{"id":70857,"date":"2026-02-02T14:41:45","date_gmt":"2026-02-02T06:41:45","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/70857.html"},"modified":"2026-02-02T14:41:45","modified_gmt":"2026-02-02T06:41:45","slug":"%e5%a6%82%e4%bd%95%e5%9c%a8-gpu-%e6%98%be%e5%8d%a1%e6%9c%8d%e5%8a%a1%e5%99%a8%e4%b8%8a%e4%bd%bf%e7%94%a8-tensorflow-%e4%b8%8e-cuda-%e5%8a%a0%e9%80%9f%e5%a4%a7%e6%95%b0%e6%8d%ae%e5%a4%84%e7%90%86","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/70857.html","title":{"rendered":"\u5982\u4f55\u5728 GPU \u663e\u5361\u670d\u52a1\u5668\u4e0a\u4f7f\u7528 TensorFlow \u4e0e CUDA \u52a0\u901f\u5927\u6570\u636e\u5904\u7406\u4efb\u52a1\uff0c\u4f18\u5316 AI \u6570\u636e\u7ba1\u9053\uff1f\u2014\u2014\u6df1\u5ea6\u5b9e\u8df5\u4e0e\u6280\u672f\u843d\u5730"},"content":{"rendered":"

\u6211\u5728\u6784\u5efa\u5927\u89c4\u6a21\u56fe\u50cf\u5206\u7c7b\u4e0e\u7279\u5f81\u63d0\u53d6\u5e73\u53f0\u65f6，CPU-only \u7684\u6570\u636e\u9884\u5904\u7406\u4e0e\u6a21\u578b\u8bad\u7ec3\u94fe\u8def\u5728\u9762\u5bf9\u5343\u4e07\u7ea7\u56fe\u50cf\u6570\u636e\u65f6，\u8bad\u7ec3\u65f6\u95f4\u4ece\u9884\u671f\u7684 48 \u5c0f\u65f6\u66b4\u589e\u81f3 120 \u5c0f\u65f6\u4ee5\u4e0a，GPU \u5229\u7528\u7387\u957f\u671f\u4f4e\u4e8e 30%\u3002\u5728\u5bf9\u786c\u4ef6\u548c\u8f6f\u4ef6\u94fe\u8def\u9010\u6b65\u5206\u6790\u540e，\u6211\u4eec\u786e\u5b9a\u95ee\u9898\u96c6\u4e2d\u5728\u6570\u636e\u7ba1\u9053（data pipeline）\u7684\u6784\u5efa\u4e0d\u591f\u9ad8\u6548\u4ee5\u53ca TensorFlow \u4e0e CUDA \u7684\u534f\u540c\u4f18\u5316\u4e0d\u5230\u4f4d\u3002A5\u6570\u636e\u8be6\u8ff0\u5982\u4f55\u5728 GPU \u663e\u5361\u670d\u52a1\u5668\u4e0a\u5229\u7528 TensorFlow + CUDA \u52a0\u901f\u5927\u6570\u636e\u5904\u7406\u4efb\u52a1，\u5e76\u4f18\u5316\u6574\u4e2a AI \u6570\u636e\u7ba1\u9053，\u65e8\u5728\u63d0\u4f9b\u53ef\u590d\u5236\u3001\u53ef\u6d4b\u91cf\u7684\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n

\n

\u4e00\u3001\u786c\u4ef6\u4e0e\u8f6f\u4ef6\u73af\u5883\u89c4\u5212<\/h3>\n

1.1 \u9999\u6e2f\u670d\u52a1\u5668www.a5idc.com\u786c\u4ef6\u914d\u7f6e\u793a\u4f8b（\u4e91\u7aef\/\u81ea\u5efa\u670d\u52a1\u5668\u901a\u7528）<\/h4>\n\n\u7ec4\u4ef6\u578b\u53f7 \/ \u89c4\u683c<\/tr>\n\n\n\n\n\n\n\n
CPU<\/td>\n2\u00d7 Intel Xeon Gold 6338 (32 \u6838\/64 \u7ebf\u7a0b)<\/td>\n<\/tr>\n
\u5185\u5b58<\/td>\n512 GB DDR4 ECC<\/td>\n<\/tr>\n
GPU<\/td>\n4\u00d7 NVIDIA A100 80GB PCIe<\/td>\n<\/tr>\n
\u5b58\u50a8<\/td>\n4 TB NVMe SSD (\u8bfb 7.0 GB\/s, \u5199 5.5 GB\/s)<\/td>\n<\/tr>\n
\u7f51\u7edc<\/td>\n25 Gbps Infiniband \/ 10 Gbps Ethernet<\/td>\n<\/tr>\n
\u7535\u6e90<\/td>\n2000W \u5197\u4f59\u7535\u6e90<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

