`.
:vartype correlation: dict[str, str]
"""
_attribute_map = {
'code': {'key': 'code', 'type': 'str'},
'status_code': {'key': 'statusCode', 'type': 'int'},
'message': {'key': 'message', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
'details': {'key': 'details', 'type': '[InnerErrorDetails]'},
'correlation': {'key': 'correlation', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword code:
:paramtype code: str
:keyword status_code:
:paramtype status_code: int
:keyword message:
:paramtype message: str
:keyword target:
:paramtype target: str
:keyword details:
:paramtype details: list[~flow.models.InnerErrorDetails]
:keyword correlation: Dictionary of :code:`<string>`.
:paramtype correlation: dict[str, str]
"""
super(ModelManagementErrorResponse, self).__init__(**kwargs)
self.code = kwargs.get('code', None)
self.status_code = kwargs.get('status_code', None)
self.message = kwargs.get('message', None)
self.target = kwargs.get('target', None)
self.details = kwargs.get('details', None)
self.correlation = kwargs.get('correlation', None)
class ModifyPipelineJobScheduleDto(msrest.serialization.Model):
"""ModifyPipelineJobScheduleDto.
:ivar pipeline_job_name:
:vartype pipeline_job_name: str
:ivar pipeline_job_runtime_settings:
:vartype pipeline_job_runtime_settings: ~flow.models.PipelineJobRuntimeBasicSettings
:ivar display_name:
:vartype display_name: str
:ivar trigger_type: Possible values include: "Recurrence", "Cron".
:vartype trigger_type: str or ~flow.models.TriggerType
:ivar recurrence:
:vartype recurrence: ~flow.models.Recurrence
:ivar cron:
:vartype cron: ~flow.models.Cron
:ivar status: Possible values include: "Enabled", "Disabled".
:vartype status: str or ~flow.models.ScheduleStatus
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
"""
_attribute_map = {
'pipeline_job_name': {'key': 'pipelineJobName', 'type': 'str'},
'pipeline_job_runtime_settings': {'key': 'pipelineJobRuntimeSettings', 'type': 'PipelineJobRuntimeBasicSettings'},
'display_name': {'key': 'displayName', 'type': 'str'},
'trigger_type': {'key': 'triggerType', 'type': 'str'},
'recurrence': {'key': 'recurrence', 'type': 'Recurrence'},
'cron': {'key': 'cron', 'type': 'Cron'},
'status': {'key': 'status', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword pipeline_job_name:
:paramtype pipeline_job_name: str
:keyword pipeline_job_runtime_settings:
:paramtype pipeline_job_runtime_settings: ~flow.models.PipelineJobRuntimeBasicSettings
:keyword display_name:
:paramtype display_name: str
:keyword trigger_type: Possible values include: "Recurrence", "Cron".
:paramtype trigger_type: str or ~flow.models.TriggerType
:keyword recurrence:
:paramtype recurrence: ~flow.models.Recurrence
:keyword cron:
:paramtype cron: ~flow.models.Cron
:keyword status: Possible values include: "Enabled", "Disabled".
:paramtype status: str or ~flow.models.ScheduleStatus
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
"""
super(ModifyPipelineJobScheduleDto, self).__init__(**kwargs)
self.pipeline_job_name = kwargs.get('pipeline_job_name', None)
self.pipeline_job_runtime_settings = kwargs.get('pipeline_job_runtime_settings', None)
self.display_name = kwargs.get('display_name', None)
self.trigger_type = kwargs.get('trigger_type', None)
self.recurrence = kwargs.get('recurrence', None)
self.cron = kwargs.get('cron', None)
self.status = kwargs.get('status', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
class ModuleDto(msrest.serialization.Model):
"""ModuleDto.
:ivar namespace:
:vartype namespace: str
:ivar tags: A set of tags.
:vartype tags: list[str]
:ivar display_name:
:vartype display_name: str
:ivar dict_tags: Dictionary of :code:`<string>`.
:vartype dict_tags: dict[str, str]
:ivar module_version_id:
:vartype module_version_id: str
:ivar feed_name:
:vartype feed_name: str
:ivar registry_name:
:vartype registry_name: str
:ivar module_name:
:vartype module_name: str
:ivar module_version:
:vartype module_version: str
:ivar description:
:vartype description: str
:ivar owner:
:vartype owner: str
:ivar job_type:
:vartype job_type: str
:ivar default_version:
:vartype default_version: str
:ivar family_id:
:vartype family_id: str
:ivar help_document:
:vartype help_document: str
:ivar codegen_by:
:vartype codegen_by: str
:ivar arm_id:
:vartype arm_id: str
:ivar module_scope: Possible values include: "All", "Global", "Workspace", "Anonymous", "Step",
"Draft", "Feed", "Registry", "SystemAutoCreated".
:vartype module_scope: str or ~flow.models.ModuleScope
:ivar module_entity:
:vartype module_entity: ~flow.models.ModuleEntity
:ivar input_types:
:vartype input_types: list[str]
:ivar output_types:
:vartype output_types: list[str]
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
:ivar yaml_link:
:vartype yaml_link: str
:ivar yaml_link_with_commit_sha:
:vartype yaml_link_with_commit_sha: str
:ivar module_source_type: Possible values include: "Unknown", "Local", "GithubFile",
"GithubFolder", "DevopsArtifactsZip", "SerializedModuleInfo".
:vartype module_source_type: str or ~flow.models.ModuleSourceType
:ivar registered_by:
:vartype registered_by: str
:ivar versions:
:vartype versions: list[~flow.models.AzureMLModuleVersionDescriptor]
:ivar is_default_module_version:
:vartype is_default_module_version: bool
:ivar system_data:
:vartype system_data: ~flow.models.SystemData
:ivar system_meta:
:vartype system_meta: ~flow.models.SystemMeta
:ivar snapshot_id:
:vartype snapshot_id: str
:ivar entry:
:vartype entry: str
:ivar os_type:
:vartype os_type: str
:ivar require_gpu:
:vartype require_gpu: bool
:ivar module_python_interface:
:vartype module_python_interface: ~flow.models.ModulePythonInterface
:ivar environment_asset_id:
:vartype environment_asset_id: str
:ivar run_setting_parameters:
:vartype run_setting_parameters: list[~flow.models.RunSettingParameter]
:ivar supported_ui_input_data_delivery_modes: Dictionary of
<components·9qwi7e·schemas·moduledto·properties·supporteduiinputdatadeliverymodes·additionalproperties>.
:vartype supported_ui_input_data_delivery_modes: dict[str, list[str or
~flow.models.UIInputDataDeliveryMode]]
:ivar output_setting_specs: Dictionary of :code:`<OutputSettingSpec>`.
:vartype output_setting_specs: dict[str, ~flow.models.OutputSettingSpec]
:ivar yaml_str:
:vartype yaml_str: str
"""
_attribute_map = {
'namespace': {'key': 'namespace', 'type': 'str'},
'tags': {'key': 'tags', 'type': '[str]'},
'display_name': {'key': 'displayName', 'type': 'str'},
'dict_tags': {'key': 'dictTags', 'type': '{str}'},
'module_version_id': {'key': 'moduleVersionId', 'type': 'str'},
'feed_name': {'key': 'feedName', 'type': 'str'},
'registry_name': {'key': 'registryName', 'type': 'str'},
'module_name': {'key': 'moduleName', 'type': 'str'},
'module_version': {'key': 'moduleVersion', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'owner': {'key': 'owner', 'type': 'str'},
'job_type': {'key': 'jobType', 'type': 'str'},
'default_version': {'key': 'defaultVersion', 'type': 'str'},
'family_id': {'key': 'familyId', 'type': 'str'},
'help_document': {'key': 'helpDocument', 'type': 'str'},
'codegen_by': {'key': 'codegenBy', 'type': 'str'},
'arm_id': {'key': 'armId', 'type': 'str'},
'module_scope': {'key': 'moduleScope', 'type': 'str'},
'module_entity': {'key': 'moduleEntity', 'type': 'ModuleEntity'},
'input_types': {'key': 'inputTypes', 'type': '[str]'},
'output_types': {'key': 'outputTypes', 'type': '[str]'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
'yaml_link': {'key': 'yamlLink', 'type': 'str'},
'yaml_link_with_commit_sha': {'key': 'yamlLinkWithCommitSha', 'type': 'str'},
'module_source_type': {'key': 'moduleSourceType', 'type': 'str'},
'registered_by': {'key': 'registeredBy', 'type': 'str'},
'versions': {'key': 'versions', 'type': '[AzureMLModuleVersionDescriptor]'},
'is_default_module_version': {'key': 'isDefaultModuleVersion', 'type': 'bool'},
'system_data': {'key': 'systemData', 'type': 'SystemData'},
'system_meta': {'key': 'systemMeta', 'type': 'SystemMeta'},
'snapshot_id': {'key': 'snapshotId', 'type': 'str'},
'entry': {'key': 'entry', 'type': 'str'},
'os_type': {'key': 'osType', 'type': 'str'},
'require_gpu': {'key': 'requireGpu', 'type': 'bool'},
'module_python_interface': {'key': 'modulePythonInterface', 'type': 'ModulePythonInterface'},
'environment_asset_id': {'key': 'environmentAssetId', 'type': 'str'},
'run_setting_parameters': {'key': 'runSettingParameters', 'type': '[RunSettingParameter]'},
'supported_ui_input_data_delivery_modes': {'key': 'supportedUIInputDataDeliveryModes', 'type': '{[str]}'},
'output_setting_specs': {'key': 'outputSettingSpecs', 'type': '{OutputSettingSpec}'},
'yaml_str': {'key': 'yamlStr', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword namespace:
:paramtype namespace: str
:keyword tags: A set of tags.
:paramtype tags: list[str]
:keyword display_name:
:paramtype display_name: str
:keyword dict_tags: Dictionary of :code:`<string>`.
:paramtype dict_tags: dict[str, str]
:keyword module_version_id:
:paramtype module_version_id: str
:keyword feed_name:
:paramtype feed_name: str
:keyword registry_name:
:paramtype registry_name: str
:keyword module_name:
:paramtype module_name: str
:keyword module_version:
:paramtype module_version: str
:keyword description:
:paramtype description: str
:keyword owner:
:paramtype owner: str
:keyword job_type:
:paramtype job_type: str
:keyword default_version:
:paramtype default_version: str
:keyword family_id:
:paramtype family_id: str
:keyword help_document:
:paramtype help_document: str
:keyword codegen_by:
:paramtype codegen_by: str
:keyword arm_id:
:paramtype arm_id: str
:keyword module_scope: Possible values include: "All", "Global", "Workspace", "Anonymous",
"Step", "Draft", "Feed", "Registry", "SystemAutoCreated".
:paramtype module_scope: str or ~flow.models.ModuleScope
:keyword module_entity:
:paramtype module_entity: ~flow.models.ModuleEntity
:keyword input_types:
:paramtype input_types: list[str]
:keyword output_types:
:paramtype output_types: list[str]
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
:keyword yaml_link:
:paramtype yaml_link: str
:keyword yaml_link_with_commit_sha:
:paramtype yaml_link_with_commit_sha: str
:keyword module_source_type: Possible values include: "Unknown", "Local", "GithubFile",
"GithubFolder", "DevopsArtifactsZip", "SerializedModuleInfo".
:paramtype module_source_type: str or ~flow.models.ModuleSourceType
:keyword registered_by:
:paramtype registered_by: str
:keyword versions:
:paramtype versions: list[~flow.models.AzureMLModuleVersionDescriptor]
:keyword is_default_module_version:
:paramtype is_default_module_version: bool
:keyword system_data:
:paramtype system_data: ~flow.models.SystemData
:keyword system_meta:
:paramtype system_meta: ~flow.models.SystemMeta
:keyword snapshot_id:
:paramtype snapshot_id: str
:keyword entry:
:paramtype entry: str
:keyword os_type:
:paramtype os_type: str
:keyword require_gpu:
:paramtype require_gpu: bool
:keyword module_python_interface:
:paramtype module_python_interface: ~flow.models.ModulePythonInterface
:keyword environment_asset_id:
:paramtype environment_asset_id: str
:keyword run_setting_parameters:
:paramtype run_setting_parameters: list[~flow.models.RunSettingParameter]
:keyword supported_ui_input_data_delivery_modes: Dictionary of
<components·9qwi7e·schemas·moduledto·properties·supporteduiinputdatadeliverymodes·additionalproperties>.
:paramtype supported_ui_input_data_delivery_modes: dict[str, list[str or
~flow.models.UIInputDataDeliveryMode]]
:keyword output_setting_specs: Dictionary of :code:`<OutputSettingSpec>`.
:paramtype output_setting_specs: dict[str, ~flow.models.OutputSettingSpec]
:keyword yaml_str:
:paramtype yaml_str: str
"""
super(ModuleDto, self).__init__(**kwargs)
self.namespace = kwargs.get('namespace', None)
self.tags = kwargs.get('tags', None)
self.display_name = kwargs.get('display_name', None)
self.dict_tags = kwargs.get('dict_tags', None)
self.module_version_id = kwargs.get('module_version_id', None)
self.feed_name = kwargs.get('feed_name', None)
self.registry_name = kwargs.get('registry_name', None)
self.module_name = kwargs.get('module_name', None)
self.module_version = kwargs.get('module_version', None)
self.description = kwargs.get('description', None)
self.owner = kwargs.get('owner', None)
self.job_type = kwargs.get('job_type', None)
self.default_version = kwargs.get('default_version', None)
self.family_id = kwargs.get('family_id', None)
self.help_document = kwargs.get('help_document', None)
self.codegen_by = kwargs.get('codegen_by', None)
self.arm_id = kwargs.get('arm_id', None)
self.module_scope = kwargs.get('module_scope', None)
self.module_entity = kwargs.get('module_entity', None)
self.input_types = kwargs.get('input_types', None)
self.output_types = kwargs.get('output_types', None)
self.entity_status = kwargs.get('entity_status', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
self.yaml_link = kwargs.get('yaml_link', None)
self.yaml_link_with_commit_sha = kwargs.get('yaml_link_with_commit_sha', None)
self.module_source_type = kwargs.get('module_source_type', None)
self.registered_by = kwargs.get('registered_by', None)
self.versions = kwargs.get('versions', None)
self.is_default_module_version = kwargs.get('is_default_module_version', None)
self.system_data = kwargs.get('system_data', None)
self.system_meta = kwargs.get('system_meta', None)
self.snapshot_id = kwargs.get('snapshot_id', None)
self.entry = kwargs.get('entry', None)
self.os_type = kwargs.get('os_type', None)
self.require_gpu = kwargs.get('require_gpu', None)
self.module_python_interface = kwargs.get('module_python_interface', None)
self.environment_asset_id = kwargs.get('environment_asset_id', None)
self.run_setting_parameters = kwargs.get('run_setting_parameters', None)
self.supported_ui_input_data_delivery_modes = kwargs.get('supported_ui_input_data_delivery_modes', None)
self.output_setting_specs = kwargs.get('output_setting_specs', None)
self.yaml_str = kwargs.get('yaml_str', None)
class ModuleDtoWithErrors(msrest.serialization.Model):
"""ModuleDtoWithErrors.
:ivar version_id_to_module_dto: This is a dictionary.
:vartype version_id_to_module_dto: dict[str, ~flow.models.ModuleDto]
:ivar name_and_version_to_module_dto:
:vartype name_and_version_to_module_dto:
list[~flow.models.KeyValuePairComponentNameMetaInfoModuleDto]
:ivar version_id_to_error: This is a dictionary.
:vartype version_id_to_error: dict[str, ~flow.models.ErrorResponse]
:ivar name_and_version_to_error:
:vartype name_and_version_to_error:
list[~flow.models.KeyValuePairComponentNameMetaInfoErrorResponse]
"""
_attribute_map = {
'version_id_to_module_dto': {'key': 'versionIdToModuleDto', 'type': '{ModuleDto}'},
'name_and_version_to_module_dto': {'key': 'nameAndVersionToModuleDto', 'type': '[KeyValuePairComponentNameMetaInfoModuleDto]'},
'version_id_to_error': {'key': 'versionIdToError', 'type': '{ErrorResponse}'},
'name_and_version_to_error': {'key': 'nameAndVersionToError', 'type': '[KeyValuePairComponentNameMetaInfoErrorResponse]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword version_id_to_module_dto: This is a dictionary.
:paramtype version_id_to_module_dto: dict[str, ~flow.models.ModuleDto]
:keyword name_and_version_to_module_dto:
:paramtype name_and_version_to_module_dto:
list[~flow.models.KeyValuePairComponentNameMetaInfoModuleDto]
:keyword version_id_to_error: This is a dictionary.
:paramtype version_id_to_error: dict[str, ~flow.models.ErrorResponse]
:keyword name_and_version_to_error:
:paramtype name_and_version_to_error:
list[~flow.models.KeyValuePairComponentNameMetaInfoErrorResponse]
"""
super(ModuleDtoWithErrors, self).__init__(**kwargs)
self.version_id_to_module_dto = kwargs.get('version_id_to_module_dto', None)
self.name_and_version_to_module_dto = kwargs.get('name_and_version_to_module_dto', None)
self.version_id_to_error = kwargs.get('version_id_to_error', None)
self.name_and_version_to_error = kwargs.get('name_and_version_to_error', None)
class ModuleDtoWithValidateStatus(msrest.serialization.Model):
"""ModuleDtoWithValidateStatus.
:ivar existing_module_entity:
:vartype existing_module_entity: ~flow.models.ModuleEntity
:ivar status: Possible values include: "NewModule", "NewVersion", "Conflict", "ParseError",
"ProcessRequestError".
:vartype status: str or ~flow.models.ModuleInfoFromYamlStatusEnum
:ivar status_details:
:vartype status_details: str
:ivar error_details:
:vartype error_details: list[str]
:ivar serialized_module_info:
:vartype serialized_module_info: str
:ivar namespace:
:vartype namespace: str
:ivar tags: A set of tags.
:vartype tags: list[str]
:ivar display_name:
:vartype display_name: str
:ivar dict_tags: Dictionary of :code:`<string>`.
:vartype dict_tags: dict[str, str]
:ivar module_version_id:
:vartype module_version_id: str
:ivar feed_name:
:vartype feed_name: str
:ivar registry_name:
:vartype registry_name: str
:ivar module_name:
:vartype module_name: str
:ivar module_version:
:vartype module_version: str
:ivar description:
:vartype description: str
:ivar owner:
:vartype owner: str
:ivar job_type:
:vartype job_type: str
:ivar default_version:
:vartype default_version: str
:ivar family_id:
:vartype family_id: str
:ivar help_document:
:vartype help_document: str
:ivar codegen_by:
:vartype codegen_by: str
:ivar arm_id:
:vartype arm_id: str
:ivar module_scope: Possible values include: "All", "Global", "Workspace", "Anonymous", "Step",
"Draft", "Feed", "Registry", "SystemAutoCreated".
:vartype module_scope: str or ~flow.models.ModuleScope
:ivar module_entity:
:vartype module_entity: ~flow.models.ModuleEntity
:ivar input_types:
:vartype input_types: list[str]
:ivar output_types:
:vartype output_types: list[str]
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
:ivar yaml_link:
:vartype yaml_link: str
:ivar yaml_link_with_commit_sha:
:vartype yaml_link_with_commit_sha: str
:ivar module_source_type: Possible values include: "Unknown", "Local", "GithubFile",
"GithubFolder", "DevopsArtifactsZip", "SerializedModuleInfo".
:vartype module_source_type: str or ~flow.models.ModuleSourceType
:ivar registered_by:
:vartype registered_by: str
:ivar versions:
:vartype versions: list[~flow.models.AzureMLModuleVersionDescriptor]
:ivar is_default_module_version:
:vartype is_default_module_version: bool
:ivar system_data:
:vartype system_data: ~flow.models.SystemData
:ivar system_meta:
:vartype system_meta: ~flow.models.SystemMeta
:ivar snapshot_id:
:vartype snapshot_id: str
:ivar entry:
:vartype entry: str
:ivar os_type:
:vartype os_type: str
:ivar require_gpu:
:vartype require_gpu: bool
:ivar module_python_interface:
:vartype module_python_interface: ~flow.models.ModulePythonInterface
:ivar environment_asset_id:
:vartype environment_asset_id: str
:ivar run_setting_parameters:
:vartype run_setting_parameters: list[~flow.models.RunSettingParameter]
:ivar supported_ui_input_data_delivery_modes: Dictionary of
<components·8o5zaj·schemas·moduledtowithvalidatestatus·properties·supporteduiinputdatadeliverymodes·additionalproperties>.
:vartype supported_ui_input_data_delivery_modes: dict[str, list[str or
~flow.models.UIInputDataDeliveryMode]]
:ivar output_setting_specs: Dictionary of :code:`<OutputSettingSpec>`.
:vartype output_setting_specs: dict[str, ~flow.models.OutputSettingSpec]
:ivar yaml_str:
:vartype yaml_str: str
"""
_attribute_map = {
'existing_module_entity': {'key': 'existingModuleEntity', 'type': 'ModuleEntity'},
'status': {'key': 'status', 'type': 'str'},
'status_details': {'key': 'statusDetails', 'type': 'str'},
'error_details': {'key': 'errorDetails', 'type': '[str]'},
'serialized_module_info': {'key': 'serializedModuleInfo', 'type': 'str'},
'namespace': {'key': 'namespace', 'type': 'str'},
'tags': {'key': 'tags', 'type': '[str]'},
'display_name': {'key': 'displayName', 'type': 'str'},
'dict_tags': {'key': 'dictTags', 'type': '{str}'},
'module_version_id': {'key': 'moduleVersionId', 'type': 'str'},
'feed_name': {'key': 'feedName', 'type': 'str'},
'registry_name': {'key': 'registryName', 'type': 'str'},
'module_name': {'key': 'moduleName', 'type': 'str'},
'module_version': {'key': 'moduleVersion', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'owner': {'key': 'owner', 'type': 'str'},
'job_type': {'key': 'jobType', 'type': 'str'},
'default_version': {'key': 'defaultVersion', 'type': 'str'},
'family_id': {'key': 'familyId', 'type': 'str'},
'help_document': {'key': 'helpDocument', 'type': 'str'},
'codegen_by': {'key': 'codegenBy', 'type': 'str'},
'arm_id': {'key': 'armId', 'type': 'str'},
'module_scope': {'key': 'moduleScope', 'type': 'str'},
'module_entity': {'key': 'moduleEntity', 'type': 'ModuleEntity'},
'input_types': {'key': 'inputTypes', 'type': '[str]'},
'output_types': {'key': 'outputTypes', 'type': '[str]'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
'yaml_link': {'key': 'yamlLink', 'type': 'str'},
'yaml_link_with_commit_sha': {'key': 'yamlLinkWithCommitSha', 'type': 'str'},
'module_source_type': {'key': 'moduleSourceType', 'type': 'str'},
'registered_by': {'key': 'registeredBy', 'type': 'str'},
'versions': {'key': 'versions', 'type': '[AzureMLModuleVersionDescriptor]'},
'is_default_module_version': {'key': 'isDefaultModuleVersion', 'type': 'bool'},
'system_data': {'key': 'systemData', 'type': 'SystemData'},
'system_meta': {'key': 'systemMeta', 'type': 'SystemMeta'},
'snapshot_id': {'key': 'snapshotId', 'type': 'str'},
'entry': {'key': 'entry', 'type': 'str'},
'os_type': {'key': 'osType', 'type': 'str'},
'require_gpu': {'key': 'requireGpu', 'type': 'bool'},
'module_python_interface': {'key': 'modulePythonInterface', 'type': 'ModulePythonInterface'},
'environment_asset_id': {'key': 'environmentAssetId', 'type': 'str'},
'run_setting_parameters': {'key': 'runSettingParameters', 'type': '[RunSettingParameter]'},
'supported_ui_input_data_delivery_modes': {'key': 'supportedUIInputDataDeliveryModes', 'type': '{[str]}'},
'output_setting_specs': {'key': 'outputSettingSpecs', 'type': '{OutputSettingSpec}'},
'yaml_str': {'key': 'yamlStr', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword existing_module_entity:
:paramtype existing_module_entity: ~flow.models.ModuleEntity
:keyword status: Possible values include: "NewModule", "NewVersion", "Conflict", "ParseError",
"ProcessRequestError".
:paramtype status: str or ~flow.models.ModuleInfoFromYamlStatusEnum
:keyword status_details:
:paramtype status_details: str
:keyword error_details:
:paramtype error_details: list[str]
:keyword serialized_module_info:
:paramtype serialized_module_info: str
:keyword namespace:
:paramtype namespace: str
:keyword tags: A set of tags.
:paramtype tags: list[str]
:keyword display_name:
:paramtype display_name: str
:keyword dict_tags: Dictionary of :code:`<string>`.
:paramtype dict_tags: dict[str, str]
:keyword module_version_id:
:paramtype module_version_id: str
:keyword feed_name:
:paramtype feed_name: str
:keyword registry_name:
:paramtype registry_name: str
:keyword module_name:
:paramtype module_name: str
:keyword module_version:
:paramtype module_version: str
:keyword description:
:paramtype description: str
:keyword owner:
:paramtype owner: str
:keyword job_type:
:paramtype job_type: str
:keyword default_version:
:paramtype default_version: str
:keyword family_id:
:paramtype family_id: str
:keyword help_document:
:paramtype help_document: str
:keyword codegen_by:
:paramtype codegen_by: str
:keyword arm_id:
:paramtype arm_id: str
:keyword module_scope: Possible values include: "All", "Global", "Workspace", "Anonymous",
"Step", "Draft", "Feed", "Registry", "SystemAutoCreated".
:paramtype module_scope: str or ~flow.models.ModuleScope
:keyword module_entity:
:paramtype module_entity: ~flow.models.ModuleEntity
:keyword input_types:
:paramtype input_types: list[str]
:keyword output_types:
:paramtype output_types: list[str]
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
:keyword yaml_link:
:paramtype yaml_link: str
:keyword yaml_link_with_commit_sha:
:paramtype yaml_link_with_commit_sha: str
:keyword module_source_type: Possible values include: "Unknown", "Local", "GithubFile",
"GithubFolder", "DevopsArtifactsZip", "SerializedModuleInfo".
:paramtype module_source_type: str or ~flow.models.ModuleSourceType
:keyword registered_by:
:paramtype registered_by: str
:keyword versions:
:paramtype versions: list[~flow.models.AzureMLModuleVersionDescriptor]
:keyword is_default_module_version:
:paramtype is_default_module_version: bool
:keyword system_data:
:paramtype system_data: ~flow.models.SystemData
:keyword system_meta:
:paramtype system_meta: ~flow.models.SystemMeta
:keyword snapshot_id:
:paramtype snapshot_id: str
:keyword entry:
:paramtype entry: str
:keyword os_type:
:paramtype os_type: str
:keyword require_gpu:
:paramtype require_gpu: bool
:keyword module_python_interface:
:paramtype module_python_interface: ~flow.models.ModulePythonInterface
:keyword environment_asset_id:
:paramtype environment_asset_id: str
:keyword run_setting_parameters:
:paramtype run_setting_parameters: list[~flow.models.RunSettingParameter]
:keyword supported_ui_input_data_delivery_modes: Dictionary of
<components·8o5zaj·schemas·moduledtowithvalidatestatus·properties·supporteduiinputdatadeliverymodes·additionalproperties>.
:paramtype supported_ui_input_data_delivery_modes: dict[str, list[str or
~flow.models.UIInputDataDeliveryMode]]
:keyword output_setting_specs: Dictionary of :code:`<OutputSettingSpec>`.
:paramtype output_setting_specs: dict[str, ~flow.models.OutputSettingSpec]
:keyword yaml_str:
:paramtype yaml_str: str
"""
super(ModuleDtoWithValidateStatus, self).__init__(**kwargs)
self.existing_module_entity = kwargs.get('existing_module_entity', None)
self.status = kwargs.get('status', None)
self.status_details = kwargs.get('status_details', None)
self.error_details = kwargs.get('error_details', None)
self.serialized_module_info = kwargs.get('serialized_module_info', None)
self.namespace = kwargs.get('namespace', None)
self.tags = kwargs.get('tags', None)
self.display_name = kwargs.get('display_name', None)
self.dict_tags = kwargs.get('dict_tags', None)
self.module_version_id = kwargs.get('module_version_id', None)
self.feed_name = kwargs.get('feed_name', None)
self.registry_name = kwargs.get('registry_name', None)
self.module_name = kwargs.get('module_name', None)
self.module_version = kwargs.get('module_version', None)
self.description = kwargs.get('description', None)
self.owner = kwargs.get('owner', None)
self.job_type = kwargs.get('job_type', None)
self.default_version = kwargs.get('default_version', None)
self.family_id = kwargs.get('family_id', None)
self.help_document = kwargs.get('help_document', None)
self.codegen_by = kwargs.get('codegen_by', None)
self.arm_id = kwargs.get('arm_id', None)
self.module_scope = kwargs.get('module_scope', None)
self.module_entity = kwargs.get('module_entity', None)
self.input_types = kwargs.get('input_types', None)
self.output_types = kwargs.get('output_types', None)
self.entity_status = kwargs.get('entity_status', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
self.yaml_link = kwargs.get('yaml_link', None)
self.yaml_link_with_commit_sha = kwargs.get('yaml_link_with_commit_sha', None)
self.module_source_type = kwargs.get('module_source_type', None)
self.registered_by = kwargs.get('registered_by', None)
self.versions = kwargs.get('versions', None)
self.is_default_module_version = kwargs.get('is_default_module_version', None)
self.system_data = kwargs.get('system_data', None)
self.system_meta = kwargs.get('system_meta', None)
self.snapshot_id = kwargs.get('snapshot_id', None)
self.entry = kwargs.get('entry', None)
self.os_type = kwargs.get('os_type', None)
self.require_gpu = kwargs.get('require_gpu', None)
self.module_python_interface = kwargs.get('module_python_interface', None)
self.environment_asset_id = kwargs.get('environment_asset_id', None)
self.run_setting_parameters = kwargs.get('run_setting_parameters', None)
self.supported_ui_input_data_delivery_modes = kwargs.get('supported_ui_input_data_delivery_modes', None)
self.output_setting_specs = kwargs.get('output_setting_specs', None)
self.yaml_str = kwargs.get('yaml_str', None)
class ModuleEntity(msrest.serialization.Model):
"""ModuleEntity.
:ivar display_name:
:vartype display_name: str
:ivar module_execution_type:
:vartype module_execution_type: str
:ivar module_type: Possible values include: "None", "BatchInferencing".
:vartype module_type: str or ~flow.models.ModuleType
:ivar module_type_version:
:vartype module_type_version: str
:ivar upload_state: Possible values include: "Uploading", "Completed", "Canceled", "Failed".
:vartype upload_state: str or ~flow.models.UploadState
:ivar is_deterministic:
:vartype is_deterministic: bool
:ivar structured_interface:
:vartype structured_interface: ~flow.models.StructuredInterface
:ivar data_location:
:vartype data_location: ~flow.models.DataLocation
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hash_v2:
:vartype identifier_hash_v2: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar created_by:
:vartype created_by: ~flow.models.CreatedBy
:ivar last_updated_by:
:vartype last_updated_by: ~flow.models.CreatedBy
:ivar runconfig:
:vartype runconfig: str
:ivar cloud_settings:
:vartype cloud_settings: ~flow.models.CloudSettings
:ivar category:
:vartype category: str
:ivar step_type:
:vartype step_type: str
:ivar stage:
:vartype stage: str
:ivar name:
:vartype name: str
:ivar hash:
:vartype hash: str
:ivar description:
:vartype description: str
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'display_name': {'key': 'displayName', 'type': 'str'},
'module_execution_type': {'key': 'moduleExecutionType', 'type': 'str'},
'module_type': {'key': 'moduleType', 'type': 'str'},
'module_type_version': {'key': 'moduleTypeVersion', 'type': 'str'},
'upload_state': {'key': 'uploadState', 'type': 'str'},
'is_deterministic': {'key': 'isDeterministic', 'type': 'bool'},
'structured_interface': {'key': 'structuredInterface', 'type': 'StructuredInterface'},
'data_location': {'key': 'dataLocation', 'type': 'DataLocation'},
'identifier_hash': {'key': 'identifierHash', 'type': 'str'},
'identifier_hash_v2': {'key': 'identifierHashV2', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'created_by': {'key': 'createdBy', 'type': 'CreatedBy'},
'last_updated_by': {'key': 'lastUpdatedBy', 'type': 'CreatedBy'},
'runconfig': {'key': 'runconfig', 'type': 'str'},
'cloud_settings': {'key': 'cloudSettings', 'type': 'CloudSettings'},
'category': {'key': 'category', 'type': 'str'},
'step_type': {'key': 'stepType', 'type': 'str'},
'stage': {'key': 'stage', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'hash': {'key': 'hash', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword display_name:
:paramtype display_name: str
:keyword module_execution_type:
:paramtype module_execution_type: str
:keyword module_type: Possible values include: "None", "BatchInferencing".
:paramtype module_type: str or ~flow.models.ModuleType
:keyword module_type_version:
:paramtype module_type_version: str
:keyword upload_state: Possible values include: "Uploading", "Completed", "Canceled", "Failed".
:paramtype upload_state: str or ~flow.models.UploadState
:keyword is_deterministic:
:paramtype is_deterministic: bool
:keyword structured_interface:
:paramtype structured_interface: ~flow.models.StructuredInterface
:keyword data_location:
:paramtype data_location: ~flow.models.DataLocation
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hash_v2:
:paramtype identifier_hash_v2: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword created_by:
:paramtype created_by: ~flow.models.CreatedBy
:keyword last_updated_by:
:paramtype last_updated_by: ~flow.models.CreatedBy
:keyword runconfig:
:paramtype runconfig: str
:keyword cloud_settings:
:paramtype cloud_settings: ~flow.models.CloudSettings
:keyword category:
:paramtype category: str
:keyword step_type:
:paramtype step_type: str
:keyword stage:
:paramtype stage: str
:keyword name:
:paramtype name: str
:keyword hash:
:paramtype hash: str
:keyword description:
:paramtype description: str
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(ModuleEntity, self).__init__(**kwargs)
self.display_name = kwargs.get('display_name', None)
self.module_execution_type = kwargs.get('module_execution_type', None)
self.module_type = kwargs.get('module_type', None)
self.module_type_version = kwargs.get('module_type_version', None)
self.upload_state = kwargs.get('upload_state', None)
self.is_deterministic = kwargs.get('is_deterministic', None)
self.structured_interface = kwargs.get('structured_interface', None)
self.data_location = kwargs.get('data_location', None)
self.identifier_hash = kwargs.get('identifier_hash', None)
self.identifier_hash_v2 = kwargs.get('identifier_hash_v2', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
self.created_by = kwargs.get('created_by', None)
self.last_updated_by = kwargs.get('last_updated_by', None)
self.runconfig = kwargs.get('runconfig', None)
self.cloud_settings = kwargs.get('cloud_settings', None)
self.category = kwargs.get('category', None)
self.step_type = kwargs.get('step_type', None)
self.stage = kwargs.get('stage', None)
self.name = kwargs.get('name', None)
self.hash = kwargs.get('hash', None)
self.description = kwargs.get('description', None)
self.entity_status = kwargs.get('entity_status', None)
self.id = kwargs.get('id', None)
self.etag = kwargs.get('etag', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
class ModulePythonInterface(msrest.serialization.Model):
"""ModulePythonInterface.
:ivar inputs:
:vartype inputs: list[~flow.models.PythonInterfaceMapping]
:ivar outputs:
:vartype outputs: list[~flow.models.PythonInterfaceMapping]
:ivar parameters:
:vartype parameters: list[~flow.models.PythonInterfaceMapping]
"""
_attribute_map = {
'inputs': {'key': 'inputs', 'type': '[PythonInterfaceMapping]'},
'outputs': {'key': 'outputs', 'type': '[PythonInterfaceMapping]'},
'parameters': {'key': 'parameters', 'type': '[PythonInterfaceMapping]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword inputs:
:paramtype inputs: list[~flow.models.PythonInterfaceMapping]
:keyword outputs:
:paramtype outputs: list[~flow.models.PythonInterfaceMapping]
:keyword parameters:
:paramtype parameters: list[~flow.models.PythonInterfaceMapping]
"""
super(ModulePythonInterface, self).__init__(**kwargs)
self.inputs = kwargs.get('inputs', None)
self.outputs = kwargs.get('outputs', None)
self.parameters = kwargs.get('parameters', None)
class MpiConfiguration(msrest.serialization.Model):
"""MpiConfiguration.
:ivar process_count_per_node:
:vartype process_count_per_node: int
"""
_attribute_map = {
'process_count_per_node': {'key': 'processCountPerNode', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword process_count_per_node:
:paramtype process_count_per_node: int
"""
super(MpiConfiguration, self).__init__(**kwargs)
self.process_count_per_node = kwargs.get('process_count_per_node', None)
class NCrossValidations(msrest.serialization.Model):
"""NCrossValidations.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.NCrossValidationMode
:ivar value:
:vartype value: int
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'value': {'key': 'value', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.NCrossValidationMode
:keyword value:
:paramtype value: int
"""
super(NCrossValidations, self).__init__(**kwargs)
self.mode = kwargs.get('mode', None)
self.value = kwargs.get('value', None)
class Node(msrest.serialization.Model):
"""Node.
:ivar name:
:vartype name: str
:ivar type: Possible values include: "llm", "python", "action", "prompt", "custom_llm",
"csharp".
:vartype type: str or ~flow.models.ToolType
:ivar source:
:vartype source: ~flow.models.NodeSource
:ivar inputs: Dictionary of :code:`<any>`.
:vartype inputs: dict[str, any]
:ivar tool:
:vartype tool: str
:ivar reduce:
:vartype reduce: bool
:ivar activate:
:vartype activate: ~flow.models.Activate
:ivar comment:
:vartype comment: str
:ivar api:
:vartype api: str
:ivar provider:
:vartype provider: str
:ivar connection:
:vartype connection: str
:ivar module:
:vartype module: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'source': {'key': 'source', 'type': 'NodeSource'},
'inputs': {'key': 'inputs', 'type': '{object}'},
'tool': {'key': 'tool', 'type': 'str'},
'reduce': {'key': 'reduce', 'type': 'bool'},
'activate': {'key': 'activate', 'type': 'Activate'},
'comment': {'key': 'comment', 'type': 'str'},
'api': {'key': 'api', 'type': 'str'},
'provider': {'key': 'provider', 'type': 'str'},
'connection': {'key': 'connection', 'type': 'str'},
'module': {'key': 'module', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type: Possible values include: "llm", "python", "action", "prompt", "custom_llm",
"csharp".
:paramtype type: str or ~flow.models.ToolType
:keyword source:
:paramtype source: ~flow.models.NodeSource
:keyword inputs: Dictionary of :code:`<any>`.
:paramtype inputs: dict[str, any]
:keyword tool:
:paramtype tool: str
:keyword reduce:
:paramtype reduce: bool
:keyword activate:
:paramtype activate: ~flow.models.Activate
:keyword comment:
:paramtype comment: str
:keyword api:
:paramtype api: str
:keyword provider:
:paramtype provider: str
:keyword connection:
:paramtype connection: str
:keyword module:
:paramtype module: str
"""
super(Node, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.type = kwargs.get('type', None)
self.source = kwargs.get('source', None)
self.inputs = kwargs.get('inputs', None)
self.tool = kwargs.get('tool', None)
self.reduce = kwargs.get('reduce', None)
self.activate = kwargs.get('activate', None)
self.comment = kwargs.get('comment', None)
self.api = kwargs.get('api', None)
self.provider = kwargs.get('provider', None)
self.connection = kwargs.get('connection', None)
self.module = kwargs.get('module', None)
class NodeInputPort(msrest.serialization.Model):
"""NodeInputPort.
:ivar name:
:vartype name: str
:ivar documentation:
:vartype documentation: str
:ivar data_types_ids:
:vartype data_types_ids: list[str]
:ivar is_optional:
:vartype is_optional: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'documentation': {'key': 'documentation', 'type': 'str'},
'data_types_ids': {'key': 'dataTypesIds', 'type': '[str]'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword documentation:
:paramtype documentation: str
:keyword data_types_ids:
:paramtype data_types_ids: list[str]
:keyword is_optional:
:paramtype is_optional: bool
"""
super(NodeInputPort, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.documentation = kwargs.get('documentation', None)
self.data_types_ids = kwargs.get('data_types_ids', None)
self.is_optional = kwargs.get('is_optional', None)
class NodeLayout(msrest.serialization.Model):
"""NodeLayout.
:ivar x:
:vartype x: float
:ivar y:
:vartype y: float
:ivar width:
:vartype width: float
:ivar height:
:vartype height: float
:ivar extended_data:
:vartype extended_data: str
"""
_attribute_map = {
'x': {'key': 'x', 'type': 'float'},
'y': {'key': 'y', 'type': 'float'},
'width': {'key': 'width', 'type': 'float'},
'height': {'key': 'height', 'type': 'float'},
'extended_data': {'key': 'extendedData', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword x:
:paramtype x: float
:keyword y:
:paramtype y: float
:keyword width:
:paramtype width: float
:keyword height:
:paramtype height: float
:keyword extended_data:
:paramtype extended_data: str
"""
super(NodeLayout, self).__init__(**kwargs)
self.x = kwargs.get('x', None)
self.y = kwargs.get('y', None)
self.width = kwargs.get('width', None)
self.height = kwargs.get('height', None)
self.extended_data = kwargs.get('extended_data', None)
class NodeOutputPort(msrest.serialization.Model):
"""NodeOutputPort.
:ivar name:
:vartype name: str
:ivar documentation:
:vartype documentation: str
:ivar data_type_id:
:vartype data_type_id: str
:ivar pass_through_input_name:
:vartype pass_through_input_name: str
:ivar early_available:
:vartype early_available: bool
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AEVADataStoreMode
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'documentation': {'key': 'documentation', 'type': 'str'},
'data_type_id': {'key': 'dataTypeId', 'type': 'str'},
'pass_through_input_name': {'key': 'passThroughInputName', 'type': 'str'},
'early_available': {'key': 'EarlyAvailable', 'type': 'bool'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword documentation:
:paramtype documentation: str
:keyword data_type_id:
:paramtype data_type_id: str
:keyword pass_through_input_name:
:paramtype pass_through_input_name: str
:keyword early_available:
:paramtype early_available: bool
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AEVADataStoreMode
"""
super(NodeOutputPort, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.documentation = kwargs.get('documentation', None)
self.data_type_id = kwargs.get('data_type_id', None)
self.pass_through_input_name = kwargs.get('pass_through_input_name', None)
self.early_available = kwargs.get('early_available', None)
self.data_store_mode = kwargs.get('data_store_mode', None)
class NodePortInterface(msrest.serialization.Model):
"""NodePortInterface.
:ivar inputs:
:vartype inputs: list[~flow.models.NodeInputPort]
:ivar outputs:
:vartype outputs: list[~flow.models.NodeOutputPort]
:ivar control_outputs:
:vartype control_outputs: list[~flow.models.ControlOutput]
"""
_attribute_map = {
'inputs': {'key': 'inputs', 'type': '[NodeInputPort]'},
'outputs': {'key': 'outputs', 'type': '[NodeOutputPort]'},
'control_outputs': {'key': 'controlOutputs', 'type': '[ControlOutput]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword inputs:
:paramtype inputs: list[~flow.models.NodeInputPort]
:keyword outputs:
:paramtype outputs: list[~flow.models.NodeOutputPort]
:keyword control_outputs:
:paramtype control_outputs: list[~flow.models.ControlOutput]
"""
super(NodePortInterface, self).__init__(**kwargs)
self.inputs = kwargs.get('inputs', None)
self.outputs = kwargs.get('outputs', None)
self.control_outputs = kwargs.get('control_outputs', None)
class Nodes(msrest.serialization.Model):
"""Nodes.
All required parameters must be populated in order to send to Azure.
:ivar nodes_value_type: Required. Possible values include: "All", "Custom".
:vartype nodes_value_type: str or ~flow.models.NodesValueType
:ivar values:
:vartype values: list[int]
"""
_validation = {
'nodes_value_type': {'required': True},
}
_attribute_map = {
'nodes_value_type': {'key': 'nodes_value_type', 'type': 'str'},
'values': {'key': 'values', 'type': '[int]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword nodes_value_type: Required. Possible values include: "All", "Custom".
:paramtype nodes_value_type: str or ~flow.models.NodesValueType
:keyword values:
:paramtype values: list[int]
"""
super(Nodes, self).__init__(**kwargs)
self.nodes_value_type = kwargs['nodes_value_type']
self.values = kwargs.get('values', None)
class NodeSource(msrest.serialization.Model):
"""NodeSource.
:ivar type:
:vartype type: str
:ivar tool:
:vartype tool: str
:ivar path:
:vartype path: str
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'tool': {'key': 'tool', 'type': 'str'},
'path': {'key': 'path', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type:
:paramtype type: str
:keyword tool:
:paramtype tool: str
:keyword path:
:paramtype path: str
"""
super(NodeSource, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.tool = kwargs.get('tool', None)
self.path = kwargs.get('path', None)
class NodeTelemetryMetaInfo(msrest.serialization.Model):
"""NodeTelemetryMetaInfo.
:ivar pipeline_run_id:
:vartype pipeline_run_id: str
:ivar node_id:
:vartype node_id: str
:ivar version_id:
:vartype version_id: str
:ivar node_type:
:vartype node_type: str
:ivar node_source:
:vartype node_source: str
:ivar is_anonymous:
:vartype is_anonymous: bool
:ivar is_pipeline_component:
:vartype is_pipeline_component: bool
"""
_attribute_map = {
'pipeline_run_id': {'key': 'pipelineRunId', 'type': 'str'},
'node_id': {'key': 'nodeId', 'type': 'str'},
'version_id': {'key': 'versionId', 'type': 'str'},
'node_type': {'key': 'nodeType', 'type': 'str'},
'node_source': {'key': 'nodeSource', 'type': 'str'},
'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
'is_pipeline_component': {'key': 'isPipelineComponent', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword pipeline_run_id:
:paramtype pipeline_run_id: str
:keyword node_id:
:paramtype node_id: str
:keyword version_id:
:paramtype version_id: str
:keyword node_type:
:paramtype node_type: str
:keyword node_source:
:paramtype node_source: str
:keyword is_anonymous:
:paramtype is_anonymous: bool
:keyword is_pipeline_component:
:paramtype is_pipeline_component: bool
"""
super(NodeTelemetryMetaInfo, self).__init__(**kwargs)
self.pipeline_run_id = kwargs.get('pipeline_run_id', None)
self.node_id = kwargs.get('node_id', None)
self.version_id = kwargs.get('version_id', None)
self.node_type = kwargs.get('node_type', None)
self.node_source = kwargs.get('node_source', None)
self.is_anonymous = kwargs.get('is_anonymous', None)
self.is_pipeline_component = kwargs.get('is_pipeline_component', None)
class NodeVariant(msrest.serialization.Model):
"""NodeVariant.
:ivar variants: This is a dictionary.
:vartype variants: dict[str, ~flow.models.VariantNode]
:ivar default_variant_id:
:vartype default_variant_id: str
"""
_attribute_map = {
'variants': {'key': 'variants', 'type': '{VariantNode}'},
'default_variant_id': {'key': 'defaultVariantId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword variants: This is a dictionary.
:paramtype variants: dict[str, ~flow.models.VariantNode]
:keyword default_variant_id:
:paramtype default_variant_id: str
"""
super(NodeVariant, self).__init__(**kwargs)
self.variants = kwargs.get('variants', None)
self.default_variant_id = kwargs.get('default_variant_id', None)
class NoteBookTaskDto(msrest.serialization.Model):
"""NoteBookTaskDto.
:ivar notebook_path:
:vartype notebook_path: str
:ivar base_parameters: Dictionary of :code:`<string>`.
:vartype base_parameters: dict[str, str]
"""
_attribute_map = {
'notebook_path': {'key': 'notebook_path', 'type': 'str'},
'base_parameters': {'key': 'base_parameters', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword notebook_path:
:paramtype notebook_path: str
:keyword base_parameters: Dictionary of :code:`<string>`.
:paramtype base_parameters: dict[str, str]
"""
super(NoteBookTaskDto, self).__init__(**kwargs)
self.notebook_path = kwargs.get('notebook_path', None)
self.base_parameters = kwargs.get('base_parameters', None)
class NotificationSetting(msrest.serialization.Model):
"""NotificationSetting.
:ivar emails:
:vartype emails: list[str]
:ivar email_on:
:vartype email_on: list[str or ~flow.models.EmailNotificationEnableType]
:ivar webhooks: Dictionary of :code:`<Webhook>`.
:vartype webhooks: dict[str, ~flow.models.Webhook]
"""
_attribute_map = {
'emails': {'key': 'emails', 'type': '[str]'},
'email_on': {'key': 'emailOn', 'type': '[str]'},
'webhooks': {'key': 'webhooks', 'type': '{Webhook}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword emails:
:paramtype emails: list[str]
:keyword email_on:
:paramtype email_on: list[str or ~flow.models.EmailNotificationEnableType]
:keyword webhooks: Dictionary of :code:`<Webhook>`.
:paramtype webhooks: dict[str, ~flow.models.Webhook]
"""
super(NotificationSetting, self).__init__(**kwargs)
self.emails = kwargs.get('emails', None)
self.email_on = kwargs.get('email_on', None)
self.webhooks = kwargs.get('webhooks', None)
class ODataError(msrest.serialization.Model):
"""Represents OData v4 error object.
:ivar code: Gets or sets a language-independent, service-defined error code.
This code serves as a sub-status for the HTTP error code specified
in the response.
:vartype code: str
:ivar message: Gets or sets a human-readable, language-dependent representation of the error.
The ``Content-Language`` header MUST contain the language code from [RFC5646]
corresponding to the language in which the value for message is written.
:vartype message: str
:ivar target: Gets or sets the target of the particular error
(for example, the name of the property in error).
:vartype target: str
:ivar details: Gets or sets additional details about the error.
:vartype details: list[~flow.models.ODataErrorDetail]
:ivar innererror: The contents of this object are service-defined.
Usually this object contains information that will help debug the service
and SHOULD only be used in development environments in order to guard
against potential security concerns around information disclosure.
:vartype innererror: ~flow.models.ODataInnerError
"""
_attribute_map = {
'code': {'key': 'code', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
'details': {'key': 'details', 'type': '[ODataErrorDetail]'},
'innererror': {'key': 'innererror', 'type': 'ODataInnerError'},
}
def __init__(
self,
**kwargs
):
"""
:keyword code: Gets or sets a language-independent, service-defined error code.
This code serves as a sub-status for the HTTP error code specified
in the response.
:paramtype code: str
:keyword message: Gets or sets a human-readable, language-dependent representation of the
error.
The ``Content-Language`` header MUST contain the language code from [RFC5646]
corresponding to the language in which the value for message is written.
:paramtype message: str
:keyword target: Gets or sets the target of the particular error
(for example, the name of the property in error).
:paramtype target: str
:keyword details: Gets or sets additional details about the error.
:paramtype details: list[~flow.models.ODataErrorDetail]
:keyword innererror: The contents of this object are service-defined.
Usually this object contains information that will help debug the service
and SHOULD only be used in development environments in order to guard
against potential security concerns around information disclosure.
:paramtype innererror: ~flow.models.ODataInnerError
"""
super(ODataError, self).__init__(**kwargs)
self.code = kwargs.get('code', None)
self.message = kwargs.get('message', None)
self.target = kwargs.get('target', None)
self.details = kwargs.get('details', None)
self.innererror = kwargs.get('innererror', None)
class ODataErrorDetail(msrest.serialization.Model):
"""Represents additional error details.
:ivar code: Gets or sets a language-independent, service-defined error code.
:vartype code: str
:ivar message: Gets or sets a human-readable, language-dependent representation of the error.
:vartype message: str
:ivar target: Gets or sets the target of the particular error
(for example, the name of the property in error).
:vartype target: str
"""
_attribute_map = {
'code': {'key': 'code', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword code: Gets or sets a language-independent, service-defined error code.
:paramtype code: str
:keyword message: Gets or sets a human-readable, language-dependent representation of the
error.
:paramtype message: str
:keyword target: Gets or sets the target of the particular error
(for example, the name of the property in error).
:paramtype target: str
"""
super(ODataErrorDetail, self).__init__(**kwargs)
self.code = kwargs.get('code', None)
self.message = kwargs.get('message', None)
self.target = kwargs.get('target', None)
class ODataErrorResponse(msrest.serialization.Model):
"""Represents OData v4 compliant error response message.
:ivar error: Represents OData v4 error object.
:vartype error: ~flow.models.ODataError
"""
_attribute_map = {
'error': {'key': 'error', 'type': 'ODataError'},
}
def __init__(
self,
**kwargs
):
"""
:keyword error: Represents OData v4 error object.
:paramtype error: ~flow.models.ODataError
"""
super(ODataErrorResponse, self).__init__(**kwargs)
self.error = kwargs.get('error', None)
class ODataInnerError(msrest.serialization.Model):
"""The contents of this object are service-defined.
Usually this object contains information that will help debug the service
and SHOULD only be used in development environments in order to guard
against potential security concerns around information disclosure.
:ivar client_request_id: Gets or sets the client provided request ID.
:vartype client_request_id: str
:ivar service_request_id: Gets or sets the server generated request ID.
:vartype service_request_id: str
:ivar trace: Gets or sets the exception stack trace.
DO NOT INCLUDE IT IN PRODUCTION ENVIRONMENT.
:vartype trace: str
:ivar context: Gets or sets additional context for the exception.
DO NOT INCLUDE IT IN PRODUCTION ENVIRONMENT.
:vartype context: str
"""
_attribute_map = {
'client_request_id': {'key': 'clientRequestId', 'type': 'str'},
'service_request_id': {'key': 'serviceRequestId', 'type': 'str'},
'trace': {'key': 'trace', 'type': 'str'},
'context': {'key': 'context', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword client_request_id: Gets or sets the client provided request ID.
:paramtype client_request_id: str
:keyword service_request_id: Gets or sets the server generated request ID.
:paramtype service_request_id: str
:keyword trace: Gets or sets the exception stack trace.
DO NOT INCLUDE IT IN PRODUCTION ENVIRONMENT.
:paramtype trace: str
:keyword context: Gets or sets additional context for the exception.
DO NOT INCLUDE IT IN PRODUCTION ENVIRONMENT.
:paramtype context: str
"""
super(ODataInnerError, self).__init__(**kwargs)
self.client_request_id = kwargs.get('client_request_id', None)
self.service_request_id = kwargs.get('service_request_id', None)
self.trace = kwargs.get('trace', None)
self.context = kwargs.get('context', None)
class OutputData(msrest.serialization.Model):
"""OutputData.
:ivar output_location:
:vartype output_location: ~flow.models.ExecutionDataLocation
:ivar mechanism: Possible values include: "Upload", "Mount", "Hdfs", "Link", "Direct".
:vartype mechanism: str or ~flow.models.OutputMechanism
:ivar additional_options:
:vartype additional_options: ~flow.models.OutputOptions
:ivar environment_variable_name:
:vartype environment_variable_name: str
"""
_attribute_map = {
'output_location': {'key': 'outputLocation', 'type': 'ExecutionDataLocation'},
'mechanism': {'key': 'mechanism', 'type': 'str'},
'additional_options': {'key': 'additionalOptions', 'type': 'OutputOptions'},
'environment_variable_name': {'key': 'environmentVariableName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword output_location:
:paramtype output_location: ~flow.models.ExecutionDataLocation
:keyword mechanism: Possible values include: "Upload", "Mount", "Hdfs", "Link", "Direct".
:paramtype mechanism: str or ~flow.models.OutputMechanism
:keyword additional_options:
:paramtype additional_options: ~flow.models.OutputOptions
:keyword environment_variable_name:
:paramtype environment_variable_name: str
"""
super(OutputData, self).__init__(**kwargs)
self.output_location = kwargs.get('output_location', None)
self.mechanism = kwargs.get('mechanism', None)
self.additional_options = kwargs.get('additional_options', None)
self.environment_variable_name = kwargs.get('environment_variable_name', None)
class OutputDataBinding(msrest.serialization.Model):
"""OutputDataBinding.
:ivar datastore_id:
:vartype datastore_id: str
:ivar path_on_datastore:
:vartype path_on_datastore: str
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar description:
:vartype description: str
:ivar uri:
:vartype uri: ~flow.models.MfeInternalUriReference
:ivar mode: Possible values include: "Mount", "Download", "Upload", "ReadOnlyMount",
"ReadWriteMount", "Direct", "EvalMount", "EvalDownload".
:vartype mode: str or ~flow.models.DataBindingMode
:ivar asset_uri:
:vartype asset_uri: str
:ivar is_asset_job_output:
:vartype is_asset_job_output: bool
:ivar job_output_type: Possible values include: "Uri", "Dataset", "UriFile", "UriFolder",
"MLTable", "CustomModel", "MLFlowModel", "TritonModel".
:vartype job_output_type: str or ~flow.models.JobOutputType
:ivar asset_name:
:vartype asset_name: str
:ivar asset_version:
:vartype asset_version: str
:ivar auto_delete_setting:
:vartype auto_delete_setting: ~flow.models.AutoDeleteSetting
"""
_attribute_map = {
'datastore_id': {'key': 'datastoreId', 'type': 'str'},
'path_on_datastore': {'key': 'pathOnDatastore', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'MfeInternalUriReference'},
'mode': {'key': 'mode', 'type': 'str'},
'asset_uri': {'key': 'assetUri', 'type': 'str'},
'is_asset_job_output': {'key': 'isAssetJobOutput', 'type': 'bool'},
'job_output_type': {'key': 'jobOutputType', 'type': 'str'},
'asset_name': {'key': 'assetName', 'type': 'str'},
'asset_version': {'key': 'assetVersion', 'type': 'str'},
'auto_delete_setting': {'key': 'autoDeleteSetting', 'type': 'AutoDeleteSetting'},
}
def __init__(
self,
**kwargs
):
"""
:keyword datastore_id:
:paramtype datastore_id: str
:keyword path_on_datastore:
:paramtype path_on_datastore: str
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword description:
:paramtype description: str
:keyword uri:
:paramtype uri: ~flow.models.MfeInternalUriReference
:keyword mode: Possible values include: "Mount", "Download", "Upload", "ReadOnlyMount",
"ReadWriteMount", "Direct", "EvalMount", "EvalDownload".
:paramtype mode: str or ~flow.models.DataBindingMode
:keyword asset_uri:
:paramtype asset_uri: str
:keyword is_asset_job_output:
:paramtype is_asset_job_output: bool
:keyword job_output_type: Possible values include: "Uri", "Dataset", "UriFile", "UriFolder",
"MLTable", "CustomModel", "MLFlowModel", "TritonModel".
:paramtype job_output_type: str or ~flow.models.JobOutputType
:keyword asset_name:
:paramtype asset_name: str
:keyword asset_version:
:paramtype asset_version: str
:keyword auto_delete_setting:
:paramtype auto_delete_setting: ~flow.models.AutoDeleteSetting
"""
super(OutputDataBinding, self).__init__(**kwargs)
self.datastore_id = kwargs.get('datastore_id', None)
self.path_on_datastore = kwargs.get('path_on_datastore', None)
self.path_on_compute = kwargs.get('path_on_compute', None)
self.description = kwargs.get('description', None)
self.uri = kwargs.get('uri', None)
self.mode = kwargs.get('mode', None)
self.asset_uri = kwargs.get('asset_uri', None)
self.is_asset_job_output = kwargs.get('is_asset_job_output', None)
self.job_output_type = kwargs.get('job_output_type', None)
self.asset_name = kwargs.get('asset_name', None)
self.asset_version = kwargs.get('asset_version', None)
self.auto_delete_setting = kwargs.get('auto_delete_setting', None)
class OutputDatasetLineage(msrest.serialization.Model):
"""OutputDatasetLineage.
:ivar identifier:
:vartype identifier: ~flow.models.DatasetIdentifier
:ivar output_type: Possible values include: "RunOutput", "Reference".
:vartype output_type: str or ~flow.models.DatasetOutputType
:ivar output_details:
:vartype output_details: ~flow.models.DatasetOutputDetails
"""
_attribute_map = {
'identifier': {'key': 'identifier', 'type': 'DatasetIdentifier'},
'output_type': {'key': 'outputType', 'type': 'str'},
'output_details': {'key': 'outputDetails', 'type': 'DatasetOutputDetails'},
}
def __init__(
self,
**kwargs
):
"""
:keyword identifier:
:paramtype identifier: ~flow.models.DatasetIdentifier
:keyword output_type: Possible values include: "RunOutput", "Reference".
:paramtype output_type: str or ~flow.models.DatasetOutputType
:keyword output_details:
:paramtype output_details: ~flow.models.DatasetOutputDetails
"""
super(OutputDatasetLineage, self).__init__(**kwargs)
self.identifier = kwargs.get('identifier', None)
self.output_type = kwargs.get('output_type', None)
self.output_details = kwargs.get('output_details', None)
class OutputDefinition(msrest.serialization.Model):
"""OutputDefinition.
:ivar name:
:vartype name: str
:ivar type:
:vartype type: list[str or ~flow.models.ValueType]
:ivar description:
:vartype description: str
:ivar is_property:
:vartype is_property: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': '[str]'},
'description': {'key': 'description', 'type': 'str'},
'is_property': {'key': 'isProperty', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type:
:paramtype type: list[str or ~flow.models.ValueType]
:keyword description:
:paramtype description: str
:keyword is_property:
:paramtype is_property: bool
"""
super(OutputDefinition, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.type = kwargs.get('type', None)
self.description = kwargs.get('description', None)
self.is_property = kwargs.get('is_property', None)
class OutputOptions(msrest.serialization.Model):
"""OutputOptions.
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar registration_options:
:vartype registration_options: ~flow.models.RegistrationOptions
:ivar upload_options:
:vartype upload_options: ~flow.models.UploadOptions
:ivar mount_options: Dictionary of :code:`<string>`.
:vartype mount_options: dict[str, str]
"""
_attribute_map = {
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'registration_options': {'key': 'registrationOptions', 'type': 'RegistrationOptions'},
'upload_options': {'key': 'uploadOptions', 'type': 'UploadOptions'},
'mount_options': {'key': 'mountOptions', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword registration_options:
:paramtype registration_options: ~flow.models.RegistrationOptions
:keyword upload_options:
:paramtype upload_options: ~flow.models.UploadOptions
:keyword mount_options: Dictionary of :code:`<string>`.
:paramtype mount_options: dict[str, str]
"""
super(OutputOptions, self).__init__(**kwargs)
self.path_on_compute = kwargs.get('path_on_compute', None)
self.registration_options = kwargs.get('registration_options', None)
self.upload_options = kwargs.get('upload_options', None)
self.mount_options = kwargs.get('mount_options', None)
class OutputSetting(msrest.serialization.Model):
"""OutputSetting.
:ivar name:
:vartype name: str
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_store_name_parameter_assignment:
:vartype data_store_name_parameter_assignment: ~flow.models.ParameterAssignment
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AEVADataStoreMode
:ivar data_store_mode_parameter_assignment:
:vartype data_store_mode_parameter_assignment: ~flow.models.ParameterAssignment
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar path_on_compute_parameter_assignment:
:vartype path_on_compute_parameter_assignment: ~flow.models.ParameterAssignment
:ivar overwrite:
:vartype overwrite: bool
:ivar data_reference_name:
:vartype data_reference_name: str
:ivar web_service_port:
:vartype web_service_port: str
:ivar dataset_registration:
:vartype dataset_registration: ~flow.models.DatasetRegistration
:ivar dataset_output_options:
:vartype dataset_output_options: ~flow.models.DatasetOutputOptions
:ivar asset_output_settings:
:vartype asset_output_settings: ~flow.models.AssetOutputSettings
:ivar parameter_name:
:vartype parameter_name: str
:ivar asset_output_settings_parameter_name:
:vartype asset_output_settings_parameter_name: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_store_name_parameter_assignment': {'key': 'DataStoreNameParameterAssignment', 'type': 'ParameterAssignment'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'data_store_mode_parameter_assignment': {'key': 'DataStoreModeParameterAssignment', 'type': 'ParameterAssignment'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'path_on_compute_parameter_assignment': {'key': 'PathOnComputeParameterAssignment', 'type': 'ParameterAssignment'},
'overwrite': {'key': 'overwrite', 'type': 'bool'},
'data_reference_name': {'key': 'dataReferenceName', 'type': 'str'},
'web_service_port': {'key': 'webServicePort', 'type': 'str'},
'dataset_registration': {'key': 'datasetRegistration', 'type': 'DatasetRegistration'},
'dataset_output_options': {'key': 'datasetOutputOptions', 'type': 'DatasetOutputOptions'},
'asset_output_settings': {'key': 'AssetOutputSettings', 'type': 'AssetOutputSettings'},
'parameter_name': {'key': 'parameterName', 'type': 'str'},
'asset_output_settings_parameter_name': {'key': 'AssetOutputSettingsParameterName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_store_name_parameter_assignment:
:paramtype data_store_name_parameter_assignment: ~flow.models.ParameterAssignment
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AEVADataStoreMode
:keyword data_store_mode_parameter_assignment:
:paramtype data_store_mode_parameter_assignment: ~flow.models.ParameterAssignment
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword path_on_compute_parameter_assignment:
:paramtype path_on_compute_parameter_assignment: ~flow.models.ParameterAssignment
:keyword overwrite:
:paramtype overwrite: bool
:keyword data_reference_name:
:paramtype data_reference_name: str
:keyword web_service_port:
:paramtype web_service_port: str
:keyword dataset_registration:
:paramtype dataset_registration: ~flow.models.DatasetRegistration
:keyword dataset_output_options:
:paramtype dataset_output_options: ~flow.models.DatasetOutputOptions
:keyword asset_output_settings:
:paramtype asset_output_settings: ~flow.models.AssetOutputSettings
:keyword parameter_name:
:paramtype parameter_name: str
:keyword asset_output_settings_parameter_name:
:paramtype asset_output_settings_parameter_name: str
"""
super(OutputSetting, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.data_store_name = kwargs.get('data_store_name', None)
self.data_store_name_parameter_assignment = kwargs.get('data_store_name_parameter_assignment', None)
self.data_store_mode = kwargs.get('data_store_mode', None)
self.data_store_mode_parameter_assignment = kwargs.get('data_store_mode_parameter_assignment', None)
self.path_on_compute = kwargs.get('path_on_compute', None)
self.path_on_compute_parameter_assignment = kwargs.get('path_on_compute_parameter_assignment', None)
self.overwrite = kwargs.get('overwrite', None)
self.data_reference_name = kwargs.get('data_reference_name', None)
self.web_service_port = kwargs.get('web_service_port', None)
self.dataset_registration = kwargs.get('dataset_registration', None)
self.dataset_output_options = kwargs.get('dataset_output_options', None)
self.asset_output_settings = kwargs.get('asset_output_settings', None)
self.parameter_name = kwargs.get('parameter_name', None)
self.asset_output_settings_parameter_name = kwargs.get('asset_output_settings_parameter_name', None)
class OutputSettingSpec(msrest.serialization.Model):
"""OutputSettingSpec.
:ivar supported_data_store_modes:
:vartype supported_data_store_modes: list[str or ~flow.models.AEVADataStoreMode]
:ivar default_asset_output_path:
:vartype default_asset_output_path: str
"""
_attribute_map = {
'supported_data_store_modes': {'key': 'supportedDataStoreModes', 'type': '[str]'},
'default_asset_output_path': {'key': 'defaultAssetOutputPath', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword supported_data_store_modes:
:paramtype supported_data_store_modes: list[str or ~flow.models.AEVADataStoreMode]
:keyword default_asset_output_path:
:paramtype default_asset_output_path: str
"""
super(OutputSettingSpec, self).__init__(**kwargs)
self.supported_data_store_modes = kwargs.get('supported_data_store_modes', None)
self.default_asset_output_path = kwargs.get('default_asset_output_path', None)
class PaginatedDataInfoList(msrest.serialization.Model):
"""A paginated list of DataInfos.
:ivar value: An array of objects of type DataInfo.
:vartype value: list[~flow.models.DataInfo]
:ivar continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:vartype continuation_token: str
:ivar next_link: The link to the next page constructed using the continuationToken. If null,
there are no additional pages.
:vartype next_link: str
"""
_attribute_map = {
'value': {'key': 'value', 'type': '[DataInfo]'},
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'next_link': {'key': 'nextLink', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword value: An array of objects of type DataInfo.
:paramtype value: list[~flow.models.DataInfo]
:keyword continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:paramtype continuation_token: str
:keyword next_link: The link to the next page constructed using the continuationToken. If
null, there are no additional pages.
:paramtype next_link: str
"""
super(PaginatedDataInfoList, self).__init__(**kwargs)
self.value = kwargs.get('value', None)
self.continuation_token = kwargs.get('continuation_token', None)
self.next_link = kwargs.get('next_link', None)
class PaginatedModelDtoList(msrest.serialization.Model):
"""A paginated list of ModelDtos.
:ivar value: An array of objects of type ModelDto.
:vartype value: list[~flow.models.ModelDto]
:ivar continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:vartype continuation_token: str
:ivar next_link: The link to the next page constructed using the continuationToken. If null,
there are no additional pages.
:vartype next_link: str
"""
_attribute_map = {
'value': {'key': 'value', 'type': '[ModelDto]'},
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'next_link': {'key': 'nextLink', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword value: An array of objects of type ModelDto.
:paramtype value: list[~flow.models.ModelDto]
:keyword continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:paramtype continuation_token: str
:keyword next_link: The link to the next page constructed using the continuationToken. If
null, there are no additional pages.
:paramtype next_link: str
"""
super(PaginatedModelDtoList, self).__init__(**kwargs)
self.value = kwargs.get('value', None)
self.continuation_token = kwargs.get('continuation_token', None)
self.next_link = kwargs.get('next_link', None)
class PaginatedModuleDtoList(msrest.serialization.Model):
"""A paginated list of ModuleDtos.
:ivar value: An array of objects of type ModuleDto.
:vartype value: list[~flow.models.ModuleDto]
:ivar continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:vartype continuation_token: str
:ivar next_link: The link to the next page constructed using the continuationToken. If null,
there are no additional pages.
:vartype next_link: str
"""
_attribute_map = {
'value': {'key': 'value', 'type': '[ModuleDto]'},
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'next_link': {'key': 'nextLink', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword value: An array of objects of type ModuleDto.
:paramtype value: list[~flow.models.ModuleDto]
:keyword continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:paramtype continuation_token: str
:keyword next_link: The link to the next page constructed using the continuationToken. If
null, there are no additional pages.
:paramtype next_link: str
"""
super(PaginatedModuleDtoList, self).__init__(**kwargs)
self.value = kwargs.get('value', None)
self.continuation_token = kwargs.get('continuation_token', None)
self.next_link = kwargs.get('next_link', None)
class PaginatedPipelineDraftSummaryList(msrest.serialization.Model):
"""A paginated list of PipelineDraftSummarys.
:ivar value: An array of objects of type PipelineDraftSummary.
:vartype value: list[~flow.models.PipelineDraftSummary]
:ivar continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:vartype continuation_token: str
:ivar next_link: The link to the next page constructed using the continuationToken. If null,
there are no additional pages.
:vartype next_link: str
"""
_attribute_map = {
'value': {'key': 'value', 'type': '[PipelineDraftSummary]'},
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'next_link': {'key': 'nextLink', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword value: An array of objects of type PipelineDraftSummary.
:paramtype value: list[~flow.models.PipelineDraftSummary]
:keyword continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:paramtype continuation_token: str
:keyword next_link: The link to the next page constructed using the continuationToken. If
null, there are no additional pages.
:paramtype next_link: str
"""
super(PaginatedPipelineDraftSummaryList, self).__init__(**kwargs)
self.value = kwargs.get('value', None)
self.continuation_token = kwargs.get('continuation_token', None)
self.next_link = kwargs.get('next_link', None)
class PaginatedPipelineEndpointSummaryList(msrest.serialization.Model):
"""A paginated list of PipelineEndpointSummarys.
:ivar value: An array of objects of type PipelineEndpointSummary.
:vartype value: list[~flow.models.PipelineEndpointSummary]
:ivar continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:vartype continuation_token: str
:ivar next_link: The link to the next page constructed using the continuationToken. If null,
there are no additional pages.
:vartype next_link: str
"""
_attribute_map = {
'value': {'key': 'value', 'type': '[PipelineEndpointSummary]'},
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'next_link': {'key': 'nextLink', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword value: An array of objects of type PipelineEndpointSummary.
:paramtype value: list[~flow.models.PipelineEndpointSummary]
:keyword continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:paramtype continuation_token: str
:keyword next_link: The link to the next page constructed using the continuationToken. If
null, there are no additional pages.
:paramtype next_link: str
"""
super(PaginatedPipelineEndpointSummaryList, self).__init__(**kwargs)
self.value = kwargs.get('value', None)
self.continuation_token = kwargs.get('continuation_token', None)
self.next_link = kwargs.get('next_link', None)
class PaginatedPipelineRunSummaryList(msrest.serialization.Model):
"""A paginated list of PipelineRunSummarys.
:ivar value: An array of objects of type PipelineRunSummary.
:vartype value: list[~flow.models.PipelineRunSummary]
:ivar continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:vartype continuation_token: str
:ivar next_link: The link to the next page constructed using the continuationToken. If null,
there are no additional pages.
:vartype next_link: str
"""
_attribute_map = {
'value': {'key': 'value', 'type': '[PipelineRunSummary]'},
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'next_link': {'key': 'nextLink', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword value: An array of objects of type PipelineRunSummary.
:paramtype value: list[~flow.models.PipelineRunSummary]
:keyword continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:paramtype continuation_token: str
:keyword next_link: The link to the next page constructed using the continuationToken. If
null, there are no additional pages.
:paramtype next_link: str
"""
super(PaginatedPipelineRunSummaryList, self).__init__(**kwargs)
self.value = kwargs.get('value', None)
self.continuation_token = kwargs.get('continuation_token', None)
self.next_link = kwargs.get('next_link', None)
class PaginatedPublishedPipelineSummaryList(msrest.serialization.Model):
"""A paginated list of PublishedPipelineSummarys.
:ivar value: An array of objects of type PublishedPipelineSummary.
:vartype value: list[~flow.models.PublishedPipelineSummary]
:ivar continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:vartype continuation_token: str
:ivar next_link: The link to the next page constructed using the continuationToken. If null,
there are no additional pages.
:vartype next_link: str
"""
_attribute_map = {
'value': {'key': 'value', 'type': '[PublishedPipelineSummary]'},
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'next_link': {'key': 'nextLink', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword value: An array of objects of type PublishedPipelineSummary.
:paramtype value: list[~flow.models.PublishedPipelineSummary]
:keyword continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:paramtype continuation_token: str
:keyword next_link: The link to the next page constructed using the continuationToken. If
null, there are no additional pages.
:paramtype next_link: str
"""
super(PaginatedPublishedPipelineSummaryList, self).__init__(**kwargs)
self.value = kwargs.get('value', None)
self.continuation_token = kwargs.get('continuation_token', None)
self.next_link = kwargs.get('next_link', None)
class ParallelForControlFlowInfo(msrest.serialization.Model):
"""ParallelForControlFlowInfo.
:ivar parallel_for_items_input:
:vartype parallel_for_items_input: ~flow.models.ParameterAssignment
"""
_attribute_map = {
'parallel_for_items_input': {'key': 'parallelForItemsInput', 'type': 'ParameterAssignment'},
}
def __init__(
self,
**kwargs
):
"""
:keyword parallel_for_items_input:
:paramtype parallel_for_items_input: ~flow.models.ParameterAssignment
"""
super(ParallelForControlFlowInfo, self).__init__(**kwargs)
self.parallel_for_items_input = kwargs.get('parallel_for_items_input', None)
class ParallelTaskConfiguration(msrest.serialization.Model):
"""ParallelTaskConfiguration.
:ivar max_retries_per_worker:
:vartype max_retries_per_worker: int
:ivar worker_count_per_node:
:vartype worker_count_per_node: int
:ivar terminal_exit_codes:
:vartype terminal_exit_codes: list[int]
:ivar configuration: Dictionary of :code:`<string>`.
:vartype configuration: dict[str, str]
"""
_attribute_map = {
'max_retries_per_worker': {'key': 'maxRetriesPerWorker', 'type': 'int'},
'worker_count_per_node': {'key': 'workerCountPerNode', 'type': 'int'},
'terminal_exit_codes': {'key': 'terminalExitCodes', 'type': '[int]'},
'configuration': {'key': 'configuration', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword max_retries_per_worker:
:paramtype max_retries_per_worker: int
:keyword worker_count_per_node:
:paramtype worker_count_per_node: int
:keyword terminal_exit_codes:
:paramtype terminal_exit_codes: list[int]
:keyword configuration: Dictionary of :code:`<string>`.
:paramtype configuration: dict[str, str]
"""
super(ParallelTaskConfiguration, self).__init__(**kwargs)
self.max_retries_per_worker = kwargs.get('max_retries_per_worker', None)
self.worker_count_per_node = kwargs.get('worker_count_per_node', None)
self.terminal_exit_codes = kwargs.get('terminal_exit_codes', None)
self.configuration = kwargs.get('configuration', None)
class Parameter(msrest.serialization.Model):
"""Parameter.
:ivar name:
:vartype name: str
:ivar documentation:
:vartype documentation: str
:ivar default_value:
:vartype default_value: str
:ivar is_optional:
:vartype is_optional: bool
:ivar min_max_rules:
:vartype min_max_rules: list[~flow.models.MinMaxParameterRule]
:ivar enum_rules:
:vartype enum_rules: list[~flow.models.EnumParameterRule]
:ivar type: Possible values include: "Int", "Double", "Bool", "String", "Undefined".
:vartype type: str or ~flow.models.ParameterType
:ivar label:
:vartype label: str
:ivar group_names:
:vartype group_names: list[str]
:ivar argument_name:
:vartype argument_name: str
:ivar ui_hint:
:vartype ui_hint: ~flow.models.UIParameterHint
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'documentation': {'key': 'documentation', 'type': 'str'},
'default_value': {'key': 'defaultValue', 'type': 'str'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
'min_max_rules': {'key': 'minMaxRules', 'type': '[MinMaxParameterRule]'},
'enum_rules': {'key': 'enumRules', 'type': '[EnumParameterRule]'},
'type': {'key': 'type', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'group_names': {'key': 'groupNames', 'type': '[str]'},
'argument_name': {'key': 'argumentName', 'type': 'str'},
'ui_hint': {'key': 'uiHint', 'type': 'UIParameterHint'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword documentation:
:paramtype documentation: str
:keyword default_value:
:paramtype default_value: str
:keyword is_optional:
:paramtype is_optional: bool
:keyword min_max_rules:
:paramtype min_max_rules: list[~flow.models.MinMaxParameterRule]
:keyword enum_rules:
:paramtype enum_rules: list[~flow.models.EnumParameterRule]
:keyword type: Possible values include: "Int", "Double", "Bool", "String", "Undefined".
:paramtype type: str or ~flow.models.ParameterType
:keyword label:
:paramtype label: str
:keyword group_names:
:paramtype group_names: list[str]
:keyword argument_name:
:paramtype argument_name: str
:keyword ui_hint:
:paramtype ui_hint: ~flow.models.UIParameterHint
"""
super(Parameter, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.documentation = kwargs.get('documentation', None)
self.default_value = kwargs.get('default_value', None)
self.is_optional = kwargs.get('is_optional', None)
self.min_max_rules = kwargs.get('min_max_rules', None)
self.enum_rules = kwargs.get('enum_rules', None)
self.type = kwargs.get('type', None)
self.label = kwargs.get('label', None)
self.group_names = kwargs.get('group_names', None)
self.argument_name = kwargs.get('argument_name', None)
self.ui_hint = kwargs.get('ui_hint', None)
class ParameterAssignment(msrest.serialization.Model):
"""ParameterAssignment.
:ivar value_type: Possible values include: "Literal", "GraphParameterName", "Concatenate",
"Input", "DataPath", "DataSetDefinition".
:vartype value_type: str or ~flow.models.ParameterValueType
:ivar assignments_to_concatenate:
:vartype assignments_to_concatenate: list[~flow.models.ParameterAssignment]
:ivar data_path_assignment:
:vartype data_path_assignment: ~flow.models.LegacyDataPath
:ivar data_set_definition_value_assignment:
:vartype data_set_definition_value_assignment: ~flow.models.DataSetDefinitionValue
:ivar name:
:vartype name: str
:ivar value:
:vartype value: str
"""
_attribute_map = {
'value_type': {'key': 'valueType', 'type': 'str'},
'assignments_to_concatenate': {'key': 'assignmentsToConcatenate', 'type': '[ParameterAssignment]'},
'data_path_assignment': {'key': 'dataPathAssignment', 'type': 'LegacyDataPath'},
'data_set_definition_value_assignment': {'key': 'dataSetDefinitionValueAssignment', 'type': 'DataSetDefinitionValue'},
'name': {'key': 'name', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword value_type: Possible values include: "Literal", "GraphParameterName", "Concatenate",
"Input", "DataPath", "DataSetDefinition".
:paramtype value_type: str or ~flow.models.ParameterValueType
:keyword assignments_to_concatenate:
:paramtype assignments_to_concatenate: list[~flow.models.ParameterAssignment]
:keyword data_path_assignment:
:paramtype data_path_assignment: ~flow.models.LegacyDataPath
:keyword data_set_definition_value_assignment:
:paramtype data_set_definition_value_assignment: ~flow.models.DataSetDefinitionValue
:keyword name:
:paramtype name: str
:keyword value:
:paramtype value: str
"""
super(ParameterAssignment, self).__init__(**kwargs)
self.value_type = kwargs.get('value_type', None)
self.assignments_to_concatenate = kwargs.get('assignments_to_concatenate', None)
self.data_path_assignment = kwargs.get('data_path_assignment', None)
self.data_set_definition_value_assignment = kwargs.get('data_set_definition_value_assignment', None)
self.name = kwargs.get('name', None)
self.value = kwargs.get('value', None)
class ParameterDefinition(msrest.serialization.Model):
"""ParameterDefinition.
:ivar name:
:vartype name: str
:ivar type:
:vartype type: str
:ivar value:
:vartype value: str
:ivar is_optional:
:vartype is_optional: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type:
:paramtype type: str
:keyword value:
:paramtype value: str
:keyword is_optional:
:paramtype is_optional: bool
"""
super(ParameterDefinition, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.type = kwargs.get('type', None)
self.value = kwargs.get('value', None)
self.is_optional = kwargs.get('is_optional', None)
class PatchFlowRequest(msrest.serialization.Model):
"""PatchFlowRequest.
:ivar flow_patch_operation_type: Possible values include: "ArchiveFlow", "RestoreFlow",
"ExportFlowToFile".
:vartype flow_patch_operation_type: str or ~flow.models.FlowPatchOperationType
:ivar flow_definition_file_path:
:vartype flow_definition_file_path: str
"""
_attribute_map = {
'flow_patch_operation_type': {'key': 'flowPatchOperationType', 'type': 'str'},
'flow_definition_file_path': {'key': 'flowDefinitionFilePath', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword flow_patch_operation_type: Possible values include: "ArchiveFlow", "RestoreFlow",
"ExportFlowToFile".
:paramtype flow_patch_operation_type: str or ~flow.models.FlowPatchOperationType
:keyword flow_definition_file_path:
:paramtype flow_definition_file_path: str
"""
super(PatchFlowRequest, self).__init__(**kwargs)
self.flow_patch_operation_type = kwargs.get('flow_patch_operation_type', None)
self.flow_definition_file_path = kwargs.get('flow_definition_file_path', None)
class Pipeline(msrest.serialization.Model):
"""Pipeline.
:ivar run_id:
:vartype run_id: str
:ivar continue_run_on_step_failure:
:vartype continue_run_on_step_failure: bool
:ivar default_datastore_name:
:vartype default_datastore_name: str
:ivar component_jobs: This is a dictionary.
:vartype component_jobs: dict[str, ~flow.models.ComponentJob]
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, ~flow.models.PipelineInput]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, ~flow.models.PipelineOutput]
"""
_attribute_map = {
'run_id': {'key': 'runId', 'type': 'str'},
'continue_run_on_step_failure': {'key': 'continueRunOnStepFailure', 'type': 'bool'},
'default_datastore_name': {'key': 'defaultDatastoreName', 'type': 'str'},
'component_jobs': {'key': 'componentJobs', 'type': '{ComponentJob}'},
'inputs': {'key': 'inputs', 'type': '{PipelineInput}'},
'outputs': {'key': 'outputs', 'type': '{PipelineOutput}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword run_id:
:paramtype run_id: str
:keyword continue_run_on_step_failure:
:paramtype continue_run_on_step_failure: bool
:keyword default_datastore_name:
:paramtype default_datastore_name: str
:keyword component_jobs: This is a dictionary.
:paramtype component_jobs: dict[str, ~flow.models.ComponentJob]
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, ~flow.models.PipelineInput]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, ~flow.models.PipelineOutput]
"""
super(Pipeline, self).__init__(**kwargs)
self.run_id = kwargs.get('run_id', None)
self.continue_run_on_step_failure = kwargs.get('continue_run_on_step_failure', None)
self.default_datastore_name = kwargs.get('default_datastore_name', None)
self.component_jobs = kwargs.get('component_jobs', None)
self.inputs = kwargs.get('inputs', None)
self.outputs = kwargs.get('outputs', None)
class PipelineDraft(msrest.serialization.Model):
"""PipelineDraft.
:ivar graph_draft_id:
:vartype graph_draft_id: str
:ivar source_pipeline_run_id:
:vartype source_pipeline_run_id: str
:ivar latest_pipeline_run_id:
:vartype latest_pipeline_run_id: str
:ivar latest_run_experiment_name:
:vartype latest_run_experiment_name: str
:ivar latest_run_experiment_id:
:vartype latest_run_experiment_id: str
:ivar is_latest_run_experiment_archived:
:vartype is_latest_run_experiment_archived: bool
:ivar status:
:vartype status: ~flow.models.PipelineStatus
:ivar graph_detail:
:vartype graph_detail: ~flow.models.PipelineRunGraphDetail
:ivar real_time_endpoint_info:
:vartype real_time_endpoint_info: ~flow.models.RealTimeEndpointInfo
:ivar linked_pipelines_info:
:vartype linked_pipelines_info: list[~flow.models.LinkedPipelineInfo]
:ivar nodes_in_draft:
:vartype nodes_in_draft: list[str]
:ivar studio_migration_info:
:vartype studio_migration_info: ~flow.models.StudioMigrationInfo
:ivar flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:vartype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:ivar pipeline_run_setting_parameters:
:vartype pipeline_run_setting_parameters: list[~flow.models.RunSettingParameter]
:ivar pipeline_run_settings:
:vartype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar continue_run_on_step_failure:
:vartype continue_run_on_step_failure: bool
:ivar continue_run_on_failed_optional_input:
:vartype continue_run_on_failed_optional_input: bool
:ivar default_compute:
:vartype default_compute: ~flow.models.ComputeSetting
:ivar default_datastore:
:vartype default_datastore: ~flow.models.DatastoreSetting
:ivar default_cloud_priority:
:vartype default_cloud_priority: ~flow.models.CloudPrioritySetting
:ivar enforce_rerun:
:vartype enforce_rerun: bool
:ivar pipeline_parameters: This is a dictionary.
:vartype pipeline_parameters: dict[str, str]
:ivar data_path_assignments: This is a dictionary.
:vartype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:ivar data_set_definition_value_assignments: This is a dictionary.
:vartype data_set_definition_value_assignments: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar asset_output_settings_assignments: This is a dictionary.
:vartype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:ivar pipeline_timeout:
:vartype pipeline_timeout: int
:ivar identity_config:
:vartype identity_config: ~flow.models.IdentitySetting
:ivar graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:vartype graph_components_mode: str or ~flow.models.GraphComponentsMode
:ivar name:
:vartype name: str
:ivar last_edited_by:
:vartype last_edited_by: str
:ivar created_by:
:vartype created_by: str
:ivar description:
:vartype description: str
:ivar pipeline_type: Possible values include: "TrainingPipeline", "RealTimeInferencePipeline",
"BatchInferencePipeline", "Unknown".
:vartype pipeline_type: str or ~flow.models.PipelineType
:ivar pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:vartype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'graph_draft_id': {'key': 'graphDraftId', 'type': 'str'},
'source_pipeline_run_id': {'key': 'sourcePipelineRunId', 'type': 'str'},
'latest_pipeline_run_id': {'key': 'latestPipelineRunId', 'type': 'str'},
'latest_run_experiment_name': {'key': 'latestRunExperimentName', 'type': 'str'},
'latest_run_experiment_id': {'key': 'latestRunExperimentId', 'type': 'str'},
'is_latest_run_experiment_archived': {'key': 'isLatestRunExperimentArchived', 'type': 'bool'},
'status': {'key': 'status', 'type': 'PipelineStatus'},
'graph_detail': {'key': 'graphDetail', 'type': 'PipelineRunGraphDetail'},
'real_time_endpoint_info': {'key': 'realTimeEndpointInfo', 'type': 'RealTimeEndpointInfo'},
'linked_pipelines_info': {'key': 'linkedPipelinesInfo', 'type': '[LinkedPipelineInfo]'},
'nodes_in_draft': {'key': 'nodesInDraft', 'type': '[str]'},
'studio_migration_info': {'key': 'studioMigrationInfo', 'type': 'StudioMigrationInfo'},
'flattened_sub_graphs': {'key': 'flattenedSubGraphs', 'type': '{PipelineSubDraft}'},
'pipeline_run_setting_parameters': {'key': 'pipelineRunSettingParameters', 'type': '[RunSettingParameter]'},
'pipeline_run_settings': {'key': 'pipelineRunSettings', 'type': '[RunSettingParameterAssignment]'},
'continue_run_on_step_failure': {'key': 'continueRunOnStepFailure', 'type': 'bool'},
'continue_run_on_failed_optional_input': {'key': 'continueRunOnFailedOptionalInput', 'type': 'bool'},
'default_compute': {'key': 'defaultCompute', 'type': 'ComputeSetting'},
'default_datastore': {'key': 'defaultDatastore', 'type': 'DatastoreSetting'},
'default_cloud_priority': {'key': 'defaultCloudPriority', 'type': 'CloudPrioritySetting'},
'enforce_rerun': {'key': 'enforceRerun', 'type': 'bool'},
'pipeline_parameters': {'key': 'pipelineParameters', 'type': '{str}'},
'data_path_assignments': {'key': 'dataPathAssignments', 'type': '{LegacyDataPath}'},
'data_set_definition_value_assignments': {'key': 'dataSetDefinitionValueAssignments', 'type': '{DataSetDefinitionValue}'},
'asset_output_settings_assignments': {'key': 'assetOutputSettingsAssignments', 'type': '{AssetOutputSettings}'},
'pipeline_timeout': {'key': 'pipelineTimeout', 'type': 'int'},
'identity_config': {'key': 'identityConfig', 'type': 'IdentitySetting'},
'graph_components_mode': {'key': 'graphComponentsMode', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'last_edited_by': {'key': 'lastEditedBy', 'type': 'str'},
'created_by': {'key': 'createdBy', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'pipeline_type': {'key': 'pipelineType', 'type': 'str'},
'pipeline_draft_mode': {'key': 'pipelineDraftMode', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword graph_draft_id:
:paramtype graph_draft_id: str
:keyword source_pipeline_run_id:
:paramtype source_pipeline_run_id: str
:keyword latest_pipeline_run_id:
:paramtype latest_pipeline_run_id: str
:keyword latest_run_experiment_name:
:paramtype latest_run_experiment_name: str
:keyword latest_run_experiment_id:
:paramtype latest_run_experiment_id: str
:keyword is_latest_run_experiment_archived:
:paramtype is_latest_run_experiment_archived: bool
:keyword status:
:paramtype status: ~flow.models.PipelineStatus
:keyword graph_detail:
:paramtype graph_detail: ~flow.models.PipelineRunGraphDetail
:keyword real_time_endpoint_info:
:paramtype real_time_endpoint_info: ~flow.models.RealTimeEndpointInfo
:keyword linked_pipelines_info:
:paramtype linked_pipelines_info: list[~flow.models.LinkedPipelineInfo]
:keyword nodes_in_draft:
:paramtype nodes_in_draft: list[str]
:keyword studio_migration_info:
:paramtype studio_migration_info: ~flow.models.StudioMigrationInfo
:keyword flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:paramtype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:keyword pipeline_run_setting_parameters:
:paramtype pipeline_run_setting_parameters: list[~flow.models.RunSettingParameter]
:keyword pipeline_run_settings:
:paramtype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword continue_run_on_step_failure:
:paramtype continue_run_on_step_failure: bool
:keyword continue_run_on_failed_optional_input:
:paramtype continue_run_on_failed_optional_input: bool
:keyword default_compute:
:paramtype default_compute: ~flow.models.ComputeSetting
:keyword default_datastore:
:paramtype default_datastore: ~flow.models.DatastoreSetting
:keyword default_cloud_priority:
:paramtype default_cloud_priority: ~flow.models.CloudPrioritySetting
:keyword enforce_rerun:
:paramtype enforce_rerun: bool
:keyword pipeline_parameters: This is a dictionary.
:paramtype pipeline_parameters: dict[str, str]
:keyword data_path_assignments: This is a dictionary.
:paramtype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:keyword data_set_definition_value_assignments: This is a dictionary.
:paramtype data_set_definition_value_assignments: dict[str,
~flow.models.DataSetDefinitionValue]
:keyword asset_output_settings_assignments: This is a dictionary.
