电脑有两个GPU,GPU0和GPU1,这两个分别是什么意思呀,还有就是想问问要是想下载pytorch 的GPU版这种情况下下载完之后能用吗
GPU1:
GPU0:
让阿豪来帮你解答,本回答参考chatgpt3.5编写提供,如果还有疑问可以评论或留言回答: GPU表示图形处理器,是电脑中用于加速图形和视频处理的芯片,也可以用于深度学习等计算密集型任务。GPU0和GPU1是电脑中两个不同的显卡设备,可以分别处理不同的任务,或者同时协同处理任务以提升性能。 下载pytorch的GPU版之后,如果电脑中存在GPU设备,那么就可以利用GPU来进行深度学习任务的加速。需要注意的是,使用GPU版的pytorch需要在代码中指定将Tensor数据放到GPU上进行计算,否则将默认使用CPU进行计算。以下是使用pytorch的GPU版进行mnist手写字符识别的代码示例:
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torchvision.datasets as dset
import torchvision.transforms as transforms
# step 1: 定义设备
use_cuda = True
device = torch.device("cuda" if use_cuda else "cpu")
# step 2:载入数据
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
train_root = './data/mnist_train'
test_root = './data/mnist_test'
train_set = dset.MNIST(root=train_root, train=True, transform=transform, download=True)
test_set = dset.MNIST(root=test_root, train=False, transform=transform, download=True)
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
train_loader = torch.utils.data.DataLoader(train_set, batch_size=100, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=100, shuffle=False, **kwargs)
# step 3:定义模型
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.deconv1 = nn.ConvTranspose2d(100, 32 * 8, 7, 1, 0)
self.bn1 = nn.BatchNorm2d(32 * 8)
self.deconv2 = nn.ConvTranspose2d(32 * 8, 32 * 4, 4, 2, 1)
self.bn2 = nn.BatchNorm2d(32 * 4)
self.deconv3 = nn.ConvTranspose2d(32 * 4, 32 * 2, 4, 2, 1)
self.bn3 = nn.BatchNorm2d(32 * 2)
self.deconv4 = nn.ConvTranspose2d(32 * 2, 1, 4, 2, 1)
def forward(self, x):
x = nn.functional.relu(self.bn1(self.deconv1(x)))
x = nn.functional.relu(self.bn2(self.deconv2(x)))
x = nn.functional.relu(self.bn3(self.deconv3(x)))
x = nn.functional.tanh(self.deconv4(x))
return x
model = Model().to(device)
# step 4:定义优化器和损失函数
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
# step 5:训练模型
def train(epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = Variable(data.to(device)), Variable(target.to(device))
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
if batch_idx % 100 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
# step 6:测试模型
def test():
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = Variable(data.to(device)), Variable(target.to(device))
output = model(data)
test_loss += criterion(output, target).item() # sum up batch loss
pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
for epoch in range(10):
train(epoch)
test()
在以上代码中,use_cuda
变量控制是否使用GPU计算;在step 1中,如果use_cuda
为True,则将模型和数据移动到GPU上进行计算。在step 5和step 6中,将数据和模型转换到GPU上。