{"id":96622,"date":"2026-08-28T02:35:15","date_gmt":"2026-08-27T18:35:15","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/96622.html"},"modified":"2026-08-28T02:35:15","modified_gmt":"2026-08-27T18:35:15","slug":"%e5%9c%a8-cuda-12-5-%e6%9c%8d%e5%8a%a1%e5%99%a8%e4%b8%8a%e5%a4%8d%e7%8e%b0%e8%87%aa%e5%8a%a8%e9%a9%be%e9%a9%b6%e9%a1%b9%e7%9b%ae%ef%bc%9apytorch-1-13cu116%e3%80%81mmcv-1-7-1-%e7%8e%af%e5%a2%83","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/96622.html","title":{"rendered":"\u5728 CUDA 12.5 \u670d\u52a1\u5668\u4e0a\u590d\u73b0\u81ea\u52a8\u9a7e\u9a76\u9879\u76ee\uff1aPyTorch 1.13+cu116\u3001mmcv 1.7.1 \u73af\u5883\u8e29\u5751\u8bb0\u5f55\uff08\u542b cusolverDn.h \u62a5\u9519\u89e3\u51b3\uff09"},"content":{"rendered":"<p>\u6700\u8fd1\u5728\u670d\u52a1\u5668\u4e0a\u590d\u73b0\u4e00\u4e2a\u81ea\u52a8\u9a7e\u9a76 3D \u68c0\u6d4b\u9879\u76ee&#xff08;\u57fa\u4e8e mmdet3d &#043; \u81ea\u5b9a\u4e49 CUDA ops&#xff09;&#xff0c;\u7ed3\u679c\u5728\u73af\u5883\u642d\u5efa\u9636\u6bb5\u5c31\u88ab CUDA \u548c mmcv \u6559\u80b2\u4e86\u4e00\u756a\u3002<\/p>\n<p>\u6838\u5fc3\u573a\u666f\u662f&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u670d\u52a1\u5668\u7cfb\u7edf CUDA \u662f 12.5&#xff08;\u5f88\u65b0&#xff09;&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u9879\u76ee\u8981\u6c42 PyTorch 1.13 &#043; cu116&#xff1b;<\/p>\n<\/li>\n<li>\n<p>mmcv \u7248\u672c\u662f mmcv-full 1.7.1&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u5b89\u88c5 mmcv \u7528\u7684\u662f OpenMMLab \u5b98\u65b9 wheel&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u771f\u6b63\u81f4\u547d\u7684\u9519\u8bef\u662f&#xff1a;\u5728\u7f16\u8bd1 mmdet\/mmdet3d \u63d2\u4ef6\u65f6\u9047\u5230<\/p>\n<p>fatal error: cusolverDn.h: No such file or directory<\/p>\n<\/li>\n<\/ul>\n<p>\u4e0b\u9762\u662f\u5b8c\u6574\u7684\u8e29\u5751\u548c\u89e3\u51b3\u8fc7\u7a0b\u3002<\/p>\n<hr \/>\n<h3>\u4e00\u3001\u9879\u76ee\u80cc\u666f\u4e0e\u73af\u5883\u8981\u6c42<\/h3>\n<p>\u9879\u76ee\u7c7b\u578b&#xff1a;\u81ea\u52a8\u9a7e\u9a76 3D \u76ee\u6807\u68c0\u6d4b<br \/>\n\u6280\u672f\u6808&#xff1a;mmdet3d &#043; \u82e5\u5e72\u81ea\u5b9a\u4e49 CUDA \u63d2\u4ef6&#xff08;\u4f8b\u5982 projects\/mmdet3d_plugin\/ops \u4e0b\u7684 deformable_aggregation_ext&#xff09;\u3002<\/p>\n<p>\u9879\u76ee\u8981\u6c42\u73af\u5883\u5927\u81f4\u5982\u4e0b&#xff1a;<\/p>\n<ul>\n<li>Python&#xff1a;3.8<\/li>\n<li>PyTorch&#xff1a;1.13.0 &#043; cu116<\/li>\n<li>mmcv-full&#xff1a;1.7.1<\/li>\n<li>mmdet3d&#xff1a;\u67d0\u4e2a\u4e0e mmcv 1.7.1\u3001PyTorch 1.13 \u517c\u5bb9\u7684\u7248\u672c<\/li>\n<li>\u989d\u5916 CUDA ops&#xff1a;mmdet3d_plugin\/ops \u5185\u90e8\u7684\u81ea\u5b9a\u4e49\u7b97\u5b50<\/li>\n<\/ul>\n<p>\u670d\u52a1\u5668\u5b9e\u9645\u60c5\u51b5&#xff1a;<\/p>\n<ul>\n<li>\u7cfb\u7edf&#xff1a;Linux x86_64<\/li>\n<li>\u7cfb\u7edf CUDA \u5bf9\u5e94\u9a71\u52a8\u7248\u672c&#xff1a;12.5&#xff08;\u53ea\u627f\u62c5\u201c\u80fd\u8dd1\u8d77\u6765\u201d\u7684\u8d23\u4efb&#xff09;<\/li>\n<li>\u73af\u5883\u7ba1\u7406&#xff1a;conda&#xff0c;\u76ee\u6807\u73af\u5883\u53eb bridgead<\/li>\n<\/ul>\n<hr \/>\n<h3>\u4e8c\u3001PyTorch \u5fc5\u987b\u7528 conda \u5b89\u88c5<\/h3>\n<p>\u56e0\u4e3a\u9879\u76ee\u91cc\u6709\u5927\u91cf CUDA \u6269\u5c55&#xff08;mmcv\u3001\u81ea\u5b9a\u4e49 ops&#xff09;&#xff0c;\u6240\u4ee5\u4e00\u5f00\u59cb\u5c31\u51b3\u5b9a&#xff1a;<\/p>\n<p>PyTorch \u575a\u51b3\u7528 conda \u5b89\u88c5&#xff0c;\u4e0d\u7528 pip \u4e71\u6765\u3002<\/p>\n<p>\u6211\u7684\u6b65\u9aa4&#xff1a;<\/p>\n<p><span class=\"token comment\"># 1. \u521b\u5efa\u73af\u5883<\/span><br \/>\nconda create -n bridgead <span class=\"token assign-left variable\">python<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token number\">3.8<\/span> -y<br \/>\nconda activate bridgead<\/p>\n<p><span class=\"token comment\"># 2. \u7528 conda \u5b89\u88c5 PyTorch 1.13 &#043; cu116<\/span><br \/>\nconda <span class=\"token function\">install<\/span> <span class=\"token assign-left variable\">pytorch<\/span><span class=\"token operator\">&#061;&#061;<\/span><span class=\"token number\">1.13<\/span>.1 <span class=\"token assign-left variable\">torchvision<\/span><span class=\"token operator\">&#061;&#061;<\/span><span class=\"token number\">0.14<\/span>.1 <span class=\"token assign-left variable\">torchaudio<\/span><span class=\"token operator\">&#061;&#061;<\/span><span class=\"token number\">0.13<\/span>.1 pytorch-cuda<span class=\"token operator\">&#061;<\/span><span class=\"token number\">11.6<\/span> -c pytorch -c nvidia -y<\/p>\n<p>\u5b89\u88c5\u5b8c\u68c0\u67e5\u4e00\u4e0b&#xff1a;<\/p>\n<p>python &#8211; <span class=\"token operator\">&lt;&lt;<\/span> <span class=\"token string\">&#039;EOF&#039;<br \/>\nimport torch<br \/>\nprint(&#034;torch:&#034;, torch.