i'm running some code with cudas, and I need to test the same code on CPU to compare running time. To decide between regular pytorch tensor and cuda float tensor, the library I use calls torch.cuda.is_available(). Is there an easy method to make this function return false? I tried changing the Cuda visible devices with
os.environ["CUDA_VISIBLE_DEVICES"]=""
but torch.cuda.is_available() still return True. I went into pytorch source code, and in my case, torch.cuda.is_avaible returns
torch._C._cuda_getDeviceCount() > 0
I assume I should be able to "hide" my GPU at the start of my notebook, so the device count is equal to zero, but i didn't get any success so far. Any help is appreciated :)
my code
Instead of trying to trick it, why not rewrite your code? For example,
use_gpu = torch.cuda.is_available() and not os.environ['USE_CPU']
Then you can start your program as python runme.py to run on GPU if available, and USE_CPU=1 python3 runme.py to force CPU execution (or make it semi-permanent by export USE_CPU=1).
I tried changing the Cuda visible devices with
You can also try running your code with CUDA_VISIBLE_DEVICES="" python3 runme.py; if you're setting the environment variable inside your code, it might be set later than PyTorch initialisation, and have no effect.