{"id":117751,"date":"2026-10-25T09:32:51","date_gmt":"2026-10-25T01:32:51","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/117751.html"},"modified":"2026-10-25T09:32:51","modified_gmt":"2026-10-25T01:32:51","slug":"%e5%a6%82%e4%bd%95%e5%9c%a8gpu%e6%98%be%e5%8d%a1%e6%9c%8d%e5%8a%a1%e5%99%a8%e4%b8%8a%e4%bc%98%e5%8c%96%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%e8%ae%ad%e7%bb%83%e4%bb%bb%e5%8a%a1%ef%bc%8c%e6%8f%90","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/117751.html","title":{"rendered":"\u5982\u4f55\u5728GPU\u663e\u5361\u670d\u52a1\u5668\u4e0a\u4f18\u5316\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u4efb\u52a1\uff0c\u63d0\u5347\u5927\u89c4\u6a21\u6a21\u578b\u7684\u8ba1\u7b97\u6548\u7387"},"content":{"rendered":"

\/* MD \/ \u5bcc\u6587\u672c\u4e2d\u7684 .toc\uff08\u542b\u535a\u5ba2\u56ed\u642c\u5bb6\u7b49\u5d4c\u5957\u7ed3\u6784\uff09\uff1b.toc-box \u5728\u4fa7\u680f\uff0c\u4e0d\u53d7\u5f71\u54cd *\/
\n #content_views .toc,
\n \/* \u7f16\u8f91\u5668\u5e38\u5728\u76ee\u5f55\u524d\u540e\u63d2\u5165\u7a7a p\uff08:empty \u4ecd\u5360 20px\uff09\uff0c\u4e00\u5e76\u53bb\u6389\u907f\u514d\u9876\u7a7a\u9699 *\/
\n #content_views.markdown_views > p:empty:has(+ .toc),
\n #content_views.markdown_views > .toc + p:empty,
\n \/* \u5bcc\u6587\u672c\u65e7\u7248\u76ee\u5f55\u6807\u8bb0 *\/
\n #content_views.htmledit_views #main-toc,
\n #content_views.htmledit_views #hr-toc,
\n #content_views.htmledit_views p[id*=\\”-toc\\”] {
\n display: none !important;
\n }
\n \/* \u76ee\u5f55\u53bb\u6389\u540e\uff0c\u7d27\u8ddf\u7684\u9996\u4e2a\u6807\u9898\u4e0d\u518d\u591a\u51fa\u4e00\u5757\u4e0a\u8fb9\u8ddd *\/
\n #content_views.markdown_views > .toc + h1,
\n #content_views.markdown_views > .toc + h2,
\n #content_views.markdown_views > .toc + h3,
\n #content_views.markdown_views > .toc + h4,
\n #content_views.markdown_views > .toc + p:empty + h1,
\n #content_views.markdown_views > .toc + p:empty + h2,
\n #content_views.markdown_views > .toc + p:empty + h3,
\n #content_views.markdown_views > .toc + p:empty + h4 {
\n margin-top: 0 !important;
\n }<\/p>\n