__version__)<br \/>\nprint(&#034;torch.cuda:&#034;, torch.version.cuda)<br \/>\nprint(&#034;is_available:&#034;, torch.cuda.is_available())<br \/>\nEOF<\/span><\/p>\n<p>\u9884\u671f\u8f93\u51fa\u7c7b\u4f3c&#xff1a;<\/p>\n<ul>\n<li>torch: 1.13.0<\/li>\n<li>torch.cuda: 11.6<\/li>\n<li>is_available: True<\/li>\n<\/ul>\n<p>\u5230\u8fd9\u4e00\u6b65\u4e3a\u6b62&#xff0c;PyTorch &#043; cu116 \u73af\u5883\u662f\u6b63\u786e\u7684\u3002<\/p>\n<hr \/>\n<h3>\u4e09\u3001\u5b89\u88c5 mmcv-full 1.7.1&#xff1a;\u901a\u8fc7\u5b98\u65b9 wheel \u8df3\u8fc7\u7f16\u8bd1<\/h3>\n<p>mmcv 1.x \u672c\u8eab\u6bd4\u8f83\u201c\u8106\u201d&#xff0c;\u5c24\u5176 mmcv-full&#xff0c;\u6e90\u7801\u7f16\u8bd1\u975e\u5e38\u4f9d\u8d56 CUDA \u73af\u5883\u3002\u4e3a\u4e86\u51cf\u5c11\u4e0d\u5fc5\u8981\u7684\u9ebb\u70e6&#xff0c;\u6211\u76f4\u63a5\u7528 OpenMMLab \u63d0\u4f9b\u7684 wheel&#xff1a;<\/p>\n<p>pip <span class=\"token function\">install<\/span> mmcv-full<span class=\"token operator\">&#061;&#061;<\/span><span class=\"token number\">1.7<\/span>.1 <span class=\"token punctuation\">\\\\<\/span><br \/>\n  -f https:\/\/download.openmmlab.com\/mmcv\/dist\/cu116\/torch1.13\/index.html <span class=\"token punctuation\">\\\\<\/span><br \/>\n  &#8211;trusted-host download.openmmlab.com<\/p>\n<p>\u8fd9\u6761\u547d\u4ee4\u4f1a\u4ece\u6307\u5b9a\u7684 index \u627e\u5230\u548c&#xff1a;<\/p>\n<ul>\n<li>torch&#061;&#061;1.13.x<\/li>\n<li>cu116<\/li>\n<\/ul>\n<p>\u7cbe\u51c6\u5339\u914d\u7684 mmcv-full 1.7.1 wheel&#xff0c;\u6574\u4e2a\u8fc7\u7a0b\u4e0d\u9700\u8981\u672c\u5730\u7f16\u8bd1 CUDA \u4ee3\u7801\u3002<\/p>\n<p>\u5b89\u88c5\u5b8c\u6210\u540e\u7b80\u5355\u9a8c\u8bc1\u4e00\u4e0b&#xff1a;<\/p>\n<p>python &#8211; <span class=\"token operator\">&lt;&lt;<\/span> <span class=\"token string\">&#039;EOF&#039;<br \/>\nimport mmcv<br \/>\nprint(&#034;mmcv:&#034;, mmcv.__version__)<br \/>\nEOF<\/span><\/p>\n<p>\u53ea\u8981\u770b\u5230 mmcv: 1.7.1&#xff0c;\u8fd9\u4e00\u6b65\u5c31\u7b97\u5b8c\u6210\u4e86\u3002<\/p>\n<p>\u8fd9\u91cc\u5f3a\u8c03\u4e00\u4e0b&#xff1a;<br \/>\nmmcv \u5e76\u6ca1\u6709\u89e6\u53d1 cusolverDn.h \u7684\u62a5\u9519&#xff0c;\u662f\u901a\u8fc7\u9884\u7f16\u8bd1 wheel \u5b89\u88c5\u6210\u529f\u7684\u3002<br \/>\n\u771f\u6b63\u7684\u5751\u53d1\u751f\u5728\u540e\u9762\u7f16\u8bd1 mmdet\/mmdet3d \u63d2\u4ef6\u65f6\u3002<\/p>\n<hr \/>\n<h3>\u56db\u3001\u771f\u6b63\u7ffb\u8f66\u7684\u5730\u65b9&#xff1a;\u7f16\u8bd1 mmdet3d \u63d2\u4ef6\u65f6\u7684 cusolverDn.h \u62a5\u9519<\/h3>\n<p>\u81ea\u52a8\u9a7e\u9a76\u9879\u76ee\u91cc\u6709\u81ea\u5b9a\u4e49\u7684 CUDA \u63d2\u4ef6&#xff0c;\u4f4d\u4e8e&#xff1a;<\/p>\n<p>BridgeAD\/projects\/mmdet3d_plugin\/ops<\/p>\n<p>\u5b89\u88c5\u65b9\u5f0f\u662f&#xff1a;<\/p>\n<p><span class=\"token builtin class-name\">cd<\/span> ~\/BridgeAD\/projects\/mmdet3d_plugin\/ops<br \/>\npython3 setup.py develop<\/p>\n<p>\u8fd9\u4e00\u6b65\u4f1a\u8c03\u7528 