lightning/CHANGELOG.md

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog.

[1.6.5] - 2022-07-12

Fixed

• Fixed estimated_stepping_batches requiring distributed comms in configure_optimizers for the DeepSpeedStrategy (#13350)
• Fixed bug with Python version check that prevented use with development versions of Python (#13420)
• The loops now call .set_epoch() also on batch samplers if the dataloader has one wrapped in a distributed sampler (#13396)
• Fixed the restoration of log step during restart (#13467)

[1.6.4] - 2022-06-01

Added

• Added all DDP params to be exposed through hpu parallel strategy (#13067)

Changed

• Keep torch.backends.cudnn.benchmark=False by default (unlike in v1.6.{0-4}) after speed and memory problems depending on the data used. Please consider tuning Trainer(benchmark) manually. (#13154)
• Prevent modification of torch.backends.cudnn.benchmark when Trainer(benchmark=...) is not set (#13154)

Fixed

• Fixed an issue causing zero-division error for empty dataloaders (#12885)
• Fixed mismatching default values for the types of some arguments in the DeepSpeed and Fully-Sharded strategies which made the CLI unable to use them (#12989)
• Avoid redundant callback restore warning while tuning (#13026)
• Fixed Trainer(precision=64) during evaluation which now uses the wrapped precision module (#12983)
• Fixed an issue to use wrapped LightningModule for evaluation during trainer.fit for BaguaStrategy (#12983)
• Fixed an issue wrt unnecessary usage of habana mixed precision package for fp32 types (#13028)
• Fixed the number of references of LightningModule so it can be deleted (#12897)
• Fixed materialize_module setting a module's child recursively (#12870)
• Fixed issue where the CLI could not pass a Profiler to the Trainer (#13084)
• Fixed torchelastic detection with non-distributed installations (#13142)
• Fixed logging's step values when multiple dataloaders are used during evaluation (#12184)
• Fixed epoch logging on train epoch end (#13025)
• Fixed DDPStrategy and DDPSpawnStrategy to initialize optimizers only after moving the module to the device (#11952)

[1.6.3] - 2022-05-03

Fixed

• Use only a single instance of rich.console.Console throughout codebase (#12886)
• Fixed an issue to ensure all the checkpoint states are saved in a common filepath with DeepspeedStrategy (#12887)
• Fixed trainer.logger deprecation message (#12671)
• Fixed an issue where sharded grad scaler is passed in when using BF16 with the ShardedStrategy (#12915)
• Fixed an issue wrt recursive invocation of DDP configuration in hpu parallel plugin (#12912)
• Fixed printing of ragged dictionaries in Trainer.validate and Trainer.test (#12857)
• Fixed threading support for legacy loading of checkpoints (#12814)
• Fixed pickling of KFoldLoop (#12441)
• Stopped optimizer_zero_grad from being called after IPU execution (#12913)
• Fixed fuse_modules to be qat-aware for torch>=1.11 (#12891)
• Enforced eval shuffle warning only for default samplers in DataLoader (#12653)
• Enable mixed precision in DDPFullyShardedStrategy when precision=16 (#12965)
• Fixed TQDMProgressBar reset and update to show correct time estimation (#12889)
• Fixed fit loop restart logic to enable resume using the checkpoint (#12821

[1.6.2] - 2022-04-27

Fixed

• Fixed ImportError when torch.distributed is not available. (#12794)
• When using custom DataLoaders in LightningDataModule, multiple inheritance is resolved properly (#12716)
• Fixed encoding issues on terminals that do not support unicode characters (#12828)
• Fixed support for ModelCheckpoint monitors with dots (#12783)

[1.6.1] - 2022-04-13

Changed

• Support strategy argument being case insensitive ([#12528](https://github.com/PyTorchLightning/pytorch-lightning/pull/12528

Fixed

• Run main progress bar updates independent of val progress bar updates in TQDMProgressBar (#12563)
• Avoid calling average_parameters multiple times per optimizer step (#12452)
• Properly pass some Logger's parent's arguments to super().__init__() (#12609)
• Fixed an issue where incorrect type warnings appear when the overridden LightningLite.run method accepts user-defined arguments (#12629)
• Fixed rank_zero_only decorator in LSF environments (#12587)
• Don't raise a warning when nn.Module is not saved under hparams (#12669)
• Raise MisconfigurationException when the accelerator is available but the user passes invalid ([]/0/"0") values to the devices flag (#12708)
• Support auto_select_gpus with the accelerator and devices API (#12608)

[1.6.0] - 2022-03-29

Added

• Allow logging to an existing run ID in MLflow with MLFlowLogger (#12290)
• Enable gradient accumulation using Horovod's backward_passes_per_step (#11911)
• Add new DETAIL log level to provide useful logs for improving monitoring and debugging of batch jobs (#11008)
• Added a flag SLURMEnvironment(auto_requeue=True|False) to control whether Lightning handles the requeuing (#10601)
• Fault Tolerant Manual
  • Add _Stateful protocol to detect if classes are stateful (#10646)
  • Add _FaultTolerantMode enum used to track different supported fault tolerant modes (#10645)
  • Add a _rotate_worker_indices utility to reload the state according the latest worker (#10647)
  • Add stateful workers (#10674)
  • Add an utility to collect the states across processes (#10639)
  • Add logic to reload the states across data loading components (#10699)
  • Cleanup some fault tolerant utilities (#10703)
  • Enable Fault Tolerant Manual Training (#10707)
  • Broadcast the _terminate_gracefully to all processes and add support for DDP (#10638)
• Added support for re-instantiation of custom (subclasses of) DataLoaders returned in the *_dataloader() methods, i.e., automatic replacement of samplers now works with custom types of DataLoader (#10680)
• Added a function to validate if fault tolerant training is supported. (#10465)
• Added a private callback to manage the creation and deletion of fault-tolerance checkpoints (#11862)
• Show a better error message when a custom DataLoader implementation is not well implemented and we need to reconstruct it (#10719)
• Show a better error message when frozen dataclass is used as a batch (#10927)
• Save the Loop's state by default in the checkpoint (#10784)
• Added Loop.replace to easily switch one loop for another (#10324)
• Added support for --lr_scheduler=ReduceLROnPlateau to the LightningCLI (#10860)
• Added LightningCLI.configure_optimizers to override the configure_optimizers return value (#10860)
• Added LightningCLI(auto_registry) flag to register all subclasses of the registerable components automatically (#12108)
• Added a warning that shows when max_epochs in the Trainer is not set (#10700)
• Added support for returning a single Callback from LightningModule.configure_callbacks without wrapping it into a list (#11060)
• Added console_kwargs for RichProgressBar to initialize inner Console (#10875)
• Added support for shorthand notation to instantiate loggers with the LightningCLI (#11533)
• Added a LOGGER_REGISTRY instance to register custom loggers to the LightningCLI (#11533)
• Added info message when the Trainer arguments limit_*_batches, overfit_batches, or val_check_interval are set to 1 or 1.0 (#11950)
• Added a PrecisionPlugin.teardown method (#10990)
• Added LightningModule.lr_scheduler_step (#10249)
• Added support for no pre-fetching to DataFetcher (#11606)
• Added support for optimizer step progress tracking with manual optimization (#11848)
• Return the output of the optimizer.step. This can be useful for LightningLite users, manual optimization users, or users overriding LightningModule.optimizer_step (#11711)
• Teardown the active loop and strategy on exception (#11620)
• Added a MisconfigurationException if user provided opt_idx in scheduler config doesn't match with actual optimizer index of its respective optimizer (#11247)
• Added a loggers property to Trainer which returns a list of loggers provided by the user (#11683)
• Added a loggers property to LightningModule which retrieves the loggers property from Trainer (#11683)
• Added support for DDP when using a CombinedLoader for the training data (#11648)
• Added a warning when using DistributedSampler during validation/testing (#11479)
• Added support for Bagua training strategy (#11146)
• Added support for manually returning a poptorch.DataLoader in a *_dataloader hook (#12116)
• Added rank_zero module to centralize utilities (#11747)
• Added a _Stateful support for LightningDataModule (#11637)
• Added _Stateful support for PrecisionPlugin (#11638)
• Added Accelerator.is_available to check device availability (#11797)
• Enabled static type-checking on the signature of Trainer (#11888)
• Added utility functions for moving optimizers to devices (#11758)
• Added a warning when saving an instance of nn.Module with save_hyperparameters() (#12068)
• Added estimated_stepping_batches property to Trainer (#11599)
• Added support for pluggable Accelerators (#12030)
• Added profiling for on_load_checkpoint/on_save_checkpoint callback and LightningModule hooks (#12149)
• Added LayerSync and NativeSyncBatchNorm plugins (#11754)
• Added optional storage_options argument to Trainer.save_checkpoint() to pass to custom CheckpointIO implementations (#11891)
• Added support to explicitly specify the process group backend for parallel strategies (#11745)
• Added device_ids and num_devices property to Trainer (#12151)
• Added Callback.state_dict() and Callback.load_state_dict() methods (#12232)
• Added AcceleratorRegistry (#12180)
• Added support for Habana Accelerator (HPU) (#11808)
• Added support for dataclasses in apply_to_collections (#11889)

Changed

• Drop PyTorch 1.7 support (#12191), (#12432)
• Make benchmark flag optional and set its value based on the deterministic flag (#11944)
• Implemented a new native and rich format in _print_results method of the EvaluationLoop (#11332)
• Do not print an empty table at the end of the EvaluationLoop (#12427)
• Set the prog_bar flag to False in LightningModule.log_grad_norm (#11472)
• Raised exception in init_dist_connection() when torch distributed is not available (#10418)
• The monitor argument in the EarlyStopping callback is no longer optional (#10328)
• Do not fail if batch size could not be inferred for logging when using DeepSpeed (#10438)
• Raised MisconfigurationException when enable_progress_bar=False and a progress bar instance has been passed in the callback list (#10520)
• Moved trainer.connectors.env_vars_connector._defaults_from_env_vars to utilities.argsparse._defaults_from_env_vars (#10501)
• Changes in LightningCLI required for the new major release of jsonargparse v4.0.0 (#10426)
• Renamed refresh_rate_per_second parameter to refresh_rate for RichProgressBar signature (#10497)
• Moved ownership of the PrecisionPlugin into TrainingTypePlugin and updated all references (#10570)
• Fault Tolerant relies on signal.SIGTERM to gracefully exit instead of signal.SIGUSR1 (#10605)
• Loop.restarting=... now sets the value recursively for all subloops (#11442)
• Raised an error if the batch_size cannot be inferred from the current batch if it contained a string or was a custom batch object (#10541)
• The validation loop is now disabled when overfit_batches > 0 is set in the Trainer (#9709)
• Moved optimizer related logics from Accelerator to TrainingTypePlugin (#10596)
• Moved ownership of the lightning optimizers from the Trainer to the Strategy (#11444)
• Moved ownership of the data fetchers from the DataConnector to the Loops (#11621)
• Moved batch_to_device method from Accelerator to TrainingTypePlugin (#10649)
• The DDPSpawnPlugin no longer overrides the post_dispatch plugin hook (#10034)
• Integrate the progress bar implementation with progress tracking (#11213)
• The LightningModule.{add_to_queue,get_from_queue} hooks no longer get a torch.multiprocessing.SimpleQueue and instead receive a list based queue (#10034)
• Changed training_step, validation_step, test_step and predict_step method signatures in Accelerator and updated input from caller side (#10908)
• Changed the name of the temporary checkpoint that the DDPSpawnPlugin and related plugins save (#10934)
• LoggerCollection returns only unique logger names and versions (#10976)
• Redesigned process creation for spawn-based plugins (DDPSpawnPlugin, TPUSpawnPlugin, etc.) (#10896)
  • All spawn-based plugins now spawn processes immediately upon calling Trainer.{fit,validate,test,predict}
  • The hooks/callbacks prepare_data, setup, configure_sharded_model and teardown now run under initialized process group for spawn-based plugins just like their non-spawn counterparts
  • Some configuration errors that were previously raised as MisconfigurationExceptions will now be raised as ProcessRaisedException (torch>=1.8) or as Exception (torch<1.8)
  • Removed the TrainingTypePlugin.pre_dispatch() method and merged it with TrainingTypePlugin.setup() (#11137)
• Changed profiler to index and display the names of the hooks with a new pattern []. (#11026)
• Changed batch_to_device entry in profiling from stage-specific to generic, to match profiling of other hooks (#11031)
• Changed the info message for finalizing ddp-spawn worker processes to a debug-level message (#10864)
• Removed duplicated file extension when uploading model checkpoints with NeptuneLogger (#11015)
• Removed __getstate__ and __setstate__ of RichProgressBar (#11100)
• The DDPPlugin and DDPSpawnPlugin and their subclasses now remove the SyncBatchNorm wrappers in teardown() to enable proper support at inference after fitting (#11078)
• Moved ownership of the Accelerator instance to the TrainingTypePlugin; all training-type plugins now take an optional parameter accelerator (#11022)
• Renamed the TrainingTypePlugin to Strategy (#11120)
  • Renamed the ParallelPlugin to ParallelStrategy (#11123)
  • Renamed the DataParallelPlugin to DataParallelStrategy (#11183)
  • Renamed the DDPPlugin to DDPStrategy (#11142)
  • Renamed the DDP2Plugin to DDP2Strategy (#11185)
  • Renamed the DDPShardedPlugin to DDPShardedStrategy (#11186)
  • Renamed the DDPFullyShardedPlugin to DDPFullyShardedStrategy (#11143)
  • Renamed the DDPSpawnPlugin to DDPSpawnStrategy (#11145)
  • Renamed the DDPSpawnShardedPlugin to DDPSpawnShardedStrategy (#11210)
  • Renamed the DeepSpeedPlugin to DeepSpeedStrategy (#11194)
  • Renamed the HorovodPlugin to HorovodStrategy (#11195)
  • Renamed the TPUSpawnPlugin to TPUSpawnStrategy (#11190)
  • Renamed the IPUPlugin to IPUStrategy (#11193)
  • Renamed the SingleDevicePlugin to SingleDeviceStrategy (#11182)
  • Renamed the SingleTPUPlugin to SingleTPUStrategy (#11182)
  • Renamed the TrainingTypePluginsRegistry to StrategyRegistry (#11233)
• Marked the ResultCollection, ResultMetric, and ResultMetricCollection classes as protected (#11130)
• Marked trainer.checkpoint_connector as protected (#11550)
• The epoch start/end hooks are now called by the FitLoop instead of the TrainingEpochLoop (#11201)
• DeepSpeed does not require lightning module zero 3 partitioning (#10655)
• Moved Strategy classes to the strategies directory (#11226)
• Renamed training_type_plugin file to strategy (#11239)
• Changed DeviceStatsMonitor to group metrics based on the logger's group_separator (#11254)
• Raised UserWarning if evaluation is triggered with best ckpt and trainer is configured with multiple checkpoint callbacks (#11274)
• Trainer.logged_metrics now always contains scalar tensors, even when a Python scalar was logged (#11270)
• The tuner now uses the checkpoint connector to copy and restore its state (#11518)
• Changed MisconfigurationException to ModuleNotFoundError when rich isn't available (#11360)
• The trainer.current_epoch value is now increased by 1 during and after on_train_end (#8578)
• The trainer.global_step value now accounts for multiple optimizers and TBPTT splits (#11805)
• The trainer.global_step value is now increased right after the optimizer.step() call which will impact users who access it during an intra-training validation hook (#11805)
• The filename of checkpoints created with ModelCheckpoint(filename='{step}') is different compared to previous versions. A checkpoint saved after 1 step will be named step=1.ckpt instead of step=0.ckpt (#11805)
• Inherit from ABC for Accelerator: Users need to implement auto_device_count (#11521)
• Changed parallel_devices property in ParallelStrategy to be lazy initialized (#11572)
• Updated TQDMProgressBar to run a separate progress bar for each eval dataloader (#11657)
• Sorted SimpleProfiler(extended=False) summary based on mean duration for each hook (#11671)
• Avoid enforcing shuffle=False for eval dataloaders (#11575)
• When using DP (data-parallel), Lightning will no longer automatically reduce all tensors returned in training_step; it will only reduce the loss unless training_step_end is overridden (#11594)
• When using DP (data-parallel), the training_epoch_end hook will no longer receive reduced outputs from training_step and instead get the full tensor of results from all GPUs (#11594)
• Changed default logger name to lightning_logs for consistency (#11762)
• Rewrote accelerator_connector (#11448)
• When manual optimization is used with DDP, we no longer force find_unused_parameters=True (#12425)
• Disable loading dataloades if corresponding limit_batches=0 (#11576)
• Removed is_global_zero check in training_epoch_loop before logger.save. If you have a custom logger that implements save the Trainer will now call save on all ranks by default. To change this behavior add @rank_zero_only to your save implementation (#12134)
• Disabled tuner with distributed strategies (#12179)
• Marked trainer.logger_connector as protected (#12195)
• Move Strategy.process_dataloader function call from fit/evaluation/predict_loop.py to data_connector.py (#12251)
• ModelCheckpoint(save_last=True, every_n_epochs=N) now saves a "last" checkpoint every epoch (disregarding every_n_epochs) instead of only once at the end of training (#12418)
• The strategies that support sync_batchnorm now only apply it when fitting (#11919)
• Avoided fallback on CPU if no devices are provided for other accelerators (#12410)
• Modified supporters.py so that in the accumulator element (for loss) is created directly on the device (#12430)
• Removed EarlyStopping.on_save_checkpoint and EarlyStopping.on_load_checkpoint in favor of EarlyStopping.state_dict and EarlyStopping.load_state_dict (#11887)
• Removed BaseFinetuning.on_save_checkpoint and BaseFinetuning.on_load_checkpoint in favor of BaseFinetuning.state_dict and BaseFinetuning.load_state_dict (#11887)
• Removed BackboneFinetuning.on_save_checkpoint and BackboneFinetuning.on_load_checkpoint in favor of BackboneFinetuning.state_dict and BackboneFinetuning.load_state_dict (#11887)
• Removed ModelCheckpoint.on_save_checkpoint and ModelCheckpoint.on_load_checkpoint in favor of ModelCheckpoint.state_dict and ModelCheckpoint.load_state_dict (#11887)
• Removed Timer.on_save_checkpoint and Timer.on_load_checkpoint in favor of Timer.state_dict and Timer.load_state_dict (#11887)
• Replaced PostLocalSGDOptimizer with a dedicated model averaging component (#12378)

