Releases · Lightning-AI/pytorch-lightning

Today we are excited to announce Lightning 1.3, containing highly anticipated new features including a new Lightning CLI, improved TPU support, integrations such as PyTorch profiler, new early stopping strategies, predict and validate trainer routines, and more.

https://medium.com/pytorch/pytorch-lightning-1-3-lightning-cli-pytorch-profiler-improved-early-stopping-6e0ffd8deb29

[1.3.0] - 2021-05-06

Added

Added support for the EarlyStopping callback to run at the end of the training epoch (#6944)
Added synchronization points before and after setup hooks are run (#7202)
Added a teardown hook to ClusterEnvironment (#6942)
Added utils for metrics to scalar conversions (#7180)
Added utils for NaN/Inf detection for gradients and parameters (#6834)
Added more explicit exception message when trying to execute trainer.test() or trainer.validate() with fast_dev_run=True (#6667)
Added LightningCLI class to provide simple reproducibility with minimum boilerplate training CLI (#4492, #6862, #7156, #7299)
Added gradient_clip_algorithm argument to Trainer for gradient clipping by value (#6123).
Added a way to print to terminal without breaking up the progress bar (#5470)
Added support to checkpoint after training steps in ModelCheckpoint callback (#6146)
Added TrainerStatus.{INITIALIZING,RUNNING,FINISHED,INTERRUPTED} (#7173)
Added Trainer.validate() method to perform one evaluation epoch over the validation set (#4948)
Added LightningEnvironment for Lightning-specific DDP (#5915)
Added teardown() hook to LightningDataModule (#4673)
Added auto_insert_metric_name parameter to ModelCheckpoint (#6277)
Added arg to self.log that enables users to give custom names when dealing with multiple dataloaders (#6274)
Added teardown method to BaseProfiler to enable subclasses defining post-profiling steps outside of __del__ (#6370)
Added setup method to BaseProfiler to enable subclasses defining pre-profiling steps for every process (#6633)
Added no return warning to predict (#6139)
Added Trainer.predict config validation (#6543)
Added AbstractProfiler interface (#6621)
Added support for including module names for forward in the autograd trace of PyTorchProfiler (#6349)
Added support for the PyTorch 1.8.1 autograd profiler (#6618)
Added outputs parameter to callback's on_validation_epoch_end & on_test_epoch_end hooks (#6120)
Added configure_sharded_model hook (#6679)
Added support for precision=64, enabling training with double precision (#6595)
Added support for DDP communication hooks (#6736)
Added artifact_location argument to MLFlowLogger which will be passed to the MlflowClient.create_experiment call (#6677)
Added model parameter to precision plugins' clip_gradients signature (#6764, #7231)
Added is_last_batch attribute to Trainer (#6825)
Added LightningModule.lr_schedulers() for manual optimization (#6567)
Added MpModelWrapper in TPU Spawn (#7045)
Added max_time Trainer argument to limit training time (#6823)
Added on_predict_{batch,epoch}_{start,end} hooks (#7141)
Added new EarlyStopping parameters stopping_threshold and divergence_threshold (#6868)
Added debug flag to TPU Training Plugins (PT_XLA_DEBUG) (#7219)
Added new UnrepeatedDistributedSampler and IndexBatchSamplerWrapper for tracking distributed predictions (#7215)
Added trainer.predict(return_predictions=None|False|True) (#7215)
Added BasePredictionWriter callback to implement prediction saving (#7127)
Added trainer.tune(scale_batch_size_kwargs, lr_find_kwargs) arguments to configure the tuning algorithms (#7258)
Added tpu_distributed check for TPU Spawn barrier (#7241)
Added device updates to TPU Spawn for Pod training (#7243)
Added warning when missing Callback and using resume_from_checkpoint (#7254)
DeepSpeed single file saving (#6900)
Added Training type Plugins Registry (#6982, #7063, #7214, #7224)
Add ignore param to save_hyperparameters (#6056)

