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()

1. 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。