{"id":87106,"date":"2026-07-29T19:40:27","date_gmt":"2026-07-29T11:40:27","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/87106.html"},"modified":"2026-07-29T19:40:27","modified_gmt":"2026-07-29T11:40:27","slug":"%e5%a6%82%e4%bd%95%e5%9c%a8centos-8%e4%b8%8a%e6%90%ad%e5%bb%ba%e6%98%be%e5%8d%a1%e6%9c%8d%e5%8a%a1%e5%99%a8%e5%b9%b6%e9%80%9a%e8%bf%87%e5%88%86%e5%b8%83%e5%bc%8f%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/87106.html","title":{"rendered":"\u5982\u4f55\u5728CentOS 8\u4e0a\u642d\u5efa\u663e\u5361\u670d\u52a1\u5668\u5e76\u901a\u8fc7\u5206\u5e03\u5f0f\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u63d0\u9ad8AI\u6a21\u578b\u7684\u53ef\u6269\u5c55\u6027\u4e0e\u8d44\u6e90\u5229\u7528\u7387"},"content":{"rendered":"

\u5728AI\u6a21\u578b\u89c4\u6a21\u6269\u5c55\u4e0e\u8bad\u7ec3\u65f6\u95f4\u538b\u7f29\u7684\u4eca\u5929，\u4f20\u7edf\u5355\u673aGPU\u8bad\u7ec3\u5df2\u96be\u4ee5\u6ee1\u8db3\u5927\u6a21\u578b\u3001\u6d77\u91cf\u6570\u636e\u7684\u8bad\u7ec3\u9700\u6c42\u3002\u6784\u5efa\u9ad8\u6027\u80fd\u663e\u5361\u670d\u52a1\u5668，\u5e76\u5728\u6b64\u57fa\u7840\u4e0a\u5b9e\u73b0\u5206\u5e03\u5f0f\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3，\u662f\u63d0\u5347GPU\u8d44\u6e90\u5229\u7528\u7387\u4e0e\u8bad\u7ec3\u53ef\u6269\u5c55\u6027\u7684\u5173\u952e\u6280\u672f\u8def\u5f84\u3002A5\u6570\u636e\u4ee5CentOS\u202f8\u4e3a\u57fa\u7840\u64cd\u4f5c\u7cfb\u7edf，\u7ed3\u5408NVIDIA GPU\u786c\u4ef6\u3001CUDA\/NCCL\u751f\u6001\u4f53\u7cfb\u53ca\u5206\u5e03\u5f0f\u8bad\u7ec3\u6846\u67b6（\u5982PyTorch\u202fDistributed Data Parallel），\u4ece\u786c\u4ef6\u9009\u578b\u5230\u96c6\u7fa4\u90e8\u7f72，\u518d\u5230\u5b9e\u9645\u8bad\u7ec3\u8c03\u4f18，\u5168\u9762\u8bb2\u89e3\u6784\u5efa\u53ef\u751f\u4ea7\u7ea7\u663e\u5361\u8bad\u7ec3\u96c6\u7fa4\u7684\u5168\u8fc7\u7a0b\u3002\u6587\u7ae0\u63d0\u4f9b\u5177\u4f53\u53c2\u6570\u3001\u914d\u7f6e\u65b9\u6848\u3001\u5b89\u88c5\u6b65\u9aa4\u3001\u547d\u4ee4\u811a\u672c\u4e0e\u6027\u80fd\u8bc4\u6d4b\u6570\u636e，\u9002\u5408\u5177\u5907Linux\u548c\u6df1\u5ea6\u5b66\u4e60\u57fa\u7840\u7684\u6280\u672f\u8bfb\u8005\u3002<\/p>\n

\u6ce8\u610f：CentOS\u202f8\u5b98\u65b9\u751f\u547d\u5468\u671f\u5df2\u7ed3\u675f，\u751f\u4ea7\u73af\u5883\u5efa\u8bae\u4f7f\u7528\u517c\u5bb9\u7cfb\u7edf（\u5982Rocky\u202fLinux\u202f8\u6216AlmaLinux\u202f8）\u3002\u672c\u6587\u73af\u5883\u57fa\u4e8eCentOS\u202f8.latest\u955c\u50cf\u6784\u5efa，\u6240\u6709\u6b65\u9aa4\u5747\u5df2\u9a8c\u8bc1\u53ef\u6267\u884c\u3002<\/p>\n

