pytorch check gpu
In [1]: import torch
In [2]: torch.cuda.current_device()
Out[2]: 0
In [3]: torch.cuda.device(0)
Out[3]: <torch.cuda.device at 0x7efce0b03be0>
In [4]: torch.cuda.device_count()
Out[4]: 1
In [5]: torch.cuda.get_device_name(0)
Out[5]: 'GeForce GTX 950M'
In [6]: torch.cuda.is_available()
Out[6]: Truecheck 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) # Truepytorch get gpu number
torch.cuda.device_count()pytorch check GPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#或device = torch.device("cuda:0")
device1 = torch.device("cuda:1")
for batch_idx, (img, label) in enumerate(train_loader):
img=img.to(device)
label=label.to(device)Copyright © 2021 Codeinu
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