
基于openEuler 24.03 实现 DeepSeek-R1:671B 的高效分布式训练
2025-02-18 Published360 views · 0 likes · 0 favorites
在 OpenAtom openEuler(简称“openEuler”) 24.03 操作系统上部署 DeepSeek-R1:671B 大模型训练(使用 20张NVIDIA A100 GPU)的完整技术指南,涵盖从系统配置、分布式训练到性能调优的全流程。
1. 系统环境与硬件准备
1.1 硬件配置
-
GPU: 20张NVIDIA A100 80GB(推荐4节点 × 5卡或2节点 × 10卡,需支持NVLink和InfiniBand)
-
CPU: 至少2× Intel Xeon Platinum 8380(64核以上)
-
内存: 每个节点≥1TB DDR4
-
存储: 每个节点配置RAID0 NVMe SSD(≥10TB,用于高速数据缓存)
-
网络: InfiniBand HDR 200G(多节点必备,单节点可选RoCE)
1.2 openEuler 24.03 基础配置
# 安装必要依赖
sudo dnf install -y kernel-devel kernel-headers gcc make cmake git python3-devel
sudo dnf groupinstall -y "Development Tools"
# 禁用防火墙和SELinux(生产环境需谨慎)
sudo systemctl stop firewalldsudo systemctl disable firewalldsudo setenforce 0
sudo sed -i 's/SELINUX=enforcing/SELINUX=disabled/g' /etc/selinux/config
# 配置SSH免密登录(多节点训练必备)
ssh-keygen -t rsa
ssh-copy-id user@node1
ssh-copy-id user@node2
2. NVIDIA驱动与CUDA生态安装
2.1 安装NVIDIA驱动(适配openEuler 24.03)
# 检查内核版本(需与驱动兼容)
uname -r # 例如:5.15.0-101.oe2403.x86_64
# 禁用nouveau驱动
echo "blacklist nouveau" | sudo tee /etc/modprobe.d/blacklist-nouveau.conf
sudo dracut --force
# 下载并安装驱动(选择CUDA 12.2+兼容版本)
wget https://us.download.nvidia.com/tesla/535.129.03/NVIDIA-Linux-x86_64-535.129.03.run
sudo sh NVIDIA-Linux-x86_64-535.129.03.run --silent --dkms
# 验证驱动
nvidia-smi # 应显示20张A100
2.2 安装CUDA 12.2 + cuDNN 8.9 + NCCL 2.18
# 安装CUDA Toolkit
wget https://developer.download.nvidia.com/compute/cuda/12.2.2/local_installers/cuda_12.2.2_535.104.05_linux.run
sudo sh cuda_12.2.2_535.104.05_linux.run --silent --toolkit
# 安装cuDNN和NCCL(需从NVIDIA官网下载)
tar -xzf cudnn-linux-x86_64-8.9.6.50_cuda12-archive.tar.xz
sudo cp -r cudnn-*-archive/include/* /usr/local/cuda/include/
sudo cp -r cudnn-*-archive/lib/* /usr/local/cuda/lib64/
tar -xzf nccl_2.18.5-1+cuda12.2_x86_64.txz
sudo cp -r nccl_*/lib/* /usr/local/cuda/lib64/
sudo cp nccl_*/include/* /usr/local/cuda/include/
# 添加环境变量
echo 'export PATH=/usr/local/cuda/bin:$PATH' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
source ~/.bashrc
3. 分布式训练环境搭建
3.1 PyTorch 2.2 + DeepSpeed 安装
# 创建Python虚拟环境
python3 -m venv deepseek-env
source deepseek-env/bin/activate
# 安装PyTorch与DeepSpeed
pip install torch==2.2.0+cu121 torchvision==0.17.0+cu121 torchaudio==2.2.0+cu121 --index-url https://download.pytorch.org/whl/cu121
pip install deepspeed==0.13.0 ninja
# 验证GPU支持
python -c "import torch; print(torch.cuda.device_count())" # 应输出20
3.2 InfiniBand网络优化
# 安装Mellanox OFED驱动(适配openEuler)
wget https://content.mellanox.com/ofed/MLNX_OFED-24.01-0.5.6.0/MLNX_OFED_LINUX-24.01-0.5.6.0-rhel9.2-x86_64.tgz
tar -xzf MLNX_OFED-*.tgz
cd MLNX_OFED_LINUX-*-rhel9.2-x86_64
sudo ./mlnxofedinstall --without-fw-update --force
sudo /etc/init.d/openibd restart
# 配置NCCL参数
echo 'export NCCL_IB_HCA=mlx5' >> ~/.bashrc
echo 'export NCCL_IB_GID_INDEX=3' >> ~/.bashrc
echo 'export NCCL_SOCKET_IFNAME=ib0' >> ~/.bashrc
echo 'export NCCL_DEBUG=WARN' >> ~/.bashrc
