I was just upgrading my PyTorch install to the CUDA 9.1 one that was recently released and noticed that training’s become super slow. Approx. 100 times slower than prior to the upgrade.
For the upgrade, I performed a conda uninstall pytorch and then installed the cuda 9.1 version via conda install pytorch torchvision cuda91 -c pytorch.
I then just went ahead and did the reverse, conda uninstall pytorch and then  conda install pytorch torchvision cuda90 -c pytorch.  The speed is back to “normal” now.
In all my scripts, I am including a line
print(torch.cuda.is_available())

which evaluated to true in all of these cases. However, since the speed via the CUDA 9.1 install matched approx the speed I get when I run the code locally on a non-GPU laptop, I am assuming that this install was not using the GPU properly. Could it be that the card is incompatible to CUDA 9.1? Is there a way to check that other than torch.cuda.is_available()?