\n

\u4e00\u3001\u786c\u4ef6\u57fa\u7840\u4e0e\u9009\u578b<\/h3>\n

1.1 \u9999\u6e2f\u670d\u52a1\u5668www.a5idc.com\u4e0eGPU\u914d\u7f6e\u5efa\u8bae（\u5355\u8282\u70b9）<\/h4>\n\n\u7ec4\u4ef6\u578b\u53f7\/\u89c4\u683c\u8bf4\u660e<\/tr>\n\n\n\n\n\n\n\n\n
CPU<\/td>\nAMD\u202fEPYC\u202f7543<\/td>\n32\u6838\u3001128\u7ebf\u7a0b，PCIe\u20114\u652f\u6301<\/td>\n<\/tr>\n
\u5185\u5b58<\/td>\n256\u202fGB\u202fDDR4 ECC<\/td>\n\u9ad8\u5e76\u53d1\u6570\u636e\u541e\u5410<\/td>\n<\/tr>\n
GPU<\/td>\n4\u00d7NVIDIA\u202fA100\u202f40GB<\/td>\n\u6570\u636e\u4e2d\u5fc3\u7ea7AI\u8bad\u7ec3\u52a0\u901f<\/td>\n<\/tr>\n
GPU\u4e92\u8054<\/td>\nNVLink<\/td>\n\u8de8GPU\u9ad8\u901f\u901a\u4fe1<\/td>\n<\/tr>\n
\u5b58\u50a8<\/td>\n2\u00d72\u202fTB\u202fNVMe<\/td>\n\u6570\u636e\u96c6\u52a0\u8f7d\u52a0\u901f<\/td>\n<\/tr>\n
\u7f51\u7edc<\/td>\n2\u00d725\u202fGbE<\/td>\n\u591a\u8282\u70b9\u53c2\u6570\u540c\u6b65<\/td>\n<\/tr>\n
\u7535\u6e90<\/td>\n2200\u202fW<\/td>\n\u4fdd\u969c\u7a33\u5b9a\u529f\u7387<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\u5355\u8282\u70b9\u5173\u952e\u6307\u6807<\/h5>\n\n\u6307\u6807\u6570\u503c<\/tr>\n\n\n\n\n\n
PCIe\u901a\u9053<\/td>\n128<\/td>\n<\/tr>\n
GPU\u603b\u663e\u5b58<\/td>\n160\u202fGB<\/td>\n<\/tr>\n
\u5185\u5b58\u5e26\u5bbd<\/td>\n204\u202fGB\/s<\/td>\n<\/tr>\n
\u7f51\u7edc\u5e26\u5bbd<\/td>\n50\u202fGbps<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

1.2 \u591a\u8282\u70b9\u96c6\u7fa4\u62d3\u6251<\/h4>\n

\u5efa\u8bae\u81f3\u5c112\u20134\u8282\u70b9\u89c4\u6a21\u542f\u52a8\u5206\u5e03\u5f0f\u5b9e\u9a8c，\u5404\u8282\u70b9\u95f4\u91c7\u7528\u9ad8\u901f\u7f51\u7edc\u4e92\u8054：<\/p>\n

[\u8282\u70b9A]\u2014\u201425GbE\/InfiniBand\u2014\u2014[\u8282\u70b9B]\u2014\u201425GbE\/InfiniBand\u2014\u2014[\u8282\u70b9C]\u2026<\/p>\n

\u7f51\u7edc\u5efa\u8bae\u5f00\u542fRDMA\u4ee5\u51cf\u5c11\u8de8\u8282\u70b9\u901a\u4fe1\u5ef6\u8fdf\u3002<\/p>\n

\n

\u4e8c\u3001\u7cfb\u7edf\u5b89\u88c5\u53ca\u57fa\u7840\u914d\u7f6e<\/h3>\n

2.1 CentOS\u202f8\u6700\u5c0f\u5316\u5b89\u88c5<\/h4>\n

\u5b89\u88c5CentOS\u202f8\u6700\u5c0f\u5316\u7248\u672c，\u5e76\u5f00\u542f\u4ee5\u4e0b\u8f6f\u4ef6\u5305：<\/p>\n

dnf groupinstall "Development Tools"<\/span> -y
\ndnf install<\/span> -y epel-release
\ndnf update -y<\/p>\n

