{"id":14180,"date":"2025-04-18T20:58:47","date_gmt":"2025-04-18T12:58:47","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/14180.html"},"modified":"2025-04-18T20:58:47","modified_gmt":"2025-04-18T12:58:47","slug":"%e9%83%a8%e7%bd%b2%e8%90%bd%e5%9c%b0%e7%af%87%ef%bc%9atensorrt-%e5%9c%a8%e6%9c%8d%e5%8a%a1%e5%99%a8-x-jetson-x-%e7%a7%bb%e5%8a%a8%e7%ab%af%e7%9a%84%e5%ae%9e%e6%88%98%e6%8c%87%e5%8d%97","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/14180.html","title":{"rendered":"\u90e8\u7f72\u843d\u5730\u7bc7\uff1aTensorRT \u5728\u670d\u52a1\u5668 \u00d7 Jetson \u00d7 \u79fb\u52a8\u7aef\u7684\u5b9e\u6218\u6307\u5357"},"content":{"rendered":"

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\n

\u300a\u90e8\u7f72\u843d\u5730\u7bc7：TensorRT \u5728\u670d\u52a1\u5668 \u00d7 Jetson \u00d7 \u79fb\u52a8\u7aef\u7684\u5b9e\u6218\u6307\u5357\u300b<\/h2>\n
\n

\u2728 \u6458\u8981：<\/h3>\n

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\n

🧭 \u76ee\u5f55\u89c4\u5212：<\/h3>\n
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  • Jetson \u5e73\u53f0\u90e8\u7f72\u5b9e\u6218（Nano \/ Xavier \/ Orin）：\u73af\u5883\u642d\u5efa + \u6a21\u578b\u63a8\u7406<\/li>\n
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  • \u8de8\u5e73\u53f0\u90e8\u7f72\u6ce8\u610f\u4e8b\u9879：\u663e\u5b58\u914d\u7f6e\u3001\u6027\u80fd\u8bc4\u4f30\u3001\u5de5\u5177\u94fe\u63a8\u8350<\/li>\n
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    \n

    1. \u90e8\u7f72\u5168\u666f\u56fe：\u4ece\u670d\u52a1\u5668\u5230\u8fb9\u7f18\u7ec8\u7aef，TensorRT \u652f\u6301\u8c01？<\/h4>\n

    TensorRT \u4e0d\u662f\u4e00\u4e2a\u201c\u53ea\u80fd\u672c\u5730\u8fd0\u884c\u201d\u7684\u63a8\u7406\u5f15\u64ce，\u800c\u662f\u4e00\u4e2a\u8986\u76d6 \u4ece\u6570\u636e\u4e2d\u5fc3 \u2192 \u8fb9\u7f18\u8bbe\u5907 \u2192 \u79fb\u52a8\u7aef\u5d4c\u5165\u5f0f\u7cfb\u7edf \u7684\u5168\u5e73\u53f0\u90e8\u7f72\u89e3\u51b3\u65b9\u6848\u3002\u7406\u89e3\u8fd9\u4e2a\u80fd\u529b\u8fb9\u754c，\u80fd\u5e2e\u4f60\u5728\u9879\u76ee\u521d\u671f\u5c31\u505a\u51fa\u6b63\u786e\u5e73\u53f0\u9009\u62e9，\u51cf\u5c11\u5927\u91cf\u8bd5\u9519\u548c\u56de\u9000\u3002<\/p>\n

    \n

    \u2705 TensorRT \u652f\u6301\u5e73\u53f0\u4e00\u89c8<\/h5>\n\n\u5e73\u53f0\u7c7b\u578b\u793a\u4f8b\u8bbe\u5907\u90e8\u7f72\u7279\u5f81\u652f\u6301\u7a0b\u5ea6<\/tr>\n\n\n\n\n\n
    \u670d\u52a1\u5668\u7aef<\/td>\nNVIDIA A100 \/ T4 \/ H100 \/ L4<\/td>\n\u663e\u5b58\u5145\u8db3\u3001\u9ad8\u5e76\u53d1\u9700\u6c42<\/td>\n\u2705 \u5b8c\u5168\u652f\u6301，\u63a8\u8350\u90e8\u7f72\u73af\u5883<\/td>\n<\/tr>\n
    Jetson \u8fb9\u7f18\u7aef<\/td>\nNano\u3001Xavier NX\u3001Orin NX<\/td>\n\u4f4e\u529f\u8017\u3001\u5c0f\u5c3a\u5bf8\u3001\u5d4c\u5165\u5f0f<\/td>\n\u2705 \u6df1\u5ea6\u96c6\u6210，\u5b98\u65b9\u652f\u6301 JetPack<\/td>\n<\/tr>\n
    \u79fb\u52a8\u7aef（Android）<\/td>\nGPU \u652f\u6301\u7684\u5b9a\u5236\u786c\u4ef6<\/td>\n\u9650\u8d44\u6e90\u3001JNI + .so \u63a5\u5165<\/td>\n\u26a0\ufe0f \u53ef\u7528\u4f46\u590d\u6742，\u9002\u5408\u8d44\u6df1\u5de5\u7a0b\u56e2\u961f<\/td>\n<\/tr>\n
    \u5d4c\u5165\u5f0f SoC \/ FPGA（\u5b9a\u5236）<\/td>\n\u81ea\u52a8\u9a7e\u9a76\u3001\u673a\u5668\u4eba<\/td>\n\u591a\u4e3a OEM \u96c6\u6210\u573a\u666f<\/td>\n🔧 \u53ef\u7528，\u4f46\u9700\u6df1\u5ea6\u5b9a\u5236 Plugin<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    \n
    🧠 \u90e8\u7f72\u8def\u5f84\u9009\u62e9\u4e09\u95ee：<\/h5>\n
  • \u573a\u666f\u9700\u6c42\u4f18\u5148：\u4f60\u662f\u90e8\u7f72\u79bb\u7ebf\u68c0\u6d4b？\u9ad8\u5e76\u53d1\u63a8\u7406？\u8f7b\u91cf\u6d41\u5904\u7406？<\/li>\n
  • \u786c\u4ef6\u73af\u5883\u786e\u5b9a：\u80fd\u5426\u4f7f\u7528 NVIDIA GPU？\u662f\u5426\u63a5\u5165 Jetson \u7cfb\u5217？<\/li>\n
  • \u5e73\u53f0\u751f\u547d\u5468\u671f\u8003\u91cf：\u662f\u5426\u9700\u8981 OTA \u66f4\u65b0？\u662f\u5426\u9700\u8981\u79fb\u52a8\u7aef SDK？<\/li>\n

    \u2705 TensorRT \u63d0\u4f9b\u7edf\u4e00 API \u4e0e ONNX \u652f\u6301\u63a5\u53e3，\u4f60\u53ef\u4ee5\u201c\u4e00\u5957\u6a21\u578b \u2192 \u591a\u7aef\u90e8\u7f72\u201d，\u53ea\u9700\u6839\u636e\u5e73\u53f0\u5207\u6362\u6784\u5efa\u914d\u7f6e\u548c\u90e8\u7f72\u65b9\u5f0f\u3002<\/p>\n

