以下是使用 PyTorch 进行多 GPU 训练的示例代码： ```python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader from torchvision.datasets import CIFAR10 from torchvision.transforms import transforms # 定义模型 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(torch.relu(self.conv1(x))) x = self.pool(torch.relu(self.conv2(x))) x = x.view(-1, 16 * 5 * 5) x = torch.relu(self.fc1(x)) x = torch.relu(self.fc2(x)) x = self.fc3(x) return x # 定义训练函数 def train(model, device, train_loader, optimizer, criterion): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = criterion(output, target) loss.backward() optimizer.step() # 定义测试函数 def test(model, device, test_loader, criterion): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += criterion(output, target).item() pred = output.argmax(dim=1, keepdim=True) correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) accuracy = 100. * correct / len(test_loader.dataset) print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format( test_loss, correct, len(test_loader.dataset), accuracy)) # 定义主函数 def main(): # 设置超参数 batch_size = 128 epochs = 10 lr = 0.01 momentum = 0.9 num_workers = 4 num_gpus = torch.cuda.device_count() # 加载数据集 transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) train_dataset = CIFAR10(root='./data', train=True, download=True, transform=transform) test_dataset = CIFAR10(root='./data', train=False, download=True, transform=transform) train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers) test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers) # 初始化模型和优化器 model = Net() if num_gpus > 1: model = nn.DataParallel(model) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum) criterion = nn.CrossEntropyLoss() # 训练和测试 for epoch in range(1, epochs + 1): train(model, device, train_loader, optimizer, criterion) test(model, device, test_loader, criterion) if __name__ == '__main__': main() ``` 这个示例代码可以在多个 GPU 上并行训练模型，如果只有一个 GPU，可以将 `num_gpus` 设置为 1。