Deprecated

• Deprecated training_type_plugin property in favor of strategy in Trainer and updated the references (#11141)
• Deprecated Trainer.{validated,tested,predicted}_ckpt_path and replaced with read-only property Trainer.ckpt_path set when checkpoints loaded via Trainer.{fit,validate,test,predict} (#11696)
• Deprecated ClusterEnvironment.master_{address,port} in favor of ClusterEnvironment.main_{address,port} (#10103)
• Deprecated DistributedType in favor of _StrategyType (#10505)
• Deprecated the precision_plugin constructor argument from Accelerator (#10570)
• Deprecated DeviceType in favor of _AcceleratorType (#10503)
• Deprecated the property Trainer.slurm_job_id in favor of the new SLURMEnvironment.job_id() method (#10622)
• Deprecated the access to the attribute IndexBatchSamplerWrapper.batch_indices in favor of IndexBatchSamplerWrapper.seen_batch_indices (#10870)
• Deprecated on_init_start and on_init_end callback hooks (#10940)
• Deprecated Trainer.call_hook in favor of Trainer._call_callback_hooks, Trainer._call_lightning_module_hook, Trainer._call_ttp_hook, and Trainer._call_accelerator_hook (#10979)
• Deprecated TrainingTypePlugin.post_dispatch in favor of TrainingTypePlugin.teardown (#10939)
• Deprecated ModelIO.on_hpc_{save/load} in favor of CheckpointHooks.on_{save/load}_checkpoint (#10911)
• Deprecated Trainer.run_stage in favor of Trainer.{fit,validate,test,predict} (#11000)
• Deprecated Trainer.lr_schedulers in favor of Trainer.lr_scheduler_configs which returns a list of dataclasses instead of dictionaries (#11443)
• Deprecated Trainer.verbose_evaluate in favor of EvaluationLoop(verbose=...) (#10931)
• Deprecated Trainer.should_rank_save_checkpoint Trainer property (#11068)
• Deprecated Trainer.lightning_optimizers (#11444)
• Deprecated TrainerOptimizersMixin and moved functionality to core/optimizer.py(#11155)
• Deprecated the on_train_batch_end(outputs) format when multiple optimizers are used and TBPTT is enabled (#12182)
• Deprecated the training_epoch_end(outputs) format when multiple optimizers are used and TBPTT is enabled (#12182)
• Deprecated TrainerCallbackHookMixin (#11148)
• Deprecated TrainerDataLoadingMixin and moved functionality to Trainer and DataConnector (#11282)
• Deprecated function pytorch_lightning.callbacks.device_stats_monitor.prefix_metric_keys (#11254)
• Deprecated Callback.on_epoch_start hook in favour of Callback.on_{train/val/test}_epoch_start (#11578)
• Deprecated Callback.on_epoch_end hook in favour of Callback.on_{train/val/test}_epoch_end (#11578)
• Deprecated LightningModule.on_epoch_start hook in favor of LightningModule.on_{train/val/test}_epoch_start (#11578)
• Deprecated LightningModule.on_epoch_end hook in favor of LightningModule.on_{train/val/test}_epoch_end (#11578)
• Deprecated on_before_accelerator_backend_setup callback hook in favour of setup (#11568)
• Deprecated on_batch_start and on_batch_end callback hooks in favor of on_train_batch_start and on_train_batch_end (#11577)
• Deprecated on_configure_sharded_model callback hook in favor of setup (#11627)
• Deprecated pytorch_lightning.utilities.distributed.rank_zero_only in favor of pytorch_lightning.utilities.rank_zero.rank_zero_only (#11747)
• Deprecated pytorch_lightning.utilities.distributed.rank_zero_debug in favor of pytorch_lightning.utilities.rank_zero.rank_zero_debug (#11747)
• Deprecated pytorch_lightning.utilities.distributed.rank_zero_info in favor of pytorch_lightning.utilities.rank_zero.rank_zero_info (#11747)
• Deprecated pytorch_lightning.utilities.warnings.rank_zero_warn in favor of pytorch_lightning.utilities.rank_zero.rank_zero_warn (#11747)
• Deprecated pytorch_lightning.utilities.warnings.rank_zero_deprecation in favor of pytorch_lightning.utilities.rank_zero.rank_zero_deprecation (#11747)
• Deprecated pytorch_lightning.utilities.warnings.LightningDeprecationWarning in favor of pytorch_lightning.utilities.rank_zero.LightningDeprecationWarning
• Deprecated on_pretrain_routine_start and on_pretrain_routine_end callback hooks in favor of on_fit_start (#11794)
• Deprecated LightningModule.on_pretrain_routine_start and LightningModule.on_pretrain_routine_end hooks in favor of on_fit_start (#12122)
• Deprecated agg_key_funcs and agg_default_func parameters from LightningLoggerBase (#11871)
• Deprecated LightningLoggerBase.update_agg_funcs (#11871)
• Deprecated LightningLoggerBase.agg_and_log_metrics in favor of LightningLoggerBase.log_metrics (#11832)
• Deprecated passing weights_save_path to the Trainer constructor in favor of adding the ModelCheckpoint callback with dirpath directly to the list of callbacks (#12084)
• Deprecated pytorch_lightning.profiler.AbstractProfiler in favor of pytorch_lightning.profiler.Profiler (#12106)
• Deprecated pytorch_lightning.profiler.BaseProfiler in favor of pytorch_lightning.profiler.Profiler (#12150)
• Deprecated BaseProfiler.profile_iterable (#12102)
• Deprecated LoggerCollection in favor of trainer.loggers (#12147)
• Deprecated PrecisionPlugin.on_{save,load}_checkpoint in favor of PrecisionPlugin.{state_dict,load_state_dict} (#11978)
• Deprecated LightningDataModule.on_save/load_checkpoint in favor of state_dict/load_state_dict (#11893)
• Deprecated Trainer.use_amp in favor of Trainer.amp_backend (#12312)
• Deprecated LightingModule.use_amp in favor of Trainer.amp_backend (#12315)
• Deprecated specifying the process group backend through the environment variable PL_TORCH_DISTRIBUTED_BACKEND (#11745)
• Deprecated ParallelPlugin.torch_distributed_backend in favor of DDPStrategy.process_group_backend property (#11745)
• Deprecated ModelCheckpoint.save_checkpoint in favor of Trainer.save_checkpoint (#12456)
• Deprecated Trainer.devices in favor of Trainer.num_devices and Trainer.device_ids (#12151)
• Deprecated Trainer.root_gpu in favor of Trainer.strategy.root_device.index when GPU is used (#12262)
• Deprecated Trainer.num_gpus in favor of Trainer.num_devices when GPU is used (#12384)
• Deprecated Trainer.ipus in favor of Trainer.num_devices when IPU is used (#12386)
• Deprecated Trainer.num_processes in favor of Trainer.num_devices (#12388)
• Deprecated Trainer.data_parallel_device_ids in favor of Trainer.device_ids (#12072)
• Deprecated returning state from Callback.on_save_checkpoint in favor of returning state in Callback.state_dict for checkpointing (#11887)
• Deprecated passing only the callback state to Callback.on_load_checkpoint(callback_state) in favor of passing the callback state to Callback.load_state_dict and in 1.8, passing the entire checkpoint dictionary to Callback.on_load_checkpoint(checkpoint) (#11887)
• Deprecated Trainer.gpus in favor of Trainer.device_ids or Trainer.num_devices (#12436)
• Deprecated Trainer.tpu_cores in favor of Trainer.num_devices (#12437)

Removed

• Removed deprecated parameter method in pytorch_lightning.utilities.model_helpers.is_overridden (#10507)
• Remove deprecated method ClusterEnvironment.creates_children (#10339)
• Removed deprecated TrainerModelHooksMixin.is_function_implemented and TrainerModelHooksMixin.has_arg (#10322)
• Removed deprecated pytorch_lightning.utilities.device_dtype_mixin.DeviceDtypeModuleMixin in favor of pytorch_lightning.core.mixins.device_dtype_mixin.DeviceDtypeModuleMixin (#10442)
• Removed deprecated LightningModule.loaded_optimizer_states_dict property (#10346)
• Removed deprecated Trainer.fit(train_dataloader=), Trainer.validate(val_dataloaders=), and Trainer.test(test_dataloader=) (#10325)
• Removed deprecated has_prepared_data, has_setup_fit, has_setup_validate, has_setup_test, has_setup_predict, has_teardown_fit, has_teardown_validate, has_teardown_test and has_teardown_predict datamodule lifecycle properties (#10350)
• Removed deprecated every_n_val_epochs parameter of ModelCheckpoint (#10366)
• Removed deprecated import pytorch_lightning.profiler.profilers in favor of import pytorch_lightning.profiler (#10443)
• Removed deprecated property configure_slurm_dpp from accelerator connector (#10370)
• Removed deprecated arguments num_nodes and sync_batchnorm from DDPPlugin, DDPSpawnPlugin, DeepSpeedPlugin (#10357)
• Removed deprecated property is_slurm_managing_tasks from AcceleratorConnector (#10353)
• Removed deprecated LightningModule.log(tbptt_reduce_fx, tbptt_reduce_token, sync_dist_op) (#10423)
• Removed deprecated Plugin.task_idx (#10441)
• Removed deprecated method master_params from PrecisionPlugin (#10372)
• Removed the automatic detachment of "extras" returned from training_step. For example, return {'loss': ..., 'foo': foo.detach()} will now be necessary if foo has gradients which you do not want to store (#10424)
• Removed deprecated passthrough methods and properties from Accelerator base class:
  • (#10403)
  • (#10448)
• Removed deprecated signature for transfer_batch_to_device hook. The new argument dataloader_idx is now required (#10480)
• Removed deprecated utilities.distributed.rank_zero_{warn/deprecation} (#10451)
• Removed deprecated mode argument from ModelSummary class (#10449)
• Removed deprecated Trainer.train_loop property in favor of Trainer.fit_loop (#10482)
• Removed deprecated Trainer.train_loop property in favor of Trainer.fit_loop (#10482)
• Removed deprecated disable_validation property from Trainer (#10450)
• Removed deprecated CheckpointConnector.hpc_load property in favor of CheckpointConnector.restore (#10525)
• Removed deprecated reload_dataloaders_every_epoch from Trainer in favour of reload_dataloaders_every_n_epochs (#10481)
• Removed the precision_plugin attribute from Accelerator in favor of its equivalent attribute precision_plugin in the TrainingTypePlugin (#10570)
• Removed DeepSpeedPlugin.{precision,amp_type,amp_level} properties (#10657)
• Removed patching of on_before_batch_transfer, transfer_batch_to_device and on_after_batch_transfer hooks in LightningModule (#10603)
• Removed argument return_result from the DDPSpawnPlugin.spawn() method (#10867)
• Removed the property TrainingTypePlugin.results and corresponding properties in subclasses (#10034)
• Removed the mp_queue attribute from DDPSpawnPlugin and TPUSpawnPlugin (#10034)
• Removed unnecessary _move_optimizer_state method overrides from TPUSpawnPlugin and SingleTPUPlugin (#10849)
• Removed should_rank_save_checkpoint property from TrainingTypePlugin (#11070)
• Removed model_sharded_context method from Accelerator (#10886)
• Removed method pre_dispatch from the PrecisionPlugin (#10887)
• Removed method setup_optimizers_in_pre_dispatch from the strategies and achieve the same logic in setup and pre_dispatch methods (#10906)
• Removed methods pre_dispatch, dispatch and post_dispatch from the Accelerator (#10885)
• Removed method training_step, test_step, validation_step and predict_step from the Accelerator (#10890)
• Removed TrainingTypePlugin.start_{training,evaluating,predicting} hooks and the same in all subclasses (#10989, #10896)
• Removed Accelerator.on_train_start (#10999)
• Removed support for Python 3.6 (#11117)
• Removed Strategy.init_optimizers in favor of Strategy.setup_optimizers (#11236)
• Removed profile("training_step_and_backward") in Closure class since we already profile calls training_step and backward (#11222)
• Removed Strategy.optimizer_zero_grad (#11246)
• Removed Strategy.on_gpu (#11537)
• Removed Strategy.on_tpu property (#11536)
• Removed the abstract property LightningLoggerBase.experiment (#11603)
• Removed FitLoop.current_epoch getter and setter (#11562)
• Removed access to _short_id in NeptuneLogger (#11517)
• Removed log_text and log_image from the LightningLoggerBase API (#11857)
• Removed calls to profile("model_forward") in favor of profiling training_step (#12032)
• Removed get_mp_spawn_kwargs from DDPSpawnStrategy and TPUSpawnStrategy in favor of configuration in the _SpawnLauncher (#11966)
• Removed _aggregate_metrics, _reduce_agg_metrics, and _finalize_agg_metrics from LightningLoggerBase (#12053)
• Removed the AcceleratorConnector.device_type property (#12081)
• Removed AcceleratorConnector.num_nodes (#12107)
• Removed AcceleratorConnector.has_ipu property (#12111)
• Removed AcceleratorConnector.use_ipu property (#12110)
• Removed AcceleratorConnector.has_tpu property (#12109)
• Removed AcceleratorConnector.use_dp property (#12112)
• Removed configure_sync_batchnorm from ParallelStrategy and all other strategies that inherit from it (#11754)
• Removed public attribute sync_batchnorm from strategies (#11754)
• Removed AcceleratorConnector.root_gpu property (#12262)
• Removed AcceleratorConnector.tpu_id property (#12387)
• Removed AcceleratorConnector.num_gpus property (#12384)
• Removed AcceleratorConnector.num_ipus property (#12386)
• Removed AcceleratorConnector.num_processes property (#12388)
• Removed AcceleratorConnector.parallel_device_ids property (#12072)
• Removed AcceleratorConnector.devices property (#12435)
• Removed AcceleratorConnector.parallel_devices property (#12075)
• Removed AcceleratorConnector.tpu_cores property (#12437)

Fixed

• Fixed an issue where ModelCheckpoint could delete last checkpoint from the old directory when dirpath has changed during resumed training (#12225)
• Fixed an issue where ModelCheckpoint could delete older checkpoints when dirpath has changed during resumed training (#12045)
• Fixed an issue where HorovodStrategy.teardown() did not complete gracefully if an exception was thrown during callback setup #11752
• Fixed security vulnerabilities CVE-2020-1747 and CVE-2020-14343 caused by the PyYAML dependency (#11099)
• Fixed security vulnerability "CWE-94: Improper Control of Generation of Code (Code Injection)" (#12212)
• Fixed logging on {test,validation}_epoch_end with multiple dataloaders (#11132)
• Reset the validation progress tracking state after sanity checking (#11218)
• Fixed double evaluation bug with fault-tolerance enabled where the second call was completely skipped (#11119)
• Fixed an issue with the TPUSpawnPlugin handling the XLA_USE_BF16 environment variable incorrectly (#10990)
• Fixed wrong typehint for Trainer.lightning_optimizers (#11155)
• Fixed the lr-scheduler state not being dumped to checkpoint when using the deepspeed strategy (#11307)
• Fixed bug that forced overriding configure_optimizers with the CLI (#11672)
• Fixed type promotion when tensors of higher category than float are logged (#11401)
• Fixed SimpleProfiler summary (#11414)
• No longer set a DistributedSampler to the poptorch.DataLoader when IPUs are used (#12114)
• Fixed bug where progress bar was not being disabled when not in rank zero during predict (#11377)
• Fixed the mid-epoch warning call while resuming training (#11556)
• Fixed LightningModule.{un,}toggle_model when only 1 optimizer is used (#12088)
• Fixed an issue in RichProgressbar to display the metrics logged only on main progress bar (#11690)
• Fixed RichProgressBar progress when refresh rate does not evenly divide the total counter (#11668)
• Fixed RichProgressBar progress validation bar total when using multiple validation runs within a single training epoch (#11668)
• Configure native Deepspeed schedulers with interval='step' (#11788), (#12031)
• Update RichProgressBarTheme styles after detecting light theme on colab (#10993)
• Fixed passing _ddp_params_and_buffers_to_ignore (#11949)
• Fixed an AttributeError when calling save_hyperparameters and no parameters need saving (#11827)
• Fixed environment variable priority for global rank determination (#11406)
• Fixed an issue that caused the Trainer to produce identical results on subsequent runs without explicit re-seeding (#11870)
• Fixed an issue that caused the Tuner to affect the random state (#11870)
• Fixed to avoid common hook warning if no hook is overridden (#12131)
• Fixed deepspeed keeping old sub-folders in same ckpt path (#12194)
• Fixed returning logged metrics instead of callback metrics during evaluation (#12224)
• Fixed the case where logger=None is passed to the Trainer (#12249)
• Fixed bug where the global step tracked by ModelCheckpoint was still set even if no checkpoint was saved (#12418)
• Fixed bug where ModelCheckpoint was overriding the epoch and step logged values (#12418)
• Fixed bug where monitoring the default epoch and step values with ModelCheckpoint would fail (#12418)
• Fixed initializing optimizers unnecessarily in DDPFullyShardedStrategy (#12267)
• Fixed check for horovod module (#12377)
• Fixed logging to loggers with multiple eval dataloaders (#12454)
• Fixed an issue with resuming from a checkpoint trained with QAT (#11346)

[1.5.10] - 2022-02-08

Fixed

• Fixed an issue to avoid validation loop run on restart (#11552)
• The RichProgressBar now correctly shows the on_epoch logged values on train epoch end (#11689)
• Fixed an issue to make the step argument in WandbLogger.log_image work (#11716)
• Fixed restore_optimizers for mapping states (#11757)
• With DPStrategy, the batch is not explicitly moved to the device (#11780)
• Fixed an issue to avoid val bar disappear after trainer.validate() (#11700)
• Fixed supporting remote filesystems with Trainer.weights_save_path for fault-tolerant training (#11776)
• Fixed check for available modules (#11526)
• Fixed bug where the path for "last" checkpoints was not getting saved correctly which caused newer runs to not remove the previous "last" checkpoint (#11481)
• Fixed bug where the path for best checkpoints was not getting saved correctly when no metric was monitored which caused newer runs to not use the best checkpoint (#11481)

[1.5.9] - 2022-01-20

Fixed

• Pinned sphinx-autodoc-typehints with <v1.15 (#11400)
• Skipped testing with PyTorch 1.7 and Python 3.9 on Ubuntu (#11217)
• Fixed type promotion when tensors of higher category than float are logged (#11401)
• Fixed the format of the configuration saved automatically by the CLI's SaveConfigCallback (#11532)

Changed

• Changed LSFEnvironment to use LSB_DJOB_RANKFILE environment variable instead of LSB_HOSTS for determining node rank and main address (#10825)
• Disabled sampler replacement when using IterableDataset (#11507)

[1.5.8] - 2022-01-05

Fixed

• Fixed LightningCLI race condition while saving the config (#11199)
• Fixed the default value used with log(reduce_fx=min|max) (#11310)
• Fixed data fetcher selection (#11294)
• Fixed a race condition that could result in incorrect (zero) values being observed in prediction writer callbacks (#11288)
• Fixed dataloaders not getting reloaded the correct amount of times when setting reload_dataloaders_every_n_epochs and check_val_every_n_epoch (#10948)
• Fixed deepspeed strategy not restoring the lr-scheduler states when lr-scheduler(s) are configured through LightningModule.configure_optimizer (#11322)

[1.5.7] - 2021-12-21

Fixed

• Fixed NeptuneLogger when using DDP (#11030)
• Fixed a bug to disable logging hyperparameters in logger if there are no hparams (#11105)
• Avoid the deprecated onnx.export(example_outputs=...) in torch 1.10 (#11116)
• Fixed an issue when torch-scripting a LightningModule after training with Trainer(sync_batchnorm=True) (#11078)
• Fixed an AttributeError occurring when using a CombinedLoader (multiple dataloaders) for prediction (#11111)
• Fixed bug where Trainer(track_grad_norm=..., logger=False) would fail (#11114)
• Fixed an incorrect warning being produced by the model summary when using bf16 precision on CPU (#11161)

Changed

• DeepSpeed does not require lightning module zero 3 partitioning (#10655)
• The ModelCheckpoint callback now saves and restores attributes best_k_models, kth_best_model_path, kth_value, and last_model_path (#10995)

[1.5.6] - 2021-12-15

Fixed

• Fixed a bug where the DeepSpeedPlugin arguments cpu_checkpointing and contiguous_memory_optimization were not being forwarded to deepspeed correctly (#10874)
• Fixed an issue with NeptuneLogger causing checkpoints to be uploaded with a duplicated file extension (#11015)
• Fixed support for logging within callbacks returned from LightningModule (#10991)
• Fixed running sanity check with RichProgressBar (#10913)
• Fixed support for CombinedLoader while checking for warning raised with eval dataloaders (#10994)
• The TQDM progress bar now correctly shows the on_epoch logged values on train epoch end (#11069)
• Fixed bug where the TQDM updated the training progress bar during trainer.validate (#11069)

[1.5.5] - 2021-12-07

Fixed

• Disabled batch_size extraction for torchmetric instances because they accumulate the metrics internally (#10815)
• Fixed an issue with SignalConnector not restoring the default signal handlers on teardown when running on SLURM or with fault-tolerant training enabled (#10611)
• Fixed SignalConnector._has_already_handler check for callable type (#10483)
• Fixed an issue to return the results for each dataloader separately instead of duplicating them for each (#10810)
• Improved exception message if rich version is less than 10.2.2 (#10839)
• Fixed uploading best model checkpoint in NeptuneLogger (#10369)
• Fixed early schedule reset logic in PyTorch profiler that was causing data leak (#10837)
• Fixed a bug that caused incorrect batch indices to be passed to the BasePredictionWriter hooks when using a dataloader with num_workers > 0 (#10870)
• Fixed an issue with item assignment on the logger on rank > 0 for those who support it (#10917)
• Fixed importing torch_xla.debug for torch-xla<1.8 (#10836)
• Fixed an issue with DDPSpawnPlugin and related plugins leaving a temporary checkpoint behind (#10934)
• Fixed a TypeError occurring in the SingalConnector.teardown() method (#10961)

[1.5.4] - 2021-11-30

Fixed

• Fixed support for --key.help=class with the LightningCLI (#10767)
• Fixed _compare_version for python packages (#10762)
• Fixed TensorBoardLogger SummaryWriter not close before spawning the processes (#10777)
• Fixed a consolidation error in Lite when attempting to save the state dict of a sharded optimizer (#10746)
• Fixed the default logging level for batch hooks associated with training from on_step=False, on_epoch=True to on_step=True, on_epoch=False (#10756)

Removed

• Removed PyTorch 1.6 support (#10367, #10738)

[1.5.3] - 2021-11-24

Fixed

• Fixed ShardedTensor state dict hook registration to check if torch distributed is available (#10621)
• Fixed an issue with self.log not respecting a tensor's dtype when applying computations (#10076)
• Fixed LigtningLite _wrap_init popping unexisting keys from DataLoader signature parameters (#10613)
• Fixed signals being registered within threads (#10610)
• Fixed an issue that caused Lightning to extract the batch size even though it was set by the user in LightningModule.log (#10408)
• Fixed Trainer(move_metrics_to_cpu=True) not moving the evaluation logged results to CPU (#10631)
• Fixed the {validation,test}_step outputs getting moved to CPU with Trainer(move_metrics_to_cpu=True) (#10631)
• Fixed an issue with collecting logged test results with multiple dataloaders (#10522)

[1.5.2] - 2021-11-16

Fixed

• Fixed CombinedLoader and max_size_cycle didn't receive a DistributedSampler (#10374)
• Fixed an issue where class or init-only variables of dataclasses were passed to the dataclass constructor in utilities.apply_to_collection (#9702)
• Fixed isinstance not working with init_meta_context, materialized model not being moved to the device (#10493)
• Fixed an issue that prevented the Trainer to shutdown workers when execution is interrupted due to failure(#10463)
• Squeeze the early stopping monitor to remove empty tensor dimensions (#10461)
• Fixed sampler replacement logic with overfit_batches to only replace the sample when SequentialSampler is not used (#10486)
• Fixed scripting causing false positive deprecation warnings (#10470, #10555)
• Do not fail if batch size could not be inferred for logging when using DeepSpeed (#10438)
• Fixed propagation of device and dtype information to submodules of LightningLite when they inherit from DeviceDtypeModuleMixin (#10559)

