pytorch的DP神经网络代码

下面是一个简单的 PyTorch 分布式数据并行神经网络代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.distributed as dist
import torchvision.datasets as datasets
import torchvision.transforms as transforms

# 初始化分布式进程
dist.init_process_group(backend='nccl', init_method='env://')

# 定义神经网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.fc1 = nn.Linear(256 * 4 * 4, 512)
        self.fc2 = nn.Linear(512, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv3(x))
        x = F.max_pool2d(x, 2)
        x = x.view(-1, 256 * 4 * 4)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 定义训练函数
def train(rank, world_size):
    # 加载数据
    train_set = datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms.ToTensor())
    train_sampler = torch.utils.data.distributed.DistributedSampler(train_set, num_replicas=world_size, rank=rank)
    train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, sampler=train_sampler)

    # 初始化模型、优化器和损失函数
    model = Net()
    model = nn.parallel.DistributedDataParallel(model, device_ids=[rank])
    optimizer = optim.SGD(model.parameters(), lr=0.01)
    criterion = nn.CrossEntropyLoss()

    # 训练循环
    for epoch in range(10):
        train_loss = 0.0
        for data, target in train_loader:
            optimizer.zero_grad()
            output = model(data)
            loss = criterion(output, target)
            loss.backward()
            optimizer.step()
            train_loss += loss.item() * data.size(0)
        train_loss /= len(train_loader.dataset)
        if rank == 0:
            print('Epoch: {}, Train Loss: {:.6f}'.format(epoch+1, train_loss))

# 获取本地进程信息
rank = dist.get_rank()
world_size = dist.get_world_size()

# 开始训练
train(rank, world_size)

# 清理分布式进程
dist.destroy_process_group()

在这个示例中，我们定义了一个简单的卷积神经网络模型 `Net`，并在 `train` 函数中使用 PyTorch 的分布式数据并行来训练模型。我们使用 CIFAR10 数据集进行训练，并使用 SGD 优化器和交叉熵损失函数。我们使用 `DistributedSampler` 和 `DataLoader` 来加载数据，并使用 `DistributedDataParallel` 来并行化模型。最后，我们在每个 epoch 结束时打印训练损失，并在进程 0 上输出。