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        "description": "<!--\nCopyright 2019 Yahoo Inc.\nLicensed under the terms of the Apache 2.0 license.\nPlease see LICENSE file in the project root for terms.\n-->\n# TensorFlowOnSpark\n> _TensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark\nclusters._\n\n[![Build Status](https://travis-ci.org/yahoo/TensorFlowOnSpark.svg?branch=master)](https://travis-ci.org/yahoo/TensorFlowOnSpark) [![PyPI version](https://badge.fury.io/py/tensorflowonspark.svg)](https://badge.fury.io/py/tensorflowonspark)\n\nBy 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\ndeep learning on a cluster of GPU and CPU servers.\n\nIt enables both distributed TensorFlow training and\ninferencing on Spark clusters, with a goal to minimize the amount\nof code changes required to run existing TensorFlow programs on a\nshared grid.  Its Spark-compatible API helps manage the TensorFlow\ncluster with the following steps:\n\n1. **Startup** - launches the Tensorflow main function on the executors, along with listeners for data/control messages.\n1. **Data ingestion**\n   - **InputMode.TENSORFLOW** - leverages TensorFlow's built-in APIs to read data files directly from HDFS.\n   - **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.\n1. **Shutdown** - shuts down the Tensorflow workers and PS nodes on the executors.\n\n## Table of Contents\n\n- [Background](#background)\n- [Install](#install)\n- [Usage](#usage)\n- [API](#api)\n- [Contribute](#contribute)\n- [License](#license)\n\n## Background\n\nTensorFlowOnSpark was developed by Yahoo for large-scale distributed\ndeep learning on our Hadoop clusters in Yahoo's private cloud.\n\nTensorFlowOnSpark provides some important benefits (see [our\nblog](http://yahoohadoop.tumblr.com/post/157196317141/open-sourcing-tensorflowonspark-distributed-deep))\nover alternative deep learning solutions.\n   * Easily migrate existing TensorFlow programs with <10 lines of code change.\n   * Support all TensorFlow functionalities: synchronous/asynchronous training, model/data parallelism, inferencing and TensorBoard.\n   * Server-to-server direct communication achieves faster learning when available.\n   * Allow datasets on HDFS and other sources pushed by Spark or pulled by TensorFlow.\n   * Easily integrate with your existing Spark data processing pipelines.\n   * Easily deployed on cloud or on-premise and on CPUs or GPUs.\n\n## Install\n\nTensorFlowOnSpark is provided as a pip package, which can be installed on single machines via:\n```\n# for tensorflow>=2.0.0\npip install tensorflowonspark\n\n# for tensorflow<2.0.0\npip install tensorflowonspark==1.4.4\n```\n\nFor 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).\n\n## Usage\n\nTo 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.\n\n**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.**\n\n## API\n\n[API Documentation](https://yahoo.github.io/TensorFlowOnSpark/) is automatically generated from the code.\n\n## Contribute\n\nPlease 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.\n\nContributions are always welcome.  For more information, please see our [guide for getting involved](Contributing.md).\n\n## License\n\nThe use and distribution terms for this software are covered by the Apache 2.0 license.\nSee [LICENSE](LICENSE) file for terms.\n\n\n",
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