Release v0.4.0 · mlverse/luz

Breaking changes

drop_last=TRUE is now the default for training dataloaders created by luz (when eg. you pass a list or a torch dataset as data input) (#117)
The default profile callback no longer tracks intra step timings as it adds a non ignorable overhead. (#125)

Added support for arm Mac's and the MPS device. (#104)
Refactor checkpointing in luz - we now also serialize optimizer state and callbacks state. (#107)
Added a luz_callback_autoresume() allowing to easily resume trainining runs that might have crashed. (#107)
Added th luz_callback_resume_from_checkpoint() allowing one to resume a training run from a checkpoint file. (#107)
Users can now chose if metrics should be called on both training and validation,
only training or only validation. See luz_metric_set() for more information. (#112)
Improved how errors raised on user code, eg while calling metrics or callbacks
are raised. This helps a lot when debuging errors in callbacks and metrics. (#112)
loss_fn is now a field of the context, thus callbacks can override it when needed. (#112)
luz_callback_mixup now supports the run_valid and auto_loss arguments. (#112)
ctx now aliases to the default opt and opt_name when a single optimizer is specified (ie. most cases) (#114)
Added tfevents callback for logging the loss and getting weights histograms. (#118)
You can now specify metrics to be evaluated during evaluate. (#123)

Bug fix: accelerators cpu argument is always respected. (#119)
Handled rlang and ggplot2 deprecations. (#120)
Better handling of metrics environments.
Faster garbage collection of dataloaders iterators, so we use less memory. (#122)
Much faster loss averaging at every step. Can have hight influence in training times for large number of iterations per epoch. (#124)