automatic import of python-tensorlayeropeneuler20.03

3 files changed, 98 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..6b0ca2b 100644
--- a/.gitignore
+++ b/.gitignore
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+/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
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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
diff --git a/sources b/sources
new file mode 100644
index 0000000..9e5f17f
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+179e0fef52ab04a45c6ffbc11198a73a  tensorlayer-2.2.5.tar.gz