Spaces:
Runtime error
Runtime error
File size: 6,950 Bytes
04a30fc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 |
import pathlib
import shutil
from typing import Optional, List
from substra import Client, BackendType
from substra.sdk.schemas import (
DatasetSpec,
Permissions,
DataSampleSpec
)
from substrafl.strategies import Strategy
from substrafl.dependency import Dependency
from substrafl.remote.register import add_metric
from substrafl.index_generator import NpIndexGenerator
from substrafl.algorithms.pytorch import TorchFedAvgAlgo
from substrafl.nodes import TrainDataNode, AggregationNode, TestDataNode
from substrafl.evaluation_strategy import EvaluationStrategy
from substrafl.experiment import execute_experiment
from substra.sdk.models import ComputePlan
from datasets import load_dataset, Dataset
from sklearn.metrics import accuracy_score
import numpy as np
import torch
class SubstraRunner:
def __init__(self):
self.num_clients = 3
self.clients = {}
self.algo_provider: Optional[Client] = None
self.datasets: List[Dataset] = []
self.test_dataset: Optional[Dataset] = None
self.path = pathlib.Path(__file__).parent.resolve()
self.dataset_keys = {}
self.train_data_sample_keys = {}
self.test_data_sample_keys = {}
self.metric_key: Optional[str] = None
NUM_UPDATES = 100
BATCH_SIZE = 32
self.index_generator = NpIndexGenerator(
batch_size=BATCH_SIZE,
num_updates=NUM_UPDATES,
)
self.algorithm: Optional[TorchFedAvgAlgo] = None
self.strategy: Optional[Strategy] = None
self.aggregation_node: Optional[AggregationNode] = None
self.train_data_nodes = list()
self.test_data_nodes = list()
self.eval_strategy: Optional[EvaluationStrategy] = None
self.NUM_ROUNDS = 3
self.compute_plan: Optional[ComputePlan] = None
self.experiment_folder = self.path / "experiment_summaries"
def set_up_clients(self):
self.algo_provider = Client(backend_type=BackendType.LOCAL_SUBPROCESS)
self.clients = {
c.organization_info().organization_id: c
for c in [Client(backend_type=BackendType.LOCAL_SUBPROCESS) for _ in range(self.num_clients - 1)]
}
def prepare_data(self):
dataset = load_dataset("mnist", split="train").shuffle()
self.datasets = [dataset.shard(num_shards=self.num_clients - 1, index=i) for i in range(self.num_clients - 1)]
self.test_dataset = load_dataset("mnist", split="test")
data_path = self.path / "data"
if data_path.exists() and data_path.is_dir():
shutil.rmtree(data_path)
for i, client_id in enumerate(self.clients):
ds = self.datasets[i]
ds.save_to_disk(data_path / client_id / "train")
self.test_dataset.save_to_disk(data_path / client_id / "test")
def register_data(self):
for client_id, client in self.clients.items():
permissions_dataset = Permissions(public=False, authorized_ids=[
self.algo_provider.organization_info().organization_id
])
dataset = DatasetSpec(
name="MNIST",
type="npy",
data_opener=self.path / pathlib.Path("dataset_assets/opener.py"),
description=self.path / pathlib.Path("dataset_assets/description.md"),
permissions=permissions_dataset,
logs_permission=permissions_dataset,
)
self.dataset_keys[client_id] = client.add_dataset(dataset)
assert self.dataset_keys[client_id], "Missing dataset key"
self.train_data_sample_keys[client_id] = client.add_data_sample(DataSampleSpec(
data_manager_keys=[self.dataset_keys[client_id]],
path=self.path / "data" / client_id / "train",
))
data_sample = DataSampleSpec(
data_manager_keys=[self.dataset_keys[client_id]],
path=self.path / "data" / client_id / "test",
)
self.test_data_sample_keys[client_id] = client.add_data_sample(data_sample)
def register_metric(self):
permissions_metric = Permissions(
public=False,
authorized_ids=[
self.algo_provider.organization_info().organization_id
] + list(self.clients.keys())
)
metric_deps = Dependency(pypi_dependencies=["numpy==1.23.1", "scikit-learn==1.1.1"])
def accuracy(datasamples, predictions_path):
y_true = datasamples["label"]
y_pred = np.load(predictions_path)
return accuracy_score(y_true, np.argmax(y_pred, axis=1))
self.metric_key = add_metric(
client=self.algo_provider,
metric_function=accuracy,
permissions=permissions_metric,
dependencies=metric_deps,
)
def set_aggregation(self):
self.aggregation_node = AggregationNode(self.algo_provider.organization_info().organization_id)
for org_id in self.clients:
train_data_node = TrainDataNode(
organization_id=org_id,
data_manager_key=self.dataset_keys[org_id],
data_sample_keys=[self.train_data_sample_keys[org_id]],
)
self.train_data_nodes.append(train_data_node)
def set_testing(self):
for org_id in self.clients:
test_data_node = TestDataNode(
organization_id=org_id,
data_manager_key=self.dataset_keys[org_id],
test_data_sample_keys=[self.test_data_sample_keys[org_id]],
metric_keys=[self.metric_key],
)
self.test_data_nodes.append(test_data_node)
self.eval_strategy = EvaluationStrategy(test_data_nodes=self.test_data_nodes, rounds=1)
def run_compute_plan(self):
algo_deps = Dependency(pypi_dependencies=["numpy==1.23.1", "torch==1.11.0"])
self.compute_plan = execute_experiment(
client=self.algo_provider,
algo=self.algorithm,
strategy=self.strategy,
train_data_nodes=self.train_data_nodes,
evaluation_strategy=self.eval_strategy,
aggregation_node=self.aggregation_node,
num_rounds=self.NUM_ROUNDS,
experiment_folder=self.experiment_folder,
dependencies=algo_deps,
)
def algo_generator(model, criterion, optimizer, index_generator, dataset, seed):
class MyAlgo(TorchFedAvgAlgo):
def __init__(self):
super().__init__(
model=model,
criterion=criterion,
optimizer=optimizer,
index_generator=index_generator,
dataset=dataset,
seed=seed,
)
return MyAlgo
|