luz 0.4.0

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)

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)

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)

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)

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)

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)