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"""PyTorch example for the SDK Participant implementation."""
from pathlib import Path
import sys
from typing import List, Optional, Tuple
import numpy as np
import torch
from torch import utils
from torchvision import datasets, transforms
from cnn_class import Net
from xain_sdk.config import Config, InvalidConfig
from xain_sdk.logger import StructLogger, get_logger
from xain_sdk.participant import Participant as ABCParticipant
from xain_sdk.participant_state_machine import start_participant
logger: StructLogger = get_logger(__name__)
class Participant(ABCParticipant):
"""An example of a PyTorch implementation of a participant for federated learning.
The attributes for the model and the datasets are only for convenience, they might
as well be loaded elsewhere.
model: The model to be trained.
trainset: A dataset for training.
testset: A dataset for testing.
trainloader: A pytorch data loader obtained from train data set.
testloader: A pytorch data loader obtained from test data set.
flattened: A flattened vector of models weights.
shape: CNN model architecture.
indices: Indices of split points in the flattened vector.
def __init__(self) -> None:
"""Initialize the custom participant.
The model and the datasets are defined here only for convenience, they might as
well be loaded elsewhere. Due to the nature of this example, the model is a
simple dense neural network and the datasets are randomly generated.
super(Participant, self).__init__()
# define or load a model to be trained
# define or load datasets to be trained on
def train_round( # pylint: disable=unused-argument
self, weights: Optional[np.ndarray], epochs: int, epoch_base: int
) -> Tuple[np.ndarray, int]:
"""Train a model in a federated learning round.
A model is given in terms of its weights and the model is trained on the
participant's dataset for a number of epochs. The weights of the updated model
are returned in combination with the number of samples of the train dataset.
Any metrics that should be returned to the coordinator must be gathered via the
participant's update_metrics() utility method per epoch.
If the weights given are None, then the participant is expected to initialize
the weights according to its model definition and return them without training.
weights: The weights of the model to be trained.
epochs: The number of epochs to be trained.
epoch_base: The global training epoch number.
The updated model weights and the number of training samples.
if weights is not None:
# load the weights of the global model into the local model
weights=weights, shapes=self.model_shapes, model=self.model
# train the local model for the specified no. of epochs and gather metrics
number_samples: int = len(self.trainloader)
# TODO: return metric values from `train_n_epochs`
self.model.train_n_epochs(self.trainloader, epochs)
# initialize the weights of the local model
number_samples = 0
# return the updated model weights, the number of training samples
weights = self.get_pytorch_weights(model=self.model)
return weights, number_samples
def init_model(self) -> None:
"""Initialize a model."""
# define model layers
self.model: Net = Net()
# get the shapes of the model weights
self.model.forward(torch.zeros((4, 3, 32, 32))) # pylint: disable=no-member
self.model_shapes: List[Tuple[int, ...]] = self.get_pytorch_shapes(
def init_datasets(self) -> None:
"""Initialize datasets."""
transform = transforms.Compose(
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
self.trainset = datasets.CIFAR10(
root="./data", train=True, download=True, transform=transform
self.trainloader =
self.trainset, batch_size=4, shuffle=True, num_workers=2
self.testset = datasets.CIFAR10(
root="./data", train=False, download=True, transform=transform
self.testloader =
self.testset, batch_size=4, shuffle=False, num_workers=2
def main() -> None:
"""Entry point to start a participant."""
participant: Participant = Participant()
config_path = Path(__file__).parent.absolute().joinpath("config.toml")
config = Config.load(config_path)
except InvalidConfig as err:
logger.error("Invalid config", error=str(err))
start_participant(participant, config)
if __name__ == "__main__":
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