\u6211\u66fe\u4f7f\u7528\u591a\u5361GPU\u670d\u52a1\u5668\u8bad\u7ec3\u4e00\u4e2a\u5305\u542b\u8d85\u8fc710\u4ebf\u53c2\u6570\u7684\u63a8\u8350\u6a21\u578b\u3002\u521d\u671f\u8bad\u7ec3\u6548\u7387\u6781\u4f4e，\u5355\u4e2aEpoch\u7684\u65f6\u95f4\u5e38\u5e38\u8d85\u8fc712\u5c0f\u65f6，GPU\u5229\u7528\u7387\u53ea\u670930%\u201340%，\u7f51\u7edc\u5e26\u5bbd\u548c\u6570\u636e\u52a0\u8f7d\u6210\u4e3a\u660e\u663e\u74f6\u9888\u3002\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e9b\u95ee\u9898，\u6211\u68b3\u7406\u5e76\u5b9e\u8df5\u4e86\u4e00\u5957\u884c\u4e4b\u6709\u6548\u7684\u4f18\u5316\u65b9\u6848\u3002A5\u6570\u636e\u5c06\u4ece\u786c\u4ef6\u914d\u7f6e\u3001\u8f6f\u4ef6\u6808\u3001\u6570\u636e\u7ba1\u7ebf\u3001\u6df7\u5408\u7cbe\u5ea6\u3001\u5206\u5e03\u5f0f\u8bad\u7ec3\u5230\u6027\u80fd\u5206\u6790\u7b49\u591a\u4e2a\u5c42\u9762\u7cfb\u7edf\u603b\u7ed3\u4e86\u63d0\u5347GPU\u670d\u52a1\u5668\u8bad\u7ec3\u6548\u7387\u7684\u6280\u672f\u7ec6\u8282，\u5e76\u901a\u8fc7\u5177\u4f53\u53c2\u6570\u3001\u4ee3\u7801\u793a\u4f8b\u548c\u8bc4\u6d4b\u6570\u636e\u5448\u73b0\u4f18\u5316\u6548\u679c\u3002<\/p>\n

\u672c\u6587\u9002\u7528\u4e8e\u4e2d\u5927\u578b\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u4efb\u52a1（\u5982\u5927\u89c4\u6a21\u63a8\u8350\u7cfb\u7edf\u3001\u89c6\u89c9\/\u8bed\u8a00\u5927\u6a21\u578b）\u5728GPU\u670d\u52a1\u5668\u4e0a\u7684\u5b9e\u8df5\u4ea4\u6d41，\u91cd\u70b9\u9762\u5411\u771f\u5b9e\u751f\u4ea7\u573a\u666f\u3002<\/p>\n

\n

\u4e00\u3001GPU\u670d\u52a1\u5668\u786c\u4ef6\u914d\u7f6e\u8be6\u89e3<\/h3>\n

\u5728\u4f18\u5316\u524d，\u7406\u89e3\u8bad\u7ec3\u670d\u52a1\u5668www.a5idc.com\u7684\u786c\u4ef6\u7ed3\u6784\u662f\u5173\u952e\u3002\u4e0b\u8868\u6c47\u603b\u4e86\u6211\u4eec\u4f7f\u7528\u7684\u4e24\u7c7bGPU\u670d\u52a1\u5668\u5178\u578b\u914d\u7f6e\u53c2\u6570（A100\u4e0eH100）：<\/p>\n\n\u786c\u4ef6\u90e8\u4ef6\u89c4\u683c\u53c2\u6570（A100 \u670d\u52a1\u5668）\u89c4\u683c\u53c2\u6570（H100 \u670d\u52a1\u5668）<\/tr>\n\n\n\n\n\n\n\n\n\n\n
GPU\u578b\u53f7<\/td>\nNVIDIA A100 SXM4 80GB \u00d7 8<\/td>\nNVIDIA H100 SXM 80GB \u00d7 8<\/td>\n<\/tr>\n
GPU\u603b\u663e\u5b58<\/td>\n640GB<\/td>\n640GB<\/td>\n<\/tr>\n
GPU\u4e92\u8054<\/td>\nNVLink 600GB\/s \u5168\u4e92\u8054<\/td>\nNVLink 900GB\/s \u5168\u4e92\u8054<\/td>\n<\/tr>\n
CPU<\/td>\n2 \u00d7 AMD EPYC 7742 (64 \u6838, 2.25GHz)<\/td>\n2 \u00d7 AMD EPYC 7763 (64 \u6838, 2.45GHz)<\/td>\n<\/tr>\n
\u7cfb\u7edf\u5185\u5b58<\/td>\n1.5TB DDR4<\/td>\n2TB DDR4<\/td>\n<\/tr>\n
\u5b58\u50a8<\/td>\n8 \u00d7 2TB NVMe SSD (RAID 0\/1 \u53ef\u9009)<\/td>\n8 \u00d7 2TB NVMe SSD (RAID 0\/1 \u53ef\u9009)<\/td>\n<\/tr>\n
\u7f51\u7edc<\/td>\n200Gb\/s InfiniBand HDR<\/td>\n400Gb\/s InfiniBand NDR<\/td>\n<\/tr>\n
\u7535\u6e90<\/td>\n3 \u00d7 2200W Redundant<\/td>\n3 \u00d7 2200W Redundant<\/td>\n<\/tr>\n
\u64cd\u4f5c\u7cfb\u7edf<\/td>\nUbuntu 22.04 LTS<\/td>\nUbuntu 22.04 LTS<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u914d\u7f6e\u8bf4\u660e\u4e0e\u5bf9\u8bad\u7ec3\u6548\u7387\u7684\u5f71\u54cd<\/h4>\n
  • \n

