automatic import of python-tensorflowonspark

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diff --git a/.gitignore b/.gitignore
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+/tensorflowonspark-2.2.5.tar.gz
diff --git a/python-tensorflowonspark.spec b/python-tensorflowonspark.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-tensorflowonspark
+Version:	2.2.5
+Release:	1
+Summary:	Deep learning with TensorFlow on Apache Spark clusters
+License:	Apache 2.0
+URL:		https://github.com/yahoo/TensorFlowOnSpark
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/95/e3/e75b54b6e5d77b8a7dff55908655b5684d7b48cc04e7e66f359a37fb3202/tensorflowonspark-2.2.5.tar.gz
+BuildArch:	noarch
+
+
+%description
+<!--
+Copyright 2019 Yahoo Inc.
+Licensed under the terms of the Apache 2.0 license.
+Please see LICENSE file in the project root for terms.
+-->
+# TensorFlowOnSpark
+> _TensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark
+clusters._
+
+[![Build Status](https://cd.screwdriver.cd/pipelines/6384/badge)](https://cd.screwdriver.cd/pipelines/6384)
+[![Package](https://img.shields.io/badge/package-pypi-blue.svg)](https://pypi.org/project/tensorflowonspark/)
+[![Downloads](https://img.shields.io/pypi/dm/tensorflowonspark.svg)](https://img.shields.io/pypi/dm/tensorflowonspark.svg)
+[![Documentation](https://img.shields.io/badge/Documentation-latest-blue.svg)](https://yahoo.github.io/TensorFlowOnSpark/)
+
+By combining salient features from the [TensorFlow](https://www.tensorflow.org) deep learning framework with [Apache Spark](http://spark.apache.org) and [Apache Hadoop](http://hadoop.apache.org), TensorFlowOnSpark enables distributed
+deep learning on a cluster of GPU and CPU servers.
+
+It enables both distributed TensorFlow training and
+inferencing on Spark clusters, with a goal to minimize the amount
+of code changes required to run existing TensorFlow programs on a
+shared grid.  Its Spark-compatible API helps manage the TensorFlow
+cluster with the following steps:
+
+1. **Startup** - launches the Tensorflow main function on the executors, along with listeners for data/control messages.
+1. **Data ingestion**
+   - **InputMode.TENSORFLOW** - leverages TensorFlow's built-in APIs to read data files directly from HDFS.
+   - **InputMode.SPARK** - sends Spark RDD data to the TensorFlow nodes via a `TFNode.DataFeed` class.  Note that we leverage the [Hadoop Input/Output Format](https://github.com/tensorflow/ecosystem/tree/master/hadoop) to access TFRecords on HDFS.
+1. **Shutdown** - shuts down the Tensorflow workers and PS nodes on the executors.
+
+## Table of Contents
+
+- [Background](#background)
+- [Install](#install)
+- [Usage](#usage)
+- [API](#api)
+- [Contribute](#contribute)
+- [License](#license)
+
+## Background
+
+TensorFlowOnSpark was developed by Yahoo for large-scale distributed
+deep learning on our Hadoop clusters in Yahoo's private cloud.
+
+TensorFlowOnSpark provides some important benefits (see [our
+blog](https://developer.yahoo.com/blogs/157196317141/))
+over alternative deep learning solutions.
+   * Easily migrate existing TensorFlow programs with <10 lines of code change.
+   * Support all TensorFlow functionalities: synchronous/asynchronous training, model/data parallelism, inferencing and TensorBoard.
+   * Server-to-server direct communication achieves faster learning when available.
+   * Allow datasets on HDFS and other sources pushed by Spark or pulled by TensorFlow.
+   * Easily integrate with your existing Spark data processing pipelines.
+   * Easily deployed on cloud or on-premise and on CPUs or GPUs.
