High Performance POSIX - Deep learning workloads with object storage

The advents of big data-sets and high-speed GPUs are fueling the growth in analytics, machine learning and deep learning techniques. In this talk we explore how to run high throughput analytics on data that resides in inexpensive object storage without the need to pre-load or stage the data. We will show how to leverage s3fs, an open source FUSE based file-system gateway for object storage to run deep learning analytics and demonstrate how this can be done effectively while providing sufficient throughput to high performance analytic engines and dedicated accelerators, such as GPUs, FPGAs, and Tensor Processing Units. Learning outcomes 1. Enabling transparent access to object storage to POSIX workloads without application changes 2. Achieving high throughput while lowering storage costs using inexpensive cloud based object storage 3. Keeping high speed GPUs busy in in machine learning workloads using object storage