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from typing import List, Optional, Union |
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from pydantic import BaseModel, ConfigDict, Field |
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from inference.core.managers.entities import ModelDescription |
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class ServerVersionInfo(BaseModel): |
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"""Server version information. |
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Attributes: |
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name (str): Server name. |
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version (str): Server version. |
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uuid (str): Server UUID. |
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""" |
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name: str = Field(examples=["Roboflow Inference Server"]) |
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version: str = Field(examples=["0.0.1"]) |
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uuid: str = Field(examples=["9c18c6f4-2266-41fb-8a0f-c12ae28f6fbe"]) |
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class ModelDescriptionEntity(BaseModel): |
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model_config = ConfigDict(protected_namespaces=()) |
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model_id: str = Field( |
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description="Identifier of the model", examples=["some-project/3"] |
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) |
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task_type: str = Field( |
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description="Type of the task that the model performs", |
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examples=["classification"], |
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) |
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batch_size: Optional[Union[int, str]] = Field( |
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None, |
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description="Batch size accepted by the model (if registered).", |
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) |
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input_height: Optional[int] = Field( |
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None, |
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description="Image input height accepted by the model (if registered).", |
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) |
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input_width: Optional[int] = Field( |
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None, |
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description="Image input width accepted by the model (if registered).", |
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) |
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@classmethod |
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def from_model_description( |
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cls, model_description: ModelDescription |
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) -> "ModelDescriptionEntity": |
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return cls( |
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model_id=model_description.model_id, |
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task_type=model_description.task_type, |
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batch_size=model_description.batch_size, |
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input_height=model_description.input_height, |
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input_width=model_description.input_width, |
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) |
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class ModelsDescriptions(BaseModel): |
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models: List[ModelDescriptionEntity] = Field( |
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description="List of models that are loaded by model manager.", |
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) |
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@classmethod |
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def from_models_descriptions( |
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cls, models_descriptions: List[ModelDescription] |
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) -> "ModelsDescriptions": |
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return cls( |
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models=[ |
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ModelDescriptionEntity.from_model_description( |
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model_description=model_description |
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) |
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for model_description in models_descriptions |
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] |
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) |
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