:paramtype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:keyword pipeline_timeout:
:paramtype pipeline_timeout: int
:keyword identity_config:
:paramtype identity_config: ~flow.models.IdentitySetting
:keyword graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:paramtype graph_components_mode: str or ~flow.models.GraphComponentsMode
:keyword name:
:paramtype name: str
:keyword last_edited_by:
:paramtype last_edited_by: str
:keyword created_by:
:paramtype created_by: str
:keyword description:
:paramtype description: str
:keyword pipeline_type: Possible values include: "TrainingPipeline",
"RealTimeInferencePipeline", "BatchInferencePipeline", "Unknown".
:paramtype pipeline_type: str or ~flow.models.PipelineType
:keyword pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:paramtype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PipelineDraft, self).__init__(**kwargs)
self.graph_draft_id = kwargs.get('graph_draft_id', None)
self.source_pipeline_run_id = kwargs.get('source_pipeline_run_id', None)
self.latest_pipeline_run_id = kwargs.get('latest_pipeline_run_id', None)
self.latest_run_experiment_name = kwargs.get('latest_run_experiment_name', None)
self.latest_run_experiment_id = kwargs.get('latest_run_experiment_id', None)
self.is_latest_run_experiment_archived = kwargs.get('is_latest_run_experiment_archived', None)
self.status = kwargs.get('status', None)
self.graph_detail = kwargs.get('graph_detail', None)
self.real_time_endpoint_info = kwargs.get('real_time_endpoint_info', None)
self.linked_pipelines_info = kwargs.get('linked_pipelines_info', None)
self.nodes_in_draft = kwargs.get('nodes_in_draft', None)
self.studio_migration_info = kwargs.get('studio_migration_info', None)
self.flattened_sub_graphs = kwargs.get('flattened_sub_graphs', None)
self.pipeline_run_setting_parameters = kwargs.get('pipeline_run_setting_parameters', None)
self.pipeline_run_settings = kwargs.get('pipeline_run_settings', None)
self.continue_run_on_step_failure = kwargs.get('continue_run_on_step_failure', None)
self.continue_run_on_failed_optional_input = kwargs.get('continue_run_on_failed_optional_input', None)
self.default_compute = kwargs.get('default_compute', None)
self.default_datastore = kwargs.get('default_datastore', None)
self.default_cloud_priority = kwargs.get('default_cloud_priority', None)
self.enforce_rerun = kwargs.get('enforce_rerun', None)
self.pipeline_parameters = kwargs.get('pipeline_parameters', None)
self.data_path_assignments = kwargs.get('data_path_assignments', None)
self.data_set_definition_value_assignments = kwargs.get('data_set_definition_value_assignments', None)
self.asset_output_settings_assignments = kwargs.get('asset_output_settings_assignments', None)
self.pipeline_timeout = kwargs.get('pipeline_timeout', None)
self.identity_config = kwargs.get('identity_config', None)
self.graph_components_mode = kwargs.get('graph_components_mode', None)
self.name = kwargs.get('name', None)
self.last_edited_by = kwargs.get('last_edited_by', None)
self.created_by = kwargs.get('created_by', None)
self.description = kwargs.get('description', None)
self.pipeline_type = kwargs.get('pipeline_type', None)
self.pipeline_draft_mode = kwargs.get('pipeline_draft_mode', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
self.entity_status = kwargs.get('entity_status', None)
self.id = kwargs.get('id', None)
self.etag = kwargs.get('etag', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
class PipelineDraftStepDetails(msrest.serialization.Model):
"""PipelineDraftStepDetails.
:ivar run_id:
:vartype run_id: str
:ivar target:
:vartype target: str
:ivar status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:vartype status: str or ~flow.models.RunStatus
:ivar status_detail:
:vartype status_detail: str
:ivar parent_run_id:
:vartype parent_run_id: str
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar is_reused:
:vartype is_reused: bool
:ivar reused_run_id:
:vartype reused_run_id: str
:ivar reused_pipeline_run_id:
:vartype reused_pipeline_run_id: str
:ivar logs: This is a dictionary.
:vartype logs: dict[str, str]
:ivar output_log:
:vartype output_log: str
:ivar run_configuration:
:vartype run_configuration: ~flow.models.RunConfiguration
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, str]
:ivar port_outputs: This is a dictionary.
:vartype port_outputs: dict[str, ~flow.models.PortOutputInfo]
:ivar is_experiment_archived:
:vartype is_experiment_archived: bool
"""
_attribute_map = {
'run_id': {'key': 'runId', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
'parent_run_id': {'key': 'parentRunId', 'type': 'str'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'is_reused': {'key': 'isReused', 'type': 'bool'},
'reused_run_id': {'key': 'reusedRunId', 'type': 'str'},
'reused_pipeline_run_id': {'key': 'reusedPipelineRunId', 'type': 'str'},
'logs': {'key': 'logs', 'type': '{str}'},
'output_log': {'key': 'outputLog', 'type': 'str'},
'run_configuration': {'key': 'runConfiguration', 'type': 'RunConfiguration'},
'outputs': {'key': 'outputs', 'type': '{str}'},
'port_outputs': {'key': 'portOutputs', 'type': '{PortOutputInfo}'},
'is_experiment_archived': {'key': 'isExperimentArchived', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword run_id:
:paramtype run_id: str
:keyword target:
:paramtype target: str
:keyword status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:paramtype status: str or ~flow.models.RunStatus
:keyword status_detail:
:paramtype status_detail: str
:keyword parent_run_id:
:paramtype parent_run_id: str
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
:keyword is_reused:
:paramtype is_reused: bool
:keyword reused_run_id:
:paramtype reused_run_id: str
:keyword reused_pipeline_run_id:
:paramtype reused_pipeline_run_id: str
:keyword logs: This is a dictionary.
:paramtype logs: dict[str, str]
:keyword output_log:
:paramtype output_log: str
:keyword run_configuration:
:paramtype run_configuration: ~flow.models.RunConfiguration
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, str]
:keyword port_outputs: This is a dictionary.
:paramtype port_outputs: dict[str, ~flow.models.PortOutputInfo]
:keyword is_experiment_archived:
:paramtype is_experiment_archived: bool
"""
super(PipelineDraftStepDetails, self).__init__(**kwargs)
self.run_id = kwargs.get('run_id', None)
self.target = kwargs.get('target', None)
self.status = kwargs.get('status', None)
self.status_detail = kwargs.get('status_detail', None)
self.parent_run_id = kwargs.get('parent_run_id', None)
self.start_time = kwargs.get('start_time', None)
self.end_time = kwargs.get('end_time', None)
self.is_reused = kwargs.get('is_reused', None)
self.reused_run_id = kwargs.get('reused_run_id', None)
self.reused_pipeline_run_id = kwargs.get('reused_pipeline_run_id', None)
self.logs = kwargs.get('logs', None)
self.output_log = kwargs.get('output_log', None)
self.run_configuration = kwargs.get('run_configuration', None)
self.outputs = kwargs.get('outputs', None)
self.port_outputs = kwargs.get('port_outputs', None)
self.is_experiment_archived = kwargs.get('is_experiment_archived', None)
class PipelineDraftSummary(msrest.serialization.Model):
"""PipelineDraftSummary.
:ivar name:
:vartype name: str
:ivar last_edited_by:
:vartype last_edited_by: str
:ivar created_by:
:vartype created_by: str
:ivar description:
:vartype description: str
:ivar pipeline_type: Possible values include: "TrainingPipeline", "RealTimeInferencePipeline",
"BatchInferencePipeline", "Unknown".
:vartype pipeline_type: str or ~flow.models.PipelineType
:ivar pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:vartype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'last_edited_by': {'key': 'lastEditedBy', 'type': 'str'},
'created_by': {'key': 'createdBy', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'pipeline_type': {'key': 'pipelineType', 'type': 'str'},
'pipeline_draft_mode': {'key': 'pipelineDraftMode', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword last_edited_by:
:paramtype last_edited_by: str
:keyword created_by:
:paramtype created_by: str
:keyword description:
:paramtype description: str
:keyword pipeline_type: Possible values include: "TrainingPipeline",
"RealTimeInferencePipeline", "BatchInferencePipeline", "Unknown".
:paramtype pipeline_type: str or ~flow.models.PipelineType
:keyword pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:paramtype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PipelineDraftSummary, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.last_edited_by = kwargs.get('last_edited_by', None)
self.created_by = kwargs.get('created_by', None)
self.description = kwargs.get('description', None)
self.pipeline_type = kwargs.get('pipeline_type', None)
self.pipeline_draft_mode = kwargs.get('pipeline_draft_mode', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
self.entity_status = kwargs.get('entity_status', None)
self.id = kwargs.get('id', None)
self.etag = kwargs.get('etag', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
class PipelineEndpoint(msrest.serialization.Model):
"""PipelineEndpoint.
:ivar default_version:
:vartype default_version: str
:ivar default_pipeline_id:
:vartype default_pipeline_id: str
:ivar default_graph_id:
:vartype default_graph_id: str
:ivar rest_endpoint:
:vartype rest_endpoint: str
:ivar published_date:
:vartype published_date: ~datetime.datetime
:ivar published_by:
:vartype published_by: str
:ivar parameters: This is a dictionary.
:vartype parameters: dict[str, str]
:ivar data_set_definition_value_assignment: This is a dictionary.
:vartype data_set_definition_value_assignment: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar default_pipeline_name:
:vartype default_pipeline_name: str
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar updated_by:
:vartype updated_by: str
:ivar swagger_url:
:vartype swagger_url: str
:ivar last_run_time:
:vartype last_run_time: ~datetime.datetime
:ivar last_run_status: Possible values include: "NotStarted", "Running", "Failed", "Finished",
"Canceled", "Queued", "CancelRequested".
:vartype last_run_status: str or ~flow.models.PipelineRunStatusCode
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'default_version': {'key': 'defaultVersion', 'type': 'str'},
'default_pipeline_id': {'key': 'defaultPipelineId', 'type': 'str'},
'default_graph_id': {'key': 'defaultGraphId', 'type': 'str'},
'rest_endpoint': {'key': 'restEndpoint', 'type': 'str'},
'published_date': {'key': 'publishedDate', 'type': 'iso-8601'},
'published_by': {'key': 'publishedBy', 'type': 'str'},
'parameters': {'key': 'parameters', 'type': '{str}'},
'data_set_definition_value_assignment': {'key': 'dataSetDefinitionValueAssignment', 'type': '{DataSetDefinitionValue}'},
'default_pipeline_name': {'key': 'defaultPipelineName', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'updated_by': {'key': 'updatedBy', 'type': 'str'},
'swagger_url': {'key': 'swaggerUrl', 'type': 'str'},
'last_run_time': {'key': 'lastRunTime', 'type': 'iso-8601'},
'last_run_status': {'key': 'lastRunStatus', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword default_version:
:paramtype default_version: str
:keyword default_pipeline_id:
:paramtype default_pipeline_id: str
:keyword default_graph_id:
:paramtype default_graph_id: str
:keyword rest_endpoint:
:paramtype rest_endpoint: str
:keyword published_date:
:paramtype published_date: ~datetime.datetime
:keyword published_by:
:paramtype published_by: str
:keyword parameters: This is a dictionary.
:paramtype parameters: dict[str, str]
:keyword data_set_definition_value_assignment: This is a dictionary.
:paramtype data_set_definition_value_assignment: dict[str, ~flow.models.DataSetDefinitionValue]
:keyword default_pipeline_name:
:paramtype default_pipeline_name: str
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword updated_by:
:paramtype updated_by: str
:keyword swagger_url:
:paramtype swagger_url: str
:keyword last_run_time:
:paramtype last_run_time: ~datetime.datetime
:keyword last_run_status: Possible values include: "NotStarted", "Running", "Failed",
"Finished", "Canceled", "Queued", "CancelRequested".
:paramtype last_run_status: str or ~flow.models.PipelineRunStatusCode
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PipelineEndpoint, self).__init__(**kwargs)
self.default_version = kwargs.get('default_version', None)
self.default_pipeline_id = kwargs.get('default_pipeline_id', None)
self.default_graph_id = kwargs.get('default_graph_id', None)
self.rest_endpoint = kwargs.get('rest_endpoint', None)
self.published_date = kwargs.get('published_date', None)
self.published_by = kwargs.get('published_by', None)
self.parameters = kwargs.get('parameters', None)
self.data_set_definition_value_assignment = kwargs.get('data_set_definition_value_assignment', None)
self.default_pipeline_name = kwargs.get('default_pipeline_name', None)
self.name = kwargs.get('name', None)
self.description = kwargs.get('description', None)
self.updated_by = kwargs.get('updated_by', None)
self.swagger_url = kwargs.get('swagger_url', None)
self.last_run_time = kwargs.get('last_run_time', None)
self.last_run_status = kwargs.get('last_run_status', None)
self.tags = kwargs.get('tags', None)
self.entity_status = kwargs.get('entity_status', None)
self.id = kwargs.get('id', None)
self.etag = kwargs.get('etag', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
class PipelineEndpointSummary(msrest.serialization.Model):
"""PipelineEndpointSummary.
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar updated_by:
:vartype updated_by: str
:ivar swagger_url:
:vartype swagger_url: str
:ivar last_run_time:
:vartype last_run_time: ~datetime.datetime
:ivar last_run_status: Possible values include: "NotStarted", "Running", "Failed", "Finished",
"Canceled", "Queued", "CancelRequested".
:vartype last_run_status: str or ~flow.models.PipelineRunStatusCode
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'updated_by': {'key': 'updatedBy', 'type': 'str'},
'swagger_url': {'key': 'swaggerUrl', 'type': 'str'},
'last_run_time': {'key': 'lastRunTime', 'type': 'iso-8601'},
'last_run_status': {'key': 'lastRunStatus', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword updated_by:
:paramtype updated_by: str
:keyword swagger_url:
:paramtype swagger_url: str
:keyword last_run_time:
:paramtype last_run_time: ~datetime.datetime
:keyword last_run_status: Possible values include: "NotStarted", "Running", "Failed",
"Finished", "Canceled", "Queued", "CancelRequested".
:paramtype last_run_status: str or ~flow.models.PipelineRunStatusCode
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PipelineEndpointSummary, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.description = kwargs.get('description', None)
self.updated_by = kwargs.get('updated_by', None)
self.swagger_url = kwargs.get('swagger_url', None)
self.last_run_time = kwargs.get('last_run_time', None)
self.last_run_status = kwargs.get('last_run_status', None)
self.tags = kwargs.get('tags', None)
self.entity_status = kwargs.get('entity_status', None)
self.id = kwargs.get('id', None)
self.etag = kwargs.get('etag', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
class PipelineGraph(msrest.serialization.Model):
"""PipelineGraph.
:ivar graph_module_dtos:
:vartype graph_module_dtos: list[~flow.models.ModuleDto]
:ivar graph_data_sources:
:vartype graph_data_sources: list[~flow.models.DataInfo]
:ivar graphs: This is a dictionary.
:vartype graphs: dict[str, ~flow.models.PipelineGraph]
:ivar graph_drafts: This is a dictionary.
:vartype graph_drafts: dict[str, ~flow.models.PipelineGraph]
:ivar module_node_run_settings:
:vartype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:ivar module_node_ui_input_settings:
:vartype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:ivar sub_pipelines_info:
:vartype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:ivar referenced_node_id:
:vartype referenced_node_id: str
:ivar pipeline_run_setting_parameters:
:vartype pipeline_run_setting_parameters: list[~flow.models.RunSettingParameter]
:ivar pipeline_run_settings:
:vartype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar real_time_endpoint_info:
:vartype real_time_endpoint_info: ~flow.models.RealTimeEndpointInfo
:ivar node_telemetry_meta_infos:
:vartype node_telemetry_meta_infos: list[~flow.models.NodeTelemetryMetaInfo]
:ivar graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:vartype graph_components_mode: str or ~flow.models.GraphComponentsMode
:ivar module_nodes:
:vartype module_nodes: list[~flow.models.GraphModuleNode]
:ivar dataset_nodes:
:vartype dataset_nodes: list[~flow.models.GraphDatasetNode]
:ivar sub_graph_nodes:
:vartype sub_graph_nodes: list[~flow.models.GraphReferenceNode]
:ivar control_reference_nodes:
:vartype control_reference_nodes: list[~flow.models.GraphControlReferenceNode]
:ivar control_nodes:
:vartype control_nodes: list[~flow.models.GraphControlNode]
:ivar edges:
:vartype edges: list[~flow.models.GraphEdge]
:ivar entity_interface:
:vartype entity_interface: ~flow.models.EntityInterface
:ivar graph_layout:
:vartype graph_layout: ~flow.models.GraphLayout
:ivar created_by:
:vartype created_by: ~flow.models.CreatedBy
:ivar last_updated_by:
:vartype last_updated_by: ~flow.models.CreatedBy
:ivar default_compute:
:vartype default_compute: ~flow.models.ComputeSetting
:ivar default_datastore:
:vartype default_datastore: ~flow.models.DatastoreSetting
:ivar default_cloud_priority:
:vartype default_cloud_priority: ~flow.models.CloudPrioritySetting
:ivar extended_properties: This is a dictionary.
:vartype extended_properties: dict[str, str]
:ivar parent_sub_graph_module_ids:
:vartype parent_sub_graph_module_ids: list[str]
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'graph_module_dtos': {'key': 'graphModuleDtos', 'type': '[ModuleDto]'},
'graph_data_sources': {'key': 'graphDataSources', 'type': '[DataInfo]'},
'graphs': {'key': 'graphs', 'type': '{PipelineGraph}'},
'graph_drafts': {'key': 'graphDrafts', 'type': '{PipelineGraph}'},
'module_node_run_settings': {'key': 'moduleNodeRunSettings', 'type': '[GraphModuleNodeRunSetting]'},
'module_node_ui_input_settings': {'key': 'moduleNodeUIInputSettings', 'type': '[GraphModuleNodeUIInputSetting]'},
'sub_pipelines_info': {'key': 'subPipelinesInfo', 'type': 'SubPipelinesInfo'},
'referenced_node_id': {'key': 'referencedNodeId', 'type': 'str'},
'pipeline_run_setting_parameters': {'key': 'pipelineRunSettingParameters', 'type': '[RunSettingParameter]'},
'pipeline_run_settings': {'key': 'pipelineRunSettings', 'type': '[RunSettingParameterAssignment]'},
'real_time_endpoint_info': {'key': 'realTimeEndpointInfo', 'type': 'RealTimeEndpointInfo'},
'node_telemetry_meta_infos': {'key': 'nodeTelemetryMetaInfos', 'type': '[NodeTelemetryMetaInfo]'},
'graph_components_mode': {'key': 'graphComponentsMode', 'type': 'str'},
'module_nodes': {'key': 'moduleNodes', 'type': '[GraphModuleNode]'},
'dataset_nodes': {'key': 'datasetNodes', 'type': '[GraphDatasetNode]'},
'sub_graph_nodes': {'key': 'subGraphNodes', 'type': '[GraphReferenceNode]'},
'control_reference_nodes': {'key': 'controlReferenceNodes', 'type': '[GraphControlReferenceNode]'},
'control_nodes': {'key': 'controlNodes', 'type': '[GraphControlNode]'},
'edges': {'key': 'edges', 'type': '[GraphEdge]'},
'entity_interface': {'key': 'entityInterface', 'type': 'EntityInterface'},
'graph_layout': {'key': 'graphLayout', 'type': 'GraphLayout'},
'created_by': {'key': 'createdBy', 'type': 'CreatedBy'},
'last_updated_by': {'key': 'lastUpdatedBy', 'type': 'CreatedBy'},
'default_compute': {'key': 'defaultCompute', 'type': 'ComputeSetting'},
'default_datastore': {'key': 'defaultDatastore', 'type': 'DatastoreSetting'},
'default_cloud_priority': {'key': 'defaultCloudPriority', 'type': 'CloudPrioritySetting'},
'extended_properties': {'key': 'extendedProperties', 'type': '{str}'},
'parent_sub_graph_module_ids': {'key': 'parentSubGraphModuleIds', 'type': '[str]'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword graph_module_dtos:
:paramtype graph_module_dtos: list[~flow.models.ModuleDto]
:keyword graph_data_sources:
:paramtype graph_data_sources: list[~flow.models.DataInfo]
:keyword graphs: This is a dictionary.
:paramtype graphs: dict[str, ~flow.models.PipelineGraph]
:keyword graph_drafts: This is a dictionary.
:paramtype graph_drafts: dict[str, ~flow.models.PipelineGraph]
:keyword module_node_run_settings:
:paramtype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:keyword module_node_ui_input_settings:
:paramtype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:keyword sub_pipelines_info:
:paramtype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:keyword referenced_node_id:
:paramtype referenced_node_id: str
:keyword pipeline_run_setting_parameters:
:paramtype pipeline_run_setting_parameters: list[~flow.models.RunSettingParameter]
:keyword pipeline_run_settings:
:paramtype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword real_time_endpoint_info:
:paramtype real_time_endpoint_info: ~flow.models.RealTimeEndpointInfo
:keyword node_telemetry_meta_infos:
:paramtype node_telemetry_meta_infos: list[~flow.models.NodeTelemetryMetaInfo]
:keyword graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:paramtype graph_components_mode: str or ~flow.models.GraphComponentsMode
:keyword module_nodes:
:paramtype module_nodes: list[~flow.models.GraphModuleNode]
:keyword dataset_nodes:
:paramtype dataset_nodes: list[~flow.models.GraphDatasetNode]
:keyword sub_graph_nodes:
:paramtype sub_graph_nodes: list[~flow.models.GraphReferenceNode]
:keyword control_reference_nodes:
:paramtype control_reference_nodes: list[~flow.models.GraphControlReferenceNode]
:keyword control_nodes:
:paramtype control_nodes: list[~flow.models.GraphControlNode]
:keyword edges:
:paramtype edges: list[~flow.models.GraphEdge]
:keyword entity_interface:
:paramtype entity_interface: ~flow.models.EntityInterface
:keyword graph_layout:
:paramtype graph_layout: ~flow.models.GraphLayout
:keyword created_by:
:paramtype created_by: ~flow.models.CreatedBy
:keyword last_updated_by:
:paramtype last_updated_by: ~flow.models.CreatedBy
:keyword default_compute:
:paramtype default_compute: ~flow.models.ComputeSetting
:keyword default_datastore:
:paramtype default_datastore: ~flow.models.DatastoreSetting
:keyword default_cloud_priority:
:paramtype default_cloud_priority: ~flow.models.CloudPrioritySetting
:keyword extended_properties: This is a dictionary.
:paramtype extended_properties: dict[str, str]
:keyword parent_sub_graph_module_ids:
:paramtype parent_sub_graph_module_ids: list[str]
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PipelineGraph, self).__init__(**kwargs)
self.graph_module_dtos = kwargs.get('graph_module_dtos', None)
self.graph_data_sources = kwargs.get('graph_data_sources', None)
self.graphs = kwargs.get('graphs', None)
self.graph_drafts = kwargs.get('graph_drafts', None)
self.module_node_run_settings = kwargs.get('module_node_run_settings', None)
self.module_node_ui_input_settings = kwargs.get('module_node_ui_input_settings', None)
self.sub_pipelines_info = kwargs.get('sub_pipelines_info', None)
self.referenced_node_id = kwargs.get('referenced_node_id', None)
self.pipeline_run_setting_parameters = kwargs.get('pipeline_run_setting_parameters', None)
self.pipeline_run_settings = kwargs.get('pipeline_run_settings', None)
self.real_time_endpoint_info = kwargs.get('real_time_endpoint_info', None)
self.node_telemetry_meta_infos = kwargs.get('node_telemetry_meta_infos', None)
self.graph_components_mode = kwargs.get('graph_components_mode', None)
self.module_nodes = kwargs.get('module_nodes', None)
self.dataset_nodes = kwargs.get('dataset_nodes', None)
self.sub_graph_nodes = kwargs.get('sub_graph_nodes', None)
self.control_reference_nodes = kwargs.get('control_reference_nodes', None)
self.control_nodes = kwargs.get('control_nodes', None)
self.edges = kwargs.get('edges', None)
self.entity_interface = kwargs.get('entity_interface', None)
self.graph_layout = kwargs.get('graph_layout', None)
self.created_by = kwargs.get('created_by', None)
self.last_updated_by = kwargs.get('last_updated_by', None)
self.default_compute = kwargs.get('default_compute', None)
self.default_datastore = kwargs.get('default_datastore', None)
self.default_cloud_priority = kwargs.get('default_cloud_priority', None)
self.extended_properties = kwargs.get('extended_properties', None)
self.parent_sub_graph_module_ids = kwargs.get('parent_sub_graph_module_ids', None)
self.id = kwargs.get('id', None)
self.etag = kwargs.get('etag', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
class PipelineInput(msrest.serialization.Model):
"""PipelineInput.
:ivar data:
:vartype data: ~flow.models.InputData
"""
_attribute_map = {
'data': {'key': 'data', 'type': 'InputData'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data:
:paramtype data: ~flow.models.InputData
"""
super(PipelineInput, self).__init__(**kwargs)
self.data = kwargs.get('data', None)
class PipelineJob(msrest.serialization.Model):
"""PipelineJob.
:ivar job_type: Possible values include: "Command", "Sweep", "Labeling", "Pipeline", "Data",
"AutoML", "Spark", "Base".
:vartype job_type: str or ~flow.models.JobType
:ivar pipeline_job_type: The only acceptable values to pass in are None and "AzureML". The
default value is None.
:vartype pipeline_job_type: str
:ivar pipeline:
:vartype pipeline: ~flow.models.Pipeline
:ivar compute_id:
:vartype compute_id: str
:ivar run_id:
:vartype run_id: str
:ivar settings: Anything.
:vartype settings: any
:ivar component_jobs: This is a dictionary.
:vartype component_jobs: dict[str, ~flow.models.MfeInternalV20211001ComponentJob]
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, ~flow.models.JobInput]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, ~flow.models.JobOutput]
:ivar bindings:
:vartype bindings: list[~flow.models.Binding]
:ivar jobs: This is a dictionary.
:vartype jobs: dict[str, any]
:ivar input_bindings: This is a dictionary.
:vartype input_bindings: dict[str, ~flow.models.InputDataBinding]
:ivar output_bindings: This is a dictionary.
:vartype output_bindings: dict[str, ~flow.models.OutputDataBinding]
:ivar source_job_id:
:vartype source_job_id: str
:ivar provisioning_state: Possible values include: "Succeeded", "Failed", "Canceled",
"InProgress".
:vartype provisioning_state: str or ~flow.models.JobProvisioningState
:ivar parent_job_name:
:vartype parent_job_name: str
:ivar display_name:
:vartype display_name: str
:ivar experiment_name:
:vartype experiment_name: str
:ivar status: Possible values include: "NotStarted", "Starting", "Provisioning", "Preparing",
"Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed", "Canceled",
"NotResponding", "Paused", "Unknown", "Scheduled".
:vartype status: str or ~flow.models.JobStatus
:ivar interaction_endpoints: Dictionary of :code:`<JobEndpoint>`.
:vartype interaction_endpoints: dict[str, ~flow.models.JobEndpoint]
:ivar identity:
:vartype identity: ~flow.models.MfeInternalIdentityConfiguration
:ivar compute:
:vartype compute: ~flow.models.ComputeConfiguration
:ivar priority:
:vartype priority: int
:ivar output:
:vartype output: ~flow.models.JobOutputArtifacts
:ivar is_archived:
:vartype is_archived: bool
:ivar schedule:
:vartype schedule: ~flow.models.ScheduleBase
:ivar component_id:
:vartype component_id: str
:ivar notification_setting:
:vartype notification_setting: ~flow.models.NotificationSetting
:ivar secrets_configuration: Dictionary of :code:`<MfeInternalSecretConfiguration>`.
:vartype secrets_configuration: dict[str, ~flow.models.MfeInternalSecretConfiguration]
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
"""
_attribute_map = {
'job_type': {'key': 'jobType', 'type': 'str'},
'pipeline_job_type': {'key': 'pipelineJobType', 'type': 'str'},
'pipeline': {'key': 'pipeline', 'type': 'Pipeline'},
'compute_id': {'key': 'computeId', 'type': 'str'},
'run_id': {'key': 'runId', 'type': 'str'},
'settings': {'key': 'settings', 'type': 'object'},
'component_jobs': {'key': 'componentJobs', 'type': '{MfeInternalV20211001ComponentJob}'},
'inputs': {'key': 'inputs', 'type': '{JobInput}'},
'outputs': {'key': 'outputs', 'type': '{JobOutput}'},
'bindings': {'key': 'bindings', 'type': '[Binding]'},
'jobs': {'key': 'jobs', 'type': '{object}'},
'input_bindings': {'key': 'inputBindings', 'type': '{InputDataBinding}'},
'output_bindings': {'key': 'outputBindings', 'type': '{OutputDataBinding}'},
'source_job_id': {'key': 'sourceJobId', 'type': 'str'},
'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
'parent_job_name': {'key': 'parentJobName', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'interaction_endpoints': {'key': 'interactionEndpoints', 'type': '{JobEndpoint}'},
'identity': {'key': 'identity', 'type': 'MfeInternalIdentityConfiguration'},
'compute': {'key': 'compute', 'type': 'ComputeConfiguration'},
'priority': {'key': 'priority', 'type': 'int'},
'output': {'key': 'output', 'type': 'JobOutputArtifacts'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
'schedule': {'key': 'schedule', 'type': 'ScheduleBase'},
'component_id': {'key': 'componentId', 'type': 'str'},
'notification_setting': {'key': 'notificationSetting', 'type': 'NotificationSetting'},
'secrets_configuration': {'key': 'secretsConfiguration', 'type': '{MfeInternalSecretConfiguration}'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword job_type: Possible values include: "Command", "Sweep", "Labeling", "Pipeline", "Data",
"AutoML", "Spark", "Base".
:paramtype job_type: str or ~flow.models.JobType
:keyword pipeline_job_type: The only acceptable values to pass in are None and "AzureML". The
default value is None.
:paramtype pipeline_job_type: str
:keyword pipeline:
:paramtype pipeline: ~flow.models.Pipeline
:keyword compute_id:
:paramtype compute_id: str
:keyword run_id:
:paramtype run_id: str
:keyword settings: Anything.
:paramtype settings: any
:keyword component_jobs: This is a dictionary.
:paramtype component_jobs: dict[str, ~flow.models.MfeInternalV20211001ComponentJob]
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, ~flow.models.JobInput]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, ~flow.models.JobOutput]
:keyword bindings:
:paramtype bindings: list[~flow.models.Binding]
:keyword jobs: This is a dictionary.
:paramtype jobs: dict[str, any]
:keyword input_bindings: This is a dictionary.
:paramtype input_bindings: dict[str, ~flow.models.InputDataBinding]
:keyword output_bindings: This is a dictionary.
:paramtype output_bindings: dict[str, ~flow.models.OutputDataBinding]
:keyword source_job_id:
:paramtype source_job_id: str
:keyword provisioning_state: Possible values include: "Succeeded", "Failed", "Canceled",
"InProgress".
:paramtype provisioning_state: str or ~flow.models.JobProvisioningState
:keyword parent_job_name:
:paramtype parent_job_name: str
:keyword display_name:
:paramtype display_name: str
:keyword experiment_name:
:paramtype experiment_name: str
:keyword status: Possible values include: "NotStarted", "Starting", "Provisioning",
"Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed",
"Canceled", "NotResponding", "Paused", "Unknown", "Scheduled".
:paramtype status: str or ~flow.models.JobStatus
:keyword interaction_endpoints: Dictionary of :code:`<JobEndpoint>`.
:paramtype interaction_endpoints: dict[str, ~flow.models.JobEndpoint]
:keyword identity:
:paramtype identity: ~flow.models.MfeInternalIdentityConfiguration
:keyword compute:
:paramtype compute: ~flow.models.ComputeConfiguration
:keyword priority:
:paramtype priority: int
:keyword output:
:paramtype output: ~flow.models.JobOutputArtifacts
:keyword is_archived:
:paramtype is_archived: bool
:keyword schedule:
:paramtype schedule: ~flow.models.ScheduleBase
:keyword component_id:
:paramtype component_id: str
:keyword notification_setting:
:paramtype notification_setting: ~flow.models.NotificationSetting
:keyword secrets_configuration: Dictionary of :code:`<MfeInternalSecretConfiguration>`.
:paramtype secrets_configuration: dict[str, ~flow.models.MfeInternalSecretConfiguration]
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
"""
super(PipelineJob, self).__init__(**kwargs)
self.job_type = kwargs.get('job_type', None)
self.pipeline_job_type = kwargs.get('pipeline_job_type', None)
self.pipeline = kwargs.get('pipeline', None)
self.compute_id = kwargs.get('compute_id', None)
self.run_id = kwargs.get('run_id', None)
self.settings = kwargs.get('settings', None)
self.component_jobs = kwargs.get('component_jobs', None)
self.inputs = kwargs.get('inputs', None)
self.outputs = kwargs.get('outputs', None)
self.bindings = kwargs.get('bindings', None)
self.jobs = kwargs.get('jobs', None)
self.input_bindings = kwargs.get('input_bindings', None)
self.output_bindings = kwargs.get('output_bindings', None)
self.source_job_id = kwargs.get('source_job_id', None)
self.provisioning_state = kwargs.get('provisioning_state', None)
self.parent_job_name = kwargs.get('parent_job_name', None)
self.display_name = kwargs.get('display_name', None)
self.experiment_name = kwargs.get('experiment_name', None)
self.status = kwargs.get('status', None)
self.interaction_endpoints = kwargs.get('interaction_endpoints', None)
self.identity = kwargs.get('identity', None)
self.compute = kwargs.get('compute', None)
self.priority = kwargs.get('priority', None)
self.output = kwargs.get('output', None)
self.is_archived = kwargs.get('is_archived', None)
self.schedule = kwargs.get('schedule', None)
self.component_id = kwargs.get('component_id', None)
self.notification_setting = kwargs.get('notification_setting', None)
self.secrets_configuration = kwargs.get('secrets_configuration', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
class PipelineJobRuntimeBasicSettings(msrest.serialization.Model):
"""PipelineJobRuntimeBasicSettings.
:ivar pipeline_run_settings:
:vartype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar experiment_name:
:vartype experiment_name: str
:ivar pipeline_job_name:
:vartype pipeline_job_name: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar display_name:
:vartype display_name: str
:ivar description:
:vartype description: str
:ivar trigger_time_string:
:vartype trigger_time_string: str
:ivar pipeline_parameters: This is a dictionary.
:vartype pipeline_parameters: dict[str, str]
:ivar data_path_assignments: This is a dictionary.
:vartype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:ivar data_set_definition_value_assignments: This is a dictionary.
:vartype data_set_definition_value_assignments: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar asset_output_settings_assignments: This is a dictionary.
:vartype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
"""
_attribute_map = {
'pipeline_run_settings': {'key': 'pipelineRunSettings', 'type': '[RunSettingParameterAssignment]'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'pipeline_job_name': {'key': 'pipelineJobName', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'display_name': {'key': 'displayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'trigger_time_string': {'key': 'triggerTimeString', 'type': 'str'},
'pipeline_parameters': {'key': 'pipelineParameters', 'type': '{str}'},
'data_path_assignments': {'key': 'dataPathAssignments', 'type': '{LegacyDataPath}'},
'data_set_definition_value_assignments': {'key': 'dataSetDefinitionValueAssignments', 'type': '{DataSetDefinitionValue}'},
'asset_output_settings_assignments': {'key': 'assetOutputSettingsAssignments', 'type': '{AssetOutputSettings}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword pipeline_run_settings:
:paramtype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword experiment_name:
:paramtype experiment_name: str
:keyword pipeline_job_name:
:paramtype pipeline_job_name: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword display_name:
:paramtype display_name: str
:keyword description:
:paramtype description: str
:keyword trigger_time_string:
:paramtype trigger_time_string: str
:keyword pipeline_parameters: This is a dictionary.
:paramtype pipeline_parameters: dict[str, str]
:keyword data_path_assignments: This is a dictionary.
:paramtype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:keyword data_set_definition_value_assignments: This is a dictionary.
:paramtype data_set_definition_value_assignments: dict[str,
~flow.models.DataSetDefinitionValue]
:keyword asset_output_settings_assignments: This is a dictionary.
:paramtype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
"""
super(PipelineJobRuntimeBasicSettings, self).__init__(**kwargs)
self.pipeline_run_settings = kwargs.get('pipeline_run_settings', None)
self.experiment_name = kwargs.get('experiment_name', None)
self.pipeline_job_name = kwargs.get('pipeline_job_name', None)
self.tags = kwargs.get('tags', None)
self.display_name = kwargs.get('display_name', None)
self.description = kwargs.get('description', None)
self.trigger_time_string = kwargs.get('trigger_time_string', None)
self.pipeline_parameters = kwargs.get('pipeline_parameters', None)
self.data_path_assignments = kwargs.get('data_path_assignments', None)
self.data_set_definition_value_assignments = kwargs.get('data_set_definition_value_assignments', None)
self.asset_output_settings_assignments = kwargs.get('asset_output_settings_assignments', None)
class PipelineJobScheduleDto(msrest.serialization.Model):
"""PipelineJobScheduleDto.
:ivar system_data:
:vartype system_data: ~flow.models.SystemData
:ivar name:
:vartype name: str
:ivar pipeline_job_name:
:vartype pipeline_job_name: str
:ivar pipeline_job_runtime_settings:
:vartype pipeline_job_runtime_settings: ~flow.models.PipelineJobRuntimeBasicSettings
:ivar display_name:
:vartype display_name: str
:ivar trigger_type: Possible values include: "Recurrence", "Cron".
:vartype trigger_type: str or ~flow.models.TriggerType
:ivar recurrence:
:vartype recurrence: ~flow.models.Recurrence
:ivar cron:
:vartype cron: ~flow.models.Cron
:ivar status: Possible values include: "Enabled", "Disabled".
:vartype status: str or ~flow.models.ScheduleStatus
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
"""
_attribute_map = {
'system_data': {'key': 'systemData', 'type': 'SystemData'},
'name': {'key': 'name', 'type': 'str'},
'pipeline_job_name': {'key': 'pipelineJobName', 'type': 'str'},
'pipeline_job_runtime_settings': {'key': 'pipelineJobRuntimeSettings', 'type': 'PipelineJobRuntimeBasicSettings'},
'display_name': {'key': 'displayName', 'type': 'str'},
'trigger_type': {'key': 'triggerType', 'type': 'str'},
'recurrence': {'key': 'recurrence', 'type': 'Recurrence'},
'cron': {'key': 'cron', 'type': 'Cron'},
'status': {'key': 'status', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword system_data:
:paramtype system_data: ~flow.models.SystemData
:keyword name:
:paramtype name: str
:keyword pipeline_job_name:
:paramtype pipeline_job_name: str
:keyword pipeline_job_runtime_settings:
:paramtype pipeline_job_runtime_settings: ~flow.models.PipelineJobRuntimeBasicSettings
:keyword display_name:
:paramtype display_name: str
:keyword trigger_type: Possible values include: "Recurrence", "Cron".
:paramtype trigger_type: str or ~flow.models.TriggerType
:keyword recurrence:
:paramtype recurrence: ~flow.models.Recurrence
:keyword cron:
:paramtype cron: ~flow.models.Cron
:keyword status: Possible values include: "Enabled", "Disabled".
:paramtype status: str or ~flow.models.ScheduleStatus
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
"""
super(PipelineJobScheduleDto, self).__init__(**kwargs)
self.system_data = kwargs.get('system_data', None)
self.name = kwargs.get('name', None)
self.pipeline_job_name = kwargs.get('pipeline_job_name', None)
self.pipeline_job_runtime_settings = kwargs.get('pipeline_job_runtime_settings', None)
self.display_name = kwargs.get('display_name', None)
self.trigger_type = kwargs.get('trigger_type', None)
self.recurrence = kwargs.get('recurrence', None)
self.cron = kwargs.get('cron', None)
self.status = kwargs.get('status', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
class PipelineOutput(msrest.serialization.Model):
"""PipelineOutput.
:ivar data:
:vartype data: ~flow.models.MfeInternalOutputData
"""
_attribute_map = {
'data': {'key': 'data', 'type': 'MfeInternalOutputData'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data:
:paramtype data: ~flow.models.MfeInternalOutputData
"""
super(PipelineOutput, self).__init__(**kwargs)
self.data = kwargs.get('data', None)
class PipelineRun(msrest.serialization.Model):
"""PipelineRun.
:ivar pipeline_id:
:vartype pipeline_id: str
:ivar run_source:
:vartype run_source: str
:ivar run_type: Possible values include: "HTTP", "SDK", "Schedule", "Portal".
:vartype run_type: str or ~flow.models.RunType
:ivar parameters: This is a dictionary.
:vartype parameters: dict[str, str]
:ivar data_path_assignments: This is a dictionary.
:vartype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:ivar data_set_definition_value_assignment: This is a dictionary.
:vartype data_set_definition_value_assignment: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar asset_output_settings_assignments: This is a dictionary.
:vartype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:ivar total_steps:
:vartype total_steps: int
:ivar logs: This is a dictionary.
:vartype logs: dict[str, str]
:ivar user_alias:
:vartype user_alias: str
:ivar enforce_rerun:
:vartype enforce_rerun: bool
:ivar continue_run_on_failed_optional_input:
:vartype continue_run_on_failed_optional_input: bool
:ivar default_compute:
:vartype default_compute: ~flow.models.ComputeSetting
:ivar default_datastore:
:vartype default_datastore: ~flow.models.DatastoreSetting
:ivar default_cloud_priority:
:vartype default_cloud_priority: ~flow.models.CloudPrioritySetting
:ivar pipeline_timeout_seconds:
:vartype pipeline_timeout_seconds: int
:ivar continue_run_on_step_failure:
:vartype continue_run_on_step_failure: bool
:ivar identity_config:
:vartype identity_config: ~flow.models.IdentitySetting
:ivar description:
:vartype description: str
:ivar display_name:
:vartype display_name: str
:ivar run_number:
:vartype run_number: int
:ivar status_code: Possible values include: "NotStarted", "InDraft", "Preparing", "Running",
"Failed", "Finished", "Canceled", "Throttled", "Unknown".
:vartype status_code: str or ~flow.models.PipelineStatusCode
:ivar run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:vartype run_status: str or ~flow.models.RunStatus
:ivar status_detail:
:vartype status_detail: str
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar graph_id:
:vartype graph_id: str
:ivar experiment_id:
:vartype experiment_id: str
:ivar experiment_name:
:vartype experiment_name: str
:ivar is_experiment_archived:
:vartype is_experiment_archived: bool
:ivar submitted_by:
:vartype submitted_by: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar step_tags: This is a dictionary.
:vartype step_tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar aether_start_time:
:vartype aether_start_time: ~datetime.datetime
:ivar aether_end_time:
:vartype aether_end_time: ~datetime.datetime
:ivar run_history_start_time:
:vartype run_history_start_time: ~datetime.datetime
:ivar run_history_end_time:
:vartype run_history_end_time: ~datetime.datetime
:ivar unique_child_run_compute_targets:
:vartype unique_child_run_compute_targets: list[str]
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_validation = {
'unique_child_run_compute_targets': {'unique': True},
}
_attribute_map = {
'pipeline_id': {'key': 'pipelineId', 'type': 'str'},
'run_source': {'key': 'runSource', 'type': 'str'},
'run_type': {'key': 'runType', 'type': 'str'},
'parameters': {'key': 'parameters', 'type': '{str}'},
'data_path_assignments': {'key': 'dataPathAssignments', 'type': '{LegacyDataPath}'},
'data_set_definition_value_assignment': {'key': 'dataSetDefinitionValueAssignment', 'type': '{DataSetDefinitionValue}'},
'asset_output_settings_assignments': {'key': 'assetOutputSettingsAssignments', 'type': '{AssetOutputSettings}'},
'total_steps': {'key': 'totalSteps', 'type': 'int'},
'logs': {'key': 'logs', 'type': '{str}'},
'user_alias': {'key': 'userAlias', 'type': 'str'},
'enforce_rerun': {'key': 'enforceRerun', 'type': 'bool'},
'continue_run_on_failed_optional_input': {'key': 'continueRunOnFailedOptionalInput', 'type': 'bool'},
'default_compute': {'key': 'defaultCompute', 'type': 'ComputeSetting'},
'default_datastore': {'key': 'defaultDatastore', 'type': 'DatastoreSetting'},
'default_cloud_priority': {'key': 'defaultCloudPriority', 'type': 'CloudPrioritySetting'},
'pipeline_timeout_seconds': {'key': 'pipelineTimeoutSeconds', 'type': 'int'},
'continue_run_on_step_failure': {'key': 'continueRunOnStepFailure', 'type': 'bool'},
'identity_config': {'key': 'identityConfig', 'type': 'IdentitySetting'},
'description': {'key': 'description', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'run_number': {'key': 'runNumber', 'type': 'int'},
'status_code': {'key': 'statusCode', 'type': 'str'},
'run_status': {'key': 'runStatus', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'graph_id': {'key': 'graphId', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'is_experiment_archived': {'key': 'isExperimentArchived', 'type': 'bool'},
'submitted_by': {'key': 'submittedBy', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'step_tags': {'key': 'stepTags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'aether_start_time': {'key': 'aetherStartTime', 'type': 'iso-8601'},
'aether_end_time': {'key': 'aetherEndTime', 'type': 'iso-8601'},
'run_history_start_time': {'key': 'runHistoryStartTime', 'type': 'iso-8601'},
'run_history_end_time': {'key': 'runHistoryEndTime', 'type': 'iso-8601'},
'unique_child_run_compute_targets': {'key': 'uniqueChildRunComputeTargets', 'type': '[str]'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword pipeline_id:
:paramtype pipeline_id: str
:keyword run_source:
:paramtype run_source: str
:keyword run_type: Possible values include: "HTTP", "SDK", "Schedule", "Portal".
:paramtype run_type: str or ~flow.models.RunType
:keyword parameters: This is a dictionary.
:paramtype parameters: dict[str, str]
:keyword data_path_assignments: This is a dictionary.
:paramtype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:keyword data_set_definition_value_assignment: This is a dictionary.
:paramtype data_set_definition_value_assignment: dict[str, ~flow.models.DataSetDefinitionValue]
:keyword asset_output_settings_assignments: This is a dictionary.
:paramtype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:keyword total_steps:
:paramtype total_steps: int
:keyword logs: This is a dictionary.
:paramtype logs: dict[str, str]
:keyword user_alias:
:paramtype user_alias: str
:keyword enforce_rerun:
:paramtype enforce_rerun: bool
:keyword continue_run_on_failed_optional_input:
:paramtype continue_run_on_failed_optional_input: bool
:keyword default_compute:
:paramtype default_compute: ~flow.models.ComputeSetting
:keyword default_datastore:
:paramtype default_datastore: ~flow.models.DatastoreSetting
:keyword default_cloud_priority:
:paramtype default_cloud_priority: ~flow.models.CloudPrioritySetting
:keyword pipeline_timeout_seconds:
:paramtype pipeline_timeout_seconds: int
:keyword continue_run_on_step_failure:
:paramtype continue_run_on_step_failure: bool
:keyword identity_config:
:paramtype identity_config: ~flow.models.IdentitySetting
:keyword description:
:paramtype description: str
:keyword display_name:
:paramtype display_name: str
:keyword run_number:
:paramtype run_number: int
:keyword status_code: Possible values include: "NotStarted", "InDraft", "Preparing", "Running",
"Failed", "Finished", "Canceled", "Throttled", "Unknown".
:paramtype status_code: str or ~flow.models.PipelineStatusCode
:keyword run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:paramtype run_status: str or ~flow.models.RunStatus
:keyword status_detail:
:paramtype status_detail: str
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
:keyword graph_id:
:paramtype graph_id: str
:keyword experiment_id:
:paramtype experiment_id: str
:keyword experiment_name:
:paramtype experiment_name: str
:keyword is_experiment_archived:
:paramtype is_experiment_archived: bool
:keyword submitted_by:
:paramtype submitted_by: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword step_tags: This is a dictionary.
:paramtype step_tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword aether_start_time:
:paramtype aether_start_time: ~datetime.datetime
:keyword aether_end_time:
:paramtype aether_end_time: ~datetime.datetime
:keyword run_history_start_time:
:paramtype run_history_start_time: ~datetime.datetime
:keyword run_history_end_time:
:paramtype run_history_end_time: ~datetime.datetime
:keyword unique_child_run_compute_targets:
:paramtype unique_child_run_compute_targets: list[str]
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PipelineRun, self).__init__(**kwargs)
self.pipeline_id = kwargs.get('pipeline_id', None)
self.run_source = kwargs.get('run_source', None)
self.run_type = kwargs.get('run_type', None)
self.parameters = kwargs.get('parameters', None)
self.data_path_assignments = kwargs.get('data_path_assignments', None)
self.data_set_definition_value_assignment = kwargs.get('data_set_definition_value_assignment', None)
self.asset_output_settings_assignments = kwargs.get('asset_output_settings_assignments', None)
self.total_steps = kwargs.get('total_steps', None)
self.logs = kwargs.get('logs', None)
self.user_alias = kwargs.get('user_alias', None)
self.enforce_rerun = kwargs.get('enforce_rerun', None)
self.continue_run_on_failed_optional_input = kwargs.get('continue_run_on_failed_optional_input', None)
self.default_compute = kwargs.get('default_compute', None)
self.default_datastore = kwargs.get('default_datastore', None)
self.default_cloud_priority = kwargs.get('default_cloud_priority', None)
self.pipeline_timeout_seconds = kwargs.get('pipeline_timeout_seconds', None)
self.continue_run_on_step_failure = kwargs.get('continue_run_on_step_failure', None)
self.identity_config = kwargs.get('identity_config', None)
self.description = kwargs.get('description', None)
self.display_name = kwargs.get('display_name', None)
self.run_number = kwargs.get('run_number', None)
self.status_code = kwargs.get('status_code', None)
self.run_status = kwargs.get('run_status', None)
self.status_detail = kwargs.get('status_detail', None)
self.start_time = kwargs.get('start_time', None)
self.end_time = kwargs.get('end_time', None)
self.graph_id = kwargs.get('graph_id', None)
self.experiment_id = kwargs.get('experiment_id', None)
self.experiment_name = kwargs.get('experiment_name', None)
self.is_experiment_archived = kwargs.get('is_experiment_archived', None)
self.submitted_by = kwargs.get('submitted_by', None)
self.tags = kwargs.get('tags', None)
self.step_tags = kwargs.get('step_tags', None)
self.properties = kwargs.get('properties', None)
self.aether_start_time = kwargs.get('aether_start_time', None)
self.aether_end_time = kwargs.get('aether_end_time', None)
self.run_history_start_time = kwargs.get('run_history_start_time', None)
self.run_history_end_time = kwargs.get('run_history_end_time', None)
self.unique_child_run_compute_targets = kwargs.get('unique_child_run_compute_targets', None)
self.entity_status = kwargs.get('entity_status', None)
self.id = kwargs.get('id', None)
self.etag = kwargs.get('etag', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
class PipelineRunGraphDetail(msrest.serialization.Model):
"""PipelineRunGraphDetail.
:ivar graph:
:vartype graph: ~flow.models.PipelineGraph
:ivar graph_nodes_status: This is a dictionary.
:vartype graph_nodes_status: dict[str, ~flow.models.GraphNodeStatusInfo]
"""
_attribute_map = {
'graph': {'key': 'graph', 'type': 'PipelineGraph'},
'graph_nodes_status': {'key': 'graphNodesStatus', 'type': '{GraphNodeStatusInfo}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword graph:
:paramtype graph: ~flow.models.PipelineGraph
:keyword graph_nodes_status: This is a dictionary.
:paramtype graph_nodes_status: dict[str, ~flow.models.GraphNodeStatusInfo]
"""
super(PipelineRunGraphDetail, self).__init__(**kwargs)
self.graph = kwargs.get('graph', None)
self.graph_nodes_status = kwargs.get('graph_nodes_status', None)
class PipelineRunGraphStatus(msrest.serialization.Model):
"""PipelineRunGraphStatus.
:ivar status:
:vartype status: ~flow.models.PipelineStatus
:ivar graph_nodes_status: This is a dictionary.
:vartype graph_nodes_status: dict[str, ~flow.models.GraphNodeStatusInfo]
:ivar experiment_id:
:vartype experiment_id: str
:ivar is_experiment_archived:
:vartype is_experiment_archived: bool
"""
_attribute_map = {
'status': {'key': 'status', 'type': 'PipelineStatus'},
'graph_nodes_status': {'key': 'graphNodesStatus', 'type': '{GraphNodeStatusInfo}'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'is_experiment_archived': {'key': 'isExperimentArchived', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword status:
:paramtype status: ~flow.models.PipelineStatus
:keyword graph_nodes_status: This is a dictionary.
:paramtype graph_nodes_status: dict[str, ~flow.models.GraphNodeStatusInfo]
:keyword experiment_id:
:paramtype experiment_id: str
:keyword is_experiment_archived:
:paramtype is_experiment_archived: bool
"""
super(PipelineRunGraphStatus, self).__init__(**kwargs)
self.status = kwargs.get('status', None)
self.graph_nodes_status = kwargs.get('graph_nodes_status', None)
self.experiment_id = kwargs.get('experiment_id', None)
self.is_experiment_archived = kwargs.get('is_experiment_archived', None)
class PipelineRunProfile(msrest.serialization.Model):
"""PipelineRunProfile.
:ivar run_id:
:vartype run_id: str
:ivar node_id:
:vartype node_id: str
:ivar run_url:
:vartype run_url: str
:ivar experiment_name:
:vartype experiment_name: str
:ivar experiment_id:
:vartype experiment_id: str
:ivar description:
:vartype description: str
:ivar status:
:vartype status: ~flow.models.PipelineRunStatus
:ivar create_time:
:vartype create_time: long
:ivar start_time:
:vartype start_time: long
:ivar end_time:
:vartype end_time: long
:ivar profiling_time:
:vartype profiling_time: long
:ivar step_runs_profile:
:vartype step_runs_profile: list[~flow.models.StepRunProfile]
:ivar sub_pipeline_run_profile:
:vartype sub_pipeline_run_profile: list[~flow.models.PipelineRunProfile]
"""
_attribute_map = {
'run_id': {'key': 'runId', 'type': 'str'},
'node_id': {'key': 'nodeId', 'type': 'str'},
'run_url': {'key': 'runUrl', 'type': 'str'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'status': {'key': 'status', 'type': 'PipelineRunStatus'},
'create_time': {'key': 'createTime', 'type': 'long'},
'start_time': {'key': 'startTime', 'type': 'long'},
'end_time': {'key': 'endTime', 'type': 'long'},
'profiling_time': {'key': 'profilingTime', 'type': 'long'},
'step_runs_profile': {'key': 'stepRunsProfile', 'type': '[StepRunProfile]'},
'sub_pipeline_run_profile': {'key': 'subPipelineRunProfile', 'type': '[PipelineRunProfile]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword run_id:
:paramtype run_id: str
:keyword node_id:
:paramtype node_id: str
:keyword run_url:
:paramtype run_url: str
:keyword experiment_name:
:paramtype experiment_name: str
:keyword experiment_id:
:paramtype experiment_id: str
:keyword description:
:paramtype description: str
:keyword status:
:paramtype status: ~flow.models.PipelineRunStatus
:keyword create_time:
:paramtype create_time: long
:keyword start_time:
:paramtype start_time: long
:keyword end_time:
:paramtype end_time: long
:keyword profiling_time:
:paramtype profiling_time: long
:keyword step_runs_profile:
:paramtype step_runs_profile: list[~flow.models.StepRunProfile]
:keyword sub_pipeline_run_profile:
:paramtype sub_pipeline_run_profile: list[~flow.models.PipelineRunProfile]
"""
super(PipelineRunProfile, self).__init__(**kwargs)
self.run_id = kwargs.get('run_id', None)
self.node_id = kwargs.get('node_id', None)
self.run_url = kwargs.get('run_url', None)
self.experiment_name = kwargs.get('experiment_name', None)
self.experiment_id = kwargs.get('experiment_id', None)
self.description = kwargs.get('description', None)
self.status = kwargs.get('status', None)
self.create_time = kwargs.get('create_time', None)
self.start_time = kwargs.get('start_time', None)
self.end_time = kwargs.get('end_time', None)
self.profiling_time = kwargs.get('profiling_time', None)
self.step_runs_profile = kwargs.get('step_runs_profile', None)
self.sub_pipeline_run_profile = kwargs.get('sub_pipeline_run_profile', None)
class PipelineRunStatus(msrest.serialization.Model):
"""PipelineRunStatus.
:ivar status_code: Possible values include: "NotStarted", "Running", "Failed", "Finished",
"Canceled", "Queued", "CancelRequested".
:vartype status_code: str or ~flow.models.PipelineRunStatusCode
:ivar status_detail:
:vartype status_detail: str
:ivar creation_time:
:vartype creation_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
"""
_attribute_map = {
'status_code': {'key': 'statusCode', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
'creation_time': {'key': 'creationTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword status_code: Possible values include: "NotStarted", "Running", "Failed", "Finished",
"Canceled", "Queued", "CancelRequested".
:paramtype status_code: str or ~flow.models.PipelineRunStatusCode
:keyword status_detail:
:paramtype status_detail: str
:keyword creation_time:
:paramtype creation_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
"""
super(PipelineRunStatus, self).__init__(**kwargs)
self.status_code = kwargs.get('status_code', None)
self.status_detail = kwargs.get('status_detail', None)
self.creation_time = kwargs.get('creation_time', None)
self.end_time = kwargs.get('end_time', None)
class PipelineRunStepDetails(msrest.serialization.Model):
"""PipelineRunStepDetails.
:ivar run_id:
:vartype run_id: str
:ivar target:
:vartype target: str
:ivar status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:vartype status: str or ~flow.models.RunStatus
:ivar status_detail:
:vartype status_detail: str
:ivar parent_run_id:
:vartype parent_run_id: str
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar is_reused:
:vartype is_reused: bool
:ivar logs: This is a dictionary.
:vartype logs: dict[str, str]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, str]
:ivar snapshot_info:
:vartype snapshot_info: ~flow.models.SnapshotInfo
:ivar input_datasets:
:vartype input_datasets: list[~flow.models.DatasetLineage]
:ivar output_datasets:
:vartype output_datasets: list[~flow.models.OutputDatasetLineage]
"""
_validation = {
'input_datasets': {'unique': True},
'output_datasets': {'unique': True},
}
_attribute_map = {
'run_id': {'key': 'runId', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
'parent_run_id': {'key': 'parentRunId', 'type': 'str'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'is_reused': {'key': 'isReused', 'type': 'bool'},
'logs': {'key': 'logs', 'type': '{str}'},
'outputs': {'key': 'outputs', 'type': '{str}'},
'snapshot_info': {'key': 'snapshotInfo', 'type': 'SnapshotInfo'},
'input_datasets': {'key': 'inputDatasets', 'type': '[DatasetLineage]'},
'output_datasets': {'key': 'outputDatasets', 'type': '[OutputDatasetLineage]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword run_id:
:paramtype run_id: str
:keyword target:
:paramtype target: str
:keyword status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:paramtype status: str or ~flow.models.RunStatus
:keyword status_detail:
:paramtype status_detail: str
:keyword parent_run_id:
:paramtype parent_run_id: str
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
:keyword is_reused:
:paramtype is_reused: bool
:keyword logs: This is a dictionary.
:paramtype logs: dict[str, str]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, str]
:keyword snapshot_info:
:paramtype snapshot_info: ~flow.models.SnapshotInfo
:keyword input_datasets:
:paramtype input_datasets: list[~flow.models.DatasetLineage]
:keyword output_datasets:
:paramtype output_datasets: list[~flow.models.OutputDatasetLineage]
"""
super(PipelineRunStepDetails, self).__init__(**kwargs)
self.run_id = kwargs.get('run_id', None)
self.target = kwargs.get('target', None)
self.status = kwargs.get('status', None)
self.status_detail = kwargs.get('status_detail', None)
self.parent_run_id = kwargs.get('parent_run_id', None)
self.start_time = kwargs.get('start_time', None)
self.end_time = kwargs.get('end_time', None)
self.is_reused = kwargs.get('is_reused', None)
self.logs = kwargs.get('logs', None)
self.outputs = kwargs.get('outputs', None)
self.snapshot_info = kwargs.get('snapshot_info', None)
self.input_datasets = kwargs.get('input_datasets', None)
self.output_datasets = kwargs.get('output_datasets', None)
class PipelineRunSummary(msrest.serialization.Model):
"""PipelineRunSummary.
:ivar description:
:vartype description: str
:ivar display_name:
:vartype display_name: str
:ivar run_number:
:vartype run_number: int
:ivar status_code: Possible values include: "NotStarted", "InDraft", "Preparing", "Running",
"Failed", "Finished", "Canceled", "Throttled", "Unknown".
:vartype status_code: str or ~flow.models.PipelineStatusCode
:ivar run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:vartype run_status: str or ~flow.models.RunStatus
:ivar status_detail:
:vartype status_detail: str
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar graph_id:
:vartype graph_id: str
:ivar experiment_id:
:vartype experiment_id: str
:ivar experiment_name:
:vartype experiment_name: str
:ivar is_experiment_archived:
:vartype is_experiment_archived: bool
:ivar submitted_by:
:vartype submitted_by: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar step_tags: This is a dictionary.
:vartype step_tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar aether_start_time:
:vartype aether_start_time: ~datetime.datetime
:ivar aether_end_time:
:vartype aether_end_time: ~datetime.datetime
:ivar run_history_start_time:
:vartype run_history_start_time: ~datetime.datetime
:ivar run_history_end_time:
:vartype run_history_end_time: ~datetime.datetime
:ivar unique_child_run_compute_targets:
:vartype unique_child_run_compute_targets: list[str]
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_validation = {
'unique_child_run_compute_targets': {'unique': True},
}
_attribute_map = {
'description': {'key': 'description', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'run_number': {'key': 'runNumber', 'type': 'int'},
'status_code': {'key': 'statusCode', 'type': 'str'},
'run_status': {'key': 'runStatus', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'graph_id': {'key': 'graphId', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'is_experiment_archived': {'key': 'isExperimentArchived', 'type': 'bool'},
'submitted_by': {'key': 'submittedBy', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'step_tags': {'key': 'stepTags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'aether_start_time': {'key': 'aetherStartTime', 'type': 'iso-8601'},
'aether_end_time': {'key': 'aetherEndTime', 'type': 'iso-8601'},
'run_history_start_time': {'key': 'runHistoryStartTime', 'type': 'iso-8601'},
'run_history_end_time': {'key': 'runHistoryEndTime', 'type': 'iso-8601'},
'unique_child_run_compute_targets': {'key': 'uniqueChildRunComputeTargets', 'type': '[str]'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword description:
:paramtype description: str
:keyword display_name:
:paramtype display_name: str
:keyword run_number:
:paramtype run_number: int
:keyword status_code: Possible values include: "NotStarted", "InDraft", "Preparing", "Running",
"Failed", "Finished", "Canceled", "Throttled", "Unknown".
:paramtype status_code: str or ~flow.models.PipelineStatusCode
:keyword run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:paramtype run_status: str or ~flow.models.RunStatus
:keyword status_detail:
:paramtype status_detail: str
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
:keyword graph_id:
:paramtype graph_id: str
:keyword experiment_id:
:paramtype experiment_id: str
:keyword experiment_name:
:paramtype experiment_name: str
:keyword is_experiment_archived:
:paramtype is_experiment_archived: bool
:keyword submitted_by:
:paramtype submitted_by: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword step_tags: This is a dictionary.
:paramtype step_tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword aether_start_time:
:paramtype aether_start_time: ~datetime.datetime
:keyword aether_end_time:
:paramtype aether_end_time: ~datetime.datetime
:keyword run_history_start_time:
:paramtype run_history_start_time: ~datetime.datetime
:keyword run_history_end_time:
:paramtype run_history_end_time: ~datetime.datetime
:keyword unique_child_run_compute_targets:
:paramtype unique_child_run_compute_targets: list[str]
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PipelineRunSummary, self).__init__(**kwargs)
self.description = kwargs.get('description', None)
self.display_name = kwargs.get('display_name', None)
self.run_number = kwargs.get('run_number', None)
self.status_code = kwargs.get('status_code', None)
self.run_status = kwargs.get('run_status', None)
self.status_detail = kwargs.get('status_detail', None)
self.start_time = kwargs.get('start_time', None)
self.end_time = kwargs.get('end_time', None)
self.graph_id = kwargs.get('graph_id', None)
self.experiment_id = kwargs.get('experiment_id', None)
self.experiment_name = kwargs.get('experiment_name', None)
self.is_experiment_archived = kwargs.get('is_experiment_archived', None)
self.submitted_by = kwargs.get('submitted_by', None)
self.tags = kwargs.get('tags', None)
self.step_tags = kwargs.get('step_tags', None)
self.properties = kwargs.get('properties', None)
self.aether_start_time = kwargs.get('aether_start_time', None)
self.aether_end_time = kwargs.get('aether_end_time', None)
self.run_history_start_time = kwargs.get('run_history_start_time', None)
self.run_history_end_time = kwargs.get('run_history_end_time', None)
self.unique_child_run_compute_targets = kwargs.get('unique_child_run_compute_targets', None)
self.entity_status = kwargs.get('entity_status', None)
self.id = kwargs.get('id', None)
self.etag = kwargs.get('etag', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
class PipelineStatus(msrest.serialization.Model):
"""PipelineStatus.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar status_code: Possible values include: "NotStarted", "InDraft", "Preparing", "Running",
"Failed", "Finished", "Canceled", "Throttled", "Unknown".
:vartype status_code: str or ~flow.models.PipelineStatusCode
:ivar run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:vartype run_status: str or ~flow.models.RunStatus
:ivar status_detail:
:vartype status_detail: str
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar is_terminal_state:
:vartype is_terminal_state: bool
"""
_validation = {
'is_terminal_state': {'readonly': True},
}
_attribute_map = {
'status_code': {'key': 'statusCode', 'type': 'str'},
'run_status': {'key': 'runStatus', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'is_terminal_state': {'key': 'isTerminalState', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword status_code: Possible values include: "NotStarted", "InDraft", "Preparing", "Running",
"Failed", "Finished", "Canceled", "Throttled", "Unknown".
:paramtype status_code: str or ~flow.models.PipelineStatusCode
:keyword run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:paramtype run_status: str or ~flow.models.RunStatus
:keyword status_detail:
:paramtype status_detail: str
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
"""
super(PipelineStatus, self).__init__(**kwargs)
self.status_code = kwargs.get('status_code', None)
self.run_status = kwargs.get('run_status', None)
self.status_detail = kwargs.get('status_detail', None)
self.start_time = kwargs.get('start_time', None)
self.end_time = kwargs.get('end_time', None)
self.is_terminal_state = None
class PipelineStepRun(msrest.serialization.Model):
"""PipelineStepRun.
:ivar step_name:
:vartype step_name: str
:ivar run_number:
:vartype run_number: int
:ivar run_id:
:vartype run_id: str
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:vartype run_status: str or ~flow.models.RunStatus
:ivar compute_target:
:vartype compute_target: str
:ivar compute_type:
:vartype compute_type: str
:ivar run_type:
:vartype run_type: str
:ivar step_type:
:vartype step_type: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar is_reused:
:vartype is_reused: bool
:ivar display_name:
:vartype display_name: str
"""
_attribute_map = {
'step_name': {'key': 'stepName', 'type': 'str'},
'run_number': {'key': 'runNumber', 'type': 'int'},
'run_id': {'key': 'runId', 'type': 'str'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'run_status': {'key': 'runStatus', 'type': 'str'},
'compute_target': {'key': 'computeTarget', 'type': 'str'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'run_type': {'key': 'runType', 'type': 'str'},
'step_type': {'key': 'stepType', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'is_reused': {'key': 'isReused', 'type': 'bool'},
'display_name': {'key': 'displayName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword step_name:
:paramtype step_name: str
:keyword run_number:
:paramtype run_number: int
:keyword run_id:
:paramtype run_id: str
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
:keyword run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:paramtype run_status: str or ~flow.models.RunStatus
:keyword compute_target:
:paramtype compute_target: str
:keyword compute_type:
:paramtype compute_type: str
:keyword run_type:
:paramtype run_type: str
:keyword step_type:
:paramtype step_type: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword is_reused:
:paramtype is_reused: bool
:keyword display_name:
:paramtype display_name: str
"""
super(PipelineStepRun, self).__init__(**kwargs)
self.step_name = kwargs.get('step_name', None)
self.run_number = kwargs.get('run_number', None)
self.run_id = kwargs.get('run_id', None)
self.start_time = kwargs.get('start_time', None)
self.end_time = kwargs.get('end_time', None)
self.run_status = kwargs.get('run_status', None)
self.compute_target = kwargs.get('compute_target', None)
self.compute_type = kwargs.get('compute_type', None)
self.run_type = kwargs.get('run_type', None)
self.step_type = kwargs.get('step_type', None)
self.tags = kwargs.get('tags', None)
self.is_reused = kwargs.get('is_reused', None)
self.display_name = kwargs.get('display_name', None)
class PipelineStepRunOutputs(msrest.serialization.Model):
"""PipelineStepRunOutputs.
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, str]
:ivar port_outputs: This is a dictionary.
:vartype port_outputs: dict[str, ~flow.models.PortOutputInfo]
"""
_attribute_map = {
'outputs': {'key': 'outputs', 'type': '{str}'},
'port_outputs': {'key': 'portOutputs', 'type': '{PortOutputInfo}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, str]
:keyword port_outputs: This is a dictionary.
:paramtype port_outputs: dict[str, ~flow.models.PortOutputInfo]
"""
super(PipelineStepRunOutputs, self).__init__(**kwargs)
self.outputs = kwargs.get('outputs', None)
self.port_outputs = kwargs.get('port_outputs', None)
class PipelineSubDraft(msrest.serialization.Model):
"""PipelineSubDraft.
:ivar parent_graph_draft_id:
:vartype parent_graph_draft_id: str
:ivar parent_node_id:
:vartype parent_node_id: str
:ivar graph_detail:
:vartype graph_detail: ~flow.models.PipelineRunGraphDetail
:ivar module_dto:
:vartype module_dto: ~flow.models.ModuleDto
:ivar name:
:vartype name: str
:ivar last_edited_by:
:vartype last_edited_by: str
:ivar created_by:
:vartype created_by: str
:ivar description:
:vartype description: str
:ivar pipeline_type: Possible values include: "TrainingPipeline", "RealTimeInferencePipeline",
"BatchInferencePipeline", "Unknown".
:vartype pipeline_type: str or ~flow.models.PipelineType
:ivar pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:vartype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'parent_graph_draft_id': {'key': 'parentGraphDraftId', 'type': 'str'},
'parent_node_id': {'key': 'parentNodeId', 'type': 'str'},
'graph_detail': {'key': 'graphDetail', 'type': 'PipelineRunGraphDetail'},
'module_dto': {'key': 'moduleDto', 'type': 'ModuleDto'},
'name': {'key': 'name', 'type': 'str'},
'last_edited_by': {'key': 'lastEditedBy', 'type': 'str'},
'created_by': {'key': 'createdBy', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'pipeline_type': {'key': 'pipelineType', 'type': 'str'},
'pipeline_draft_mode': {'key': 'pipelineDraftMode', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword parent_graph_draft_id:
:paramtype parent_graph_draft_id: str
:keyword parent_node_id:
:paramtype parent_node_id: str
:keyword graph_detail:
:paramtype graph_detail: ~flow.models.PipelineRunGraphDetail
:keyword module_dto:
:paramtype module_dto: ~flow.models.ModuleDto
:keyword name:
:paramtype name: str
:keyword last_edited_by:
:paramtype last_edited_by: str
:keyword created_by:
:paramtype created_by: str
:keyword description:
:paramtype description: str
:keyword pipeline_type: Possible values include: "TrainingPipeline",
"RealTimeInferencePipeline", "BatchInferencePipeline", "Unknown".
:paramtype pipeline_type: str or ~flow.models.PipelineType
:keyword pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:paramtype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PipelineSubDraft, self).__init__(**kwargs)
self.parent_graph_draft_id = kwargs.get('parent_graph_draft_id', None)
self.parent_node_id = kwargs.get('parent_node_id', None)
self.graph_detail = kwargs.get('graph_detail', None)
self.module_dto = kwargs.get('module_dto', None)
self.name = kwargs.get('name', None)
self.last_edited_by = kwargs.get('last_edited_by', None)
self.created_by = kwargs.get('created_by', None)
self.description = kwargs.get('description', None)
self.pipeline_type = kwargs.get('pipeline_type', None)
self.pipeline_draft_mode = kwargs.get('pipeline_draft_mode', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
self.entity_status = kwargs.get('entity_status', None)
self.id = kwargs.get('id', None)
self.etag = kwargs.get('etag', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
class PolicyValidationResponse(msrest.serialization.Model):
"""PolicyValidationResponse.
:ivar error_response: The error response.
:vartype error_response: ~flow.models.ErrorResponse
:ivar next_action_interval_in_seconds:
:vartype next_action_interval_in_seconds: int
:ivar action_type: Possible values include: "SendValidationRequest", "GetValidationStatus",
"SubmitBulkRun", "LogRunResult", "LogRunTerminatedEvent".
:vartype action_type: str or ~flow.models.ActionType
"""
_attribute_map = {
'error_response': {'key': 'errorResponse', 'type': 'ErrorResponse'},
'next_action_interval_in_seconds': {'key': 'nextActionIntervalInSeconds', 'type': 'int'},
'action_type': {'key': 'actionType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword error_response: The error response.
:paramtype error_response: ~flow.models.ErrorResponse
:keyword next_action_interval_in_seconds:
:paramtype next_action_interval_in_seconds: int
:keyword action_type: Possible values include: "SendValidationRequest", "GetValidationStatus",
"SubmitBulkRun", "LogRunResult", "LogRunTerminatedEvent".
:paramtype action_type: str or ~flow.models.ActionType
"""
super(PolicyValidationResponse, self).__init__(**kwargs)
self.error_response = kwargs.get('error_response', None)
self.next_action_interval_in_seconds = kwargs.get('next_action_interval_in_seconds', None)
self.action_type = kwargs.get('action_type', None)
class PortInfo(msrest.serialization.Model):
"""PortInfo.
:ivar node_id:
:vartype node_id: str
:ivar port_name:
:vartype port_name: str
:ivar graph_port_name:
:vartype graph_port_name: str
:ivar is_parameter:
:vartype is_parameter: bool
:ivar web_service_port:
:vartype web_service_port: str
"""
_attribute_map = {
'node_id': {'key': 'nodeId', 'type': 'str'},
'port_name': {'key': 'portName', 'type': 'str'},
'graph_port_name': {'key': 'graphPortName', 'type': 'str'},
'is_parameter': {'key': 'isParameter', 'type': 'bool'},
'web_service_port': {'key': 'webServicePort', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword node_id:
:paramtype node_id: str
:keyword port_name:
:paramtype port_name: str
:keyword graph_port_name:
:paramtype graph_port_name: str
:keyword is_parameter:
:paramtype is_parameter: bool
:keyword web_service_port:
:paramtype web_service_port: str
"""
super(PortInfo, self).__init__(**kwargs)
self.node_id = kwargs.get('node_id', None)
self.port_name = kwargs.get('port_name', None)
self.graph_port_name = kwargs.get('graph_port_name', None)
self.is_parameter = kwargs.get('is_parameter', None)
self.web_service_port = kwargs.get('web_service_port', None)
class PortOutputInfo(msrest.serialization.Model):
"""PortOutputInfo.
:ivar container_uri:
:vartype container_uri: str
:ivar relative_path:
:vartype relative_path: str
:ivar preview_params:
:vartype preview_params: str
:ivar model_output_path:
:vartype model_output_path: str
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_reference_type: Possible values include: "None", "AzureBlob", "AzureDataLake",
"AzureFiles", "AzureSqlDatabase", "AzurePostgresDatabase", "AzureDataLakeGen2", "DBFS",
"AzureMySqlDatabase", "Custom", "Hdfs".
:vartype data_reference_type: str or ~flow.models.DataReferenceType
:ivar is_file:
:vartype is_file: bool
:ivar supported_actions:
:vartype supported_actions: list[str or ~flow.models.PortAction]
"""
_attribute_map = {
'container_uri': {'key': 'containerUri', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'preview_params': {'key': 'previewParams', 'type': 'str'},
'model_output_path': {'key': 'modelOutputPath', 'type': 'str'},
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_reference_type': {'key': 'dataReferenceType', 'type': 'str'},
'is_file': {'key': 'isFile', 'type': 'bool'},
'supported_actions': {'key': 'supportedActions', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword container_uri:
:paramtype container_uri: str
:keyword relative_path:
:paramtype relative_path: str
:keyword preview_params:
:paramtype preview_params: str
:keyword model_output_path:
:paramtype model_output_path: str
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_reference_type: Possible values include: "None", "AzureBlob", "AzureDataLake",
"AzureFiles", "AzureSqlDatabase", "AzurePostgresDatabase", "AzureDataLakeGen2", "DBFS",
"AzureMySqlDatabase", "Custom", "Hdfs".
:paramtype data_reference_type: str or ~flow.models.DataReferenceType
:keyword is_file:
:paramtype is_file: bool
:keyword supported_actions:
:paramtype supported_actions: list[str or ~flow.models.PortAction]
"""
super(PortOutputInfo, self).__init__(**kwargs)
self.container_uri = kwargs.get('container_uri', None)
self.relative_path = kwargs.get('relative_path', None)
self.preview_params = kwargs.get('preview_params', None)
self.model_output_path = kwargs.get('model_output_path', None)
self.data_store_name = kwargs.get('data_store_name', None)
self.data_reference_type = kwargs.get('data_reference_type', None)
self.is_file = kwargs.get('is_file', None)
self.supported_actions = kwargs.get('supported_actions', None)
class PriorityConfig(msrest.serialization.Model):
"""PriorityConfig.
:ivar job_priority:
:vartype job_priority: int
:ivar is_preemptible:
:vartype is_preemptible: bool
:ivar node_count_set:
:vartype node_count_set: list[int]
:ivar scale_interval:
:vartype scale_interval: int
"""
_attribute_map = {
'job_priority': {'key': 'jobPriority', 'type': 'int'},
'is_preemptible': {'key': 'isPreemptible', 'type': 'bool'},
'node_count_set': {'key': 'nodeCountSet', 'type': '[int]'},
'scale_interval': {'key': 'scaleInterval', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword job_priority:
:paramtype job_priority: int
:keyword is_preemptible:
:paramtype is_preemptible: bool
:keyword node_count_set:
:paramtype node_count_set: list[int]
:keyword scale_interval:
:paramtype scale_interval: int
"""
super(PriorityConfig, self).__init__(**kwargs)
self.job_priority = kwargs.get('job_priority', None)
self.is_preemptible = kwargs.get('is_preemptible', None)
self.node_count_set = kwargs.get('node_count_set', None)
self.scale_interval = kwargs.get('scale_interval', None)
class PriorityConfiguration(msrest.serialization.Model):
"""PriorityConfiguration.
:ivar cloud_priority:
:vartype cloud_priority: int
:ivar string_type_priority:
:vartype string_type_priority: str
"""
_attribute_map = {
'cloud_priority': {'key': 'cloudPriority', 'type': 'int'},
'string_type_priority': {'key': 'stringTypePriority', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword cloud_priority:
:paramtype cloud_priority: int
:keyword string_type_priority:
:paramtype string_type_priority: str
"""
super(PriorityConfiguration, self).__init__(**kwargs)
self.cloud_priority = kwargs.get('cloud_priority', None)
self.string_type_priority = kwargs.get('string_type_priority', None)
class PromoteDataSetRequest(msrest.serialization.Model):
"""PromoteDataSetRequest.
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar module_node_id:
:vartype module_node_id: str
:ivar step_run_id:
:vartype step_run_id: str
:ivar output_port_name:
:vartype output_port_name: str
:ivar model_output_path:
:vartype model_output_path: str
:ivar data_type_id:
:vartype data_type_id: str
:ivar dataset_type:
:vartype dataset_type: str
:ivar data_store_name:
:vartype data_store_name: str
:ivar output_relative_path:
:vartype output_relative_path: str
:ivar pipeline_run_id:
:vartype pipeline_run_id: str
:ivar root_pipeline_run_id:
:vartype root_pipeline_run_id: str
:ivar experiment_name:
:vartype experiment_name: str
:ivar experiment_id:
:vartype experiment_id: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'module_node_id': {'key': 'moduleNodeId', 'type': 'str'},
'step_run_id': {'key': 'stepRunId', 'type': 'str'},
'output_port_name': {'key': 'outputPortName', 'type': 'str'},
'model_output_path': {'key': 'modelOutputPath', 'type': 'str'},
'data_type_id': {'key': 'dataTypeId', 'type': 'str'},
'dataset_type': {'key': 'datasetType', 'type': 'str'},
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'output_relative_path': {'key': 'outputRelativePath', 'type': 'str'},
'pipeline_run_id': {'key': 'pipelineRunId', 'type': 'str'},
'root_pipeline_run_id': {'key': 'rootPipelineRunId', 'type': 'str'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword module_node_id:
:paramtype module_node_id: str
:keyword step_run_id:
:paramtype step_run_id: str
:keyword output_port_name:
:paramtype output_port_name: str
:keyword model_output_path:
:paramtype model_output_path: str
:keyword data_type_id:
:paramtype data_type_id: str
:keyword dataset_type:
:paramtype dataset_type: str
:keyword data_store_name:
:paramtype data_store_name: str
:keyword output_relative_path:
:paramtype output_relative_path: str
:keyword pipeline_run_id:
:paramtype pipeline_run_id: str
:keyword root_pipeline_run_id:
:paramtype root_pipeline_run_id: str
:keyword experiment_name:
:paramtype experiment_name: str
:keyword experiment_id:
:paramtype experiment_id: str
"""
super(PromoteDataSetRequest, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.description = kwargs.get('description', None)
self.module_node_id = kwargs.get('module_node_id', None)
self.step_run_id = kwargs.get('step_run_id', None)
self.output_port_name = kwargs.get('output_port_name', None)
self.model_output_path = kwargs.get('model_output_path', None)
self.data_type_id = kwargs.get('data_type_id', None)
self.dataset_type = kwargs.get('dataset_type', None)
self.data_store_name = kwargs.get('data_store_name', None)
self.output_relative_path = kwargs.get('output_relative_path', None)
self.pipeline_run_id = kwargs.get('pipeline_run_id', None)
self.root_pipeline_run_id = kwargs.get('root_pipeline_run_id', None)
self.experiment_name = kwargs.get('experiment_name', None)
self.experiment_id = kwargs.get('experiment_id', None)
class ProviderEntity(msrest.serialization.Model):
"""ProviderEntity.
:ivar provider:
:vartype provider: str
:ivar module:
:vartype module: str
:ivar connection_type:
:vartype connection_type: list[str or ~flow.models.ConnectionType]
:ivar apis:
:vartype apis: list[~flow.models.ApiAndParameters]
"""
_attribute_map = {
'provider': {'key': 'provider', 'type': 'str'},
'module': {'key': 'module', 'type': 'str'},
'connection_type': {'key': 'connection_type', 'type': '[str]'},
'apis': {'key': 'apis', 'type': '[ApiAndParameters]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword provider:
:paramtype provider: str
:keyword module:
:paramtype module: str
:keyword connection_type:
:paramtype connection_type: list[str or ~flow.models.ConnectionType]
:keyword apis:
:paramtype apis: list[~flow.models.ApiAndParameters]
"""
super(ProviderEntity, self).__init__(**kwargs)
self.provider = kwargs.get('provider', None)
self.module = kwargs.get('module', None)
self.connection_type = kwargs.get('connection_type', None)
self.apis = kwargs.get('apis', None)
class PublishedPipeline(msrest.serialization.Model):
"""PublishedPipeline.
:ivar total_run_steps:
:vartype total_run_steps: int
:ivar total_runs:
:vartype total_runs: int
:ivar parameters: This is a dictionary.
:vartype parameters: dict[str, str]
:ivar data_set_definition_value_assignment: This is a dictionary.
:vartype data_set_definition_value_assignment: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar rest_endpoint:
:vartype rest_endpoint: str
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar graph_id:
:vartype graph_id: str
:ivar published_date:
:vartype published_date: ~datetime.datetime
:ivar last_run_time:
:vartype last_run_time: ~datetime.datetime
:ivar last_run_status: Possible values include: "NotStarted", "Running", "Failed", "Finished",
"Canceled", "Queued", "CancelRequested".
:vartype last_run_status: str or ~flow.models.PipelineRunStatusCode
:ivar published_by:
:vartype published_by: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar version:
:vartype version: str
:ivar is_default:
:vartype is_default: bool
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'total_run_steps': {'key': 'totalRunSteps', 'type': 'int'},
'total_runs': {'key': 'totalRuns', 'type': 'int'},
'parameters': {'key': 'parameters', 'type': '{str}'},
'data_set_definition_value_assignment': {'key': 'dataSetDefinitionValueAssignment', 'type': '{DataSetDefinitionValue}'},
'rest_endpoint': {'key': 'restEndpoint', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'graph_id': {'key': 'graphId', 'type': 'str'},
'published_date': {'key': 'publishedDate', 'type': 'iso-8601'},
'last_run_time': {'key': 'lastRunTime', 'type': 'iso-8601'},
'last_run_status': {'key': 'lastRunStatus', 'type': 'str'},
'published_by': {'key': 'publishedBy', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'version': {'key': 'version', 'type': 'str'},
'is_default': {'key': 'isDefault', 'type': 'bool'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword total_run_steps:
:paramtype total_run_steps: int
:keyword total_runs:
:paramtype total_runs: int
:keyword parameters: This is a dictionary.
:paramtype parameters: dict[str, str]
:keyword data_set_definition_value_assignment: This is a dictionary.
:paramtype data_set_definition_value_assignment: dict[str, ~flow.models.DataSetDefinitionValue]
:keyword rest_endpoint:
:paramtype rest_endpoint: str
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword graph_id:
:paramtype graph_id: str
:keyword published_date:
:paramtype published_date: ~datetime.datetime
:keyword last_run_time:
:paramtype last_run_time: ~datetime.datetime
:keyword last_run_status: Possible values include: "NotStarted", "Running", "Failed",
"Finished", "Canceled", "Queued", "CancelRequested".
:paramtype last_run_status: str or ~flow.models.PipelineRunStatusCode
:keyword published_by:
:paramtype published_by: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword version:
:paramtype version: str
:keyword is_default:
:paramtype is_default: bool
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PublishedPipeline, self).__init__(**kwargs)
self.total_run_steps = kwargs.get('total_run_steps', None)
self.total_runs = kwargs.get('total_runs', None)
self.parameters = kwargs.get('parameters', None)
self.data_set_definition_value_assignment = kwargs.get('data_set_definition_value_assignment', None)
self.rest_endpoint = kwargs.get('rest_endpoint', None)
self.name = kwargs.get('name', None)
self.description = kwargs.get('description', None)
self.graph_id = kwargs.get('graph_id', None)
self.published_date = kwargs.get('published_date', None)
self.last_run_time = kwargs.get('last_run_time', None)
self.last_run_status = kwargs.get('last_run_status', None)
self.published_by = kwargs.get('published_by', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
self.version = kwargs.get('version', None)
self.is_default = kwargs.get('is_default', None)
self.entity_status = kwargs.get('entity_status', None)
self.id = kwargs.get('id', None)
self.etag = kwargs.get('etag', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
class PublishedPipelineSummary(msrest.serialization.Model):
"""PublishedPipelineSummary.
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar graph_id:
:vartype graph_id: str
:ivar published_date:
:vartype published_date: ~datetime.datetime
:ivar last_run_time:
:vartype last_run_time: ~datetime.datetime
:ivar last_run_status: Possible values include: "NotStarted", "Running", "Failed", "Finished",
"Canceled", "Queued", "CancelRequested".
:vartype last_run_status: str or ~flow.models.PipelineRunStatusCode
:ivar published_by:
:vartype published_by: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar version:
:vartype version: str
:ivar is_default:
:vartype is_default: bool
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'graph_id': {'key': 'graphId', 'type': 'str'},
'published_date': {'key': 'publishedDate', 'type': 'iso-8601'},
'last_run_time': {'key': 'lastRunTime', 'type': 'iso-8601'},
'last_run_status': {'key': 'lastRunStatus', 'type': 'str'},
'published_by': {'key': 'publishedBy', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'version': {'key': 'version', 'type': 'str'},
'is_default': {'key': 'isDefault', 'type': 'bool'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword graph_id:
:paramtype graph_id: str
:keyword published_date:
:paramtype published_date: ~datetime.datetime
:keyword last_run_time:
:paramtype last_run_time: ~datetime.datetime
:keyword last_run_status: Possible values include: "NotStarted", "Running", "Failed",
"Finished", "Canceled", "Queued", "CancelRequested".
:paramtype last_run_status: str or ~flow.models.PipelineRunStatusCode
:keyword published_by:
:paramtype published_by: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword version:
:paramtype version: str
:keyword is_default:
:paramtype is_default: bool
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PublishedPipelineSummary, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.description = kwargs.get('description', None)
self.graph_id = kwargs.get('graph_id', None)
self.published_date = kwargs.get('published_date', None)
self.last_run_time = kwargs.get('last_run_time', None)
self.last_run_status = kwargs.get('last_run_status', None)
self.published_by = kwargs.get('published_by', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
self.version = kwargs.get('version', None)
self.is_default = kwargs.get('is_default', None)
self.entity_status = kwargs.get('entity_status', None)
self.id = kwargs.get('id', None)
self.etag = kwargs.get('etag', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
class PythonInterfaceMapping(msrest.serialization.Model):
"""PythonInterfaceMapping.
:ivar name:
:vartype name: str
:ivar name_in_yaml:
:vartype name_in_yaml: str
:ivar argument_name:
:vartype argument_name: str
:ivar command_line_option:
:vartype command_line_option: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'name_in_yaml': {'key': 'nameInYaml', 'type': 'str'},
'argument_name': {'key': 'argumentName', 'type': 'str'},
'command_line_option': {'key': 'commandLineOption', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword name_in_yaml:
:paramtype name_in_yaml: str
:keyword argument_name:
:paramtype argument_name: str
:keyword command_line_option:
:paramtype command_line_option: str
"""
super(PythonInterfaceMapping, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.name_in_yaml = kwargs.get('name_in_yaml', None)
self.argument_name = kwargs.get('argument_name', None)
self.command_line_option = kwargs.get('command_line_option', None)
class PythonPyPiOrRCranLibraryDto(msrest.serialization.Model):
"""PythonPyPiOrRCranLibraryDto.
:ivar package:
:vartype package: str
:ivar repo:
:vartype repo: str
"""
_attribute_map = {
'package': {'key': 'package', 'type': 'str'},
'repo': {'key': 'repo', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword package:
:paramtype package: str
:keyword repo:
:paramtype repo: str
"""
super(PythonPyPiOrRCranLibraryDto, self).__init__(**kwargs)
self.package = kwargs.get('package', None)
self.repo = kwargs.get('repo', None)
class PythonSection(msrest.serialization.Model):
"""PythonSection.
:ivar interpreter_path:
:vartype interpreter_path: str
:ivar user_managed_dependencies:
:vartype user_managed_dependencies: bool
:ivar conda_dependencies: Anything.
:vartype conda_dependencies: any
:ivar base_conda_environment:
:vartype base_conda_environment: str
"""
_attribute_map = {
'interpreter_path': {'key': 'interpreterPath', 'type': 'str'},
'user_managed_dependencies': {'key': 'userManagedDependencies', 'type': 'bool'},
'conda_dependencies': {'key': 'condaDependencies', 'type': 'object'},
'base_conda_environment': {'key': 'baseCondaEnvironment', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword interpreter_path:
:paramtype interpreter_path: str
:keyword user_managed_dependencies:
:paramtype user_managed_dependencies: bool
:keyword conda_dependencies: Anything.
:paramtype conda_dependencies: any
:keyword base_conda_environment:
:paramtype base_conda_environment: str
"""
super(PythonSection, self).__init__(**kwargs)
self.interpreter_path = kwargs.get('interpreter_path', None)
self.user_managed_dependencies = kwargs.get('user_managed_dependencies', None)
self.conda_dependencies = kwargs.get('conda_dependencies', None)
self.base_conda_environment = kwargs.get('base_conda_environment', None)
class PyTorchConfiguration(msrest.serialization.Model):
"""PyTorchConfiguration.
:ivar communication_backend:
:vartype communication_backend: str
:ivar process_count:
:vartype process_count: int
"""
_attribute_map = {
'communication_backend': {'key': 'communicationBackend', 'type': 'str'},
'process_count': {'key': 'processCount', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword communication_backend:
:paramtype communication_backend: str
:keyword process_count:
:paramtype process_count: int
"""
super(PyTorchConfiguration, self).__init__(**kwargs)
self.communication_backend = kwargs.get('communication_backend', None)
self.process_count = kwargs.get('process_count', None)
class QueueingInfo(msrest.serialization.Model):
"""QueueingInfo.
:ivar code:
:vartype code: str
:ivar message:
:vartype message: str
:ivar last_refresh_timestamp:
:vartype last_refresh_timestamp: ~datetime.datetime
"""
_attribute_map = {
'code': {'key': 'code', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
'last_refresh_timestamp': {'key': 'lastRefreshTimestamp', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword code:
:paramtype code: str
:keyword message:
:paramtype message: str
:keyword last_refresh_timestamp:
:paramtype last_refresh_timestamp: ~datetime.datetime
"""
super(QueueingInfo, self).__init__(**kwargs)
self.code = kwargs.get('code', None)
self.message = kwargs.get('message', None)
self.last_refresh_timestamp = kwargs.get('last_refresh_timestamp', None)
class RawComponentDto(msrest.serialization.Model):
"""RawComponentDto.