    GPU\u663e\u5b58\u4e0e\u4e92\u8054\u5e26\u5bbd \u5927\u663e\u5b58（80GB\/\u5361）\u5141\u8bb8\u52a0\u8f7d\u66f4\u5927\u6279\u6b21（batch size）\u548c\u66f4\u5927\u6a21\u578b\u3002NVLink\u5e26\u5bbd\u8d8a\u9ad8，\u8de8GPU\u901a\u4fe1\u6548\u7387\u8d8a\u9ad8，\u5c24\u5176\u5728\u5206\u5e03\u5f0f\u8bad\u7ec3\u4e2d\u63d0\u5347\u663e\u8457\u3002<\/p>\n<\/li>\n

  • \n

    CPU\u4e0e\u5185\u5b58 \u5145\u8db3\u7684CPU\u6838\u5fc3\u6570\u548c\u5927\u5185\u5b58\u652f\u6301\u9ad8\u5e76\u53d1\u6570\u636e\u9884\u5904\u7406，\u4e0d\u4f1a\u6210\u4e3a\u6570\u636e\u4f9b\u7ed9\u7aef\u74f6\u9888\u3002<\/p>\n<\/li>\n

  • \n

    \u7f51\u7edc\u4e92\u8054（InfiniBand） \u5728\u591a\u8282\u70b9\u8bad\u7ec3\u4e2d，\u9ad8\u901fRDMA\u7f51\u7edc\u80fd\u663e\u8457\u964d\u4f4eAllReduce\u7b49\u901a\u4fe1\u5ef6\u8fdf，\u63d0\u5347\u6269\u5c55\u6027\u3002<\/p>\n<\/li>\n

    \n

    \u4e8c\u3001\u8f6f\u4ef6\u6808\u4e0e\u73af\u5883\u51c6\u5907<\/h3>\n

    \u6211\u4eec\u7edf\u4e00\u5728\u6240\u6709\u670d\u52a1\u5668\u4e0a\u90e8\u7f72\u4ee5\u4e0b\u8f6f\u4ef6\u73af\u5883：<\/p>\n\n\u8f6f\u4ef6\u7ec4\u4ef6\u7248\u672c<\/tr>\n\n\n\n\n\n\n\n\n\n
    Linux \u5185\u6838<\/td>\n5.15.x<\/td>\n<\/tr>\n
    NVIDIA \u9a71\u52a8<\/td>\n535.x<\/td>\n<\/tr>\n
    CUDA<\/td>\n12.2<\/td>\n<\/tr>\n
    cuDNN<\/td>\n8.9<\/td>\n<\/tr>\n
    NCCL<\/td>\n2.18<\/td>\n<\/tr>\n
    PyTorch<\/td>\n2.1.0<\/td>\n<\/tr>\n
    Python<\/td>\n3.10<\/td>\n<\/tr>\n
    \u6570\u636e\u5e93\/\u5b58\u50a8<\/td>\nS3 \u517c\u5bb9\u5bf9\u8c61\u5b58\u50a8（MinIO）<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u5b89\u88c5\u793a\u4f8b（\u4ee5 CUDA 12.2 + PyTorch 2.1 \u4e3a\u4f8b）：<\/p>\n

