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Lightning-AI / lightning-Habana

Lightning support for Intel Habana accelerators.
Apache License 2.0
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build(deps): bump pytorch-lightning from 2.3.3 to 2.4.0 #224

Closed dependabot[bot] closed 3 months ago

dependabot[bot] commented 3 months ago

Bumps pytorch-lightning from 2.3.3 to 2.4.0.

Release notes

Sourced from pytorch-lightning's releases.

Lightning v2.4

Lightning AI :zap: is excited to announce the release of Lightning 2.4. This is mainly a compatibility upgrade for PyTorch 2.4 and Python 3.12, with a sprinkle of a few features and bug fixes.

Did you know? The Lightning philosophy extends beyond a boilerplate-free deep learning framework: We've been hard at work bringing you Lightning Studio. Code together, prototype, train, deploy, host AI web apps. All from your browser, with zero setup.

Changes

PyTorch Lightning

  • Made saving non-distributed checkpoints fully atomic (#20011)
  • Added dump_stats flag to AdvancedProfiler (#19703)
  • Added a flag verbose to the seed_everything() function (#20108)
  • Added support for PyTorch 2.4 (#20010)
  • Added support for Python 3.12 (20078)
  • The TQDMProgressBar now provides an option to retain prior training epoch bars (#19578)
  • Added the count of modules in train and eval mode to the printed ModelSummary table (#20159)
  • Triggering KeyboardInterrupt (Ctrl+C) during .fit(), .evaluate(), .test() or .predict() now terminates all processes launched by the Trainer and exits the program (#19976)
  • Changed the implementation of how seeds are chosen for dataloader workers when using seed_everything(..., workers=True) (#20055)
  • NumPy is no longer a required dependency (#20090)
  • Removed support for PyTorch 2.1 (#20009)
  • Removed support for Python 3.8 (#20071)
  • Avoid LightningCLI saving hyperparameters with class_path and init_args since this would be a breaking change (#20068)
  • Fixed an issue that would cause too many printouts of the seed info when using seed_everything() (#20108)
  • Fixed _LoggerConnector's _ResultMetric to move all registered keys to the device of the logged value if needed (#19814)
  • Fixed _optimizer_to_device logic for special 'step' key in optimizer state causing performance regression (#20019)
  • Fixed parameter counts in ModelSummary when model has distributed parameters (DTensor) (#20163)

Lightning Fabric

... (truncated)

Commits


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