torch.utils.cpp_extension &#043; ninja \u53bb\u7f16\u8bd1 CUDA \u6e90\u7801&#xff0c;\u7ed3\u679c\u62a5\u9519&#xff08;\u5173\u952e\u90e8\u5206&#xff09;&#xff1a;<\/p>\n<p>In file included from \/data\/&#8230;\/mmdet3d_plugin\/ops\/src\/deformable_aggregation_cuda.cu:3:<br \/>\n\/data\/xxx\/miniconda3\/envs\/bridgead\/lib\/python3.8\/site-packages\/torch\/include\/ATen\/cuda\/CUDAContext.h:10:10: fatal error: cusolverDn.h: No such file or directory<br \/>\n   10 | #include &lt;cusolverDn.h&gt;<br \/>\n      |          ^~~~~~~~~~~~~~<br \/>\ncompilation terminated.<\/p>\n<p>ninja: build stopped: subcommand failed.<br \/>\nRuntimeError: Error compiling objects for extension<\/p>\n<p>\u8fd9\u8bf4\u660e&#xff1a;<\/p>\n<ul>\n<li>\u81ea\u5b9a\u4e49 CUDA op \u7684 .cu \u6587\u4ef6\u91cc include \u4e86 PyTorch \u63d0\u4f9b\u7684\u5934\u6587\u4ef6&#xff1b;<\/li>\n<li>PyTorch \u7684 ATen\/cuda\/CUDAContext.h \u53c8\u4f1a #include &lt;cusolverDn.h&gt;&#xff1b;<\/li>\n<li>\u4f46\u662f\u5f53\u524d\u7684\u7f16\u8bd1\u73af\u5883&#xff08;conda \u91cc\u7684 CUDA \u5de5\u5177\u94fe&#xff09;\u91cc&#xff0c;\u6839\u672c\u627e\u4e0d\u5230 cusolverDn.h\u3002<\/li>\n<\/ul>\n<p>\u6362\u53e5\u8bdd\u8bf4&#xff0c;\u672c\u8d28\u95ee\u9898\u662f&#xff1a;<\/p>\n<p>conda \u73af\u5883\u4e2d\u7684 CUDA 11.6 \u88c5\u5f97\u4e0d\u5b8c\u6574&#xff0c;\u7f3a\u4e86 cuSOLVER \u7684\u5f00\u53d1\u5934\u6587\u4ef6\u3002<\/p>\n<p>\u548c\u7cfb\u7edf CUDA \u662f 12.5 \u6ca1\u592a\u5927\u5173\u7cfb&#xff0c;\u7f16\u8bd1\u65f6\u7528\u7684\u662f conda \u73af\u5883\u91cc\u7684 nvcc \u548c\u5934\u6587\u4ef6\u3002<\/p>\n<hr \/>\n<h3>\u4e94\u3001\u68c0\u67e5 conda \u73af\u5883&#xff1a;cuda&#061;11.6 \u5df2\u88c5&#xff0c;\u4f46\u5934\u6587\u4ef6\u7f3a\u5931<\/h3>\n<p>\u5f53\u65f6\u7684\u76f4\u89c9\u662f&#xff1a;\u65e2\u7136 PyTorch \u662f cu116&#xff0c;\u90a3\u6211\u5c31\u5728\u73af\u5883\u91cc\u88c5\u4e00\u4e2a cuda&#061;11.6&#xff1a;<\/p>\n<p>conda activate bridgead<\/p>\n<p>conda <span class=\"token function\">install<\/span> -c <span class=\"token string\">&#034;nvidia\/label\/cuda-11.6.0&#034;<\/span> cuda<br \/>\nconda <span class=\"token function\">install<\/span> -c nvidia <span class=\"token assign-left variable\">cuda<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token number\">11.6<\/span><\/p>\n<p>conda \u63d0\u793a&#xff1a;<\/p>\n<p># All requested packages already installed.