    # \u5b89\u88c5 NVIDIA \u9a71\u52a8\u4e0e CUDA<\/span>
    \nsudo<\/span> apt<\/span> update &&<\/span> sudo<\/span> apt<\/span> install<\/span> -y build-essential
    \nwget<\/span> https:\/\/developer.download.nvidia.com\/compute\/cuda\/repos\/ubuntu2204\/x86_64\/cuda-ubuntu2204.pin
    \nsudo<\/span> mv<\/span> cuda-ubuntu2204.pin \/etc\/apt\/preferences.d\/cuda-repository-pin-600
    \nsudo<\/span> apt-key adv –fetch-keys https:\/\/developer.download.nvidia.com\/compute\/cuda\/repos\/ubuntu2204\/x86_64\/3bf863cc.pub
    \nsudo<\/span> add-apt-repository "deb https:\/\/developer.download.nvidia.com\/compute\/cuda\/repos\/ubuntu2204\/x86_64\/ \/"<\/span>
    \nsudo<\/span> apt<\/span> update &&<\/span> sudo<\/span> apt<\/span> install<\/span> -y cuda-toolkit-12-2<\/p>\n

    # \u5b89\u88c5 Python \u4e0e PyTorch<\/span>
    \nconda create -n dl_env python<\/span>=<\/span>3.10<\/span> -y
    \nconda activate dl_env
    \npip install<\/span> torch<\/span>==<\/span>2.1<\/span>.0 torchvision torchaudio –index-url https:\/\/download.pytorch.org\/whl\/cu122<\/p>\n

    \u786e\u4fdd NCCL \u652f\u6301\u591a\u5361\u5206\u5e03\u5f0f\u8bad\u7ec3，\u5e76\u8c03\u6574\u7cfb\u7edf\u53c2\u6570：<\/p>\n

    # \u542f\u7528\u5927\u9875\u5185\u5b58\u4e0e\u5173\u95ed\u4ea4\u6362<\/span>
    \nsudo<\/span> swapoff -a
    \necho<\/span> "vm.nr_hugepages=1024"<\/span> |<\/span> sudo<\/span> tee<\/span> -a \/etc\/sysctl.conf
    \nsudo<\/span> sysctl -p<\/p>\n

    \n

    \u4e09\u3001\u6570\u636e\u7ba1\u7ebf\u4f18\u5316<\/h3>\n

    \u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u7684\u6570\u636e\u9884\u5904\u7406\u5f80\u5f80\u5236\u7ea6GPU\u7684\u6709\u6548\u5229\u7528\u7387\u3002\u4f18\u5316\u76ee\u6807\u662f\u8ba9GPU\u59cb\u7ec8\u6709\u6570\u636e\u53ef\u7528\u3002<\/p>\n

    3.1 \u4f7f\u7528\u591a\u7ebf\u7a0b\u52a0\u8f7d\u4e0e\u9884\u53d6<\/h4>\n

    \u5728 PyTorch \u4e2d：<\/p>\n

    train_loader =<\/span> DataLoader(<\/span>
    \n dataset=<\/span>train_dataset,<\/span>
    \n batch_size=<\/span>256<\/span>,<\/span>
    \n shuffle=<\/span>True<\/span>,<\/span>
    \n num_workers=<\/span>16<\/span>,<\/span> # \u8bfb\u53d6\u6570\u636e\u7684 worker \u6570\u91cf<\/span>
    \n pin_memory=<\/span>True<\/span>,<\/span> # \u9501\u9875\u5185\u5b58\u4ee5\u52a0\u5feb\u5230 GPU \u7684\u62f7\u8d1d<\/span>
    \n prefetch_factor=<\/span>4<\/span>
    \n)<\/span><\/p>\n

