English

Extremely Large Minibatch SGD: Training ResNet-50 on ImageNet in 15 Minutes

Distributed, Parallel, and Cluster Computing 2017-11-15 v1 Computer Vision and Pattern Recognition Machine Learning

Abstract

We demonstrate that training ResNet-50 on ImageNet for 90 epochs can be achieved in 15 minutes with 1024 Tesla P100 GPUs. This was made possible by using a large minibatch size of 32k. To maintain accuracy with this large minibatch size, we employed several techniques such as RMSprop warm-up, batch normalization without moving averages, and a slow-start learning rate schedule. This paper also describes the details of the hardware and software of the system used to achieve the above performance.

Keywords

Cite

@article{arxiv.1711.04325,
  title  = {Extremely Large Minibatch SGD: Training ResNet-50 on ImageNet in 15 Minutes},
  author = {Takuya Akiba and Shuji Suzuki and Keisuke Fukuda},
  journal= {arXiv preprint arXiv:1711.04325},
  year   = {2017}
}

Comments

NIPS'17 Workshop: Deep Learning at Supercomputer Scale