[1.5.1] - 2021-11-09

Fixed

• Fixed apply_to_collection(defaultdict) (#10316)
• Fixed failure when DataLoader(batch_size=None) is passed (#10345)
• Fixed interception of __init__ arguments for sub-classed DataLoader re-instantiation in Lite (#10334)
• Fixed issue with pickling CSVLogger after a call to CSVLogger.save (#10388)
• Fixed an import error being caused by PostLocalSGD when torch.distributed not available (#10359)
• Fixed the logging with on_step=True in epoch-level hooks causing unintended side-effects. Logging with on_step=True in epoch-level hooks will now correctly raise an error (#10409)
• Fixed deadlocks for distributed training with RichProgressBar (#10428)
• Fixed an issue where the model wrapper in Lite converted non-floating point tensors to float (#10429)
• Fixed an issue with inferring the dataset type in fault-tolerant training (#10432)
• Fixed dataloader workers with persistent_workers being deleted on every iteration (#10434)

[1.5.0] - 2021-11-02

Added

• Added support for monitoring the learning rate without schedulers in LearningRateMonitor (#9786)
• Added registration of ShardedTensor state dict hooks in LightningModule.__init__ if the PyTorch version supports ShardedTensor (#8944)
• Added error handling including calling of on_keyboard_interrupt() and on_exception() for all entrypoints (fit, validate, test, predict) (#8819)
• Added a flavor of training_step that takes dataloader_iter as an argument (#8807)
• Added a state_key property to the Callback base class (#6886)
• Added progress tracking to loops:
  • Integrated TrainingEpochLoop.total_batch_idx (#8598)
  • Added BatchProgress and integrated TrainingEpochLoop.is_last_batch (#9657)
  • Avoid optional Tracker attributes (#9320)
  • Reset current progress counters when restarting an epoch loop that had already finished (#9371)
  • Call reset_on_restart in the loop's reset hook instead of when loading a checkpoint (#9561)
  • Use completed over processed in reset_on_restart (#9656)
  • Renamed reset_on_epoch to reset_on_run (#9658)
• Added batch_size and rank_zero_only arguments for log_dict to match log (#8628)
• Added a check for unique GPU ids (#8666)
• Added ResultCollection state_dict to the Loop state_dict and added support for distributed reload (#8641)
• Added DeepSpeed collate checkpoint utility function (#8701)
• Added a handles_accumulate_grad_batches property to the training type plugins (#8856)
• Added a warning to WandbLogger when reusing a wandb run (#8714)
• Added log_graph argument for watch method of WandbLogger (#8662)
• LightningCLI additions:
  • Added LightningCLI(run=False|True) to choose whether to run a Trainer subcommand (#8751)
  • Added support to call any trainer function from the LightningCLI via subcommands (#7508)
  • Allow easy trainer re-instantiation (#7508)
  • Automatically register all optimizers and learning rate schedulers (#9565)
  • Allow registering custom optimizers and learning rate schedulers without subclassing the CLI (#9565)
  • Support shorthand notation to instantiate optimizers and learning rate schedulers (#9565)
  • Support passing lists of callbacks via command line (#8815)
  • Support shorthand notation to instantiate models (#9588)
  • Support shorthand notation to instantiate datamodules (#10011)
  • Added multifile option to LightningCLI to enable/disable config saving to preserve multiple files structure (#9073)
• Fault-tolerant training:
  • Added FastForwardSampler and CaptureIterableDataset injection to data loading utilities (#8366)
  • Added DataFetcher to control fetching flow (#8890)
  • Added SharedCycleIteratorState to prevent infinite loop (#8889)
  • Added CaptureMapDataset for state management in map-style datasets (#8891)
  • Added Fault Tolerant Training to DataFetcher (#8891)
  • Replaced old prefetch iterator with new DataFetcher in training loop (#8953)
  • Added partial support for global random state fault-tolerance in map-style datasets (#8950)
  • Converted state to tuple explicitly when setting Python random state (#9401)
  • Added support for restarting an optimizer loop (multiple optimizers) (#9537)
  • Added support for restarting within Evaluation Loop (#9563)
  • Added mechanism to detect that a signal has been sent so the Trainer can gracefully exit (#9566)
  • Added support for skipping ahead to validation during the auto-restart of fitting (#9681)
  • Added support for auto-restart if a fault-tolerant checkpoint is available (#9722)
• Checkpoint saving and loading extensibility:
  • Added CheckpointIO plugin to expose checkpoint IO from training type plugin (#8743)
  • Refactored CheckpointConnector to offload validation logic to the CheckpointIO plugin (#9045)
  • Added remove_checkpoint to CheckpointIO plugin by moving the responsibility out of the ModelCheckpoint callback (#9373)
  • Added XLACheckpointIO plugin (#9972)
• Loop customization:
  • Added Closure and AbstractClosure classes (#8642)
  • Refactored TrainingBatchLoop and extracted OptimizerLoop, splitting off automatic optimization into its own loop (#9191)
  • Removed TrainingBatchLoop.backward(); manual optimization now calls directly into Accelerator.backward() and automatic optimization handles backward in new OptimizerLoop (#9265)
  • Extracted ManualOptimization logic from TrainingBatchLoop into its own separate loop class (#9266)
  • Added OutputResult and ManualResult classes (#9437, #9424)
  • Marked OptimizerLoop.backward as protected (#9514)
  • Marked FitLoop.should_accumulate as protected (#9515)
  • Marked several methods in PredictionLoop as protected: on_predict_start, on_predict_epoch_end, on_predict_end, on_predict_model_eval (#9516)
  • Marked several methods in EvaluationLoop as protected: get_max_batches, on_evaluation_model_eval, on_evaluation_model_train, on_evaluation_start, on_evaluation_epoch_start, on_evaluation_epoch_end, on_evaluation_end, reload_evaluation_dataloaders (#9516)
  • Marked several methods in EvaluationEpochLoop as protected: on_evaluation_batch_start, evaluation_step, evaluation_step_end (#9516)
  • Added yielding_training_step example (#9983)
• Added support for saving and loading state of multiple callbacks of the same type (#7187)
• Added DeepSpeed Stage 1 support (#8974)
• Added Python dataclass support for LightningDataModule (#8272)
• Added sanitization of tensors when they get logged as hyperparameters in TensorBoardLogger (#9031)
• Added InterBatchParallelDataFetcher (#9020)
• Added DataLoaderIterDataFetcher (#9020)
• Added DataFetcher within Fit / Evaluation Loop (#9047)
• Added a friendly error message when DDP attempts to spawn new distributed processes with rank > 0 (#9005)
• Added Rich integration:
  • Added Rich progress bar (#8929, #9559)
  • Added Support for iterable datasets (#9734)
  • Added RichModelSummary callback (#9546)
  • Added configure_columns method to RichProgressBar (#10288)
  • Added leave argument to RichProgressBar (#10301)
• Added input validation logic for precision (#9080)
• Added support for CPU AMP autocast (#9084)
• Added on_exception callback hook (#9183)
• Added a warning to DeepSpeed when inferring batch size (#9221)
• Added ModelSummary callback (#9344)
• Added log_images, log_text and log_table to WandbLogger (#9545)
• Added PL_RECONCILE_PROCESS environment variable to enable process reconciliation regardless of cluster environment settings (#9389)
• Added get_device_stats to the Accelerator interface and added its implementation for GPU and TPU (#9586)
• Added a warning when an unknown key is encountered in the optimizer configuration, and when OneCycleLR is used with "interval": "epoch" (#9666)
• Added DeviceStatsMonitor callback (#9712)
• Added enable_progress_bar to the Trainer constructor (#9664)
• Added pl_legacy_patch load utility for loading old checkpoints that have pickled legacy Lightning attributes (#9166)
• Added support for torch.use_deterministic_algorithms (#9121)
• Added automatic parameters tying for TPUs (#9525)
• Added support for torch.autograd.set_detect_anomaly through Trainer constructor argument detect_anomaly (#9848)
• Added enable_model_summary flag to Trainer (#9699)
• Added strategy argument to Trainer (#8597)
• Added init_meta_context, materialize_module utilities (#9920)
• Added TPUPrecisionPlugin (#10020)
• Added torch.bfloat16 support:
  • Added bfloat16 support for Lightning Trainer (#9049)
  • Renamed TPUHalfPrecisionPlugin to TPUBf16PrecisionPlugin (#10026)
  • Default to precision=bf16 on CPU when precision=16 is passed (#10033)
  • Added support for torch.autocast (#10053)
• Added kfold example for loop customization (#9965)
• LightningLite:
  • Added PrecisionPlugin.forward_context, making it the default implementation for all {train,val,test,predict}_step_context() methods (#9988)
  • Added DDPSpawnPlugin.spawn() for spawning new processes of a given function (#10018, #10022)
  • Added TrainingTypePlugin.{_setup_model, _setup_optimizer} methods (#9994, #10064)
  • Implemented DataParallelPlugin._setup_model (#10010)
  • Implemented DeepSpeedPlugin._setup_model_and_optimizers (#10009, #10064)
  • Implemented {DDPShardedPlugin,DDPShardedSpawnPlugin}._setup_model_and_optimizers (#10028, #10064)
  • Added optional model argument to the optimizer_step methods in accelerators and plugins (#10023)
  • Updated precision attributes in DeepSpeedPlugin (#10164)
  • Added the ability to return a result from rank 0 in DDPSpawnPlugin.spawn (#10162)
  • Added pytorch_lightning.lite package (#10175)
  • Added LightningLite documentation (#10043)
  • Added LightningLite examples (#9987)
  • Make the _LiteDataLoader an iterator and add supports for custom dataloader (#10279)
• Added use_omegaconf argument to save_hparams_to_yaml plugin (#9170)
• Added ckpt_path argument for Trainer.fit() (#10061)
• Added auto_device_count method to Accelerators (#10222)
• Added support for devices="auto" (#10264)
• Added a filename argument in ModelCheckpoint.format_checkpoint_name (#9818)
• Added support for empty gpus list to run on CPU (#10246)
• Added a warning if multiple batch sizes are found from ambiguous batch (#10247)

Changed

• Trainer now raises a MisconfigurationException when its methods are called with ckpt_path="best" but a checkpoint callback isn't configured (#9841)
• Setting Trainer(accelerator="ddp_cpu") now does not spawn a subprocess if num_processes is kept 1 along with num_nodes > 1 (#9603)
• Module imports are now catching ModuleNotFoundError instead of ImportError (#9867)
• pytorch_lightning.loggers.neptune.NeptuneLogger is now consistent with the new neptune-client API; the old neptune-client API is supported by NeptuneClient from the neptune-contrib repo (#6867)
• Parsing of enums type hyperparameters to be saved in the haprams.yaml file by TensorBoard and CSV loggers has been fixed and made in line with how OmegaConf parses it (#9170)
• Parsing of the gpus Trainer argument has changed: gpus="n" (str) no longer selects the GPU index n and instead selects the first n devices (#8770)
• iteration_count and other index attributes in the loops has been replaced with progress dataclasses (#8477)
• The trainer.lightning_module reference is now properly set at the very beginning of a run (#8536)
• The model weights now get loaded in all cases when the checkpoint path gets provided in validate/test/predict, regardless of whether the model instance is provided or not (#8352)
• The Trainer functions reset_{train,val,test,predict}_dataloader, reset_train_val_dataloaders, and request_dataloader model argument is now optional (#8536)
• Saved checkpoints will no longer use the type of a Callback as the key to avoid issues with unpickling (#6886)
• Improved string conversion for ResultCollection (#8622)
• LightningCLI changes:
  • LightningCLI.init_parser now returns the parser instance (#8721)
  • LightningCLI.add_core_arguments_to_parser, LightningCLI.parse_arguments now take a parser argument (#8721)
  • LightningCLI.instantiate_trainer now takes a config and a list of callbacks (#8721)
  • Split LightningCLI.add_core_arguments_to_parser into LightningCLI.add_default_arguments_to_parser + LightningCLI.add_core_arguments_to_parser (#8721)
• The accelerator and training type plugin setup hooks no longer have a model argument (#8536)
• The accelerator and training type plugin update_global_step hook has been removed (#8856)
• The coverage of self.log-ing in any LightningModule or Callback hook has been improved (#8498)
• self.log-ing without a Trainer reference now raises a warning instead of an exception (#9733)
• Removed restrictions in the Trainer that loggers can only log from rank 0; the existing logger behavior has not changed (#8608)
• Trainer.request_dataloader now takes a RunningStage enum instance (#8858)
• Changed rank_zero_warn to NotImplementedError in the {train, val, test, predict}_dataloader hooks that Lightning(Data)Module uses (#9161)
• Moved block_ddp_sync_behaviour out of TrainingBatchLoop to loop utilities (#9192)
• Executing the optimizer_closure is now required when overriding the optimizer_step hook (#9360)
• Changed logging of LightningModule and LightningDataModule hyperparameters to raise an exception only if there are colliding keys with different values (#9496)
• seed_everything now fails when an invalid seed value is passed instead of selecting a random seed (#8787)
• The Trainer now calls TrainingTypePlugin collective APIs directly instead of going through the Accelerator reference (#9677, #9901)
• The tuner now uses a unique filename to save a temporary checkpoint (#9682)
• Changed HorovodPlugin.all_gather to return a torch.Tensor instead of a list (#9696)
• Changed Trainer connectors to be protected attributes:
  • Configuration Validator (#9779)
• The current_epoch and global_step attributes now get restored irrespective of the Trainer task (#9413)
• Trainer now raises an exception when requesting amp_level with native amp_backend (#9755)
• Update the logic to check for accumulation steps with deepspeed (#9826)
• pytorch_lightning.utilities.grads.grad_norm now raises an exception if parameter norm_type <= 0 (#9765)
• Updated error message for interactive incompatible plugins (#9896)
• Moved the optimizer_step and clip_gradients hook from the Accelerator and TrainingTypePlugin into the PrecisionPlugin (#10143, #10029)
• NativeMixedPrecisionPlugin and its subclasses now take an optional GradScaler instance (#10055)
• Trainer is now raising a MisconfigurationException instead of a warning if Trainer.{validate/test} is missing required methods (#10016)
• Changed default value of the max_steps Trainer argument from None to -1 (#9460)
• LightningModule now raises an error when calling log(on_step=False, on_epoch=False) (#10227)
• Quantization aware training observers are now disabled by default during validating/testing/predicting stages (#8540)
• Raised MisconfigurationException when total length of dataloader across ranks is zero, and give warning when total length is non-zero, but only local rank length is zero. (#9827)
• Changed the model size calculation using ByteCounter (#10123)
• Enabled on_load_checkpoint for LightningDataModule for all trainer_fn (#10238)
• Allowed separate config files for parameters with class type when LightningCLI is in subclass_mode=False (#10286)

Deprecated

• Deprecated Trainer argument terminate_on_nan in favor of detect_anomaly(#9175)
• Deprecated Trainer.terminate_on_nan public attribute access (#9849)
• Deprecated LightningModule.summarize() in favor of pytorch_lightning.utilities.model_summary.summarize() (#8513)
• Deprecated LightningModule.model_size (#8343)
• Deprecated DataModule properties: train_transforms, val_transforms, test_transforms, size, dims (#8851)
• Deprecated add_to_queue, get_from_queue from LightningModule in favor of corresponding methods in the DDPSpawnPlugin (#9118)
• Deprecated LightningModule.get_progress_bar_dict and Trainer.progress_bar_dict in favor of pytorch_lightning.callbacks.progress.base.get_standard_metrics and ProgressBarBase.get_metrics (#8985)
• Deprecated prepare_data_per_node flag on Trainer and set it as a property of DataHooks, accessible in the LightningModule and LightningDataModule (#8958)
• Deprecated the TestTubeLogger (#9065)
• Deprecated on_{train/val/test/predict}_dataloader() from LightningModule and LightningDataModule (#9098)
• Deprecated on_keyboard_interrupt callback hook in favor of new on_exception hook (#9260)
• Deprecated passing process_position to the Trainer constructor in favor of adding the ProgressBar callback with process_position directly to the list of callbacks (#9222)
• Deprecated passing flush_logs_every_n_steps as a Trainer argument, instead pass it to the logger init if supported (#9366)
• Deprecated LightningLoggerBase.close, LoggerCollection.close in favor of LightningLoggerBase.finalize, LoggerCollection.finalize (#9422)
• Deprecated passing progress_bar_refresh_rate to the Trainer constructor in favor of adding the ProgressBar callback with refresh_rate directly to the list of callbacks, or passing enable_progress_bar=False to disable the progress bar (#9616)
• Deprecated LightningDistributed and moved the broadcast logic to DDPPlugin and DDPSpawnPlugin directly (#9691)
• Deprecated passing stochastic_weight_avg to the Trainer constructor in favor of adding the StochasticWeightAveraging callback directly to the list of callbacks (#8989)
• Deprecated Accelerator collective API barrier, broadcast, and all_gather in favor of calling the TrainingTypePlugin collective API directly (#9677)
• Deprecated checkpoint_callback from the Trainer constructor in favor of enable_checkpointing (#9754)
• Deprecated the LightningModule.on_post_move_to_device method (#9525)
• Deprecated pytorch_lightning.core.decorators.parameter_validation in favor of pytorch_lightning.utilities.parameter_tying.set_shared_parameters (#9525)
• Deprecated passing weights_summary to the Trainer constructor in favor of adding the ModelSummary callback with max_depth directly to the list of callbacks (#9699)
• Deprecated log_gpu_memory, gpu_metrics, and util funcs in favor of DeviceStatsMonitor callback (#9921)
• Deprecated GPUStatsMonitor and XLAStatsMonitor in favor of DeviceStatsMonitor callback (#9924)
• Deprecated setting Trainer(max_steps=None); To turn off the limit, set Trainer(max_steps=-1) (default) (#9460)
• Deprecated access to the AcceleratorConnector.is_slurm_managing_tasks attribute and marked it as protected (#10101)
• Deprecated access to the AcceleratorConnector.configure_slurm_ddp method and marked it as protected (#10101)
• Deprecated passing resume_from_checkpoint to the Trainer constructor in favor of trainer.fit(ckpt_path=) (#10061)
• Deprecated ClusterEnvironment.creates_children() in favor of ClusterEnvironment.creates_processes_externally (property) (#10106)
• Deprecated PrecisionPlugin.master_params() in favor of PrecisionPlugin.main_params() (#10105)
• Deprecated lr_sch_names from LearningRateMonitor (#10066)
• Deprecated ProgressBar callback in favor of TQDMProgressBar (#10134)

Removed

• Removed deprecated metrics (#8586)
• Removed the deprecated outputs argument in both the LightningModule.on_train_epoch_end and Callback.on_train_epoch_end hooks (#8587)
• Removed the deprecated TrainerLoggingMixin class (#8609)
• Removed the deprecated TrainerTrainingTricksMixin class (#8679)
• Removed the deprecated optimizer_idx from training_step as an accepted argument in manual optimization (#8576)
• Removed support for the deprecated on_save_checkpoint signature. The hook now takes a checkpoint positional parameter (#8697)
• Removed support for the deprecated on_load_checkpoint signature. The hook now takes a pl_module positional parameter (#8697)
• Removed the deprecated save_function property in ModelCheckpoint (#8680)
• Removed the deprecated model argument from ModelCheckpoint.save_checkpoint (#8688)
• Removed the deprecated sync_step argument from WandbLogger (#8763)
• Removed the deprecated Trainer.truncated_bptt_steps in favor of LightningModule.truncated_bptt_steps (#8826)
• Removed LightningModule.write_predictions and LightningModule.write_predictions_dict (#8850)
• Removed on_reset_*_dataloader hooks in TrainingType Plugins and Accelerators (#8858)
• Removed deprecated GradInformation module in favor of pytorch_lightning.utilities.grads (#8831)
• Removed TrainingTypePlugin.on_save and Accelerator.on_save (#9023)
• Removed {Accelerator,TrainingTypePlugin,PrecisionPlugin}.post_optimizer_step (#9746)
• Removed deprecated connect_precision_plugin and connect_training_type_plugin from Accelerator (#9019)
• Removed on_train_epoch_end from Accelerator (#9035)
• Removed InterBatchProcessor in favor of DataLoaderIterDataFetcher (#9052)
• Removed Plugin in base_plugin.py in favor of accessing TrainingTypePlugin and PrecisionPlugin directly instead (#9066)
• Removed teardown from ParallelPlugin (#8943)
• Removed deprecated profiled_functions argument from PyTorchProfiler (#9178)
• Removed deprecated pytorch_lighting.utilities.argparse_utils module (#9166)
• Removed deprecated property Trainer.running_sanity_check in favor of Trainer.sanity_checking (#9209)
• Removed deprecated BaseProfiler.output_filename arg from it and its descendants in favor of dirpath and filename (#9214)
• Removed deprecated property ModelCheckpoint.period in favor of ModelCheckpoint.every_n_epochs (#9213)
• Removed deprecated auto_move_data decorator (#9231)
• Removed deprecated property LightningModule.datamodule in favor of Trainer.datamodule (#9233)
• Removed deprecated properties DeepSpeedPlugin.cpu_offload* in favor of offload_optimizer, offload_parameters and pin_memory (#9244)
• Removed deprecated property AcceleratorConnector.is_using_torchelastic in favor of TorchElasticEnvironment.is_using_torchelastic() (#9729)
• Removed pytorch_lightning.utilities.debugging.InternalDebugger (#9680)
• Removed call_configure_sharded_model_hook property from Accelerator and TrainingTypePlugin (#9612)
• Removed TrainerProperties mixin and moved property definitions directly into Trainer (#9495)
• Removed a redundant warning with ModelCheckpoint(monitor=None) callback (#9875)
• Remove epoch from trainer.logged_metrics (#9904)
• Remove deprecated distributed_backend from Trainer (#10017)
• Removed process_idx from the {DDPSpawnPlugin,TPUSpawnPlugin}.new_process methods (#10022)
• Removed automatic patching of {train,val,test,predict}_dataloader() on the LightningModule (#9764)
• Removed pytorch_lightning.trainer.connectors.OptimizerConnector (#10120)