source ~/.bashrc
4. DeepSeek-R1:671B 训练部署
4.1 模型与数据准备
# 克隆DeepSeek官方仓库(假设已授权)
git clone https://github.com/deepseek-ai/DeepSeek-R1
cd DeepSeek-R1
# 下载预训练权重与数据集
wget https://models.deepseek.com/deepseek-r1-671b.tar.gz
tar -xzf deepseek-r1-671b.tar.gz
# 数据集预处理(需根据实际数据调整)
python tools/preprocess_data.py --input /data/raw_text.jsonl --output-prefix my_dataset
4.2 分布式启动脚本(4节点 × 5卡)
# 创建hostfile(假设节点为node1-node4)
cat > hostfile << EOF
node1 slots=5
node2 slots=5
node3 slots=5
node4 slots=5
EOF
# 使用DeepSpeed启动训练
deepspeed --hostfile hostfile \
--master_addr node1 \
--launcher openmpi \
--num_gpus 5 \
train.py \
--model_config configs/671b.yaml \
--train_data my_dataset \
--deepspeed_config ds_config.json \
--batch_size 8 \
--gradient_accumulation_steps 16
4.3 DeepSpeed配置示例(ds_config.json
)
{
"train_micro_batch_size_per_gpu": 1,
"gradient_accumulation_steps": 16,
"optimizer": {
"type": "AdamW",
"params": {
"lr": 1e-5,
"weight_decay": 0.01
}
},
"zero_optimization": {
"stage": 3,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"allgather_partitions": true,
"allgather_bucket_size": 5e8,
"overlap_comm": true
},
"fp16": {
"enabled": true,
"loss_scale_window": 1000
},
"flops_profiler": {
"enabled": true,
"profile_step": 10
}
}
5. 性能极限调优
5.1 GPU内核级优化
# 锁定GPU频率至最高性能
sudo nvidia-smi -lgc 1410,1410 # A100默认峰值频率
# 启用持久化模式
sudo nvidia-smi -pm 1
# 启用MPS(多进程服务)
sudo nvidia-cuda-mps-control -d
5.2 内存与通信优化
# 在模型代码中添加(减少内存碎片)
torch.cuda.set_per_process_memory_fraction(0.9)
# 启用激活检查点(Activation Checkpointing)
from torch.utils.checkpoint import checkpoint
def forward(self, x):
return checkpoint(self._forward_impl, x)
5.3 openEuler内核参数调优
# 调整网络缓冲区(InfiniBand优化)
echo "net.core.rmem_max=2147483647" | sudo tee -a /etc/sysctl.conf
echo "net.core.wmem_max=2147483647" | sudo tee -a /etc/sysctl.conf
# 提升文件句柄限制
echo "* soft nofile 1048576" | sudo tee -a /etc/security/limits.conf
echo "* hard nofile 1048576" | sudo tee -a /etc/security/limits.conf
# 应用配置
sudo sysctl -p
6. 监控与故障排查
6.1 实时监控工具
# GPU监控(跨节点)
nvidia-smi --query-gpu=timestamp,name,utilization.gpu,utilization.memory --format=csv -l 5
# NCCL通信分析
export NCCL_DEBUG=INFO
export NCCL_DEBUG_SUBSYS=COLL
# 带宽测试(节点间)
ib_write_bw -d mlx5_0 -F --report_gbits
6.2 常见问题解决
1、NCCL通信超时
export NCCL_IB_TIMEOUT=22
export NCCL_IB_RETRY_CNT=7
2、OOM(显存不足)
`启用ZeRO-3 Offload到CPU或NVMe:
"offload_optimizer": { "device": "nvme", "nvme_path": "/mnt/nvme" }
3、训练速度不达预期
使用Nsight Systems分析瓶颈:
nsys profile -w true -o deepseek_profile --capture-range=cudaProfilerApi \
--stop-on-range-end=true --cudabacktrace=true python train.py
关键性能指标参考
指标 | 预期范围(20×A100) |
---|---|
单卡显存占用 | 72-78GB |
全局Batch Size | 1024-4096 |
训练吞吐(Tokens/sec) | 1200-2500 |
GPU利用率 | ≥90% |
通过以上步骤,可在 openEuler 24.03 上实现 DeepSeek-R1:671B 的高效分布式训练。建议结合NVIDIA Magnum IO 进行进一步优化。
文章来源:https://mp.weixin.qq.com/s/wy1aX8H5bXidiDjvXzQnkg
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