luz (development version)

• 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)

luz 0.3.1

CRAN release: 2022-09-06

• Re-submission to fix vignette rendering.

luz 0.3.0

CRAN release: 2022-08-19

Breaking changes

• lr_finder() now by default divides the range between start_lr and end_lr into log-spaced intervals, following the fast.ai implementation. Cf. Sylvain Gugger’s post: https://sgugger.github.io/how-do-you-find-a-good-learning-rate.html. The previous behavior can be achieved passing log_spaced_intervals=FALSE to the function. (#82, @skeydan)
• plot.lr_records() now in addition plots an exponentially weighted moving average of the loss (again, see Sylvain Gugger’s post), with a weighting coefficient of 0.9 (which seems a reasonable value for the default setting of 100 learning-rate-incrementing intervals). (#82, @skeydan)

New features

• Added MixUp callback and helper loss function and functional logic. (#82, @skeydan).
• Added a luz_callback_gradient_clip inspired by FastAI’s implementation. (#90)
• Added a backward argument to setup allowing one to customize how backward is called for the loss scalar value. (#93)
• Added the luz_callback_keep_best_model() to reload the weights from the best model after training is finished. (#95)

luz 0.2.0

CRAN release: 2021-10-07

• Allow users to provide the minimum and maximum number of epochs when calling fit.luz_module_generator(). Removed ctx$epochs from context object and replaced it with ctx$min_epochs and ctx$max_epochs (#53, @mattwarkentin). • Early stopping will now only occur if the minimum number of training epochs has been met (#53, @mattwarkentin). • Added cuda_index argument to accelerator to allow selecting an specific GPU when multiple are present (#58, @cmcmaster1). • Implemented lr_finder (#59, @cmcmaster1). • We now handle different kinds of data arguments passed to fit using the as_dataloader() method (#66). • valid_data can now be scalar value indicating the proportion of data that will be used for fitting. This only works if data is a torch dataset or a list. (#69) • You can now supply dataloader_options to fit to pass additional information to as_dataloader(). (#71) • Implemented the evaluate function allowing users to get metrics from a model in a new dataset. (#73) Bug fixes • Fixed bug in CSV logger callback that was saving the logs as a space delimited file (#52, @mattwarkentin). • Fixed bug in the length of the progress bar for the validation dataset (#52, @mattwarkentin). • Fixed bugs in early stopping callback related to them not working properly when patience = 1 and when they are specified before other logging callbacks. (#76) Internal changes • ctx$data now refers to the current in use data instead of always refering to ctx\$train_data. (#54)
• Refactored the ctx object to make it safer and avoid returing it in the output. (#73)

luz 0.1.0

CRAN release: 2021-06-17

• Added a NEWS.md file to track changes to the package.