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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
Categories
Machine LearningLicense
Apache License V2.0Follow TensorFlowOnSpark
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