gpu partially detected, torch tensor is not uploaded to gpu

Question

My gpu is detected on

torch.cuda.is_available()

torch.randn(1).cuda()

tensorflow.test.is_built_with_cuda()

torch.cuda.device_count()

but not on

device_lib.list_local_devices()

tensorflow.config.list_local_devices('GPU')

the code I used is:

print(torch.cuda.is_available())

print(torch.randn(1).cuda())

print(device_lib.list_local_devices())

print(tf.test.is_built_with_cuda())

print(tf.__version__)

print(tf.config.list_physical_devices('GPU'))

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
print(torch.cuda.device_count())

and the result is:

True
tensor([0.7429], device='cuda:0')
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 11914755976629927437
xla_global_id: -1
]
True
2.10.0
[]
cuda:0
1

I'm using NVIDIA GTX 1070 Ti, Nvidia graphics driver 460.89, cuda 11.2, cudnn 8.1.1, torch 1.7.1+cu110, torchvision 0.8.2+cu110.

Although the result above, my deeplearning model successfully got in cuda. The problem is that I cannot get my tensor data to gpu. When I checked the datatype with print(type(x)) , it returned <class 'torch.Tensor'> .

I then tried both x.to(device) and x.cuda() .

But in both cases, it returns false to x.is_cuda .

When I tried to make this data go through my model, it returned this: RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same

Which means my model is on gpu. I can't figure out why my tensor is on cpu but my model isn't.

My data is a tensor converted from image, and is a shape of [3, 3, 512, 512]. My model is a GAN.

Answer 1

Never mind.

I didn't reassign x.

In case someone bump into this, using x = x.cuda() instead of x.cuda() made it fixed.