%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 - 1.3.2-1 - Package Spec generated