Extremely Large Minibatch SGD: Training ResNet-50 on ImageNet in 15 Minutes
November 12, 2017 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Takuya Akiba, Shuji Suzuki, Keisuke Fukuda
arXiv ID
1711.04325
Category
cs.DC: Distributed Computing
Cross-listed
cs.CV,
cs.LG
Citations
319
Venue
arXiv.org
Last Checked
3 months ago
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.
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