Pytorch gpu使用率很低

Question

我正在尝试来自https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html的pytorch示例。 当我运行这个示例时，GPU的使用率约为1%，完成时间为130秒 而对于CPU情况，CPU使用率约为90%，完成时间为79s 我的CPU是Intel（R）Core（TM）i7-8700 我的GPU是NVIDIA GeForce RTX 2070

我想问一下CPU运行速度比GPU快是否正常？ 因为GPU的使用量很小（与我从另一个网站看到的相比） 下面是我运行的代码（类似于网站）

import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import torch.nn as nn
import torch.nn.functional as F

def run():
    # ==================================================================================
    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)

    classes = ('plane', 'car', 'bird', 'cat',
               'deer', 'dog', 'frog', 'horse', 'ship', 'truck')


    # ==================================================================================
    # # functions to show an image


    # def imshow(img):
    #     img = img / 2 + 0.5     # unnormalize
    #     npimg = img.numpy()
    #     plt.imshow(np.transpose(npimg, (1, 2, 0)))
    #     plt.show()


    # # get some random training images
    # dataiter = iter(trainloader)
    # images, labels = dataiter.next()


    # # show images
    # imshow(torchvision.utils.make_grid(images))
    # # print labels
    # print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
    # ==================================================================================
    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()
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    print(device)
    net.to(device)
    # ==================================================================================
    import torch.optim as optim

    criterion = nn.CrossEntropyLoss().to(device)
    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, data in enumerate(trainloader, 0):
            # get the inputs; data is a list of [inputs, labels]
            inputs, labels = data[0].to(device), data[1].to(device)


            # zero the parameter gradients
            optimizer.zero_grad()

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

    print('Finished Training')
    # ==================================================================================
    PATH = './cifar_net.pth'
    torch.save(net.state_dict(), PATH)

    # ==================================================================================
    # ==================================================================================
    # ==================================================================================


if __name__ == '__main__':
    run()