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@Fabien-GELUS Fabien-GELUS released this 29 Sep 07:26
· 220 commits to main since this release
0.21.0
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Learn about vigilant mode.

Substra 0.21.0 (first OS release) - 2022-09-12

This is our first open source release since 2021! When the product was
closed source it used to be named Connect. It is now renamed Substra.
This is an overview of the main changes, please have a look at the
changelog of every repos to have a full grasp on what has changed:

Main changes

  • Admin and user roles have been introduced. The user role is the same
    as the previous role. The admin role can, in addition, manage users
    and define their roles. The admin can create users and reset their
    password in the GUI.
  • BREAKING CHANGE: remove the shared local folder of the compute
    plan
  • BREAKING CHANGE: pass the algo method to execute under the
    --method-name argument within the within the cli of the task
    execution. If the interface between substra and the backend is
    handled via substratools, there are no changes to apply within the
    substra code but algo and metric Dockerfiles should expose a
    --method-name argument in the ENTRYPOINT.
  • BREAKING CHANGE: an extra argument predictions_path has been
    added to both predict and _local_predict methods from all
    TorchAlgo classes. The user now have to use the
    _save_predictions method to save its predictions in
    _local_predict. The user defined metrics will load those saved
    prediction with np.load(inputs['predictions']). The
    _save_predictions method can be overwritten.

Default _local_predict method from substrafl algorithms went from:

def _local_predict(self, predict_dataset: torch.utils.data.Dataset):
if self._index_generator is not None:
predict_loader = torch.utils.data.DataLoader(predict_dataset, batch_size=self._index_generator.batch_size)
else:
raise BatchSizeNotFoundError(
\"No default batch size has been found to perform local prediction. \"
\"Please overwrite the _local_predict function of your algorithm.\"
)
self._model.eval()
predictions = torch.Tensor([])
with torch.inference_mode():
for x in predict_loader:
predictions = torch.cat((predictions, self._model(x)), 0)
return predictions}

to

def _local_predict(self, predict_dataset: torch.utils.data.Dataset, predictions_path: Path):
if self._index_generator is not None:
predict_loader = torch.utils.data.DataLoader(predict_dataset, batch_size=self._index_generator.batch_size)
else:
raise BatchSizeNotFoundError(
\"No default batch size has been found to perform local prediction. \"
\"Please overwrite the _local_predict function of your algorithm.\"
)
self._model.eval()
predictions = torch.Tensor([])
with torch.inference_mode():
for x in predict_loader:
predictions = torch.cat((predictions, self._model(x)), 0)
self._save_predictions(predictions, predictions_path)
return predictions}

GUI

  • GUI: the page size has been increased from 10 to 30 items displayed
  • GUI: Fixed: keep filtering/ordering setup when refreshing an asset
    list page
  • GUI: Fixed: filtering on compute plan duration
  • GUI: Fixed: the columns name, status and dates are displayed
    by default in the compute plans page
  • GUI: Fixed: broken unselection of compute plans in comparison page
  • GUI: Fixed: CP columns and favorites disappear on logout
  • GUI: the CP workflow graph now displays CPs with up to 1000 tasks,
    instead of 300
  • The test task rank now have the same behaviour as for other tasks
    (parent task rank + 1)

Substra

  • added list_model to the SDK client

  • Download function of the client return the path of downloaded file

  • Local mode: add a check, a task output of type performance must have
    public permissions

  • Fix the filters on status for compute plans and tasks. This fix also
    introduces some changes: the value for the filters on status must
    now be a list (like for other filters, there is a OR condition
    between elements of the list) and its value must be
    substra.models.ComputePlanStatus.{name of the status}.value for
    compute plans and substra.models.Status.{name of the status}.value
    for tasks.

    Example:

# Return all the composite traintuples with the status \"doing\"
client.list_composite_traintuple(filters={\"status\": [substra.models.Status.doing.value]})}
  • changed the metrics and algo definition relying on substra
    tools. All the methods of those objects now take inputs and
    outputs as arguments; which are TypedDict.

Substrafl

  • Throw an error if pytorch 1.12.0 is used. There is a regression
    bug in torch 1.12.0, that impacts optimizers that have been
    pickled and unpickled. This bug occurs for Adam optimizer for
    example (but not for SGD). Here is a link to one issue covering it:
    pytorch/pytorch#80345
  • In the PyTorch algorithms, move the data to the device (GPU or CPU)
    in the training loop and predict function so that the user does not
    need to do it.
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