0.22.0

Substra 0.22.0 - 2022-10-20

Main changes

• BREAKING CHANGE: the backend type is now set in the Client, the env variable DEBUG_SPAWNER is not used anymore. Default value is deployed.

before:

  export DEBUG_SPAWNER=subprocess
  client = substra.Client(debug=True)

after:

  client = substra.Client(backend_type=substra.BackendType.LOCAL_SUBPROCESS)

• BREAKING CHANGE: schemas.ComputePlanSpec.clean_models property is now removed, the transient property on tasks outputs should be used instead.
• BREAKING CHANGE: Model.category field has been removed.
• BREAKING CHANGE: train and predict methods of all substrafl algos now takes datasamples as argument instead of X and 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 TorchAlgo classes. The user dataset should be adapted accordingly:

  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,
          )

• BREAKING CHANGE: Algo.category: do not rely on categories anymore, all algo categories will be returned as UNKNOWN.
• BREAKING CHANGE: Replaced algo by algo_key in ComputeTask.

GUI

• Improved user management: the last admin cannot be deleted anymore.

Substra

• Algo categories are not checked anymore in local mode. Validations based on inputs and outputs are sufficient.
• Pass substra-tools arguments via a file instead of the command line. This fixes an issue where compute plan would not run if there was too many data samples.

Substrafl

• 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())