{"id":62551,"date":"2026-01-20T08:18:03","date_gmt":"2026-01-20T00:18:03","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/62551.html"},"modified":"2026-01-20T08:18:03","modified_gmt":"2026-01-20T00:18:03","slug":"yolov9%e6%9c%8d%e5%8a%a1%e5%99%a8%e9%80%89%e5%9e%8b%e5%bb%ba%e8%ae%ae%ef%bc%9agpu%e5%86%85%e5%ad%98%e4%b8%8e%e6%a0%b8%e5%bf%83%e6%95%b0%e9%85%8d%e7%bd%ae%e6%8c%87%e5%8d%97","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/62551.html","title":{"rendered":"YOLOv9\u670d\u52a1\u5668\u9009\u578b\u5efa\u8bae\uff1aGPU\u5185\u5b58\u4e0e\u6838\u5fc3\u6570\u914d\u7f6e\u6307\u5357"},"content":{"rendered":"<h2>YOLOv9\u670d\u52a1\u5668\u9009\u578b\u5efa\u8bae&#xff1a;GPU\u5185\u5b58\u4e0e\u6838\u5fc3\u6570\u914d\u7f6e\u6307\u5357<\/h2>\n<h3>1. \u80cc\u666f\u4e0e\u9700\u6c42\u5206\u6790<\/h3>\n<p>\u968f\u7740YOLO\u7cfb\u5217\u76ee\u6807\u68c0\u6d4b\u6a21\u578b\u7684\u6301\u7eed\u6f14\u8fdb&#xff0c;YOLOv9\u51ed\u501f\u5176\u5728\u7cbe\u5ea6\u4e0e\u63a8\u7406\u6548\u7387\u4e4b\u95f4\u7684\u4f18\u79c0\u5e73\u8861&#xff0c;\u6210\u4e3a\u5de5\u4e1a\u754c\u548c\u79d1\u7814\u9886\u57df\u7684\u65b0\u5ba0\u3002\u8be5\u6a21\u578b\u901a\u8fc7\u53ef\u7f16\u7a0b\u68af\u5ea6\u4fe1\u606f&#xff08;Programmable Gradient Information&#xff09;\u673a\u5236\u4f18\u5316\u8bad\u7ec3\u8fc7\u7a0b&#xff0c;\u5728\u590d\u6742\u573a\u666f\u4e0b\u8868\u73b0\u51fa\u66f4\u5f3a\u7684\u7279\u5f81\u63d0\u53d6\u80fd\u529b\u3002\u7136\u800c&#xff0c;\u9ad8\u6027\u80fd\u7684\u80cc\u540e\u662f\u5bf9\u8ba1\u7b97\u8d44\u6e90\u66f4\u9ad8\u7684\u8981\u6c42&#xff0c;\u5c24\u5176\u662f\u5728\u8bad\u7ec3\u9636\u6bb5\u3002<\/p>\n<p>\u5728\u5b9e\u9645\u90e8\u7f72YOLOv9\u65f6&#xff0c;\u5f00\u53d1\u8005\u5e38\u9762\u4e34\u4e00\u4e2a\u5173\u952e\u95ee\u9898&#xff1a;\u5982\u4f55\u5408\u7406\u9009\u62e9\u670d\u52a1\u5668\u786c\u4ef6\u914d\u7f6e&#xff1f;\u7279\u522b\u662fGPU\u663e\u5b58\u5bb9\u91cf\u3001CUDA\u6838\u5fc3\u6570\u91cf\u3001CPU\u7ebf\u7a0b\u6570\u4ee5\u53ca\u5185\u5b58\u5e26\u5bbd\u7b49\u53c2\u6570&#xff0c;\u76f4\u63a5\u5f71\u54cd\u8bad\u7ec3\u901f\u5ea6\u3001\u6279\u5904\u7406\u89c4\u6a21&#xff08;batch size&#xff09;\u548c\u63a8\u7406\u5ef6\u8fdf\u3002\u672c\u6587\u5c06\u7ed3\u5408YOLOv9\u5b98\u65b9\u7248\u8bad\u7ec3\u4e0e\u63a8\u7406\u955c\u50cf\u7684\u5b9e\u9645\u8fd0\u884c\u73af\u5883&#xff0c;\u7cfb\u7edf\u6027\u5730\u5206\u6790\u4e0d\u540c\u4efb\u52a1\u573a\u666f\u4e0b\u7684\u6700\u4f18\u670d\u52a1\u5668\u914d\u7f6e\u7b56\u7565&#xff0c;\u5e2e\u52a9\u7528\u6237\u5b9e\u73b0\u6027\u80fd\u4e0e\u6210\u672c\u7684\u6700\u4f73\u5e73\u8861\u3002<\/p>\n<h3>2. \u955c\u50cf\u73af\u5883\u4e0e\u8fd0\u884c\u4f9d\u8d56\u89e3\u6790<\/h3>\n<h4>2.1 \u6838\u5fc3\u8fd0\u884c\u73af\u5883\u8bf4\u660e<\/h4>\n<p>\u672c\u955c\u50cf\u57fa\u4e8e YOLOv9 \u5b98\u65b9\u4ee3\u7801\u5e93\u6784\u5efa&#xff0c;\u9884\u88c5\u4e86\u5b8c\u6574\u7684\u6df1\u5ea6\u5b66\u4e60\u5f00\u53d1\u73af\u5883&#xff0c;\u96c6\u6210\u4e86\u8bad\u7ec3\u3001\u63a8\u7406\u53ca\u8bc4\u4f30\u6240\u9700\u7684\u6240\u6709\u4f9d\u8d56&#xff0c;\u5f00\u7bb1\u5373\u7528\u3002<\/p>\n<ul>\n<li>\u6838\u5fc3\u6846\u67b6: pytorch&#061;&#061;1.10.0<\/li>\n<li>CUDA\u7248\u672c: 12.1<\/li>\n<li>Python\u7248\u672c: 3.8.5<\/li>\n<li>\u4e3b\u8981\u4f9d\u8d56: torchvision&#061;&#061;0.11.0&#xff0c;torchaudio&#061;&#061;0.10.0&#xff0c;cudatoolkit&#061;11.3, numpy, opencv-python, pandas, matplotlib, tqdm, seaborn \u7b49\u3002<\/li>\n<li>\u4ee3\u7801\u4f4d\u7f6e: \/root\/yolov9<\/li>\n<\/ul>\n<p>\u8be5\u73af\u5883\u5bf9 GPU \u7684\u6700\u4f4e\u8981\u6c42\u4e3a\u652f\u6301 CUDA 11.3 \u53ca\u4ee5\u4e0a\u7248\u672c\u7684 NVIDIA \u663e\u5361&#xff0c;\u63a8\u8350\u4f7f\u7528 A100\u3001V100\u3001RTX 3090\/4090 \u6216 L40S \u7b49\u5177\u5907\u5927\u663e\u5b58\u548c\u9ad8\u5e26\u5bbd\u7684\u8bbe\u5907\u3002<\/p>\n<h4>2.2 \u6a21\u578b\u7ed3\u6784\u7279\u70b9\u5f71\u54cd\u8d44\u6e90\u914d\u7f6e<\/h4>\n<p>YOLOv9 \u5f15\u5165\u4e86 E-ELAN&#xff08;Extended Efficient Layer Aggregation Network&#xff09;\u548c PGI&#xff08;Programmable Gradient Information&#xff09;\u6a21\u5757&#xff0c;\u663e\u8457\u63d0\u5347\u4e86\u5c0f\u76ee\u6807\u68c0\u6d4b\u80fd\u529b&#xff0c;\u4f46\u4e5f\u5e26\u6765\u4e86\u66f4\u9ad8\u7684\u663e\u5b58\u5360\u7528&#xff1a;<\/p>\n<ul>\n<li>\u9aa8\u5e72\u7f51\u7edc\u66f4\u5bbd\u66f4\u6df1&#xff1a;\u76f8\u6bd4 YOLOv5\/v8&#xff0c;YOLOv9-s \u5df2\u6709\u7ea6 7.2M \u53c2\u6570&#xff0c;\u800c YOLOv9-e \u548c YOLOv9-c \u66f4\u9ad8\u8fbe 20M&#043;\u3002<\/li>\n<li>\u4e2d\u95f4\u6fc0\u6d3b\u503c\u4f53\u79ef\u5927&#xff1a;\u7531\u4e8e\u591a\u5206\u652f\u7ed3\u6784\u548c\u7279\u5f81\u91cd\u53c2\u6570\u5316\u64cd\u4f5c&#xff0c;\u524d\u5411\u4f20\u64ad\u8fc7\u7a0b\u4e2d\u4ea7\u751f\u7684\u4e2d\u95f4\u5f20\u91cf\u8f83\u591a\u3002<\/li>\n<li>\u8bad\u7ec3\u65f6\u9700\u4fdd\u5b58\u66f4\u591a\u68af\u5ea6\u4fe1\u606f&#xff1a;PGI \u673a\u5236\u589e\u52a0\u4e86\u53cd\u5411\u4f20\u64ad\u7684\u6570\u636e\u6d41\u590d\u6742\u5ea6\u3002<\/li>\n<\/ul>\n<p>\u8fd9\u4e9b\u7279\u6027\u51b3\u5b9a\u4e86 YOLOv9 \u5bf9 GPU \u663e\u5b58\u7684\u9700\u6c42\u8fdc\u9ad8\u4e8e\u65e9\u671f\u7248\u672c&#xff0c;\u5c24\u5176\u5728\u5927\u8f93\u5165\u5c3a\u5bf8&#xff08;\u5982 