PyTorch DDP训练部署验证

PyTorch DDP训练部署验证

PyTorch Distributed Data Parallel (DDP) 是实现多机多卡训练的核心框架。本文将通过实际案例演示如何配置和优化DDP训练环境。

环境准备

# 安装必要依赖
pip install torch torchvision torchaudio
pip install horovod  # 可选，用于混合并行

基础配置代码

import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP
import os

def setup(rank, world_size):
    # 初始化分布式环境
    os.environ['MASTER_ADDR'] = '127.0.0.1'
    os.environ['MASTER_PORT'] = '12355'
    dist.init_process_group("nccl", rank=rank, world_size=world_size)

def cleanup():
    dist.destroy_process_group()

# 训练函数
def train(rank, world_size):
    setup(rank, world_size)
    
    # 创建模型并移动到GPU
    model = torch.nn.Linear(1000, 10).to(rank)
    model = DDP(model, device_ids=[rank])
    
    # 创建数据加载器
    dataset = torch.utils.data.TensorDataset(
        torch.randn(1000, 1000),
        torch.randint(0, 10, (1000,))
    )
    dataloader = torch.utils.data.DataLoader(dataset, batch_size=32)
    
    # 定义优化器
    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
    criterion = torch.nn.CrossEntropyLoss()
    
    # 训练循环
    for epoch in range(5):
        for batch_idx, (data, target) in enumerate(dataloader):
            data, target = data.to(rank), target.to(rank)
            optimizer.zero_grad()
            output = model(data)
            loss = criterion(output, target)
            loss.backward()
            optimizer.step()
    
    cleanup()

# 启动多进程训练
if __name__ == "__main__":
    world_size = torch.cuda.device_count()
    mp.spawn(train, args=(world_size,), nprocs=world_size, join=True)

性能优化要点

1. 混合精度训练：使用torch.cuda.amp自动混合精度
2. 梯度压缩：在大规模分布式中启用梯度压缩减少通信开销
3. 数据预取：使用torch.utils.data.DataLoader的prefetch_factor参数
4. NCCL优化：设置NCCL_BLOCKING_WAIT=1提升通信效率

通过以上配置可实现高效的多机多卡训练部署。