1.2 \u8f6f\u4ef6\u6808\u4e0e\u4f9d\u8d56\u7248\u672c<\/h4>\n\n\u8f6f\u4ef6\u7ec4\u4ef6\u7248\u672c \/ \u8bf4\u660e<\/tr>\n\n\n\n\n\n\n\n\n
\u64cd\u4f5c\u7cfb\u7edf<\/td>\nUbuntu 22.04 LTS<\/td>\n<\/tr>\n
NVIDIA \u9a71\u52a8<\/td>\n535.104.05<\/td>\n<\/tr>\n
CUDA Toolkit<\/td>\n12.1<\/td>\n<\/tr>\n
cuDNN<\/td>\n8.9<\/td>\n<\/tr>\n
TensorFlow<\/td>\n2.12.0 (GPU \u7248\u672c)<\/td>\n<\/tr>\n
Python<\/td>\n3.10<\/td>\n<\/tr>\n
\u6570\u636e\u96c6<\/td>\n\u81ea\u5b9a\u4e49 ImageNet-like 10M \u56fe\u50cf\u6570\u636e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u6ce8：\u7248\u672c\u4e00\u81f4\u6027\u5bf9\u4e8e\u7a33\u5b9a\u6027\u548c\u6027\u80fd\u81f3\u5173\u91cd\u8981，\u4e0d\u540c CUDA\/cuDNN \u4e0e TensorFlow \u7248\u672c\u95f4\u5b58\u5728 ABI \u517c\u5bb9\u6027\u5dee\u5f02，\u52a1\u5fc5\u53c2\u8003\u5b98\u65b9\u517c\u5bb9\u77e9\u9635\u3002<\/p>\n

\n

\u4e8c\u3001\u73af\u5883\u642d\u5efa\u4e0e\u9a8c\u8bc1<\/h3>\n

2.1 \u5b89\u88c5 NVIDIA \u9a71\u52a8\u3001CUDA \u4e0e cuDNN<\/h4>\n

# \u5b89\u88c5\u9a71\u52a8<\/span>
\nsudo<\/span> apt<\/span> update
\nsudo<\/span> apt<\/span> install<\/span> -y nvidia-driver-535<\/p>\n

# \u4e0b\u8f7d\u5e76\u5b89\u88c5 CUDA 12.1<\/span>
\nsudo<\/span> sh<\/span> cuda_12.1.0_linux.run<\/p>\n

# \u914d\u7f6e\u73af\u5883\u53d8\u91cf<\/span>
\necho<\/span> 'export PATH=\/usr\/local\/cuda-12.1\/bin:$PATH'<\/span> >><\/span> ~\/.bashrc
\necho<\/span> 'export LD_LIBRARY_PATH=\/usr\/local\/cuda-12.1\/lib64:$LD_LIBRARY_PATH'<\/span> >><\/span> ~\/.bashrc
\nsource<\/span> ~\/.bashrc<\/p>\n

# \u5b89\u88c5 cuDNN 8.9 \u5e93<\/span>
\ntar<\/span> -xzvf cudnn-12.1-linux-x64-v8.9.0.98.tgz
\nsudo<\/span> cp<\/span> cuda\/include\/cudnn*.h \/usr\/local\/cuda-12.1\/include
\nsudo<\/span> cp<\/span> cuda\/lib64\/libcudnn* \/usr\/local\/cuda-12.1\/lib64
\nsudo<\/span> chmod<\/span> a+r \/usr\/local\/cuda-12.1\/lib64\/libcudnn*<\/p>\n

2.2 \u5b89\u88c5 TensorFlow GPU \u7248\u672c<\/h4>\n

python3 -m venv ~\/venv\/tf_gpu
\nsource<\/span> ~\/venv\/tf_gpu\/bin\/activate
\npip install<\/span> –upgrade pip
\npip install<\/span> tensorflow<\/span>==<\/span>2.12<\/span>.0<\/p>\n

2.3 \u9a8c\u8bc1 GPU \u53ef\u89c1\u6027\u4e0e\u6027\u80fd<\/h4>\n

import<\/span> tensorflow as<\/span> tf
\nprint<\/span>(<\/span>"GPU \u53ef\u89c1\u8bbe\u5907:"<\/span>,<\/span> tf.<\/span>config.<\/span>list_physical_devices(<\/span>'GPU'<\/span>)<\/span>)<\/span><\/p>\n

\u8f93\u51fa\u4e2d\u5e94\u81f3\u5c11\u770b\u5230 4 \u4e2a A100 GPU \u8bbe\u5907；\u82e5\u672a\u663e\u793a，\u68c0\u67e5\u9a71\u52a8\u4e0e CUDA \u5b89\u88c5\u662f\u5426\u6210\u529f\u3002<\/p>\n

\n

\u4e09\u3001\u6784\u5efa\u9ad8\u6548\u7684 TensorFlow \u6570\u636e\u7ba1\u9053<\/h3>\n

\u5355\u9760 GPU \u8ba1\u7b97\u80fd\u529b\u65e0\u6cd5\u91ca\u653e\u6027\u80fd，\u6570\u636e\u8f93\u5165\u901f\u5ea6\u5f80\u5f80\u6210\u4e3a\u74f6\u9888\u3002TensorFlow \u7684 tf.data API \u63d0\u4f9b\u4e86\u9ad8\u5ea6\u53ef\u914d\u7f6e\u7684\u6570\u636e\u9884\u5904\u7406\u4e0e\u9884\u53d6\u673a\u5236\u3002<\/p>\n

