test cuda pytorch
import torch
torch.cuda.is_available()
>>> True
torch.cuda.current_device()
>>> 0
torch.cuda.device(0)
>>> <torch.cuda.device at 0x7efce0b03be0>
torch.cuda.device_count()
>>> 1
torch.cuda.get_device_name(0)
>>> 'GeForce GTX 950M'check if pytorch is using gpu minimal example
import torch
import torch.nn as nn
dev = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
t1 = torch.randn(1,2)
t2 = torch.randn(1,2).to(dev)
print(t1) # tensor([[-0.2678, 1.9252]])
print(t2) # tensor([[ 0.5117, -3.6247]], device='cuda:0')
t1.to(dev)
print(t1) # tensor([[-0.2678, 1.9252]])
print(t1.is_cuda) # False
t1 = t1.to(dev)
print(t1) # tensor([[-0.2678, 1.9252]], device='cuda:0')
print(t1.is_cuda) # True
class M(nn.Module):
def __init__(self):
super().__init__()
self.l1 = nn.Linear(1,2)
def forward(self, x):
x = self.l1(x)
return x
model = M() # not on cuda
model.to(dev) # is on cuda (all parameters)
print(next(model.parameters()).is_cuda) # Truecheck cuda version pytorch
torch.version.cudaCopyright © 2021 Codeinu
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