If I call model.cuda() in pytorch where model is a subclass of nn.Module, and say if I have four GPUs, how it will utilize the four GPUs and how do I know which GPUs that are using?
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2 Answers
If you have a custom module derived from nn.Module after model.cuda() all model parameters, (model.parameters() iterator can show you these) will end on your cuda.
To check where are your parameters just print them (cuda:0) in my case:
class M(nn.Module):
'custom module'
def __init__(self):
super().__init__()
self.lin = nn.Linear(784, 10)
m = M()
m.cuda()
for _ in m.parameters():
print(_)
# Parameter containing:
# tensor([[-0.0201, 0.0282, -0.0258, ..., 0.0056, 0.0146, 0.0220],
# [ 0.0098, -0.0264, 0.0283, ..., 0.0286, -0.0052, 0.0007],
# [-0.0036, -0.0045, -0.0227, ..., -0.0048, -0.0003, -0.0330],
# ...,
# [ 0.0217, -0.0008, 0.0029, ..., -0.0213, 0.0005, 0.0050],
# [-0.0050, 0.0320, 0.0013, ..., -0.0057, -0.0213, 0.0045],
# [-0.0302, 0.0315, 0.0356, ..., 0.0259, 0.0166, -0.0114]],
# device='cuda:0', requires_grad=True)
# Parameter containing:
# tensor([-0.0027, -0.0353, -0.0349, -0.0236, -0.0230, 0.0176, -0.0156, 0.0037,
# 0.0222, -0.0332], device='cuda:0', requires_grad=True)
You can also specify the device like this:
m.cuda('cuda:0')
With torch.cuda.device_count() you may check how many devices you have.
Comments
To expand on prosti's answer to split your computations among multiple GPUs you should use torch.nn.DataParallel or DistributedDataParallel.