Fixed

• Fixed ImageNet evaluation in example (#10179)
• Fixed an issue with logger outputs not being finalized correctly after prediction runs (#8685)
• Fixed move_metrics_to_cpu moving the loss to CPU while training on device (#9308)
• Fixed incorrect main progress bar indicator when resuming training mid-epoch (#9310)
• Fixed an issue with freeing memory of datafetchers during teardown (#9387)
• Fixed a bug where the training step output needed to be deepcopy-ed (#9349)
• Fixed an issue with freeing memory allocated by the data iterators in Loop.on_run_end (#9386, #9915)
• Fixed BasePredictionWriter not returning the batch indices in a non-distributed setting (#9432)
• Fixed an error when running in XLA environments with no TPU attached (#9572)
• Fixed check on torchmetrics logged whose compute() output is a multielement tensor (#9582)
• Fixed gradient accumulation for DDPShardedPlugin (#9122)
• Fixed missing DeepSpeed distributed call (#9540)
• Fixed an issue with wrapped LightningModule during evaluation; The LightningModule no longer gets wrapped with data-parallel modules when not fitting in DDPPlugin, DDPSpawnPlugin, DDPShardedPlugin, DDPSpawnShardedPlugin (#9096)
• Fixed trainer.accumulate_grad_batches to be an int on init. The default value for it is now None inside Trainer (#9652)
• Fixed broadcast in DDPPlugin and DDPSpawnPlugin to respect the src input (#9691)
• Fixed self.log(on_epoch=True, reduce_fx=sum)) for the on_batch_start and on_train_batch_start hooks (#9791)
• Fixed self.log(on_epoch=True) for the on_batch_start and on_train_batch_start hooks (#9780)
• Fixed restoring training state during Trainer.fit only (#9413)
• Fixed DeepSpeed and Lightning both calling the scheduler (#9788)
• Fixed missing arguments when saving hyperparameters from the parent class but not from the child class (#9800)
• Fixed DeepSpeed GPU device IDs (#9847)
• Reset val_dataloader in tuner/batch_size_scaling (#9857)
• Fixed use of LightningCLI in computer_vision_fine_tuning.py example (#9934)
• Fixed issue with non-init dataclass fields in apply_to_collection (#9963)
• Reset val_dataloader in tuner/batch_size_scaling for binsearch (#9975)
• Fixed logic to check for spawn in dataloader TrainerDataLoadingMixin._worker_check (#9902)
• Fixed train_dataloader getting loaded twice when resuming from a checkpoint during Trainer.fit() (#9671)
• Fixed LearningRateMonitor logging with multiple param groups optimizer with no scheduler (#10044)
• Fixed undesired side effects being caused by Trainer patching dataloader methods on the LightningModule (#9764)
• Fixed gradients not being unscaled when clipping or logging the gradient norm (#9287)
• Fixed on_before_optimizer_step getting called before the optimizer closure (including backward) has run (#10167)
• Fixed monitor value in ModelCheckpoint getting moved to the wrong device in a special case where it becomes NaN (#10118)
• Fixed creation of dirpath in BaseProfiler if it doesn't exist (#10073)
• Fixed incorrect handling of sigterm (#10189)
• Fixed bug where log(on_step=True, on_epoch=True, sync_dist=True) wouldn't reduce the value on step (#10227)
• Fixed an issue with pl.utilities.seed.reset_seed converting the PL_SEED_WORKERS environment variable to bool (#10099)
• Fixed iterating over a logger collection when fast_dev_run > 0 (#10232)
• Fixed batch_size in ResultCollection not being reset to 1 on epoch end (#10242)
• Fixed distrib_type not being set when training plugin instances are being passed to the Trainer (#10251)

[1.4.9] - 2021-09-30

• Fixed lr_find to generate same results on multiple calls (#9704)
• Fixed reset metrics on validation epoch end (#9717)
• Fixed input validation for gradient_clip_val, gradient_clip_algorithm, track_grad_norm and terminate_on_nan Trainer arguments (#9595)
• Reset metrics before each task starts (#9410)

[1.4.8] - 2021-09-22

• Fixed error reporting in DDP process reconciliation when processes are launched by an external agent (#9389)
• Added PL_RECONCILE_PROCESS environment variable to enable process reconciliation regardless of cluster environment settings (#9389)
• Fixed add_argparse_args raising TypeError when args are typed as typing.Generic in Python 3.6 (#9554)
• Fixed back-compatibility for saving hyperparameters from a single container and inferring its argument name by reverting #9125 (#9642)

[1.4.7] - 2021-09-14

• Fixed logging of nan parameters (#9364)
• Fixed replace_sampler missing the batch size under specific conditions (#9367)
• Pass init args to ShardedDataParallel (#9483)
• Fixed collision of user argument when using ShardedDDP (#9512)
• Fixed DeepSpeed crash for RNNs (#9489)

[1.4.6] - 2021-09-07

• Fixed an issues with export to ONNX format when a model has multiple inputs (#8800)
• Removed deprecation warnings being called for on_{task}_dataloader (#9279)
• Fixed save/load/resume from checkpoint for DeepSpeed Plugin ( #8397, #8644, #8627)
• Fixed EarlyStopping running on train epoch end when check_val_every_n_epoch>1 is set (#9156)
• Fixed an issue with logger outputs not being finalized correctly after prediction runs (#8333)
• Fixed the Apex and DeepSpeed plugin closure running after the on_before_optimizer_step hook (#9288)
• Fixed the Native AMP plugin closure not running with manual optimization (#9288)
• Fixed bug where data-loading functions where not getting the correct running stage passed (#8858)
• Fixed intra-epoch evaluation outputs staying in memory when the respective *_epoch_end hook wasn't overridden (#9261)
• Fixed error handling in DDP process reconciliation when _sync_dir was not initialized (#9267)
• Fixed PyTorch Profiler not enabled for manual optimization (#9316)
• Fixed inspection of other args when a container is specified in save_hyperparameters (#9125)
• Fixed signature of Timer.on_train_epoch_end and StochasticWeightAveraging.on_train_epoch_end to prevent unwanted deprecation warnings (#9347)

[1.4.5] - 2021-08-31

• Fixed reduction using self.log(sync_dict=True, reduce_fx={mean,max}) (#9142)
• Fixed not setting a default value for max_epochs if max_time was specified on the Trainer constructor (#9072)
• Fixed the CometLogger, no longer modifies the metrics in place. Instead creates a copy of metrics before performing any operations (#9150)
• Fixed DDP "CUDA error: initialization error" due to a copy instead of deepcopy on ResultCollection (#9239)

[1.4.4] - 2021-08-24

• Fixed a bug in the binary search mode of auto batch size scaling where exception was raised if the first trainer run resulted in OOM (#8954)
• Fixed a bug causing logging with log_gpu_memory='min_max' not working (#9013)

[1.4.3] - 2021-08-17

• Fixed plateau scheduler stepping on incomplete epoch (#8861)
• Fixed infinite loop with CycleIterator and multiple loaders (#8889)
• Fixed StochasticWeightAveraging with a list of learning rates not applying them to each param group (#8747)
• Restore original loaders if replaced by entrypoint (#8885)
• Fixed lost reference to _Metadata object in ResultMetricCollection (#8932)
• Ensure the existence of DDPPlugin._sync_dir in reconciliate_processes (#8939)

[1.4.2] - 2021-08-10

• Fixed recursive call for apply_to_collection(include_none=False) (#8719)
• Fixed truncated backprop through time enablement when set as a property on the LightningModule and not the Trainer (#8804)
• Fixed comments and exception message for metrics_to_scalars (#8782)
• Fixed typo error in LightningLoggerBase.after_save_checkpoint docstring (#8737)

[1.4.1] - 2021-08-03

• Fixed trainer.fit_loop.split_idx always returning None (#8601)
• Fixed references for ResultCollection.extra (#8622)
• Fixed reference issues during epoch end result collection (#8621)
• Fixed horovod auto-detection when horovod is not installed and the launcher is mpirun (#8610)
• Fixed an issue with training_step outputs not getting collected correctly for training_epoch_end (#8613)
• Fixed distributed types support for CPUs (#8667)
• Fixed a deadlock issue with DDP and torchelastic (#8655)
• Fixed accelerator=ddp choice for CPU (#8645)

[1.4.0] - 2021-07-27

Added

• Added extract_batch_size utility and corresponding tests to extract batch dimension from multiple batch types (#8357)
• Added support for named parameter groups in LearningRateMonitor (#7987)
• Added dataclass support for pytorch_lightning.utilities.apply_to_collection (#7935)
• Added support to LightningModule.to_torchscript for saving to custom filesystems with fsspec (#7617)
• Added KubeflowEnvironment for use with the PyTorchJob operator in Kubeflow
• Added LightningCLI support for config files on object stores (#7521)
• Added ModelPruning(prune_on_train_epoch_end=True|False) to choose when to apply pruning (#7704)
• Added support for checkpointing based on a provided time interval during training (#7515)
• Progress tracking
  • Added dataclasses for progress tracking (#6603, #7574, #8140, #8362)
  • Add {,load_}state_dict to the progress tracking dataclasses (#8140)
  • Connect the progress tracking dataclasses to the loops (#8244, #8362)
  • Do not reset the progress tracking dataclasses total counters (#8475)
• Added support for passing a LightningDataModule positionally as the second argument to trainer.{validate,test,predict} (#7431)
• Added argument trainer.predict(ckpt_path) (#7430)
• Added clip_grad_by_value support for TPUs (#7025)
• Added support for passing any class to is_overridden (#7918)
• Added sub_dir parameter to TensorBoardLogger (#6195)
• Added correct dataloader_idx to batch transfer hooks (#6241)
• Added include_none=bool argument to apply_to_collection (#7769)
• Added apply_to_collections to apply a function to two zipped collections (#7769)
• Added ddp_fully_sharded support (#7487)
• Added should_rank_save_checkpoint property to Training Plugins (#7684)
• Added log_grad_norm hook to LightningModule to customize the logging of gradient norms (#7873)
• Added save_config_filename init argument to LightningCLI to ease resolving name conflicts (#7741)
• Added save_config_overwrite init argument to LightningCLI to ease overwriting existing config files (#8059)
• Added reset dataloader hooks to Training Plugins and Accelerators (#7861)
• Added trainer stage hooks for Training Plugins and Accelerators (#7864)
• Added the on_before_optimizer_step hook (#8048)
• Added IPU Accelerator (#7867)
• Fault-tolerant training
  • Added {,load_}state_dict to ResultCollection (#7948)
  • Added {,load_}state_dict to Loops (#8197)
  • Added FastForwardSampler and CaptureIterableDataset (#8307)
  • Set Loop.restarting=False at the end of the first iteration (#8362)
  • Save the loops state with the checkpoint (opt-in) (#8362)
  • Save a checkpoint to restore the state on exception (opt-in) (#8362)
  • Added state_dict and load_state_dict utilities for CombinedLoader + utilities for dataloader (#8364)
• Added rank_zero_only to LightningModule.log function (#7966)
• Added metric_attribute to LightningModule.log function (#7966)
• Added a warning if Trainer(log_every_n_steps) is a value too high for the training dataloader (#7734)
• Added LightningCLI support for argument links applied on instantiation (#7895)
• Added LightningCLI support for configurable callbacks that should always be present (#7964)
• Added DeepSpeed Infinity Support, and updated to DeepSpeed 0.4.0 (#7234)
• Added support for torch.nn.UninitializedParameter in ModelSummary (#7642)
• Added support LightningModule.save_hyperparameters when LightningModule is a dataclass (#7992)
• Added support for overriding optimizer_zero_grad and optimizer_step when using accumulate_grad_batches (#7980)
• Added logger boolean flag to save_hyperparameters (#7960)
• Added support for calling scripts using the module syntax (python -m package.script) (#8073)
• Added support for optimizers and learning rate schedulers to LightningCLI (#8093)
• Added XLA Profiler (#8014)
• Added PrecisionPlugin.{pre,post}_backward (#8328)
• Added on_load_checkpoint and on_save_checkpoint hooks to the PrecisionPlugin base class (#7831)
• Added max_depth parameter in ModelSummary (#8062)
• Added XLAStatsMonitor callback (#8235)
• Added restore function and restarting attribute to base Loop (#8247)
• Added support for save_hyperparameters in LightningDataModule (#3792)
• Added the ModelCheckpoint(save_on_train_epoch_end) to choose when to run the saving logic (#8389)
• Added LSFEnvironment for distributed training with the LSF resource manager jsrun (#5102)
• Added support for accelerator='cpu'|'gpu'|'tpu'|'ipu'|'auto' (#7808)
• Added tpu_spawn_debug to plugin registry (#7933)
• Enabled traditional/manual launching of DDP processes through LOCAL_RANK and NODE_RANK environment variable assignments (#7480)
• Added quantize_on_fit_end argument to QuantizationAwareTraining (#8464)
• Added experimental support for loop specialization (#8226)
• Added support for devices flag to Trainer (#8440)
• Added private prevent_trainer_and_dataloaders_deepcopy context manager on the LightningModule (#8472)
• Added support for providing callables to the Lightning CLI instead of types (#8400)

Changed

• Decoupled device parsing logic from Accelerator connector to Trainer (#8180)
• Changed the Trainer's checkpoint_callback argument to allow only boolean values (#7539)
• Log epoch metrics before the on_evaluation_end hook (#7272)
• Explicitly disallow calling self.log(on_epoch=False) during epoch-only or single-call hooks (#7874)
• Changed these Trainer methods to be protected: call_setup_hook, call_configure_sharded_model, pre_dispatch, dispatch, post_dispatch, call_teardown_hook, run_train, run_sanity_check, run_evaluate, run_evaluation, run_predict, track_output_for_epoch_end
• Changed metrics_to_scalars to work with any collection or value (#7888)
• Changed clip_grad_norm to use torch.nn.utils.clip_grad_norm_ (#7025)
• Validation is now always run inside the training epoch scope (#7357)
• ModelCheckpoint now runs at the end of the training epoch by default (#8389)
• EarlyStopping now runs at the end of the training epoch by default (#8286)
• Refactored Loops
  • Moved attributes global_step, current_epoch, max/min_steps, max/min_epochs, batch_idx, and total_batch_idx to TrainLoop (#7437)
  • Refactored result handling in training loop (#7506)
  • Moved attributes hiddens and split_idx to TrainLoop (#7507)
  • Refactored the logic around manual and automatic optimization inside the optimizer loop (#7526)
  • Simplified "should run validation" logic (#7682)
  • Simplified logic for updating the learning rate for schedulers (#7682)
  • Removed the on_epoch guard from the "should stop" validation check (#7701)
  • Refactored internal loop interface; added new classes FitLoop, TrainingEpochLoop, TrainingBatchLoop (#7871, #8077)
  • Removed pytorch_lightning/trainer/training_loop.py (#7985)
  • Refactored evaluation loop interface; added new classes DataLoaderLoop, EvaluationLoop, EvaluationEpochLoop (#7990, #8077)
  • Removed pytorch_lightning/trainer/evaluation_loop.py (#8056)
  • Restricted public access to several internal functions (#8024)
  • Refactored trainer _run_* functions and separate evaluation loops (#8065)
  • Refactored prediction loop interface; added new classes PredictionLoop, PredictionEpochLoop (#7700, #8077)
  • Removed pytorch_lightning/trainer/predict_loop.py (#8094)
  • Moved result teardown to the loops (#8245)
  • Improve Loop API to better handle children state_dict and progress (#8334)
• Refactored logging
  • Renamed and moved core/step_result.py to trainer/connectors/logger_connector/result.py (#7736)
  • Dramatically simplify the LoggerConnector (#7882)
  • trainer.{logged,progress_bar,callback}_metrics are now updated on-demand (#7882)
  • Completely overhaul the Result object in favor of ResultMetric (#7882)
  • Improve epoch-level reduction time and overall memory usage (#7882)
  • Allow passing self.log(batch_size=...) (#7891)
  • Each of the training loops now keeps its own results collection (#7891)
  • Remove EpochResultStore and HookResultStore in favor of ResultCollection (#7909)
  • Remove MetricsHolder (#7909)
• Moved ignore_scalar_return_in_dp warning suppression to the DataParallelPlugin class (#7421)
• Changed the behaviour when logging evaluation step metrics to no longer append /epoch_* to the metric name (#7351)
• Raised ValueError when a None value is self.log-ed (#7771)
• Changed resolve_training_type_plugins to allow setting num_nodes and sync_batchnorm from Trainer setting (#7026)
• Default seed_everything(workers=True) in the LightningCLI (#7504)
• Changed model.state_dict() in CheckpointConnector to allow training_type_plugin to customize the model's state_dict() (#7474)
• MLflowLogger now uses the env variable MLFLOW_TRACKING_URI as default tracking URI (#7457)
• Changed Trainer arg and functionality from reload_dataloaders_every_epoch to reload_dataloaders_every_n_epochs (#5043)
• Changed WandbLogger(log_model={True/'all'}) to log models as artifacts (#6231)
• MLFlowLogger now accepts run_name as an constructor argument (#7622)
• Changed teardown() in Accelerator to allow training_type_plugin to customize teardown logic (#7579)
• Trainer.fit now raises an error when using manual optimization with unsupported features such as gradient_clip_val or accumulate_grad_batches (#7788)
• Accelerator hooks are called regardless if LightningModule overrides the same hooks (#7826)
• Moved profilers to their own file (#7822)
• The on_after_backward hook is now called on accumulating iterations. Use the on_before_optimizer_step hook to mimic the old behaviour (#8328)
• The mixed precision loss is no longer unscaled before the on_after_backward hook. Use the on_before_optimizer_step hook to mimic the old behaviour (#8328)
• The TrainingTypePlugin.{pre,post}_backward hooks no longer take the optimizer, opt_idx, should_accumulate arguments (#8328)
• The PrecisionPlugin.backward hooks no longer returns a value (#8328)
• The PrecisionPlugin.backward hooks no longer takes a should_accumulate argument (#8328)
• Added the on_before_backward hook (#7865)
• LightningCLI now aborts with a clearer message if config already exists and disables save config during fast_dev_run(#7963)
• Saved the LightningCLI config on setup and only on the main process (#8017)
• Dropped the LightningCLI ArgumentParser when pickling (#8017)
• Skip broadcast if distributed not initialized for the spawn plugins (#8017)
• Trainer(resume_from_checkpoint=...) now restores the model directly after LightningModule.setup(), which is before LightningModule.configure_sharded_model() (#7652)
• Moved torch.cuda.set_device() to enable collective calls earlier in setup (#8312)
• Used XLA utility API to move data to CPU (Single TPU core) (#8078)
• Improved error messages in replace_sampler when the DataLoader attributes are not included in the signature or the signature is missing optional arguments (#8519)
• Moved DeviceDtypeModuleMixin and HyperparametersMixin mixin to core (#8396)
• Return the default_root_dir as the log_dir when the logger is a LoggerCollection (#8187)

Deprecated

• Deprecated LightningModule.loaded_optimizer_states_dict (#8229)
• Standardized the dataloaders arguments of trainer.{fit,valdiate,test,tune} (#7431)
• Deprecated DataModule properties: has_prepared_data, has_setup_fit, has_setup_validate, has_setup_test, has_setup_predict, has_teardown_fit, has_teardown_validate, has_teardown_test, has_teardown_predict (#7657)
• Deprecated TrainerModelHooksMixin in favor of pytorch_lightning.utilities.signature_utils (#7422)
• Deprecated num_nodes and sync_batchnorm arguments in DDPPlugin and DDPSpawnPlugin (#7026)
• Deprecated self.log(sync_dist_op) in favor of self.log(reduce_fx). (#7891)
• Deprecated is_overridden(model=...) in favor of is_overridden(instance=...) (#7918)
• Deprecated automatically detaching returned extras with grads (#7994)
• Deprecated default value of monitor argument in EarlyStopping callback to enforce monitor as a required argument (#7907)
• Deprecated importing rank_zero_{warn,deprecation} directly from pytorch_lightning.utilities.distributed (#8085)
• Deprecated the use of CheckpointConnector.hpc_load() in favor of CheckpointConnector.restore() (#7652)
• Deprecated ModelCheckpoint(every_n_val_epochs) in favor of ModelCheckpoint(every_n_epochs) (#8383)
• Deprecated DDPPlugin.task_idx in favor of DDPPlugin.local_rank (#8203)
• Deprecated the Trainer.train_loop property in favor of Trainer.fit_loop (#8025)
• Deprecated the Trainer.disable_validation property in favor of not Trainer.enable_validation (#8291)
• Deprecated mode parameter in ModelSummary in favor of max_depth (#8062)
• Deprecated reload_dataloaders_every_epoch argument of Trainer in favor of reload_dataloaders_every_n_epochs (#5043)
• Deprecated distributed_backend argument for Trainer (#8575)