:ivar component_schema:
:vartype component_schema: str
:ivar is_anonymous:
:vartype is_anonymous: bool
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
:ivar type: Possible values include: "Unknown", "CommandComponent", "Command".
:vartype type: str or ~flow.models.ComponentType
:ivar component_type_version:
:vartype component_type_version: str
:ivar display_name:
:vartype display_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar is_deterministic:
:vartype is_deterministic: bool
:ivar successful_return_code:
:vartype successful_return_code: str
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, ~flow.models.ComponentInput]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, ~flow.models.ComponentOutput]
:ivar command:
:vartype command: str
:ivar environment_name:
:vartype environment_name: str
:ivar environment_version:
:vartype environment_version: str
:ivar snapshot_id:
:vartype snapshot_id: str
:ivar created_by:
:vartype created_by: ~flow.models.SchemaContractsCreatedBy
:ivar last_modified_by:
:vartype last_modified_by: ~flow.models.SchemaContractsCreatedBy
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
:ivar component_internal_id:
:vartype component_internal_id: str
"""
_attribute_map = {
'component_schema': {'key': 'componentSchema', 'type': 'str'},
'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'component_type_version': {'key': 'componentTypeVersion', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'is_deterministic': {'key': 'isDeterministic', 'type': 'bool'},
'successful_return_code': {'key': 'successfulReturnCode', 'type': 'str'},
'inputs': {'key': 'inputs', 'type': '{ComponentInput}'},
'outputs': {'key': 'outputs', 'type': '{ComponentOutput}'},
'command': {'key': 'command', 'type': 'str'},
'environment_name': {'key': 'environmentName', 'type': 'str'},
'environment_version': {'key': 'environmentVersion', 'type': 'str'},
'snapshot_id': {'key': 'snapshotId', 'type': 'str'},
'created_by': {'key': 'createdBy', 'type': 'SchemaContractsCreatedBy'},
'last_modified_by': {'key': 'lastModifiedBy', 'type': 'SchemaContractsCreatedBy'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
'component_internal_id': {'key': 'componentInternalId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword component_schema:
:paramtype component_schema: str
:keyword is_anonymous:
:paramtype is_anonymous: bool
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
:keyword type: Possible values include: "Unknown", "CommandComponent", "Command".
:paramtype type: str or ~flow.models.ComponentType
:keyword component_type_version:
:paramtype component_type_version: str
:keyword display_name:
:paramtype display_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword is_deterministic:
:paramtype is_deterministic: bool
:keyword successful_return_code:
:paramtype successful_return_code: str
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, ~flow.models.ComponentInput]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, ~flow.models.ComponentOutput]
:keyword command:
:paramtype command: str
:keyword environment_name:
:paramtype environment_name: str
:keyword environment_version:
:paramtype environment_version: str
:keyword snapshot_id:
:paramtype snapshot_id: str
:keyword created_by:
:paramtype created_by: ~flow.models.SchemaContractsCreatedBy
:keyword last_modified_by:
:paramtype last_modified_by: ~flow.models.SchemaContractsCreatedBy
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
:keyword component_internal_id:
:paramtype component_internal_id: str
"""
super(RawComponentDto, self).__init__(**kwargs)
self.component_schema = kwargs.get('component_schema', None)
self.is_anonymous = kwargs.get('is_anonymous', None)
self.name = kwargs.get('name', None)
self.version = kwargs.get('version', None)
self.type = kwargs.get('type', None)
self.component_type_version = kwargs.get('component_type_version', None)
self.display_name = kwargs.get('display_name', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
self.is_deterministic = kwargs.get('is_deterministic', None)
self.successful_return_code = kwargs.get('successful_return_code', None)
self.inputs = kwargs.get('inputs', None)
self.outputs = kwargs.get('outputs', None)
self.command = kwargs.get('command', None)
self.environment_name = kwargs.get('environment_name', None)
self.environment_version = kwargs.get('environment_version', None)
self.snapshot_id = kwargs.get('snapshot_id', None)
self.created_by = kwargs.get('created_by', None)
self.last_modified_by = kwargs.get('last_modified_by', None)
self.created_date = kwargs.get('created_date', None)
self.last_modified_date = kwargs.get('last_modified_date', None)
self.component_internal_id = kwargs.get('component_internal_id', None)
class RayConfiguration(msrest.serialization.Model):
"""RayConfiguration.
:ivar port:
:vartype port: int
:ivar address:
:vartype address: str
:ivar include_dashboard:
:vartype include_dashboard: bool
:ivar dashboard_port:
:vartype dashboard_port: int
:ivar head_node_additional_args:
:vartype head_node_additional_args: str
:ivar worker_node_additional_args:
:vartype worker_node_additional_args: str
"""
_attribute_map = {
'port': {'key': 'port', 'type': 'int'},
'address': {'key': 'address', 'type': 'str'},
'include_dashboard': {'key': 'includeDashboard', 'type': 'bool'},
'dashboard_port': {'key': 'dashboardPort', 'type': 'int'},
'head_node_additional_args': {'key': 'headNodeAdditionalArgs', 'type': 'str'},
'worker_node_additional_args': {'key': 'workerNodeAdditionalArgs', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword port:
:paramtype port: int
:keyword address:
:paramtype address: str
:keyword include_dashboard:
:paramtype include_dashboard: bool
:keyword dashboard_port:
:paramtype dashboard_port: int
:keyword head_node_additional_args:
:paramtype head_node_additional_args: str
:keyword worker_node_additional_args:
:paramtype worker_node_additional_args: str
"""
super(RayConfiguration, self).__init__(**kwargs)
self.port = kwargs.get('port', None)
self.address = kwargs.get('address', None)
self.include_dashboard = kwargs.get('include_dashboard', None)
self.dashboard_port = kwargs.get('dashboard_port', None)
self.head_node_additional_args = kwargs.get('head_node_additional_args', None)
self.worker_node_additional_args = kwargs.get('worker_node_additional_args', None)
class RCranPackage(msrest.serialization.Model):
"""RCranPackage.
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
:ivar repository:
:vartype repository: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'repository': {'key': 'repository', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
:keyword repository:
:paramtype repository: str
"""
super(RCranPackage, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.version = kwargs.get('version', None)
self.repository = kwargs.get('repository', None)
class RealTimeEndpoint(msrest.serialization.Model):
"""RealTimeEndpoint.
:ivar created_by:
:vartype created_by: str
:ivar kv_tags: Dictionary of :code:`<string>`.
:vartype kv_tags: dict[str, str]
:ivar state: Possible values include: "Transitioning", "Healthy", "Unhealthy", "Failed",
"Unschedulable".
:vartype state: str or ~flow.models.WebServiceState
:ivar error:
:vartype error: ~flow.models.ModelManagementErrorResponse
:ivar compute_type: Possible values include: "ACI", "AKS", "AMLCOMPUTE", "IOT", "AKSENDPOINT",
"MIRSINGLEMODEL", "MIRAMLCOMPUTE", "MIRGA", "AMLARC", "BATCHAMLCOMPUTE", "UNKNOWN".
:vartype compute_type: str or ~flow.models.ComputeEnvironmentType
:ivar image_id:
:vartype image_id: str
:ivar cpu:
:vartype cpu: float
:ivar memory_in_gb:
:vartype memory_in_gb: float
:ivar max_concurrent_requests_per_container:
:vartype max_concurrent_requests_per_container: int
:ivar num_replicas:
:vartype num_replicas: int
:ivar event_hub_enabled:
:vartype event_hub_enabled: bool
:ivar storage_enabled:
:vartype storage_enabled: bool
:ivar app_insights_enabled:
:vartype app_insights_enabled: bool
:ivar auto_scale_enabled:
:vartype auto_scale_enabled: bool
:ivar min_replicas:
:vartype min_replicas: int
:ivar max_replicas:
:vartype max_replicas: int
:ivar target_utilization:
:vartype target_utilization: int
:ivar refresh_period_in_seconds:
:vartype refresh_period_in_seconds: int
:ivar scoring_uri:
:vartype scoring_uri: str
:ivar deployment_status:
:vartype deployment_status: ~flow.models.AKSReplicaStatus
:ivar scoring_timeout_ms:
:vartype scoring_timeout_ms: int
:ivar auth_enabled:
:vartype auth_enabled: bool
:ivar aad_auth_enabled:
:vartype aad_auth_enabled: bool
:ivar region:
:vartype region: str
:ivar primary_key:
:vartype primary_key: str
:ivar secondary_key:
:vartype secondary_key: str
:ivar swagger_uri:
:vartype swagger_uri: str
:ivar linked_pipeline_draft_id:
:vartype linked_pipeline_draft_id: str
:ivar linked_pipeline_run_id:
:vartype linked_pipeline_run_id: str
:ivar warning:
:vartype warning: str
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar id:
:vartype id: str
:ivar created_time:
:vartype created_time: ~datetime.datetime
:ivar updated_time:
:vartype updated_time: ~datetime.datetime
:ivar compute_name:
:vartype compute_name: str
:ivar updated_by:
:vartype updated_by: str
"""
_attribute_map = {
'created_by': {'key': 'createdBy', 'type': 'str'},
'kv_tags': {'key': 'kvTags', 'type': '{str}'},
'state': {'key': 'state', 'type': 'str'},
'error': {'key': 'error', 'type': 'ModelManagementErrorResponse'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'image_id': {'key': 'imageId', 'type': 'str'},
'cpu': {'key': 'cpu', 'type': 'float'},
'memory_in_gb': {'key': 'memoryInGB', 'type': 'float'},
'max_concurrent_requests_per_container': {'key': 'maxConcurrentRequestsPerContainer', 'type': 'int'},
'num_replicas': {'key': 'numReplicas', 'type': 'int'},
'event_hub_enabled': {'key': 'eventHubEnabled', 'type': 'bool'},
'storage_enabled': {'key': 'storageEnabled', 'type': 'bool'},
'app_insights_enabled': {'key': 'appInsightsEnabled', 'type': 'bool'},
'auto_scale_enabled': {'key': 'autoScaleEnabled', 'type': 'bool'},
'min_replicas': {'key': 'minReplicas', 'type': 'int'},
'max_replicas': {'key': 'maxReplicas', 'type': 'int'},
'target_utilization': {'key': 'targetUtilization', 'type': 'int'},
'refresh_period_in_seconds': {'key': 'refreshPeriodInSeconds', 'type': 'int'},
'scoring_uri': {'key': 'scoringUri', 'type': 'str'},
'deployment_status': {'key': 'deploymentStatus', 'type': 'AKSReplicaStatus'},
'scoring_timeout_ms': {'key': 'scoringTimeoutMs', 'type': 'int'},
'auth_enabled': {'key': 'authEnabled', 'type': 'bool'},
'aad_auth_enabled': {'key': 'aadAuthEnabled', 'type': 'bool'},
'region': {'key': 'region', 'type': 'str'},
'primary_key': {'key': 'primaryKey', 'type': 'str'},
'secondary_key': {'key': 'secondaryKey', 'type': 'str'},
'swagger_uri': {'key': 'swaggerUri', 'type': 'str'},
'linked_pipeline_draft_id': {'key': 'linkedPipelineDraftId', 'type': 'str'},
'linked_pipeline_run_id': {'key': 'linkedPipelineRunId', 'type': 'str'},
'warning': {'key': 'warning', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'created_time': {'key': 'createdTime', 'type': 'iso-8601'},
'updated_time': {'key': 'updatedTime', 'type': 'iso-8601'},
'compute_name': {'key': 'computeName', 'type': 'str'},
'updated_by': {'key': 'updatedBy', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword created_by:
:paramtype created_by: str
:keyword kv_tags: Dictionary of :code:`<string>`.
:paramtype kv_tags: dict[str, str]
:keyword state: Possible values include: "Transitioning", "Healthy", "Unhealthy", "Failed",
"Unschedulable".
:paramtype state: str or ~flow.models.WebServiceState
:keyword error:
:paramtype error: ~flow.models.ModelManagementErrorResponse
:keyword compute_type: Possible values include: "ACI", "AKS", "AMLCOMPUTE", "IOT",
"AKSENDPOINT", "MIRSINGLEMODEL", "MIRAMLCOMPUTE", "MIRGA", "AMLARC", "BATCHAMLCOMPUTE",
"UNKNOWN".
:paramtype compute_type: str or ~flow.models.ComputeEnvironmentType
:keyword image_id:
:paramtype image_id: str
:keyword cpu:
:paramtype cpu: float
:keyword memory_in_gb:
:paramtype memory_in_gb: float
:keyword max_concurrent_requests_per_container:
:paramtype max_concurrent_requests_per_container: int
:keyword num_replicas:
:paramtype num_replicas: int
:keyword event_hub_enabled:
:paramtype event_hub_enabled: bool
:keyword storage_enabled:
:paramtype storage_enabled: bool
:keyword app_insights_enabled:
:paramtype app_insights_enabled: bool
:keyword auto_scale_enabled:
:paramtype auto_scale_enabled: bool
:keyword min_replicas:
:paramtype min_replicas: int
:keyword max_replicas:
:paramtype max_replicas: int
:keyword target_utilization:
:paramtype target_utilization: int
:keyword refresh_period_in_seconds:
:paramtype refresh_period_in_seconds: int
:keyword scoring_uri:
:paramtype scoring_uri: str
:keyword deployment_status:
:paramtype deployment_status: ~flow.models.AKSReplicaStatus
:keyword scoring_timeout_ms:
:paramtype scoring_timeout_ms: int
:keyword auth_enabled:
:paramtype auth_enabled: bool
:keyword aad_auth_enabled:
:paramtype aad_auth_enabled: bool
:keyword region:
:paramtype region: str
:keyword primary_key:
:paramtype primary_key: str
:keyword secondary_key:
:paramtype secondary_key: str
:keyword swagger_uri:
:paramtype swagger_uri: str
:keyword linked_pipeline_draft_id:
:paramtype linked_pipeline_draft_id: str
:keyword linked_pipeline_run_id:
:paramtype linked_pipeline_run_id: str
:keyword warning:
:paramtype warning: str
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword id:
:paramtype id: str
:keyword created_time:
:paramtype created_time: ~datetime.datetime
:keyword updated_time:
:paramtype updated_time: ~datetime.datetime
:keyword compute_name:
:paramtype compute_name: str
:keyword updated_by:
:paramtype updated_by: str
"""
super(RealTimeEndpoint, self).__init__(**kwargs)
self.created_by = kwargs.get('created_by', None)
self.kv_tags = kwargs.get('kv_tags', None)
self.state = kwargs.get('state', None)
self.error = kwargs.get('error', None)
self.compute_type = kwargs.get('compute_type', None)
self.image_id = kwargs.get('image_id', None)
self.cpu = kwargs.get('cpu', None)
self.memory_in_gb = kwargs.get('memory_in_gb', None)
self.max_concurrent_requests_per_container = kwargs.get('max_concurrent_requests_per_container', None)
self.num_replicas = kwargs.get('num_replicas', None)
self.event_hub_enabled = kwargs.get('event_hub_enabled', None)
self.storage_enabled = kwargs.get('storage_enabled', None)
self.app_insights_enabled = kwargs.get('app_insights_enabled', None)
self.auto_scale_enabled = kwargs.get('auto_scale_enabled', None)
self.min_replicas = kwargs.get('min_replicas', None)
self.max_replicas = kwargs.get('max_replicas', None)
self.target_utilization = kwargs.get('target_utilization', None)
self.refresh_period_in_seconds = kwargs.get('refresh_period_in_seconds', None)
self.scoring_uri = kwargs.get('scoring_uri', None)
self.deployment_status = kwargs.get('deployment_status', None)
self.scoring_timeout_ms = kwargs.get('scoring_timeout_ms', None)
self.auth_enabled = kwargs.get('auth_enabled', None)
self.aad_auth_enabled = kwargs.get('aad_auth_enabled', None)
self.region = kwargs.get('region', None)
self.primary_key = kwargs.get('primary_key', None)
self.secondary_key = kwargs.get('secondary_key', None)
self.swagger_uri = kwargs.get('swagger_uri', None)
self.linked_pipeline_draft_id = kwargs.get('linked_pipeline_draft_id', None)
self.linked_pipeline_run_id = kwargs.get('linked_pipeline_run_id', None)
self.warning = kwargs.get('warning', None)
self.name = kwargs.get('name', None)
self.description = kwargs.get('description', None)
self.id = kwargs.get('id', None)
self.created_time = kwargs.get('created_time', None)
self.updated_time = kwargs.get('updated_time', None)
self.compute_name = kwargs.get('compute_name', None)
self.updated_by = kwargs.get('updated_by', None)
class RealTimeEndpointInfo(msrest.serialization.Model):
"""RealTimeEndpointInfo.
:ivar web_service_inputs:
:vartype web_service_inputs: list[~flow.models.WebServicePort]
:ivar web_service_outputs:
:vartype web_service_outputs: list[~flow.models.WebServicePort]
:ivar deployments_info:
:vartype deployments_info: list[~flow.models.DeploymentInfo]
"""
_attribute_map = {
'web_service_inputs': {'key': 'webServiceInputs', 'type': '[WebServicePort]'},
'web_service_outputs': {'key': 'webServiceOutputs', 'type': '[WebServicePort]'},
'deployments_info': {'key': 'deploymentsInfo', 'type': '[DeploymentInfo]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword web_service_inputs:
:paramtype web_service_inputs: list[~flow.models.WebServicePort]
:keyword web_service_outputs:
:paramtype web_service_outputs: list[~flow.models.WebServicePort]
:keyword deployments_info:
:paramtype deployments_info: list[~flow.models.DeploymentInfo]
"""
super(RealTimeEndpointInfo, self).__init__(**kwargs)
self.web_service_inputs = kwargs.get('web_service_inputs', None)
self.web_service_outputs = kwargs.get('web_service_outputs', None)
self.deployments_info = kwargs.get('deployments_info', None)
class RealTimeEndpointStatus(msrest.serialization.Model):
"""RealTimeEndpointStatus.
:ivar last_operation: Possible values include: "Create", "Update", "Delete".
:vartype last_operation: str or ~flow.models.RealTimeEndpointOpCode
:ivar last_operation_status: Possible values include: "Ongoing", "Succeeded", "Failed",
"SucceededWithWarning".
:vartype last_operation_status: str or ~flow.models.RealTimeEndpointOpStatusCode
:ivar internal_step: Possible values include: "AboutToDeploy", "WaitAksComputeReady",
"RegisterModels", "CreateServiceFromModels", "UpdateServiceFromModels", "WaitServiceCreating",
"FetchServiceRelatedInfo", "TestWithSampleData", "AboutToDelete", "DeleteDeployment",
"DeleteAsset", "DeleteImage", "DeleteModel", "DeleteServiceRecord".
:vartype internal_step: str or ~flow.models.RealTimeEndpointInternalStepCode
:ivar status_detail:
:vartype status_detail: str
:ivar deployment_state:
:vartype deployment_state: str
:ivar service_id:
:vartype service_id: str
:ivar linked_pipeline_draft_id:
:vartype linked_pipeline_draft_id: str
"""
_attribute_map = {
'last_operation': {'key': 'lastOperation', 'type': 'str'},
'last_operation_status': {'key': 'lastOperationStatus', 'type': 'str'},
'internal_step': {'key': 'internalStep', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
'deployment_state': {'key': 'deploymentState', 'type': 'str'},
'service_id': {'key': 'serviceId', 'type': 'str'},
'linked_pipeline_draft_id': {'key': 'linkedPipelineDraftId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword last_operation: Possible values include: "Create", "Update", "Delete".
:paramtype last_operation: str or ~flow.models.RealTimeEndpointOpCode
:keyword last_operation_status: Possible values include: "Ongoing", "Succeeded", "Failed",
"SucceededWithWarning".
:paramtype last_operation_status: str or ~flow.models.RealTimeEndpointOpStatusCode
:keyword internal_step: Possible values include: "AboutToDeploy", "WaitAksComputeReady",
"RegisterModels", "CreateServiceFromModels", "UpdateServiceFromModels", "WaitServiceCreating",
"FetchServiceRelatedInfo", "TestWithSampleData", "AboutToDelete", "DeleteDeployment",
"DeleteAsset", "DeleteImage", "DeleteModel", "DeleteServiceRecord".
:paramtype internal_step: str or ~flow.models.RealTimeEndpointInternalStepCode
:keyword status_detail:
:paramtype status_detail: str
:keyword deployment_state:
:paramtype deployment_state: str
:keyword service_id:
:paramtype service_id: str
:keyword linked_pipeline_draft_id:
:paramtype linked_pipeline_draft_id: str
"""
super(RealTimeEndpointStatus, self).__init__(**kwargs)
self.last_operation = kwargs.get('last_operation', None)
self.last_operation_status = kwargs.get('last_operation_status', None)
self.internal_step = kwargs.get('internal_step', None)
self.status_detail = kwargs.get('status_detail', None)
self.deployment_state = kwargs.get('deployment_state', None)
self.service_id = kwargs.get('service_id', None)
self.linked_pipeline_draft_id = kwargs.get('linked_pipeline_draft_id', None)
class RealTimeEndpointSummary(msrest.serialization.Model):
"""RealTimeEndpointSummary.
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar id:
:vartype id: str
:ivar created_time:
:vartype created_time: ~datetime.datetime
:ivar updated_time:
:vartype updated_time: ~datetime.datetime
:ivar compute_type: Possible values include: "ACI", "AKS", "AMLCOMPUTE", "IOT", "AKSENDPOINT",
"MIRSINGLEMODEL", "MIRAMLCOMPUTE", "MIRGA", "AMLARC", "BATCHAMLCOMPUTE", "UNKNOWN".
:vartype compute_type: str or ~flow.models.ComputeEnvironmentType
:ivar compute_name:
:vartype compute_name: str
:ivar updated_by:
:vartype updated_by: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'created_time': {'key': 'createdTime', 'type': 'iso-8601'},
'updated_time': {'key': 'updatedTime', 'type': 'iso-8601'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'compute_name': {'key': 'computeName', 'type': 'str'},
'updated_by': {'key': 'updatedBy', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword id:
:paramtype id: str
:keyword created_time:
:paramtype created_time: ~datetime.datetime
:keyword updated_time:
:paramtype updated_time: ~datetime.datetime
:keyword compute_type: Possible values include: "ACI", "AKS", "AMLCOMPUTE", "IOT",
"AKSENDPOINT", "MIRSINGLEMODEL", "MIRAMLCOMPUTE", "MIRGA", "AMLARC", "BATCHAMLCOMPUTE",
"UNKNOWN".
:paramtype compute_type: str or ~flow.models.ComputeEnvironmentType
:keyword compute_name:
:paramtype compute_name: str
:keyword updated_by:
:paramtype updated_by: str
"""
super(RealTimeEndpointSummary, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.description = kwargs.get('description', None)
self.id = kwargs.get('id', None)
self.created_time = kwargs.get('created_time', None)
self.updated_time = kwargs.get('updated_time', None)
self.compute_type = kwargs.get('compute_type', None)
self.compute_name = kwargs.get('compute_name', None)
self.updated_by = kwargs.get('updated_by', None)
class RealTimeEndpointTestRequest(msrest.serialization.Model):
"""RealTimeEndpointTestRequest.
:ivar end_point:
:vartype end_point: str
:ivar auth_key:
:vartype auth_key: str
:ivar payload:
:vartype payload: str
"""
_attribute_map = {
'end_point': {'key': 'endPoint', 'type': 'str'},
'auth_key': {'key': 'authKey', 'type': 'str'},
'payload': {'key': 'payload', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword end_point:
:paramtype end_point: str
:keyword auth_key:
:paramtype auth_key: str
:keyword payload:
:paramtype payload: str
"""
super(RealTimeEndpointTestRequest, self).__init__(**kwargs)
self.end_point = kwargs.get('end_point', None)
self.auth_key = kwargs.get('auth_key', None)
self.payload = kwargs.get('payload', None)
class Recurrence(msrest.serialization.Model):
"""Recurrence.
:ivar frequency: Possible values include: "Month", "Week", "Day", "Hour", "Minute".
:vartype frequency: str or ~flow.models.Frequency
:ivar interval:
:vartype interval: int
:ivar schedule:
:vartype schedule: ~flow.models.RecurrenceSchedule
:ivar end_time:
:vartype end_time: str
:ivar start_time:
:vartype start_time: str
:ivar time_zone:
:vartype time_zone: str
"""
_attribute_map = {
'frequency': {'key': 'frequency', 'type': 'str'},
'interval': {'key': 'interval', 'type': 'int'},
'schedule': {'key': 'schedule', 'type': 'RecurrenceSchedule'},
'end_time': {'key': 'endTime', 'type': 'str'},
'start_time': {'key': 'startTime', 'type': 'str'},
'time_zone': {'key': 'timeZone', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword frequency: Possible values include: "Month", "Week", "Day", "Hour", "Minute".
:paramtype frequency: str or ~flow.models.Frequency
:keyword interval:
:paramtype interval: int
:keyword schedule:
:paramtype schedule: ~flow.models.RecurrenceSchedule
:keyword end_time:
:paramtype end_time: str
:keyword start_time:
:paramtype start_time: str
:keyword time_zone:
:paramtype time_zone: str
"""
super(Recurrence, self).__init__(**kwargs)
self.frequency = kwargs.get('frequency', None)
self.interval = kwargs.get('interval', None)
self.schedule = kwargs.get('schedule', None)
self.end_time = kwargs.get('end_time', None)
self.start_time = kwargs.get('start_time', None)
self.time_zone = kwargs.get('time_zone', None)
class RecurrencePattern(msrest.serialization.Model):
"""RecurrencePattern.
:ivar hours:
:vartype hours: list[int]
:ivar minutes:
:vartype minutes: list[int]
:ivar weekdays:
:vartype weekdays: list[str or ~flow.models.Weekday]
"""
_attribute_map = {
'hours': {'key': 'hours', 'type': '[int]'},
'minutes': {'key': 'minutes', 'type': '[int]'},
'weekdays': {'key': 'weekdays', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword hours:
:paramtype hours: list[int]
:keyword minutes:
:paramtype minutes: list[int]
:keyword weekdays:
:paramtype weekdays: list[str or ~flow.models.Weekday]
"""
super(RecurrencePattern, self).__init__(**kwargs)
self.hours = kwargs.get('hours', None)
self.minutes = kwargs.get('minutes', None)
self.weekdays = kwargs.get('weekdays', None)
class RecurrenceSchedule(msrest.serialization.Model):
"""RecurrenceSchedule.
:ivar hours:
:vartype hours: list[int]
:ivar minutes:
:vartype minutes: list[int]
:ivar week_days:
:vartype week_days: list[str or ~flow.models.WeekDays]
:ivar month_days:
:vartype month_days: list[int]
"""
_attribute_map = {
'hours': {'key': 'hours', 'type': '[int]'},
'minutes': {'key': 'minutes', 'type': '[int]'},
'week_days': {'key': 'weekDays', 'type': '[str]'},
'month_days': {'key': 'monthDays', 'type': '[int]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword hours:
:paramtype hours: list[int]
:keyword minutes:
:paramtype minutes: list[int]
:keyword week_days:
:paramtype week_days: list[str or ~flow.models.WeekDays]
:keyword month_days:
:paramtype month_days: list[int]
"""
super(RecurrenceSchedule, self).__init__(**kwargs)
self.hours = kwargs.get('hours', None)
self.minutes = kwargs.get('minutes', None)
self.week_days = kwargs.get('week_days', None)
self.month_days = kwargs.get('month_days', None)
class RegenerateServiceKeysRequest(msrest.serialization.Model):
"""RegenerateServiceKeysRequest.
:ivar key_type: Possible values include: "Primary", "Secondary".
:vartype key_type: str or ~flow.models.KeyType
:ivar key_value:
:vartype key_value: str
"""
_attribute_map = {
'key_type': {'key': 'keyType', 'type': 'str'},
'key_value': {'key': 'keyValue', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword key_type: Possible values include: "Primary", "Secondary".
:paramtype key_type: str or ~flow.models.KeyType
:keyword key_value:
:paramtype key_value: str
"""
super(RegenerateServiceKeysRequest, self).__init__(**kwargs)
self.key_type = kwargs.get('key_type', None)
self.key_value = kwargs.get('key_value', None)
class RegisterComponentMetaInfo(msrest.serialization.Model):
"""RegisterComponentMetaInfo.
:ivar aml_module_name:
:vartype aml_module_name: str
:ivar name_only_display_info:
:vartype name_only_display_info: str
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
:ivar module_version_id:
:vartype module_version_id: str
:ivar snapshot_id:
:vartype snapshot_id: str
:ivar component_registration_type: Possible values include: "Normal", "AnonymousAmlModule",
"AnonymousAmlModuleVersion", "ModuleEntityOnly".
:vartype component_registration_type: str or ~flow.models.ComponentRegistrationTypeEnum
:ivar module_entity_from_yaml:
:vartype module_entity_from_yaml: ~flow.models.ModuleEntity
:ivar set_as_default_version:
:vartype set_as_default_version: bool
:ivar data_types_from_yaml:
:vartype data_types_from_yaml: list[~flow.models.DataTypeCreationInfo]
:ivar data_type_mechanism: Possible values include: "ErrorWhenNotExisting",
"RegisterWhenNotExisting", "RegisterBuildinDataTypeOnly".
:vartype data_type_mechanism: str or ~flow.models.DataTypeMechanism
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hashes:
:vartype identifier_hashes: ~flow.models.RegisterComponentMetaInfoIdentifierHashes
:ivar content_hash:
:vartype content_hash: str
:ivar extra_hash:
:vartype extra_hash: str
:ivar extra_hashes:
:vartype extra_hashes: ~flow.models.RegisterComponentMetaInfoExtraHashes
:ivar registration:
:vartype registration: bool
:ivar validate_only:
:vartype validate_only: bool
:ivar skip_workspace_related_check:
:vartype skip_workspace_related_check: bool
:ivar intellectual_property_protected_workspace_component_registration_allowed_publisher:
:vartype intellectual_property_protected_workspace_component_registration_allowed_publisher:
list[str]
:ivar system_managed_registration:
:vartype system_managed_registration: bool
:ivar allow_dup_name_between_input_and_ouput_port:
:vartype allow_dup_name_between_input_and_ouput_port: bool
:ivar module_source:
:vartype module_source: str
:ivar module_scope:
:vartype module_scope: str
:ivar module_additional_includes_count:
:vartype module_additional_includes_count: int
:ivar module_os_type:
:vartype module_os_type: str
:ivar module_codegen_by:
:vartype module_codegen_by: str
:ivar module_client_source:
:vartype module_client_source: str
:ivar module_is_builtin:
:vartype module_is_builtin: bool
:ivar module_register_event_extension_fields: Dictionary of :code:`<string>`.
:vartype module_register_event_extension_fields: dict[str, str]
"""
_attribute_map = {
'aml_module_name': {'key': 'amlModuleName', 'type': 'str'},
'name_only_display_info': {'key': 'nameOnlyDisplayInfo', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'module_version_id': {'key': 'moduleVersionId', 'type': 'str'},
'snapshot_id': {'key': 'snapshotId', 'type': 'str'},
'component_registration_type': {'key': 'componentRegistrationType', 'type': 'str'},
'module_entity_from_yaml': {'key': 'moduleEntityFromYaml', 'type': 'ModuleEntity'},
'set_as_default_version': {'key': 'setAsDefaultVersion', 'type': 'bool'},
'data_types_from_yaml': {'key': 'dataTypesFromYaml', 'type': '[DataTypeCreationInfo]'},
'data_type_mechanism': {'key': 'dataTypeMechanism', 'type': 'str'},
'identifier_hash': {'key': 'identifierHash', 'type': 'str'},
'identifier_hashes': {'key': 'identifierHashes', 'type': 'RegisterComponentMetaInfoIdentifierHashes'},
'content_hash': {'key': 'contentHash', 'type': 'str'},
'extra_hash': {'key': 'extraHash', 'type': 'str'},
'extra_hashes': {'key': 'extraHashes', 'type': 'RegisterComponentMetaInfoExtraHashes'},
'registration': {'key': 'registration', 'type': 'bool'},
'validate_only': {'key': 'validateOnly', 'type': 'bool'},
'skip_workspace_related_check': {'key': 'skipWorkspaceRelatedCheck', 'type': 'bool'},
'intellectual_property_protected_workspace_component_registration_allowed_publisher': {'key': 'intellectualPropertyProtectedWorkspaceComponentRegistrationAllowedPublisher', 'type': '[str]'},
'system_managed_registration': {'key': 'systemManagedRegistration', 'type': 'bool'},
'allow_dup_name_between_input_and_ouput_port': {'key': 'allowDupNameBetweenInputAndOuputPort', 'type': 'bool'},
'module_source': {'key': 'moduleSource', 'type': 'str'},
'module_scope': {'key': 'moduleScope', 'type': 'str'},
'module_additional_includes_count': {'key': 'moduleAdditionalIncludesCount', 'type': 'int'},
'module_os_type': {'key': 'moduleOSType', 'type': 'str'},
'module_codegen_by': {'key': 'moduleCodegenBy', 'type': 'str'},
'module_client_source': {'key': 'moduleClientSource', 'type': 'str'},
'module_is_builtin': {'key': 'moduleIsBuiltin', 'type': 'bool'},
'module_register_event_extension_fields': {'key': 'moduleRegisterEventExtensionFields', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword aml_module_name:
:paramtype aml_module_name: str
:keyword name_only_display_info:
:paramtype name_only_display_info: str
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
:keyword module_version_id:
:paramtype module_version_id: str
:keyword snapshot_id:
:paramtype snapshot_id: str
:keyword component_registration_type: Possible values include: "Normal", "AnonymousAmlModule",
"AnonymousAmlModuleVersion", "ModuleEntityOnly".
:paramtype component_registration_type: str or ~flow.models.ComponentRegistrationTypeEnum
:keyword module_entity_from_yaml:
:paramtype module_entity_from_yaml: ~flow.models.ModuleEntity
:keyword set_as_default_version:
:paramtype set_as_default_version: bool
:keyword data_types_from_yaml:
:paramtype data_types_from_yaml: list[~flow.models.DataTypeCreationInfo]
:keyword data_type_mechanism: Possible values include: "ErrorWhenNotExisting",
"RegisterWhenNotExisting", "RegisterBuildinDataTypeOnly".
:paramtype data_type_mechanism: str or ~flow.models.DataTypeMechanism
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hashes:
:paramtype identifier_hashes: ~flow.models.RegisterComponentMetaInfoIdentifierHashes
:keyword content_hash:
:paramtype content_hash: str
:keyword extra_hash:
:paramtype extra_hash: str
:keyword extra_hashes:
:paramtype extra_hashes: ~flow.models.RegisterComponentMetaInfoExtraHashes
:keyword registration:
:paramtype registration: bool
:keyword validate_only:
:paramtype validate_only: bool
:keyword skip_workspace_related_check:
:paramtype skip_workspace_related_check: bool
:keyword intellectual_property_protected_workspace_component_registration_allowed_publisher:
:paramtype intellectual_property_protected_workspace_component_registration_allowed_publisher:
list[str]
:keyword system_managed_registration:
:paramtype system_managed_registration: bool
:keyword allow_dup_name_between_input_and_ouput_port:
:paramtype allow_dup_name_between_input_and_ouput_port: bool
:keyword module_source:
:paramtype module_source: str
:keyword module_scope:
:paramtype module_scope: str
:keyword module_additional_includes_count:
:paramtype module_additional_includes_count: int
:keyword module_os_type:
:paramtype module_os_type: str
:keyword module_codegen_by:
:paramtype module_codegen_by: str
:keyword module_client_source:
:paramtype module_client_source: str
:keyword module_is_builtin:
:paramtype module_is_builtin: bool
:keyword module_register_event_extension_fields: Dictionary of :code:`<string>`.
:paramtype module_register_event_extension_fields: dict[str, str]
"""
super(RegisterComponentMetaInfo, self).__init__(**kwargs)
self.aml_module_name = kwargs.get('aml_module_name', None)
self.name_only_display_info = kwargs.get('name_only_display_info', None)
self.name = kwargs.get('name', None)
self.version = kwargs.get('version', None)
self.module_version_id = kwargs.get('module_version_id', None)
self.snapshot_id = kwargs.get('snapshot_id', None)
self.component_registration_type = kwargs.get('component_registration_type', None)
self.module_entity_from_yaml = kwargs.get('module_entity_from_yaml', None)
self.set_as_default_version = kwargs.get('set_as_default_version', None)
self.data_types_from_yaml = kwargs.get('data_types_from_yaml', None)
self.data_type_mechanism = kwargs.get('data_type_mechanism', None)
self.identifier_hash = kwargs.get('identifier_hash', None)
self.identifier_hashes = kwargs.get('identifier_hashes', None)
self.content_hash = kwargs.get('content_hash', None)
self.extra_hash = kwargs.get('extra_hash', None)
self.extra_hashes = kwargs.get('extra_hashes', None)
self.registration = kwargs.get('registration', None)
self.validate_only = kwargs.get('validate_only', None)
self.skip_workspace_related_check = kwargs.get('skip_workspace_related_check', None)
self.intellectual_property_protected_workspace_component_registration_allowed_publisher = kwargs.get('intellectual_property_protected_workspace_component_registration_allowed_publisher', None)
self.system_managed_registration = kwargs.get('system_managed_registration', None)
self.allow_dup_name_between_input_and_ouput_port = kwargs.get('allow_dup_name_between_input_and_ouput_port', None)
self.module_source = kwargs.get('module_source', None)
self.module_scope = kwargs.get('module_scope', None)
self.module_additional_includes_count = kwargs.get('module_additional_includes_count', None)
self.module_os_type = kwargs.get('module_os_type', None)
self.module_codegen_by = kwargs.get('module_codegen_by', None)
self.module_client_source = kwargs.get('module_client_source', None)
self.module_is_builtin = kwargs.get('module_is_builtin', None)
self.module_register_event_extension_fields = kwargs.get('module_register_event_extension_fields', None)
class RegisterComponentMetaInfoExtraHashes(msrest.serialization.Model):
"""RegisterComponentMetaInfoExtraHashes.
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hash_v2:
:vartype identifier_hash_v2: str
"""
_attribute_map = {
'identifier_hash': {'key': 'IdentifierHash', 'type': 'str'},
'identifier_hash_v2': {'key': 'IdentifierHashV2', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hash_v2:
:paramtype identifier_hash_v2: str
"""
super(RegisterComponentMetaInfoExtraHashes, self).__init__(**kwargs)
self.identifier_hash = kwargs.get('identifier_hash', None)
self.identifier_hash_v2 = kwargs.get('identifier_hash_v2', None)
class RegisterComponentMetaInfoIdentifierHashes(msrest.serialization.Model):
"""RegisterComponentMetaInfoIdentifierHashes.
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hash_v2:
:vartype identifier_hash_v2: str
"""
_attribute_map = {
'identifier_hash': {'key': 'IdentifierHash', 'type': 'str'},
'identifier_hash_v2': {'key': 'IdentifierHashV2', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hash_v2:
:paramtype identifier_hash_v2: str
"""
super(RegisterComponentMetaInfoIdentifierHashes, self).__init__(**kwargs)
self.identifier_hash = kwargs.get('identifier_hash', None)
self.identifier_hash_v2 = kwargs.get('identifier_hash_v2', None)
class RegisteredDataSetReference(msrest.serialization.Model):
"""RegisteredDataSetReference.
:ivar id:
:vartype id: str
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
"""
super(RegisteredDataSetReference, self).__init__(**kwargs)
self.id = kwargs.get('id', None)
self.name = kwargs.get('name', None)
self.version = kwargs.get('version', None)
class RegisterRegistryComponentMetaInfo(msrest.serialization.Model):
"""RegisterRegistryComponentMetaInfo.
:ivar registry_name:
:vartype registry_name: str
:ivar intellectual_property_publisher_information:
:vartype intellectual_property_publisher_information:
~flow.models.IntellectualPropertyPublisherInformation
:ivar blob_reference_data: This is a dictionary.
:vartype blob_reference_data: dict[str, ~flow.models.RegistryBlobReferenceData]
:ivar aml_module_name:
:vartype aml_module_name: str
:ivar name_only_display_info:
:vartype name_only_display_info: str
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
:ivar module_version_id:
:vartype module_version_id: str
:ivar snapshot_id:
:vartype snapshot_id: str
:ivar component_registration_type: Possible values include: "Normal", "AnonymousAmlModule",
"AnonymousAmlModuleVersion", "ModuleEntityOnly".
:vartype component_registration_type: str or ~flow.models.ComponentRegistrationTypeEnum
:ivar module_entity_from_yaml:
:vartype module_entity_from_yaml: ~flow.models.ModuleEntity
:ivar set_as_default_version:
:vartype set_as_default_version: bool
:ivar data_types_from_yaml:
:vartype data_types_from_yaml: list[~flow.models.DataTypeCreationInfo]
:ivar data_type_mechanism: Possible values include: "ErrorWhenNotExisting",
"RegisterWhenNotExisting", "RegisterBuildinDataTypeOnly".
:vartype data_type_mechanism: str or ~flow.models.DataTypeMechanism
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hashes:
:vartype identifier_hashes: ~flow.models.RegisterRegistryComponentMetaInfoIdentifierHashes
:ivar content_hash:
:vartype content_hash: str
:ivar extra_hash:
:vartype extra_hash: str
:ivar extra_hashes:
:vartype extra_hashes: ~flow.models.RegisterRegistryComponentMetaInfoExtraHashes
:ivar registration:
:vartype registration: bool
:ivar validate_only:
:vartype validate_only: bool
:ivar skip_workspace_related_check:
:vartype skip_workspace_related_check: bool
:ivar intellectual_property_protected_workspace_component_registration_allowed_publisher:
:vartype intellectual_property_protected_workspace_component_registration_allowed_publisher:
list[str]
:ivar system_managed_registration:
:vartype system_managed_registration: bool
:ivar allow_dup_name_between_input_and_ouput_port:
:vartype allow_dup_name_between_input_and_ouput_port: bool
:ivar module_source:
:vartype module_source: str
:ivar module_scope:
:vartype module_scope: str
:ivar module_additional_includes_count:
:vartype module_additional_includes_count: int
:ivar module_os_type:
:vartype module_os_type: str
:ivar module_codegen_by:
:vartype module_codegen_by: str
:ivar module_client_source:
:vartype module_client_source: str
:ivar module_is_builtin:
:vartype module_is_builtin: bool
:ivar module_register_event_extension_fields: Dictionary of :code:`<string>`.
:vartype module_register_event_extension_fields: dict[str, str]
"""
_attribute_map = {
'registry_name': {'key': 'registryName', 'type': 'str'},
'intellectual_property_publisher_information': {'key': 'intellectualPropertyPublisherInformation', 'type': 'IntellectualPropertyPublisherInformation'},
'blob_reference_data': {'key': 'blobReferenceData', 'type': '{RegistryBlobReferenceData}'},
'aml_module_name': {'key': 'amlModuleName', 'type': 'str'},
'name_only_display_info': {'key': 'nameOnlyDisplayInfo', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'module_version_id': {'key': 'moduleVersionId', 'type': 'str'},
'snapshot_id': {'key': 'snapshotId', 'type': 'str'},
'component_registration_type': {'key': 'componentRegistrationType', 'type': 'str'},
'module_entity_from_yaml': {'key': 'moduleEntityFromYaml', 'type': 'ModuleEntity'},
'set_as_default_version': {'key': 'setAsDefaultVersion', 'type': 'bool'},
'data_types_from_yaml': {'key': 'dataTypesFromYaml', 'type': '[DataTypeCreationInfo]'},
'data_type_mechanism': {'key': 'dataTypeMechanism', 'type': 'str'},
'identifier_hash': {'key': 'identifierHash', 'type': 'str'},
'identifier_hashes': {'key': 'identifierHashes', 'type': 'RegisterRegistryComponentMetaInfoIdentifierHashes'},
'content_hash': {'key': 'contentHash', 'type': 'str'},
'extra_hash': {'key': 'extraHash', 'type': 'str'},
'extra_hashes': {'key': 'extraHashes', 'type': 'RegisterRegistryComponentMetaInfoExtraHashes'},
'registration': {'key': 'registration', 'type': 'bool'},
'validate_only': {'key': 'validateOnly', 'type': 'bool'},
'skip_workspace_related_check': {'key': 'skipWorkspaceRelatedCheck', 'type': 'bool'},
'intellectual_property_protected_workspace_component_registration_allowed_publisher': {'key': 'intellectualPropertyProtectedWorkspaceComponentRegistrationAllowedPublisher', 'type': '[str]'},
'system_managed_registration': {'key': 'systemManagedRegistration', 'type': 'bool'},
'allow_dup_name_between_input_and_ouput_port': {'key': 'allowDupNameBetweenInputAndOuputPort', 'type': 'bool'},
'module_source': {'key': 'moduleSource', 'type': 'str'},
'module_scope': {'key': 'moduleScope', 'type': 'str'},
'module_additional_includes_count': {'key': 'moduleAdditionalIncludesCount', 'type': 'int'},
'module_os_type': {'key': 'moduleOSType', 'type': 'str'},
'module_codegen_by': {'key': 'moduleCodegenBy', 'type': 'str'},
'module_client_source': {'key': 'moduleClientSource', 'type': 'str'},
'module_is_builtin': {'key': 'moduleIsBuiltin', 'type': 'bool'},
'module_register_event_extension_fields': {'key': 'moduleRegisterEventExtensionFields', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword registry_name:
:paramtype registry_name: str
:keyword intellectual_property_publisher_information:
:paramtype intellectual_property_publisher_information:
~flow.models.IntellectualPropertyPublisherInformation
:keyword blob_reference_data: This is a dictionary.
:paramtype blob_reference_data: dict[str, ~flow.models.RegistryBlobReferenceData]
:keyword aml_module_name:
:paramtype aml_module_name: str
:keyword name_only_display_info:
:paramtype name_only_display_info: str
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
:keyword module_version_id:
:paramtype module_version_id: str
:keyword snapshot_id:
:paramtype snapshot_id: str
:keyword component_registration_type: Possible values include: "Normal", "AnonymousAmlModule",
"AnonymousAmlModuleVersion", "ModuleEntityOnly".
:paramtype component_registration_type: str or ~flow.models.ComponentRegistrationTypeEnum
:keyword module_entity_from_yaml:
:paramtype module_entity_from_yaml: ~flow.models.ModuleEntity
:keyword set_as_default_version:
:paramtype set_as_default_version: bool
:keyword data_types_from_yaml:
:paramtype data_types_from_yaml: list[~flow.models.DataTypeCreationInfo]
:keyword data_type_mechanism: Possible values include: "ErrorWhenNotExisting",
"RegisterWhenNotExisting", "RegisterBuildinDataTypeOnly".
:paramtype data_type_mechanism: str or ~flow.models.DataTypeMechanism
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hashes:
:paramtype identifier_hashes: ~flow.models.RegisterRegistryComponentMetaInfoIdentifierHashes
:keyword content_hash:
:paramtype content_hash: str
:keyword extra_hash:
:paramtype extra_hash: str
:keyword extra_hashes:
:paramtype extra_hashes: ~flow.models.RegisterRegistryComponentMetaInfoExtraHashes
:keyword registration:
:paramtype registration: bool
:keyword validate_only:
:paramtype validate_only: bool
:keyword skip_workspace_related_check:
:paramtype skip_workspace_related_check: bool
:keyword intellectual_property_protected_workspace_component_registration_allowed_publisher:
:paramtype intellectual_property_protected_workspace_component_registration_allowed_publisher:
list[str]
:keyword system_managed_registration:
:paramtype system_managed_registration: bool
:keyword allow_dup_name_between_input_and_ouput_port:
:paramtype allow_dup_name_between_input_and_ouput_port: bool
:keyword module_source:
:paramtype module_source: str
:keyword module_scope:
:paramtype module_scope: str
:keyword module_additional_includes_count:
:paramtype module_additional_includes_count: int
:keyword module_os_type:
:paramtype module_os_type: str
:keyword module_codegen_by:
:paramtype module_codegen_by: str
:keyword module_client_source:
:paramtype module_client_source: str
:keyword module_is_builtin:
:paramtype module_is_builtin: bool
:keyword module_register_event_extension_fields: Dictionary of :code:`<string>`.
:paramtype module_register_event_extension_fields: dict[str, str]
"""
super(RegisterRegistryComponentMetaInfo, self).__init__(**kwargs)
self.registry_name = kwargs.get('registry_name', None)
self.intellectual_property_publisher_information = kwargs.get('intellectual_property_publisher_information', None)
self.blob_reference_data = kwargs.get('blob_reference_data', None)
self.aml_module_name = kwargs.get('aml_module_name', None)
self.name_only_display_info = kwargs.get('name_only_display_info', None)
self.name = kwargs.get('name', None)
self.version = kwargs.get('version', None)
self.module_version_id = kwargs.get('module_version_id', None)
self.snapshot_id = kwargs.get('snapshot_id', None)
self.component_registration_type = kwargs.get('component_registration_type', None)
self.module_entity_from_yaml = kwargs.get('module_entity_from_yaml', None)
self.set_as_default_version = kwargs.get('set_as_default_version', None)
self.data_types_from_yaml = kwargs.get('data_types_from_yaml', None)
self.data_type_mechanism = kwargs.get('data_type_mechanism', None)
self.identifier_hash = kwargs.get('identifier_hash', None)
self.identifier_hashes = kwargs.get('identifier_hashes', None)
self.content_hash = kwargs.get('content_hash', None)
self.extra_hash = kwargs.get('extra_hash', None)
self.extra_hashes = kwargs.get('extra_hashes', None)
self.registration = kwargs.get('registration', None)
self.validate_only = kwargs.get('validate_only', None)
self.skip_workspace_related_check = kwargs.get('skip_workspace_related_check', None)
self.intellectual_property_protected_workspace_component_registration_allowed_publisher = kwargs.get('intellectual_property_protected_workspace_component_registration_allowed_publisher', None)
self.system_managed_registration = kwargs.get('system_managed_registration', None)
self.allow_dup_name_between_input_and_ouput_port = kwargs.get('allow_dup_name_between_input_and_ouput_port', None)
self.module_source = kwargs.get('module_source', None)
self.module_scope = kwargs.get('module_scope', None)
self.module_additional_includes_count = kwargs.get('module_additional_includes_count', None)
self.module_os_type = kwargs.get('module_os_type', None)
self.module_codegen_by = kwargs.get('module_codegen_by', None)
self.module_client_source = kwargs.get('module_client_source', None)
self.module_is_builtin = kwargs.get('module_is_builtin', None)
self.module_register_event_extension_fields = kwargs.get('module_register_event_extension_fields', None)
class RegisterRegistryComponentMetaInfoExtraHashes(msrest.serialization.Model):
"""RegisterRegistryComponentMetaInfoExtraHashes.
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hash_v2:
:vartype identifier_hash_v2: str
"""
_attribute_map = {
'identifier_hash': {'key': 'IdentifierHash', 'type': 'str'},
'identifier_hash_v2': {'key': 'IdentifierHashV2', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hash_v2:
:paramtype identifier_hash_v2: str
"""
super(RegisterRegistryComponentMetaInfoExtraHashes, self).__init__(**kwargs)
self.identifier_hash = kwargs.get('identifier_hash', None)
self.identifier_hash_v2 = kwargs.get('identifier_hash_v2', None)
class RegisterRegistryComponentMetaInfoIdentifierHashes(msrest.serialization.Model):
"""RegisterRegistryComponentMetaInfoIdentifierHashes.
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hash_v2:
:vartype identifier_hash_v2: str
"""
_attribute_map = {
'identifier_hash': {'key': 'IdentifierHash', 'type': 'str'},
'identifier_hash_v2': {'key': 'IdentifierHashV2', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hash_v2:
:paramtype identifier_hash_v2: str
"""
super(RegisterRegistryComponentMetaInfoIdentifierHashes, self).__init__(**kwargs)
self.identifier_hash = kwargs.get('identifier_hash', None)
self.identifier_hash_v2 = kwargs.get('identifier_hash_v2', None)
class RegistrationOptions(msrest.serialization.Model):
"""RegistrationOptions.
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar properties: Dictionary of :code:`<string>`.
:vartype properties: dict[str, str]
:ivar dataset_registration_options:
:vartype dataset_registration_options: ~flow.models.DatasetRegistrationOptions
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'dataset_registration_options': {'key': 'datasetRegistrationOptions', 'type': 'DatasetRegistrationOptions'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword properties: Dictionary of :code:`<string>`.
:paramtype properties: dict[str, str]
:keyword dataset_registration_options:
:paramtype dataset_registration_options: ~flow.models.DatasetRegistrationOptions
"""
super(RegistrationOptions, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.version = kwargs.get('version', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
self.dataset_registration_options = kwargs.get('dataset_registration_options', None)
class RegistryBlobReferenceData(msrest.serialization.Model):
"""RegistryBlobReferenceData.
:ivar data_reference_id:
:vartype data_reference_id: str
:ivar data:
:vartype data: str
"""
_attribute_map = {
'data_reference_id': {'key': 'dataReferenceId', 'type': 'str'},
'data': {'key': 'data', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_reference_id:
:paramtype data_reference_id: str
:keyword data:
:paramtype data: str
"""
super(RegistryBlobReferenceData, self).__init__(**kwargs)
self.data_reference_id = kwargs.get('data_reference_id', None)
self.data = kwargs.get('data', None)
class RegistryIdentity(msrest.serialization.Model):
"""RegistryIdentity.
:ivar resource_id:
:vartype resource_id: str
:ivar client_id:
:vartype client_id: str
"""
_attribute_map = {
'resource_id': {'key': 'resourceId', 'type': 'str'},
'client_id': {'key': 'clientId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword resource_id:
:paramtype resource_id: str
:keyword client_id:
:paramtype client_id: str
"""
super(RegistryIdentity, self).__init__(**kwargs)
self.resource_id = kwargs.get('resource_id', None)
self.client_id = kwargs.get('client_id', None)
class Relationship(msrest.serialization.Model):
"""Relationship.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar relation_type:
:vartype relation_type: str
:ivar target_entity_id:
:vartype target_entity_id: str
:ivar asset_id:
:vartype asset_id: str
:ivar entity_type:
:vartype entity_type: str
:ivar direction:
:vartype direction: str
:ivar entity_container_id:
:vartype entity_container_id: str
"""
_validation = {
'entity_type': {'readonly': True},
'entity_container_id': {'readonly': True},
}
_attribute_map = {
'relation_type': {'key': 'relationType', 'type': 'str'},
'target_entity_id': {'key': 'targetEntityId', 'type': 'str'},
'asset_id': {'key': 'assetId', 'type': 'str'},
'entity_type': {'key': 'entityType', 'type': 'str'},
'direction': {'key': 'direction', 'type': 'str'},
'entity_container_id': {'key': 'entityContainerId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword relation_type:
:paramtype relation_type: str
:keyword target_entity_id:
:paramtype target_entity_id: str
:keyword asset_id:
:paramtype asset_id: str
:keyword direction:
:paramtype direction: str
"""
super(Relationship, self).__init__(**kwargs)
self.relation_type = kwargs.get('relation_type', None)
self.target_entity_id = kwargs.get('target_entity_id', None)
self.asset_id = kwargs.get('asset_id', None)
self.entity_type = None
self.direction = kwargs.get('direction', None)
self.entity_container_id = None
class RemoteDockerComputeInfo(msrest.serialization.Model):
"""RemoteDockerComputeInfo.
:ivar address:
:vartype address: str
:ivar username:
:vartype username: str
:ivar password:
:vartype password: str
:ivar private_key:
:vartype private_key: str
"""
_attribute_map = {
'address': {'key': 'address', 'type': 'str'},
'username': {'key': 'username', 'type': 'str'},
'password': {'key': 'password', 'type': 'str'},
'private_key': {'key': 'privateKey', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword address:
:paramtype address: str
:keyword username:
:paramtype username: str
:keyword password:
:paramtype password: str
:keyword private_key:
:paramtype private_key: str
"""
super(RemoteDockerComputeInfo, self).__init__(**kwargs)
self.address = kwargs.get('address', None)
self.username = kwargs.get('username', None)
self.password = kwargs.get('password', None)
self.private_key = kwargs.get('private_key', None)
class ResourceConfig(msrest.serialization.Model):
"""ResourceConfig.
:ivar gpu_count:
:vartype gpu_count: int
:ivar cpu_count:
:vartype cpu_count: int
:ivar memory_request_in_gb:
:vartype memory_request_in_gb: int
"""
_attribute_map = {
'gpu_count': {'key': 'gpuCount', 'type': 'int'},
'cpu_count': {'key': 'cpuCount', 'type': 'int'},
'memory_request_in_gb': {'key': 'memoryRequestInGB', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword gpu_count:
:paramtype gpu_count: int
:keyword cpu_count:
:paramtype cpu_count: int
:keyword memory_request_in_gb:
:paramtype memory_request_in_gb: int
"""
super(ResourceConfig, self).__init__(**kwargs)
self.gpu_count = kwargs.get('gpu_count', None)
self.cpu_count = kwargs.get('cpu_count', None)
self.memory_request_in_gb = kwargs.get('memory_request_in_gb', None)
class ResourceConfiguration(msrest.serialization.Model):
"""ResourceConfiguration.
:ivar gpu_count:
:vartype gpu_count: int
:ivar cpu_count:
:vartype cpu_count: int
:ivar memory_request_in_gb:
:vartype memory_request_in_gb: int
"""
_attribute_map = {
'gpu_count': {'key': 'gpuCount', 'type': 'int'},
'cpu_count': {'key': 'cpuCount', 'type': 'int'},
'memory_request_in_gb': {'key': 'memoryRequestInGB', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword gpu_count:
:paramtype gpu_count: int
:keyword cpu_count:
:paramtype cpu_count: int
:keyword memory_request_in_gb:
:paramtype memory_request_in_gb: int
"""
super(ResourceConfiguration, self).__init__(**kwargs)
self.gpu_count = kwargs.get('gpu_count', None)
self.cpu_count = kwargs.get('cpu_count', None)
self.memory_request_in_gb = kwargs.get('memory_request_in_gb', None)
class ResourcesSetting(msrest.serialization.Model):
"""ResourcesSetting.
:ivar instance_size:
:vartype instance_size: str
:ivar spark_version:
:vartype spark_version: str
"""
_attribute_map = {
'instance_size': {'key': 'instanceSize', 'type': 'str'},
'spark_version': {'key': 'sparkVersion', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword instance_size:
:paramtype instance_size: str
:keyword spark_version:
:paramtype spark_version: str
"""
super(ResourcesSetting, self).__init__(**kwargs)
self.instance_size = kwargs.get('instance_size', None)
self.spark_version = kwargs.get('spark_version', None)
class RetrieveToolFuncResultRequest(msrest.serialization.Model):
"""RetrieveToolFuncResultRequest.
:ivar func_path:
:vartype func_path: str
:ivar func_kwargs: This is a dictionary.
:vartype func_kwargs: dict[str, any]
:ivar func_call_scenario: Possible values include: "generated_by", "reverse_generated_by",
"dynamic_list".
:vartype func_call_scenario: str or ~flow.models.ToolFuncCallScenario
"""
_attribute_map = {
'func_path': {'key': 'func_path', 'type': 'str'},
'func_kwargs': {'key': 'func_kwargs', 'type': '{object}'},
'func_call_scenario': {'key': 'func_call_scenario', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword func_path:
:paramtype func_path: str
:keyword func_kwargs: This is a dictionary.
:paramtype func_kwargs: dict[str, any]
:keyword func_call_scenario: Possible values include: "generated_by", "reverse_generated_by",
"dynamic_list".
:paramtype func_call_scenario: str or ~flow.models.ToolFuncCallScenario
"""
super(RetrieveToolFuncResultRequest, self).__init__(**kwargs)
self.func_path = kwargs.get('func_path', None)
self.func_kwargs = kwargs.get('func_kwargs', None)
self.func_call_scenario = kwargs.get('func_call_scenario', None)
class RetryConfiguration(msrest.serialization.Model):
"""RetryConfiguration.
:ivar max_retry_count:
:vartype max_retry_count: int
"""
_attribute_map = {
'max_retry_count': {'key': 'maxRetryCount', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword max_retry_count:
:paramtype max_retry_count: int
"""
super(RetryConfiguration, self).__init__(**kwargs)
self.max_retry_count = kwargs.get('max_retry_count', None)
class RGitHubPackage(msrest.serialization.Model):
"""RGitHubPackage.
:ivar repository:
:vartype repository: str
:ivar auth_token:
:vartype auth_token: str
"""
_attribute_map = {
'repository': {'key': 'repository', 'type': 'str'},
'auth_token': {'key': 'authToken', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword repository:
:paramtype repository: str
:keyword auth_token:
:paramtype auth_token: str
"""
super(RGitHubPackage, self).__init__(**kwargs)
self.repository = kwargs.get('repository', None)
self.auth_token = kwargs.get('auth_token', None)
class RootError(msrest.serialization.Model):
"""The root error.
:ivar code: The service-defined error code. Supported error codes: ServiceError, UserError,
ValidationError, AzureStorageError, TransientError, RequestThrottled.
:vartype code: str
:ivar severity: The Severity of error.
:vartype severity: int
:ivar message: A human-readable representation of the error.
:vartype message: str
:ivar message_format: An unformatted version of the message with no variable substitution.
:vartype message_format: str
:ivar message_parameters: Value substitutions corresponding to the contents of MessageFormat.
:vartype message_parameters: dict[str, str]
:ivar reference_code: This code can optionally be set by the system generating the error.
It should be used to classify the problem and identify the module and code area where the
failure occured.
:vartype reference_code: str
:ivar details_uri: A URI which points to more details about the context of the error.
:vartype details_uri: str
:ivar target: The target of the error (e.g., the name of the property in error).
:vartype target: str
:ivar details: The related errors that occurred during the request.
:vartype details: list[~flow.models.RootError]
:ivar inner_error: A nested structure of errors.
:vartype inner_error: ~flow.models.InnerErrorResponse
:ivar additional_info: The error additional info.
:vartype additional_info: list[~flow.models.ErrorAdditionalInfo]
"""
_attribute_map = {
'code': {'key': 'code', 'type': 'str'},
'severity': {'key': 'severity', 'type': 'int'},
'message': {'key': 'message', 'type': 'str'},
'message_format': {'key': 'messageFormat', 'type': 'str'},
'message_parameters': {'key': 'messageParameters', 'type': '{str}'},
'reference_code': {'key': 'referenceCode', 'type': 'str'},
'details_uri': {'key': 'detailsUri', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
'details': {'key': 'details', 'type': '[RootError]'},
'inner_error': {'key': 'innerError', 'type': 'InnerErrorResponse'},
'additional_info': {'key': 'additionalInfo', 'type': '[ErrorAdditionalInfo]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword code: The service-defined error code. Supported error codes: ServiceError, UserError,
ValidationError, AzureStorageError, TransientError, RequestThrottled.
:paramtype code: str
:keyword severity: The Severity of error.
:paramtype severity: int
:keyword message: A human-readable representation of the error.
:paramtype message: str
:keyword message_format: An unformatted version of the message with no variable substitution.
:paramtype message_format: str
:keyword message_parameters: Value substitutions corresponding to the contents of
MessageFormat.
:paramtype message_parameters: dict[str, str]
:keyword reference_code: This code can optionally be set by the system generating the error.
It should be used to classify the problem and identify the module and code area where the
failure occured.
:paramtype reference_code: str
:keyword details_uri: A URI which points to more details about the context of the error.
:paramtype details_uri: str
:keyword target: The target of the error (e.g., the name of the property in error).
:paramtype target: str
:keyword details: The related errors that occurred during the request.
:paramtype details: list[~flow.models.RootError]
:keyword inner_error: A nested structure of errors.
:paramtype inner_error: ~flow.models.InnerErrorResponse
:keyword additional_info: The error additional info.
:paramtype additional_info: list[~flow.models.ErrorAdditionalInfo]
"""
super(RootError, self).__init__(**kwargs)
self.code = kwargs.get('code', None)
self.severity = kwargs.get('severity', None)
self.message = kwargs.get('message', None)
self.message_format = kwargs.get('message_format', None)
self.message_parameters = kwargs.get('message_parameters', None)
self.reference_code = kwargs.get('reference_code', None)
self.details_uri = kwargs.get('details_uri', None)
self.target = kwargs.get('target', None)
self.details = kwargs.get('details', None)
self.inner_error = kwargs.get('inner_error', None)
self.additional_info = kwargs.get('additional_info', None)
class RSection(msrest.serialization.Model):
"""RSection.
:ivar r_version:
:vartype r_version: str
:ivar user_managed:
:vartype user_managed: bool
:ivar rscript_path:
:vartype rscript_path: str
:ivar snapshot_date:
:vartype snapshot_date: str
:ivar cran_packages:
:vartype cran_packages: list[~flow.models.RCranPackage]
:ivar git_hub_packages:
:vartype git_hub_packages: list[~flow.models.RGitHubPackage]
:ivar custom_url_packages:
:vartype custom_url_packages: list[str]
:ivar bio_conductor_packages:
:vartype bio_conductor_packages: list[str]
"""
_attribute_map = {
'r_version': {'key': 'rVersion', 'type': 'str'},
'user_managed': {'key': 'userManaged', 'type': 'bool'},
'rscript_path': {'key': 'rscriptPath', 'type': 'str'},
'snapshot_date': {'key': 'snapshotDate', 'type': 'str'},
'cran_packages': {'key': 'cranPackages', 'type': '[RCranPackage]'},
'git_hub_packages': {'key': 'gitHubPackages', 'type': '[RGitHubPackage]'},
'custom_url_packages': {'key': 'customUrlPackages', 'type': '[str]'},
'bio_conductor_packages': {'key': 'bioConductorPackages', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword r_version:
:paramtype r_version: str
:keyword user_managed:
:paramtype user_managed: bool
:keyword rscript_path:
:paramtype rscript_path: str
:keyword snapshot_date:
:paramtype snapshot_date: str
:keyword cran_packages:
:paramtype cran_packages: list[~flow.models.RCranPackage]
:keyword git_hub_packages:
:paramtype git_hub_packages: list[~flow.models.RGitHubPackage]
:keyword custom_url_packages:
:paramtype custom_url_packages: list[str]
:keyword bio_conductor_packages:
:paramtype bio_conductor_packages: list[str]
"""
super(RSection, self).__init__(**kwargs)
self.r_version = kwargs.get('r_version', None)
self.user_managed = kwargs.get('user_managed', None)
self.rscript_path = kwargs.get('rscript_path', None)
self.snapshot_date = kwargs.get('snapshot_date', None)
self.cran_packages = kwargs.get('cran_packages', None)
self.git_hub_packages = kwargs.get('git_hub_packages', None)
self.custom_url_packages = kwargs.get('custom_url_packages', None)
self.bio_conductor_packages = kwargs.get('bio_conductor_packages', None)
class RunAnnotations(msrest.serialization.Model):
"""RunAnnotations.
:ivar display_name:
:vartype display_name: str
:ivar status:
:vartype status: str
:ivar primary_metric_name:
:vartype primary_metric_name: str
:ivar estimated_cost:
:vartype estimated_cost: float
:ivar primary_metric_summary:
:vartype primary_metric_summary: ~flow.models.RunIndexMetricSummary
:ivar metrics: Dictionary of :code:`<RunIndexMetricSummarySystemObject>`.
:vartype metrics: dict[str, ~flow.models.RunIndexMetricSummarySystemObject]
:ivar parameters: Dictionary of :code:`<any>`.
:vartype parameters: dict[str, any]
:ivar settings: Dictionary of :code:`<string>`.
:vartype settings: dict[str, str]
:ivar modified_time:
:vartype modified_time: ~datetime.datetime
:ivar retain_for_lifetime_of_workspace:
:vartype retain_for_lifetime_of_workspace: bool
:ivar error:
:vartype error: ~flow.models.IndexedErrorResponse
:ivar resource_metric_summary:
:vartype resource_metric_summary: ~flow.models.RunIndexResourceMetricSummary
:ivar job_cost:
:vartype job_cost: ~flow.models.JobCost
:ivar compute_duration:
:vartype compute_duration: str
:ivar compute_duration_milliseconds:
:vartype compute_duration_milliseconds: float
:ivar effective_start_time_utc:
:vartype effective_start_time_utc: ~datetime.datetime
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar archived:
:vartype archived: bool
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
"""
_attribute_map = {
'display_name': {'key': 'displayName', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'primary_metric_name': {'key': 'primaryMetricName', 'type': 'str'},
'estimated_cost': {'key': 'estimatedCost', 'type': 'float'},
'primary_metric_summary': {'key': 'primaryMetricSummary', 'type': 'RunIndexMetricSummary'},
'metrics': {'key': 'metrics', 'type': '{RunIndexMetricSummarySystemObject}'},
'parameters': {'key': 'parameters', 'type': '{object}'},
'settings': {'key': 'settings', 'type': '{str}'},
'modified_time': {'key': 'modifiedTime', 'type': 'iso-8601'},
'retain_for_lifetime_of_workspace': {'key': 'retainForLifetimeOfWorkspace', 'type': 'bool'},
'error': {'key': 'error', 'type': 'IndexedErrorResponse'},
'resource_metric_summary': {'key': 'resourceMetricSummary', 'type': 'RunIndexResourceMetricSummary'},
'job_cost': {'key': 'jobCost', 'type': 'JobCost'},
'compute_duration': {'key': 'computeDuration', 'type': 'str'},
'compute_duration_milliseconds': {'key': 'computeDurationMilliseconds', 'type': 'float'},
'effective_start_time_utc': {'key': 'effectiveStartTimeUtc', 'type': 'iso-8601'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'archived': {'key': 'archived', 'type': 'bool'},
'tags': {'key': 'tags', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword display_name:
:paramtype display_name: str
:keyword status:
:paramtype status: str
:keyword primary_metric_name:
:paramtype primary_metric_name: str
:keyword estimated_cost:
:paramtype estimated_cost: float
:keyword primary_metric_summary:
:paramtype primary_metric_summary: ~flow.models.RunIndexMetricSummary
:keyword metrics: Dictionary of :code:`<RunIndexMetricSummarySystemObject>`.
:paramtype metrics: dict[str, ~flow.models.RunIndexMetricSummarySystemObject]
:keyword parameters: Dictionary of :code:`<any>`.
:paramtype parameters: dict[str, any]
:keyword settings: Dictionary of :code:`<string>`.
:paramtype settings: dict[str, str]
:keyword modified_time:
:paramtype modified_time: ~datetime.datetime
:keyword retain_for_lifetime_of_workspace:
:paramtype retain_for_lifetime_of_workspace: bool
:keyword error:
:paramtype error: ~flow.models.IndexedErrorResponse
:keyword resource_metric_summary:
:paramtype resource_metric_summary: ~flow.models.RunIndexResourceMetricSummary
:keyword job_cost:
:paramtype job_cost: ~flow.models.JobCost
:keyword compute_duration:
:paramtype compute_duration: str
:keyword compute_duration_milliseconds:
:paramtype compute_duration_milliseconds: float
:keyword effective_start_time_utc:
:paramtype effective_start_time_utc: ~datetime.datetime
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword archived:
:paramtype archived: bool
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
"""
super(RunAnnotations, self).__init__(**kwargs)
self.display_name = kwargs.get('display_name', None)
self.status = kwargs.get('status', None)
self.primary_metric_name = kwargs.get('primary_metric_name', None)
self.estimated_cost = kwargs.get('estimated_cost', None)
self.primary_metric_summary = kwargs.get('primary_metric_summary', None)
self.metrics = kwargs.get('metrics', None)
self.parameters = kwargs.get('parameters', None)
self.settings = kwargs.get('settings', None)
self.modified_time = kwargs.get('modified_time', None)
self.retain_for_lifetime_of_workspace = kwargs.get('retain_for_lifetime_of_workspace', None)
self.error = kwargs.get('error', None)
self.resource_metric_summary = kwargs.get('resource_metric_summary', None)
self.job_cost = kwargs.get('job_cost', None)
self.compute_duration = kwargs.get('compute_duration', None)
self.compute_duration_milliseconds = kwargs.get('compute_duration_milliseconds', None)
self.effective_start_time_utc = kwargs.get('effective_start_time_utc', None)
self.name = kwargs.get('name', None)
self.description = kwargs.get('description', None)
self.archived = kwargs.get('archived', None)
self.tags = kwargs.get('tags', None)
class RunConfiguration(msrest.serialization.Model):
"""RunConfiguration.
:ivar script:
:vartype script: str
:ivar script_type: Possible values include: "Python", "Notebook".
:vartype script_type: str or ~flow.models.ScriptType
:ivar command:
:vartype command: str
:ivar use_absolute_path:
:vartype use_absolute_path: bool
:ivar arguments:
:vartype arguments: list[str]
:ivar framework: Possible values include: "Python", "PySpark", "Cntk", "TensorFlow", "PyTorch",
"PySparkInteractive", "R".
:vartype framework: str or ~flow.models.Framework
:ivar communicator: Possible values include: "None", "ParameterServer", "Gloo", "Mpi", "Nccl",
"ParallelTask".
:vartype communicator: str or ~flow.models.Communicator
:ivar target:
:vartype target: str
:ivar auto_cluster_compute_specification:
:vartype auto_cluster_compute_specification: ~flow.models.AutoClusterComputeSpecification
:ivar data_references: Dictionary of :code:`<DataReferenceConfiguration>`.
:vartype data_references: dict[str, ~flow.models.DataReferenceConfiguration]
:ivar data: Dictionary of :code:`<Data>`.
:vartype data: dict[str, ~flow.models.Data]
:ivar input_assets: Dictionary of :code:`<InputAsset>`.
:vartype input_assets: dict[str, ~flow.models.InputAsset]
:ivar output_data: Dictionary of :code:`<OutputData>`.
:vartype output_data: dict[str, ~flow.models.OutputData]
:ivar datacaches:
:vartype datacaches: list[~flow.models.DatacacheConfiguration]
:ivar job_name:
:vartype job_name: str
:ivar max_run_duration_seconds:
:vartype max_run_duration_seconds: long
:ivar node_count:
:vartype node_count: int
:ivar max_node_count:
:vartype max_node_count: int
:ivar instance_types:
:vartype instance_types: list[str]
:ivar priority:
:vartype priority: int
:ivar credential_passthrough:
:vartype credential_passthrough: bool
:ivar identity:
:vartype identity: ~flow.models.IdentityConfiguration
:ivar environment:
:vartype environment: ~flow.models.EnvironmentDefinition
:ivar history:
:vartype history: ~flow.models.HistoryConfiguration
:ivar spark:
:vartype spark: ~flow.models.SparkConfiguration
:ivar parallel_task:
:vartype parallel_task: ~flow.models.ParallelTaskConfiguration
:ivar tensorflow:
:vartype tensorflow: ~flow.models.TensorflowConfiguration
:ivar mpi:
:vartype mpi: ~flow.models.MpiConfiguration
:ivar py_torch:
:vartype py_torch: ~flow.models.PyTorchConfiguration
:ivar ray:
:vartype ray: ~flow.models.RayConfiguration
:ivar hdi:
:vartype hdi: ~flow.models.HdiConfiguration
:ivar docker:
:vartype docker: ~flow.models.DockerConfiguration
:ivar command_return_code_config:
:vartype command_return_code_config: ~flow.models.CommandReturnCodeConfig
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
:ivar application_endpoints: Dictionary of :code:`<ApplicationEndpointConfiguration>`.
:vartype application_endpoints: dict[str, ~flow.models.ApplicationEndpointConfiguration]
:ivar parameters:
:vartype parameters: list[~flow.models.ParameterDefinition]
:ivar autologger_settings:
:vartype autologger_settings: ~flow.models.AutologgerSettings
:ivar data_bricks:
:vartype data_bricks: ~flow.models.DatabricksConfiguration
:ivar training_diagnostic_config:
:vartype training_diagnostic_config: ~flow.models.TrainingDiagnosticConfiguration
:ivar secrets_configuration: Dictionary of :code:`<SecretConfiguration>`.
:vartype secrets_configuration: dict[str, ~flow.models.SecretConfiguration]
"""
_attribute_map = {
'script': {'key': 'script', 'type': 'str'},
'script_type': {'key': 'scriptType', 'type': 'str'},
'command': {'key': 'command', 'type': 'str'},
'use_absolute_path': {'key': 'useAbsolutePath', 'type': 'bool'},
'arguments': {'key': 'arguments', 'type': '[str]'},
'framework': {'key': 'framework', 'type': 'str'},
'communicator': {'key': 'communicator', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
'auto_cluster_compute_specification': {'key': 'autoClusterComputeSpecification', 'type': 'AutoClusterComputeSpecification'},
'data_references': {'key': 'dataReferences', 'type': '{DataReferenceConfiguration}'},
'data': {'key': 'data', 'type': '{Data}'},
'input_assets': {'key': 'inputAssets', 'type': '{InputAsset}'},
'output_data': {'key': 'outputData', 'type': '{OutputData}'},
'datacaches': {'key': 'datacaches', 'type': '[DatacacheConfiguration]'},
'job_name': {'key': 'jobName', 'type': 'str'},
'max_run_duration_seconds': {'key': 'maxRunDurationSeconds', 'type': 'long'},
'node_count': {'key': 'nodeCount', 'type': 'int'},
'max_node_count': {'key': 'maxNodeCount', 'type': 'int'},
'instance_types': {'key': 'instanceTypes', 'type': '[str]'},
'priority': {'key': 'priority', 'type': 'int'},
'credential_passthrough': {'key': 'credentialPassthrough', 'type': 'bool'},
'identity': {'key': 'identity', 'type': 'IdentityConfiguration'},
'environment': {'key': 'environment', 'type': 'EnvironmentDefinition'},
'history': {'key': 'history', 'type': 'HistoryConfiguration'},
'spark': {'key': 'spark', 'type': 'SparkConfiguration'},
'parallel_task': {'key': 'parallelTask', 'type': 'ParallelTaskConfiguration'},
'tensorflow': {'key': 'tensorflow', 'type': 'TensorflowConfiguration'},
'mpi': {'key': 'mpi', 'type': 'MpiConfiguration'},
'py_torch': {'key': 'pyTorch', 'type': 'PyTorchConfiguration'},
'ray': {'key': 'ray', 'type': 'RayConfiguration'},
'hdi': {'key': 'hdi', 'type': 'HdiConfiguration'},
'docker': {'key': 'docker', 'type': 'DockerConfiguration'},
'command_return_code_config': {'key': 'commandReturnCodeConfig', 'type': 'CommandReturnCodeConfig'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'application_endpoints': {'key': 'applicationEndpoints', 'type': '{ApplicationEndpointConfiguration}'},
'parameters': {'key': 'parameters', 'type': '[ParameterDefinition]'},
'autologger_settings': {'key': 'autologgerSettings', 'type': 'AutologgerSettings'},
'data_bricks': {'key': 'dataBricks', 'type': 'DatabricksConfiguration'},
'training_diagnostic_config': {'key': 'trainingDiagnosticConfig', 'type': 'TrainingDiagnosticConfiguration'},
'secrets_configuration': {'key': 'secretsConfiguration', 'type': '{SecretConfiguration}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword script:
:paramtype script: str
:keyword script_type: Possible values include: "Python", "Notebook".
:paramtype script_type: str or ~flow.models.ScriptType
:keyword command:
:paramtype command: str
:keyword use_absolute_path:
:paramtype use_absolute_path: bool
:keyword arguments:
:paramtype arguments: list[str]
:keyword framework: Possible values include: "Python", "PySpark", "Cntk", "TensorFlow",
"PyTorch", "PySparkInteractive", "R".
:paramtype framework: str or ~flow.models.Framework
:keyword communicator: Possible values include: "None", "ParameterServer", "Gloo", "Mpi",
"Nccl", "ParallelTask".
:paramtype communicator: str or ~flow.models.Communicator
:keyword target:
:paramtype target: str
:keyword auto_cluster_compute_specification:
:paramtype auto_cluster_compute_specification: ~flow.models.AutoClusterComputeSpecification
:keyword data_references: Dictionary of :code:`<DataReferenceConfiguration>`.
:paramtype data_references: dict[str, ~flow.models.DataReferenceConfiguration]
:keyword data: Dictionary of :code:`<Data>`.
:paramtype data: dict[str, ~flow.models.Data]
:keyword input_assets: Dictionary of :code:`<InputAsset>`.
:paramtype input_assets: dict[str, ~flow.models.InputAsset]
:keyword output_data: Dictionary of :code:`<OutputData>`.
:paramtype output_data: dict[str, ~flow.models.OutputData]
:keyword datacaches:
:paramtype datacaches: list[~flow.models.DatacacheConfiguration]
:keyword job_name:
:paramtype job_name: str
:keyword max_run_duration_seconds:
:paramtype max_run_duration_seconds: long
:keyword node_count:
:paramtype node_count: int
:keyword max_node_count:
:paramtype max_node_count: int
:keyword instance_types:
:paramtype instance_types: list[str]
:keyword priority:
:paramtype priority: int
:keyword credential_passthrough:
:paramtype credential_passthrough: bool
:keyword identity:
:paramtype identity: ~flow.models.IdentityConfiguration
:keyword environment:
:paramtype environment: ~flow.models.EnvironmentDefinition
:keyword history:
:paramtype history: ~flow.models.HistoryConfiguration
:keyword spark:
:paramtype spark: ~flow.models.SparkConfiguration
:keyword parallel_task:
:paramtype parallel_task: ~flow.models.ParallelTaskConfiguration
:keyword tensorflow:
:paramtype tensorflow: ~flow.models.TensorflowConfiguration
:keyword mpi:
:paramtype mpi: ~flow.models.MpiConfiguration
:keyword py_torch:
:paramtype py_torch: ~flow.models.PyTorchConfiguration
:keyword ray:
:paramtype ray: ~flow.models.RayConfiguration
:keyword hdi:
:paramtype hdi: ~flow.models.HdiConfiguration
:keyword docker:
:paramtype docker: ~flow.models.DockerConfiguration
:keyword command_return_code_config:
:paramtype command_return_code_config: ~flow.models.CommandReturnCodeConfig
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
:keyword application_endpoints: Dictionary of :code:`<ApplicationEndpointConfiguration>`.
:paramtype application_endpoints: dict[str, ~flow.models.ApplicationEndpointConfiguration]
:keyword parameters:
:paramtype parameters: list[~flow.models.ParameterDefinition]
:keyword autologger_settings:
:paramtype autologger_settings: ~flow.models.AutologgerSettings
:keyword data_bricks:
:paramtype data_bricks: ~flow.models.DatabricksConfiguration
:keyword training_diagnostic_config:
:paramtype training_diagnostic_config: ~flow.models.TrainingDiagnosticConfiguration
:keyword secrets_configuration: Dictionary of :code:`<SecretConfiguration>`.
:paramtype secrets_configuration: dict[str, ~flow.models.SecretConfiguration]
"""
super(RunConfiguration, self).__init__(**kwargs)
self.script = kwargs.get('script', None)
self.script_type = kwargs.get('script_type', None)
self.command = kwargs.get('command', None)
self.use_absolute_path = kwargs.get('use_absolute_path', None)
self.arguments = kwargs.get('arguments', None)
self.framework = kwargs.get('framework', None)
self.communicator = kwargs.get('communicator', None)
self.target = kwargs.get('target', None)
self.auto_cluster_compute_specification = kwargs.get('auto_cluster_compute_specification', None)
self.data_references = kwargs.get('data_references', None)
self.data = kwargs.get('data', None)
self.input_assets = kwargs.get('input_assets', None)
self.output_data = kwargs.get('output_data', None)
self.datacaches = kwargs.get('datacaches', None)
self.job_name = kwargs.get('job_name', None)
self.max_run_duration_seconds = kwargs.get('max_run_duration_seconds', None)
self.node_count = kwargs.get('node_count', None)
self.max_node_count = kwargs.get('max_node_count', None)
self.instance_types = kwargs.get('instance_types', None)
self.priority = kwargs.get('priority', None)
self.credential_passthrough = kwargs.get('credential_passthrough', None)
self.identity = kwargs.get('identity', None)
self.environment = kwargs.get('environment', None)
self.history = kwargs.get('history', None)
self.spark = kwargs.get('spark', None)
self.parallel_task = kwargs.get('parallel_task', None)
self.tensorflow = kwargs.get('tensorflow', None)
self.mpi = kwargs.get('mpi', None)
self.py_torch = kwargs.get('py_torch', None)
self.ray = kwargs.get('ray', None)
self.hdi = kwargs.get('hdi', None)
self.docker = kwargs.get('docker', None)
self.command_return_code_config = kwargs.get('command_return_code_config', None)
self.environment_variables = kwargs.get('environment_variables', None)
self.application_endpoints = kwargs.get('application_endpoints', None)
self.parameters = kwargs.get('parameters', None)
self.autologger_settings = kwargs.get('autologger_settings', None)
self.data_bricks = kwargs.get('data_bricks', None)
self.training_diagnostic_config = kwargs.get('training_diagnostic_config', None)
self.secrets_configuration = kwargs.get('secrets_configuration', None)
class RunDatasetReference(msrest.serialization.Model):
"""RunDatasetReference.
:ivar id:
:vartype id: str
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
"""
super(RunDatasetReference, self).__init__(**kwargs)
self.id = kwargs.get('id', None)
self.name = kwargs.get('name', None)
self.version = kwargs.get('version', None)
class RunDefinition(msrest.serialization.Model):
"""RunDefinition.
:ivar configuration:
:vartype configuration: ~flow.models.RunConfiguration
:ivar snapshot_id:
:vartype snapshot_id: str
:ivar snapshots:
:vartype snapshots: list[~flow.models.Snapshot]
:ivar parent_run_id:
:vartype parent_run_id: str
:ivar run_type:
:vartype run_type: str
:ivar display_name:
:vartype display_name: str
:ivar environment_asset_id:
:vartype environment_asset_id: str
:ivar primary_metric_name:
:vartype primary_metric_name: str
:ivar description:
:vartype description: str
:ivar cancel_reason:
:vartype cancel_reason: str
:ivar properties: Dictionary of :code:`<string>`.
:vartype properties: dict[str, str]
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
"""
_attribute_map = {
'configuration': {'key': 'configuration', 'type': 'RunConfiguration'},
'snapshot_id': {'key': 'snapshotId', 'type': 'str'},
'snapshots': {'key': 'snapshots', 'type': '[Snapshot]'},
'parent_run_id': {'key': 'parentRunId', 'type': 'str'},
'run_type': {'key': 'runType', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'environment_asset_id': {'key': 'environmentAssetId', 'type': 'str'},
'primary_metric_name': {'key': 'primaryMetricName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'cancel_reason': {'key': 'cancelReason', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'tags': {'key': 'tags', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword configuration:
:paramtype configuration: ~flow.models.RunConfiguration
:keyword snapshot_id:
:paramtype snapshot_id: str
:keyword snapshots:
:paramtype snapshots: list[~flow.models.Snapshot]
:keyword parent_run_id:
:paramtype parent_run_id: str
:keyword run_type:
:paramtype run_type: str
:keyword display_name:
:paramtype display_name: str
:keyword environment_asset_id:
:paramtype environment_asset_id: str
:keyword primary_metric_name:
:paramtype primary_metric_name: str
:keyword description:
:paramtype description: str
:keyword cancel_reason:
:paramtype cancel_reason: str
:keyword properties: Dictionary of :code:`<string>`.
:paramtype properties: dict[str, str]
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
"""
super(RunDefinition, self).__init__(**kwargs)
self.configuration = kwargs.get('configuration', None)
self.snapshot_id = kwargs.get('snapshot_id', None)
self.snapshots = kwargs.get('snapshots', None)
self.parent_run_id = kwargs.get('parent_run_id', None)
self.run_type = kwargs.get('run_type', None)
self.display_name = kwargs.get('display_name', None)
self.environment_asset_id = kwargs.get('environment_asset_id', None)
self.primary_metric_name = kwargs.get('primary_metric_name', None)
self.description = kwargs.get('description', None)
self.cancel_reason = kwargs.get('cancel_reason', None)
self.properties = kwargs.get('properties', None)
self.tags = kwargs.get('tags', None)
class RunDetailsDto(msrest.serialization.Model):
"""RunDetailsDto.
:ivar run_id:
:vartype run_id: str
:ivar run_uuid:
:vartype run_uuid: str
:ivar parent_run_uuid:
:vartype parent_run_uuid: str
:ivar root_run_uuid:
:vartype root_run_uuid: str
:ivar target:
:vartype target: str
:ivar status:
:vartype status: str
:ivar parent_run_id:
:vartype parent_run_id: str
:ivar data_container_id:
:vartype data_container_id: str
:ivar created_time_utc:
:vartype created_time_utc: ~datetime.datetime
:ivar start_time_utc:
:vartype start_time_utc: ~datetime.datetime
:ivar end_time_utc:
:vartype end_time_utc: ~datetime.datetime
:ivar error: The error response.
:vartype error: ~flow.models.ErrorResponse
:ivar warnings:
:vartype warnings: list[~flow.models.RunDetailsWarningDto]
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar parameters: Dictionary of :code:`<any>`.
:vartype parameters: dict[str, any]
:ivar services: This is a dictionary.
:vartype services: dict[str, ~flow.models.EndpointSetting]
:ivar input_datasets:
:vartype input_datasets: list[~flow.models.DatasetLineage]
:ivar output_datasets:
:vartype output_datasets: list[~flow.models.OutputDatasetLineage]
:ivar run_definition: Anything.
:vartype run_definition: any
:ivar log_files: This is a dictionary.
:vartype log_files: dict[str, str]
:ivar job_cost:
:vartype job_cost: ~flow.models.JobCost
:ivar revision:
:vartype revision: long
:ivar run_type_v2:
:vartype run_type_v2: ~flow.models.RunTypeV2
:ivar settings: This is a dictionary.
:vartype settings: dict[str, str]
:ivar compute_request:
:vartype compute_request: ~flow.models.ComputeRequest
:ivar compute:
:vartype compute: ~flow.models.Compute
:ivar created_by:
:vartype created_by: ~flow.models.User
:ivar compute_duration:
:vartype compute_duration: str
:ivar effective_start_time_utc:
:vartype effective_start_time_utc: ~datetime.datetime
:ivar run_number:
:vartype run_number: int
:ivar root_run_id:
:vartype root_run_id: str
:ivar experiment_id:
:vartype experiment_id: str
:ivar user_id:
:vartype user_id: str
:ivar status_revision:
:vartype status_revision: long
:ivar current_compute_time:
:vartype current_compute_time: str
:ivar last_start_time_utc:
:vartype last_start_time_utc: ~datetime.datetime
:ivar last_modified_by:
:vartype last_modified_by: ~flow.models.User
:ivar last_modified_utc:
:vartype last_modified_utc: ~datetime.datetime
:ivar duration:
:vartype duration: str
:ivar inputs: Dictionary of :code:`<TypedAssetReference>`.
:vartype inputs: dict[str, ~flow.models.TypedAssetReference]
:ivar outputs: Dictionary of :code:`<TypedAssetReference>`.
:vartype outputs: dict[str, ~flow.models.TypedAssetReference]
:ivar current_attempt_id:
:vartype current_attempt_id: int
"""
_validation = {
'input_datasets': {'unique': True},
'output_datasets': {'unique': True},
}
_attribute_map = {
'run_id': {'key': 'runId', 'type': 'str'},
'run_uuid': {'key': 'runUuid', 'type': 'str'},
'parent_run_uuid': {'key': 'parentRunUuid', 'type': 'str'},
'root_run_uuid': {'key': 'rootRunUuid', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'parent_run_id': {'key': 'parentRunId', 'type': 'str'},
'data_container_id': {'key': 'dataContainerId', 'type': 'str'},
'created_time_utc': {'key': 'createdTimeUtc', 'type': 'iso-8601'},
'start_time_utc': {'key': 'startTimeUtc', 'type': 'iso-8601'},
'end_time_utc': {'key': 'endTimeUtc', 'type': 'iso-8601'},
'error': {'key': 'error', 'type': 'ErrorResponse'},
'warnings': {'key': 'warnings', 'type': '[RunDetailsWarningDto]'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'parameters': {'key': 'parameters', 'type': '{object}'},
'services': {'key': 'services', 'type': '{EndpointSetting}'},
'input_datasets': {'key': 'inputDatasets', 'type': '[DatasetLineage]'},
'output_datasets': {'key': 'outputDatasets', 'type': '[OutputDatasetLineage]'},
'run_definition': {'key': 'runDefinition', 'type': 'object'},
'log_files': {'key': 'logFiles', 'type': '{str}'},
'job_cost': {'key': 'jobCost', 'type': 'JobCost'},
'revision': {'key': 'revision', 'type': 'long'},
'run_type_v2': {'key': 'runTypeV2', 'type': 'RunTypeV2'},
'settings': {'key': 'settings', 'type': '{str}'},
'compute_request': {'key': 'computeRequest', 'type': 'ComputeRequest'},
'compute': {'key': 'compute', 'type': 'Compute'},
'created_by': {'key': 'createdBy', 'type': 'User'},
'compute_duration': {'key': 'computeDuration', 'type': 'str'},
'effective_start_time_utc': {'key': 'effectiveStartTimeUtc', 'type': 'iso-8601'},
'run_number': {'key': 'runNumber', 'type': 'int'},
'root_run_id': {'key': 'rootRunId', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'user_id': {'key': 'userId', 'type': 'str'},
'status_revision': {'key': 'statusRevision', 'type': 'long'},
'current_compute_time': {'key': 'currentComputeTime', 'type': 'str'},
'last_start_time_utc': {'key': 'lastStartTimeUtc', 'type': 'iso-8601'},
'last_modified_by': {'key': 'lastModifiedBy', 'type': 'User'},
'last_modified_utc': {'key': 'lastModifiedUtc', 'type': 'iso-8601'},
'duration': {'key': 'duration', 'type': 'str'},
'inputs': {'key': 'inputs', 'type': '{TypedAssetReference}'},
'outputs': {'key': 'outputs', 'type': '{TypedAssetReference}'},
'current_attempt_id': {'key': 'currentAttemptId', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword run_id:
:paramtype run_id: str
:keyword run_uuid:
:paramtype run_uuid: str
:keyword parent_run_uuid:
:paramtype parent_run_uuid: str
:keyword root_run_uuid:
:paramtype root_run_uuid: str
:keyword target:
:paramtype target: str
:keyword status:
:paramtype status: str
:keyword parent_run_id:
:paramtype parent_run_id: str
:keyword data_container_id:
:paramtype data_container_id: str
:keyword created_time_utc:
:paramtype created_time_utc: ~datetime.datetime
:keyword start_time_utc:
:paramtype start_time_utc: ~datetime.datetime
:keyword end_time_utc:
:paramtype end_time_utc: ~datetime.datetime
:keyword error: The error response.
