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|
%global _empty_manifest_terminate_build 0
Name: python-hydra-core
Version: 1.3.2
Release: 1
Summary: A framework for elegantly configuring complex applications
License: MIT
URL: https://github.com/facebookresearch/hydra
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/6d/8e/07e42bc434a847154083b315779b0a81d567154504624e181caf2c71cd98/hydra-core-1.3.2.tar.gz
BuildArch: noarch
Requires: python3-omegaconf
Requires: python3-antlr4-python3-runtime
Requires: python3-packaging
Requires: python3-importlib-resources
%description
### 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}
}
```
%package -n python3-hydra-core
Summary: A framework for elegantly configuring complex applications
Provides: python-hydra-core
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-hydra-core
### 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}
}
```
%package help
Summary: Development documents and examples for hydra-core
Provides: python3-hydra-core-doc
%description help
### 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}
}
```
%prep
%autosetup -n hydra-core-1.3.2
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-hydra-core -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.3.2-1
- Package Spec generated
|