Removed

• Dropped official support/testing for PyTorch <1.6 (#8288)
• Removed ProfilerConnector (#7654)
• Pruned deprecated classif. metrics from pytorch_lightning.metrics.functional.classification (#7499)
• Removed deprecated data parallel classes LightningDataParallel and LightningDistributedDataParallel from pytorch_lightning.overrides.data_parallel (#7510)
• Removed deprecated trainer attributes - get_model and accelerator_backend (#7502)
• Removed support for automatically monitoring the val_loss key with ModelCheckpoint. Pass your monitor of choice to the ModelCheckpoint instance instead (#8293)
• Removed support for self.log(tbptt_reduce_fx) and self.log(tbptt_pad_token). Please, open a discussion explaining your use-case if you relied on these. (#7644)
• Removed deprecated utils modules model_utils, warning_utils, xla_device_utils and partially argparse_utils (#7503)
• Removed RPCPlugin and RPCSequentialPlugin. If you were successfully using these plugins, please open a GitHub discussion about your use case (#8101)
• Removed deprecated trainer attributes - on_cpu, on_tpu, use_tpu, on_gpu, use_dp, use_ddp, use_ddp2, use_horovod, use_single_gpu (#7501)
• Removed deprecated optimizer argument in LightningModule.manual_backward(); Toggling optimizers in manual optimization should be done using LightningModule.{un}toggle_optimizer() (#8287)
• Removed DeepSpeed FP16 Exception as FP32 is now supported (#8462)
• Removed environment variable PL_EXP_VERSION from DDP subprocesses (7403)

Fixed

• Fixed the GPUStatsMonitor callbacks to use the correct GPU IDs if CUDA_VISIBLE_DEVICES set (#8260)
• Fixed lr_scheduler checkpointed state by calling update_lr_schedulers before saving checkpoints (#7877)
• Fixed ambiguous warning when both overfit and train dataloader shuffling are enabled (#7685)
• Fixed dev debugger memory growing due to tracking events even when disabled (#7875)
• Fixed None loss keys getting added in training_epoch_end when using manual optimization and not returning a loss (#7772)
• Fixed a bug where precision=64 with accelerator='ddp_spawn' would throw a pickle error (#6924)
• Do not override the existing epoch value in logged_metrics when already logged by the user (#7982)
• Support for manual optimization with DeepSpeed (#7970)
• Fixed dataloader_idx argument value when predicting with only one DataLoader (#7941)
• Fixed passing the stage argument of Callback.{setup,teardown} as a keyword (#7973)
• Fixed metrics generated during validation sanity checking are cleaned on end (#8171)
• Fixed log_gpu_memory metrics not being added to logging when nothing else is logged (#8174)
• Fixed a bug where calling log with a Metric instance would raise an error if it was a nested attribute of the model (#8181)
• Fixed a bug where using precision=64 would cause buffers with complex dtype to be cast to real (#8208)
• Fixed is_overridden returning true for wrapped functions with no changes (#8296)
• Fixed a bug where truncated_bptt_steps would throw an AttributeError when the target RNN has multiple hidden states (#8145)
• Fixed self.optimizers() not returning a single optimizer if it had been wrapped (#8326)
• Fixed the on_after_backward hook not getting called when using manual optimization and no plugins (#8328)
• Fixed the LightningModule.backward hook only getting called with the apex plugin when using manual optimization (#8328)
• Fixed moving batch to device before sending it to the on_*_batch_start/on_*_batch_end callbacks and model hooks (#7378)
• Fixed passing a custom DDPPlugin when choosing accelerator="ddp_cpu" for the accelerator (#6208)
• Fixed missing call to LightningModule.untoggle_optimizer in training loop when running gradient accumulation with multiple optimizers (#8284)
• Fixed hash of LightningEnum to work with value instead of name (#8421).
• Fixed a bug where an extra checkpoint was saved at the end of training if the val_check_interval did not align with the number of training batches (#7724)
• Fixed hash of LightningEnum to work with value instead of name(#8421).
• Fixed move_data_to_device to return the batch if the object to function didn't return self (#8433)
• Fixed progress bar updates for Pod Training (#8258)
• Fixed clearing dataloader references before attaching new dataloaders in consecutive `Trainer.{fit,validate,test,predict}´ runs (#8442)
• Fixed memory leaks on GPU by moving optimizer_states, ResultCollection.extra, ResultMetric attributes, and LoggerConnector metrics to cpu. Also, delete the DDP wrapper on teardown (#8490)
• Fixed SWA callback using LightningModule prevent_trainer_and_dataloaders_deepcopy to avoid OOM (#8472)
• Fixed ModelPruning callback on_save_checkpoint to avoid making a deepcopy potentially leading to OOM (#8472)
• Fixed the sampler replacement logic for DataLoaders which do not define all DataLoader attributes as __init__ parameters (#8519)
• Fixed DeepSpeed Windows support (#8488)
• Fixed DeepSpeed not properly setting the trainer lr_schedulers attribute (#8527)
• Fixed experiment version and log-dir divergence in DDP when using multiple Trainer instances in sequence (7403)
• Enabled manual optimization for TPUs (#8458)
• Fixed accumulate_grad_batches not been recomputed during model reload (#5334)
• Fixed a TypeError when wrapping optimizers in the HorovodPlugin and running Trainer.test (#7840)
• Fixed BackboneFinetuning restoration (#8501)
• Fixed lr_scheduler with metric (e.g. torch.optim.lr_scheduler.ReduceLROnPlateau) when using automatic_optimization = False (#7643)
• Fixed DeepSpeed breaking with no schedulers (#8580)

[1.3.8] - 2021-07-01

Fixed

• Fixed a sync deadlock when checkpointing a LightningModule that uses a torchmetrics 0.4 Metric (#8218)
• Fixed compatibility TorchMetrics v0.4 (#8206)
• Added torchelastic check when sanitizing GPUs (#8095)
• Fixed a DDP info message that was never shown (#8111)
• Fixed metrics deprecation message at module import level (#8163)
• Fixed a bug where an infinite recursion would be triggered when using the BaseFinetuning callback on a model that contains a ModuleDict (#8170)
• Added a mechanism to detect deadlock for DDP when only 1 process trigger an Exception. The mechanism will kill the processes when it happens (#8167)
• Fixed NCCL error when selecting non-consecutive device ids (#8165)
• Fixed SWA to also work with IterableDataset (#8172)

[1.3.7] - 2021-06-22

Fixed

• Fixed a bug where skipping an optimizer while using amp causes amp to trigger an assertion error (#7975)
• Fixed deprecation messages not showing due to incorrect stacklevel (#8002, #8005)
• Fixed setting a DistributedSampler when using a distributed plugin in a custom accelerator (#7814)
• Improved PyTorchProfiler chrome traces names (#8009)
• Fixed moving the best score to device in EarlyStopping callback for TPU devices (#7959)
• Fixes access to callback_metrics in ddp_spawn (#7916)

[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)

[1.3.5] - 2021-06-08

Added

• Added warning to Training Step output (#7779)

Fixed

• Fixed LearningRateMonitor and 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)

[1.3.4] - 2021-06-01

Fixed

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

[1.3.3] - 2021-05-27

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)

[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)

[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)

[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 arguments from ModelCheckpoint.format_checkpoint_name(), these are now included in the metrics argument (#7344)
• Removed legacy references for magic keys in the Result object (#6016)
• Removed deprecated LightningModule hparams setter (#6207)
• Removed legacy code to log or include metrics in the progress bar by returning them in a dict with the "log"/"progress_bar" magic keys. Use self.log instead (#6734)
• Removed trainer.fit() return value of 1. It has no return now (#7237)
• Removed logger_connector legacy code (#6733)
• Removed unused mixin attributes (#6487)

Fixed

• Fixed NaN errors in progress bars when training with iterable datasets with no length defined (#7306)
• Fixed attaching train and validation dataloaders when reload_dataloaders_every_epoch=True and num_sanity_val_steps=0 (#7207)
• Added a barrier in the accelerator teardown to synchronize processes before execution finishes (#6814)
• Fixed multi-node DDP sub-process launch by using local_rank instead of global_rank for main process assertion (#7061)
• Fixed incorrect removal of WORLD_SIZE environment variable in DDP training when launching with torch distributed/torchelastic (#6942)
• Made the Plugin.reduce method more consistent across all Plugins to reflect a mean-reduction by default (#6011)
• Move lightning module to correct device type when using LightningDistributedWrapper (#6070)
• Do not print top-k verbose log with ModelCheckpoint(monitor=None) (#6109)
• Fixed ModelCheckpoint(save_top_k=0, save_last=True) not saving the last checkpoint (#6136)
• Fixed .teardown(stage='fit') and .on_fit_{start,end}() getting called during trainer.test (#6386)
• Fixed LightningModule all_gather on cpu tensors (#6416)
• Fixed torch distributed not available in setup hook for DDP (#6506)
• Fixed trainer.tuner.{lr_find,scale_batch_size} not setting the Trainer state properly (#7258)
• Fixed bug where the learning rate schedulers did not follow the optimizer frequencies (#4868)
• Fixed pickle error checker to now check for pickle.PickleError to catch all pickle errors (#6917)
• Fixed a bug where the outputs object passed to LightningModule.training_epoch_end was different from the object passed to the on_train_end_epoch hook (#6969)
• Fixed a bug where the outputs passed to train_batch_end would be lists even when using a single optimizer and no truncated backprop through time steps (#6969)
• Fixed bug for trainer error handling which would cause hang for distributed training (#6864)
• Fixed self.device not returning the correct device in replicas of data-parallel (#6414)
• Fixed lr_find trying beyond num_training steps and suggesting a too high learning rate (#7076)
• Fixed logger creating incorrect version folder in DDP with repeated Trainer.fit calls (#7077)
• Fixed metric objects passed directly to self.log not being reset correctly (#7055)
• Fixed CombinedLoader in distributed settings for validation / testing (#7102)
• Fixed the save_dir in WandbLogger when the run was initiated externally (#7106)
• Fixed num_sanity_val_steps affecting reproducibility of training data shuffling (#7014)
• Fixed resetting device after fitting/evaluating/predicting (#7188)
• Fixed bug where trainer.tuner.scale_batch_size(max_trials=0) would not return the correct batch size result (#7262)
• Fixed metrics not being properly logged with precision=16 and manual_optimization (#7228)
• Fixed BaseFinetuning properly reloading optimizer_states when using resume_from_checkpoint (#6891)
• Fixed parameters_to_ignore not properly set to DDPWrapper (#7239)
• Fixed parsing of fast_dev_run=True with the built-in ArgumentParser (#7240)
• Fixed handling an IterableDataset that fails to produce a batch at the beginning of an epoch (#7294)
• Fixed LightningModule.save_hyperparameters() when attempting to save an empty container (#7268)
• Fixed apex not properly instantiated when running with ddp (#7274)
• Fixed optimizer state not moved to GPU (#7277)
• Fixed custom init args for WandbLogger (#6989)
• Fixed a bug where an error would be raised if the train dataloader sometimes produced None for a batch (#7342)
• Fixed examples ( #6600, #6638, #7096, #7246, #6357, #6476, #6294, #6373, #6088, #7398 )
• Resolved schedule step bug for PyTorch Profiler (#6674, #6681)
• Updated logic for checking TPUs availability (#6767)
• Resolve TPU miss rendezvous (#6781)
• Fixed auto-scaling mode when calling tune method on trainer (#7321)
• Fixed finetuning complex models correctly unfreezes (#6880)
• Ensure we set the eval/train flag correctly on accelerator model (#6877)
• Set better defaults for rank_zero_only.rank when training is launched with SLURM and torchelastic (#6802)
• Fixed matching the number of outputs of backward with forward for AllGatherGrad (#6625)
• Fixed the gradient_clip_algorithm has no effect (#6928)
• Fixed CUDA OOM detection and handling (#6934)
• Fixed unfreeze_and_add_param_group expects modules rather than module (#6822)
• Fixed DPP + SyncBN when move on device (#6838)
• Fixed missing arguments in lr_find call (#6784)
• Fixed set_default_tensor_type to torch.DoubleTensor with precision=64 (#7108)
• Fixed NeptuneLogger.log_text(step=None) (#7194)
• Fixed importing torchtext batch (#6365, #6323, #6211)

[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)

[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)

[1.2.7] - 2021-04-06

Fixed

• Fixed resolve a bug with omegaconf and xm.save (#6741)
• Fixed an issue with IterableDataset when len is not defined (#6828)
• Sanitize None params during pruning (#6836)
• Enforce an epoch scheduler interval when using SWA (#6588)
• Fixed TPU Colab hang issue, post training (#6816)
• Fixed a bug where TensorBoardLogger would give a warning and not log correctly to a symbolic link save_dir (#6730)
• Fixed bug where predict could not be used when progress_bar_refresh_rate=0 (#6884)

[1.2.6] - 2021-03-30

Changed

• Changed the behavior of on_epoch_start to run at the beginning of validation & test epoch (#6498)

Removed

• Removed legacy code to include step dictionary returns in callback_metrics. Use self.log_dict instead. (#6682)

Fixed

• Fixed DummyLogger.log_hyperparams raising a TypeError when running with fast_dev_run=True (#6398)
• Fixed error on TPUs when there was no ModelCheckpoint (#6654)
• Fixed trainer.test freeze on TPUs (#6654)
• Fixed a bug where gradients were disabled after calling Trainer.predict (#6657)
• Fixed bug where no TPUs were detected in a TPU pod env (#6719)

[1.2.5] - 2021-03-23

Changed

• Update Gradient Clipping for the TPU Accelerator (#6576)
• Refactored setup for typing friendly (#6590)

Fixed

• Fixed a bug where all_gather would not work correctly with tpu_cores=8 (#6587)
• Fixed comparing required versions (#6434)
• Fixed duplicate logs appearing in console when using the python logging module (#6275)
• Added Autocast in validation, test and predict modes for Native AMP (#6565)

[1.2.4] - 2021-03-16

Changed

• Changed the default of find_unused_parameters back to True in DDP and DDP Spawn (#6438)

Fixed

• Expose DeepSpeed loss parameters to allow users to fix loss instability (#6115)
• Fixed DP reduction with collection (#6324)
• Fixed an issue where the tuner would not tune the learning rate if also tuning the batch size (#4688)
• Fixed broadcast to use PyTorch broadcast_object_list and add reduce_decision (#6410)
• Fixed logger creating directory structure too early in DDP (#6380)
• Fixed DeepSpeed additional memory use on rank 0 when default device not set early enough (#6460)
• Fixed an issue with Tuner.scale_batch_size not finding the batch size attribute in the datamodule (#5968)
• Fixed an exception in the layer summary when the model contains torch.jit scripted submodules (#6511)
• Fixed when Train loop config was run during Trainer.predict (#6541)

[1.2.3] - 2021-03-09

Fixed

• Fixed ModelPruning(make_pruning_permanent=True) pruning buffers getting removed when saved during training (#6073)
• Fixed when _stable_1d_sort to work when n >= N (#6177)
• Fixed AttributeError when logger=None on TPU (#6221)
• Fixed PyTorch Profiler with emit_nvtx (#6260)
• Fixed trainer.test from best_path hangs after calling trainer.fit (#6272)
• Fixed SingleTPU calling all_gather (#6296)
• Ensure we check DeepSpeed/Sharded in multi-node DDP (#6297
• Check LightningOptimizer doesn't delete optimizer hooks (#6305
• Resolve memory leak for evaluation (#6326
• Ensure that clip gradients is only called if the value is greater than 0 (#6330
• Fixed Trainer not resetting lightning_optimizers when calling Trainer.fit() multiple times (#6372)

[1.2.2] - 2021-03-02

Added

• Added checkpoint parameter to callback's on_save_checkpoint hook (#6072)

Changed

• Changed the order of backward, step, zero_grad to zero_grad, backward, step (#6147)
• Changed default for DeepSpeed CPU Offload to False, due to prohibitively slow speeds at smaller scale (#6262)

Fixed

• Fixed epoch level schedulers not being called when val_check_interval < 1.0 (#6075)
• Fixed multiple early stopping callbacks (#6197)
• Fixed incorrect usage of detach(), cpu(), to() (#6216)
• Fixed LBFGS optimizer support which didn't converge in automatic optimization (#6147)
• Prevent WandbLogger from dropping values (#5931)
• Fixed error thrown when using valid distributed mode in multi node (#6297

[1.2.1] - 2021-02-23

Fixed

• Fixed incorrect yield logic for the amp autocast context manager (#6080)
• Fixed priority of plugin/accelerator when setting distributed mode (#6089)
• Fixed error message for AMP + CPU incompatibility (#6107)
• Disabled batch transfer in DP mode (#6093)

[1.2.0] - 2021-02-18

Added

• Added DataType, AverageMethod and MDMCAverageMethod enum in metrics (#5657)
• Added support for summarized model total params size in megabytes (#5590)
• Added support for multiple train loaders (#1959)
• Added Accuracy metric now generalizes to Top-k accuracy for (multi-dimensional) multi-class inputs using the top_k parameter (#4838)
• Added Accuracy metric now enables the computation of subset accuracy for multi-label or multi-dimensional multi-class inputs with the subset_accuracy parameter (#4838)
• Added HammingDistance metric to compute the hamming distance (loss) (#4838)
• Added max_fpr parameter to auroc metric for computing partial auroc metric (#3790)
• Added StatScores metric to compute the number of true positives, false positives, true negatives and false negatives (#4839)
• Added R2Score metric (#5241)
• Added LambdaCallback (#5347)
• Added BackboneLambdaFinetuningCallback (#5377)
• Accelerator all_gather supports collection (#5221)
• Added image_gradients functional metric to compute the image gradients of a given input image. (#5056)
• Added MetricCollection (#4318)
• Added .clone() method to metrics (#4318)
• Added IoU class interface (#4704)
• Support to tie weights after moving model to TPU via on_post_move_to_device hook
• Added missing val/test hooks in LightningModule (#5467)
• The Recall and Precision metrics (and their functional counterparts recall and precision) can now be generalized to Recall@K and Precision@K with the use of top_k parameter (#4842)
• Added ModelPruning Callback (#5618, #5825, #6045)
• Added PyTorchProfiler (#5560)
• Added compositional metrics (#5464)
• Added Trainer method predict(...) for high performance predictions (#5579)
• Added on_before_batch_transfer and on_after_batch_transfer data hooks (#3671)
• Added AUC/AUROC class interface (#5479)
• Added PredictLoop object (#5752)
• Added QuantizationAwareTraining callback (#5706, #6040)
• Added LightningModule.configure_callbacks to enable the definition of model-specific callbacks (#5621)
• Added dim to PSNR metric for mean-squared-error reduction (#5957)
• Added promxial policy optimization template to pl_examples (#5394)
• Added log_graph to CometLogger (#5295)
• Added possibility for nested loaders (#5404)
• Added sync_step to Wandb logger (#5351)
• Added StochasticWeightAveraging callback (#5640)
• Added LightningDataModule.from_datasets(...) (#5133)
• Added PL_TORCH_DISTRIBUTED_BACKEND env variable to select backend (#5981)
• Added Trainer flag to activate Stochastic Weight Averaging (SWA) Trainer(stochastic_weight_avg=True) (#6038)
• Added DeepSpeed integration (#5954, #6042)

Changed

• Changed stat_scores metric now calculates stat scores over all classes and gains new parameters, in line with the new StatScores metric (#4839)
• Changed computer_vision_fine_tunning example to use BackboneLambdaFinetuningCallback (#5377)
• Changed automatic casting for LoggerConnector metrics (#5218)
• Changed iou [func] to allow float input (#4704)
• Metric compute() method will no longer automatically call reset() (#5409)
• Set PyTorch 1.4 as min requirements, also for testing and examples torchvision>=0.5 and torchtext>=0.5 (#5418)
• Changed callbacks argument in Trainer to allow Callback input (#5446)
• Changed the default of find_unused_parameters to False in DDP (#5185)
• Changed ModelCheckpoint version suffixes to start at 1 (#5008)
• Progress bar metrics tensors are now converted to float (#5692)
• Changed the default value for the progress_bar_refresh_rate Trainer argument in Google COLAB notebooks to 20 (#5516)
• Extended support for purely iteration-based training (#5726)
• Made LightningModule.global_rank, LightningModule.local_rank and LightningModule.logger read-only properties (#5730)
• Forced ModelCheckpoint callbacks to run after all others to guarantee all states are saved to the checkpoint (#5731)
• Refactored Accelerators and Plugins:
  • Added base classes for plugins (#5715)
  • Added parallel plugins for DP, DDP, DDPSpawn, DDP2 and Horovod (#5714)
  • Precision Plugins (#5718)
  • Added new Accelerators for CPU, GPU and TPU (#5719)
  • Added RPC and Sharded plugins (#5732)
  • Added missing LightningModule-wrapper logic to new plugins and accelerator (#5734)
  • Moved device-specific teardown logic from training loop to accelerator (#5973)
  • Moved accelerator_connector.py to the connectors subfolder (#6033)
  • Trainer only references accelerator (#6039)
  • Made parallel devices optional across all plugins (#6051)
  • Cleaning (#5948, #5949, #5950)
• Enabled self.log in callbacks (#5094)
• Renamed xxx_AVAILABLE as protected (#5082)
• Unified module names in Utils (#5199)
• Separated utils: imports & enums (#5256 #5874)
• Refactor: clean trainer device & distributed getters (#5300)
• Simplified training phase as LightningEnum (#5419)
• Updated metrics to use LightningEnum (#5689)
• Changed the seq of on_train_batch_end, on_batch_end & on_train_epoch_end, on_epoch_end hooks (#5688)
• Refactored setup_training and remove test_mode (#5388)
• Disabled training with zero num_training_batches when insufficient limit_train_batches (#5703)
• Refactored EpochResultStore (#5522)
• Update lr_finder to check for attribute if not running fast_dev_run (#5990)
• LightningOptimizer manual optimizer is more flexible and expose toggle_model (#5771)
• MlflowLogger limit parameter value length to 250 char (#5893)
• Re-introduced fix for Hydra directory sync with multiple process (#5993)