<\/p>\n<p>\u770b\u4e0a\u53bb\u4e00\u5207\u6b63\u5e38\u3002\u4f46\u4e3a\u4e86\u9a8c\u8bc1&#xff0c;\u67e5\u4e86\u4e00\u4e0b cusolverDn.h&#xff1a;<\/p>\n<p><span class=\"token function\">find<\/span> <span class=\"token string\">&#034;<span class=\"token variable\">$CONDA_PREFIX<\/span>&#034;<\/span> -name <span class=\"token string\">&#034;cusolverDn.h&#034;<\/span><\/p>\n<p>\u4e00\u5f00\u59cb\u662f\u67e5\u4e0d\u5230\u4efb\u4f55\u7ed3\u679c\u7684\u3002<\/p>\n<p>\u8fd9\u5c31\u8bf4\u660e&#xff1a;\u5373\u4fbf cuda&#061;11.6 meta-package \u5df2\u7ecf\u88c5\u4e0a&#xff0c;\u5e76\u4e0d\u4ee3\u8868 cuSOLVER \u7684 dev \u5934\u6587\u4ef6\u4e00\u5b9a\u5728&#xff0c;\u5c24\u5176\u662f cusolverDn.h\u3002<\/p>\n<p>\u7ed3\u8bba&#xff1a;<\/p>\n<p>cuda&#061;11.6 \u2260 \u201c\u6240\u6709 CUDA \u5f00\u53d1\u5934\u6587\u4ef6\u90fd\u9f50\u5168\u201d\u3002<\/p>\n<hr \/>\n<h3>\u516d\u3001\u5173\u952e\u8865\u4e01&#xff1a;\u5b89\u88c5 libcusolver-dev&#xff0c;\u8ba9 cusolverDn.h \u771f\u6b63\u51fa\u73b0<\/h3>\n<p>\u771f\u6b63\u89e3\u51b3\u95ee\u9898\u7684\u662f\u5355\u72ec\u5b89\u88c5 cuSOLVER \u7684\u5f00\u53d1\u5305 libcusolver-dev\u3002<\/p>\n<p>\u5728 bridgead \u73af\u5883\u91cc\u6267\u884c&#xff1a;<\/p>\n<p>conda activate bridgead<\/p>\n<p><span class=\"token comment\"># \u5b89\u88c5 cuSOLVER \u5f00\u53d1\u5305<\/span><br \/>\nconda <span class=\"token function\">install<\/span> -c nvidia libcusolver-dev<\/p>\n<p>\u5b89\u88c5\u5b8c\u6210\u540e\u518d\u67e5\u4e00\u904d cusolverDn.h&#xff1a;<\/p>\n<p><span class=\"token function\">find<\/span> <span class=\"token string\">&#034;<span class=\"token variable\">$CONDA_PREFIX<\/span>&#034;<\/span> -name <span class=\"token string\">&#034;cusolverDn.h&#034;<\/span><\/p>\n<p>\u8fd9\u6b21\u8f93\u51fa\u53d8\u6210\u4e86&#xff1a;<\/p>\n<p>\/data\/xxx\/miniconda3\/envs\/bridgead\/include\/cusolverDn.h<\/p>\n<p>\u8bf4\u660e cusolverDn.h \u5df2\u7ecf\u5b58\u5728\u4e8e\u5f53\u524d conda \u73af\u5883\u7684&#xff1a;<\/p>\n<p>$CONDA_PREFIX\/include<\/p>\n<p>\u800c PyTorch \u5728\u7f16\u8bd1\u6269\u5c55\u65f6\u7684 nvcc \u547d\u4ee4\u91cc&#xff0c;\u672c\u6765\u5c31\u5305\u542b\u4e86&#xff1a;<\/p>\n<p>-I\/data\/xxx\/miniconda3\/envs\/bridgead\/include<\/p>\n<p>\u6240\u4ee5\u8fd9\u4e00\u6b65\u5b8c\u6210\u540e&#xff0c;\u7f16\u8bd1\u5668\u518d\u4e5f\u4e0d\u4f1a\u62a5 \u201ccusolverDn.h: No such file or directory\u201d\u3002<\/p>\n<hr \/>\n<h3>\u4e03\u3001\u91cd\u65b0\u7f16\u8bd1 mmdet3d \u63d2\u4ef6&#xff1a;\u4e00\u6b21\u8fc7<\/h3>\n<p>\u5934\u6587\u4ef6\u8865\u9f50\u4e4b\u540e&#xff0c;\u518d\u56de\u5230 ops \u76ee\u5f55&#xff1a;<\/p>\n<p>conda activate bridgead<br \/>\n<span class=\"token builtin class-name\">cd<\/span> ~\/BridgeAD\/projects\/mmdet3d_plugin\/ops<\/p>\n<p>python3 setup.py