:paramtype error: ~flow.models.ErrorResponse
:keyword warnings:
:paramtype warnings: list[~flow.models.RunDetailsWarningDto]
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword parameters: Dictionary of :code:`<any>`.
:paramtype parameters: dict[str, any]
:keyword services: This is a dictionary.
:paramtype services: dict[str, ~flow.models.EndpointSetting]
:keyword input_datasets:
:paramtype input_datasets: list[~flow.models.DatasetLineage]
:keyword output_datasets:
:paramtype output_datasets: list[~flow.models.OutputDatasetLineage]
:keyword run_definition: Anything.
:paramtype run_definition: any
:keyword log_files: This is a dictionary.
:paramtype log_files: dict[str, str]
:keyword job_cost:
:paramtype job_cost: ~flow.models.JobCost
:keyword revision:
:paramtype revision: long
:keyword run_type_v2:
:paramtype run_type_v2: ~flow.models.RunTypeV2
:keyword settings: This is a dictionary.
:paramtype settings: dict[str, str]
:keyword compute_request:
:paramtype compute_request: ~flow.models.ComputeRequest
:keyword compute:
:paramtype compute: ~flow.models.Compute
:keyword created_by:
:paramtype created_by: ~flow.models.User
:keyword compute_duration:
:paramtype compute_duration: str
:keyword effective_start_time_utc:
:paramtype effective_start_time_utc: ~datetime.datetime
:keyword run_number:
:paramtype run_number: int
:keyword root_run_id:
:paramtype root_run_id: str
:keyword experiment_id:
:paramtype experiment_id: str
:keyword user_id:
:paramtype user_id: str
:keyword status_revision:
:paramtype status_revision: long
:keyword current_compute_time:
:paramtype current_compute_time: str
:keyword last_start_time_utc:
:paramtype last_start_time_utc: ~datetime.datetime
:keyword last_modified_by:
:paramtype last_modified_by: ~flow.models.User
:keyword last_modified_utc:
:paramtype last_modified_utc: ~datetime.datetime
:keyword duration:
:paramtype duration: str
:keyword inputs: Dictionary of :code:`<TypedAssetReference>`.
:paramtype inputs: dict[str, ~flow.models.TypedAssetReference]
:keyword outputs: Dictionary of :code:`<TypedAssetReference>`.
:paramtype outputs: dict[str, ~flow.models.TypedAssetReference]
:keyword current_attempt_id:
:paramtype current_attempt_id: int
"""
super(RunDetailsDto, self).__init__(**kwargs)
self.run_id = kwargs.get('run_id', None)
self.run_uuid = kwargs.get('run_uuid', None)
self.parent_run_uuid = kwargs.get('parent_run_uuid', None)
self.root_run_uuid = kwargs.get('root_run_uuid', None)
self.target = kwargs.get('target', None)
self.status = kwargs.get('status', None)
self.parent_run_id = kwargs.get('parent_run_id', None)
self.data_container_id = kwargs.get('data_container_id', None)
self.created_time_utc = kwargs.get('created_time_utc', None)
self.start_time_utc = kwargs.get('start_time_utc', None)
self.end_time_utc = kwargs.get('end_time_utc', None)
self.error = kwargs.get('error', None)
self.warnings = kwargs.get('warnings', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
self.parameters = kwargs.get('parameters', None)
self.services = kwargs.get('services', None)
self.input_datasets = kwargs.get('input_datasets', None)
self.output_datasets = kwargs.get('output_datasets', None)
self.run_definition = kwargs.get('run_definition', None)
self.log_files = kwargs.get('log_files', None)
self.job_cost = kwargs.get('job_cost', None)
self.revision = kwargs.get('revision', None)
self.run_type_v2 = kwargs.get('run_type_v2', None)
self.settings = kwargs.get('settings', None)
self.compute_request = kwargs.get('compute_request', None)
self.compute = kwargs.get('compute', None)
self.created_by = kwargs.get('created_by', None)
self.compute_duration = kwargs.get('compute_duration', None)
self.effective_start_time_utc = kwargs.get('effective_start_time_utc', None)
self.run_number = kwargs.get('run_number', None)
self.root_run_id = kwargs.get('root_run_id', None)
self.experiment_id = kwargs.get('experiment_id', None)
self.user_id = kwargs.get('user_id', None)
self.status_revision = kwargs.get('status_revision', None)
self.current_compute_time = kwargs.get('current_compute_time', None)
self.last_start_time_utc = kwargs.get('last_start_time_utc', None)
self.last_modified_by = kwargs.get('last_modified_by', None)
self.last_modified_utc = kwargs.get('last_modified_utc', None)
self.duration = kwargs.get('duration', None)
self.inputs = kwargs.get('inputs', None)
self.outputs = kwargs.get('outputs', None)
self.current_attempt_id = kwargs.get('current_attempt_id', None)
class RunDetailsWarningDto(msrest.serialization.Model):
"""RunDetailsWarningDto.
:ivar source:
:vartype source: str
:ivar message:
:vartype message: str
"""
_attribute_map = {
'source': {'key': 'source', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword source:
:paramtype source: str
:keyword message:
:paramtype message: str
"""
super(RunDetailsWarningDto, self).__init__(**kwargs)
self.source = kwargs.get('source', None)
self.message = kwargs.get('message', None)
class RunDto(msrest.serialization.Model):
"""RunDto.
:ivar run_number:
:vartype run_number: int
:ivar root_run_id:
:vartype root_run_id: str
:ivar created_utc:
:vartype created_utc: ~datetime.datetime
:ivar created_by:
:vartype created_by: ~flow.models.User
:ivar user_id:
:vartype user_id: str
:ivar token:
:vartype token: str
:ivar token_expiry_time_utc:
:vartype token_expiry_time_utc: ~datetime.datetime
:ivar error: The error response.
:vartype error: ~flow.models.ErrorResponse
:ivar warnings:
:vartype warnings: list[~flow.models.RunDetailsWarningDto]
:ivar revision:
:vartype revision: long
:ivar status_revision:
:vartype status_revision: long
:ivar run_uuid:
:vartype run_uuid: str
:ivar parent_run_uuid:
:vartype parent_run_uuid: str
:ivar root_run_uuid:
:vartype root_run_uuid: str
:ivar last_start_time_utc:
:vartype last_start_time_utc: ~datetime.datetime
:ivar current_compute_time:
:vartype current_compute_time: str
:ivar compute_duration:
:vartype compute_duration: str
:ivar effective_start_time_utc:
:vartype effective_start_time_utc: ~datetime.datetime
:ivar last_modified_by:
:vartype last_modified_by: ~flow.models.User
:ivar last_modified_utc:
:vartype last_modified_utc: ~datetime.datetime
:ivar duration:
:vartype duration: str
:ivar cancelation_reason:
:vartype cancelation_reason: str
:ivar current_attempt_id:
:vartype current_attempt_id: int
:ivar run_id:
:vartype run_id: str
:ivar parent_run_id:
:vartype parent_run_id: str
:ivar experiment_id:
:vartype experiment_id: str
:ivar status:
:vartype status: str
:ivar start_time_utc:
:vartype start_time_utc: ~datetime.datetime
:ivar end_time_utc:
:vartype end_time_utc: ~datetime.datetime
:ivar schedule_id:
:vartype schedule_id: str
:ivar display_name:
:vartype display_name: str
:ivar name:
:vartype name: str
:ivar data_container_id:
:vartype data_container_id: str
:ivar description:
:vartype description: str
:ivar hidden:
:vartype hidden: bool
:ivar run_type:
:vartype run_type: str
:ivar run_type_v2:
:vartype run_type_v2: ~flow.models.RunTypeV2
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar parameters: Dictionary of :code:`<any>`.
:vartype parameters: dict[str, any]
:ivar action_uris: Dictionary of :code:`<string>`.
:vartype action_uris: dict[str, str]
:ivar script_name:
:vartype script_name: str
:ivar target:
:vartype target: str
:ivar unique_child_run_compute_targets:
:vartype unique_child_run_compute_targets: list[str]
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar settings: Dictionary of :code:`<string>`.
:vartype settings: dict[str, str]
:ivar services: Dictionary of :code:`<EndpointSetting>`.
:vartype services: dict[str, ~flow.models.EndpointSetting]
:ivar input_datasets:
:vartype input_datasets: list[~flow.models.DatasetLineage]
:ivar output_datasets:
:vartype output_datasets: list[~flow.models.OutputDatasetLineage]
:ivar run_definition: Anything.
:vartype run_definition: any
:ivar job_specification: Anything.
:vartype job_specification: any
:ivar primary_metric_name:
:vartype primary_metric_name: str
:ivar created_from:
:vartype created_from: ~flow.models.CreatedFromDto
:ivar cancel_uri:
:vartype cancel_uri: str
:ivar complete_uri:
:vartype complete_uri: str
:ivar diagnostics_uri:
:vartype diagnostics_uri: str
:ivar compute_request:
:vartype compute_request: ~flow.models.ComputeRequest
:ivar compute:
:vartype compute: ~flow.models.Compute
:ivar retain_for_lifetime_of_workspace:
:vartype retain_for_lifetime_of_workspace: bool
:ivar queueing_info:
:vartype queueing_info: ~flow.models.QueueingInfo
:ivar inputs: Dictionary of :code:`<TypedAssetReference>`.
:vartype inputs: dict[str, ~flow.models.TypedAssetReference]
:ivar outputs: Dictionary of :code:`<TypedAssetReference>`.
:vartype outputs: dict[str, ~flow.models.TypedAssetReference]
"""
_validation = {
'unique_child_run_compute_targets': {'unique': True},
'input_datasets': {'unique': True},
'output_datasets': {'unique': True},
}
_attribute_map = {
'run_number': {'key': 'runNumber', 'type': 'int'},
'root_run_id': {'key': 'rootRunId', 'type': 'str'},
'created_utc': {'key': 'createdUtc', 'type': 'iso-8601'},
'created_by': {'key': 'createdBy', 'type': 'User'},
'user_id': {'key': 'userId', 'type': 'str'},
'token': {'key': 'token', 'type': 'str'},
'token_expiry_time_utc': {'key': 'tokenExpiryTimeUtc', 'type': 'iso-8601'},
'error': {'key': 'error', 'type': 'ErrorResponse'},
'warnings': {'key': 'warnings', 'type': '[RunDetailsWarningDto]'},
'revision': {'key': 'revision', 'type': 'long'},
'status_revision': {'key': 'statusRevision', 'type': 'long'},
'run_uuid': {'key': 'runUuid', 'type': 'str'},
'parent_run_uuid': {'key': 'parentRunUuid', 'type': 'str'},
'root_run_uuid': {'key': 'rootRunUuid', 'type': 'str'},
'last_start_time_utc': {'key': 'lastStartTimeUtc', 'type': 'iso-8601'},
'current_compute_time': {'key': 'currentComputeTime', 'type': 'str'},
'compute_duration': {'key': 'computeDuration', 'type': 'str'},
'effective_start_time_utc': {'key': 'effectiveStartTimeUtc', 'type': 'iso-8601'},
'last_modified_by': {'key': 'lastModifiedBy', 'type': 'User'},
'last_modified_utc': {'key': 'lastModifiedUtc', 'type': 'iso-8601'},
'duration': {'key': 'duration', 'type': 'str'},
'cancelation_reason': {'key': 'cancelationReason', 'type': 'str'},
'current_attempt_id': {'key': 'currentAttemptId', 'type': 'int'},
'run_id': {'key': 'runId', 'type': 'str'},
'parent_run_id': {'key': 'parentRunId', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'start_time_utc': {'key': 'startTimeUtc', 'type': 'iso-8601'},
'end_time_utc': {'key': 'endTimeUtc', 'type': 'iso-8601'},
'schedule_id': {'key': 'scheduleId', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'data_container_id': {'key': 'dataContainerId', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'hidden': {'key': 'hidden', 'type': 'bool'},
'run_type': {'key': 'runType', 'type': 'str'},
'run_type_v2': {'key': 'runTypeV2', 'type': 'RunTypeV2'},
'properties': {'key': 'properties', 'type': '{str}'},
'parameters': {'key': 'parameters', 'type': '{object}'},
'action_uris': {'key': 'actionUris', 'type': '{str}'},
'script_name': {'key': 'scriptName', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
'unique_child_run_compute_targets': {'key': 'uniqueChildRunComputeTargets', 'type': '[str]'},
'tags': {'key': 'tags', 'type': '{str}'},
'settings': {'key': 'settings', 'type': '{str}'},
'services': {'key': 'services', 'type': '{EndpointSetting}'},
'input_datasets': {'key': 'inputDatasets', 'type': '[DatasetLineage]'},
'output_datasets': {'key': 'outputDatasets', 'type': '[OutputDatasetLineage]'},
'run_definition': {'key': 'runDefinition', 'type': 'object'},
'job_specification': {'key': 'jobSpecification', 'type': 'object'},
'primary_metric_name': {'key': 'primaryMetricName', 'type': 'str'},
'created_from': {'key': 'createdFrom', 'type': 'CreatedFromDto'},
'cancel_uri': {'key': 'cancelUri', 'type': 'str'},
'complete_uri': {'key': 'completeUri', 'type': 'str'},
'diagnostics_uri': {'key': 'diagnosticsUri', 'type': 'str'},
'compute_request': {'key': 'computeRequest', 'type': 'ComputeRequest'},
'compute': {'key': 'compute', 'type': 'Compute'},
'retain_for_lifetime_of_workspace': {'key': 'retainForLifetimeOfWorkspace', 'type': 'bool'},
'queueing_info': {'key': 'queueingInfo', 'type': 'QueueingInfo'},
'inputs': {'key': 'inputs', 'type': '{TypedAssetReference}'},
'outputs': {'key': 'outputs', 'type': '{TypedAssetReference}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword run_number:
:paramtype run_number: int
:keyword root_run_id:
:paramtype root_run_id: str
:keyword created_utc:
:paramtype created_utc: ~datetime.datetime
:keyword created_by:
:paramtype created_by: ~flow.models.User
:keyword user_id:
:paramtype user_id: str
:keyword token:
:paramtype token: str
:keyword token_expiry_time_utc:
:paramtype token_expiry_time_utc: ~datetime.datetime
:keyword error: The error response.
:paramtype error: ~flow.models.ErrorResponse
:keyword warnings:
:paramtype warnings: list[~flow.models.RunDetailsWarningDto]
:keyword revision:
:paramtype revision: long
:keyword status_revision:
:paramtype status_revision: long
:keyword run_uuid:
:paramtype run_uuid: str
:keyword parent_run_uuid:
:paramtype parent_run_uuid: str
:keyword root_run_uuid:
:paramtype root_run_uuid: str
:keyword last_start_time_utc:
:paramtype last_start_time_utc: ~datetime.datetime
:keyword current_compute_time:
:paramtype current_compute_time: str
:keyword compute_duration:
:paramtype compute_duration: str
:keyword effective_start_time_utc:
:paramtype effective_start_time_utc: ~datetime.datetime
:keyword last_modified_by:
:paramtype last_modified_by: ~flow.models.User
:keyword last_modified_utc:
:paramtype last_modified_utc: ~datetime.datetime
:keyword duration:
:paramtype duration: str
:keyword cancelation_reason:
:paramtype cancelation_reason: str
:keyword current_attempt_id:
:paramtype current_attempt_id: int
:keyword run_id:
:paramtype run_id: str
:keyword parent_run_id:
:paramtype parent_run_id: str
:keyword experiment_id:
:paramtype experiment_id: str
:keyword status:
:paramtype status: str
:keyword start_time_utc:
:paramtype start_time_utc: ~datetime.datetime
:keyword end_time_utc:
:paramtype end_time_utc: ~datetime.datetime
:keyword schedule_id:
:paramtype schedule_id: str
:keyword display_name:
:paramtype display_name: str
:keyword name:
:paramtype name: str
:keyword data_container_id:
:paramtype data_container_id: str
:keyword description:
:paramtype description: str
:keyword hidden:
:paramtype hidden: bool
:keyword run_type:
:paramtype run_type: str
:keyword run_type_v2:
:paramtype run_type_v2: ~flow.models.RunTypeV2
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword parameters: Dictionary of :code:`<any>`.
:paramtype parameters: dict[str, any]
:keyword action_uris: Dictionary of :code:`<string>`.
:paramtype action_uris: dict[str, str]
:keyword script_name:
:paramtype script_name: str
:keyword target:
:paramtype target: str
:keyword unique_child_run_compute_targets:
:paramtype unique_child_run_compute_targets: list[str]
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword settings: Dictionary of :code:`<string>`.
:paramtype settings: dict[str, str]
:keyword services: Dictionary of :code:`<EndpointSetting>`.
:paramtype services: dict[str, ~flow.models.EndpointSetting]
:keyword input_datasets:
:paramtype input_datasets: list[~flow.models.DatasetLineage]
:keyword output_datasets:
:paramtype output_datasets: list[~flow.models.OutputDatasetLineage]
:keyword run_definition: Anything.
:paramtype run_definition: any
:keyword job_specification: Anything.
:paramtype job_specification: any
:keyword primary_metric_name:
:paramtype primary_metric_name: str
:keyword created_from:
:paramtype created_from: ~flow.models.CreatedFromDto
:keyword cancel_uri:
:paramtype cancel_uri: str
:keyword complete_uri:
:paramtype complete_uri: str
:keyword diagnostics_uri:
:paramtype diagnostics_uri: str
:keyword compute_request:
:paramtype compute_request: ~flow.models.ComputeRequest
:keyword compute:
:paramtype compute: ~flow.models.Compute
:keyword retain_for_lifetime_of_workspace:
:paramtype retain_for_lifetime_of_workspace: bool
:keyword queueing_info:
:paramtype queueing_info: ~flow.models.QueueingInfo
:keyword inputs: Dictionary of :code:`<TypedAssetReference>`.
:paramtype inputs: dict[str, ~flow.models.TypedAssetReference]
:keyword outputs: Dictionary of :code:`<TypedAssetReference>`.
:paramtype outputs: dict[str, ~flow.models.TypedAssetReference]
"""
super(RunDto, self).__init__(**kwargs)
self.run_number = kwargs.get('run_number', None)
self.root_run_id = kwargs.get('root_run_id', None)
self.created_utc = kwargs.get('created_utc', None)
self.created_by = kwargs.get('created_by', None)
self.user_id = kwargs.get('user_id', None)
self.token = kwargs.get('token', None)
self.token_expiry_time_utc = kwargs.get('token_expiry_time_utc', None)
self.error = kwargs.get('error', None)
self.warnings = kwargs.get('warnings', None)
self.revision = kwargs.get('revision', None)
self.status_revision = kwargs.get('status_revision', None)
self.run_uuid = kwargs.get('run_uuid', None)
self.parent_run_uuid = kwargs.get('parent_run_uuid', None)
self.root_run_uuid = kwargs.get('root_run_uuid', None)
self.last_start_time_utc = kwargs.get('last_start_time_utc', None)
self.current_compute_time = kwargs.get('current_compute_time', None)
self.compute_duration = kwargs.get('compute_duration', None)
self.effective_start_time_utc = kwargs.get('effective_start_time_utc', None)
self.last_modified_by = kwargs.get('last_modified_by', None)
self.last_modified_utc = kwargs.get('last_modified_utc', None)
self.duration = kwargs.get('duration', None)
self.cancelation_reason = kwargs.get('cancelation_reason', None)
self.current_attempt_id = kwargs.get('current_attempt_id', None)
self.run_id = kwargs.get('run_id', None)
self.parent_run_id = kwargs.get('parent_run_id', None)
self.experiment_id = kwargs.get('experiment_id', None)
self.status = kwargs.get('status', None)
self.start_time_utc = kwargs.get('start_time_utc', None)
self.end_time_utc = kwargs.get('end_time_utc', None)
self.schedule_id = kwargs.get('schedule_id', None)
self.display_name = kwargs.get('display_name', None)
self.name = kwargs.get('name', None)
self.data_container_id = kwargs.get('data_container_id', None)
self.description = kwargs.get('description', None)
self.hidden = kwargs.get('hidden', None)
self.run_type = kwargs.get('run_type', None)
self.run_type_v2 = kwargs.get('run_type_v2', None)
self.properties = kwargs.get('properties', None)
self.parameters = kwargs.get('parameters', None)
self.action_uris = kwargs.get('action_uris', None)
self.script_name = kwargs.get('script_name', None)
self.target = kwargs.get('target', None)
self.unique_child_run_compute_targets = kwargs.get('unique_child_run_compute_targets', None)
self.tags = kwargs.get('tags', None)
self.settings = kwargs.get('settings', None)
self.services = kwargs.get('services', None)
self.input_datasets = kwargs.get('input_datasets', None)
self.output_datasets = kwargs.get('output_datasets', None)
self.run_definition = kwargs.get('run_definition', None)
self.job_specification = kwargs.get('job_specification', None)
self.primary_metric_name = kwargs.get('primary_metric_name', None)
self.created_from = kwargs.get('created_from', None)
self.cancel_uri = kwargs.get('cancel_uri', None)
self.complete_uri = kwargs.get('complete_uri', None)
self.diagnostics_uri = kwargs.get('diagnostics_uri', None)
self.compute_request = kwargs.get('compute_request', None)
self.compute = kwargs.get('compute', None)
self.retain_for_lifetime_of_workspace = kwargs.get('retain_for_lifetime_of_workspace', None)
self.queueing_info = kwargs.get('queueing_info', None)
self.inputs = kwargs.get('inputs', None)
self.outputs = kwargs.get('outputs', None)
class RunIndexEntity(msrest.serialization.Model):
"""RunIndexEntity.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar schema_id:
:vartype schema_id: str
:ivar entity_id:
:vartype entity_id: str
:ivar kind: Possible values include: "Invalid", "LineageRoot", "Versioned", "Unversioned".
:vartype kind: str or ~flow.models.EntityKind
:ivar annotations:
:vartype annotations: ~flow.models.RunAnnotations
:ivar properties:
:vartype properties: ~flow.models.RunProperties
:ivar internal: Any object.
:vartype internal: any
:ivar update_sequence:
:vartype update_sequence: long
:ivar type:
:vartype type: str
:ivar version:
:vartype version: str
:ivar entity_container_id:
:vartype entity_container_id: str
:ivar entity_object_id:
:vartype entity_object_id: str
:ivar resource_type:
:vartype resource_type: str
:ivar relationships:
:vartype relationships: list[~flow.models.Relationship]
:ivar asset_id:
:vartype asset_id: str
"""
_validation = {
'version': {'readonly': True},
'entity_container_id': {'readonly': True},
'entity_object_id': {'readonly': True},
'resource_type': {'readonly': True},
}
_attribute_map = {
'schema_id': {'key': 'schemaId', 'type': 'str'},
'entity_id': {'key': 'entityId', 'type': 'str'},
'kind': {'key': 'kind', 'type': 'str'},
'annotations': {'key': 'annotations', 'type': 'RunAnnotations'},
'properties': {'key': 'properties', 'type': 'RunProperties'},
'internal': {'key': 'internal', 'type': 'object'},
'update_sequence': {'key': 'updateSequence', 'type': 'long'},
'type': {'key': 'type', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'entity_container_id': {'key': 'entityContainerId', 'type': 'str'},
'entity_object_id': {'key': 'entityObjectId', 'type': 'str'},
'resource_type': {'key': 'resourceType', 'type': 'str'},
'relationships': {'key': 'relationships', 'type': '[Relationship]'},
'asset_id': {'key': 'assetId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword schema_id:
:paramtype schema_id: str
:keyword entity_id:
:paramtype entity_id: str
:keyword kind: Possible values include: "Invalid", "LineageRoot", "Versioned", "Unversioned".
:paramtype kind: str or ~flow.models.EntityKind
:keyword annotations:
:paramtype annotations: ~flow.models.RunAnnotations
:keyword properties:
:paramtype properties: ~flow.models.RunProperties
:keyword internal: Any object.
:paramtype internal: any
:keyword update_sequence:
:paramtype update_sequence: long
:keyword type:
:paramtype type: str
:keyword relationships:
:paramtype relationships: list[~flow.models.Relationship]
:keyword asset_id:
:paramtype asset_id: str
"""
super(RunIndexEntity, self).__init__(**kwargs)
self.schema_id = kwargs.get('schema_id', None)
self.entity_id = kwargs.get('entity_id', None)
self.kind = kwargs.get('kind', None)
self.annotations = kwargs.get('annotations', None)
self.properties = kwargs.get('properties', None)
self.internal = kwargs.get('internal', None)
self.update_sequence = kwargs.get('update_sequence', None)
self.type = kwargs.get('type', None)
self.version = None
self.entity_container_id = None
self.entity_object_id = None
self.resource_type = None
self.relationships = kwargs.get('relationships', None)
self.asset_id = kwargs.get('asset_id', None)
class RunIndexMetricSummary(msrest.serialization.Model):
"""RunIndexMetricSummary.
:ivar count:
:vartype count: long
:ivar last_value: Anything.
:vartype last_value: any
:ivar minimum_value: Anything.
:vartype minimum_value: any
:ivar maximum_value: Anything.
:vartype maximum_value: any
:ivar metric_type:
:vartype metric_type: str
"""
_attribute_map = {
'count': {'key': 'count', 'type': 'long'},
'last_value': {'key': 'lastValue', 'type': 'object'},
'minimum_value': {'key': 'minimumValue', 'type': 'object'},
'maximum_value': {'key': 'maximumValue', 'type': 'object'},
'metric_type': {'key': 'metricType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword count:
:paramtype count: long
:keyword last_value: Anything.
:paramtype last_value: any
:keyword minimum_value: Anything.
:paramtype minimum_value: any
:keyword maximum_value: Anything.
:paramtype maximum_value: any
:keyword metric_type:
:paramtype metric_type: str
"""
super(RunIndexMetricSummary, self).__init__(**kwargs)
self.count = kwargs.get('count', None)
self.last_value = kwargs.get('last_value', None)
self.minimum_value = kwargs.get('minimum_value', None)
self.maximum_value = kwargs.get('maximum_value', None)
self.metric_type = kwargs.get('metric_type', None)
class RunIndexMetricSummarySystemObject(msrest.serialization.Model):
"""RunIndexMetricSummarySystemObject.
:ivar count:
:vartype count: long
:ivar last_value: Anything.
:vartype last_value: any
:ivar minimum_value: Anything.
:vartype minimum_value: any
:ivar maximum_value: Anything.
:vartype maximum_value: any
:ivar metric_type:
:vartype metric_type: str
"""
_attribute_map = {
'count': {'key': 'count', 'type': 'long'},
'last_value': {'key': 'lastValue', 'type': 'object'},
'minimum_value': {'key': 'minimumValue', 'type': 'object'},
'maximum_value': {'key': 'maximumValue', 'type': 'object'},
'metric_type': {'key': 'metricType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword count:
:paramtype count: long
:keyword last_value: Anything.
:paramtype last_value: any
:keyword minimum_value: Anything.
:paramtype minimum_value: any
:keyword maximum_value: Anything.
:paramtype maximum_value: any
:keyword metric_type:
:paramtype metric_type: str
"""
super(RunIndexMetricSummarySystemObject, self).__init__(**kwargs)
self.count = kwargs.get('count', None)
self.last_value = kwargs.get('last_value', None)
self.minimum_value = kwargs.get('minimum_value', None)
self.maximum_value = kwargs.get('maximum_value', None)
self.metric_type = kwargs.get('metric_type', None)
class RunIndexResourceMetricSummary(msrest.serialization.Model):
"""RunIndexResourceMetricSummary.
:ivar gpu_utilization_percent_last_hour:
:vartype gpu_utilization_percent_last_hour: float
:ivar gpu_memory_utilization_percent_last_hour:
:vartype gpu_memory_utilization_percent_last_hour: float
:ivar gpu_energy_joules:
:vartype gpu_energy_joules: float
:ivar resource_metric_names:
:vartype resource_metric_names: list[str]
"""
_attribute_map = {
'gpu_utilization_percent_last_hour': {'key': 'gpuUtilizationPercentLastHour', 'type': 'float'},
'gpu_memory_utilization_percent_last_hour': {'key': 'gpuMemoryUtilizationPercentLastHour', 'type': 'float'},
'gpu_energy_joules': {'key': 'gpuEnergyJoules', 'type': 'float'},
'resource_metric_names': {'key': 'resourceMetricNames', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword gpu_utilization_percent_last_hour:
:paramtype gpu_utilization_percent_last_hour: float
:keyword gpu_memory_utilization_percent_last_hour:
:paramtype gpu_memory_utilization_percent_last_hour: float
:keyword gpu_energy_joules:
:paramtype gpu_energy_joules: float
:keyword resource_metric_names:
:paramtype resource_metric_names: list[str]
"""
super(RunIndexResourceMetricSummary, self).__init__(**kwargs)
self.gpu_utilization_percent_last_hour = kwargs.get('gpu_utilization_percent_last_hour', None)
self.gpu_memory_utilization_percent_last_hour = kwargs.get('gpu_memory_utilization_percent_last_hour', None)
self.gpu_energy_joules = kwargs.get('gpu_energy_joules', None)
self.resource_metric_names = kwargs.get('resource_metric_names', None)
class RunMetricDto(msrest.serialization.Model):
"""RunMetricDto.
:ivar run_id:
:vartype run_id: str
:ivar metric_id:
:vartype metric_id: str
:ivar data_container_id:
:vartype data_container_id: str
:ivar metric_type:
:vartype metric_type: str
:ivar created_utc:
:vartype created_utc: ~datetime.datetime
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar label:
:vartype label: str
:ivar num_cells:
:vartype num_cells: int
:ivar data_location:
:vartype data_location: str
:ivar cells:
:vartype cells: list[dict[str, any]]
:ivar schema:
:vartype schema: ~flow.models.MetricSchemaDto
"""
_attribute_map = {
'run_id': {'key': 'runId', 'type': 'str'},
'metric_id': {'key': 'metricId', 'type': 'str'},
'data_container_id': {'key': 'dataContainerId', 'type': 'str'},
'metric_type': {'key': 'metricType', 'type': 'str'},
'created_utc': {'key': 'createdUtc', 'type': 'iso-8601'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'num_cells': {'key': 'numCells', 'type': 'int'},
'data_location': {'key': 'dataLocation', 'type': 'str'},
'cells': {'key': 'cells', 'type': '[{object}]'},
'schema': {'key': 'schema', 'type': 'MetricSchemaDto'},
}
def __init__(
self,
**kwargs
):
"""
:keyword run_id:
:paramtype run_id: str
:keyword metric_id:
:paramtype metric_id: str
:keyword data_container_id:
:paramtype data_container_id: str
:keyword metric_type:
:paramtype metric_type: str
:keyword created_utc:
:paramtype created_utc: ~datetime.datetime
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword label:
:paramtype label: str
:keyword num_cells:
:paramtype num_cells: int
:keyword data_location:
:paramtype data_location: str
:keyword cells:
:paramtype cells: list[dict[str, any]]
:keyword schema:
:paramtype schema: ~flow.models.MetricSchemaDto
"""
super(RunMetricDto, self).__init__(**kwargs)
self.run_id = kwargs.get('run_id', None)
self.metric_id = kwargs.get('metric_id', None)
self.data_container_id = kwargs.get('data_container_id', None)
self.metric_type = kwargs.get('metric_type', None)
self.created_utc = kwargs.get('created_utc', None)
self.name = kwargs.get('name', None)
self.description = kwargs.get('description', None)
self.label = kwargs.get('label', None)
self.num_cells = kwargs.get('num_cells', None)
self.data_location = kwargs.get('data_location', None)
self.cells = kwargs.get('cells', None)
self.schema = kwargs.get('schema', None)
class RunMetricsTypesDto(msrest.serialization.Model):
"""RunMetricsTypesDto.
:ivar name:
:vartype name: str
:ivar type:
:vartype type: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type:
:paramtype type: str
"""
super(RunMetricsTypesDto, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.type = kwargs.get('type', None)
class RunProperties(msrest.serialization.Model):
"""RunProperties.
:ivar data_container_id:
:vartype data_container_id: str
:ivar target_name:
:vartype target_name: str
:ivar run_name:
:vartype run_name: str
:ivar experiment_name:
:vartype experiment_name: str
:ivar run_id:
:vartype run_id: str
:ivar parent_run_id:
:vartype parent_run_id: str
:ivar root_run_id:
:vartype root_run_id: str
:ivar run_type:
:vartype run_type: str
:ivar run_type_v2:
:vartype run_type_v2: ~flow.models.RunTypeV2Index
:ivar script_name:
:vartype script_name: str
:ivar experiment_id:
:vartype experiment_id: str
:ivar run_uuid:
:vartype run_uuid: str
:ivar parent_run_uuid:
:vartype parent_run_uuid: str
:ivar run_number:
:vartype run_number: int
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar compute_request:
:vartype compute_request: ~flow.models.ComputeRequest
:ivar compute:
:vartype compute: ~flow.models.Compute
:ivar user_properties: This is a dictionary.
:vartype user_properties: dict[str, str]
:ivar action_uris: This is a dictionary.
:vartype action_uris: dict[str, str]
:ivar duration:
:vartype duration: str
:ivar duration_milliseconds:
:vartype duration_milliseconds: float
:ivar creation_context:
:vartype creation_context: ~flow.models.CreationContext
"""
_attribute_map = {
'data_container_id': {'key': 'dataContainerId', 'type': 'str'},
'target_name': {'key': 'targetName', 'type': 'str'},
'run_name': {'key': 'runName', 'type': 'str'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'run_id': {'key': 'runId', 'type': 'str'},
'parent_run_id': {'key': 'parentRunId', 'type': 'str'},
'root_run_id': {'key': 'rootRunId', 'type': 'str'},
'run_type': {'key': 'runType', 'type': 'str'},
'run_type_v2': {'key': 'runTypeV2', 'type': 'RunTypeV2Index'},
'script_name': {'key': 'scriptName', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'run_uuid': {'key': 'runUuid', 'type': 'str'},
'parent_run_uuid': {'key': 'parentRunUuid', 'type': 'str'},
'run_number': {'key': 'runNumber', 'type': 'int'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'compute_request': {'key': 'computeRequest', 'type': 'ComputeRequest'},
'compute': {'key': 'compute', 'type': 'Compute'},
'user_properties': {'key': 'userProperties', 'type': '{str}'},
'action_uris': {'key': 'actionUris', 'type': '{str}'},
'duration': {'key': 'duration', 'type': 'str'},
'duration_milliseconds': {'key': 'durationMilliseconds', 'type': 'float'},
'creation_context': {'key': 'creationContext', 'type': 'CreationContext'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_container_id:
:paramtype data_container_id: str
:keyword target_name:
:paramtype target_name: str
:keyword run_name:
:paramtype run_name: str
:keyword experiment_name:
:paramtype experiment_name: str
:keyword run_id:
:paramtype run_id: str
:keyword parent_run_id:
:paramtype parent_run_id: str
:keyword root_run_id:
:paramtype root_run_id: str
:keyword run_type:
:paramtype run_type: str
:keyword run_type_v2:
:paramtype run_type_v2: ~flow.models.RunTypeV2Index
:keyword script_name:
:paramtype script_name: str
:keyword experiment_id:
:paramtype experiment_id: str
:keyword run_uuid:
:paramtype run_uuid: str
:keyword parent_run_uuid:
:paramtype parent_run_uuid: str
:keyword run_number:
:paramtype run_number: int
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
:keyword compute_request:
:paramtype compute_request: ~flow.models.ComputeRequest
:keyword compute:
:paramtype compute: ~flow.models.Compute
:keyword user_properties: This is a dictionary.
:paramtype user_properties: dict[str, str]
:keyword action_uris: This is a dictionary.
:paramtype action_uris: dict[str, str]
:keyword duration:
:paramtype duration: str
:keyword duration_milliseconds:
:paramtype duration_milliseconds: float
:keyword creation_context:
:paramtype creation_context: ~flow.models.CreationContext
"""
super(RunProperties, self).__init__(**kwargs)
self.data_container_id = kwargs.get('data_container_id', None)
self.target_name = kwargs.get('target_name', None)
self.run_name = kwargs.get('run_name', None)
self.experiment_name = kwargs.get('experiment_name', None)
self.run_id = kwargs.get('run_id', None)
self.parent_run_id = kwargs.get('parent_run_id', None)
self.root_run_id = kwargs.get('root_run_id', None)
self.run_type = kwargs.get('run_type', None)
self.run_type_v2 = kwargs.get('run_type_v2', None)
self.script_name = kwargs.get('script_name', None)
self.experiment_id = kwargs.get('experiment_id', None)
self.run_uuid = kwargs.get('run_uuid', None)
self.parent_run_uuid = kwargs.get('parent_run_uuid', None)
self.run_number = kwargs.get('run_number', None)
self.start_time = kwargs.get('start_time', None)
self.end_time = kwargs.get('end_time', None)
self.compute_request = kwargs.get('compute_request', None)
self.compute = kwargs.get('compute', None)
self.user_properties = kwargs.get('user_properties', None)
self.action_uris = kwargs.get('action_uris', None)
self.duration = kwargs.get('duration', None)
self.duration_milliseconds = kwargs.get('duration_milliseconds', None)
self.creation_context = kwargs.get('creation_context', None)
class RunSettingParameter(msrest.serialization.Model):
"""RunSettingParameter.
:ivar name:
:vartype name: str
:ivar label:
:vartype label: str
:ivar parameter_type: Possible values include: "Undefined", "Int", "Double", "Bool", "String",
"JsonString", "YamlString", "StringList".
:vartype parameter_type: str or ~flow.models.RunSettingParameterType
:ivar is_optional:
:vartype is_optional: bool
:ivar default_value:
:vartype default_value: str
:ivar lower_bound:
:vartype lower_bound: str
:ivar upper_bound:
:vartype upper_bound: str
:ivar description:
:vartype description: str
:ivar run_setting_ui_hint:
:vartype run_setting_ui_hint: ~flow.models.RunSettingUIParameterHint
:ivar argument_name:
:vartype argument_name: str
:ivar section_name:
:vartype section_name: str
:ivar section_description:
:vartype section_description: str
:ivar section_argument_name:
:vartype section_argument_name: str
:ivar examples:
:vartype examples: list[str]
:ivar enum_values:
:vartype enum_values: list[str]
:ivar enum_values_to_argument_strings: This is a dictionary.
:vartype enum_values_to_argument_strings: dict[str, str]
:ivar enabled_by_parameter_name:
:vartype enabled_by_parameter_name: str
:ivar enabled_by_parameter_values:
:vartype enabled_by_parameter_values: list[str]
:ivar disabled_by_parameters:
:vartype disabled_by_parameters: list[str]
:ivar module_run_setting_type: Possible values include: "All", "Released", "Default",
"Testing", "Legacy", "Preview", "UxFull", "Integration", "UxIntegration", "Full".
:vartype module_run_setting_type: str or ~flow.models.ModuleRunSettingTypes
:ivar linked_parameter_default_value_mapping: Dictionary of :code:`<string>`.
:vartype linked_parameter_default_value_mapping: dict[str, str]
:ivar linked_parameter_key_name:
:vartype linked_parameter_key_name: str
:ivar support_link_setting:
:vartype support_link_setting: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'parameter_type': {'key': 'parameterType', 'type': 'str'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
'default_value': {'key': 'defaultValue', 'type': 'str'},
'lower_bound': {'key': 'lowerBound', 'type': 'str'},
'upper_bound': {'key': 'upperBound', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'run_setting_ui_hint': {'key': 'runSettingUIHint', 'type': 'RunSettingUIParameterHint'},
'argument_name': {'key': 'argumentName', 'type': 'str'},
'section_name': {'key': 'sectionName', 'type': 'str'},
'section_description': {'key': 'sectionDescription', 'type': 'str'},
'section_argument_name': {'key': 'sectionArgumentName', 'type': 'str'},
'examples': {'key': 'examples', 'type': '[str]'},
'enum_values': {'key': 'enumValues', 'type': '[str]'},
'enum_values_to_argument_strings': {'key': 'enumValuesToArgumentStrings', 'type': '{str}'},
'enabled_by_parameter_name': {'key': 'enabledByParameterName', 'type': 'str'},
'enabled_by_parameter_values': {'key': 'enabledByParameterValues', 'type': '[str]'},
'disabled_by_parameters': {'key': 'disabledByParameters', 'type': '[str]'},
'module_run_setting_type': {'key': 'moduleRunSettingType', 'type': 'str'},
'linked_parameter_default_value_mapping': {'key': 'linkedParameterDefaultValueMapping', 'type': '{str}'},
'linked_parameter_key_name': {'key': 'linkedParameterKeyName', 'type': 'str'},
'support_link_setting': {'key': 'supportLinkSetting', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword label:
:paramtype label: str
:keyword parameter_type: Possible values include: "Undefined", "Int", "Double", "Bool",
"String", "JsonString", "YamlString", "StringList".
:paramtype parameter_type: str or ~flow.models.RunSettingParameterType
:keyword is_optional:
:paramtype is_optional: bool
:keyword default_value:
:paramtype default_value: str
:keyword lower_bound:
:paramtype lower_bound: str
:keyword upper_bound:
:paramtype upper_bound: str
:keyword description:
:paramtype description: str
:keyword run_setting_ui_hint:
:paramtype run_setting_ui_hint: ~flow.models.RunSettingUIParameterHint
:keyword argument_name:
:paramtype argument_name: str
:keyword section_name:
:paramtype section_name: str
:keyword section_description:
:paramtype section_description: str
:keyword section_argument_name:
:paramtype section_argument_name: str
:keyword examples:
:paramtype examples: list[str]
:keyword enum_values:
:paramtype enum_values: list[str]
:keyword enum_values_to_argument_strings: This is a dictionary.
:paramtype enum_values_to_argument_strings: dict[str, str]
:keyword enabled_by_parameter_name:
:paramtype enabled_by_parameter_name: str
:keyword enabled_by_parameter_values:
:paramtype enabled_by_parameter_values: list[str]
:keyword disabled_by_parameters:
:paramtype disabled_by_parameters: list[str]
:keyword module_run_setting_type: Possible values include: "All", "Released", "Default",
"Testing", "Legacy", "Preview", "UxFull", "Integration", "UxIntegration", "Full".
:paramtype module_run_setting_type: str or ~flow.models.ModuleRunSettingTypes
:keyword linked_parameter_default_value_mapping: Dictionary of :code:`<string>`.
:paramtype linked_parameter_default_value_mapping: dict[str, str]
:keyword linked_parameter_key_name:
:paramtype linked_parameter_key_name: str
:keyword support_link_setting:
:paramtype support_link_setting: bool
"""
super(RunSettingParameter, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.label = kwargs.get('label', None)
self.parameter_type = kwargs.get('parameter_type', None)
self.is_optional = kwargs.get('is_optional', None)
self.default_value = kwargs.get('default_value', None)
self.lower_bound = kwargs.get('lower_bound', None)
self.upper_bound = kwargs.get('upper_bound', None)
self.description = kwargs.get('description', None)
self.run_setting_ui_hint = kwargs.get('run_setting_ui_hint', None)
self.argument_name = kwargs.get('argument_name', None)
self.section_name = kwargs.get('section_name', None)
self.section_description = kwargs.get('section_description', None)
self.section_argument_name = kwargs.get('section_argument_name', None)
self.examples = kwargs.get('examples', None)
self.enum_values = kwargs.get('enum_values', None)
self.enum_values_to_argument_strings = kwargs.get('enum_values_to_argument_strings', None)
self.enabled_by_parameter_name = kwargs.get('enabled_by_parameter_name', None)
self.enabled_by_parameter_values = kwargs.get('enabled_by_parameter_values', None)
self.disabled_by_parameters = kwargs.get('disabled_by_parameters', None)
self.module_run_setting_type = kwargs.get('module_run_setting_type', None)
self.linked_parameter_default_value_mapping = kwargs.get('linked_parameter_default_value_mapping', None)
self.linked_parameter_key_name = kwargs.get('linked_parameter_key_name', None)
self.support_link_setting = kwargs.get('support_link_setting', None)
class RunSettingParameterAssignment(msrest.serialization.Model):
"""RunSettingParameterAssignment.
:ivar use_graph_default_compute:
:vartype use_graph_default_compute: bool
:ivar mlc_compute_type:
:vartype mlc_compute_type: str
:ivar compute_run_settings:
:vartype compute_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar linked_parameter_name:
:vartype linked_parameter_name: str
:ivar value_type: Possible values include: "Literal", "GraphParameterName", "Concatenate",
"Input", "DataPath", "DataSetDefinition".
:vartype value_type: str or ~flow.models.ParameterValueType
:ivar assignments_to_concatenate:
:vartype assignments_to_concatenate: list[~flow.models.ParameterAssignment]
:ivar data_path_assignment:
:vartype data_path_assignment: ~flow.models.LegacyDataPath
:ivar data_set_definition_value_assignment:
:vartype data_set_definition_value_assignment: ~flow.models.DataSetDefinitionValue
:ivar name:
:vartype name: str
:ivar value:
:vartype value: str
"""
_attribute_map = {
'use_graph_default_compute': {'key': 'useGraphDefaultCompute', 'type': 'bool'},
'mlc_compute_type': {'key': 'mlcComputeType', 'type': 'str'},
'compute_run_settings': {'key': 'computeRunSettings', 'type': '[RunSettingParameterAssignment]'},
'linked_parameter_name': {'key': 'linkedParameterName', 'type': 'str'},
'value_type': {'key': 'valueType', 'type': 'str'},
'assignments_to_concatenate': {'key': 'assignmentsToConcatenate', 'type': '[ParameterAssignment]'},
'data_path_assignment': {'key': 'dataPathAssignment', 'type': 'LegacyDataPath'},
'data_set_definition_value_assignment': {'key': 'dataSetDefinitionValueAssignment', 'type': 'DataSetDefinitionValue'},
'name': {'key': 'name', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword use_graph_default_compute:
:paramtype use_graph_default_compute: bool
:keyword mlc_compute_type:
:paramtype mlc_compute_type: str
:keyword compute_run_settings:
:paramtype compute_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword linked_parameter_name:
:paramtype linked_parameter_name: str
:keyword value_type: Possible values include: "Literal", "GraphParameterName", "Concatenate",
"Input", "DataPath", "DataSetDefinition".
:paramtype value_type: str or ~flow.models.ParameterValueType
:keyword assignments_to_concatenate:
:paramtype assignments_to_concatenate: list[~flow.models.ParameterAssignment]
:keyword data_path_assignment:
:paramtype data_path_assignment: ~flow.models.LegacyDataPath
:keyword data_set_definition_value_assignment:
:paramtype data_set_definition_value_assignment: ~flow.models.DataSetDefinitionValue
:keyword name:
:paramtype name: str
:keyword value:
:paramtype value: str
"""
super(RunSettingParameterAssignment, self).__init__(**kwargs)
self.use_graph_default_compute = kwargs.get('use_graph_default_compute', None)
self.mlc_compute_type = kwargs.get('mlc_compute_type', None)
self.compute_run_settings = kwargs.get('compute_run_settings', None)
self.linked_parameter_name = kwargs.get('linked_parameter_name', None)
self.value_type = kwargs.get('value_type', None)
self.assignments_to_concatenate = kwargs.get('assignments_to_concatenate', None)
self.data_path_assignment = kwargs.get('data_path_assignment', None)
self.data_set_definition_value_assignment = kwargs.get('data_set_definition_value_assignment', None)
self.name = kwargs.get('name', None)
self.value = kwargs.get('value', None)
class RunSettingUIParameterHint(msrest.serialization.Model):
"""RunSettingUIParameterHint.
:ivar ui_widget_type: Possible values include: "Default", "ComputeSelection", "JsonEditor",
"Mode", "SearchSpaceParameter", "SectionToggle", "YamlEditor", "EnableRuntimeSweep",
"DataStoreSelection", "Checkbox", "MultipleSelection", "HyperparameterConfiguration",
"JsonTextBox", "Connection", "Static".
:vartype ui_widget_type: str or ~flow.models.RunSettingUIWidgetTypeEnum
:ivar json_editor:
:vartype json_editor: ~flow.models.UIJsonEditor
:ivar yaml_editor:
:vartype yaml_editor: ~flow.models.UIYamlEditor
:ivar compute_selection:
:vartype compute_selection: ~flow.models.UIComputeSelection
:ivar hyperparameter_configuration:
:vartype hyperparameter_configuration: ~flow.models.UIHyperparameterConfiguration
:ivar ux_ignore:
:vartype ux_ignore: bool
:ivar anonymous:
:vartype anonymous: bool
:ivar support_reset:
:vartype support_reset: bool
"""
_attribute_map = {
'ui_widget_type': {'key': 'uiWidgetType', 'type': 'str'},
'json_editor': {'key': 'jsonEditor', 'type': 'UIJsonEditor'},
'yaml_editor': {'key': 'yamlEditor', 'type': 'UIYamlEditor'},
'compute_selection': {'key': 'computeSelection', 'type': 'UIComputeSelection'},
'hyperparameter_configuration': {'key': 'hyperparameterConfiguration', 'type': 'UIHyperparameterConfiguration'},
'ux_ignore': {'key': 'uxIgnore', 'type': 'bool'},
'anonymous': {'key': 'anonymous', 'type': 'bool'},
'support_reset': {'key': 'supportReset', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword ui_widget_type: Possible values include: "Default", "ComputeSelection", "JsonEditor",
"Mode", "SearchSpaceParameter", "SectionToggle", "YamlEditor", "EnableRuntimeSweep",
"DataStoreSelection", "Checkbox", "MultipleSelection", "HyperparameterConfiguration",
"JsonTextBox", "Connection", "Static".
:paramtype ui_widget_type: str or ~flow.models.RunSettingUIWidgetTypeEnum
:keyword json_editor:
:paramtype json_editor: ~flow.models.UIJsonEditor
:keyword yaml_editor:
:paramtype yaml_editor: ~flow.models.UIYamlEditor
:keyword compute_selection:
:paramtype compute_selection: ~flow.models.UIComputeSelection
:keyword hyperparameter_configuration:
:paramtype hyperparameter_configuration: ~flow.models.UIHyperparameterConfiguration
:keyword ux_ignore:
:paramtype ux_ignore: bool
:keyword anonymous:
:paramtype anonymous: bool
:keyword support_reset:
:paramtype support_reset: bool
"""
super(RunSettingUIParameterHint, self).__init__(**kwargs)
self.ui_widget_type = kwargs.get('ui_widget_type', None)
self.json_editor = kwargs.get('json_editor', None)
self.yaml_editor = kwargs.get('yaml_editor', None)
self.compute_selection = kwargs.get('compute_selection', None)
self.hyperparameter_configuration = kwargs.get('hyperparameter_configuration', None)
self.ux_ignore = kwargs.get('ux_ignore', None)
self.anonymous = kwargs.get('anonymous', None)
self.support_reset = kwargs.get('support_reset', None)
class RunStatusPeriod(msrest.serialization.Model):
"""RunStatusPeriod.
:ivar status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:vartype status: str or ~flow.models.RunStatus
:ivar sub_periods:
:vartype sub_periods: list[~flow.models.SubStatusPeriod]
:ivar start:
:vartype start: long
:ivar end:
:vartype end: long
"""
_attribute_map = {
'status': {'key': 'status', 'type': 'str'},
'sub_periods': {'key': 'subPeriods', 'type': '[SubStatusPeriod]'},
'start': {'key': 'start', 'type': 'long'},
'end': {'key': 'end', 'type': 'long'},
}
def __init__(
self,
**kwargs
):
"""
:keyword status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:paramtype status: str or ~flow.models.RunStatus
:keyword sub_periods:
:paramtype sub_periods: list[~flow.models.SubStatusPeriod]
:keyword start:
:paramtype start: long
:keyword end:
:paramtype end: long
"""
super(RunStatusPeriod, self).__init__(**kwargs)
self.status = kwargs.get('status', None)
self.sub_periods = kwargs.get('sub_periods', None)
self.start = kwargs.get('start', None)
self.end = kwargs.get('end', None)
class RuntimeConfiguration(msrest.serialization.Model):
"""RuntimeConfiguration.
:ivar base_image:
:vartype base_image: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'base_image': {'key': 'baseImage', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword base_image:
:paramtype base_image: str
:keyword version:
:paramtype version: str
"""
super(RuntimeConfiguration, self).__init__(**kwargs)
self.base_image = kwargs.get('base_image', None)
self.version = kwargs.get('version', None)
class RunTypeV2(msrest.serialization.Model):
"""RunTypeV2.
:ivar orchestrator:
:vartype orchestrator: str
:ivar traits:
:vartype traits: list[str]
:ivar attribution:
:vartype attribution: str
:ivar compute_type:
:vartype compute_type: str
"""
_validation = {
'traits': {'unique': True},
}
_attribute_map = {
'orchestrator': {'key': 'orchestrator', 'type': 'str'},
'traits': {'key': 'traits', 'type': '[str]'},
'attribution': {'key': 'attribution', 'type': 'str'},
'compute_type': {'key': 'computeType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword orchestrator:
:paramtype orchestrator: str
:keyword traits:
:paramtype traits: list[str]
:keyword attribution:
:paramtype attribution: str
:keyword compute_type:
:paramtype compute_type: str
"""
super(RunTypeV2, self).__init__(**kwargs)
self.orchestrator = kwargs.get('orchestrator', None)
self.traits = kwargs.get('traits', None)
self.attribution = kwargs.get('attribution', None)
self.compute_type = kwargs.get('compute_type', None)
class RunTypeV2Index(msrest.serialization.Model):
"""RunTypeV2Index.
:ivar orchestrator:
:vartype orchestrator: str
:ivar traits: Dictionary of :code:`<string>`.
:vartype traits: dict[str, str]
:ivar attribution:
:vartype attribution: str
:ivar compute_type:
:vartype compute_type: str
"""
_attribute_map = {
'orchestrator': {'key': 'orchestrator', 'type': 'str'},
'traits': {'key': 'traits', 'type': '{str}'},
'attribution': {'key': 'attribution', 'type': 'str'},
'compute_type': {'key': 'computeType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword orchestrator:
:paramtype orchestrator: str
:keyword traits: Dictionary of :code:`<string>`.
:paramtype traits: dict[str, str]
:keyword attribution:
:paramtype attribution: str
:keyword compute_type:
:paramtype compute_type: str
"""
super(RunTypeV2Index, self).__init__(**kwargs)
self.orchestrator = kwargs.get('orchestrator', None)
self.traits = kwargs.get('traits', None)
self.attribution = kwargs.get('attribution', None)
self.compute_type = kwargs.get('compute_type', None)
class SampleMeta(msrest.serialization.Model):
"""SampleMeta.
:ivar image:
:vartype image: str
:ivar id:
:vartype id: str
:ivar display_name:
:vartype display_name: str
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar doc_link:
:vartype doc_link: str
:ivar tags: A set of tags.
:vartype tags: list[str]
:ivar created_at:
:vartype created_at: ~datetime.datetime
:ivar updated_at:
:vartype updated_at: ~datetime.datetime
:ivar feed_name:
:vartype feed_name: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'image': {'key': 'image', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'doc_link': {'key': 'docLink', 'type': 'str'},
'tags': {'key': 'tags', 'type': '[str]'},
'created_at': {'key': 'createdAt', 'type': 'iso-8601'},
'updated_at': {'key': 'updatedAt', 'type': 'iso-8601'},
'feed_name': {'key': 'feedName', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword image:
:paramtype image: str
:keyword id:
:paramtype id: str
:keyword display_name:
:paramtype display_name: str
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword doc_link:
:paramtype doc_link: str
:keyword tags: A set of tags.
:paramtype tags: list[str]
:keyword created_at:
:paramtype created_at: ~datetime.datetime
:keyword updated_at:
:paramtype updated_at: ~datetime.datetime
:keyword feed_name:
:paramtype feed_name: str
:keyword version:
:paramtype version: str
"""
super(SampleMeta, self).__init__(**kwargs)
self.image = kwargs.get('image', None)
self.id = kwargs.get('id', None)
self.display_name = kwargs.get('display_name', None)
self.name = kwargs.get('name', None)
self.description = kwargs.get('description', None)
self.doc_link = kwargs.get('doc_link', None)
self.tags = kwargs.get('tags', None)
self.created_at = kwargs.get('created_at', None)
self.updated_at = kwargs.get('updated_at', None)
self.feed_name = kwargs.get('feed_name', None)
self.version = kwargs.get('version', None)
class SavedDataSetReference(msrest.serialization.Model):
"""SavedDataSetReference.
:ivar id:
:vartype id: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword id:
:paramtype id: str
"""
super(SavedDataSetReference, self).__init__(**kwargs)
self.id = kwargs.get('id', None)
class SavePipelineDraftRequest(msrest.serialization.Model):
"""SavePipelineDraftRequest.
:ivar ui_widget_meta_infos:
:vartype ui_widget_meta_infos: list[~flow.models.UIWidgetMetaInfo]
:ivar web_service_inputs:
:vartype web_service_inputs: list[~flow.models.WebServicePort]
:ivar web_service_outputs:
:vartype web_service_outputs: list[~flow.models.WebServicePort]
:ivar nodes_in_draft:
:vartype nodes_in_draft: list[str]
:ivar name:
:vartype name: str
:ivar pipeline_type: Possible values include: "TrainingPipeline", "RealTimeInferencePipeline",
"BatchInferencePipeline", "Unknown".
:vartype pipeline_type: str or ~flow.models.PipelineType
:ivar pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:vartype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:ivar graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:vartype graph_components_mode: str or ~flow.models.GraphComponentsMode
:ivar sub_pipelines_info:
:vartype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:ivar flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:vartype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:ivar pipeline_parameters: This is a dictionary.
:vartype pipeline_parameters: dict[str, str]
:ivar data_path_assignments: This is a dictionary.
:vartype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:ivar data_set_definition_value_assignments: This is a dictionary.
:vartype data_set_definition_value_assignments: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar asset_output_settings_assignments: This is a dictionary.
:vartype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:ivar graph:
:vartype graph: ~flow.models.GraphDraftEntity
:ivar pipeline_run_settings:
:vartype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar module_node_run_settings:
:vartype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:ivar module_node_ui_input_settings:
:vartype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar continue_run_on_step_failure:
:vartype continue_run_on_step_failure: bool
:ivar description:
:vartype description: str
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar enforce_rerun:
:vartype enforce_rerun: bool
:ivar dataset_access_modes: Possible values include: "Default", "DatasetInDpv2", "AssetInDpv2",
"DatasetInDesignerUI", "DatasetInDpv2WithDatasetInDesignerUI", "Dataset",
"AssetInDpv2WithDatasetInDesignerUI", "DatasetAndAssetInDpv2WithDatasetInDesignerUI",
"AssetInDesignerUI", "AssetInDpv2WithAssetInDesignerUI", "Asset".
:vartype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
_attribute_map = {
'ui_widget_meta_infos': {'key': 'uiWidgetMetaInfos', 'type': '[UIWidgetMetaInfo]'},
'web_service_inputs': {'key': 'webServiceInputs', 'type': '[WebServicePort]'},
'web_service_outputs': {'key': 'webServiceOutputs', 'type': '[WebServicePort]'},
'nodes_in_draft': {'key': 'nodesInDraft', 'type': '[str]'},
'name': {'key': 'name', 'type': 'str'},
'pipeline_type': {'key': 'pipelineType', 'type': 'str'},
'pipeline_draft_mode': {'key': 'pipelineDraftMode', 'type': 'str'},
'graph_components_mode': {'key': 'graphComponentsMode', 'type': 'str'},
'sub_pipelines_info': {'key': 'subPipelinesInfo', 'type': 'SubPipelinesInfo'},
'flattened_sub_graphs': {'key': 'flattenedSubGraphs', 'type': '{PipelineSubDraft}'},
'pipeline_parameters': {'key': 'pipelineParameters', 'type': '{str}'},
'data_path_assignments': {'key': 'dataPathAssignments', 'type': '{LegacyDataPath}'},
'data_set_definition_value_assignments': {'key': 'dataSetDefinitionValueAssignments', 'type': '{DataSetDefinitionValue}'},
'asset_output_settings_assignments': {'key': 'assetOutputSettingsAssignments', 'type': '{AssetOutputSettings}'},
'graph': {'key': 'graph', 'type': 'GraphDraftEntity'},
'pipeline_run_settings': {'key': 'pipelineRunSettings', 'type': '[RunSettingParameterAssignment]'},
'module_node_run_settings': {'key': 'moduleNodeRunSettings', 'type': '[GraphModuleNodeRunSetting]'},
'module_node_ui_input_settings': {'key': 'moduleNodeUIInputSettings', 'type': '[GraphModuleNodeUIInputSetting]'},
'tags': {'key': 'tags', 'type': '{str}'},
'continue_run_on_step_failure': {'key': 'continueRunOnStepFailure', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'enforce_rerun': {'key': 'enforceRerun', 'type': 'bool'},
'dataset_access_modes': {'key': 'datasetAccessModes', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword ui_widget_meta_infos:
:paramtype ui_widget_meta_infos: list[~flow.models.UIWidgetMetaInfo]
:keyword web_service_inputs:
:paramtype web_service_inputs: list[~flow.models.WebServicePort]
:keyword web_service_outputs:
:paramtype web_service_outputs: list[~flow.models.WebServicePort]
:keyword nodes_in_draft:
:paramtype nodes_in_draft: list[str]
:keyword name:
:paramtype name: str
:keyword pipeline_type: Possible values include: "TrainingPipeline",
"RealTimeInferencePipeline", "BatchInferencePipeline", "Unknown".
:paramtype pipeline_type: str or ~flow.models.PipelineType
:keyword pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:paramtype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:keyword graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:paramtype graph_components_mode: str or ~flow.models.GraphComponentsMode
:keyword sub_pipelines_info:
:paramtype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:keyword flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:paramtype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:keyword pipeline_parameters: This is a dictionary.
:paramtype pipeline_parameters: dict[str, str]
:keyword data_path_assignments: This is a dictionary.
:paramtype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:keyword data_set_definition_value_assignments: This is a dictionary.
:paramtype data_set_definition_value_assignments: dict[str,
~flow.models.DataSetDefinitionValue]
:keyword asset_output_settings_assignments: This is a dictionary.
:paramtype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:keyword graph:
:paramtype graph: ~flow.models.GraphDraftEntity
:keyword pipeline_run_settings:
:paramtype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword module_node_run_settings:
:paramtype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:keyword module_node_ui_input_settings:
:paramtype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword continue_run_on_step_failure:
:paramtype continue_run_on_step_failure: bool
:keyword description:
:paramtype description: str
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword enforce_rerun:
:paramtype enforce_rerun: bool
:keyword dataset_access_modes: Possible values include: "Default", "DatasetInDpv2",
"AssetInDpv2", "DatasetInDesignerUI", "DatasetInDpv2WithDatasetInDesignerUI", "Dataset",
"AssetInDpv2WithDatasetInDesignerUI", "DatasetAndAssetInDpv2WithDatasetInDesignerUI",
"AssetInDesignerUI", "AssetInDpv2WithAssetInDesignerUI", "Asset".
:paramtype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
super(SavePipelineDraftRequest, self).__init__(**kwargs)
self.ui_widget_meta_infos = kwargs.get('ui_widget_meta_infos', None)
self.web_service_inputs = kwargs.get('web_service_inputs', None)
self.web_service_outputs = kwargs.get('web_service_outputs', None)
self.nodes_in_draft = kwargs.get('nodes_in_draft', None)
self.name = kwargs.get('name', None)
self.pipeline_type = kwargs.get('pipeline_type', None)
self.pipeline_draft_mode = kwargs.get('pipeline_draft_mode', None)
self.graph_components_mode = kwargs.get('graph_components_mode', None)
self.sub_pipelines_info = kwargs.get('sub_pipelines_info', None)
self.flattened_sub_graphs = kwargs.get('flattened_sub_graphs', None)
self.pipeline_parameters = kwargs.get('pipeline_parameters', None)
self.data_path_assignments = kwargs.get('data_path_assignments', None)
self.data_set_definition_value_assignments = kwargs.get('data_set_definition_value_assignments', None)
self.asset_output_settings_assignments = kwargs.get('asset_output_settings_assignments', None)
self.graph = kwargs.get('graph', None)
self.pipeline_run_settings = kwargs.get('pipeline_run_settings', None)
self.module_node_run_settings = kwargs.get('module_node_run_settings', None)
self.module_node_ui_input_settings = kwargs.get('module_node_ui_input_settings', None)
self.tags = kwargs.get('tags', None)
self.continue_run_on_step_failure = kwargs.get('continue_run_on_step_failure', None)
self.description = kwargs.get('description', None)
self.properties = kwargs.get('properties', None)
self.enforce_rerun = kwargs.get('enforce_rerun', None)
self.dataset_access_modes = kwargs.get('dataset_access_modes', None)
class ScheduleBase(msrest.serialization.Model):
"""ScheduleBase.
:ivar schedule_status: Possible values include: "Enabled", "Disabled".
:vartype schedule_status: str or ~flow.models.MfeInternalScheduleStatus
:ivar schedule_type: Possible values include: "Cron", "Recurrence".
:vartype schedule_type: str or ~flow.models.ScheduleType
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar time_zone:
:vartype time_zone: str
:ivar expression:
:vartype expression: str
:ivar frequency: Possible values include: "Minute", "Hour", "Day", "Week", "Month".
:vartype frequency: str or ~flow.models.RecurrenceFrequency
:ivar interval:
:vartype interval: int
:ivar pattern:
:vartype pattern: ~flow.models.RecurrencePattern
"""
_attribute_map = {
'schedule_status': {'key': 'scheduleStatus', 'type': 'str'},
'schedule_type': {'key': 'scheduleType', 'type': 'str'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'time_zone': {'key': 'timeZone', 'type': 'str'},
'expression': {'key': 'expression', 'type': 'str'},
'frequency': {'key': 'frequency', 'type': 'str'},
'interval': {'key': 'interval', 'type': 'int'},
'pattern': {'key': 'pattern', 'type': 'RecurrencePattern'},
}
def __init__(
self,
**kwargs
):
"""
:keyword schedule_status: Possible values include: "Enabled", "Disabled".
:paramtype schedule_status: str or ~flow.models.MfeInternalScheduleStatus
:keyword schedule_type: Possible values include: "Cron", "Recurrence".
:paramtype schedule_type: str or ~flow.models.ScheduleType
:keyword end_time:
:paramtype end_time: ~datetime.datetime
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword time_zone:
:paramtype time_zone: str
:keyword expression:
:paramtype expression: str
:keyword frequency: Possible values include: "Minute", "Hour", "Day", "Week", "Month".
:paramtype frequency: str or ~flow.models.RecurrenceFrequency
:keyword interval:
:paramtype interval: int
:keyword pattern:
:paramtype pattern: ~flow.models.RecurrencePattern
"""
super(ScheduleBase, self).__init__(**kwargs)
self.schedule_status = kwargs.get('schedule_status', None)
self.schedule_type = kwargs.get('schedule_type', None)
self.end_time = kwargs.get('end_time', None)
self.start_time = kwargs.get('start_time', None)
self.time_zone = kwargs.get('time_zone', None)
self.expression = kwargs.get('expression', None)
self.frequency = kwargs.get('frequency', None)
self.interval = kwargs.get('interval', None)
self.pattern = kwargs.get('pattern', None)
class SchemaContractsCreatedBy(msrest.serialization.Model):
"""SchemaContractsCreatedBy.
:ivar user_object_id:
:vartype user_object_id: str
:ivar user_tenant_id:
:vartype user_tenant_id: str
:ivar user_name:
:vartype user_name: str
:ivar user_principal_name:
:vartype user_principal_name: str
"""
_attribute_map = {
'user_object_id': {'key': 'userObjectId', 'type': 'str'},
'user_tenant_id': {'key': 'userTenantId', 'type': 'str'},
'user_name': {'key': 'userName', 'type': 'str'},
'user_principal_name': {'key': 'userPrincipalName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword user_object_id:
:paramtype user_object_id: str
:keyword user_tenant_id:
:paramtype user_tenant_id: str
:keyword user_name:
:paramtype user_name: str
:keyword user_principal_name:
:paramtype user_principal_name: str
"""
super(SchemaContractsCreatedBy, self).__init__(**kwargs)
self.user_object_id = kwargs.get('user_object_id', None)
self.user_tenant_id = kwargs.get('user_tenant_id', None)
self.user_name = kwargs.get('user_name', None)
self.user_principal_name = kwargs.get('user_principal_name', None)
class ScopeCloudConfiguration(msrest.serialization.Model):
"""ScopeCloudConfiguration.
:ivar input_path_suffixes: This is a dictionary.
:vartype input_path_suffixes: dict[str, ~flow.models.ArgumentAssignment]
:ivar output_path_suffixes: This is a dictionary.
:vartype output_path_suffixes: dict[str, ~flow.models.ArgumentAssignment]
:ivar user_alias:
:vartype user_alias: str
:ivar tokens:
:vartype tokens: int
:ivar auto_token:
:vartype auto_token: int
:ivar vcp:
:vartype vcp: float
"""
_attribute_map = {
'input_path_suffixes': {'key': 'inputPathSuffixes', 'type': '{ArgumentAssignment}'},
'output_path_suffixes': {'key': 'outputPathSuffixes', 'type': '{ArgumentAssignment}'},
'user_alias': {'key': 'userAlias', 'type': 'str'},
'tokens': {'key': 'tokens', 'type': 'int'},
'auto_token': {'key': 'autoToken', 'type': 'int'},
'vcp': {'key': 'vcp', 'type': 'float'},
}
def __init__(
self,
**kwargs
):
"""
:keyword input_path_suffixes: This is a dictionary.
:paramtype input_path_suffixes: dict[str, ~flow.models.ArgumentAssignment]
:keyword output_path_suffixes: This is a dictionary.
:paramtype output_path_suffixes: dict[str, ~flow.models.ArgumentAssignment]
:keyword user_alias:
:paramtype user_alias: str
:keyword tokens:
:paramtype tokens: int
:keyword auto_token:
:paramtype auto_token: int
:keyword vcp:
:paramtype vcp: float
"""
super(ScopeCloudConfiguration, self).__init__(**kwargs)
self.input_path_suffixes = kwargs.get('input_path_suffixes', None)
self.output_path_suffixes = kwargs.get('output_path_suffixes', None)
self.user_alias = kwargs.get('user_alias', None)
self.tokens = kwargs.get('tokens', None)
self.auto_token = kwargs.get('auto_token', None)
self.vcp = kwargs.get('vcp', None)
class Seasonality(msrest.serialization.Model):
"""Seasonality.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.SeasonalityMode
:ivar value:
:vartype value: int
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'value': {'key': 'value', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.SeasonalityMode
:keyword value:
:paramtype value: int
"""
super(Seasonality, self).__init__(**kwargs)
self.mode = kwargs.get('mode', None)
self.value = kwargs.get('value', None)
class SecretConfiguration(msrest.serialization.Model):
"""SecretConfiguration.
:ivar workspace_secret_name:
:vartype workspace_secret_name: str
:ivar uri:
:vartype uri: str
"""
_attribute_map = {
'workspace_secret_name': {'key': 'workspace_secret_name', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword workspace_secret_name:
:paramtype workspace_secret_name: str
:keyword uri:
:paramtype uri: str
"""
super(SecretConfiguration, self).__init__(**kwargs)
self.workspace_secret_name = kwargs.get('workspace_secret_name', None)
self.uri = kwargs.get('uri', None)
class SegmentedResult1(msrest.serialization.Model):
"""SegmentedResult1.
:ivar value:
:vartype value: list[~flow.models.FlowIndexEntity]
:ivar continuation_token:
:vartype continuation_token: str
:ivar count:
:vartype count: int
:ivar next_link:
:vartype next_link: str
"""
_attribute_map = {
'value': {'key': 'value', 'type': '[FlowIndexEntity]'},
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'count': {'key': 'count', 'type': 'int'},
'next_link': {'key': 'nextLink', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword value:
:paramtype value: list[~flow.models.FlowIndexEntity]
:keyword continuation_token:
:paramtype continuation_token: str
:keyword count:
:paramtype count: int
:keyword next_link:
:paramtype next_link: str
"""
super(SegmentedResult1, self).__init__(**kwargs)
self.value = kwargs.get('value', None)
self.continuation_token = kwargs.get('continuation_token', None)
self.count = kwargs.get('count', None)
self.next_link = kwargs.get('next_link', None)
class ServiceLogRequest(msrest.serialization.Model):
"""ServiceLogRequest.
:ivar log_level: Possible values include: "Trace", "Debug", "Information", "Warning", "Error",
"Critical", "None".
:vartype log_level: str or ~flow.models.LogLevel
:ivar message:
:vartype message: str
:ivar timestamp:
:vartype timestamp: ~datetime.datetime
"""
_attribute_map = {
'log_level': {'key': 'logLevel', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
'timestamp': {'key': 'timestamp', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword log_level: Possible values include: "Trace", "Debug", "Information", "Warning",
"Error", "Critical", "None".
:paramtype log_level: str or ~flow.models.LogLevel
:keyword message:
:paramtype message: str
:keyword timestamp:
:paramtype timestamp: ~datetime.datetime
"""
super(ServiceLogRequest, self).__init__(**kwargs)
self.log_level = kwargs.get('log_level', None)
self.message = kwargs.get('message', None)
self.timestamp = kwargs.get('timestamp', None)
class SessionApplication(msrest.serialization.Model):
"""SessionApplication.
:ivar image:
:vartype image: str
:ivar env_vars: Dictionary of :code:`<string>`.
:vartype env_vars: dict[str, str]
:ivar python_pip_requirements:
:vartype python_pip_requirements: list[str]
:ivar setup_results:
:vartype setup_results: list[~flow.models.SessionApplicationRunCommandResult]
"""
_attribute_map = {
'image': {'key': 'image', 'type': 'str'},
'env_vars': {'key': 'envVars', 'type': '{str}'},
'python_pip_requirements': {'key': 'pythonPipRequirements', 'type': '[str]'},
'setup_results': {'key': 'setupResults', 'type': '[SessionApplicationRunCommandResult]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword image:
:paramtype image: str
:keyword env_vars: Dictionary of :code:`<string>`.
:paramtype env_vars: dict[str, str]
:keyword python_pip_requirements:
:paramtype python_pip_requirements: list[str]
:keyword setup_results:
:paramtype setup_results: list[~flow.models.SessionApplicationRunCommandResult]
"""
super(SessionApplication, self).__init__(**kwargs)
self.image = kwargs.get('image', None)
self.env_vars = kwargs.get('env_vars', None)
self.python_pip_requirements = kwargs.get('python_pip_requirements', None)
self.setup_results = kwargs.get('setup_results', None)
class SessionApplicationRunCommandResult(msrest.serialization.Model):
"""SessionApplicationRunCommandResult.
:ivar command:
:vartype command: str
:ivar arguments:
:vartype arguments: list[str]
:ivar exit_code:
:vartype exit_code: int
:ivar std_out:
:vartype std_out: str
:ivar std_err:
:vartype std_err: str
"""
_attribute_map = {
'command': {'key': 'command', 'type': 'str'},
'arguments': {'key': 'arguments', 'type': '[str]'},
'exit_code': {'key': 'exitCode', 'type': 'int'},
'std_out': {'key': 'stdOut', 'type': 'str'},
'std_err': {'key': 'stdErr', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword command:
:paramtype command: str
:keyword arguments:
:paramtype arguments: list[str]
:keyword exit_code:
:paramtype exit_code: int
:keyword std_out:
:paramtype std_out: str
:keyword std_err:
:paramtype std_err: str
"""
super(SessionApplicationRunCommandResult, self).__init__(**kwargs)
self.command = kwargs.get('command', None)
self.arguments = kwargs.get('arguments', None)
self.exit_code = kwargs.get('exit_code', None)
self.std_out = kwargs.get('std_out', None)
self.std_err = kwargs.get('std_err', None)
class SessionProperties(msrest.serialization.Model):
"""SessionProperties.
:ivar session_id:
:vartype session_id: str
:ivar subscription_id:
:vartype subscription_id: str
:ivar resource_group_name:
:vartype resource_group_name: str
:ivar workspace_name:
:vartype workspace_name: str
:ivar user_object_id:
:vartype user_object_id: str
:ivar user_tenant_id:
:vartype user_tenant_id: str
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar application:
:vartype application: ~flow.models.SessionApplication
:ivar last_alive_time:
:vartype last_alive_time: ~datetime.datetime
"""
_attribute_map = {
'session_id': {'key': 'sessionId', 'type': 'str'},
'subscription_id': {'key': 'subscriptionId', 'type': 'str'},
'resource_group_name': {'key': 'resourceGroupName', 'type': 'str'},
'workspace_name': {'key': 'workspaceName', 'type': 'str'},
'user_object_id': {'key': 'userObjectId', 'type': 'str'},
'user_tenant_id': {'key': 'userTenantId', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'application': {'key': 'application', 'type': 'SessionApplication'},
'last_alive_time': {'key': 'lastAliveTime', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword session_id:
:paramtype session_id: str
:keyword subscription_id:
:paramtype subscription_id: str
:keyword resource_group_name:
:paramtype resource_group_name: str
:keyword workspace_name:
:paramtype workspace_name: str
:keyword user_object_id:
:paramtype user_object_id: str
:keyword user_tenant_id:
:paramtype user_tenant_id: str
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword application:
:paramtype application: ~flow.models.SessionApplication
:keyword last_alive_time:
:paramtype last_alive_time: ~datetime.datetime
"""
super(SessionProperties, self).__init__(**kwargs)
self.session_id = kwargs.get('session_id', None)
self.subscription_id = kwargs.get('subscription_id', None)
self.resource_group_name = kwargs.get('resource_group_name', None)
self.workspace_name = kwargs.get('workspace_name', None)
self.user_object_id = kwargs.get('user_object_id', None)
self.user_tenant_id = kwargs.get('user_tenant_id', None)
self.vm_size = kwargs.get('vm_size', None)
self.max_idle_time_seconds = kwargs.get('max_idle_time_seconds', None)
self.application = kwargs.get('application', None)
self.last_alive_time = kwargs.get('last_alive_time', None)
class SetupFlowSessionRequest(msrest.serialization.Model):
"""SetupFlowSessionRequest.
:ivar action: Possible values include: "Install", "Reset", "Update", "Delete".
:vartype action: str or ~flow.models.SetupFlowSessionAction
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar identity:
:vartype identity: str
"""
_attribute_map = {
'action': {'key': 'action', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'identity': {'key': 'identity', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword action: Possible values include: "Install", "Reset", "Update", "Delete".
:paramtype action: str or ~flow.models.SetupFlowSessionAction
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword identity:
:paramtype identity: str
"""
super(SetupFlowSessionRequest, self).__init__(**kwargs)
self.action = kwargs.get('action', None)
self.vm_size = kwargs.get('vm_size', None)
self.max_idle_time_seconds = kwargs.get('max_idle_time_seconds', None)
self.identity = kwargs.get('identity', None)
class SharingScope(msrest.serialization.Model):
"""SharingScope.
:ivar type: Possible values include: "Global", "Tenant", "Subscription", "ResourceGroup",
"Workspace".
:vartype type: str or ~flow.models.ScopeType
:ivar identifier:
:vartype identifier: str
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'identifier': {'key': 'identifier', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword type: Possible values include: "Global", "Tenant", "Subscription", "ResourceGroup",
"Workspace".
:paramtype type: str or ~flow.models.ScopeType
:keyword identifier:
:paramtype identifier: str
"""
super(SharingScope, self).__init__(**kwargs)
self.type = kwargs.get('type', None)
self.identifier = kwargs.get('identifier', None)
class Snapshot(msrest.serialization.Model):
"""Snapshot.
:ivar id:
:vartype id: str
:ivar directory_name:
:vartype directory_name: str
:ivar snapshot_asset_id:
:vartype snapshot_asset_id: str
:ivar snapshot_entity_id:
:vartype snapshot_entity_id: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'directory_name': {'key': 'directoryName', 'type': 'str'},
'snapshot_asset_id': {'key': 'snapshotAssetId', 'type': 'str'},
'snapshot_entity_id': {'key': 'snapshotEntityId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword directory_name:
:paramtype directory_name: str
:keyword snapshot_asset_id:
:paramtype snapshot_asset_id: str
:keyword snapshot_entity_id:
:paramtype snapshot_entity_id: str
"""
super(Snapshot, self).__init__(**kwargs)
self.id = kwargs.get('id', None)
self.directory_name = kwargs.get('directory_name', None)
self.snapshot_asset_id = kwargs.get('snapshot_asset_id', None)
self.snapshot_entity_id = kwargs.get('snapshot_entity_id', None)
class SnapshotInfo(msrest.serialization.Model):
"""SnapshotInfo.
:ivar root_download_url:
:vartype root_download_url: str
:ivar snapshots: This is a dictionary.
:vartype snapshots: dict[str, ~flow.models.DownloadResourceInfo]
"""
_attribute_map = {
'root_download_url': {'key': 'rootDownloadUrl', 'type': 'str'},
'snapshots': {'key': 'snapshots', 'type': '{DownloadResourceInfo}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword root_download_url:
:paramtype root_download_url: str
:keyword snapshots: This is a dictionary.
:paramtype snapshots: dict[str, ~flow.models.DownloadResourceInfo]
"""
super(SnapshotInfo, self).__init__(**kwargs)
self.root_download_url = kwargs.get('root_download_url', None)
self.snapshots = kwargs.get('snapshots', None)
class SourceCodeDataReference(msrest.serialization.Model):
"""SourceCodeDataReference.
:ivar data_store_name:
:vartype data_store_name: str
:ivar path:
:vartype path: str
"""
_attribute_map = {
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'path': {'key': 'path', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_store_name:
:paramtype data_store_name: str
:keyword path:
:paramtype path: str
"""
super(SourceCodeDataReference, self).__init__(**kwargs)
self.data_store_name = kwargs.get('data_store_name', None)
self.path = kwargs.get('path', None)
class SparkConfiguration(msrest.serialization.Model):
"""SparkConfiguration.
:ivar configuration: Dictionary of :code:`<string>`.
:vartype configuration: dict[str, str]
:ivar files:
:vartype files: list[str]
:ivar archives:
:vartype archives: list[str]
:ivar jars:
:vartype jars: list[str]
:ivar py_files:
:vartype py_files: list[str]
:ivar spark_pool_resource_id:
:vartype spark_pool_resource_id: str
"""
_attribute_map = {
'configuration': {'key': 'configuration', 'type': '{str}'},
'files': {'key': 'files', 'type': '[str]'},
'archives': {'key': 'archives', 'type': '[str]'},
'jars': {'key': 'jars', 'type': '[str]'},
'py_files': {'key': 'pyFiles', 'type': '[str]'},
'spark_pool_resource_id': {'key': 'sparkPoolResourceId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword configuration: Dictionary of :code:`<string>`.
:paramtype configuration: dict[str, str]
:keyword files:
:paramtype files: list[str]
:keyword archives:
:paramtype archives: list[str]
:keyword jars:
:paramtype jars: list[str]
:keyword py_files:
:paramtype py_files: list[str]
:keyword spark_pool_resource_id:
:paramtype spark_pool_resource_id: str
"""
super(SparkConfiguration, self).__init__(**kwargs)
self.configuration = kwargs.get('configuration', None)
self.files = kwargs.get('files', None)
self.archives = kwargs.get('archives', None)
self.jars = kwargs.get('jars', None)
self.py_files = kwargs.get('py_files', None)
self.spark_pool_resource_id = kwargs.get('spark_pool_resource_id', None)
class SparkJarTaskDto(msrest.serialization.Model):
"""SparkJarTaskDto.
:ivar main_class_name:
:vartype main_class_name: str
:ivar parameters:
:vartype parameters: list[str]
"""
_attribute_map = {
'main_class_name': {'key': 'main_class_name', 'type': 'str'},
'parameters': {'key': 'parameters', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword main_class_name:
:paramtype main_class_name: str
:keyword parameters:
:paramtype parameters: list[str]
"""
super(SparkJarTaskDto, self).__init__(**kwargs)
self.main_class_name = kwargs.get('main_class_name', None)
self.parameters = kwargs.get('parameters', None)
class SparkJob(msrest.serialization.Model):
"""SparkJob.
:ivar job_type: Possible values include: "Command", "Sweep", "Labeling", "Pipeline", "Data",
"AutoML", "Spark", "Base".
:vartype job_type: str or ~flow.models.JobType
:ivar resources:
:vartype resources: ~flow.models.SparkResourceConfiguration
:ivar args:
:vartype args: str
:ivar code_id:
:vartype code_id: str
:ivar entry:
:vartype entry: ~flow.models.SparkJobEntry
:ivar py_files:
:vartype py_files: list[str]
:ivar jars:
:vartype jars: list[str]
:ivar files:
:vartype files: list[str]
:ivar archives:
:vartype archives: list[str]
:ivar environment_id:
:vartype environment_id: str
:ivar input_data_bindings: Dictionary of :code:`<InputDataBinding>`.
:vartype input_data_bindings: dict[str, ~flow.models.InputDataBinding]
:ivar output_data_bindings: Dictionary of :code:`<OutputDataBinding>`.
:vartype output_data_bindings: dict[str, ~flow.models.OutputDataBinding]
:ivar conf: Dictionary of :code:`<string>`.
:vartype conf: dict[str, str]
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
:ivar provisioning_state: Possible values include: "Succeeded", "Failed", "Canceled",
"InProgress".
:vartype provisioning_state: str or ~flow.models.JobProvisioningState
:ivar parent_job_name:
:vartype parent_job_name: str
:ivar display_name:
:vartype display_name: str
:ivar experiment_name:
:vartype experiment_name: str
:ivar status: Possible values include: "NotStarted", "Starting", "Provisioning", "Preparing",
"Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed", "Canceled",
"NotResponding", "Paused", "Unknown", "Scheduled".
:vartype status: str or ~flow.models.JobStatus
:ivar interaction_endpoints: Dictionary of :code:`<JobEndpoint>`.
:vartype interaction_endpoints: dict[str, ~flow.models.JobEndpoint]
:ivar identity:
:vartype identity: ~flow.models.MfeInternalIdentityConfiguration
:ivar compute:
:vartype compute: ~flow.models.ComputeConfiguration
:ivar priority:
:vartype priority: int
:ivar output:
:vartype output: ~flow.models.JobOutputArtifacts
:ivar is_archived:
:vartype is_archived: bool
:ivar schedule:
:vartype schedule: ~flow.models.ScheduleBase
:ivar component_id:
:vartype component_id: str
:ivar notification_setting:
:vartype notification_setting: ~flow.models.NotificationSetting
:ivar secrets_configuration: Dictionary of :code:`<MfeInternalSecretConfiguration>`.
:vartype secrets_configuration: dict[str, ~flow.models.MfeInternalSecretConfiguration]
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
"""
_attribute_map = {
'job_type': {'key': 'jobType', 'type': 'str'},
'resources': {'key': 'resources', 'type': 'SparkResourceConfiguration'},
'args': {'key': 'args', 'type': 'str'},
'code_id': {'key': 'codeId', 'type': 'str'},
'entry': {'key': 'entry', 'type': 'SparkJobEntry'},
'py_files': {'key': 'pyFiles', 'type': '[str]'},
'jars': {'key': 'jars', 'type': '[str]'},
'files': {'key': 'files', 'type': '[str]'},
'archives': {'key': 'archives', 'type': '[str]'},
'environment_id': {'key': 'environmentId', 'type': 'str'},
'input_data_bindings': {'key': 'inputDataBindings', 'type': '{InputDataBinding}'},
'output_data_bindings': {'key': 'outputDataBindings', 'type': '{OutputDataBinding}'},
'conf': {'key': 'conf', 'type': '{str}'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
'parent_job_name': {'key': 'parentJobName', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'interaction_endpoints': {'key': 'interactionEndpoints', 'type': '{JobEndpoint}'},
'identity': {'key': 'identity', 'type': 'MfeInternalIdentityConfiguration'},
'compute': {'key': 'compute', 'type': 'ComputeConfiguration'},
'priority': {'key': 'priority', 'type': 'int'},
'output': {'key': 'output', 'type': 'JobOutputArtifacts'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
'schedule': {'key': 'schedule', 'type': 'ScheduleBase'},
'component_id': {'key': 'componentId', 'type': 'str'},
'notification_setting': {'key': 'notificationSetting', 'type': 'NotificationSetting'},
'secrets_configuration': {'key': 'secretsConfiguration', 'type': '{MfeInternalSecretConfiguration}'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword job_type: Possible values include: "Command", "Sweep", "Labeling", "Pipeline", "Data",
"AutoML", "Spark", "Base".
:paramtype job_type: str or ~flow.models.JobType
:keyword resources:
:paramtype resources: ~flow.models.SparkResourceConfiguration
:keyword args:
:paramtype args: str
:keyword code_id:
:paramtype code_id: str
:keyword entry:
:paramtype entry: ~flow.models.SparkJobEntry
:keyword py_files:
:paramtype py_files: list[str]
:keyword jars:
:paramtype jars: list[str]
:keyword files:
:paramtype files: list[str]
:keyword archives:
:paramtype archives: list[str]
:keyword environment_id:
:paramtype environment_id: str
:keyword input_data_bindings: Dictionary of :code:`<InputDataBinding>`.
:paramtype input_data_bindings: dict[str, ~flow.models.InputDataBinding]
:keyword output_data_bindings: Dictionary of :code:`<OutputDataBinding>`.
:paramtype output_data_bindings: dict[str, ~flow.models.OutputDataBinding]
:keyword conf: Dictionary of :code:`<string>`.
:paramtype conf: dict[str, str]
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
:keyword provisioning_state: Possible values include: "Succeeded", "Failed", "Canceled",
"InProgress".
:paramtype provisioning_state: str or ~flow.models.JobProvisioningState
:keyword parent_job_name:
:paramtype parent_job_name: str
:keyword display_name:
:paramtype display_name: str
:keyword experiment_name:
:paramtype experiment_name: str
:keyword status: Possible values include: "NotStarted", "Starting", "Provisioning",
"Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed",
"Canceled", "NotResponding", "Paused", "Unknown", "Scheduled".
:paramtype status: str or ~flow.models.JobStatus
:keyword interaction_endpoints: Dictionary of :code:`<JobEndpoint>`.
:paramtype interaction_endpoints: dict[str, ~flow.models.JobEndpoint]
:keyword identity:
:paramtype identity: ~flow.models.MfeInternalIdentityConfiguration
:keyword compute:
:paramtype compute: ~flow.models.ComputeConfiguration
:keyword priority:
:paramtype priority: int
:keyword output:
:paramtype output: ~flow.models.JobOutputArtifacts
:keyword is_archived:
:paramtype is_archived: bool
:keyword schedule:
:paramtype schedule: ~flow.models.ScheduleBase
:keyword component_id:
:paramtype component_id: str
:keyword notification_setting:
:paramtype notification_setting: ~flow.models.NotificationSetting
:keyword secrets_configuration: Dictionary of :code:`<MfeInternalSecretConfiguration>`.
:paramtype secrets_configuration: dict[str, ~flow.models.MfeInternalSecretConfiguration]
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
"""
super(SparkJob, self).__init__(**kwargs)
self.job_type = kwargs.get('job_type', None)
self.resources = kwargs.get('resources', None)
self.args = kwargs.get('args', None)
self.code_id = kwargs.get('code_id', None)
self.entry = kwargs.get('entry', None)
self.py_files = kwargs.get('py_files', None)
self.jars = kwargs.get('jars', None)
self.files = kwargs.get('files', None)
self.archives = kwargs.get('archives', None)
self.environment_id = kwargs.get('environment_id', None)
self.input_data_bindings = kwargs.get('input_data_bindings', None)
self.output_data_bindings = kwargs.get('output_data_bindings', None)
self.conf = kwargs.get('conf', None)
self.environment_variables = kwargs.get('environment_variables', None)
self.provisioning_state = kwargs.get('provisioning_state', None)
self.parent_job_name = kwargs.get('parent_job_name', None)
self.display_name = kwargs.get('display_name', None)
self.experiment_name = kwargs.get('experiment_name', None)
self.status = kwargs.get('status', None)
self.interaction_endpoints = kwargs.get('interaction_endpoints', None)
self.identity = kwargs.get('identity', None)
self.compute = kwargs.get('compute', None)
self.priority = kwargs.get('priority', None)
self.output = kwargs.get('output', None)
self.is_archived = kwargs.get('is_archived', None)
self.schedule = kwargs.get('schedule', None)
self.component_id = kwargs.get('component_id', None)
self.notification_setting = kwargs.get('notification_setting', None)
self.secrets_configuration = kwargs.get('secrets_configuration', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
class SparkJobEntry(msrest.serialization.Model):
"""SparkJobEntry.
