0.30.2

0.30.2 - 2022-12-13

• feat: expose the CP submission batch size in execute_experiment

0.30.1 - 2022-10-14

• fix: install your current package as local dependency

0.30.0 - 2022-09-26

Removed

• Return statement of both predict and _local_predict methods from Torch Algorithms.

Tests

• Update the Client, it takes a backend type instead of debug=True + env variable to set the spawner - (#210)
• Do not use Model.category since this field is being removed from the SDK
• Update the tests and benchmark with the change on Metrics from substratools (#24)

Changed

• NOTABLE CHANGES due to breaking changes in substra-tools:
  • the opener only exposes get_data and fake_data methods
  • the results of the above method is passed under the datasamples keys within the inputs dict arg of all
    tools methods (train, predict, aggregate, score)
  • all method (train, predict, aggregate, score) now takes a task_properties argument (dict) in addition to
    inputs and outputs
  • The rank of a task previously passed under the rank key within the inputs is now given in the task_properties
    dict under the rank key

This means that all opener.py file should be changed from:

import substratools as tools

class TestOpener(tools.Opener):
    def get_X(self, folders):
      ...

    def get_y(self, folders):
      ...

    def fake_X(self, n_samples=None):
      ...

    def fake_y(self, n_samples=None):
      ...

to:

import substratools as tools

class TestOpener(tools.Opener):
    def get_data(self, folders):
      ...

    def fake_data(self, n_samples=None):
      ...

This also implies that metrics has now access to the results of get_data
and not only get_y as previously. The user should adapt all of his metrics file accordingly e.g.:

class AUC(tools.Metrics):
    def score(self, inputs, outputs):
        """AUC"""
        y_true = inputs["y"]
        ...

    def get_predictions(self, path):
        return np.load(path)

if __name__ == "__main__":
    tools.metrics.execute(AUC())

could be replace with:

class AUC(tools.Metrics):
    def score(self, inputs, outputs, task_properties):
        """AUC"""
        datasamples = inputs["datasamples"]
        y_true = ... # getting target from the whole datasamples

    def get_predictions(self, path):
        return np.load(path)

if __name__ == "__main__":
    tools.metrics.execute(AUC())

• BREAKING CHANGE: train and predict method of all substrafl algos now takes datasamples as argument instead of X abd y. This is impacting the user code only
  if he or she overwrite those methods instead of using the _local_train and _local_predict methods.
• BREAKING CHANGE: The result of the get_data method from the opener is automatically provided to the given dataset as __init__ arg
  instead of x and y within the train and predict methods of all Torch*Algo classes. The user dataset should be adapted
  accordingly e.g.:

from torch.utils.data import Dataset

class MyDataset(Dataset):
    def __init__(self, x, y, is_inference=False) -> None:
        ...

class MyAlgo(TorchFedAvgAlgo):
    def __init__(
        self,
    ):
        torch.manual_seed(seed)
        super().__init__(
            model=my_model,
            criterion=criterion,
            optimizer=optimizer,
            index_generator=index_generator,
            dataset=MyDataset,
        )

should be replaced with

from torch.utils.data import Dataset

class MyDataset(Dataset):
    def __init__(self, datasamples, is_inference=False) -> None:
        ...

class MyAlgo(TorchFedAvgAlgo):
    def __init__(
        self,
    ):
        torch.manual_seed(seed)
        super().__init__(
            model=my_model,
            criterion=criterion,
            optimizer=optimizer,
            index_generator=index_generator,
            dataset=MyDataset,
        )