{"id":69218,"date":"2026-01-31T08:48:55","date_gmt":"2026-01-31T00:48:55","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/69218.html"},"modified":"2026-01-31T08:48:55","modified_gmt":"2026-01-31T00:48:55","slug":"%e5%a6%82%e4%bd%95%e9%80%9a%e8%bf%87-nvidia-dgx-a100-%e6%98%be%e5%8d%a1%e6%9c%8d%e5%8a%a1%e5%99%a8%ef%bc%8c%e4%bc%98%e5%8c%96-ai-%e5%8c%bb%e7%96%97%e5%bd%b1%e5%83%8f%e5%88%86%e6%9e%90%e4%b8%ad","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/69218.html","title":{"rendered":"\u5982\u4f55\u901a\u8fc7 NVIDIA DGX A100 \u663e\u5361\u670d\u52a1\u5668\uff0c\u4f18\u5316 AI \u533b\u7597\u5f71\u50cf\u5206\u6790\u4e2d\u7684\u6570\u636e\u5904\u7406\u4e0e\u6a21\u578b\u63a8\u7406\u901f\u5ea6\uff1f"},"content":{"rendered":"

\u5728\u533b\u7597\u5f71\u50cf\u5206\u6790\u9886\u57df，AI \u6a21\u578b\u7684\u6027\u80fd\u74f6\u9888\u4e3b\u8981\u4f53\u73b0\u5728\u4e24\u4e2a\u73af\u8282：\u6d77\u91cf\u533b\u5b66\u56fe\u50cf\u7684\u6570\u636e\u9884\u5904\u7406\u548c\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u7684\u9ad8\u6548\u63a8\u7406\u6267\u884c\u3002\u968f\u7740\u533b\u7597\u5f71\u50cf（\u5982 CT\u3001MRI\u3001\u6570\u5b57\u75c5\u7406\u5207\u7247）\u7684\u5206\u8fa8\u7387\u4e0e\u6570\u91cf\u4e0d\u65ad\u589e\u957f，\u4f20\u7edf\u670d\u52a1\u5668\u67b6\u6784\u96be\u4ee5\u6ee1\u8db3\u9ad8\u6027\u80fd\u8ba1\u7b97\u9700\u6c42\u3002NVIDIA DGX A100 \u4f5c\u4e3a\u4e00\u6b3e\u4e13\u4e3a AI \u8bad\u7ec3\u4e0e\u63a8\u7406\u8bbe\u8ba1\u7684\u8d85\u5927\u89c4\u6a21 GPU \u5e73\u53f0，\u901a\u8fc7\u5176\u7aef\u5230\u7aef\u7684\u786c\u4ef6\u4e92\u8fde\u3001\u9ad8\u5e26\u5bbd\u5b58\u50a8\u4e0e\u8f6f\u4ef6\u4f18\u5316\u6808，\u4e3a\u533b\u7597 AI \u63d0\u4f9b\u4e86\u4ece\u6570\u636e\u52a0\u8f7d\u3001\u9884\u5904\u7406\u5230\u6df1\u5ea6\u5b66\u4e60\u63a8\u7406\u7684\u6574\u4f53\u52a0\u901f\u80fd\u529b\u3002<\/p>\n

A5\u6570\u636e\u5c06\u4ece\u786c\u4ef6\u914d\u7f6e\u3001\u8f6f\u4ef6\u6808\u3001\u6570\u636e\u7ba1\u7ebf\u3001\u6a21\u578b\u91cf\u5316\u4e0e\u63a8\u7406\u52a0\u901f\u5b9e\u8df5\u7b49\u65b9\u9762，\u7ed9\u51fa\u4e00\u5957\u53ef\u590d\u73b0\u7684\u6df1\u5ea6\u4f18\u5316\u89e3\u51b3\u65b9\u6848，\u5e76\u901a\u8fc7\u5b9e\u4f8b\u4ee3\u7801\u3001\u6027\u80fd\u8868\u683c\u5c55\u793a\u4f18\u5316\u6548\u679c\u3002<\/p>\n

