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Scaling the distributed deep learning to a massive GPU cluster level ischallenging due to the instability of the large mini-batch training and theoverhead of the gradient synchronization. We address the instability of thelarge mini-batch training with batch-size control and label smoothing. Weaddress the overhead of the gradient synchronization with 2D-Torus all-reduce.Specifically, 2D-Torus all-reduce arranges GPUs in a logical 2D grid andperforms a series of collective operation in different orientations. These twotechniques are implemented with Neural Network Libraries (NNL). We havesuccessfully trained ImageNet/ResNet-50 in 122 seconds without significantaccuracy loss on ABCI cluster.

Massively Distributed SGD: ImageNet/ResNet-50 Training in a Flash