Standard weekly patch release

[1.3.6] - 2021-06-15

Fixed

• Fixed logs overwriting issue for remote filesystems (#7889)
• Fixed DataModule.prepare_data could only be called on the global rank 0 process (#7945)
• Fixed setting worker_init_fn to seed dataloaders correctly when using DDP (#7942)
• Fixed BaseFinetuning callback to properly handle parent modules w/ parameters (#7931)

Contributors

@awaelchli @Borda @kaushikb11 @Queuecumber @SeanNaren @senarvi @speediedan

Standard weekly patch release

[1.3.5] - 2021-06-08

Added

• Added warning to Training Step output (#7779)

Fixed

• Fixed LearningRateMonitor + BackboneFinetuning (#7835)
• Minor improvements to apply_to_collection and type signature of log_dict (#7851)
• Fixed docker versions (#7834)
• Fixed sharded training check for fp16 precision (#7825)
• Fixed support for torch Module type hints in LightningCLI (#7807)

Changed

• Move training_output validation to after train_step_end (#7868)

Contributors

@Borda, @justusschock, @kandluis, @mauvilsa, @shuyingsunshine21, @tchaton

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Standard weekly patch release

[1.3.4] - 2021-06-01

Fixed

• Fixed info message when max training time reached (#7780)
• Fixed missing __len__ method to IndexBatchSamplerWrapper (#7681)

Contributors

@awaelchli @kaushikb11

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Standard weekly patch release

[1.3.3] - 2021-05-26

Changed

• Changed calling of untoggle_optimizer(opt_idx) out of the closure function (#7563)

Fixed

• Fixed ProgressBar pickling after calling trainer.predict (#7608)
• Fixed broadcasting in multi-node, multi-gpu DDP using torch 1.7 (#7592)
• Fixed dataloaders are not reset when tuning the model (#7566)
• Fixed print errors in ProgressBar when trainer.fit is not called (#7674)
• Fixed global step update when the epoch is skipped (#7677)
• Fixed training loop total batch counter when accumulate grad batches was enabled (#7692)

Contributors

@carmocca @kaushikb11 @ryanking13 @Lucklyric @ajtritt @yifuwang

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Standard weekly patch release

[1.3.2] - 2021-05-18

Changed

• DataModules now avoid duplicate {setup,teardown,prepare_data} calls for the same stage (#7238)

Fixed

• Fixed parsing of multiple training dataloaders (#7433)
• Fixed recursive passing of wrong_type keyword argument in pytorch_lightning.utilities.apply_to_collection (#7433)
• Fixed setting correct DistribType for ddp_cpu (spawn) backend (#7492)
• Fixed incorrect number of calls to LR scheduler when check_val_every_n_epoch > 1 (#7032)

Contributors

@alanhdu @carmocca @justusschock @tkng

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Standard weekly patch release

[1.3.1] - 2021-05-11

Fixed

• Fixed DeepSpeed with IterableDatasets (#7362)
• Fixed Trainer.current_epoch not getting restored after tuning (#7434)
• Fixed local rank displayed in console log (#7395)

Contributors

@akihironitta @awaelchli @leezu

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Lightning CLI, PyTorch Profiler, Improved Early Stopping

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...

Quick patch release

Fixing missing packaging package in dependencies, which was affecting the only installation to a very blank system.

Standard weekly patch release

[1.2.9] - 2021-04-20

Fixed

• Fixed the order to call for world ranks & the root_device property in TPUSpawnPlugin (#7074)
• Fixed multi-gpu join for Horovod (#6954)
• Fixed parsing for pre-release package versions (#6999)

Contributors

@irasit @Borda @kaushikb11

Standard weekly patch release

[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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