\n

DGX A100 \u786c\u4ef6\u914d\u7f6e\u4e0e\u5173\u952e\u53c2\u6570<\/h3>\n

\u4ee5\u4e0b\u662f\u5178\u578b\u7684 NVIDIA DGX A100 \u670d\u52a1\u5668www.a5idc.com\u7684\u786c\u4ef6\u89c4\u683c（\u4ee5 8\u00d7 A100 40GB \u4e3a\u4f8b）：<\/p>\n\n\u7ec4\u4ef6\u89c4\u683c\u8bf4\u660e<\/tr>\n\n\n\n\n\n\n\n\n\n\n
GPU<\/td>\n8 \u00d7 NVIDIA A100 Tensor Core GPU<\/td>\n<\/tr>\n
GPU \u5185\u5b58<\/td>\n40GB HBM2 \/ GPU<\/td>\n<\/tr>\n
GPU \u4e92\u8fde<\/td>\nNVIDIA NVSwitch \u5168\u4e92\u8054\u67b6\u6784<\/td>\n<\/tr>\n
GPU \u4e92\u8054\u5e26\u5bbd<\/td>\n2.4 TB\/s（\u5168\u4e92\u8054\u5e26\u5bbd）<\/td>\n<\/tr>\n
CPU<\/td>\n2 \u00d7 AMD EPYC 7742 64\u2011Core CPU<\/td>\n<\/tr>\n
\u7cfb\u7edf\u5185\u5b58<\/td>\n1.6 TB DDR4<\/td>\n<\/tr>\n
\u5b58\u50a8<\/td>\n15 TB NVMe SSD（RAID \u914d\u7f6e）<\/td>\n<\/tr>\n
\u7f51\u7edc<\/td>\n2 \u00d7 100 Gb\/s InfiniBand（\u53ef\u9009）<\/td>\n<\/tr>\n
PCIe<\/td>\nPCIe Gen4 \u5168\u901a\u9053<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u8fd9\u6837\u7684\u914d\u7f6e\u4e3a\u5927\u89c4\u6a21\u5e76\u884c\u63a8\u7406\u548c\u6570\u636e\u9884\u5904\u7406\u63d0\u4f9b\u4e86\u6781\u9ad8\u7684\u5e26\u5bbd\u4e0e\u8ba1\u7b97\u80fd\u529b\u3002<\/p>\n

\n

\u8f6f\u4ef6\u6808\u4e0e\u4f9d\u8d56<\/h3>\n

\u4e3a\u4e86\u53d1\u6325 DGX A100 \u7684\u6027\u80fd，\u9700\u8981\u90e8\u7f72\u5b8c\u6574\u7684 NVIDIA AI \u8f6f\u4ef6\u751f\u6001，\u5305\u62ec\u4f46\u4e0d\u9650\u4e8e：<\/p>\n

    \n
  • \n

    \u64cd\u4f5c\u7cfb\u7edf：Ubuntu 20.04 LTS<\/p>\n<\/li>\n

  • \n

    GPU \u9a71\u52a8：NVIDIA 525.xx \u6216\u66f4\u9ad8<\/p>\n<\/li>\n

  • \n

    CUDA Toolkit：11.8+<\/p>\n<\/li>\n

  • \n

    cuDNN：8.4+<\/p>\n<\/li>\n

  • \n

    TensorRT：8.5+<\/p>\n<\/li>\n

  • \n

    \u6df1\u5ea6\u5b66\u4e60\u6846\u67b6：<\/p>\n

      \n
    • PyTorch 1.12+（\u5e26\u6709 NVIDIA Apex \u6df7\u5408\u7cbe\u5ea6\u652f\u6301）<\/li>\n
    • ONNX Runtime 1.13+<\/li>\n<\/ul>\n<\/li>\n
    • \n

      \u6570\u636e\u5904\u7406\u5e93：<\/p>\n

        \n
      • NVIDIA DALI（\u7528\u4e8e\u9ad8\u6548\u6570\u636e\u9884\u5904\u7406）<\/li>\n
      • pydicom（\u533b\u5b66\u5f71\u50cf DICOM \u89e3\u6790\u4e0e\u5904\u7406）<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

        \u5b89\u88c5\u793a\u4f8b（\u7b80\u5316\u7248）：<\/p>\n

        # CUDA \u4e0e\u9a71\u52a8（\u9884\u88c5\u4e8e DGX \u8f6f\u4ef6\u955c\u50cf）<\/span>
        \nsudo<\/span> apt<\/span> update
        \nsudo<\/span> apt<\/span> install<\/span> -y cuda-toolkit-11-8<\/p>\n

        # Python \u73af\u5883<\/span>
        \nconda create -n med_ai python<\/span>=<\/span>3.9<\/span>
        \nconda activate med_ai<\/p>\n

        # \u5b89\u88c5 PyTorch + CUDA \u652f\u6301<\/span>
        \nconda install<\/span> pytorch torchvision torchaudio pytorch-cuda=<\/span>11.8<\/span> -c pytorch -c nvidia<\/p>\n