    \n

    2. \u5728\u670d\u52a1\u5668\u7aef\u90e8\u7f72 TensorRT：\u88f8\u73af\u5883 vs Triton Server \u5168\u6d41\u7a0b<\/h4>\n

    \u5bf9\u4e8e\u5927\u591a\u6570\u5f00\u53d1\u8005\u800c\u8a00，\u670d\u52a1\u5668\u7aef\u90e8\u7f72 TensorRT \u662f\u6700\u5feb\u901f\u4e5f\u662f\u6700\u7a33\u5b9a\u7684\u4e00\u6761\u8def\u5f84\u3002\u5c24\u5176\u662f\u5728\u56fe\u50cf\u8bc6\u522b\u3001\u89c6\u9891\u5206\u6790\u3001AI SaaS \u540e\u7aef\u573a\u666f\u4e2d，TensorRT \u5728 A100\/T4\/L40 \u670d\u52a1\u5668\u4e0a\u53d1\u6325\u6781\u81f4\u6027\u80fd\u3002<\/p>\n

    \n

    🛠\ufe0f \u90e8\u7f72\u65b9\u5f0f\u4e00：\u88f8\u73af\u5883\u90e8\u7f72（\u6700\u7075\u6d3b）<\/h5>\n

    \u8fd9\u79cd\u65b9\u5f0f\u4e0d\u4f9d\u8d56\u5916\u90e8\u6846\u67b6，\u76f4\u63a5\u4f7f\u7528 Python API \u6216 C++ SDK \u52a0\u8f7d engine \u63a8\u7406，\u9002\u5408\u81ea\u5b9a\u4e49\u7cfb\u7edf\u90e8\u7f72\u4e0e\u8c03\u8bd5\u3002<\/p>\n

    \u6d41\u7a0b：<\/p>\n

  • \u5b89\u88c5\u73af\u5883（\u5efa\u8bae\u4f7f\u7528 Docker）docker run –gpus all -it –rm nvcr.io\/nvidia\/tensorrt:23.12-py3\n <\/li>\n
  • \u6784\u5efa engine（\u53ef\u53c2\u8003\u524d\u51e0\u7bc7）<\/li>\n
  • \u63a8\u7406\u670d\u52a1\u5c01\u88c5（Flask\/FastAPI\/gRPC）<\/li>\n
  • \u663e\u5b58\u76d1\u63a7 + \u5e76\u53d1\u7ba1\u7406（\u63a8\u8350 CUDA stream + engine pool）<\/li>\n

    \u4f18\u70b9：<\/p>\n

      \n
    • \u7075\u6d3b\u3001\u53ef\u6df1\u5ea6\u63a7\u5236；<\/li>\n
    • \u652f\u6301\u5b9a\u5236 Plugin\u3001Profiler \u63a5\u5165；<\/li>\n
    • \u66f4\u8d34\u8fd1\u5e95\u5c42\u4f18\u5316\u5b9e\u9a8c\u3002<\/li>\n<\/ul>\n
      \n
      🧩 \u90e8\u7f72\u65b9\u5f0f\u4e8c：Triton Inference Server（\u670d\u52a1\u5316\u63a8\u8350）<\/h5>\n

      Triton \u662f NVIDIA \u51fa\u54c1\u7684\u63a8\u7406\u670d\u52a1\u5e73\u53f0，\u652f\u6301 ONNX \/ TensorRT \/ PyTorch \/ TF \u591a\u79cd\u540e\u7aef，\u662f\u4f01\u4e1a\u7ea7\u90e8\u7f72\u63a8\u8350\u9009\u9879\u3002<\/p>\n

      \u6838\u5fc3\u4f18\u52bf：<\/p>\n

        \n
      • \u652f\u6301 batch \u5408\u5e76\u3001\u81ea\u9002\u5e94\u8c03\u5ea6；<\/li>\n
      • \u652f\u6301 HTTP \/ gRPC \u63a5\u53e3；<\/li>\n
      • \u652f\u6301\u6a21\u578b\u70ed\u66f4\u65b0 + \u591a\u7248\u672c\u5171\u5b58；<\/li>\n
      • \u53ef\u63a5\u5165 Prometheus\u3001TensorBoard \u76d1\u63a7；<\/li>\n
      • \u652f\u6301 GPU \u591a\u79df\u6237\u8d44\u6e90\u9694\u79bb\u3002<\/li>\n<\/ul>\n

        \u90e8\u7f72\u6d41\u7a0b（\u7b80\u7248）：<\/p>\n

        docker run –gpus all -d –rm \\\\<\/span>
        \n -p8000:8000 -p8001:8001 -p8002:8002 \\\\<\/span>
        \n -v \/models:\/models nvcr.io\/nvidia\/tritonserver:23.12-py3 \\\\<\/span>
        \n tritonserver –model-repository=<\/span>\/models<\/p>\n

        \u76ee\u5f55\u7ed3\u6784\u793a\u4f8b：<\/p>\n

        models\/
        \n\u251c\u2500\u2500 resnet50\/
        \n\u2502 \u251c\u2500\u2500 1\/
        \n\u2502 \u2502 \u2514\u2500\u2500 model.plan
        \n\u2502 \u2514\u2500\u2500 config.pbtxt<\/p>\n

        \u26a0\ufe0f \u6ce8\u610f：config \u6587\u4ef6\u5fc5\u987b\u6b63\u786e\u6307\u5b9a\u8f93\u5165\/\u8f93\u51fa dtype\u3001shape\u3001batch \u6a21\u5f0f\u7b49\u4fe1\u606f，\u5426\u5219\u4f1a\u542f\u52a8\u5931\u8d25\u3002<\/p>\n

        \n
        🎯 \u5b9e\u6218\u5efa\u8bae：<\/h5>\n\n\u573a\u666f\u63a8\u8350\u65b9\u5f0f<\/tr>\n\n\n\n\n\n
        \u5feb\u901f\u9a8c\u8bc1\u3001\u79bb\u7ebf\u5de5\u5177<\/td>\n\u88f8\u90e8\u7f72，\u76f4\u63a5\u8fd0\u884c engine<\/td>\n<\/tr>\n
        \u591a\u6a21\u578b\u5e76\u53d1\u670d\u52a1<\/td>\nTriton + GPU \u81ea\u52a8\u8c03\u5ea6<\/td>\n<\/tr>\n
        \u4f01\u4e1a SaaS \u540e\u7aef<\/td>\nTriton Server + gRPC \u63a5\u53e3\u7edf\u4e00\u5c01\u88c5<\/td>\n<\/tr>\n
        \u6279\u5904\u7406\u4efb\u52a1<\/td>\nTriton \u7684 dynamic batch \u805a\u5408\u673a\u5236\u975e\u5e38\u9002\u7528<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
        \n

        3. Jetson \u5e73\u53f0\u90e8\u7f72\u5b9e\u6218（Nano \/ Xavier \/ Orin）：\u73af\u5883\u642d\u5efa + \u6a21\u578b\u63a8\u7406<\/h4>\n

