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@@ -0,0 +1 @@ +/tensorlayer-2.2.5.tar.gz diff --git a/python-tensorlayer.spec b/python-tensorlayer.spec new file mode 100644 index 0000000..5ba7eeb --- /dev/null +++ b/python-tensorlayer.spec @@ -0,0 +1,96 @@ +%global _empty_manifest_terminate_build 0 +Name: python-tensorlayer +Version: 2.2.5 +Release: 1 +Summary: High Level Tensorflow Deep Learning Library for Researcher and Engineer. +License: apache +URL: https://github.com/tensorlayer/tensorlayer +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/44/9e/2806af7a4c4e6948342247444e8341df20eee806d98a68b1f1274faf5cb0/tensorlayer-2.2.5.tar.gz +BuildArch: noarch + + +%description +TensorLayer is a new deep learning library designed with simplicity, flexibility and high-performance in mind. +- **Simplicity** : TensorLayer has a high-level layer/model abstraction which is effortless to learn. You can learn how deep learning can benefit your AI tasks in minutes through the massive [examples](https://github.com/tensorlayer/awesome-tensorlayer). +- **Flexibility** : TensorLayer APIs are transparent and flexible, inspired by the emerging PyTorch library. Compared to the Keras abstraction, TensorLayer makes it much easier to build and train complex AI models. +- **Zero-cost Abstraction** : Though simple to use, TensorLayer does not require you to make any compromise in the performance of TensorFlow (Check the following benchmark section for more details). +TensorLayer stands at a unique spot in the TensorFlow wrappers. Other wrappers like Keras and TFLearn +hide many powerful features of TensorFlow and provide little support for writing custom AI models. Inspired by PyTorch, TensorLayer APIs are simple, flexible and Pythonic, +making it easy to learn while being flexible enough to cope with complex AI tasks. +TensorLayer has a fast-growing community. It has been used by researchers and engineers all over the world, including those from Peking University, +Imperial College London, UC Berkeley, Carnegie Mellon University, Stanford University, and companies like Google, Microsoft, Alibaba, Tencent, Xiaomi, and Bloomberg. + +%package -n python3-tensorlayer +Summary: High Level Tensorflow Deep Learning Library for Researcher and Engineer. +Provides: python-tensorlayer +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-tensorlayer +TensorLayer is a new deep learning library designed with simplicity, flexibility and high-performance in mind. +- **Simplicity** : TensorLayer has a high-level layer/model abstraction which is effortless to learn. You can learn how deep learning can benefit your AI tasks in minutes through the massive [examples](https://github.com/tensorlayer/awesome-tensorlayer). +- **Flexibility** : TensorLayer APIs are transparent and flexible, inspired by the emerging PyTorch library. Compared to the Keras abstraction, TensorLayer makes it much easier to build and train complex AI models. +- **Zero-cost Abstraction** : Though simple to use, TensorLayer does not require you to make any compromise in the performance of TensorFlow (Check the following benchmark section for more details). +TensorLayer stands at a unique spot in the TensorFlow wrappers. Other wrappers like Keras and TFLearn +hide many powerful features of TensorFlow and provide little support for writing custom AI models. Inspired by PyTorch, TensorLayer APIs are simple, flexible and Pythonic, +making it easy to learn while being flexible enough to cope with complex AI tasks. +TensorLayer has a fast-growing community. It has been used by researchers and engineers all over the world, including those from Peking University, +Imperial College London, UC Berkeley, Carnegie Mellon University, Stanford University, and companies like Google, Microsoft, Alibaba, Tencent, Xiaomi, and Bloomberg. + +%package help +Summary: Development documents and examples for tensorlayer +Provides: python3-tensorlayer-doc +%description help +TensorLayer is a new deep learning library designed with simplicity, flexibility and high-performance in mind. +- **Simplicity** : TensorLayer has a high-level layer/model abstraction which is effortless to learn. You can learn how deep learning can benefit your AI tasks in minutes through the massive [examples](https://github.com/tensorlayer/awesome-tensorlayer). +- **Flexibility** : TensorLayer APIs are transparent and flexible, inspired by the emerging PyTorch library. Compared to the Keras abstraction, TensorLayer makes it much easier to build and train complex AI models. +- **Zero-cost Abstraction** : Though simple to use, TensorLayer does not require you to make any compromise in the performance of TensorFlow (Check the following benchmark section for more details). +TensorLayer stands at a unique spot in the TensorFlow wrappers. Other wrappers like Keras and TFLearn +hide many powerful features of TensorFlow and provide little support for writing custom AI models. Inspired by PyTorch, TensorLayer APIs are simple, flexible and Pythonic, +making it easy to learn while being flexible enough to cope with complex AI tasks. +TensorLayer has a fast-growing community. It has been used by researchers and engineers all over the world, including those from Peking University, +Imperial College London, UC Berkeley, Carnegie Mellon University, Stanford University, and companies like Google, Microsoft, Alibaba, Tencent, Xiaomi, and Bloomberg. + +%prep +%autosetup -n tensorlayer-2.2.5 + +%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-tensorlayer -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 2.2.5-1 +- Package Spec generated @@ -0,0 +1 @@ +179e0fef52ab04a45c6ffbc11198a73a tensorlayer-2.2.5.tar.gz |