[eggie5]

Here's my results:

Testing new Tesla V100 on AWS. Fine-tuning VGG on DeepSent dataset for 10 epochs.

GRID 520K (4GB) (baseline):

* 780s/epoch @ minibatch 8 (GPU saturated)

V100(16Gb):

* 30s/epoch @ minibatch 8 (GPU not saturated)

* 6s/epoch @ minibatch 32 (GPU more saturated)

* 6s/epoch @ minibatch 256 (GPU saturated)

[dharma1]

Thanks! Curious how this would scale on the 8x or 16x instances

[eggie5]

what do you mean? 8 or 16 GPUs? That's require changing the code to use distributed tensorflow...

[dharma1]

Yes exactly. The instances with 8 or 16 GPUs. Does the training time reduce linearly, is the GPU utilisation 100%, is it plug and play with TF