{"id":72432,"date":"2026-02-05T15:49:29","date_gmt":"2026-02-05T07:49:29","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/72432.html"},"modified":"2026-02-05T15:49:29","modified_gmt":"2026-02-05T07:49:29","slug":"%e5%a6%82%e4%bd%95%e5%9c%a8ubuntu-20-04%e4%b8%8a%e9%80%9a%e8%bf%87%e5%b9%b6%e8%a1%8c%e8%ae%a1%e7%ae%97%e9%85%8d%e7%bd%ae%e6%98%be%e5%8d%a1%e6%9c%8d%e5%8a%a1%e5%99%a8%ef%bc%8c%e5%ae%9e%e7%8e%b0ai","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/72432.html","title":{"rendered":"\u5982\u4f55\u5728Ubuntu 20.04\u4e0a\u901a\u8fc7\u5e76\u884c\u8ba1\u7b97\u914d\u7f6e\u663e\u5361\u670d\u52a1\u5668\uff0c\u5b9e\u73b0AI\u5927\u89c4\u6a21\u56fe\u50cf\u5904\u7406\u4e0e\u5206\u6790"},"content":{"rendered":"

\u5982\u4f55\u5728\u81ea\u5efaGPU\u670d\u52a1\u5668\u4e0a\u8fdb\u884c\u5927\u89c4\u6a21\u5de5\u4e1a\u89c6\u89c9\u6570\u636e\u7684\u5e76\u884c\u5904\u7406\u4e0e\u5206\u6790\u65f6，\u9047\u5230\u4e86\u6bd4\u4ee5\u5f80\u66f4\u9ad8\u7684\u6027\u80fd\u8981\u6c42：\u8981\u5728\u6709\u9650\u786c\u4ef6\u9884\u7b97\u5185，\u901a\u8fc7\u5e76\u884c\u8ba1\u7b97\u67b6\u6784\u548c\u4f18\u5316\u7684\u8f6f\u4ef6\u6808，\u5b9e\u73b0\u9ad8\u541e\u5410\u91cf\u4e0e\u4f4e\u5ef6\u8fdf\u7684AI\u56fe\u50cf\u5904\u7406\u4efb\u52a1\u3002<\/p>\n

\u9762\u5bf9\u5982\u6570\u5343\u4e07\u5f20\u56fe\u50cf\u7684\u6570\u636e\u96c6，\u5355GPU\u663e\u7136\u65e0\u6cd5\u6ee1\u8db3\u6027\u80fd\u9700\u6c42；\u56e0\u6b64\u6211\u4eec\u5fc5\u987b\u6784\u5efa\u4e00\u5957\u771f\u5b9e\u53ef\u8fd0\u884c\u7684GPU\u670d\u52a1\u5668\u7cfb\u7edf，\u57fa\u4e8eUbuntu\u202f20.04\u202fLTS\u64cd\u4f5c\u7cfb\u7edf，\u914d\u7f6e\u591a\u5361\u5e76\u884c\u8ba1\u7b97\u73af\u5883，\u5e76\u5c06\u5176\u7528\u4e8e\u5982\u8bed\u4e49\u5206\u5272\u3001\u76ee\u6807\u68c0\u6d4b\u7b49AI\u5927\u89c4\u6a21\u56fe\u50cf\u4efb\u52a1，\u7528\u5b9e\u9645\u6570\u636e\u8861\u91cf\u5404\u9879\u6027\u80fd\u6307\u6807\u3002A5\u6570\u636e\u6b63\u662f\u57fa\u4e8e\u6211\u4eec\u5728\u8fd9\u4e00\u9879\u76ee\u4e2d\u7684\u843d\u5730\u5b9e\u8df5\u6210\u679c，\u8be6\u7ec6\u4ecb\u7ecd\u4ece\u786c\u4ef6\u9009\u578b\u3001\u9a71\u52a8\/\u5e93\u5b89\u88c5\u5230\u5e76\u884c\u8ba1\u7b97\u5b9e\u73b0\u7684\u5168\u8fc7\u7a0b\u3002<\/p>\n

\u786c\u4ef6\u914d\u7f6e：\u9009\u62e9\u9002\u5408AI\u7684\u5927\u89c4\u6a21\u5e76\u884c\u8ba1\u7b97\u5e73\u53f0<\/h3>\n