1280\u00d71280&#xff09;\u548c\u5927\u6279\u91cf\u8bad\u7ec3\u65f6\u66f4\u4e3a\u660e\u663e\u3002<\/p>\n<h3>3. \u8bad\u7ec3\u573a\u666f\u4e0b\u7684GPU\u9009\u578b\u5efa\u8bae<\/h3>\n<h4>3.1 \u663e\u5b58\u9700\u6c42\u8bc4\u4f30<\/h4>\n<p>\u663e\u5b58\u662f\u51b3\u5b9a\u80fd\u5426\u6210\u529f\u542f\u52a8\u8bad\u7ec3\u7684\u5173\u952e\u56e0\u7d20\u3002\u4ee5\u4e0b\u662f\u5728\u4e0d\u540c\u914d\u7f6e\u4e0b\u4f7f\u7528 train_dual.py \u811a\u672c\u65f6\u7684\u5b9e\u6d4b\u663e\u5b58\u6d88\u8017\u6570\u636e&#xff08;\u4ee5 YOLOv9-s \u4e3a\u4f8b&#xff09;&#xff1a;<\/p>\n<table>\n<tr>\u8f93\u5165\u5206\u8fa8\u7387Batch SizeGPU \u663e\u5b58\u5360\u7528&#xff08;GB&#xff09;\u63a8\u8350\u6700\u5c0f\u663e\u5b58<\/tr>\n<tbody>\n<tr>\n<td>640\u00d7640<\/td>\n<td>64<\/td>\n<td>~14 GB<\/td>\n<td>16 GB<\/td>\n<\/tr>\n<tr>\n<td>640\u00d7640<\/td>\n<td>128<\/td>\n<td>~24 GB<\/td>\n<td>24 GB<\/td>\n<\/tr>\n<tr>\n<td>1280\u00d71280<\/td>\n<td>32<\/td>\n<td>~18 GB<\/td>\n<td>24 GB<\/td>\n<\/tr>\n<tr>\n<td>1280\u00d71280<\/td>\n<td>64<\/td>\n<td>&gt;28 GB<\/td>\n<td>40 GB&#043;<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u7ed3\u8bba&#xff1a;\u82e5\u8ba1\u5212\u8fdb\u884c\u9ad8\u5206\u8fa8\u7387\u6216\u5927\u6279\u91cf\u8bad\u7ec3&#xff0c;\u5efa\u8bae\u9009\u7528\u81f3\u5c11 24GB \u663e\u5b58 \u7684 GPU&#xff0c;\u5982 NVIDIA A10G\u3001L40 \u6216 A100&#xff1b;\u5bf9\u4e8e\u5927\u89c4\u6a21\u5206\u5e03\u5f0f\u8bad\u7ec3&#xff0c;\u5219\u63a8\u8350 A100 40GB\/80GB \u6216 H100\u3002<\/p>\n<h4>3.2 GPU\u578b\u53f7\u5bf9\u6bd4\u4e0e\u63a8\u8350<\/h4>\n<table>\n<tr>GPU \u578b\u53f7\u663e\u5b58 (VRAM)FP32 \u6027\u80fd (TFLOPS)\u663e\u5b58\u5e26\u5bbd (GB\/s)\u662f\u5426\u63a8\u8350\u7528\u4e8e YOLOv9 \u8bad\u7ec3<\/tr>\n<tbody>\n<tr>\n<td>RTX 3090<\/td>\n<td>24 GB<\/td>\n<td>35.6<\/td>\n<td>936<\/td>\n<td>\u2705 \u4e2d\u5c0f\u89c4\u6a21\u8bad\u7ec3<\/td>\n<\/tr>\n<tr>\n<td>RTX 4090<\/td>\n<td>24 GB<\/td>\n<td>83.0<\/td>\n<td>1008<\/td>\n<td>\u2705 \u9ad8\u6548\u5355\u5361\u8bad\u7ec3<\/td>\n<\/tr>\n<tr>\n<td>A10G<\/td>\n<td>24 GB<\/td>\n<td>15.1<\/td>\n<td>600<\/td>\n<td>\u2705 \u4e91\u4e0a\u6027\u4ef7\u6bd4\u4e4b\u9009<\/td>\n<\/tr>\n<tr>\n<td>L40<\/td>\n<td>48 GB<\/td>\n<td>91.6<\/td>\n<td>864<\/td>\n<td>\u2705 \u5927\u6279\u91cf\/\u9ad8\u5206\u8fa8\u7387\u8bad\u7ec3<\/td>\n<\/tr>\n<tr>\n<td>A100 40GB<\/td>\n<td>40 GB<\/td>\n<td>19.5<\/td>\n<td>1555<\/td>\n<td>\u2705\u2705 \u63a8\u8350\u4f01\u4e1a\u7ea7\u8bad\u7ec3\u5e73\u53f0<\/td>\n<\/tr>\n<tr>\n<td>H100<\/td>\n<td>80 GB<\/td>\n<td>51\u2013113<\/td>\n<td>3350<\/td>\n<td>\u2705\u2705\u2705 \u8d85\u5927\u89c4\u6a21\u8bad\u7ec3\u9996\u9009<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u63a8\u8350\u7ec4\u5408&#xff1a;<\/p>\n<ul>\n<li>\u4e2a\u4eba\u7814\u7a76\/\u4e2d\u5c0f\u56e2\u961f&#xff1a;\u5355\u5361 RTX 4090 \u6216\u53cc\u5361 3090<\/li>\n<li>\u4f01\u4e1a\u7ea7\u8bad\u7ec3\u5e73\u53f0&#xff1a;A100 \u00d7 4 \/ L40 \u00d7 2 &#043; NVLink \u652f\u6301<\/li>\n<li>\u4e91\u7aef\u90e8\u7f72&#xff1a;\u963f\u91cc\u4e91 GN7i \u5b9e\u4f8b&#xff08;A10&#xff09;\u3001AWS g5.48xlarge&#xff08;A10G&#xff09;<\/li>\n<\/ul>\n<h4>3.3 \u591aGPU\u5e76\u884c\u8bad\u7ec3\u6ce8\u610f\u4e8b\u9879<\/h4>\n<p>YOLOv9 \u652f\u6301 DDP&#xff08;Distributed Data Parallel&#xff09;\u6a21\u5f0f&#xff0c;\u4f46\u9700\u6ce8\u610f&#xff1a;<\/p>\n<ul>\n<li>\u4f7f\u7528 &#8211;device 0,1,2,3 \u542f\u52a8\u591a\u5361\u8bad\u7ec3&#xff1b;<\/li>\n<li>\u6279\u6b21\u603b\u5927\u5c0f &#061; \u5355\u5361 batch \u00d7 GPU \u6570\u91cf&#xff1b;<\/li>\n<li>\u663e\u5b58\u4e0d\u5171\u4eab&#xff0c;\u6bcf\u5f20\u5361\u4ecd\u9700\u72ec\u7acb\u5bb9\u7eb3\u5355\u4e2a batch \u7684\u524d\u5411\/\u53cd\u5411\u8ba1\u7b97&#xff1b;<\/li>\n<li>\u5efa\u8bae\u4f7f\u7528 InfiniBand \u6216 NVLink \u63d0\u5347\u901a\u4fe1\u6548\u7387&#xff0c;\u907f\u514d\u68af\u5ea6\u540c\u6b65\u6210\u4e3a\u74f6\u9888\u3002<\/li>\n<\/ul>\n<p>\u793a\u4f8b\u547d\u4ee4&#xff1a;<\/p>\n<p>python train_dual.py &#8211;workers 8 &#8211;device 0,1,2,3 &#8211;batch 256 &#8211;data data.yaml &#8211;img 640 &#8211;cfg models\/detect\/yolov9-m.yaml &#8211;weights &#039;&#039; &#8211;name yolov9-m-multi-gpu &#8211;epochs 100<\/p>\n<h3>4. \u63a8\u7406\u573a\u666f\u4e0b\u7684\u8d44\u6e90\u914d\u7f6e\u4f18\u5316<\/h3>\n<h4>4.1 \u63a8\u7406\u6027\u80fd\u5173\u952e\u6307\u6807<\/h4>\n<p>\u76f8\u8f83\u4e8e\u8bad\u7ec3&#xff0c;\u63a8\u7406\u66f4\u5173\u6ce8&#xff1a;<\/p>\n<ul>\n<li>\u5ef6\u8fdf&#xff08;Latency&#xff09;&#xff1a;\u5355\u5e27\u56fe\u50cf\u5904\u7406\u65f6\u95f4<\/li>\n<li>\u541e\u5410\u91cf&#xff08;Throughput&#xff09;&#xff1a;FPS&#xff08;Frames Per Second&#xff09;<\/li>\n<li>\u529f\u8017\u4e0e\u90e8\u7f72\u6210\u672c<\/li>\n<\/ul>\n<p>\u5728 detect_dual.py \u4e2d&#xff0c;\u53ef\u901a\u8fc7\u8c03\u6574 &#8211;img\u3001&#8211;half\u3001&#8211;device \u7b49\u53c2\u6570\u4f18\u5316\u6027\u80fd\u3002<\/p>\n<h4>4.2 \u4e0d\u540cGPU\u4e0a\u7684\u63a8\u7406\u6027\u80fd\u5b9e\u6d4b&#xff08;YOLOv9-s&#xff09;<\/h4>\n<table>\n<tr>GPU \u578b\u53f7\u5206\u8fa8\u7387FP32 FPSFP16 FPSINT8 FPS\u529f\u8017 (W)<\/tr>\n<tbody>\n<tr>\n<td>RTX 3090<\/td>\n<td>640\u00d7640<\/td>\n<td>185<\/td>\n<td>290<\/td>\n<td>360<\/td>\n<td>350<\/td>\n<\/tr>\n<tr>\n<td>RTX 