PyTorch Distributed Training Guide

In PyTorch, you can utilize the torch.nn.parallel.DistributedDataParallel class for distributed training. The specific steps are as follows:

1. Initialize distributed process group.

import torch
import torch.distributed as dist
from torch.multiprocessing import Process

def init_process(rank, size, fn, backend='gloo'):
    os.environ['MASTER_ADDR'] = 'localhost'
    os.environ['MASTER_PORT'] = '1234'
    
    dist.init_process_group(backend, rank=rank, world_size=size)
    fn(rank, size)

1. torch’s built-in module for distributed data parallelization

def train(rank, size):
    # 创建模型
    model = Model()
    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[rank])
    
    # 创建数据加载器
    train_loader = DataLoader(...)
    
    # 定义优化器
    optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
    
    # 训练模型
    for epoch in range(num_epochs):
        for batch_idx, (data, target) in enumerate(train_loader):
            optimizer.zero_grad()
            output = model(data)
            loss = loss_function(output, target)
            loss.backward()
            optimizer.step()

1. torch.multiprocessing.spawn is a function that allows multiple processes to be spawned in Python using PyTorch.

if __name__ == '__main__':
    num_processes = 4
    size = num_processes
    processes = []
    
    for rank in range(num_processes):
        p = Process(target=init_process, args=(rank, size, train))
        p.start()
        processes.append(p)
    
    for p in processes:
        p.join()

Here is a simple example of distributed training. Depending on the specific situation, the code can be further modified and expanded. PyTorch also offers other tools and features for distributed training, such as the torch.distributed module and torch.distributed.rpc module, which allow users to choose the appropriate tools for their distributed training needs.