初见PyTorch.pdf

    1. import  torch
    2. from    torch import autograd
    5. x = torch.tensor(1.)
    6. a = torch.tensor(1., requires_grad=True)
    7. b = torch.tensor(2., requires_grad=True)
    8. c = torch.tensor(3., requires_grad=True)
    10. y = a**2 * x + b * x + c
    12. print('before:', a.grad, b.grad, c.grad)
    13. grads = autograd.grad(y, [a, b, c])
    14. print('after :', grads[0], grads[1], grads[2])
    1. import     torch
    2. import  time
    3. print(torch.__version__)
    4. print(torch.cuda.is_available())
    5. # print('hello, world.')
    8. a = torch.randn(10000, 1000)
    9. b = torch.randn(1000, 2000)
    11. t0 = time.time()
    12. c = torch.matmul(a, b)
    13. t1 = time.time()
    14. print(a.device, t1 - t0, c.norm(2))
    16. device = torch.device('cuda')
    17. a = a.to(device)
    18. b = b.to(device)
    20. t0 = time.time()
    21. c = torch.matmul(a, b)
    22. t2 = time.time()
    23. print(a.device, t2 - t0, c.norm(2))
    25. t0 = time.time()
    26. c = torch.matmul(a, b)
    27. t2 = time.time()
    28. print(a.device, t2 - t0, c.norm(2))