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.

翻译：视觉Transformer（ViTs）通过利用自注意力机制捕捉数据中的全局关系，在图像处理任务中展现出卓越潜力。然而，其可扩展性受到显著计算和内存需求的限制，特别是对于参数众多的大规模模型。本研究旨在利用DeepSpeed——一种常用于语言模型的高效分布式训练框架——来提升ViTs的可扩展性和性能。我们在CIFAR-10和CIFAR-100等多个数据集上，评估了不同GPU配置下的节点内与节点间训练效率，探究分布式数据并行对训练速度、通信开销及整体可扩展性（强扩展与弱扩展）的影响。通过系统调整批量大小和梯度累积等软件参数，我们识别出影响分布式训练性能的关键因素。本研究的实验为将DeepSpeed应用于图像相关任务提供了基础依据。未来工作将扩展这些研究，以深化对DeepSpeed局限性的理解，并探索优化视觉Transformer分布式训练流程的策略。