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<p align="center">
<i>A framework for elegantly configuring complex applications.</i>
</p>
<p align="center">
<i>Check the <a href="https://hydra.cc/">website</a> for more information,<br>
or click the thumbnail below for a one-minute video introduction to Hydra.</i>
</p>
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<a href="http://www.youtube.com/watch?feature=player_embedded&v=Slc3gRQpnBI" target="_blank">
<img src="http://img.youtube.com/vi/Slc3gRQpnBI/hqdefault.jpg" alt="1 minute overview" width="240" height="180" border="10" />
</a>
</p>
</p>
----------------------
### 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}
}
```
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