develop<\/p>\n<p>\u8fd9\u6b21\u8f93\u51fa\u7c7b\u4f3c&#xff1a;<\/p>\n<p>building &#039;..deformable_aggregation_ext&#039; extension<br \/>\nEmitting ninja build file &#8230;\/build.ninja&#8230;<br \/>\nCompiling objects&#8230;<br \/>\n[1\/1] \/data\/xxx\/miniconda3\/envs\/bridgead\/bin\/nvcc &#8230; deformable_aggregation_cuda.cu -o &#8230;\/deformable_aggregation_cuda.o &#8230;<br \/>\ncreating build\/lib.linux-x86_64-cpython-38<br \/>\ng&#043;&#043; &#8230; -o build\/lib.linux-x86_64-cpython-38\/deformable_aggregation_ext.cpython-38-x86_64-linux-gnu.so<br \/>\ncopying build\/lib.linux-x86_64-cpython-38\/deformable_aggregation_ext.cpython-38-x86_64-linux-gnu.so -&gt;<br \/>\nCreating &#8230;\/deformable-aggregation-ext.egg-link (link to .)<br \/>\nAdding deformable-aggregation-ext 0.0.0 to easy-install.pth file<\/p>\n<p>Installed \/data\/xxx\/BridgeAD\/projects\/mmdet3d_plugin\/ops<br \/>\nProcessing dependencies for deformable-aggregation-ext&#061;&#061;0.0.0<br \/>\nFinished processing dependencies for deformable-aggregation-ext&#061;&#061;0.0.0<\/p>\n<p>\u53ef\u4ee5\u770b\u5230&#xff1a;<\/p>\n<ul>\n<li>nvcc \u6210\u529f\u7f16\u8bd1\u4e86 CUDA \u6e90\u7801&#xff1b;<\/li>\n<li>g&#043;&#043; \u6210\u529f\u94fe\u63a5 .so&#xff1b;<\/li>\n<li>\u63d2\u4ef6\u4ee5 develop \u6a21\u5f0f\u5b89\u88c5\u5230\u5f53\u524d\u73af\u5883\u3002<\/li>\n<\/ul>\n<p>\u7b80\u5355\u9a8c\u8bc1\u4e00\u4e0b&#xff1a;<\/p>\n<p>python &#8211; <span class=\"token operator\">&lt;&lt;<\/span> <span class=\"token string\">&#039;EOF&#039;<br \/>\nimport deformable_aggregation_ext<br \/>\nprint(&#034;deformable_aggregation_ext loaded:&#034;, deformable_aggregation_ext)<br \/>\nEOF<\/span><\/p>\n<p>\u5982\u679c\u6ca1\u6709 ImportError&#xff0c;\u5c31\u8bf4\u660e\u81ea\u52a8\u9a7e\u9a76\u9879\u76ee\u91cc\u7684\u8fd9\u4e2a CUDA \u63d2\u4ef6\u5df2\u7ecf\u53ef\u4ee5\u6b63\u5e38\u4f7f\u7528\u4e86\u3002<\/p>\n<hr \/>\n<h3>\u516b\u3001\u987a\u4fbf\u5410\u69fd&#xff1a;mmcv \u5c24\u5176\u662f 1.x \u8001\u7248\u672c\u771f\u7684\u96be\u88c5<\/h3>\n<p>\u501f\u8fd9\u4e2a\u673a\u4f1a\u987a\u4fbf\u5410\u69fd\u4e00\u4e0b&#xff1a;mmcv \u672c\u8eab\u7684\u5b89\u88c5\u4f53\u9a8c&#xff0c;\u5c24\u5176\u662f 1.x \u8001\u7248\u672c&#xff0c;\u771f\u7684\u4e0d\u7b97\u53cb\u597d\u3002<\/p>\n<p>\u7ed3\u5408\u8fd9\u6b21\u81ea\u52a8\u9a7e\u9a76\u9879\u76ee&#xff0c;\u5927\u6982\u6709\u51e0\u70b9\u611f\u53d7&#xff1a;<\/p>\n<li>\n<p>\u7248\u672c\u5f3a\u7ed1\u5b9a&#xff1a;PyTorch \/ CUDA \/ mmcv \/ mmdet \/ mmdet3d \u4e00\u4e32\u7ed1\u6b7b<\/p>\n<ul>\n<li>\u672c\u9879\u76ee\u7684\u7ec4\u5408\u662f&#xff1a;PyTorch 