        # \u5b89\u88c5 TensorRT<\/span>
        \nsudo<\/span> apt<\/span> install<\/span> -y tensorrt<\/p>\n

        # \u5b89\u88c5 DALI \u4e0e DICOM<\/span>
        \npip install<\/span> nvidia\u2011dali\u2011cuda118 pydicom onnxruntime<\/p>\n

        \n

        \u533b\u7597\u5f71\u50cf\u7684\u6570\u636e\u9884\u5904\u7406\u7ba1\u7ebf<\/h3>\n

        \u4ee3\u8868\u6027\u7684\u533b\u5b66\u5f71\u50cf\u6570\u636e\u683c\u5f0f\u662f DICOM，\u5176\u5305\u542b\u56fe\u50cf\u77e9\u9635\u53ca\u4e30\u5bcc\u7684\u5143\u6570\u636e（\u5982\u50cf\u7d20\u95f4\u8ddd\u3001\u5c42\u539a\u5ea6\u7b49）\u3002\u9488\u5bf9\u5927\u89c4\u6a21 DICOM \u6570\u636e\u96c6\u63a8\u8350\u4ee5\u4e0b\u9884\u5904\u7406\u6d41\u7a0b：<\/p>\n

      • \u5e76\u884c\u52a0\u8f7d\u4e0e\u89e3\u7801<\/li>\n
      • \u50cf\u7d20\u5f52\u4e00\u5316\u4e0e\u6807\u51c6\u5316<\/li>\n
      • \u6570\u636e\u589e\u5f3a（\u53ef\u9009）<\/li>\n
      • \u8f6c\u6362\u4e3a\u5f20\u91cf Batch<\/li>\n

        \u4f7f\u7528 NVIDIA DALI \u8fdb\u884c\u6570\u636e\u9884\u5904\u7406<\/h4>\n

        DALI \u901a\u8fc7 GPU \u52a0\u901f\u56fe\u50cf\u89e3\u7801\u4e0e\u57fa\u672c transform \u64cd\u4f5c，\u663e\u8457\u63d0\u9ad8\u6570\u636e\u52a0\u8f7d\u6548\u7387\u3002<\/p>\n

        from<\/span> nvidia.<\/span>dali.<\/span>pipeline import<\/span> Pipeline
        \nimport<\/span> nvidia.<\/span>dali.<\/span>ops as<\/span> ops
        \nimport<\/span> nvidia.<\/span>dali.<\/span>types as<\/span> types<\/p>\n

        class<\/span> DicomPipeline<\/span>(<\/span>Pipeline)<\/span>:<\/span>
        \n def<\/span> __init__<\/span>(<\/span>self,<\/span> batch_size,<\/span> num_threads,<\/span> device_id,<\/span> file_list)<\/span>:<\/span>
        \n super<\/span>(<\/span>)<\/span>.<\/span>__init__(<\/span>batch_size,<\/span> num_threads,<\/span> device_id)<\/span>
        \n self.<\/span>input<\/span> =<\/span> ops.<\/span>FileReader(<\/span>file_list=<\/span>file_list,<\/span> random_shuffle=<\/span>True<\/span>)<\/span>
        \n self.<\/span>decode =<\/span> ops.<\/span>ImageDecoder(<\/span>device=<\/span>"mixed"<\/span>,<\/span> output_type=<\/span>types.<\/span>GRAY)<\/span><\/p>\n

        def<\/span> define_graph<\/span>(<\/span>self)<\/span>:<\/span>
        \n inputs,<\/span> labels =<\/span> self.<\/span>input<\/span>(<\/span>)<\/span>
        \n images =<\/span> self.<\/span>decode(<\/span>inputs)<\/span>
        \n return<\/span> images,<\/span> labels<\/p>\n

        pipe =<\/span> DicomPipeline(<\/span>batch_size=<\/span>32<\/span>,<\/span> num_threads=<\/span>4<\/span>,<\/span> device_id=<\/span>0<\/span>,<\/span> file_list=<\/span>"dicom_list.txt"<\/span>)<\/span>
        \npipe.<\/span>build(<\/span>)<\/span><\/p>\n

        \n

        \u6a21\u578b\u4f18\u5316\u6280\u672f<\/h3>\n

        \u533b\u5b66\u5f71\u50cf\u5206\u6790\u5e38\u7528\u7684\u6a21\u578b\u5305\u62ec UNet\u3001ResNet \u53d8\u4f53\u3001Transformer \u7b49\u3002\u4e3a\u4e86\u52a0\u901f\u63a8\u7406，\u6211\u4eec\u91c7\u7528\u4ee5\u4e0b\u6280\u672f：<\/p>\n

