Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Data @ Scale


Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. 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. Using commodity hardware, our implementation achieves ∼90% scaling efficiency when moving from 8 to 256 GPUs. This system enables us to train visual recognition models on internet-scale data with high efficiency.

Related Publications

All Publications

EACL - April 20, 2021

FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary

Terra Blevins, Mandar Joshi, Luke Zettlemoyer

CVPR - June 19, 2021

Robust Audio-Visual Instance Discrimination

Pedro Morgado, Ishan Misra, Nuno Vasconcelos

CVPR - June 19, 2021

Audio-Visual Instance Discrimination with Cross-Modal Agreement

Pedro Morgado, Nuno Vasconcelos, Ishan Misra

The Springer Series on Challenges in Machine Learning - December 12, 2019

The Second Conversational Intelligence Challenge (ConvAI2)

Emily Dinan, Varvara Logacheva, Valentin Malykh, Alexander Miller, Kurt Shuster, Jack Urbanek, Douwe Kiela, Arthur Szlam, Iulian Serban, Ryan Lowe, Shrimai Prabhumoye, Alan W. Black, Alexander Rudnicky, Jason Williams, Joelle Pineau, Jason Weston

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy