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On small netowrk (ResNet 18) data augmentation on CPU will be the bottleneck and the GPU will not be used fully leading to funky solutions like: https://www.sagivtech.com/2017/09/19/optimizing-pytorch-training-code/ (which uses multiprocessing to bypass Python GIL and resort to killing spawned thread)
On small netowrk (ResNet 18) data augmentation on CPU will be the bottleneck and the GPU will not be used fully leading to funky solutions like: https://www.sagivtech.com/2017/09/19/optimizing-pytorch-training-code/ (which uses multiprocessing to bypass Python GIL and resort to killing spawned thread)
Image loading need to be fast. Benchmarks like https://t0rakka.silvrback.com/jpeg-decoding-benchmark shows that libjpeg-turbo can be a bottleneck. See repo https://github.com/t0rakka/mango/tree/master/source/mango/jpeg and Nvidia nvJPEG https://developer.nvidia.com/nvjpeg and Nvidia DALI (Data Augmentation Library) https://github.com/NVIDIA/DALI.
Alternative libraries to look at:
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