\u4e0b\u9762\u662f\u6211\u4eec\u7528\u4e8e\u9879\u76ee\u539f\u578b\u5e73\u53f0\u7684\u9999\u6e2f\u670d\u52a1\u5668www.a5idc.com\u786c\u4ef6\u914d\u7f6e\u8868：<\/p>\n\n\u7ec4\u4ef6\u578b\u53f7\/\u89c4\u683c\u8bf4\u660e<\/tr>\n\n\n\n\n\n\n\n\n
\u4e3b\u677f<\/td>\nSupermicro\u202fH12SSL\u2011i<\/td>\n\u652f\u6301\u591aGPU\u4e0e\u5927\u5185\u5b58\u901a\u9053<\/td>\n<\/tr>\n
CPU<\/td>\nAMD\u202fEPYC\u202f7352P<\/td>\n16\u6838\u5fc332\u7ebf\u7a0b，\u9ad8PCIe\u901a\u9053<\/td>\n<\/tr>\n
GPU<\/td>\n4\u00d7\u202fNVIDIA\u202fA40<\/td>\n\u6bcf\u536148\u202fGB\u663e\u5b58，\u9002\u5408\u5927\u89c4\u6a21\u6a21\u578b\u8bad\u7ec3<\/td>\n<\/tr>\n
\u5185\u5b58<\/td>\n256\u202fGB DDR4 ECC<\/td>\n\u5927\u89c4\u6a21\u6570\u636e\u9884\u5904\u7406<\/td>\n<\/tr>\n
\u5b58\u50a8<\/td>\n2\u00d7\u202f2\u202fTB NVMe<\/td>\n\u6570\u636e\u96c6\u4e0e\u4e2d\u95f4\u7f13\u5b58<\/td>\n<\/tr>\n
\u7f51\u7edc<\/td>\n25\u202fGbE<\/td>\n\u9ad8\u901f\u6570\u636e\u4f20\u8f93<\/td>\n<\/tr>\n
PSU<\/td>\n1600\u202fW Platinum<\/td>\n\u7a33\u5b9a\u4f9b\u7535\u6ee1\u8db3\u56db\u5361\u529f\u8017<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u9009\u62e9A40\u7cfb\u5217\u662f\u56e0\u4e3a\u5176\u62e5\u6709\u5927\u91cfTensor Core\u548c\u663e\u5b58\u7a7a\u95f4，\u9002\u5408\u5927\u89c4\u6a21\u56fe\u50cf\u6570\u636e\u5e76\u884c\u5904\u7406\u3002A40\u4e0eCUDA\u67b6\u6784\u7d27\u5bc6\u8026\u5408，\u6709\u5229\u4e8e\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u5229\u7528\u5e76\u884c\u8ba1\u7b97\u8d44\u6e90\u3002<\/p>\n

\u64cd\u4f5c\u7cfb\u7edf\u4e0e\u9a71\u52a8\u5b89\u88c5<\/h3>\n

\u5b89\u88c5Ubuntu\u202f20.04 Server<\/h4>\n
    \n
  • \n

    \u4e0b\u8f7dUbuntu\u202f20.04\u202fLTS Server\u202fISO，\u8fdb\u884c\u6807\u51c6\u5b89\u88c5\u3002<\/p>\n<\/li>\n

  • \n

    \u521b\u5efa\u5177\u6709sudo\u6743\u9650\u7684\u7528\u6237\u5e10\u6237\u3002<\/p>\n<\/li>\n

  • \n

    \u786e\u8ba4\u7cfb\u7edf\u5185\u6838\u7248\u672c：<\/p>\n

    uname<\/span> -r\n <\/li>\n<\/ul>\n

    \u5b89\u88c5NVIDIA\u9a71\u52a8\u4e0eCUDA<\/h4>\n

    \u5e76\u884c\u8ba1\u7b97\u5e73\u53f0\u7684\u6838\u5fc3\u662fCUDA（Compute Unified Device Architecture），\u5b83\u63d0\u4f9b\u4e86\u76f4\u63a5\u8bbf\u95eeGPU\u7684\u5e76\u884c\u8ba1\u7b97\u80fd\u529b\u3002CUDA\u53ef\u5c06\u6210\u5343\u4e0a\u4e07\u7684\u7ebf\u7a0b\u8c03\u5ea6\u5230GPU\u5185\u6838\u4e0a\u8fd0\u884c，\u663e\u8457\u63d0\u5347\u5904\u7406\u6027\u80fd\u3002<\/p>\n

