多个GPU的pytorch处理思路

import torch
import torch.nn as nn
import torch.optim as optim
import torch.distributed as dist
import torch.multiprocessing as mp

# 定义模型
class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.fc = nn.Linear(10, 1)

    def forward(self, x):
        return self.fc(x)

# 定义训练函数
def train(model, device, data):
    optimizer = optim.SGD(model.parameters(), lr=0.01)
    criterion = nn.MSELoss()

    x, y = data
    x = x.to(device)
    y = y.to(device)

    optimizer.zero_grad()
    output = model(x)
    loss = criterion(output, y)
    loss.backward()
    optimizer.step()

# 定义并行训练函数
def parallel_train(model, data):
    ngpus = torch.cuda.device_count()
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    model.to(device)
    model = torch.nn.DataParallel(model)

    train(model, device, data)

# 主函数
if __name__ == '__main__':
    # 数据切分和分配
    data = torch.randn(100, 10), torch.randn(100, 1)
    parallel_train(Model(), data)