Accelerating I/O bound deep learning – RiseML Blog

blog.riseml.com

An increasing trend is that pre-processing and especially reading the training data from disk becomes the bottleneck. This is caused by multiple factors, including faster GPUs, more efficient model architectures, and larger datasets, especially for video and image processing. As a result, the GPUs sit idle a lot of time, waiting for the next batch of data to work on. After optimising the pre-processing pipeline, I/O often becomes the next bottleneck.

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