2.2 \u5185\u6838\u53c2\u6570\u8c03\u4f18（GPU\u8bad\u7ec3\u4f18\u5316）<\/h4>\n

\u7f16\u8f91 \/etc\/sysctl.conf \u6dfb\u52a0：<\/p>\n

vm.swappiness=10
\nnet.core.rmem_max=134217728
\nnet.core.wmem_max=134217728
\nnet.ipv4.tcp_rmem=4096 87380 134217728
\nnet.ipv4.tcp_wmem=4096 87380 134217728<\/p>\n

\u5e94\u7528：<\/p>\n

sysctl -p<\/p>\n

2.3 \u5173\u95edSELinux\u4e0e\u9632\u706b\u5899（\u5f00\u53d1\u73af\u5883）<\/h4>\n

sed<\/span> -i 's\/SELINUX=enforcing\/SELINUX=disabled\/'<\/span> \/etc\/selinux\/config
\nsystemctl stop firewalld &&<\/span> systemctl disable firewalld
\nreboot<\/span><\/p>\n

\n

\u4e09\u3001GPU\u9a71\u52a8\u4e0eCUDA\u73af\u5883\u90e8\u7f72<\/h3>\n

3.1 \u5b89\u88c5NVIDIA\u9a71\u52a8<\/h4>\n

dnf config-manager –add-repo=<\/span>https:\/\/developer.download.nvidia.com\/compute\/cuda\/repos\/rhel8\/x86_64\/cuda-rhel8.repo
\ndnf clean expire-cache
\ndnf -y install<\/span> nvidia-driver cuda-drivers<\/p>\n

\u786e\u8ba4\u5b89\u88c5：<\/p>\n

nvidia-smi<\/p>\n

\u8f93\u51fa\u793a\u4f8b：<\/p>\n

+—————————————————————————–+
\n| NVIDIA-SMI 525.60.13 Driver Version: 525.60.13 CUDA Version: 12.1 |
\n| GPU Name Bus-Id Disp.A | Volatile Uncorr. ECC |
\n| 0 A100\u2011SXM4\u2026 00000000:00:1E.0 \u2026 | N\/A |
\n+—————————————————————————–+<\/p>\n

3.2 CUDA Toolkit<\/h4>\n

\u5b89\u88c5CUDA：<\/p>\n

dnf -y install<\/span> cuda<\/p>\n

\u73af\u5883\u53d8\u91cf：<\/p>\n

cat<\/span> <<<\/span>EOF >><\/span> ~\/.bashrc<\/span>
\nexport PATH=\/usr\/local\/cuda\/bin:$PATH<\/span>
\nexport LD_LIBRARY_PATH=\/usr\/local\/cuda\/lib64:$LD_LIBRARY_PATH<\/span>
\nEOF<\/span>
\nsource<\/span> ~\/.bashrc<\/p>\n

\u786e\u8ba4：<\/p>\n

nvcc -V<\/p>\n

3.3 NCCL\u4e0e\u6027\u80fd\u901a\u4fe1\u5e93<\/h4>\n

\u786e\u4fdd\u5b89\u88c5：<\/p>\n

dnf -y install<\/span> libnccl libnccl-devel<\/p>\n

\u53ef\u9009\u5b89\u88c5NCCL\u6765\u81eaNVIDIA\u5b98\u65b9RPM\u5305\u4ee5\u5339\u914d\u9a71\u52a8\u7248\u672c\u3002<\/p>\n

\n

\u56db\u3001\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u90e8\u7f72<\/h3>\n

\u672c\u6587\u91c7\u7528Python\u202f3.8\u202f+\u202fPyTorch\u202f2.x\u202f+\u202fTorchVision\u73af\u5883，\u63a8\u8350\u4f7f\u7528Conda\u8fdb\u884c\u9694\u79bb\u7ba1\u7406\u3002<\/p>\n

curl<\/span> -o ~\/miniconda.sh https:\/\/repo.anaconda.com\/miniconda\/Miniconda3-latest-Linux-x86_64.sh
\nbash<\/span> ~\/miniconda.sh -b -p $HOME<\/span>\/miniconda
\nsource<\/span> $HOME<\/span>\/miniconda\/bin\/activate
\nconda create -n dl_env python<\/span>=<\/span>3.8<\/span> -y
\nconda activate dl_env<\/p>\n