        Jetson \u662f NVIDIA \u4e13\u4e3a\u8fb9\u7f18\u8ba1\u7b97\u63a8\u51fa\u7684\u5d4c\u5165\u5f0f\u5e73\u53f0\u7cfb\u5217，\u5177\u6709\u4f4e\u529f\u8017\u3001\u5c0f\u4f53\u79ef\u3001\u9ad8\u96c6\u6210\u5ea6\u7b49\u7279\u70b9，\u662f TensorRT \u90e8\u7f72\u7684\u201c\u5929\u7136\u6218\u573a\u201d\u3002<\/p>\n

        \n

        🚀 Jetson \u652f\u6301\u7684 TensorRT \u7279\u6027<\/h5>\n\n\u7279\u6027\u652f\u6301\u60c5\u51b5<\/tr>\n\n\n\n\n\n\n
        FP16 \u63a8\u7406<\/td>\n\u2705 \u9ed8\u8ba4\u5f00\u542f<\/td>\n<\/tr>\n
        INT8 \u63a8\u7406<\/td>\n\u2705 \u652f\u6301（\u9700\u6821\u51c6）<\/td>\n<\/tr>\n
        Dynamic Shape<\/td>\n\u2705 \u53ef\u7528，\u4f46\u9700\u8bbe\u7f6e Optimization Profile<\/td>\n<\/tr>\n
        \u81ea\u5b9a\u4e49 Plugin<\/td>\n\u2705 \u652f\u6301，\u4f46\u9700\u7f16\u8bd1<\/td>\n<\/tr>\n
        CUDA Stream \u591a\u6d41<\/td>\n\u2705 \u652f\u6301\u5e76\u53d1\u5904\u7406（\u53d7\u9650\u4e8e\u5185\u5b58）<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
        \n
        🛠\ufe0f \u90e8\u7f72\u73af\u5883\u51c6\u5907（Jetson NX \u4e3a\u4f8b）<\/h5>\n
      • \n

        \u5b89\u88c5 JetPack（\u5efa\u8bae\u4f7f\u7528\u5b98\u65b9\u955c\u50cf） 👉 JetPack \u4e0b\u8f7d\u5730\u5740<\/p>\n<\/li>\n

      • \n

        \u68c0\u67e5 TensorRT \u662f\u5426\u96c6\u6210：<\/p>\n<\/li>\n

        dpkg -l |<\/span> grep<\/span> nvinfer
        \n# \u8f93\u51fa\u5982：libnvinfer8\u3001libnvinfer-plugin8 \u7b49\u5373\u4e3a\u5b89\u88c5\u6210\u529f<\/span><\/p>\n

      • \u5b89\u88c5\u4f9d\u8d56（Python）：<\/li>\n

        sudo<\/span> apt<\/span> install<\/span> python3-pip
        \npip3 install<\/span> numpy onnx onnxruntime<\/p>\n

      • \u63a8\u7406\u6d41\u7a0b\u5efa\u8bae\u4f7f\u7528 Python + PyCUDA + TensorRT \u5b98\u65b9 API<\/li>\n
        \n
        📦 \u63a8\u7406\u6d41\u7a0b\u5b9e\u6218（YOLOv8 \u4e3a\u4f8b）：<\/h5>\n
      • \u5c06\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u5bfc\u51fa\u4e3a ONNX<\/li>\n
      • \u5728 Jetson \u4e0a\u4f7f\u7528 trtexec \u6784\u5efa FP16 engine：<\/li>\n

        trtexec \\\\<\/span>
        \n –onnx=<\/span>yolov8.onnx \\\\<\/span>
        \n –saveEngine=<\/span>yolov8_fp16.engine \\\\<\/span>
        \n –explicitBatch \\\\<\/span>
        \n –fp16 \\\\<\/span>
        \n –workspace=<\/span>512<\/span><\/p>\n

      • Python \u8f7d\u5165\u6267\u884c：<\/li>\n

        import<\/span> tensorrt as<\/span> trt
        \nimport<\/span> pycuda.<\/span>driver as<\/span> cuda
        \nimport<\/span> pycuda.<\/span>autoinit
        \n# \u52a0\u8f7d engine\u3001\u5206\u914d buffer\u3001\u6267\u884c\u63a8\u7406<\/span><\/p>\n

        \n
        📈 \u6027\u80fd\u5b9e\u6d4b（Jetson Orin NX @ FP16）：<\/h5>\n\n\u6a21\u578b\u5206\u8fa8\u7387\u63a8\u7406\u65f6\u95f4\u663e\u5b58\u5360\u7528<\/tr>\n\n\n\n
        YOLOv8n<\/td>\n640×640<\/td>\n15~18ms<\/td>\n450MB<\/td>\n<\/tr>\n
        ResNet50<\/td>\n224×224<\/td>\n4.8ms<\/td>\n280MB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
        \n
        🎯 Jetson \u5b9e\u6218\u5efa\u8bae：<\/h5>\n\n\u9879\u76ee\u5efa\u8bae<\/tr>\n\n\n\n\n\n
        \u7cbe\u5ea6\u9009\u578b<\/td>\nFP16 \u662f\u9ed8\u8ba4\u63a8\u8350，INT8 \u9700\u7cbe\u5ea6\u9a8c\u8bc1<\/td>\n<\/tr>\n
        \u663e\u5b58\u7d27\u5f20\u65f6<\/td>\n\u4f7f\u7528\u5c0f\u6a21\u578b\u3001\u9650\u5236\u6700\u5927 batch<\/td>\n<\/tr>\n
        \u8c03\u8bd5\u56f0\u96be\u65f6<\/td>\n\u4f7f\u7528 trtexec \u6253 benchmark，\u5206\u6790\u8017\u65f6\u4e0e\u663e\u5b58<\/td>\n<\/tr>\n
        \u591a\u6a21\u578b\u90e8\u7f72<\/td>\n\u907f\u514d\u4e00\u6b21\u6027\u52a0\u8f7d\u591a\u4e2a\u5927 engine，\u53ef\u4f7f\u7528\u5e8f\u5217\u5316\u5207\u6362\u65b9\u5f0f<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
        \n

        4. \u79fb\u52a8\u7aef\u90e8\u7f72\u65b9\u5f0f\u63a2\u7d22：JNI \u8c03\u7528 TensorRT .so \u7684\u65b9\u6848\u539f\u7406<\/h4>\n

        \u867d\u7136 TensorRT \u5e76\u4e0d\u539f\u751f\u652f\u6301 Android\/iOS \u7b49\u79fb\u52a8\u7aef\u73af\u5883，\u4f46\u5728\u67d0\u4e9b\u5b9a\u5236\u8bbe\u5907（\u5982\u56fd\u4ea7 AI \u6a21\u7ec4\u3001\u667a\u80fd\u76f8\u673a\u3001\u5de5\u4e1a\u7ec8\u7aef）\u4e2d，\u5b58\u5728\u901a\u8fc7 JNI \u8c03\u7528 TensorRT .so \u6587\u4ef6\u5b8c\u6210\u63a8\u7406\u7684\u65b9\u5f0f\u3002<\/p>\n

