Changelog

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

The format is based on Keep a Changelog.

[1.9.6] - 2023-04-DD

[1.9.6] - Fixed

• Fixed issue where Model.load_from_checkpoint("checkpoint.ckpt", map_location=map_location) would always return model on CPU (#17308)

[1.9.5] - 2023-03-30

[1.9.5] - Changed

• Changed to the NeptuneLogger (#16761):

  • It now supports neptune-client 0.16.16 and neptune >=1.0, and we have replaced the log() method with append() and extend().

  • It now accepts a namespace Handler as an alternative to Run for the run argument. This means that you can call it like NeptuneLogger(run=run["some/namespace"]) to log everything to the some/namespace/ location of the run.

• Allow sys.argv and args in LightningCLI (#16808)

• Moveed HPU broadcast override to the HPU strategy file (#17011)

• Changed the is_picklable util function to handle the edge case that throws a TypeError (#17270)

[1.9.5] - Depercated

• Removed registration of ShardedTensor state dict hooks in LightningModule.__init__ with torch>=2.1 (#16892)

• Removed the lightning.pytorch.core.saving.ModelIO class interface (#16974)

[1.9.5] - Fixed

• Fixed num_nodes not being set for DDPFullyShardedNativeStrategy (#17160)

• Fixed parsing the precision config for inference in DeepSpeedStrategy (#16973)

• Fixed the availability check for rich that prevented Lightning to be imported in Google Colab (#17156)

• Fixed for all _cuda_clearCublasWorkspaces on teardown (#16907)

• The psutil package is now required for CPU monitoring (#17010)

• Improved the error message for installing tensorboard or tensorboardx (#17053)

[1.9.4] - 2023-03-01

[1.9.4] - Added

• Added Fabric(strategy="auto") support. It will choose DDP over DDP-spawn, contrary to strategy=None (default) (#16916)

[1.9.4] - Fixed

• Fixed DDP spawn hang on TPU Pods (#16844)

• Fixed edge cases in parsing device ids using NVML (#16795)

• Fixed backwards compatibility for lightning.pytorch.utilities.parsing.get_init_args (#16851)

[1.9.3] - 2023-02-21

[1.9.3] - Fixed

• Fixed an issue causing a wrong environment plugin to be selected when accelerator=tpu and devices > 1 (#16806)

[1.9.2] - 2023-02-15

[1.9.2] - Changed

• Disabled strict loading in multiprocessing launcher (“ddp_spawn”, etc.) when loading weights back into the main process (#16365)

[1.9.2] - Fixed

• Fixed an attribute error and improved input validation for invalid strategy types being passed to Trainer (#16693)

• Fixed early stopping triggering extra validation runs after reaching min_epochs or min_steps (#16719)

[1.9.1] - 2023-02-10

[1.9.1] - Fixed

• Fixed an unintended limitation for calling save_hyperparameters on mixin classes that don’t subclass LightningModule/LightningDataModule (#16369)

• Fixed an issue with MLFlowLogger logging the wrong keys with .log_hyperparams() (#16418)

• Fixed logging more than 100 parameters with MLFlowLogger and long values are truncated (#16451)

• Fixed strict availability check for torch_xla requirement (#16476)

• Fixed an issue where PL would wrap DataLoaders with XLA’s MpDeviceLoader more than once (#16571)

• Fixed the batch_sampler reference for DataLoaders wrapped with XLA’s MpDeviceLoader (#16571)

• Fixed an import error when torch.distributed is not available (#16658)

[1.9.0] - 2023-01-17

[1.9.0] - Added

• Added support for native logging of MetricCollection with enabled compute groups (#15580)

• Added support for custom artifact names in pl.loggers.WandbLogger (#16173)

• Added support for DDP with LRFinder (#15304)

• Added utilities to migrate checkpoints from one Lightning version to another (#15237)

• Added support to upgrade all checkpoints in a folder using the pl.utilities.upgrade_checkpoint script (#15333)

• Add an axes argument ax to the .lr_find().plot() to enable writing to a user-defined axes in a matplotlib figure (#15652)

• Added log_model parameter to MLFlowLogger (#9187)

• Added a check to validate that wrapped FSDP models are used while initializing optimizers (#15301)

• Added a warning when self.log(..., logger=True) is called without a configured logger (#15814)

• Added support for colossalai 0.1.11 (#15888)

• Added LightningCLI support for optimizer and learning schedulers via callable type dependency injection (#15869)

• Added support for activation checkpointing for the DDPFullyShardedNativeStrategy strategy (#15826)

• Added the option to set DDPFullyShardedNativeStrategy(cpu_offload=True|False) via bool instead of needing to pass a configuration object (#15832)

• Added info message for Ampere CUDA GPU users to enable tf32 matmul precision (#16037)

• Added support for returning optimizer-like classes in LightningModule.configure_optimizers (#16189)

[1.9.0] - Changed

• Drop PyTorch 1.9 support (#15347)

• Switch from tensorboard to tensorboardx in TensorBoardLogger (#15728)

• From now on, Lightning Trainer and LightningModule.load_from_checkpoint automatically upgrade the loaded checkpoint if it was produced in an old version of Lightning (#15237)

• Trainer.{validate,test,predict}(ckpt_path=...) no longer restores the Trainer.global_step and trainer.current_epoch value from the checkpoints - From now on, only Trainer.fit will restore this value (#15532)

• The ModelCheckpoint.save_on_train_epoch_end attribute is now computed dynamically every epoch, accounting for changes to the validation dataloaders (#15300)

• The Trainer now raises an error if it is given multiple stateful callbacks of the same time with colliding state keys (#15634)

• MLFlowLogger now logs hyperparameters and metrics in batched API calls (#15915)

• Overriding the on_train_batch_{start,end} hooks in conjunction with taking a dataloader_iter in the training_step no longer errors out and instead shows a warning (#16062)

• Move tensorboardX to extra dependencies. Use the CSVLogger by default (#16349)

[1.9.0] - Deprecated

• Deprecated description, env_prefix and env_parse parameters in LightningCLI.__init__ in favour of giving them through parser_kwargs (#15651)

• Deprecated pytorch_lightning.profiler in favor of pytorch_lightning.profilers (#16059)

• Deprecated Trainer(auto_select_gpus=...) in favor of pytorch_lightning.accelerators.find_usable_cuda_devices (#16147)

• Deprecated pytorch_lightning.tuner.auto_gpu_select.{pick_single_gpu,pick_multiple_gpus} in favor of pytorch_lightning.accelerators.find_usable_cuda_devices (#16147)

• nvidia/apex deprecation (#16039)

  • Deprecated pytorch_lightning.plugins.NativeMixedPrecisionPlugin in favor of pytorch_lightning.plugins.MixedPrecisionPlugin

  • Deprecated the LightningModule.optimizer_step(using_native_amp=...) argument

  • Deprecated the Trainer(amp_backend=...) argument

  • Deprecated the Trainer.amp_backend property

  • Deprecated the Trainer(amp_level=...) argument

  • Deprecated the pytorch_lightning.plugins.ApexMixedPrecisionPlugin class

  • Deprecates the pytorch_lightning.utilities.enums.AMPType enum

  • Deprecates the DeepSpeedPrecisionPlugin(amp_type=..., amp_level=...) arguments

• horovod deprecation (#16141)

  • Deprecated Trainer(strategy="horovod")

  • Deprecated the HorovodStrategy class

• Deprecated pytorch_lightning.lite.LightningLite in favor of lightning.fabric.Fabric (#16314)

• FairScale deprecation (in favor of PyTorch’s FSDP implementation) (#16353)

  • Deprecated the pytorch_lightning.overrides.fairscale.LightningShardedDataParallel class

  • Deprecated the pytorch_lightning.plugins.precision.fully_sharded_native_amp.FullyShardedNativeMixedPrecisionPlugin class

  • Deprecated the pytorch_lightning.plugins.precision.sharded_native_amp.ShardedNativeMixedPrecisionPlugin class

  • Deprecated the pytorch_lightning.strategies.fully_sharded.DDPFullyShardedStrategy class

  • Deprecated the pytorch_lightning.strategies.sharded.DDPShardedStrategy class

  • Deprecated the pytorch_lightning.strategies.sharded_spawn.DDPSpawnShardedStrategy class

[1.9.0] - Removed

• Removed deprecated pytorch_lightning.utilities.memory.get_gpu_memory_map in favor of pytorch_lightning.accelerators.cuda.get_nvidia_gpu_stats (#15617)

• Temporarily removed support for Hydra multi-run (#15737)

• Removed deprecated pytorch_lightning.profiler.base.AbstractProfiler in favor of pytorch_lightning.profilers.profiler.Profiler (#15637)

• Removed deprecated pytorch_lightning.profiler.base.BaseProfiler in favor of pytorch_lightning.profilers.profiler.Profiler (#15637)

• Removed deprecated code in pytorch_lightning.utilities.meta (#16038)

• Removed the deprecated LightningDeepSpeedModule (#16041)

• Removed the deprecated pytorch_lightning.accelerators.GPUAccelerator in favor of pytorch_lightning.accelerators.CUDAAccelerator (#16050)

• Removed the deprecated pytorch_lightning.profiler.* classes in favor of pytorch_lightning.profilers (#16059)

• Removed the deprecated pytorch_lightning.utilities.cli module in favor of pytorch_lightning.cli (#16116)

• Removed the deprecated pytorch_lightning.loggers.base module in favor of pytorch_lightning.loggers.logger (#16120)

• Removed the deprecated pytorch_lightning.loops.base module in favor of pytorch_lightning.loops.loop (#16142)

• Removed the deprecated pytorch_lightning.core.lightning module in favor of pytorch_lightning.core.module (#16318)

• Removed the deprecated pytorch_lightning.callbacks.base module in favor of pytorch_lightning.callbacks.callback (#16319)

• Removed the deprecated Trainer.reset_train_val_dataloaders() in favor of Trainer.reset_{train,val}_dataloader (#16131)

• Removed support for LightningCLI(seed_everything_default=None) (#16131)

• Removed support in LightningLite for FairScale’s sharded training (strategy='ddp_sharded'|'ddp_sharded_spawn'). Use Fully-Sharded Data Parallel instead (strategy='fsdp') (#16329)

[1.9.0] - Fixed

• Enhanced reduce_boolean_decision to accommodate any-analogous semantics expected by the EarlyStopping callback (#15253)

• Fixed the incorrect optimizer step synchronization when running across multiple TPU devices (#16020)

• Fixed a type error when dividing the chunk size in the ColossalAI strategy (#16212)

• Fixed bug where the interval key of the scheduler would be ignored during manual optimization, making the LearningRateMonitor callback fail to log the learning rate (#16308)

• Fixed an issue with MLFlowLogger not finalizing correctly when status code ‘finished’ was passed (#16340)

[1.8.6] - 2022-12-21

• minor cleaning

[1.8.5] - 2022-12-15

• Add function to remove checkpoint to allow override for extended classes (#16067)

[1.8.4] - 2022-12-08

[1.8.4] - Changed

• Direct support for compiled models ( #15922, #15957 )

[1.8.4] - Fixed

• Fixed issue with unsupported torch.inference_mode() on hpu backends (#15918)

• Fixed LRScheduler import for PyTorch 2.0 (#15940)

• Fixed fit_loop.restarting to be False for lr finder (#15620)

• Fixed torch.jit.script-ing a LightningModule causing an unintended error message about deprecated use_amp property (#15947)

• Fixed the XLAProfiler not recording anything due to mismatching of action names (#15885)

[1.8.3] - 2022-11-22

[1.8.3] - Changed

• Temporarily removed support for Hydra multi-run (#15737)

• Switch from tensorboard to tensorboardx in TensorBoardLogger (#15728)

[1.8.2] - 2022-11-17

[1.8.2] - Fixed

• Make sure save_dir can be empty str (#15638)

• Fixed the automatic fallback from Trainer(strategy="ddp_spawn", ...) to Trainer(strategy="ddp", ...) when on an LSF cluster (#15103)

[1.8.1] - 2022-11-10

[1.8.1] - Added

• Added back the accidentally removed pytorch_lightning.utilities.distributed.rank_zero_only function (#15536)

[1.8.1] - Deprecated

• Deprecated pytorch_lightning.utilities.distributed.rank_zero_only in favor of pytorch_lightning.utilities.rank_zero_only (#15536)

[1.8.1] - Fixed

• Fixed TensorBoardLogger not validating the input array type when logging the model graph (#15323)

• Fixed an attribute error in ColossalAIStrategy at import time when torch.distributed is not available (#15535)

• Fixed an issue when calling fs.listdir with file URI instead of path in CheckpointConnector (#15413)

• Fixed an issue with the BaseFinetuning callback not setting the track_running_stats attribute for batch normaliztion layers (#15063)

• Fixed an issue with WandbLogger(log_model=True|'all) raising an error and not being able to serialize tensors in the metadata (#15544)

• Fixed the gradient unscaling logic when using Trainer(precision=16) and fused optimizers such as Adam(..., fused=True) (#15544)

• Fixed model state transfer in multiprocessing launcher when running multi-node (#15567)

• Fixed manual optimization raising AttributeError with Bagua Strategy (#12534)

• Fixed the import of pytorch_lightning causing a warning ‘Redirects are currently not supported in Windows or MacOs’ (#15610)

[1.8.0] - 2022-11-01

[1.8.0] - Added

• Added support for requeueing slurm array jobs (#15040)

• Added native AMP support for ddp_fork (and associated alias strategies) with CUDA GPUs (#14983)

• Added BatchSizeFinder callback (#11089)

• Added LearningRateFinder callback (#13802)

• Tuner now supports a new method argument which will determine when to run the BatchSizeFinder: one of fit, validate, test or predict (#11089)

• Added prefix to log message in seed_everything with rank info (#14031)

• Added support for auto wrapping for DDPFullyShardedNativeStrategy (#14252)

• Added support for passing extra init-parameters to the LightningDataModule.from_datasets (#14185)

• Added support for saving sharded optimizer state dict outside of DDPShardedStrategy (#14208)

• Added support for auto wrapping for DDPFullyShardedStrategy (#14383)

• Integrate the lightning_utilities package ( #14475, #14537, #14556, #14558, #14575, #14620)