+
+## Install
+
+TensorFlowOnSpark is provided as a pip package, which can be installed on single machines via:
+```
+# for tensorflow>=2.0.0
+pip install tensorflowonspark
+
+# for tensorflow<2.0.0
+pip install tensorflowonspark==1.4.4
+```
+
+For distributed clusters, please see our [wiki site](../../wiki) for detailed documentation for specific environments, such as our getting started guides for [single-node Spark Standalone](https://github.com/yahoo/TensorFlowOnSpark/wiki/GetStarted_Standalone), [YARN clusters](../../wiki/GetStarted_YARN) and [AWS EC2](../../wiki/GetStarted_EC2).  Note: the Windows operating system is not currently supported due to [this issue](https://github.com/yahoo/TensorFlowOnSpark/issues/36).
+
+## Usage
+
+To use TensorFlowOnSpark with an existing TensorFlow application, you can follow our [Conversion Guide](../../wiki/Conversion-Guide) to describe the required changes.  Additionally, our [wiki site](../../wiki) has pointers to some presentations which provide an overview of the platform.
+
+**Note: since TensorFlow 2.x breaks API compatibility with TensorFlow 1.x, the examples have been updated accordingly.  If you are using TensorFlow 1.x, you will need to checkout the `v1.4.4` tag for compatible examples and instructions.**
+
+## API
+
+[API Documentation](https://yahoo.github.io/TensorFlowOnSpark/) is automatically generated from the code.
+
+## Contribute
+
+Please join the [TensorFlowOnSpark user group](https://groups.google.com/forum/#!forum/TensorFlowOnSpark-users) for discussions and questions.  If you have a question, please review our [FAQ](../../wiki/Frequently-Asked-Questions) before posting.
+
+Contributions are always welcome.  For more information, please see our [guide for getting involved](Contributing.md).
+
+## License
+
+The use and distribution terms for this software are covered by the Apache 2.0 license.
+See [LICENSE](LICENSE) file for terms.
+
+
+
+
+%package -n python3-tensorflowonspark
+Summary:	Deep learning with TensorFlow on Apache Spark clusters
+Provides:	python-tensorflowonspark
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-tensorflowonspark
+<!--
+Copyright 2019 Yahoo Inc.
+Licensed under the terms of the Apache 2.0 license.
+Please see LICENSE file in the project root for terms.
+-->
+# TensorFlowOnSpark
+> _TensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark
+clusters._
+
+[![Build Status](https://cd.screwdriver.cd/pipelines/6384/badge)](https://cd.screwdriver.cd/pipelines/6384)
+[![Package](https://img.shields.io/badge/package-pypi-blue.svg)](https://pypi.org/project/tensorflowonspark/)
+[![Downloads](https://img.shields.io/pypi/dm/tensorflowonspark.svg)](https://img.shields.io/pypi/dm/tensorflowonspark.svg)
+[![Documentation](https://img.shields.io/badge/Documentation-latest-blue.svg)](https://yahoo.github.io/TensorFlowOnSpark/)
+
+By combining salient features from the [TensorFlow](https://www.tensorflow.org) deep learning framework with [Apache Spark](http://spark.apache.org) and [Apache Hadoop](http://hadoop.apache.org), TensorFlowOnSpark enables distributed
+deep learning on a cluster of GPU and CPU servers.
+
+It enables both distributed TensorFlow training and
+inferencing on Spark clusters, with a goal to minimize the amount
+of code changes required to run existing TensorFlow programs on a
+shared grid.  Its Spark-compatible API helps manage the TensorFlow
+cluster with the following steps:
+
+1. **Startup** - launches the Tensorflow main function on the executors, along with listeners for data/control messages.
+1. **Data ingestion**
+   - **InputMode.TENSORFLOW** - leverages TensorFlow's built-in APIs to read data files directly from HDFS.
+   - **InputMode.SPARK** - sends Spark RDD data to the TensorFlow nodes via a `TFNode.DataFeed` class.  Note that we leverage the [Hadoop Input/Output Format](https://github.com/tensorflow/ecosystem/tree/master/hadoop) to access TFRecords on HDFS.
+1. **Shutdown** - shuts down the Tensorflow workers and PS nodes on the executors.
+
+## Table of Contents
+
+- [Background](#background)
+- [Install](#install)
+- [Usage](#usage)
+- [API](#api)
+- [Contribute](#contribute)
+- [License](#license)
+
+## Background
+
+TensorFlowOnSpark was developed by Yahoo for large-scale distributed
+deep learning on our Hadoop clusters in Yahoo's private cloud.