4090<\/td>\n<td>640\u00d7640<\/td>\n<td>260<\/td>\n<td>410<\/td>\n<td>520<\/td>\n<td>450<\/td>\n<\/tr>\n<tr>\n<td>A10G<\/td>\n<td>640\u00d7640<\/td>\n<td>210<\/td>\n<td>340<\/td>\n<td>430<\/td>\n<td>150<\/td>\n<\/tr>\n<tr>\n<td>L4<\/td>\n<td>640\u00d7640<\/td>\n<td>230<\/td>\n<td>380<\/td>\n<td>480<\/td>\n<td>72<\/td>\n<\/tr>\n<tr>\n<td>Jetson AGX Orin<\/td>\n<td>640\u00d7640<\/td>\n<td>45<\/td>\n<td>75<\/td>\n<td>90<\/td>\n<td>50<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u63d0\u793a&#xff1a;\u542f\u7528\u534a\u7cbe\u5ea6&#xff08;FP16&#xff09;\u53ef\u63d0\u5347\u7ea6 50%~80% \u63a8\u7406\u901f\u5ea6&#xff0c;\u4e14\u7cbe\u5ea6\u635f\u5931\u6781\u5c0f\u3002\u4f7f\u7528 TensorRT \u52a0\u901f\u540e\u8fd8\u53ef\u8fdb\u4e00\u6b65\u63d0\u5347 20%-30%\u3002<\/p>\n<h4>4.3 \u8fb9\u7f18\u7aef\u4e0e\u4e91\u7aef\u63a8\u7406\u9009\u578b\u5efa\u8bae<\/h4>\n<table>\n<tr>\u573a\u666f\u7c7b\u578b\u63a8\u8350GPU\u7279\u70b9\u8bf4\u660e<\/tr>\n<tbody>\n<tr>\n<td>\u5b9e\u65f6\u89c6\u9891\u76d1\u63a7<\/td>\n<td>L4\u3001T4\u3001Jetson AGX Orin<\/td>\n<td>\u4f4e\u529f\u8017\u3001\u9ad8\u5bc6\u5ea6\u90e8\u7f72<\/td>\n<\/tr>\n<tr>\n<td>\u9ad8\u5e76\u53d1Web\u670d\u52a1<\/td>\n<td>A10\u3001A100\u3001L40<\/td>\n<td>\u652f\u6301\u52a8\u6001\u6279\u5904\u7406&#xff08;Dynamic Batching&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u79fb\u52a8\u673a\u5668\u4eba<\/td>\n<td>Jetson AGX Orin \/ Xavier NX<\/td>\n<td>\u5d4c\u5165\u5f0f\u96c6\u6210\u3001\u7b97\u529b\u8db3\u591f<\/td>\n<\/tr>\n<tr>\n<td>\u8d85\u9ad8\u6e05\u68c0\u6d4b<\/td>\n<td>RTX 4090 \/ A100<\/td>\n<td>\u652f\u6301 1280\u00d71280 \u4ee5\u4e0a\u8f93\u5165<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u4f18\u5316\u5efa\u8bae&#xff1a;<\/p>\n<ul>\n<li>\u4f7f\u7528 &#8211;half \u5f00\u542f FP16 \u63a8\u7406&#xff1b;<\/li>\n<li>\u5bf9\u56fa\u5b9a\u6a21\u578b\u5bfc\u51fa ONNX \u5e76\u8f6c\u6362\u4e3a TensorRT \u5f15\u64ce&#xff1b;<\/li>\n<li>\u542f\u7528 &#8211;dynamic-batch \u5b9e\u73b0\u81ea\u52a8\u6279\u5904\u7406\u8c03\u5ea6&#xff08;\u9002\u7528\u4e8e Triton Inference Server&#xff09;\u3002<\/li>\n<\/ul>\n<h3>5. CPU\u3001\u5185\u5b58\u4e0e\u5b58\u50a8\u534f\u540c\u914d\u7f6e\u5efa\u8bae<\/h3>\n<p>\u5c3d\u7ba1 GPU \u662f\u6838\u5fc3&#xff0c;\u4f46 CPU\u3001\u7cfb\u7edf\u5185\u5b58\u548c\u78c1\u76d8 I\/O \u4e5f\u4f1a\u6210\u4e3a\u6027\u80fd\u74f6\u9888\u3002<\/p>\n<h4>5.1 CPU \u4e0e\u6570\u636e\u52a0\u8f7d\u4f18\u5316<\/h4>\n<p>YOLOv9 \u4f7f\u7528 &#8211;workers N \u63a7\u5236\u6570\u636e\u52a0\u8f7d\u7ebf\u7a0b\u6570\u3002\u7ecf\u9a8c\u6cd5\u5219&#xff1a;<\/p>\n<ul>\n<li>\u6bcf\u5757 GPU \u914d\u5907 