{"id":36244,"date":"2025-05-08T22:35:54","date_gmt":"2025-05-08T14:35:54","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/36244.html"},"modified":"2025-05-08T22:35:54","modified_gmt":"2025-05-08T14:35:54","slug":"%e6%9c%8d%e5%8a%a1%e5%99%a8%e4%b8%8a%e9%85%8d%e7%bd%aecuda%e3%80%81cudnn%e3%80%81anaconda%e5%92%8cpython%e7%8e%af%e5%a2%83%e7%9a%84%e5%ae%8c%e6%95%b4%e8%bf%87%e7%a8%8b","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/36244.html","title":{"rendered":"\u670d\u52a1\u5668\u4e0a\u914d\u7f6eCUDA\u3001cuDNN\u3001Anaconda\u548cPython\u73af\u5883\u7684\u5b8c\u6574\u8fc7\u7a0b"},"content":{"rendered":"

\u5728\u6df1\u5ea6\u5b66\u4e60\u548c\u79d1\u5b66\u8ba1\u7b97\u9886\u57df，\u6b63\u786e\u914d\u7f6eGPU\u52a0\u901f\u73af\u5883\u5bf9\u4e8e\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\u81f3\u5173\u91cd\u8981\u3002\u6211\u8bb0\u5f55\u4e86\u5728\u670d\u52a1\u5668\u4e0a\u914d\u7f6eCUDA\u3001cuDNN\u3001Anaconda\u4ee5\u53ca\u76f8\u5e94\u7684Python\u73af\u5883\u7684\u8fc7\u7a0b\u3002<\/p>\n

\u4e00\u3001\u51c6\u5907\u5de5\u4f5c<\/h3>\n

\u5728\u5f00\u59cb\u5b89\u88c5\u524d，\u9700\u8981\u786e\u8ba4\u670d\u52a1\u5668\u7684\u57fa\u672c\u914d\u7f6e：<\/p>\n

  • \u786e\u8ba4\u670d\u52a1\u5668\u4e0a\u5b89\u88c5\u4e86NVIDIA GPU<\/li>\n
  • \u68c0\u67e5GPU\u9a71\u52a8\u662f\u5426\u5df2\u5b89\u88c5<\/li>\n
  • \u786e\u8ba4\u670d\u52a1\u5668\u64cd\u4f5c\u7cfb\u7edf\u7248\u672c<\/li>\n

    \u68c0\u67e5GPU\u548c\u9a71\u52a8<\/h4>\n

    nvidia-smi<\/p>\n

    \u5982\u679c\u663e\u793aGPU\u4fe1\u606f，\u8bf4\u660e\u9a71\u52a8\u5df2\u6b63\u786e\u5b89\u88c5\u3002\u5982\u679c\u6ca1\u6709，\u9700\u8981\u5148\u5b89\u88c5NVIDIA\u9a71\u52a8\u3002<\/p>\n

    \u4e8c\u3001\u5b89\u88c5CUDA<\/h3>\n

    CUDA\u662fNVIDIA\u63a8\u51fa\u7684\u5e76\u884c\u8ba1\u7b97\u5e73\u53f0\u548c\u7f16\u7a0b\u6a21\u578b，\u662f\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u4f7f\u7528GPU\u52a0\u901f\u7684\u57fa\u7840\u3002<\/p>\n

    1. \u4e0b\u8f7dCUDA\u5b89\u88c5\u5305<\/h4>\n

    \u8bbf\u95eeNVIDIA\u5b98\u65b9\u7f51\u7ad9\u4e0b\u8f7d\u9002\u5408\u4f60\u7cfb\u7edf\u7684CUDA\u7248\u672c\u3002\u4ee5CUDA 11.7\u4e3a\u4f8b：<\/p>\n

    wget<\/span> https:\/\/developer.download.nvidia.com\/compute\/cuda\/11.7.0\/local_installers\/cuda_11.7.0_515.43.04_linux.run<\/p>\n

    2. \u5b89\u88c5CUDA<\/h4>\n

    sudo<\/span> sh<\/span> cuda_11.7.0_515.43.04_linux.run<\/p>\n

    \u5b89\u88c5\u8fc7\u7a0b\u4e2d：<\/p>\n

      \n
    • \u63a5\u53d7\u8bb8\u53ef\u534f\u8bae<\/li>\n
    • \u5982\u679c\u5df2\u5b89\u88c5\u9a71\u52a8，\u53ef\u4ee5\u53d6\u6d88\u9009\u62e9\u9a71\u52a8\u5b89\u88c5<\/li>\n
    • \u9009\u62e9\u5b89\u88c5CUDA Toolkit\u548c\u76f8\u5173\u7ec4\u4ef6<\/li>\n<\/ul>\n

