PyTorch Lightning
| PyTorch Lightning | |
|---|---|
 | |
| Original author(s) | William Falcon |
| Developer(s) | various |
| Initial release | 2019-05-31 |
| Repository | https://github.com/PyTorchLightning/pytorch-lightning |
| Platform | Cross-platform |
| License | Apache 2.0 |
| Website | https://www.pytorchlightning.ai/ |
PyTorch Lightning is an open-source Python library that provides a high-level interface for PyTorch, a popular deep learning framework.[1] It is a lightweight and high-performance framework that organizes PyTorch code to decouple the research from the engineering, making deep learning experiments easier to read and reproduce. It is designed to create scalable deep learning models that can easily run on distributed hardware while keeping the models hardware agnostic. PyTorch Lightning provides the ultimate flexibility for any type of research, and reduces the engineering boilerplate required to implement state-of-the-art AI features.[promotion?]
In 2019, Lightning was adopted by the NeurIPS Reproducibility Challenge as a standard for submitting PyTorch code to the conference.[2]
References
- ^ "GitHub - PyTorch Lightning". 2019-12-01.
{{cite web}}: CS1 maint: url-status (link) - ^ "Reproducibility Challenge @NeurIPS 2019". NeurIPS. 2019-12-01. Retrieved 2019-12-01.
{{cite web}}: CS1 maint: url-status (link)
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