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%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
|
