TensorFlowOnSpark

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

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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

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