In this paper, we primarily focus on understanding the data preprocessing
pipeline for DNN Training in the public cloud. First, we run experiments to
test the performance implications of the two major data preprocessing methods
using either raw data or record files. The preliminary results show that data
preprocessing is a clear bottleneck, even with the most efficient software and
hardware configuration enabled by NVIDIA DALI, a high-optimized data
preprocessing library. Second, we identify the potential causes, exercise a
variety of optimization methods, and present their pros and cons. We hope this
work will shed light on the new co-design of ``data storage, loading pipeline''
and ``training framework'' and flexible resource configurations between them so
that the resources can be fully exploited and performance can be maximized.

本篇论文主要关注于了解公共云中深度神经网络（DNN）训练的数据预处理流程。我们运行实验来测试使用原始数据或记录文件两种主要数据预处理方法所带来的性能影响。初步结果表明，即使使用 NVIDIA DALI 这种高度优化的数据预处理库，数据预处理仍然是明显的瓶颈。其次，我们确定了潜在的原因，采用了多种优化方法，并介绍了它们的优缺点。希望本研究可以为 “数据存储和加载管道” 与 “训练框架” 的新协同设计以及它们之间的灵活资源配置提供参考，使资源得到充分利用并提高性能。