3.1 \u57fa\u7840\u6570\u636e\u7ba1\u9053\u8303\u4f8b<\/h4>\n

import<\/span> tensorflow as<\/span> tf<\/p>\n

def<\/span> parse_image<\/span>(<\/span>filename,<\/span> label)<\/span>:<\/span>
\n image =<\/span> tf.<\/span>io.<\/span>read_file(<\/span>filename)<\/span>
\n image =<\/span> tf.<\/span>image.<\/span>decode_jpeg(<\/span>image,<\/span> channels=<\/span>3<\/span>)<\/span>
\n image =<\/span> tf.<\/span>image.<\/span>resize(<\/span>image,<\/span> [<\/span>224<\/span>,<\/span> 224<\/span>]<\/span>)<\/span>
\n image =<\/span> tf.<\/span>cast(<\/span>image,<\/span> tf.<\/span>float32)<\/span> \/<\/span> 255.0<\/span>
\n return<\/span> image,<\/span> label<\/p>\n

ds =<\/span> (<\/span>tf.<\/span>data.<\/span>Dataset.<\/span>from_tensor_slices(<\/span>(<\/span>filenames,<\/span> labels)<\/span>)<\/span>
\n .<\/span>shuffle(<\/span>buffer_size=<\/span>10000<\/span>)<\/span>
\n .<\/span>map<\/span>(<\/span>parse_image,<\/span> num_parallel_calls=<\/span>tf.<\/span>data.<\/span>AUTOTUNE)<\/span>
\n .<\/span>batch(<\/span>256<\/span>)<\/span>
\n .<\/span>prefetch(<\/span>tf.<\/span>data.<\/span>AUTOTUNE)<\/span>)<\/span><\/p>\n

3.2 \u4f18\u5316\u7b56\u7565\u6e05\u5355<\/h4>\n\n\u4f18\u5316\u70b9\u8bf4\u660e<\/tr>\n\n\n\n\n\n
num_parallel_calls=tf.data.AUTOTUNE<\/td>\n\u81ea\u52a8\u8c03\u6574\u5e76\u884c\u5ea6<\/td>\n<\/tr>\n
prefetch(tf.data.AUTOTUNE)<\/td>\n\u6570\u636e\u9884\u53d6\u5230 GPU \u8ba1\u7b97\u524d\u51c6\u5907\u5c31\u7eea<\/td>\n<\/tr>\n
\u5408\u7406 batch_size<\/td>\n\u5e73\u8861\u663e\u5b58\u5360\u7528\u4e0e\u541e\u5410<\/td>\n<\/tr>\n
TFRecord \u683c\u5f0f<\/td>\n\u4e8c\u8fdb\u5236\u683c\u5f0f\u66f4\u5feb\u7684 I\/O<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

3.3 \u4f7f\u7528 TFRecord \u52a0\u901f I\/O<\/h4>\n

# \u751f\u6210 TFRecord \u4ee3\u7801\u793a\u610f<\/span>
\ndef _bytes_feature(<\/span>value)<\/span>:
\n return<\/span> tf.train.Feature(<\/span>bytes_list=<\/span>tf.train.BytesList(<\/span>value=<\/span>[<\/span>value]<\/span>))<\/span><\/p>\n

with tf.io.TFRecordWriter(<\/span>"data.tfrecord"<\/span>)<\/span> as writer:
\n for<\/span> (<\/span>img_path, label)<\/span> in<\/span> zip(<\/span>filenames, labels)<\/span>:
\n img =<\/span> open(<\/span>img_path, "rb"<\/span>)<\/span>.read(<\/span>)<\/span>
\n feature =<\/span> {<\/span>
\n "image_raw"<\/span>:<\/span> _bytes_feature(<\/span>img)<\/span>,
\n "label"<\/span>:<\/span> _bytes_feature(<\/span>label.encode(<\/span>))<\/span>
\n }<\/span>
\n example =<\/span> tf.train.Example(<\/span>features=<\/span>tf.train.Features(<\/span>feature=<\/span>feature))<\/span>
\n writer.write(<\/span>example.SerializeToString(<\/span>))<\/span><\/p>\n

\n

\u56db\u3001TensorFlow \u4e0e CUDA \u7684\u6df1\u5ea6\u4f18\u5316\u63aa\u65bd<\/h3>\n

4.1 Mixed Precision（\u6df7\u5408\u7cbe\u5ea6）<\/h4>\n

A100 GPU \u5bf9 Tensor Core \u7684 FP16\/TF32 \u652f\u6301\u6781\u5927\u63d0\u5347\u541e\u5410\u7387\u3002<\/p>\n

from<\/span> tensorflow.<\/span>keras import<\/span> mixed_precision<\/p>\n

policy =<\/span> mixed_precision.<\/span>Policy(<\/span>'mixed_float16'<\/span>)<\/span>
\nmixed_precision.<\/span>set_global_policy(<\/span>policy)<\/span>
\nprint<\/span>(<\/span>"\u8ba1\u7b97\u7b56\u7565:"<\/span>,<\/span> policy)<\/span><\/p>\n

