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Status: Unchecked ×Keyword: learning rate scaling ×Clear all

3 claims from 1 paper

1. Computer Science › Stochastic Gradient Optimization Techniques

   Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

   Goyal, Dollár, Girshick et al. · arXiv (Cornell University) · 2017

   The paper shows that large-minibatch SGD on ImageNet can match small-minibatch accuracy, using a linear learning-rate scaling rule and warmup, training ResNet-50 in one hour on 256 GPUs.

   Unchecked3 claims
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   1. UncheckedOn ImageNet, the authors report no loss of accuracy when training with minibatches of up to 8192 images, using adjusted learning rates and a warmup scheme.“Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images.”
   2. UncheckedUsing a linear learning-rate scaling rule and a warmup scheme, ResNet-50 trained with minibatches of 8192 on 256 GPUs in one hour, matching small-minibatch accuracy.“With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy.”
   3. UncheckedThe authors' implementation reaches about 90% scaling efficiency on commodity hardware when going from 8 to 256 GPUs for ImageNet training.“Using commodity hardware, our implementation achieves ~90% scaling efficiency when moving from 8 to 256 GPUs.”