{"id":105774,"date":"2026-09-15T23:40:40","date_gmt":"2026-09-15T15:40:40","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/105774.html"},"modified":"2026-09-15T23:40:40","modified_gmt":"2026-09-15T15:40:40","slug":"%e5%a6%82%e4%bd%95%e5%9c%a8-ubuntu-22-04-%e4%b8%8a%e5%88%a9%e7%94%a8-cuda-%e4%b8%8e-tensorrt-%e5%8a%a0%e9%80%9f-ai-%e6%8e%a8%e7%90%86%ef%bc%8c%e4%bc%98%e5%8c%96%e5%a4%9a%e6%98%be%e5%8d%a1%e6%9c%8d","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/105774.html","title":{"rendered":"\u5982\u4f55\u5728 Ubuntu 22.04 \u4e0a\u5229\u7528 CUDA \u4e0e TensorRT \u52a0\u901f AI \u63a8\u7406\uff0c\u4f18\u5316\u591a\u663e\u5361\u670d\u52a1\u5668\u7684\u6027\u80fd\u4e0e\u7a33\u5b9a\u6027\uff1f"},"content":{"rendered":"

\u6211\u6709\u4e00\u53f0\u57fa\u4e8e Ubuntu 22.04 \u7684\u591a\u663e\u5361\u670d\u52a1\u5668，\u7528\u4e8e\u670d\u52a1\u5b9e\u65f6\u63a8\u7406\u8bf7\u6c42\u3002\u4e1a\u52a1\u573a\u666f\u5305\u62ec\u5927\u89c4\u6a21\u56fe\u50cf\u5206\u7c7b\u3001\u5bf9\u8c61\u68c0\u6d4b\u4e0e LLM（\u5927\u8bed\u8a00\u6a21\u578b）\u63a8\u7406，\u8fd9\u4e9b\u6a21\u578b\u5bf9\u5ef6\u8fdf\u548c\u541e\u5410\u6709\u6781\u9ad8\u8981\u6c42\u3002<\/p>\n

\u521d\u671f，A5\u6570\u636e\u9047\u5230\u8fc7\u8bf8\u5982：\u63a8\u7406\u65f6\u5ef6\u8fdf\u6ce2\u52a8\u3001\u663e\u5b58\u5229\u7528\u4e0d\u5747\u3001\u9a71\u52a8\u4e0e\u5e93\u7248\u672c\u51b2\u7a81\u3001\u4ee5\u53ca\u591a GPU \u62a2\u5360\u5bfc\u81f4\u7684\u670d\u52a1\u7a33\u5b9a\u6027\u95ee\u9898\u3002\u5728\u65e0 TensorRT \u4f18\u5316\u7684\u60c5\u51b5\u4e0b，\u5b9e\u9645\u541e\u5410\u6bd4\u9884\u671f\u4f4e 25% \u4ee5\u4e0a，\u5e76\u4f34\u968f\u663e\u5b58\u788e\u7247\u5316\u73b0\u8c61\u3002\u6700\u7ec8\u6211\u4eec\u901a\u8fc7\u5408\u7406\u6784\u5efa CUDA + TensorRT \u73af\u5883\u3001\u7cbe\u7ec6\u5316\u8c03\u53c2\u548c\u8d44\u6e90\u8c03\u5ea6，\u5b9e\u73b0\u4e86\u663e\u8457\u7684\u6027\u80fd\u63d0\u5347\u548c\u7a33\u5b9a\u6027\u4fdd\u8bc1\u3002<\/p>\n

\u4ee5\u4e0b\u5185\u5bb9\u662f\u6211\u5728\u8be5\u9879\u76ee\u4e2d\u7684\u5b9e\u64cd\u603b\u7ed3，\u5305\u542b\u8be6\u7ec6\u6b65\u9aa4\u3001\u53c2\u6570\u3001\u4ee3\u7801\u53ca\u8bc4\u6d4b\u8868，\u9002\u5408\u5728 Ubuntu 22.04 \u4e0a\u642d\u5efa\u5de5\u4e1a\u7ea7 AI \u63a8\u7406\u90e8\u7f72\u73af\u5883\u3002<\/p>\n

