Reducing the training time of deep learning models using synchronous SGD and large batch size
Salah Eddine Loukili, Abdellah Ezzati, Said Ben Alla, Brahim Zraibi · 2022
Recently, deep learning research has demonstrated that being able to train big models improves performance substantially. In this work, we consider the problem of training a deep neural network with millions of parameters using multiple CPU cores. On a single machine with a modern CPU platform, training a benchmark dataset of Dogs vs Cats can take up to hours; however, distributing training across numerous machines has been seen to dramatically reduce this time. The current state of the art for a modern distributed training framework is presented in this study, which covers the many methods and strategies utilized to distribute training. We concentrate on synchronous versions of distributed Stochastic Gradient Descent, different All Reduce gradient aggregation algorithms, and best practices for achieving higher throughput and reduced latency, such as gradient compression and large batch sizes. We show that using the same approaches, we can train a smaller deep network for an image classification problem in a shorter time. Although we focus on and report on the effectiveness of these approaches when used to train convolutional neural networks, the underlying methods may be used to train any gradient-based machine-learning algorithm.