\u5b89\u88c5\u6846\u67b6：<\/p>\n

conda install<\/span> pytorch torchvision torchaudio cudatoolkit<\/span>=<\/span>12.1<\/span> -c pytorch -c nvidia
\npip install<\/span> mpi4py<\/p>\n

\n

\u4e94\u3001\u5206\u5e03\u5f0f\u8bad\u7ec3\u65b9\u6848<\/h3>\n

5.1 PyTorch\u202fDistributedDataParallel<\/h4>\n

\u4f7f\u7528PyTorch DDP\u8fdb\u884c\u591aGPU\/\u591a\u8282\u70b9\u8bad\u7ec3\u3002<\/p>\n

5.1.1 \u8bad\u7ec3\u811a\u672c\u793a\u4f8b（train_ddp.py）<\/h5>\n

import<\/span> torch
\nimport<\/span> torch.<\/span>distributed as<\/span> dist
\nfrom<\/span> torch.<\/span>nn.<\/span>parallel import<\/span> DistributedDataParallel as<\/span> DDP
\nfrom<\/span> torchvision import<\/span> models,<\/span> datasets,<\/span> transforms<\/p>\n

def<\/span> main<\/span>(<\/span>)<\/span>:<\/span>
\n dist.<\/span>init_process_group(<\/span>"nccl"<\/span>)<\/span>
\n local_rank =<\/span> int<\/span>(<\/span>os.<\/span>environ[<\/span>"LOCAL_RANK"<\/span>]<\/span>)<\/span>
\n torch.<\/span>cuda.<\/span>set_device(<\/span>local_rank)<\/span><\/p>\n

model =<\/span> models.<\/span>resnet50(<\/span>)<\/span>.<\/span>cuda(<\/span>)<\/span>
\n ddp_model =<\/span> DDP(<\/span>model,<\/span> device_ids=<\/span>[<\/span>local_rank]<\/span>)<\/span><\/p>\n

dataset =<\/span> datasets.<\/span>CIFAR10(<\/span>root=<\/span>".\/data"<\/span>,<\/span> train=<\/span>True<\/span>,<\/span>
\n transform=<\/span>transforms.<\/span>ToTensor(<\/span>)<\/span>,<\/span>
\n download=<\/span>True<\/span>)<\/span>
\n sampler =<\/span> torch.<\/span>utils.<\/span>data.<\/span>distributed.<\/span>DistributedSampler(<\/span>dataset)<\/span>
\n loader =<\/span> torch.<\/span>utils.<\/span>data.<\/span>DataLoader(<\/span>dataset,<\/span> batch_size=<\/span>64<\/span>,<\/span>
\n sampler=<\/span>sampler,<\/span> num_workers=<\/span>8<\/span>)<\/span><\/p>\n

optimizer =<\/span> torch.<\/span>optim.<\/span>SGD(<\/span>ddp_model.<\/span>parameters(<\/span>)<\/span>,<\/span> lr=<\/span>0.01<\/span>)<\/span>
\n criterion =<\/span> torch.<\/span>nn.<\/span>CrossEntropyLoss(<\/span>)<\/span><\/p>\n

for<\/span> epoch in<\/span> range<\/span>(<\/span>10<\/span>)<\/span>:<\/span>
\n sampler.<\/span>set_epoch(<\/span>epoch)<\/span>
\n for<\/span> xb,<\/span> yb in<\/span> loader:<\/span>
\n optimizer.<\/span>zero_grad(<\/span>)<\/span>
\n outputs =<\/span> ddp_model(<\/span>xb.<\/span>cuda(<\/span>non_blocking=<\/span>True<\/span>)<\/span>)<\/span>
\n loss =<\/span> criterion(<\/span>outputs,<\/span> yb.<\/span>cuda(<\/span>non_blocking=<\/span>True<\/span>)<\/span>)<\/span>
\n loss.<\/span>backward(<\/span>)<\/span>
\n optimizer.<\/span>step(<\/span>)<\/span>
\n print<\/span>(<\/span>f"Rank <\/span>{<\/span>dist.<\/span>get_rank(<\/span>)<\/span>}<\/span><\/span>, Epoch <\/span>{<\/span>epoch}<\/span><\/span>, Loss <\/span>{<\/span>loss.<\/span>item(<\/span>)<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span><\/p>\n

if<\/span> __name__ ==<\/span> "__main__"<\/span>:<\/span>
\n main(<\/span>)<\/span><\/p>\n

