tensorflowonspark 2.2.5
Deep learning with TensorFlow on Apache Spark clusters
License: Apache 2.0
Requires Python: >="3.6"

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# TensorFlowOnSpark
> _TensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark
clusters._

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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.