Deprecated

• Function stat_scores_multiple_classes is deprecated in favor of stat_scores (#4839)
• Moved accelerators and plugins to its legacy pkg (#5645)
• Deprecated LightningDistributedDataParallel in favor of new wrapper module LightningDistributedModule (#5185)
• Deprecated LightningDataParallel in favor of new wrapper module LightningParallelModule (#5670)
• Renamed utils modules (#5199)
  • argparse_utils >> argparse
  • model_utils >> model_helpers
  • warning_utils >> warnings
  • xla_device_utils >> xla_device
• Deprecated using 'val_loss' to set the ModelCheckpoint monitor (#6012)
• Deprecated .get_model() with explicit .lightning_module property (#6035)
• Deprecated Trainer attribute accelerator_backend in favor of accelerator (#6034)

Removed

• Removed deprecated checkpoint argument filepath (#5321)
• Removed deprecated Fbeta, f1_score and fbeta_score metrics (#5322)
• Removed deprecated TrainResult (#5323)
• Removed deprecated EvalResult (#5633)
• Removed LoggerStages (#5673)

Fixed

• Fixed distributed setting and ddp_cpu only with num_processes>1 (#5297)
• Fixed num_workers for Windows example (#5375)
• Fixed loading yaml (#5619)
• Fixed support custom DataLoader with DDP if they can be re-instantiated (#5745)
• Fixed repeated .fit() calls ignore max_steps iteration bound (#5936)
• Fixed throwing MisconfigurationError on unknown mode (#5255)
• Resolve bug with Finetuning (#5744)
• Fixed ModelCheckpoint race condition in file existence check (#5155)
• Fixed some compatibility with PyTorch 1.8 (#5864)
• Fixed forward cache (#5895)
• Fixed recursive detach of tensors to CPU (#6007)
• Fixed passing wrong strings for scheduler interval doesn't throw an error (#5923)
• Fixed wrong requires_grad state after return None with multiple optimizers (#5738)
• Fixed add on_epoch_end hook at the end of validation, test epoch (#5986)
• Fixed missing process_dataloader call for TPUSpawn when in distributed mode (#6015)
• Fixed progress bar flickering by appending 0 to floats/strings (#6009)
• Fixed synchronization issues with TPU training (#6027)
• Fixed hparams.yaml saved twice when using TensorBoardLogger (#5953)
• Fixed basic examples (#5912, #5985)
• Fixed fairscale compatible with PT 1.8 (#5996)
• Ensured process_dataloader is called when tpu_cores > 1 to use Parallel DataLoader (#6015)
• Attempted SLURM auto resume call when non-shell call fails (#6002)
• Fixed wrapping optimizers upon assignment (#6006)
• Fixed allowing hashing of metrics with lists in their state (#5939)

[1.1.8] - 2021-02-08

Fixed

• Separate epoch validation from step validation (#5208)
• Fixed toggle_optimizers not handling all optimizer parameters (#5775)

[1.1.7] - 2021-02-03

Fixed

• Fixed TensorBoardLogger not closing SummaryWriter on finalize (#5696)
• Fixed filtering of pytorch "unsqueeze" warning when using DP (#5622)
• Fixed num_classes argument in F1 metric (#5663)
• Fixed log_dir property (#5537)
• Fixed a race condition in ModelCheckpoint when checking if a checkpoint file exists (#5144)
• Remove unnecessary intermediate layers in Dockerfiles (#5697)
• Fixed auto learning rate ordering (#5638)

[1.1.6] - 2021-01-26

Changed

• Increased TPU check timeout from 20s to 100s (#5598)
• Ignored step param in Neptune logger's log_metric method (#5510)
• Pass batch outputs to on_train_batch_end instead of epoch_end outputs (#4369)

Fixed

• Fixed toggle_optimizer to reset requires_grad state (#5574)
• Fixed FileNotFoundError for best checkpoint when using DDP with Hydra (#5629)
• Fixed an error when logging a progress bar metric with a reserved name (#5620)
• Fixed Metric's state_dict not included when child modules (#5614)
• Fixed Neptune logger creating multiple experiments when GPUs > 1 (#3256)
• Fixed duplicate logs appearing in console when using the python logging module (#5509)
• Fixed tensor printing in trainer.test() (#5138)
• Fixed not using dataloader when hparams present (#4559)

[1.1.5] - 2021-01-19

Fixed

• Fixed a visual bug in the progress bar display initialization (#4579)
• Fixed logging on_train_batch_end in a callback with multiple optimizers (#5521)
• Fixed reinit_scheduler_properties with correct optimizer (#5519)
• Fixed val_check_interval with fast_dev_run (#5540)

[1.1.4] - 2021-01-12

Added

• Add automatic optimization property setter to lightning module (#5169)

Changed

• Changed deprecated enable_pl_optimizer=True (#5244)

Fixed

• Fixed transfer_batch_to_device for DDP with len(devices_ids) == 1 (#5195)
• Logging only on not should_accumulate() during training (#5417)
• Resolve interpolation bug with Hydra (#5406)
• Check environ before selecting a seed to prevent warning message (#4743)
• Fixed signature mismatch in model_to_device of DDPCPUHPCAccelerator (#5505)

[1.1.3] - 2021-01-05

Added

• Added a check for optimizer attached to lr_scheduler (#5338)
• Added support for passing non-existing filepaths to resume_from_checkpoint (#4402)

Changed

• Skip restore from resume_from_checkpoint while testing (#5161)
• Allowed log_momentum for adaptive optimizers in LearningRateMonitor (#5333)
• Disabled checkpointing, earlystopping and logging with fast_dev_run (#5277)
• Distributed group defaults to WORLD if None (#5125)

Fixed

• Fixed trainer.test returning non-test metrics (#5214)
• Fixed metric state reset (#5273)
• Fixed --num-nodes on DDPSequentialPlugin (#5327)
• Fixed invalid value for weights_summary (#5296)
• Fixed Trainer.test not using the latest best_model_path (#5161)
• Fixed existence check for hparams not using underlying filesystem (#5250)
• Fixed LightningOptimizer AMP bug (#5191)
• Fixed casted key to string in _flatten_dict (#5354)

[1.1.2] - 2020-12-23

Added

• Support number for logging with sync_dist=True (#5080)
• Added offset logging step when resuming for Wandb logger (#5050)

Removed

• enable_pl_optimizer=False by default to temporarily fix AMP issues (#5163)

Fixed

• Metric reduction with Logging (#5150)
• Remove nan loss in manual optimization (#5121)
• Un-balanced logging properly supported (#5119)
• Fix hanging in DDP HPC accelerators (#5157)
• Fix reset TensorRunningAccum (#5106)
• Updated DALIClassificationLoader to not use deprecated arguments (#4925)
• Corrected call to torch.no_grad (#5124)

[1.1.1] - 2020-12-15

Added

• Add a notebook example to reach a quick baseline of ~94% accuracy on CIFAR10 using Resnet in Lightning (#4818)

Changed

• Simplify accelerator steps (#5015)
• Refactor load in checkpoint connector (#4593)
• Fixed the saved filename in ModelCheckpoint when it already exists (#4861)

Removed

• Drop duplicate metrics (#5014)
• Remove beta arg from F1 class and functional (#5076)

Fixed

• Fixed trainer by default None in DDPAccelerator (#4915)
• Fixed LightningOptimizer to expose optimizer attributes (#5095)
• Do not warn when the name key is used in the lr_scheduler dict (#5057)
• Check if optimizer supports closure (#4981)
• Add deprecated metric utility functions back to functional ( #5067, #5068)
• Allow any input in to_onnx and to_torchscript (#4378)
• Fixed DDPHPCAccelerator hangs in DDP construction by calling init_device (#5157)

[1.1.0] - 2020-12-09

Added

• Added "monitor" key to saved ModelCheckpoints (#4383)
• Added ConfusionMatrix class interface (#4348)
• Added multiclass AUROC metric (#4236)
• Added global step indexing to the checkpoint name for a better sub-epoch checkpointing experience (#3807)
• Added optimizer hooks in callbacks (#4379)
• Added option to log momentum (#4384)
• Added current_score to ModelCheckpoint.on_save_checkpoint (#4721)
• Added logging using self.log in train and evaluation for epoch end hooks ( #4552, #4495, #4439, #4684, #4913)
• Added ability for DDP plugin to modify optimizer state saving (#4675)
• Added prefix argument in loggers (#4557)
• Added printing of total num of params, trainable and non-trainable params in ModelSummary (#4521)
• Added PrecisionRecallCurve, ROC, AveragePrecision class metric (#4549)
• Added custom Apex and NativeAMP as Precision plugins (#4355)
• Added DALI MNIST example (#3721)
• Added sharded plugin for DDP for multi-gpu training memory optimizations ( #4639, #4686, #4737, #4773)
• Added experiment_id to the NeptuneLogger (#3462)
• Added Pytorch Geometric integration example with Lightning (#4568)
• Added all_gather method to LightningModule which allows gradient based tensor synchronizations for use-cases such as negative sampling. (#5012)
• Enabled self.log in most functions (#4969)
• Added changeable extension variable for ModelCheckpoint (#4977)

Changed

• Tuner algorithms will be skipped if fast_dev_run=True (#3903)
• WandbLogger does not force wandb reinit arg to True anymore and creates a run only when needed (#4648)
• Changed automatic_optimization to be a model attribute (#4602)
• Changed Simple Profiler report to order by percentage time spent + num calls (#4880)
• Simplify optimization Logic (#4984)
• Classification metrics overhaul (#4837)
• Updated fast_dev_run to accept integer representing num_batches (#4629)
• Refactored optimizer (#4658)

Deprecated

• Deprecated prefix argument in ModelCheckpoint (#4765)
• Deprecated the old way of assigning hyper-parameters through self.hparams = ... (#4813)
• Deprecated mode='auto' from ModelCheckpoint and EarlyStopping (#4695)

Removed

• Removed reorder parameter of the auc metric (#5004)
• Removed multiclass_roc and multiclass_precision_recall_curve, use roc and precision_recall_curve instead (#4549)

Fixed

• Added feature to move tensors to CPU before saving (#4309)
• Fixed LoggerConnector to have logged metrics on root device in DP (#4138)
• Auto convert tensors to contiguous format when gather_all (#4907)
• Fixed PYTHONPATH for ddp test model (#4528)
• Fixed allowing logger to support indexing (#4595)
• Fixed DDP and manual_optimization (#4976)

[1.0.8] - 2020-11-24

Added

• Added casting to python types for numpy scalars when logging hparams (#4647)
• Added warning when progress bar refresh rate is less than 20 on Google Colab to prevent crashing (#4654)
• Added F1 class metric (#4656)

Changed

• Consistently use step=trainer.global_step in LearningRateMonitor independently of logging_interval (#4376)
• Metric states are no longer as default added to state_dict (#4685)
• Renamed class metric Fbeta >> FBeta (#4656)
• Model summary: add 1 decimal place (#4745)
• Do not override PYTHONWARNINGS (#4700)
• Changed init_ddp_connection moved from DDP to DDPPlugin (#4407)

Fixed

• Fixed checkpoint hparams dict casting when omegaconf is available (#4770)
• Fixed incomplete progress bars when total batches not divisible by refresh rate (#4577)
• Updated SSIM metric (#4566)
• Fixed batch_arg_name - add batch_arg_name to all calls to _adjust_batch_sizebug (#4812)
• Fixed torchtext data to GPU (#4785)
• Fixed a crash bug in MLFlow logger (#4716)

[1.0.7] - 2020-11-17

Added

• Added lambda closure to manual_optimizer_step (#4618)

Changed

• Change Metrics persistent default mode to False (#4685)
• LoggerConnector log_metrics will use total_batch_idx instead of global_step when logging on training step (#4738)

Fixed

• Prevent crash if sync_dist=True on CPU (#4626)
• Fixed average pbar Metrics (#4534)
• Fixed setup callback hook to correctly pass the LightningModule through (#4608)
• Allowing decorate model init with saving hparams inside (#4662)
• Fixed split_idx set by LoggerConnector in on_trainer_init to Trainer (#4697)

[1.0.6] - 2020-11-11

Added

• Added metrics aggregation in Horovod and fixed early stopping (#3775)
• Added manual_optimizer_step which work with AMP Native and accumulated_grad_batches (#4485)
• Added persistent(mode) method to metrics, to enable and disable metric states being added to state_dict (#4482)
• Added congratulations at the end of our notebooks (#4555)
• Added parameters move_metrics_to_cpu in Trainer to disable gpu leak (#4592)

Changed

• Changed fsspec to tuner (#4458)
• Unify SLURM/TorchElastic under backend plugin (#4578, #4580, #4581, #4582, #4583)

Fixed

• Fixed feature-lack in hpc_load (#4526)
• Fixed metrics states being overridden in DDP mode (#4482)
• Fixed lightning_getattr, lightning_hasattr not finding the correct attributes in datamodule (#4347)
• Fixed automatic optimization AMP by manual_optimization_step (#4485)
• Replace MisconfigurationException with warning in ModelCheckpoint Callback (#4560)
• Fixed logged keys in mlflow logger (#4412)
• Fixed is_picklable by catching AttributeError (#4508)
• Fixed multi test dataloaders dict AttributeError error (#4480)
• Fixed show progress bar only for progress_rank 0 on DDP_SLURM (#4437)

[1.0.5] - 2020-11-03

Added

• Added PyTorch 1.7 Stable support (#3821)
• Added timeout for tpu_device_exists to ensure process does not hang indefinitely (#4340)

Changed

• W&B log in sync with Trainer step (#4405)
• Hook on_after_backward is called only when optimizer_step is being called (#4439)
• Moved track_and_norm_grad into training loop and called only when optimizer_step is being called (#4439)
• Changed type checker with explicit cast of ref_model object (#4457)
• Changed distributed_backend -> accelerator (#4429)

Deprecated

• Deprecated passing ModelCheckpoint instance to checkpoint_callback Trainer argument (#4336)

Fixed

• Disable saving checkpoints if not trained (#4372)
• Fixed error using auto_select_gpus=True with gpus=-1 (#4209)
• Disabled training when limit_train_batches=0 (#4371)
• Fixed that metrics do not store computational graph for all seen data (#4313)
• Fixed AMP unscale for on_after_backward (#4439)
• Fixed TorchScript export when module includes Metrics (#4428)
• Fixed TorchScript trace method's data to device and docstring (#4360)
• Fixed CSV logger warning (#4419)
• Fixed skip DDP parameter sync (#4301)
• Fixed WandbLogger _sanitize_callable function (#4422)
• Fixed AMP Native _unscale gradient (#4441)

[1.0.4] - 2020-10-27

Added

• Added dirpath and filename parameter in ModelCheckpoint (#4213)
• Added plugins docs and DDPPlugin to customize ddp across all accelerators (#4258)
• Added strict option to the scheduler dictionary (#3586)
• Added fsspec support for profilers (#4162)
• Added autogenerated helptext to Trainer.add_argparse_args (#4344)
• Added support for string values in Trainer's profiler parameter (#3656)
• Added optimizer_closure to optimizer.step when supported (#4190)
• Added unification of regression metrics (#4166)
• Added checkpoint load from Bytes (#4314)

Changed

• Improved error messages for invalid configure_optimizers returns (#3587)
• Allow changing the logged step value in validation_step (#4130)
• Allow setting replace_sampler_ddp=True with a distributed sampler already added (#4273)
• Fixed sanitized parameters for WandbLogger.log_hyperparams (#4320)

Deprecated

• Deprecated filepath in ModelCheckpoint (#4213)
• Deprecated reorder parameter of the auc metric (#4237)
• Deprecated bool values in Trainer's profiler parameter (#3656)

Fixed

• Fixed setting device ids in DDP (#4297)
• Fixed synchronization of best model path in ddp_accelerator (#4323)
• Fixed WandbLogger not uploading checkpoint artifacts at the end of training (#4341)
• Fixed FBeta computation (#4183)
• Fixed accumulation across batches has completed before breaking training loop (#4278)
• Fixed ModelCheckpoint don't increase current_epoch and global_step when not training (#4291)
• Fixed COMET_EXPERIMENT_KEY environment variable usage in comet logger (#4230)

[1.0.3] - 2020-10-20

Added

• Added persistent flag to Metric.add_state (#4195)

Changed

• Used checkpoint_connector.hpc_save in SLURM (#4217)
• Moved base req. to root (#4219)

Fixed

• Fixed hparams assign in init (#4189)
• Fixed overwrite check for model hooks (#4010)

[1.0.2] - 2020-10-15

Added

• Added trace functionality to the function to_torchscript (#4142)

Changed

• Called on_load_checkpoint before loading state_dict (#4057)

Removed

• Removed duplicate metric vs step log for train loop (#4173)

Fixed

• Fixed the self.log problem in validation_step() (#4169)
• Fixed hparams saving - save the state when save_hyperparameters() is called [in __init__] (#4163)
• Fixed runtime failure while exporting hparams to yaml (#4158)

[1.0.1] - 2020-10-14

Added

• Added getstate/setstate method for torch.save serialization (#4127)

[1.0.0] - 2020-10-13

Added

• Added Explained Variance Metric + metric fix (#4013)
• Added Metric <-> Lightning Module integration tests (#4008)
• Added parsing OS env vars in Trainer (#4022)
• Added classification metrics (#4043)
• Updated explained variance metric (#4024)
• Enabled plugins (#4041)
• Enabled custom clusters (#4048)
• Enabled passing in custom accelerators (#4050)
• Added LightningModule.toggle_optimizer (#4058)
• Added LightningModule.manual_backward (#4063)
• Added output argument to *_batch_end hooks (#3965, #3966)
• Added output argument to *_epoch_end hooks (#3967)

Changed

• Integrated metrics API with self.log (#3961)
• Decoupled Apex (#4052, #4054, #4055, #4056, #4058, #4060, #4061, #4062, #4063, #4064, #4065)
• Renamed all backends to Accelerator (#4066)
• Enabled manual returns (#4089)

Removed

• Removed support for EvalResult and TrainResult (#3968)
• Removed deprecated trainer flags: overfit_pct, log_save_interval, row_log_interval (#3969)
• Removed deprecated early_stop_callback (#3982)
• Removed deprecated model hooks (#3980)
• Removed deprecated callbacks (#3979)
• Removed trainer argument in LightningModule.backward #4056)

Fixed

• Fixed current_epoch property update to reflect true epoch number inside LightningDataModule, when reload_dataloaders_every_epoch=True. (#3974)
• Fixed to print scaler value in progress bar (#4053)
• Fixed mismatch between docstring and code regarding when on_load_checkpoint hook is called (#3996)

[0.10.0] - 2020-10-07

Added

• Added new Metrics API. (#3868, #3921)
• Enable PyTorch 1.7 compatibility (#3541)
• Added LightningModule.to_torchscript to support exporting as ScriptModule (#3258)
• Added warning when dropping unpicklable hparams (#2874)
• Added EMB similarity (#3349)
• Added ModelCheckpoint.to_yaml method (#3048)
• Allow ModelCheckpoint monitor to be None, meaning it will always save (#3630)
• Disabled optimizers setup during testing (#3059)
• Added support for datamodules to save and load checkpoints when training (#3563)
• Added support for datamodule in learning rate finder (#3425)
• Added gradient clip test for native AMP (#3754)
• Added dist lib to enable syncing anything across devices (#3762)
• Added broadcast to TPUBackend (#3814)
• Added XLADeviceUtils class to check XLA device type (#3274)