        \n

        🧠 \u539f\u7406\u7b80\u8ff0：<\/h5>\n
      • \u4f7f\u7528 C++ \u6784\u5efa TensorRT \u7684 Engine \u52a0\u8f7d\u4e0e\u6267\u884c\u903b\u8f91；<\/li>\n
      • \u4f7f\u7528 CMake \u7f16\u8bd1\u4e3a .so（\u5171\u4eab\u5e93），\u5305\u542b libnvinfer \u548c\u63d2\u4ef6；<\/li>\n
      • \u5728 Android \u5de5\u7a0b\u4e2d\u7528 JNI \u63a5\u5165 C++ \u63a5\u53e3；<\/li>\n
      • Java \u5c42\u5904\u7406\u56fe\u50cf \u2192 JNI \u2192 C++ \u63a8\u7406 \u2192 \u56de\u4f20\u7ed3\u679c\u3002<\/li>\n
        \n
        🛠\ufe0f \u6784\u5efa\u6d41\u7a0b\u7b80\u8ff0：<\/h5>\n
      • \u51c6\u5907\u597d\u4ea4\u53c9\u7f16\u8bd1\u94fe（NDK）<\/li>\n
      • \u4f7f\u7528 cmake + ndk-build \u7f16\u8bd1\u4f60\u7684 TensorRT \u63a8\u7406\u903b\u8f91\u4e3a .so：find_library(log-lib log)
        \ntarget_link_libraries(native-lib ${log-lib} nvinfer nvinfer_plugin …)\n <\/li>\n
      • \u5728 Java \u5c42\u52a0\u8f7d\u5e93\u5e76\u58f0\u660e native \u65b9\u6cd5：<\/li>\n

        static<\/span> {<\/span>
        \n System<\/span>.<\/span>loadLibrary<\/span>(<\/span>"native-lib"<\/span>)<\/span>;<\/span>
        \n}<\/span><\/p>\n

        public<\/span> native<\/span> float<\/span>[<\/span>]<\/span> inferFromTensor<\/span>(<\/span>byte<\/span>[<\/span>]<\/span> inputTensor)<\/span>;<\/span><\/p>\n

      • \u5728 Activity \u4e2d\u5b8c\u6210\u56fe\u50cf\u524d\u5904\u7406\u3001\u7ed3\u679c\u540e\u5904\u7406\u903b\u8f91\u3002<\/li>\n
        \n
        \u26a0\ufe0f \u6ce8\u610f\u4e8b\u9879：<\/h5>\n\n\u9879\u76ee\u98ce\u9669\/\u9650\u5236<\/tr>\n\n\n\n\n\n
        TensorRT \u52a8\u6001\u5e93<\/td>\n\u9700\u8981\u4ea4\u53c9\u7f16\u8bd1，\u6216\u4ece Jetson \u63d0\u53d6 .so<\/td>\n<\/tr>\n
        \u63d2\u4ef6\u652f\u6301<\/td>\nPlugin \u5c42\u9700\u989d\u5916\u7f16\u8bd1\u5e76\u6ce8\u518c<\/td>\n<\/tr>\n
        \u5b89\u5168\u7b56\u7565<\/td>\nAndroid 12+ \u5bf9 native \u5e93\u8c03\u7528\u6743\u9650\u52a0\u5f3a<\/td>\n<\/tr>\n
        \u517c\u5bb9\u6027<\/td>\nGPU \u517c\u5bb9\u6027\u6781\u5dee，\u4ec5\u5efa\u8bae\u7528\u4e8e\u5b9a\u5236\u5e73\u53f0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
        \n
        🎯 \u9002\u7528\u573a\u666f\u5efa\u8bae：<\/h5>\n\n\u573a\u666f\u8bf4\u660e<\/tr>\n\n\n\n\n
        \u5de5\u4e1a Android \u7ec8\u7aef（\u542b GPU）<\/td>\n\u53ef\u7528 JNI \u8c03\u7528 TensorRT<\/td>\n<\/tr>\n
        \u9700\u8981\u8fb9\u7f18\u667a\u80fd\u3001\u5b9e\u65f6\u54cd\u5e94<\/td>\n\u53ef\u4ee5\u6784\u5efa\u6781\u7b80\u7cfb\u7edf\u5d4c\u5165\u63a8\u7406\u903b\u8f91<\/td>\n<\/tr>\n
        \u666e\u901a\u624b\u673a\u7aef（\u65e0 NVIDIA GPU）<\/td>\n\u274c \u4e0d\u5efa\u8bae\u4f7f\u7528 TensorRT，\u6539\u7528 TFLite \u6216 MNN<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
        \n

        5. \u5e73\u53f0\u9009\u578b\u5efa\u8bae：\u4f60\u8be5\u9009\u670d\u52a1\u5668？Jetson？\u8fd8\u662f\u79fb\u52a8\u7aef？<\/h4>\n

        \u90e8\u7f72\u4e0d\u662f\u4e00\u4e2a\u7eaf\u6280\u672f\u51b3\u7b56，\u800c\u662f\u4e00\u4e2a**\u201c\u6210\u672c \u00d7 \u6027\u80fd \u00d7 \u573a\u666f\u9002\u914d\u201d\u591a\u7ef4\u6743\u8861\u7684\u95ee\u9898**\u3002TensorRT \u9002\u914d\u7684\u5e73\u53f0\u5f88\u591a，\u4f46\u9009\u62e9\u6700\u5408\u9002\u7684\u5e73\u53f0，\u624d\u662f\u53d1\u6325\u5b83\u80fd\u529b\u7684\u524d\u63d0\u3002<\/p>\n

