Changelog

All notable changes to metatensor-learn are documented here, following the keep a changelog format. This project follows Semantic Versioning.

Unreleased

Version 0.2.3 - 2024-08-28

Changed

• We now require Python >= 3.9

• Dataset and DataLoader can now handle fields with a name which is not a valid Python identifier.

Version 0.2.2 - 2024-05-16

Added

• Added torch-style activation function module maps to metatensor.learn.nn: ReLU, InvariantReLU, SiLU, and InvariantSiLU (#597)

• Added torch-style neural network module maps to metatensor.learn.nn: LayerNorm, InvariantLayerNorm, EquivariantLinear, Sequential, Tanh, and InvariantTanh (#513)

Fixed

• metatensor.learn.nn modules LayerNorm and InvariantLayerNorm now applies sample-independent transformations to input tensors.

• Set correct device for output of when torch default device is different than input device (#595)

Version 0.2.1 - 2024-03-01

Changed

• metatensor-learn is no longer re-exported from metatensor and metatensor.torch, all functions are still available inside metatensor.learn and metatensor.torch.learn.

Fixed

• Make sure the Dataset class is iterable (#500)

Version 0.2.0 - 2024-02-07

Changed

• Pluralization removed for special kwarg sample_ids of IndexedDataset -> sample_id, and provided collate functions group and group_and_join updated accordingly.

Fixed

• Removal of usage of Labels.range in nn modules to support torch.jit.save (#410)

Version 0.1.0 - 2024-01-26

Added

• ModuleMap and Linear modules, following torch.nn.ModuleDict and torch.nn.Linear in PyTorch but adapted for TensorMap’s (#427)

• Dataset and DataLoader facilities, following the corresponding classes in PyTorch (#428)