By combining salient features from the TensorFlow deep learning framework with Apache Spark and Apache Hadoop, 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.

Features

  • 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

Project Samples

Project Activity

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Categories

Machine Learning

License

Apache License V2.0

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Additional Project Details

Operating Systems

Linux, Mac, Windows

Programming Language

Python

Related Categories

Python Machine Learning Software

Registered

2024-08-05