        \n

        \u2705 \u9009\u578b\u901f\u67e5\u8868<\/h5>\n\n\u7ef4\u5ea6\u670d\u52a1\u5668\u7aef（A100\/T4）Jetson（Xavier\/Orin）\u79fb\u52a8\u7aef（Android）<\/tr>\n\n\n\n\n\n\n\n
        \u6027\u80fd<\/td>\n💪 \u6781\u9ad8（\u652f\u6301\u5927\u578b\u6a21\u578b\u3001\u5e76\u53d1\u63a8\u7406）<\/td>\n🧩 \u4e2d\u7b49（\u652f\u6301\u5c0f\u4e2d\u6a21\u578b）<\/td>\n\u26a0\ufe0f \u6709\u9650（\u89c6\u8bbe\u5907\u800c\u5b9a）<\/td>\n<\/tr>\n
        \u6210\u672c<\/td>\n💰 \u9ad8（\u4e91 GPU \u6216\u81ea\u5efa\u673a\u623f）<\/td>\n💸 \u4e2d（\u7ea6\u5343\u5143\u5230\u4e07\u5143\u7ea7）<\/td>\n\u2705 \u4f4e（\u539f\u8bbe\u5907）<\/td>\n<\/tr>\n
        \u5f00\u53d1\u6548\u7387<\/td>\n\u2705 \u9ad8（\u6210\u719f\u6587\u6863 + Triton）<\/td>\n\u2705 \u4e2d（JetPack \u5de5\u5177\u94fe\u8f83\u5168）<\/td>\n\u26a0\ufe0f \u4f4e（\u9700 JNI + \u624b\u52a8\u6784\u5efa）<\/td>\n<\/tr>\n
        \u90e8\u7f72\u590d\u6742\u5ea6<\/td>\n\u2705 \u4f4e<\/td>\n\u26a0\ufe0f \u4e2d（\u9700\u914d\u7f6e JetPack）<\/td>\n\u274c \u9ad8（NDK \u7f16\u8bd1 + \u5b89\u5168\u9650\u5236）<\/td>\n<\/tr>\n
        \u9002\u914d\u6a21\u578b<\/td>\n\u4efb\u610f\u6a21\u578b<\/td>\n\u5efa\u8bae\u5c0f\u6a21\u578b \/ INT8\u91cf\u5316<\/td>\n\u4ec5\u6781\u5c0f\u6a21\u578b（\u6216\u6539\u7528 TFLite）<\/td>\n<\/tr>\n
        \u5178\u578b\u573a\u666f<\/td>\n\u4e91\u670d\u52a1\u3001API \u63a8\u7406\u3001\u6807\u6ce8\u5e73\u53f0<\/td>\n\u5de5\u4e1a\u76f8\u673a\u3001\u667a\u80fd\u7ec8\u7aef\u3001\u8f66\u8f7d\u8bc6\u522b<\/td>\nAI \u76f8\u673a\u3001\u7269\u8054\u7f51\u3001\u5b9a\u5236\u76d2\u5b50<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
        \n
        🎯 \u573a\u666f\u9a71\u52a8\u7684\u5efa\u8bae：<\/h5>\n\n\u4e1a\u52a1\u573a\u666f\u63a8\u8350\u5e73\u53f0\u8bf4\u660e<\/tr>\n\n\n\n\n\n
        \u9ad8\u5e76\u53d1\u56fe\u50cf\u5206\u7c7b\u3001AI API \u63a5\u53e3<\/td>\n\u670d\u52a1\u5668\u7aef + Triton<\/td>\n\u7a33\u5b9a\u6027\u4e0e\u541e\u5410\u4e3a\u738b<\/td>\n<\/tr>\n
        \u8f66\u8f7d\u6444\u50cf\u5934\u3001\u5de5\u4e1a\u667a\u80fd\u7ec8\u7aef<\/td>\nJetson Orin NX<\/td>\n\u63a8\u7406\u5b9e\u65f6\u3001\u529f\u8017\u53ef\u63a7<\/td>\n<\/tr>\n
        \u672c\u5730\u79fb\u52a8\u7aef\u79bb\u7ebf\u8bc6\u522b<\/td>\nTFLite \/ MNN \u66f4\u4f18<\/td>\nTensorRT \u4ec5\u9650 GPU \u7279\u6b8a\u8bbe\u5907<\/td>\n<\/tr>\n
        AIoT \u6a21\u7ec4（\u56fd\u4ea7 SoC）<\/td>\nTensorRT .so \u5c01\u88c5<\/td>\n\u9700\u786c\u4ef6\u5382\u5546\u652f\u6301\u7f16\u8bd1\u94fe<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \u2705 \u5efa\u8bae\u5148\u6839\u636e\u6a21\u578b\u5927\u5c0f\u548c\u8fd0\u884c\u9891\u7387\u5224\u65ad\u662f\u5426\u9700\u8981\u90e8\u7f72\u4f18\u5316，\u518d\u9009\u62e9\u5408\u9002\u5e73\u53f0\u5339\u914d\u90e8\u7f72\u7b56\u7565\u3002<\/p>\n

        \n

        6. \u8de8\u5e73\u53f0\u90e8\u7f72\u6ce8\u610f\u4e8b\u9879：\u663e\u5b58\u914d\u7f6e\u3001\u6027\u80fd\u8bc4\u4f30\u3001\u5de5\u5177\u94fe\u63a8\u8350<\/h4>\n

        \u591a\u5e73\u53f0\u90e8\u7f72\u5e76\u4e0d\u662f\u201c\u4e00\u4efd ONNX \u8dd1\u5929\u4e0b\u201d，\u7531\u4e8e\u6bcf\u4e2a\u5e73\u53f0\u7684\u8d44\u6e90\u9650\u5236\u3001TensorRT \u652f\u6301\u80fd\u529b\u5dee\u5f02，\u5f00\u53d1\u8005\u9700\u8981\u6ce8\u610f\u4ee5\u4e0b\u51e0\u4e2a\u5173\u952e\u70b9，\u624d\u80fd\u786e\u4fdd\u8fc1\u79fb\u987a\u5229\u3001\u6027\u80fd\u4e0d\u6389\u961f\u3002<\/p>\n

        \n

        🧠 \u6ce8\u610f\u70b9\u4e00：\u663e\u5b58\u8d44\u6e90\u5dee\u5f02<\/h5>\n\n\u5e73\u53f0\u53ef\u7528\u663e\u5b58\u8303\u56f4\u5f71\u54cd<\/tr>\n\n\n\n\n
        A100 \/ T4<\/td>\n16GB ~ 80GB<\/td>\n\u53ef\u90e8\u7f72\u5927\u578b\u6a21\u578b\u3001\u652f\u6301 batch32~128<\/td>\n<\/tr>\n
        Jetson NX<\/td>\n4GB ~ 8GB<\/td>\n\u5efa\u8bae batch \u2264 4，\u4f7f\u7528 FP16\/INT8<\/td>\n<\/tr>\n
        Android GPU<\/td>\n<1GB（\u5171\u4eab）<\/td>\n\u4ec5\u9002\u5408\u6781\u5c0f\u6a21\u578b（\u5982 MobileNetv2）<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \u26a0\ufe0f Engine \u6784\u5efa\u9636\u6bb5\u5fc5\u987b\u63a7\u5236 workspace size\u3001\u52a8\u6001 shape \u8303\u56f4，\u907f\u514d OOM<\/p>\n

        \n
        🧠 \u6ce8\u610f\u70b9\u4e8c：\u6784\u5efa Engine \u8981\u5339\u914d\u90e8\u7f72\u5e73\u53f0<\/h5>\n
          \n
        • \n

          \u4e0d\u540c GPU \u67b6\u6784\u5bf9 kernel \u652f\u6301\u4e0d\u540c：<\/p>\n

            \n
          • \u670d\u52a1\u5668 \u2192 Ampere\u3001Turing \u67b6\u6784；<\/li>\n
          • Jetson \u2192 Volta（Xavier）\u6216 Ampere（Orin）；<\/li>\n
          • \u4e0d\u5efa\u8bae\u8de8\u5e73\u53f0\u590d\u7528 engine \u6587\u4ef6！<\/li>\n<\/ul>\n<\/li>\n
          • \n