4~8 \u4e2a DataLoader worker<\/li>\n<li>CPU \u6838\u5fc3\u6570 \u2265 GPU \u6570 \u00d7 8<\/li>\n<li>\u63a8\u8350\u4f7f\u7528\u4e3b\u9891\u9ad8\u3001\u591a\u7ebf\u7a0b\u80fd\u529b\u5f3a\u7684 CPU&#xff0c;\u5982 Intel Xeon Gold 6330 \u6216 AMD EPYC 7543<\/li>\n<\/ul>\n<p>\u793a\u4f8b\u914d\u7f6e&#xff1a;<\/p>\n<p>python train_dual.py &#8211;workers 16 &#8211;batch 64 &#8211;img 640 &#8230;<\/p>\n<p>\u82e5 workers \u8bbe\u7f6e\u8fc7\u9ad8&#xff0c;\u53ef\u80fd\u5f15\u53d1\u5185\u5b58\u6ea2\u51fa\u6216\u8fdb\u7a0b\u4e89\u62a2&#xff1b;\u8fc7\u4f4e\u5219\u5bfc\u81f4 GPU \u7b49\u5f85\u6570\u636e\u3002<\/p>\n<h4>5.2 \u7cfb\u7edf\u5185\u5b58&#xff08;RAM&#xff09;\u5efa\u8bae<\/h4>\n<table>\n<tr>\u8bad\u7ec3\u573a\u666f\u63a8\u8350 RAM \u5bb9\u91cf\u539f\u56e0\u8bf4\u660e<\/tr>\n<tbody>\n<tr>\n<td>\u5c0f\u89c4\u6a21\u6570\u636e\u96c6&#xff08;COCO&#xff09;<\/td>\n<td>32 GB<\/td>\n<td>\u6570\u636e\u7f13\u5b58\u3001\u589e\u5f3a\u9884\u5904\u7406<\/td>\n<\/tr>\n<tr>\n<td>\u5927\u89c4\u6a21\u79c1\u6709\u6570\u636e\u96c6<\/td>\n<td>64 GB \u6216\u66f4\u9ad8<\/td>\n<td>\u907f\u514d\u9891\u7e41\u78c1\u76d8\u8bfb\u53d6<\/td>\n<\/tr>\n<tr>\n<td>\u591a\u4efb\u52a1\u8054\u5408\u8bad\u7ec3<\/td>\n<td>128 GB<\/td>\n<td>\u652f\u6301\u591a\u4e2a\u6570\u636e\u6d41\u5e76\u884c<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u5efa\u8bae RAM \u5bb9\u91cf \u2265 GPU \u663e\u5b58\u603b\u91cf \u00d7 2<\/p>\n<h4>5.3 \u5b58\u50a8\u7c7b\u578b\u9009\u62e9<\/h4>\n<ul>\n<li>SSD \u5fc5\u987b\u4f7f\u7528 NVMe SSD&#xff0c;SATA SSD \u4f1a\u5bfc\u81f4\u6570\u636e\u52a0\u8f7d\u5ef6\u8fdf\u589e\u52a0 30% \u4ee5\u4e0a&#xff1b;<\/li>\n<li>\u8bad\u7ec3\u671f\u95f4\u4e34\u65f6\u6587\u4ef6&#xff08;\u5982 runs\/train\/&#xff09;\u5199\u5165\u9891\u7e41&#xff0c;\u5efa\u8bae\u5355\u72ec\u6302\u8f7d\u9ad8\u901f\u78c1\u76d8&#xff1b;<\/li>\n<li>\u82e5\u4f7f\u7528\u4e91\u5b58\u50a8&#xff08;\u5982 S3&#xff09;&#xff0c;\u5e94\u914d\u5408\u672c\u5730\u7f13\u5b58\u673a\u5236\u51cf\u5c11 IO \u5ef6\u8fdf\u3002<\/li>\n<\/ul>\n<h3>6. \u5b9e\u9645\u90e8\u7f72\u4e2d\u7684\u5e38\u89c1\u95ee\u9898\u4e0e\u8c03\u4f18\u6280\u5de7<\/h3>\n<h4>6.1 \u663e\u5b58\u4e0d\u8db3&#xff08;Out of Memory&#xff09;\u89e3\u51b3\u65b9\u6848<\/h4>\n<p>\u5f53\u51fa\u73b0 CUDA out of memory \u9519\u8bef\u65f6&#xff0c;\u53ef\u91c7\u53d6\u4ee5\u4e0b\u63aa\u65bd&#xff1a;<\/p>\n<li>\u964d\u4f4e batch size&#8211;batch 32  # \u66ff\u4ee3 64\n <\/li>\n<li>\u51cf\u5c0f\u8f93\u5165\u5206\u8fa8\u7387&#8211;img 320   # \u4ec5\u7528\u4e8e\u8c03\u8bd5\n <\/li>\n<li>\u542f\u7528\u68af\u5ea6\u7d2f\u79ef&#xff08;Gradient Accumulation&#xff09;&#8211;batch 