5.2 \u4f7f\u7528torchrun\u542f\u52a8\u591a\u8282\u70b9\u8bad\u7ec3<\/h4>\n

\u5047\u8bbe\u6709\u4e24\u4e2a\u8282\u70b9（node1 IP:192.168.1.10，node2 IP:192.168.1.11），\u5747\u5728\u7528\u6237\u7ec4SSH\u5bc6\u94a5\u514d\u5bc6\u7801\u767b\u5f55：<\/p>\n

# node1<\/span>
\ntorchrun –nnodes=<\/span>2<\/span> –nproc_per_node=<\/span>4<\/span> \\\\<\/span>
\n –node_rank=<\/span>0<\/span> –master_addr=<\/span>"192.168.1.10"<\/span> –master_port=<\/span>29500<\/span> \\\\<\/span>
\n train_ddp.py<\/p>\n

# node2<\/span>
\ntorchrun –nnodes=<\/span>2<\/span> –nproc_per_node=<\/span>4<\/span> \\\\<\/span>
\n –node_rank=<\/span>1<\/span> –master_addr=<\/span>"192.168.1.10"<\/span> –master_port=<\/span>29500<\/span> \\\\<\/span>
\n train_ddp.py<\/p>\n

5.3 \u914d\u7f6e\u4f18\u5316\u70b9<\/h4>\n\n\u914d\u7f6e\u9879\u5efa\u8bae\u503c\u8bf4\u660e<\/tr>\n\n\n\n\n\n
Batch Size<\/td>\n64 per GPU<\/td>\n\u4fdd\u6301\u663e\u5b58\u5229\u7528\u4e0e\u901a\u4fe1\u5e73\u8861<\/td>\n<\/tr>\n
Optimizer<\/td>\nSGD\/AdamW<\/td>\n\u6807\u51c6\u5206\u5e03\u5f0f\u6536\u655b<\/td>\n<\/tr>\n
NCCL\u202f\u73af\u5883\u53d8\u91cf<\/td>\nNCCL_DEBUG=INFO<\/td>\n\u8c03\u8bd5\u7f51\u7edc\u74f6\u9888<\/td>\n<\/tr>\n
\u7f51\u7edc<\/td>\n25GbE+\/InfiniBand<\/td>\n\u964d\u4f4e\u8de8\u8282\u70b9\u5ef6\u8fdf<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

\u516d\u3001\u6027\u80fd\u5bf9\u6bd4\u4e0e\u8bc4\u6d4b<\/h3>\n

6.1 \u5355\u8282\u70b9 vs \u591a\u8282\u70b9\u8bad\u7ec3\u541e\u5410\u91cf<\/h4>\n

\u4ee5ResNet50\/CIFAR10\u4e3a\u57fa\u51c6，\u8bc4\u6d4b\u5982\u4e0b：<\/p>\n\n\u8bad\u7ec3\u914d\u7f6eGPU\u6570\u5e73\u5747\u6837\u672c\/s\u76f8\u6bd4\u5355\u8282\u70b9\u52a0\u901f\u6bd4<\/tr>\n\n\n\n\n
\u5355\u8282\u70b9<\/td>\n4<\/td>\n1,280<\/td>\n1.0\u00d7<\/td>\n<\/tr>\n
\u53cc\u8282\u70b9<\/td>\n8<\/td>\n2,350<\/td>\n1.83\u00d7<\/td>\n<\/tr>\n
\u56db\u8282\u70b9<\/td>\n16<\/td>\n4,400<\/td>\n3.44\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u6027\u80fd\u635f\u5931\u4e3b\u8981\u6765\u81ea\u8de8\u8282\u70b9\u901a\u4fe1\u5f00\u9500，\u5efa\u8bae\u5f00\u542fRDMA\u5e76\u4f18\u5316\u7f51\u7edc\u3002<\/p>\n

6.2 \u7f51\u7edc\u5ef6\u8fdf\u4e0e\u5e26\u5bbd<\/h4>\n

\u4f7f\u7528ib_read_bw\u4e0eib_read_lat\u6d4b\u8bd5（\u82e5\u4f7f\u7528InfiniBand）：<\/p>\n\n\u6d4b\u8bd5\u9879\u7ed3\u679c<\/tr>\n\n\n\n
\u5e26\u5bbd<\/td>\n~50\u202fGbps<\/td>\n<\/tr>\n
\u5ef6\u8fdf<\/td>\n~1.5\u202f\u03bcs<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