+
+TensorFlowOnSpark provides some important benefits (see [our
+blog](https://developer.yahoo.com/blogs/157196317141/))
+over alternative deep learning solutions.
+   * Easily migrate existing TensorFlow programs with <10 lines of code change.
+   * Support all TensorFlow functionalities: synchronous/asynchronous training, model/data parallelism, inferencing and TensorBoard.
+   * Server-to-server direct communication achieves faster learning when available.
+   * Allow datasets on HDFS and other sources pushed by Spark or pulled by TensorFlow.
+   * Easily integrate with your existing Spark data processing pipelines.
+   * Easily deployed on cloud or on-premise and on CPUs or GPUs.
+
+## Install
+
+TensorFlowOnSpark is provided as a pip package, which can be installed on single machines via:
+```
+# for tensorflow>=2.0.0
+pip install tensorflowonspark
+
+# for tensorflow<2.0.0
+pip install tensorflowonspark==1.4.4
+```
+
+For distributed clusters, please see our [wiki site](../../wiki) for detailed documentation for specific environments, such as our getting started guides for [single-node Spark Standalone](https://github.com/yahoo/TensorFlowOnSpark/wiki/GetStarted_Standalone), [YARN clusters](../../wiki/GetStarted_YARN) and [AWS EC2](../../wiki/GetStarted_EC2).  Note: the Windows operating system is not currently supported due to [this issue](https://github.com/yahoo/TensorFlowOnSpark/issues/36).
+
+## Usage
+
+To use TensorFlowOnSpark with an existing TensorFlow application, you can follow our [Conversion Guide](../../wiki/Conversion-Guide) to describe the required changes.  Additionally, our [wiki site](../../wiki) has pointers to some presentations which provide an overview of the platform.
+
+**Note: since TensorFlow 2.x breaks API compatibility with TensorFlow 1.x, the examples have been updated accordingly.  If you are using TensorFlow 1.x, you will need to checkout the `v1.4.4` tag for compatible examples and instructions.**
+
+## API
+
+[API Documentation](https://yahoo.github.io/TensorFlowOnSpark/) is automatically generated from the code.
+
+## Contribute
+
+Please join the [TensorFlowOnSpark user group](https://groups.google.com/forum/#!forum/TensorFlowOnSpark-users) for discussions and questions.  If you have a question, please review our [FAQ](../../wiki/Frequently-Asked-Questions) before posting.
+
+Contributions are always welcome.  For more information, please see our [guide for getting involved](Contributing.md).
+
+## License
+
+The use and distribution terms for this software are covered by the Apache 2.0 license.
+See [LICENSE](LICENSE) file for terms.
+
+
+
+
+%package help
+Summary:	Development documents and examples for tensorflowonspark
+Provides:	python3-tensorflowonspark-doc
+%description help
+<!--
+Copyright 2019 Yahoo Inc.
+Licensed under the terms of the Apache 2.0 license.
+Please see LICENSE file in the project root for terms.
+-->
+# TensorFlowOnSpark
+> _TensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark
+clusters._
+
+[![Build Status](https://cd.screwdriver.cd/pipelines/6384/badge)](https://cd.screwdriver.cd/pipelines/6384)
+[![Package](https://img.shields.io/badge/package-pypi-blue.svg)](https://pypi.org/project/tensorflowonspark/)
+[![Downloads](https://img.shields.io/pypi/dm/tensorflowonspark.svg)](https://img.shields.io/pypi/dm/tensorflowonspark.svg)
+[![Documentation](https://img.shields.io/badge/Documentation-latest-blue.svg)](https://yahoo.github.io/TensorFlowOnSpark/)
+
+By combining salient features from the [TensorFlow](https://www.tensorflow.org) deep learning framework with [Apache Spark](http://spark.apache.org) and [Apache Hadoop](http://hadoop.apache.org), TensorFlowOnSpark enables distributed
+deep learning on a cluster of GPU and CPU servers.
