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luz (development version)

  • Added mixed precision callback. (#127)
  • Added support for torch iterable datasets. (#135)
  • Fixed a bug when trying to resume models trained with learning rate schedulers. (#137)
  • Added support for learning rate schedulers that take the current loss as arguments. (#140)

luz 0.4.0

CRAN release: 2023-04-17

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)

New features

  • 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 fixes

  • 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)

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 implementation. Cf. Sylvain Gugger’s post: 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

New features

  • 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 file to track changes to the package.