Large Batch Training of Convolutional Networks with Layer-wise Adaptive Rate Scaling

Boris Ginsburg, Igor Gitman, Yang You

Abstract: A common way to speed up training of large convolutional networks is to add computational units. Training is then performed using data-parallel synchronous Stochastic Gradient Descent (SGD) with a mini-batch divided between computational units. With an increase in the number of nodes, the batch size grows. However, training with a large batch often results in lower model accuracy. We argue that the current recipe for large batch training (linear learning rate scaling with warm-up) is not general enough and training may diverge. To overcome these optimization difficulties, we propose a new training algorithm based on Layer-wise Adaptive Rate Scaling (LARS). Using LARS, we scaled AlexNet and ResNet-50 to a batch size of 16K.

TL;DR: A new large batch training algorithm based on Layer-wise Adaptive Rate Scaling (LARS); using LARS, we scaled AlexNet and ResNet-50 to a batch of 16K.

Keywords: large batch, LARS, adaptive rate scaling