• Added args parameter to LightningCLI to ease running from within Python (#14596)

• Added WandbLogger.download_artifact and WandbLogger.use_artifact for managing artifacts with Weights and Biases (#14551)

• Added an option to configure the signal SLURM sends when a job is preempted or requeued (#14626)

• Added a warning when the model passed to LightningLite.setup() does not have all parameters on the same device (#14822)

• The CometLogger now flags the Comet Experiments as being created from Lightning for analytics purposes (#14906)

• Introduce ckpt_path="hpc" keyword for checkpoint loading (#14911)

• Added a more descriptive error message when attempting to fork processes with pre-initialized CUDA context (#14709)

• Added support for custom parameters in subclasses of SaveConfigCallback (#14998)

• Added inference_mode flag to Trainer to let users enable/disable inference mode during evaluation (#15034)

• Added LightningLite.no_backward_sync for control over efficient gradient accumulation with distributed strategies (#14966)

• Added a sanity check that scripts are executed with the srun command in SLURM and that environment variables are not conflicting (#15011)

• Added an error message when attempting to launch processes with python -i and an interactive-incompatible strategy (#15293)

[1.8.0] - Changed

• The Trainer.{fit,validate,test,predict,tune} methods now raise a useful error message if the input is not a LightningModule (#13892)

• Raised a MisconfigurationException if batch transfer hooks are overriden with IPUAccelerator (#13961)

• Replaced the unwrapping logic in strategies with direct access to unwrapped LightningModule (#13738)

• Enabled on_before_batch_transfer for DPStrategy and IPUAccelerator (#14023)

• When resuming training with Apex enabled, the Trainer will now raise an error (#14341)

• Included torch.cuda rng state to the aggregate _collect_rng_states() and _set_rng_states() (#14384)

• Changed trainer.should_stop to not stop in between an epoch and run until min_steps/min_epochs only (#13890)

• The pyDeprecate dependency is no longer installed (#14472)

• When using multiple loggers, by default checkpoints and profiler output now get saved to the log dir of the first logger in the list (#14325)

• In Lightning Lite, state-dict access to the module wrapper now gets passed through to the original module reference (#14629)

• Removed fall-back to LightningEnvironment when number of SLURM tasks does not correspond to number of processes in Trainer (#14300)

• Aligned DDP and DDPSpawn strategies in setting up the environment (#11073)

• Integrated the Lite Precision plugins into the PL Precision plugins - the base class in PL now extends the lightning_lite.precision.Precision base class (#14798)

  • The PrecisionPlugin.backward signature changed: The closure_loss argument was renamed to tensor

  • The PrecisionPlugin.{pre_,post_}backward signature changed: The closure_loss argument was renamed to tensor and moved as the first argument

  • The PrecisionPlugin.optimizer_step signature changed: The model, optimizer_idx and closure arguments need to be passed as keyword arguments now

• Trainer queries the CUDA devices through NVML if available to avoid initializing CUDA before forking, which eliminates the need for the PL_DISABLE_FORK environment variable introduced in v1.7.4 (#14631)

• The MLFlowLogger.finalize() now sets the status to FAILED when an exception occurred in Trainer, and sets the status to FINISHED on successful completion (#12292)

• It is no longer needed to call model.double() when using precision=64 in Lightning Lite (#14827)

• HPC checkpoints are now loaded automatically only in slurm environment when no specific value for ckpt_path has been set (#14911)

• The Callback.on_load_checkpoint now gets the full checkpoint dictionary and the callback_state argument was renamed checkpoint (#14835)

• Moved the warning about saving nn.Module in save_hyperparameters() to before the deepcopy (#15132)

• To avoid issues with forking processes, from PyTorch 1.13 and higher, Lightning will directly use the PyTorch NVML-based check for torch.cuda.device_count and from PyTorch 2.0 and higher, Lightning will configure PyTorch to use a NVML-based check for torch.cuda.is_available. (#15110, #15133)

• The NeptuneLogger now uses neptune.init_run instead of the deprecated neptune.init to initialize a run (#15393)

[1.8.0] - Deprecated

• Deprecated LightningDeepSpeedModule (#14000)

• Deprecated amp_level from Trainer in favour of passing it explictly via precision plugin (#13898)

• Deprecated the calls to pytorch_lightning.utiltiies.meta functions in favor of built-in https://github.com/pytorch/torchdistx support (#13868)

• Deprecated the unwrap_lightning_module and unwrap_lightning_module_sharded utility functions in favor of accessing the unwrapped LightningModule on the strategy directly (#13738)

• Deprecated the pl_module argument in LightningParallelModule, LightningDistributedModule, LightningShardedDataParallel, LightningBaguaModule and LightningDeepSpeedModule wrapper classes (#13738)

• Deprecated the on_colab_kaggle function (#14247)

• Deprecated the internal pl.core.mixins.DeviceDtypeModuleMixin class (#14511, #14548)

• Deprecated all functions in pytorch_lightning.utilities.xla_device (#14514, #14550)

  • Deprecated the internal inner_f function

  • Deprecated the internal pl_multi_process function

  • Deprecated the internal XLADeviceUtils.xla_available staticmethod

  • Deprecated the XLADeviceUtils.tpu_device_exists staticmethod in favor of pytorch_lightning.accelerators.TPUAccelerator.is_available()

• Deprecated pytorch_lightning.utilities.distributed.tpu_distributed in favor of lightning_lite.accelerators.tpu.tpu_distributed (#14550)

• Deprecated all functions in pytorch_lightning.utilities.cloud_io in favor of lightning_lite.utilities.cloud_io (#14515)

• Deprecated the functions in pytorch_lightning.utilities.apply_func in favor of lightning_utilities.core.apply_func (#14516, #14537)

• Deprecated all functions in pytorch_lightning.utilities.device_parser (#14492, #14753)

  • Deprecated the pytorch_lightning.utilities.device_parser.determine_root_gpu_device in favor of lightning_lite.utilities.device_parser.determine_root_gpu_device

  • Deprecated the pytorch_lightning.utilities.device_parser.parse_gpu_ids in favor of lightning_lite.utilities.device_parser.parse_gpu_ids

  • Deprecated the pytorch_lightning.utilities.device_parser.is_cuda_available in favor of lightning_lite.accelerators.cuda.is_cuda_available

  • Deprecated the pytorch_lightning.utilities.device_parser.num_cuda_devices in favor of lightning_lite.accelerators.cuda.num_cuda_devices

  • Deprecated the pytorch_lightning.utilities.device_parser.parse_cpu_cores in favor of lightning_lite.accelerators.cpu.parse_cpu_cores

  • Deprecated the pytorch_lightning.utilities.device_parser.parse_tpu_cores in favor of lightning_lite.accelerators.tpu.parse_tpu_cores

  • Deprecated the pytorch_lightning.utilities.device_parser.parse_hpus in favor of pytorch_lightning.accelerators.hpu.parse_hpus

• Deprecated duplicate SaveConfigCallback parameters in LightningCLI.__init__: save_config_kwargs, save_config_overwrite and save_config_multifile. New save_config_kwargs parameter should be used instead (#14998)

• Deprecated TrainerFn.TUNING, RunningStage.TUNING and trainer.tuning property (#15100)