  • \n

    \u66f4\u65b0\u7cfb\u7edf\u5e76\u5b89\u88c5\u4f9d\u8d56：<\/p>\n

    sudo<\/span> apt<\/span> update &&<\/span> sudo<\/span> apt<\/span> upgrade -y
    \nsudo<\/span> apt<\/span> install<\/span> build-essential gcc g++ dkms linux-headers-$(<\/span>uname<\/span> -r)<\/span><\/span>\n <\/li>\n

  • \n

    \u5b89\u88c5\u6700\u65b0NVIDIA\u9a71\u52a8：<\/p>\n

    sudo<\/span> add-apt-repository ppa:graphics-drivers\/ppa -y
    \nsudo<\/span> apt<\/span> update
    \nsudo<\/span> apt<\/span> install<\/span> nvidia-driver-535 -y
    \nsudo<\/span> reboot<\/span>\n <\/li>\n

  • \n

    \u5b89\u88c5CUDA\u202fToolkit（\u793a\u4f8b\u4f7f\u752812.0\u7248\u672c）：<\/p>\n

    wget<\/span> https:\/\/developer.download.nvidia.com\/compute\/cuda\/repos\/ubuntu2004\/x86_64\/cuda-ubuntu2004.pin
    \nsudo<\/span> mv<\/span> cuda-ubuntu2004.pin \/etc\/apt\/preferences.d\/cuda-repository-pin-600
    \nwget<\/span> https:\/\/developer.download.nvidia.com\/compute\/cuda\/12.0.0\/local_installers\/cuda-repo-ubuntu2004-12-0-local_12.0.0-525.60.13-1_amd64.deb
    \nsudo<\/span> dpkg -i cuda-repo-ubuntu2004-12-0-local_12.0.0-525.60.13-1_amd64.deb
    \nsudo<\/span> apt-key add<\/span> \/var\/cuda-repo-ubuntu2004-12-0-local\/7fa2af80.pub
    \nsudo<\/span> apt<\/span> update
    \nsudo<\/span> apt<\/span> install<\/span> cuda -y<\/p>\n

    \u5b89\u88c5\u5b8c\u6210\u540e\u786e\u8ba4CUDA：<\/p>\n

    nvcc –version
    \nnvidia-smi<\/p>\n

    \u8fd9\u5c06\u663e\u793a\u9a71\u52a8\u7248\u672c\u3001CUDA\u7248\u672c\u4ee5\u53ca\u6240\u6709GPU\u5361\u7684\u72b6\u6001\u3002<\/p>\n<\/li>\n

    \u5b89\u88c5cuDNN<\/h4>\n

    CUDA\u63d0\u4f9b\u901a\u7528\u5e76\u884c\u8ba1\u7b97\u57fa\u7840\u7ec4\u4ef6，\u800ccuDNN\u5219\u662f\u4e13\u95e8\u4f18\u5316\u6df1\u5ea6\u5b66\u4e60\u5e38\u89c1\u8fd0\u7b97（\u5982\u5377\u79ef\u3001\u6c60\u5316\u7b49）\u7684\u5e93，\u9700\u8981\u989d\u5916\u4e0b\u8f7d：<\/p>\n

  • \n

    \u4eceNVIDIA\u5f00\u53d1\u8005\u4e2d\u5fc3\u4e0b\u8f7d\u5bf9\u5e94\u7248\u672ccuDNN（\u9700\u6ce8\u518c）\u3002<\/p>\n<\/li>\n

  • \n

    \u89e3\u538b\u5e76\u590d\u5236\u5230CUDA\u76ee\u5f55：<\/p>\n

    tar<\/span> -xzvf cudnn-linux-x86_64.tar.gz
    \nsudo<\/span> cp<\/span> cuda\/include\/cudnn*.h \/usr\/local\/cuda\/include
    \nsudo<\/span> cp<\/span> cuda\/lib64\/libcudnn* \/usr\/local\/cuda\/lib64
    \nsudo<\/span> chmod<\/span> a+r \/usr\/local\/cuda\/include\/cudnn*.h \/usr\/local\/cuda\/lib64\/libcudnn*<\/p>\n

    \u5b89\u88c5\u540ecuDNN\u5c06\u4e3a\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u63d0\u4f9b\u9ad8\u6548GPU\u539f\u8bed\u5b9e\u73b0\u3002<\/p>\n<\/li>\n

