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My training of Resnet-18 network on Imagenet using Tesla V100 seems to be quite slow (1 epoch is about 2,5 hours, batch 128). Increasing the number of GPUs does not seem to help.

What is your training time of Resnet-18/Resnet-50 on Imagenet? How many epochs do you train for to obtain the desired accuracy? I am wondering what I should expect.

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  • $\begingroup$ Do you double the batch size after increasing the number of gpus? So weird that the training speed still constant $\endgroup$
    – CuCaRot
    May 2 at 15:58

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Currently, when using the code on this branch: https://github.com/benchopt/benchmark_resnet_classif/pull/53, I use 35 minutes per epoch to train a ResNet-18 on ImageNet in TensorFlow with a V100 GPU and a batch size of 128, with standard data augmentations.

I haven't found other mentions of the training times for a ResNEt-18 with a standard training policy, so I am just mentioning this to kick off the conversation without claiming that this is the best one can get.

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