@fchollet check out @bzamecnik 's perf. analysis of #Keras multi-GPU training speedups - many detailed measurements https://github.com/rossumai/keras-multi-gpu/blob/master/blog/docs/measurements.md …https://twitter.com/RossumAi/status/924985495851012096 …
I did my own benchmarks with Xception and ResNet50 (nothing fancy) and got 85% efficiency with 8 GPUs on EC2 (6.8x speedup)
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When training from an ImageDataGenerator, performance was worse (65% or so efficiency). Potentially because of slow disk I/O on EC2
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Adrian got 97% efficiency on a GoogleNet-like with 4 GPUs (74 min to 19 min): https://www.pyimagesearch.com/2017/10/30/how-to-multi-gpu-training-with-keras-python-and-deep-learning/ … -with 4 GPUs, I had low 90s efficiency
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