Storage Performance Basics for Deep Learning

This is an interesting write-up from James Mauro from Nvidia on Storage Performance Basics for Deep Learning.

The complexity of the workloads plus the volume of data required to feed deep-learning training creates a challenging performance environment. Deep learning workloads cut across a broad array of data sources (images, binary data, etc), imposing different disk IO load attributes, depending on the model and a myriad of parameters and variables.”

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