Saving and Loading PyTorch Checkpoints#

Similarly to the previous example but with a few extra steps, we’ll show how to store a PyTorch checkpoint we’ll use the function. Firstly, aggregate_fit returns a Parameters object that has to be transformed into a list of NumPy ndarray’s, then those are transformed into the PyTorch state_dict following the OrderedDict class structure.

net = cifar.Net().to(DEVICE)
class SaveModelStrategy(fl.server.strategy.FedAvg):
    def aggregate_fit(
        server_round: int,
        results: List[Tuple[fl.server.client_proxy.ClientProxy, fl.common.FitRes]],
        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
        """Aggregate model weights using weighted average and store checkpoint"""

        # Call aggregate_fit from base class (FedAvg) to aggregate parameters and metrics
        aggregated_parameters, aggregated_metrics = super().aggregate_fit(server_round, results, failures)

        if aggregated_parameters is not None:
            print(f"Saving round {server_round} aggregated_parameters...")

            # Convert `Parameters` to `List[np.ndarray]`
            aggregated_ndarrays: List[np.ndarray] = fl.common.parameters_to_ndarrays(aggregated_parameters)

            # Convert `List[np.ndarray]` to PyTorch`state_dict`
            params_dict = zip(net.state_dict().keys(), aggregated_ndarrays)
            state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
            net.load_state_dict(state_dict, strict=True)

            # Save the model
  , f"model_round_{server_round}.pth")

        return aggregated_parameters, aggregated_metrics

To load your progress, you simply append the following lines to your code. Note that this will iterate over all saved checkpoints and load the latest one:

list_of_files = [fname for fname in glob.glob("./model_round_*")]
latest_round_file = max(list_of_files, key=os.path.getctime)
print("Loading pre-trained model from: ", latest_round_file)
state_dict = torch.load(latest_round_file)