Changelog

[1.2.0] More Efficient Graph Dataset Storing/Loading and Minor Improvements

Requirements

Pytorch >= 2.5.0
Pytorch-Geometric >= 2.6.0

Added

Automatic check that data splits created by any splitter do not overlap. You can skip this check by passing --skip-data-splits-check to mlwiz-data

Fixed

Loading and storing graphs still makes use of torch.load and torch.save. Relies on PyG defining safe_globals after the recent Pytorch update (2.4)

[1.1.2] Random Search Fix

Added

Now you can execute an arbitrary configuration first in debug mode. Just pass the argument --debug --execute-config-id [config_id] to the mlwiz-exp command.

[1.1.1] Random Search Fix

Fixed

Random search breaks when you need to pass args to a class

[1.1.0] Data Augmentation Improvements

Fixed

Bug in pre-transform and transform_train/eval not being parsed correctly

Changed

Ray always creates a new local instance when initialized, except when working with a cluster of machines.
DatasetInterface __init__() has changed to differentiate between runtime transform_train and transform_eval. This helps when training vision models that need to perform data augmentation in the training set. Note that the IterableDatasetInterface has a slightly different logic here than DatasetInterface. The latter relies on two custom SubsetTrain and SubsetEval classes used by the DataProvider.

[1.0.1] Improvements

Changed

Substituted torch.load with dill in some places to resolve annoying warnings
Replaced data_root attributes with storage_folder for consistency
Added tests for post-training utilities
Improvements to workflow files

Fixed

Fixed post-training utilities

[1.0.0] First Release