    3.2 TFRecord\/LMDB \u7b49\u683c\u5f0f<\/h4>\n

    \u5728\u5927\u89c4\u6a21\u56fe\u50cf\/\u89c6\u89c9\u4efb\u52a1\u4e2d，\u91c7\u7528 TFRecord \u6216 LMDB \u683c\u5f0f\u80fd\u663e\u8457\u63d0\u5347\u8bfb\u53d6\u6548\u7387\u3002\u793a\u4f8b\u4ee3\u7801\u7565\u3002<\/p>\n

    \n

    \u56db\u3001\u6df7\u5408\u7cbe\u5ea6\u8bad\u7ec3（Automatic Mixed Precision）<\/h3>\n

    \u6df7\u5408\u7cbe\u5ea6\u8bad\u7ec3\u662f\u63d0\u5347\u8bad\u7ec3\u6548\u7387\u7684\u5173\u952e\u624b\u6bb5\u4e4b\u4e00，\u5728\u4fdd\u8bc1\u6a21\u578b\u7cbe\u5ea6\u7684\u524d\u63d0\u4e0b，\u51cf\u5c11\u663e\u5b58\u5360\u7528\u4e0e\u52a0\u901f\u8ba1\u7b97\u3002<\/p>\n

    4.1 PyTorch AMP \u793a\u4f8b<\/h4>\n

    scaler =<\/span> torch.<\/span>cuda.<\/span>amp.<\/span>GradScaler(<\/span>)<\/span><\/p>\n

    for<\/span> inputs,<\/span> labels in<\/span> train_loader:<\/span>
    \n optimizer.<\/span>zero_grad(<\/span>)<\/span>
    \n with<\/span> torch.<\/span>cuda.<\/span>amp.<\/span>autocast(<\/span>)<\/span>:<\/span>
    \n outputs =<\/span> model(<\/span>inputs)<\/span>
    \n loss =<\/span> loss_fn(<\/span>outputs,<\/span> labels)<\/span>
    \n scaler.<\/span>scale(<\/span>loss)<\/span>.<\/span>backward(<\/span>)<\/span>
    \n scaler.<\/span>step(<\/span>optimizer)<\/span>
    \n scaler.<\/span>update(<\/span>)<\/span><\/p>\n

    4.2 \u4f18\u5316\u5efa\u8bae<\/h4>\n
      \n
    • \u5728\u652f\u6301 Tensor Core \u7684 GPU（\u5982 A100\/H100）\u4e0a\u5f00\u542f AMP，\u5927\u591a\u6570\u60c5\u51b5\u4e0b\u53ef\u5c06\u663e\u5b58\u5360\u7528\u51cf\u5c11 30%\u201350% \u3002<\/li>\n
    • \u5bf9\u6570\u503c\u654f\u611f\u90e8\u5206（\u5982 Softmax\u3001LayerNorm）\u4fdd\u6301 FP32 \u7cbe\u5ea6\u3002<\/li>\n<\/ul>\n
      \n

      \u4e94\u3001\u5206\u5e03\u5f0f\u8bad\u7ec3\u4e0e\u901a\u4fe1\u4f18\u5316<\/h3>\n

      \u5728\u5355\u670d\u52a1\u5668\u591aGPU\u6216\u591a\u8282\u70b9\u591aGPU\u573a\u666f\u4e2d，\u5206\u5e03\u5f0f\u8bad\u7ec3\u662f\u63d0\u5347\u8bad\u7ec3\u901f\u5ea6\u7684\u6838\u5fc3\u3002<\/p>\n

      5.1 \u5355\u8282\u70b9\u591a\u5361（PyTorch DDP）<\/h4>\n

      \u542f\u52a8\u811a\u672c：<\/p>\n

      python -m torch.distributed.launch \\\\<\/span>
      \n –nproc_per_node=<\/span>8<\/span> \\\\<\/span>
      \n train.py –epochs 100<\/span> –batch-size 256<\/span><\/p>\n

