Thank you for the answer .
I don’t have any ready model really so I picked up the following code instead and uncomment the GPU part to make sure the GPU will be used:
: dtype = torch.float
: #device = torch.device("cpu")
: device = torch.device("cuda:0") # Uncomment this to run on GPU
:
: # N is batch size; D_in is input dimension;
: # H is hidden dimension; D_out is output dimension.
: N, D_in, H, D_out = 64, 1000, 100, 10
:
: # Create random input and output data
: x = torch.randn(N, D_in, device=device, dtype=dtype)
: y = torch.randn(N, D_out, device=device, dtype=dtype)
:
: # Randomly initialize weights
: w1 = torch.randn(D_in, H, device=device, dtype=dtype)
: w2 = torch.randn(H, D_out, device=device, dtype=dtype)
:
: learning_rate = 1e-6
: for t in range(500):
:     # Forward pass: compute predicted y
:     h = x.mm(w1)
:     h_relu = h.clamp(min=0)
:     y_pred = h_relu.mm(w2)
:
:     # Compute and print loss
:     loss = (y_pred - y).pow(2).sum().item()
:     if t % 100 == 99:
:         print(t, loss)
:
:     # Backprop to compute gradients of w1 and w2 with respect to loss
:     grad_y_pred = 2.0 * (y_pred - y)
:     grad_w2 = h_relu.t().mm(grad_y_pred)
:     grad_h_relu = grad_y_pred.mm(w2.t())
:     grad_h = grad_h_relu.clone()
:     grad_h[h < 0] = 0
:     grad_w1 = x.t().mm(grad_h)
:
:     # Update weights using gradient descent
:     w1 -= learning_rate * grad_w1
:     w2 -= learning_rate * grad_w2

Got the following:
99 384.6168518066406
199 2.1987862586975098
299 0.02469571679830551
399 0.0005685320356860757
499 7.920750067569315e-05