:ivar file:
:vartype file: str
:ivar class_name:
:vartype class_name: str
"""
_attribute_map = {
'file': {'key': 'file', 'type': 'str'},
'class_name': {'key': 'className', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword file:
:paramtype file: str
:keyword class_name:
:paramtype class_name: str
"""
super(SparkJobEntry, self).__init__(**kwargs)
self.file = kwargs.get('file', None)
self.class_name = kwargs.get('class_name', None)
class SparkMavenPackage(msrest.serialization.Model):
"""SparkMavenPackage.
:ivar group:
:vartype group: str
:ivar artifact:
:vartype artifact: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'group': {'key': 'group', 'type': 'str'},
'artifact': {'key': 'artifact', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword group:
:paramtype group: str
:keyword artifact:
:paramtype artifact: str
:keyword version:
:paramtype version: str
"""
super(SparkMavenPackage, self).__init__(**kwargs)
self.group = kwargs.get('group', None)
self.artifact = kwargs.get('artifact', None)
self.version = kwargs.get('version', None)
class SparkPythonTaskDto(msrest.serialization.Model):
"""SparkPythonTaskDto.
:ivar python_file:
:vartype python_file: str
:ivar parameters:
:vartype parameters: list[str]
"""
_attribute_map = {
'python_file': {'key': 'python_file', 'type': 'str'},
'parameters': {'key': 'parameters', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword python_file:
:paramtype python_file: str
:keyword parameters:
:paramtype parameters: list[str]
"""
super(SparkPythonTaskDto, self).__init__(**kwargs)
self.python_file = kwargs.get('python_file', None)
self.parameters = kwargs.get('parameters', None)
class SparkResourceConfiguration(msrest.serialization.Model):
"""SparkResourceConfiguration.
:ivar instance_type:
:vartype instance_type: str
:ivar runtime_version:
:vartype runtime_version: str
"""
_attribute_map = {
'instance_type': {'key': 'instanceType', 'type': 'str'},
'runtime_version': {'key': 'runtimeVersion', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword instance_type:
:paramtype instance_type: str
:keyword runtime_version:
:paramtype runtime_version: str
"""
super(SparkResourceConfiguration, self).__init__(**kwargs)
self.instance_type = kwargs.get('instance_type', None)
self.runtime_version = kwargs.get('runtime_version', None)
class SparkSection(msrest.serialization.Model):
"""SparkSection.
:ivar repositories:
:vartype repositories: list[str]
:ivar packages:
:vartype packages: list[~flow.models.SparkMavenPackage]
:ivar precache_packages:
:vartype precache_packages: bool
"""
_attribute_map = {
'repositories': {'key': 'repositories', 'type': '[str]'},
'packages': {'key': 'packages', 'type': '[SparkMavenPackage]'},
'precache_packages': {'key': 'precachePackages', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword repositories:
:paramtype repositories: list[str]
:keyword packages:
:paramtype packages: list[~flow.models.SparkMavenPackage]
:keyword precache_packages:
:paramtype precache_packages: bool
"""
super(SparkSection, self).__init__(**kwargs)
self.repositories = kwargs.get('repositories', None)
self.packages = kwargs.get('packages', None)
self.precache_packages = kwargs.get('precache_packages', None)
class SparkSubmitTaskDto(msrest.serialization.Model):
"""SparkSubmitTaskDto.
:ivar parameters:
:vartype parameters: list[str]
"""
_attribute_map = {
'parameters': {'key': 'parameters', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword parameters:
:paramtype parameters: list[str]
"""
super(SparkSubmitTaskDto, self).__init__(**kwargs)
self.parameters = kwargs.get('parameters', None)
class SqlDataPath(msrest.serialization.Model):
"""SqlDataPath.
:ivar sql_table_name:
:vartype sql_table_name: str
:ivar sql_query:
:vartype sql_query: str
:ivar sql_stored_procedure_name:
:vartype sql_stored_procedure_name: str
:ivar sql_stored_procedure_params:
:vartype sql_stored_procedure_params: list[~flow.models.StoredProcedureParameter]
"""
_attribute_map = {
'sql_table_name': {'key': 'sqlTableName', 'type': 'str'},
'sql_query': {'key': 'sqlQuery', 'type': 'str'},
'sql_stored_procedure_name': {'key': 'sqlStoredProcedureName', 'type': 'str'},
'sql_stored_procedure_params': {'key': 'sqlStoredProcedureParams', 'type': '[StoredProcedureParameter]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword sql_table_name:
:paramtype sql_table_name: str
:keyword sql_query:
:paramtype sql_query: str
:keyword sql_stored_procedure_name:
:paramtype sql_stored_procedure_name: str
:keyword sql_stored_procedure_params:
:paramtype sql_stored_procedure_params: list[~flow.models.StoredProcedureParameter]
"""
super(SqlDataPath, self).__init__(**kwargs)
self.sql_table_name = kwargs.get('sql_table_name', None)
self.sql_query = kwargs.get('sql_query', None)
self.sql_stored_procedure_name = kwargs.get('sql_stored_procedure_name', None)
self.sql_stored_procedure_params = kwargs.get('sql_stored_procedure_params', None)
class StackEnsembleSettings(msrest.serialization.Model):
"""StackEnsembleSettings.
:ivar stack_meta_learner_type: Possible values include: "None", "LogisticRegression",
"LogisticRegressionCV", "LightGBMClassifier", "ElasticNet", "ElasticNetCV",
"LightGBMRegressor", "LinearRegression".
:vartype stack_meta_learner_type: str or ~flow.models.StackMetaLearnerType
:ivar stack_meta_learner_train_percentage:
:vartype stack_meta_learner_train_percentage: float
:ivar stack_meta_learner_k_wargs: Anything.
:vartype stack_meta_learner_k_wargs: any
"""
_attribute_map = {
'stack_meta_learner_type': {'key': 'stackMetaLearnerType', 'type': 'str'},
'stack_meta_learner_train_percentage': {'key': 'stackMetaLearnerTrainPercentage', 'type': 'float'},
'stack_meta_learner_k_wargs': {'key': 'stackMetaLearnerKWargs', 'type': 'object'},
}
def __init__(
self,
**kwargs
):
"""
:keyword stack_meta_learner_type: Possible values include: "None", "LogisticRegression",
"LogisticRegressionCV", "LightGBMClassifier", "ElasticNet", "ElasticNetCV",
"LightGBMRegressor", "LinearRegression".
:paramtype stack_meta_learner_type: str or ~flow.models.StackMetaLearnerType
:keyword stack_meta_learner_train_percentage:
:paramtype stack_meta_learner_train_percentage: float
:keyword stack_meta_learner_k_wargs: Anything.
:paramtype stack_meta_learner_k_wargs: any
"""
super(StackEnsembleSettings, self).__init__(**kwargs)
self.stack_meta_learner_type = kwargs.get('stack_meta_learner_type', None)
self.stack_meta_learner_train_percentage = kwargs.get('stack_meta_learner_train_percentage', None)
self.stack_meta_learner_k_wargs = kwargs.get('stack_meta_learner_k_wargs', None)
class StandbyPoolProperties(msrest.serialization.Model):
"""StandbyPoolProperties.
:ivar name:
:vartype name: str
:ivar count:
:vartype count: int
:ivar vm_size:
:vartype vm_size: str
:ivar standby_available_instances:
:vartype standby_available_instances: list[~flow.models.StandbyPoolResourceStatus]
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'count': {'key': 'count', 'type': 'int'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'standby_available_instances': {'key': 'standbyAvailableInstances', 'type': '[StandbyPoolResourceStatus]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword count:
:paramtype count: int
:keyword vm_size:
:paramtype vm_size: str
:keyword standby_available_instances:
:paramtype standby_available_instances: list[~flow.models.StandbyPoolResourceStatus]
"""
super(StandbyPoolProperties, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.count = kwargs.get('count', None)
self.vm_size = kwargs.get('vm_size', None)
self.standby_available_instances = kwargs.get('standby_available_instances', None)
class StandbyPoolResourceStatus(msrest.serialization.Model):
"""StandbyPoolResourceStatus.
:ivar status:
:vartype status: str
:ivar error:
:vartype error: ~flow.models.CloudError
"""
_attribute_map = {
'status': {'key': 'status', 'type': 'str'},
'error': {'key': 'error', 'type': 'CloudError'},
}
def __init__(
self,
**kwargs
):
"""
:keyword status:
:paramtype status: str
:keyword error:
:paramtype error: ~flow.models.CloudError
"""
super(StandbyPoolResourceStatus, self).__init__(**kwargs)
self.status = kwargs.get('status', None)
self.error = kwargs.get('error', None)
class StartRunResult(msrest.serialization.Model):
"""StartRunResult.
All required parameters must be populated in order to send to Azure.
:ivar run_id: Required.
:vartype run_id: str
"""
_validation = {
'run_id': {'required': True, 'min_length': 1},
}
_attribute_map = {
'run_id': {'key': 'runId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword run_id: Required.
:paramtype run_id: str
"""
super(StartRunResult, self).__init__(**kwargs)
self.run_id = kwargs['run_id']
class StepRunProfile(msrest.serialization.Model):
"""StepRunProfile.
:ivar step_run_id:
:vartype step_run_id: str
:ivar step_run_number:
:vartype step_run_number: int
:ivar run_url:
:vartype run_url: str
:ivar compute_target:
:vartype compute_target: str
:ivar compute_target_url:
:vartype compute_target_url: str
:ivar node_id:
:vartype node_id: str
:ivar node_name:
:vartype node_name: str
:ivar step_name:
:vartype step_name: str
:ivar create_time:
:vartype create_time: long
:ivar start_time:
:vartype start_time: long
:ivar end_time:
:vartype end_time: long
:ivar status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:vartype status: str or ~flow.models.RunStatus
:ivar status_detail:
:vartype status_detail: str
:ivar is_reused:
:vartype is_reused: bool
:ivar reused_pipeline_run_id:
:vartype reused_pipeline_run_id: str
:ivar reused_step_run_id:
:vartype reused_step_run_id: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar status_timeline:
:vartype status_timeline: list[~flow.models.RunStatusPeriod]
"""
_attribute_map = {
'step_run_id': {'key': 'stepRunId', 'type': 'str'},
'step_run_number': {'key': 'stepRunNumber', 'type': 'int'},
'run_url': {'key': 'runUrl', 'type': 'str'},
'compute_target': {'key': 'computeTarget', 'type': 'str'},
'compute_target_url': {'key': 'computeTargetUrl', 'type': 'str'},
'node_id': {'key': 'nodeId', 'type': 'str'},
'node_name': {'key': 'nodeName', 'type': 'str'},
'step_name': {'key': 'stepName', 'type': 'str'},
'create_time': {'key': 'createTime', 'type': 'long'},
'start_time': {'key': 'startTime', 'type': 'long'},
'end_time': {'key': 'endTime', 'type': 'long'},
'status': {'key': 'status', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
'is_reused': {'key': 'isReused', 'type': 'bool'},
'reused_pipeline_run_id': {'key': 'reusedPipelineRunId', 'type': 'str'},
'reused_step_run_id': {'key': 'reusedStepRunId', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'status_timeline': {'key': 'statusTimeline', 'type': '[RunStatusPeriod]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword step_run_id:
:paramtype step_run_id: str
:keyword step_run_number:
:paramtype step_run_number: int
:keyword run_url:
:paramtype run_url: str
:keyword compute_target:
:paramtype compute_target: str
:keyword compute_target_url:
:paramtype compute_target_url: str
:keyword node_id:
:paramtype node_id: str
:keyword node_name:
:paramtype node_name: str
:keyword step_name:
:paramtype step_name: str
:keyword create_time:
:paramtype create_time: long
:keyword start_time:
:paramtype start_time: long
:keyword end_time:
:paramtype end_time: long
:keyword status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:paramtype status: str or ~flow.models.RunStatus
:keyword status_detail:
:paramtype status_detail: str
:keyword is_reused:
:paramtype is_reused: bool
:keyword reused_pipeline_run_id:
:paramtype reused_pipeline_run_id: str
:keyword reused_step_run_id:
:paramtype reused_step_run_id: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword status_timeline:
:paramtype status_timeline: list[~flow.models.RunStatusPeriod]
"""
super(StepRunProfile, self).__init__(**kwargs)
self.step_run_id = kwargs.get('step_run_id', None)
self.step_run_number = kwargs.get('step_run_number', None)
self.run_url = kwargs.get('run_url', None)
self.compute_target = kwargs.get('compute_target', None)
self.compute_target_url = kwargs.get('compute_target_url', None)
self.node_id = kwargs.get('node_id', None)
self.node_name = kwargs.get('node_name', None)
self.step_name = kwargs.get('step_name', None)
self.create_time = kwargs.get('create_time', None)
self.start_time = kwargs.get('start_time', None)
self.end_time = kwargs.get('end_time', None)
self.status = kwargs.get('status', None)
self.status_detail = kwargs.get('status_detail', None)
self.is_reused = kwargs.get('is_reused', None)
self.reused_pipeline_run_id = kwargs.get('reused_pipeline_run_id', None)
self.reused_step_run_id = kwargs.get('reused_step_run_id', None)
self.tags = kwargs.get('tags', None)
self.status_timeline = kwargs.get('status_timeline', None)
class StorageInfo(msrest.serialization.Model):
"""StorageInfo.
:ivar storage_auth_type: Possible values include: "MSI", "ConnectionString", "SAS".
:vartype storage_auth_type: str or ~flow.models.StorageAuthType
:ivar connection_string:
:vartype connection_string: str
:ivar sas_token:
:vartype sas_token: str
:ivar account_name:
:vartype account_name: str
"""
_attribute_map = {
'storage_auth_type': {'key': 'storageAuthType', 'type': 'str'},
'connection_string': {'key': 'connectionString', 'type': 'str'},
'sas_token': {'key': 'sasToken', 'type': 'str'},
'account_name': {'key': 'accountName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword storage_auth_type: Possible values include: "MSI", "ConnectionString", "SAS".
:paramtype storage_auth_type: str or ~flow.models.StorageAuthType
:keyword connection_string:
:paramtype connection_string: str
:keyword sas_token:
:paramtype sas_token: str
:keyword account_name:
:paramtype account_name: str
"""
super(StorageInfo, self).__init__(**kwargs)
self.storage_auth_type = kwargs.get('storage_auth_type', None)
self.connection_string = kwargs.get('connection_string', None)
self.sas_token = kwargs.get('sas_token', None)
self.account_name = kwargs.get('account_name', None)
class StoredProcedureParameter(msrest.serialization.Model):
"""StoredProcedureParameter.
:ivar name:
:vartype name: str
:ivar value:
:vartype value: str
:ivar type: Possible values include: "String", "Int", "Decimal", "Guid", "Boolean", "Date".
:vartype type: str or ~flow.models.StoredProcedureParameterType
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword value:
:paramtype value: str
:keyword type: Possible values include: "String", "Int", "Decimal", "Guid", "Boolean", "Date".
:paramtype type: str or ~flow.models.StoredProcedureParameterType
"""
super(StoredProcedureParameter, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.value = kwargs.get('value', None)
self.type = kwargs.get('type', None)
class Stream(msrest.serialization.Model):
"""Stream.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar can_read:
:vartype can_read: bool
:ivar can_write:
:vartype can_write: bool
:ivar can_seek:
:vartype can_seek: bool
:ivar can_timeout:
:vartype can_timeout: bool
:ivar length:
:vartype length: long
:ivar position:
:vartype position: long
:ivar read_timeout:
:vartype read_timeout: int
:ivar write_timeout:
:vartype write_timeout: int
"""
_validation = {
'can_read': {'readonly': True},
'can_write': {'readonly': True},
'can_seek': {'readonly': True},
'can_timeout': {'readonly': True},
'length': {'readonly': True},
}
_attribute_map = {
'can_read': {'key': 'canRead', 'type': 'bool'},
'can_write': {'key': 'canWrite', 'type': 'bool'},
'can_seek': {'key': 'canSeek', 'type': 'bool'},
'can_timeout': {'key': 'canTimeout', 'type': 'bool'},
'length': {'key': 'length', 'type': 'long'},
'position': {'key': 'position', 'type': 'long'},
'read_timeout': {'key': 'readTimeout', 'type': 'int'},
'write_timeout': {'key': 'writeTimeout', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword position:
:paramtype position: long
:keyword read_timeout:
:paramtype read_timeout: int
:keyword write_timeout:
:paramtype write_timeout: int
"""
super(Stream, self).__init__(**kwargs)
self.can_read = None
self.can_write = None
self.can_seek = None
self.can_timeout = None
self.length = None
self.position = kwargs.get('position', None)
self.read_timeout = kwargs.get('read_timeout', None)
self.write_timeout = kwargs.get('write_timeout', None)
class StructuredInterface(msrest.serialization.Model):
"""StructuredInterface.
:ivar command_line_pattern:
:vartype command_line_pattern: str
:ivar inputs:
:vartype inputs: list[~flow.models.StructuredInterfaceInput]
:ivar outputs:
:vartype outputs: list[~flow.models.StructuredInterfaceOutput]
:ivar control_outputs:
:vartype control_outputs: list[~flow.models.ControlOutput]
:ivar parameters:
:vartype parameters: list[~flow.models.StructuredInterfaceParameter]
:ivar metadata_parameters:
:vartype metadata_parameters: list[~flow.models.StructuredInterfaceParameter]
:ivar arguments:
:vartype arguments: list[~flow.models.ArgumentAssignment]
"""
_attribute_map = {
'command_line_pattern': {'key': 'commandLinePattern', 'type': 'str'},
'inputs': {'key': 'inputs', 'type': '[StructuredInterfaceInput]'},
'outputs': {'key': 'outputs', 'type': '[StructuredInterfaceOutput]'},
'control_outputs': {'key': 'controlOutputs', 'type': '[ControlOutput]'},
'parameters': {'key': 'parameters', 'type': '[StructuredInterfaceParameter]'},
'metadata_parameters': {'key': 'metadataParameters', 'type': '[StructuredInterfaceParameter]'},
'arguments': {'key': 'arguments', 'type': '[ArgumentAssignment]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword command_line_pattern:
:paramtype command_line_pattern: str
:keyword inputs:
:paramtype inputs: list[~flow.models.StructuredInterfaceInput]
:keyword outputs:
:paramtype outputs: list[~flow.models.StructuredInterfaceOutput]
:keyword control_outputs:
:paramtype control_outputs: list[~flow.models.ControlOutput]
:keyword parameters:
:paramtype parameters: list[~flow.models.StructuredInterfaceParameter]
:keyword metadata_parameters:
:paramtype metadata_parameters: list[~flow.models.StructuredInterfaceParameter]
:keyword arguments:
:paramtype arguments: list[~flow.models.ArgumentAssignment]
"""
super(StructuredInterface, self).__init__(**kwargs)
self.command_line_pattern = kwargs.get('command_line_pattern', None)
self.inputs = kwargs.get('inputs', None)
self.outputs = kwargs.get('outputs', None)
self.control_outputs = kwargs.get('control_outputs', None)
self.parameters = kwargs.get('parameters', None)
self.metadata_parameters = kwargs.get('metadata_parameters', None)
self.arguments = kwargs.get('arguments', None)
class StructuredInterfaceInput(msrest.serialization.Model):
"""StructuredInterfaceInput.
:ivar name:
:vartype name: str
:ivar label:
:vartype label: str
:ivar data_type_ids_list:
:vartype data_type_ids_list: list[str]
:ivar is_optional:
:vartype is_optional: bool
:ivar description:
:vartype description: str
:ivar skip_processing:
:vartype skip_processing: bool
:ivar is_resource:
:vartype is_resource: bool
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AEVADataStoreMode
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar overwrite:
:vartype overwrite: bool
:ivar data_reference_name:
:vartype data_reference_name: str
:ivar dataset_types:
:vartype dataset_types: list[str or ~flow.models.DatasetType]
"""
_validation = {
'dataset_types': {'unique': True},
}
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'data_type_ids_list': {'key': 'dataTypeIdsList', 'type': '[str]'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'skip_processing': {'key': 'skipProcessing', 'type': 'bool'},
'is_resource': {'key': 'isResource', 'type': 'bool'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'overwrite': {'key': 'overwrite', 'type': 'bool'},
'data_reference_name': {'key': 'dataReferenceName', 'type': 'str'},
'dataset_types': {'key': 'datasetTypes', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword label:
:paramtype label: str
:keyword data_type_ids_list:
:paramtype data_type_ids_list: list[str]
:keyword is_optional:
:paramtype is_optional: bool
:keyword description:
:paramtype description: str
:keyword skip_processing:
:paramtype skip_processing: bool
:keyword is_resource:
:paramtype is_resource: bool
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AEVADataStoreMode
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword overwrite:
:paramtype overwrite: bool
:keyword data_reference_name:
:paramtype data_reference_name: str
:keyword dataset_types:
:paramtype dataset_types: list[str or ~flow.models.DatasetType]
"""
super(StructuredInterfaceInput, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.label = kwargs.get('label', None)
self.data_type_ids_list = kwargs.get('data_type_ids_list', None)
self.is_optional = kwargs.get('is_optional', None)
self.description = kwargs.get('description', None)
self.skip_processing = kwargs.get('skip_processing', None)
self.is_resource = kwargs.get('is_resource', None)
self.data_store_mode = kwargs.get('data_store_mode', None)
self.path_on_compute = kwargs.get('path_on_compute', None)
self.overwrite = kwargs.get('overwrite', None)
self.data_reference_name = kwargs.get('data_reference_name', None)
self.dataset_types = kwargs.get('dataset_types', None)
class StructuredInterfaceOutput(msrest.serialization.Model):
"""StructuredInterfaceOutput.
:ivar name:
:vartype name: str
:ivar label:
:vartype label: str
:ivar data_type_id:
:vartype data_type_id: str
:ivar pass_through_data_type_input_name:
:vartype pass_through_data_type_input_name: str
:ivar description:
:vartype description: str
:ivar skip_processing:
:vartype skip_processing: bool
:ivar is_artifact:
:vartype is_artifact: bool
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AEVADataStoreMode
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar overwrite:
:vartype overwrite: bool
:ivar data_reference_name:
:vartype data_reference_name: str
:ivar training_output:
:vartype training_output: ~flow.models.TrainingOutput
:ivar dataset_output:
:vartype dataset_output: ~flow.models.DatasetOutput
:ivar asset_output_settings:
:vartype asset_output_settings: ~flow.models.AssetOutputSettings
:ivar early_available:
:vartype early_available: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'data_type_id': {'key': 'dataTypeId', 'type': 'str'},
'pass_through_data_type_input_name': {'key': 'passThroughDataTypeInputName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'skip_processing': {'key': 'skipProcessing', 'type': 'bool'},
'is_artifact': {'key': 'IsArtifact', 'type': 'bool'},
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'overwrite': {'key': 'overwrite', 'type': 'bool'},
'data_reference_name': {'key': 'dataReferenceName', 'type': 'str'},
'training_output': {'key': 'trainingOutput', 'type': 'TrainingOutput'},
'dataset_output': {'key': 'datasetOutput', 'type': 'DatasetOutput'},
'asset_output_settings': {'key': 'AssetOutputSettings', 'type': 'AssetOutputSettings'},
'early_available': {'key': 'EarlyAvailable', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword label:
:paramtype label: str
:keyword data_type_id:
:paramtype data_type_id: str
:keyword pass_through_data_type_input_name:
:paramtype pass_through_data_type_input_name: str
:keyword description:
:paramtype description: str
:keyword skip_processing:
:paramtype skip_processing: bool
:keyword is_artifact:
:paramtype is_artifact: bool
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AEVADataStoreMode
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword overwrite:
:paramtype overwrite: bool
:keyword data_reference_name:
:paramtype data_reference_name: str
:keyword training_output:
:paramtype training_output: ~flow.models.TrainingOutput
:keyword dataset_output:
:paramtype dataset_output: ~flow.models.DatasetOutput
:keyword asset_output_settings:
:paramtype asset_output_settings: ~flow.models.AssetOutputSettings
:keyword early_available:
:paramtype early_available: bool
"""
super(StructuredInterfaceOutput, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.label = kwargs.get('label', None)
self.data_type_id = kwargs.get('data_type_id', None)
self.pass_through_data_type_input_name = kwargs.get('pass_through_data_type_input_name', None)
self.description = kwargs.get('description', None)
self.skip_processing = kwargs.get('skip_processing', None)
self.is_artifact = kwargs.get('is_artifact', None)
self.data_store_name = kwargs.get('data_store_name', None)
self.data_store_mode = kwargs.get('data_store_mode', None)
self.path_on_compute = kwargs.get('path_on_compute', None)
self.overwrite = kwargs.get('overwrite', None)
self.data_reference_name = kwargs.get('data_reference_name', None)
self.training_output = kwargs.get('training_output', None)
self.dataset_output = kwargs.get('dataset_output', None)
self.asset_output_settings = kwargs.get('asset_output_settings', None)
self.early_available = kwargs.get('early_available', None)
class StructuredInterfaceParameter(msrest.serialization.Model):
"""StructuredInterfaceParameter.
:ivar name:
:vartype name: str
:ivar label:
:vartype label: str
:ivar parameter_type: Possible values include: "Int", "Double", "Bool", "String", "Undefined".
:vartype parameter_type: str or ~flow.models.ParameterType
:ivar is_optional:
:vartype is_optional: bool
:ivar default_value:
:vartype default_value: str
:ivar lower_bound:
:vartype lower_bound: str
:ivar upper_bound:
:vartype upper_bound: str
:ivar enum_values:
:vartype enum_values: list[str]
:ivar enum_values_to_argument_strings: This is a dictionary.
:vartype enum_values_to_argument_strings: dict[str, str]
:ivar description:
:vartype description: str
:ivar set_environment_variable:
:vartype set_environment_variable: bool
:ivar environment_variable_override:
:vartype environment_variable_override: str
:ivar enabled_by_parameter_name:
:vartype enabled_by_parameter_name: str
:ivar enabled_by_parameter_values:
:vartype enabled_by_parameter_values: list[str]
:ivar ui_hint:
:vartype ui_hint: ~flow.models.UIParameterHint
:ivar group_names:
:vartype group_names: list[str]
:ivar argument_name:
:vartype argument_name: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'parameter_type': {'key': 'parameterType', 'type': 'str'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
'default_value': {'key': 'defaultValue', 'type': 'str'},
'lower_bound': {'key': 'lowerBound', 'type': 'str'},
'upper_bound': {'key': 'upperBound', 'type': 'str'},
'enum_values': {'key': 'enumValues', 'type': '[str]'},
'enum_values_to_argument_strings': {'key': 'enumValuesToArgumentStrings', 'type': '{str}'},
'description': {'key': 'description', 'type': 'str'},
'set_environment_variable': {'key': 'setEnvironmentVariable', 'type': 'bool'},
'environment_variable_override': {'key': 'environmentVariableOverride', 'type': 'str'},
'enabled_by_parameter_name': {'key': 'enabledByParameterName', 'type': 'str'},
'enabled_by_parameter_values': {'key': 'enabledByParameterValues', 'type': '[str]'},
'ui_hint': {'key': 'uiHint', 'type': 'UIParameterHint'},
'group_names': {'key': 'groupNames', 'type': '[str]'},
'argument_name': {'key': 'argumentName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword label:
:paramtype label: str
:keyword parameter_type: Possible values include: "Int", "Double", "Bool", "String",
"Undefined".
:paramtype parameter_type: str or ~flow.models.ParameterType
:keyword is_optional:
:paramtype is_optional: bool
:keyword default_value:
:paramtype default_value: str
:keyword lower_bound:
:paramtype lower_bound: str
:keyword upper_bound:
:paramtype upper_bound: str
:keyword enum_values:
:paramtype enum_values: list[str]
:keyword enum_values_to_argument_strings: This is a dictionary.
:paramtype enum_values_to_argument_strings: dict[str, str]
:keyword description:
:paramtype description: str
:keyword set_environment_variable:
:paramtype set_environment_variable: bool
:keyword environment_variable_override:
:paramtype environment_variable_override: str
:keyword enabled_by_parameter_name:
:paramtype enabled_by_parameter_name: str
:keyword enabled_by_parameter_values:
:paramtype enabled_by_parameter_values: list[str]
:keyword ui_hint:
:paramtype ui_hint: ~flow.models.UIParameterHint
:keyword group_names:
:paramtype group_names: list[str]
:keyword argument_name:
:paramtype argument_name: str
"""
super(StructuredInterfaceParameter, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.label = kwargs.get('label', None)
self.parameter_type = kwargs.get('parameter_type', None)
self.is_optional = kwargs.get('is_optional', None)
self.default_value = kwargs.get('default_value', None)
self.lower_bound = kwargs.get('lower_bound', None)
self.upper_bound = kwargs.get('upper_bound', None)
self.enum_values = kwargs.get('enum_values', None)
self.enum_values_to_argument_strings = kwargs.get('enum_values_to_argument_strings', None)
self.description = kwargs.get('description', None)
self.set_environment_variable = kwargs.get('set_environment_variable', None)
self.environment_variable_override = kwargs.get('environment_variable_override', None)
self.enabled_by_parameter_name = kwargs.get('enabled_by_parameter_name', None)
self.enabled_by_parameter_values = kwargs.get('enabled_by_parameter_values', None)
self.ui_hint = kwargs.get('ui_hint', None)
self.group_names = kwargs.get('group_names', None)
self.argument_name = kwargs.get('argument_name', None)
class StudioMigrationInfo(msrest.serialization.Model):
"""StudioMigrationInfo.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar source_workspace_id:
:vartype source_workspace_id: str
:ivar source_experiment_id:
:vartype source_experiment_id: str
:ivar source_experiment_link:
:vartype source_experiment_link: str
:ivar failed_node_id_list:
:vartype failed_node_id_list: list[str]
:ivar error_message:
:vartype error_message: str
"""
_validation = {
'error_message': {'readonly': True},
}
_attribute_map = {
'source_workspace_id': {'key': 'sourceWorkspaceId', 'type': 'str'},
'source_experiment_id': {'key': 'sourceExperimentId', 'type': 'str'},
'source_experiment_link': {'key': 'sourceExperimentLink', 'type': 'str'},
'failed_node_id_list': {'key': 'failedNodeIdList', 'type': '[str]'},
'error_message': {'key': 'errorMessage', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword source_workspace_id:
:paramtype source_workspace_id: str
:keyword source_experiment_id:
:paramtype source_experiment_id: str
:keyword source_experiment_link:
:paramtype source_experiment_link: str
:keyword failed_node_id_list:
:paramtype failed_node_id_list: list[str]
"""
super(StudioMigrationInfo, self).__init__(**kwargs)
self.source_workspace_id = kwargs.get('source_workspace_id', None)
self.source_experiment_id = kwargs.get('source_experiment_id', None)
self.source_experiment_link = kwargs.get('source_experiment_link', None)
self.failed_node_id_list = kwargs.get('failed_node_id_list', None)
self.error_message = None
class SubGraphConcatenateAssignment(msrest.serialization.Model):
"""SubGraphConcatenateAssignment.
:ivar concatenate_parameter:
:vartype concatenate_parameter: list[~flow.models.ParameterAssignment]
:ivar parameter_assignments:
:vartype parameter_assignments: ~flow.models.SubPipelineParameterAssignment
"""
_attribute_map = {
'concatenate_parameter': {'key': 'concatenateParameter', 'type': '[ParameterAssignment]'},
'parameter_assignments': {'key': 'parameterAssignments', 'type': 'SubPipelineParameterAssignment'},
}
def __init__(
self,
**kwargs
):
"""
:keyword concatenate_parameter:
:paramtype concatenate_parameter: list[~flow.models.ParameterAssignment]
:keyword parameter_assignments:
:paramtype parameter_assignments: ~flow.models.SubPipelineParameterAssignment
"""
super(SubGraphConcatenateAssignment, self).__init__(**kwargs)
self.concatenate_parameter = kwargs.get('concatenate_parameter', None)
self.parameter_assignments = kwargs.get('parameter_assignments', None)
class SubGraphConfiguration(msrest.serialization.Model):
"""SubGraphConfiguration.
:ivar graph_id:
:vartype graph_id: str
:ivar graph_draft_id:
:vartype graph_draft_id: str
:ivar default_cloud_priority:
:vartype default_cloud_priority: ~flow.models.CloudPrioritySetting
:ivar is_dynamic:
:vartype is_dynamic: bool
"""
_attribute_map = {
'graph_id': {'key': 'graphId', 'type': 'str'},
'graph_draft_id': {'key': 'graphDraftId', 'type': 'str'},
'default_cloud_priority': {'key': 'DefaultCloudPriority', 'type': 'CloudPrioritySetting'},
'is_dynamic': {'key': 'IsDynamic', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword graph_id:
:paramtype graph_id: str
:keyword graph_draft_id:
:paramtype graph_draft_id: str
:keyword default_cloud_priority:
:paramtype default_cloud_priority: ~flow.models.CloudPrioritySetting
:keyword is_dynamic:
:paramtype is_dynamic: bool
"""
super(SubGraphConfiguration, self).__init__(**kwargs)
self.graph_id = kwargs.get('graph_id', None)
self.graph_draft_id = kwargs.get('graph_draft_id', None)
self.default_cloud_priority = kwargs.get('default_cloud_priority', None)
self.is_dynamic = kwargs.get('is_dynamic', False)
class SubGraphConnectionInfo(msrest.serialization.Model):
"""SubGraphConnectionInfo.
:ivar node_id:
:vartype node_id: str
:ivar port_name:
:vartype port_name: str
"""
_attribute_map = {
'node_id': {'key': 'nodeId', 'type': 'str'},
'port_name': {'key': 'portName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword node_id:
:paramtype node_id: str
:keyword port_name:
:paramtype port_name: str
"""
super(SubGraphConnectionInfo, self).__init__(**kwargs)
self.node_id = kwargs.get('node_id', None)
self.port_name = kwargs.get('port_name', None)
class SubGraphDataPathParameterAssignment(msrest.serialization.Model):
"""SubGraphDataPathParameterAssignment.
:ivar data_set_path_parameter:
:vartype data_set_path_parameter: ~flow.models.DataSetPathParameter
:ivar data_set_path_parameter_assignments:
:vartype data_set_path_parameter_assignments: list[str]
"""
_attribute_map = {
'data_set_path_parameter': {'key': 'dataSetPathParameter', 'type': 'DataSetPathParameter'},
'data_set_path_parameter_assignments': {'key': 'dataSetPathParameterAssignments', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword data_set_path_parameter:
:paramtype data_set_path_parameter: ~flow.models.DataSetPathParameter
:keyword data_set_path_parameter_assignments:
:paramtype data_set_path_parameter_assignments: list[str]
"""
super(SubGraphDataPathParameterAssignment, self).__init__(**kwargs)
self.data_set_path_parameter = kwargs.get('data_set_path_parameter', None)
self.data_set_path_parameter_assignments = kwargs.get('data_set_path_parameter_assignments', None)
class SubGraphInfo(msrest.serialization.Model):
"""SubGraphInfo.
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar default_compute_target:
:vartype default_compute_target: ~flow.models.ComputeSetting
:ivar default_data_store:
:vartype default_data_store: ~flow.models.DatastoreSetting
:ivar id:
:vartype id: str
:ivar parent_graph_id:
:vartype parent_graph_id: str
:ivar pipeline_definition_id:
:vartype pipeline_definition_id: str
:ivar sub_graph_parameter_assignment:
:vartype sub_graph_parameter_assignment: list[~flow.models.SubGraphParameterAssignment]
:ivar sub_graph_concatenate_assignment:
:vartype sub_graph_concatenate_assignment: list[~flow.models.SubGraphConcatenateAssignment]
:ivar sub_graph_data_path_parameter_assignment:
:vartype sub_graph_data_path_parameter_assignment:
list[~flow.models.SubGraphDataPathParameterAssignment]
:ivar sub_graph_default_compute_target_nodes:
:vartype sub_graph_default_compute_target_nodes: list[str]
:ivar sub_graph_default_data_store_nodes:
:vartype sub_graph_default_data_store_nodes: list[str]
:ivar inputs:
:vartype inputs: list[~flow.models.SubGraphPortInfo]
:ivar outputs:
:vartype outputs: list[~flow.models.SubGraphPortInfo]
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'default_compute_target': {'key': 'defaultComputeTarget', 'type': 'ComputeSetting'},
'default_data_store': {'key': 'defaultDataStore', 'type': 'DatastoreSetting'},
'id': {'key': 'id', 'type': 'str'},
'parent_graph_id': {'key': 'parentGraphId', 'type': 'str'},
'pipeline_definition_id': {'key': 'pipelineDefinitionId', 'type': 'str'},
'sub_graph_parameter_assignment': {'key': 'subGraphParameterAssignment', 'type': '[SubGraphParameterAssignment]'},
'sub_graph_concatenate_assignment': {'key': 'subGraphConcatenateAssignment', 'type': '[SubGraphConcatenateAssignment]'},
'sub_graph_data_path_parameter_assignment': {'key': 'subGraphDataPathParameterAssignment', 'type': '[SubGraphDataPathParameterAssignment]'},
'sub_graph_default_compute_target_nodes': {'key': 'subGraphDefaultComputeTargetNodes', 'type': '[str]'},
'sub_graph_default_data_store_nodes': {'key': 'subGraphDefaultDataStoreNodes', 'type': '[str]'},
'inputs': {'key': 'inputs', 'type': '[SubGraphPortInfo]'},
'outputs': {'key': 'outputs', 'type': '[SubGraphPortInfo]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword default_compute_target:
:paramtype default_compute_target: ~flow.models.ComputeSetting
:keyword default_data_store:
:paramtype default_data_store: ~flow.models.DatastoreSetting
:keyword id:
:paramtype id: str
:keyword parent_graph_id:
:paramtype parent_graph_id: str
:keyword pipeline_definition_id:
:paramtype pipeline_definition_id: str
:keyword sub_graph_parameter_assignment:
:paramtype sub_graph_parameter_assignment: list[~flow.models.SubGraphParameterAssignment]
:keyword sub_graph_concatenate_assignment:
:paramtype sub_graph_concatenate_assignment: list[~flow.models.SubGraphConcatenateAssignment]
:keyword sub_graph_data_path_parameter_assignment:
:paramtype sub_graph_data_path_parameter_assignment:
list[~flow.models.SubGraphDataPathParameterAssignment]
:keyword sub_graph_default_compute_target_nodes:
:paramtype sub_graph_default_compute_target_nodes: list[str]
:keyword sub_graph_default_data_store_nodes:
:paramtype sub_graph_default_data_store_nodes: list[str]
:keyword inputs:
:paramtype inputs: list[~flow.models.SubGraphPortInfo]
:keyword outputs:
:paramtype outputs: list[~flow.models.SubGraphPortInfo]
"""
super(SubGraphInfo, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.description = kwargs.get('description', None)
self.default_compute_target = kwargs.get('default_compute_target', None)
self.default_data_store = kwargs.get('default_data_store', None)
self.id = kwargs.get('id', None)
self.parent_graph_id = kwargs.get('parent_graph_id', None)
self.pipeline_definition_id = kwargs.get('pipeline_definition_id', None)
self.sub_graph_parameter_assignment = kwargs.get('sub_graph_parameter_assignment', None)
self.sub_graph_concatenate_assignment = kwargs.get('sub_graph_concatenate_assignment', None)
self.sub_graph_data_path_parameter_assignment = kwargs.get('sub_graph_data_path_parameter_assignment', None)
self.sub_graph_default_compute_target_nodes = kwargs.get('sub_graph_default_compute_target_nodes', None)
self.sub_graph_default_data_store_nodes = kwargs.get('sub_graph_default_data_store_nodes', None)
self.inputs = kwargs.get('inputs', None)
self.outputs = kwargs.get('outputs', None)
class SubGraphParameterAssignment(msrest.serialization.Model):
"""SubGraphParameterAssignment.
:ivar parameter:
:vartype parameter: ~flow.models.Parameter
:ivar parameter_assignments:
:vartype parameter_assignments: list[~flow.models.SubPipelineParameterAssignment]
"""
_attribute_map = {
'parameter': {'key': 'parameter', 'type': 'Parameter'},
'parameter_assignments': {'key': 'parameterAssignments', 'type': '[SubPipelineParameterAssignment]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword parameter:
:paramtype parameter: ~flow.models.Parameter
:keyword parameter_assignments:
:paramtype parameter_assignments: list[~flow.models.SubPipelineParameterAssignment]
"""
super(SubGraphParameterAssignment, self).__init__(**kwargs)
self.parameter = kwargs.get('parameter', None)
self.parameter_assignments = kwargs.get('parameter_assignments', None)
class SubGraphPortInfo(msrest.serialization.Model):
"""SubGraphPortInfo.
:ivar name:
:vartype name: str
:ivar internal:
:vartype internal: list[~flow.models.SubGraphConnectionInfo]
:ivar external:
:vartype external: list[~flow.models.SubGraphConnectionInfo]
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'internal': {'key': 'internal', 'type': '[SubGraphConnectionInfo]'},
'external': {'key': 'external', 'type': '[SubGraphConnectionInfo]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword internal:
:paramtype internal: list[~flow.models.SubGraphConnectionInfo]
:keyword external:
:paramtype external: list[~flow.models.SubGraphConnectionInfo]
"""
super(SubGraphPortInfo, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.internal = kwargs.get('internal', None)
self.external = kwargs.get('external', None)
class SubmitBulkRunRequest(msrest.serialization.Model):
"""SubmitBulkRunRequest.
:ivar flow_definition_file_path:
:vartype flow_definition_file_path: str
:ivar flow_definition_resource_id:
:vartype flow_definition_resource_id: str
:ivar flow_definition_data_store_name:
:vartype flow_definition_data_store_name: str
:ivar flow_definition_blob_path:
:vartype flow_definition_blob_path: str
:ivar flow_definition_data_uri:
:vartype flow_definition_data_uri: str
:ivar run_id:
:vartype run_id: str
:ivar run_display_name:
:vartype run_display_name: str
:ivar run_experiment_name:
:vartype run_experiment_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar node_variant:
:vartype node_variant: str
:ivar variant_run_id:
:vartype variant_run_id: str
:ivar baseline_run_id:
:vartype baseline_run_id: str
:ivar batch_data_input:
:vartype batch_data_input: ~flow.models.BatchDataInput
:ivar inputs_mapping: This is a dictionary.
:vartype inputs_mapping: dict[str, str]
:ivar connections: This is a dictionary.
:vartype connections: dict[str, dict[str, str]]
:ivar environment_variables: This is a dictionary.
:vartype environment_variables: dict[str, str]
:ivar aml_compute_name:
:vartype aml_compute_name: str
:ivar runtime_name:
:vartype runtime_name: str
:ivar session_id:
:vartype session_id: str
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar session_setup_mode: Possible values include: "ClientWait", "SystemWait".
:vartype session_setup_mode: str or ~flow.models.SessionSetupModeEnum
:ivar output_data_store:
:vartype output_data_store: str
:ivar flow_lineage_id:
:vartype flow_lineage_id: str
:ivar run_display_name_generation_type: Possible values include: "AutoAppend",
"UserProvidedMacro".
:vartype run_display_name_generation_type: str or ~flow.models.RunDisplayNameGenerationType
"""
_attribute_map = {
'flow_definition_file_path': {'key': 'flowDefinitionFilePath', 'type': 'str'},
'flow_definition_resource_id': {'key': 'flowDefinitionResourceId', 'type': 'str'},
'flow_definition_data_store_name': {'key': 'flowDefinitionDataStoreName', 'type': 'str'},
'flow_definition_blob_path': {'key': 'flowDefinitionBlobPath', 'type': 'str'},
'flow_definition_data_uri': {'key': 'flowDefinitionDataUri', 'type': 'str'},
'run_id': {'key': 'runId', 'type': 'str'},
'run_display_name': {'key': 'runDisplayName', 'type': 'str'},
'run_experiment_name': {'key': 'runExperimentName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'node_variant': {'key': 'nodeVariant', 'type': 'str'},
'variant_run_id': {'key': 'variantRunId', 'type': 'str'},
'baseline_run_id': {'key': 'baselineRunId', 'type': 'str'},
'batch_data_input': {'key': 'batchDataInput', 'type': 'BatchDataInput'},
'inputs_mapping': {'key': 'inputsMapping', 'type': '{str}'},
'connections': {'key': 'connections', 'type': '{{str}}'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'aml_compute_name': {'key': 'amlComputeName', 'type': 'str'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'session_id': {'key': 'sessionId', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'session_setup_mode': {'key': 'sessionSetupMode', 'type': 'str'},
'output_data_store': {'key': 'outputDataStore', 'type': 'str'},
'flow_lineage_id': {'key': 'flowLineageId', 'type': 'str'},
'run_display_name_generation_type': {'key': 'runDisplayNameGenerationType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword flow_definition_file_path:
:paramtype flow_definition_file_path: str
:keyword flow_definition_resource_id:
:paramtype flow_definition_resource_id: str
:keyword flow_definition_data_store_name:
:paramtype flow_definition_data_store_name: str
:keyword flow_definition_blob_path:
:paramtype flow_definition_blob_path: str
:keyword flow_definition_data_uri:
:paramtype flow_definition_data_uri: str
:keyword run_id:
:paramtype run_id: str
:keyword run_display_name:
:paramtype run_display_name: str
:keyword run_experiment_name:
:paramtype run_experiment_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword node_variant:
:paramtype node_variant: str
:keyword variant_run_id:
:paramtype variant_run_id: str
:keyword baseline_run_id:
:paramtype baseline_run_id: str
:keyword batch_data_input:
:paramtype batch_data_input: ~flow.models.BatchDataInput
:keyword inputs_mapping: This is a dictionary.
:paramtype inputs_mapping: dict[str, str]
:keyword connections: This is a dictionary.
:paramtype connections: dict[str, dict[str, str]]
:keyword environment_variables: This is a dictionary.
:paramtype environment_variables: dict[str, str]
:keyword aml_compute_name:
:paramtype aml_compute_name: str
:keyword runtime_name:
:paramtype runtime_name: str
:keyword session_id:
:paramtype session_id: str
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword session_setup_mode: Possible values include: "ClientWait", "SystemWait".
:paramtype session_setup_mode: str or ~flow.models.SessionSetupModeEnum
:keyword output_data_store:
:paramtype output_data_store: str
:keyword flow_lineage_id:
:paramtype flow_lineage_id: str
:keyword run_display_name_generation_type: Possible values include: "AutoAppend",
"UserProvidedMacro".
:paramtype run_display_name_generation_type: str or ~flow.models.RunDisplayNameGenerationType
"""
super(SubmitBulkRunRequest, self).__init__(**kwargs)
self.flow_definition_file_path = kwargs.get('flow_definition_file_path', None)
self.flow_definition_resource_id = kwargs.get('flow_definition_resource_id', None)
self.flow_definition_data_store_name = kwargs.get('flow_definition_data_store_name', None)
self.flow_definition_blob_path = kwargs.get('flow_definition_blob_path', None)
self.flow_definition_data_uri = kwargs.get('flow_definition_data_uri', None)
self.run_id = kwargs.get('run_id', None)
self.run_display_name = kwargs.get('run_display_name', None)
self.run_experiment_name = kwargs.get('run_experiment_name', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.properties = kwargs.get('properties', None)
self.node_variant = kwargs.get('node_variant', None)
self.variant_run_id = kwargs.get('variant_run_id', None)
self.baseline_run_id = kwargs.get('baseline_run_id', None)
self.batch_data_input = kwargs.get('batch_data_input', None)
self.inputs_mapping = kwargs.get('inputs_mapping', None)
self.connections = kwargs.get('connections', None)
self.environment_variables = kwargs.get('environment_variables', None)
self.aml_compute_name = kwargs.get('aml_compute_name', None)
self.runtime_name = kwargs.get('runtime_name', None)
self.session_id = kwargs.get('session_id', None)
self.vm_size = kwargs.get('vm_size', None)
self.max_idle_time_seconds = kwargs.get('max_idle_time_seconds', None)
self.session_setup_mode = kwargs.get('session_setup_mode', None)
self.output_data_store = kwargs.get('output_data_store', None)
self.flow_lineage_id = kwargs.get('flow_lineage_id', None)
self.run_display_name_generation_type = kwargs.get('run_display_name_generation_type', None)
class SubmitBulkRunResponse(msrest.serialization.Model):
"""SubmitBulkRunResponse.
:ivar next_action_interval_in_seconds:
:vartype next_action_interval_in_seconds: int
:ivar action_type: Possible values include: "SendValidationRequest", "GetValidationStatus",
"SubmitBulkRun", "LogRunResult", "LogRunTerminatedEvent".
:vartype action_type: str or ~flow.models.ActionType
:ivar flow_runs:
:vartype flow_runs: list[any]
:ivar node_runs:
:vartype node_runs: list[any]
:ivar error_response: The error response.
:vartype error_response: ~flow.models.ErrorResponse
:ivar flow_name:
:vartype flow_name: str
:ivar flow_run_display_name:
:vartype flow_run_display_name: str
:ivar flow_run_id:
:vartype flow_run_id: str
:ivar flow_graph:
:vartype flow_graph: ~flow.models.FlowGraph
:ivar flow_graph_layout:
:vartype flow_graph_layout: ~flow.models.FlowGraphLayout
:ivar flow_run_resource_id:
:vartype flow_run_resource_id: str
:ivar bulk_test_id:
:vartype bulk_test_id: str
:ivar batch_inputs:
:vartype batch_inputs: list[dict[str, any]]
:ivar batch_data_input:
:vartype batch_data_input: ~flow.models.BatchDataInput
:ivar created_by:
:vartype created_by: ~flow.models.SchemaContractsCreatedBy
:ivar created_on:
:vartype created_on: ~datetime.datetime
:ivar flow_run_type: Possible values include: "FlowRun", "EvaluationRun",
"PairwiseEvaluationRun", "SingleNodeRun", "FromNodeRun".
:vartype flow_run_type: str or ~flow.models.FlowRunTypeEnum
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar runtime_name:
:vartype runtime_name: str
:ivar aml_compute_name:
:vartype aml_compute_name: str
:ivar flow_run_logs: Dictionary of :code:`<string>`.
:vartype flow_run_logs: dict[str, str]
:ivar flow_test_mode: Possible values include: "Sync", "Async".
:vartype flow_test_mode: str or ~flow.models.FlowTestMode
:ivar flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:vartype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:ivar working_directory:
:vartype working_directory: str
:ivar flow_dag_file_relative_path:
:vartype flow_dag_file_relative_path: str
:ivar flow_snapshot_id:
:vartype flow_snapshot_id: str
:ivar variant_run_to_evaluation_runs_id_mapping: Dictionary of
<components·1mlssi7·schemas·submitbulkrunresponse·properties·variantruntoevaluationrunsidmapping·additionalproperties>.
:vartype variant_run_to_evaluation_runs_id_mapping: dict[str, list[str]]
"""
_attribute_map = {
'next_action_interval_in_seconds': {'key': 'nextActionIntervalInSeconds', 'type': 'int'},
'action_type': {'key': 'actionType', 'type': 'str'},
'flow_runs': {'key': 'flow_runs', 'type': '[object]'},
'node_runs': {'key': 'node_runs', 'type': '[object]'},
'error_response': {'key': 'errorResponse', 'type': 'ErrorResponse'},
'flow_name': {'key': 'flowName', 'type': 'str'},
'flow_run_display_name': {'key': 'flowRunDisplayName', 'type': 'str'},
'flow_run_id': {'key': 'flowRunId', 'type': 'str'},
'flow_graph': {'key': 'flowGraph', 'type': 'FlowGraph'},
'flow_graph_layout': {'key': 'flowGraphLayout', 'type': 'FlowGraphLayout'},
'flow_run_resource_id': {'key': 'flowRunResourceId', 'type': 'str'},
'bulk_test_id': {'key': 'bulkTestId', 'type': 'str'},
'batch_inputs': {'key': 'batchInputs', 'type': '[{object}]'},
'batch_data_input': {'key': 'batchDataInput', 'type': 'BatchDataInput'},
'created_by': {'key': 'createdBy', 'type': 'SchemaContractsCreatedBy'},
'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
'flow_run_type': {'key': 'flowRunType', 'type': 'str'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'aml_compute_name': {'key': 'amlComputeName', 'type': 'str'},
'flow_run_logs': {'key': 'flowRunLogs', 'type': '{str}'},
'flow_test_mode': {'key': 'flowTestMode', 'type': 'str'},
'flow_test_infos': {'key': 'flowTestInfos', 'type': '{FlowTestInfo}'},
'working_directory': {'key': 'workingDirectory', 'type': 'str'},
'flow_dag_file_relative_path': {'key': 'flowDagFileRelativePath', 'type': 'str'},
'flow_snapshot_id': {'key': 'flowSnapshotId', 'type': 'str'},
'variant_run_to_evaluation_runs_id_mapping': {'key': 'variantRunToEvaluationRunsIdMapping', 'type': '{[str]}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword next_action_interval_in_seconds:
:paramtype next_action_interval_in_seconds: int
:keyword action_type: Possible values include: "SendValidationRequest", "GetValidationStatus",
"SubmitBulkRun", "LogRunResult", "LogRunTerminatedEvent".
:paramtype action_type: str or ~flow.models.ActionType
:keyword flow_runs:
:paramtype flow_runs: list[any]
:keyword node_runs:
:paramtype node_runs: list[any]
:keyword error_response: The error response.
:paramtype error_response: ~flow.models.ErrorResponse
:keyword flow_name:
:paramtype flow_name: str
:keyword flow_run_display_name:
:paramtype flow_run_display_name: str
:keyword flow_run_id:
:paramtype flow_run_id: str
:keyword flow_graph:
:paramtype flow_graph: ~flow.models.FlowGraph
:keyword flow_graph_layout:
:paramtype flow_graph_layout: ~flow.models.FlowGraphLayout
:keyword flow_run_resource_id:
:paramtype flow_run_resource_id: str
:keyword bulk_test_id:
:paramtype bulk_test_id: str
:keyword batch_inputs:
:paramtype batch_inputs: list[dict[str, any]]
:keyword batch_data_input:
:paramtype batch_data_input: ~flow.models.BatchDataInput
:keyword created_by:
:paramtype created_by: ~flow.models.SchemaContractsCreatedBy
:keyword created_on:
:paramtype created_on: ~datetime.datetime
:keyword flow_run_type: Possible values include: "FlowRun", "EvaluationRun",
"PairwiseEvaluationRun", "SingleNodeRun", "FromNodeRun".
:paramtype flow_run_type: str or ~flow.models.FlowRunTypeEnum
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword runtime_name:
:paramtype runtime_name: str
:keyword aml_compute_name:
:paramtype aml_compute_name: str
:keyword flow_run_logs: Dictionary of :code:`<string>`.
:paramtype flow_run_logs: dict[str, str]
:keyword flow_test_mode: Possible values include: "Sync", "Async".
:paramtype flow_test_mode: str or ~flow.models.FlowTestMode
:keyword flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:paramtype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:keyword working_directory:
:paramtype working_directory: str
:keyword flow_dag_file_relative_path:
:paramtype flow_dag_file_relative_path: str
:keyword flow_snapshot_id:
:paramtype flow_snapshot_id: str
:keyword variant_run_to_evaluation_runs_id_mapping: Dictionary of
<components·1mlssi7·schemas·submitbulkrunresponse·properties·variantruntoevaluationrunsidmapping·additionalproperties>.
:paramtype variant_run_to_evaluation_runs_id_mapping: dict[str, list[str]]
"""
super(SubmitBulkRunResponse, self).__init__(**kwargs)
self.next_action_interval_in_seconds = kwargs.get('next_action_interval_in_seconds', None)
self.action_type = kwargs.get('action_type', None)
self.flow_runs = kwargs.get('flow_runs', None)
self.node_runs = kwargs.get('node_runs', None)
self.error_response = kwargs.get('error_response', None)
self.flow_name = kwargs.get('flow_name', None)
self.flow_run_display_name = kwargs.get('flow_run_display_name', None)
self.flow_run_id = kwargs.get('flow_run_id', None)
self.flow_graph = kwargs.get('flow_graph', None)
self.flow_graph_layout = kwargs.get('flow_graph_layout', None)
self.flow_run_resource_id = kwargs.get('flow_run_resource_id', None)
self.bulk_test_id = kwargs.get('bulk_test_id', None)
self.batch_inputs = kwargs.get('batch_inputs', None)
self.batch_data_input = kwargs.get('batch_data_input', None)
self.created_by = kwargs.get('created_by', None)
self.created_on = kwargs.get('created_on', None)
self.flow_run_type = kwargs.get('flow_run_type', None)
self.flow_type = kwargs.get('flow_type', None)
self.runtime_name = kwargs.get('runtime_name', None)
self.aml_compute_name = kwargs.get('aml_compute_name', None)
self.flow_run_logs = kwargs.get('flow_run_logs', None)
self.flow_test_mode = kwargs.get('flow_test_mode', None)
self.flow_test_infos = kwargs.get('flow_test_infos', None)
self.working_directory = kwargs.get('working_directory', None)
self.flow_dag_file_relative_path = kwargs.get('flow_dag_file_relative_path', None)
self.flow_snapshot_id = kwargs.get('flow_snapshot_id', None)
self.variant_run_to_evaluation_runs_id_mapping = kwargs.get('variant_run_to_evaluation_runs_id_mapping', None)
class SubmitFlowRequest(msrest.serialization.Model):
"""SubmitFlowRequest.
:ivar flow_run_id:
:vartype flow_run_id: str
:ivar flow_run_display_name:
:vartype flow_run_display_name: str
:ivar flow_id:
:vartype flow_id: str
:ivar flow:
:vartype flow: ~flow.models.Flow
:ivar flow_submit_run_settings:
:vartype flow_submit_run_settings: ~flow.models.FlowSubmitRunSettings
:ivar async_submission:
:vartype async_submission: bool
:ivar use_workspace_connection:
:vartype use_workspace_connection: bool
:ivar use_flow_snapshot_to_submit:
:vartype use_flow_snapshot_to_submit: bool
:ivar enable_blob_run_artifacts:
:vartype enable_blob_run_artifacts: bool
:ivar enable_async_flow_test:
:vartype enable_async_flow_test: bool
:ivar flow_runtime_submission_api_version: Possible values include: "Version1", "Version2".
:vartype flow_runtime_submission_api_version: str or
~flow.models.FlowRuntimeSubmissionApiVersion
:ivar run_display_name_generation_type: Possible values include: "AutoAppend",
"UserProvidedMacro".
:vartype run_display_name_generation_type: str or ~flow.models.RunDisplayNameGenerationType
"""
_attribute_map = {
'flow_run_id': {'key': 'flowRunId', 'type': 'str'},
'flow_run_display_name': {'key': 'flowRunDisplayName', 'type': 'str'},
'flow_id': {'key': 'flowId', 'type': 'str'},
'flow': {'key': 'flow', 'type': 'Flow'},
'flow_submit_run_settings': {'key': 'flowSubmitRunSettings', 'type': 'FlowSubmitRunSettings'},
'async_submission': {'key': 'asyncSubmission', 'type': 'bool'},
'use_workspace_connection': {'key': 'useWorkspaceConnection', 'type': 'bool'},
'use_flow_snapshot_to_submit': {'key': 'useFlowSnapshotToSubmit', 'type': 'bool'},
'enable_blob_run_artifacts': {'key': 'enableBlobRunArtifacts', 'type': 'bool'},
'enable_async_flow_test': {'key': 'enableAsyncFlowTest', 'type': 'bool'},
'flow_runtime_submission_api_version': {'key': 'flowRuntimeSubmissionApiVersion', 'type': 'str'},
'run_display_name_generation_type': {'key': 'runDisplayNameGenerationType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword flow_run_id:
:paramtype flow_run_id: str
:keyword flow_run_display_name:
:paramtype flow_run_display_name: str
:keyword flow_id:
:paramtype flow_id: str
:keyword flow:
:paramtype flow: ~flow.models.Flow
:keyword flow_submit_run_settings:
:paramtype flow_submit_run_settings: ~flow.models.FlowSubmitRunSettings
:keyword async_submission:
:paramtype async_submission: bool
:keyword use_workspace_connection:
:paramtype use_workspace_connection: bool
:keyword use_flow_snapshot_to_submit:
:paramtype use_flow_snapshot_to_submit: bool
:keyword enable_blob_run_artifacts:
:paramtype enable_blob_run_artifacts: bool
:keyword enable_async_flow_test:
:paramtype enable_async_flow_test: bool
:keyword flow_runtime_submission_api_version: Possible values include: "Version1", "Version2".
:paramtype flow_runtime_submission_api_version: str or
~flow.models.FlowRuntimeSubmissionApiVersion
:keyword run_display_name_generation_type: Possible values include: "AutoAppend",
"UserProvidedMacro".
:paramtype run_display_name_generation_type: str or ~flow.models.RunDisplayNameGenerationType
"""
super(SubmitFlowRequest, self).__init__(**kwargs)
self.flow_run_id = kwargs.get('flow_run_id', None)
self.flow_run_display_name = kwargs.get('flow_run_display_name', None)
self.flow_id = kwargs.get('flow_id', None)
self.flow = kwargs.get('flow', None)
self.flow_submit_run_settings = kwargs.get('flow_submit_run_settings', None)
self.async_submission = kwargs.get('async_submission', None)
self.use_workspace_connection = kwargs.get('use_workspace_connection', None)
self.use_flow_snapshot_to_submit = kwargs.get('use_flow_snapshot_to_submit', None)
self.enable_blob_run_artifacts = kwargs.get('enable_blob_run_artifacts', None)
self.enable_async_flow_test = kwargs.get('enable_async_flow_test', None)
self.flow_runtime_submission_api_version = kwargs.get('flow_runtime_submission_api_version', None)
self.run_display_name_generation_type = kwargs.get('run_display_name_generation_type', None)
class SubmitPipelineRunRequest(msrest.serialization.Model):
"""SubmitPipelineRunRequest.
:ivar compute_target:
:vartype compute_target: str
:ivar flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:vartype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:ivar step_tags: This is a dictionary.
:vartype step_tags: dict[str, str]
:ivar experiment_name:
:vartype experiment_name: str
:ivar pipeline_parameters: This is a dictionary.
:vartype pipeline_parameters: dict[str, str]
:ivar data_path_assignments: This is a dictionary.
:vartype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:ivar data_set_definition_value_assignments: This is a dictionary.
:vartype data_set_definition_value_assignments: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar asset_output_settings_assignments: This is a dictionary.
:vartype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:ivar enable_notification:
:vartype enable_notification: bool
:ivar sub_pipelines_info:
:vartype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:ivar display_name:
:vartype display_name: str
:ivar run_id:
:vartype run_id: str
:ivar parent_run_id:
:vartype parent_run_id: str
:ivar graph:
:vartype graph: ~flow.models.GraphDraftEntity
:ivar pipeline_run_settings:
:vartype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar module_node_run_settings:
:vartype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:ivar module_node_ui_input_settings:
:vartype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar continue_run_on_step_failure:
:vartype continue_run_on_step_failure: bool
:ivar description:
:vartype description: str
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar enforce_rerun:
:vartype enforce_rerun: bool
:ivar dataset_access_modes: Possible values include: "Default", "DatasetInDpv2", "AssetInDpv2",
"DatasetInDesignerUI", "DatasetInDpv2WithDatasetInDesignerUI", "Dataset",
"AssetInDpv2WithDatasetInDesignerUI", "DatasetAndAssetInDpv2WithDatasetInDesignerUI",
"AssetInDesignerUI", "AssetInDpv2WithAssetInDesignerUI", "Asset".
:vartype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
_attribute_map = {
'compute_target': {'key': 'computeTarget', 'type': 'str'},
'flattened_sub_graphs': {'key': 'flattenedSubGraphs', 'type': '{PipelineSubDraft}'},
'step_tags': {'key': 'stepTags', 'type': '{str}'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'pipeline_parameters': {'key': 'pipelineParameters', 'type': '{str}'},
'data_path_assignments': {'key': 'dataPathAssignments', 'type': '{LegacyDataPath}'},
'data_set_definition_value_assignments': {'key': 'dataSetDefinitionValueAssignments', 'type': '{DataSetDefinitionValue}'},
'asset_output_settings_assignments': {'key': 'assetOutputSettingsAssignments', 'type': '{AssetOutputSettings}'},
'enable_notification': {'key': 'enableNotification', 'type': 'bool'},
'sub_pipelines_info': {'key': 'subPipelinesInfo', 'type': 'SubPipelinesInfo'},
'display_name': {'key': 'displayName', 'type': 'str'},
'run_id': {'key': 'runId', 'type': 'str'},
'parent_run_id': {'key': 'parentRunId', 'type': 'str'},
'graph': {'key': 'graph', 'type': 'GraphDraftEntity'},
'pipeline_run_settings': {'key': 'pipelineRunSettings', 'type': '[RunSettingParameterAssignment]'},
'module_node_run_settings': {'key': 'moduleNodeRunSettings', 'type': '[GraphModuleNodeRunSetting]'},
'module_node_ui_input_settings': {'key': 'moduleNodeUIInputSettings', 'type': '[GraphModuleNodeUIInputSetting]'},
'tags': {'key': 'tags', 'type': '{str}'},
'continue_run_on_step_failure': {'key': 'continueRunOnStepFailure', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'enforce_rerun': {'key': 'enforceRerun', 'type': 'bool'},
'dataset_access_modes': {'key': 'datasetAccessModes', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword compute_target:
:paramtype compute_target: str
:keyword flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:paramtype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:keyword step_tags: This is a dictionary.
:paramtype step_tags: dict[str, str]
:keyword experiment_name:
:paramtype experiment_name: str
:keyword pipeline_parameters: This is a dictionary.
:paramtype pipeline_parameters: dict[str, str]
:keyword data_path_assignments: This is a dictionary.
:paramtype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:keyword data_set_definition_value_assignments: This is a dictionary.
:paramtype data_set_definition_value_assignments: dict[str,
~flow.models.DataSetDefinitionValue]
:keyword asset_output_settings_assignments: This is a dictionary.
:paramtype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:keyword enable_notification:
:paramtype enable_notification: bool
:keyword sub_pipelines_info:
:paramtype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:keyword display_name:
:paramtype display_name: str
:keyword run_id:
:paramtype run_id: str
:keyword parent_run_id:
:paramtype parent_run_id: str
:keyword graph:
:paramtype graph: ~flow.models.GraphDraftEntity
:keyword pipeline_run_settings:
:paramtype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword module_node_run_settings:
:paramtype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:keyword module_node_ui_input_settings:
:paramtype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword continue_run_on_step_failure:
:paramtype continue_run_on_step_failure: bool
:keyword description:
:paramtype description: str
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword enforce_rerun:
:paramtype enforce_rerun: bool
:keyword dataset_access_modes: Possible values include: "Default", "DatasetInDpv2",
"AssetInDpv2", "DatasetInDesignerUI", "DatasetInDpv2WithDatasetInDesignerUI", "Dataset",
"AssetInDpv2WithDatasetInDesignerUI", "DatasetAndAssetInDpv2WithDatasetInDesignerUI",
"AssetInDesignerUI", "AssetInDpv2WithAssetInDesignerUI", "Asset".
:paramtype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
super(SubmitPipelineRunRequest, self).__init__(**kwargs)
self.compute_target = kwargs.get('compute_target', None)
self.flattened_sub_graphs = kwargs.get('flattened_sub_graphs', None)
self.step_tags = kwargs.get('step_tags', None)
self.experiment_name = kwargs.get('experiment_name', None)
self.pipeline_parameters = kwargs.get('pipeline_parameters', None)
self.data_path_assignments = kwargs.get('data_path_assignments', None)
self.data_set_definition_value_assignments = kwargs.get('data_set_definition_value_assignments', None)
self.asset_output_settings_assignments = kwargs.get('asset_output_settings_assignments', None)
self.enable_notification = kwargs.get('enable_notification', None)
self.sub_pipelines_info = kwargs.get('sub_pipelines_info', None)
self.display_name = kwargs.get('display_name', None)
self.run_id = kwargs.get('run_id', None)
self.parent_run_id = kwargs.get('parent_run_id', None)
self.graph = kwargs.get('graph', None)
self.pipeline_run_settings = kwargs.get('pipeline_run_settings', None)
self.module_node_run_settings = kwargs.get('module_node_run_settings', None)
self.module_node_ui_input_settings = kwargs.get('module_node_ui_input_settings', None)
self.tags = kwargs.get('tags', None)
self.continue_run_on_step_failure = kwargs.get('continue_run_on_step_failure', None)
self.description = kwargs.get('description', None)
self.properties = kwargs.get('properties', None)
self.enforce_rerun = kwargs.get('enforce_rerun', None)
self.dataset_access_modes = kwargs.get('dataset_access_modes', None)
class SubPipelineDefinition(msrest.serialization.Model):
"""SubPipelineDefinition.
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar default_compute_target:
:vartype default_compute_target: ~flow.models.ComputeSetting
:ivar default_data_store:
:vartype default_data_store: ~flow.models.DatastoreSetting
:ivar pipeline_function_name:
:vartype pipeline_function_name: str
:ivar id:
:vartype id: str
:ivar parent_definition_id:
:vartype parent_definition_id: str
:ivar from_module_name:
:vartype from_module_name: str
:ivar parameter_list:
:vartype parameter_list: list[~flow.models.Kwarg]
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'default_compute_target': {'key': 'defaultComputeTarget', 'type': 'ComputeSetting'},
'default_data_store': {'key': 'defaultDataStore', 'type': 'DatastoreSetting'},
'pipeline_function_name': {'key': 'pipelineFunctionName', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'parent_definition_id': {'key': 'parentDefinitionId', 'type': 'str'},
'from_module_name': {'key': 'fromModuleName', 'type': 'str'},
'parameter_list': {'key': 'parameterList', 'type': '[Kwarg]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword default_compute_target:
:paramtype default_compute_target: ~flow.models.ComputeSetting
:keyword default_data_store:
:paramtype default_data_store: ~flow.models.DatastoreSetting
:keyword pipeline_function_name:
:paramtype pipeline_function_name: str
:keyword id:
:paramtype id: str
:keyword parent_definition_id:
:paramtype parent_definition_id: str
:keyword from_module_name:
:paramtype from_module_name: str
:keyword parameter_list:
:paramtype parameter_list: list[~flow.models.Kwarg]
"""
super(SubPipelineDefinition, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.description = kwargs.get('description', None)
self.default_compute_target = kwargs.get('default_compute_target', None)
self.default_data_store = kwargs.get('default_data_store', None)
self.pipeline_function_name = kwargs.get('pipeline_function_name', None)
self.id = kwargs.get('id', None)
self.parent_definition_id = kwargs.get('parent_definition_id', None)
self.from_module_name = kwargs.get('from_module_name', None)
self.parameter_list = kwargs.get('parameter_list', None)
class SubPipelineParameterAssignment(msrest.serialization.Model):
"""SubPipelineParameterAssignment.
:ivar node_id:
:vartype node_id: str
:ivar parameter_name:
:vartype parameter_name: str
"""
_attribute_map = {
'node_id': {'key': 'nodeId', 'type': 'str'},
'parameter_name': {'key': 'parameterName', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword node_id:
:paramtype node_id: str
:keyword parameter_name:
:paramtype parameter_name: str
"""
super(SubPipelineParameterAssignment, self).__init__(**kwargs)
self.node_id = kwargs.get('node_id', None)
self.parameter_name = kwargs.get('parameter_name', None)
class SubPipelinesInfo(msrest.serialization.Model):
"""SubPipelinesInfo.
:ivar sub_graph_info:
:vartype sub_graph_info: list[~flow.models.SubGraphInfo]
:ivar node_id_to_sub_graph_id_mapping: Dictionary of :code:`<string>`.
:vartype node_id_to_sub_graph_id_mapping: dict[str, str]
:ivar sub_pipeline_definition:
:vartype sub_pipeline_definition: list[~flow.models.SubPipelineDefinition]
"""
_attribute_map = {
'sub_graph_info': {'key': 'subGraphInfo', 'type': '[SubGraphInfo]'},
'node_id_to_sub_graph_id_mapping': {'key': 'nodeIdToSubGraphIdMapping', 'type': '{str}'},
'sub_pipeline_definition': {'key': 'subPipelineDefinition', 'type': '[SubPipelineDefinition]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword sub_graph_info:
:paramtype sub_graph_info: list[~flow.models.SubGraphInfo]
:keyword node_id_to_sub_graph_id_mapping: Dictionary of :code:`<string>`.
:paramtype node_id_to_sub_graph_id_mapping: dict[str, str]
:keyword sub_pipeline_definition:
:paramtype sub_pipeline_definition: list[~flow.models.SubPipelineDefinition]
"""
super(SubPipelinesInfo, self).__init__(**kwargs)
self.sub_graph_info = kwargs.get('sub_graph_info', None)
self.node_id_to_sub_graph_id_mapping = kwargs.get('node_id_to_sub_graph_id_mapping', None)
self.sub_pipeline_definition = kwargs.get('sub_pipeline_definition', None)
class SubStatusPeriod(msrest.serialization.Model):
"""SubStatusPeriod.
:ivar name:
:vartype name: str
:ivar sub_periods:
:vartype sub_periods: list[~flow.models.SubStatusPeriod]
:ivar start:
:vartype start: long
:ivar end:
:vartype end: long
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'sub_periods': {'key': 'subPeriods', 'type': '[SubStatusPeriod]'},
'start': {'key': 'start', 'type': 'long'},
'end': {'key': 'end', 'type': 'long'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword sub_periods:
:paramtype sub_periods: list[~flow.models.SubStatusPeriod]
:keyword start:
:paramtype start: long
:keyword end:
:paramtype end: long
"""
super(SubStatusPeriod, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.sub_periods = kwargs.get('sub_periods', None)
self.start = kwargs.get('start', None)
self.end = kwargs.get('end', None)
class SweepEarlyTerminationPolicy(msrest.serialization.Model):
"""SweepEarlyTerminationPolicy.
:ivar policy_type: Possible values include: "Bandit", "MedianStopping", "TruncationSelection".
:vartype policy_type: str or ~flow.models.EarlyTerminationPolicyType
:ivar evaluation_interval:
:vartype evaluation_interval: int
:ivar delay_evaluation:
:vartype delay_evaluation: int
:ivar slack_factor:
:vartype slack_factor: float
:ivar slack_amount:
:vartype slack_amount: float
:ivar truncation_percentage:
:vartype truncation_percentage: int
"""
_attribute_map = {
'policy_type': {'key': 'policyType', 'type': 'str'},
'evaluation_interval': {'key': 'evaluationInterval', 'type': 'int'},
'delay_evaluation': {'key': 'delayEvaluation', 'type': 'int'},
'slack_factor': {'key': 'slackFactor', 'type': 'float'},
'slack_amount': {'key': 'slackAmount', 'type': 'float'},
'truncation_percentage': {'key': 'truncationPercentage', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword policy_type: Possible values include: "Bandit", "MedianStopping",
"TruncationSelection".
:paramtype policy_type: str or ~flow.models.EarlyTerminationPolicyType
:keyword evaluation_interval:
:paramtype evaluation_interval: int
:keyword delay_evaluation:
:paramtype delay_evaluation: int
:keyword slack_factor:
:paramtype slack_factor: float
:keyword slack_amount:
:paramtype slack_amount: float
:keyword truncation_percentage:
:paramtype truncation_percentage: int
"""
super(SweepEarlyTerminationPolicy, self).__init__(**kwargs)
self.policy_type = kwargs.get('policy_type', None)
self.evaluation_interval = kwargs.get('evaluation_interval', None)
self.delay_evaluation = kwargs.get('delay_evaluation', None)
self.slack_factor = kwargs.get('slack_factor', None)
self.slack_amount = kwargs.get('slack_amount', None)
self.truncation_percentage = kwargs.get('truncation_percentage', None)
class SweepSettings(msrest.serialization.Model):
"""SweepSettings.
:ivar limits:
:vartype limits: ~flow.models.SweepSettingsLimits
:ivar search_space:
:vartype search_space: list[dict[str, str]]
:ivar sampling_algorithm: Possible values include: "Random", "Grid", "Bayesian".
:vartype sampling_algorithm: str or ~flow.models.SamplingAlgorithmType
:ivar early_termination:
:vartype early_termination: ~flow.models.SweepEarlyTerminationPolicy
"""
_attribute_map = {
'limits': {'key': 'limits', 'type': 'SweepSettingsLimits'},
'search_space': {'key': 'searchSpace', 'type': '[{str}]'},
'sampling_algorithm': {'key': 'samplingAlgorithm', 'type': 'str'},
'early_termination': {'key': 'earlyTermination', 'type': 'SweepEarlyTerminationPolicy'},
}
def __init__(
self,
**kwargs
):
"""
:keyword limits:
:paramtype limits: ~flow.models.SweepSettingsLimits
:keyword search_space:
:paramtype search_space: list[dict[str, str]]
:keyword sampling_algorithm: Possible values include: "Random", "Grid", "Bayesian".
:paramtype sampling_algorithm: str or ~flow.models.SamplingAlgorithmType
:keyword early_termination:
:paramtype early_termination: ~flow.models.SweepEarlyTerminationPolicy
"""
super(SweepSettings, self).__init__(**kwargs)
self.limits = kwargs.get('limits', None)
self.search_space = kwargs.get('search_space', None)
self.sampling_algorithm = kwargs.get('sampling_algorithm', None)
self.early_termination = kwargs.get('early_termination', None)
class SweepSettingsLimits(msrest.serialization.Model):
"""SweepSettingsLimits.
:ivar max_total_trials:
:vartype max_total_trials: int
:ivar max_concurrent_trials:
:vartype max_concurrent_trials: int
"""
_attribute_map = {
'max_total_trials': {'key': 'maxTotalTrials', 'type': 'int'},
'max_concurrent_trials': {'key': 'maxConcurrentTrials', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword max_total_trials:
:paramtype max_total_trials: int
:keyword max_concurrent_trials:
:paramtype max_concurrent_trials: int
"""
super(SweepSettingsLimits, self).__init__(**kwargs)
self.max_total_trials = kwargs.get('max_total_trials', None)
self.max_concurrent_trials = kwargs.get('max_concurrent_trials', None)
class SystemData(msrest.serialization.Model):
"""SystemData.
:ivar created_at:
:vartype created_at: ~datetime.datetime
:ivar created_by:
:vartype created_by: str
:ivar created_by_type: Possible values include: "User", "Application", "ManagedIdentity",
"Key".
:vartype created_by_type: str or ~flow.models.UserType
:ivar last_modified_at:
:vartype last_modified_at: ~datetime.datetime
:ivar last_modified_by:
:vartype last_modified_by: str
:ivar last_modified_by_type: Possible values include: "User", "Application", "ManagedIdentity",
"Key".
:vartype last_modified_by_type: str or ~flow.models.UserType
"""
_attribute_map = {
'created_at': {'key': 'createdAt', 'type': 'iso-8601'},
'created_by': {'key': 'createdBy', 'type': 'str'},
'created_by_type': {'key': 'createdByType', 'type': 'str'},
'last_modified_at': {'key': 'lastModifiedAt', 'type': 'iso-8601'},
'last_modified_by': {'key': 'lastModifiedBy', 'type': 'str'},
'last_modified_by_type': {'key': 'lastModifiedByType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword created_at:
:paramtype created_at: ~datetime.datetime
:keyword created_by:
:paramtype created_by: str
:keyword created_by_type: Possible values include: "User", "Application", "ManagedIdentity",
"Key".
:paramtype created_by_type: str or ~flow.models.UserType
:keyword last_modified_at:
:paramtype last_modified_at: ~datetime.datetime
:keyword last_modified_by:
:paramtype last_modified_by: str
:keyword last_modified_by_type: Possible values include: "User", "Application",
"ManagedIdentity", "Key".
:paramtype last_modified_by_type: str or ~flow.models.UserType
"""
super(SystemData, self).__init__(**kwargs)
self.created_at = kwargs.get('created_at', None)
self.created_by = kwargs.get('created_by', None)
self.created_by_type = kwargs.get('created_by_type', None)
self.last_modified_at = kwargs.get('last_modified_at', None)
self.last_modified_by = kwargs.get('last_modified_by', None)
self.last_modified_by_type = kwargs.get('last_modified_by_type', None)
class SystemMeta(msrest.serialization.Model):
"""SystemMeta.
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar extra_hash:
:vartype extra_hash: str
:ivar content_hash:
:vartype content_hash: str
:ivar identifier_hashes:
:vartype identifier_hashes: ~flow.models.SystemMetaIdentifierHashes
:ivar extra_hashes:
:vartype extra_hashes: ~flow.models.SystemMetaExtraHashes
"""
_attribute_map = {
'identifier_hash': {'key': 'identifierHash', 'type': 'str'},
'extra_hash': {'key': 'extraHash', 'type': 'str'},
'content_hash': {'key': 'contentHash', 'type': 'str'},
'identifier_hashes': {'key': 'identifierHashes', 'type': 'SystemMetaIdentifierHashes'},
'extra_hashes': {'key': 'extraHashes', 'type': 'SystemMetaExtraHashes'},
}
def __init__(
self,
**kwargs
):
"""
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword extra_hash:
:paramtype extra_hash: str
:keyword content_hash:
:paramtype content_hash: str
:keyword identifier_hashes:
:paramtype identifier_hashes: ~flow.models.SystemMetaIdentifierHashes
:keyword extra_hashes:
:paramtype extra_hashes: ~flow.models.SystemMetaExtraHashes
"""
super(SystemMeta, self).__init__(**kwargs)
self.identifier_hash = kwargs.get('identifier_hash', None)
self.extra_hash = kwargs.get('extra_hash', None)
self.content_hash = kwargs.get('content_hash', None)
self.identifier_hashes = kwargs.get('identifier_hashes', None)
self.extra_hashes = kwargs.get('extra_hashes', None)
class SystemMetaExtraHashes(msrest.serialization.Model):
"""SystemMetaExtraHashes.
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hash_v2:
:vartype identifier_hash_v2: str
"""
_attribute_map = {
'identifier_hash': {'key': 'IdentifierHash', 'type': 'str'},
'identifier_hash_v2': {'key': 'IdentifierHashV2', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hash_v2:
:paramtype identifier_hash_v2: str
"""
super(SystemMetaExtraHashes, self).__init__(**kwargs)
self.identifier_hash = kwargs.get('identifier_hash', None)
self.identifier_hash_v2 = kwargs.get('identifier_hash_v2', None)
class SystemMetaIdentifierHashes(msrest.serialization.Model):
"""SystemMetaIdentifierHashes.
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hash_v2:
:vartype identifier_hash_v2: str
"""
_attribute_map = {
'identifier_hash': {'key': 'IdentifierHash', 'type': 'str'},
'identifier_hash_v2': {'key': 'IdentifierHashV2', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hash_v2:
:paramtype identifier_hash_v2: str
"""
super(SystemMetaIdentifierHashes, self).__init__(**kwargs)
self.identifier_hash = kwargs.get('identifier_hash', None)
self.identifier_hash_v2 = kwargs.get('identifier_hash_v2', None)
class TargetLags(msrest.serialization.Model):
"""TargetLags.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.TargetLagsMode
:ivar values:
:vartype values: list[int]
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'values': {'key': 'values', 'type': '[int]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.TargetLagsMode
:keyword values:
:paramtype values: list[int]
"""
super(TargetLags, self).__init__(**kwargs)
self.mode = kwargs.get('mode', None)
self.values = kwargs.get('values', None)
class TargetRollingWindowSize(msrest.serialization.Model):
"""TargetRollingWindowSize.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.TargetRollingWindowSizeMode
:ivar value:
:vartype value: int
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'value': {'key': 'value', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.TargetRollingWindowSizeMode
:keyword value:
:paramtype value: int
"""
super(TargetRollingWindowSize, self).__init__(**kwargs)
self.mode = kwargs.get('mode', None)
self.value = kwargs.get('value', None)
class TargetSelectorConfiguration(msrest.serialization.Model):
"""TargetSelectorConfiguration.
:ivar low_priority_vm_tolerant:
:vartype low_priority_vm_tolerant: bool
:ivar cluster_block_list:
:vartype cluster_block_list: list[str]
:ivar compute_type:
:vartype compute_type: str
:ivar instance_type:
:vartype instance_type: list[str]
:ivar instance_types:
:vartype instance_types: list[str]
:ivar my_resource_only:
:vartype my_resource_only: bool
:ivar plan_id:
:vartype plan_id: str
:ivar plan_region_id:
:vartype plan_region_id: str
:ivar region:
:vartype region: list[str]
:ivar regions:
:vartype regions: list[str]
:ivar vc_block_list:
:vartype vc_block_list: list[str]
"""
_attribute_map = {
'low_priority_vm_tolerant': {'key': 'lowPriorityVMTolerant', 'type': 'bool'},
'cluster_block_list': {'key': 'clusterBlockList', 'type': '[str]'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'instance_type': {'key': 'instanceType', 'type': '[str]'},
'instance_types': {'key': 'instanceTypes', 'type': '[str]'},
'my_resource_only': {'key': 'myResourceOnly', 'type': 'bool'},
'plan_id': {'key': 'planId', 'type': 'str'},
'plan_region_id': {'key': 'planRegionId', 'type': 'str'},
'region': {'key': 'region', 'type': '[str]'},
'regions': {'key': 'regions', 'type': '[str]'},
'vc_block_list': {'key': 'vcBlockList', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword low_priority_vm_tolerant:
:paramtype low_priority_vm_tolerant: bool
:keyword cluster_block_list:
:paramtype cluster_block_list: list[str]
:keyword compute_type:
:paramtype compute_type: str
:keyword instance_type:
:paramtype instance_type: list[str]
:keyword instance_types:
:paramtype instance_types: list[str]
:keyword my_resource_only:
:paramtype my_resource_only: bool
:keyword plan_id:
:paramtype plan_id: str
:keyword plan_region_id:
:paramtype plan_region_id: str
:keyword region:
:paramtype region: list[str]
:keyword regions:
:paramtype regions: list[str]
:keyword vc_block_list:
:paramtype vc_block_list: list[str]
"""
super(TargetSelectorConfiguration, self).__init__(**kwargs)
self.low_priority_vm_tolerant = kwargs.get('low_priority_vm_tolerant', None)
self.cluster_block_list = kwargs.get('cluster_block_list', None)
self.compute_type = kwargs.get('compute_type', None)
self.instance_type = kwargs.get('instance_type', None)
self.instance_types = kwargs.get('instance_types', None)
self.my_resource_only = kwargs.get('my_resource_only', None)
self.plan_id = kwargs.get('plan_id', None)
self.plan_region_id = kwargs.get('plan_region_id', None)
self.region = kwargs.get('region', None)
self.regions = kwargs.get('regions', None)
self.vc_block_list = kwargs.get('vc_block_list', None)
class Task(msrest.serialization.Model):
"""Task.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar id:
:vartype id: int
:ivar exception: Anything.
:vartype exception: any
:ivar status: Possible values include: "Created", "WaitingForActivation", "WaitingToRun",
"Running", "WaitingForChildrenToComplete", "RanToCompletion", "Canceled", "Faulted".
:vartype status: str or ~flow.models.TaskStatus
:ivar is_canceled:
:vartype is_canceled: bool
:ivar is_completed:
:vartype is_completed: bool
:ivar is_completed_successfully:
:vartype is_completed_successfully: bool
:ivar creation_options: Possible values include: "None", "PreferFairness", "LongRunning",
"AttachedToParent", "DenyChildAttach", "HideScheduler", "RunContinuationsAsynchronously".
:vartype creation_options: str or ~flow.models.TaskCreationOptions
:ivar async_state: Anything.
:vartype async_state: any
:ivar is_faulted:
:vartype is_faulted: bool
"""
_validation = {
'id': {'readonly': True},
'exception': {'readonly': True},
'is_canceled': {'readonly': True},
'is_completed': {'readonly': True},
'is_completed_successfully': {'readonly': True},
'async_state': {'readonly': True},
'is_faulted': {'readonly': True},
}
_attribute_map = {
'id': {'key': 'id', 'type': 'int'},
'exception': {'key': 'exception', 'type': 'object'},
'status': {'key': 'status', 'type': 'str'},
'is_canceled': {'key': 'isCanceled', 'type': 'bool'},
'is_completed': {'key': 'isCompleted', 'type': 'bool'},
'is_completed_successfully': {'key': 'isCompletedSuccessfully', 'type': 'bool'},
'creation_options': {'key': 'creationOptions', 'type': 'str'},
'async_state': {'key': 'asyncState', 'type': 'object'},
'is_faulted': {'key': 'isFaulted', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword status: Possible values include: "Created", "WaitingForActivation", "WaitingToRun",
"Running", "WaitingForChildrenToComplete", "RanToCompletion", "Canceled", "Faulted".
:paramtype status: str or ~flow.models.TaskStatus
:keyword creation_options: Possible values include: "None", "PreferFairness", "LongRunning",
"AttachedToParent", "DenyChildAttach", "HideScheduler", "RunContinuationsAsynchronously".
:paramtype creation_options: str or ~flow.models.TaskCreationOptions
"""
super(Task, self).__init__(**kwargs)
self.id = None
self.exception = None
self.status = kwargs.get('status', None)
self.is_canceled = None
self.is_completed = None
self.is_completed_successfully = None
self.creation_options = kwargs.get('creation_options', None)
self.async_state = None
self.is_faulted = None
class TaskControlFlowInfo(msrest.serialization.Model):
"""TaskControlFlowInfo.
:ivar control_flow_type: Possible values include: "None", "DoWhile", "ParallelFor".
:vartype control_flow_type: str or ~flow.models.ControlFlowType
:ivar iteration_index:
:vartype iteration_index: int
:ivar item_name:
:vartype item_name: str
:ivar parameters_overwritten: Dictionary of :code:`<string>`.
:vartype parameters_overwritten: dict[str, str]
:ivar is_reused:
:vartype is_reused: bool
"""
_attribute_map = {
'control_flow_type': {'key': 'controlFlowType', 'type': 'str'},
'iteration_index': {'key': 'iterationIndex', 'type': 'int'},
'item_name': {'key': 'itemName', 'type': 'str'},
'parameters_overwritten': {'key': 'parametersOverwritten', 'type': '{str}'},
'is_reused': {'key': 'isReused', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword control_flow_type: Possible values include: "None", "DoWhile", "ParallelFor".
:paramtype control_flow_type: str or ~flow.models.ControlFlowType
:keyword iteration_index:
:paramtype iteration_index: int
:keyword item_name:
:paramtype item_name: str
:keyword parameters_overwritten: Dictionary of :code:`<string>`.
:paramtype parameters_overwritten: dict[str, str]
:keyword is_reused:
:paramtype is_reused: bool
"""
super(TaskControlFlowInfo, self).__init__(**kwargs)
self.control_flow_type = kwargs.get('control_flow_type', None)
self.iteration_index = kwargs.get('iteration_index', None)
self.item_name = kwargs.get('item_name', None)
self.parameters_overwritten = kwargs.get('parameters_overwritten', None)
self.is_reused = kwargs.get('is_reused', None)
class TaskReuseInfo(msrest.serialization.Model):
"""TaskReuseInfo.
:ivar experiment_id:
:vartype experiment_id: str
:ivar pipeline_run_id:
:vartype pipeline_run_id: str
:ivar node_id:
:vartype node_id: str
:ivar request_id:
:vartype request_id: str
:ivar run_id:
:vartype run_id: str
:ivar node_start_time:
:vartype node_start_time: ~datetime.datetime
:ivar node_end_time:
:vartype node_end_time: ~datetime.datetime
"""
_attribute_map = {
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'pipeline_run_id': {'key': 'pipelineRunId', 'type': 'str'},
'node_id': {'key': 'nodeId', 'type': 'str'},
'request_id': {'key': 'requestId', 'type': 'str'},
'run_id': {'key': 'runId', 'type': 'str'},
'node_start_time': {'key': 'nodeStartTime', 'type': 'iso-8601'},
'node_end_time': {'key': 'nodeEndTime', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword experiment_id:
:paramtype experiment_id: str
:keyword pipeline_run_id:
:paramtype pipeline_run_id: str
:keyword node_id:
:paramtype node_id: str
:keyword request_id:
:paramtype request_id: str
:keyword run_id:
:paramtype run_id: str
:keyword node_start_time:
:paramtype node_start_time: ~datetime.datetime
:keyword node_end_time:
:paramtype node_end_time: ~datetime.datetime
"""
super(TaskReuseInfo, self).__init__(**kwargs)
self.experiment_id = kwargs.get('experiment_id', None)
self.pipeline_run_id = kwargs.get('pipeline_run_id', None)
self.node_id = kwargs.get('node_id', None)
self.request_id = kwargs.get('request_id', None)
self.run_id = kwargs.get('run_id', None)
self.node_start_time = kwargs.get('node_start_time', None)
self.node_end_time = kwargs.get('node_end_time', None)
class TensorflowConfiguration(msrest.serialization.Model):
"""TensorflowConfiguration.
:ivar worker_count:
:vartype worker_count: int
:ivar parameter_server_count:
:vartype parameter_server_count: int
"""
_attribute_map = {
'worker_count': {'key': 'workerCount', 'type': 'int'},
'parameter_server_count': {'key': 'parameterServerCount', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword worker_count:
:paramtype worker_count: int
:keyword parameter_server_count:
:paramtype parameter_server_count: int
"""
super(TensorflowConfiguration, self).__init__(**kwargs)
self.worker_count = kwargs.get('worker_count', None)
self.parameter_server_count = kwargs.get('parameter_server_count', None)
class TestDataSettings(msrest.serialization.Model):
"""TestDataSettings.