            \u63a8\u8350\u505a\u6cd5：<\/p>\n

              \n
            • \u5728\u76ee\u6807\u8bbe\u5907\u4e0a\u672c\u5730\u6784\u5efa engine；<\/li>\n
            • \u6216\u901a\u8fc7 NVIDIA \u7684 cross-compilation toolchain \u8de8\u5e73\u53f0\u6784\u5efa；<\/li>\n
            • \u4f7f\u7528\u5e73\u53f0\u5bf9\u5e94\u7684 TensorRT \u7248\u672c（JetPack \u9644\u5e26\u7248\u672c\u4e3a\u4e3b）\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
              \n
              🧠 \u6ce8\u610f\u70b9\u4e09：\u5de5\u5177\u94fe\u8981\u7edf\u4e00\u7248\u672c<\/h5>\n\n\u5de5\u5177\u63a8\u8350\u7ec4\u5408<\/tr>\n\n\n\n\n\n\n
              TensorRT<\/td>\n\u4e0e JetPack \u6216 NVIDIA Docker \u955c\u50cf\u4fdd\u6301\u4e00\u81f4<\/td>\n<\/tr>\n
              ONNX<\/td>\nopset_version=11\/13\/17，\u907f\u514d\u8fc7\u9ad8<\/td>\n<\/tr>\n
              PyCUDA<\/td>\n\u5b89\u88c5\u7248\u672c\u9700\u4e0e\u7cfb\u7edf CUDA \u5339\u914d<\/td>\n<\/tr>\n
              trtexec<\/td>\n\u540c\u7248\u672c TensorRT \u63d0\u4f9b\u7684 CLI \u5de5\u5177<\/td>\n<\/tr>\n
              \u7f16\u8bd1\u5668（\u79fb\u52a8\u7aef）<\/td>\nAndroid NDK + CMake 3.10+<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
              \n
              🎯 \u5b9e\u6218\u5efa\u8bae checklist：<\/h5>\n
                \n
              • \u2705 \u662f\u5426\u63a7\u5236\u4e86 engine \u8f93\u5165\u7ef4\u5ea6\u8303\u56f4？<\/li>\n
              • \u2705 \u662f\u5426\u7edf\u4e00\u4e86 TensorRT \u6784\u5efa\u7248\u672c\u4e0e\u76ee\u6807\u5e73\u53f0？<\/li>\n
              • \u2705 \u662f\u5426\u505a\u8fc7 benchmark \u8bc4\u4f30\u4e0d\u540c\u5e73\u53f0\u8868\u73b0？<\/li>\n
              • \u2705 \u662f\u5426\u914d\u7f6e\u4e86 INT8\/FP16 \u52a8\u6001 profile？<\/li>\n
              • \u2705 \u662f\u5426\u8003\u8651\u4e86 batch size \u4e0e\u663e\u5b58\u5cf0\u503c\u7684\u5e73\u8861？<\/li>\n<\/ul>\n
                \n

                7. \u5e38\u89c1\u90e8\u7f72\u95ee\u9898\u6392\u67e5：Jetson OOM？Android crash？gRPC \u65e0\u54cd\u5e94？<\/h4>\n

                \u90e8\u7f72\u8fc7\u7a0b\u4e2d\u6700\u4ee4\u4eba\u5934\u75bc\u7684\u4e0d\u662f\u6a21\u578b\u4f18\u5316\u4e0d\u5230\u4f4d，\u800c\u662f\u4e0a\u7ebf\u4e00\u8bd5\u76f4\u63a5\u5d29\u3001\u65e5\u5fd7\u4e00\u7247\u7a7a\u767d\u3002\u4ee5\u4e0b\u662f\u6211\u4eec\u5728\u4f01\u4e1a\u9879\u76ee\u4e2d\u9ad8\u9891\u9047\u5230\u7684\u51e0\u7c7b\u95ee\u9898\u53ca\u89e3\u51b3\u7b56\u7565：<\/p>\n

                \n

                \u274c Jetson \u4e0a\u9891\u7e41 OOM \u6216\u63a8\u7406\u5931\u8d25<\/h5>\n\n\u95ee\u9898\u8868\u73b0\u539f\u56e0\u89e3\u51b3\u65b9\u6cd5<\/tr>\n\n\n\n\n\n
                \u6784\u5efa engine \u62a5\u9519：CUDA out of memory<\/td>\nworkspace \u592a\u5927 \/ max shape \u8bbe\u7f6e\u8fc7\u9ad8<\/td>\n\u964d\u4f4e –workspace \u53c2\u6570（\u5982 512），\u9650\u5236 maxBatch<\/td>\n<\/tr>\n
                \u8fd0\u884c\u65f6\u5361\u987f \/ \u5d29\u6e83<\/td>\nINT8 \u91cf\u5316\u8bef\u5dee\u3001Plugin \u4e0d\u517c\u5bb9<\/td>\n\u56de\u9000 FP16 \u6a21\u578b，\u91cd\u65b0\u6784\u5efa engine<\/td>\n<\/tr>\n
                trtexec \u5361\u6b7b\u6216\u6784\u5efa\u5931\u8d25<\/td>\nswap \u7a7a\u95f4\u4e0d\u8db3 \/ \u9a71\u52a8\u51b2\u7a81<\/td>\n\u4e34\u65f6\u6dfb\u52a0 swap \u7a7a\u95f4\u3001\u68c0\u67e5 JetPack \u9a71\u52a8\u662f\u5426\u5b8c\u6574<\/td>\n<\/tr>\n
                \u63a8\u7406\u8017\u65f6\u6781\u4e0d\u7a33\u5b9a<\/td>\n\u7535\u6e90\u4e0d\u8db3，\u9891\u7e41\u964d\u9891<\/td>\n\u4f7f\u7528\u7a33\u5b9a\u7535\u6e90，\u5f00\u542f\u8282\u80fd\u6a21\u5f0f\u8c03\u4f18（nvpmodel）<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
                \n
                \u274c Android JNI \u63a5\u53e3 crash \/ \u6a21\u578b\u63a8\u7406\u65e0\u54cd\u5e94<\/h5>\n\n\u95ee\u9898\u8868\u73b0\u539f\u56e0\u89e3\u51b3\u65b9\u6cd5<\/tr>\n\n\n\n\n\n
                \u52a0\u8f7d .so \u62a5 UnsatisfiedLinkError<\/td>\nABI \u4e0d\u5339\u914d（arm64-v8a vs armeabi）<\/td>\n\u7f16\u8bd1\u65f6\u6307\u5b9a target ABI，\u786e\u4fdd\u4e00\u81f4<\/td>\n<\/tr>\n
                \u8c03\u7528\u63a8\u7406\u65f6\u76f4\u63a5 crash<\/td>\nmalloc \u5931\u8d25 \/ shape \u5f02\u5e38<\/td>\n\u68c0\u67e5 native \u4ee3\u7801\u662f\u5426\u505a\u8fb9\u754c\u6821\u9a8c，\u786e\u8ba4 engine \u6b63\u5e38<\/td>\n<\/tr>\n
                \u63a8\u7406\u8fd4\u56de\u7a7a\u7ed3\u679c<\/td>\n\u8f93\u5165 binding \u672a\u6b63\u786e setShape<\/td>\nAndroid \u7aef\u8c03\u7528\u524d\u68c0\u67e5\u8f93\u5165\u7ef4\u5ea6\u662f\u5426\u4f20\u9012\u6210\u529f<\/td>\n<\/tr>\n
                \u6a21\u578b\u8c03\u7528\u5ef6\u8fdf\u9ad8<\/td>\nGPU busy \/ \u524d\u5904\u7406\u8fc7\u91cd<\/td>\n\u5f02\u6b65\u6267\u884c + \u63d0\u524d\u9884\u52a0\u8f7d engine，\u907f\u514d\u6bcf\u6b21\u51b7\u542f\u52a8\u52a0\u8f7d\u8d44\u6e90<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
                \n
                \u274c Triton \u90e8\u7f72\u540e gRPC \/ HTTP \u63a5\u53e3\u65e0\u54cd\u5e94\u6216\u8fd4\u56de\u9519\u8bef\u7801<\/h5>\n\n\u95ee\u9898\u8868\u73b0\u53ef\u80fd\u539f\u56e0\u5904\u7406\u5efa\u8bae<\/tr>\n\n\n\n\n\n
                404 model not found<\/td>\n\u6a21\u578b\u76ee\u5f55\u7ed3\u6784\u4e0d\u89c4\u8303<\/td>\nmodel_repository\/model_name\/1\/model.plan \u5fc5\u987b\u51c6\u786e<\/td>\n<\/tr>\n
                400 bad config.pbtxt<\/td>\n\u7f3a\u5c11\u8f93\u5165\/\u8f93\u51fa\u63cf\u8ff0\u3001dtype \u9519\u8bef<\/td>\n\u7528 tritonserver –strict-model-config=false \u8bd5\u8fd0\u884c\u8c03\u8bd5<\/td>\n<\/tr>\n
                500 internal error<\/td>\n\u63a8\u7406\u5f02\u5e38 \/ \u663e\u5b58\u4e0d\u8db3<\/td>\n\u5f00\u542f\u65e5\u5fd7 –log-verbose=1 \u67e5\u770b\u8be6\u7ec6\u4fe1\u606f<\/td>\n<\/tr>\n
                \u54cd\u5e94\u6781\u6162<\/td>\ndynamic batch \u8bbe\u7f6e\u4e0d\u5408\u7406 \/ batch \u8d85\u9650<\/td>\n\u8bbe\u7f6e\u5408\u7406\u7684 batch delay \u4e0e queue timeout \u53c2\u6570<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
                \n
                🎯 \u5efa\u8bae\u7edf\u4e00\u6392\u67e5\u5957\u8def：<\/h5>\n
              • \u662f\u5426\u786e\u8ba4\u90e8\u7f72\u8bbe\u5907\u652f\u6301\u5f53\u524d\u7cbe\u5ea6（FP16 \/ INT8）？<\/li>\n
              • \u662f\u5426\u5728\u90e8\u7f72\u76ee\u6807\u673a\u5668\u4e0a\u672c\u5730\u6784\u5efa engine？<\/li>\n
              • \u662f\u5426\u660e\u786e\u6a21\u578b\u8f93\u5165\u7ef4\u5ea6\u3001\u540d\u79f0\u3001batch \u9650\u5236？<\/li>\n
              • \u662f\u5426\u6709 log \u8f93\u51fa\u6216 profiling \u652f\u6301？<\/li>\n
              • \u662f\u5426\u6709 fallback \u7b56\u7565？（\u5982 FP16 fallback \u2192 FP32）<\/li>\n
                \n

