pytorch多gpu并行训练怎么实现

在PyTorch中实现多GPU并行训练可以通过使用torch.nn.DataParallel模块或torch.nn.parallel.DistributedDataParallel模块来实现。下面分别介绍这两种方法的实现步骤：

import torchimport torch.nn as nnfrom torch.utils.data import DataLoader# 构建模型model = nn.Sequential(    nn.Linear(10, 100),    nn.ReLU(),    nn.Linear(100, 1))# 将模型放到多个GPU上model = nn.DataParallel(model)# 定义损失函数和优化器criterion = nn.MSELoss()optimizer = torch.optim.SGD(model.parameters(), lr=0.01)# 构建数据加载器train_loader = DataLoader(dataset, batch_size=64, shuffle=True)# 开始训练for epoch in range(num_epochs):    for inputs, targets in train_loader:        outputs = model(inputs)        loss = criterion(outputs, targets)                optimizer.zero_grad()        loss.backward()        optimizer.step()

import torchimport torch.nn as nnfrom torch.utils.data import DataLoaderimport torch.distributed as dist# 初始化进程组dist.init_process_group(backend='nccl')# 构建模型model = nn.Sequential(    nn.Linear(10, 100),    nn.ReLU(),    nn.Linear(100, 1))# 将模型放到多个GPU上model = nn.parallel.DistributedDataParallel(model)# 定义损失函数和优化器criterion = nn.MSELoss()optimizer = torch.optim.SGD(model.parameters(), lr=0.01)# 构建数据加载器train_loader = DataLoader(dataset, batch_size=64, shuffle=True)# 开始训练for epoch in range(num_epochs):    for inputs, targets in train_loader:        outputs = model(inputs)        loss = criterion(outputs, targets)                optimizer.zero_grad()        loss.backward()        optimizer.step()

以上是使用torch.nn.DataParallel和torch.nn.parallel.DistributedDataParallel模块在PyTorch中实现多GPU并行训练的方法。根据具体需求选择合适的模块来实现多GPU训练。