arXiv:1811.06992cs.LG
Image Classification at Supercomputer Scale
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Abstract
Deep learning is extremely computationally intensive, and hardware vendors have responded by building faster accelerators in large clusters. Training deep learning models at petaFLOPS scale requires overcoming both algorithmic and systems software challenges. In this paper, we discuss three systems-related optimizations: (1) distributed batch normalization to control per-replica batch sizes, (2) input pipeline optimizations to sustain model throughput, and (3) 2-D torus all-reduce to speed up gradient summation. We combine these optimizations to train ResNet-50 on ImageNet to 76.3% accuracy in 2.2 minutes on a 1024-chip TPU v3 Pod with a training throughput of over 1.05 million images/second and no accuracy drop.
The authors trained ResNet-50 in 7.3 minutes at 75.3% accuracy. As a comparison, a Google TPUv3 pod with 1024 chips got to 75.2% accuracy with ResNet-50 in 1.8 minutes, and 76.2% accuracy in 2.2 minutes with an optimizer change and distributed batch normalization [1]. [1]…
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