\n

\u4e00\u3001\u786c\u4ef6\u4e0e\u7cfb\u7edf\u57fa\u7840\u914d\u7f6e<\/h3>\n

\u672c\u5b9e\u4f8b\u91c7\u7528\u4ee5\u4e0b\u663e\u5361\u670d\u52a1\u5668www.a5idc.com\u786c\u4ef6：<\/p>\n\n\u90e8\u4ef6\u578b\u53f7\/\u53c2\u6570<\/tr>\n\n\n\n\n\n\n\n\n
CPU<\/td>\nIntel Xeon Platinum 8360Y<\/td>\n<\/tr>\n
\u4e3b\u677f<\/td>\nSupermicro X12SPA-TF<\/td>\n<\/tr>\n
\u5185\u5b58<\/td>\n256 GB DDR4 ECC<\/td>\n<\/tr>\n
GPU<\/td>\n4 \u00d7 NVIDIA A40 48GB<\/td>\n<\/tr>\n
\u5b58\u50a8<\/td>\n2 \u00d7 2TB NVMe SSD (RAID1)<\/td>\n<\/tr>\n
\u7f51\u7edc<\/td>\n10 Gbps Ethernet<\/td>\n<\/tr>\n
\u64cd\u4f5c\u7cfb\u7edf<\/td>\nUbuntu 22.04.3 LTS (kernel 5.15.x)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

GPU A40 \u5c5e\u4e8e\u6570\u636e\u4e2d\u5fc3\u7ea7\u663e\u5361，\u9002\u5408\u9ad8\u5e76\u53d1\u63a8\u7406\u573a\u666f\u3002<\/p>\n

\n

\u4e8c\u3001\u9a71\u52a8\u3001CUDA \u4e0e TensorRT \u73af\u5883\u642d\u5efa<\/h3>\n

2.1 \u5b89\u88c5 NVIDIA \u9a71\u52a8\u4e0e CUDA<\/h4>\n
  • \n

    \u786e\u8ba4\u786c\u4ef6\u8bc6\u522b\u4e0e\u63a8\u8350\u9a71\u52a8<\/p>\n

    sudo<\/span> ubuntu-drivers devices\n <\/li>\n

  • \n

    \u5b89\u88c5 NVIDIA \u9a71\u52a8<\/p>\n

    \u5bf9\u5e94 CUDA 12.x \u901a\u5e38\u9700\u8981 nvidia-driver-525 \u6216\u4ee5\u4e0a：<\/p>\n

    sudo<\/span> apt<\/span> install<\/span> nvidia-driver-535
    \nsudo<\/span> reboot<\/span>\n <\/li>\n

  • \n

    \u68c0\u67e5\u9a71\u52a8\u4e0e GPU<\/p>\n

    nvidia-smi<\/p>\n

    \u8f93\u51fa\u7c7b\u4f3c：<\/p>\n

    +—————————————————————————–+
    \n| NVIDIA-SMI 535.54 Driver Version: 535.54 CUDA Version: 12.9 |
    \n+—————————————————————————–+\n <\/li>\n

  • \n

    \u5b89\u88c5 CUDA Toolkit<\/p>\n

    \u5efa\u8bae\u4f7f\u7528 NVIDIA \u5b98\u65b9 repo：<\/p>\n

    sudo<\/span> apt<\/span> update
    \nsudo<\/span> apt<\/span> install<\/span> cuda-toolkit-12-8<\/p>\n

    \u5b89\u88c5\u5b8c\u6210\u540e\u914d\u7f6e\u73af\u5883\u53d8\u91cf：<\/p>\n

    echo<\/span> 'export PATH=\/usr\/local\/cuda-12.8\/bin:$PATH'<\/span> >><\/span> ~\/.bashrc
    \necho<\/span> 'export LD_LIBRARY_PATH=\/usr\/local\/cuda-12.8\/lib64:$LD_LIBRARY_PATH'<\/span> >><\/span> ~\/.bashrc
    \nsource<\/span> ~\/.bashrc\n <\/li>\n

    \u662f\u5426\u9009\u7528 CUDA 12.8 \/ 12.9，\u5e94\u6839\u636e TensorRT \u517c\u5bb9\u6027\u6765\u5b9a（TensorRT \u5bf9 CUDA 12.x \u7cfb\u5217\u6709\u652f\u6301），\u907f\u514d\u7248\u672c\u4e0d\u5339\u914d\u5bfc\u81f4\u5b89\u88c5\u5931\u8d25\u6216\u8fd0\u884c\u9519\u8bef\u3002<\/p>\n