      \u5728 train.py \u4e2d：<\/p>\n

      dist.<\/span>init_process_group(<\/span>backend=<\/span>'nccl'<\/span>)<\/span>
      \nmodel =<\/span> torch.<\/span>nn.<\/span>parallel.<\/span>DistributedDataParallel(<\/span>model,<\/span> device_ids=<\/span>[<\/span>local_rank]<\/span>)<\/span><\/p>\n

      5.2 \u591a\u8282\u70b9\u8bad\u7ec3（NCCL + InfiniBand）<\/h4>\n

      \u786e\u4fdd NCCL \u4f7f\u7528 InfiniBand：<\/p>\n

      export<\/span> NCCL_NET_GDR_LEVEL<\/span>=<\/span>2<\/span>
      \nexport<\/span> NCCL_IB_HCA<\/span>=<\/span>mlx5
      \nexport<\/span> NCCL_IB_GID_INDEX<\/span>=<\/span>3<\/span>
      \nexport<\/span> NCCL_IB_SL<\/span>=<\/span>5<\/span><\/p>\n

      \n

      \u516d\u3001\u6027\u80fd\u5206\u6790\u4e0e\u74f6\u9888\u5b9a\u4f4d<\/h3>\n

      \u4ec5\u505a\u4f18\u5316\u800c\u4e0d\u505a\u6027\u80fd\u5206\u6790\u662f\u76f2\u76ee\u7684\u3002\u6211\u4eec\u4e3b\u8981\u4f7f\u7528\u4ee5\u4e0b\u5de5\u5177：<\/p>\n\n\u5206\u6790\u5de5\u5177\u4f5c\u7528<\/tr>\n\n\n\n\n
      PyTorch Profiler<\/td>\n\u91c7\u96c6\u6a21\u578b\u524d\u5411\/\u53cd\u5411\u8ba1\u7b97\u65f6\u95f4<\/td>\n<\/tr>\n
      nvprof \/ Nsight Systems<\/td>\nGPU \u5c42\u9762\u5206\u6790（\u5185\u5b58\u5e26\u5bbd\u3001\u7b97\u529b\u5229\u7528\u7b49）<\/td>\n<\/tr>\n
      nvidia-smi<\/td>\nGPU \u5229\u7528\u7387\u3001\u663e\u5b58\u5360\u7528\u5b9e\u65f6\u76d1\u63a7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      6.1 PyTorch Profiler \u793a\u4f8b<\/h4>\n

      with<\/span> torch.<\/span>profiler.<\/span>profile(<\/span>
      \n schedule=<\/span>torch.<\/span>profiler.<\/span>schedule(<\/span>wait=<\/span>1<\/span>,<\/span> warmup=<\/span>1<\/span>,<\/span> active=<\/span>3<\/span>)<\/span>,<\/span>
      \n on_trace_ready=<\/span>torch.<\/span>profiler.<\/span>tensorboard_trace_handler(<\/span>'.\/log'<\/span>)<\/span>
      \n)<\/span> as<\/span> prof:<\/span>
      \n train_step(<\/span>)<\/span><\/p>\n

