pythonfix
pytorch-lightning 2.4.0
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: 28156, Watchers: 28156, Forks: 3372, Open Issues: 852The Lightning-AI/pytorch-lightning repo was created 5 years ago and the last code push was 3 days ago.
The project is extremely popular with a mindblowing 28156 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 852 open issues on GitHub
- Pin
coverage<6.3 - GPU testing is temporarily unavailable
test_signal_handlers_restored_in_teardownfailing on mac and linux- CombinedLoader for training data does not work in DDP
- Update requirements.txt for
pyDeprecateversion flexibility - Deprecate
on_configure_sharded_modelcallback hook for v1.6 - [Feature Request] Simple method to display image batch
- Deprecate trainer.num_processe/trainer.num_gpus and remove incorrect tests
- Add eager mode PTQ callback
- Default config file fails to initialize module.
- Move data fetcher ownership to the loops
- Teardown all internal components on exception
- Change pyDeprecate version from 0.3.1 to 0.3.2.
- LightningModule.save_hyperparameters leaks parameters of surrounding classes into model hparams
- Improving Hydra+DDP support
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