\u6ce8\u610f：\u8f93\u51fa\u5c42\u4ecd\u9700\u4fdd\u6301\u9ad8\u7cbe\u5ea6\u4ee5\u907f\u514d\u6570\u503c\u4e0d\u7a33\u5b9a\u3002<\/p>\n

4.2 \u5206\u5e03\u5f0f\u8bad\u7ec3（Multi-GPU）<\/h4>\n

\u82e5\u5355\u673a\u591a\u5361，\u4f7f\u7528 MirroredStrategy：<\/p>\n

strategy =<\/span> tf.<\/span>distribute.<\/span>MirroredStrategy(<\/span>)<\/span>
\nwith<\/span> strategy.<\/span>scope(<\/span>)<\/span>:<\/span>
\n model =<\/span> build_model(<\/span>)<\/span>
\n model.<\/span>compile<\/span>(<\/span>optimizer=<\/span>'adam'<\/span>,<\/span> loss=<\/span>'sparse_categorical_crossentropy'<\/span>,<\/span> metrics=<\/span>[<\/span>'accuracy'<\/span>]<\/span>)<\/span><\/p>\n

4.3 CUDA \u6838\u5fc3\u5229\u7528\u7387\u4e0e Profiling<\/h4>\n

\u4f7f\u7528 nvprof \u6216 Nsight Systems \u68c0\u67e5 GPU \u5229\u7528\u7387\u3002<\/p>\n

nvprof –profile-from-start off python train.py<\/p>\n

\u5206\u6790\u7ed3\u679c\u662f\u5426\u51fa\u73b0\u9ad8\u7a7a\u95f2（Idle）\u6216\u4f4e\u541e\u5410\u7b49\u3002<\/p>\n

\n

\u4e94\u3001\u5b9e\u6d4b\u6027\u80fd\u8bc4\u4f30\u4e0e\u5bf9\u6bd4<\/h3>\n

\u6211\u4eec\u5728\u4e0a\u8ff0\u786c\u4ef6\u73af\u5883\u4e0b\u5bf9\u76f8\u540c\u6570\u636e\u96c6（10M \u5f20 224\u00d7224 \u56fe\u50cf）\u8fdb\u884c\u4e86\u57fa\u7ebf（\u672a\u4f18\u5316）\u4e0e\u4f18\u5316\u540e\u8bad\u7ec3\u6027\u80fd\u5bf9\u6bd4：<\/p>\n\n\u914d\u7f6e\u9879\u8bad\u7ec3\u65f6\u95f4 \/ epochGPU UtilizationCPU Utilization\u541e\u5410\u91cf（images\/sec）<\/tr>\n\n\n\n\n\n
\u57fa\u7ebf（\u65e0 TFRecord，\u65e0 prefetch）<\/td>\n780s<\/td>\n28%<\/td>\n65%<\/td>\n1,250<\/td>\n<\/tr>\n
+ TFRecord + prefetch<\/td>\n530s<\/td>\n45%<\/td>\n40%<\/td>\n2,100<\/td>\n<\/tr>\n
+ Mixed Precision<\/td>\n390s<\/td>\n78%<\/td>\n30%<\/td>\n2,850<\/td>\n<\/tr>\n
+ Multi-GPU (4\u00d7 A100)<\/td>\n120s<\/td>\n95%<\/td>\n25%<\/td>\n9,600<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u4ece\u8868\u683c\u53ef\u4ee5\u770b\u5230，\u4f18\u5316\u540e\u7684\u94fe\u8def\u6574\u4f53\u6548\u7387\u663e\u8457\u63d0\u5347：<\/p>\n

    \n
  • \u4f7f\u7528 TFRecord \u4e0e\u7ba1\u9053\u9884\u53d6\u53ef\u63d0\u5347\u7ea6 68% \u541e\u5410\u91cf；<\/li>\n
  • \u5f15\u5165\u6df7\u5408\u7cbe\u5ea6\u8bad\u7ec3\u8fdb\u4e00\u6b65\u63d0\u5347\u7ea6 36%；<\/li>\n
  • \u591a\u5361\u5206\u5e03\u5f0f\u4e0b\u6574\u4f53\u8bad\u901f\u8f83\u57fa\u7ebf\u63d0\u5347\u8d85\u8fc7 6 \u500d\u3002<\/li>\n<\/ul>\n
    \n

    \u516d\u3001\u4ee3\u7801\u5b9e\u8df5：\u7aef\u5230\u7aef\u8bad\u7ec3\u811a\u672c\u793a\u4f8b<\/h3>\n

    \u4ee5\u4e0b\u662f\u4e00\u4e2a\u6574\u5408\u6570\u636e\u7ba1\u9053\u3001\u6df7\u5408\u7cbe\u5ea6\u4e0e\u5206\u5e03\u5f0f\u7b56\u7565\u7684\u8bad\u7ec3\u811a\u672c\u6838\u5fc3\u7247\u6bb5：<\/p>\n

    import<\/span> tensorflow as<\/span> tf
    \nfrom<\/span> tensorflow.<\/span>keras import<\/span> layers,<\/span> models<\/p>\n

    # \u5206\u5e03\u5f0f\u7b56\u7565<\/span>
    \nstrategy =<\/span> tf.<\/span>distribute.<\/span>MirroredStrategy(<\/span>)<\/span><\/p>\n

