The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

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)

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

• Re-submission to fix vignette rendering.

luz 0.3.0

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)

Documentation

• Many wording improvements in the getting started guides (#81 #94, @jonthegeek).

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

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

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.