TensorFlowOnSpark
Enables distributed TensorFlow training and inferencing on Apache Spark and Hadoop clusters with minimal code changes.
What is TensorFlowOnSpark?
TensorFlowOnSpark is an open-source framework that enables distributed TensorFlow programs to run on Apache Spark and Hadoop clusters. It allows data scientists and engineers to perform scalable deep learning training and inferencing directly within their existing big data infrastructure, eliminating the need for separate machine learning clusters. The framework minimizes code changes required to adapt existing TensorFlow applications for distributed execution.
Data engineers and machine learning practitioners working in organizations with existing Hadoop/Spark clusters who need to run TensorFlow workloads at scale. It's particularly valuable for teams looking to integrate deep learning into their big data pipelines without managing separate infrastructure.
Developers choose TensorFlowOnSpark because it provides a seamless bridge between TensorFlow and big data ecosystems, allowing them to leverage existing cluster resources while maintaining full TensorFlow functionality. Its minimal code change requirement and support for flexible data ingestion make it practical for production deployments.
Overview
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.
Use Cases
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