:ivar test_data_size:
:vartype test_data_size: float
"""
_attribute_map = {
'test_data_size': {'key': 'testDataSize', 'type': 'float'},
}
def __init__(
self,
**kwargs
):
"""
:keyword test_data_size:
:paramtype test_data_size: float
"""
super(TestDataSettings, self).__init__(**kwargs)
self.test_data_size = kwargs.get('test_data_size', None)
class Tool(msrest.serialization.Model):
"""Tool.
:ivar name:
:vartype name: str
:ivar type: Possible values include: "llm", "python", "action", "prompt", "custom_llm",
"csharp".
:vartype type: str or ~flow.models.ToolType
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, ~flow.models.InputDefinition]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, ~flow.models.OutputDefinition]
:ivar description:
:vartype description: str
:ivar connection_type:
:vartype connection_type: list[str or ~flow.models.ConnectionType]
:ivar module:
:vartype module: str
:ivar class_name:
:vartype class_name: str
:ivar source:
:vartype source: str
:ivar lkg_code:
:vartype lkg_code: str
:ivar code:
:vartype code: str
:ivar function:
:vartype function: str
:ivar action_type:
:vartype action_type: str
:ivar provider_config: This is a dictionary.
:vartype provider_config: dict[str, ~flow.models.InputDefinition]
:ivar function_config: This is a dictionary.
:vartype function_config: dict[str, ~flow.models.InputDefinition]
:ivar icon: Anything.
:vartype icon: any
:ivar category:
:vartype category: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, any]
:ivar is_builtin:
:vartype is_builtin: bool
:ivar package:
:vartype package: str
:ivar package_version:
:vartype package_version: str
:ivar default_prompt:
:vartype default_prompt: str
:ivar enable_kwargs:
:vartype enable_kwargs: bool
:ivar deprecated_tools:
:vartype deprecated_tools: list[str]
:ivar tool_state: Possible values include: "Stable", "Preview", "Deprecated".
:vartype tool_state: str or ~flow.models.ToolState
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'inputs': {'key': 'inputs', 'type': '{InputDefinition}'},
'outputs': {'key': 'outputs', 'type': '{OutputDefinition}'},
'description': {'key': 'description', 'type': 'str'},
'connection_type': {'key': 'connection_type', 'type': '[str]'},
'module': {'key': 'module', 'type': 'str'},
'class_name': {'key': 'class_name', 'type': 'str'},
'source': {'key': 'source', 'type': 'str'},
'lkg_code': {'key': 'lkgCode', 'type': 'str'},
'code': {'key': 'code', 'type': 'str'},
'function': {'key': 'function', 'type': 'str'},
'action_type': {'key': 'action_type', 'type': 'str'},
'provider_config': {'key': 'provider_config', 'type': '{InputDefinition}'},
'function_config': {'key': 'function_config', 'type': '{InputDefinition}'},
'icon': {'key': 'icon', 'type': 'object'},
'category': {'key': 'category', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{object}'},
'is_builtin': {'key': 'is_builtin', 'type': 'bool'},
'package': {'key': 'package', 'type': 'str'},
'package_version': {'key': 'package_version', 'type': 'str'},
'default_prompt': {'key': 'default_prompt', 'type': 'str'},
'enable_kwargs': {'key': 'enable_kwargs', 'type': 'bool'},
'deprecated_tools': {'key': 'deprecated_tools', 'type': '[str]'},
'tool_state': {'key': 'tool_state', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type: Possible values include: "llm", "python", "action", "prompt", "custom_llm",
"csharp".
:paramtype type: str or ~flow.models.ToolType
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, ~flow.models.InputDefinition]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, ~flow.models.OutputDefinition]
:keyword description:
:paramtype description: str
:keyword connection_type:
:paramtype connection_type: list[str or ~flow.models.ConnectionType]
:keyword module:
:paramtype module: str
:keyword class_name:
:paramtype class_name: str
:keyword source:
:paramtype source: str
:keyword lkg_code:
:paramtype lkg_code: str
:keyword code:
:paramtype code: str
:keyword function:
:paramtype function: str
:keyword action_type:
:paramtype action_type: str
:keyword provider_config: This is a dictionary.
:paramtype provider_config: dict[str, ~flow.models.InputDefinition]
:keyword function_config: This is a dictionary.
:paramtype function_config: dict[str, ~flow.models.InputDefinition]
:keyword icon: Anything.
:paramtype icon: any
:keyword category:
:paramtype category: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, any]
:keyword is_builtin:
:paramtype is_builtin: bool
:keyword package:
:paramtype package: str
:keyword package_version:
:paramtype package_version: str
:keyword default_prompt:
:paramtype default_prompt: str
:keyword enable_kwargs:
:paramtype enable_kwargs: bool
:keyword deprecated_tools:
:paramtype deprecated_tools: list[str]
:keyword tool_state: Possible values include: "Stable", "Preview", "Deprecated".
:paramtype tool_state: str or ~flow.models.ToolState
"""
super(Tool, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.type = kwargs.get('type', None)
self.inputs = kwargs.get('inputs', None)
self.outputs = kwargs.get('outputs', None)
self.description = kwargs.get('description', None)
self.connection_type = kwargs.get('connection_type', None)
self.module = kwargs.get('module', None)
self.class_name = kwargs.get('class_name', None)
self.source = kwargs.get('source', None)
self.lkg_code = kwargs.get('lkg_code', None)
self.code = kwargs.get('code', None)
self.function = kwargs.get('function', None)
self.action_type = kwargs.get('action_type', None)
self.provider_config = kwargs.get('provider_config', None)
self.function_config = kwargs.get('function_config', None)
self.icon = kwargs.get('icon', None)
self.category = kwargs.get('category', None)
self.tags = kwargs.get('tags', None)
self.is_builtin = kwargs.get('is_builtin', None)
self.package = kwargs.get('package', None)
self.package_version = kwargs.get('package_version', None)
self.default_prompt = kwargs.get('default_prompt', None)
self.enable_kwargs = kwargs.get('enable_kwargs', None)
self.deprecated_tools = kwargs.get('deprecated_tools', None)
self.tool_state = kwargs.get('tool_state', None)
class ToolFuncResponse(msrest.serialization.Model):
"""ToolFuncResponse.
:ivar result: Anything.
:vartype result: any
:ivar logs: This is a dictionary.
:vartype logs: dict[str, str]
"""
_attribute_map = {
'result': {'key': 'result', 'type': 'object'},
'logs': {'key': 'logs', 'type': '{str}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword result: Anything.
:paramtype result: any
:keyword logs: This is a dictionary.
:paramtype logs: dict[str, str]
"""
super(ToolFuncResponse, self).__init__(**kwargs)
self.result = kwargs.get('result', None)
self.logs = kwargs.get('logs', None)
class ToolInputDynamicList(msrest.serialization.Model):
"""ToolInputDynamicList.
:ivar func_path:
:vartype func_path: str
:ivar func_kwargs:
:vartype func_kwargs: list[dict[str, any]]
"""
_attribute_map = {
'func_path': {'key': 'func_path', 'type': 'str'},
'func_kwargs': {'key': 'func_kwargs', 'type': '[{object}]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword func_path:
:paramtype func_path: str
:keyword func_kwargs:
:paramtype func_kwargs: list[dict[str, any]]
"""
super(ToolInputDynamicList, self).__init__(**kwargs)
self.func_path = kwargs.get('func_path', None)
self.func_kwargs = kwargs.get('func_kwargs', None)
class ToolInputGeneratedBy(msrest.serialization.Model):
"""ToolInputGeneratedBy.
:ivar func_path:
:vartype func_path: str
:ivar func_kwargs:
:vartype func_kwargs: list[dict[str, any]]
:ivar reverse_func_path:
:vartype reverse_func_path: str
"""
_attribute_map = {
'func_path': {'key': 'func_path', 'type': 'str'},
'func_kwargs': {'key': 'func_kwargs', 'type': '[{object}]'},
'reverse_func_path': {'key': 'reverse_func_path', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword func_path:
:paramtype func_path: str
:keyword func_kwargs:
:paramtype func_kwargs: list[dict[str, any]]
:keyword reverse_func_path:
:paramtype reverse_func_path: str
"""
super(ToolInputGeneratedBy, self).__init__(**kwargs)
self.func_path = kwargs.get('func_path', None)
self.func_kwargs = kwargs.get('func_kwargs', None)
self.reverse_func_path = kwargs.get('reverse_func_path', None)
class ToolMetaDto(msrest.serialization.Model):
"""ToolMetaDto.
:ivar tools: This is a dictionary.
:vartype tools: dict[str, ~flow.models.Tool]
:ivar errors: This is a dictionary.
:vartype errors: dict[str, ~flow.models.ErrorResponse]
"""
_attribute_map = {
'tools': {'key': 'tools', 'type': '{Tool}'},
'errors': {'key': 'errors', 'type': '{ErrorResponse}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword tools: This is a dictionary.
:paramtype tools: dict[str, ~flow.models.Tool]
:keyword errors: This is a dictionary.
:paramtype errors: dict[str, ~flow.models.ErrorResponse]
"""
super(ToolMetaDto, self).__init__(**kwargs)
self.tools = kwargs.get('tools', None)
self.errors = kwargs.get('errors', None)
class ToolSetting(msrest.serialization.Model):
"""ToolSetting.
:ivar providers:
:vartype providers: list[~flow.models.ProviderEntity]
"""
_attribute_map = {
'providers': {'key': 'providers', 'type': '[ProviderEntity]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword providers:
:paramtype providers: list[~flow.models.ProviderEntity]
"""
super(ToolSetting, self).__init__(**kwargs)
self.providers = kwargs.get('providers', None)
class ToolSourceMeta(msrest.serialization.Model):
"""ToolSourceMeta.
:ivar tool_type:
:vartype tool_type: str
"""
_attribute_map = {
'tool_type': {'key': 'tool_type', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword tool_type:
:paramtype tool_type: str
"""
super(ToolSourceMeta, self).__init__(**kwargs)
self.tool_type = kwargs.get('tool_type', None)
class TorchDistributedConfiguration(msrest.serialization.Model):
"""TorchDistributedConfiguration.
:ivar process_count_per_node:
:vartype process_count_per_node: int
"""
_attribute_map = {
'process_count_per_node': {'key': 'processCountPerNode', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword process_count_per_node:
:paramtype process_count_per_node: int
"""
super(TorchDistributedConfiguration, self).__init__(**kwargs)
self.process_count_per_node = kwargs.get('process_count_per_node', None)
class TrainingDiagnosticConfiguration(msrest.serialization.Model):
"""TrainingDiagnosticConfiguration.
:ivar job_heart_beat_timeout_seconds:
:vartype job_heart_beat_timeout_seconds: int
"""
_attribute_map = {
'job_heart_beat_timeout_seconds': {'key': 'jobHeartBeatTimeoutSeconds', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword job_heart_beat_timeout_seconds:
:paramtype job_heart_beat_timeout_seconds: int
"""
super(TrainingDiagnosticConfiguration, self).__init__(**kwargs)
self.job_heart_beat_timeout_seconds = kwargs.get('job_heart_beat_timeout_seconds', None)
class TrainingOutput(msrest.serialization.Model):
"""TrainingOutput.
:ivar training_output_type: Possible values include: "Metrics", "Model".
:vartype training_output_type: str or ~flow.models.TrainingOutputType
:ivar iteration:
:vartype iteration: int
:ivar metric:
:vartype metric: str
:ivar model_file:
:vartype model_file: str
"""
_attribute_map = {
'training_output_type': {'key': 'trainingOutputType', 'type': 'str'},
'iteration': {'key': 'iteration', 'type': 'int'},
'metric': {'key': 'metric', 'type': 'str'},
'model_file': {'key': 'modelFile', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword training_output_type: Possible values include: "Metrics", "Model".
:paramtype training_output_type: str or ~flow.models.TrainingOutputType
:keyword iteration:
:paramtype iteration: int
:keyword metric:
:paramtype metric: str
:keyword model_file:
:paramtype model_file: str
"""
super(TrainingOutput, self).__init__(**kwargs)
self.training_output_type = kwargs.get('training_output_type', None)
self.iteration = kwargs.get('iteration', None)
self.metric = kwargs.get('metric', None)
self.model_file = kwargs.get('model_file', None)
class TrainingSettings(msrest.serialization.Model):
"""TrainingSettings.
:ivar block_list_models:
:vartype block_list_models: list[str]
:ivar allow_list_models:
:vartype allow_list_models: list[str]
:ivar enable_dnn_training:
:vartype enable_dnn_training: bool
:ivar enable_onnx_compatible_models:
:vartype enable_onnx_compatible_models: bool
:ivar stack_ensemble_settings:
:vartype stack_ensemble_settings: ~flow.models.StackEnsembleSettings
:ivar enable_stack_ensemble:
:vartype enable_stack_ensemble: bool
:ivar enable_vote_ensemble:
:vartype enable_vote_ensemble: bool
:ivar ensemble_model_download_timeout:
:vartype ensemble_model_download_timeout: str
:ivar enable_model_explainability:
:vartype enable_model_explainability: bool
:ivar training_mode: Possible values include: "Distributed", "NonDistributed", "Auto".
:vartype training_mode: str or ~flow.models.TabularTrainingMode
"""
_attribute_map = {
'block_list_models': {'key': 'blockListModels', 'type': '[str]'},
'allow_list_models': {'key': 'allowListModels', 'type': '[str]'},
'enable_dnn_training': {'key': 'enableDnnTraining', 'type': 'bool'},
'enable_onnx_compatible_models': {'key': 'enableOnnxCompatibleModels', 'type': 'bool'},
'stack_ensemble_settings': {'key': 'stackEnsembleSettings', 'type': 'StackEnsembleSettings'},
'enable_stack_ensemble': {'key': 'enableStackEnsemble', 'type': 'bool'},
'enable_vote_ensemble': {'key': 'enableVoteEnsemble', 'type': 'bool'},
'ensemble_model_download_timeout': {'key': 'ensembleModelDownloadTimeout', 'type': 'str'},
'enable_model_explainability': {'key': 'enableModelExplainability', 'type': 'bool'},
'training_mode': {'key': 'trainingMode', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword block_list_models:
:paramtype block_list_models: list[str]
:keyword allow_list_models:
:paramtype allow_list_models: list[str]
:keyword enable_dnn_training:
:paramtype enable_dnn_training: bool
:keyword enable_onnx_compatible_models:
:paramtype enable_onnx_compatible_models: bool
:keyword stack_ensemble_settings:
:paramtype stack_ensemble_settings: ~flow.models.StackEnsembleSettings
:keyword enable_stack_ensemble:
:paramtype enable_stack_ensemble: bool
:keyword enable_vote_ensemble:
:paramtype enable_vote_ensemble: bool
:keyword ensemble_model_download_timeout:
:paramtype ensemble_model_download_timeout: str
:keyword enable_model_explainability:
:paramtype enable_model_explainability: bool
:keyword training_mode: Possible values include: "Distributed", "NonDistributed", "Auto".
:paramtype training_mode: str or ~flow.models.TabularTrainingMode
"""
super(TrainingSettings, self).__init__(**kwargs)
self.block_list_models = kwargs.get('block_list_models', None)
self.allow_list_models = kwargs.get('allow_list_models', None)
self.enable_dnn_training = kwargs.get('enable_dnn_training', None)
self.enable_onnx_compatible_models = kwargs.get('enable_onnx_compatible_models', None)
self.stack_ensemble_settings = kwargs.get('stack_ensemble_settings', None)
self.enable_stack_ensemble = kwargs.get('enable_stack_ensemble', None)
self.enable_vote_ensemble = kwargs.get('enable_vote_ensemble', None)
self.ensemble_model_download_timeout = kwargs.get('ensemble_model_download_timeout', None)
self.enable_model_explainability = kwargs.get('enable_model_explainability', None)
self.training_mode = kwargs.get('training_mode', None)
class TriggerAsyncOperationStatus(msrest.serialization.Model):
"""TriggerAsyncOperationStatus.
:ivar id:
:vartype id: str
:ivar operation_type: Possible values include: "Create", "Update", "Delete", "CreateOrUpdate".
:vartype operation_type: str or ~flow.models.TriggerOperationType
:ivar provisioning_status: Possible values include: "Creating", "Updating", "Deleting",
"Succeeded", "Failed", "Canceled".
:vartype provisioning_status: str or ~flow.models.ScheduleProvisioningStatus
:ivar created_time:
:vartype created_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar error: The error response.
:vartype error: ~flow.models.ErrorResponse
:ivar status_code: Possible values include: "Continue", "SwitchingProtocols", "Processing",
"EarlyHints", "OK", "Created", "Accepted", "NonAuthoritativeInformation", "NoContent",
"ResetContent", "PartialContent", "MultiStatus", "AlreadyReported", "IMUsed",
"MultipleChoices", "Ambiguous", "MovedPermanently", "Moved", "Found", "Redirect", "SeeOther",
"RedirectMethod", "NotModified", "UseProxy", "Unused", "TemporaryRedirect", "RedirectKeepVerb",
"PermanentRedirect", "BadRequest", "Unauthorized", "PaymentRequired", "Forbidden", "NotFound",
"MethodNotAllowed", "NotAcceptable", "ProxyAuthenticationRequired", "RequestTimeout",
"Conflict", "Gone", "LengthRequired", "PreconditionFailed", "RequestEntityTooLarge",
"RequestUriTooLong", "UnsupportedMediaType", "RequestedRangeNotSatisfiable",
"ExpectationFailed", "MisdirectedRequest", "UnprocessableEntity", "Locked", "FailedDependency",
"UpgradeRequired", "PreconditionRequired", "TooManyRequests", "RequestHeaderFieldsTooLarge",
"UnavailableForLegalReasons", "InternalServerError", "NotImplemented", "BadGateway",
"ServiceUnavailable", "GatewayTimeout", "HttpVersionNotSupported", "VariantAlsoNegotiates",
"InsufficientStorage", "LoopDetected", "NotExtended", "NetworkAuthenticationRequired".
:vartype status_code: str or ~flow.models.HttpStatusCode
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'operation_type': {'key': 'operationType', 'type': 'str'},
'provisioning_status': {'key': 'provisioningStatus', 'type': 'str'},
'created_time': {'key': 'createdTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'error': {'key': 'error', 'type': 'ErrorResponse'},
'status_code': {'key': 'statusCode', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword operation_type: Possible values include: "Create", "Update", "Delete",
"CreateOrUpdate".
:paramtype operation_type: str or ~flow.models.TriggerOperationType
:keyword provisioning_status: Possible values include: "Creating", "Updating", "Deleting",
"Succeeded", "Failed", "Canceled".
:paramtype provisioning_status: str or ~flow.models.ScheduleProvisioningStatus
:keyword created_time:
:paramtype created_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
:keyword error: The error response.
:paramtype error: ~flow.models.ErrorResponse
:keyword status_code: Possible values include: "Continue", "SwitchingProtocols", "Processing",
"EarlyHints", "OK", "Created", "Accepted", "NonAuthoritativeInformation", "NoContent",
"ResetContent", "PartialContent", "MultiStatus", "AlreadyReported", "IMUsed",
"MultipleChoices", "Ambiguous", "MovedPermanently", "Moved", "Found", "Redirect", "SeeOther",
"RedirectMethod", "NotModified", "UseProxy", "Unused", "TemporaryRedirect", "RedirectKeepVerb",
"PermanentRedirect", "BadRequest", "Unauthorized", "PaymentRequired", "Forbidden", "NotFound",
"MethodNotAllowed", "NotAcceptable", "ProxyAuthenticationRequired", "RequestTimeout",
"Conflict", "Gone", "LengthRequired", "PreconditionFailed", "RequestEntityTooLarge",
"RequestUriTooLong", "UnsupportedMediaType", "RequestedRangeNotSatisfiable",
"ExpectationFailed", "MisdirectedRequest", "UnprocessableEntity", "Locked", "FailedDependency",
"UpgradeRequired", "PreconditionRequired", "TooManyRequests", "RequestHeaderFieldsTooLarge",
"UnavailableForLegalReasons", "InternalServerError", "NotImplemented", "BadGateway",
"ServiceUnavailable", "GatewayTimeout", "HttpVersionNotSupported", "VariantAlsoNegotiates",
"InsufficientStorage", "LoopDetected", "NotExtended", "NetworkAuthenticationRequired".
:paramtype status_code: str or ~flow.models.HttpStatusCode
"""
super(TriggerAsyncOperationStatus, self).__init__(**kwargs)
self.id = kwargs.get('id', None)
self.operation_type = kwargs.get('operation_type', None)
self.provisioning_status = kwargs.get('provisioning_status', None)
self.created_time = kwargs.get('created_time', None)
self.end_time = kwargs.get('end_time', None)
self.error = kwargs.get('error', None)
self.status_code = kwargs.get('status_code', None)
class TuningNodeSetting(msrest.serialization.Model):
"""TuningNodeSetting.
:ivar variant_ids:
:vartype variant_ids: list[str]
"""
_attribute_map = {
'variant_ids': {'key': 'variantIds', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword variant_ids:
:paramtype variant_ids: list[str]
"""
super(TuningNodeSetting, self).__init__(**kwargs)
self.variant_ids = kwargs.get('variant_ids', None)
class TypedAssetReference(msrest.serialization.Model):
"""TypedAssetReference.
:ivar asset_id:
:vartype asset_id: str
:ivar type:
:vartype type: str
"""
_attribute_map = {
'asset_id': {'key': 'assetId', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword asset_id:
:paramtype asset_id: str
:keyword type:
:paramtype type: str
"""
super(TypedAssetReference, self).__init__(**kwargs)
self.asset_id = kwargs.get('asset_id', None)
self.type = kwargs.get('type', None)
class UIAzureOpenAIDeploymentNameSelector(msrest.serialization.Model):
"""UIAzureOpenAIDeploymentNameSelector.
:ivar capabilities:
:vartype capabilities: ~flow.models.UIAzureOpenAIModelCapabilities
"""
_attribute_map = {
'capabilities': {'key': 'Capabilities', 'type': 'UIAzureOpenAIModelCapabilities'},
}
def __init__(
self,
**kwargs
):
"""
:keyword capabilities:
:paramtype capabilities: ~flow.models.UIAzureOpenAIModelCapabilities
"""
super(UIAzureOpenAIDeploymentNameSelector, self).__init__(**kwargs)
self.capabilities = kwargs.get('capabilities', None)
class UIAzureOpenAIModelCapabilities(msrest.serialization.Model):
"""UIAzureOpenAIModelCapabilities.
:ivar completion:
:vartype completion: bool
:ivar chat_completion:
:vartype chat_completion: bool
:ivar embeddings:
:vartype embeddings: bool
"""
_attribute_map = {
'completion': {'key': 'Completion', 'type': 'bool'},
'chat_completion': {'key': 'ChatCompletion', 'type': 'bool'},
'embeddings': {'key': 'Embeddings', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword completion:
:paramtype completion: bool
:keyword chat_completion:
:paramtype chat_completion: bool
:keyword embeddings:
:paramtype embeddings: bool
"""
super(UIAzureOpenAIModelCapabilities, self).__init__(**kwargs)
self.completion = kwargs.get('completion', None)
self.chat_completion = kwargs.get('chat_completion', None)
self.embeddings = kwargs.get('embeddings', None)
class UIColumnPicker(msrest.serialization.Model):
"""UIColumnPicker.
:ivar column_picker_for:
:vartype column_picker_for: str
:ivar column_selection_categories:
:vartype column_selection_categories: list[str]
:ivar single_column_selection:
:vartype single_column_selection: bool
"""
_attribute_map = {
'column_picker_for': {'key': 'columnPickerFor', 'type': 'str'},
'column_selection_categories': {'key': 'columnSelectionCategories', 'type': '[str]'},
'single_column_selection': {'key': 'singleColumnSelection', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword column_picker_for:
:paramtype column_picker_for: str
:keyword column_selection_categories:
:paramtype column_selection_categories: list[str]
:keyword single_column_selection:
:paramtype single_column_selection: bool
"""
super(UIColumnPicker, self).__init__(**kwargs)
self.column_picker_for = kwargs.get('column_picker_for', None)
self.column_selection_categories = kwargs.get('column_selection_categories', None)
self.single_column_selection = kwargs.get('single_column_selection', None)
class UIComputeSelection(msrest.serialization.Model):
"""UIComputeSelection.
:ivar compute_types:
:vartype compute_types: list[str]
:ivar require_gpu:
:vartype require_gpu: bool
:ivar os_types:
:vartype os_types: list[str]
:ivar support_serverless:
:vartype support_serverless: bool
:ivar compute_run_settings_mapping: Dictionary of
<components·10my8oj·schemas·uicomputeselection·properties·computerunsettingsmapping·additionalproperties>.
:vartype compute_run_settings_mapping: dict[str, list[~flow.models.RunSettingParameter]]
"""
_attribute_map = {
'compute_types': {'key': 'computeTypes', 'type': '[str]'},
'require_gpu': {'key': 'requireGpu', 'type': 'bool'},
'os_types': {'key': 'osTypes', 'type': '[str]'},
'support_serverless': {'key': 'supportServerless', 'type': 'bool'},
'compute_run_settings_mapping': {'key': 'computeRunSettingsMapping', 'type': '{[RunSettingParameter]}'},
}
def __init__(
self,
**kwargs
):
"""
:keyword compute_types:
:paramtype compute_types: list[str]
:keyword require_gpu:
:paramtype require_gpu: bool
:keyword os_types:
:paramtype os_types: list[str]
:keyword support_serverless:
:paramtype support_serverless: bool
:keyword compute_run_settings_mapping: Dictionary of
<components·10my8oj·schemas·uicomputeselection·properties·computerunsettingsmapping·additionalproperties>.
:paramtype compute_run_settings_mapping: dict[str, list[~flow.models.RunSettingParameter]]
"""
super(UIComputeSelection, self).__init__(**kwargs)
self.compute_types = kwargs.get('compute_types', None)
self.require_gpu = kwargs.get('require_gpu', None)
self.os_types = kwargs.get('os_types', None)
self.support_serverless = kwargs.get('support_serverless', None)
self.compute_run_settings_mapping = kwargs.get('compute_run_settings_mapping', None)
class UIHyperparameterConfiguration(msrest.serialization.Model):
"""UIHyperparameterConfiguration.
:ivar model_name_to_hyper_parameter_and_distribution_mapping: Dictionary of
<components·1nrp69t·schemas·uihyperparameterconfiguration·properties·modelnametohyperparameteranddistributionmapping·additionalproperties>.
:vartype model_name_to_hyper_parameter_and_distribution_mapping: dict[str, dict[str,
list[str]]]
:ivar distribution_parameters_mapping: Dictionary of
<components·d9plq4·schemas·uihyperparameterconfiguration·properties·distributionparametersmapping·additionalproperties>.
:vartype distribution_parameters_mapping: dict[str, list[~flow.models.DistributionParameter]]
:ivar json_schema:
:vartype json_schema: str
"""
_attribute_map = {
'model_name_to_hyper_parameter_and_distribution_mapping': {'key': 'modelNameToHyperParameterAndDistributionMapping', 'type': '{{[str]}}'},
'distribution_parameters_mapping': {'key': 'distributionParametersMapping', 'type': '{[DistributionParameter]}'},
'json_schema': {'key': 'jsonSchema', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword model_name_to_hyper_parameter_and_distribution_mapping: Dictionary of
<components·1nrp69t·schemas·uihyperparameterconfiguration·properties·modelnametohyperparameteranddistributionmapping·additionalproperties>.
:paramtype model_name_to_hyper_parameter_and_distribution_mapping: dict[str, dict[str,
list[str]]]
:keyword distribution_parameters_mapping: Dictionary of
<components·d9plq4·schemas·uihyperparameterconfiguration·properties·distributionparametersmapping·additionalproperties>.
:paramtype distribution_parameters_mapping: dict[str, list[~flow.models.DistributionParameter]]
:keyword json_schema:
:paramtype json_schema: str
"""
super(UIHyperparameterConfiguration, self).__init__(**kwargs)
self.model_name_to_hyper_parameter_and_distribution_mapping = kwargs.get('model_name_to_hyper_parameter_and_distribution_mapping', None)
self.distribution_parameters_mapping = kwargs.get('distribution_parameters_mapping', None)
self.json_schema = kwargs.get('json_schema', None)
class UIInputSetting(msrest.serialization.Model):
"""UIInputSetting.
:ivar name:
:vartype name: str
:ivar data_delivery_mode: Possible values include: "Read-only mount", "Read-write mount",
"Download", "Direct", "Evaluate mount", "Evaluate download", "Hdfs".
:vartype data_delivery_mode: str or ~flow.models.UIInputDataDeliveryMode
:ivar path_on_compute:
:vartype path_on_compute: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'data_delivery_mode': {'key': 'dataDeliveryMode', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword data_delivery_mode: Possible values include: "Read-only mount", "Read-write mount",
"Download", "Direct", "Evaluate mount", "Evaluate download", "Hdfs".
:paramtype data_delivery_mode: str or ~flow.models.UIInputDataDeliveryMode
:keyword path_on_compute:
:paramtype path_on_compute: str
"""
super(UIInputSetting, self).__init__(**kwargs)
self.name = kwargs.get('name', None)
self.data_delivery_mode = kwargs.get('data_delivery_mode', None)
self.path_on_compute = kwargs.get('path_on_compute', None)
class UIJsonEditor(msrest.serialization.Model):
"""UIJsonEditor.
:ivar json_schema:
:vartype json_schema: str
"""
_attribute_map = {
'json_schema': {'key': 'jsonSchema', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword json_schema:
:paramtype json_schema: str
"""
super(UIJsonEditor, self).__init__(**kwargs)
self.json_schema = kwargs.get('json_schema', None)
class UIParameterHint(msrest.serialization.Model):
"""UIParameterHint.
:ivar ui_widget_type: Possible values include: "Default", "Mode", "ColumnPicker", "Credential",
"Script", "ComputeSelection", "JsonEditor", "SearchSpaceParameter", "SectionToggle",
"YamlEditor", "EnableRuntimeSweep", "DataStoreSelection", "InstanceTypeSelection",
"ConnectionSelection", "PromptFlowConnectionSelection", "AzureOpenAIDeploymentNameSelection".
:vartype ui_widget_type: str or ~flow.models.UIWidgetTypeEnum
:ivar column_picker:
:vartype column_picker: ~flow.models.UIColumnPicker
:ivar ui_script_language: Possible values include: "None", "Python", "R", "Json", "Sql".
:vartype ui_script_language: str or ~flow.models.UIScriptLanguageEnum
:ivar json_editor:
:vartype json_editor: ~flow.models.UIJsonEditor
:ivar prompt_flow_connection_selector:
:vartype prompt_flow_connection_selector: ~flow.models.UIPromptFlowConnectionSelector
:ivar azure_open_ai_deployment_name_selector:
:vartype azure_open_ai_deployment_name_selector:
~flow.models.UIAzureOpenAIDeploymentNameSelector
:ivar ux_ignore:
:vartype ux_ignore: bool
:ivar anonymous:
:vartype anonymous: bool
"""
_attribute_map = {
'ui_widget_type': {'key': 'uiWidgetType', 'type': 'str'},
'column_picker': {'key': 'columnPicker', 'type': 'UIColumnPicker'},
'ui_script_language': {'key': 'uiScriptLanguage', 'type': 'str'},
'json_editor': {'key': 'jsonEditor', 'type': 'UIJsonEditor'},
'prompt_flow_connection_selector': {'key': 'PromptFlowConnectionSelector', 'type': 'UIPromptFlowConnectionSelector'},
'azure_open_ai_deployment_name_selector': {'key': 'AzureOpenAIDeploymentNameSelector', 'type': 'UIAzureOpenAIDeploymentNameSelector'},
'ux_ignore': {'key': 'UxIgnore', 'type': 'bool'},
'anonymous': {'key': 'Anonymous', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword ui_widget_type: Possible values include: "Default", "Mode", "ColumnPicker",
"Credential", "Script", "ComputeSelection", "JsonEditor", "SearchSpaceParameter",
"SectionToggle", "YamlEditor", "EnableRuntimeSweep", "DataStoreSelection",
"InstanceTypeSelection", "ConnectionSelection", "PromptFlowConnectionSelection",
"AzureOpenAIDeploymentNameSelection".
:paramtype ui_widget_type: str or ~flow.models.UIWidgetTypeEnum
:keyword column_picker:
:paramtype column_picker: ~flow.models.UIColumnPicker
:keyword ui_script_language: Possible values include: "None", "Python", "R", "Json", "Sql".
:paramtype ui_script_language: str or ~flow.models.UIScriptLanguageEnum
:keyword json_editor:
:paramtype json_editor: ~flow.models.UIJsonEditor
:keyword prompt_flow_connection_selector:
:paramtype prompt_flow_connection_selector: ~flow.models.UIPromptFlowConnectionSelector
:keyword azure_open_ai_deployment_name_selector:
:paramtype azure_open_ai_deployment_name_selector:
~flow.models.UIAzureOpenAIDeploymentNameSelector
:keyword ux_ignore:
:paramtype ux_ignore: bool
:keyword anonymous:
:paramtype anonymous: bool
"""
super(UIParameterHint, self).__init__(**kwargs)
self.ui_widget_type = kwargs.get('ui_widget_type', None)
self.column_picker = kwargs.get('column_picker', None)
self.ui_script_language = kwargs.get('ui_script_language', None)
self.json_editor = kwargs.get('json_editor', None)
self.prompt_flow_connection_selector = kwargs.get('prompt_flow_connection_selector', None)
self.azure_open_ai_deployment_name_selector = kwargs.get('azure_open_ai_deployment_name_selector', None)
self.ux_ignore = kwargs.get('ux_ignore', None)
self.anonymous = kwargs.get('anonymous', None)
class UIPromptFlowConnectionSelector(msrest.serialization.Model):
"""UIPromptFlowConnectionSelector.
:ivar prompt_flow_connection_type:
:vartype prompt_flow_connection_type: str
"""
_attribute_map = {
'prompt_flow_connection_type': {'key': 'PromptFlowConnectionType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword prompt_flow_connection_type:
:paramtype prompt_flow_connection_type: str
"""
super(UIPromptFlowConnectionSelector, self).__init__(**kwargs)
self.prompt_flow_connection_type = kwargs.get('prompt_flow_connection_type', None)
class UIWidgetMetaInfo(msrest.serialization.Model):
"""UIWidgetMetaInfo.
:ivar module_node_id:
:vartype module_node_id: str
:ivar meta_module_id:
:vartype meta_module_id: str
:ivar parameter_name:
:vartype parameter_name: str
:ivar ui_widget_type: Possible values include: "Default", "Mode", "ColumnPicker", "Credential",
"Script", "ComputeSelection", "JsonEditor", "SearchSpaceParameter", "SectionToggle",
"YamlEditor", "EnableRuntimeSweep", "DataStoreSelection", "InstanceTypeSelection",
"ConnectionSelection", "PromptFlowConnectionSelection", "AzureOpenAIDeploymentNameSelection".
:vartype ui_widget_type: str or ~flow.models.UIWidgetTypeEnum
"""
_attribute_map = {
'module_node_id': {'key': 'moduleNodeId', 'type': 'str'},
'meta_module_id': {'key': 'metaModuleId', 'type': 'str'},
'parameter_name': {'key': 'parameterName', 'type': 'str'},
'ui_widget_type': {'key': 'uiWidgetType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword module_node_id:
:paramtype module_node_id: str
:keyword meta_module_id:
:paramtype meta_module_id: str
:keyword parameter_name:
:paramtype parameter_name: str
:keyword ui_widget_type: Possible values include: "Default", "Mode", "ColumnPicker",
"Credential", "Script", "ComputeSelection", "JsonEditor", "SearchSpaceParameter",
"SectionToggle", "YamlEditor", "EnableRuntimeSweep", "DataStoreSelection",
"InstanceTypeSelection", "ConnectionSelection", "PromptFlowConnectionSelection",
"AzureOpenAIDeploymentNameSelection".
:paramtype ui_widget_type: str or ~flow.models.UIWidgetTypeEnum
"""
super(UIWidgetMetaInfo, self).__init__(**kwargs)
self.module_node_id = kwargs.get('module_node_id', None)
self.meta_module_id = kwargs.get('meta_module_id', None)
self.parameter_name = kwargs.get('parameter_name', None)
self.ui_widget_type = kwargs.get('ui_widget_type', None)
class UIYamlEditor(msrest.serialization.Model):
"""UIYamlEditor.
:ivar json_schema:
:vartype json_schema: str
"""
_attribute_map = {
'json_schema': {'key': 'jsonSchema', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword json_schema:
:paramtype json_schema: str
"""
super(UIYamlEditor, self).__init__(**kwargs)
self.json_schema = kwargs.get('json_schema', None)
class UnversionedEntityRequestDto(msrest.serialization.Model):
"""UnversionedEntityRequestDto.
:ivar unversioned_entity_ids:
:vartype unversioned_entity_ids: list[str]
"""
_attribute_map = {
'unversioned_entity_ids': {'key': 'unversionedEntityIds', 'type': '[str]'},
}
def __init__(
self,
**kwargs
):
"""
:keyword unversioned_entity_ids:
:paramtype unversioned_entity_ids: list[str]
"""
super(UnversionedEntityRequestDto, self).__init__(**kwargs)
self.unversioned_entity_ids = kwargs.get('unversioned_entity_ids', None)
class UnversionedEntityResponseDto(msrest.serialization.Model):
"""UnversionedEntityResponseDto.
:ivar unversioned_entities:
:vartype unversioned_entities: list[~flow.models.FlowIndexEntity]
:ivar unversioned_entity_json_schema: Anything.
:vartype unversioned_entity_json_schema: any
:ivar normalized_request_charge:
:vartype normalized_request_charge: float
:ivar normalized_request_charge_period:
:vartype normalized_request_charge_period: str
"""
_attribute_map = {
'unversioned_entities': {'key': 'unversionedEntities', 'type': '[FlowIndexEntity]'},
'unversioned_entity_json_schema': {'key': 'unversionedEntityJsonSchema', 'type': 'object'},
'normalized_request_charge': {'key': 'normalizedRequestCharge', 'type': 'float'},
'normalized_request_charge_period': {'key': 'normalizedRequestChargePeriod', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword unversioned_entities:
:paramtype unversioned_entities: list[~flow.models.FlowIndexEntity]
:keyword unversioned_entity_json_schema: Anything.
:paramtype unversioned_entity_json_schema: any
:keyword normalized_request_charge:
:paramtype normalized_request_charge: float
:keyword normalized_request_charge_period:
:paramtype normalized_request_charge_period: str
"""
super(UnversionedEntityResponseDto, self).__init__(**kwargs)
self.unversioned_entities = kwargs.get('unversioned_entities', None)
self.unversioned_entity_json_schema = kwargs.get('unversioned_entity_json_schema', None)
self.normalized_request_charge = kwargs.get('normalized_request_charge', None)
self.normalized_request_charge_period = kwargs.get('normalized_request_charge_period', None)
class UnversionedRebuildIndexDto(msrest.serialization.Model):
"""UnversionedRebuildIndexDto.
:ivar continuation_token:
:vartype continuation_token: str
:ivar entity_count:
:vartype entity_count: int
:ivar entity_container_type:
:vartype entity_container_type: str
:ivar entity_type:
:vartype entity_type: str
:ivar resource_id:
:vartype resource_id: str
:ivar workspace_id:
:vartype workspace_id: str
:ivar immutable_resource_id:
:vartype immutable_resource_id: str
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
"""
_attribute_map = {
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'entity_count': {'key': 'entityCount', 'type': 'int'},
'entity_container_type': {'key': 'entityContainerType', 'type': 'str'},
'entity_type': {'key': 'entityType', 'type': 'str'},
'resource_id': {'key': 'resourceId', 'type': 'str'},
'workspace_id': {'key': 'workspaceId', 'type': 'str'},
'immutable_resource_id': {'key': 'immutableResourceId', 'type': 'str'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
}
def __init__(
self,
**kwargs
):
"""
:keyword continuation_token:
:paramtype continuation_token: str
:keyword entity_count:
:paramtype entity_count: int
:keyword entity_container_type:
:paramtype entity_container_type: str
:keyword entity_type:
:paramtype entity_type: str
:keyword resource_id:
:paramtype resource_id: str
:keyword workspace_id:
:paramtype workspace_id: str
:keyword immutable_resource_id:
:paramtype immutable_resource_id: str
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
"""
super(UnversionedRebuildIndexDto, self).__init__(**kwargs)
self.continuation_token = kwargs.get('continuation_token', None)
self.entity_count = kwargs.get('entity_count', None)
self.entity_container_type = kwargs.get('entity_container_type', None)
self.entity_type = kwargs.get('entity_type', None)
self.resource_id = kwargs.get('resource_id', None)
self.workspace_id = kwargs.get('workspace_id', None)
self.immutable_resource_id = kwargs.get('immutable_resource_id', None)
self.start_time = kwargs.get('start_time', None)
self.end_time = kwargs.get('end_time', None)
class UnversionedRebuildResponseDto(msrest.serialization.Model):
"""UnversionedRebuildResponseDto.
:ivar entities:
:vartype entities: ~flow.models.SegmentedResult1
:ivar unversioned_entity_schema: Anything.
:vartype unversioned_entity_schema: any
:ivar normalized_request_charge:
:vartype normalized_request_charge: float
:ivar normalized_request_charge_period:
:vartype normalized_request_charge_period: str
"""
_attribute_map = {
'entities': {'key': 'entities', 'type': 'SegmentedResult1'},
'unversioned_entity_schema': {'key': 'unversionedEntitySchema', 'type': 'object'},
'normalized_request_charge': {'key': 'normalizedRequestCharge', 'type': 'float'},
'normalized_request_charge_period': {'key': 'normalizedRequestChargePeriod', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword entities:
:paramtype entities: ~flow.models.SegmentedResult1
:keyword unversioned_entity_schema: Anything.
:paramtype unversioned_entity_schema: any
:keyword normalized_request_charge:
:paramtype normalized_request_charge: float
:keyword normalized_request_charge_period:
:paramtype normalized_request_charge_period: str
"""
super(UnversionedRebuildResponseDto, self).__init__(**kwargs)
self.entities = kwargs.get('entities', None)
self.unversioned_entity_schema = kwargs.get('unversioned_entity_schema', None)
self.normalized_request_charge = kwargs.get('normalized_request_charge', None)
self.normalized_request_charge_period = kwargs.get('normalized_request_charge_period', None)
class UpdateComponentRequest(msrest.serialization.Model):
"""UpdateComponentRequest.
:ivar display_name:
:vartype display_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar module_update_operation_type: Possible values include: "SetDefaultVersion",
"EnableModule", "DisableModule", "UpdateDisplayName", "UpdateDescription", "UpdateTags".
:vartype module_update_operation_type: str or ~flow.models.ModuleUpdateOperationType
:ivar module_version:
:vartype module_version: str
"""
_attribute_map = {
'display_name': {'key': 'displayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'module_update_operation_type': {'key': 'moduleUpdateOperationType', 'type': 'str'},
'module_version': {'key': 'moduleVersion', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword display_name:
:paramtype display_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword module_update_operation_type: Possible values include: "SetDefaultVersion",
"EnableModule", "DisableModule", "UpdateDisplayName", "UpdateDescription", "UpdateTags".
:paramtype module_update_operation_type: str or ~flow.models.ModuleUpdateOperationType
:keyword module_version:
:paramtype module_version: str
"""
super(UpdateComponentRequest, self).__init__(**kwargs)
self.display_name = kwargs.get('display_name', None)
self.description = kwargs.get('description', None)
self.tags = kwargs.get('tags', None)
self.module_update_operation_type = kwargs.get('module_update_operation_type', None)
self.module_version = kwargs.get('module_version', None)
class UpdateFlowRequest(msrest.serialization.Model):
"""UpdateFlowRequest.
:ivar flow_run_result:
:vartype flow_run_result: ~flow.models.FlowRunResult
:ivar flow_test_mode: Possible values include: "Sync", "Async".
:vartype flow_test_mode: str or ~flow.models.FlowTestMode
:ivar flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:vartype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:ivar flow_name:
:vartype flow_name: str
:ivar description:
:vartype description: str
:ivar details:
:vartype details: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar flow:
:vartype flow: ~flow.models.Flow
:ivar flow_definition_file_path:
:vartype flow_definition_file_path: str
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar flow_run_settings:
:vartype flow_run_settings: ~flow.models.FlowRunSettings
:ivar is_archived:
:vartype is_archived: bool
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar identity:
:vartype identity: str
"""
_attribute_map = {
'flow_run_result': {'key': 'flowRunResult', 'type': 'FlowRunResult'},
'flow_test_mode': {'key': 'flowTestMode', 'type': 'str'},
'flow_test_infos': {'key': 'flowTestInfos', 'type': '{FlowTestInfo}'},
'flow_name': {'key': 'flowName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'details': {'key': 'details', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'flow': {'key': 'flow', 'type': 'Flow'},
'flow_definition_file_path': {'key': 'flowDefinitionFilePath', 'type': 'str'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'flow_run_settings': {'key': 'flowRunSettings', 'type': 'FlowRunSettings'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'identity': {'key': 'identity', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword flow_run_result:
:paramtype flow_run_result: ~flow.models.FlowRunResult
:keyword flow_test_mode: Possible values include: "Sync", "Async".
:paramtype flow_test_mode: str or ~flow.models.FlowTestMode
:keyword flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:paramtype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:keyword flow_name:
:paramtype flow_name: str
:keyword description:
:paramtype description: str
:keyword details:
:paramtype details: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword flow:
:paramtype flow: ~flow.models.Flow
:keyword flow_definition_file_path:
:paramtype flow_definition_file_path: str
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword flow_run_settings:
:paramtype flow_run_settings: ~flow.models.FlowRunSettings
:keyword is_archived:
:paramtype is_archived: bool
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword identity:
:paramtype identity: str
"""
super(UpdateFlowRequest, self).__init__(**kwargs)
self.flow_run_result = kwargs.get('flow_run_result', None)
self.flow_test_mode = kwargs.get('flow_test_mode', None)
self.flow_test_infos = kwargs.get('flow_test_infos', None)
self.flow_name = kwargs.get('flow_name', None)
self.description = kwargs.get('description', None)
self.details = kwargs.get('details', None)
self.tags = kwargs.get('tags', None)
self.flow = kwargs.get('flow', None)
self.flow_definition_file_path = kwargs.get('flow_definition_file_path', None)
self.flow_type = kwargs.get('flow_type', None)
self.flow_run_settings = kwargs.get('flow_run_settings', None)
self.is_archived = kwargs.get('is_archived', None)
self.vm_size = kwargs.get('vm_size', None)
self.max_idle_time_seconds = kwargs.get('max_idle_time_seconds', None)
self.identity = kwargs.get('identity', None)
class UpdateFlowRuntimeRequest(msrest.serialization.Model):
"""UpdateFlowRuntimeRequest.
:ivar runtime_description:
:vartype runtime_description: str
:ivar environment:
:vartype environment: str
:ivar instance_count:
:vartype instance_count: int
"""
_attribute_map = {
'runtime_description': {'key': 'runtimeDescription', 'type': 'str'},
'environment': {'key': 'environment', 'type': 'str'},
'instance_count': {'key': 'instanceCount', 'type': 'int'},
}
def __init__(
self,
**kwargs
):
"""
:keyword runtime_description:
:paramtype runtime_description: str
:keyword environment:
:paramtype environment: str
:keyword instance_count:
:paramtype instance_count: int
"""
super(UpdateFlowRuntimeRequest, self).__init__(**kwargs)
self.runtime_description = kwargs.get('runtime_description', None)
self.environment = kwargs.get('environment', None)
self.instance_count = kwargs.get('instance_count', None)
class UpdateRegistryComponentRequest(msrest.serialization.Model):
"""UpdateRegistryComponentRequest.
:ivar registry_name:
:vartype registry_name: str
:ivar component_name:
:vartype component_name: str
:ivar component_version:
:vartype component_version: str
:ivar update_type: The only acceptable values to pass in are None and "SetDefaultVersion". The
default value is None.
:vartype update_type: str
"""
_attribute_map = {
'registry_name': {'key': 'registryName', 'type': 'str'},
'component_name': {'key': 'componentName', 'type': 'str'},
'component_version': {'key': 'componentVersion', 'type': 'str'},
'update_type': {'key': 'updateType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword registry_name:
:paramtype registry_name: str
:keyword component_name:
:paramtype component_name: str
:keyword component_version:
:paramtype component_version: str
:keyword update_type: The only acceptable values to pass in are None and "SetDefaultVersion".
The default value is None.
:paramtype update_type: str
"""
super(UpdateRegistryComponentRequest, self).__init__(**kwargs)
self.registry_name = kwargs.get('registry_name', None)
self.component_name = kwargs.get('component_name', None)
self.component_version = kwargs.get('component_version', None)
self.update_type = kwargs.get('update_type', None)
class UploadOptions(msrest.serialization.Model):
"""UploadOptions.
:ivar overwrite:
:vartype overwrite: bool
:ivar source_globs:
:vartype source_globs: ~flow.models.ExecutionGlobsOptions
"""
_attribute_map = {
'overwrite': {'key': 'overwrite', 'type': 'bool'},
'source_globs': {'key': 'sourceGlobs', 'type': 'ExecutionGlobsOptions'},
}
def __init__(
self,
**kwargs
):
"""
:keyword overwrite:
:paramtype overwrite: bool
:keyword source_globs:
:paramtype source_globs: ~flow.models.ExecutionGlobsOptions
"""
super(UploadOptions, self).__init__(**kwargs)
self.overwrite = kwargs.get('overwrite', None)
self.source_globs = kwargs.get('source_globs', None)
class UriReference(msrest.serialization.Model):
"""UriReference.
:ivar path:
:vartype path: str
:ivar is_file:
:vartype is_file: bool
"""
_attribute_map = {
'path': {'key': 'path', 'type': 'str'},
'is_file': {'key': 'isFile', 'type': 'bool'},
}
def __init__(
self,
**kwargs
):
"""
:keyword path:
:paramtype path: str
:keyword is_file:
:paramtype is_file: bool
"""
super(UriReference, self).__init__(**kwargs)
self.path = kwargs.get('path', None)
self.is_file = kwargs.get('is_file', None)
class User(msrest.serialization.Model):
"""User.
:ivar user_object_id: A user or service principal's object ID.
This is EUPI and may only be logged to warm path telemetry.
:vartype user_object_id: str
:ivar user_pu_id: A user or service principal's PuID.
This is PII and should never be logged.
:vartype user_pu_id: str
:ivar user_idp: A user identity provider. Eg live.com
This is PII and should never be logged.
:vartype user_idp: str
:ivar user_alt_sec_id: A user alternate sec id. This represents the user in a different
identity provider system Eg.1:live.com:puid
This is PII and should never be logged.
:vartype user_alt_sec_id: str
:ivar user_iss: The issuer which issed the token for this user.
This is PII and should never be logged.
:vartype user_iss: str
:ivar user_tenant_id: A user or service principal's tenant ID.
:vartype user_tenant_id: str
:ivar user_name: A user's full name or a service principal's app ID.
This is PII and should never be logged.
:vartype user_name: str
:ivar upn: A user's Principal name (upn)
This is PII andshould never be logged.
:vartype upn: str
"""
_attribute_map = {
'user_object_id': {'key': 'userObjectId', 'type': 'str'},
'user_pu_id': {'key': 'userPuId', 'type': 'str'},
'user_idp': {'key': 'userIdp', 'type': 'str'},
'user_alt_sec_id': {'key': 'userAltSecId', 'type': 'str'},
'user_iss': {'key': 'userIss', 'type': 'str'},
'user_tenant_id': {'key': 'userTenantId', 'type': 'str'},
'user_name': {'key': 'userName', 'type': 'str'},
'upn': {'key': 'upn', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword user_object_id: A user or service principal's object ID.
This is EUPI and may only be logged to warm path telemetry.
:paramtype user_object_id: str
:keyword user_pu_id: A user or service principal's PuID.
This is PII and should never be logged.
:paramtype user_pu_id: str
:keyword user_idp: A user identity provider. Eg live.com
This is PII and should never be logged.
:paramtype user_idp: str
:keyword user_alt_sec_id: A user alternate sec id. This represents the user in a different
identity provider system Eg.1:live.com:puid
This is PII and should never be logged.
:paramtype user_alt_sec_id: str
:keyword user_iss: The issuer which issed the token for this user.
This is PII and should never be logged.
:paramtype user_iss: str
:keyword user_tenant_id: A user or service principal's tenant ID.
:paramtype user_tenant_id: str
:keyword user_name: A user's full name or a service principal's app ID.
This is PII and should never be logged.
:paramtype user_name: str
:keyword upn: A user's Principal name (upn)
This is PII andshould never be logged.
:paramtype upn: str
"""
super(User, self).__init__(**kwargs)
self.user_object_id = kwargs.get('user_object_id', None)
self.user_pu_id = kwargs.get('user_pu_id', None)
self.user_idp = kwargs.get('user_idp', None)
self.user_alt_sec_id = kwargs.get('user_alt_sec_id', None)
self.user_iss = kwargs.get('user_iss', None)
self.user_tenant_id = kwargs.get('user_tenant_id', None)
self.user_name = kwargs.get('user_name', None)
self.upn = kwargs.get('upn', None)
class UserAssignedIdentity(msrest.serialization.Model):
"""UserAssignedIdentity.
:ivar principal_id:
:vartype principal_id: str
:ivar client_id:
:vartype client_id: str
"""
_attribute_map = {
'principal_id': {'key': 'principalId', 'type': 'str'},
'client_id': {'key': 'clientId', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword principal_id:
:paramtype principal_id: str
:keyword client_id:
:paramtype client_id: str
"""
super(UserAssignedIdentity, self).__init__(**kwargs)
self.principal_id = kwargs.get('principal_id', None)
self.client_id = kwargs.get('client_id', None)
class ValidationDataSettings(msrest.serialization.Model):
"""ValidationDataSettings.
:ivar n_cross_validations:
:vartype n_cross_validations: ~flow.models.NCrossValidations
:ivar validation_data_size:
:vartype validation_data_size: float
:ivar cv_split_column_names:
:vartype cv_split_column_names: list[str]
:ivar validation_type:
:vartype validation_type: str
"""
_attribute_map = {
'n_cross_validations': {'key': 'nCrossValidations', 'type': 'NCrossValidations'},
'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
'cv_split_column_names': {'key': 'cvSplitColumnNames', 'type': '[str]'},
'validation_type': {'key': 'validationType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword n_cross_validations:
:paramtype n_cross_validations: ~flow.models.NCrossValidations
:keyword validation_data_size:
:paramtype validation_data_size: float
:keyword cv_split_column_names:
:paramtype cv_split_column_names: list[str]
:keyword validation_type:
:paramtype validation_type: str
"""
super(ValidationDataSettings, self).__init__(**kwargs)
self.n_cross_validations = kwargs.get('n_cross_validations', None)
self.validation_data_size = kwargs.get('validation_data_size', None)
self.cv_split_column_names = kwargs.get('cv_split_column_names', None)
self.validation_type = kwargs.get('validation_type', None)
class VariantNode(msrest.serialization.Model):
"""VariantNode.
:ivar node:
:vartype node: ~flow.models.Node
:ivar description:
:vartype description: str
"""
_attribute_map = {
'node': {'key': 'node', 'type': 'Node'},
'description': {'key': 'description', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword node:
:paramtype node: ~flow.models.Node
:keyword description:
:paramtype description: str
"""
super(VariantNode, self).__init__(**kwargs)
self.node = kwargs.get('node', None)
self.description = kwargs.get('description', None)
class Webhook(msrest.serialization.Model):
"""Webhook.
:ivar webhook_type: The only acceptable values to pass in are None and "AzureDevOps". The
default value is None.
:vartype webhook_type: str
:ivar event_type:
:vartype event_type: str
"""
_attribute_map = {
'webhook_type': {'key': 'webhookType', 'type': 'str'},
'event_type': {'key': 'eventType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword webhook_type: The only acceptable values to pass in are None and "AzureDevOps". The
default value is None.
:paramtype webhook_type: str
:keyword event_type:
:paramtype event_type: str
"""
super(Webhook, self).__init__(**kwargs)
self.webhook_type = kwargs.get('webhook_type', None)
self.event_type = kwargs.get('event_type', None)
class WebServiceComputeMetaInfo(msrest.serialization.Model):
"""WebServiceComputeMetaInfo.
:ivar node_count:
:vartype node_count: int
:ivar is_ssl_enabled:
:vartype is_ssl_enabled: bool
:ivar aks_not_found:
:vartype aks_not_found: bool
:ivar cluster_purpose:
:vartype cluster_purpose: str
:ivar public_ip_address:
:vartype public_ip_address: str
:ivar vm_size:
:vartype vm_size: str
:ivar location:
:vartype location: str
:ivar provisioning_state:
:vartype provisioning_state: str
:ivar state:
:vartype state: str
:ivar os_type:
:vartype os_type: str
:ivar id:
:vartype id: str
:ivar name:
:vartype name: str
:ivar created_by_studio:
:vartype created_by_studio: bool
:ivar is_gpu_type:
:vartype is_gpu_type: bool
:ivar resource_id:
:vartype resource_id: str
:ivar compute_type:
:vartype compute_type: str
"""
_attribute_map = {
'node_count': {'key': 'nodeCount', 'type': 'int'},
'is_ssl_enabled': {'key': 'isSslEnabled', 'type': 'bool'},
'aks_not_found': {'key': 'aksNotFound', 'type': 'bool'},
'cluster_purpose': {'key': 'clusterPurpose', 'type': 'str'},
'public_ip_address': {'key': 'publicIpAddress', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
'state': {'key': 'state', 'type': 'str'},
'os_type': {'key': 'osType', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'created_by_studio': {'key': 'createdByStudio', 'type': 'bool'},
'is_gpu_type': {'key': 'isGpuType', 'type': 'bool'},
'resource_id': {'key': 'resourceId', 'type': 'str'},
'compute_type': {'key': 'computeType', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword node_count:
:paramtype node_count: int
:keyword is_ssl_enabled:
:paramtype is_ssl_enabled: bool
:keyword aks_not_found:
:paramtype aks_not_found: bool
:keyword cluster_purpose:
:paramtype cluster_purpose: str
:keyword public_ip_address:
:paramtype public_ip_address: str
:keyword vm_size:
:paramtype vm_size: str
:keyword location:
:paramtype location: str
:keyword provisioning_state:
:paramtype provisioning_state: str
:keyword state:
:paramtype state: str
:keyword os_type:
:paramtype os_type: str
:keyword id:
:paramtype id: str
:keyword name:
:paramtype name: str
:keyword created_by_studio:
:paramtype created_by_studio: bool
:keyword is_gpu_type:
:paramtype is_gpu_type: bool
:keyword resource_id:
:paramtype resource_id: str
:keyword compute_type:
:paramtype compute_type: str
"""
super(WebServiceComputeMetaInfo, self).__init__(**kwargs)
self.node_count = kwargs.get('node_count', None)
self.is_ssl_enabled = kwargs.get('is_ssl_enabled', None)
self.aks_not_found = kwargs.get('aks_not_found', None)
self.cluster_purpose = kwargs.get('cluster_purpose', None)
self.public_ip_address = kwargs.get('public_ip_address', None)
self.vm_size = kwargs.get('vm_size', None)
self.location = kwargs.get('location', None)
self.provisioning_state = kwargs.get('provisioning_state', None)
self.state = kwargs.get('state', None)
self.os_type = kwargs.get('os_type', None)
self.id = kwargs.get('id', None)
self.name = kwargs.get('name', None)
self.created_by_studio = kwargs.get('created_by_studio', None)
self.is_gpu_type = kwargs.get('is_gpu_type', None)
self.resource_id = kwargs.get('resource_id', None)
self.compute_type = kwargs.get('compute_type', None)
class WebServicePort(msrest.serialization.Model):
"""WebServicePort.
:ivar node_id:
:vartype node_id: str
:ivar port_name:
:vartype port_name: str
:ivar name:
:vartype name: str
"""
_attribute_map = {
'node_id': {'key': 'nodeId', 'type': 'str'},
'port_name': {'key': 'portName', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword node_id:
:paramtype node_id: str
:keyword port_name:
:paramtype port_name: str
:keyword name:
:paramtype name: str
"""
super(WebServicePort, self).__init__(**kwargs)
self.node_id = kwargs.get('node_id', None)
self.port_name = kwargs.get('port_name', None)
self.name = kwargs.get('name', None)
class WorkspaceConnectionSpec(msrest.serialization.Model):
"""WorkspaceConnectionSpec.
:ivar connection_category: Possible values include: "PythonFeed", "ACR", "Git", "S3",
"Snowflake", "AzureSqlDb", "AzureSynapseAnalytics", "AzureMySqlDb", "AzurePostgresDb",
"AzureDataLakeGen2", "Redis", "ApiKey", "AzureOpenAI", "CognitiveSearch", "CognitiveService",
"CustomKeys", "AzureBlob", "AzureOneLake", "CosmosDb", "CosmosDbMongoDbApi",
"AzureDataExplorer", "AzureMariaDb", "AzureDatabricksDeltaLake", "AzureSqlMi",
"AzureTableStorage", "AmazonRdsForOracle", "AmazonRdsForSqlServer", "AmazonRedshift", "Db2",
"Drill", "GoogleBigQuery", "Greenplum", "Hbase", "Hive", "Impala", "Informix", "MariaDb",
"MicrosoftAccess", "MySql", "Netezza", "Oracle", "Phoenix", "PostgreSql", "Presto",
"SapOpenHub", "SapBw", "SapHana", "SapTable", "Spark", "SqlServer", "Sybase", "Teradata",
"Vertica", "Cassandra", "Couchbase", "MongoDbV2", "MongoDbAtlas", "AmazonS3Compatible",
"FileServer", "FtpServer", "GoogleCloudStorage", "Hdfs", "OracleCloudStorage", "Sftp",
"GenericHttp", "ODataRest", "Odbc", "GenericRest", "AmazonMws", "Concur", "Dynamics",
"DynamicsAx", "DynamicsCrm", "GoogleAdWords", "Hubspot", "Jira", "Magento", "Marketo",
"Office365", "Eloqua", "Responsys", "OracleServiceCloud", "PayPal", "QuickBooks", "Salesforce",
"SalesforceServiceCloud", "SalesforceMarketingCloud", "SapCloudForCustomer", "SapEcc",
"ServiceNow", "SharePointOnlineList", "Shopify", "Square", "WebTable", "Xero", "Zoho",
"GenericContainerRegistry".
:vartype connection_category: str or ~flow.models.ConnectionCategory
:ivar flow_value_type: Possible values include: "int", "double", "bool", "string", "secret",
"prompt_template", "object", "list", "BingConnection", "OpenAIConnection",
"AzureOpenAIConnection", "AzureContentModeratorConnection", "CustomConnection",
"AzureContentSafetyConnection", "SerpConnection", "CognitiveSearchConnection",
"SubstrateLLMConnection", "PineconeConnection", "QdrantConnection", "WeaviateConnection",
"function_list", "function_str", "FormRecognizerConnection", "file_path", "image".
:vartype flow_value_type: str or ~flow.models.ValueType
:ivar connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:vartype connection_type: str or ~flow.models.ConnectionType
:ivar connection_type_display_name:
:vartype connection_type_display_name: str
:ivar config_specs:
:vartype config_specs: list[~flow.models.ConnectionConfigSpec]
:ivar module:
:vartype module: str
"""
_attribute_map = {
'connection_category': {'key': 'connectionCategory', 'type': 'str'},
'flow_value_type': {'key': 'flowValueType', 'type': 'str'},
'connection_type': {'key': 'connectionType', 'type': 'str'},
'connection_type_display_name': {'key': 'connectionTypeDisplayName', 'type': 'str'},
'config_specs': {'key': 'configSpecs', 'type': '[ConnectionConfigSpec]'},
'module': {'key': 'module', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
"""
:keyword connection_category: Possible values include: "PythonFeed", "ACR", "Git", "S3",
"Snowflake", "AzureSqlDb", "AzureSynapseAnalytics", "AzureMySqlDb", "AzurePostgresDb",
"AzureDataLakeGen2", "Redis", "ApiKey", "AzureOpenAI", "CognitiveSearch", "CognitiveService",
"CustomKeys", "AzureBlob", "AzureOneLake", "CosmosDb", "CosmosDbMongoDbApi",
"AzureDataExplorer", "AzureMariaDb", "AzureDatabricksDeltaLake", "AzureSqlMi",
"AzureTableStorage", "AmazonRdsForOracle", "AmazonRdsForSqlServer", "AmazonRedshift", "Db2",
"Drill", "GoogleBigQuery", "Greenplum", "Hbase", "Hive", "Impala", "Informix", "MariaDb",
"MicrosoftAccess", "MySql", "Netezza", "Oracle", "Phoenix", "PostgreSql", "Presto",
"SapOpenHub", "SapBw", "SapHana", "SapTable", "Spark", "SqlServer", "Sybase", "Teradata",
"Vertica", "Cassandra", "Couchbase", "MongoDbV2", "MongoDbAtlas", "AmazonS3Compatible",
"FileServer", "FtpServer", "GoogleCloudStorage", "Hdfs", "OracleCloudStorage", "Sftp",
"GenericHttp", "ODataRest", "Odbc", "GenericRest", "AmazonMws", "Concur", "Dynamics",
"DynamicsAx", "DynamicsCrm", "GoogleAdWords", "Hubspot", "Jira", "Magento", "Marketo",
"Office365", "Eloqua", "Responsys", "OracleServiceCloud", "PayPal", "QuickBooks", "Salesforce",
"SalesforceServiceCloud", "SalesforceMarketingCloud", "SapCloudForCustomer", "SapEcc",
"ServiceNow", "SharePointOnlineList", "Shopify", "Square", "WebTable", "Xero", "Zoho",
"GenericContainerRegistry".
:paramtype connection_category: str or ~flow.models.ConnectionCategory
:keyword flow_value_type: Possible values include: "int", "double", "bool", "string", "secret",
"prompt_template", "object", "list", "BingConnection", "OpenAIConnection",
"AzureOpenAIConnection", "AzureContentModeratorConnection", "CustomConnection",
"AzureContentSafetyConnection", "SerpConnection", "CognitiveSearchConnection",
"SubstrateLLMConnection", "PineconeConnection", "QdrantConnection", "WeaviateConnection",
"function_list", "function_str", "FormRecognizerConnection", "file_path", "image".
:paramtype flow_value_type: str or ~flow.models.ValueType
:keyword connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:paramtype connection_type: str or ~flow.models.ConnectionType
:keyword connection_type_display_name:
:paramtype connection_type_display_name: str
:keyword config_specs:
:paramtype config_specs: list[~flow.models.ConnectionConfigSpec]
:keyword module:
:paramtype module: str
"""
super(WorkspaceConnectionSpec, self).__init__(**kwargs)
self.connection_category = kwargs.get('connection_category', None)
self.flow_value_type = kwargs.get('flow_value_type', None)
self.connection_type = kwargs.get('connection_type', None)
self.connection_type_display_name = kwargs.get('connection_type_display_name', None)
self.config_specs = kwargs.get('config_specs', None)
self.module = kwargs.get('module', None)
| promptflow/src/promptflow/promptflow/azure/_restclient/flow/models/_models.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_restclient/flow/models/_models.py",
"repo_id": "promptflow",
"token_count": 668286
} | 49 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from threading import Lock
from promptflow.azure._restclient.flow_service_caller import FlowServiceCaller
class _FlowServiceCallerFactory:
caller_cache_by_workspace_id = {}
_instance_lock = Lock()
@classmethod
def get_instance(cls, workspace, credential, operation_scope, region=None, **kwargs) -> FlowServiceCaller:
"""Get instance of flow service caller.
:param workspace: workspace
"""
cache_id = workspace.id if workspace else region
cache = cls.caller_cache_by_workspace_id
if cache_id not in cache:
with _FlowServiceCallerFactory._instance_lock:
if cache_id not in cache:
cache[cache_id] = FlowServiceCaller(workspace, credential=credential, operation_scope=operation_scope, region=region, **kwargs)
return cache[cache_id]
| promptflow/src/promptflow/promptflow/azure/_restclient/service_caller_factory.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_restclient/service_caller_factory.py",
"repo_id": "promptflow",
"token_count": 347
} | 50 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
# flake8: noqa
from ._base_executor_proxy import AbstractExecutorProxy, APIBasedExecutorProxy
from ._batch_engine import BatchEngine
from ._csharp_executor_proxy import CSharpExecutorProxy
from ._python_executor_proxy import PythonExecutorProxy
from ._result import BatchResult
__all__ = [
"AbstractExecutorProxy",
"APIBasedExecutorProxy",
"BatchEngine",
"CSharpExecutorProxy",
"PythonExecutorProxy",
"BatchResult",
]
| promptflow/src/promptflow/promptflow/batch/__init__.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/batch/__init__.py",
"repo_id": "promptflow",
"token_count": 170
} | 51 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
import logging
from dataclasses import asdict, dataclass
from enum import Enum
from typing import Any, Dict, List, Optional, Type, TypeVar
from promptflow._constants import CONNECTION_NAME_PROPERTY
from .multimedia import Image
from .types import AssistantDefinition, FilePath, PromptTemplate, Secret
logger = logging.getLogger(__name__)
T = TypeVar("T", bound="Enum")
def _deserialize_enum(cls: Type[T], val) -> T:
if not all(isinstance(i.value, str) for i in cls):
return val
typ = next((i for i in cls if val.lower() == i.value.lower()), None)
# Keep string value for unknown type, as they may be resolved later after some requisites imported.
# Type resolve will be ensured in 'ensure_node_inputs_type' before execution.
return typ if typ else val
class ValueType(str, Enum):
"""Value types."""
INT = "int"
DOUBLE = "double"
BOOL = "bool"
STRING = "string"
SECRET = "secret"
PROMPT_TEMPLATE = "prompt_template"
LIST = "list"
OBJECT = "object"
FILE_PATH = "file_path"
IMAGE = "image"
ASSISTANT_DEFINITION = "assistant_definition"
@staticmethod
def from_value(t: Any) -> "ValueType":
"""Get :class:`~promptflow.contracts.tool.ValueType` by value.
:param t: The value needs to get its :class:`~promptflow.contracts.tool.ValueType`
:type t: Any
:return: The :class:`~promptflow.contracts.tool.ValueType` of the given value
:rtype: ~promptflow.contracts.tool.ValueType
"""
if isinstance(t, Secret):
return ValueType.SECRET
if isinstance(t, PromptTemplate):
return ValueType.PROMPT_TEMPLATE
if isinstance(t, bool):
return ValueType.BOOL
if isinstance(t, int):
return ValueType.INT
if isinstance(t, float):
return ValueType.DOUBLE
# FilePath is a subclass of str, so it must be checked before str
if isinstance(t, FilePath):
return ValueType.FILE_PATH
if isinstance(t, str):
return ValueType.STRING
if isinstance(t, list):
return ValueType.LIST
if isinstance(t, AssistantDefinition):
return ValueType.ASSISTANT_DEFINITION
return ValueType.OBJECT
@staticmethod
def from_type(t: type) -> "ValueType":
"""Get :class:`~promptflow.contracts.tool.ValueType` by type.
:param t: The type needs to get its :class:`~promptflow.contracts.tool.ValueType`
:type t: type
:return: The :class:`~promptflow.contracts.tool.ValueType` of the given type
:rtype: ~promptflow.contracts.tool.ValueType
"""
if t == int:
return ValueType.INT
if t == float:
return ValueType.DOUBLE
if t == bool:
return ValueType.BOOL
if t == str:
return ValueType.STRING
if t == list:
return ValueType.LIST
if t == Secret:
return ValueType.SECRET
if t == PromptTemplate:
return ValueType.PROMPT_TEMPLATE
if t == FilePath:
return ValueType.FILE_PATH
if t == Image:
return ValueType.IMAGE
if t == AssistantDefinition:
return ValueType.ASSISTANT_DEFINITION
return ValueType.OBJECT
def parse(self, v: Any) -> Any: # noqa: C901
"""Parse value to the given :class:`~promptflow.contracts.tool.ValueType`.
:param v: The value needs to be parsed to the given :class:`~promptflow.contracts.tool.ValueType`
:type v: Any
:return: The parsed value
:rtype: Any
"""
if self == ValueType.INT:
return int(v)
if self == ValueType.DOUBLE:
return float(v)
if self == ValueType.BOOL:
if isinstance(v, bool):
return v
if isinstance(v, str) and v.lower() in {"true", "false"}:
return v.lower() == "true"
raise ValueError(f"Invalid boolean value {v!r}")
if self == ValueType.STRING:
return str(v)
if self == ValueType.LIST:
if isinstance(v, str):
v = json.loads(v)
if not isinstance(v, list):
raise ValueError(f"Invalid list value {v!r}")
return v
if self == ValueType.OBJECT:
if isinstance(v, str):
try:
return json.loads(v)
except Exception:
# Ignore the exception since it might really be a string
pass
# TODO: parse other types
return v
class ConnectionType:
"""This class provides methods to interact with connection types."""
@staticmethod
def get_connection_class(type_name: str) -> Optional[type]:
"""Get connection type by type name.
:param type_name: The type name of the connection
:type type_name: str
:return: The connection type
:rtype: type
"""
# Note: This function must be called after ensure_flow_valid, as required modules may not be imported yet,
# and connections may not be registered yet.
from promptflow._core.tools_manager import connections
if not isinstance(type_name, str):
return None
return connections.get(type_name)
@staticmethod
def is_connection_class_name(type_name: str) -> bool:
"""Check if the given type name is a connection type.
:param type_name: The type name of the connection
:type type_name: str
:return: Whether the given type name is a connection type
:rtype: bool
"""
return ConnectionType.get_connection_class(type_name) is not None
@staticmethod
def is_connection_value(val: Any) -> bool:
"""Check if the given value is a connection.
:param val: The value to check
:type val: Any
:return: Whether the given value is a connection
:rtype: bool
"""
# Note: This function must be called after ensure_flow_valid, as required modules may not be imported yet,
# and connections may not be registered yet.
from promptflow._core.tools_manager import connections
val = type(val) if not isinstance(val, type) else val
return val in connections.values() or ConnectionType.is_custom_strong_type(val)
@staticmethod
def is_custom_strong_type(val: Any) -> bool:
"""Check if the given value is a custom strong type connection.
:param val: The value to check
:type val: Any
:return: Whether the given value is a custom strong type
:rtype: bool
"""
from promptflow.connections import CustomStrongTypeConnection
val = type(val) if not isinstance(val, type) else val
try:
return issubclass(val, CustomStrongTypeConnection)
except TypeError as e:
# TypeError is not expected to happen, but if it does, we will log it for debugging and return False.
# The try-except block cannot be confidently removed due to the uncertainty of TypeError that may occur.
logger.warning(f"Failed to check if {val} is a custom strong type: {e}")
return False
@staticmethod
def serialize_conn(connection: Any) -> dict:
"""Serialize the given connection.
:param connection: The connection to serialize
:type connection: Any
:return: A dictionary representation of the connection.
:rtype: dict
"""
if not ConnectionType.is_connection_value(connection):
raise ValueError(f"Invalid connection value {connection!r}")
return getattr(connection, CONNECTION_NAME_PROPERTY, type(connection).__name__)
class ToolType(str, Enum):
"""Tool types."""
LLM = "llm"
PYTHON = "python"
CSHARP = "csharp"
PROMPT = "prompt"
_ACTION = "action"
CUSTOM_LLM = "custom_llm"
@dataclass
class InputDefinition:
"""Input definition."""
type: List[ValueType]
default: str = None
description: str = None
enum: List[str] = None
# Param 'custom_type' is currently used for inputs of custom strong type connection.
# For a custom strong type connection input, the type should be 'CustomConnection',
# while the custom_type should be the custom strong type connection class name.
custom_type: List[str] = None
def serialize(self) -> dict:
"""Serialize input definition to dict.
:return: The serialized input definition
:rtype: dict
"""
data = {}
data["type"] = [t.value for t in self.type]
if len(self.type) == 1:
data["type"] = self.type[0].value
if self.default:
data["default"] = str(self.default)
if self.description:
data["description"] = self.description
if self.enum:
data["enum"] = self.enum
if self.custom_type:
data["custom_type"] = self.custom_type
return data
@staticmethod
def deserialize(data: dict) -> "InputDefinition":
"""Deserialize dict to input definition.
:param data: The dict needs to be deserialized
:type data: dict
:return: The deserialized input definition
:rtype: ~promptflow.contracts.tool.InputDefinition
"""
def _deserialize_type(v):
v = [v] if not isinstance(v, list) else v
# Note: Connection type will be keep as string value,
# as they may be resolved later after some requisites imported.
return [_deserialize_enum(ValueType, item) for item in v]
return InputDefinition(
_deserialize_type(data["type"]),
data.get("default", ""),
data.get("description", ""),
data.get("enum", []),
data.get("custom_type", []),
)
def to_flow_input_definition(self):
""" Used for eager flow to convert input definition to flow input definition.
"""
from .flow import FlowInputDefinition
# TODO: To align with tool resolver we respect the first type if multiple types are provided,
# still need more discussion on this. Should we raise error if multiple types are provided?
return FlowInputDefinition(
type=self.type[0], default=self.default, description=self.description, enum=self.enum
)
@dataclass
class OutputDefinition:
"""Output definition."""
type: List["ValueType"]
description: str = ""
is_property: bool = False
def serialize(self) -> dict:
"""Serialize output definition to dict.
:return: The serialized output definition
:rtype: dict
"""
data = {"type": [t.value for t in self.type], "is_property": self.is_property}
if len(data["type"]) == 1:
data["type"] = data["type"][0]
if self.description:
data["description"] = self.description
return data
@staticmethod
def deserialize(data: dict) -> "OutputDefinition":
"""Deserialize dict to output definition.
:param data: The dict needs to be deserialized
:type data: dict
:return: The deserialized output definition
:rtype: ~promptflow.contracts.tool.OutputDefinition
"""
return OutputDefinition(
[ValueType(t) for t in data["type"]] if isinstance(data["type"], list) else [ValueType(data["type"])],
data.get("description", ""),
data.get("is_property", False),
)
@dataclass
class Tool:
"""Tool definition.
:param name: The name of the tool
:type name: str
:param type: The type of the tool
:type type: ~promptflow.contracts.tool.ToolType
:param inputs: The inputs of the tool
:type inputs: Dict[str, ~promptflow.contracts.tool.InputDefinition]
:param outputs: The outputs of the tool
:type outputs: Optional[Dict[str, ~promptflow.contracts.tool.OutputDefinition]]
:param description: The description of the tool
:type description: Optional[str]
:param module: The module of the tool
:type module: Optional[str]
:param class_name: The class name of the tool
:type class_name: Optional[str]
:param source: The source of the tool
:type source: Optional[str]
:param code: The code of the tool
:type code: Optional[str]
:param function: The function of the tool
:type function: Optional[str]
:param connection_type: The connection type of the tool
:type connection_type: Optional[List[str]]
:param is_builtin: Whether the tool is a built-in tool
:type is_builtin: Optional[bool]
:param stage: The stage of the tool
:type stage: Optional[str]
:param enable_kwargs: Whether to enable kwargs, only available for customer python tool
:type enable_kwargs: Optional[bool]
:param deprecated_tools: A list of old tool IDs that are mapped to the current tool ID.
:type deprecated_tools: Optional[List[str]]
"""
name: str
type: ToolType
inputs: Dict[str, InputDefinition]
outputs: Optional[Dict[str, OutputDefinition]] = None
description: Optional[str] = None
module: Optional[str] = None
class_name: Optional[str] = None
source: Optional[str] = None
code: Optional[str] = None
function: Optional[str] = None
connection_type: Optional[List[str]] = None
is_builtin: Optional[bool] = None
stage: Optional[str] = None
enable_kwargs: Optional[bool] = False
deprecated_tools: Optional[List[str]] = None
def serialize(self) -> dict:
"""Serialize tool to dict and skip None fields.
:return: The serialized tool
:rtype: dict
"""
data = asdict(self, dict_factory=lambda x: {k: v for (k, v) in x if v is not None and k != "outputs"})
if not self.type == ToolType._ACTION:
return data
# Pop unused field for action
skipped_fields = ["type", "inputs", "outputs"]
return {k: v for k, v in data.items() if k not in skipped_fields}
@staticmethod
def deserialize(data: dict) -> "Tool":
"""Deserialize dict to tool.
:param data: The dict needs to be deserialized
:type data: dict
:return: The deserialized tool
:rtype: ~promptflow.contracts.tool.Tool
"""
return Tool(
name=data["name"],
description=data.get("description", ""),
type=_deserialize_enum(ToolType, data["type"]),
inputs={k: InputDefinition.deserialize(i) for k, i in data.get("inputs", {}).items()},
outputs={k: OutputDefinition.deserialize(o) for k, o in data.get("outputs", {}).items()},
module=data.get("module"),
class_name=data.get("class_name"),
source=data.get("source"),
code=data.get("code"),
function=data.get("function"),
connection_type=data.get("connection_type"),
is_builtin=data.get("is_builtin"),
stage=data.get("stage"),
enable_kwargs=data.get("enable_kwargs", False),
deprecated_tools=data.get("deprecated_tools"),
)
def _require_connection(self) -> bool:
return self.type is ToolType.LLM or isinstance(self.connection_type, list) and len(self.connection_type) > 0
class ToolFuncCallScenario(str, Enum):
GENERATED_BY = "generated_by"
REVERSE_GENERATED_BY = "reverse_generated_by"
DYNAMIC_LIST = "dynamic_list"
| promptflow/src/promptflow/promptflow/contracts/tool.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/contracts/tool.py",
"repo_id": "promptflow",
"token_count": 6405
} | 52 |
import asyncio
import inspect
import uuid
from pathlib import Path
from typing import Any, Callable, Mapping, Optional
from promptflow._constants import LINE_NUMBER_KEY
from promptflow._core.operation_context import OperationContext
from promptflow._core.run_tracker import RunTracker
from promptflow._core.tool_meta_generator import PythonLoadError, load_python_module_from_file
from promptflow._core.tracer import _traced, Tracer
from promptflow._utils.dataclass_serializer import convert_eager_flow_output_to_dict
from promptflow._utils.logger_utils import logger
from promptflow._utils.tool_utils import function_to_interface
from promptflow.contracts.flow import Flow
from promptflow.contracts.run_mode import RunMode
from promptflow.executor._result import LineResult
from promptflow.storage import AbstractRunStorage
from promptflow.storage._run_storage import DefaultRunStorage
from .flow_executor import FlowExecutor
class ScriptExecutor(FlowExecutor):
def __init__(
self,
flow_file: Path,
entry: str,
connections: Optional[dict] = None,
working_dir: Optional[Path] = None,
*,
storage: Optional[AbstractRunStorage] = None,
):
logger.debug(f"Start initializing the executor with {flow_file}.")
self._flow_file = flow_file
# TODO: Refine the logic here
m = load_python_module_from_file(flow_file)
func: Callable = getattr(m, entry, None)
if func is None or not inspect.isfunction(func):
raise PythonLoadError(
message_format="Failed to load python function '{entry}' from file '{flow_file}'.",
entry=entry,
flow_file=flow_file,
)
# If the function is not decorated with trace, add trace for it.
if not hasattr(func, "__original_function"):
func = _traced(func)
inputs, _, _, _ = function_to_interface(func)
self._func = func
self._inputs = {k: v.to_flow_input_definition() for k, v in inputs.items()}
self._entry = entry
self._is_async = inspect.iscoroutinefunction(self._func)
self._connections = connections
self._working_dir = Flow._resolve_working_dir(flow_file, working_dir)
self._storage = storage or DefaultRunStorage()
self._flow_id = None
self._log_interval = 60
self._line_timeout_sec = 600
def exec_line(
self,
inputs: Mapping[str, Any],
index: Optional[int] = None,
run_id: Optional[str] = None,
**kwargs,
) -> LineResult:
operation_context = OperationContext.get_instance()
operation_context.run_mode = operation_context.get("run_mode", None) or RunMode.Test.name
run_id = run_id or str(uuid.uuid4())
line_run_id = run_id if index is None else f"{run_id}_{index}"
default_flow_id = "default_flow_id"
run_tracker = RunTracker(self._storage)
run_info = run_tracker.start_flow_run(
flow_id=default_flow_id,
root_run_id=run_id,
run_id=line_run_id,
parent_run_id=run_id,
inputs=inputs,
index=index,
)
# Executor will add line_number to batch inputs if there is no line_number in the original inputs,
# which should be removed, so, we only preserve the inputs that are contained in self._inputs.
inputs = {k: inputs[k] for k in self._inputs if k in inputs}
output = None
traces = []
try:
Tracer.start_tracing(line_run_id)
if self._is_async:
output = asyncio.run(self._func(**inputs))
else:
output = self._func(**inputs)
traces = Tracer.end_tracing(line_run_id)
# Should convert output to dict before storing it to run info, since we will add key 'line_number' to it,
# so it must be a dict.
output_dict = convert_eager_flow_output_to_dict(output)
run_tracker.end_run(line_run_id, result=output_dict, traces=traces)
except Exception as e:
if not traces:
traces = Tracer.end_tracing(line_run_id)
run_tracker.end_run(line_run_id, ex=e, traces=traces)
finally:
run_tracker.persist_flow_run(run_info)
line_result = LineResult(output, {}, run_info, {})
# Return line result with index
if index is not None and isinstance(line_result.output, dict):
line_result.output[LINE_NUMBER_KEY] = index
return line_result
def enable_streaming_for_llm_flow(self, stream_required: Callable[[], bool]):
# TODO(2901157): check if eager mode should have streaming
return
def get_inputs_definition(self):
return self._inputs
| promptflow/src/promptflow/promptflow/executor/_script_executor.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/executor/_script_executor.py",
"repo_id": "promptflow",
"token_count": 2020
} | 53 |
import importlib
import json
import os
import tempfile
from multiprocessing import Lock
from pathlib import Path
from unittest.mock import MagicMock, patch
import pytest
from _constants import (
CONNECTION_FILE,
DEFAULT_REGISTRY_NAME,
DEFAULT_RESOURCE_GROUP_NAME,
DEFAULT_RUNTIME_NAME,
DEFAULT_SUBSCRIPTION_ID,
DEFAULT_WORKSPACE_NAME,
ENV_FILE,
)
from _pytest.monkeypatch import MonkeyPatch
from dotenv import load_dotenv
from filelock import FileLock
from pytest_mock import MockerFixture
from sdk_cli_azure_test.recording_utilities import SanitizedValues, is_replay
from promptflow._cli._utils import AzureMLWorkspaceTriad
from promptflow._constants import PROMPTFLOW_CONNECTIONS
from promptflow._core.connection_manager import ConnectionManager
from promptflow._core.openai_injector import inject_openai_api
from promptflow._utils.context_utils import _change_working_dir
from promptflow.connections import AzureOpenAIConnection
load_dotenv()
@pytest.fixture(scope="session", autouse=True)
def modify_work_directory():
os.chdir(Path(__file__).parent.parent.absolute())
@pytest.fixture(autouse=True, scope="session")
def mock_build_info():
"""Mock BUILD_INFO environment variable in pytest.
BUILD_INFO is set as environment variable in docker image, but not in local test.
So we need to mock it in test senario. Rule - build_number is set as
ci-<BUILD_BUILDNUMBER> in CI pipeline, and set as local in local dev test."""
if "BUILD_INFO" not in os.environ:
m = MonkeyPatch()
build_number = os.environ.get("BUILD_BUILDNUMBER", "")
buid_info = {"build_number": f"ci-{build_number}" if build_number else "local-pytest"}
m.setenv("BUILD_INFO", json.dumps(buid_info))
yield m
@pytest.fixture(autouse=True, scope="session")
def inject_api():
"""Inject OpenAI API during test session.
