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Recent deep learning models are difficult to train using a large batch size,because commodity machines may not have enough memory to accommodate both themodel and a large data batch size. The batch size is one of thehyper-parameters used in the training model, and it is dependent on and islimited by the target machine memory capacity because the batch size can onlyfit into the remaining memory after the model is uploaded. Moreover, the dataitem size is also an important factor because if each data item size is largerthen the batch size that can fit into the remaining memory becomes smaller.This paper proposes a framework called Micro-Batch Streaming (MBS) to addressthis problem. This method helps deep learning models to train by providing abatch streaming method that splits a batch into a size that can fit in theremaining memory and streams them sequentially. A loss normalization algorithmbased on the gradient accumulation is used to maintain the performance. Thepurpose of our method is to allow deep learning models to train using largerbatch sizes that exceed the memory capacity of a system without increasing thememory size or using multiple devices (GPUs).

Micro Batch Streaming: Allowing the Training of DNN Models to Use a large Batch Size in Memory Constrained Environments