SparkNet: Training Deep Networks in Spark
This addresses the problem of time-consuming deep network training for researchers and practitioners by enabling efficient distributed training in Spark, though it is incremental as it builds on existing frameworks like Caffe.
The paper tackles the slow training of deep networks by introducing SparkNet, a framework for distributed deep learning in Spark, which scales well with cluster size and tolerates high-latency communication, achieving speedups quantified on the ImageNet dataset.
Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a cluster to speed up training is an important area of work. However, widely-popular batch-processing computational frameworks like MapReduce and Spark were not designed to support the asynchronous and communication-intensive workloads of existing distributed deep learning systems. We introduce SparkNet, a framework for training deep networks in Spark. Our implementation includes a convenient interface for reading data from Spark RDDs, a Scala interface to the Caffe deep learning framework, and a lightweight multi-dimensional tensor library. Using a simple parallelization scheme for stochastic gradient descent, SparkNet scales well with the cluster size and tolerates very high-latency communication. Furthermore, it is easy to deploy and use with no parameter tuning, and it is compatible with existing Caffe models. We quantify the dependence of the speedup obtained by SparkNet on the number of machines, the communication frequency, and the cluster's communication overhead, and we benchmark our system's performance on the ImageNet dataset.