pythonfix
pytorch-lightning 2.6.1
PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write le
PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate.
Stars: 30845, Watchers: 30845, 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 30845 github stars!
How to Install pytorch-lightning
You can install pytorch-lightning using pip
pip install pytorch-lightning
or add it to a project with poetry
poetry add pytorch-lightning
Package Details
- Author
- Lightning AI et al.
- License
- Apache-2.0
- Homepage
- https://github.com/Lightning-AI/lightning
- PyPi:
- https://pypi.org/project/pytorch-lightning/
- Documentation:
- https://pytorch-lightning.rtfd.io/en/latest/
- GitHub Repo:
- https://github.com/PyTorchLightning/pytorch-lightning
Classifiers
- Scientific/Engineering/Artificial Intelligence
- Scientific/Engineering/Image Recognition
- Scientific/Engineering/Information Analysis
Related Packages
Errors
A list of common pytorch-lightning errors.
Code Examples
Here are some pytorch-lightning code examples and snippets.
GitHub Issues
The pytorch-lightning package has 951 open issues on GitHub
- 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
- build(deps): update sphinx requirement from <6.0,>5.0 to >5.0,<10.0 in /requirements
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