Yahoo supercharges TensorFlow with Apache Spark
Yahoo, model Apache Spark citizen and developer of CaffeOnSpark, which made it easier for developers building deep learning models in Caffe to scale with parallel processing, is open sourcing a new project called TensorFlowOnSpark. The pairing of Spark and TensorFlow should make the deep learning framework more attractive to developers who are creating models that need to run on large computing clusters.
For those that zoned out during the big-data boom, Apache Spark is an open source framework designed to increase the efficiency of parallel computing. Following in the steps of tools like Hadoop, Spark made it possible for companies like Netflix to process huge amounts of user data to offer up recommendations at scale.
Machine learning frameworks like Google’s TensorFlow and Caffe help people create deep learning models without the rigorous skill-set of a machine learning specialist. The open source libraries exist at a higher level of abstraction that enable developers to create models without getting lost in the weeds reinventing the wheel.
Naturally, Spark and machine learning go hand-in-hand. Deep learning in particular leans heavily on large amounts of compute as a crutch. Yahoo created CaffeOnSpark for its own use. But mixing Caffe and Spark only benefited a portion of the machine learning community. TensorFlow remains the most popular framework, so Yahoo decided to bring the same pairing to it and hopefully pick up some developer respect along the way.