Micro Batch Streaming: Allowing the Training of DNN models Using a large batch size on Small Memory Systems
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The size of the deep learning models has greatly increased over the past decade. Such models are difficult to train using a large batch size, because commodity machines do not have enough memory to accommodate both the model and a large data size. The batch size is one of the hyper-parameters used in the training model, and it is dependent on and is limited by the target machine memory capacity and it is dependent on the remaining memory after the model is uploaded. A smaller batch size usually results in performance degradation. This paper proposes a framework called Micro-Batch Streaming (MBS) to address this problem. This method helps deep learning models to train by providing a batch streaming algorithm that splits a batch into the appropriate size for the remaining memory size and streams them sequentially to the target machine. A loss normalization algorithm based on the gradient accumulation is used to maintain the performance. The purpose of our method is to allow deep learning models to train using mathematically determined optimal batch sizes that cannot fit into the memory of a target system.
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