Update pytorch-lightning requirement from <1.4.0,>=1.3.0 to >=1.3.0,<1.5.0

[dependabot[bot]]

Updates the requirements on pytorch-lightning to permit the latest version.

Release notes

Sourced from pytorch-lightning's releases.

TPU Pod Training, IPU Accelerator, DeepSpeed Infinity, Fully Sharded Data Parallel

Today we are excited to announce Lightning 1.4, introducing support for TPU pods, XLA profiling, IPUs, and new plugins to reach 10+ billion parameters, including Deep Speed Infinity, Fully Sharded Data-Parallel and more!

https://devblog.pytorchlightning.ai/announcing-lightning-1-4-8cd20482aee9

[1.4.0] - 2021-07-27

Added

• Added extract_batch_size utility and corresponding tests to extract batch dimension from multiple batch types (#8357)
• Added support for named parameter groups in LearningRateMonitor (#7987)
• Added dataclass support for pytorch_lightning.utilities.apply_to_collection (#7935)
• Added support to LightningModule.to_torchscript for saving to custom filesystems with fsspec (#7617)
• Added KubeflowEnvironment for use with the PyTorchJob operator in Kubeflow
• Added LightningCLI support for config files on object stores (#7521)
• Added ModelPruning(prune_on_train_epoch_end=True|False) to choose when to apply pruning (#7704)
• Added support for checkpointing based on a provided time interval during training (#7515)
• Progress tracking
  • Added dataclasses for progress tracking (#6603, #7574, #8140, #8362)
  • Add {,load_}state_dict to the progress tracking dataclasses (#8140)
  • Connect the progress tracking dataclasses to the loops (#8244, #8362)
  • Do not reset the progress tracking dataclasses total counters (#8475)
• Added support for passing a LightningDataModule positionally as the second argument to trainer.{validate,test,predict} (#7431)
• Added argument trainer.predict(ckpt_path) (#7430)
• Added clip_grad_by_value support for TPUs (#7025)
• Added support for passing any class to is_overridden (#7918)
• Added sub_dir parameter to TensorBoardLogger (#6195)
• Added correct dataloader_idx to batch transfer hooks (#6241)
• Added include_none=bool argument to apply_to_collection (#7769)
• Added apply_to_collections to apply a function to two zipped collections (#7769)
• Added ddp_fully_sharded support (#7487)
• Added should_rank_save_checkpoint property to Training Plugins (#7684)
• Added log_grad_norm hook to LightningModule to customize the logging of gradient norms (#7873)
• Added save_config_filename init argument to LightningCLI to ease resolving name conflicts (#7741)
• Added save_config_overwrite init argument to LightningCLI to ease overwriting existing config files (#8059)
• Added reset dataloader hooks to Training Plugins and Accelerators (#7861)
• Added trainer stage hooks for Training Plugins and Accelerators (#7864)
• Added the on_before_optimizer_step hook (#8048)
• Added IPU Accelerator (#7867)
• Fault-tolerant training
  • Added {,load_}state_dict to ResultCollection (#7948)
  • Added {,load_}state_dict to Loops (#8197)
  • 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)

... (truncated)

Changelog

Sourced from pytorch-lightning's changelog.

[1.4.0] - 2021-07-27

Added

• Added extract_batch_size utility and corresponding tests to extract batch dimension from multiple batch types. (#8357)
• Added support for named parameter groups in LearningRateMonitor (#7987)
• Added dataclass support for pytorch_lightning.utilities.apply_to_collection (#7935)
• Added support to LightningModule.to_torchscript for saving to custom filesystems with fsspec (#7617)
• Added KubeflowEnvironment for use with the PyTorchJob operator in Kubeflow
• Added LightningCLI support for config files on object stores (#7521)
• Added ModelPruning(prune_on_train_epoch_end=True|False) to choose when to apply pruning (#7704)
• Added support for checkpointing based on a provided time interval during training (#7515)
• Progress tracking
  • Added dataclasses for progress tracking (#6603, #7574, #8140, #8362)
  • Add {,load_}state_dict to the progress tracking dataclasses (#8140)
  • Connect the progress tracking dataclasses to the loops (#8244, #8362)
  • Do not reset the progress tracking dataclasses total counters (#8475)
• Added support for passing a LightningDataModule positionally as the second argument to trainer.{validate,test,predict} (#7431)
• Added argument trainer.predict(ckpt_path) (#7430)
• Added clip_grad_by_value support for TPUs (#7025)
• Added support for passing any class to is_overridden (#7918)
• Added sub_dir parameter to TensorBoardLogger (#6195)
• Added correct dataloader_idx to batch transfer hooks (#6241)
• Added include_none=bool argument to apply_to_collection (#7769)
• Added apply_to_collections to apply a function to two zipped collections (#7769)
• Added ddp_fully_sharded support (#7487)
• Added should_rank_save_checkpoint property to Training Plugins (#7684)
• Added log_grad_norm hook to LightningModule to customize the logging of gradient norms (#7873)
• Added save_config_filename init argument to LightningCLI to ease resolving name conflicts (#7741)
• Added save_config_overwrite init argument to LightningCLI to ease overwriting existing config files (#8059)
• Added reset dataloader hooks to Training Plugins and Accelerators (#7861)
• Added trainer stage hooks for Training Plugins and Accelerators (#7864)
• Added the on_before_optimizer_step hook (#8048)
• Added IPU Accelerator (#7867)
• Fault-tolerant training
  • Added {,load_}state_dict to ResultCollection (#7948)
  • Added {,load_}state_dict to Loops (#8197)
  • 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)

... (truncated)

Commits

• c7f8c8c [bugfix] DeepSpeed with no schedulers (#8580)
• 39de7fe Lightning Release v1.4 (#8579)
• c7e5743 Update cloud docs (#8569)
• 4b7f78e Add deprecation warning & test for distributed_backend flag (#8575)
• 4605e8a Add missing highlighting for Python snippets (#8411)
• d90cb7f Bugfix: Scheduler monitor for manual optimization (#7643)
• eaa16c7 docs: explain how Lightning uses closures for automatic optimization (#8551)
• 75e18a5 v1.4.0rc2 (#8553)
• f457f97 Raise exception for ddp_cpu not supported for TPUs (#8530)
• c5167e9 Fix profiler test on Windows minimal (#8556)
• Additional commits viewable in compare view

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[LoicGrobol]

@dependabot rebase

[LoicGrobol]

@dependabot rebase

[LoicGrobol]

@dependabot rebase

For real this time please