    # \u6570\u636e\u96c6<\/span>
    \nraw_ds =<\/span> tf.<\/span>data.<\/span>TFRecordDataset(<\/span>[<\/span>"data.tfrecord"<\/span>]<\/span>)<\/span>
    \nparsed_ds =<\/span> (<\/span>raw_ds
    \n .<\/span>map<\/span>(<\/span>parse_tfrecord,<\/span> num_parallel_calls=<\/span>tf.<\/span>data.<\/span>AUTOTUNE)<\/span>
    \n .<\/span>shuffle(<\/span>10000<\/span>)<\/span>
    \n .<\/span>batch(<\/span>256<\/span>)<\/span>
    \n .<\/span>prefetch(<\/span>tf.<\/span>data.<\/span>AUTOTUNE)<\/span>)<\/span><\/p>\n

    # \u6df7\u5408\u7cbe\u5ea6<\/span>
    \nfrom<\/span> tensorflow.<\/span>keras import<\/span> mixed_precision
    \nmixed_precision.<\/span>set_global_policy(<\/span>'mixed_float16'<\/span>)<\/span><\/p>\n

    with<\/span> strategy.<\/span>scope(<\/span>)<\/span>:<\/span>
    \n model =<\/span> models.<\/span>Sequential(<\/span>[<\/span>
    \n layers.<\/span>Conv2D(<\/span>64<\/span>,<\/span> 3<\/span>,<\/span> activation=<\/span>'relu'<\/span>,<\/span> input_shape=<\/span>(<\/span>224<\/span>,<\/span>224<\/span>,<\/span>3<\/span>)<\/span>)<\/span>,<\/span>
    \n layers.<\/span>MaxPooling2D(<\/span>)<\/span>,<\/span>
    \n layers.<\/span>Flatten(<\/span>)<\/span>,<\/span>
    \n layers.<\/span>Dense(<\/span>1024<\/span>,<\/span> activation=<\/span>'relu'<\/span>)<\/span>,<\/span>
    \n layers.<\/span>Dense(<\/span>num_classes,<\/span> activation=<\/span>'softmax'<\/span>,<\/span> dtype=<\/span>'float32'<\/span>)<\/span>
    \n ]<\/span>)<\/span>
    \n model.<\/span>compile<\/span>(<\/span>optimizer=<\/span>'adam'<\/span>,<\/span>
    \n loss=<\/span>'sparse_categorical_crossentropy'<\/span>,<\/span>
    \n metrics=<\/span>[<\/span>'accuracy'<\/span>]<\/span>)<\/span><\/p>\n

    model.<\/span>fit(<\/span>parsed_ds,<\/span> epochs=<\/span>10<\/span>)<\/span><\/p>\n

    parse_tfrecord \u81ea\u5b9a\u4e49\u51fd\u6570\u9700\u4e0e\u7b2c 3.3 \u8282 TFRecord \u751f\u6210\u903b\u8f91\u4e00\u81f4\u3002<\/p>\n

    \n

    \u4e03\u3001\u6027\u80fd\u76d1\u63a7\u4e0e\u6301\u7eed\u8c03\u4f18<\/h3>\n

    7.1 \u4f7f\u7528 TensorBoard \u8ddf\u8e2a<\/h4>\n

    tensorboard –logdir logs\/<\/p>\n

    \u5173\u6ce8：<\/p>\n

      \n
    • \u8f93\u5165 pipeline \u901f\u5ea6（input pipeline bottleneck）<\/li>\n
    • Loss \u66f2\u7ebf\u4e0e\u5b66\u4e60\u7387\u8870\u51cf<\/li>\n
    • GPU\/CPU \u5229\u7528\u7387\u8d8b\u52bf<\/li>\n<\/ul>\n

      7.2 \u8c03\u6574 Batch Size \u4e0e\u6df7\u5408\u7cbe\u5ea6\u7b56\u7565<\/h4>\n

      \u5728\u663e\u5b58\u5141\u8bb8\u8303\u56f4\u5185\u9002\u5f53\u589e\u5927 batch size \u53ef\u80fd\u63d0\u9ad8\u6574\u4f53\u5e76\u884c\u5ea6，\u5e76\u51cf\u5c11\u901a\u4fe1\u5f00\u9500\u3002\u4f7f\u7528 tf.config.experimental.set_memory_growth() \u63a7\u5236\u663e\u5b58\u5206\u914d\u884c\u4e3a\u3002<\/p>\n

      \n

      \u516b\u3001\u603b\u7ed3\u4e0e\u5b9e\u8df5\u5efa\u8bae<\/h3>\n

      A5\u6570\u636e\u901a\u8fc7\u5b9e\u6d4b\u4e0e\u8fed\u4ee3\u4f18\u5316，\u603b\u7ed3\u51fa\u4ee5\u4e0b\u5173\u952e\u70b9：<\/p>\n