+
+It enables both distributed TensorFlow training and
+inferencing on Spark clusters, with a goal to minimize the amount
+of code changes required to run existing TensorFlow programs on a
+shared grid.  Its Spark-compatible API helps manage the TensorFlow
+cluster with the following steps:
+
+1. **Startup** - launches the Tensorflow main function on the executors, along with listeners for data/control messages.
+1. **Data ingestion**
+   - **InputMode.TENSORFLOW** - leverages TensorFlow's built-in APIs to read data files directly from HDFS.
+   - **InputMode.SPARK** - sends Spark RDD data to the TensorFlow nodes via a `TFNode.DataFeed` class.  Note that we leverage the [Hadoop Input/Output Format](https://github.com/tensorflow/ecosystem/tree/master/hadoop) to access TFRecords on HDFS.
+1. **Shutdown** - shuts down the Tensorflow workers and PS nodes on the executors.
+
+## Table of Contents
+
+- [Background](#background)
+- [Install](#install)
+- [Usage](#usage)
+- [API](#api)
+- [Contribute](#contribute)
+- [License](#license)
+
+## Background
+
+TensorFlowOnSpark was developed by Yahoo for large-scale distributed
+deep learning on our Hadoop clusters in Yahoo's private cloud.
+
+TensorFlowOnSpark provides some important benefits (see [our
+blog](https://developer.yahoo.com/blogs/157196317141/))
+over alternative deep learning solutions.
+   * Easily migrate existing TensorFlow programs with <10 lines of code change.
+   * Support all TensorFlow functionalities: synchronous/asynchronous training, model/data parallelism, inferencing and TensorBoard.
+   * Server-to-server direct communication achieves faster learning when available.
+   * Allow datasets on HDFS and other sources pushed by Spark or pulled by TensorFlow.
+   * Easily integrate with your existing Spark data processing pipelines.
+   * Easily deployed on cloud or on-premise and on CPUs or GPUs.
+
+## Install
+
+TensorFlowOnSpark is provided as a pip package, which can be installed on single machines via:
+```
+# for tensorflow>=2.0.0
+pip install tensorflowonspark
+
+# for tensorflow<2.0.0
+pip install tensorflowonspark==1.4.4
+```
+
+For distributed clusters, please see our [wiki site](../../wiki) for detailed documentation for specific environments, such as our getting started guides for [single-node Spark Standalone](https://github.com/yahoo/TensorFlowOnSpark/wiki/GetStarted_Standalone), [YARN clusters](../../wiki/GetStarted_YARN) and [AWS EC2](../../wiki/GetStarted_EC2).  Note: the Windows operating system is not currently supported due to [this issue](https://github.com/yahoo/TensorFlowOnSpark/issues/36).
+
+## Usage
+
+To use TensorFlowOnSpark with an existing TensorFlow application, you can follow our [Conversion Guide](../../wiki/Conversion-Guide) to describe the required changes.  Additionally, our [wiki site](../../wiki) has pointers to some presentations which provide an overview of the platform.
+
+**Note: since TensorFlow 2.x breaks API compatibility with TensorFlow 1.x, the examples have been updated accordingly.  If you are using TensorFlow 1.x, you will need to checkout the `v1.4.4` tag for compatible examples and instructions.**
+
+## API
+
+[API Documentation](https://yahoo.github.io/TensorFlowOnSpark/) is automatically generated from the code.
+
+## Contribute
+
+Please join the [TensorFlowOnSpark user group](https://groups.google.com/forum/#!forum/TensorFlowOnSpark-users) for discussions and questions.  If you have a question, please review our [FAQ](../../wiki/Frequently-Asked-Questions) before posting.
+
+Contributions are always welcome.  For more information, please see our [guide for getting involved](Contributing.md).
+
+## License
+
+The use and distribution terms for this software are covered by the Apache 2.0 license.
+See [LICENSE](LICENSE) file for terms.
+
+
+
+
+%prep
+%autosetup -n tensorflowonspark-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-tensorflowonspark -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon Apr 10 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..50f9dd3
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+6e1e3ae35e7ffec8aefa11132dedc04b  tensorflowonspark-2.2.5.tar.gz