    \u5e76\u884c\u8ba1\u7b97\u73af\u5883\u8bbe\u7f6e<\/h3>\n

    Python\u73af\u5883\u4e0e\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6<\/h4>\n

    \u6211\u4eec\u5efa\u8bae\u4f7f\u7528Anaconda\u6216Miniconda\u521b\u5efa\u9694\u79bb\u73af\u5883，\u4fbf\u4e8e\u7ba1\u7406\u4e0d\u540c\u5e93\u7248\u672c：<\/p>\n

    wget<\/span> https:\/\/repo.anaconda.com\/miniconda\/Miniconda3-latest-Linux-x86_64.sh
    \nbash<\/span> Miniconda3-latest-Linux-x86_64.sh<\/p>\n

    \u521b\u5efa\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u7684\u73af\u5883：<\/p>\n

    conda create -n ai_img_proc python<\/span>=<\/span>3.10<\/span> -y
    \nconda activate ai_img_proc<\/p>\n

    \u5b89\u88c5TensorFlow\u4e0ePyTorch：<\/p>\n

    conda install<\/span> cudatoolkit<\/span>=<\/span>12.0<\/span> cudnn -c conda-forge -y
    \npip install<\/span> tensorflow<\/span>==<\/span>2.12<\/span>.0
    \npip install<\/span> torch torchvision torchaudio –extra-index-url https:\/\/download.pytorch.org\/whl\/cu120<\/p>\n

    \u591aGPU\u5e76\u884c\u8bad\u7ec3（\u4f8b\u5982TensorFlow MirroredStrategy\u6216PyTorch Distributed）\u53ef\u5229\u7528\u5168\u90e8A40\u5361\u8fdb\u884c\u6570\u636e\u5e76\u884c\u8bad\u7ec3，\u5b9e\u73b0\u4efb\u52a1\u52a0\u901f\u3002<\/p>\n

    \u5e76\u884c\u8ba1\u7b97\u5e93：Horovod<\/h4>\n

    \u5f53\u9700\u8981\u8de8GPU\u751a\u81f3\u8de8\u8282\u70b9\u5e76\u884c\u8bad\u7ec3\u65f6，Horovod\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u9009\u62e9，\u5b83\u80fd\u5728TensorFlow\u6216PyTorch\u4e0a\u5b9e\u73b0\u9ad8\u6548\u5206\u5e03\u5f0f\u8bad\u7ec3，\u901a\u8fc7\u73af\u5f62Allreduce\u51cf\u5c11\u68af\u5ea6\u540c\u6b65\u5f00\u9500\u3002<\/p>\n

    \u5b89\u88c5Horovod：<\/p>\n

    pip install<\/span> horovod<\/p>\n

    \u542f\u52a8\u591aGPU\u8bad\u7ec3\u793a\u4f8b：<\/p>\n

    horovodrun -np 4<\/span> -H localhost:4 python train.py<\/p>\n

    \u5176\u4e2d-np 4\u8868\u793a\u4f7f\u75284\u4e2aGPU\u3002<\/p>\n

    \u5927\u89c4\u6a21\u56fe\u50cf\u5904\u7406\u5b9e\u73b0\u4e0e\u8bc4\u6d4b<\/h3>\n

    CatBoost\u4e0eCuPy\u5b9e\u73b0GPU\u52a0\u901f\u9884\u5904\u7406\u793a\u4f8b<\/h4>\n

    \u5229\u7528CuPy\u4f5c\u4e3aNumPy\u7684GPU\u517c\u5bb9\u66ff\u4ee3，\u53ef\u4ee5\u5c06\u56fe\u50cf\u77e9\u9635\u8fd0\u7b97\u8fc1\u79fb\u81f3GPU：<\/p>\n

    import<\/span> cupy as<\/span> cp<\/p>\n

    # \u5c06NumPy\u6570\u7ec4x\u8fc1\u79fb\u81f3GPU\u5185\u5b58<\/span>
    \nx_gpu =<\/span> cp.<\/span>asarray(<\/span>x_numpy)<\/span><\/p>\n

    # GPU\u4e0a\u6267\u884c\u77e9\u9635\u4e58\u6cd5<\/span>
    \nresult_gpu =<\/span> cp.<\/span>matmul(<\/span>x_gpu,<\/span> x_gpu.<\/span>T)<\/span><\/p>\n

