pythonfix
lightning 2.6.1
The Deep Learning framework to train, deploy, and ship AI products Lightning fast.
The Deep Learning framework to train, deploy, and ship AI products Lightning fast.
Stars: 30847, Watchers: 30847, Forks: 3670, Open Issues: 951The Lightning-AI/pytorch-lightning repo was created 6 years ago and the last code push was Yesterday.
The project is extremely popular with a mindblowing 30847 github stars!
How to Install lightning
You can install lightning using pip
pip install lightning
or add it to a project with poetry
poetry add lightning
Package Details
- Author
- Lightning AI et al.
- License
- Apache-2.0
- Homepage
- https://github.com/Lightning-AI/lightning
- PyPi:
- https://pypi.org/project/lightning/
- Documentation:
- https://lightning.ai/lightning-docs
- GitHub Repo:
- https://github.com/Lightning-AI/lightning
Classifiers
- Scientific/Engineering/Artificial Intelligence
- Scientific/Engineering/Information Analysis
Related Packages
Errors
A list of common lightning errors.
Code Examples
Here are some lightning code examples and snippets.
GitHub Issues
The lightning package has 951 open issues on GitHub
- torch.compile doesn't work with self.toggle_optimizer() in lightning module
- build(deps): update setuptools requirement from <80.9.1 to <80.10.3 in /requirements
- build(deps): update torchvision requirement from <0.25.0,>=0.16.0 to >=0.16.0,<0.26.0 in /requirements
- build(deps): update wheel requirement from <0.46.0 to <0.47.0 in /requirements
- build(deps): update pandas requirement from <2.4.0,>2.0 to >2.0,<3.1.0 in /requirements
- build(deps): bump coverage from 7.13.1 to 7.13.2 in /requirements
- log_dict: support MetricCollection containing ClasswiseWrapper
- feat(logger): add SwanLabLogger for enhanced logging capabilities
- DO NOT MERGE: trigger install-pkg CI
- Coerce tensorboard metrics into numpy arrays
- Tensorboard logging breaks with certain scalar values with numpy >= 2.4.0
- CI: fix doctest failure from PyTorch LeafSpec FutureWarning
- chore(tests): update PyTorch versions in CI workflows to include 2.10
- Add batch interval support for learning rate schedulers
- [Optimization] Use fs.pipe() or fs.put() instead of f.write() in atomic_save for faster cloud checkpointing
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