[tune](deps): Bump pytorch-lightning from 1.0.3 to 1.4.1 in /python/requirements/tune

[dependabot]

Bumps pytorch-lightning from 1.0.3 to 1.4.1.

Release notes

Sourced from pytorch-lightning's releases.

Standard weekly patch release

[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)

Contributors

@​awaelchli, @​borda, @​carmocca, @​kaushikb11, @​tchaton

If we forgot someone due to not matching commit email with GitHub account, let us know :]

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)

... (truncated)

Changelog

Sourced from pytorch-lightning's changelog.

[1.4.1] - 2021-08-03

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

[1.4.0] - 2021-07-27

Added

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

... (truncated)

Commits

• e6b0277 release commit
• f0c8959 CI: validate JSON & fix benchmark (#8567)
• 7522e4b bump version 1.3 to 1.4 in README (#8582)
• 7239a42 Fix ddp accelerator choice for cpu (#8645)
• 7374bc8 Torch Elastic DDP DeadLock bug fix (#8655)
• 7c042f3 black: magic trailing comma (#8560)
• cee7043 Fix distributed types support for CPUs (#8667)
• c4500f7 update (#8674)
• f4d0279 fix collecting training_step outputs (#8613)
• 4f12334 Fix reference issues during epoch end result collection (#8621)
• Additional commits viewable in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

---

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

• @dependabot rebase will rebase this PR
• @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
• @dependabot merge will merge this PR after your CI passes on it
• @dependabot squash and merge will squash and merge this PR after your CI passes on it
• @dependabot cancel merge will cancel a previously requested merge and block automerging
• @dependabot reopen will reopen this PR if it is closed
• @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
• @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)

[dependabot]

Superseded by #47.