AOAI call in promptflow should involve trace logging and header injection. Inject
function to API call in test scenario."""
inject_openai_api()
@pytest.fixture
def dev_connections() -> dict:
with open(CONNECTION_FILE, "r") as f:
return json.load(f)
@pytest.fixture
def use_secrets_config_file(mocker: MockerFixture):
mocker.patch.dict(os.environ, {PROMPTFLOW_CONNECTIONS: CONNECTION_FILE})
@pytest.fixture
def env_with_secrets_config_file():
_lock = Lock()
with _lock:
with open(ENV_FILE, "w") as f:
f.write(f"{PROMPTFLOW_CONNECTIONS}={CONNECTION_FILE}\n")
yield ENV_FILE
if os.path.exists(ENV_FILE):
os.remove(ENV_FILE)
@pytest.fixture
def azure_open_ai_connection() -> AzureOpenAIConnection:
return ConnectionManager().get("azure_open_ai_connection")
@pytest.fixture
def temp_output_dir() -> str:
with tempfile.TemporaryDirectory() as temp_dir:
yield temp_dir
@pytest.fixture
def prepare_symbolic_flow() -> str:
flows_dir = Path(__file__).parent / "test_configs" / "flows"
target_folder = flows_dir / "web_classification_with_symbolic"
source_folder = flows_dir / "web_classification"
with _change_working_dir(target_folder):
for file_name in os.listdir(source_folder):
if not Path(file_name).exists():
os.symlink(source_folder / file_name, file_name)
return target_folder
@pytest.fixture(scope="session")
def install_custom_tool_pkg():
# The tests could be running in parallel. Use a lock to prevent race conditions.
lock = FileLock("custom_tool_pkg_installation.lock")
with lock:
try:
import my_tool_package # noqa: F401
except ImportError:
import subprocess
import sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "test-custom-tools==0.0.2"])
@pytest.fixture
def mocked_ws_triple() -> AzureMLWorkspaceTriad:
return AzureMLWorkspaceTriad("mock_subscription_id", "mock_resource_group", "mock_workspace_name")
@pytest.fixture(scope="session")
def mock_list_func():
"""Mock function object for dynamic list testing."""
def my_list_func(prefix: str = "", size: int = 10, **kwargs):
return [
{
"value": "fig0",
"display_value": "My_fig0",
"hyperlink": "https://www.bing.com/search?q=fig0",
"description": "this is 0 item",
},
{
"value": "kiwi1",
"display_value": "My_kiwi1",
"hyperlink": "https://www.bing.com/search?q=kiwi1",
"description": "this is 1 item",
},
]
return my_list_func
@pytest.fixture(scope="session")
def mock_module_with_list_func(mock_list_func):
"""Mock module object for dynamic list testing."""
mock_module = MagicMock()
mock_module.my_list_func = mock_list_func
mock_module.my_field = 1
original_import_module = importlib.import_module # Save this to prevent recursion
with patch.object(importlib, "import_module") as mock_import:
def side_effect(module_name, *args, **kwargs):
if module_name == "my_tool_package.tools.tool_with_dynamic_list_input":
return mock_module
else:
return original_import_module(module_name, *args, **kwargs)
mock_import.side_effect = side_effect
yield
# below fixtures are used for pfazure and global config tests
@pytest.fixture(scope="session")
def subscription_id() -> str:
if is_replay():
return SanitizedValues.SUBSCRIPTION_ID
else:
return os.getenv("PROMPT_FLOW_SUBSCRIPTION_ID", DEFAULT_SUBSCRIPTION_ID)
@pytest.fixture(scope="session")
def resource_group_name() -> str:
if is_replay():
return SanitizedValues.RESOURCE_GROUP_NAME
else:
return os.getenv("PROMPT_FLOW_RESOURCE_GROUP_NAME", DEFAULT_RESOURCE_GROUP_NAME)
@pytest.fixture(scope="session")
def workspace_name() -> str:
if is_replay():
return SanitizedValues.WORKSPACE_NAME
else:
return os.getenv("PROMPT_FLOW_WORKSPACE_NAME", DEFAULT_WORKSPACE_NAME)
@pytest.fixture(scope="session")
def runtime_name() -> str:
return os.getenv("PROMPT_FLOW_RUNTIME_NAME", DEFAULT_RUNTIME_NAME)
@pytest.fixture(scope="session")
def registry_name() -> str:
return os.getenv("PROMPT_FLOW_REGISTRY_NAME", DEFAULT_REGISTRY_NAME)
@pytest.fixture
def enable_logger_propagate():
"""This is for test cases that need to check the log output."""
from promptflow._utils.logger_utils import get_cli_sdk_logger
logger = get_cli_sdk_logger()
original_value = logger.propagate
logger.propagate = True
yield
logger.propagate = original_value
| promptflow/src/promptflow/tests/conftest.py/0 | {
"file_path": "promptflow/src/promptflow/tests/conftest.py",
"repo_id": "promptflow",
"token_count": 2688
} | 54 |
from pathlib import Path
from tempfile import mkdtemp
import pytest
from promptflow._utils.logger_utils import LogContext
from promptflow.batch import BatchEngine
from promptflow.batch._result import BatchResult
from promptflow.contracts.run_info import Status
from promptflow.contracts.run_mode import RunMode
from promptflow.executor import FlowExecutor
from ..utils import (
get_flow_folder,
get_flow_inputs_file,
get_flow_sample_inputs,
get_yaml_file,
load_content,
load_jsonl,
)
TEST_LOGS_FLOW = ["print_input_flow"]
SAMPLE_FLOW_WITH_TEN_INPUTS = "simple_flow_with_ten_inputs"
OUTPUT_FILE_NAME = "output.jsonl"
def submit_batch_run(
flow_folder,
inputs_mapping,
*,
input_dirs={},
input_file_name="samples.json",
run_id=None,
connections={},
storage=None,
return_output_dir=False,
):
batch_engine = BatchEngine(
get_yaml_file(flow_folder), get_flow_folder(flow_folder), connections=connections, storage=storage
)
if not input_dirs and inputs_mapping:
input_dirs = {"data": get_flow_inputs_file(flow_folder, file_name=input_file_name)}
output_dir = Path(mkdtemp())
if return_output_dir:
return batch_engine.run(input_dirs, inputs_mapping, output_dir, run_id=run_id), output_dir
return batch_engine.run(input_dirs, inputs_mapping, output_dir, run_id=run_id)
def get_batch_inputs_line(flow_folder, sample_inputs_file="samples.json"):
inputs = get_flow_sample_inputs(flow_folder, sample_inputs_file=sample_inputs_file)
return len(inputs)
@pytest.mark.usefixtures("dev_connections")
@pytest.mark.e2etest
class TestExecutorLogs:
def assert_node_run_info(self, node_run_info, content):
assert node_run_info.status == Status.Completed
assert content in node_run_info.logs["stdout"]
assert "STDOUT:" in node_run_info.logs["stdout"]
assert content in node_run_info.logs["stderr"]
assert "STDERR:" in node_run_info.logs["stderr"]
def assert_flow_result(self, flow_result, content):
assert isinstance(flow_result.output, dict)
assert flow_result.run_info.status == Status.Completed
for node_run_info in flow_result.node_run_infos.values():
self.assert_node_run_info(node_run_info, content)
def submit_bulk_run(self, folder_name):
batch_engine = BatchEngine(get_yaml_file(folder_name), get_flow_folder(folder_name), connections={})
input_dirs = {"data": get_flow_inputs_file(folder_name)}
inputs_mapping = {"text": "${data.text}"}
output_dir = Path(mkdtemp())
return batch_engine.run(input_dirs, inputs_mapping, output_dir)
@pytest.mark.parametrize(
"folder_name",
TEST_LOGS_FLOW,
)
def test_node_logs(self, folder_name):
# Test node logs in flow run
executor = FlowExecutor.create(get_yaml_file(folder_name), {})
content = "line_text"
flow_result = executor.exec_line({"text": content})
node_run_ids = [node_run_info.run_id for node_run_info in flow_result.node_run_infos.values()]
for node_run_id in node_run_ids:
logs = executor._run_tracker.node_log_manager.get_logs(node_run_id)
assert logs["stderr"] is None and logs["stdout"] is None, f"Logs for node {node_run_id} is cleared."
self.assert_flow_result(flow_result, content)
# Test node logs in single node run
content = "single_node_text"
node_run_info = FlowExecutor.load_and_exec_node(
get_yaml_file(folder_name),
"print_input",
flow_inputs={"text": content},
)
self.assert_node_run_info(node_run_info, content)
@pytest.mark.parametrize(
"folder_name",
TEST_LOGS_FLOW,
)
def test_executor_logs(self, folder_name):
logs_directory = Path(mkdtemp())
flow_run_log_path = str(logs_directory / "test_flow_run.log")
bulk_run_log_path = str(logs_directory / "test_bulk_run.log")
# flow run: test exec_line
with LogContext(flow_run_log_path):
executor = FlowExecutor.create(get_yaml_file(folder_name), {})
executor.exec_line({"text": "line_text"})
log_content = load_content(flow_run_log_path)
loggers_name_list = ["execution", "execution.flow"]
assert all(logger in log_content for logger in loggers_name_list)
# bulk run: test batch_engine.run
# setting run_mode to BulkTest is a requirement to use bulk_logger
with LogContext(bulk_run_log_path, run_mode=RunMode.Batch):
self.submit_bulk_run(folder_name)
log_content = load_content(bulk_run_log_path)
loggers_name_list = ["execution", "execution.bulk"]
# bulk logger will print the average execution time and estimated time
bulk_logs_keywords = ["Average execution time for completed lines", "Estimated time for incomplete lines"]
assert all(logger in log_content for logger in loggers_name_list)
assert all(keyword in log_content for keyword in bulk_logs_keywords)
@pytest.mark.parametrize(
"folder_name",
TEST_LOGS_FLOW,
)
def test_node_logs_in_executor_logs(self, folder_name):
logs_directory = Path(mkdtemp())
flow_run_log_path = str(logs_directory / "test_flow_run.log")
bulk_run_log_path = str(logs_directory / "test_bulk_run.log")
# flow run: test exec_line
with LogContext(flow_run_log_path, run_mode=RunMode.Test):
executor = FlowExecutor.create(get_yaml_file(folder_name), {})
executor.exec_line({"text": "line_text"})
log_content = load_content(flow_run_log_path)
node_logs_list = ["print_input in line", "stdout> STDOUT:", "stderr> STDERR:"]
assert all(node_log in log_content for node_log in node_logs_list)
# bulk run: test batch_engine.run
# setting run_mode to BulkTest is a requirement to use bulk_logger
with LogContext(bulk_run_log_path, run_mode=RunMode.Batch):
self.submit_bulk_run(folder_name)
log_content = load_content(bulk_run_log_path)
node_logs_list = ["print_input in line", "stderr> STDERR:"]
assert all(node_log in log_content for node_log in node_logs_list)
def test_long_run_log(self):
executor = FlowExecutor.create(get_yaml_file("long_run"), {})
file_path = Path(mkdtemp()) / "flow.log"
with LogContext(file_path):
flow_result = executor.exec_line({}, index=0)
node_run = flow_result.node_run_infos["long_run_node"]
assert node_run.status == Status.Completed
with open(file_path) as fin:
lines = fin.readlines()
lines = [line for line in lines if line.strip()]
target_texts = [
"INFO Start executing nodes in thread pool mode.",
"INFO Start to run 1 nodes with concurrency level 16.",
"INFO Executing node long_run_node.",
"WARNING long_run_node in line 0 has been running for 60 seconds, stacktrace of thread",
", line 16, in long_run_func",
"return f2()",
", line 11, in f2",
"return f1()",
", line 6, in f1",
"time.sleep(61)",
"INFO Node long_run_node completes.",
]
msg = f"Got {len(lines)} lines in {file_path}, expected {len(target_texts)}."
assert len(lines) == len(target_texts), msg
for actual, expected in zip(lines, target_texts):
assert expected in actual, f"Expected {expected} in {actual}"
@pytest.mark.parametrize(
"flow_folder, inputs_mapping",
[
(
SAMPLE_FLOW_WITH_TEN_INPUTS,
{"input": "${data.input}", "index": "${data.index}"},
)
],
)
def test_log_progress(self, flow_folder, inputs_mapping, dev_connections):
logs_directory = Path(mkdtemp())
bulk_run_log_path = str(logs_directory / "test_bulk_run.log")
with LogContext(bulk_run_log_path, run_mode=RunMode.Batch):
batch_result, output_dir = submit_batch_run(
flow_folder, inputs_mapping, connections=dev_connections, return_output_dir=True
)
nlines = get_batch_inputs_line(flow_folder)
log_content = load_content(bulk_run_log_path)
for i in range(1, nlines + 1):
assert f"Finished {i} / {nlines} lines." in log_content
assert isinstance(batch_result, BatchResult)
assert batch_result.total_lines == nlines
assert batch_result.completed_lines == nlines
assert batch_result.start_time < batch_result.end_time
assert batch_result.system_metrics.duration > 0
outputs = load_jsonl(output_dir / OUTPUT_FILE_NAME)
assert len(outputs) == nlines
for i, output in enumerate(outputs):
assert isinstance(output, dict)
assert "line_number" in output, f"line_number is not in {i}th output {output}"
assert output["line_number"] == i, f"line_number is not correct in {i}th output {output}"
def test_activate_config_log(self):
logs_directory = Path(mkdtemp())
log_path = str(logs_directory / "flow.log")
# flow run: test exec_line
with LogContext(log_path, run_mode=RunMode.Test):
executor = FlowExecutor.create(get_yaml_file("activate_flow"), {})
# use default inputs
executor.exec_line({})
log_content = load_content(log_path)
logs_list = [
"execution.flow",
"The node 'nodeA' will be bypassed because the activate condition is not met, "
"i.e. '${flow.text}' is not equal to 'hello'.",
"The node 'nodeB' will be bypassed because it depends on the node 'nodeA' "
"which has already been bypassed in the activate config.",
"The node 'nodeC' will be bypassed because all nodes ['nodeB'] it depends on are bypassed.",
"The node 'nodeD' will be executed because the activate condition is met, "
"i.e. '${flow.text}' is equal to 'world'.",
]
assert all(log in log_content for log in logs_list)
| promptflow/src/promptflow/tests/executor/e2etests/test_logs.py/0 | {
"file_path": "promptflow/src/promptflow/tests/executor/e2etests/test_logs.py",
"repo_id": "promptflow",
"token_count": 4611
} | 55 |
{
"tool_with_connection": {
"function": "tool_with_test_conn",
"inputs": {
"conn": {"type": ["TestConnection"]}
},
"module": "tool_with_connection",
"name": "Test Tool with Connection",
"type": "python"
}
}
| promptflow/src/promptflow/tests/executor/package_tools/tool_with_connection/package_tool_definition.json/0 | {
"file_path": "promptflow/src/promptflow/tests/executor/package_tools/tool_with_connection/package_tool_definition.json",
"repo_id": "promptflow",
"token_count": 135
} | 56 |
from pathlib import Path
from tempfile import mkdtemp
from unittest.mock import Mock, patch
import pytest
from promptflow._core._errors import UnexpectedError
from promptflow.batch import APIBasedExecutorProxy, BatchEngine, CSharpExecutorProxy, PythonExecutorProxy
from promptflow.contracts.run_info import Status
from promptflow.exceptions import ErrorTarget
from promptflow.executor._errors import ConnectionNotFound
from promptflow.executor._result import AggregationResult
from ...utils import MemoryRunStorage, get_yaml_file, load_jsonl
from .test_result import get_line_results, get_node_run_infos
@pytest.mark.unittest
class TestBatchEngine:
@pytest.mark.parametrize(
"side_effect, ex_type, ex_target, ex_codes, ex_msg",
[
(
Exception("test error"),
UnexpectedError,
ErrorTarget.BATCH,
["SystemError", "UnexpectedError"],
"Unexpected error occurred while executing the batch run. Error: (Exception) test error.",
),
(
ConnectionNotFound(message="Connection 'aoai_conn' not found"),
ConnectionNotFound,
ErrorTarget.EXECUTOR,
["UserError", "ValidationError", "InvalidRequest", "ConnectionNotFound"],
"Connection 'aoai_conn' not found",
),
],
)
def test_batch_engine_run_error(self, side_effect, ex_type, ex_target, ex_codes, ex_msg):
batch_engine = BatchEngine(get_yaml_file("print_input_flow"))
with patch("promptflow.batch._batch_engine.BatchEngine._exec_in_task") as mock_func:
mock_func.side_effect = side_effect
with patch(
"promptflow.batch._batch_inputs_processor.BatchInputsProcessor.process_batch_inputs", new=Mock()
):
with pytest.raises(ex_type) as e:
batch_engine.run({}, {}, Path("."))
assert e.value.target == ex_target
assert e.value.error_codes == ex_codes
assert e.value.message == ex_msg
def test_register_executor(self):
# assert original values
assert BatchEngine.executor_proxy_classes["python"] == PythonExecutorProxy
assert BatchEngine.executor_proxy_classes["csharp"] == CSharpExecutorProxy
class MockJSExecutorProxy(APIBasedExecutorProxy):
pass
# register new proxy
BatchEngine.register_executor("js", MockJSExecutorProxy)
assert BatchEngine.executor_proxy_classes["js"] == MockJSExecutorProxy
assert len(BatchEngine.executor_proxy_classes) == 3
def test_cancel(self):
batch_engine = BatchEngine(get_yaml_file("print_input_flow"))
assert batch_engine._is_canceled is False
batch_engine.cancel()
assert batch_engine._is_canceled is True
def test_persist_run_info(self):
line_dict = {
0: {"node_0": Status.Completed, "node_1": Status.Completed, "node_2": Status.Completed},
1: {"node_0": Status.Completed, "node_1": Status.Failed, "node_2": Status.Completed},
2: {"node_0": Status.Completed, "node_1": Status.Completed, "node_2": Status.Bypassed},
}
line_results = get_line_results(line_dict)
mem_run_storge = MemoryRunStorage()
batch_engine = BatchEngine(get_yaml_file("print_input_flow"), "", storage=mem_run_storge)
batch_engine._persist_run_info(line_results)
assert len(mem_run_storge._flow_runs) == 3
assert len(mem_run_storge._node_runs) == 9
def test_persist_outputs(self):
outputs = [
{"line_number": 0, "output": "Hello World!"},
{"line_number": 1, "output": "Hello Microsoft!"},
{"line_number": 2, "output": "Hello Promptflow!"},
]
output_dir = Path(mkdtemp())
batch_engine = BatchEngine(get_yaml_file("print_input_flow"))
batch_engine._persist_outputs(outputs, output_dir)
actual_outputs = load_jsonl(output_dir / "output.jsonl")
assert actual_outputs == outputs
def test_update_aggr_result(self):
output = {"output": "Hello World!"}
metrics = {"accuracy": 0.9}
node_run_infos = get_node_run_infos({"aggr_1": Status.Completed, "aggr_2": Status.Completed})
aggre_result = AggregationResult(output={}, metrics={}, node_run_infos={})
aggr_exec_result = AggregationResult(output=output, metrics=metrics, node_run_infos=node_run_infos)
batch_engine = BatchEngine(get_yaml_file("print_input_flow"))
batch_engine._update_aggr_result(aggre_result, aggr_exec_result)
assert aggre_result.output == output
assert aggre_result.metrics == metrics
assert aggre_result.node_run_infos == node_run_infos
| promptflow/src/promptflow/tests/executor/unittests/batch/test_batch_engine.py/0 | {
"file_path": "promptflow/src/promptflow/tests/executor/unittests/batch/test_batch_engine.py",
"repo_id": "promptflow",
"token_count": 2021
} | 57 |
import pytest
from promptflow.exceptions import PromptflowException
@pytest.mark.unittest
class TestExceptions:
def test_exception_message(self):
ex = PromptflowException(
message_format="Test exception message with parameters: {param}, {param1}.",
param="test_param",
)
assert ex.message == "Test exception message with parameters: test_param, <param1>."
assert None not in ex.message_parameters
| promptflow/src/promptflow/tests/executor/unittests/executor/test_exceptions.py/0 | {
"file_path": "promptflow/src/promptflow/tests/executor/unittests/executor/test_exceptions.py",
"repo_id": "promptflow",
"token_count": 165
} | 58 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
import os
import re
from dataclasses import dataclass
from typing import Dict
import jwt
from azure.core.credentials import AccessToken
from vcr.request import Request
from .constants import ENVIRON_TEST_MODE, SanitizedValues, TestMode
def get_test_mode_from_environ() -> str:
return os.getenv(ENVIRON_TEST_MODE, TestMode.LIVE)
def is_live() -> bool:
return get_test_mode_from_environ() == TestMode.LIVE
def is_record() -> bool:
return get_test_mode_from_environ() == TestMode.RECORD
def is_replay() -> bool:
return get_test_mode_from_environ() == TestMode.REPLAY
class FakeTokenCredential:
"""Refer from Azure SDK for Python repository.
https://github.com/Azure/azure-sdk-for-python/blob/main/tools/azure-sdk-tools/devtools_testutils/fake_credentials.py
"""
def __init__(self):
token = jwt.encode(
payload={
"aud": "https://management.azure.com",
},
key="",
)
self.token = AccessToken(token, 0)
self.get_token_count = 0
def get_token(self, *args, **kwargs) -> AccessToken:
self.get_token_count += 1
return self.token
@dataclass
class MockDatastore:
"""Mock Datastore class for `DatastoreOperations.get_default().name`."""
name: str
account_name: str
container_name: str
endpoint: str
def mock_datastore_get_default(*args, **kwargs) -> MockDatastore:
return MockDatastore(
name="workspaceblobstore",
account_name=SanitizedValues.FAKE_ACCOUNT_NAME,
container_name=SanitizedValues.FAKE_CONTAINER_NAME,
endpoint="core.windows.net",
)
def mock_workspace_get(*args, **kwargs):
from azure.ai.ml.entities import Workspace
return Workspace(
name=SanitizedValues.WORKSPACE_NAME,
resource_group=SanitizedValues.RESOURCE_GROUP_NAME,
discovery_url=SanitizedValues.DISCOVERY_URL,
workspace_id=SanitizedValues.WORKSPACE_ID,
)
def get_pf_client_for_replay():
from azure.ai.ml import MLClient
from promptflow.azure import PFClient
ml_client = MLClient(
credential=FakeTokenCredential(),
subscription_id=SanitizedValues.SUBSCRIPTION_ID,
resource_group_name=SanitizedValues.RESOURCE_GROUP_NAME,
workspace_name=SanitizedValues.WORKSPACE_NAME,
)
ml_client.datastores.get_default = mock_datastore_get_default
ml_client.workspaces.get = mock_workspace_get
return PFClient(ml_client=ml_client)
def sanitize_azure_workspace_triad(value: str) -> str:
sanitized_sub = re.sub(
"/(subscriptions)/[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}",
r"/\1/{}".format("00000000-0000-0000-0000-000000000000"),
value,
flags=re.IGNORECASE,
)
# for regex pattern for resource group name and workspace name, refer from:
# https://learn.microsoft.com/en-us/rest/api/resources/resource-groups/create-or-update?tabs=HTTP
sanitized_rg = re.sub(
r"/(resourceGroups)/[-\w\._\(\)]+",
r"/\1/{}".format("00000"),
sanitized_sub,
flags=re.IGNORECASE,
)
sanitized_ws = re.sub(
r"/(workspaces)/[-\w\._\(\)]+[/?]",
r"/\1/{}/".format("00000"),
sanitized_rg,
flags=re.IGNORECASE,
)
# workspace name can be the last part of the string
# e.g. xxx/Microsoft.MachineLearningServices/workspaces/<workspace-name>
# apply a special handle here to sanitize
if sanitized_ws.startswith("https://"):
split1, split2 = sanitized_ws.split("/")[-2:]
if split1 == "workspaces":
sanitized_ws = sanitized_ws.replace(split2, SanitizedValues.WORKSPACE_NAME)
return sanitized_ws
def sanitize_experiment_id(value: str) -> str:
value = re.sub(
r"(experimentId)=[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}",
r"\1={}".format(SanitizedValues.WORKSPACE_ID),
value,
flags=re.IGNORECASE,
)
return value
def sanitize_upload_hash(value: str) -> str:
value = re.sub(
r"(az-ml-artifacts)/([0-9a-f]{32})",
r"\1/{}".format(SanitizedValues.UPLOAD_HASH),
value,
flags=re.IGNORECASE,
)
value = re.sub(
r"(LocalUpload)/([0-9a-f]{32})",
r"\1/{}".format(SanitizedValues.UPLOAD_HASH),
value,
flags=re.IGNORECASE,
)
return value
def sanitize_username(value: str) -> str:
value = re.sub(
r"/(Users%2F)([^%?]+)(%2F|\?)",
r"/\1{}\3".format(SanitizedValues.USERNAME),
value,
flags=re.IGNORECASE,
)
value = re.sub(
r"(Users/)([^/]+)(/)",
r"\1{}\3".format(SanitizedValues.USERNAME),
value,
flags=re.IGNORECASE,
)
return value
def sanitize_flow_asset_id(value: str) -> str:
# input: azureml://locations/<region>/workspaces/<workspace-id>/flows/<flow-id>
# sanitize those with angle brackets
sanitized_region = re.sub(
r"/(locations)/[^/]+",
r"/\1/{}".format(SanitizedValues.REGION),
value,
flags=re.IGNORECASE,
)
sanitized_workspace_id = re.sub(
r"/(workspaces)/[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}",
r"/\1/{}".format(SanitizedValues.WORKSPACE_ID),
sanitized_region,
flags=re.IGNORECASE,
)
sanitized_flow_id = re.sub(
r"/(flows)/[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}",
r"/\1/{}/".format(SanitizedValues.FLOW_ID),
sanitized_workspace_id,
flags=re.IGNORECASE,
)
return sanitized_flow_id
def sanitize_pfs_request_body(body: str) -> str:
# sanitize workspace triad for longhand syntax asset, e.g. "batchDataInput.dataUri"
body = sanitize_azure_workspace_triad(body)
body_dict = json.loads(body)
# /BulkRuns/submit
if "runtimeName" in body_dict:
body_dict["runtimeName"] = SanitizedValues.RUNTIME_NAME
if "sessionId" in body_dict:
body_dict["sessionId"] = SanitizedValues.SESSION_ID
if "flowLineageId" in body:
body_dict["flowLineageId"] = SanitizedValues.FLOW_LINEAGE_ID
if "flowDefinitionResourceId" in body_dict:
body_dict["flowDefinitionResourceId"] = sanitize_flow_asset_id(body_dict["flowDefinitionResourceId"])
# PFS will help handle this field, so client does not need to pass this value
if "runExperimentName" in body:
body_dict["runExperimentName"] = ""
return json.dumps(body_dict)
def sanitize_pfs_response_body(body: str) -> str:
body_dict = json.loads(body)
# BulkRuns/{flowRunId}
if "studioPortalEndpoint" in body:
body_dict["studioPortalEndpoint"] = sanitize_azure_workspace_triad(body_dict["studioPortalEndpoint"])
return json.dumps(body_dict)
def sanitize_email(value: str) -> str:
return re.sub(r"([\w\.-]+)@(microsoft.com)", r"{}@\2".format(SanitizedValues.EMAIL_USERNAME), value)
def sanitize_file_share_flow_path(value: str) -> str:
flow_folder_name = "simple_hello_world"
if flow_folder_name not in value:
return value
start_index = value.index(flow_folder_name)
flow_name_length = 38 # len("simple_hello_world-01-01-2024-00-00-00")
flow_name = value[start_index : start_index + flow_name_length]
return value.replace(flow_name, "flow_name")
def _sanitize_session_id_creating_automatic_runtime(value: str) -> str:
value = re.sub(
"/(FlowSessions)/[0-9a-f]{48}",
r"/\1/{}".format(SanitizedValues.SESSION_ID),
value,
flags=re.IGNORECASE,
)
return value
def _sanitize_operation_id_polling_automatic_runtime(value: str) -> str:
value = re.sub(
"/(operations)/[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}",
r"/\1/{}".format(SanitizedValues.UUID),
value,
flags=re.IGNORECASE,
)
return value
def sanitize_automatic_runtime_request_path(value: str) -> str:
return _sanitize_operation_id_polling_automatic_runtime(_sanitize_session_id_creating_automatic_runtime(value))
def _is_json_payload(headers: Dict, key: str) -> bool:
if not headers:
return False
content_type = headers.get(key)
if not content_type:
return False
# content-type can be an array, e.g. ["application/json; charset=utf-8"]
content_type = content_type[0] if isinstance(content_type, list) else content_type
content_type = content_type.split(";")[0].lower()
return "application/json" in content_type
def is_json_payload_request(request: Request) -> bool:
headers = request.headers
return _is_json_payload(headers, key="Content-Type")
def is_json_payload_response(response: Dict) -> bool:
headers = response.get("headers")
# PFAzureIntegrationTestRecording will lower keys in response headers
return _is_json_payload(headers, key="content-type")
def is_httpx_response(response: Dict) -> bool:
# different from other stubs in vcrpy, httpx response uses "content" instead of "body"
# this leads to different handle logic to response
# so we need a utility to check if a response is from httpx
return "content" in response
def get_created_flow_name_from_flow_path(flow_path: str) -> str:
# pytest fixture "created_flow" will create flow on file share with timestamp as suffix
# we need to extract the flow name from the path
# flow name is expected to start with "simple_hello_world" and follow with "/flow.dag.yaml"
return flow_path[flow_path.index("simple_hello_world") : flow_path.index("/flow.dag.yaml")]
| promptflow/src/promptflow/tests/sdk_cli_azure_test/recording_utilities/utils.py/0 | {
"file_path": "promptflow/src/promptflow/tests/sdk_cli_azure_test/recording_utilities/utils.py",
"repo_id": "promptflow",
"token_count": 4229
} | 59 |
import json
import pytest
@pytest.mark.usefixtures("recording_injection")
@pytest.mark.e2etest
def test_azureml_serving_api_with_encoded_connection(flow_serving_client_with_encoded_connection):
response = flow_serving_client_with_encoded_connection.get("/health")
assert b'{"status":"Healthy","version":"0.0.1"}' in response.data
response = flow_serving_client_with_encoded_connection.post("/score", data=json.dumps({"text": "hi"}))
assert (
response.status_code == 200
), f"Response code indicates error {response.status_code} - {response.data.decode()}"
assert "output_prompt" in json.loads(response.data.decode())
| promptflow/src/promptflow/tests/sdk_cli_test/e2etests/test_flow_serve_azureml_extension.py/0 | {
"file_path": "promptflow/src/promptflow/tests/sdk_cli_test/e2etests/test_flow_serve_azureml_extension.py",
"repo_id": "promptflow",
"token_count": 229
} | 60 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import pandas as pd
import pytest
from promptflow._sdk._constants import LINE_NUMBER
from promptflow._sdk.operations._local_storage_operations import LocalStorageOperations
@pytest.mark.unittest
class TestLocalStorageOperations:
def test_outputs_padding(self) -> None:
data = [
{LINE_NUMBER: 1, "col": "a"},
{LINE_NUMBER: 2, "col": "b"},
]
df = pd.DataFrame(data)
df_with_padding = LocalStorageOperations._outputs_padding(df, inputs_line_numbers=[0, 1, 2, 3, 4])
df_with_padding.fillna("", inplace=True)
assert len(df_with_padding) == 5
assert df_with_padding.iloc[0].to_dict() == {LINE_NUMBER: 0, "col": ""}
assert df_with_padding.iloc[1].to_dict() == {LINE_NUMBER: 1, "col": "a"}
assert df_with_padding.iloc[2].to_dict() == {LINE_NUMBER: 2, "col": "b"}
assert df_with_padding.iloc[3].to_dict() == {LINE_NUMBER: 3, "col": ""}
assert df_with_padding.iloc[4].to_dict() == {LINE_NUMBER: 4, "col": ""}
# in evaluation run, inputs may not have all line number
df_with_padding = LocalStorageOperations._outputs_padding(df, inputs_line_numbers=[1, 2, 4])
df_with_padding.fillna("", inplace=True)
assert len(df_with_padding) == 3
assert df_with_padding.iloc[0].to_dict() == {LINE_NUMBER: 1, "col": "a"}
assert df_with_padding.iloc[1].to_dict() == {LINE_NUMBER: 2, "col": "b"}
assert df_with_padding.iloc[2].to_dict() == {LINE_NUMBER: 4, "col": ""}
| promptflow/src/promptflow/tests/sdk_cli_test/unittests/test_local_storage_operations.py/0 | {
"file_path": "promptflow/src/promptflow/tests/sdk_cli_test/unittests/test_local_storage_operations.py",
"repo_id": "promptflow",
"token_count": 693
} | 61 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import contextlib
import getpass
import json
from typing import Any, Dict, List
from unittest import mock
import werkzeug
from flask.testing import FlaskClient
@contextlib.contextmanager
def check_activity_end_telemetry(
*,
expected_activities: List[Dict[str, Any]] = None,
**kwargs,
):
if expected_activities is None and kwargs:
expected_activities = [kwargs]
with mock.patch("promptflow._sdk._telemetry.activity.log_activity_end") as mock_telemetry:
yield
actual_activities = [call.args[0] for call in mock_telemetry.call_args_list]
assert mock_telemetry.call_count == len(expected_activities), (
f"telemetry should not be called {len(expected_activities)} times but got {mock_telemetry.call_count}:\n"
f"{json.dumps(actual_activities, indent=2)}\n"
)
default_expected_call = {
"first_call": True,
"activity_type": "PublicApi",
"completion_status": "Success",
"user_agent": f"promptflow-sdk/0.0.1 Werkzeug/{werkzeug.__version__} local_pfs/0.0.1",
}
for i, expected_activity in enumerate(expected_activities):
temp = default_expected_call.copy()
temp.update(expected_activity)
expected_activity = temp
for key, expected_value in expected_activity.items():
value = actual_activities[i][key]
assert (
value == expected_value
), f"{key} mismatch in {i+1}th call: expect {expected_value} but got {value}"
class PFSOperations:
CONNECTION_URL_PREFIX = "/v1.0/Connections"
RUN_URL_PREFIX = "/v1.0/Runs"
TELEMETRY_PREFIX = "/v1.0/Telemetries"
def __init__(self, client: FlaskClient):
self._client = client
def remote_user_header(self):
return {"X-Remote-User": getpass.getuser()}
def heartbeat(self):
return self._client.get("/heartbeat")
# connection APIs
def connection_operation_with_invalid_user(self, status_code=None):
response = self._client.get(f"{self.CONNECTION_URL_PREFIX}/", headers={"X-Remote-User": "invalid_user"})
if status_code:
assert status_code == response.status_code, response.text
return response
def list_connections(self, status_code=None):
response = self._client.get(f"{self.CONNECTION_URL_PREFIX}/", headers=self.remote_user_header())
if status_code:
assert status_code == response.status_code, response.text
return response
def delete_connection(self, name: str, status_code=None):
response = self._client.delete(f"{self.CONNECTION_URL_PREFIX}/{name}", headers=self.remote_user_header())
if status_code:
assert status_code == response.status_code, response.text
return response
def list_connections_by_provider(self, working_dir, status_code=None):
response = self._client.get(
f"{self.CONNECTION_URL_PREFIX}/",
query_string={"working_directory": working_dir},
headers=self.remote_user_header(),
)
if status_code:
assert status_code == response.status_code, response.text
return response
def get_connection(self, name: str, status_code=None):
response = self._client.get(f"{self.CONNECTION_URL_PREFIX}/{name}", headers=self.remote_user_header())
if status_code:
assert status_code == response.status_code, response.text
return response
def get_connections_by_provider(self, name: str, working_dir, status_code=None):
response = self._client.get(
f"{self.CONNECTION_URL_PREFIX}/{name}",
data={"working_directory": working_dir},
headers=self.remote_user_header(),
)
if status_code:
assert status_code == response.status_code, response.text
return response
def get_connection_with_secret(self, name: str, status_code=None):
response = self._client.get(
f"{self.CONNECTION_URL_PREFIX}/{name}/listsecrets", headers=self.remote_user_header()
)
if status_code:
assert status_code == response.status_code, response.text
return response
def get_connection_specs(self, status_code=None):
response = self._client.get(f"{self.CONNECTION_URL_PREFIX}/specs")
if status_code:
assert status_code == response.status_code, response.text
return response
# run APIs
def list_runs(self, status_code=None):
# TODO: add query parameters
response = self._client.get(f"{self.RUN_URL_PREFIX}/", headers=self.remote_user_header())
if status_code:
assert status_code == response.status_code, response.text
return response
def submit_run(self, request_body, status_code=None):
response = self._client.post(f"{self.RUN_URL_PREFIX}/submit", json=request_body)
if status_code:
assert status_code == response.status_code, response.text
return response
def update_run(
self, name: str, display_name: str = None, description: str = None, tags: str = None, status_code=None
):
request_body = {
"display_name": display_name,
"description": description,
"tags": tags,
}
response = self._client.put(f"{self.RUN_URL_PREFIX}/{name}", json=request_body)
if status_code:
assert status_code == response.status_code, response.text
return response
def archive_run(self, name: str, status_code=None):
response = self._client.get(f"{self.RUN_URL_PREFIX}/{name}/archive")
if status_code:
assert status_code == response.status_code, response.text
return response
def restore_run(self, name: str, status_code=None):
response = self._client.get(f"{self.RUN_URL_PREFIX}/{name}/restore")
if status_code:
assert status_code == response.status_code, response.text
return response
def delete_run(self, name: str, status_code=None):
response = self._client.delete(f"{self.RUN_URL_PREFIX}/{name}")
if status_code:
assert status_code == response.status_code, response.text
return response
def get_run_visualize(self, name: str, status_code=None):
response = self._client.get(f"{self.RUN_URL_PREFIX}/{name}/visualize")
if status_code:
assert status_code == response.status_code, response.text
return response
def get_run(self, name: str, status_code=None):
response = self._client.get(f"{self.RUN_URL_PREFIX}/{name}")
if status_code:
assert status_code == response.status_code, response.text
return response
def get_child_runs(self, name: str, status_code=None):
response = self._client.get(f"{self.RUN_URL_PREFIX}/{name}/childRuns")
if status_code:
assert status_code == response.status_code, response.text
return response
def get_node_runs(self, name: str, node_name: str, status_code=None):
response = self._client.get(f"{self.RUN_URL_PREFIX}/{name}/nodeRuns/{node_name}")
if status_code:
assert status_code == response.status_code, response.text
return response
def get_run_metadata(self, name: str, status_code=None):
response = self._client.get(f"{self.RUN_URL_PREFIX}/{name}/metaData")
if status_code:
assert status_code == response.status_code, response.text
return response
def get_run_log(self, name: str, status_code=None):
response = self._client.get(f"{self.RUN_URL_PREFIX}/{name}/logContent")
if status_code:
assert status_code == response.status_code, response.text
return response
def get_run_metrics(self, name: str, status_code=None):
response = self._client.get(f"{self.RUN_URL_PREFIX}/{name}/metrics")
if status_code:
assert status_code == response.status_code, response.text
return response
# telemetry APIs
def create_telemetry(self, *, body, headers, status_code=None):
response = self._client.post(
f"{self.TELEMETRY_PREFIX}/",
headers={
**self.remote_user_header(),
**headers,
},
json=body,
)
if status_code:
assert status_code == response.status_code, response.text
return response
| promptflow/src/promptflow/tests/sdk_pfs_test/utils.py/0 | {
"file_path": "promptflow/src/promptflow/tests/sdk_pfs_test/utils.py",
"repo_id": "promptflow",
"token_count": 3734
} | 62 |
$schema: https://azuremlschemas.azureedge.net/promptflow/latest/AzureOpenAIConnection.schema.json
name: my_azure_open_ai_connection
type: azure_open_ai # snake case
api_key: "******" # Use the scrub value to test key2 not being updated
api_base: "new_value"
api_type: "azure"
api_version: "2023-07-01-preview"
| promptflow/src/promptflow/tests/test_configs/connections/update_azure_openai_connection.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/connections/update_azure_openai_connection.yaml",
"repo_id": "promptflow",
"token_count": 122
} | 63 |
path: ./entry.py
entry: my_flow
| promptflow/src/promptflow/tests/test_configs/eager_flows/dummy_flow_with_trace/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/eager_flows/dummy_flow_with_trace/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 13
} | 64 |
inputs:
text:
type: string
default: world
outputs:
output1:
type: string
reference: ${nodeC.output}
output2:
type: string
reference: ${nodeD.output}
nodes:
- name: nodeA
type: python
source:
type: code
path: print_input.py
inputs:
input: ${inputs.text}
activate:
when: ${inputs.text}
is: hello
- name: nodeB
type: python
source:
type: code
path: print_input.py
inputs:
input: ${inputs.text}
activate:
when: ${nodeA.output}
is: hello
- name: nodeC
type: python
source:
type: code
path: print_input.py
inputs:
input: ${nodeB.output}
- name: nodeD
type: python
source:
type: code
path: print_input.py
inputs:
input: ${inputs.text}
activate:
when: ${inputs.text}
is: world
| promptflow/src/promptflow/tests/test_configs/flows/activate_flow/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/activate_flow/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 343
} | 65 |
from promptflow import tool
@tool
def pass_through(input1: str="Execution") -> str:
return input1 | promptflow/src/promptflow/tests/test_configs/flows/all_depedencies_bypassed_with_activate_met/pass_through.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/all_depedencies_bypassed_with_activate_met/pass_through.py",
"repo_id": "promptflow",
"token_count": 33
} | 66 |
from promptflow import tool
import asyncio
@tool
async def passthrough_str_and_wait(input1: str, wait_seconds=3, wait_seconds_in_cancellation=1) -> str:
assert isinstance(input1, str), f"input1 should be a string, got {input1}"
try:
print(f"Wait for {wait_seconds} seconds in async function")
for i in range(wait_seconds):
print(i)
await asyncio.sleep(1)
except asyncio.CancelledError:
print(f"Async function is cancelled, wait for {wait_seconds_in_cancellation}"
" in cancellation process")
for i in range(wait_seconds_in_cancellation):
print(f"Wait for {i} seconds in async tool cancellation logic")
await asyncio.sleep(1)
print(f"End time consuming cancellation process")
raise
return input1
| promptflow/src/promptflow/tests/test_configs/flows/async_tools_with_sync_tools/async_passthrough.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/async_tools_with_sync_tools/async_passthrough.py",
"repo_id": "promptflow",
"token_count": 334
} | 67 |
import random
import time
from promptflow import tool
@tool
def get_calorie_by_jogging(duration: float, temperature: float):
"""Estimate the calories burned by jogging based on duration and temperature.
:param duration: the length of the jogging in hours.
:type duration: float
:param temperature: the environment temperature in degrees Celsius.
:type temperature: float
"""
print(
f"Figure out the calories burned by jogging, with temperature of {temperature} degrees Celsius, "
f"and duration of {duration} hours."
)
# Generating a random number between 0.2 and 1 for tracing purpose
time.sleep(random.uniform(0.2, 1))
return random.randint(50, 100)
| promptflow/src/promptflow/tests/test_configs/flows/chat-with-assistant-no-file/get_calorie_by_jogging.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/chat-with-assistant-no-file/get_calorie_by_jogging.py",
"repo_id": "promptflow",
"token_count": 222
} | 68 |
from promptflow import tool
@tool
def show_answer(chat_answer: str):
raise Exception("mock exception")
| promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_exception/show_answer.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_exception/show_answer.py",
"repo_id": "promptflow",
"token_count": 33
} | 69 |
from typing import List
from promptflow import tool
@tool
def aggregation_assert(input1: List[str], input2: List[str]):
assert isinstance(input1, list)
assert isinstance(input2, list)
assert len(input1) == len(input2)
| promptflow/src/promptflow/tests/test_configs/flows/classification_accuracy_evaluation/aggregation_assert.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/classification_accuracy_evaluation/aggregation_assert.py",
"repo_id": "promptflow",
"token_count": 79
} | 70 |
from promptflow import tool
@tool
def extract_incident_info(incident: dict) -> str:
retriever_type = ["icm", "tsg", "kql"]
return {
"retriever": retriever_type[incident["incident_id"]],
"incident_content": incident["incident_content"]
} | promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/incident_info_extractor.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/incident_info_extractor.py",
"repo_id": "promptflow",
"token_count": 92
} | 71 |
import os
from promptflow import tool
from promptflow.connections import CustomConnection
from intent import extract_intent
@tool
def extract_intent_tool(
chat_prompt,
connection: CustomConnection) -> str:
# set environment variables
for key, value in dict(connection).items():
os.environ[key] = value
# call the entry function
return extract_intent(
chat_prompt=chat_prompt,
) | promptflow/src/promptflow/tests/test_configs/flows/export/linux/flow/extract_intent_tool.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/export/linux/flow/extract_intent_tool.py",
"repo_id": "promptflow",
"token_count": 141
} | 72 |
inputs:
number:
type: int
outputs:
output:
type: object
reference: ${nan_inf.output}
nodes:
- name: nan_inf
type: python
source:
type: code
path: nan_inf.py
inputs:
number: ${inputs.number}
| promptflow/src/promptflow/tests/test_configs/flows/flow-with-nan-inf/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/flow-with-nan-inf/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 97
} | 73 |
environment_variables:
env1: 2
env2: spawn
env3:
- 1
- 2
- 3
- 4
- 5
env4:
a: 1
b: "2"
inputs:
key:
type: string
outputs:
output:
type: string
reference: ${print_env.output.value}
nodes:
- name: print_env
type: python
source:
type: code
path: print_env.py
inputs:
key: ${inputs.key}
| promptflow/src/promptflow/tests/test_configs/flows/flow_with_environment_variables/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/flow_with_environment_variables/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 163
} | 74 |
inputs:
key:
type: list
outputs:
output:
type: string
reference: ${print_val.output.value}
nodes:
- name: print_val
type: python
source:
type: code
path: print_val.py
inputs:
key: ${inputs.key}
| promptflow/src/promptflow/tests/test_configs/flows/flow_with_list_input/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/flow_with_list_input/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 99
} | 75 |
inputs:
text:
type: string
default: this is an input
outputs:
out:
type: string
reference: ${my_script_tool.output}
nodes:
- name: my_script_tool
type: python
source:
type: code
path: my_script_tool.py
inputs:
connection: custom_connection_2
input_param: ${inputs.text}
| promptflow/src/promptflow/tests/test_configs/flows/flow_with_script_tool_with_custom_strong_type_connection/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/flow_with_script_tool_with_custom_strong_type_connection/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 126
} | 76 |
inputs:
text:
type: string
outputs:
answer:
type: string
reference: ${echo.output}
nodes:
- name: echo
type: python
source:
type: code
path: echo.py
inputs:
text: ${inputs.text}
| promptflow/src/promptflow/tests/test_configs/flows/generator_tools/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/generator_tools/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 91
} | 77 |
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"id": "/subscriptions/xxxx/resourceGroups/xxx/providers/Microsoft.MachineLearningServices/workspaces/xxx/connections/azure_open_ai_connection",
"name": "azure_open_ai_connection",
"type": "Microsoft.MachineLearningServices/workspaces/connections",
"properties": {
"authType": "ApiKey",
"credentials": {
"key": "api_key"
},
"category": "AzureOpenAI",
"expiryTime": null,
"target": "api_base",
"createdByWorkspaceArmId": null,
"isSharedToAll": false,
"sharedUserList": [],
"metadata": {
"azureml.flow.connection_type": "AzureOpenAI",
"azureml.flow.module": "promptflow.connections",
"ApiType": "azure",
"ApiVersion": "2023-03-15-preview"
}
},
"systemData": {
"createdAt": "2023-06-14T09:40:51.1117116Z",
"createdBy": "[email protected]",
"createdByType": "User",
"lastModifiedAt": "2023-06-14T09:40:51.1117116Z",
"lastModifiedBy": "[email protected]",
"lastModifiedByType": "User"
}
} | promptflow/src/promptflow/tests/test_configs/flows/llm_connection_override/connection_arm_template.json/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/llm_connection_override/connection_arm_template.json",
"repo_id": "promptflow",
"token_count": 538
} | 78 |
from promptflow import tool
@tool
def mod_three(number: int):
if number % 3 != 0:
raise Exception("cannot mod 3!")
return {"value": number}
| promptflow/src/promptflow/tests/test_configs/flows/mod-n/three/mod_three.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/mod-n/three/mod_three.py",
"repo_id": "promptflow",
"token_count": 58
} | 79 |
from openai.version import VERSION as OPENAI_VERSION
import openai
from promptflow import tool
from promptflow.connections import AzureOpenAIConnection
IS_LEGACY_OPENAI = OPENAI_VERSION.startswith("0.")
def get_client(connection: AzureOpenAIConnection):
api_key = connection.api_key
conn = dict(
api_key=connection.api_key,
)
if api_key.startswith("sk-"):
from openai import OpenAI as Client
else:
from openai import AzureOpenAI as Client
conn.update(
azure_endpoint=connection.api_base,
api_version=connection.api_version,
)
return Client(**conn)
@tool
def completion(connection: AzureOpenAIConnection, prompt: str, stream: bool) -> str:
if IS_LEGACY_OPENAI:
completion = openai.Completion.create(
prompt=prompt,
engine="text-ada-001",
max_tokens=256,
temperature=0.8,
top_p=1.0,
n=1,
stream=stream,
stop=None,
**dict(connection),
)
else:
completion = get_client(connection).completions.create(
prompt=prompt,
model="text-ada-001",
max_tokens=256,
temperature=0.8,
top_p=1.0,
n=1,
stream=stream,
stop=None,
)
if stream:
def generator():
for chunk in completion:
if chunk.choices:
if IS_LEGACY_OPENAI:
yield getattr(chunk.choices[0], "text", "")
else:
yield chunk.choices[0].text or ""
return "".join(generator())
else:
if IS_LEGACY_OPENAI:
return getattr(completion.choices[0], "text", "")
else:
return completion.choices[0].text or ""
| promptflow/src/promptflow/tests/test_configs/flows/openai_completion_api_flow/completion.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/openai_completion_api_flow/completion.py",
"repo_id": "promptflow",
"token_count": 956
} | 80 |
inputs:
text:
type: string
outputs:
output_prompt:
type: string
reference: ${prompt_tool_with_duplicated_inputs.output}
nodes:
- name: prompt_tool_with_duplicated_inputs
type: prompt
source:
type: code
path: prompt_with_duplicated_inputs.jinja2
inputs:
text: ${inputs.text} | promptflow/src/promptflow/tests/test_configs/flows/prompt_tool_with_duplicated_inputs/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/prompt_tool_with_duplicated_inputs/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 127
} | 81 |
{"image": {"data:image/png;path":"logo.jpg"}}
{"image": {"data:image/png;path":"logo_2.png"}} | promptflow/src/promptflow/tests/test_configs/flows/python_tool_with_simple_image/inputs.jsonl/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/python_tool_with_simple_image/inputs.jsonl",
"repo_id": "promptflow",
"token_count": 39
} | 82 |
creation_context:
created_at: xxx
created_by: xxx
created_by_type: xxx
last_modified_at: xxx
last_modified_by: xxx
last_modified_by_type: xxx
description: Create flows that use large language models to classify URLs into multiple
categories.
display_name: web_classification_4
error_threshold: -1
id: azureml:/subscriptions/xxx/resourceGroups/xxx/providers/Microsoft.MachineLearningServices/workspaces/xxx/components/xxx/versions/xxx
input_data: ${{inputs.data}}
inputs:
connections.classify_with_llm.connection:
default: azure_open_ai_connection
optional: true
type: string
connections.classify_with_llm.deployment_name:
default: text-davinci-003
optional: true
type: string
connections.classify_with_llm.model:
enum:
- text-davinci-001
- text-davinci-002
- text-davinci-003
- text-curie-001
- text-babbage-001
- text-ada-001
- code-cushman-001
- code-davinci-002
optional: true
type: string
connections.summarize_text_content.connection:
default: azure_open_ai_connection
optional: true
type: string
connections.summarize_text_content.deployment_name:
default: text-davinci-003
optional: true
type: string
connections.summarize_text_content.model:
enum:
- text-davinci-001
- text-davinci-002
- text-davinci-003
- text-curie-001
- text-babbage-001
- text-ada-001
- code-cushman-001
- code-davinci-002
optional: true
type: string
data:
optional: false
type: uri_folder
run_outputs:
optional: true
type: uri_folder
url:
default: https://www.microsoft.com/en-us/d/xbox-wireless-controller-stellar-shift-special-edition/94fbjc7h0h6h
optional: false
type: string
is_deterministic: true
logging_level: INFO
max_concurrency_per_instance: 1
mini_batch_error_threshold: 0
mini_batch_size: '1'
name: web_classification_4
outputs:
debug_info:
type: uri_folder
flow_outputs:
type: uri_folder
retry_settings:
max_retries: 2
timeout: 3600
task:
append_row_to: ${{outputs.flow_outputs}}
code: /subscriptions/xxx/resourceGroups/xxx/providers/Microsoft.MachineLearningServices/workspaces/xxx/codes/xxx/versions/xxx
entry_script: driver/azureml_user/parallel_run/prompt_flow_entry.py
environment: azureml:/subscriptions/xxx/resourceGroups/xxx/providers/Microsoft.MachineLearningServices/workspaces/xxx/environments/xxx/versions/xxx
program_arguments: --amlbi_pf_enabled True --amlbi_pf_run_mode component --amlbi_mini_batch_rows
1 --amlbi_file_format jsonl $[[--amlbi_pf_run_outputs ${{inputs.run_outputs}}]]
--amlbi_pf_debug_info ${{outputs.debug_info}} --amlbi_pf_connections "$[[classify_with_llm.connection=${{inputs.connections.classify_with_llm.connection}},]]$[[summarize_text_content.connection=${{inputs.connections.summarize_text_content.connection}},]]"
--amlbi_pf_deployment_names "$[[classify_with_llm.deployment_name=${{inputs.connections.classify_with_llm.deployment_name}},]]$[[summarize_text_content.deployment_name=${{inputs.connections.summarize_text_content.deployment_name}},]]"
--amlbi_pf_model_names "$[[classify_with_llm.model=${{inputs.connections.classify_with_llm.model}},]]$[[summarize_text_content.model=${{inputs.connections.summarize_text_content.model}},]]"
--amlbi_pf_input_url ${{inputs.url}}
type: run_function
type: parallel
version: 1.0.0
| promptflow/src/promptflow/tests/test_configs/flows/saved_component_spec/parallel.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/saved_component_spec/parallel.yaml",
"repo_id": "promptflow",
"token_count": 1314
} | 83 |
from promptflow import tool, log_metric
from typing import List
@tool
def accuracy(answer: List[str], groundtruth: List[str]):
assert isinstance(answer, list)
correct = 0
for a, g in zip(answer, groundtruth):
if a == g:
correct += 1
accuracy = float(correct) / len(answer)
log_metric("accuracy", accuracy)
return accuracy
| promptflow/src/promptflow/tests/test_configs/flows/simple_aggregation/accuracy.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/simple_aggregation/accuracy.py",
"repo_id": "promptflow",
"token_count": 136
} | 84 |
{
"package": {},
"code": {
"fetch_text_content_from_url.py": {
"type": "python",
"inputs": {
"url": {
"type": [
"string"
]
}
},
"function": "fetch_text_content_from_url"
},
"summarize_text_content.jinja2": {
"type": "llm",
"inputs": {
"text": {
"type": [
"string"
]
}
},
"description": "Summarize webpage content into a short paragraph."
},
"summarize_text_content__variant_1.jinja2": {
"type": "llm",
"inputs": {
"text": {
"type": [
"string"
]
}
}
},
"prepare_examples.py": {
"type": "python",
"function": "prepare_examples"
},
"classify_with_llm.jinja2": {
"type": "llm",
"inputs": {
"url": {
"type": [
"string"
]
},
"examples": {
"type": [
"string"
]
},
"text_content": {
"type": [
"string"
]
}
},
"description": "Multi-class classification of a given url and text content."
},
"convert_to_dict.py": {
"type": "python",
"inputs": {
"input_str": {
"type": [
"string"
]
}
},
"function": "convert_to_dict"
}
}
}
| promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow/flow.tools.json/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/.promptflow/flow.tools.json",
"repo_id": "promptflow",
"token_count": 1366
} | 85 |
import json
from promptflow import tool
@tool
def convert_to_dict(input_str: str):
try:
return json.loads(input_str)
except Exception as e:
print("input is not valid, error: {}".format(e))
return {"category": "None", "evidence": "None"}
| promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants_unordered/convert_to_dict.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants_unordered/convert_to_dict.py",
"repo_id": "promptflow",
"token_count": 104
} | 86 |
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cache-control:
- no-cache
content-length:
- '1372'
content-type:
- application/json; charset=utf-8
expires:
- '-1'
pragma:
- no-cache
strict-transport-security:
- max-age=31536000; includeSubDomains
transfer-encoding:
- chunked
vary:
- Accept-Encoding,Accept-Encoding
x-content-type-options:
- nosniff
x-request-time:
- '0.088'
status:
code: 200
message: OK
- request:
body: '{"runId": "non_exist_run", "selectRunMetadata": true, "selectRunDefinition":
true, "selectJobSpecification": true}'
headers:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate
Connection:
- keep-alive
Content-Length:
- '137'
Content-Type:
- application/json
User-Agent:
- python-requests/2.31.0
method: POST
uri: https://eastus.api.azureml.ms/history/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/rundata
response:
body:
string: '{"error": {"code": "UserError", "severity": null, "message": "Run runId=non_exist_run
was not found", "messageFormat": "Run {runId} was not found", "messageParameters":
{"runId": "runId=non_exist_run"}, "referenceCode": null, "detailsUri": null,
"target": null, "details": [], "innerError": {"code": "NotFoundError", "innerError":
null}, "debugInfo": null, "additionalInfo": null}, "correlation": {"operation":
"8ae7e1bb4942865bf920115c263c0e78", "request": "7f0871b4a154f85d"}, "environment":
"eastus", "location": "eastus", "time": "2024-01-12T07:58:30.4482197+00:00",
"componentName": "run-history", "statusCode": 404}'
headers:
connection:
- keep-alive
content-length:
- '777'
content-type:
- application/json; charset=utf-8
strict-transport-security:
- max-age=15724800; includeSubDomains; preload
transfer-encoding:
- chunked
vary:
- Accept-Encoding
x-content-type-options:
- nosniff
x-request-time:
- '0.046'
status:
code: 404
message: Run runId=f1356823-7551-4ab3-93ba-6d60270b9dc6 was not found
version: 1
| promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_stream_invalid_run_logs.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_stream_invalid_run_logs.yaml",
"repo_id": "promptflow",
"token_count": 2431
} | 87 |
flow: ../flows/print_env_var
data: ../datas/env_var_names.jsonl
# run config: env related
environment_variables:
API_BASE: ${azure_open_ai_connection.api_base}
| promptflow/src/promptflow/tests/test_configs/runs/run_with_env.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/runs/run_with_env.yaml",
"repo_id": "promptflow",
"token_count": 64
} | 88 |
inputs:
url:
type: string
default: https://play.google.com/store/apps/details?id=com.twitter.android
is_chat_input: false
outputs:
category:
type: string
reference: ${convert_to_dict.output.category}
evaluation_only: false
is_chat_output: false
evidence:
type: string
reference: ${convert_to_dict.output.evidence}
evaluation_only: false
is_chat_output: false
nodes:
- name: fetch_text_content_from_url
type: python
source:
type: code
path: fetch_text_content_from_url.py
inputs:
url: "${inputs.url}"
aggregation: false
- name: prepare_examples
type: python
source:
type: code
path: prepare_examples.py
inputs: {}
aggregation: false
- name: classify_with_llm
type: llm
source:
type: code
path: classify_with_llm.jinja2
inputs:
deployment_name: "gpt-35-turbo"
model: "gpt-3.5-turbo"
max_tokens: 128
temperature: 0.2
url: "${inputs.url}"
text_content: "${summarize_text_content.output}"
examples: "${prepare_examples.output}"
api: chat
connection: open_ai_connection
aggregation: false
- name: convert_to_dict
type: python
source:
type: code
path: convert_to_dict.py
inputs:
input_str: "${classify_with_llm.output}"
aggregation: false
- name: summarize_text_content
type: llm
source:
type: code
path: summarize_text_content.jinja2
inputs:
deployment_name: "gpt-35-turbo"
model: "gpt-3.5-turbo"
max_tokens: 128
temperature: 0.2
text: "${fetch_text_content_from_url.output}"
api: chat
connection: open_ai_connection
aggregation: false
environment:
python_requirements_txt: requirements.txt
| promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/snapshot/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/snapshot/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 663
} | 89 |
from promptflow._core.tool import ToolProvider, tool
from promptflow.connections import CustomConnection
class MyTool(ToolProvider):
"""
Doc reference :
"""
def __init__(self, connection: CustomConnection):
super().__init__()
self.connection = connection
@tool(name="My Second Tool", description="This is my second tool")
def my_tool(self, input_text: str) -> str:
# Replace with your tool code.
# Usually connection contains configs to connect to an API.
# Use CustomConnection is a dict. You can use it like: connection.api_key, connection.api_base
# Not all tools need a connection. You can remove it if you don't need it.
return "Hello " + input_text
| promptflow/src/promptflow/tests/test_configs/tools/tool_with_custom_connection.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/tools/tool_with_custom_connection.py",
"repo_id": "promptflow",
"token_count": 244
} | 90 |
inputs:
text:
type: string
outputs:
output:
type: string
reference: ${summarize_text_content.output}
nodes:
- name: summarize_text_content
type: llm
source:
type: code
path: summarize_text_content__variant_1.jinja2
inputs:
deployment_name: gpt-35-turbo
suffix: ''
max_tokens: '256'
temperature: '0.2'
top_p: '1.0'
logprobs: ''
echo: 'False'
stop: ''
presence_penalty: '0'
frequency_penalty: '0'
best_of: '1'
logit_bias: ''
text: ${inputs.text}
provider: AzureOpenAI
connection: azure_open_ai_connection_111
api: completion
module: promptflow.tools.aoai
| promptflow/src/promptflow/tests/test_configs/wrong_flows/invalid_connection/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/wrong_flows/invalid_connection/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 280
} | 91 |
from promptflow import tool
@tool
def stringify_num(num: int):
return str(num)
| promptflow/src/promptflow/tests/test_configs/wrong_flows/outputs_reference_not_valid/stringify_num.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/wrong_flows/outputs_reference_not_valid/stringify_num.py",
"repo_id": "promptflow",
"token_count": 30
} | 92 |
from promptflow import tool
1 / 0
@tool
def tool1():
pass
| promptflow/src/promptflow/tests/test_configs/wrong_tools/load_error.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/wrong_tools/load_error.py",
"repo_id": "promptflow",
"token_count": 25
} | 93 |
# Cloud
Prompt flow streamlines the process of developing AI applications based on LLM, easing prompt engineering, prototyping, evaluating, and fine-tuning for high-quality products.
Transitioning to production, however, typically requires a comprehensive **LLMOps** process, LLMOps is short for large language model operations. This can often be a complex task, demanding high availability and security, particularly vital for large-scale team collaboration and lifecycle management when deploying to production.
To assist in this journey, we've introduced **Azure AI**, a **cloud-based platform** tailored for executing LLMOps, focusing on boosting productivity for enterprises.
* Private data access and controls
* Collaborative development
* Automating iterative experimentation and CI/CD
* Deployment and optimization
* Safe and Responsible AI
## Transitioning from local to cloud (Azure AI)
In prompt flow, You can develop your flow locally and then seamlessly transition to Azure AI. Here are a few scenarios where this might be beneficial:
| Scenario | Benefit | How to|
| --- | --- |--- |
| Collaborative development | Azure AI provides a cloud-based platform for flow development and management, facilitating sharing and collaboration across multiple teams, organizations, and tenants.| [Submit a run using pfazure](./azureai/quick-start.md), based on the flow file in your code base.|
| Processing large amounts of data in parallel pipelines | Transitioning to Azure AI allows you to use your flow as a parallel component in a pipeline job, enabling you to process large amounts of data and integrate with existing pipelines. | Learn how to [Use flow in Azure ML pipeline job](./azureai/use-flow-in-azure-ml-pipeline.md).|
| Large-scale Deployment | Azure AI allows for seamless deployment and optimization when your flow is ready for production and requires high availability and security. | Use `pf flow build` to deploy your flow to [Azure App Service](./azureai/deploy-to-azure-appservice.md).|
| Data Security and Responsible AI Practices | If your flow handling sensitive data or requiring ethical AI practices, Azure AI offers robust security, responsible AI services, and features for data storage, identity, and access control. | Follow the steps mentioned in the above scenarios.|
For more resources on Azure AI, visit the cloud documentation site: [Build AI solutions with prompt flow](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/get-started-prompt-flow?view=azureml-api-2).
```{toctree}
:caption: AzureAI
:maxdepth: 1
azureai/quick-start
azureai/manage-flows
azureai/consume-connections-from-azure-ai
azureai/deploy-to-azure-appservice
azureai/use-flow-in-azure-ml-pipeline.md
azureai/faq
azureai/runtime-change-log.md
```
| promptflow/docs/cloud/index.md/0 | {
"file_path": "promptflow/docs/cloud/index.md",
"repo_id": "promptflow",
"token_count": 718
} | 0 |
# Develop chat flow
:::{admonition} Experimental feature
This is an experimental feature, and may change at any time. Learn [more](../faq.md#stable-vs-experimental).
:::
From this document, you can learn how to develop a chat flow by writing a flow yaml from scratch. You can
find additional information about flow yaml schema in [Flow YAML Schema](../../reference/flow-yaml-schema-reference.md).
## Flow input data
The most important elements that differentiate a chat flow from a standard flow are **chat input** and **chat history**. A chat flow can have multiple inputs, but **chat history** and **chat input** are required inputs in chat flow.
- **Chat Input**: Chat input refers to the messages or queries submitted by users to the chatbot. Effectively handling chat input is crucial for a successful conversation, as it involves understanding user intentions, extracting relevant information, and triggering appropriate responses.
- **Chat History**: Chat history is the record of all interactions between the user and the chatbot, including both user inputs and AI-generated outputs. Maintaining chat history is essential for keeping track of the conversation context and ensuring the AI can generate contextually relevant responses. Chat history is a special type of chat flow input, that stores chat messages in a structured format.
An example of chat history:
```python
[
{"inputs": {"question": "What types of container software there are?"}, "outputs": {"answer": "There are several types of container software available, including: Docker, Kubernetes"}},
{"inputs": {"question": "What's the different between them?"}, "outputs": {"answer": "The main difference between the various container software systems is their functionality and purpose. Here are some key differences between them..."}},
]
```
You can set **is_chat_input**/**is_chat_history** to **true** to add chat_input/chat_history to the chat flow.
```yaml
inputs:
chat_history:
type: list
is_chat_history: true
default: []
question:
type: string
is_chat_input: true
default: What is ChatGPT?
```
For more information see [develop the flow using different tools](./develop-standard-flow.md#flow-input-data).
## Develop the flow using different tools
In one flow, you can consume different kinds of tools. We now support built-in tool like
[LLM](../../reference/tools-reference/llm-tool.md), [Python](../../reference/tools-reference/python-tool.md) and
[Prompt](../../reference/tools-reference/prompt-tool.md) and
third-party tool like [Serp API](../../reference/tools-reference/serp-api-tool.md),
[Vector Search](../../reference/tools-reference/vector_db_lookup_tool.md), etc.
For more information see [develop the flow using different tools](./develop-standard-flow.md#develop-the-flow-using-different-tools).
## Chain your flow - link nodes together
Before linking nodes together, you need to define and expose an interface.
For more information see [chain your flow](./develop-standard-flow.md#chain-your-flow---link-nodes-together).
## Set flow output
**Chat output** is required output in the chat flow. It refers to the AI-generated messages that are sent to the user in response to their inputs. Generating contextually appropriate and engaging chat outputs is vital for a positive user experience.
You can set **is_chat_output** to **true** to add chat_output to the chat flow.
```yaml
outputs:
answer:
type: string
reference: ${chat.output}
is_chat_output: true
```
| promptflow/docs/how-to-guides/develop-a-flow/develop-chat-flow.md/0 | {
"file_path": "promptflow/docs/how-to-guides/develop-a-flow/develop-chat-flow.md",
"repo_id": "promptflow",
"token_count": 954
} | 1 |
# Frequency asked questions (FAQ)
## General ##
### Stable vs experimental
Prompt flow provides both stable and experimental features in the same SDK.
|Feature status | Description |
|----------------|----------------|
Stable features | **Production ready** <br/><br/> These features are recommended for most use cases and production environments. They are updated less frequently then experimental features.|
Experimental features | **Developmental** <br/><br/> These features are newly developed capabilities & updates that may not be ready or fully tested for production usage. While the features are typically functional, they can include some breaking changes. Experimental features are used to iron out SDK breaking bugs, and will only receive updates for the duration of the testing period. Experimental features are also referred to as features that are in **preview**. <br/> As the name indicates, the experimental (preview) features are for experimenting and is **not considered bug free or stable**. For this reason, we only recommend experimental features to advanced users who wish to try out early versions of capabilities and updates, and intend to participate in the reporting of bugs and glitches.
### OpenAI 1.x support
Please use the following command to upgrade promptflow for openai 1.x support:
```
pip install promptflow>=1.1.0
pip install promptflow-tools>=1.0.0
```
Note that the command above will upgrade your openai package a version later than 1.0.0,
which may introduce breaking changes to custom tool code.
Reach [OpenAI migration guide](https://github.com/openai/openai-python/discussions/742) for more details.
## Troubleshooting ##
### Connection creation failed with StoreConnectionEncryptionKeyError
```
Connection creation failed with StoreConnectionEncryptionKeyError: System keyring backend service not found in your operating system. See https://pypi.org/project/keyring/ to install requirement for different operating system, or 'pip install keyrings.alt' to use the third-party backend.
```
This error raised due to keyring can't find an available backend to store keys.
For example [macOS Keychain](https://en.wikipedia.org/wiki/Keychain_%28software%29) and [Windows Credential Locker](https://learn.microsoft.com/en-us/windows/uwp/security/credential-locker)
are valid keyring backends.
To resolve this issue, install the third-party keyring backend or write your own keyring backend, for example:
`pip install keyrings.alt`
For more detail about keyring third-party backend, please refer to 'Third-Party Backends' in [keyring](https://pypi.org/project/keyring/).
### Pf visualize show error: "tcgetpgrp failed: Not a tty"
If you are using WSL, this is a known issue for `webbrowser` under WSL; see [this issue](https://github.com/python/cpython/issues/89752) for more information. Please try to upgrade your WSL to 22.04 or later, this issue should be resolved.
If you are still facing this issue with WSL 22.04 or later, or you are not even using WSL, please open an issue to us.
### Installed tool not appearing in VSCode Extension tool list
After installing a tool package via `pip install [tool-package-name]`, the new tool may not immediately appear in the tool list within the VSCode Extension, as shown below:
This is often due to outdated cache. To refresh the tool list and make newly installed tools visible:
1. Open the VSCode Extension window.
2. Bring up the command palette by pressing "Ctrl+Shift+P".
3. Type and select the "Developer: Reload Webviews" command.
4. Wait a moment for the tool list refreshing.
Reloading clears the previous cache and populates the tool list with any newly installed tools. So that the missing tools are now visible.
### Set logging level
Promptflow uses `logging` module to log messages. You can set logging level via environment variable `PF_LOGGING_LEVEL`, valid values includes `CRITICAL`, `ERROR`, `WARNING`, `INFO`, `DEBUG`, default to `INFO`.
Below is the serving logs after setting `PF_LOGGING_LEVEL` to `DEBUG`:
Compare to the serving logs with `WARNING` level:
### Set environment variables
Currently, promptflow supports the following environment variables:
**PF_WORKER_COUNT**
Effective for batch run only, count of parallel workers in batch run execution.
The default value is 4 (was 16 when promptflow<1.4.0)
Please take the following points into consideration when changing it:
1. The concurrency should not exceed the total data rows count. Otherwise, the execution may slow down due to additional time spent on process startup and shutdown.
2. High parallelism may cause the underlying API call to reach the rate limit of your LLM endpoint. In which case you can decrease the `PF_WORKER_COUNT` or increase the rate limit. Please refer to [this doc](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/quota) on quota management. Then you can refer to this expression to set up the concurrency.
```
PF_WORKER_COUNT <= TPM * duration_seconds / token_count / 60
```
TPM: token per minute, capacity rate limit of your LLM endpoint
duration_seconds: single flow run duration in seconds
token_count: single flow run token count
For example, if your endpoint TPM (token per minute) is 50K, the single flow run takes 10k tokens and runs for 30s, pls do not set up PF_WORKER_COUNT bigger than 2. This is a rough estimation. Please also consider collboaration (teammates use the same endpoint at the same time) and tokens consumed in deployed inference endpoints, playground and other cases which might send request to your LLM endpoints.
**PF_BATCH_METHOD**
Valid for batch run only. Optional values: 'spawn', 'fork'.
**spawn**
1. The child processes will not inherit resources of the parent process, therefore, each process needs to reinitialize the resources required for the flow, which may use more system memory.
2. Starting a process is slow because it will take some time to initialize the necessary resources.
**fork**
1. Use the copy-on-write mechanism, the child processes will inherit all the resources of the parent process, thereby using less system memory.
2. The process starts faster as it doesn't need to reinitialize resources.
Note: Windows only supports spawn, Linux and macOS support both spawn and fork.
#### How to configure environment variables
1. Configure environment variables in ```flow.dag.yaml```. Example:
```
inputs: []
outputs: []
nodes: []
environment_variables:
PF_WORKER_COUNT: 2
PF_BATCH_METHOD: "spawn"
MY_CUSTOM_SETTING: my_custom_value
```
2. Specify environment variables when submitting runs.
::::{tab-set}
:::{tab-item} CLI
:sync: CLI
Use this parameter: ```--environment-variable``` to specify environment variables.
Example: ```--environment-variable PF_WORKER_COUNT="2" PF_BATCH_METHOD="spawn"```.
:::
:::{tab-item} SDK
:sync: SDK
Specify environment variables when creating run. Example:
``` python
pf = PFClient(
credential=credential,
subscription_id="<SUBSCRIPTION_ID>",
resource_group_name="<RESOURCE_GROUP>",
workspace_name="<AML_WORKSPACE_NAME>",
)
flow = "web-classification"
data = "web-classification/data.jsonl"
runtime = "example-runtime-ci"
environment_variables = {"PF_WORKER_COUNT": "2", "PF_BATCH_METHOD": "spawn"}
# create run
base_run = pf.run(
flow=flow,
data=data,
runtime=runtime,
environment_variables=environment_variables,
)
```
:::
:::{tab-item} VS Code Extension
:sync: VS Code Extension
VSCode Extension supports specifying environment variables only when submitting batch runs.
Specify environment variables in ```batch_run_create.yaml```. Example:
``` yaml
name: flow_name
display_name: display_name
flow: flow_folder
data: data_file
column_mapping:
customer_info: <Please select a data input>
history: <Please select a data input>
environment_variables:
PF_WORKER_COUNT: "2"
PF_BATCH_METHOD: "spawn"
```
:::
::::
#### Priority
The environment variables specified when submitting runs always takes precedence over the environment variables in the flow.dag.yaml file.
| promptflow/docs/how-to-guides/faq.md/0 | {
"file_path": "promptflow/docs/how-to-guides/faq.md",
"repo_id": "promptflow",
"token_count": 2390
} | 2 |
# Reference
**Current stable version:**
- [promptflow](https://pypi.org/project/promptflow):
[](https://badge.fury.io/py/promptflow)
[](https://pypi.org/project/promptflow/)
- [promptflow-tools](https://pypi.org/project/promptflow-tools/):
[](https://badge.fury.io/py/promptflow-tools)
[](https://pypi.org/project/promptflow-tools/)
```{toctree}
:caption: Command Line Interface
:maxdepth: 1
pf-command-reference.md
pfazure-command-reference.md
```
```{toctree}
:caption: Python Library Reference
:maxdepth: 4
python-library-reference/promptflow
```
```{toctree}
:caption: Tool Reference
:maxdepth: 1
tools-reference/llm-tool
tools-reference/prompt-tool
tools-reference/python-tool
tools-reference/serp-api-tool
tools-reference/faiss_index_lookup_tool
tools-reference/vector_db_lookup_tool
tools-reference/embedding_tool
tools-reference/open_model_llm_tool
tools-reference/openai-gpt-4v-tool
tools-reference/contentsafety_text_tool
tools-reference/aoai-gpt4-turbo-vision
```
```{toctree}
:caption: YAML Schema
:maxdepth: 1
flow-yaml-schema-reference.md
run-yaml-schema-reference.md
```
| promptflow/docs/reference/index.md/0 | {
"file_path": "promptflow/docs/reference/index.md",
"repo_id": "promptflow",
"token_count": 535
} | 3 |
$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
environment:
python_requirements_txt: requirements.txt
inputs:
chat_history:
type: list
is_chat_history: true
question:
type: list
default:
- data:image/png;url: https://images.idgesg.net/images/article/2019/11/edge-browser-logo_microsoft-100816808-large.jpg
- How many colors can you see?
is_chat_input: true
outputs:
answer:
type: string
reference: ${chat.output}
is_chat_output: true
nodes:
- name: chat
type: custom_llm
source:
type: package_with_prompt
tool: promptflow.tools.aoai_gpt4v.AzureOpenAI.chat
path: chat.jinja2
inputs:
connection: aoai_gpt4v_connection
deployment_name: gpt-4v
max_tokens: 512
chat_history: ${inputs.chat_history}
question: ${inputs.question}
| promptflow/examples/flows/chat/chat-with-image/flow.dag.yaml/0 | {
"file_path": "promptflow/examples/flows/chat/chat-with-image/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 340
} | 4 |
$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Run.schema.json
#name: chat_with_pdf_default_20230820_162219_559000
flow: .
data: ./data/bert-paper-qna.jsonl
#run: <Uncomment to select a run input>
column_mapping:
chat_history: ${data.chat_history}
pdf_url: ${data.pdf_url}
question: ${data.question}
config:
EMBEDDING_MODEL_DEPLOYMENT_NAME: text-embedding-ada-002
CHAT_MODEL_DEPLOYMENT_NAME: gpt-4
PROMPT_TOKEN_LIMIT: 3000
MAX_COMPLETION_TOKENS: 1024
VERBOSE: true
CHUNK_SIZE: 1024
CHUNK_OVERLAP: 64 | promptflow/examples/flows/chat/chat-with-pdf/batch_run.yaml/0 | {
"file_path": "promptflow/examples/flows/chat/chat-with-pdf/batch_run.yaml",
"repo_id": "promptflow",
"token_count": 243
} | 5 |
import argparse
from dotenv import load_dotenv
import os
from qna import qna
from find_context import find_context
from rewrite_question import rewrite_question
from build_index import create_faiss_index
from download import download
from utils.lock import acquire_lock
from constants import PDF_DIR, INDEX_DIR
def chat_with_pdf(question: str, pdf_url: str, history: list):
with acquire_lock("create_folder.lock"):
if not os.path.exists(PDF_DIR):
os.mkdir(PDF_DIR)
if not os.path.exists(INDEX_DIR):
os.makedirs(INDEX_DIR)
pdf_path = download(pdf_url)
index_path = create_faiss_index(pdf_path)
q = rewrite_question(question, history)
prompt, context = find_context(q, index_path)
stream = qna(prompt, history)
return stream, context
def print_stream_and_return_full_answer(stream):
answer = ""
for str in stream:
print(str, end="", flush=True)
answer = answer + str + ""
print(flush=True)
return answer
def main_loop(url: str):
load_dotenv(os.path.join(os.path.dirname(__file__), ".env"), override=True)
history = []
while True:
question = input("\033[92m" + "$User (type q! to quit): " + "\033[0m")
if question == "q!":
break
stream, context = chat_with_pdf(question, url, history)
print("\033[92m" + "$Bot: " + "\033[0m", end=" ", flush=True)
answer = print_stream_and_return_full_answer(stream)
history = history + [
{"role": "user", "content": question},
{"role": "assistant", "content": answer},
]
def main():
parser = argparse.ArgumentParser(description="Ask questions about a PDF file")
parser.add_argument("url", help="URL to the PDF file")
args = parser.parse_args()
main_loop(args.url)
if __name__ == "__main__":
main()
| promptflow/examples/flows/chat/chat-with-pdf/chat_with_pdf/main.py/0 | {
"file_path": "promptflow/examples/flows/chat/chat-with-pdf/chat_with_pdf/main.py",
"repo_id": "promptflow",
"token_count": 740
} | 6 |
{"pdf_url":"https://arxiv.org/pdf/1810.04805.pdf"}
| promptflow/examples/flows/chat/chat-with-pdf/data/invalid-data-missing-column.jsonl/0 | {
"file_path": "promptflow/examples/flows/chat/chat-with-pdf/data/invalid-data-missing-column.jsonl",
"repo_id": "promptflow",
"token_count": 24
} | 7 |
system:
You are a chatbot having a conversation with a human.
Given the following extracted parts of a long document and a question, create a final answer with references ("SOURCES").
If you don't know the answer, just say that you don't know. Don't try to make up an answer.
ALWAYS return a "SOURCES" part in your answer.
{{contexts}}
{% for item in chat_history %}
user:
{{item.inputs.question}}
assistant:
{{item.outputs.answer}}
{% endfor %}
user:
{{question}}
| promptflow/examples/flows/chat/chat-with-wikipedia/augmented_chat.jinja2/0 | {
"file_path": "promptflow/examples/flows/chat/chat-with-wikipedia/augmented_chat.jinja2",
"repo_id": "promptflow",
"token_count": 142
} | 8 |
from promptflow import tool
@tool
def line_process(groundtruth: str, prediction: str) -> int:
processed_result = 0
if prediction == "JSONDecodeError" or prediction.startswith("Unknown Error:"):
processed_result = -1
return processed_result
try:
groundtruth = float(groundtruth)
prediction = float(prediction)
except ValueError:
processed_result = -1
return processed_result
if round(prediction, 2) == round(groundtruth, 2):
processed_result = 1
return processed_result
if __name__ == "__main__":
processed_result = line_process("1.0", "1")
print("The processed result is", processed_result)
processed_result = line_process("3.14", "3.1415926")
print("The processed result is", processed_result)
processed_result = line_process("2.1", "2.0")
print("The processed result is", processed_result)
processed_result = line_process("1.0", "JSONDecodeError")
print("The processed result is", processed_result)
processed_result = line_process("1.0", "No module named 'numpy'")
print("The processed result is", processed_result)
| promptflow/examples/flows/evaluation/eval-accuracy-maths-to-code/line_process.py/0 | {
"file_path": "promptflow/examples/flows/evaluation/eval-accuracy-maths-to-code/line_process.py",
"repo_id": "promptflow",
"token_count": 396
} | 9 |
{"groundtruth": "App","prediction": "App"}
{"groundtruth": "Channel","prediction": "Channel"}
{"groundtruth": "Academic","prediction": "Academic"}
| promptflow/examples/flows/evaluation/eval-classification-accuracy/data.jsonl/0 | {
"file_path": "promptflow/examples/flows/evaluation/eval-classification-accuracy/data.jsonl",
"repo_id": "promptflow",
"token_count": 44
} | 10 |
$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
environment:
python_requirements_txt: requirements.txt
inputs:
question:
type: string
default: What is the name of the new language representation model introduced in
the document?
answer:
type: string
default: The document mentions multiple language representation models, so it is
unclear which one is being referred to as \"new\". Can you provide more
specific information or context?
context:
type: string
default: '["statistical language modeling. arXiv preprint arXiv:1312.3005 . Z.
Chen, H. Zhang, X. Zhang, and L. Zhao. 2018. Quora question pairs.
Christopher Clark and Matt Gardner. 2018. Simple and effective
multi-paragraph reading comprehen- sion. In ACL.Kevin Clark, Minh-Thang
Luong, Christopher D Man- ning, and Quoc Le. 2018. Semi-supervised se-
quence modeling with cross-view training. In Pro- ceedings of the 2018
Conference on Empirical Meth- ods in Natural Language Processing , pages
1914\u2013 1925. Ronan Collobert and Jason Weston. 2008. A uni\ufb01ed
architecture for natural language processing: Deep neural networks with
multitask learning. In Pro- ceedings of the 25th international conference
on Machine learning , pages 160\u2013167. ACM. Alexis Conneau, Douwe
Kiela, Holger Schwenk, Lo \u00a8\u0131c Barrault, and Antoine Bordes.
2017. Supervised learning of universal sentence representations from
natural language inference data. In Proceedings of the 2017 Conference on
Empirical Methods in Nat- ural Language Processing , pages 670\u2013680,
Copen- hagen, Denmark. Association for Computational Linguistics. Andrew M
Dai and Quoc V Le. 2015. Semi-supervised sequence learning. In Advances in
neural informa- tion processing systems , pages 3079\u20133087. J. Deng,
W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei- Fei. 2009. ImageNet: A
Large-Scale Hierarchical Image Database. In CVPR09 . William B Dolan and
Chris Brockett. 2005. Automati- cally constructing a corpus of sentential
paraphrases. InProceedings of the Third International Workshop on
Paraphrasing (IWP2005) . William Fedus, Ian Goodfellow, and Andrew M Dai.
2018. Maskgan: Better text generation via \ufb01lling in the.arXiv
preprint arXiv:1801.07736 . Dan Hendrycks and Kevin Gimpel. 2016. Bridging
nonlinearities and stochastic regularizers with gaussian error linear
units. CoRR , abs\/1606.08415. Felix Hill, Kyunghyun Cho, and Anna
Korhonen. 2016. Learning distributed representations of sentences from
unlabelled data. In Proceedings of the 2016 Conference of the North
American Chapter of the Association for Computational Linguistics: Human
Language Technologies . Association for Computa- tional Linguistics.
Jeremy Howard and Sebastian Ruder. 2018. Universal language model
\ufb01ne-tuning for text classi\ufb01cation. In ACL. Association for
Computational Linguistics. Minghao Hu, Yuxing Peng, Zhen Huang, Xipeng
Qiu, Furu Wei, and Ming Zhou. 2018. Reinforced mnemonic reader for machine
reading comprehen- sion. In IJCAI . Yacine Jernite, Samuel R. Bowman, and
David Son- tag. 2017. Discourse-based objectives for fast un- supervised
sentence representation learning. CoRR , abs\/1705.00557.Mandar Joshi,
Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017. Triviaqa: A large
scale distantly supervised challenge dataset for reading comprehen- sion.
In ACL. Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel,
Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015. Skip-thought
vectors. In Advances in neural information processing systems , pages
3294\u20133302. Quoc Le and Tomas Mikolov. 2014. Distributed rep-
resentations of sentences and documents. In Inter- national Conference on
Machine Learning , pages 1188\u20131196. Hector J Levesque, Ernest Davis,
and Leora Morgen- stern. 2011. The winograd schema challenge. In Aaai
spring symposium: Logical formalizations of commonsense reasoning , volume
46, page 47. Lajanugen Logeswaran and Honglak Lee. 2018. An ef\ufb01cient
framework for learning sentence represen- tations. In International
Conference on Learning Representations . Bryan McCann, James Bradbury,
Caiming Xiong, and Richard Socher. 2017. Learned in translation:
Con-","tool for measuring readability. Journalism Bulletin ,
30(4):415\u2013433. Erik F Tjong Kim Sang and Fien De Meulder. 2003.
Introduction to the conll-2003 shared task: Language-independent named
entity recognition. In CoNLL . Joseph Turian, Lev Ratinov, and Yoshua
Bengio. 2010. Word representations: A simple and general method for
semi-supervised learning. In Proceedings of the 48th Annual Meeting of the
Association for Compu- tational Linguistics , ACL \u201910, pages
384\u2013394. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017.
Attention is all you need. In Advances in Neural Information Pro- cessing
Systems , pages 6000\u20136010. Pascal Vincent, Hugo Larochelle, Yoshua
Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust
features with denoising autoen- coders. In Proceedings of the 25th
international conference on Machine learning , pages 1096\u20131103. ACM.
Alex Wang, Amanpreet Singh, Julian Michael, Fe- lix Hill, Omer Levy, and
Samuel Bowman. 2018a. Glue: A multi-task benchmark and analysis
platformfor natural language understanding. In Proceedings of the 2018
EMNLP Workshop BlackboxNLP: An- alyzing and Interpreting Neural Networks
for NLP , pages 353\u2013355. Wei Wang, Ming Yan, and Chen Wu. 2018b.
Multi- granularity hierarchical attention fusion networks for reading
comprehension and question answering. InProceedings of the 56th Annual
Meeting of the As- sociation for Computational Linguistics (Volume 1: Long
Papers) . Association for Computational Lin- guistics. Alex Warstadt,
Amanpreet Singh, and Samuel R Bow- man. 2018. Neural network acceptability
judg- ments. arXiv preprint arXiv:1805.12471 . Adina Williams, Nikita
Nangia, and Samuel R Bow- man. 2018. A broad-coverage challenge corpus for
sentence understanding through inference. In NAACL . Yonghui Wu, Mike
Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey,
Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016.
Google\u2019s neural ma- chine translation system: Bridging the gap
between human and machine translation. arXiv preprint arXiv:1609.08144 .
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. 2014. How
transferable are features in deep neural networks? In Advances in neural
information processing systems , pages 3320\u20133328. Adams Wei Yu, David
Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V
Le. 2018. QANet: Combining local convolution with global self-attention
for reading comprehen- sion. In ICLR . Rowan Zellers, Yonatan Bisk, Roy
Schwartz, and Yejin Choi. 2018. Swag: A large-scale adversarial dataset
for grounded commonsense inference. In Proceed- ings of the 2018
Conference on Empirical Methods in Natural Language Processing (EMNLP) .
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhut- dinov, Raquel Urtasun,
Antonio Torralba, and Sanja Fidler. 2015. Aligning books and movies:
Towards story-like visual explanations by watching movies and reading
books. In Proceedings of the IEEE international conference on computer
vision , pages 19\u201327. Appendix for \u201cBERT: Pre-training of Deep
Bidirectional Transformers for Language Understanding\u201d We organize
the appendix into three sections: \u2022 Additional implementation details
for BERT are presented in Appendix A;\u2022 Additional details for our
experiments are presented in Appendix B; and \u2022 Additional ablation
studies are presented in Appendix C. We present additional ablation
studies for BERT including: \u2013Effect of Number of Training Steps; and
\u2013Ablation for Different"]} {"question": "What is the main difference
between BERT and previous language representation models?", "variant_id":
"v1", "line_number": 2, answer":"BERT is designed to pre-train deep
bidirectional representations from unlabeled text by jointly conditioning
on both left and right context in all layers, allowing it to incorporate
context from both directions. This is unlike previous language
representation models that are unidirectional, which limits the choice of
architectures that can be used during pre-training and could be
sub-optimal for sentence-level tasks and token-level tasks such as
question answering.","context":["BERT: Pre-training of Deep Bidirectional
Transformers for Language Understanding Jacob Devlin Ming-Wei Chang Kenton
Lee Kristina Toutanova Google AI Language
fjacobdevlin,mingweichang,kentonl,kristout [email protected] Abstract We
introduce a new language representa- tion model called BERT , which stands
for Bidirectional Encoder Representations from Transformers. Unlike recent
language repre- sentation models (Peters et al., 2018a; Rad- ford et al.,
2018), BERT is designed to pre- train deep bidirectional representations
from unlabeled text by jointly conditioning on both left and right context
in all layers. As a re- sult, the pre-trained BERT model can be \ufb01ne-
tuned with just one additional output layer to create state-of-the-art
models for a wide range of tasks, such as question answering and language
inference, without substantial task- speci\ufb01c architecture
modi\ufb01cations. BERT is conceptually simple and empirically powerful.
It obtains new state-of-the-art re- sults on eleven natural language
processing tasks, including pushing the GLUE score to 80.5% (7.7% point
absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute
improvement), SQuAD v1.1 question answer- ing Test F1 to 93.2 (1.5 point
absolute im- provement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute
improvement). 1 Introduction Language model pre-training has been shown to
be effective for improving many natural language processing tasks (Dai and
Le, 2015; Peters et al., 2018a; Radford et al., 2018; Howard and Ruder,
2018). These include sentence-level tasks such as natural language
inference (Bowman et al., 2015; Williams et al., 2018) and paraphrasing
(Dolan and Brockett, 2005), which aim to predict the re- lationships
between sentences by analyzing them holistically, as well as token-level
tasks such as named entity recognition and question answering, where
models are required to produce \ufb01ne-grained output at the token level
(Tjong Kim Sang and De Meulder, 2003; Rajpurkar et al., 2016).There are
two existing strategies for apply- ing pre-trained language
representations to down- stream tasks: feature-based and\ufb01ne-tuning .
The feature-based approach, such as ELMo (Peters et al., 2018a), uses
task-speci\ufb01c architectures that include the pre-trained
representations as addi- tional features. The \ufb01ne-tuning approach,
such as the Generative Pre-trained Transformer (OpenAI GPT) (Radford et
al., 2018), introduces minimal task-speci\ufb01c parameters, and is
trained on the downstream tasks by simply \ufb01ne-tuning allpre- trained
parameters. The two approaches share the same objective function during
pre-training, where they use unidirectional language models to learn
general language representations. We argue that current techniques
restrict the power of the pre-trained representations, espe- cially for
the \ufb01ne-tuning approaches. The ma- jor limitation is that standard
language models are unidirectional, and this limits the choice of archi-
tectures that can be used during pre-training. For example, in OpenAI GPT,
the authors use a left-to- right architecture, where every token can only
at- tend to previous tokens in the self-attention layers of the
Transformer (Vaswani et al., 2017). Such re- strictions are sub-optimal
for sentence-level tasks, and could be very harmful when applying
\ufb01ne- tuning based approaches to token-level tasks such as question
answering, where it is crucial to incor- porate context from both
directions. In this paper, we improve the \ufb01ne-tuning based approaches
by proposing BERT: Bidirectional Encoder Representations from
Transformers.","the self-attention layers of the Transformer (Vaswani et
al., 2017). Such re- strictions are sub-optimal for sentence-level tasks,
and could be very harmful when applying \ufb01ne- tuning based approaches
to token-level tasks such as question answering, where it is crucial to
incor- porate context from both directions. In this paper, we improve the
\ufb01ne-tuning based approaches by proposing BERT: Bidirectional Encoder
Representations from Transformers. BERT alleviates the previously
mentioned unidi- rectionality constraint by using a \u201cmasked lan-
guage model\u201d (MLM) pre-training objective, in- spired by the Cloze
task (Taylor, 1953). The masked language model randomly masks some of the
tokens from the input, and the objective is to predict the original
vocabulary id of the maskedarXiv:1810.04805v2 [cs.CL] 24 May 2019word
based only on its context. Unlike left-to- right language model
pre-training, the MLM ob- jective enables the representation to fuse the
left and the right context, which allows us to pre- train a deep
bidirectional Transformer. In addi- tion to the masked language model, we
also use a \u201cnext sentence prediction\u201d task that jointly pre-
trains text-pair representations. The contributions of our paper are as
follows: \u2022 We demonstrate the importance of bidirectional
pre-training for language representations. Un- like Radford et al. (2018),
which uses unidirec- tional language models for pre-training, BERT uses
masked language models to enable pre- trained deep bidirectional
representations. This is also in contrast to Peters et al. (2018a), which
uses a shallow concatenation of independently trained left-to-right and
right-to-left LMs. \u2022 We show that pre-trained representations reduce
the need for many heavily-engineered task- speci\ufb01c architectures.