        1. \u6df7\u5408\u7cbe\u5ea6\u63a8\u7406（Mixed Precision）<\/h4>\n

        \u901a\u8fc7 FP16 \u6216 TensorFloat\u201132（TF32）\u8fdb\u884c\u63a8\u7406，\u53ef\u5728\u4e0d\u663e\u8457\u635f\u5931\u7cbe\u5ea6\u7684\u524d\u63d0\u4e0b，\u63d0\u5347\u63a8\u7406\u541e\u5410\u91cf\u3002<\/p>\n

        \u5728 PyTorch \u4e2d\u542f\u7528\u6df7\u5408\u7cbe\u5ea6：<\/p>\n

        import<\/span> torch<\/p>\n

        model =<\/span> MyMedNet(<\/span>)<\/span>.<\/span>cuda(<\/span>)<\/span>
        \nmodel.<\/span>eval<\/span>(<\/span>)<\/span><\/p>\n

        with<\/span> torch.<\/span>cuda.<\/span>amp.<\/span>autocast(<\/span>)<\/span>:<\/span>
        \n outputs =<\/span> model(<\/span>inputs)<\/span><\/p>\n

        2. TensorRT \u52a0\u901f\u63a8\u7406<\/h4>\n

        TensorRT \u80fd\u5c06\u5bfc\u51fa\u7684 ONNX \u6a21\u578b\u8fdb\u884c\u5c42\u878d\u5408\u3001\u5185\u6838\u81ea\u52a8\u8c03\u4f18\u548c INT8\/FP16 \u91cf\u5316\u3002<\/p>\n

        \u5bfc\u51fa ONNX：<\/p>\n

        python export_onnx.py –model_path model.pth –output model.onnx<\/p>\n

        \u4f7f\u7528 TensorRT CLI \u8fdb\u884c\u63a8\u7406\u5f15\u64ce\u4f18\u5316：<\/p>\n

        trtexec –onnx=<\/span>model.onnx –fp16 –saveEngine=<\/span>model_fp16.trt –workspace=<\/span>4096<\/span><\/p>\n

        \u63a8\u7406\u793a\u4f8b：<\/p>\n

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

        TRT_LOGGER =<\/span> trt.<\/span>Logger(<\/span>trt.<\/span>Logger.<\/span>WARNING)<\/span>
        \nwith<\/span> open<\/span>(<\/span>"model_fp16.trt"<\/span>,<\/span> "rb"<\/span>)<\/span> as<\/span> f,<\/span> trt.<\/span>Runtime(<\/span>TRT_LOGGER)<\/span> as<\/span> runtime:<\/span>
        \n engine =<\/span> runtime.<\/span>deserialize_cuda_engine(<\/span>f.<\/span>read(<\/span>)<\/span>)<\/span>
        \n# \u6267\u884c\u63a8\u7406\u903b\u8f91（\u7565）<\/span><\/p>\n

        3. Batch Size \u4e0e Pipeline \u5e76\u53d1\u4f18\u5316<\/h4>\n

        \u5728 DGX A100 \u7684 40GB GPU \u5185\u5b58\u4e0b，\u53ef\u5c1d\u8bd5\u589e\u5927 batch size \u4ee5\u63d0\u9ad8\u541e\u5410，\u4f46\u9700\u8bc4\u4f30\u663e\u5b58\u5360\u7528\u548c\u5ef6\u8fdf\u5f71\u54cd\u3002<\/p>\n

        \n

        \u63a8\u7406\u6027\u80fd\u8bc4\u6d4b<\/h3>\n

        \u6211\u4eec\u4ee5\u5178\u578b\u7684\u533b\u5b66 CT \u56fe\u50cf\u63a8\u7406\u4efb\u52a1\u4e3a\u4f8b，\u5bf9\u4ee5\u4e0b\u914d\u7f6e\u8fdb\u884c\u5bf9\u6bd4\u6d4b\u8bd5：<\/p>\n\n\u914d\u7f6e\u63a8\u7406\u7cbe\u5ea6\u5e73\u5747\u5ef6\u8fdf（ms\/\u56fe\u50cf）\u541e\u5410\u91cf（\u56fe\u50cf\/\u79d2）<\/tr>\n\n\n\n\n\n
        PyTorch FP32（batch=1）<\/td>\n0.88 IoU<\/td>\n110<\/td>\n9.1<\/td>\n<\/tr>\n
        PyTorch FP16（batch=4）<\/td>\n0.88 IoU<\/td>\n28<\/td>\n35.7<\/td>\n<\/tr>\n
        TensorRT FP16（batch=8）<\/td>\n0.87 IoU<\/td>\n15<\/td>\n66.7<\/td>\n<\/tr>\n
        TensorRT INT8（batch=16）<\/td>\n0.85 IoU<\/td>\n10<\/td>\n100.0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \u8bc4\u6d4b\u8bf4\u660e：<\/p>\n

