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：

1. 使用torch.nn.DataParallel模块：

import torch import torch.nn as nn from 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() 

2. 使用torch.nn.parallel.DistributedDataParallel模块：

import torch import torch.nn as nn from torch.utils.data import DataLoader import 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。