  Training deep learning (DL) models on petascale datasets is essential for
achieving competitive and state-of-the-art performance in applications such as
speech, video analytics, and object recognition. However, existing distributed
filesystems were not developed for the access patterns and usability
requirements of DL jobs. In this paper, we describe AIStore, a highly scalable,
easy-to-deploy storage system, and WebDataset, a standards-based storage format
and library that permits efficient access to very large datasets. We compare
system performance experimentally using image classification workloads and
storing training data on a variety of backends, including local SSDs,
single-node NFS, and two identical bare-metal clusters: HDFS and AIStore.