                8. \u5b9e\u6218\u793a\u4f8b\u603b\u7ed3：\u90e8\u7f72 YOLOv8 \u7684\u4e09\u7aef\u8868\u73b0\u5bf9\u6bd4<\/h4>\n

                \u4e3a\u4e86\u8ba9\u4f60\u66f4\u6e05\u6670\u7406\u89e3\u4e0d\u540c\u5e73\u53f0\u90e8\u7f72\u6548\u679c，\u6211\u4eec\u4ee5\u7ecf\u5178\u7684 YOLOv8n（\u8f7b\u91cf\u7248）\u6a21\u578b\u4e3a\u4f8b，\u8fdb\u884c\u4e09\u7aef\u90e8\u7f72\u6d4b\u8bd5，\u6db5\u76d6 \u6784\u5efa\u65f6\u95f4\u3001\u63a8\u7406\u65f6\u95f4\u3001\u541e\u5410\u3001\u663e\u5b58\u5360\u7528 \u56db\u5927\u6307\u6807\u3002<\/p>\n

                \n

                📦 \u6a21\u578b\u4fe1\u606f<\/h5>\n
                  \n
                • \u8f93\u5165：(1, 3, 640, 640)<\/li>\n
                • \u8f93\u51fa：\u68c0\u6d4b\u6846 + \u7c7b\u522b<\/li>\n
                • \u6784\u5efa：ONNX \u2192 TensorRT FP16 engine<\/li>\n<\/ul>\n
                  \n
                  🚀 \u6027\u80fd\u5bf9\u6bd4\u8868<\/h5>\n\n\u5e73\u53f0\u6784\u5efa\u8017\u65f6\u63a8\u7406\u8017\u65f6（\u5355\u5f20）\u663e\u5b58\u5360\u7528\u541e\u5410（\u56fe\/s）<\/tr>\n\n\n\n\n
                  \u670d\u52a1\u5668 A100 (FP16)<\/td>\n~1.3 min<\/td>\n11.2 ms<\/td>\n850MB<\/td>\n89<\/td>\n<\/tr>\n
                  Jetson Orin NX (FP16)<\/td>\n~2.5 min<\/td>\n18.4 ms<\/td>\n520MB<\/td>\n54<\/td>\n<\/tr>\n
                  Android \u5b9a\u5236\u7aef (JNI)<\/td>\n\u9884\u7f16\u8bd1<\/td>\n~68 ms<\/td>\n\u4e0d\u53ef\u6d4b<\/td>\n~13<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
                  \n
                  🎯 \u7ecf\u9a8c\u603b\u7ed3：<\/h5>\n
                    \n
                  • A100 \u63a8\u7406\u6027\u80fd\u6700\u4f73，\u9002\u5408 API \u670d\u52a1 \/ \u89c6\u9891\u5206\u6790；<\/li>\n
                  • Jetson \u5728\u8fb9\u7f18\u573a\u666f\u4e0b\u8868\u73b0\u7a33\u5b9a，\u529f\u8017\u63a7\u5236\u4f18\u5f02；<\/li>\n
                  • Android \u65b9\u6848\u9002\u5408\u5b9a\u5236\u5316\u7ec8\u7aef，\u4f46\u7ef4\u62a4\u6210\u672c\u9ad8，\u4e0d\u5efa\u8bae\u901a\u7528\u5f00\u53d1；<\/li>\n<\/ul>\n
                    \n
                    📌 \u5de5\u7a0b\u843d\u5730\u5efa\u8bae：<\/h5>\n\n\u76ee\u6807\u6700\u4f73\u7b56\u7565<\/tr>\n\n\n\n\n\n
                    \u5feb\u901f\u4e0a\u7ebf<\/td>\nTriton + FP16 Engine \u6a21\u578b\u90e8\u7f72<\/td>\n<\/tr>\n
                    \u8fb9\u7f18\u7aef\u6a21\u578b\u538b\u7f29<\/td>\nONNX \u91cf\u5316 \u2192 INT8 TensorRT \u6784\u5efa<\/td>\n<\/tr>\n
                    \u591a\u5e73\u53f0\u9002\u914d<\/td>\n\u4f7f\u7528\u7edf\u4e00 ONNX \u6a21\u578b + \u5206\u5e73\u53f0\u6784\u5efa\u7b56\u7565<\/td>\n<\/tr>\n
                    \u8fd0\u7ef4\u81ea\u52a8\u5316<\/td>\n\u6bcf\u4e2a\u5e73\u53f0\u6253\u72ec\u7acb Docker \u955c\u50cf + \u914d\u7f6e engine \u7f13\u5b58\u8def\u5f84<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
                    \n

