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]: True
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) # True
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