BERT is the \ufb01rst \ufb01ne- tuning based representation model that
achieves state-of-the-art performance on a large suite of sentence-level
andtoken-level tasks, outper- forming many task-speci\ufb01c
architectures. \u2022 BERT advances the state of the art for eleven NLP
tasks. The code and pre-trained mod- els are available at
https:\/\/github.com\/ google-research\/bert . 2 Related Work There is a
long history of pre-training general lan- guage representations, and we
brie\ufb02y review the most widely-used approaches in this section. 2.1
Unsupervised Feature-based Approaches Learning widely applicable
representations of words has been an active area of research for decades,
including non-neural (Brown et al., 1992; Ando and Zhang, 2005; Blitzer et
al., 2006) and neural (Mikolov et al., 2013; Pennington et al., 2014)
methods. Pre-trained word embeddings are an integral part of modern NLP
systems, of- fering signi\ufb01cant improvements over embeddings learned
from scratch (Turian et al., 2010). To pre- train word embedding vectors,
left-to-right lan- guage modeling objectives have been used (Mnih and
Hinton, 2009), as well as objectives to dis- criminate correct from
incorrect words in left and right context (Mikolov et al., 2013).These
approaches have been generalized to coarser granularities, such as
sentence embed- dings (Kiros et al., 2015; Logeswaran and Lee, 2018) or
paragraph embeddings (Le and Mikolov, 2014). "]'
outputs:
groundedness:
type: string
reference: ${parse_score.output}
nodes:
- name: parse_score
type: python
source:
type: code
path: calc_groundedness.py
inputs:
gpt_score: ${gpt_groundedness.output}
- name: aggregate
type: python
source:
type: code
path: aggregate.py
inputs:
groundedness_scores: ${parse_score.output}
aggregation: true
- name: gpt_groundedness
type: llm
source:
type: code
path: gpt_groundedness.md
inputs:
# This is to easily switch between openai and azure openai.
# deployment_name is required by azure openai, model is required by openai.
deployment_name: gpt-4
model: gpt-4
max_tokens: 5
answer: ${inputs.answer}
question: ${inputs.question}
context: ${inputs.context}
temperature: 0
connection: open_ai_connection
api: chat
| promptflow/examples/flows/evaluation/eval-groundedness/flow.dag.yaml/0 | {
"file_path": "promptflow/examples/flows/evaluation/eval-groundedness/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 5523
} | 11 |
$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
inputs:
question:
type: string
default: Which tent is the most waterproof?
is_chat_input: false
answer:
type: string
default: The Alpine Explorer Tent is the most waterproof.
is_chat_input: false
context:
type: string
default: From the our product list, the alpine explorer tent is the most
waterproof. The Adventure Dining Tabbe has higher weight.
is_chat_input: false
ground_truth:
type: string
default: The Alpine Explorer Tent has the highest rainfly waterproof rating at 3000m
is_chat_input: false
metrics:
type: string
default: gpt_groundedness,f1_score,ada_similarity,gpt_fluency,gpt_coherence,gpt_similarity,gpt_relevance
is_chat_input: false
outputs:
f1_score:
type: string
reference: ${concat_scores.output.f1_score}
gpt_coherence:
type: string
reference: ${concat_scores.output.gpt_coherence}
gpt_similarity:
type: string
reference: ${concat_scores.output.gpt_similarity}
gpt_fluency:
type: string
reference: ${concat_scores.output.gpt_fluency}
gpt_relevance:
type: string
reference: ${concat_scores.output.gpt_relevance}
gpt_groundedness:
type: string
reference: ${concat_scores.output.gpt_groundedness}
ada_similarity:
type: string
reference: ${concat_scores.output.ada_similarity}
nodes:
- name: gpt_coherence
type: llm
source:
type: code
path: gpt_coherence_prompt.jinja2
inputs:
deployment_name: gpt-4
temperature: 0
top_p: 1
stop: ""
max_tokens: 1
presence_penalty: 0
frequency_penalty: 0
logit_bias: ""
question: ${inputs.question}
answer: ${inputs.answer}
provider: AzureOpenAI
connection: open_ai_connection
api: chat
module: promptflow.tools.aoai
activate:
when: ${validate_input.output.gpt_coherence}
is: true
use_variants: false
- name: concat_scores
type: python
source:
type: code
path: concat_scores.py
inputs:
ada_cosine_similarity: ${ada_similarity.output}
f1_score: ${f1_score.output}
gpt_coherence_score: ${gpt_coherence.output}
gpt_fluency_score: ${gpt_fluency.output}
gpt_groundedness_score: ${gpt_groundedness.output}
gpt_relevance_score: ${gpt_relevance.output}
gpt_similarity_score: ${gpt_similarity.output}
use_variants: false
- name: gpt_similarity
type: llm
source:
type: code
path: gpt_similarity_prompt.jinja2
inputs:
deployment_name: gpt-4
temperature: 0
top_p: 1
stop: ""
max_tokens: 1
presence_penalty: 0
frequency_penalty: 0
logit_bias: ""
answer: ${inputs.answer}
ground_truth: ${inputs.ground_truth}
question: ${inputs.question}
provider: AzureOpenAI
connection: open_ai_connection
api: chat
module: promptflow.tools.aoai
activate:
when: ${validate_input.output.gpt_similarity}
is: true
use_variants: false
- name: gpt_relevance
type: llm
source:
type: code
path: gpt_relevance_prompt.jinja2
inputs:
deployment_name: gpt-4
temperature: 0
top_p: 1
stop: ""
max_tokens: 1
presence_penalty: 0
frequency_penalty: 0
logit_bias: ""
answer: ${inputs.answer}
context: ${inputs.context}
question: ${inputs.question}
provider: AzureOpenAI
connection: open_ai_connection
api: chat
module: promptflow.tools.aoai
activate:
when: ${validate_input.output.gpt_relevance}
is: true
use_variants: false
- name: gpt_fluency
type: llm
source:
type: code
path: gpt_fluency_prompt.jinja2
inputs:
deployment_name: gpt-4
temperature: 0
top_p: 1
stop: ""
max_tokens: 1
presence_penalty: 0
frequency_penalty: 0
logit_bias: ""
answer: ${inputs.answer}
question: ${inputs.question}
provider: AzureOpenAI
connection: open_ai_connection
api: chat
module: promptflow.tools.aoai
activate:
when: ${validate_input.output.gpt_fluency}
is: true
use_variants: false
- name: f1_score
type: python
source:
type: code
path: f1_score.py
inputs:
answer: ${inputs.answer}
ground_truth: ${inputs.ground_truth}
activate:
when: ${validate_input.output.f1_score}
is: true
use_variants: false
- name: gpt_groundedness
type: llm
source:
type: code
path: gpt_groundedness_prompt.jinja2
inputs:
deployment_name: gpt-4
temperature: 0
top_p: 1
stop: ""
max_tokens: 1
presence_penalty: 0
frequency_penalty: 0
logit_bias: ""
answer: ${inputs.answer}
context: ${inputs.context}
provider: AzureOpenAI
connection: open_ai_connection
api: chat
module: promptflow.tools.aoai
activate:
when: ${validate_input.output.gpt_groundedness}
is: true
use_variants: false
- name: aggregate_variants_results
type: python
source:
type: code
path: aggregate_variants_results.py
inputs:
metrics: ${inputs.metrics}
results: ${concat_scores.output}
aggregation: true
use_variants: false
- name: select_metrics
type: python
source:
type: code
path: select_metrics.py
inputs:
metrics: ${inputs.metrics}
use_variants: false
- name: embeded_ground_truth
type: python
source:
type: package
tool: promptflow.tools.embedding.embedding
inputs:
connection: open_ai_connection
deployment_name: text-embedding-ada-002
input: ${inputs.ground_truth}
activate:
when: ${validate_input.output.ada_similarity}
is: true
use_variants: false
- name: embeded_answer
type: python
source:
type: package
tool: promptflow.tools.embedding.embedding
inputs:
connection: open_ai_connection
deployment_name: text-embedding-ada-002
input: ${inputs.answer}
activate:
when: ${validate_input.output.ada_similarity}
is: true
use_variants: false
- name: ada_similarity
type: python
source:
type: code
path: ada_cosine_similarity_score.py
inputs:
a: ${embeded_ground_truth.output}
b: ${embeded_answer.output}
activate:
when: ${validate_input.output.ada_similarity}
is: true
use_variants: false
- name: validate_input
type: python
source:
type: code
path: validate_input.py
inputs:
answer: ${inputs.answer}
context: ${inputs.context}
ground_truth: ${inputs.ground_truth}
question: ${inputs.question}
selected_metrics: ${select_metrics.output}
use_variants: false
node_variants: {}
environment:
python_requirements_txt: requirements.txt
| promptflow/examples/flows/evaluation/eval-qna-non-rag/flow.dag.yaml/0 | {
"file_path": "promptflow/examples/flows/evaluation/eval-qna-non-rag/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 2623
} | 12 |
from promptflow import tool
import re
@tool
def parse_retrieval_output(retrieval_output: str) -> str:
score_response = [sent.strip() for sent in
retrieval_output.strip("\"").split("# Result")[-1].strip().split('.') if sent.strip()]
parsed_score_response = re.findall(r"\d+", score_response[-1])
if len(parsed_score_response) > 0:
score = parsed_score_response[-1].strip()
if float(score) < 1.0 or float(score) > 5.0:
score = float('nan')
else:
score = float('nan')
try:
reasoning_response, _ = retrieval_output.split("# Result")
except Exception:
reasoning_response = retrieval_output
return {"quality_score": float(score), "quality_reasoning": reasoning_response}
| promptflow/examples/flows/evaluation/eval-qna-rag-metrics/parse_retrival_score.py/0 | {
"file_path": "promptflow/examples/flows/evaluation/eval-qna-rag-metrics/parse_retrival_score.py",
"repo_id": "promptflow",
"token_count": 308
} | 13 |
$schema: https://azuremlschemas.azureedge.net/promptflow/latest/CustomConnection.schema.json
name: azure_ai_language_connection
type: custom
configs:
endpoint: "<azure-language-resource-endpoint>"
secrets:
api_key: "<to-be-replaced>" | promptflow/examples/flows/integrations/azure-ai-language/connections/azure_ai_language.yml/0 | {
"file_path": "promptflow/examples/flows/integrations/azure-ai-language/connections/azure_ai_language.yml",
"repo_id": "promptflow",
"token_count": 85
} | 14 |
You are {{name}}, {{role}}
Play to your strengths as an LLM and pursue simple strategies with no legal complications to complete all goals.
Your decisions must always be made independently without seeking user assistance.
Performance Evaluation:
1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
2. Constructively self-criticize your big-picture behavior constantly.
3. Reflect on past decisions and strategies to refine your approach.
4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
| promptflow/examples/flows/standard/autonomous-agent/system_prompt.jinja2/0 | {
"file_path": "promptflow/examples/flows/standard/autonomous-agent/system_prompt.jinja2",
"repo_id": "promptflow",
"token_count": 130
} | 15 |
from promptflow import tool
@tool
def generate_result(llm_result="", default_result="") -> str:
if llm_result:
return llm_result
else:
return default_result
| promptflow/examples/flows/standard/conditional-flow-for-if-else/generate_result.py/0 | {
"file_path": "promptflow/examples/flows/standard/conditional-flow-for-if-else/generate_result.py",
"repo_id": "promptflow",
"token_count": 71
} | 16 |
{
"package": {},
"code": {
"chat_prompt": {
"type": "prompt",
"inputs": {
"customer_info": {
"type": [
"string"
]
},
"chat_history": {
"type": [
"string"
]
}
},
"source": "user_intent_zero_shot.jinja2"
},
"extract_intent_tool.py": {
"type": "python",
"inputs": {
"chat_prompt": {
"type": [
"object"
]
},
"connection": {
"type": [
"CustomConnection"
]
}
},
"source": "extract_intent_tool.py",
"function": "extract_intent_tool"
},
"user_intent_zero_shot.jinja2": {
"type": "prompt",
"inputs": {
"customer_info": {
"type": [
"string"
]
},
"history": {
"type": [
"string"
]
}
},
"source": "user_intent_zero_shot.jinja2"
}
}
} | promptflow/examples/flows/standard/customer-intent-extraction/.promptflow/flow.tools.json/0 | {
"file_path": "promptflow/examples/flows/standard/customer-intent-extraction/.promptflow/flow.tools.json",
"repo_id": "promptflow",
"token_count": 628
} | 17 |
# Flow with additional_includes
User sometimes need to reference some common files or folders, this sample demos how to solve the problem using additional_includes. The file or folders in additional includes will be
copied to the snapshot folder by promptflow when operate this flow.
## Tools used in this flow
- LLM Tool
- Python Tool
## What you will learn
In this flow, you will learn
- how to add additional includes to the flow
## Prerequisites
Install promptflow sdk and other dependencies:
```bash
pip install -r requirements.txt
```
## Getting Started
### 1. Add additional includes to flow
You can add this field `additional_includes` into the [`flow.dag.yaml`](flow.dag.yaml).
The value of this field is a list of the relative file/folder path to the flow folder.
``` yaml
additional_includes:
- ../web-classification/classify_with_llm.jinja2
- ../web-classification/convert_to_dict.py
- ../web-classification/fetch_text_content_from_url.py
- ../web-classification/prepare_examples.py
- ../web-classification/summarize_text_content.jinja2
- ../web-classification/summarize_text_content__variant_1.jinja2
```
### 2. Test & run the flow with additional includes
In this sample, this flow will references some files in the [web-classification](../web-classification/README.md) flow.
You can execute this flow with additional_include or submit it to cloud. The snapshot generated by Promptflow contains additional include files/folders.
#### Test flow with single line data
```bash
# test with default input value in flow.dag.yaml
pf flow test --flow .
# test with user specified inputs
pf flow test --flow . --inputs url='https://www.microsoft.com/en-us/d/xbox-wireless-controller-stellar-shift-special-edition/94fbjc7h0h6h'
```
#### Run with multi-line data
```bash
# create run using command line args
pf run create --flow . --data ./data.jsonl --column-mapping url='${data.url}' --stream
# create run using yaml file
pf run create --file run.yml --stream
```
You can also skip providing `column-mapping` if provided data has same column name as the flow.
Reference [here](https://aka.ms/pf/column-mapping) for default behavior when `column-mapping` not provided in CLI.
#### Submit run to cloud
Assume we already have a connection named `open_ai_connection` in workspace.
```bash
# set default workspace
az account set -s <your_subscription_id>
az configure --defaults group=<your_resource_group_name> workspace=<your_workspace_name>
```
``` bash
# create run
pfazure run create --flow . --data ./data.jsonl --column-mapping url='${data.url}' --stream
pfazure run create --file run.yml
```
Note: Click portal_url of the run to view the final snapshot.
| promptflow/examples/flows/standard/flow-with-additional-includes/README.md/0 | {
"file_path": "promptflow/examples/flows/standard/flow-with-additional-includes/README.md",
"repo_id": "promptflow",
"token_count": 804
} | 18 |
{"source": "./divider.py"}
{"source": "./azure_open_ai.py"}
{"source": "./generate_docstring_tool.py"}
| promptflow/examples/flows/standard/gen-docstring/data.jsonl/0 | {
"file_path": "promptflow/examples/flows/standard/gen-docstring/data.jsonl",
"repo_id": "promptflow",
"token_count": 44
} | 19 |
from promptflow import tool
import ast
import json
def infinite_loop_check(code_snippet):
tree = ast.parse(code_snippet)
for node in ast.walk(tree):
if isinstance(node, ast.While):
if not node.orelse:
return True
return False
def syntax_error_check(code_snippet):
try:
ast.parse(code_snippet)
except SyntaxError:
return True
return False
def error_fix(code_snippet):
tree = ast.parse(code_snippet)
for node in ast.walk(tree):
if isinstance(node, ast.While):
if not node.orelse:
node.orelse = [ast.Pass()]
return ast.unparse(tree)
@tool
def code_refine(original_code: str) -> str:
try:
original_code = json.loads(original_code)["code"]
fixed_code = None
if infinite_loop_check(original_code):
fixed_code = error_fix(original_code)
else:
fixed_code = original_code
if syntax_error_check(fixed_code):
fixed_code = error_fix(fixed_code)
return fixed_code
except json.JSONDecodeError:
return "JSONDecodeError"
except Exception as e:
return "Unknown Error:" + str(e)
if __name__ == "__main__":
code = "{\n \"code\": \"distance_A = 10 * 0.5\\ndistance_B = 15 * t\\n\\n\
equation: distance_A = distance_B\\n\\n\10 * 0.5 = 15 * t\\n\\nt = (10 * 0.5) / 15\\n\\nprint(t)\"\n}"
code_refine = code_refine(code)
print(code_refine)
| promptflow/examples/flows/standard/maths-to-code/code_refine.py/0 | {
"file_path": "promptflow/examples/flows/standard/maths-to-code/code_refine.py",
"repo_id": "promptflow",
"token_count": 680
} | 20 |
{
"package": {},
"code": {
"fetch_text_content_from_url.py": {
"type": "python",
"inputs": {
"url": {
"type": [
"string"
]
}
},
"source": "fetch_text_content_from_url.py",
"function": "fetch_text_content_from_url"
},
"summarize_text_content.jinja2": {
"type": "llm",
"inputs": {
"text": {
"type": [
"string"
]
}
},
"source": "summarize_text_content.jinja2"
},
"summarize_text_content__variant_1.jinja2": {
"type": "llm",
"inputs": {
"text": {
"type": [
"string"
]
}
},
"source": "summarize_text_content__variant_1.jinja2"
},
"prepare_examples.py": {
"type": "python",
"source": "prepare_examples.py",
"function": "prepare_examples"
},
"classify_with_llm.jinja2": {
"type": "llm",
"inputs": {
"url": {
"type": [
"string"
]
},
"examples": {
"type": [
"string"
]
},
"text_content": {
"type": [
"string"
]
}
},
"source": "classify_with_llm.jinja2"
},
"convert_to_dict.py": {
"type": "python",
"inputs": {
"input_str": {
"type": [
"string"
]
}
},
"source": "convert_to_dict.py",
"function": "convert_to_dict"
}
}
} | promptflow/examples/flows/standard/web-classification/.promptflow/flow.tools.json/0 | {
"file_path": "promptflow/examples/flows/standard/web-classification/.promptflow/flow.tools.json",
"repo_id": "promptflow",
"token_count": 938
} | 21 |
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore
| promptflow/examples/tools/tool-package-quickstart/my_tool_package/__init__.py/0 | {
"file_path": "promptflow/examples/tools/tool-package-quickstart/my_tool_package/__init__.py",
"repo_id": "promptflow",
"token_count": 30
} | 22 |
my_tool_package.tools.tool_with_cascading_inputs.my_tool:
function: my_tool
inputs:
user_type:
type:
- string
enum:
- student
- teacher
student_id:
type:
- string
enabled_by: user_type
enabled_by_value: [student]
teacher_id:
type:
- string
enabled_by: user_type
enabled_by_value: [teacher]
module: my_tool_package.tools.tool_with_cascading_inputs
name: My Tool with Cascading Inputs
description: This is my tool with cascading inputs
type: python | promptflow/examples/tools/tool-package-quickstart/my_tool_package/yamls/tool_with_cascading_inputs.yaml/0 | {
"file_path": "promptflow/examples/tools/tool-package-quickstart/my_tool_package/yamls/tool_with_cascading_inputs.yaml",
"repo_id": "promptflow",
"token_count": 244
} | 23 |
$schema: https://azuremlschemas.azureedge.net/promptflow/latest/CustomConnection.schema.json
name: basic_custom_connection
type: custom
configs:
api_base: <to-be-replaced>
secrets: # must-have
api_key: <to-be-replaced>
| promptflow/examples/tools/use-cases/custom_llm_tool_showcase/custom_connection.yml/0 | {
"file_path": "promptflow/examples/tools/use-cases/custom_llm_tool_showcase/custom_connection.yml",
"repo_id": "promptflow",
"token_count": 86
} | 24 |
# Copyright (C) Microsoft Corporation. All rights reserved.
<#
.SYNOPSIS
Check Policheck Scan result.
.DESCRIPTION
Helper script to check the Policheck result.
If there is policheck failure, show the error and throw exception.
#>
[CmdLetbinding()]
param (
[string]$policheckResult,
[string]$raiseError = $true
)
$result = Get-Content -Path $policheckResult | Measure-Object -Line;
Write-Host("Number of errors found in this scan: " + ($result.Lines - 1));
if ($raiseError -and ($result.Lines -gt 1))
{
Get-Content -Path $policheckResult;
throw "Policheck scan completed successfully but there are issues to fix.";
}
# Read-Host "Press enter to finish the process and close this window";
| promptflow/scripts/compliance-check/Check-PolicheckScan.ps1/0 | {
"file_path": "promptflow/scripts/compliance-check/Check-PolicheckScan.ps1",
"repo_id": "promptflow",
"token_count": 242
} | 25 |
{
"type": "object",
"oneOf": [
{
"properties": {
"additional_includes": {
"title": "additional_includes",
"type": "array",
"items": {
"title": "additional_includes",
"type": "string"
}
},
"description": {
"title": "description",
"type": "string"
},
"display_name": {
"title": "display_name",
"type": "string"
},
"environment": {
"title": "environment",
"type": "object",
"additionalProperties": {}
},
"inputs": {
"title": "inputs",
"type": "object",
"additionalProperties": {
"type": "object",
"$ref": "#/definitions/FlowInputSchema"
}
},
"language": {
"title": "language",
"type": "string"
},
"node_variants": {
"title": "node_variants",
"type": "object",
"additionalProperties": {
"title": "node_variants",
"type": "object",
"additionalProperties": {}
}
},
"nodes": {
"title": "nodes",
"type": "array",
"items": {
"title": "nodes",
"type": "object",
"additionalProperties": {}
}
},
"outputs": {
"title": "outputs",
"type": "object",
"additionalProperties": {
"type": "object",
"$ref": "#/definitions/FlowOutputSchema"
}
},
"$schema": {
"title": "$schema",
"type": "string",
"readOnly": true
},
"tags": {
"title": "tags",
"type": "object",
"additionalProperties": {
"title": "tags",
"type": "string"
}
},
"type": {
"title": "type",
"type": "string",
"enum": [
"standard",
"evaluation",
"chat"
],
"enumNames": []
}
},
"type": "object",
"additionalProperties": false
},
{
"properties": {
"additional_includes": {
"title": "additional_includes",
"type": "array",
"items": {
"title": "additional_includes",
"type": "string"
}
},
"description": {
"title": "description",
"type": "string"
},
"display_name": {
"title": "display_name",
"type": "string"
},
"entry": {
"title": "entry",
"type": "string"
},
"environment": {
"title": "environment",
"type": "object",
"additionalProperties": {}
},
"language": {
"title": "language",
"type": "string"
},
"path": {
"title": "path",
"type": "string"
},
"$schema": {
"title": "$schema",
"type": "string",
"readOnly": true
},
"tags": {
"title": "tags",
"type": "object",
"additionalProperties": {
"title": "tags",
"type": "string"
}
},
"type": {
"title": "type",
"type": "string",
"enum": [
"standard",
"evaluation",
"chat"
],
"enumNames": []
}
},
"type": "object",
"required": [
"entry",
"path"
],
"additionalProperties": false
}
]
} | promptflow/scripts/json_schema/Flow.schema.json/0 | {
"file_path": "promptflow/scripts/json_schema/Flow.schema.json",
"repo_id": "promptflow",
"token_count": 3605
} | 26 |
- name: {{ step_name }}
working-directory: {{ working_dir }}
run: |
gpt_base=${{ '{{' }} secrets.AOAI_API_ENDPOINT_TEST }}
gpt_base=$(echo ${gpt_base//\//\\/})
if [[ -e run.yml ]]; then
sed -i -e "s/\${azure_open_ai_connection.api_key}/${{ '{{' }} secrets.AOAI_API_KEY_TEST }}/g" -e "s/\${azure_open_ai_connection.api_base}/$gpt_base/g" run.yml
fi
| promptflow/scripts/readme/ghactions_driver/workflow_steps/step_create_run_yml.yml.jinja2/0 | {
"file_path": "promptflow/scripts/readme/ghactions_driver/workflow_steps/step_create_run_yml.yml.jinja2",
"repo_id": "promptflow",
"token_count": 180
} | 27 |
from promptflow._sdk._load_functions import load_yaml
from promptflow._sdk._pf_client import PFClient
from ghactions_driver.readme_step import ReadmeStepsManage
from pathlib import Path
import os
import subprocess
import sys
def install(filename):
subprocess.check_call([sys.executable, "-m", "pip", "install", "-r", filename])
def main(input_glob_flow_dag):
# check if flow.dag.yaml contains schema field.
error = False
globs = set()
pf_client = PFClient()
for p in input_glob_flow_dag:
globs = globs | set(Path(ReadmeStepsManage.git_base_dir()).glob(p))
flow_dag_items = sorted([i for i in globs])
for file in flow_dag_items:
data = load_yaml(file)
if "$schema" not in data.keys():
print(f"{file} does not contain $schema field.")
error = True
if error is False:
new_links = []
if (Path(file).parent / "requirements.txt").exists():
install(Path(file).parent / "requirements.txt")
if "flow-with-symlinks" in str(file):
saved_path = os.getcwd()
os.chdir(str(file.parent))
source_folder = Path("../web-classification")
for file_name in os.listdir(source_folder):
if not Path(file_name).exists():
os.symlink(
source_folder / file_name,
file_name
)
new_links.append(file_name)
validation_result = pf_client.flows.validate(
flow=file,
)
if "flow-with-symlinks" in str(file):
for link in new_links:
os.remove(link)
os.chdir(saved_path)
print(f"VALIDATE {file}: \n" + repr(validation_result))
if not validation_result.passed:
print(f"{file} is not valid.")
error = True
if len(validation_result._warnings) > 0:
print(f"{file} has warnings.")
error = True
if error:
raise Exception("Some flow.dag.yaml validation failed.")
else:
print("All flow.dag.yaml validation completed.")
if __name__ == "__main__":
input_glob_flow_dag = [
"examples/**/flow.dag.yaml",
]
main(input_glob_flow_dag)
| promptflow/scripts/readme/schema_checker.py/0 | {
"file_path": "promptflow/scripts/readme/schema_checker.py",
"repo_id": "promptflow",
"token_count": 1200
} | 28 |
import pytest
import unittest
from promptflow.connections import CustomConnection
from {{ package_name }}.tools.{{ tool_name }} import {{ function_name }}
@pytest.fixture
def my_custom_connection() -> CustomConnection:
my_custom_connection = CustomConnection(
{
"api-key" : "my-api-key",
"api-secret" : "my-api-secret",
"api-url" : "my-api-url"
}
)
return my_custom_connection
class TestTool:
def test_{{ function_name }}(self, my_custom_connection):
result = {{ function_name }}(my_custom_connection, input_text="Microsoft")
assert result == "Hello Microsoft"
# Run the unit tests
if __name__ == "__main__":
unittest.main()
| promptflow/scripts/tool/templates/test_tool.py.j2/0 | {
"file_path": "promptflow/scripts/tool/templates/test_tool.py.j2",
"repo_id": "promptflow",
"token_count": 280
} | 29 |
[flake8]
extend-ignore = E203, E266, W503, F403, F821
max-line-length = 120
enable-extensions = E123,E133,E241,E242,E704,W505
exclude =
.git
.tox
.eggs
__pycache__
tests/fixtures/*
docs/*
venv,.pytest_cache
build
src/promptflow/promptflow/azure/_restclient
src/promptflow/tests/test_configs/*
import-order-style = google
[mypy]
ignore_missing_imports = True
disallow_untyped_defs = True
[mypy-pytest,pytest_mock]
ignore_missing_imports = True
[tool:pycln]
quiet = True
[black]
line_length = 120
[pycln]
silence = True
[isort]
# we use check for make fmt*
profile = "black"
# no need to fmt ignored
skip_gitignore = true
# needs to be the same as in black
line_length = 120
use_parentheses = true
include_trailing_comma = true
honor_noqa = true
ensure_newline_before_comments = true
skip_glob = [
docs/**,
pipelines/**,
pytest/**,
samples/**,
]
known_third_party = azure,mock,numpy,pandas,pydash,pytest,pytest_mock,requests,setuptools,six,sklearn,tqdm,urllib3,utilities,utils,yaml,jsonschema,strictyaml,jwt,pathspec,isodate,docker
known_first_party = promptflow,promptflow_test
| promptflow/setup.cfg/0 | {
"file_path": "promptflow/setup.cfg",
"repo_id": "promptflow",
"token_count": 494
} | 30 |
from enum import Enum
try:
from openai import OpenAI as OpenAIClient
except Exception:
raise Exception(
"Please upgrade your OpenAI package to version 1.0.0 or later using the command: pip install --upgrade openai.")
from promptflow.tools.common import render_jinja_template, handle_openai_error, \
parse_chat, to_bool, validate_functions, process_function_call, \
post_process_chat_api_response, normalize_connection_config
# Avoid circular dependencies: Use import 'from promptflow._internal' instead of 'from promptflow'
# since the code here is in promptflow namespace as well
from promptflow._internal import ToolProvider, tool, register_apis
from promptflow.connections import OpenAIConnection
from promptflow.contracts.types import PromptTemplate
class Engine(str, Enum):
TEXT_DAVINCI_001 = "text-davinci-001"
TEXT_DAVINCI_002 = "text-davinci-002"
TEXT_DAVINCI_003 = "text-davinci-003"
TEXT_CURIE_001 = "text-curie-001"
TEXT_BABBAGE_001 = "text-babbage-001"
TEXT_ADA_001 = "text-ada-001"
CODE_CUSHMAN_001 = "code-cushman-001"
CODE_DAVINCI_002 = "code-davinci-002"
class OpenAI(ToolProvider):
def __init__(self, connection: OpenAIConnection):
super().__init__()
self._connection_dict = normalize_connection_config(connection)
self._client = OpenAIClient(**self._connection_dict)
@tool
@handle_openai_error()
def completion(
self,
prompt: PromptTemplate,
model: Engine = Engine.TEXT_DAVINCI_003,
suffix: str = None,
max_tokens: int = 16,
temperature: float = 1.0,
top_p: float = 1.0,
n: int = 1,
# stream is a hidden to the end user, it is only supposed to be set by the executor.
stream: bool = False,
logprobs: int = None,
echo: bool = False,
stop: list = None,
presence_penalty: float = 0,
frequency_penalty: float = 0,
best_of: int = 1,
logit_bias: dict = {},
user: str = "",
**kwargs,
) -> str:
prompt = render_jinja_template(prompt, trim_blocks=True, keep_trailing_newline=True, **kwargs)
# TODO: remove below type conversion after client can pass json rather than string.
echo = to_bool(echo)
stream = to_bool(stream)
response = self._client.completions.create(
prompt=prompt,
model=model.value if isinstance(model, Enum) else model,
# empty string suffix should be treated as None.
suffix=suffix if suffix else None,
max_tokens=int(max_tokens),
temperature=float(temperature),
top_p=float(top_p),
n=int(n),
stream=stream,
logprobs=int(logprobs) if logprobs else None,
echo=echo,
stop=stop if stop else None,
presence_penalty=float(presence_penalty),
frequency_penalty=float(frequency_penalty),
best_of=int(best_of),
# Logit bias must be a dict if we passed it to openai api.
logit_bias=logit_bias if logit_bias else {},
user=user
)
if stream:
def generator():
for chunk in response:
if chunk.choices:
yield getattr(chunk.choices[0], "text", "")
# We must return the generator object, not using yield directly here.
# Otherwise, the function itself will become a generator, despite whether stream is True or False.
return generator()
else:
# get first element because prompt is single.
return response.choices[0].text
@tool
@handle_openai_error()
def chat(
self,
prompt: PromptTemplate,
model: str = "gpt-3.5-turbo",
temperature: float = 1.0,
top_p: float = 1.0,
n: int = 1,
# stream is a hidden to the end user, it is only supposed to be set by the executor.
stream: bool = False,
stop: list = None,
max_tokens: int = None,
presence_penalty: float = 0,
frequency_penalty: float = 0,
logit_bias: dict = {},
user: str = "",
# function_call can be of type str or dict.
function_call: object = None,
functions: list = None,
response_format: object = None,
**kwargs
) -> [str, dict]:
chat_str = render_jinja_template(prompt, trim_blocks=True, keep_trailing_newline=True, **kwargs)
messages = parse_chat(chat_str)
# TODO: remove below type conversion after client can pass json rather than string.
stream = to_bool(stream)
params = {
"model": model,
"messages": messages,
"temperature": float(temperature),
"top_p": float(top_p),
"n": int(n),
"stream": stream,
"stop": stop if stop else None,
"max_tokens": int(max_tokens) if max_tokens is not None and str(max_tokens).lower() != "inf" else None,
"presence_penalty": float(presence_penalty),
"frequency_penalty": float(frequency_penalty),
"logit_bias": logit_bias,
"user": user,
"response_format": response_format
}
if functions is not None:
validate_functions(functions)
params["functions"] = functions
params["function_call"] = process_function_call(function_call)
completion = self._client.chat.completions.create(**params)
return post_process_chat_api_response(completion, stream, functions)
register_apis(OpenAI)
@tool
def completion(
connection: OpenAIConnection,
prompt: PromptTemplate,
model: Engine = Engine.TEXT_DAVINCI_003,
suffix: str = None,
max_tokens: int = 16,
temperature: float = 1.0,
top_p: float = 1,
n: int = 1,
stream: bool = False,
logprobs: int = None,
echo: bool = False,
stop: list = None,
presence_penalty: float = 0,
frequency_penalty: float = 0,
best_of: int = 1,
logit_bias: dict = {},
user: str = "",
**kwargs
) -> [str, dict]:
return OpenAI(connection).completion(
prompt=prompt,
model=model,
suffix=suffix,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
n=n,
stream=stream,
logprobs=logprobs,
echo=echo,
stop=stop if stop else None,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
best_of=best_of,
logit_bias=logit_bias,
user=user,
**kwargs,
)
@tool
def chat(
connection: OpenAIConnection,
prompt: PromptTemplate,
model: str = "gpt-3.5-turbo",
temperature: float = 1,
top_p: float = 1,
n: int = 1,
stream: bool = False,
stop: list = None,
max_tokens: int = None,
presence_penalty: float = 0,
frequency_penalty: float = 0,
logit_bias: dict = {},
user: str = "",
function_call: object = None,
functions: list = None,
response_format: object = None,
**kwargs
) -> [str, dict]:
return OpenAI(connection).chat(
prompt=prompt,
model=model,
temperature=temperature,
top_p=top_p,
n=n,
stream=stream,
stop=stop if stop else None,
max_tokens=max_tokens,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
logit_bias=logit_bias,
user=user,
function_call=function_call,
functions=functions,
response_format=response_format,
**kwargs,
)
| promptflow/src/promptflow-tools/promptflow/tools/openai.py/0 | {
"file_path": "promptflow/src/promptflow-tools/promptflow/tools/openai.py",
"repo_id": "promptflow",
"token_count": 3479
} | 31 |
import pytest
from promptflow.tools.azure_content_safety import analyze_text
@pytest.mark.usefixtures("use_secrets_config_file")
class TestAzureContentSafety:
def test_azure_content_safety_analyze_happy_path(self, azure_content_safety_connection):
text = "I hate you."
result = analyze_text(
connection=azure_content_safety_connection,
text=text
)
assert "suggested_action" in result
assert "action_by_category" in result
| promptflow/src/promptflow-tools/tests/test_acs.py/0 | {
"file_path": "promptflow/src/promptflow-tools/tests/test_acs.py",
"repo_id": "promptflow",
"token_count": 194
} | 32 |
import json
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def __getattr__(self, item):
if item in self:
return self.__getitem__(item)
return super().__getattribute__(item)
def is_json_serializable(data, function_name):
try:
json.dumps(data)
except TypeError:
raise TypeError(f"{function_name} output is not JSON serializable!")
def verify_url_exists(endpoint_url: str) -> bool:
import urllib.request
from urllib.request import HTTPError
from urllib.error import URLError
try:
urllib.request.urlopen(
urllib.request.Request(endpoint_url),
timeout=50)
except HTTPError as e:
# verify that the connection is not authorized, anything else would mean the endpoint is failed
return e.code == 403
except URLError:
# Endpoint does not exist - skip the test
return False
raise Exception("Task Succeeded unexpectedly.")
| promptflow/src/promptflow-tools/tests/utils.py/0 | {
"file_path": "promptflow/src/promptflow-tools/tests/utils.py",
"repo_id": "promptflow",
"token_count": 412
} | 33 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import argparse
import json
from promptflow._cli._params import (
add_param_all_results,
add_param_archived_only,
add_param_include_archived,
add_param_max_results,
base_params,
)
from promptflow._cli._utils import activate_action, exception_handler
from promptflow._sdk._constants import get_list_view_type
from promptflow._sdk._load_functions import load_common
from promptflow._sdk._pf_client import PFClient
from promptflow._sdk.entities._experiment import Experiment, ExperimentTemplate
from promptflow._utils.logger_utils import get_cli_sdk_logger
logger = get_cli_sdk_logger()
_client = None
def _get_pf_client():
global _client
if _client is None:
_client = PFClient()
return _client
def add_param_template(parser):
parser.add_argument("--template", type=str, required=True, help="The experiment template path.")
def add_param_name(parser):
parser.add_argument("--name", "-n", type=str, help="The experiment name.")
def add_experiment_create(subparsers):
epilog = """
Examples:
# Create an experiment from a template:
pf experiment create --template flow.exp.yaml
"""
add_params = [add_param_template, add_param_name] + base_params
create_parser = activate_action(
name="create",
description=None,
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Create an experiment.",
action_param_name="sub_action",
)
return create_parser
def add_experiment_list(subparsers):
epilog = """
Examples:
# List all experiments:
pf experiment list
"""
activate_action(
name="list",
description="List all experiments.",
epilog=epilog,
add_params=[
add_param_max_results,
add_param_all_results,
add_param_archived_only,
add_param_include_archived,
]
+ base_params,
subparsers=subparsers,
help_message="List all experiments.",
action_param_name="sub_action",
)
def add_experiment_show(subparsers):
epilog = """
Examples:
# Get and show an experiment:
pf experiment show -n my_experiment
"""
activate_action(
name="show",
description="Show an experiment for promptflow.",
epilog=epilog,
add_params=[add_param_name] + base_params,
subparsers=subparsers,
help_message="Show an experiment for promptflow.",
action_param_name="sub_action",
)
def add_experiment_start(subparsers):
epilog = """
Examples:
# Start an experiment:
pf experiment start -n my_experiment
"""
activate_action(
name="start",
description="Start an experiment.",
epilog=epilog,
add_params=[add_param_name] + base_params,
subparsers=subparsers,
help_message="Start an experiment.",
action_param_name="sub_action",
)
def add_experiment_parser(subparsers):
experiment_parser = subparsers.add_parser(
"experiment",
description="[Experimental] A CLI tool to manage experiment for prompt flow.",
help="[Experimental] pf experiment. This is an experimental feature, and may change at any time.",
)
subparsers = experiment_parser.add_subparsers()
add_experiment_create(subparsers)
add_experiment_list(subparsers)
add_experiment_show(subparsers)
add_experiment_start(subparsers)
experiment_parser.set_defaults(action="experiment")
def dispatch_experiment_commands(args: argparse.Namespace):
if args.sub_action == "create":
create_experiment(args)
elif args.sub_action == "list":
list_experiment(args)
elif args.sub_action == "show":
show_experiment(args)
elif args.sub_action == "start":
start_experiment(args)
elif args.sub_action == "show-status":
pass
elif args.sub_action == "update":
pass
elif args.sub_action == "delete":
pass
elif args.sub_action == "stop":
pass
elif args.sub_action == "test":
pass
elif args.sub_action == "clone":
pass
@exception_handler("Create experiment")
def create_experiment(args: argparse.Namespace):
template_path = args.template
logger.debug("Loading experiment template from %s", template_path)
template = load_common(ExperimentTemplate, source=template_path)
logger.debug("Creating experiment from template %s", template.name)
experiment = Experiment.from_template(template, name=args.name)
logger.debug("Creating experiment %s", experiment.name)
exp = _get_pf_client()._experiments.create_or_update(experiment)
print(json.dumps(exp._to_dict(), indent=4))
@exception_handler("List experiment")
def list_experiment(args: argparse.Namespace):
list_view_type = get_list_view_type(archived_only=args.archived_only, include_archived=args.include_archived)
results = _get_pf_client()._experiments.list(args.max_results, list_view_type=list_view_type)
print(json.dumps([result._to_dict() for result in results], indent=4))
@exception_handler("Show experiment")
def show_experiment(args: argparse.Namespace):
result = _get_pf_client()._experiments.get(args.name)
print(json.dumps(result._to_dict(), indent=4))
@exception_handler("Start experiment")
def start_experiment(args: argparse.Namespace):
result = _get_pf_client()._experiments.start(args.name)
print(json.dumps(result._to_dict(), indent=4))
| promptflow/src/promptflow/promptflow/_cli/_pf/_experiment.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_cli/_pf/_experiment.py",
"repo_id": "promptflow",
"token_count": 2168
} | 34 |
The directory structure in the package tool is as follows:
```python
{{ package_name }}
│ setup.py # This file contains metadata about your project like the name, version.
│
│ MANIFEST.in # This file is used to determine which files to include in the distribution of the project.
│
└───{{ package_name }}{{" " * (24 - package_name|length)}}# This is the source directory. All of your project’s source code should be placed in this directory.
{{ tool_name }}.py{{ " " * (17 - tool_name|length)}}# The source code of tools. Using the @tool decorator to identify the function as a tool.
utils.py # Utility functions for the package. A method for listing all tools defined in the package is generated in this file.
__init__.py
```
Please refer to [tool doc](https://microsoft.github.io/promptflow/how-to-guides/develop-a-tool/index.html) for more details about how to develop a tool. | promptflow/src/promptflow/promptflow/_cli/data/package_tool/README.md.jinja2/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_cli/data/package_tool/README.md.jinja2",
"repo_id": "promptflow",
"token_count": 311
} | 35 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import asyncio
import functools
import inspect
import logging
import threading
import time
import uuid
from contextvars import ContextVar
from logging import WARNING
from typing import Callable
from promptflow._core._errors import ToolExecutionError, UnexpectedError
from promptflow._core.cache_manager import AbstractCacheManager, CacheInfo, CacheResult
from promptflow._core.operation_context import OperationContext
from promptflow._utils.logger_utils import flow_logger, logger
from promptflow._utils.thread_utils import RepeatLogTimer
from promptflow._utils.utils import generate_elapsed_time_messages
from promptflow.contracts.flow import Node
from promptflow.contracts.run_info import RunInfo
from promptflow.exceptions import PromptflowException
from .run_tracker import RunTracker
from .thread_local_singleton import ThreadLocalSingleton
from .tracer import Tracer
class FlowExecutionContext(ThreadLocalSingleton):
"""The context for a flow execution."""
CONTEXT_VAR_NAME = "Flow"
context_var = ContextVar(CONTEXT_VAR_NAME, default=None)
def __init__(
self,
name,
run_tracker: RunTracker,
cache_manager: AbstractCacheManager = None,
run_id=None,
flow_id=None,
line_number=None,
variant_id=None,
):
self._name = name
self._run_tracker = run_tracker
self._cache_manager = cache_manager or AbstractCacheManager.init_from_env()
self._run_id = run_id or str(uuid.uuid4())
self._flow_id = flow_id or self._run_id
self._line_number = line_number
self._variant_id = variant_id
def copy(self):
return FlowExecutionContext(
name=self._name,
run_tracker=self._run_tracker,
cache_manager=self._cache_manager,
run_id=self._run_id,
flow_id=self._flow_id,
line_number=self._line_number,
variant_id=self._variant_id,
)
def _update_operation_context(self):
flow_context_info = {"flow-id": self._flow_id, "root-run-id": self._run_id}
OperationContext.get_instance().update(flow_context_info)
def cancel_node_runs(self, msg):
self._run_tracker.cancel_node_runs(msg, self._run_id)
def invoke_tool(self, node: Node, f: Callable, kwargs):
run_info = self._prepare_node_run(node, f, kwargs)
node_run_id = run_info.run_id
traces = []
try:
hit_cache = False
# Get result from cache. If hit cache, no need to execute f.
cache_info: CacheInfo = self._cache_manager.calculate_cache_info(self._flow_id, f, [], kwargs)
if node.enable_cache and cache_info:
cache_result: CacheResult = self._cache_manager.get_cache_result(cache_info)
if cache_result and cache_result.hit_cache:
# Assign cached_flow_run_id and cached_run_id.
run_info.cached_flow_run_id = cache_result.cached_flow_run_id
run_info.cached_run_id = cache_result.cached_run_id
result = cache_result.result
hit_cache = True
if not hit_cache:
Tracer.start_tracing(node_run_id, node.name)
result = self._invoke_tool_with_timer(node, f, kwargs)
traces = Tracer.end_tracing(node_run_id)
self._run_tracker.end_run(node_run_id, result=result, traces=traces)
# Record result in cache so that future run might reuse its result.
if not hit_cache and node.enable_cache:
self._persist_cache(cache_info, run_info)
flow_logger.info(f"Node {node.name} completes.")
return result
except Exception as e:
logger.exception(f"Node {node.name} in line {self._line_number} failed. Exception: {e}.")
if not traces:
traces = Tracer.end_tracing(node_run_id)
self._run_tracker.end_run(node_run_id, ex=e, traces=traces)
raise
finally:
self._run_tracker.persist_node_run(run_info)
def _prepare_node_run(self, node: Node, f, kwargs={}):
# Ensure this thread has a valid operation context
self._update_operation_context()
node_run_id = self._generate_node_run_id(node)
flow_logger.info(f"Executing node {node.name}. node run id: {node_run_id}")
parent_run_id = f"{self._run_id}_{self._line_number}" if self._line_number is not None else self._run_id
run_info: RunInfo = self._run_tracker.start_node_run(
node=node.name,
flow_run_id=self._run_id,
parent_run_id=parent_run_id,
run_id=node_run_id,
index=self._line_number,
)
run_info.index = self._line_number
run_info.variant_id = self._variant_id
self._run_tracker.set_inputs(node_run_id, {key: value for key, value in kwargs.items() if key != "self"})
return run_info
async def invoke_tool_async(self, node: Node, f: Callable, kwargs):
if not inspect.iscoroutinefunction(f):
raise UnexpectedError(
message_format="Tool '{function}' in node '{node}' is not a coroutine function.",
function=f,
node=node.name,
)
run_info = self._prepare_node_run(node, f, kwargs=kwargs)
node_run_id = run_info.run_id
traces = []
try:
Tracer.start_tracing(node_run_id, node.name)
result = await self._invoke_tool_async_inner(node, f, kwargs)
traces = Tracer.end_tracing(node_run_id)
self._run_tracker.end_run(node_run_id, result=result, traces=traces)
flow_logger.info(f"Node {node.name} completes.")
return result
# User tool should reraise the CancelledError after its own handling logic,
# so that the error can propagate to the scheduler for handling.
# Otherwise, the node would end with Completed status.
except asyncio.CancelledError as e:
logger.info(f"Node {node.name} in line {self._line_number} is canceled.")
traces = Tracer.end_tracing(node_run_id)
self._run_tracker.end_run(node_run_id, ex=e, traces=traces)
raise
except Exception as e:
logger.exception(f"Node {node.name} in line {self._line_number} failed. Exception: {e}.")
traces = Tracer.end_tracing(node_run_id)
self._run_tracker.end_run(node_run_id, ex=e, traces=traces)
raise
finally:
self._run_tracker.persist_node_run(run_info)
async def _invoke_tool_async_inner(self, node: Node, f: Callable, kwargs):
module = f.func.__module__ if isinstance(f, functools.partial) else f.__module__
try:
return await f(**kwargs)
except PromptflowException as e:
# All the exceptions from built-in tools are PromptflowException.
# For these cases, raise the exception directly.
if module is not None:
e.module = module
raise e
except Exception as e:
# Otherwise, we assume the error comes from user's tool.
# For these cases, raise ToolExecutionError, which is classified as UserError
# and shows stack trace in the error message to make it easy for user to troubleshoot.
raise ToolExecutionError(node_name=node.name, module=module) from e
def _invoke_tool_with_timer(self, node: Node, f: Callable, kwargs):
module = f.func.__module__ if isinstance(f, functools.partial) else f.__module__
node_name = node.name
try:
logging_name = node_name
if self._line_number is not None:
logging_name = f"{node_name} in line {self._line_number}"
interval_seconds = 60
start_time = time.perf_counter()
thread_id = threading.current_thread().ident
with RepeatLogTimer(
interval_seconds=interval_seconds,
logger=logger,
level=WARNING,
log_message_function=generate_elapsed_time_messages,
args=(logging_name, start_time, interval_seconds, thread_id),
):
return f(**kwargs)
except PromptflowException as e:
# All the exceptions from built-in tools are PromptflowException.
# For these cases, raise the exception directly.
if module is not None:
e.module = module
raise e
except Exception as e:
# Otherwise, we assume the error comes from user's tool.
# For these cases, raise ToolExecutionError, which is classified as UserError
# and shows stack trace in the error message to make it easy for user to troubleshoot.
raise ToolExecutionError(node_name=node_name, module=module) from e
def bypass_node(self, node: Node):
"""Update teh bypassed node run info."""
node_run_id = self._generate_node_run_id(node)
flow_logger.info(f"Bypassing node {node.name}. node run id: {node_run_id}")
parent_run_id = f"{self._run_id}_{self._line_number}" if self._line_number is not None else self._run_id
run_info = self._run_tracker.bypass_node_run(
node=node.name,
flow_run_id=self._run_id,
parent_run_id=parent_run_id,
run_id=node_run_id,
index=self._line_number,
variant_id=self._variant_id,
)
self._run_tracker.persist_node_run(run_info)
def _persist_cache(self, cache_info: CacheInfo, run_info: RunInfo):
"""Record result in cache storage if hash_id is valid."""
if cache_info and cache_info.hash_id is not None and len(cache_info.hash_id) > 0:
try:
self._cache_manager.persist_result(run_info, cache_info, self._flow_id)
except Exception as ex:
# Not a critical path, swallow the exception.
logging.warning(f"Failed to persist cache result. run_id: {run_info.run_id}. Exception: {ex}")
def _generate_node_run_id(self, node: Node) -> str:
if node.aggregation:
# For reduce node, the id should be constructed by the flow run info run id
return f"{self._run_id}_{node.name}_reduce"
if self._line_number is None:
return f"{self._run_id}_{node.name}_{uuid.uuid4()}"
return f"{self._run_id}_{node.name}_{self._line_number}"
| promptflow/src/promptflow/promptflow/_core/flow_execution_context.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_core/flow_execution_context.py",
"repo_id": "promptflow",
"token_count": 4757
} | 36 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import os
from enum import Enum
from pathlib import Path
LOGGER_NAME = "promptflow"
PROMPT_FLOW_HOME_DIR_ENV_VAR = "PF_HOME_DIRECTORY"
PROMPT_FLOW_DIR_NAME = ".promptflow"
def _prepare_home_dir() -> Path:
"""Prepare prompt flow home directory.
User can configure it by setting environment variable: `PF_HOME_DIRECTORY`;
if not configured, or configured value is not valid, use default value: "~/.promptflow/".
"""
from promptflow._utils.logger_utils import get_cli_sdk_logger
logger = get_cli_sdk_logger()
if PROMPT_FLOW_HOME_DIR_ENV_VAR in os.environ:
logger.debug(
f"environment variable {PROMPT_FLOW_HOME_DIR_ENV_VAR!r} is set, honor it preparing home directory."
)
try:
pf_home_dir = Path(os.getenv(PROMPT_FLOW_HOME_DIR_ENV_VAR)).resolve()
pf_home_dir.mkdir(parents=True, exist_ok=True)
return pf_home_dir
except Exception as e: # pylint: disable=broad-except
_warning_message = (
"Invalid configuration for prompt flow home directory: "
f"{os.getenv(PROMPT_FLOW_HOME_DIR_ENV_VAR)!r}: {str(e)!r}.\n"
'Fall back to use default value: "~/.promptflow/".'
)
logger.warning(_warning_message)
try:
logger.debug("preparing home directory with default value.")
pf_home_dir = (Path.home() / PROMPT_FLOW_DIR_NAME).resolve()
pf_home_dir.mkdir(parents=True, exist_ok=True)
return pf_home_dir
except Exception as e: # pylint: disable=broad-except
_error_message = (
f"Cannot create prompt flow home directory: {str(e)!r}.\n"
"Please check if you have proper permission to operate the directory "
f"{HOME_PROMPT_FLOW_DIR.as_posix()!r}; or configure it via "
f"environment variable {PROMPT_FLOW_HOME_DIR_ENV_VAR!r}.\n"
)
logger.error(_error_message)
raise Exception(_error_message)
HOME_PROMPT_FLOW_DIR = _prepare_home_dir()
DAG_FILE_NAME = "flow.dag.yaml"
NODE_VARIANTS = "node_variants"
VARIANTS = "variants"
NODES = "nodes"
NODE = "node"
INPUTS = "inputs"
USE_VARIANTS = "use_variants"
DEFAULT_VAR_ID = "default_variant_id"
FLOW_TOOLS_JSON = "flow.tools.json"
FLOW_TOOLS_JSON_GEN_TIMEOUT = 60
PROMPT_FLOW_RUNS_DIR_NAME = ".runs"
PROMPT_FLOW_EXP_DIR_NAME = ".exps"
SERVICE_CONFIG_FILE = "pf.yaml"
PF_SERVICE_PORT_FILE = "pfs.port"
PF_SERVICE_LOG_FILE = "pfs.log"
LOCAL_MGMT_DB_PATH = (HOME_PROMPT_FLOW_DIR / "pf.sqlite").resolve()
LOCAL_MGMT_DB_SESSION_ACQUIRE_LOCK_PATH = (HOME_PROMPT_FLOW_DIR / "pf.sqlite.lock").resolve()
SCHEMA_INFO_TABLENAME = "schema_info"
RUN_INFO_TABLENAME = "run_info"
RUN_INFO_CREATED_ON_INDEX_NAME = "idx_run_info_created_on"
CONNECTION_TABLE_NAME = "connection"
EXPERIMENT_TABLE_NAME = "experiment"
EXPERIMENT_CREATED_ON_INDEX_NAME = "idx_experiment_created_on"
BASE_PATH_CONTEXT_KEY = "base_path"
SCHEMA_KEYS_CONTEXT_CONFIG_KEY = "schema_configs_keys"
SCHEMA_KEYS_CONTEXT_SECRET_KEY = "schema_secrets_keys"
PARAMS_OVERRIDE_KEY = "params_override"
FILE_PREFIX = "file:"
KEYRING_SYSTEM = "promptflow"
KEYRING_ENCRYPTION_KEY_NAME = "encryption_key"
KEYRING_ENCRYPTION_LOCK_PATH = (HOME_PROMPT_FLOW_DIR / "encryption_key.lock").resolve()
REFRESH_CONNECTIONS_DIR_LOCK_PATH = (HOME_PROMPT_FLOW_DIR / "refresh_connections_dir.lock").resolve()
# Note: Use this only for show. Reading input should regard all '*' string as scrubbed, no matter the length.
SCRUBBED_VALUE = "******"
SCRUBBED_VALUE_NO_CHANGE = "<no-change>"
SCRUBBED_VALUE_USER_INPUT = "<user-input>"
CHAT_HISTORY = "chat_history"
WORKSPACE_LINKED_DATASTORE_NAME = "workspaceblobstore"
LINE_NUMBER = "line_number"
AZUREML_PF_RUN_PROPERTIES_LINEAGE = "azureml.promptflow.input_run_id"
AZURE_WORKSPACE_REGEX_FORMAT = (
"^azureml:[/]{1,2}subscriptions/([^/]+)/resource(groups|Groups)/([^/]+)"
"(/providers/Microsoft.MachineLearningServices)?/workspaces/([^/]+)$"
)
DEFAULT_ENCODING = "utf-8"
LOCAL_STORAGE_BATCH_SIZE = 1
LOCAL_SERVICE_PORT = 5000
BULK_RUN_ERRORS = "BulkRunErrors"
RUN_MACRO = "${run}"
VARIANT_ID_MACRO = "${variant_id}"
TIMESTAMP_MACRO = "${timestamp}"
DEFAULT_VARIANT = "variant_0"
# run visualize constants
VIS_HTML_TMPL = Path(__file__).parent / "data" / "visualize.j2"
VIS_JS_BUNDLE_FILENAME = "bulkTestDetails.min.js"
VIS_PORTAL_URL_TMPL = (
"https://ml.azure.com/prompts/flow/bulkrun/runs/outputs"
"?wsid=/subscriptions/{subscription_id}/resourceGroups/{resource_group_name}"
"/providers/Microsoft.MachineLearningServices/workspaces/{workspace_name}&runId={names}"
)
REMOTE_URI_PREFIX = "azureml:"
REGISTRY_URI_PREFIX = "azureml://registries/"
FLOW_RESOURCE_ID_PREFIX = "azureml://locations/"
FLOW_DIRECTORY_MACRO_IN_CONFIG = "${flow_directory}"
# Tool meta info
UIONLY_HIDDEN = "uionly_hidden"
SKIP_FUNC_PARAMS = ["subscription_id", "resource_group_name", "workspace_name"]
ICON_DARK = "icon_dark"
ICON_LIGHT = "icon_light"
ICON = "icon"
TOOL_SCHEMA = Path(__file__).parent / "data" / "tool.schema.json"
class CustomStrongTypeConnectionConfigs:
PREFIX = "promptflow.connection."
TYPE = "custom_type"
MODULE = "module"
PACKAGE = "package"
PACKAGE_VERSION = "package_version"
PROMPTFLOW_TYPE_KEY = PREFIX + TYPE
PROMPTFLOW_MODULE_KEY = PREFIX + MODULE
PROMPTFLOW_PACKAGE_KEY = PREFIX + PACKAGE
PROMPTFLOW_PACKAGE_VERSION_KEY = PREFIX + PACKAGE_VERSION
@staticmethod
def is_custom_key(key):
return key not in [
CustomStrongTypeConnectionConfigs.PROMPTFLOW_TYPE_KEY,
CustomStrongTypeConnectionConfigs.PROMPTFLOW_MODULE_KEY,
CustomStrongTypeConnectionConfigs.PROMPTFLOW_PACKAGE_KEY,
CustomStrongTypeConnectionConfigs.PROMPTFLOW_PACKAGE_VERSION_KEY,
]
class RunTypes:
BATCH = "batch"
EVALUATION = "evaluation"
PAIRWISE_EVALUATE = "pairwise_evaluate"
COMMAND = "command"
class AzureRunTypes:
"""Run types for run entity from index service."""
BATCH = "azureml.promptflow.FlowRun"
EVALUATION = "azureml.promptflow.EvaluationRun"
PAIRWISE_EVALUATE = "azureml.promptflow.PairwiseEvaluationRun"
class RestRunTypes:
"""Run types for run entity from MT service."""
BATCH = "FlowRun"
EVALUATION = "EvaluationRun"
PAIRWISE_EVALUATE = "PairwiseEvaluationRun"
# run document statuses
class RunStatus(object):
# Ordered by transition order
QUEUED = "Queued"
NOT_STARTED = "NotStarted"
PREPARING = "Preparing"
PROVISIONING = "Provisioning"
STARTING = "Starting"
RUNNING = "Running"
CANCEL_REQUESTED = "CancelRequested"
CANCELED = "Canceled"
FINALIZING = "Finalizing"
COMPLETED = "Completed"
FAILED = "Failed"
UNAPPROVED = "Unapproved"
NOTRESPONDING = "NotResponding"
PAUSING = "Pausing"
PAUSED = "Paused"
@classmethod
def list(cls):
"""Return the list of supported run statuses."""
return [
cls.QUEUED,
cls.PREPARING,
cls.PROVISIONING,
cls.STARTING,
cls.RUNNING,
cls.CANCEL_REQUESTED,
cls.CANCELED,
cls.FINALIZING,
cls.COMPLETED,
cls.FAILED,
cls.NOT_STARTED,
cls.UNAPPROVED,
cls.NOTRESPONDING,
cls.PAUSING,
cls.PAUSED,
]
@classmethod
def get_running_statuses(cls):
"""Return the list of running statuses."""
return [
cls.NOT_STARTED,
cls.QUEUED,
cls.PREPARING,
cls.PROVISIONING,
cls.STARTING,
cls.RUNNING,
cls.UNAPPROVED,
cls.NOTRESPONDING,
cls.PAUSING,
cls.PAUSED,
]
@classmethod
def get_post_processing_statuses(cls):
"""Return the list of running statuses."""
return [cls.CANCEL_REQUESTED, cls.FINALIZING]
class FlowRunProperties:
FLOW_PATH = "flow_path"
OUTPUT_PATH = "output_path"
NODE_VARIANT = "node_variant"
RUN = "run"
SYSTEM_METRICS = "system_metrics"
# Experiment command node fields only
COMMAND = "command"
OUTPUTS = "outputs"
class CommonYamlFields:
"""Common yaml fields.
Common yaml fields are used to define the common fields in yaml files. It can be one of the following values: type,
name, $schema.
"""
TYPE = "type"
"""Type."""
NAME = "name"
"""Name."""
SCHEMA = "$schema"
"""Schema."""
MAX_LIST_CLI_RESULTS = 50 # general list
MAX_RUN_LIST_RESULTS = 50 # run list
MAX_SHOW_DETAILS_RESULTS = 100 # show details
class CLIListOutputFormat:
JSON = "json"
TABLE = "table"
class LocalStorageFilenames:
SNAPSHOT_FOLDER = "snapshot"
DAG = DAG_FILE_NAME
FLOW_TOOLS_JSON = FLOW_TOOLS_JSON
INPUTS = "inputs.jsonl"
OUTPUTS = "outputs.jsonl"
DETAIL = "detail.json"
METRICS = "metrics.json"
LOG = "logs.txt"
EXCEPTION = "error.json"
META = "meta.json"
class ListViewType(str, Enum):
ACTIVE_ONLY = "ActiveOnly"
ARCHIVED_ONLY = "ArchivedOnly"
ALL = "All"
def get_list_view_type(archived_only: bool, include_archived: bool) -> ListViewType:
if archived_only and include_archived:
raise Exception("Cannot provide both archived-only and include-archived.")
if include_archived:
return ListViewType.ALL
elif archived_only:
return ListViewType.ARCHIVED_ONLY
else:
return ListViewType.ACTIVE_ONLY
class RunInfoSources(str, Enum):
"""Run sources."""
LOCAL = "local"
INDEX_SERVICE = "index_service"
RUN_HISTORY = "run_history"
MT_SERVICE = "mt_service"
EXISTING_RUN = "existing_run"
class ConfigValueType(str, Enum):
STRING = "String"
SECRET = "Secret"
class ConnectionType(str, Enum):
_NOT_SET = "NotSet"
AZURE_OPEN_AI = "AzureOpenAI"
OPEN_AI = "OpenAI"
QDRANT = "Qdrant"
COGNITIVE_SEARCH = "CognitiveSearch"
SERP = "Serp"
AZURE_CONTENT_SAFETY = "AzureContentSafety"
FORM_RECOGNIZER = "FormRecognizer"
WEAVIATE = "Weaviate"
CUSTOM = "Custom"
ALL_CONNECTION_TYPES = set(
map(lambda x: f"{x.value}Connection", filter(lambda x: x != ConnectionType._NOT_SET, ConnectionType))
)
class ConnectionFields(str, Enum):
CONNECTION = "connection"
DEPLOYMENT_NAME = "deployment_name"
MODEL = "model"
SUPPORTED_CONNECTION_FIELDS = {
ConnectionFields.CONNECTION.value,
ConnectionFields.DEPLOYMENT_NAME.value,
ConnectionFields.MODEL.value,
}
class RunDataKeys:
PORTAL_URL = "portal_url"
DATA = "data"
RUN = "run"
OUTPUT = "output"
class RunHistoryKeys:
RunMetaData = "runMetadata"
HIDDEN = "hidden"
class ConnectionProvider(str, Enum):
LOCAL = "local"
AZUREML = "azureml"
class FlowType:
STANDARD = "standard"
EVALUATION = "evaluation"
CHAT = "chat"
@staticmethod
def get_all_values():
values = [value for key, value in vars(FlowType).items() if isinstance(value, str) and key.isupper()]
return values
CLIENT_FLOW_TYPE_2_SERVICE_FLOW_TYPE = {
FlowType.STANDARD: "default",
FlowType.EVALUATION: "evaluation",
FlowType.CHAT: "chat",
}
SERVICE_FLOW_TYPE_2_CLIENT_FLOW_TYPE = {value: key for key, value in CLIENT_FLOW_TYPE_2_SERVICE_FLOW_TYPE.items()}
class AzureFlowSource:
LOCAL = "local"
PF_SERVICE = "pf_service"
INDEX = "index"
class DownloadedRun:
SNAPSHOT_FOLDER = LocalStorageFilenames.SNAPSHOT_FOLDER
METRICS_FILE_NAME = LocalStorageFilenames.METRICS
LOGS_FILE_NAME = LocalStorageFilenames.LOG
RUN_METADATA_FILE_NAME = "run_metadata.json"
class ExperimentNodeType(object):
FLOW = "flow"
COMMAND = "command"
class ExperimentStatus(object):
NOT_STARTED = "NotStarted"
IN_PROGRESS = "InProgress"
TERMINATED = "Terminated"
| promptflow/src/promptflow/promptflow/_sdk/_constants.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_constants.py",
"repo_id": "promptflow",
"token_count": 5327
} | 37 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import inspect
from pathlib import Path
from flask import jsonify, request
import promptflow._sdk.schemas._connection as connection
from promptflow._sdk._configuration import Configuration
from promptflow._sdk._service import Namespace, Resource, fields
from promptflow._sdk._service.utils.utils import build_pfs_user_agent, local_user_only, make_response_no_content
from promptflow._sdk.entities._connection import _Connection
api = Namespace("Connections", description="Connections Management")
# azure connection
def validate_working_directory(value):
if value is None:
return
if not isinstance(value, str):
value = str(value)
if not Path(value).is_dir():
raise ValueError("Invalid working directory.")
return value
working_directory_parser = api.parser()
working_directory_parser.add_argument(
"working_directory", type=validate_working_directory, location="args", required=False
)
# Response model of list connections
list_connection_field = api.model(
"Connection",
{
"name": fields.String,
"type": fields.String,
"module": fields.String,
"expiry_time": fields.String,
"created_date": fields.String,
"last_modified_date": fields.String,
},
)
# Response model of connection operation
dict_field = api.schema_model("ConnectionDict", {"additionalProperties": True, "type": "object"})
# Response model of connection spec
connection_config_spec_model = api.model(
"ConnectionConfigSpec",
{
"name": fields.String,
"optional": fields.Boolean,
"default": fields.String,
},
)
connection_spec_model = api.model(
"ConnectionSpec",
{
"connection_type": fields.String,
"config_spec": fields.List(fields.Nested(connection_config_spec_model)),
},
)
def _get_connection_operation(working_directory=None):
from promptflow._sdk._pf_client import PFClient
connection_provider = Configuration().get_connection_provider(path=working_directory)
# get_connection_operation is a shared function, so we build user agent based on request first and
# then pass it to the function
connection_operation = PFClient(
connection_provider=connection_provider, user_agent=build_pfs_user_agent()
).connections
return connection_operation
@api.route("/")
class ConnectionList(Resource):
@api.doc(parser=working_directory_parser, description="List all connection")
@api.marshal_with(list_connection_field, skip_none=True, as_list=True)
@local_user_only
@api.response(
code=403, description="This service is available for local user only, please specify X-Remote-User in headers."
)
def get(self):
args = working_directory_parser.parse_args()
connection_op = _get_connection_operation(args.working_directory)
# parse query parameters
max_results = request.args.get("max_results", default=50, type=int)
all_results = request.args.get("all_results", default=False, type=bool)
connections = connection_op.list(max_results=max_results, all_results=all_results)
connections_dict = [connection._to_dict() for connection in connections]
return connections_dict
@api.route("/<string:name>")
@api.param("name", "The connection name.")
class Connection(Resource):
@api.doc(parser=working_directory_parser, description="Get connection")
@api.response(code=200, description="Connection details", model=dict_field)
@local_user_only
@api.response(
code=403, description="This service is available for local user only, please specify X-Remote-User in headers."
)
def get(self, name: str):
args = working_directory_parser.parse_args()
connection_op = _get_connection_operation(args.working_directory)
connection = connection_op.get(name=name, raise_error=True)
connection_dict = connection._to_dict()
return jsonify(connection_dict)
@api.doc(body=dict_field, description="Create connection")
@api.response(code=200, description="Connection details", model=dict_field)
@local_user_only
@api.response(
code=403, description="This service is available for local user only, please specify X-Remote-User in headers."
)
def post(self, name: str):
connection_op = _get_connection_operation()
connection_data = request.get_json(force=True)
connection_data["name"] = name
connection = _Connection._load(data=connection_data)
connection = connection_op.create_or_update(connection)
return jsonify(connection._to_dict())
@api.doc(body=dict_field, description="Update connection")
@api.response(code=200, description="Connection details", model=dict_field)
@local_user_only
@api.response(
code=403, description="This service is available for local user only, please specify X-Remote-User in headers."
)
def put(self, name: str):
connection_op = _get_connection_operation()
connection_dict = request.get_json(force=True)
params_override = [{k: v} for k, v in connection_dict.items()]
# TODO: check if we need to record registry for this private operation
existing_connection = connection_op._get(name)
connection = _Connection._load(data=existing_connection._to_dict(), params_override=params_override)
connection._secrets = existing_connection._secrets
connection = connection_op.create_or_update(connection)
return jsonify(connection._to_dict())
@api.doc(description="Delete connection")
@local_user_only
@api.response(code=204, description="Delete connection", model=dict_field)
@api.response(
code=403, description="This service is available for local user only, please specify X-Remote-User in headers."
)
def delete(self, name: str):
connection_op = _get_connection_operation()
connection_op.delete(name=name)
return make_response_no_content()
@api.route("/<string:name>/listsecrets")
class ConnectionWithSecret(Resource):
@api.doc(parser=working_directory_parser, description="Get connection with secret")
@api.response(code=200, description="Connection details with secret", model=dict_field)
@local_user_only
@api.response(
code=403, description="This service is available for local user only, please specify X-Remote-User in headers."
)
def get(self, name: str):
args = working_directory_parser.parse_args()
connection_op = _get_connection_operation(args.working_directory)
connection = connection_op.get(name=name, with_secrets=True, raise_error=True)
connection_dict = connection._to_dict()
return jsonify(connection_dict)
@api.route("/specs")
class ConnectionSpecs(Resource):
@api.doc(description="List connection spec")
@api.response(code=200, description="List connection spec", skip_none=True, model=connection_spec_model)
def get(self):
hide_connection_fields = ["module"]
connection_specs = []
for name, obj in inspect.getmembers(connection):
if (
inspect.isclass(obj)
and issubclass(obj, connection.ConnectionSchema)
and not isinstance(obj, connection.ConnectionSchema)
):
config_specs = []
for field_name, field in obj._declared_fields.items():
if not field.dump_only and field_name not in hide_connection_fields:
configs = {"name": field_name, "optional": field.allow_none}
if field.default:
configs["default"] = field.default
if field_name == "type":
configs["default"] = field.allowed_values[0]
config_specs.append(configs)
connection_spec = {
"connection_type": name.replace("Schema", ""),
"config_specs": config_specs,
}
connection_specs.append(connection_spec)
return jsonify(connection_specs)
| promptflow/src/promptflow/promptflow/_sdk/_service/apis/connection.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_service/apis/connection.py",
"repo_id": "promptflow",
"token_count": 3034
} | 38 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from .run_submitter import RunSubmitter
from .test_submitter import TestSubmitter
from .utils import (
overwrite_connections,
overwrite_flow,
overwrite_variant,
remove_additional_includes,
variant_overwrite_context,
)
__all__ = [
"RunSubmitter",
"TestSubmitter",
"overwrite_variant",
"variant_overwrite_context",
"remove_additional_includes",
"overwrite_connections",
"overwrite_flow",
]
| promptflow/src/promptflow/promptflow/_sdk/_submitter/__init__.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_submitter/__init__.py",
"repo_id": "promptflow",
"token_count": 185
} | 39 |
# syntax=docker/dockerfile:1
{% if env.image %}
FROM {{env.image}}
{% else %}
{% if show_comment %}
# use mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest? current image is based on Debian 11
{% endif %}
FROM docker.io/continuumio/miniconda3:latest
{% endif %}
WORKDIR /
{% if env.python_requirements_txt %}
COPY ./flow/{{env.python_requirements_txt}} /flow/{{env.python_requirements_txt}}
{% endif %}
# gcc is for build psutil in MacOS
RUN apt-get update && apt-get install -y runit gcc
# create conda environment
{% if env.conda_file %}
COPY ./flow/{{env.conda_file}} /flow/{{env.conda_file}}
RUN conda create -f flow/{{env.conda_file}} -q && \
{% else %}
RUN conda create -n {{env.conda_env_name}} python=3.9.16 pip=23.0.1 -q -y && \
{% endif %}
conda run -n {{env.conda_env_name}} \
{% if env.python_requirements_txt %}
pip install -r /flow/{{env.python_requirements_txt}} && \
{% else %}
{% if env.sdk_version %}
pip install promptflow=={{env.sdk_version}} \
{% else %}
pip install promptflow \
{% endif %}
promptflow-tools && \
{% endif %}
conda run -n {{env.conda_env_name}} pip install keyrings.alt && \
conda run -n {{env.conda_env_name}} pip install gunicorn==20.1.0 && \
conda run -n {{env.conda_env_name}} pip cache purge && \
conda clean -a -y
COPY ./flow /flow
{% if env.setup_sh %}
RUN conda run -n {{env.conda_env_name}} sh /flow/{{ env.setup_sh }}
{% endif %}
EXPOSE 8080
COPY ./connections/* /connections/
# reset runsvdir
RUN rm -rf /var/runit
COPY ./runit /var/runit
# grant permission
RUN chmod -R +x /var/runit
COPY ./start.sh /
CMD ["bash", "./start.sh"]
| promptflow/src/promptflow/promptflow/_sdk/data/docker/Dockerfile.jinja2/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/data/docker/Dockerfile.jinja2",
"repo_id": "promptflow",
"token_count": 680
} | 40 |
import base64
import json
import re
import streamlit as st
from bs4 import BeautifulSoup, NavigableString, Tag
from promptflow._utils.multimedia_utils import MIME_PATTERN, is_multimedia_dict
def show_image(image, key=None):
col1, _ = st.columns(2)
with col1:
if not image.startswith("data:image"):
st.image(key + "," + image, use_column_width="auto")
else:
st.image(image, use_column_width="auto")
def json_dumps(value):
try:
return json.dumps(value, ensure_ascii=False)
except Exception:
return value
def is_list_contains_rich_text(rich_text):
result = False
for item in rich_text:
if isinstance(item, list):
result |= is_list_contains_rich_text(item)
elif isinstance(item, dict):
result |= is_dict_contains_rich_text(item)
else:
if isinstance(item, str) and item.startswith("data:image"):
result = True
return result
def is_dict_contains_rich_text(rich_text):
result = False
for rich_text_key, rich_text_value in rich_text.items():
if isinstance(rich_text_value, list):
result |= is_list_contains_rich_text(rich_text_value)
elif isinstance(rich_text_value, dict):
result |= is_dict_contains_rich_text(rich_text_value)
elif re.match(MIME_PATTERN, rich_text_key) or (
isinstance(rich_text_value, str) and rich_text_value.startswith("data:image")
):
result = True
return result
def item_render_message(value, key=None):
if key and re.match(MIME_PATTERN, key):
show_image(value, key)
elif isinstance(value, str) and value.startswith("data:image"):
show_image(value)
else:
if key is None:
st.markdown(f"{json_dumps(value)},")
else:
st.markdown(f"{key}: {json_dumps(value)},")
def list_iter_render_message(message_items):
if is_list_contains_rich_text(message_items):
st.markdown("[ ")
for item in message_items:
if isinstance(item, list):
list_iter_render_message(item)
if isinstance(item, dict):
dict_iter_render_message(item)
else:
item_render_message(item)
st.markdown("], ")
else:
st.markdown(f"{json_dumps(message_items)},")
def dict_iter_render_message(message_items):
if is_multimedia_dict(message_items):
key = list(message_items.keys())[0]
value = message_items[key]
show_image(value, key)
elif is_dict_contains_rich_text(message_items):
st.markdown("{ ")
for key, value in message_items.items():
if re.match(MIME_PATTERN, key):
show_image(value, key)
else:
if isinstance(value, list):
st.markdown(f"{key}: ")
list_iter_render_message(value)
elif isinstance(value, dict):
st.markdown(f"{key}: ")
dict_iter_render_message(value)
else:
item_render_message(value, key)
st.markdown("}, ")
else:
st.markdown(f"{json_dumps(message_items)},")
def render_single_list_message(message_items):
# This function is added for chat flow with only single input and single output.
# So that we can show the message directly without the list and dict wrapper.
for item in message_items:
if isinstance(item, list):
render_single_list_message(item)
elif isinstance(item, dict):
render_single_dict_message(item)
elif isinstance(item, str):
st.text(item)
def render_single_dict_message(message_items):
# This function is added for chat flow with only single input and single output.
# So that we can show the message directly without the list and dict wrapper.
for key, value in message_items.items():
if re.match(MIME_PATTERN, key):
show_image(value, key)
continue
else:
if isinstance(value, list):
render_single_list_message(value)
elif isinstance(value, dict):
render_single_dict_message(value)
else:
item_render_message(value, key)
def extract_content(node):
if isinstance(node, NavigableString):
text = node.strip()
if text:
return [text]
elif isinstance(node, Tag):
if node.name == "img":
prefix, base64_str = node["src"].split(",", 1)
return [{prefix: base64_str}]
else:
result = []
for child in node.contents:
result.extend(extract_content(child))
return result
return []
def parse_list_from_html(html_content):
"""
Parse the html content to a list of strings and images.
"""
soup = BeautifulSoup(html_content, "html.parser")
result = []
for p in soup.find_all("p"):
result.extend(extract_content(p))
return result
def parse_image_content(image_content, image_type):
if image_content is not None:
file_contents = image_content.read()
image_content = base64.b64encode(file_contents).decode("utf-8")
prefix = f"data:{image_type};base64"
return {prefix: image_content}
| promptflow/src/promptflow/promptflow/_sdk/data/executable/utils.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/data/executable/utils.py",
"repo_id": "promptflow",
"token_count": 2495
} | 41 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from typing import List, Optional
from promptflow._sdk._constants import MAX_LIST_CLI_RESULTS, ListViewType
from promptflow._sdk._errors import ExperimentExistsError, ExperimentNotFoundError, ExperimentValueError
from promptflow._sdk._orm.experiment import Experiment as ORMExperiment
from promptflow._sdk._telemetry import ActivityType, TelemetryMixin, monitor_operation
from promptflow._sdk._utils import safe_parse_object_list
from promptflow._sdk.entities._experiment import Experiment
from promptflow._utils.logger_utils import get_cli_sdk_logger
logger = get_cli_sdk_logger()
class ExperimentOperations(TelemetryMixin):
"""ExperimentOperations."""
def __init__(self, client, **kwargs):
super().__init__(**kwargs)
self._client = client
@monitor_operation(activity_name="pf.experiment.list", activity_type=ActivityType.PUBLICAPI)
def list(
self,
max_results: Optional[int] = MAX_LIST_CLI_RESULTS,
*,
list_view_type: ListViewType = ListViewType.ACTIVE_ONLY,
) -> List[Experiment]:
"""List experiments.
:param max_results: Max number of results to return. Default: 50.
:type max_results: Optional[int]
:param list_view_type: View type for including/excluding (for example) archived experiments.
Default: ACTIVE_ONLY.
:type list_view_type: Optional[ListViewType]
:return: List of experiment objects.
:rtype: List[~promptflow.entities.Experiment]
"""
orm_experiments = ORMExperiment.list(max_results=max_results, list_view_type=list_view_type)
return safe_parse_object_list(
obj_list=orm_experiments,
parser=Experiment._from_orm_object,
message_generator=lambda x: f"Error parsing experiment {x.name!r}, skipped.",
)
@monitor_operation(activity_name="pf.experiment.get", activity_type=ActivityType.PUBLICAPI)
def get(self, name: str) -> Experiment:
"""Get an experiment entity.
:param name: Name of the experiment.
:type name: str
:return: experiment object retrieved from the database.
:rtype: ~promptflow.entities.Experiment
"""
try:
return Experiment._from_orm_object(ORMExperiment.get(name))
except ExperimentNotFoundError as e:
raise e
@monitor_operation(activity_name="pf.experiment.create_or_update", activity_type=ActivityType.PUBLICAPI)
def create_or_update(self, experiment: Experiment, **kwargs) -> Experiment:
"""Create or update an experiment.
:param experiment: Experiment object to create or update.
:type experiment: ~promptflow.entities.Experiment
:return: Experiment object created or updated.
:rtype: ~promptflow.entities.Experiment
"""
orm_experiment = experiment._to_orm_object()
try:
orm_experiment.dump()
return self.get(experiment.name)
except ExperimentExistsError:
logger.info(f"Experiment {experiment.name!r} already exists, updating.")
existing_experiment = orm_experiment.get(experiment.name)
existing_experiment.update(
status=orm_experiment.status,
description=orm_experiment.description,
last_start_time=orm_experiment.last_start_time,
last_end_time=orm_experiment.last_end_time,
node_runs=orm_experiment.node_runs,
)
return self.get(experiment.name)
@monitor_operation(activity_name="pf.experiment.start", activity_type=ActivityType.PUBLICAPI)
def start(self, name: str, **kwargs) -> Experiment:
"""Start an experiment.
:param name: Experiment name.
:type name: str
:return: Experiment object started.
:rtype: ~promptflow.entities.Experiment
"""
from promptflow._sdk._submitter.experiment_orchestrator import ExperimentOrchestrator
if not isinstance(name, str):
raise ExperimentValueError(f"Invalid type {type(name)} for name. Must be str.")
return ExperimentOrchestrator(self._client.runs, self).start(self.get(name), **kwargs)
| promptflow/src/promptflow/promptflow/_sdk/operations/_experiment_operations.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/operations/_experiment_operations.py",
"repo_id": "promptflow",
"token_count": 1682
} | 42 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import asyncio
from concurrent.futures import ThreadPoolExecutor
def _has_running_loop() -> bool:
"""Check if the current thread has a running event loop."""
# When using asyncio.get_running_loop(), a RuntimeError is raised if there is no running event loop.
# So, we use a try-catch block to determine whether there is currently an event loop in place.
#
# Note that this is the only way to check whether there is a running loop now, see:
# https://docs.python.org/3/library/asyncio-eventloop.html?highlight=get_running_loop#asyncio.get_running_loop
try:
asyncio.get_running_loop()
return True
except RuntimeError:
return False
def async_run_allowing_running_loop(async_func, *args, **kwargs):
"""Run an async function in a new thread, allowing the current thread to have a running event loop.
When run in an async environment (e.g., in a notebook), because each thread allows only one event
loop, using asyncio.run directly leads to a RuntimeError ("asyncio.run() cannot be called from a
running event loop").
To address this issue, we add a check for the event loop here. If the current thread already has an
event loop, we run _exec_batch in a new thread; otherwise, we run it in the current thread.
"""
if _has_running_loop():
with ThreadPoolExecutor(1) as executor:
return executor.submit(lambda: asyncio.run(async_func(*args, **kwargs))).result()
else:
return asyncio.run(async_func(*args, **kwargs))
| promptflow/src/promptflow/promptflow/_utils/async_utils.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_utils/async_utils.py",
"repo_id": "promptflow",
"token_count": 524
} | 43 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import importlib
import inspect
import logging
import re
from enum import Enum, EnumMeta
from typing import Any, Callable, Dict, List, Union, get_args, get_origin
from jinja2 import Environment, meta
from promptflow._core._errors import DuplicateToolMappingError
from promptflow._utils.utils import is_json_serializable
from promptflow.exceptions import ErrorTarget, UserErrorException
from ..contracts.tool import ConnectionType, InputDefinition, Tool, ToolFuncCallScenario, ValueType
from ..contracts.types import PromptTemplate
module_logger = logging.getLogger(__name__)
_DEPRECATED_TOOLS = "deprecated_tools"
def value_to_str(val):
if val is inspect.Parameter.empty:
# For empty case, default field will be skipped when dumping to json
return None
if val is None:
# Dump default: "" in json to avoid UI validation error
return ""
if isinstance(val, Enum):
return val.value
return str(val)
def resolve_annotation(anno) -> Union[str, list]:
"""Resolve the union annotation to type list."""
origin = get_origin(anno)
if origin != Union:
return anno
# Optional[Type] is Union[Type, NoneType], filter NoneType out
args = [arg for arg in get_args(anno) if arg != type(None)] # noqa: E721
return args[0] if len(args) == 1 else args
def param_to_definition(param, gen_custom_type_conn=False) -> (InputDefinition, bool):
default_value = param.default
# Get value type and enum from annotation
value_type = resolve_annotation(param.annotation)
enum = None
custom_type_conn = None
# Get value type and enum from default if no annotation
if default_value is not inspect.Parameter.empty and value_type == inspect.Parameter.empty:
value_type = default_value.__class__ if isinstance(default_value, Enum) else type(default_value)
# Extract enum for enum class
if isinstance(value_type, EnumMeta):
enum = [str(option.value) for option in value_type]
value_type = str
is_connection = False
if ConnectionType.is_connection_value(value_type):
if ConnectionType.is_custom_strong_type(value_type):
typ = ["CustomConnection"]
custom_type_conn = [value_type.__name__]
else:
typ = [value_type.__name__]
is_connection = True
elif isinstance(value_type, list):
if not all(ConnectionType.is_connection_value(t) for t in value_type):
typ = [ValueType.OBJECT]
else:
custom_connection_added = False
typ = []
custom_type_conn = []
for t in value_type:
# Add 'CustomConnection' to typ list when custom strong type connection exists. Collect all custom types
if ConnectionType.is_custom_strong_type(t):
if not custom_connection_added:
custom_connection_added = True
typ.append("CustomConnection")
custom_type_conn.append(t.__name__)
else:
if t.__name__ != "CustomConnection":
typ.append(t.__name__)
elif not custom_connection_added:
custom_connection_added = True
typ.append(t.__name__)
is_connection = True
else:
typ = [ValueType.from_type(value_type)]
# 1. Do not generate custom type when generating flow.tools.json for script tool.
# Extension would show custom type if it exists. While for script tool with custom strong type connection,
# we still want to show 'CustomConnection' type.
# 2. Generate custom connection type when resolving tool in _tool_resolver, since we rely on it to convert the
# custom connection to custom strong type connection.
if not gen_custom_type_conn:
custom_type_conn = None
return (
InputDefinition(
type=typ,
default=value_to_str(default_value),
description=None,
enum=enum,
custom_type=custom_type_conn,
),
is_connection,
)
def function_to_interface(
f: Callable, initialize_inputs=None, gen_custom_type_conn=False, skip_prompt_template=False
) -> tuple:
sign = inspect.signature(f)
all_inputs = {}
input_defs = {}
connection_types = []
# Collect all inputs from class and func
if initialize_inputs:
if any(k for k in initialize_inputs if k in sign.parameters):
raise Exception(f'Duplicate inputs found from {f.__name__!r} and "__init__()"!')
all_inputs = {**initialize_inputs}
enable_kwargs = any([param.kind == inspect.Parameter.VAR_KEYWORD for _, param in sign.parameters.items()])
all_inputs.update(
{
k: v
for k, v in sign.parameters.items()
if k != "self" and v.kind != v.VAR_KEYWORD and v.kind != v.VAR_POSITIONAL # TODO: Handle these cases
}
)
# Resolve inputs to definitions.
for k, v in all_inputs.items():
# Get value type from annotation
value_type = resolve_annotation(v.annotation)
if skip_prompt_template and value_type is PromptTemplate:
# custom llm tool has prompt template as input, skip it
continue
input_def, is_connection = param_to_definition(v, gen_custom_type_conn=gen_custom_type_conn)
input_defs[k] = input_def
if is_connection:
connection_types.append(input_def.type)
outputs = {}
# Note: We don't have output definition now
return input_defs, outputs, connection_types, enable_kwargs
def function_to_tool_definition(f: Callable, type=None, initialize_inputs=None) -> Tool:
"""Translate a function to tool definition.
:param f: Function to be translated.
:param type: Tool type
:param initialize_inputs: The initialize() func inputs get by get_initialize_inputs() when function
defined in class. We will merge those inputs with f() inputs.
:return: The tool definition.
"""
if hasattr(f, "__original_function"):
f = f.__original_function
inputs, outputs, _, _ = function_to_interface(f, initialize_inputs)
# Hack to get class name
class_name = None
if "." in f.__qualname__:
class_name = f.__qualname__.replace(f".{f.__name__}", "")
meta_dict = {
"name": f.__qualname__,
"description": inspect.getdoc(f) or None,
"inputs": inputs,
"outputs": outputs,
"class_name": class_name,
"function": f.__name__,
}
return Tool(type=type, module=f.__module__, **meta_dict, is_builtin=True, stage="test")
def get_inputs_for_prompt_template(template_str):
"""Get all input variable names and definitions from a jinja2 template string.
: param template_str: template string
: type t: str
: return: the input name to InputDefinition dict
: rtype t: Dict[str, ~promptflow.contracts.tool.InputDefinition]
Example:
>>> get_inputs_for_prompt_template(
template_str="A simple prompt with no variables"
)
{}
>>> get_inputs_for_prompt_template(
template_str="Prompt with only one string input {{str_input}}"
)
{"str_input": InputDefinition(type=[ValueType.STRING])}
>>> get_inputs_for_prompt_template(
template_str="Prompt with image input  and string input {{str_input}}"
)
{"image_input": InputDefinition(type=[ValueType.IMAGE]), "str_input": InputDefinition(type=[ValueType.STRING])
"""
env = Environment()
template = env.parse(template_str)
inputs = sorted(meta.find_undeclared_variables(template), key=lambda x: template_str.find(x))
result_dict = {i: InputDefinition(type=[ValueType.STRING]) for i in inputs}
# currently we only support image type
pattern = r"\!\[(\s*image\s*)\]\(\{\{\s*([^{}]+)\s*\}\}\)"
matches = re.finditer(pattern, template_str)
for match in matches:
input_name = match.group(2).strip()
result_dict[input_name] = InputDefinition([ValueType(match.group(1).strip())])
return result_dict
def get_prompt_param_name_from_func(f):
"""Get the param name of prompt template on provider."""
return next((k for k, annotation in f.__annotations__.items() if annotation == PromptTemplate), None)
def validate_dynamic_list_func_response_type(response: Any, f: str):
"""Verify response type is correct.
The response is a list of items. Each item is a dict with the following keys:
- value: for backend use. Required.
- display_value: for UI display. Optional.
- hyperlink: external link. Optional.
- description: information icon tip. Optional.
The response can not be empty.
"""
if not response:
raise ListFunctionResponseError(f"{f} response can not be empty.")
if not isinstance(response, List):
raise ListFunctionResponseError(f"{f} response must be a list.")
for item in response:
if not isinstance(item, Dict):
raise ListFunctionResponseError(f"{f} response must be a list of dict. {item} is not a dict.")
if "value" not in item:
raise ListFunctionResponseError(f"{f} response dict must have 'value' key.")
for key, value in item.items():
if not isinstance(key, str):
raise ListFunctionResponseError(f"{f} response dict key must be a string. {key} is not a string.")
if not is_json_serializable(value):
raise ListFunctionResponseError(f"{f} response dict value {value} is not json serializable.")
if not isinstance(value, (str, int, float, list, Dict)):
raise ListFunctionResponseError(
f"{f} response dict value must be a string, int, float, list or dict. {value} is not supported."
)
def validate_tool_func_result(func_call_scenario: str, result):
if func_call_scenario == ToolFuncCallScenario.REVERSE_GENERATED_BY:
if not isinstance(result, Dict):
raise RetrieveToolFuncResultValidationError(
f"ToolFuncCallScenario {func_call_scenario} response must be a dict. " f"{result} is not a dict."
)
elif func_call_scenario == ToolFuncCallScenario.DYNAMIC_LIST:
validate_dynamic_list_func_response_type(result, f"ToolFuncCallScenario {func_call_scenario}")
def append_workspace_triple_to_func_input_params(
func_sig_params: Dict, func_input_params_dict: Dict, ws_triple_dict: Dict[str, str]
):
"""Append workspace triple to func input params.
:param func_sig_params: function signature parameters, full params.
:param func_input_params_dict: user input param key-values for dynamic list function.
:param ws_triple_dict: workspace triple dict, including subscription_id, resource_group_name, workspace_name.
:return: combined func input params.
"""
# append workspace triple to func input params if any below condition are met:
# 1. func signature has kwargs param.
# 2. func signature has param named 'subscription_id','resource_group_name','workspace_name'.
ws_triple_dict = ws_triple_dict if ws_triple_dict is not None else {}
func_input_params_dict = func_input_params_dict if func_input_params_dict is not None else {}
has_kwargs_param = any([param.kind == inspect.Parameter.VAR_KEYWORD for _, param in func_sig_params.items()])
if has_kwargs_param is False:
# keep only params that are in func signature. Or run into error when calling func.
avail_ws_info_dict = {k: v for k, v in ws_triple_dict.items() if k in set(func_sig_params.keys())}
else:
avail_ws_info_dict = ws_triple_dict
# if ws triple key is in func input params, it means user has provided value for it,
# do not expect implicit override.
combined_func_input_params = dict(avail_ws_info_dict, **func_input_params_dict)
return combined_func_input_params
def load_function_from_function_path(func_path: str):
"""Load a function from a function path.
The function path should be in the format of "module_name.function_name".
"""
try:
module_name, func_name = func_path.rsplit(".", 1)
module = importlib.import_module(module_name)
f = getattr(module, func_name)
if callable(f):
return f
else:
raise FunctionPathValidationError(f"'{f}' is not callable.")
except Exception as e:
raise FunctionPathValidationError(
f"Failed to parse function from function path: '{func_path}'. Expected format: format 'my_module.my_func'. "
f"Detailed error: {e}"
)
# Handling backward compatibility and generating a mapping between the previous and new tool IDs.
def _find_deprecated_tools(package_tools) -> Dict[str, str]:
_deprecated_tools = {}
for tool_id, tool in package_tools.items():
# a list of old tool IDs that are mapped to the current tool ID.
if tool and _DEPRECATED_TOOLS in tool:
for old_tool_id in tool[_DEPRECATED_TOOLS]:
# throw error to prompt user for manual resolution of this conflict, ensuring secure operation.
if old_tool_id in _deprecated_tools:
raise DuplicateToolMappingError(
message_format=(
"The tools '{first_tool_id}', '{second_tool_id}' are both linked to the deprecated "
"tool ID '{deprecated_tool_id}'. To ensure secure operation, please either "
"remove or adjust one of these tools in your environment and fix this conflict."
),
first_tool_id=_deprecated_tools[old_tool_id],
second_tool_id=tool_id,
deprecated_tool_id=old_tool_id,
target=ErrorTarget.TOOL,
)
_deprecated_tools[old_tool_id] = tool_id
return _deprecated_tools
def _get_function_path(function):
# Validate function exist
if isinstance(function, str):
module_name, func_name = function.rsplit(".", 1)
module = importlib.import_module(module_name)
func = getattr(module, func_name)
func_path = function
elif isinstance(function, Callable):
func = function
func_path = f"{function.__module__}.{function.__name__}"
else:
raise UserErrorException("Function has invalid type, please provide callable or function name for function.")
return func, func_path
class RetrieveToolFuncResultError(UserErrorException):
"""Base exception raised for retreive tool func result errors."""
def __init__(self, message):
msg = (
f"Unable to retreive tool func result due to '{message}'. \nPlease contact the tool author/support team "
f"for troubleshooting assistance."
)
super().__init__(msg, target=ErrorTarget.FUNCTION_PATH)
class RetrieveToolFuncResultValidationError(RetrieveToolFuncResultError):
pass
class DynamicListError(UserErrorException):
"""Base exception raised for dynamic list errors."""
def __init__(self, message):
msg = (
f"Unable to display list of items due to '{message}'. \nPlease contact the tool author/support team "
f"for troubleshooting assistance."
)
super().__init__(msg, target=ErrorTarget.FUNCTION_PATH)
class ListFunctionResponseError(DynamicListError):
pass
class FunctionPathValidationError(DynamicListError):
pass
| promptflow/src/promptflow/promptflow/_utils/tool_utils.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_utils/tool_utils.py",
"repo_id": "promptflow",
"token_count": 6166
} | 44 |
# coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import Any, Callable, Dict, Generic, Optional, TypeVar
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator_async import distributed_trace_async
from ... import models as _models
from ..._vendor import _convert_request
from ...operations._flow_runtimes_workspace_independent_operations import build_get_runtime_latest_config_request
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class FlowRuntimesWorkspaceIndependentOperations:
"""FlowRuntimesWorkspaceIndependentOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace_async
async def get_runtime_latest_config(
self,
**kwargs: Any
) -> "_models.RuntimeConfiguration":
"""get_runtime_latest_config.
:keyword callable cls: A custom type or function that will be passed the direct response
:return: RuntimeConfiguration, or the result of cls(response)
:rtype: ~flow.models.RuntimeConfiguration
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.RuntimeConfiguration"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_runtime_latest_config_request(
template_url=self.get_runtime_latest_config.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('RuntimeConfiguration', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_runtime_latest_config.metadata = {'url': '/flow/api/runtimes/latestConfig'} # type: ignore
| promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/operations/_flow_runtimes_workspace_independent_operations.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/operations/_flow_runtimes_workspace_independent_operations.py",
"repo_id": "promptflow",
"token_count": 1237
} | 45 |
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