Changed

Changed LightningModule.truncated_bptt_steps to be property (#7323)
Changed EarlyStopping callback from by default running EarlyStopping.on_validation_end if only training is run. Set check_on_train_epoch_end to run the callback at the end of the train epoch instead of at the end of the validation epoch (#7069)
Renamed pytorch_lightning.callbacks.swa to pytorch_lightning.callbacks.stochastic_weight_avg (#6259)
Refactor RunningStage and TrainerState usage (#4945, #7173)
- Added RunningStage.SANITY_CHECKING
- Added TrainerFn.{FITTING,VALIDATING,TESTING,PREDICTING,TUNING}
- Changed trainer.evaluating to return True if validating or testing
Changed setup() and teardown() stage argument to take any of {fit,validate,test,predict} (#6386)
Changed profilers to save separate report files per state and rank (#6621)
The trainer no longer tries to save a checkpoint on exception or run callback's on_train_end functions (#6864)
Changed PyTorchProfiler to use torch.autograd.profiler.record_function to record functions (#6349)
Disabled lr_scheduler.step() in manual optimization (#6825)
Changed warnings and recommendations for dataloaders in ddp_spawn (#6762)
pl.seed_everything will now also set the seed on the DistributedSampler (#7024)
Changed default setting for communication of multi-node training using DDPShardedPlugin (#6937)
trainer.tune() now returns the tuning result (#7258)
LightningModule.from_datasets() now accepts IterableDataset instances as training datasets. (#7503)
Changed resume_from_checkpoint warning to an error when the checkpoint file does not exist (#7075)
Automatically set sync_batchnorm for training_type_plugin (#6536)
Allowed training type plugin to delay optimizer creation (#6331)
Removed ModelSummary validation from train loop on_trainer_init (#6610)
Moved save_function to accelerator (#6689)
Updated DeepSpeed ZeRO (#6546, #6752, #6142, #6321)
Improved verbose logging for EarlyStopping callback (#6811)
Run ddp_spawn dataloader checks on Windows (#6930)
Updated mlflow with using resolve_tags (#6746)
Moved save_hyperparameters to its own function (#7119)
Replaced _DataModuleWrapper with __new__ (#7289)
Reset current_fx properties on lightning module in teardown (#7247)
Auto-set DataLoader.worker_init_fn with seed_everything (#6960)
Remove model.trainer call inside of dataloading mixin (#7317)
Split profilers module (#6261)
Ensure accelerator is valid if running interactively (#5970)
Disabled batch transfer in DP mode (#6098)

Deprecated

Deprecated outputs in both LightningModule.on_train_epoch_end and Callback.on_train_epoch_end hooks (#7339)
Deprecated Trainer.truncated_bptt_steps in favor of LightningModule.truncated_bptt_steps (#7323)
Deprecated outputs in both LightningModule.on_train_epoch_end and Callback.on_train_epoch_end hooks (#7339)
Deprecated LightningModule.grad_norm in favor of pytorch_lightning.utilities.grads.grad_norm (#7292)
Deprecated the save_function property from the ModelCheckpoint callback (#7201)
Deprecated LightningModule.write_predictions and LightningModule.write_predictions_dict (#7066)
Deprecated TrainerLoggingMixin in favor of a separate utilities module for metric handling (#7180)
Deprecated TrainerTrainingTricksMixin in favor of a separate utilities module for NaN/Inf detection for gradients and parameters (#6834)
period has been deprecated in favor of every_n_val_epochs in the ModelCheckpoint callback (#6146)
Deprecated trainer.running_sanity_check in favor of trainer.sanity_checking (#4945)
Deprecated Profiler(output_filename) in favor of dirpath and filename (#6621)
Deprecated PytorchProfiler(profiled_functions) in favor of record_functions (#6349)
Deprecated @auto_move_data in favor of trainer.predict (#6993)
Deprecated Callback.on_load_checkpoint(checkpoint) in favor of Callback.on_load_checkpoint(trainer, pl_module, checkpoint) (#7253)
Deprecated metrics in favor of torchmetrics (#6505, #6530, #6540, #6547, #6515, #6572, #6573, #6584, #6636, #6637, #6649, #6659, #7131)
Deprecated the LightningModule.datamodule getter and setter methods; access them through Trainer.datamodule instead (#7168)
Deprecated the use of Trainer(gpus="i") (string) for selecting the i-th GPU; from v1.5 this will set the number of GPUs instead of the index (#6388)

