Update pytorch-lightning requirement from <2.3.0,>=2.0.0 to >=2.0.0,<2.5.0

[dependabot]

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

Release notes

Sourced from pytorch-lightning's releases.

Lightning v2.4

Lightning AI ⚡ 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

• 2129fdf fix(ci): resolve input str -> num conversion (#20169)
• cf24a19 fix(docs): remove dead link from readme (#20170)
• a3e60ad ci/docs: disable optional cache pkg (#20168)
• 87ffd8c ci: fix cleaning caches (#20167)
• b3ee85d Prepare Lightning 2.4.0 release (#20154)
• 631911c Add special logic for 'step' in _optimizer_to_device (#20019)
• 345450b Fix parameter count in ModelSummary when parameters are DTensors (#20163)
• 3de60f4 docs: fix typo in linkcheck_ignore (#20164)
• e9d4ef8 Add diffusion example to README (#20161)
• d4de8e2 Count number of modules in train/eval mode in ModelSummary (#20159)
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📚 Documentation preview 📚: https://pytorch-tabular--476.org.readthedocs.build/en/476/