• Deprecated custom pl.utilities.distributed.AllGatherGrad implementation in favor of PyTorch’s (#15364)

[1.8.0] - Removed

• Removed the deprecated Trainer.training_type_plugin property in favor of Trainer.strategy (#14011)

• Removed all deprecated training type plugins (#14011)

• Removed the deprecated DDP2Strategy (#14026)

• Removed the deprecated DistributedType and DeviceType enum classes (#14045)

• Removed deprecated support for passing the rank_zero_warn warning category positionally (#14470)

• Removed the legacy and unused Trainer.get_deprecated_arg_names() (#14415)

• Removed the deprecated on_train_batch_end(outputs) format when multiple optimizers are used and TBPTT is enabled (#14373)

• Removed the deprecated training_epoch_end(outputs) format when multiple optimizers are used and TBPTT is enabled (#14373)

• Removed the experimental pytorch_lightning.utiltiies.meta functions in favor of built-in https://github.com/pytorch/torchdistx support (#13868)

• Removed the deprecated LoggerCollection; Trainer.logger and LightningModule.logger now returns the first logger when more than one gets passed to the Trainer (#14283)

• Removed the deprecated the trainer.lr_schedulers (#14408)

• Removed the deprecated LightningModule.{on_hpc_load,on_hpc_save} hooks in favor of the general purpose hooks LightningModule.{on_load_checkpoint,on_save_checkpoint} (#14315)

• Removed deprecated support for old torchtext versions (#14375)

• Removed deprecated support for the old neptune-client API in the NeptuneLogger (#14727)

• Removed the deprecated weights_save_path Trainer argumnent and Trainer.weights_save_path property (#14424)

• Removed the deprecated (#14471)

  • pytorch_lightning.utilities.distributed.rank_zero_only in favor of pytorch_lightning.utilities.rank_zero.rank_zero_only

  • pytorch_lightning.utilities.distributed.rank_zero_debug in favor of pytorch_lightning.utilities.rank_zero.rank_zero_debug

  • pytorch_lightning.utilities.distributed.rank_zero_info in favor of pytorch_lightning.utilities.rank_zero.rank_zero_info

  • pytorch_lightning.utilities.warnings.rank_zero_warn in favor of pytorch_lightning.utilities.rank_zero.rank_zero_warn

  • pytorch_lightning.utilities.warnings.rank_zero_deprecation in favor of pytorch_lightning.utilities.rank_zero.rank_zero_deprecation

  • pytorch_lightning.utilities.warnings.LightningDeprecationWarning in favor of pytorch_lightning.utilities.rank_zero.LightningDeprecationWarning

• Removed deprecated Trainer.num_processes attribute in favour of Trainer.num_devices (#14423)

• Removed the deprecated Trainer.data_parallel_device_ids hook in favour of Trainer.device_ids (#14422)

• Removed the deprecated class TrainerCallbackHookMixin (#14401)

• Removed the deprecated BaseProfiler and AbstractProfiler classes (#14404)

• Removed the deprecated way to set the distributed backend via the environment variable PL_TORCH_DISTRIBUTED_BACKEND, in favor of setting the process_group_backend in the strategy constructor (#14693)

• Removed deprecated callback hooks (#14834)

  • Callback.on_configure_sharded_model in favor of Callback.setup

  • Callback.on_before_accelerator_backend_setup in favor of Callback.setup

  • Callback.on_batch_start in favor of Callback.on_train_batch_start

  • Callback.on_batch_end in favor of Callback.on_train_batch_end

  • Callback.on_epoch_start in favor of Callback.on_{train,validation,test}_epoch_start

  • Callback.on_epoch_end in favor of Callback.on_{train,validation,test}_epoch_end

  • Callback.on_pretrain_routine_{start,end} in favor of Callback.on_fit_start

• Removed the deprecated device attributes Trainer.{devices,gpus,num_gpus,ipus,tpu_cores} in favor of the accelerator-agnostic Trainer.num_devices (#14829)

• Removed the deprecated LightningIPUModule (#14830)

• Removed the deprecated Logger.agg_and_log_metrics hook in favour of Logger.log_metrics and the agg_key_funcs and agg_default_func arguments. (#14840)

• Removed the deprecated precision plugin checkpoint hooks PrecisionPlugin.on_load_checkpoint and PrecisionPlugin.on_save_checkpoint (#14833)

• Removed the deprecated Trainer.root_gpu attribute in favor of Trainer.strategy.root_device (#14829)

• Removed the deprecated Trainer.use_amp and LightningModule.use_amp attributes (#14832)

• Removed the deprecated callback hooks Callback.on_init_start and Callback.on_init_end (#14867)

• Removed the deprecated Trainer.run_stage in favor of Trainer.{fit,validate,test,predict} (#14870)

• Removed the deprecated SimpleProfiler.profile_iterable and AdvancedProfiler.profile_iterable attributes (#14864)

• Removed the deprecated Trainer.verbose_evaluate (#14884)

• Removed the deprecated Trainer.should_rank_save_checkpoint (#14885)

• Removed the deprecated TrainerOptimizersMixin (#14887)

• Removed the deprecated Trainer.lightning_optimizers (#14889)

• Removed the deprecated TrainerDataLoadingMixin (#14888)

• Removed the 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 (#14869)

• Removed the deprecated Trainer.{validated,tested,predicted}_ckpt_path (#14897)

• Removed the deprecated device_stats_monitor_prefix_metric_keys (#14890)

• Removed the deprecated LightningDataModule.on_save/load_checkpoint hooks (#14909)

• Removed support for returning a value in Callback.on_save_checkpoint in favor of implementing Callback.state_dict (#14835)

[1.8.0] - Fixed

• Fixed an issue with LightningLite.setup() not setting the .device attribute correctly on the returned wrapper (#14822)

• Fixed an attribute error when running the tuner together with the StochasticWeightAveraging callback (#14836)

• Fixed MissingFieldException in offline mode for the NeptuneLogger() (#14919)

• Fixed wandb save_dir is overridden by None dir when using CLI (#14878)

• Fixed a missing call to LightningDataModule.load_state_dict hook while restoring checkpoint using LightningDataModule.load_from_checkpoint (#14883)

• Fixed torchscript error with containers of LightningModules (#14904)

• Fixed reloading of the last checkpoint on run restart (#14907)

• SaveConfigCallback instances should only save the config once to allow having the overwrite=False safeguard when using LightningCLI(..., run=False) (#14927)

• Fixed an issue with terminating the trainer profiler when a StopIteration exception is raised while using an IterableDataset (#14940)

• Do not update on-plateau schedulers when reloading from an end-of-epoch checkpoint (#14702)

• Fixed Trainer support for PyTorch built without distributed support (#14971)

• Fixed batch normalization statistics calculation in StochasticWeightAveraging callback (#14866)

• Avoided initializing optimizers during deepspeed inference (#14944)

• Fixed LightningCLI parse_env and description in subcommands (#15138)

• Fixed an exception that would occur when creating a multiprocessing.Pool after importing Lightning (#15292)

• Fixed a pickling error when using RichProgressBar together with checkpointing (#15319)

• Fixed the RichProgressBar crashing when used with distributed strategies (#15376)