      3. \u914d\u7f6e\u73af\u5883\u53d8\u91cf<\/h4>\n

      \u7f16\u8f91~\/.bashrc\u6587\u4ef6：<\/p>\n

      nano<\/span> ~\/.bashrc<\/p>\n

      \u6dfb\u52a0\u4ee5\u4e0b\u5185\u5bb9：<\/p>\n

      export<\/span> PATH<\/span><\/span>=<\/span>\/usr\/local\/cuda-11.7\/bin:$PATH<\/span>
      \nexport<\/span> LD_LIBRARY_PATH<\/span>=<\/span>\/usr\/local\/cuda-11.7\/lib64:$LD_LIBRARY_PATH<\/span><\/p>\n

      \u4f7f\u73af\u5883\u53d8\u91cf\u751f\u6548：<\/p>\n

      source<\/span> ~\/.bashrc<\/p>\n

      4. \u9a8c\u8bc1CUDA\u5b89\u88c5<\/h4>\n

      nvcc –version<\/p>\n

      \u5982\u679c\u663e\u793aCUDA\u7248\u672c\u4fe1\u606f，\u8bf4\u660e\u5b89\u88c5\u6210\u529f\u3002<\/p>\n

      \u4e09\u3001\u5b89\u88c5cuDNN<\/h3>\n

      cuDNN\u662fNVIDIA\u5f00\u53d1\u7684\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u5e93，\u53ef\u4ee5\u52a0\u901f\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u7684\u8ba1\u7b97\u3002<\/p>\n

      1. \u4e0b\u8f7dcuDNN<\/h4>\n

      \u8bbf\u95eeNVIDIA cuDNN\u9875\u9762\u4e0b\u8f7d\u4e0eCUDA\u7248\u672c\u517c\u5bb9\u7684cuDNN（\u9700\u8981\u6ce8\u518cNVIDIA\u5f00\u53d1\u8005\u8d26\u53f7）\u3002<\/p>\n

      2. \u89e3\u538b\u5e76\u5b89\u88c5cuDNN<\/h4>\n

      tar<\/span> -xzvf cudnn-linux-x86_64-8.x.x.x_cudaX.Y-archive.tar.xz
      \nsudo<\/span> cp<\/span> cudnn-linux-x86_64-8.x.x.x_cudaX.Y-archive\/include\/cudnn*.h \/usr\/local\/cuda-11.7\/include
      \nsudo<\/span> cp<\/span> cudnn-linux-x86_64-8.x.x.x_cudaX.Y-archive\/lib\/libcudnn* \/usr\/local\/cuda-11.7\/lib64
      \nsudo<\/span> chmod<\/span> a+r \/usr\/local\/cuda-11.7\/include\/cudnn*.h \/usr\/local\/cuda-11.7\/lib64\/libcudnn*<\/p>\n

      3. \u9a8c\u8bc1cuDNN\u5b89\u88c5<\/h4>\n

      cat<\/span> \/usr\/local\/cuda\/include\/cudnn_version.h |<\/span> grep<\/span> CUDNN_MAJOR -A 2<\/span><\/p>\n

      \u56db\u3001\u5b89\u88c5Anaconda<\/h3>\n

      Anaconda\u662f\u4e00\u4e2a\u5f00\u6e90\u7684Python\u53d1\u884c\u7248，\u5305\u542b\u4e86conda\u3001Python\u7b49180\u591a\u4e2a\u79d1\u5b66\u5305\u53ca\u5176\u4f9d\u8d56\u9879\u3002<\/p>\n

      \u5b98\u7f51\u4e0b\u8f7d\u5730\u5740：https:\/\/repo.anaconda.com\/archive\/<\/p>\n

      \u4e0b\u8f7d\u5730\u5740：https:\/\/mirrors.tuna.tsinghua.edu.cn\/anaconda\/archive\/<\/p>\n

      1. \u4e0b\u8f7dAnaconda\u5b89\u88c5\u5305<\/h4>\n

      wget<\/span> https:\/\/repo.anaconda.com\/archive\/Anaconda3-2023.07-2-Linux-x86_64.sh<\/p>\n

