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https://github.com/facebookresearch/hydra
Hydra is a framework for elegantly configuring complex applications
https://github.com/facebookresearch/hydra
Last synced: 11 days ago
JSON representation
Hydra is a framework for elegantly configuring complex applications
- Host: GitHub
- URL: https://github.com/facebookresearch/hydra
- Owner: facebookresearch
- License: mit
- Created: 2019-06-12T19:33:15.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2024-10-24T21:38:26.000Z (15 days ago)
- Last Synced: 2024-10-25T02:08:00.718Z (15 days ago)
- Language: Python
- Homepage: https://hydra.cc
- Size: 271 MB
- Stars: 8,745
- Watchers: 129
- Forks: 628
- Open Issues: 295
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
A framework for elegantly configuring complex applications.
Check the website for more information,
or click the thumbnail below for a one-minute video introduction to Hydra.
----------------------
### Releases
#### Stable
**Hydra 1.3** is the stable version of Hydra.
- [Documentation](https://hydra.cc/docs/1.3/intro/)
- Installation : `pip install hydra-core --upgrade`See the [NEWS.md](NEWS.md) file for a summary of recent changes to Hydra.
### License
Hydra is licensed under [MIT License](LICENSE).## Hydra Ecosystem
#### Check out these third-party libraries that build on Hydra's functionality:
* [hydra-zen](https://github.com/mit-ll-responsible-ai/hydra-zen): Pythonic utilities for working with Hydra. Dynamic config generation capabilities, enhanced config store features, a Python API for launching Hydra jobs, and more.
* [lightning-hydra-template](https://github.com/ashleve/lightning-hydra-template): user-friendly template combining Hydra with [Pytorch-Lightning](https://github.com/Lightning-AI/lightning) for ML experimentation.
* [hydra-torch](https://github.com/pytorch/hydra-torch): [configen](https://github.com/facebookresearch/hydra/tree/main/tools/configen)-generated configuration classes enabling type-safe PyTorch configuration for Hydra apps.
* NVIDIA's DeepLearningExamples repository contains a Hydra Launcher plugin, the [distributed_launcher](https://github.com/NVIDIA/DeepLearningExamples/tree/9c34e35c218514b8607d7cf381d8a982a01175e9/Tools/PyTorch/TimeSeriesPredictionPlatform/distributed_launcher), which makes use of the pytorch [distributed.launch](https://pytorch.org/docs/stable/distributed.html#launch-utility) API.#### Ask questions in Github Discussions or StackOverflow (Use the tag #fb-hydra or #omegaconf):
* [Github Discussions](https://github.com/facebookresearch/hydra/discussions)
* [StackOverflow](https://stackexchange.com/filters/391828/hydra-questions)
* [Twitter](https://twitter.com/Hydra_Framework)Check out the Meta AI [blog post](https://ai.facebook.com/blog/reengineering-facebook-ais-deep-learning-platforms-for-interoperability/) to learn about how Hydra fits into Meta's efforts to reengineer deep learning platforms for interoperability.
### Citing Hydra
If you use Hydra in your research please use the following BibTeX entry:
```BibTeX
@Misc{Yadan2019Hydra,
author = {Omry Yadan},
title = {Hydra - A framework for elegantly configuring complex applications},
howpublished = {Github},
year = {2019},
url = {https://github.com/facebookresearch/hydra}
}
```