TensorFlowOnSpark is a powerful tool that combines the deep learning capabilities of TensorFlow with the distributed data processing of Apache Spark and Hadoop. In this guide, we’ll walk through the steps to get started, outline the installation process, and provide insights into effective usage.

Table of Contents

• Background
• Install
• Usage
• API
• Contribute
• License

Background

Originally developed by Yahoo, TensorFlowOnSpark allows enterprises to perform large-scale distributed deep learning within their existing Hadoop clusters. The benefits of using TensorFlowOnSpark include:

• Easily migrate existing TensorFlow programs with minimal code changes.
• Support for all TensorFlow functionalities, including synchronous and asynchronous training.
• Enhanced communication leading to faster learning.
• Flexible data handling from HDFS and Spark processing pipelines.
• Deployment capabilities across cloud and on-premise environments.

Install

To install TensorFlowOnSpark, you can use pip. Here are the commands for installation:

# for tensorflow=2.0.0
pip install tensorflowonspark

# for tensorflow2.0.0
pip install tensorflowonspark==1.4.4

For distributed cluster installations, check our wiki site for detailed documentation.

Note: Windows operating system is currently not supported.

Usage

If you already have a TensorFlow application, you can refer to our Conversion Guide for more information on adapting your code for TensorFlowOnSpark. Should you need examples or presentations, they are also available on our wiki site.

Keep in mind that TensorFlow 2.x may not be compatible with 1.x. If you’re using TensorFlow 1.x, ensure to check out the v1.4.4 tag for relevant examples.

API

Our API Documentation is automatically generated and serves as a useful reference for developers.

Contribute

We invite you to join our TensorFlowOnSpark user group for discussions, questions, and more. For any inquiries, make sure to consult our FAQ.

Contributions are always appreciated! To get involved, refer to our guide for getting involved.

License

The usage and distribution of this software are governed by the Apache 2.0 license. Please refer to the LICENSE file for specific terms.

Troubleshooting

If you encounter issues during installation or usage, consider the following tips:

• Verify that you are using a compatible version of TensorFlow.
• Check your network settings to ensure that all required components can communicate effectively.
• Consult the community forums for specific errors you might encounter.

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