Removed

Removed the exp_save_path property from the LightningModule (#7266)
Removed training loop explicitly calling EarlyStopping.on_validation_end if no validation is run (#7069)
Removed automatic_optimization as a property from the training loop in favor of LightningModule.automatic_optimization (#7130)
Removed evaluation loop legacy returns for *_epoch_end hooks (#6973)
Removed support for passing a bool value to profiler argument of Trainer (#6164)
Removed no return warning from val/test step (#6139)
Removed passing a ModelCheckpoint instance to Trainer(checkpoint_callback) (#6166)
Removed deprecated Trainer argument enable_pl_optimizer and automatic_optimization (#6163)
Removed deprecated metrics (#6161)
- from pytorch_lightning.metrics.functional.classification removed to_onehot, to_categorical, get_num_classes, roc, multiclass_roc, average_precision, precision_recall_curve, multiclass_precision_recall_curve
- from pytorch_lightning.metrics.functional.reduction removed reduce, class_reduce
Removed deprecated ModelCheckpoint arguments prefix, mode="auto" (#6162)
Removed mode='auto' from EarlyStopping (#6167)
Removed epoch and step argume...

[1.2.8] - 2021-04-14

Added

Added TPUSpawn + IterableDataset error message (#6875)

Fixed

Fixed process rank not being available right away after Trainer instantiation (#6941)
Fixed sync_dist for tpus (#6950)
Fixed AttributeError for require_backward_grad_sync` when running manual optimization with sharded plugin (#6915)
Fixed --gpus default for parser returned by Trainer.add_argparse_args (#6898)
Fixed TPU Spawn all gather (#6896)
Fixed EarlyStopping logic when min_epochs or min_steps requirement is not met (#6705)
Fixed csv extension check (#6436)
Fixed checkpoint issue when using Horovod distributed backend (#6958)
Fixed tensorboard exception raising (#6901)
Fixed setting the eval/train flag correctly on accelerator model (#6983)
Fixed DDP_SPAWN compatibility with bug_report_model.py (#6892)
Fixed bug where BaseFinetuning.flatten_modules() was duplicating leaf node parameters (#6879)
Set better defaults for rank_zero_only.rank when training is launched with SLURM and torchelastic:
- Support SLURM and torchelastic global rank environment variables (#5715)
- Remove hardcoding of local rank in accelerator connector (#6878)

Contributors

@ananthsub @awaelchli @ethanwharris @justusschock @kandluis @kaushikb11 @liob @SeanNaren @skmatz

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[1.3.6] - 2021-06-15

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[1.3.5] - 2021-06-08

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[1.3.4] - 2021-06-01

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[1.3.3] - 2021-05-26

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[1.3.2] - 2021-05-18

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[1.3.1] - 2021-05-11

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[1.3.0] - 2021-05-06

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[1.2.9] - 2021-04-20

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[1.2.8] - 2021-04-14

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Releases: Lightning-AI/pytorch-lightning

Standard weekly patch release

[1.3.6] - 2021-06-15

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[1.3.5] - 2021-06-08

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[1.3.4] - 2021-06-01

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[1.3.3] - 2021-05-26

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Standard weekly patch release

[1.3.2] - 2021-05-18

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Standard weekly patch release

[1.3.1] - 2021-05-11

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Lightning CLI, PyTorch Profiler, Improved Early Stopping

[1.3.0] - 2021-05-06

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Standard weekly patch release

[1.2.9] - 2021-04-20

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Standard weekly patch release

[1.2.8] - 2021-04-14

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