1.13 &#043; cu116 \u2192 mmcv-full 1.7.1 \u2192 \u67d0\u4e00\u7248\u672c\u7684 mmdet3d\u3002<\/li>\n<li>\u4e00\u65e6 PyTorch \u6216 CUDA \u504f\u79bb\u5b98\u65b9\u63a8\u8350&#xff0c;\u7acb\u523b\u5c31\u9762\u4e34 wheel \u5bf9\u4e0d\u4e0a\u3001\u53ea\u80fd\u672c\u5730\u7f16\u8bd1\u7684\u60c5\u51b5&#xff0c;\u98ce\u9669\u76f4\u7ebf\u4e0a\u5347\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>mmcv-full 1.x \u6e90\u7801\u7f16\u8bd1\u6781\u5ea6\u4f9d\u8d56 CUDA \u73af\u5883\u7ec6\u8282<\/p>\n<ul>\n<li>\u81ea\u5b9a\u4e49 CUDA \u7b97\u5b50\u5f88\u591a&#xff0c;\u5bf9 nvcc \u7248\u672c\u3001Toolkit \u5b8c\u6574\u6027\u90fd\u5f88\u654f\u611f\u3002<\/li>\n<li>\u8fd9\u6b21 cusolverDn.h \u7684\u5751\u867d\u7136\u6700\u7ec8\u662f\u5728 mmdet \u63d2\u4ef6\u4e0a\u66b4\u9732\u51fa\u6765\u7684&#xff0c;\u4f46\u540c\u6837\u4f1a\u5f71\u54cd\u5230 mmcv-full \u7684\u6e90\u7801\u7f16\u8bd1\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u5b98\u65b9\u6709\u517c\u5bb9\u77e9\u9635&#xff0c;\u4f46\u73b0\u5b9e\u73af\u5883\u5343\u5947\u767e\u602a<\/p>\n<ul>\n<li>\u6587\u6863\u4e2d\u7684\u517c\u5bb9\u77e9\u9635\u5f88\u597d&#xff0c;\u4f46\u670d\u52a1\u5668\u4e0a\u5f80\u5f80\u5df2\u7ecf\u6709\u4e00\u5957\u81ea\u5df1\u7684\u9a71\u52a8\/CUDA\/\u73af\u5883&#xff0c;\u5f88\u96be\u5b8c\u5168\u5bf9\u9f50\u3002<\/li>\n<li>\u5c24\u5176\u662f\u81ea\u52a8\u9a7e\u9a76\u9879\u76ee&#xff0c;\u518d\u53e0\u4e00\u5c42\u81ea\u5b9a\u4e49 op&#xff0c;\u5b9a\u4f4d\u95ee\u9898\u5230\u5e95\u662f mmcv\u3001mmdet \u8fd8\u662f\u81ea\u5bb6\u63d2\u4ef6&#xff0c;\u5f88\u8017\u7cbe\u529b\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>1.x \u65f6\u4ee3 wheel \u8986\u76d6\u6709\u9650&#xff0c;\u5f88\u591a\u7ec4\u5408\u5176\u5b9e\u6ca1\u6709\u73b0\u6210\u5305<\/p>\n<ul>\n<li>\u65b0\u7248 mmcv 2.x \u5bf9\u4e3b\u6d41 PyTorch\/CUDA \u7ec4\u5408\u57fa\u672c\u90fd\u6709 wheel&#xff0c;mim install \u5c31\u884c\u3002<\/li>\n<li>1.x \u8001\u7248\u672c\u5f88\u591a wheel \u4e0d\u518d\u66f4\u65b0&#xff0c;\u53ea\u8981\u73af\u5883\u7a0d\u5fae\u201c\u975e\u6807\u201d&#xff0c;\u5c31\u5fc5\u987b\u672c\u5730\u7f16\u8bd1&#xff0c;\u4f53\u9a8c\u77ac\u95f4\u56de\u5230\u201c\u7eaf\u6e90\u7801\u5e74\u4ee3\u201d\u3002<\/li>\n<\/ul>\n<\/li>\n<p>\u73b0\u5728\u6211\u7684\u7ed3\u8bba\u662f&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u80fd\u7528\u5b98\u65b9 wheel \u7684\u5730\u65b9&#xff0c;\u575a\u51b3\u7528 wheel<br \/>\n\u4f8b\u5982\u8fd9\u6b21 mmcv-full 1.7.1&#xff1a;<br \/>\npip install mmcv-full&#061;&#061;1.7.1 -f