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Deep learning has become an indispensable part of life, such as facerecognition, NLP, etc., but the training of deep model has always been achallenge, and in recent years, the complexity of training data and models hasshown explosive growth, so the training method is gradually transformed intodistributed training. Classical synchronization strategy can guarantee accuracybut frequent communication can lead to a slow training speed, althoughasynchronous strategy training speed but can not guarantee the accuracy, and inthe face of the training of the heterogeneous cluster, the above work is notefficient to work, on the one hand, can cause serious waste of resources, onthe other hand, frequent communication also made slow training speed, so thispaper proposes a semi-synchronous training strategy based on local-SDG,effectively improve the utilization efficiency of heterogeneous resourcescluster and reduce communication overhead, to accelerate the training andensure the accuracy of the model.

Oscars: Adaptive Semi-Synchronous Parallel Model for Distributed Deep Learning with Global View