• Fixed an issue with RichProgressBar not resetting the internal state for the sanity check progress (#15377)

• Fixed an issue with DataLoader re-instantiation when the attribute is an array and the default value of the corresponding argument changed (#15409)

[1.7.7] - 2022-09-22

[1.7.7] - Fixed

• Fixed the availability check for the neptune-client package (#14714)

• Break HPU Graphs into two parts (forward + backward as one and optimizer as another) for better performance (#14656)

• Fixed torchscript error with ensembles of LightningModules (#14657, #14724)

• Fixed an issue with TensorBoardLogger.finalize creating a new experiment when none was created during the Trainer’s execution (#14762)

• Fixed TypeError on import when torch.distributed is not available (#14809)

[1.7.6] - 2022-09-13

[1.7.6] - Changed

• Improved the error messaging when passing Trainer.method(model, x_dataloader=None) with no module-method implementations available (#14614)

[1.7.6] - Fixed

• Reset the dataloaders on OOM failure in batch size finder to use the last successful batch size (#14372)

• Fixed an issue to keep downscaling the batch size in case there hasn’t been even a single successful optimal batch size with mode="power" (#14372)

• Fixed an issue where self.log-ing a tensor would create a user warning from PyTorch about cloning tensors (#14599)

• Fixed compatibility when torch.distributed is not available (#14454)

[1.7.5] - 2022-09-06

[1.7.5] - Fixed

• Squeezed tensor values when logging with LightningModule.log (#14489)

• Fixed WandbLogger save_dir is not set after creation (#14326)

• Fixed Trainer.estimated_stepping_batches when maximum number of epochs is not set (#14317)

[1.7.4] - 2022-08-31

[1.7.4] - Added

• Added an environment variable PL_DISABLE_FORK that can be used to disable all forking in the Trainer (#14319)

[1.7.4] - Fixed

• Fixed LightningDataModule hparams parsing (#12806)

• Reset epoch progress with batch size scaler (#13846)

• Fixed restoring the trainer after using lr_find() so that the correct LR schedule is used for the actual training (#14113)

• Fixed incorrect values after transferring data to an MPS device (#14368)

[1.7.3] - 2022-08-25

[1.7.3] - Fixed

• Fixed an assertion error when using a ReduceOnPlateau scheduler with the Horovod strategy (#14215)

• Fixed an AttributeError when accessing LightningModule.logger and the Trainer has multiple loggers (#14234)

• Added back support for logging in the configure_gradient_clipping hook after unintended removal in v1.7.2 (#14298)

• Fixed wrong num padding for RichProgressBar (#14296)

• Fixed an issue to avoid the impact of sanity check on reload_dataloaders_every_n_epochs for validation (#13964)

[1.7.2] - 2022-08-17

[1.7.2] - Added

• Added FullyShardedNativeNativeMixedPrecisionPlugin to handle precision for DDPFullyShardedNativeStrategy (#14092)

• Added profiling to these hooks: on_before_batch_transfer, transfer_batch_to_device, on_after_batch_transfer, configure_gradient_clipping, clip_gradients (#14069)

[1.7.2] - Changed

• The WandbLogger.name property no longer returns the name of the experiment, and instead returns the project’s name (#14145)

• The default project name in WandbLogger is now “lightning_logs” (#14145)

• Updated compatibility for LightningLite to run with the latest DeepSpeed 0.7.0 (13967)

[1.7.2] - Fixed

• Fixed a bug that caused spurious AttributeError when multiple DataLoader classes are imported (#14117)

• Fixed epoch-end logging results not being reset after the end of the epoch (#14061)

• Fixed resuming from a checkpoint when using Stochastic Weight Averaging (SWA) (#9938)

• Fixed the device placement when LightningModule.cuda() gets called without specifying a device index and the current cuda device was not 0 (#14128)

• Avoided false positive warning about using sync_dist when using torchmetrics (#14143)

• Avoid metadata.entry_points deprecation warning on Python 3.10 (#14052)

• Fixed epoch-end logging results not being reset after the end of the epoch (#14061)

• Avoid raising the sampler warning if num_replicas=1 (#14097)

• Fixed saving hyperparameters in a composition where the parent class is not a LightningModule or LightningDataModule (#14151)

• Avoided requiring the FairScale package to use precision with the fsdp native strategy (#14092)

• Fixed an issue in which the default name for a run in WandbLogger would be set to the project name instead of a randomly generated string (#14145)

• Fixed not preserving set attributes on DataLoader and BatchSampler when instantiated inside *_dataloader hooks (#14212)

[1.7.1] - 2022-08-09

[1.7.1] - Fixed

• Casted only floating point tensors to fp16 with IPUs (#13983)

• Casted tensors to fp16 before moving them to device with DeepSpeedStrategy (#14000)

• Fixed the NeptuneLogger dependency being unrecognized (#13988)

• Fixed an issue where users would be warned about unset max_epochs even when fast_dev_run was set (#13262)

• Fixed MPS device being unrecognized (#13992)

• Fixed incorrect precision="mixed" being used with DeepSpeedStrategy and IPUStrategy (#14041)

• Fixed dtype inference during gradient norm computation (#14051)

• Fixed a bug that caused ddp_find_unused_parameters to be set False, whereas the intended default is True (#14095)

[1.7.0] - 2022-08-02

[1.7.0] - Added

• Added ServableModule and its associated callback called ServableModuleValidator to ensure the model can served (#13614)

• Converted validation loop config warnings to PossibleUserWarning (#13377)

• Added a flag named log_rank_zero_only to EarlyStopping to disable logging to non-zero rank processes (#13233)

• Added support for reloading the last checkpoint saved by passing ckpt_path="last" (#12816)

• Added LightningDataModule.load_from_checkpoint to support loading datamodules directly from checkpoint (#12550)

• Added a friendly error message when attempting to call Trainer.save_checkpoint() without a model attached (#12772)

• Added a friendly error message when attempting to use DeepSpeedStrategy on unsupported accelerators (#12699)

• Enabled torch.inference_mode for evaluation and prediction (#12715)

• Added support for setting val_check_interval to a value higher than the amount of training batches when check_val_every_n_epoch=None (#11993)

• Include the pytorch_lightning version as a header in the CLI config files (#12532)

• Added support for Callback registration through entry points (#12739)

• Added support for Trainer(deterministic="warn") to warn instead of fail when a non-deterministic operation is encountered (#12588)

• Added profiling to the loops’ dataloader __next__ calls (#12124)

• Hivemind Strategy

  • Added CollaborativeStrategy (#12842)

  • Renamed CollaborativeStrategy to HivemindStrategy (#13388)

  • Removed unnecessary endpoint logic, renamed collaborative to hivemind (#13392)

• Include a version suffix for new “last” checkpoints of later runs in the same directory (#12902)

• Show a better error message when a Metric that does not return a Tensor is logged (#13164)

• Added missing predict_dataset argument in LightningDataModule.from_datasets to create predict dataloaders (#12942)

• Added class name prefix to metrics logged by DeviceStatsMonitor (#12228)

• Automatically wrap custom samplers under a distributed environment by using DistributedSamplerWrapper (#12959)

• Added profiling of LightningDataModule hooks (#12971)

