Don't Use Large Mini-batches, Use Local SGD

Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel, Martin Jaggi

Abstract:

Mini-batch stochastic gradient methods (SGD) are state of the art for distributed training of deep neural networks.
Drastic increases in the mini-batch sizes have lead to key efficiency and scalability gains in recent years.
However, progress faces a major roadblock, as models trained with large batches often do not generalize well, i.e. they do not show good accuracy on new data.
As a remedy, we propose a \emph{post-local} SGD and show that it significantly improves the generalization performance compared to large-batch training on standard benchmarks while enjoying the same efficiency (time-to-accuracy) and scalability. We further provide an extensive study of the communication efficiency vs. performance trade-offs associated with a host of \emph{local SGD} variants.

Code: [ epfml/LocalSGD-Code](https://github.com/epfml/LocalSGD-Code) + [ 1 community implementation](https://paperswithcode.com/paper/?openreview=B1eyO1BFPr)

Data: [CIFAR-10](https://paperswithcode.com/dataset/cifar-10), [CIFAR-100](https://paperswithcode.com/dataset/cifar-100), [ImageNet](https://paperswithcode.com/dataset/imagenet)

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/arxiv:1808.07217/code)