Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

# THIRD
# We can create regular tensors and send them to
# the device (as we did with our data)
torch.manual_seed(42)
b = torch.randn(1, dtype=torch.float).to(device)
w = torch.randn(1, dtype=torch.float).to(device)
# and THEN set them as requiring gradients...
b.requires_grad_()
w.requires_grad_()
print(b, w)
Output
tensor([0.3367], device='cuda:0', requires_grad=True)
tensor([0.1288], device='cuda:0', requires_grad=True)
This approach worked fine; we managed to end up with gradient-requiring GPU
tensors for our parameters b and w. It seems a lot of work, though… Can we do
better still?
Yes, we can do better: We can assign tensors to a device at the moment of their
creation.
Notebook Cell 1.4 - Actually creating variables for the coefficients
# FINAL
# We can specify the device at the moment of creation
# RECOMMENDED!
# Step 0 - Initializes parameters "b" and "w" randomly
torch.manual_seed(42)
b = torch.randn(1, requires_grad=True, \
dtype=torch.float, device=device)
w = torch.randn(1, requires_grad=True, \
dtype=torch.float, device=device)
print(b, w)
84 | Chapter 1: A Simple Regression Problem