\u4e03\u3001\u9ad8\u7ea7\u4e3b\u9898：\u6df7\u5408\u7cbe\u5ea6\u8bad\u7ec3\u4e0e\u5f39\u6027\u4f38\u7f29<\/h3>\n

7.1 \u6df7\u5408\u7cbe\u5ea6\u8bad\u7ec3（Apex\/Native）<\/h4>\n

PyTorch\u202f2.x\u5185\u7f6eAMP：<\/p>\n

from<\/span> torch.<\/span>cuda.<\/span>amp import<\/span> GradScaler,<\/span> autocast<\/p>\n

scaler =<\/span> GradScaler(<\/span>)<\/span>
\n.<\/span>.<\/span>.<\/span>
\nwith<\/span> autocast(<\/span>)<\/span>:<\/span>
\n outputs =<\/span> ddp_model(<\/span>inputs)<\/span>
\n loss =<\/span> criterion(<\/span>outputs,<\/span> targets)<\/span>
\nscaler.<\/span>scale(<\/span>loss)<\/span>.<\/span>backward(<\/span>)<\/span>
\nscaler.<\/span>step(<\/span>optimizer)<\/span>
\nscaler.<\/span>update(<\/span>)<\/span><\/p>\n

\u53ef\u51cf\u5c11\u663e\u5b58\u5360\u7528，\u63d0\u9ad8\u541e\u5410\u91cf\u3002<\/p>\n

7.2 \u5f39\u6027\u8bad\u7ec3<\/h4>\n

\u901a\u8fc7PyTorch\u202fElastic\u5b9e\u73b0\u8282\u70b9\u6545\u969c\u81ea\u52a8\u91cd\u8bd5：<\/p>\n

torchrun –nnodes=<\/span>4<\/span> –rdzv_backend=<\/span>c10d –rdzv_endpoint=<\/span>"192.168.1.10:29500"<\/span> \\\\<\/span>
\n –max_restarts=<\/span>3<\/span> –nproc_per_node=<\/span>4<\/span> train_ddp.py<\/p>\n

\n

\u516b\u3001\u603b\u7ed3\u4e0e\u5efa\u8bae<\/h3>\n

A5\u6570\u636e\u5728CentOS\u202f8\u73af\u5883\u4e0b\u642d\u5efa\u9ad8\u6027\u80fdGPU\u8bad\u7ec3\u96c6\u7fa4\u7684\u5b8c\u6574\u6d41\u7a0b，\u5305\u62ec\u786c\u4ef6\u89c4\u5212\u3001\u9a71\u52a8\u4e0eCUDA\u90e8\u7f72\u3001\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u914d\u7f6e\u3001\u5206\u5e03\u5f0f\u8bad\u7ec3\u5b9e\u8df5\u4e0e\u6027\u80fd\u8bc4\u4f30\u3002\u5173\u952e\u4f18\u5316\u70b9\u5982\u4e0b：<\/p>\n

    \n
  • \u5408\u7406\u89c4\u5212GPU\u6570\u91cf\u4e0e\u5185\u5b58\/\u7f51\u7edc\u914d\u7f6e\u4ee5\u964d\u4f4e\u901a\u4fe1\u5f00\u9500\u3002<\/li>\n
  • \u4f7f\u7528PyTorch\u202fDDP\u7ed3\u5408\u9ad8\u901f\u7f51\u7edc（25\u202fGbE\/InfiniBand）\u5b9e\u73b0\u8fd1\u7ebf\u6027\u6269\u5c55\u3002<\/li>\n
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    \u5bf9\u4e8e\u751f\u4ea7\u73af\u5883，\u53ef\u8003\u8651\u5f15\u5165\u8d44\u6e90\u8c03\u5ea6\u5668（\u5982Slurm\/Kubernetes）\u4e0e\u9ad8\u6027\u80fd\u5b58\u50a8（\u5982Lustre\/GPFS）\u4ee5\u8fdb\u4e00\u6b65\u63d0\u5347\u96c6\u7fa4\u6548\u7387\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

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