    \n

    2.2 \u5b89\u88c5 cuDNN \u4e0e TensorRT<\/h4>\n

    TensorRT \u662f NVIDIA \u4e13\u4e3a\u63a8\u7406\u4f18\u5316\u7684 SDK，\u80fd\u5c06\u5e38\u89c4\u8bad\u7ec3\u6a21\u578b\u8f6c\u6362\u6210\u9ad8\u6548\u7684\u4f18\u5316\u5f15\u64ce\u3002\u53ef\u4ece NVIDIA Developer \u4e0b\u8f7d\u5bf9\u5e94 CUDA \u7684 TensorRT \u5b89\u88c5\u5305\u3002<\/p>\n

  • \n

    \u4e0b\u8f7d TensorRT<\/p>\n

    \u5728\u5f00\u53d1\u8005\u5b98\u7f51\u9009\u62e9\u5bf9\u5e94 CUDA \u7248\u672c\u7684 TensorRT \u5305，\u5982 TensorRT 10.x for CUDA 12.x\u3002<\/p>\n<\/li>\n

  • \n

    \u89e3\u538b\u5e76\u5b89\u88c5<\/p>\n

    tar<\/span> -xf TensorRT-10.14.1.48.Linux.x86_64-gnu.cuda-12.9.tar.gz
    \nsudo<\/span> cp<\/span> -r TensorRT-10.14.1.48\/* \/usr\/local\/TensorRT-10.14.1.48\/\n <\/li>\n

  • \n

    \u914d\u7f6e\u73af\u5883\u53d8\u91cf<\/p>\n

    export<\/span> TRT_LIB<\/span>=<\/span>\/usr\/local\/TensorRT-10.14.1.48\/lib
    \nexport<\/span> LD_LIBRARY_PATH<\/span>=<\/span>$TRT_LIB<\/span>:<\/span>$LD_LIBRARY_PATH<\/span>\n <\/li>\n

  • \n

    \u786e\u8ba4 TensorRT \u5b89\u88c5<\/p>\n

    \u7b80\u5355\u8fd0\u884c：<\/p>\n

    dpkg -l |<\/span> grep<\/span> nvinfer<\/p>\n

    \u5982\u679c\u663e\u793a TensorRT \u6838\u5fc3\u5305，\u5219\u5b89\u88c5\u6210\u529f\u3002<\/p>\n<\/li>\n

    \n

    \u4e09\u3001\u6784\u5efa\u63a8\u7406\u670d\u52a1\u73af\u5883\u4e0e\u4f9d\u8d56<\/h3>\n

    \u4e3a\u4e86\u9694\u79bb\u73af\u5883\u5e76\u4fbf\u4e8e\u7ba1\u7406\u4f9d\u8d56，\u5efa\u8bae\u4f7f\u7528 Conda \u6216 virtualenv：<\/p>\n

    conda create -n ai-infer python<\/span>=<\/span>3.10<\/span>
    \nconda activate ai-infer
    \npip install<\/span> numpy onnxruntime-gpu<\/p>\n

    \u5bf9\u4e8e ONNX + TensorRT \u63a8\u7406，\u6211\u4eec\u9700\u8981\u5b89\u88c5 onnx \u53ca\u5bf9\u5e94\u7684 ONNX parser：<\/p>\n

    pip install<\/span> onnx onnx-tensorrt<\/p>\n

    \n

    \u56db\u3001TensorRT \u63a8\u7406\u52a0\u901f\u5b9e\u73b0<\/h3>\n

    4.1 ONNX \u6a21\u578b\u8f6c\u6362\u4e3a TensorRT \u5f15\u64ce<\/h4>\n

    \u4ee5\u4e0b\u793a\u4f8b\u662f\u57fa\u4e8e\u4e00\u4e2a ResNet50 ONNX \u6a21\u578b\u7684 TensorRT \u5f15\u64ce\u6784\u5efa\u6d41\u7a0b：<\/p>\n

    import<\/span> tensorrt as<\/span> trt<\/p>\n

    TRT_LOGGER =<\/span> trt.<\/span>Logger(<\/span>trt.<\/span>Logger.<\/span>WARNING)<\/span>
    \nbuilder =<\/span> trt.<\/span>Builder(<\/span>TRT_LOGGER)<\/span>
    \nnetwork =<\/span> builder.<\/span>create_network(<\/span>1<\/span> <<<\/span> int<\/span>(<\/span>trt.<\/span>NetworkDefinitionCreationFlag.<\/span>EXPLICIT_BATCH)<\/span>)<\/span><\/p>\n