• Added Native FSDP Strategy (#12447)

• Added breaking of lazy graph across training, validation, test and predict steps when training with habana accelerators to ensure better performance (#12938)

• Added Checkpoint class to inherit from (#13024)

• Added CPU metric tracking to DeviceStatsMonitor (#11795)

• Added teardown() method to Accelerator (#11935)

• Added support for using custom Trainers that don’t include callbacks using the CLI (#13138)

• Added a timeout argument to DDPStrategy and DDPSpawnStrategy. (#13244, #13383)

• Added XLAEnvironment cluster environment plugin (#11330)

• Added logging messages to notify when FitLoop stopping conditions are met (#9749)

• Added support for calling unknown methods with DummyLogger (#13224

• Added support for recursively setting the Trainer reference for ensembles of LightningModules (#13638

• Added Apple Silicon Support via MPSAccelerator (#13123)

• Added support for DDP Fork (#13405)

• Added support for async checkpointing (#13658)

• Added support for HPU Device stats monitor (#13819)

[1.7.0] - Changed

• accelerator="gpu" now automatically selects an available GPU backend (CUDA and MPS currently) (#13642)

• Enable validation during overfitting (#12527)

• Added dataclass support to extract_batch_size (#12573)

• Changed checkpoints save path in the case of one logger and user-provided weights_save_path from weights_save_path/name/version/checkpoints to weights_save_path/checkpoints (#12372)

• Changed checkpoints save path in the case of multiple loggers and user-provided weights_save_path from weights_save_path/name1_name2/version1_version2/checkpoints to weights_save_path/checkpoints (#12372)

• Marked swa_lrs argument in StochasticWeightAveraging callback as required (#12556)

• LightningCLI’s shorthand notation changed to use jsonargparse native feature (#12614)

• LightningCLI changed to use jsonargparse native support for list append (#13129)

• Changed seed_everything_default argument in the LightningCLI to type Union[bool, int]. If set to True a seed is automatically generated for the parser argument --seed_everything. (#12822, #13110)

• Make positional arguments required for classes passed into the add_argparse_args function. (#12504)

• Raise an error if there are insufficient training batches when using a float value of limit_train_batches (#12885)

• DataLoader instantiated inside a *_dataloader hook will not set the passed arguments as attributes anymore (#12981)

• When a multi-element tensor is logged, an error is now raised instead of silently taking the mean of all elements (#13164)

• The WandbLogger will now use the run name in the logs folder if it is provided, and otherwise the project name (#12604)

• Enabled using any Sampler in distributed environment in Lite (#13646)

• Raised a warning instead of forcing sync_dist=True on epoch end (13364)

• Updated val_check_interval(int) to consider total train batches processed instead of _batches_that_stepped for validation check during training (#12832

• Updated Habana Accelerator’s auto_device_count, is_available & get_device_name methods based on the latest torch habana package (#13423)

• Disallowed using BatchSampler when running on multiple IPUs (#13854)

[1.7.0] - Deprecated

• Deprecated pytorch_lightning.accelerators.gpu.GPUAccelerator in favor of pytorch_lightning.accelerators.cuda.CUDAAccelerator (#13636)

• Deprecated pytorch_lightning.loggers.base.LightningLoggerBase in favor of pytorch_lightning.loggers.logger.Logger, and deprecated pytorch_lightning.loggers.base in favor of pytorch_lightning.loggers.logger (#120148)

• Deprecated pytorch_lightning.callbacks.base.Callback in favor of pytorch_lightning.callbacks.callback.Callback (#13031)

• Deprecated num_processes, gpus, tpu_cores, and ipus from the Trainer constructor in favor of using the accelerator and devices arguments (#11040)

• Deprecated setting LightningCLI(seed_everything_default=None) in favor of False (#12804).

• Deprecated pytorch_lightning.core.lightning.LightningModule in favor of pytorch_lightning.core.module.LightningModule (#12740)

• Deprecated pytorch_lightning.loops.base.Loop in favor of pytorch_lightning.loops.loop.Loop (#13043)

• Deprecated Trainer.reset_train_val_dataloaders() in favor of Trainer.reset_{train,val}_dataloader (#12184)

• Deprecated LightningCLI’s registries in favor of importing the respective package (#13221)

• Deprecated public utilities in pytorch_lightning.utilities.cli.LightningCLI in favor of equivalent copies in pytorch_lightning.cli.LightningCLI (#13767)

• Deprecated pytorch_lightning.profiler.* in favor of pytorch_lightning.profilers (#12308)

[1.7.0] - Removed

• Removed deprecated IndexBatchSamplerWrapper.batch_indices (#13565)

• Removed the deprecated LightningModule.add_to_queue and LightningModule.get_from_queue method (#13600)

• Removed deprecated pytorch_lightning.core.decorators.parameter_validation from decorators (#13514)

• Removed the deprecated Logger.close method (#13149)

• Removed the deprecated weights_summary argument from the Trainer constructor (#13070)

• Removed the deprecated flush_logs_every_n_steps argument from the Trainer constructor (#13074)

• Removed the deprecated process_position argument from the Trainer constructor (13071)

• Removed the deprecated checkpoint_callback argument from the Trainer constructor (#13027)

• Removed the deprecated on_{train,val,test,predict}_dataloader hooks from the LightningModule and LightningDataModule (#13033)

• Removed the deprecated TestTubeLogger (#12859)

• Removed the deprecated pytorch_lightning.core.memory.LayerSummary and pytorch_lightning.core.memory.ModelSummary (#12593)

• Removed the deprecated summarize method from the LightningModule (#12559)

• Removed the deprecated model_size property from the LightningModule class (#12641)

• Removed the deprecated stochastic_weight_avg argument from the Trainer constructor (#12535)

• Removed the deprecated progress_bar_refresh_rate argument from the Trainer constructor (#12514)

• Removed the deprecated prepare_data_per_node argument from the Trainer constructor (#12536)

• Removed the deprecated pytorch_lightning.core.memory.{get_gpu_memory_map,get_memory_profile} (#12659)

• Removed the deprecated terminate_on_nan argument from the Trainer constructor (#12553)

• Removed the deprecated XLAStatsMonitor callback (#12688)

• Remove deprecated pytorch_lightning.callbacks.progress.progress (#12658)

• Removed the deprecated dim and size arguments from the LightningDataModule constructor(#12780)

• Removed the deprecated train_transforms argument from the LightningDataModule constructor(#12662)

• Removed the deprecated log_gpu_memory argument from the Trainer constructor (#12657)

• Removed the deprecated automatic logging of GPU stats by the logger connector (#12657)

• Removed deprecated GPUStatsMonitor callback (#12554)

• Removed support for passing strategy names or strategy instances to the accelerator Trainer argument (#12696)

• Removed support for passing strategy names or strategy instances to the plugins Trainer argument (#12700)

• Removed the deprecated val_transforms argument from the LightningDataModule constructor (#12763)

• Removed the deprecated test_transforms argument from the LightningDataModule constructor (#12773)

• Removed deprecated Trainer(max_steps=None) (#13591)

• Removed deprecated dataloader_idx argument from on_train_batch_start/end hooks Callback and LightningModule (#12769, #12977)