      2. \u5b89\u88c5Anaconda<\/h4>\n

      bash<\/span> Anaconda3-2023.07-2-Linux-x86_64.sh<\/p>\n

      \u6309\u7167\u63d0\u793a\u5b8c\u6210\u5b89\u88c5：<\/p>\n

        \n
      • \u63a5\u53d7\u8bb8\u53ef\u534f\u8bae<\/li>\n
      • \u786e\u8ba4\u5b89\u88c5\u4f4d\u7f6e<\/li>\n
      • \u662f\u5426\u521d\u59cb\u5316Anaconda（\u5efa\u8bae\u9009\u62e9"yes"）<\/li>\n<\/ul>\n

        3. \u6fc0\u6d3bAnaconda<\/h4>\n

        source<\/span> ~\/.bashrc<\/p>\n

        4. \u9a8c\u8bc1Anaconda\u5b89\u88c5<\/h4>\n

        conda –version<\/p>\n

        \u4e94\u3001\u521b\u5efa\u5e76\u914d\u7f6ePython\u73af\u5883<\/h3>\n

        \u4f7f\u7528Anaconda\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u548c\u7ba1\u7406\u4e0d\u540c\u7684Python\u73af\u5883\u3002<\/p>\n

        1. \u521b\u5efa\u65b0\u7684Python\u73af\u5883<\/h4>\n

        conda create -n deeplearning python<\/span>=<\/span>3.9<\/span><\/p>\n

        2. \u6fc0\u6d3b\u73af\u5883<\/h4>\n

        conda activate deeplearning<\/p>\n

        \u516d\u3001\u5b89\u88c5PyTorch<\/h3>\n

        PyTorch\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6，\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684GPU\u52a0\u901f\u529f\u80fd\u3002\u4ee5\u4e0b\u662f\u51e0\u79cd\u5b89\u88c5PyTorch\u7684\u65b9\u6cd5：<\/p>\n

        1. \u4f7f\u7528conda\u5b89\u88c5PyTorch（\u63a8\u8350）<\/h4>\n

        \u6839\u636e\u4f60\u7684CUDA\u7248\u672c\u9009\u62e9\u5408\u9002\u7684PyTorch\u7248\u672c\u3002\u4ee5CUDA 11.7\u4e3a\u4f8b：<\/p>\n

        conda install<\/span> pytorch torchvision torchaudio pytorch-cuda=<\/span>11.7<\/span> -c pytorch -c nvidia<\/p>\n

        \u5982\u679c\u4f60\u60f3\u5b89\u88c5\u7279\u5b9a\u7248\u672c\u7684PyTorch，\u53ef\u4ee5\u6307\u5b9a\u7248\u672c\u53f7：<\/p>\n

        conda install<\/span> pytorch<\/span>=<\/span>1.13<\/span>.1 torchvision<\/span>=<\/span>0.14<\/span>.1 torchaudio<\/span>=<\/span>0.13<\/span>.1 pytorch-cuda=<\/span>11.7<\/span> -c pytorch -c nvidia<\/p>\n

        2. \u4f7f\u7528pip\u5b89\u88c5PyTorch<\/h4>\n

        \u4e5f\u53ef\u4ee5\u4f7f\u7528pip\u5b89\u88c5PyTorch：<\/p>\n

        pip install<\/span> torch torchvision torchaudio –index-url https:\/\/download.pytorch.org\/whl\/cu117<\/p>\n

        3. \u4ece\u6e90\u7801\u7f16\u8bd1\u5b89\u88c5（\u9ad8\u7ea7\u7528\u6237）<\/h4>\n

        \u5982\u679c\u9700\u8981\u81ea\u5b9a\u4e49PyTorch\u529f\u80fd\u6216\u9488\u5bf9\u7279\u5b9a\u786c\u4ef6\u4f18\u5316，\u53ef\u4ee5\u4ece\u6e90\u7801\u7f16\u8bd1\u5b89\u88c5：<\/p>\n

        # \u514b\u9686PyTorch\u4ed3\u5e93<\/span>
        \ngit<\/span> clone –recursive https:\/\/github.com\/pytorch\/pytorch
        \ncd<\/span> pytorch<\/p>\n

        # \u8bbe\u7f6e\u73af\u5883\u53d8\u91cf<\/span>
        \nexport<\/span> CMAKE_PREFIX_PATH<\/span>=<\/span>${CONDA_PREFIX:-<\/span>"$(dirname $(which conda))\/<\/span>..\/<\/span>"}<\/span>
        \nexport<\/span> CUDA_HOME<\/span>=<\/span>\/usr\/local\/cuda-11.7<\/p>\n