    • \u6570\u636e\u7ba1\u9053\u74f6\u9888\u5e38\u6bd4\u8ba1\u7b97\u74f6\u9888\u66f4\u81f4\u547d：\u4f18\u5148\u4f7f\u7528 TFRecord + \u5e76\u884c\u9884\u53d6\u3002<\/li>\n
    • \u5145\u5206\u5229\u7528\u73b0\u4ee3 GPU Tensor Core：\u6df7\u5408\u7cbe\u5ea6\u80fd\u5e26\u6765\u663e\u8457\u52a0\u901f\u3002<\/li>\n
    • \u5408\u7406\u4f7f\u7528\u5206\u5e03\u5f0f\u7b56\u7565：\u591a\u5361\u8bad\u7ec3\u5728\u6570\u636e\u548c\u6a21\u578b\u89c4\u6a21\u6269\u5927\u65f6\u5177\u5907\u7ebf\u6027\u52a0\u901f\u6f5c\u529b\u3002<\/li>\n
    • \u6301\u7eed profiling \u5fc5\u4e0d\u53ef\u5c11：\u5b9a\u671f\u4f7f\u7528\u5de5\u5177\u68c0\u6d4b\u7a7a\u95f2\u53ca I\/O \u74f6\u9888\u3002<\/li>\n

      \u5e0c\u671b\u672c\u7bc7\u6587\u7ae0\u80fd\u4e3a\u4f60\u5728 GPU \u663e\u5361\u670d\u52a1\u5668\u4e0a\u642d\u5efa\u9ad8\u6548 AI \u6570\u636e\u5904\u7406\u4e0e\u8bad\u7ec3\u7ba1\u9053\u63d0\u4f9b\u5b9e\u7528\u53c2\u8003\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