• Removed deprecated get_progress_bar_dict property from LightningModule (#12839)

• Removed sanity check for multi-optimizer support with habana backends (#13217)

• Removed the need to explicitly load habana module (#13338)

• Removed the deprecated Strategy.post_dispatch() hook (#13461)

• Removed deprecated pytorch_lightning.callbacks.lr_monitor.LearningRateMonitor.lr_sch_names (#13353)

• Removed deprecated Trainer.slurm_job_id in favor of SLURMEnvironment.job_id (#13459)

• Removed support for the DDP2Strategy (#12705)

• Removed deprecated LightningDistributed (#13549)

• Removed deprecated ClusterEnvironment properties master_address and master_port in favor of main_address and main_port (#13458)

• Removed deprecated ClusterEnvironment methods KubeflowEnvironment.is_using_kubelfow(), LSFEnvironment.is_using_lsf() and TorchElasticEnvironment.is_using_torchelastic() in favor of the detect() method (#13458)

• Removed deprecated Callback.on_keyboard_interrupt (#13438)

• Removed deprecated LightningModule.on_post_move_to_device (#13548)

• Removed TPUSpawnStrategy.{tpu_local_core_rank,tpu_global_core_rank} attributes in favor of TPUSpawnStrategy.{local_rank,global_rank} (#11163)

• Removed SingleTPUStrategy.{tpu_local_core_rank,tpu_global_core_rank} attributes in favor of SingleTPUStrategy.{local_rank,global_rank}(#11163)

[1.7.0] - Fixed

• Improved support for custom DataLoaders when instantiated in *_dataloader hook (#12981)

• Allowed custom BatchSamplers when instantiated in *_dataloader hook #13640)

• Fixed an issue with unsupported torch.inference_mode() on hpu backends by making it use no_grad (#13014)

• The model wrapper returned by LightningLite.setup() now properly supports pass-through when looking up attributes (#12597)

• Fixed issue where the CLI fails with certain torch objects (#13153)

• Fixed LightningCLI signature parameter resolving for some lightning classes (#13283)

• Fixed Model Summary when using DeepSpeed Stage 3 (#13427)

• Fixed pytorch_lightning.utilities.distributed.gather_all_tensors to handle tensors of different dimensions (#12630)

• Fixed the input validation for the accelerator Trainer argument when passed as a string (#13417)

• Fixed Trainer.predict(return_predictions=False) to track prediction’s batch_indices (#13629)

• Fixed and issue that prevented setting a custom CheckpointIO plugin with strategies (#13785)

• Fixed main progress bar counter when val_check_interval=int and check_val_every_n_epoch=None (#12832

• Improved support for custom ReduceLROnPlateau scheduler if reduce_on_plateau is set by the user in scheduler config (#13838)

• Used global_step while restoring logging step for old checkpoints (#13645)

• When training with precision=16 on IPU, the cast has been moved off the IPU onto the host, making the copies from host to IPU cheaper (#13880)

• Fixed error handling in learning rate finder when not enough data points are available to give a good suggestion (#13845)

• Fixed an issue that caused the learning rate finder to set the model’s learning rate to None when no suggestion was possible (#13845)

• Fixed an issue causing deterministic algorighms and other globals to get reset in spawned processes (#13921)

• Fixed default amp_level for DeepSpeedPrecisionPlugin to O2 (#13897)

• Fixed Python 3.10 compatibility for truncated back-propagation through time (TBPTT) (#13973)

• Fixed TQDMProgressBar reset and update to show correct time estimation (2/2) (#13962)

[1.6.5] - 2022-07-13

[1.6.5] - 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

[1.6.4] - Added

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

[1.6.4] - Changed

• Keep torch.backends.cudnn.benchmark=False by default (unlike in v1.6.{0-3}) 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)

[1.6.4] - 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

[1.6.3] - 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 (1/2) (#12889)

• Fixed fit loop restart logic to enable resume using the checkpoint (#12821)

[1.6.2] - 2022-04-27

[1.6.2] - 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

[1.6.1] - Changed

• Support strategy argument being case insensitive (#12528)

[1.6.1] - 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

[1.6.0] - 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)

[1.6.0] - 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)

[1.6.0] - 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 (#11747)

• 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)

[1.6.0] - 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)

[1.6.0] - 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

[1.5.10] - 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

[1.5.9] - 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)

[1.5.9] - 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

[1.5.8] - 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

[1.5.7] - 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)

[1.5.7] - 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

[1.5.6] - 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

[1.5.5] - 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

[1.5.4] - 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)

[1.5.4] - Removed

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

[1.5.3] - 2021-11-24

[1.5.3] - 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

[1.5.2] - 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

[1.5.1] - 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

[1.5.0] - 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)

[1.5.0] - 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)

[1.5.0] - 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)

[1.5.0] - 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)

[1.5.0] - 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

[1.4.0] - 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)

[1.4.0] - 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)

[1.4.0] - 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)

[1.4.0] - 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)

[1.4.0] - 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

[1.3.8] - 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

[1.3.7] - 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

[1.3.6] - 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

[1.3.5] - Added

• Added warning to Training Step output (#7779)

[1.3.5] - 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)

[1.3.5] - Changed

• Move training_output validation to after train_step_end (#7868)

[1.3.4] - 2021-06-01

[1.3.4] - Fixed

• Fixed info message when max training time reached (#7780)

• Fixed missing __len__ method to IndexBatchSamplerWrapper (#7681)

[1.3.3] - 2021-05-27

[1.3.3] - Changed

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

[1.3.3] - 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

[1.3.2] - Changed

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

[1.3.2] - 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

[1.3.1] - 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

[1.3.0] - 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)

[1.3.0] - 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)

[1.3.0] - 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)

[1.3.0] - 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)

[1.3.0] - 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

[1.2.9] - 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

[1.2.8] - Added

• Added TPUSpawn + IterableDataset error message (#6875)

[1.2.8] - 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

[1.2.7] - 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

[1.2.6] - Changed

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

[1.2.6] - Removed

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

[1.2.6] - 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

[1.2.5] - Changed

• Update Gradient Clipping for the TPU Accelerator (#6576)

• Refactored setup for typing friendly (#6590)

[1.2.5] - 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

[1.2.4] - Changed

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

[1.2.4] - 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

[1.2.3] - 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

[1.2.2] - Added

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

[1.2.2] - 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)

[1.2.2] - 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

[1.2.1] - 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

[1.2.0] - 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)

[1.2.0] - 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)

[1.2.0] - 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)

[1.2.0] - 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)

[1.2.0] - 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

[1.1.8] - Fixed

• Separate epoch validation from step validation (#5208)

• Fixed toggle_optimizers not handling all optimizer parameters (#5775)

[1.1.7] - 2021-02-03

[1.1.7] - 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

[1.1.6] - 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)

[1.1.6] - 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

[1.1.5] - 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

[1.1.4] - Added

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

[1.1.4] - Changed

• Changed deprecated enable_pl_optimizer=True (#5244)

[1.1.4] - 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

[1.1.3] - Added

• Added a check for optimizer attached to lr_scheduler (#5338)

• Added support for passing non-existing filepaths to resume_from_checkpoint (#4402)

[1.1.3] - 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)