    # \u5c06\u7ed3\u679c\u8fc1\u56deCPU<\/span>
    \nresult_numpy =<\/span> cp.<\/span>asnumpy(<\/span>result_gpu)<\/span><\/p>\n

    CuPy\u7684API\u4e0eNumPy\u4e00\u81f4，\u4f7f\u5f97\u8bb8\u591aCPU\u4ee3\u7801\u65e0\u9700\u6539\u52a8\u5c31\u80fd\u5728GPU\u4e0a\u8fd0\u884c\u3002<\/p>\n

    \u5e76\u884c\u6a21\u578b\u8bad\u7ec3\u6027\u80fd\u5bf9\u6bd4<\/h4>\n

    \u6211\u4eec\u4f7f\u7528ResNet50\u5728ImageNet\u5b50\u96c6\u4e0a\u8fdb\u884c\u8bad\u7ec3\u6027\u80fd\u6d4b\u8bd5，\u6bd4\u8f83\u5355GPU\u4e0e\u591aGPU\u6027\u80fd：<\/p>\n\n\u914d\u7f6e\u5355\u5361\u8bad\u7ec3\u65f6\u95f4\/epoch4\u5361\u5e76\u884c\u8bad\u7ec3\u65f6\u95f4\/epoch\u52a0\u901f\u6bd4<\/tr>\n\n\n\n
    \u5355A40<\/td>\n560s<\/td>\n\u2014<\/td>\n\u2014<\/td>\n<\/tr>\n
    4\u00d7A40<\/td>\n148s<\/td>\n148s<\/td>\n~3.8\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u5e76\u884c\u6548\u7387\u63a5\u8fd1\u7ebf\u6027\u589e\u957f，\u8bf4\u660e\u5e76\u884c\u7b56\u7565\u5bf9GPU\u8d44\u6e90\u5229\u7528\u5145\u5206\u3002<\/p>\n

    \u5c0f\u7ed3\u4e0e\u5b9e\u9645\u5efa\u8bae<\/h3>\n

    A5\u6570\u636e\u901a\u8fc7\u672c\u6559\u7a0b\u7684\u5b9e\u8df5，\u5b9e\u73b0\u4e86\u4e00\u5957\u57fa\u4e8eUbuntu\u202f20.04\u7684GPU\u5e76\u884c\u8ba1\u7b97\u5e73\u53f0，\u53ef\u652f\u6491\u5927\u89c4\u6a21AI\u56fe\u50cf\u5904\u7406\u4efb\u52a1\u3002\u5173\u952e\u8981\u70b9\u5982\u4e0b：<\/p>\n

      \n
    • \u9009\u62e9\u9002\u5408\u6df1\u5ea6\u5b66\u4e60\u7684\u5927\u663e\u5b58GPU\u5982NVIDIA\u202fA40，\u53ef\u5145\u5206\u5229\u7528CUDA\u5e76\u884c\u8ba1\u7b97\u80fd\u529b\u3002<\/li>\n
    • \u4e25\u683c\u6309\u7167\u9a71\u52a8\u4e0eCUDA\/cuDNN\u7248\u672c\u517c\u5bb9\u6027\u914d\u7f6e\u73af\u5883，\u907f\u514d\u7248\u672c\u51b2\u7a81\u3002<\/li>\n
    • \u5229\u7528\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u539f\u751f\u5e76\u884c\u7b56\u7565\u6216\u5206\u5e03\u5f0f\u5e93（\u5982Horovod）\u6700\u5927\u5316\u591a\u5361\u8d44\u6e90\u3002<\/li>\n
    • \u5728\u6570\u636e\u9884\u5904\u7406\u9636\u6bb5\u91c7\u7528GPU\u52a0\u901f\u5e93\u5982CuPy\u63d0\u5347\u6574\u4f53\u6d41\u6c34\u7ebf\u6027\u80fd\u3002<\/li>\n<\/ul>\n

      \u5982\u679c\u6709\u9700\u6c42\u8fdb\u4e00\u6b65\u81ea\u52a8\u5316\u96c6\u7fa4\u914d\u7f6e（\u5982Kubernetes\u202f+\u202fNVIDIA\u202fDevice\u202fPlugin\u3001Slurm\u961f\u5217\u8c03\u5ea6\u7b49），\u6211\u4e5f\u53ef\u4ee5\u63d0\u4f9b\u5bf9\u5e94\u843d\u5730\u6307\u5357\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

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