Scaling Vision Transformers: Evaluating DeepSpeed for Image-Centric Workloads

TL;DR

This paper evaluates the use of DeepSpeed to improve the scalability and training efficiency of Vision Transformers on image datasets, addressing computational challenges in large-scale models.

Contribution

It systematically assesses DeepSpeed's impact on training speed and scalability of Vision Transformers, providing insights for optimizing distributed image model training.

Findings

DeepSpeed enhances training scalability for Vision Transformers.

Distributed data parallelism improves training speed on multiple GPUs.

Key software parameters significantly influence training performance.

Abstract

Vision Transformers (ViTs) have demonstrated remarkable potential in image processing tasks by utilizing self-attention mechanisms to capture global relationships within data. However, their scalability is hindered by significant computational and memory demands, especially for large-scale models with many parameters. This study aims to leverage DeepSpeed, a highly efficient distributed training framework that is commonly used for language models, to enhance the scalability and performance of ViTs. We evaluate intra- and inter-node training efficiency across multiple GPU configurations on various datasets like CIFAR-10 and CIFAR-100, exploring the impact of distributed data parallelism on training speed, communication overhead, and overall scalability (strong and weak scaling). By systematically varying software parameters, such as batch size and gradient accumulation, we identify key factors influencing performance of distributed training. The experiments in this study provide a foundational basis for applying DeepSpeed to image-related tasks. Future work will extend these investigations to deepen our understanding of DeepSpeed's limitations and explore strategies for optimizing distributed training pipelines for Vision Transformers.