1.1.0

1.1.0

Breaking Changes

• Dependencies have been updated. Please see the README for more information.
• Helios now requires a minimum NumPY version of 2.0.0.
• The TrainingState struct was previously saved in checkpoints as a dictionary. This has now been changed to save the struct itself, so you must migrate your checkpoints to the new system.

Feature Changes

• Introduces a new plug-in system to extend the functionality of Helios.
• Introduces a new safe_torch_load function that wraps torch.load with weights_only set to true. This addresses the warnings coming from PyTorch starting with 2.4.0.
• Introduces a way to have the trainer ignore certain exception types when training so they can be caught by the calling code.
• Adds a multi-processing queue to the trainer (available only in distributed mode) that allows data to be passed back to the main process.
• Adds native integration with Optuna through the new OptunaPlugin.
• Adds a new CUDAPlugin that automatically moves batches to the set GPU device.

Bug Fixes

• When setting both CPU and GPU for the trainer, an exception is now raised instead of silently ignoring the CPU flag.
• Unit tests are now expanded to cover all supported versions of Python.
• Protobuf is no longer fixed to be less than 5.0.0.