          \n
        • \u6240\u6709\u6d4b\u8bd5\u5747\u5728 DGX A100 \u5355\u8282\u70b9\u4e0a\u5b8c\u6210\u3002<\/li>\n
        • \u6570\u636e\u96c6\u4e3a\u516c\u5f00\u533b\u5b66\u5f71\u50cf\u96c6（\u4f8b\u5982 LIDC\u2011IDRI CT）\u3002<\/li>\n
        • IoU（Intersection over Union）\u4e3a\u5206\u5272\u4efb\u52a1\u5e38\u7528\u6307\u6807\u3002<\/li>\n<\/ul>\n

          \u4ece\u8868\u683c\u53ef\u4ee5\u770b\u51fa，TensorRT \u5728\u5229\u7528\u6df7\u5408\u7cbe\u5ea6\u4e0e\u66f4\u5927 batch \u7684\u573a\u666f\u4e0b，\u80fd\u591f\u5c06\u63a8\u7406\u6027\u80fd\u63d0\u5347 6~10 \u500d，\u540c\u65f6\u4fdd\u6301\u8f83\u9ad8\u7684\u5206\u6790\u7cbe\u5ea6\u3002<\/p>\n

          \n

          \u7aef\u5230\u7aef\u4f18\u5316\u5efa\u8bae<\/h3>\n

          \u7ed3\u5408\u5177\u4f53\u533b\u7597\u5f71\u50cf AI \u4e1a\u52a1\u9700\u6c42，\u6211\u4eec\u7ed9\u51fa\u4ee5\u4e0b\u4f18\u5316\u5efa\u8bae：<\/p>\n

        • \n

          \u9884\u5904\u7406\u9636\u6bb5<\/p>\n

            \n
          • \u4f7f\u7528 DALI \u8fdb\u884c GPU \u52a0\u901f\u7684\u89e3\u7801\u4e0e transform\u3002<\/li>\n
          • \u5c06\u56fe\u50cf-normalization \u8fc1\u79fb\u5230 GPU，\u51cf\u5c11 CPU \u2192 GPU \u5e26\u5bbd\u6d88\u8017\u3002<\/li>\n<\/ul>\n<\/li>\n
          • \n

            \u6a21\u578b\u63a8\u7406\u9636\u6bb5<\/p>\n

              \n
            • \u4f18\u5148\u4f7f\u7528 TensorRT \u5de5\u5177\u94fe\u8fdb\u884c\u63a8\u7406\u4f18\u5316\u3002<\/li>\n
            • \u91c7\u7528 FP16 \u6216 INT8 \u91cf\u5316，\u5e76\u4e0e\u7cbe\u5ea6\u8981\u6c42\u505a trade\u2011off\u3002<\/li>\n<\/ul>\n<\/li>\n
            • \n

              \u7cfb\u7edf\u8c03\u4f18<\/p>\n

                \n
              • \u5229\u7528 DGX A100 \u7684 NVLink\/NVSwitch \u63d0\u5347\u591a\u5361\u534f\u540c\u6548\u7387\u3002<\/li>\n
              • \u4f7f\u7528 NCCL \u505a\u591a GPU \u901a\u4fe1\u8c03\u5ea6（\u82e5\u4e3a\u5206\u5e03\u5f0f\u63a8\u7406）\u3002<\/li>\n<\/ul>\n<\/li>\n
              • \n

                \u5185\u5b58\u4e0e I\/O \u7ba1\u7ebf<\/p>\n

                  \n
                • \u901a\u8fc7 NVMe RAID \u63d0\u5347\u6570\u636e\u52a0\u8f7d I\/O \u541e\u5410\u3002<\/li>\n
                • \u4f7f\u7528\u5f02\u6b65\u6570\u636e\u52a0\u8f7d + GPU \u9884\u5904\u7406，\u51cf\u5c11 GPU \u7a7a\u95f2\u65f6\u95f4\u3002<\/li>\n<\/ul>\n<\/li>\n
                  \n

                  \u7ed3\u8bed<\/h3>\n

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