Hiding Latencies in Network-Based Image Loading for Deep Learning

In the last decades, the computational power of GPUs has grown exponentially, allowing current deep learning (DL) applications to handle increasingly large amounts of data at a progressively higher throughput. However, network and storage latencies cannot decrease at a similar pace due to physical constraints, leading to data stalls, and creating a bottleneck for DL tasks. Additionally, managing vast quantities of data and their associated metadata has proven challenging, hampering and slowing the productivity of data scientists. Moreover, existing data loaders have limited network support, necessitating, for maximum performance, that data be stored on local filesystems close to the GPUs, overloading the storage of computing nodes. In this paper we propose a strategy, aimed at DL image applications, to address these challenges by: storing data and metadata in fast, scalable NoSQL databases; connecting the databases to state-of-the-art loaders for DL frameworks; enabling high-throughput data loading over high-latency networks through our out-of-order, incremental prefetching techniques. To evaluate our approach, we showcase our implementation and assess its data loading capabilities through local, medium and high-latency (intercontinental) experiments.