Changed

• Refactored accelerator backends:
  • moved TPU xxx_step to backend (#3118)
  • refactored DDP backend forward (#3119)
  • refactored GPU backend __step (#3120)
  • refactored Horovod backend (#3121, #3122)
  • remove obscure forward call in eval + CPU backend ___step (#3123)
  • reduced all simplified forward (#3126)
  • added hook base method (#3127)
  • refactor eval loop to use hooks - use test_mode for if so we can split later (#3129)
  • moved ___step_end hooks (#3130)
  • training forward refactor (#3134)
  • training AMP scaling refactor (#3135)
  • eval step scaling factor (#3136)
  • add eval loop object to streamline eval loop (#3138)
  • refactored dataloader process hook (#3139)
  • refactored inner eval loop (#3141)
  • final inner eval loop hooks (#3154)
  • clean up hooks in run_evaluation (#3156)
  • clean up data reset (#3161)
  • expand eval loop out (#3165)
  • moved hooks around in eval loop (#3195)
  • remove _evaluate fx (#3197)
  • Trainer.fit hook clean up (#3198)
  • DDPs train hooks (#3203)
  • refactor DDP backend (#3204, #3207, #3208, #3209, #3210)
  • reduced accelerator selection (#3211)
  • group prepare data hook (#3212)
  • added data connector (#3285)
  • modular is_overridden (#3290)
  • adding Trainer.tune() (#3293)
  • move run_pretrain_routine -> setup_training (#3294)
  • move train outside of setup training (#3297)
  • move prepare_data to data connector (#3307)
  • moved accelerator router (#3309)
  • train loop refactor - moving train loop to own object (#3310, #3312, #3313, #3314)
  • duplicate data interface definition up into DataHooks class (#3344)
  • inner train loop (#3359, #3361, #3362, #3363, #3365, #3366, #3367, #3368, #3369, #3370, #3371, #3372, #3373, #3374, #3375, #3376, #3385, #3388, #3397)
  • all logging related calls in a connector (#3395)
  • device parser (#3400, #3405)
  • added model connector (#3407)
  • moved eval loop logging to loggers (#3408)
  • moved eval loop (#3412#3408)
  • trainer/separate argparse (#3421, #3428, #3432)
  • move lr_finder (#3434)
  • organize args (##3435, #3442, #3447, #3448, #3449, #3456)
  • move specific accelerator code (#3457)
  • group connectors (#3472)
  • accelerator connector methods x/n (#3469, #3470, #3474)
  • merge backends x/n (#3476, #3477, #3478, #3480, #3482)
  • apex plugin (#3502)
  • precision plugins (#3504)
  • Result - make monitor default to checkpoint_on to simplify (#3571)
  • reference to the Trainer on the LightningDataModule (#3684)
  • add .log to lightning module (#3686, #3699, #3701, #3704, #3715)
  • enable tracking original metric when step and epoch are both true (#3685)
  • deprecated results obj, added support for simpler comms (#3681)
  • move backends back to individual files (#3712)
  • fixes logging for eval steps (#3763)
  • decoupled DDP, DDP spawn (#3733, #3766, #3767, #3774, #3802, #3806, #3817, #3819, #3927)
  • remove weight loading hack for ddp_cpu (#3808)
  • separate torchelastic from DDP (#3810)
  • separate SLURM from DDP (#3809)
  • decoupled DDP2 (#3816)
  • bug fix with logging val epoch end + monitor (#3812)
  • callback system and init DDP (#3836)
  • adding compute environments (#3837, #3842)
  • epoch can now log independently (#3843)
  • test selecting the correct backend. temp backends while slurm and TorchElastic are decoupled (#3848)
  • fixed init_slurm_connection causing hostname errors (#3856)
  • moves init apex from LM to apex connector (#3923)
  • moves sync bn to each backend (#3925)
  • moves configure ddp to each backend (#3924)
• Deprecation warning (#3844)
• Changed LearningRateLogger to LearningRateMonitor (#3251)
• Used fsspec instead of gfile for all IO (#3320)
  • Swapped torch.load for fsspec load in DDP spawn backend (#3787)
  • Swapped torch.load for fsspec load in cloud_io loading (#3692)
  • Added support for to_disk() to use remote filepaths with fsspec (#3930)
  • Updated model_checkpoint's to_yaml to use fsspec open (#3801)
  • Fixed fsspec is inconsistent when doing fs.ls (#3805)
• Refactor GPUStatsMonitor to improve training speed (#3257)
• Changed IoU score behavior for classes absent in target and pred (#3098)
• Changed IoU remove_bg bool to ignore_index optional int (#3098)
• Changed defaults of save_top_k and save_last to None in ModelCheckpoint (#3680)
• row_log_interval and log_save_interval are now based on training loop's global_step instead of epoch-internal batch index (#3667)
• Silenced some warnings. verified ddp refactors (#3483)
• Cleaning up stale logger tests (#3490)
• Allow ModelCheckpoint monitor to be None (#3633)
• Enable None model checkpoint default (#3669)
• Skipped best_model_path if checkpoint_callback is None (#2962)
• Used raise .. from .. to explicitly chain exceptions (#3750)
• Mocking loggers (#3596, #3617, #3851, #3859, #3884, #3853, #3910, #3889, #3926)
• Write predictions in LightningModule instead of EvalResult #3882

Deprecated

• Deprecated TrainResult and EvalResult, use self.log and self.write from the LightningModule to log metrics and write predictions. training_step can now only return a scalar (for the loss) or a dictionary with anything you want. (#3681)
• Deprecate early_stop_callback Trainer argument (#3845)
• Rename Trainer arguments row_log_interval >> log_every_n_steps and log_save_interval >> flush_logs_every_n_steps (#3748)

Removed

• Removed experimental Metric API (#3943, #3949, #3946), listed changes before final removal:
  • Added EmbeddingSimilarity metric (#3349, #3358)
  • Added hooks to metric module interface (#2528)
  • Added error when AUROC metric is used for multiclass problems (#3350)
  • Fixed ModelCheckpoint with save_top_k=-1 option not tracking the best models when a monitor metric is available (#3735)
  • Fixed counter-intuitive error being thrown in Accuracy metric for zero target tensor (#3764)
  • Fixed aggregation of metrics (#3517)
  • Fixed Metric aggregation (#3321)
  • Fixed RMSLE metric (#3188)
  • Renamed reduction to class_reduction in classification metrics (#3322)
  • Changed class_reduction similar to sklearn for classification metrics (#3322)
  • Renaming of precision recall metric (#3308)

Fixed

• Fixed on_train_batch_start hook to end epoch early (#3700)
• Fixed num_sanity_val_steps is clipped to limit_val_batches (#2917)
• Fixed ONNX model save on GPU (#3145)
• Fixed GpuUsageLogger to work on different platforms (#3008)
• Fixed auto-scale batch size not dumping auto_lr_find parameter (#3151)
• Fixed batch_outputs with optimizer frequencies (#3229)
• Fixed setting batch size in LightningModule.datamodule when using auto_scale_batch_size (#3266)
• Fixed Horovod distributed backend compatibility with native AMP (#3404)
• Fixed batch size auto scaling exceeding the size of the dataset (#3271)
• Fixed getting experiment_id from MLFlow only once instead of each training loop (#3394)
• Fixed overfit_batches which now correctly disables shuffling for the training loader. (#3501)
• Fixed gradient norm tracking for row_log_interval > 1 (#3489)
• Fixed ModelCheckpoint name formatting (#3164)
• Fixed example implementation of AutoEncoder (#3190)
• Fixed invalid paths when remote logging with TensorBoard (#3236)
• Fixed change t() to transpose() as XLA devices do not support .t() on 1-dim tensor (#3252)
• Fixed (weights only) checkpoints loading without PL (#3287)
• Fixed gather_all_tensors cross GPUs in DDP (#3319)
• Fixed CometML save dir (#3419)
• Fixed forward key metrics (#3467)
• Fixed normalize mode at confusion matrix (replace NaNs with zeros) (#3465)
• Fixed global step increment in training loop when training_epoch_end hook is used (#3673)
• Fixed dataloader shuffling not getting turned off with overfit_batches > 0 and distributed_backend = "ddp" (#3534)
• Fixed determinism in DDPSpawnBackend when using seed_everything in main process (#3335)
• Fixed ModelCheckpoint period to actually save every period epochs (#3630)
• Fixed val_progress_bar total with num_sanity_val_steps (#3751)
• Fixed Tuner dump: add current_epoch to dumped_params (#3261)
• Fixed current_epoch and global_step properties mismatch between Trainer and LightningModule (#3785)
• Fixed learning rate scheduler for optimizers with internal state (#3897)
• Fixed tbptt_reduce_fx when non-floating tensors are logged (#3796)
• Fixed model checkpoint frequency (#3852)
• Fixed logging non-tensor scalar with result breaks subsequent epoch aggregation (#3855)
• Fixed TrainerEvaluationLoopMixin activates model.train() at the end (#3858)
• Fixed overfit_batches when using with multiple val/test_dataloaders (#3857)
• Fixed enables training_step to return None (#3862)
• Fixed init nan for checkpointing (#3863)
• Fixed for load_from_checkpoint (#2776)
• Fixes incorrect batch_sizes when Dataloader returns a dict with multiple tensors (#3668)
• Fixed unexpected signature for validation_step (#3947)

[0.9.0] - 2020-08-20

Added

• Added SyncBN for DDP (#2801, #2838)
• Added basic CSVLogger (#2721)
• Added SSIM metrics (#2671)
• Added BLEU metrics (#2535)
• Added support to export a model to ONNX format (#2596)
• Added support for Trainer(num_sanity_val_steps=-1) to check all validation data before training (#2246)
• Added struct. output:
  • tests for val loop flow (#2605)
  • EvalResult support for train and val. loop (#2615, #2651)
  • weighted average in results obj (#2930)
  • fix result obj DP auto reduce (#3013)
• Added class LightningDataModule (#2668)
• Added support for PyTorch 1.6 (#2745)
• Added call DataModule hooks implicitly in trainer (#2755)
• Added support for Mean in DDP Sync (#2568)
• Added remaining sklearn metrics: AveragePrecision, BalancedAccuracy, CohenKappaScore, DCG, Hamming, Hinge, Jaccard, MeanAbsoluteError, MeanSquaredError, MeanSquaredLogError, MedianAbsoluteError, R2Score, MeanPoissonDeviance, MeanGammaDeviance, MeanTweedieDeviance, ExplainedVariance (#2562)
• Added support for limit_{mode}_batches (int) to work with infinite dataloader (IterableDataset) (#2840)
• Added support returning python scalars in DP (#1935)
• Added support to Tensorboard logger for OmegaConf hparams (#2846)
• Added tracking of basic states in Trainer (#2541)
• Tracks all outputs including TBPTT and multiple optimizers (#2890)
• Added GPU Usage Logger (#2932)
• Added strict=False for load_from_checkpoint (#2819)
• Added saving test predictions on multiple GPUs (#2926)
• Auto log the computational graph for loggers that support this (#3003)
• Added warning when changing monitor and using results obj (#3014)
• Added a hook transfer_batch_to_device to the LightningDataModule (#3038)

Changed

• Truncated long version numbers in progress bar (#2594)
• Enabling val/test loop disabling (#2692)
• Refactored into accelerator module:
  • GPU training (#2704)
  • TPU training (#2708)
  • DDP(2) backend (#2796)
  • Retrieve last logged val from result by key (#3049)
• Using .comet.config file for CometLogger (#1913)
• Updated hooks arguments - breaking for setup and teardown (#2850)
• Using gfile to support remote directories (#2164)
• Moved optimizer creation after device placement for DDP backends (#2904)
• Support **DictConfig for hparam serialization (#2519)
• Removed callback metrics from test results obj (#2994)
• Re-enabled naming metrics in ckpt name (#3060)
• Changed progress bar epoch counting to start from 0 (#3061)

Deprecated

• Deprecated Trainer attribute ckpt_path, which will now be set by weights_save_path (#2681)

Removed

• Removed deprecated: (#2760)
  • core decorator data_loader
  • Module hook on_sanity_check_start and loading load_from_metrics
  • package pytorch_lightning.logging
  • Trainer arguments: show_progress_bar, num_tpu_cores, use_amp, print_nan_grads
  • LR Finder argument num_accumulation_steps

Fixed

• Fixed accumulate_grad_batches for last batch (#2853)
• Fixed setup call while testing (#2624)
• Fixed local rank zero casting (#2640)
• Fixed single scalar return from training (#2587)
• Fixed Horovod backend to scale LR schedlers with the optimizer (#2626)
• Fixed dtype and device properties not getting updated in submodules (#2657)
• Fixed fast_dev_run to run for all dataloaders (#2581)
• Fixed save_dir in loggers getting ignored by default value of weights_save_path when user did not specify weights_save_path (#2681)
• Fixed weights_save_path getting ignored when logger=False is passed to Trainer (#2681)
• Fixed TPU multi-core and Float16 (#2632)
• Fixed test metrics not being logged with LoggerCollection (#2723)
• Fixed data transfer to device when using torchtext.data.Field and include_lengths is True (#2689)
• Fixed shuffle argument for distributed sampler (#2789)
• Fixed logging interval (#2694)
• Fixed loss value in the progress bar is wrong when accumulate_grad_batches > 1 (#2738)
• Fixed correct CWD for ddp sub-processes when using Hydra (#2719)
• Fixed selecting GPUs using CUDA_VISIBLE_DEVICES (#2739)
• Fixed false num_classes warning in metrics (#2781)
• Fixed shell injection vulnerability in subprocess call (#2786)
• Fixed LR finder and hparams compatibility (#2821)
• Fixed ModelCheckpoint not saving the latest information when save_last=True (#2881)
• Fixed ImageNet example: learning rate scheduler, number of workers and batch size when using DDP (#2889)
• Fixed apex gradient clipping (#2829)
• Fixed save apex scaler states (#2828)
• Fixed a model loading issue with inheritance and variable positional arguments (#2911)
• Fixed passing non_blocking=True when transferring a batch object that does not support it (#2910)
• Fixed checkpointing to remote file paths (#2925)
• Fixed adding val step argument to metrics (#2986)
• Fixed an issue that caused Trainer.test() to stall in ddp mode (#2997)
• Fixed gathering of results with tensors of varying shape (#3020)
• Fixed batch size auto-scaling feature to set the new value on the correct model attribute (#3043)
• Fixed automatic batch scaling not working with half precision (#3045)
• Fixed setting device to root gpu (#3042)

[0.8.5] - 2020-07-09

Added

• Added a PSNR metric: peak signal-to-noise ratio (#2483)
• Added functional regression metrics (#2492)

Removed

• Removed auto val reduce (#2462)

Fixed

• Flattening Wandb Hyperparameters (#2459)
• Fixed using the same DDP python interpreter and actually running (#2482)
• Fixed model summary input type conversion for models that have input dtype different from model parameters (#2510)
• Made TensorBoardLogger and CometLogger pickleable (#2518)
• Fixed a problem with MLflowLogger creating multiple run folders (#2502)
• Fixed global_step increment (#2455)
• Fixed TPU hanging example (#2488)
• Fixed argparse default value bug (#2526)
• Fixed Dice and IoU to avoid NaN by adding small eps (#2545)
• Fixed accumulate gradients schedule at epoch 0 (continued) (#2513)
• Fixed Trainer .fit() returning last not best weights in "ddp_spawn" (#2565)
• Fixed passing (do not pass) TPU weights back on test (#2566)
• Fixed DDP tests and .test() (#2512, #2570)

[0.8.4] - 2020-07-01

Added

• Added reduce ddp results on eval (#2434)
• Added a warning when an IterableDataset has __len__ defined (#2437)

Changed

• Enabled no returns from eval (#2446)

Fixed

• Fixes train outputs (#2428)
• Fixes Conda dependencies (#2412)
• Fixed Apex scaling with decoupled backward (#2433)
• Fixed crashing or wrong displaying progressbar because of missing ipywidgets (#2417)
• Fixed TPU saving dir (fc26078e, 04e68f02)
• Fixed logging on rank 0 only (#2425)

[0.8.3] - 2020-06-29

Fixed

• Fixed AMP wrong call (593837e)
• Fixed batch typo (92d1e75)

[0.8.2] - 2020-06-28

Added

• Added TorchText support for moving data to GPU (#2379)

Changed

• Changed epoch indexing from 0 instead of 1 (#2289)
• Refactor Model backward (#2276)
• Refactored training_batch + tests to verify correctness (#2327, #2328)
• Refactored training loop (#2336)
• Made optimization steps for hooks (#2363)
• Changed default apex level to 'O2' (#2362)

Removed

• Moved TrainsLogger to Bolts (#2384)

Fixed

• Fixed parsing TPU arguments and TPU tests (#2094)
• Fixed number batches in case of multiple dataloaders and limit_{*}_batches (#1920, #2226)
• Fixed an issue with forward hooks not being removed after model summary (#2298)
• Fix for load_from_checkpoint() not working with absolute path on Windows (#2294)
• Fixed an issue how _has_len handles NotImplementedError e.g. raised by torchtext.data.Iterator (#2293), (#2307)
• Fixed average_precision metric (#2319)
• Fixed ROC metric for CUDA tensors (#2304)
• Fixed lost compatibility with custom datatypes implementing .to (#2335)
• Fixed loading model with kwargs (#2387)
• Fixed sum(0) for trainer.num_val_batches (#2268)
• Fixed checking if the parameters are a DictConfig Object (#2216)
• Fixed SLURM weights saving (#2341)
• Fixed swaps LR scheduler order (#2356)
• Fixed adding tensorboard hparams logging test (#2342)
• Fixed use model ref for tear down (#2360)
• Fixed logger crash on DDP (#2388)
• Fixed several issues with early stopping and checkpoint callbacks (#1504, #2391)
• Fixed loading past checkpoints from v0.7.x (#2405)
• Fixed loading model without arguments (#2403)
• Fixed Windows compatibility issue (#2358)

[0.8.1] - 2020-06-19

Fixed

• Fixed the load_from_checkpoint path detected as URL bug (#2244)
• Fixed hooks - added barrier (#2245, #2257, #2260)
• Fixed hparams - remove frame inspection on self.hparams (#2253)
• Fixed setup and on fit calls (#2252)
• Fixed GPU template (#2255)

[0.8.0] - 2020-06-18

Added

• Added overfit_batches, limit_{val|test}_batches flags (overfit now uses training set for all three) (#2213)
• Added metrics
  • Base classes (#1326, #1877)
  • Sklearn metrics classes (#1327)
  • Native torch metrics (#1488, #2062)
  • docs for all Metrics (#2184, #2209)
  • Regression metrics (#2221)
• Allow dataloaders without sampler field present (#1907)
• Added option save_last to save the model at the end of every epoch in ModelCheckpoint (#1908)
• Early stopping checks on_validation_end (#1458)
• Speed up single-core TPU training by loading data using ParallelLoader (#2033)
• Added a model hook transfer_batch_to_device that enables moving custom data structures to the target device (#1756)
• Added black formatter for the code with code-checker on pull (#1610)
• Added back the slow spawn ddp implementation as ddp_spawn (#2115)
• Added loading checkpoints from URLs (#1667)
• Added a callback method on_keyboard_interrupt for handling KeyboardInterrupt events during training (#2134)
• Added a decorator auto_move_data that moves data to the correct device when using the LightningModule for inference (#1905)
• Added ckpt_path option to LightningModule.test(...) to load particular checkpoint (#2190)
• Added setup and teardown hooks for model (#2229)

Changed

• Allow user to select individual TPU core to train on (#1729)
• Removed non-finite values from loss in LRFinder (#1862)
• Allow passing model hyperparameters as complete kwarg list (#1896)
• Renamed ModelCheckpoint's attributes best to best_model_score and kth_best_model to kth_best_model_path (#1799)
• Re-Enable Logger's ImportErrors (#1938)
• Changed the default value of the Trainer argument weights_summary from full to top (#2029)
• Raise an error when lightning replaces an existing sampler (#2020)
• Enabled prepare_data from correct processes - clarify local vs global rank (#2166)
• Remove explicit flush from tensorboard logger (#2126)
• Changed epoch indexing from 1 instead of 0 (#2206)

Deprecated

• Deprecated flags: (#2213)
  • overfit_pct in favour of overfit_batches
  • val_percent_check in favour of limit_val_batches
  • test_percent_check in favour of limit_test_batches
• Deprecated ModelCheckpoint's attributes best and kth_best_model (#1799)
• Dropped official support/testing for older PyTorch versions <1.3 (#1917)
• Deprecated Trainer proc_rank in favour of global_rank (#2166, #2269)

Removed

• Removed unintended Trainer argument progress_bar_callback, the callback should be passed in by Trainer(callbacks=[...]) instead (#1855)
• Removed obsolete self._device in Trainer (#1849)
• Removed deprecated API (#2073)
  • Packages: pytorch_lightning.pt_overrides, pytorch_lightning.root_module
  • Modules: pytorch_lightning.logging.comet_logger, pytorch_lightning.logging.mlflow_logger, pytorch_lightning.logging.test_tube_logger, pytorch_lightning.overrides.override_data_parallel, pytorch_lightning.core.model_saving, pytorch_lightning.core.root_module
  • Trainer arguments: add_row_log_interval, default_save_path, gradient_clip, nb_gpu_nodes, max_nb_epochs, min_nb_epochs, nb_sanity_val_steps
  • Trainer attributes: nb_gpu_nodes, num_gpu_nodes, gradient_clip, max_nb_epochs, min_nb_epochs, nb_sanity_val_steps, default_save_path, tng_tqdm_dic

Fixed

• Run graceful training teardown on interpreter exit (#1631)
• Fixed user warning when apex was used together with learning rate schedulers (#1873)
• Fixed multiple calls of EarlyStopping callback (#1863)
• Fixed an issue with Trainer.from_argparse_args when passing in unknown Trainer args (#1932)
• Fixed bug related to logger not being reset correctly for model after tuner algorithms (#1933)
• Fixed root node resolution for SLURM cluster with dash in host name (#1954)
• Fixed LearningRateLogger in multi-scheduler setting (#1944)
• Fixed test configuration check and testing (#1804)
• Fixed an issue with Trainer constructor silently ignoring unknown/misspelled arguments (#1820)
• Fixed save_weights_only in ModelCheckpoint (#1780)
• Allow use of same WandbLogger instance for multiple training loops (#2055)
• Fixed an issue with _auto_collect_arguments collecting local variables that are not constructor arguments and not working for signatures that have the instance not named self (#2048)
• Fixed mistake in parameters' grad norm tracking (#2012)
• Fixed CPU and hanging GPU crash (#2118)
• Fixed an issue with the model summary and example_input_array depending on a specific ordering of the submodules in a LightningModule (#1773)
• Fixed Tpu logging (#2230)
• Fixed Pid port + duplicate rank_zero logging (#2140, #2231)

