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https://github.com/facebookresearch/hydra

Hydra is a framework for elegantly configuring complex applications
https://github.com/facebookresearch/hydra

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Hydra is a framework for elegantly configuring complex applications

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    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.

  


  


   

     1 minute overview

   

  



----------------------



### 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}

}

```