pytorch-在“with statement”中使用设备

不幸的是,在目前的实施中 with-device 语句不能以这种方式工作,它只能用于在cuda设备之间切换。

你还得使用 device .cuda() 将张量移动到指定的GPU),使用如下术语:

# allocates a tensor on GPU 1
a = torch.tensor([1., 2.], device=cuda)

所以要访问 cuda:1 :

cuda = torch.device('cuda')

with torch.cuda.device(1):
    # allocates a tensor on GPU 1
    a = torch.tensor([1., 2.], device=cuda)

以及访问 cuda:2 :

cuda = torch.device('cuda')

with torch.cuda.device(2):
    # allocates a tensor on GPU 2
    a = torch.tensor([1., 2.], device=cuda)

但是张量没有 装置 参数仍将是CPU张量:

cuda = torch.device('cuda')

with torch.cuda.device(1):
    # allocates a tensor on CPU
    a = torch.tensor([1., 2.])

不-不幸的是,它是在当前的执行 带设备 问题。

documentation :

cuda = torch.device('cuda')     # Default CUDA device
cuda0 = torch.device('cuda:0')
cuda2 = torch.device('cuda:2')  # GPU 2 (these are 0-indexed)

x = torch.tensor([1., 2.], device=cuda0)
# x.device is device(type='cuda', index=0)
y = torch.tensor([1., 2.]).cuda()
# y.device is device(type='cuda', index=0)

with torch.cuda.device(1):
    # allocates a tensor on GPU 1
    a = torch.tensor([1., 2.], device=cuda)

    # transfers a tensor from CPU to GPU 1
    b = torch.tensor([1., 2.]).cuda()
    # a.device and b.device are device(type='cuda', index=1)

    # You can also use ``Tensor.to`` to transfer a tensor:
    b2 = torch.tensor([1., 2.]).to(device=cuda)
    # b.device and b2.device are device(type='cuda', index=1)

    c = a + b
    # c.device is device(type='cuda', index=1)

    z = x + y
    # z.device is device(type='cuda', index=0)

    # even within a context, you can specify the device
    # (or give a GPU index to the .cuda call)
    d = torch.randn(2, device=cuda2)
    e = torch.randn(2).to(cuda2)
    f = torch.randn(2).cuda(cuda2)
    # d.device, e.device, and f.device are all device(type='cuda', index=2)