[0.7.6] - 2020-05-16

Added

• Added callback for logging learning rates (#1498)
• Added transfer learning example (for a binary classification task in computer vision) (#1564)
• Added type hints in Trainer.fit() and Trainer.test() to reflect that also a list of dataloaders can be passed in (#1723).
• Added auto scaling of batch size (#1638)
• The progress bar metrics now also get updated in training_epoch_end (#1724)
• Enable NeptuneLogger to work with distributed_backend=ddp (#1753)
• Added option to provide seed to random generators to ensure reproducibility (#1572)
• Added override for hparams in load_from_ckpt (#1797)
• Added support multi-node distributed execution under torchelastic (#1811, #1818)
• Added using store_true for bool args (#1822, #1842)
• Added dummy logger for internally disabling logging for some features (#1836)

Changed

• Enable non-blocking for device transfers to GPU (#1843)
• Replace mata_tags.csv with hparams.yaml (#1271)
• Reduction when batch_size < num_gpus (#1609)
• Updated LightningTemplateModel to look more like Colab example (#1577)
• Don't convert namedtuple to tuple when transferring the batch to target device (#1589)
• Allow passing hparams as keyword argument to LightningModule when loading from checkpoint (#1639)
• Args should come after the last positional argument (#1807)
• Made ddp the default if no backend specified with multiple GPUs (#1789)

Deprecated

• Deprecated tags_csv in favor of hparams_file (#1271)

Fixed

• Fixed broken link in PR template (#1675)
• Fixed ModelCheckpoint not None checking filepath (#1654)
• Trainer now calls on_load_checkpoint() when resuming from a checkpoint (#1666)
• Fixed sampler logic for ddp with iterable dataset (#1734)
• Fixed _reset_eval_dataloader() for IterableDataset (#1560)
• Fixed Horovod distributed backend to set the root_gpu property (#1669)
• Fixed wandb logger global_step affects other loggers (#1492)
• Fixed disabling progress bar on non-zero ranks using Horovod backend (#1709)
• Fixed bugs that prevent lr finder to be used together with early stopping and validation dataloaders (#1676)
• Fixed a bug in Trainer that prepended the checkpoint path with version_ when it shouldn't (#1748)
• Fixed lr key name in case of param groups in LearningRateLogger (#1719)
• Fixed accumulation parameter and suggestion method for learning rate finder (#1801)
• Fixed num processes wasn't being set properly and auto sampler was ddp failing (#1819)
• Fixed bugs in semantic segmentation example (#1824)
• Fixed saving native AMP scaler state (#1777)
• Fixed native amp + ddp (#1788)
• Fixed hparam logging with metrics (#1647)

[0.7.5] - 2020-04-27

Changed

• Allow logging of metrics together with hparams (#1630)

Removed

• Removed Warning from trainer loop (#1634)

Fixed

• Fixed ModelCheckpoint not being fixable (#1632)
• Fixed CPU DDP breaking change and DDP change (#1635)
• Tested pickling (#1636)

[0.7.4] - 2020-04-26

Added

• Added flag replace_sampler_ddp to manually disable sampler replacement in DDP (#1513)
• Added auto_select_gpus flag to trainer that enables automatic selection of available GPUs on exclusive mode systems.
• Added learning rate finder (#1347)
• Added support for DDP mode in clusters without SLURM (#1387)
• Added test_dataloaders parameter to Trainer.test() (#1434)
• Added terminate_on_nan flag to trainer that performs a NaN check with each training iteration when set to True (#1475)
• Added speed parity tests (max 1 sec difference per epoch)(#1482)
• Added ddp_cpu backend for testing ddp without GPUs (#1158)
• Added Horovod support as a distributed backend Trainer(distributed_backend='horovod') (#1529)
• Added support for 8 core distributed training on Kaggle TPU's (#1568)
• Added support for native AMP (#1561, #1580)

Changed

• Changed the default behaviour to no longer include a NaN check with each training iteration (#1475)
• Decoupled the progress bar from trainer` it is a callback now and can be customized or even be replaced entirely (#1450).
• Changed lr schedule step interval behavior to update every backwards pass instead of every forwards pass (#1477)
• Defines shared proc. rank, remove rank from instances (e.g. loggers) (#1408)
• Updated semantic segmentation example with custom U-Net and logging (#1371)
• Disabled val and test shuffling (#1600)

Deprecated

• Deprecated training_tqdm_dict in favor of progress_bar_dict (#1450).

Removed

• Removed test_dataloaders parameter from Trainer.fit() (#1434)

Fixed

• Added the possibility to pass nested metrics dictionaries to loggers (#1582)
• Fixed memory leak from opt return (#1528)
• Fixed saving checkpoint before deleting old ones (#1453)
• Fixed loggers - flushing last logged metrics even before continue, e.g. trainer.test() results (#1459)
• Fixed optimizer configuration when configure_optimizers returns dict without lr_scheduler (#1443)
• Fixed LightningModule - mixing hparams and arguments in LightningModule.__init__() crashes load_from_checkpoint() (#1505)
• Added a missing call to the on_before_zero_grad model hook (#1493).
• Allow use of sweeps with WandbLogger (#1512)
• Fixed a bug that caused the callbacks Trainer argument to reference a global variable (#1534).
• Fixed a bug that set all boolean CLI arguments from Trainer.add_argparse_args always to True (#1571)
• Fixed do not copy the batch when training on a single GPU (#1576, #1579)
• Fixed soft checkpoint removing on DDP (#1408)
• Fixed automatic parser bug (#1585)
• Fixed bool conversion from string (#1606)

[0.7.3] - 2020-04-09

Added

• Added rank_zero_warn for warning only in rank 0 (#1428)

Fixed

• Fixed default DistributedSampler for DDP training (#1425)
• Fixed workers warning not on windows (#1430)
• Fixed returning tuple from run_training_batch (#1431)
• Fixed gradient clipping (#1438)
• Fixed pretty print (#1441)

[0.7.2] - 2020-04-07

Added

• Added same step loggers' metrics aggregation (#1278)
• Added parity test between a vanilla MNIST model and lightning model (#1284)
• Added parity test between a vanilla RNN model and lightning model (#1351)
• Added Reinforcement Learning - Deep Q-network (DQN) lightning example (#1232)
• Added support for hierarchical dict (#1152)
• Added TrainsLogger class (#1122)
• Added type hints to pytorch_lightning.core (#946)
• Added support for IterableDataset in validation and testing (#1104)
• Added support for non-primitive types in hparams for TensorboardLogger (#1130)
• Added a check that stops the training when loss or weights contain NaN or inf values. (#1097)
• Added support for IterableDataset when val_check_interval=1.0 (default), this will trigger validation at the end of each epoch. (#1283)
• Added summary method to Profilers. (#1259)
• Added informative errors if user defined dataloader has zero length (#1280)
• Added testing for python 3.8 (#915)
• Added model configuration checking (#1199)
• Added support for optimizer frequencies through LightningModule.configure_optimizers() (#1269)
• Added option to run without an optimizer by returning None from configure_optimizers. (#1279)
• Added a warning when the number of data loader workers is small. (#1378)

Changed

• Changed (renamed and refatored) TensorRunningMean -> TensorRunningAccum: running accumulations were generalized. (#1278)
• Changed progress_bar_refresh_rate trainer flag to disable progress bar when set to 0. (#1108)
• Enhanced load_from_checkpoint to also forward params to the model (#1307)
• Updated references to self.forward() to instead use the __call__ interface. (#1211)
• Changed default behaviour of configure_optimizers to use no optimizer rather than Adam. (#1279)
• Allow to upload models on W&B (#1339)
• On DP and DDP2 unsqueeze is automated now (#1319)
• Did not always create a DataLoader during reinstantiation, but the same type as before (if subclass of DataLoader) (#1346)
• Did not interfere with a default sampler (#1318)
• Remove default Adam optimizer (#1317)
• Give warnings for unimplemented required lightning methods (#1317)
• Made evaluate method private >> Trainer._evaluate(...). (#1260)
• Simplify the PL examples structure (shallower and more readable) (#1247)
• Changed min max gpu memory to be on their own plots (#1358)
• Remove .item which causes sync issues (#1254)
• Changed smoothing in TQDM to decrease variability of time remaining between training / eval (#1194)
• Change default logger to dedicated one (#1064)

Deprecated

• Deprecated Trainer argument print_nan_grads (#1097)
• Deprecated Trainer argument show_progress_bar (#1108)

Removed

• Removed test for no test dataloader in .fit (#1495)
• Removed duplicated module pytorch_lightning.utilities.arg_parse for loading CLI arguments (#1167)
• Removed wandb logger's finalize method (#1193)
• Dropped torchvision dependency in tests and added own MNIST dataset class instead (#986)

Fixed

• Fixed model_checkpoint when saving all models (#1359)
• Trainer.add_argparse_args classmethod fixed. Now it adds a type for the arguments (#1147)
• Fixed bug related to type checking of ReduceLROnPlateau lr schedulers(#1126)
• Fixed a bug to ensure lightning checkpoints to be backward compatible (#1132)
• Fixed a bug that created an extra dataloader with active reload_dataloaders_every_epoch (#1196)
• Fixed all warnings and errors in the docs build process (#1191)
• Fixed an issue where val_percent_check=0 would not disable validation (#1251)
• Fixed average of incomplete TensorRunningMean (#1309)
• Fixed WandbLogger.watch with wandb.init() (#1311)
• Fixed an issue with early stopping that would prevent it from monitoring training metrics when validation is disabled / not implemented (#1235).
• Fixed a bug that would cause trainer.test() to run on the validation set when overloading validation_epoch_end and test_end (#1353)
• Fixed WandbLogger.watch - use of the watch method without importing wandb (#1311)
• Fixed WandbLogger to be used with 'ddp' - allow reinits in sub-processes (#1149, #1360)
• Made training_epoch_end behave like validation_epoch_end (#1357)
• Fixed fast_dev_run running validation twice (#1365)
• Fixed pickle error from quick patch __code__ (#1352)
• Fixed memory leak on GPU0 (#1094, #1349)
• Fixed checkpointing interval (#1272)
• Fixed validation and training loops run the partial dataset (#1192)
• Fixed running on_validation_end only on main process in DDP (#1125)
• Fixed load_spawn_weights only in proc rank 0 (#1385)
• Fixes using deprecated use_amp attribute (#1145)
• Fixed Tensorboard logger error: lightning_logs directory not exists in multi-node DDP on nodes with rank != 0 (#1377)
• Fixed Unimplemented backend XLA error on TPU (#1387)

[0.7.1] - 2020-03-07

Fixed

• Fixes print issues and data_loader (#1080)

[0.7.0] - 2020-03-06

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. Throttle 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

• Improved NeptuneLogger by adding close_after_fit argument to allow logging after training(#908)
• 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 checkpointer 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)

[0.6.0] - 2020-01-21

Added

• Added support for resuming from a specific checkpoint via resume_from_checkpoint argument (#516)
• Added support for ReduceLROnPlateau scheduler (#320)
• Added support for Apex mode O2 in conjunction with Data Parallel (#493)
• Added option (save_top_k) to save the top k models in the ModelCheckpoint class (#128)
• Added on_train_start and on_train_end hooks to ModelHooks (#598)
• Added TensorBoardLogger (#607)
• Added support for weight summary of model with multiple inputs (#543)
• Added map_location argument to load_from_metrics and load_from_checkpoint (#625)
• Added option to disable validation by setting val_percent_check=0 (#649)
• Added NeptuneLogger class (#648)
• Added WandbLogger class (#627)

Changed

• Changed the default progress bar to print to stdout instead of stderr (#531)
• Renamed step_idx to step, epoch_idx to epoch, max_num_epochs to max_epochs and min_num_epochs to min_epochs (#589)
• Renamed total_batch_nb to total_batches, nb_val_batches to num_val_batches, nb_training_batches to num_training_batches, max_nb_epochs to max_epochs, min_nb_epochs to min_epochs, nb_test_batches to num_test_batches, and nb_val_batches to num_val_batches (#567)
• Changed gradient logging to use parameter names instead of indexes (#660)
• Changed the default logger to TensorBoardLogger (#609)
• Changed the directory for tensorboard logging to be the same as model checkpointing (#706)

Deprecated

• Deprecated max_nb_epochs and min_nb_epochs (#567)
• Deprecated the on_sanity_check_start hook in ModelHooks (#598)

Removed

• Removed the save_best_only argument from ModelCheckpoint, use save_top_k=1 instead (#128)

Fixed

• Fixed a bug which occurred when using Adagrad with cuda (#554)
• Fixed a bug where training would be on the GPU despite setting gpus=0 or gpus=[] (#561)
• Fixed an error with print_nan_gradients when some parameters do not require gradient (#579)
• Fixed a bug where the progress bar would show an incorrect number of total steps during the validation sanity check when using multiple validation data loaders (#597)
• Fixed support for PyTorch 1.1.0 (#552)
• Fixed an issue with early stopping when using a val_check_interval < 1.0 in Trainer (#492)
• Fixed bugs relating to the CometLogger object that would cause it to not work properly (#481)
• Fixed a bug that would occur when returning -1 from on_batch_start following an early exit or when the batch was None (#509)
• Fixed a potential race condition with several processes trying to create checkpoint directories (#530)
• Fixed a bug where batch 'segments' would remain on the GPU when using truncated_bptt > 1 (#532)
• Fixed a bug when using IterableDataset (#547)
• Fixed a bug where .item was called on non-tensor objects (#602)
• Fixed a bug where Trainer.train would crash on an uninitialized variable if the trainer was run after resuming from a checkpoint that was already at max_epochs (#608)
• Fixed a bug where early stopping would begin two epochs early (#617)
• Fixed a bug where num_training_batches and num_test_batches would sometimes be rounded down to zero (#649)
• Fixed a bug where an additional batch would be processed when manually setting num_training_batches (#653)
• Fixed a bug when batches did not have a .copy method (#701)
• Fixed a bug when using log_gpu_memory=True in Python 3.6 (#715)
• Fixed a bug where checkpoint writing could exit before completion, giving incomplete checkpoints (#689)
• Fixed a bug where on_train_end was not called when ealy stopping (#723)

[0.5.3] - 2019-11-06

Added

• Added option to disable default logger, checkpointer, and early stopping by passing logger=False, checkpoint_callback=False and early_stop_callback=False respectively
• Added CometLogger for use with Comet.ml
• Added val_check_interval argument to Trainer allowing validition to be performed at every given number of batches
• Added functionality to save and load hyperparameters using the standard checkpoint mechanism
• Added call to torch.cuda.empty_cache before training starts
• Added option for user to override the call t backward
• Added support for truncated backprop through time via the truncated_bptt_steps argument in Trainer
• Added option to operate on all outputs from training_step in DDP2
• Added a hook for modifying DDP init
• Added a hook for modifying Apex

Changed

• Changed experiment version to be padded with zeros (e.g. /dir/version_9 becomes /dir/version_0009)
• Changed callback metrics to include any metrics given in logs or progress bar
• Changed the default for save_best_only in ModelCheckpoint to True
• Added tng_data_loader for backwards compatibility
• Renamed MLFlowLogger.client to MLFlowLogger.experiment for consistency
• Moved global_step increment to happen after the batch has been processed
• Changed weights restore to first attempt HPC weights before restoring normally, preventing both weights being restored and running out of memory
• Changed progress bar functionality to add multiple progress bars for train/val/test
• Changed calls to print to use logging instead

Deprecated

• Deprecated tng_dataloader

Fixed

• Fixed an issue where the number of batches was off by one during training
• Fixed a bug that occurred when setting a ckeckpoint callback and early_stop_callback=False
• Fixed an error when importing CometLogger
• Fixed a bug where the gpus argument had some unexpected behaviour
• Fixed a bug where the computed total number of batches was sometimes incorrect
• Fixed a bug where the progress bar would sometimes not show the total number of batches in test mode
• Fixed a bug when using the log_gpu_memory='min_max' option in Trainer
• Fixed a bug where checkpointing would sometimes erase the current directory

[0.5.2] - 2019-10-10

Added

• Added weights_summary argument to Trainer to be set to full (full summary), top (just top level modules) or other
• Added tags argument to MLFlowLogger

Changed

• Changed default for amp_level to O1

Removed

• Removed the print_weights_summary argument from Trainer

Fixed

• Fixed a bug where logs were not written properly
• Fixed a bug where logger.finalize wasn't called after training is complete
• Fixed callback metric errors in DDP
• Fixed a bug where TestTubeLogger didn't log to the correct directory

[0.5.1] - 2019-10-05

Added

• Added the LightningLoggerBase class for experiment loggers
• Added MLFlowLogger for logging with mlflow
• Added TestTubeLogger for logging with test_tube
• Added a different implementation of DDP (distributed_backed='ddp2') where every node has one model using all GPUs
• Added support for optimisers which require a closure (e.g. LBFGS)
• Added automatic MASTER_PORT default for DDP when not set manually
• Added new GPU memory logging options 'min_max' (log only the min/max utilization) and 'all' (log all the GPU memory)

Changed

• Changed schedulers to always be called with the current epoch
• Changed test_tube to an optional dependency
• Changed data loaders to internally use a getter instead of a python property
• Disabled auto GPU loading when restoring weights to prevent out of memory errors
• Changed logging, early stopping and checkpointing to occur by default

Fixed

• Fixed a bug with samplers that do not specify set_epoch
• Fixed a bug when using the MLFlowLogger with unsupported data types, this will now raise a warning
• Fixed a bug where gradient norms were always zero using track_grad_norm
• Fixed a bug which causes a crash when logging memory

[0.5.0] - 2019-09-26

Changed

• Changed data_batch argument to batch throughout
• Changed batch_i argument to batch_idx throughout
• Changed tng_dataloader method to train_dataloader
• Changed on_tng_metrics method to on_training_metrics
• Changed gradient_clip argument to gradient_clip_val
• Changed add_log_row_interval to row_log_interval

Fixed

• Fixed a bug with tensorboard logging in multi-gpu setup

[0.4.9] - 2019-09-16

Added

• Added the flag log_gpu_memory to Trainer to deactivate logging of GPU memory utilization
• Added SLURM resubmit functionality (port from test-tube)
• Added optional weight_save_path to trainer to remove the need for a checkpoint_callback when using cluster training
• Added option to use single gpu per node with DistributedDataParallel

Changed

• Changed functionality of validation_end and test_end with multiple dataloaders to be given all of the dataloaders at once rather than in separate calls
• Changed print_nan_grads to only print the parameter value and gradients when they contain NaN
• Changed gpu API to take integers as well (e.g. gpus=2 instead of gpus=[0, 1])
• All models now loaded on to CPU to avoid device and out of memory issues in PyTorch

Fixed

• Fixed a bug where data types that implement .to but not .cuda would not be properly moved onto the GPU
• Fixed a bug where data would not be re-shuffled every epoch when using a DistributedSampler

[0.4.8] - 2019-08-31

Added

• Added test_step and test_end methods, used when Trainer.test is called
• Added GradientAccumulationScheduler callback which can be used to schedule changes to the number of accumulation batches
• Added option to skip the validation sanity check by setting nb_sanity_val_steps = 0

Fixed

• Fixed a bug when setting nb_sanity_val_steps = 0

[0.4.7] - 2019-08-24

Changed

• Changed the default val_check_interval to 1.0
• Changed defaults for nb_val_batches, nb_tng_batches and nb_test_batches to 0

Fixed

• Fixed a bug where the full validation set as used despite setting val_percent_check
• Fixed a bug where an Exception was thrown when using a data set containing a single batch
• Fixed a bug where an Exception was thrown if no val_dataloader was given
• Fixed a bug where tuples were not properly transferred to the GPU
• Fixed a bug where data of a non standard type was not properly handled by the trainer
• Fixed a bug when loading data as a tuple
• Fixed a bug where AttributeError could be suppressed by the Trainer

[0.4.6] - 2019-08-15

Added

• Added support for data to be given as a dict or list with a single gpu
• Added support for configure_optimizers to return a single optimizer, two list (optimizers and schedulers), or a single list

Fixed

• Fixed a bug where returning just an optimizer list (i.e. without schedulers) from configure_optimizers would throw an Exception

[0.4.5] - 2019-08-13

Added

• Added optimizer_step method that can be overridden to change the standard optimizer behaviour

[0.4.4] - 2019-08-12

Added

• Added supoort for multiple validation dataloaders
• Added support for latest test-tube logger (optimised for torch==1.2.0)

Changed

• validation_step and val_dataloader are now optional
• lr_scheduler is now activated after epoch

Fixed

• Fixed a bug where a warning would show when using lr_scheduler in torch>1.1.0
• Fixed a bug where an Exception would be thrown if using torch.DistributedDataParallel without using a DistributedSampler, this now throws a Warning instead

[0.4.3] - 2019-08-10

Fixed

• Fixed a bug where accumulate gradients would scale the loss incorrectly

[0.4.2] - 2019-08-08

Changed

• Changed install requirement to torch==1.2.0

[0.4.1] - 2019-08-08

Changed

• Changed install requirement to torch==1.1.0

[0.4.0] - 2019-08-08

Added

• Added 16-bit support for a single GPU
• Added support for training continuation (preserves epoch, global step etc.)

Changed

• Changed training_step and validation_step, outputs will no longer be automatically reduced

Removed

• Removed need for Experiment object in Trainer

Fixed

• Fixed issues with reducing outputs from generative models (such as images and text)

[0.3.6] - 2019-07-25

Added

• Added a decorator to do lazy data loading internally

Fixed

• Fixed a bug where Experiment object was not process safe, potentially causing logs to be overwritten

[0.3.5] - 2019-07-25

[0.3.4] - 2019-07-22

[0.3.3] - 2019-07-22

[0.3.2] - 2019-07-21

[0.3.1] - 2019-07-21

[0.2.x] - 2019-07-09

[0.1.x] - 2019-06-DD