Lightning-AI/pytorch-lightning 0.7.0

TPU support & profiling

on GitHub

Overview

This is the first joint release between pytorch-bearer and Lightning, here we come ...

This release adds support for training models on Tensor Processing Units (TPU). We can now train models on GPUs and TPUs by changing a single parameter in Trainer (see docs). We are also bringing the flexibility of Bearer into Lightning by allowing for arbitrary user-defined callbacks, see docs.

We are also including a profiler that allows Lightning users to identify training bottlenecks (see docs).

This release also includes automatic sampler setup depending on the selected backend, Lightning configures the sampler correctly (no need for user input).

The loggers have also been extended to support for multiple concurrent loggers to be passed to Trainer as an iterable, docs and added support for step-based learning rate scheduling.

At last, lots of bug fixes (see below).

Detail changes

Added

• Added automatic sampler setup. Depending on DDP or TPU, lightning configures the sampler correctly (user needs to do nothing) (#926)
• Added reload_dataloaders_every_epoch=False flag for trainer. Some users require reloading data every epoch (#926)
• Added progress_bar_refresh_rate=50 flag for trainer. The refresh rate on notebooks (#926)
• Updated governance docs
• Added a check to ensure that the metric used for early stopping exists before training commences (#542)
• Added optimizer_idx argument to backward hook (#733)
• Added entity argument to WandbLogger to be passed to wandb.init (#783)
• Added a tool for profiling training runs (#782)
• Improved flexibility for naming of TensorBoard logs, can now set version to a str to just save to that directory, and use name='' to prevent experiment-name directory (#804)
• Added option to specify step key when logging metrics (#808)
• Added train_dataloader, val_dataloader and test_dataloader arguments to Trainer.fit(), for alternative data parsing (#759)
• Added Tensor Processing Unit (TPU) support (#868)
• Added semantic segmentation example (#751, #876, #881)
• Split callbacks in multiple files (#849)
• Support for user-defined callbacks (#889 and #950)
• Added support for multiple loggers to be passed to Trainer as an iterable (e.g. list, tuple, etc.) (#903)
• Added support for step-based learning rate scheduling (#941)
• Added support for logging hparams as dict (#1029)
• Checkpoint and early stopping now work without val. step (#1041)
• Support graceful training cleanup after Keyboard Interrupt (#856, #1019)
• Added type hints for function arguments (#912)
• Added default argparser for Trainer (#952, #1023)
• Added TPU gradient clipping (#963)
• Added max/min number of steps in Trainer (#728)

Changed

• Changed default TQDM to use tqdm.auto for prettier outputs in IPython notebooks (#752)
• Changed pytorch_lightning.logging to pytorch_lightning.loggers (#767)
• Moved the default tqdm_dict definition from Trainer to LightningModule, so it can be overridden by the user (#749)
• Moved functionality of LightningModule.load_from_metrics into LightningModule.load_from_checkpoint (#995)
• Changed Checkpoint path parameter from filepath to dirpath (#1016)
• Freezed models hparams as Namespace property (#1029)
• Dropped logging config in package init (#1015)
• Renames model steps (#1051)
  • training_end >> training_epoch_end
  • validation_end >> validation_epoch_end
  • test_end >> test_epoch_end
• Refactor dataloading, supports infinite dataloader (#955)
• Create single file in TensorBoardLogger (#777)

Deprecated

• Deprecated pytorch_lightning.logging (#767)
• Deprecated LightningModule.load_from_metrics in favour of LightningModule.load_from_checkpoint (#995, #1079)
• Deprecated @data_loader decorator (#926)
• Deprecated model steps training_end, validation_end and test_end (#1051, #1056)

Removed

• Removed dependency on pandas (#736)
• Removed dependency on torchvision (#797)
• Removed dependency on scikit-learn (#801)

Fixed

• Fixed a bug where early stopping on_end_epoch would be called inconsistently when check_val_every_n_epoch == 0 (#743)
• Fixed a bug where the model checkpoint didn't write to the same directory as the logger (#771)
• Fixed a bug where the TensorBoardLogger class would create an additional empty log file during fitting (#777)
• Fixed a bug where global_step was advanced incorrectly when using accumulate_grad_batches > 1 (#832)
• Fixed a bug when calling self.logger.experiment with multiple loggers (#1009)
• Fixed a bug when calling logger.append_tags on a NeptuneLogger with a single tag (#1009)
• Fixed sending back data from .spawn by saving and loading the trained model in/out of the process (#1017)
• Fixed port collision on DDP (#1010)
• Fixed/tested pass overrides (#918)
• Fixed comet logger to log after train (#892)
• Remove deprecated args to learning rate step function (#890)

Contributors

@airglow, @akshaykvnit, @AljoSt, @AntixK, @awaelchli, @baeseongsu, @bobkemp, @Borda, @calclavia, @Calysto, @djbyrne, @ethanwharris, @fdelrio89, @hadim, @hanbyul-kim, @jeremyjordan, @kuynzereb, @luiscape, @MattPainter01, @neggert, @onkyo14taro, @peteriz, @shoarora, @SkafteNicki, @smallzzy, @srush, @theevann, @tullie, @williamFalcon, @xeTaiz, @xssChauhan, @yukw777

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