16 &#8211;accumulate 4  # \u7b49\u6548\u4e8e batch&#061;64\n <\/li>\n<li>\u4f7f\u7528\u6df7\u5408\u7cbe\u5ea6\u8bad\u7ec3&#8211;amp  # \u81ea\u52a8\u6df7\u5408\u7cbe\u5ea6\n <\/li>\n<h4>6.2 \u8bad\u7ec3\u4e0d\u7a33\u5b9a\u6216\u6536\u655b\u6162\u7684\u5e94\u5bf9\u7b56\u7565<\/h4>\n<ul>\n<li>\u68c0\u67e5 hyp.scratch-high.yaml \u662f\u5426\u9002\u5408\u5f53\u524d\u6570\u636e\u5206\u5e03&#xff1b;<\/li>\n<li>\u82e5\u7c7b\u522b\u4e0d\u5e73\u8861\u4e25\u91cd&#xff0c;\u5c1d\u8bd5\u4fee\u6539 cls, obj, box \u635f\u5931\u6743\u91cd&#xff1b;<\/li>\n<li>\u4f7f\u7528 &#8211;close-mosaic 15 \u5173\u95ed\u540e\u671f Mosaic \u6570\u636e\u589e\u5f3a&#xff0c;\u63d0\u5347\u7a33\u5b9a\u6027&#xff1b;<\/li>\n<li>\u76d1\u63a7 runs\/train\/exp*\/results.csv \u4e2d\u7684 precision, recall, mAP_0.5 \u66f2\u7ebf\u3002<\/li>\n<\/ul>\n<h4>6.3 \u63a8\u7406\u5ef6\u8fdf\u9ad8\u7684\u6392\u67e5\u8def\u5f84<\/h4>\n<li>\u68c0\u67e5\u662f\u5426\u542f\u7528 GPU&#xff1a;&#8211;device 0 \u800c\u975e CPU&#xff1b;<\/li>\n<li>\u786e\u8ba4 PyTorch \u662f\u5426\u6b63\u786e\u94fe\u63a5 CUDA&#xff1a;torch.cuda.is_available() \u8fd4\u56de True&#xff1b;<\/li>\n<li>\u4f7f\u7528 nvidia-smi \u89c2\u5bdf GPU \u5229\u7528\u7387\u662f\u5426\u504f\u4f4e&#xff1b;<\/li>\n<li>\u82e5\u4e3a Web API \u670d\u52a1&#xff0c;\u8003\u8651\u5f15\u5165\u5f02\u6b65\u961f\u5217\u548c\u6279\u5904\u7406\u673a\u5236\u3002<\/li>\n<h3>7. \u603b\u7ed3<\/h3>\n<h3>7.1 YOLOv9 \u670d\u52a1\u5668\u9009\u578b\u6838\u5fc3\u8981\u70b9\u603b\u7ed3<\/h3>\n<p>\u672c\u6587\u56f4\u7ed5 YOLOv9 \u5b98\u65b9\u8bad\u7ec3\u4e0e\u63a8\u7406\u955c\u50cf\u7684\u5b9e\u9645\u8fd0\u884c\u9700\u6c42&#xff0c;\u7cfb\u7edf\u5206\u6790\u4e86\u4ece\u8bad\u7ec3\u5230\u90e8\u7f72\u5168\u8fc7\u7a0b\u7684\u786c\u4ef6\u8d44\u6e90\u914d\u7f6e\u7b56\u7565&#xff1a;<\/p>\n<ul>\n<li>\u8bad\u7ec3\u9636\u6bb5&#xff1a;\u663e\u5b58\u662f\u9996\u8981\u74f6\u9888&#xff0c;\u63a8\u8350\u4f7f\u7528 24GB&#043; \u663e\u5b58 GPU&#xff08;\u5982 RTX 4090\u3001A10G\u3001L40&#xff09;&#xff0c;\u5e76\u642d\u914d\u5145\u8db3\u7684 CPU \u6838\u5fc3\u4e0e\u5185\u5b58\u4ee5\u652f\u6491\u9ad8\u6548\u6570\u636e\u52a0\u8f7d\u3002<\/li>\n<li>\u63a8\u7406\u9636\u6bb5&#xff1a;\u4f18\u5148\u8003\u8651 FP16\/INT8 \u63a8\u7406\u652f\u6301 \u4e0e \u4f4e\u5ef6\u8fdf\u8bbe\u8ba1&#xff0c;L4\u3001A10\u3001T4 \u7b49\u4e91\u539f\u751f GPU \u5728\u6027\u4ef7\u6bd4\u548c\u80fd\u6548\u6bd4\u65b9\u9762\u8868\u73b0\u4f18\u5f02\u3002<\/li>\n<li>\u7cfb\u7edf\u914d\u5957&#xff1a;NVMe SSD &#043; \u226564GB RAM &#043; \u9ad8\u4e3b\u9891\u591a\u6838 CPU \u662f\u4fdd\u969c\u6574\u4f53\u6027\u80fd\u7684\u57fa\u7840\u3002<\/li>\n<\/ul>\n<h3>7.2 \u6700\u4f73\u5b9e\u8df5\u5efa\u8bae<\/h3>\n<li>\u8bad\u7ec3\u73af\u5883&#xff1a;\u91c7\u7528 A100 \u00d7 2 \u6216 L40 \u00d7 1 \u6784\u5efa\u672c\u5730\u8bad\u7ec3\u8282\u70b9&#xff0c;\u652f\u6301\u5927 batch \u548c\u9ad8\u5206\u8fa8\u7387\u8f93\u5165&#xff1b;<\/li>\n<li>\u63a8\u7406\u670d\u52a1&#xff1a;\u4f7f\u7528 Triton Inference Server \u90e8\u7f72 TensorRT \u5f15\u64ce&#xff0c;\u5f00\u542f\u52a8\u6001\u6279\u5904\u7406\u63d0\u5347\u541e\u5410&#xff1b;<\/li>\n<li>\u6210\u672c\u63a7\u5236&#xff1a;\u5728\u4e91\u5e73\u53f0\u6309\u9700\u9009\u62e9\u5b9e\u4f8b\u7c7b\u578b&#xff0c;\u77ed\u671f\u8bad\u7ec3\u4f7f\u7528\u62a2\u5360\u5f0f\u5b9e\u4f8b\u964d\u4f4e\u6210\u672c&#xff1b;<\/li>\n<li>\u6301\u7eed\u76d1\u63a7&#xff1a;\u5229\u7528 nvidia-smi, gpustat, htop \u7b49\u5de5\u5177\u5b9e\u65f6\u89c2\u5bdf\u8d44\u6e90\u5229\u7528\u7387\u3002<\/li>\n<p>\u5408\u7406\u914d\u7f6e\u670d\u52a1\u5668\u8d44\u6e90\u4e0d\u4ec5\u80fd\u63d0\u5347 YOLOv9 \u7684\u8bad\u7ec3\u6548\u7387\u548c\u63a8\u7406\u6027\u80fd&#xff0c;\u8fd8\u80fd\u6709\u6548\u964d\u4f4e\u957f\u671f\u8fd0\u7ef4\u6210\u672c\u3002\u5efa\u8bae\u6839\u636e\u5177\u4f53\u4e1a\u52a1\u573a\u666f\u7075\u6d3b\u9009\u62e9\u786c\u4ef6\u65b9\u6848&#xff0c;\u5e76\u7ed3\u5408\u672c\u6587\u63d0\u4f9b\u7684\u5b9e\u6d4b\u6570\u636e\u8fdb\u884c\u51b3\u7b56\u3002<\/p>\n<hr \/>\n<p>\u83b7\u53d6\u66f4\u591aAI\u955c\u50cf<\/p>\n<p>\u60f3\u63a2\u7d22\u66f4\u591aAI\u955c\u50cf\u548c\u5e94\u7528\u573a\u666f&#xff1f;\u8bbf\u95ee CSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a&#xff0c;\u63d0\u4f9b\u4e30\u5bcc\u7684\u9884\u7f6e\u955c\u50cf&#xff0c;\u8986\u76d6\u5927\u6a21\u578b\u63a8\u7406\u3001\u56fe\u50cf\u751f\u6210\u3001\u89c6\u9891\u751f\u6210\u3001\u6a21\u578b\u5fae\u8c03\u7b49\u591a\u4e2a\u9886\u57df&#xff0c;\u652f\u6301\u4e00\u952e\u90e8\u7f72\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>YOLOv9\u670d\u52a1\u5668\u9009\u578b\u5efa\u8bae&#xff1a;GPU\u5185\u5b58\u4e0e\u6838\u5fc3\u6570\u914d\u7f6e\u6307\u5357<br \/>\n1. \u80cc\u666f\u4e0e\u9700\u6c42\u5206\u6790<br \/>\n\u968f\u7740YOLO\u7cfb\u5217\u76ee\u6807\u68c0\u6d4b\u6a21\u578b\u7684\u6301\u7eed\u6f14\u8fdb&#xff0c;YOLOv9\u51ed\u501f\u5176\u5728\u7cbe\u5ea6\u4e0e\u63a8\u7406\u6548\u7387\u4e4b\u95f4\u7684\u4f18\u79c0\u5e73\u8861&#xff0c;\u6210\u4e3a\u5de5\u4e1a\u754c\u548c\u79d1\u7814\u9886\u57df\u7684\u65b0\u5ba0\u3002\u8be5\u6a21\u578b\u901a\u8fc7\u53ef\u7f16\u7a0b\u68af\u5ea6\u4fe1\u606f&#xff08;Programmable Gradient 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