分布式训练环境稳定性测试

在多机多卡分布式训练环境中，环境稳定性是保证训练任务成功的关键因素。本文将通过实际案例演示如何系统性地测试分布式训练环境的稳定性。

测试目标

验证Horovod和PyTorch Distributed在不同负载下的稳定性和容错能力。

环境准备

1. 硬件配置

• 4台服务器，每台2张GPU
• 10G网络带宽
• 同步的NTP时间服务

2. 软件环境

pip install torch==2.0.1
pip install horovod==0.28.1

测试案例：Horovod稳定性测试

import torch
import torch.nn as nn
import horovod.torch as hvd
import time

def test_stability():
    # 初始化Horovod
    hvd.init()
    
    # 设置设备
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    torch.manual_seed(42)
    
    # 创建简单模型
    model = nn.Linear(1000, 10).to(device)
    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
    
    # 模拟训练过程
    for epoch in range(10):
        try:
            # 模拟数据生成
            x = torch.randn(64, 1000).to(device)
            y = torch.randint(0, 10, (64,)).to(device)
            
            # 前向传播
            output = model(x)
            loss = nn.CrossEntropyLoss()(output, y)
            
            # 反向传播
            optimizer.zero_grad()
            loss.backward()
            
            # 同步梯度
            hvd.allreduce_gradients(model)
            
            # 更新参数
            optimizer.step()
            
            print(f"Epoch {epoch}, Loss: {loss.item()}")
            
            # 模拟网络抖动测试
            if epoch == 5:
                time.sleep(2)  # 模拟网络延迟
                
        except Exception as e:
            print(f"Error in epoch {epoch}: {e}")
            continue
    
    print("Training completed successfully")

if __name__ == "__main__":
    test_stability()

PyTorch Distributed稳定性测试

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):
    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()

def test_ddp_stability(rank, world_size):
    setup(rank, world_size)
    
    device = torch.device('cuda', rank)
    
    # 创建模型
    model = nn.Linear(1000, 10).to(device)
    ddp_model = DDP(model, device_ids=[rank])
    
    try:
        for epoch in range(20):
            x = torch.randn(64, 1000).to(device)
            y = torch.randint(0, 10, (64,)).to(device)
            
            output = ddp_model(x)
            loss = nn.CrossEntropyLoss()(output, y)
            
            loss.backward()
            
            print(f"Rank {rank}, Epoch {epoch}, Loss: {loss.item()}")
            
            # 模拟异常情况
            if epoch == 10:
                time.sleep(3)
                
        print(f"Rank {rank} completed training")
    except Exception as e:
        print(f"Error in rank {rank}: {e}")
    finally:
        cleanup()

# 启动测试
if __name__ == "__main__":
    world_size = 2
    mp.spawn(test_ddp_stability, args=(world_size,), nprocs=world_size, join=True)

测试方法

1. 基础功能验证：确保基本训练流程正常
2. 异常处理测试：模拟网络中断、节点故障等场景
3. 性能监控：记录各epoch耗时和资源使用率
4. 数据一致性检查：验证多节点训练结果一致性

复现步骤

1. 准备4台服务器的Horovod环境
2. 部署测试代码到所有节点
3. 使用以下命令运行：

   horovodrun -np 8 python test_stability.py
   

通过以上测试，可以全面评估分布式训练环境的稳定性，并为生产环境提供可靠的配置依据。