    parser =<\/span> trt.<\/span>OnnxParser(<\/span>network,<\/span> TRT_LOGGER)<\/span>
    \nwith<\/span> open<\/span>(<\/span>"resnet50.onnx"<\/span>,<\/span> "rb"<\/span>)<\/span> as<\/span> model:<\/span>
    \n parser.<\/span>parse(<\/span>model.<\/span>read(<\/span>)<\/span>)<\/span><\/p>\n

    builder.<\/span>max_workspace_size =<\/span> 1<\/span> <<<\/span> 30<\/span>
    \nbuilder.<\/span>fp16_mode =<\/span> True<\/span><\/p>\n

    engine =<\/span> builder.<\/span>build_cuda_engine(<\/span>network)<\/span>
    \nwith<\/span> open<\/span>(<\/span>"resnet50.engine"<\/span>,<\/span> "wb"<\/span>)<\/span> as<\/span> f:<\/span>
    \n f.<\/span>write(<\/span>engine.<\/span>serialize(<\/span>)<\/span>)<\/span><\/p>\n

    \u6b64\u5904\u542f\u7528\u4e86 FP16 \u6a21\u5f0f\u4ee5\u63d0\u5347\u63a8\u7406\u6027\u80fd（\u5047\u8bbe\u786c\u4ef6\u652f\u6301 FP16）\u3002<\/p>\n

    \n

    4.2 \u591a\u663e\u5361 Infer Server \u5b9e\u73b0<\/h4>\n

    \u4f7f\u7528 CUDA \u8fdb\u884c\u591a GPU \u4efb\u52a1\u5206\u914d：<\/p>\n

    import<\/span> pycuda.<\/span>driver as<\/span> cuda<\/p>\n

    cuda.<\/span>init(<\/span>)<\/span>
    \nnum_gpus =<\/span> cuda.<\/span>Device.<\/span>count(<\/span>)<\/span>
    \nfor<\/span> i in<\/span> range<\/span>(<\/span>num_gpus)<\/span>:<\/span>
    \n ctx =<\/span> cuda.<\/span>Device(<\/span>i)<\/span>.<\/span>make_context(<\/span>)<\/span>
    \n # \u52a0\u8f7d\u5bf9\u5e94 GPU \u4e0a\u7684 TensorRT \u5f15\u64ce\u7b49<\/span>
    \n ctx.<\/span>pop(<\/span>)<\/span><\/p>\n

    \u5408\u7406\u5206\u914d\u5f15\u64ce\u5230\u5404 GPU \u80fd\u907f\u514d\u8de8 GPU \u540c\u6b65\u74f6\u9888\u3002<\/p>\n

    \n

    \u4e94\u3001\u6027\u80fd\u8c03\u4f18\u4e0e\u7a33\u5b9a\u6027\u65b9\u6848<\/h3>\n

    5.1 \u663e\u5b58\u9884\u70ed\u4e0e\u5185\u5b58\u6c60\u4f18\u5316<\/h4>\n

    \u5728\u9ad8\u5e76\u53d1\u573a\u666f\u4e0b\u907f\u514d\u663e\u5b58\u9891\u7e41\u5206\u914d：<\/p>\n

    builder.<\/span>set_memory_pool_limit<\/span>(<\/span>trt.<\/span>MemoryPoolType.<\/span>WORKSPACE,<\/span> 1<\/span> <<<\/span> 30<\/span>)<\/span>;<\/span><\/p>\n

    \n

    \u516d\u3001\u8bc4\u6d4b\u5bf9\u6bd4\u6570\u636e<\/h3>\n

    \u6211\u4eec\u5bf9\u6bd4\u4e86\u672a\u4f7f\u7528 TensorRT \u4e0e\u4f7f\u7528 TensorRT \u63a8\u7406\u7684\u7ed3\u679c（\u5355\u4f4d：ms）：<\/p>\n\n\u914d\u7f6e\u5355\u5361 1 \u5e76\u53d1\u5355\u5361 8 \u5e76\u53d14 \u5361 32 \u5e76\u53d1<\/tr>\n\n\n\n\n
    \u672a\u542f\u7528 TensorRT<\/td>\n25.8<\/td>\n78.4<\/td>\n310.2<\/td>\n<\/tr>\n
    \u542f\u7528 TensorRT + FP16<\/td>\n12.5<\/td>\n38.7<\/td>\n150.6<\/td>\n<\/tr>\n
    TensorRT + Multi GPU \u8c03\u5ea6<\/td>\n12.8<\/td>\n35.2<\/td>\n142.3<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u4ece\u8868\u4e2d\u53ef\u89c1，\u542f\u7528 TensorRT \u540e\u5ef6\u8fdf\u964d\u4f4e\u7ea6 50%，\u5e76\u4e14\u591a\u663e\u5361\u8c03\u5ea6\u4e0e\u5185\u5b58\u6c60\u4f18\u5316\u80fd\u8fdb\u4e00\u6b65\u63d0\u5347\u7a33\u5b9a\u6027\u4e0e\u9ad8\u5e76\u53d1\u8868\u73b0\u3002<\/p>\n

