v1.17.3

What's Changed

• Fix _TORCH_GREATER_EQUAL_1_12 with lightning_utilities by @atremblay in #174
• Add support for Python 3.12 by @freud14 in #175

Full Changelog: v1.17.2...v1.17.3

v1.17.2

What's Changed

• fix: np.Inf was deprecated forever and is now gone by @dhdaines in #171
• Update Black, isort, PyLint and flake8. by @freud14 in #172
• Update Github Actions by @freud14 in #173

New Contributors

• @dhdaines made their first contribution in #171

Full Changelog: v1.17.1...v1.17.2

v1.17.1

• Fix _XLA_AVAILABLE import with old versions of torchmetrics.
• Fix WandB tests.

v1.17

• FBeta is using the non-deterministic torch function bincount. Either by passing the argument make_deterministic to the FBeta class or by using one of the PyTorch functions torch.set_deterministic_debug_mode or torch.use_deterministic_algorithms, you can now make this function deterministic. Note that this might make your code slower.

v1.16

• Add run_id and terminate_on_end arguments to MLFlowLogger.

Breaking change:

• In MLFlowLogger, except for experiment_name, all arguments must now be passed as keyword arguments. Passing experiment_name as a positional argument is also deprecated and will be removed in future versions.

v1.15

• Remove support for Python 3.7

v1.14

• Update examples using classification metrics from torchmetrics to add the now required task argument.
• Fix the no LR scheduler bug when using PyTorch 2.0.

v1.13

Breaking changes:

• The deprecated torch_metrics keyword argument has been removed. Users should use the batch_metrics or epoch_metrics keyword argument for torchmetrics' metrics.
• The deprecated EpochMetric class has been removed. Users should implement the Metric class instead.

v1.12.1

• Fix memory leak when using a recursive structure (of tuples or lists) as data in the Model.fit() or the ModelBundle.train_data() methods.

v1.12

• Fix a bug when transfering the optimizer on another device caused by a new feature in PyTorch 1.12, i.e. the "capturable" parameter in Adam and AdamW.
• Add utilitary functions for saving (save_random_states) and loading (load_random_states) Python, Numpy and Pytorch's (both CPU and GPU) random states. Furthermore, we also add the RandomStatesCheckpoint callback. This callback is now used in ModelBundle.