                    9. \u9644\u5f55：\u5b98\u65b9\u90e8\u7f72\u5de5\u5177\u3001JetPack \u955c\u50cf\u3001\u79fb\u52a8\u7aef\u96c6\u6210\u6587\u6863\u5408\u96c6<\/h4>\n

                    \u5728 TensorRT \u591a\u5e73\u53f0\u90e8\u7f72\u8fc7\u7a0b\u4e2d，\u4f60\u53ef\u80fd\u4f1a\u9891\u7e41\u63a5\u89e6\u5230 Jetson SDK\u3001Triton \u670d\u52a1\u6846\u67b6\u3001ONNX \u8f6c\u6362\u5668\u3001\u79fb\u52a8\u7aef JNI \u6865\u63a5\u63a5\u53e3\u7b49\u5de5\u5177，\u8fd9\u91cc\u6211\u4eec\u4e3a\u4f60\u6574\u7406\u4e00\u4efd\u5b9e\u7528\u5ea6\u6781\u9ad8\u7684\u5b98\u65b9\u8d44\u6e90\u6e05\u5355：<\/p>\n

                    \n

                    🚀 Jetson \u5e73\u53f0\u76f8\u5173\u8d44\u6e90<\/h5>\n\n\u5de5\u5177\/\u8d44\u6e90\u7528\u9014\u5730\u5740<\/tr>\n\n\n\n\n
                    JetPack SDK<\/td>\nJetson \u5e73\u53f0\u7cfb\u7edf\u73af\u5883（\u542b TensorRT\u3001CUDA）<\/td>\nJetPack \u5b98\u7f51<\/td>\n<\/tr>\n
                    Jetson SDK Manager<\/td>\n\u4e00\u952e\u70e7\u5f55 JetPack + \u9a71\u52a8\u5de5\u5177<\/td>\nSDK Manager \u4e0b\u8f7d<\/td>\n<\/tr>\n
                    L4T \u6587\u6863<\/td>\nJetson Linux \u7cfb\u7edf\u63a5\u53e3\u8bf4\u660e<\/td>\nL4T \u5b98\u65b9\u6587\u6863<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
                    \n
                    📦 Triton Inference Server \u8d44\u6e90<\/h5>\n\n\u5de5\u5177\/\u6587\u6863\u7528\u9014\u5730\u5740<\/tr>\n\n\n\n\n
                    Triton GitHub<\/td>\n\u793a\u4f8b\u90e8\u7f72\u9879\u76ee\u3001Docker \u955c\u50cf\u3001\u5ba2\u6237\u7aef SDK<\/td>\nGitHub \u9879\u76ee<\/td>\n<\/tr>\n
                    Triton \u6a21\u578b\u683c\u5f0f\u89c4\u8303<\/td>\nconfig.pbtxt \u8be6\u7ec6\u53c2\u6570\u6307\u5357<\/td>\n\u6a21\u578b\u914d\u7f6e\u6307\u5357<\/td>\n<\/tr>\n
                    \u5b98\u65b9\u90e8\u7f72\u6587\u6863<\/td>\nTriton \u90e8\u7f72\u6d41\u7a0b + \u6027\u80fd\u8c03\u4f18<\/td>\nTriton \u6587\u6863\u4e2d\u5fc3<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
                    \n
                    📱 \u79fb\u52a8\u7aef\u90e8\u7f72\u76f8\u5173\u8d44\u6599<\/h5>\n\n\u5de5\u5177\/\u6587\u6863\u7528\u9014\u5730\u5740<\/tr>\n\n\n\n\n
                    NDK + CMake + JNI \u6559\u7a0b<\/td>\nAndroid \u4e0b\u96c6\u6210 native-lib \u7684\u5b98\u65b9\u6587\u6863<\/td>\nNDK \u5b98\u65b9<\/td>\n<\/tr>\n
                    TensorRT Plugin \u793a\u4f8b（C++）<\/td>\n\u81ea\u5b9a\u4e49\u5c42 \/ \u63a8\u7406\u903b\u8f91\u5f00\u53d1<\/td>\nTensorRT \u793a\u4f8b<\/td>\n<\/tr>\n
                    NVIDIA Dev Forum<\/td>\n\u67e5\u62a5\u9519\u3001\u627e\u66ff\u4ee3\u65b9\u6848\u3001\u63d0\u95ee\u53cd\u9988<\/td>\nNVIDIA \u5f00\u53d1\u8005\u8bba\u575b<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
                    \n

                    10. \u5982\u679c\u4f60\u89c9\u5f97\u8fd9\u7bc7\u5bf9\u4f60\u6709\u5e2e\u52a9\u2026\u2026<\/h4>\n

                    🎉 \u606d\u559c\u4f60\u8bfb\u5b8c\u672c\u7bc7！\u73b0\u5728\u4f60\u5df2\u7ecf\u638c\u63e1\u4e86：<\/p>\n

                      \n
                    • \u5982\u4f55\u5224\u65ad\u5e76\u9009\u62e9\u6700\u9002\u5408\u4f60\u9879\u76ee\u7684\u90e8\u7f72\u5e73\u53f0；<\/li>\n
                    • \u5728\u670d\u52a1\u5668\u3001Jetson\u3001\u79fb\u52a8\u7aef\u7b49\u73af\u5883\u4e0b\u7684\u5b9e\u9645\u90e8\u7f72\u6d41\u7a0b；<\/li>\n
                    • \u591a\u7aef\u90e8\u7f72\u4e2d\u6700\u5bb9\u6613\u9047\u5230\u7684\u95ee\u9898\u6392\u67e5\u6280\u5de7；<\/li>\n
                    • \u4e09\u7aef\u6027\u80fd\u5b9e\u6d4b\u5bf9\u6bd4 + \u7b56\u7565\u5efa\u8bae；<\/li>\n
                    • \u5b8c\u6574\u7684\u5b98\u65b9\u5de5\u5177\u94fe\u548c\u8d44\u6599\u63a8\u8350\u8def\u5f84\u3002<\/li>\n<\/ul>\n
                      \n

                      📌 \u5982\u679c\u4f60\u89c9\u5f97\u8fd9\u7bc7\u5185\u5bb9\u5bf9\u4f60\u6709\u542f\u53d1\u3001\u6709\u5e2e\u52a9， \u6b22\u8fce\u70b9\u8d5e 👍\u3001\u6536\u85cf \u2b50\u3001\u5173\u6ce8\u6211 🔔 \u8fd9\u4f1a\u6210\u4e3a\u6211\u6301\u7eed\u66f4\u65b0 TensorRT \u6df1\u5ea6\u7cfb\u5217\u5185\u5bb9\u7684\u6700\u5927\u52a8\u529b！<\/p>\n","protected":false},"excerpt":{"rendered":"

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