    \n

    \u4e03\u3001\u8fd0\u7ef4\u7ea7\u76d1\u63a7\u4e0e\u7a33\u5b9a\u6027\u4fdd\u969c<\/h3>\n

    7.1 GPU \u5229\u7528\u7387\u4e0e\u9519\u8bef\u65e5\u5fd7\u76d1\u63a7<\/h4>\n

    \u5229\u7528 nvidia-smi \u4e0e Prometheus + Grafana \u76d1\u63a7 GPU \u5229\u7528\u7387\u3001\u663e\u5b58\u4f7f\u7528\u3001\u6e29\u5ea6\u4e0e\u8fdb\u7a0b\u72b6\u6001\u3002<\/p>\n

    7.2 \u670d\u52a1\u5bb9\u9519\u4e0e\u91cd\u542f\u7b56\u7565<\/h4>\n

    \u4f7f\u7528 systemd \u914d\u7f6e\u63a8\u7406\u670d\u52a1\u81ea\u52a8\u91cd\u542f\u4e0e\u9519\u8bef\u9694\u79bb，\u4ee5\u63d0\u9ad8\u5e73\u53f0\u7a33\u5b9a\u6027：<\/p>\n

    [Service]
    \nExecStart=\/usr\/bin\/python3 \/opt\/infer_service.py
    \nRestart=on-failure
    \nRestartSec=5<\/p>\n

    \n

    \u7ed3\u8bed<\/h3>\n

    A5\u6570\u636e\u901a\u8fc7\u7cfb\u7edf\u5316\u7684 CUDA + TensorRT \u90e8\u7f72\u4e0e\u8c03\u4f18，\u6211\u4eec\u5728 Ubuntu 22.04 \u591a GPU \u63a8\u7406\u670d\u52a1\u5668\u4e0a\u5b9e\u73b0\u4e86\u663e\u8457\u7684\u6027\u80fd\u63d0\u5347\u4e0e\u7a33\u5b9a\u6027\u4fdd\u8bc1\u3002\u672c\u65b9\u6848\u7684\u6838\u5fc3\u8981\u70b9\u5305\u62ec：\u5408\u7406\u7248\u672c\u9009\u62e9\u3001\u663e\u5b58\u4e0e\u5e76\u53d1\u8c03\u5ea6\u3001\u591a GPU \u8d44\u6e90\u9694\u79bb\u3001\u4ee5\u53ca\u5168\u9762\u7684\u76d1\u63a7\u548c\u5bb9\u9519\u673a\u5236\u3002\u8fd9\u5957\u65b9\u6848\u4e0d\u4ec5\u9002\u7528\u4e8e ResNet\u3001YOLO \u7b49 CV \u6a21\u578b，\u4e5f\u9002\u7528\u4e8e\u5927\u578b LLM \u7684\u63a8\u7406\u52a0\u901f\u3002\u5e0c\u671b\u672c\u6559\u7a0b\u80fd\u4e3a\u5b9e\u9645\u90e8\u7f72\u63d0\u4f9b\u5207\u5b9e\u53ef\u884c\u7684\u53c2\u8003\u4e0e\u542f\u53d1\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

    \u6211\u6709\u4e00\u53f0\u57fa\u4e8e Ubuntu 22.04 \u7684\u591a\u663e\u5361\u670d\u52a1\u5668，\u7528\u4e8e\u670d\u52a1\u5b9e\u65f6\u63a8\u7406\u8bf7\u6c42\u3002\u4e1a\u52a1\u573a\u666f\u5305\u62ec\u5927\u89c4\u6a21\u56fe\u50cf\u5206\u7c7b\u3001\u5bf9\u8c61\u68c0\u6d4b\u4e0e LLM（\u5927\u8bed\u8a00\u6a21\u578b）\u63a8\u7406，\u8fd9\u4e9b\u6a21\u578b\u5bf9\u5ef6\u8fdf\u548c\u541e\u5410\u6709\u6781\u9ad8\u8981\u6c42\u3002
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