        # \u5b89\u88c5\u4f9d\u8d56<\/span>
        \nconda install<\/span> -y astunparse numpy ninja pyyaml setuptools cmake cffi typing_extensions future six requests dataclasses<\/p>\n

        # \u7f16\u8bd1\u5b89\u88c5<\/span>
        \npython setup.py install<\/span><\/p>\n

        4. \u9a8c\u8bc1PyTorch\u5b89\u88c5<\/h4>\n

        \u5728Python\u73af\u5883\u4e2d\u8fd0\u884c\u4ee5\u4e0b\u4ee3\u7801\u9a8c\u8bc1PyTorch\u662f\u5426\u6b63\u786e\u5b89\u88c5\u5e76\u80fd\u591f\u4f7f\u7528GPU：<\/p>\n

        import<\/span> torch
        \nprint<\/span>(<\/span>"PyTorch\u7248\u672c:"<\/span>,<\/span> torch.<\/span>__version__)<\/span>
        \nprint<\/span>(<\/span>"CUDA\u662f\u5426\u53ef\u7528:"<\/span>,<\/span> torch.<\/span>cuda.<\/span>is_available(<\/span>)<\/span>)<\/span>
        \nprint<\/span>(<\/span>"CUDA\u7248\u672c:"<\/span>,<\/span> torch.<\/span>version.<\/span>cuda)<\/span>
        \nprint<\/span>(<\/span>"cuDNN\u7248\u672c:"<\/span>,<\/span> torch.<\/span>backends.<\/span>cudnn.<\/span>version(<\/span>)<\/span>)<\/span>
        \nprint<\/span>(<\/span>"GPU\u6570\u91cf:"<\/span>,<\/span> torch.<\/span>cuda.<\/span>device_count(<\/span>)<\/span>)<\/span>
        \nprint<\/span>(<\/span>"\u5f53\u524dGPU:"<\/span>,<\/span> torch.<\/span>cuda.<\/span>current_device(<\/span>)<\/span>)<\/span>
        \nprint<\/span>(<\/span>"GPU\u540d\u79f0:"<\/span>,<\/span> torch.<\/span>cuda.<\/span>get_device_name(<\/span>0<\/span>)<\/span>)<\/span><\/p>\n

        \u5982\u679c\u8f93\u51fa\u663e\u793aCUDA\u53ef\u7528，\u5e76\u4e14\u80fd\u591f\u6b63\u786e\u663e\u793aGPU\u4fe1\u606f，\u8bf4\u660ePyTorch\u5df2\u6210\u529f\u5b89\u88c5\u5e76\u652f\u6301GPU\u52a0\u901f\u3002<\/p>\n

        5. \u5b89\u88c5\u5e38\u7528\u7684PyTorch\u76f8\u5173\u5e93<\/h4>\n

        # \u5b89\u88c5\u5e38\u7528\u7684\u6570\u636e\u5904\u7406\u548c\u53ef\u89c6\u5316\u5e93<\/span>
        \nconda install<\/span> pandas matplotlib scikit-learn jupyter<\/p>\n

        # \u5b89\u88c5\u5176\u4ed6PyTorch\u751f\u6001\u7cfb\u7edf\u5de5\u5177<\/span>
        \npip install<\/span> pytorch-lightning
        \npip install<\/span> transformers
        \npip install<\/span> timm # \u8ba1\u7b97\u673a\u89c6\u89c9\u6a21\u578b\u5e93<\/span><\/p>\n

        \u4e03\u3001\u5b89\u88c5\u5176\u4ed6\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6（\u53ef\u9009）<\/h3>\n

        \u5b89\u88c5TensorFlow<\/h4>\n

        conda install<\/span> -c conda-forge tensorflow-gpu<\/p>\n

        \u9a8c\u8bc1TensorFlow GPU\u652f\u6301：<\/p>\n

        import<\/span> tensorflow as<\/span> tf
        \nprint<\/span>(<\/span>"TensorFlow\u7248\u672c:"<\/span>,<\/span> tf.<\/span>__version__)<\/span>
        \nprint<\/span>(<\/span>"GPU\u662f\u5426\u53ef\u7528:"<\/span>,<\/span> tf.<\/span>config.<\/span>list_physical_devices(<\/span>'GPU'<\/span>)<\/span>)<\/span><\/p>\n