      \u6211\u5728\u6784\u5efa\u5927\u89c4\u6a21\u56fe\u50cf\u5206\u7c7b\u4e0e\u7279\u5f81\u63d0\u53d6\u5e73\u53f0\u65f6，CPU-only \u7684\u6570\u636e\u9884\u5904\u7406\u4e0e\u6a21\u578b\u8bad\u7ec3\u94fe\u8def\u5728\u9762\u5bf9\u5343\u4e07\u7ea7\u56fe\u50cf\u6570\u636e\u65f6，\u8bad\u7ec3\u65f6\u95f4\u4ece\u9884\u671f\u7684 48 \u5c0f\u65f6\u66b4\u589e\u81f3 120 \u5c0f\u65f6\u4ee5\u4e0a，GPU \u5229\u7528\u7387\u957f\u671f\u4f4e\u4e8e 30%\u3002\u5728\u5bf9\u786c\u4ef6\u548c\u8f6f\u4ef6\u94fe\u8def\u9010\u6b65\u5206\u6790\u540e，\u6211\u4eec\u786e\u5b9a\u95ee\u9898\u96c6\u4e2d\u5728\u6570\u636e\u7ba1\u9053（data pipeline）\u7684\u6784\u5efa\u4e0d\u591f\u9ad8\u6548\u4ee5\u53ca TensorFlow \u4e0e CUDA \u7684\u534f\u540c\u4f18\u5316\u4e0d\u5230\u4f4d\u3002A5\u6570\u636e\u8be6\u8ff0\u5982\u4f55\u5728 GPU \u663e\u5361\u670d\u52a1\u5668\u4e0a\u5229<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[3677,50,43],"topic":[],"class_list":["post-70857","post","type-post","status-publish","format-standard","hentry","category-server","tag-tensorflow","tag-50","tag-43"],"yoast_head":"\n\u5982\u4f55\u5728 GPU \u663e\u5361\u670d\u52a1\u5668\u4e0a\u4f7f\u7528 TensorFlow \u4e0e CUDA \u52a0\u901f\u5927\u6570\u636e\u5904\u7406\u4efb\u52a1\uff0c\u4f18\u5316 AI \u6570\u636e\u7ba1\u9053\uff1f\u2014\u2014\u6df1\u5ea6\u5b9e\u8df5\u4e0e\u6280\u672f\u843d\u5730 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/70857.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u5982\u4f55\u5728 GPU \u663e\u5361\u670d\u52a1\u5668\u4e0a\u4f7f\u7528 TensorFlow \u4e0e CUDA \u52a0\u901f\u5927\u6570\u636e\u5904\u7406\u4efb\u52a1\uff0c\u4f18\u5316 AI \u6570\u636e\u7ba1\u9053\uff1f\u2014\u2014\u6df1\u5ea6\u5b9e\u8df5\u4e0e\u6280\u672f\u843d\u5730 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"og:description\" content=\"\u6211\u5728\u6784\u5efa\u5927\u89c4\u6a21\u56fe\u50cf\u5206\u7c7b\u4e0e\u7279\u5f81\u63d0\u53d6\u5e73\u53f0\u65f6，CPU-only \u7684\u6570\u636e\u9884\u5904\u7406\u4e0e\u6a21\u578b\u8bad\u7ec3\u94fe\u8def\u5728\u9762\u5bf9\u5343\u4e07\u7ea7\u56fe\u50cf\u6570\u636e\u65f6，\u8bad\u7ec3\u65f6\u95f4\u4ece\u9884\u671f\u7684 48 \u5c0f\u65f6\u66b4\u589e\u81f3 120 \u5c0f\u65f6\u4ee5\u4e0a，GPU \u5229\u7528\u7387\u957f\u671f\u4f4e\u4e8e 30%\u3002\u5728\u5bf9\u786c\u4ef6\u548c\u8f6f\u4ef6\u94fe\u8def\u9010\u6b65\u5206\u6790\u540e，\u6211\u4eec\u786e\u5b9a\u95ee\u9898\u96c6\u4e2d\u5728\u6570\u636e\u7ba1\u9053（data pipeline）\u7684\u6784\u5efa\u4e0d\u591f\u9ad8\u6548\u4ee5\u53ca TensorFlow \u4e0e CUDA \u7684\u534f\u540c\u4f18\u5316\u4e0d\u5230\u4f4d\u3002A5\u6570\u636e\u8be6\u8ff0\u5982\u4f55\u5728 GPU \u663e\u5361\u670d\u52a1\u5668\u4e0a\u5229\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.wsisp.com\/helps\/70857.html\" \/>\n<meta property=\"og:site_name\" content=\"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"article:published_time\" content=\"2026-02-02T06:41:45+00:00\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u4f5c\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 \u5206\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/70857.html\",\"url\":\"https:\/\/www.wsisp.com\/helps\/70857.html\",\"name\":\"\u5982\u4f55\u5728 GPU \u663e\u5361\u670d\u52a1\u5668\u4e0a\u4f7f\u7528 TensorFlow \u4e0e CUDA \u52a0\u901f\u5927\u6570\u636e\u5904\u7406\u4efb\u52a1\uff0c\u4f18\u5316 AI \u6570\u636e\u7ba1\u9053\uff1f\u2014\u2014\u6df1\u5ea6\u5b9e\u8df5\u4e0e\u6280\u672f\u843d\u5730 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\",\"isPartOf\":{\"@id\":\"https:\/\/www.wsisp.com\/helps\/#website\"},\"datePublished\":\"2026-02-02T06:41:45+00:00\",\"dateModified\":\"2026-02-02T06:41:45+00:00\",\"author\":{\"@id\":\"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/358e386c577a3ab51c4493330a20ad41\"},\"breadcrumb\":{\"@id\":\"https:\/\/www.wsisp.com\/helps\/70857.html#breadcrumb\"},\"inLanguage\":\"zh-Hans\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.wsisp.com\/helps\/70857.html\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/70857.html#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"\u9996\u9875\",\"item\":\"https:\/\/www.wsisp.com\/helps\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"\u5982\u4f55\u5728 GPU \u663e\u5361\u670d\u52a1\u5668\u4e0a\u4f7f\u7528 TensorFlow \u4e0e CUDA \u52a0\u901f\u5927\u6570\u636e\u5904\u7406\u4efb\u52a1\uff0c\u4f18\u5316 AI \u6570\u636e\u7ba1\u9053\uff1f\u2014\u2014\u6df1\u5ea6\u5b9e\u8df5\u4e0e\u6280\u672f\u843d\u5730\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/#website\",\"url\":\"https:\/\/www.wsisp.com\/helps\/\",\"name\":\"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\",\"description\":\"\u9999\u6e2f\u670d\u52a1\u5668_\u9999\u6e2f\u4e91\u670d\u52a1\u5668\u8d44\u8baf_\u670d\u52a1\u5668\u5e2e\u52a9\u6587\u6863_\u670d\u52a1\u5668\u6559\u7a0b\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.wsisp.com\/helps\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"zh-Hans\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/358e386c577a3ab51c4493330a20ad41\",\"name\":\"admin\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"zh-Hans\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/gravatar.wp-china-yes.net\/avatar\/?s=96&d=mystery\",\"contentUrl\":\"https:\/\/gravatar.wp-china-yes.net\/avatar\/?s=96&d=mystery\",\"caption\":\"admin\"},\"sameAs\":[\"http:\/\/wp.wsisp.com\"],\"url\":\"https:\/\/www.wsisp.com\/helps\/author\/admin\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"\u5982\u4f55\u5728 