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How is the distributed performance of Apache Spark different from Distributed TensorFlow?

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I will assume first that the distributed version of TensorFlow will eventually be released (which it has not as of this writing) and will make the comparison based on that hypothetical release. TensorFlow is designed to support Deep Learning Neural Network training. Spark is designed to support...
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I will assume first that the distributed version of TensorFlow will eventually be released (which it has not as of this writing) and will make the comparison based on that hypothetical release. TensorFlow is designed to support Deep Learning Neural Network training. Spark is designed to support Distributed Computation in general. Therefore one would logically expect that TensorFlow will out perform Spark in the specific domain of Deep Learning. However, does the TensorFlow paradigm translate to other distributed computation or does it translate to other Machine Learning algorithms? Spark and TensorFlow both are based on the DataFlow paradigm of Parallel Computation. That is, if data dependencies are explicitly known, then optimizations can be made to parallelize the computation. The nature of the optimizations will of course depend on the kind of algorithms that the framework supports. Furthermore, the complexity of distribution requires the reduction of the messaging required to send data between computational nodes. General distributed computation and other ML algorithms will not have different characteristics than Neural Networks. Therefore it should be safe to assume that for computation other than Deep Learning training that Spark will remain to be valuable. Final note: Google has another framework called DataFlow (formerly FlumeJava) that has an implementation that uses Spark. I doubt that TensorFlow was designed to replace DataFlow.. read less
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