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

Understand Data Preprocessing for Effective End-to-End Training of Deep Neural Networks