Can i run PyTorch in tpu?

imrankhan_pythonLang: <p><strong>Is any way to run this code in tpu</strong></p>
<pre><code class="lang-auto">
import torch
import torchvision
import torchvision.transforms as transforms

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)




import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)
        
    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

net = Net()




import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)




for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, (inputs, labels) in enumerate(trainloader):
        # get the inputs; data is a list of [inputs, labels]
        #inputs, labels = data
        
        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        

        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

</code></pre>

surya00060: <p>Hi</p>
<p>You can execute this code on Google TPUs but you need to make certain changes to your code. You can find an example <a href="https://colab.research.google.com/github/pytorch/xla/blob/master/contrib/colab/resnet18-training.ipynb" rel="nofollow noopener">here</a>. Also you can find more examples/tutorials on executing PyTorch codes on Google TPUs on the following GitHub repository <a href="https://github.com/pytorch/xla" rel="nofollow noopener">https://github.com/pytorch/xla</a></p>

imrankhan_pythonLang: <p>i see it… it’s not simple as TensorFlow simply i don’t understand any single line of code that you provide an example notebook. <img src="https://discuss.pytorch.org/images/emoji/apple/confused.png?v=9" title=":confused:" class="emoji" alt=":confused:"> <img src="https://discuss.pytorch.org/images/emoji/apple/confused.png?v=9" title=":confused:" class="emoji" alt=":confused:"> <img src="https://discuss.pytorch.org/images/emoji/apple/confused.png?v=9" title=":confused:" class="emoji" alt=":confused:"></p>