https:\/\/download.openmmlab.com\/mmcv\/dist\/cu116\/torch1.13\/index.html<\/p>\n<\/li>\n<li>\n<p>\u5fc5\u987b\u52a8\u624b\u7f16\u8bd1\u7684\u65f6\u5019&#xff0c;\u4e00\u5b9a\u8981\u5148\u628a CUDA dev \u5934\u6587\u4ef6\u914d\u9f50<br \/>\n\u6bd4\u5982\u672c\u6b21\u5c31\u662f\u663e\u5f0f\u88c5\u4e86 cuda&#061;11.6 &#043; libcusolver-dev&#xff0c;\u518d\u7f16 mmdet3d \u7684 CUDA \u63d2\u4ef6\u624d\u987a\u5229\u901a\u8fc7\u3002<\/p>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>\u6700\u8fd1\u5728\u670d\u52a1\u5668\u4e0a\u590d\u73b0\u4e00\u4e2a\u81ea\u52a8\u9a7e\u9a76 3D \u68c0\u6d4b\u9879\u76ee&#xff08;\u57fa\u4e8e mmdet3d  \u81ea\u5b9a\u4e49 CUDA ops&#xff09;&#xff0c;\u7ed3\u679c\u5728\u73af\u5883\u642d\u5efa\u9636\u6bb5\u5c31\u88ab CUDA \u548c mmcv \u6559\u80b2\u4e86\u4e00\u756a\u3002<br \/>\n\u6838\u5fc3\u573a\u666f\u662f&#xff1a;\u670d\u52a1\u5668\u7cfb\u7edf CUDA \u662f 12.5&#xff08;\u5f88\u65b0&#xff09;&#xff1b;\u9879\u76ee\u8981\u6c42 PyTorch 1.13  cu116&#xff1b;mmcv \u7248\u672c\u662f mmcv-full 1.7.1&#xff1b;\u5b89\u88c5 mmcv \u7528\u7684\u662f OpenMM<\/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":[152,43,2051],"topic":[],"class_list":["post-96622","post","type-post","status-publish","format-standard","hentry","category-server","tag-pytorch","tag-43","tag-2051"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u5728 CUDA 12.5 \u670d\u52a1\u5668\u4e0a\u590d\u73b0\u81ea\u52a8\u9a7e\u9a76\u9879\u76ee\uff1aPyTorch 1.13+cu116\u3001mmcv 1.7.1 \u73af\u5883\u8e29\u5751\u8bb0\u5f55\uff08\u542b cusolverDn.h \u62a5\u9519\u89e3\u51b3\uff09 - \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\/96622.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u5728 CUDA 12.5 \u670d\u52a1\u5668\u4e0a\u590d\u73b0\u81ea\u52a8\u9a7e\u9a76\u9879\u76ee\uff1aPyTorch 1.13+cu116\u3001mmcv 1.7.1 \u73af\u5883\u8e29\u5751\u8bb0\u5f55\uff08\u542b cusolverDn.h \u62a5\u9519\u89e3\u51b3\uff09 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"og:description\" content=\"\u6700\u8fd1\u5728\u670d\u52a1\u5668\u4e0a\u590d\u73b0\u4e00\u4e2a\u81ea\u52a8\u9a7e\u9a76 3D \u68c0\u6d4b\u9879\u76ee&#xff08;\u57fa\u4e8e mmdet3d \u81ea\u5b9a\u4e49 CUDA ops&#xff09;&#xff0c;\u7ed3\u679c\u5728\u73af\u5883\u642d\u5efa\u9636\u6bb5\u5c31\u88ab CUDA \u548c mmcv \u6559\u80b2\u4e86\u4e00\u756a\u3002 \u6838\u5fc3\u573a\u666f\u662f&#xff1a;\u670d\u52a1\u5668\u7cfb\u7edf CUDA \u662f 12.5&#xff08;\u5f88\u65b0&#xff09;&#xff1b;\u9879\u76ee\u8981\u6c42 PyTorch 1.13 cu116&#xff1b;mmcv \u7248\u672c\u662f mmcv-full 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