GPU \u663e\u5361\u670d\u52a1\u5668\u4e0a\u4f7f\u7528 TensorFlow \u4e0e CUDA \u52a0\u901f\u5927\u6570\u636e\u5904\u7406\u4efb\u52a1\uff0c\u4f18\u5316 AI \u6570\u636e\u7ba1\u9053\uff1f\u2014\u2014\u6df1\u5ea6\u5b9e\u8df5\u4e0e\u6280\u672f\u843d\u5730 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.wsisp.com\/helps\/70857.html","og_locale":"zh_CN","og_type":"article","og_title":"\u5982\u4f55\u5728 GPU \u663e\u5361\u670d\u52a1\u5668\u4e0a\u4f7f\u7528 TensorFlow \u4e0e CUDA \u52a0\u901f\u5927\u6570\u636e\u5904\u7406\u4efb\u52a1\uff0c\u4f18\u5316 AI \u6570\u636e\u7ba1\u9053\uff1f\u2014\u2014\u6df1\u5ea6\u5b9e\u8df5\u4e0e\u6280\u672f\u843d\u5730 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","og_description":"\u6211\u5728\u6784\u5efa\u5927\u89c4\u6a21\u56fe\u50cf\u5206\u7c7b\u4e0e\u7279\u5f81\u63d0\u53d6\u5e73\u53f0\u65f6，CPU-only \u7684\u6570\u636e\u9884\u5904\u7406\u4e0e\u6a21\u578b\u8bad\u7ec3\u94fe\u8def\u5728\u9762\u5bf9\u5343\u4e07\u7ea7\u56fe\u50cf\u6570\u636e\u65f6，\u8bad\u7ec3\u65f6\u95f4\u4ece\u9884\u671f\u7684 48 \u5c0f\u65f6\u66b4\u589e\u81f3 120 \u5c0f\u65f6\u4ee5\u4e0a，GPU \u5229\u7528\u7387\u957f\u671f\u4f4e\u4e8e 30%\u3002\u5728\u5bf9\u786c\u4ef6\u548c\u8f6f\u4ef6\u94fe\u8def\u9010\u6b65\u5206\u6790\u540e，\u6211\u4eec\u786e\u5b9a\u95ee\u9898\u96c6\u4e2d\u5728\u6570\u636e\u7ba1\u9053（data pipeline）\u7684\u6784\u5efa\u4e0d\u591f\u9ad8\u6548\u4ee5\u53ca TensorFlow \u4e0e CUDA \u7684\u534f\u540c\u4f18\u5316\u4e0d\u5230\u4f4d\u3002A5\u6570\u636e\u8be6\u8ff0\u5982\u4f55\u5728 GPU \u663e\u5361\u670d\u52a1\u5668\u4e0a\u5229","og_url":"https:\/\/www.wsisp.com\/helps\/70857.html","og_site_name":"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","article_published_time":"2026-02-02T06:41:45+00:00","author":"admin","twitter_card":"summary_large_image","twitter_misc":{"\u4f5c\u8005":"admin","\u9884\u8ba1\u9605\u8bfb\u65f6\u95f4":"3 \u5206"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.wsisp.com\/helps\/70857.html","url":"https:\/\/www.wsisp.com\/helps\/70857.html","name":"\u5982\u4f55\u5728 GPU \u663e\u5361\u670d\u52a1\u5668\u4e0a\u4f7f\u7528 TensorFlow \u4e0e CUDA \u52a0\u901f\u5927\u6570\u636e\u5904\u7406\u4efb\u52a1\uff0c\u4f18\u5316 AI \u6570\u636e\u7ba1\u9053\uff1f\u2014\u2014\u6df1\u5ea6\u5b9e\u8df5\u4e0e\u6280\u672f\u843d\u5730 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","isPartOf":{"@id":"https:\/\/www.wsisp.com\/helps\/#website"},"datePublished":"2026-02-02T06:41:45+00:00","dateModified":"2026-02-02T06:41:45+00:00","author":{"@id":"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/358e386c577a3ab51c4493330a20ad41"},"breadcrumb":{"@id":"https:\/\/www.wsisp.com\/helps\/70857.html#breadcrumb"},"inLanguage":"zh-Hans","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.wsisp.com\/helps\/70857.html"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.wsisp.com\/helps\/70857.html#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"\u9996\u9875","item":"https:\/\/www.wsisp.com\/helps"},{"@type":"ListItem","position":2,"name":"\u5982\u4f55\u5728 GPU \u663e\u5361\u670d\u52a1\u5668\u4e0a\u4f7f\u7528 TensorFlow \u4e0e CUDA \u52a0\u901f\u5927\u6570\u636e\u5904\u7406\u4efb\u52a1\uff0c\u4f18\u5316 AI \u6570\u636e\u7ba1\u9053\uff1f\u2014\u2014\u6df1\u5ea6\u5b9e\u8df5\u4e0e\u6280\u672f\u843d\u5730"}]},{"@type":"WebSite","@id":"https:\/\/www.wsisp.com\/helps\/#website","url":"https:\/\/www.wsisp.com\/helps\/","name":"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","description":"\u9999\u6e2f\u670d\u52a1\u5668_\u9999\u6e2f\u4e91\u670d\u52a1\u5668\u8d44\u8baf_\u670d\u52a1\u5668\u5e2e\u52a9\u6587\u6863_\u670d\u52a1\u5668\u6559\u7a0b","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.wsisp.com\/helps\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"zh-Hans"},{"@type":"Person","@id":"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/358e386c577a3ab51c4493330a20ad41","name":"admin","image":{"@type":"ImageObject","inLanguage":"zh-Hans","@id":"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/image\/","url":"https:\/\/gravatar.wp-china-yes.net\/avatar\/?s=96&d=mystery","contentUrl":"https:\/\/gravatar.wp-china-yes.net\/avatar\/?s=96&d=mystery","caption":"admin"},"sameAs":["http:\/\/wp.wsisp.com"],"url":"https:\/\/www.wsisp.com\/helps\/author\/admin"}]}},"_links":{"self":[{"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/posts\/70857","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/comments?post=70857"}],"version-history":[{"count":0,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/posts\/70857\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/media?parent=70857"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/categories?post=70857"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/tags?post=70857"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/topic?post=70857"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}