[1.1.3] - 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

[1.1.2] - Added

• Support number for logging with sync_dist=True (#5080)

• Added offset logging step when resuming for Wandb logger (#5050)

[1.1.2] - Removed

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

[1.1.2] - 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

[1.1.1] - Added

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

[1.1.1] - Changed

• Simplify accelerator steps (#5015)

• Refactor load in checkpoint connector (#4593)

• Fixed the saved filename in ModelCheckpoint when it already exists (#4861)

[1.1.1] - Removed

• Drop duplicate metrics (#5014)

• Remove beta arg from F1 class and functional (#5076)

[1.1.1] - 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

[1.1.0] - 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)

[1.1.0] - 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)

[1.1.0] - 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)

[1.1.0] - 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)

[1.1.0] - 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

[1.0.8] - 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)

[1.0.8] - 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)

[1.0.8] - 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

[1.0.7] - Added

• Added lambda closure to manual_optimizer_step (#4618)

[1.0.7] - 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)

[1.0.7] - 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

[1.0.6] - 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)

[1.0.6] - Changed

• Changed fsspec to tuner (#4458)

• Unify SLURM/TorchElastic under backend plugin (#4578, #4580, #4581, #4582, #4583)

[1.0.6] - 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

[1.0.5] - Added

• Added PyTorch 1.7 Stable support (#3821)

• Added timeout for tpu_device_exists to ensure process does not hang indefinitely (#4340)

[1.0.5] - 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)

[1.0.5] - Deprecated

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

[1.0.5] - 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

[1.0.4] - 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)

[1.0.4] - 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)

[1.0.4] - Deprecated

• Deprecated filepath in ModelCheckpoint (#4213)

• Deprecated reorder parameter of the auc metric (#4237)

• Deprecated bool values in Trainer’s profiler parameter (#3656)

[1.0.4] - 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

[1.0.3] - Added

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

[1.0.3] - Changed

• Used checkpoint_connector.hpc_save in SLURM (#4217)

• Moved base req. to root (#4219)

[1.0.3] - Fixed

• Fixed hparams assign in init (#4189)

• Fixed overwrite check for model hooks (#4010)

[1.0.2] - 2020-10-15

[1.0.2] - Added

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

[1.0.2] - Changed

• Called on_load_checkpoint before loading state_dict (#4057)

[1.0.2] - Removed

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

[1.0.2] - 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

[1.0.1] - Added

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

[1.0.0] - 2020-10-13

[1.0.0] - 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)

[1.0.0] - 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)

[1.0.0] - 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)

[1.0.0] - 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

[0.10.0] - 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)

[0.10.0] - 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

[0.10.0] - 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)

[0.10.0] - 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)

[0.10.0] - 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

[0.9.0] - 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)

[0.9.0] - 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)

[0.9.0] - Deprecated

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

[0.9.0] - 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

[0.9.0] - 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

[0.8.5] - Added

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

• Added functional regression metrics (#2492)

[0.8.5] - Removed

• Removed auto val reduce (#2462)

[0.8.5] - 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

[0.8.4] - Added

• Added reduce ddp results on eval (#2434)

• Added a warning when an IterableDataset has __len__ defined (#2437)

[0.8.4] - Changed

• Enabled no returns from eval (#2446)

[0.8.4] - 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

[0.8.3] - Fixed

• Fixed AMP wrong call (593837e)

• Fixed batch typo (92d1e75)

[0.8.2] - 2020-06-28

[0.8.2] - Added

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

[0.8.2] - 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)

[0.8.2] - Removed

• Moved TrainsLogger to Bolts (#2384)

[0.8.2] - 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

[0.8.1] - 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

[0.8.0] - 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)

[0.8.0] - 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)

[0.8.0] - 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)

[0.8.0] - 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

[0.8.0] - 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

[0.7.6] - 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)

[0.7.6] - 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)

[0.7.6] - Deprecated

• Deprecated tags_csv in favor of hparams_file (#1271)

[0.7.6] - 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

[0.7.5] - Changed

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

[0.7.5] - Removed

• Removed Warning from trainer loop (#1634)

[0.7.5] - 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

[0.7.4] - 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)

[0.7.4] - 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)

[0.7.4] - Deprecated

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

[0.7.4] - Removed

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

[0.7.4] - 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

[0.7.3] - Added

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

[0.7.3] - 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

[0.7.2] - 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)

[0.7.2] - 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)

[0.7.2] - Deprecated

• Deprecated Trainer argument print_nan_grads (#1097)

• Deprecated Trainer argument show_progress_bar (#1108)

[0.7.2] - 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)

[0.7.2] - 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

[0.7.1] - Fixed

• Fixes print issues and data_loader (#1080)

[0.7.0] - 2020-03-06

[0.7.0] - 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)

[0.7.0] - 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)

[0.7.0] - 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)

[0.7.0] - Removed

• Removed dependency on pandas (#736)

• Removed dependency on torchvision (#797)

• Removed dependency on scikit-learn (#801)

[0.7.0] - 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

[0.6.0] - 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)

[0.6.0] - 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)

[0.6.0] - Deprecated

• Deprecated max_nb_epochs and min_nb_epochs (#567)

• Deprecated the on_sanity_check_start hook in ModelHooks (#598)

[0.6.0] - Removed

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

[0.6.0] - 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

[0.5.3] - 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

[0.5.3] - 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

[0.5.3] - Deprecated

• Deprecated tng_dataloader

[0.5.3] - 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

[0.5.2] - 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

[0.5.2] - Changed

• Changed default for amp_level to O1

[0.5.2] - Removed

• Removed the print_weights_summary argument from Trainer

[0.5.2] - 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

[0.5.1] - 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)

[0.5.1] - 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

[0.5.1] - 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

[0.5.0] - 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

[0.5.0] - Fixed

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

[0.4.9] - 2019-09-16

[0.4.9] - 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

[0.4.9] - 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

[0.4.9] - 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

[0.4.8] - 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

[0.4.8] - Fixed

• Fixed a bug when setting nb_sanity_val_steps = 0

[0.4.7] - 2019-08-24

[0.4.7] - 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

[0.4.7] - 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

[0.4.6] - 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

[0.4.6] - 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

[0.4.5] - Added

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

[0.4.4] - 2019-08-12

[0.4.4] - Added

• Added supoort for multiple validation dataloaders

• Added support for latest test-tube logger (optimised for torch==1.2.0)

[0.4.4] - Changed

• validation_step and val_dataloader are now optional

• lr_scheduler is now activated after epoch

[0.4.4] - 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

[0.4.3] - Fixed

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

[0.4.2] - 2019-08-08

[0.4.2] - Changed

• Changed install requirement to torch==1.2.0

[0.4.1] - 2019-08-08

[0.4.1] - Changed

• Changed install requirement to torch==1.1.0

[0.4.0] - 2019-08-08

[0.4.0] - Added

• Added 16-bit support for a single GPU

• Added support for training continuation (preserves epoch, global step etc.)

[0.4.0] - Changed

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

[0.4.0] - Removed

• Removed need for Experiment object in Trainer

[0.4.0] - Fixed

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

[0.3.6] - 2019-07-25

[0.3.6] - Added

• Added a decorator to do lazy data loading internally

[0.3.6] - 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