        \u516b\u3001\u5e38\u89c1\u95ee\u9898\u53ca\u89e3\u51b3\u65b9\u6848<\/h3>\n

        1. CUDA\u7248\u672c\u4e0e\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u4e0d\u517c\u5bb9<\/h4>\n

        \u786e\u4fdd\u5b89\u88c5\u7684CUDA\u3001cuDNN\u7248\u672c\u4e0e\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u517c\u5bb9\u3002\u53ef\u4ee5\u53c2\u8003\u6846\u67b6\u5b98\u65b9\u6587\u6863\u4e2d\u7684\u7248\u672c\u517c\u5bb9\u6027\u8bf4\u660e\u3002<\/p>\n

        2. \u73af\u5883\u53d8\u91cf\u914d\u7f6e\u9519\u8bef<\/h4>\n

        \u68c0\u67e5~\/.bashrc\u6587\u4ef6\u4e2d\u7684\u73af\u5883\u53d8\u91cf\u914d\u7f6e\u662f\u5426\u6b63\u786e，\u8def\u5f84\u662f\u5426\u5bf9\u5e94\u5b9e\u9645\u5b89\u88c5\u4f4d\u7f6e\u3002<\/p>\n

        3. \u591a\u4e2aCUDA\u7248\u672c\u5171\u5b58<\/h4>\n

        \u5982\u679c\u9700\u8981\u5728\u670d\u52a1\u5668\u4e0a\u5b89\u88c5\u591a\u4e2aCUDA\u7248\u672c，\u53ef\u4ee5\u901a\u8fc7\u73af\u5883\u53d8\u91cf\u63a7\u5236\u4f7f\u7528\u54ea\u4e2a\u7248\u672c：<\/p>\n

        export<\/span> CUDA_HOME<\/span>=<\/span>\/usr\/local\/cuda-X.Y
        \nexport<\/span> PATH<\/span><\/span>=<\/span>$CUDA_HOME<\/span>\/bin:$PATH<\/span>
        \nexport<\/span> LD_LIBRARY_PATH<\/span>=<\/span>$CUDA_HOME<\/span>\/lib64:$LD_LIBRARY_PATH<\/span><\/p>\n

        4. \u5185\u5b58\u4e0d\u8db3<\/h4>\n

        \u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u8bad\u7ec3\u53ef\u80fd\u9700\u8981\u5927\u91cfGPU\u5185\u5b58，\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u6cd5\u89e3\u51b3：<\/p>\n

          \n
        • \u51cf\u5c0f\u6279\u91cf\u5927\u5c0f(batch size)<\/li>\n
        • \u4f7f\u7528\u6df7\u5408\u7cbe\u5ea6\u8bad\u7ec3<\/li>\n
        • \u5b9e\u73b0\u68af\u5ea6\u7d2f\u79ef<\/li>\n
        • \u4f7f\u7528\u6a21\u578b\u5e76\u884c\u6216\u6570\u636e\u5e76\u884c<\/li>\n<\/ul>\n

          5. PyTorch\u5b89\u88c5\u540e\u65e0\u6cd5\u8bc6\u522bGPU<\/h4>\n

          \u5982\u679cPyTorch\u5b89\u88c5\u540e\u65e0\u6cd5\u8bc6\u522bGPU，\u53ef\u80fd\u7684\u539f\u56e0\u5305\u62ec：<\/p>\n

            \n
          • CUDA\u7248\u672c\u4e0ePyTorch\u4e0d\u517c\u5bb9<\/li>\n
          • \u73af\u5883\u53d8\u91cf\u8bbe\u7f6e\u9519\u8bef<\/li>\n
          • GPU\u9a71\u52a8\u7248\u672c\u8fc7\u4f4e<\/li>\n<\/ul>\n

            \u89e3\u51b3\u65b9\u6cd5：<\/p>\n

            # \u68c0\u67e5CUDA\u662f\u5426\u6b63\u786e\u5b89\u88c5<\/span>
            \nnvcc –version<\/p>\n

            # \u786e\u4fdd\u5b89\u88c5\u4e86\u4e0eCUDA\u7248\u672c\u517c\u5bb9\u7684PyTorch<\/span>
            \n# \u4f8b\u5982，\u5bf9\u4e8eCUDA 11.7，\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u91cd\u65b0\u5b89\u88c5PyTorch<\/span>
            \nconda install<\/span> pytorch torchvision torchaudio pytorch-cuda=<\/span>11.7<\/span> -c pytorch -c nvidia<\/p>\n

            # \u68c0\u67e5\u73af\u5883\u53d8\u91cf<\/span>
            \necho<\/span> $PATH<\/span> |<\/span> grep<\/span> cuda
            \necho<\/span> $LD_LIBRARY_PATH<\/span> |<\/span> grep<\/span> cuda<\/p>\n","protected":false},"excerpt":{"rendered":"

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