From practical standpoint just one minor digression:

import torch
dev = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

This dev now knows if cuda or cpu.

And there is a difference in how you deal with models and with tensors when moving to cuda. It is a bit strange at first.

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

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

This tells me the GPU GeForce GTX 950M is being used by PyTorch.

This should work:

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'

This tells me CUDA is available and can be used in one of your devices (GPUs). And currently, Device 0 or the GPU GeForce GTX 950M is being used by PyTorch.