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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._
[](https://cd.screwdriver.cd/pipelines/6384)
[](https://pypi.org/project/tensorflowonspark/)
[](https://img.shields.io/pypi/dm/tensorflowonspark.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._
[](https://cd.screwdriver.cd/pipelines/6384)
[](https://pypi.org/project/tensorflowonspark/)
[](https://img.shields.io/pypi/dm/tensorflowonspark.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._
[](https://cd.screwdriver.cd/pipelines/6384)
[](https://pypi.org/project/tensorflowonspark/)
[](https://img.shields.io/pypi/dm/tensorflowonspark.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
|