      \n

      \u4e03\u3001\u8bc4\u6d4b：\u4f18\u5316\u524d\u540e\u5bf9\u6bd4<\/h3>\n

      \u6211\u4eec\u5728\u7edf\u4e00\u6570\u636e\u96c6（\u5982 ImageNet1K）\u4e0a\u8bc4\u6d4b\u4e86\u4e0d\u540c\u4f18\u5316\u7b56\u7565\u7684\u6548\u679c\u3002<\/p>\n\n\u4f18\u5316\u9879\u5355\u5361\u8bad\u7ec3\u65f6\u95f4\/EpochGPU \u5229\u7528\u7387\u5185\u5b58\u5360\u7528<\/tr>\n\n\n\n\n\n\n
      \u57fa\u7ebf（\u65e0\u4f18\u5316）<\/td>\n85 min<\/td>\n35%<\/td>\n75GB<\/td>\n<\/tr>\n
      + \u591a\u7ebf\u7a0b\u6570\u636e\u52a0\u8f7d<\/td>\n70 min<\/td>\n48%<\/td>\n75GB<\/td>\n<\/tr>\n
      + \u6df7\u5408\u7cbe\u5ea6 AMP<\/td>\n55 min<\/td>\n60%<\/td>\n42GB<\/td>\n<\/tr>\n
      + DDP (8 \u5361)<\/td>\n12 min<\/td>\n78%<\/td>\n42GB\/\u5361<\/td>\n<\/tr>\n
      + DDP + InfiniBand \u4f18\u5316<\/td>\n9 min<\/td>\n82%<\/td>\n42GB\/\u5361<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u53ef\u4ee5\u770b\u51fa，\u5355\u8282\u70b9 DDP \u63d0\u5347\u663e\u8457，\u591a\u8282\u70b9\u901a\u4fe1\u4f18\u5316\u8fdb\u4e00\u6b65\u63d0\u9ad8\u6269\u5c55\u6548\u7387\u3002<\/p>\n

      \n

      \u516b\u3001\u6784\u5efa\u9ad8\u6548\u7684\u8bad\u7ec3\u4f53\u7cfb<\/h3>\n

      A5\u6570\u636e\u901a\u8fc7\u672c\u6587\u7684\u7cfb\u7edf\u4f18\u5316\u601d\u8def，\u6211\u4eec\u5728\u771f\u5b9e\u4e1a\u52a1\u4e2d\u5c06\u5927\u89c4\u6a21\u6a21\u578b\u8bad\u7ec3\u65f6\u95f4\u4ece\u4e00\u5468\u7ea7\u522b\u7f29\u77ed\u81f3\u4e00\u5929\u5185\u5b8c\u6210，GPU \u7684\u5229\u7528\u7387\u548c\u6574\u4f53\u8bad\u7ec3\u6548\u7387\u5927\u5e45\u63d0\u5347\u3002\u5173\u952e\u5728\u4e8e：<\/p>\n

    • \u5408\u7406\u9009\u62e9\u548c\u914d\u7f6e\u9ad8\u6027\u80fd\u786c\u4ef6（\u5982 A100\/H100 GPU\u3001NVLink\u3001InfiniBand）\u3002<\/li>\n
    • \u6784\u5efa\u7a33\u5b9a\u3001\u4e0e\u786c\u4ef6\u5339\u914d\u7684\u8f6f\u4ef6\u6808（CUDA\/cuDNN\/NCCL）\u3002<\/li>\n
    • \u4f18\u5316\u6570\u636e\u7ba1\u7ebf\u3001\u5f15\u5165\u6df7\u5408\u7cbe\u5ea6\u3001\u5f00\u542f\u5206\u5e03\u5f0f\u8bad\u7ec3\u3002<\/li>\n
    • \u5b9a\u671f\u5206\u6790\u6027\u80fd\u74f6\u9888，\u5e76\u636e\u6b64\u8c03\u6574\u7b56\u7565\u3002<\/li>\n

      \u5982\u9700\u9488\u5bf9\u7279\u5b9a\u6a21\u578b（\u5982 Transformer \u5927\u89c4\u6a21\u8bed\u8a00\u6a21\u578b）\u8fdb\u4e00\u6b65\u4f18\u5316\u8bad\u7ec3\u6548\u7387，\u53ef\u7ee7\u7eed\u63a2\u8ba8\u68af\u5ea6\u7d2f\u79ef\u3001\u53c2\u6570\u5206\u7247（ZeRO）\u3001\u6a21\u578b\u5e76\u884c\u7b49\u9ad8\u7ea7\u6280\u672f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

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