Moving computations to GPU
Hi there,
thanks for providing this implementation!
I'm having some trouble performing inference using GPUs. Specifically, despite having a cuda enabled machine, by default the computations in the example take place on CPU (due to the gpus=None argument). However, upon setting gpus=[0] I find that still none of the computations are moved to the GPU and indeed when i check with nvidia-smi there is no GPU usage. I've been playing around trying to send models/data to device by just simply adding in `.to(device) methods into the test function but with no success so far. I was wondering whether I am missing something very obvious which is causing these difficulties.
Thanks!
The code was tested on many GPU-enabled servers and worked fine, so I suppose there might be issues with installing CUDA-enabled Pytorch on your end?
- Do you have a Pytorch 2.1+ with CUDA 11.8+?
- Does Pytorch see a GPU on that machine in
torch.cuda.is_available()?
Yep to both of those! Eventually the code was made to utilise CUDA using a mixture of to(device) and the ToDevice class from torch_geometric.transforms
It could potentially be issues with the VM - it was spun up on GCP and used a Deep Learning on Linux machine image running Debian 10. Can post full specs if useful but given the fixes above perhaps it's unnecessary.