{"id":3532,"date":"2025-06-09T18:00:10","date_gmt":"2025-06-09T10:00:10","guid":{"rendered":"https:\/\/www.wunen.com\/index.php\/2025\/06\/09\/%e8%b6%85%e8%af%a6%e7%bb%86%e6%90%ad%e5%bb%ba%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%e7%8e%af%e5%a2%83%e3%80%90anacondapycharmnvidia-%e6%98%be%e5%8d%a1%e9%a9%b1%e5%8a%a8pytorch%e5%90%abgpu%e7%89%88\/"},"modified":"2025-06-09T18:00:10","modified_gmt":"2025-06-09T10:00:10","slug":"%e8%b6%85%e8%af%a6%e7%bb%86%e6%90%ad%e5%bb%ba%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%e7%8e%af%e5%a2%83%e3%80%90anacondapycharmnvidia-%e6%98%be%e5%8d%a1%e9%a9%b1%e5%8a%a8pytorch%e5%90%abgpu%e7%89%88","status":"publish","type":"post","link":"http:\/\/www.wunen.com\/index.php\/2025\/06\/09\/%e8%b6%85%e8%af%a6%e7%bb%86%e6%90%ad%e5%bb%ba%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%e7%8e%af%e5%a2%83%e3%80%90anacondapycharmnvidia-%e6%98%be%e5%8d%a1%e9%a9%b1%e5%8a%a8pytorch%e5%90%abgpu%e7%89%88\/","title":{"rendered":"(\u8d85\u8be6\u7ec6)\u642d\u5efa\u6df1\u5ea6\u5b66\u4e60\u73af\u5883\u3010Anaconda+Pycharm+NVIDIA \u663e\u5361\u9a71\u52a8+PyTorch(\u542bGPU\u7248)+CUDA+cuDNN+win10\u3011+ \u914d\u7f6e&#8230;"},"content":{"rendered":"<div class=\"article_content clearfix\" id=\"article_content\">\n <link href=\"https:\/\/csdnimg.cn\/release\/blogv2\/dist\/mdeditor\/css\/editerView\/kdoc_html_views-1a98987dfd.css\" rel=\"stylesheet\"\/>\n <link href=\"https:\/\/csdnimg.cn\/release\/blogv2\/dist\/mdeditor\/css\/editerView\/ck_htmledit_views-704d5b9767.css\" rel=\"stylesheet\"\/>\n<div class=\"htmledit_views atom-one-dark\" id=\"content_views\">\n<p>\n   \u4e0d\u8981\u76f2\u76ee\u4e00\u4e0b\u5b50\u5c31\u8ddf\u7740\u505a\u54e6\uff0c\u5148\u4ece\u5934\u5230\u5c3e\u770b\u4e00\u904d\u54e6\uff0c\u5408\u9002\u518d\u6765\uff01\u6216\u8005\u591a\u53c2\u8003\u4e00\u4e0b\u5f88\u591a\u5927\u4f6c\u7684\uff01\n  <\/p>\n<h3>\n  <\/h3>\n<h2>\n   \u524d\u8a00<br \/>\n  <\/h2>\n<p>\n   <strong><br \/>\n    Anaconda+PyCharm+NVIDIA\u663e\u5361\u9a71\u52a8+PyTorch\uff08\u542bGPU\u7248\uff09+CUDA+cuDNN<br \/>\n   <\/strong><br \/>\n   \u8fd9\u79cd\u7ec4\u5408\u5c31\u50cf\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u201c\u5f00\u53d1\u5de5\u5177\u7bb1\u201d\uff0c\u6bcf\u4e00\u4e2a\u90e8\u5206\u90fd\u6709\u5176\u72ec\u7279\u7684\u529f\u80fd\uff0c\u4f46\u5f53\u5b83\u4eec\u7ec4\u5408\u5728\u4e00\u8d77\u65f6\uff0c\u5b83\u4eec\u80fd\u591f\u534f\u540c\u5de5\u4f5c\uff0c\u4f7f\u6df1\u5ea6\u5b66\u4e60\u548c\u5176\u4ed6\u8ba1\u7b97\u5bc6\u96c6\u578b\u4efb\u52a1\u53d8\u5f97\u66f4\u52a0\u9ad8\u6548\u548c\u5bb9\u6613\u3002\n  <\/p>\n<p>\n   \u8fd9\u4e9b\u8f6f\u4ef6\u4e4b\u95f4\u7684\u534f\u540c\u5de5\u4f5c\u53ef\u4ee5\u89c6\u4e3a\u4e00\u4e2a\u9ad8\u6548\u7684\u201c<br \/>\n   <span style=\"color:#fe2c24\"><br \/>\n    \u6df1\u5ea6\u5b66\u4e60\u751f\u4ea7\u7ebf<br \/>\n   <\/span><br \/>\n   \u201d\uff0c\u6bcf\u4e2a\u73af\u8282\u90fd\u626e\u6f14\u7740\u4e0d\u53ef\u6216\u7f3a\u7684\u89d2\u8272\uff0c\u5171\u540c\u63a8\u52a8\u6df1\u5ea6\u5b66\u4e60\u4efb\u52a1\u7684\u9ad8\u6548\u5b8c\u6210\u3002\n  <\/p>\n<p>\n   \u9996\u5148\uff0c<br \/>\n   <strong><br \/>\n    Anaconda<br \/>\n   <\/strong><br \/>\n   \u4f5c\u4e3a\u8fd9\u4e2a\u751f\u4ea7\u7ebf\u7684\u201c<br \/>\n   <span style=\"color:#fe2c24\"><br \/>\n    \u57fa\u7840\u5e73\u53f0<br \/>\n   <\/span><br \/>\n   \u201d\uff0c\u4e3a\u6574\u4e2a\u73af\u5883\u63d0\u4f9b\u4e86\u7a33\u5b9a\u4e14\u5f3a\u5927\u7684Python\u652f\u6301\u3002\u5b83\u786e\u4fdd\u4e86\u6240\u6709\u9700\u8981\u7684Python\u5305\uff08\u5982NumPy\u3001Pandas\u7b49\uff09\u90fd\u80fd\u5f97\u5230\u59a5\u5584\u7ba1\u7406\uff0c\u5e76\u901a\u8fc7conda\u8fd9\u4e2a\u5305\u548c\u73af\u5883\u7ba1\u7406\u5668\uff0c\u5e2e\u52a9\u7528\u6237\u521b\u5efa\u548c\u5207\u6362\u4e0d\u540c\u7684\u5de5\u4f5c\u73af\u5883\uff0c\u786e\u4fdd\u4e0d\u540c\u9879\u76ee\u4e4b\u95f4\u7684\u4f9d\u8d56\u5173\u7cfb\u4e0d\u4f1a\u76f8\u4e92\u5e72\u6270\u3002\n  <\/p>\n<p>\n   \u63a5\u7740\uff0c<br \/>\n   <strong><br \/>\n    PyCharm<br \/>\n   <\/strong><br \/>\n   \u4f5c\u4e3a\u201c<br \/>\n   <span style=\"color:#fe2c24\"><br \/>\n    \u5f00\u53d1\u8f66\u95f4<br \/>\n   <\/span><br \/>\n   \u201d\uff0c\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u4ee3\u7801\u7f16\u8f91\u3001\u8c03\u8bd5\u548c\u6d4b\u8bd5\u529f\u80fd\u3002\u5f00\u53d1\u8005\u53ef\u4ee5\u5728\u8fd9\u4e2a\u8212\u9002\u7684\u754c\u9762\u4e2d\u7f16\u5199\u3001\u8c03\u8bd5\u548c\u4f18\u5316Python\u4ee3\u7801\uff0c\u786e\u4fdd\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7684\u903b\u8f91\u6b63\u786e\u548c\u6027\u80fd\u4f18\u5316\u3002\n  <\/p>\n<p>\n   \u800c<br \/>\n   <strong><br \/>\n    NVIDIA\u663e\u5361\u9a71\u52a8<br \/>\n   <\/strong><br \/>\n   \u5219\u662f\u8fd9\u4e2a\u751f\u4ea7\u7ebf\u7684\u201c<br \/>\n   <span style=\"color:#fe2c24\"><br \/>\n    \u52a8\u529b\u6e90\u6cc9<br \/>\n   <\/span><br \/>\n   \u201d\u3002\u5b83\u786e\u4fdd\u4e86\u663e\u5361\u80fd\u591f\u6b63\u5e38\u5de5\u4f5c\uff0c\u5e76\u4e0e\u64cd\u4f5c\u7cfb\u7edf\u53ca\u5176\u4ed6\u8f6f\u4ef6\u65e0\u7f1d\u5bf9\u63a5\u3002\u5bf9\u4e8e\u6df1\u5ea6\u5b66\u4e60\u4efb\u52a1\u6765\u8bf4\uff0c\u663e\u5361\u7684\u8ba1\u7b97\u80fd\u529b\u81f3\u5173\u91cd\u8981\uff0c\u800c\u663e\u5361\u9a71\u52a8\u5c31\u662f\u6fc0\u53d1\u8fd9\u79cd\u80fd\u529b\u7684\u5173\u952e\u3002\n  <\/p>\n<p>\n   <strong><br \/>\n    PyTorch\uff08\u542bGPU\u7248\uff09<br \/>\n   <\/strong><br \/>\n   \u5219\u662f\u8fd9\u4e2a\u751f\u4ea7\u7ebf\u7684\u201c<br \/>\n   <span style=\"color:#fe2c24\"><br \/>\n    \u6838\u5fc3\u751f\u4ea7\u7ebf<br \/>\n   <\/span><br \/>\n   \u201d\uff0c\u8d1f\u8d23\u6784\u5efa\u548c\u4f18\u5316\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u3002GPU\u7248\u672c\u7684PyTorch\u80fd\u591f\u5145\u5206\u5229\u7528\u663e\u5361\u7684\u5e76\u884c\u8ba1\u7b97\u80fd\u529b\uff0c\u663e\u8457\u52a0\u901f\u6a21\u578b\u7684\u8bad\u7ec3\u548c\u63a8\u7406\u8fc7\u7a0b\u3002\u5728\u8fd9\u91cc\uff0c\u6a21\u578b\u7684\u7ed3\u6784\u3001\u53c2\u6570\u4ee5\u53ca\u8bad\u7ec3\u8fc7\u7a0b\u90fd\u7531PyTorch\u7cbe\u5fc3\u7ba1\u7406\u3002\n  <\/p>\n<p>\n   <strong><br \/>\n    CUDA<br \/>\n   <\/strong><br \/>\n   \u548c<br \/>\n   <strong><br \/>\n    cuDNN<br \/>\n   <\/strong><br \/>\n   \u5219\u50cf\u662f\u751f\u4ea7\u7ebf\u4e0a\u7684\u201c<br \/>\n   <span style=\"color:#fe2c24\"><br \/>\n    \u9ad8\u7ea7\u6280\u5de5<br \/>\n   <\/span><br \/>\n   \u201d\u3002CUDA\u63d0\u4f9b\u4e86\u4e0e\u663e\u5361\u901a\u4fe1\u7684\u63a5\u53e3\uff0c\u4f7f\u5f97PyTorch\u80fd\u591f\u8c03\u7528\u663e\u5361\u8fdb\u884c\u8ba1\u7b97\u3002\u800ccuDNN\u5219\u9488\u5bf9\u6df1\u5ea6\u5b66\u4e60\u4e2d\u5e38\u89c1\u7684\u57fa\u672c\u64cd\u4f5c\u8fdb\u884c\u4e86\u4f18\u5316\uff0c\u4e3aPyTorch\u63d0\u4f9b\u4e86\u66f4\u52a0\u9ad8\u6548\u7684\u8ba1\u7b97\u5de5\u5177\u3002\u8fd9\u4e24\u8005\u7684\u534f\u540c\u5de5\u4f5c\uff0c\u4f7f\u5f97\u6df1\u5ea6\u5b66\u4e60\u4efb\u52a1\u80fd\u591f\u66f4\u5feb\u5730\u5b8c\u6210\u3002\n  <\/p>\n<p>\n   \u5728\u6574\u4e2a\u534f\u540c\u5de5\u4f5c\u7684\u8fc7\u7a0b\u4e2d\uff0c\u8fd9\u4e9b\u8f6f\u4ef6\u4e4b\u95f4\u7684\u6570\u636e\u6d41\u52a8\u662f\u65e0\u7f1d\u7684\u3002PyTorch\u6784\u5efa\u7684\u6a21\u578b\u53ef\u4ee5\u5728PyCharm\u4e2d\u8fdb\u884c\u8c03\u8bd5\u548c\u4f18\u5316\uff0c\u7136\u540e\u901a\u8fc7CUDA\u548ccuDNN\u5728\u663e\u5361\u4e0a\u8fdb\u884c\u9ad8\u6548\u7684\u8ba1\u7b97\u3002\u800cAnaconda\u5219\u786e\u4fdd\u4e86\u6574\u4e2a\u8fc7\u7a0b\u4e2dPython\u73af\u5883\u7684\u7a33\u5b9a\u548c\u4e00\u81f4\u3002\n  <\/p>\n<p>\n   \u603b\u7684\u6765\u8bf4\uff0c\u8fd9\u4e9b\u8f6f\u4ef6\u4e4b\u95f4\u7684\u534f\u540c\u5de5\u4f5c\u6784\u6210\u4e86\u4e00\u4e2a\u9ad8\u6548\u3001\u7a33\u5b9a\u7684\u6df1\u5ea6\u5b66\u4e60\u5f00\u53d1\u73af\u5883\uff0c\u4f7f\u5f97\u5f00\u53d1\u8005\u80fd\u591f\u66f4\u8f7b\u677e\u3001\u66f4\u9ad8\u6548\u5730\u8fdb\u884c\u6df1\u5ea6\u5b66\u4e60\u4efb\u52a1\u7684\u5f00\u53d1\u548c\u5b9e\u9a8c\u3002\n  <\/p>\n<h3>\n  <\/h3>\n<h2>\n   \u4e00\u3001\u5b89\u88c5Anaconda3\u5e76\u914d\u7f6e\u73af\u5883<br \/>\n  <\/h2>\n<h3>\n   1.\u8fd9\u662f\u6211\u5b89\u88c5Anaconda\u7684\u6559\u7a0b<br \/>\n  <\/h3>\n<p>\n   \u94fe\u63a5\uff1a<br \/>\n   <a href=\"https:\/\/blog.csdn.net\/2302_76846184\/article\/details\/137974636?spm=1001.2014.3001.5502\" title=\"Anaconda3 \u5b89\u88c5\u6559\u7a0b-CSDN\u535a\u5ba2\"><br \/>\n    Anaconda3 \u5b89\u88c5\u6559\u7a0b-CSDN\u535a\u5ba2<br \/>\n   <\/a>\n  <\/p>\n<h3>\n   2. \u8fd9\u91cc\u6709\u8be6\u7ec6\u5b89\u88c5\u73af\u5883\u4e3apython3.7\u7684\u6559\u7a0b\uff0c\u7528\u4e8e\u4f20\u7edf\u673a\u5668\u5b66\u4e60<br \/>\n  <\/h3>\n<p>\n   \u94fe\u63a5\uff1a<br \/>\n   <a href=\"https:\/\/blog.csdn.net\/2302_76846184\/article\/details\/138009420\" title=\"Anaconda3+Pycharm \u642d\u5efa\u6df1\u5ea6\u5b66\u4e60\u73af\u5883\uff1b\u5b89\u88c5\u4e0d\u540c\u6846\u67b6\uff1b\u914d\u7f6e\u4e24\u4e2a\u73af\u5883\uff1bAnaconda\u914d\u7f6e\u56fd\u5185\u955c\u50cf\u6e90-CSDN\u535a\u5ba2\"><br \/>\n    Anaconda3+Pycharm \u642d\u5efa\u6df1\u5ea6\u5b66\u4e60\u73af\u5883\uff1b\u5b89\u88c5\u4e0d\u540c\u6846\u67b6\uff1b\u914d\u7f6e\u4e24\u4e2a\u73af\u5883\uff1bAnaconda\u914d\u7f6e\u56fd\u5185\u955c\u50cf\u6e90-CSDN\u535a\u5ba2<br \/>\n   <\/a>\n  <\/p>\n<h3>\n   3. \u914d\u7f6epython3.8\u7248\u672c\u7684\u73af\u5883<br \/>\n  <\/h3>\n<p>\n   3. \u4f46\u76ee\u524d\uff0c\u9879\u76ee\u9700\u8981python3.8 \uff0c\u56e0\u4e3apython3.8\u7528\u4e8e\u627f\u8f7dpytorch\u7684\u7a33\u5b9a\u6027\u76f8\u5bf9\u4e8e\u5176\u5b83\u7248\u672c\u8f83\u4e3a\u826f\u597d\uff0c\u6240\u4ee5\u8fd9\u91cc\u9644\u4e0a\u6211\u5b89\u88c5python3.8\u73af\u5883\u7684\u8be6\u89e3\uff0c\u6211\u7684\u73af\u5883\u540d\u4e3ayolo\n  <\/p>\n<p>\n   3.1 \u9ed8\u8ba4\u5df2\u7ecf\u5b89\u88c5\u597dAnaconda\u540e\uff0c\u5f00\u59cb\u83dc\u5355\u627e\u5230\u201cAnaconda Prompt(Anaconda)\u201d\u6253\u5f00\u5b83\n  <\/p>\n<\/p>\n<p>\n   3.2 \u521b\u5efa\u65b0\u73af\u5883\uff0c\u8fd9\u91ccyolo\u662f\u6211\u7684\u73af\u5883\u540d\u5b57\uff0c\u53d6\u5565\u540d\u4efb\u610f\u54c8\u5404\u4f4d~\uff1b\n  <\/p>\n<pre><code class=\"language-python\">conda create --name yolo python=3.8<\/code><\/pre>\n<\/p>\n<p>\n   3.3 \u8f93\u5165\u201cy\u201d\u540e\uff0c\u56de\u8f66\u786e\u8ba4\uff1b\n  <\/p>\n<\/p>\n<p>\n   3.4 \u9a8c\u8bc1\u662f\u5426\u521b\u5efa\u6210\u529f\uff1b\n  <\/p>\n<pre><code class=\"language-python\">conda env list  # \u67e5\u770b\u6240\u6709\u73af\u5883<\/code><\/pre>\n<\/p>\n<p>\n   3.5 \u8f93\u5165\u4e0b\u9762\u6307\u4ee4\uff0c\u56de\u8f66\u786e\u8ba4\uff0c\u6fc0\u6d3b\u4f60\u7684\u73af\u5883\uff1b\n  <\/p>\n<pre><code class=\"language-python\">conda activate yolo  # \u6fc0\u6d3b\u73af\u5883\u4f60\u7684\u73af\u5883<\/code><\/pre>\n<\/p>\n<p>\n   3.6 \u63a5\u4e0b\u6765\u5c31\u662f\u6211\u4eec\u00a0PyTorch\u6846\u67b6\u7684\u5b89\u88c5\u4e86\uff0c\u4f46\u5982\u679c\u662f\u8981\u8ddf\u6211\u4e00\u6837\u8981\u8dd1\u6570\u636e\u96c6\u7684\uff0c\u90a3\u4e48~\uff0c\u9700\u8981\u5b89\u88c5GPU\u7248\u7684\uff0c\u90a3\u4e48~\uff0c\u5efa\u8bae\u6309\u7167\u6211\u4e0b\u9762\u7684\u6b65\u9aa4\u8d70\uff0c\u4e0d\u8981\u6025\u7740\u5b89\u88c5\u65b0\u7248\u7684\uff0c\u56e0\u4e3a\u6bcf\u4e2a\u9879\u76ee\u9002\u5e94\u7684\u7248\u672c\u4e0d\u4e00\u6837~\n  <\/p>\n<\/p>\n<h2>\n   \u4e8c\u3001\u5b89\u88c5Pycharm<br \/>\n  <\/h2>\n<p>\n   \u8fd9\u662f\u6211\u5b89\u88c5\u7684\u6559\u7a0b\u94fe\u63a5\uff1a<br \/>\n   <a href=\"https:\/\/blog.csdn.net\/2302_76846184\/article\/details\/137996008?spm=1001.2014.3001.5502\" title=\"Pycharm\u5b89\u88c5\u6559\u7a0b-CSDN\u535a\u5ba2\"><br \/>\n    Pycharm\u5b89\u88c5\u6559\u7a0b-CSDN\u535a\u5ba2<br \/>\n   <\/a>\n  <\/p>\n<h2>\n   \u4e09\u3001\u5b89\u88c5Nvidia\u663e\u5361\u9a71\u52a8<br \/>\n  <\/h2>\n<p>\n   \u53ef\u4ee5\u5148\u67e5\u770b\u81ea\u5df1\u7535\u8111\u663e\u5361\u9a71\u52a8\u7684\u7248\u672c\uff0c\u5728\u8fd9\u91cc\u6211\u5b89\u88c5\u9a71\u52a8\u4e4b\u524d\uff0c\u6211\u5148\u67e5\u770b\u6211\u7535\u8111\u7684\u9a71\u52a8\uff08\u5728\u4e0b\u9762<br \/>\n   <strong><br \/>\n    2.3.1<br \/>\n   <\/strong><br \/>\n   \u5c31\u662f\uff09\uff0c\u4e0d\u4f1a\u67e5\u770b\u7684\u670b\u53cb\u53ef\u4ee5\u770b\u4e0b\u9762\u7684\u300a2. \u9a8c\u8bc1\u662f\u5426\u6210\u529f\u5b89\u88c5\u597d\u9a71\u52a8\u300b\u3002\n  <\/p>\n<p>\n   <strong><br \/>\n    \u5728\u8fd9\u91cc\u5148\u4ecb\u7ecd\u4e00\u4e0b<br \/>\n   <\/strong>\n  <\/p>\n<p>\n   \u5b89\u88c5<br \/>\n   <strong><br \/>\n    \u65b0\u7248<br \/>\n   <\/strong><br \/>\n   NVIDIA\u663e\u5361\u9a71\u52a8\u5bf9\u4e8e<br \/>\n   <span style=\"color:#fe2c24\"><br \/>\n    \u8fd0\u884c\u6570\u636e\u96c6<br \/>\n   <\/span><br \/>\n   \u6765\u8bf4\u662f<br \/>\n   <span style=\"color:#fe2c24\"><br \/>\n    \u975e\u5e38\u91cd\u8981\u7684<br \/>\n   <\/span><br \/>\n   \uff0c\u539f\u56e0\u4e3b\u8981\u6709\u4ee5\u4e0b\u51e0\u70b9\uff1a\n  <\/p>\n<p>\n   \u9996\u5148\uff0c\u663e\u5361\u9a71\u52a8\u662f\u663e\u5361\u4e0e\u8ba1\u7b97\u673a\u64cd\u4f5c\u7cfb\u7edf\u4e4b\u95f4\u7684\u63a5\u53e3\uff0c\u5b83\u4f7f\u5f97\u663e\u5361\u80fd\u591f\u6b63\u5e38\u5de5\u4f5c\u5e76\u53d1\u6325\u5176\u5e94\u6709\u7684\u6027\u80fd\u3002\u5f53\u4f60\u8fd0\u884c\u4e00\u4e2a\u6570\u636e\u96c6\u65f6\uff0c\u5c24\u5176\u662f\u6d89\u53ca\u56fe\u5f62\u5904\u7406\u6216\u6df1\u5ea6\u5b66\u4e60\u7b49\u8ba1\u7b97\u5bc6\u96c6\u578b\u4efb\u52a1\u65f6\uff0c\u663e\u5361\u7684\u6027\u80fd\u81f3\u5173\u91cd\u8981\u3002\u56e0\u6b64\uff0c\u5b89\u88c5\u6700\u65b0\u7248\u7684\u663e\u5361\u9a71\u52a8\u53ef\u4ee5\u786e\u4fdd\u663e\u5361\u4e0e\u64cd\u4f5c\u7cfb\u7edf\u4e4b\u95f4\u7684\u517c\u5bb9\u6027\uff0c\u4ece\u800c\u63d0\u4f9b\u6700\u4f73\u7684\u6027\u80fd\u548c\u7a33\u5b9a\u6027\u3002\n  <\/p>\n<p>\n   \u5176\u6b21\uff0c\u65b0\u7248\u663e\u5361\u9a71\u52a8\u901a\u5e38\u4f1a\u4fee\u590d\u65e7\u7248\u672c\u4e2d\u7684\u4e00\u4e9b\u9519\u8bef\u548c\u6f0f\u6d1e\uff0c\u63d0\u5347\u663e\u5361\u7684\u6027\u80fd\u548c\u7a33\u5b9a\u6027\u3002\u8fd9\u610f\u5473\u7740\u5728\u8fd0\u884c\u6570\u636e\u96c6\u65f6\uff0c\u65b0\u7248\u9a71\u52a8\u80fd\u591f\u66f4\u6709\u6548\u5730\u5229\u7528\u663e\u5361\u7684\u8d44\u6e90\uff0c\u51cf\u5c11\u51fa\u9519\u7684\u53ef\u80fd\u6027\uff0c\u5e76\u63d0\u5347\u8ba1\u7b97\u901f\u5ea6\u3002\n  <\/p>\n<p>\n   \u6b64\u5916\uff0c\u968f\u7740\u6280\u672f\u7684\u53d1\u5c55\u548c\u65b0\u7684\u7b97\u6cd5\u3001\u6280\u672f\u7684\u51fa\u73b0\uff0c\u65b0\u7248\u663e\u5361\u9a71\u52a8\u8fd8\u53ef\u80fd\u652f\u6301\u65b0\u7684\u7279\u6027\u548c\u529f\u80fd\u3002\u8fd9\u4e9b\u65b0\u7279\u6027\u53ef\u80fd\u6709\u52a9\u4e8e\u66f4\u597d\u5730\u5904\u7406\u6570\u636e\u96c6\uff0c\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\uff0c\u6216\u8005\u4e3a\u5f00\u53d1\u8005\u63d0\u4f9b\u66f4\u591a\u7684\u7075\u6d3b\u6027\u548c\u9009\u62e9\u3002\n  <\/p>\n<p>\n   \u7efc\u4e0a\u6240\u8ff0\uff0c\u5b89\u88c5\u65b0\u7248NVIDIA\u663e\u5361\u9a71\u52a8\u53ef\u4ee5\u786e\u4fdd\u663e\u5361\u4e0e\u64cd\u4f5c\u7cfb\u7edf\u7684\u517c\u5bb9\u6027\uff0c\u63d0\u5347\u663e\u5361\u7684\u6027\u80fd\u548c\u7a33\u5b9a\u6027\uff0c\u5e76\u53ef\u80fd\u652f\u6301\u65b0\u7684\u7279\u6027\u548c\u529f\u80fd\uff0c\u4ece\u800c\u6709\u52a9\u4e8e\u66f4\u597d\u5730\u8fd0\u884c\u548c\u5904\u7406\u6570\u636e\u96c6\u3002\n  <\/p>\n<\/p>\n<h3>\n   1. \u5b89\u88c5\u5bf9\u5e94\u81ea\u5df1\u7535\u8111\u7684\u9a71\u52a8<br \/>\n  <\/h3>\n<p>\n   1.1 \u5b98\u65b9\u94fe\u63a5\uff1a<br \/>\n   <a href=\"https:\/\/www.nvidia.cn\/Download\/index.aspx?lang=cn\" rel=\"nofollow\" title=\"\u5b98\u65b9\u9a71\u52a8 | NVIDIA\"><br \/>\n    \u5b98\u65b9\u9a71\u52a8 | NVIDIA<br \/>\n   <\/a><br \/>\n   \uff1b\n  <\/p>\n<\/p>\n<p>\n   1.2\u00a0\u9996\u5148\u8981\u5728\u8bbe\u5907\u7ba1\u7406\u5668\u4e2d\u67e5\u770b\u4f60\u7684\u663e\u5361\u578b\u53f7\uff1b\n  <\/p>\n<\/p>\n<p>\n   1.3 \u5728\u8bbe\u522b\u7ba1\u7406\u5668\u4e2d\u53ef\u4ee5\u770b\u5230\u6211\u4eec\u8bbe\u5907\u7684\u663e\u5361\u578b\u53f7\u5566^_^\n  <\/p>\n<\/p>\n<p>\n   1.4\u00a0\u5728\u5b98\u7f51\u4e0a\u627e\u81ea\u5df1\u7684\u663e\u5361\u578b\u53f7\u7cfb\u5217\uff1b\n  <\/p>\n<p>\n   <strong><br \/>\n   <\/strong><br \/>\n   \u6839\u636e\u81ea\u5df1\u7684\u7535\u8111\u7684\u914d\u7f6e\u53bb\u9009\u62e9\u9a71\u52a8\u3002\u8fd9\u91ccNotebooks\u662f\u7b14\u8bb0\u672c\u7684\u610f\u601d\uff0c\u6240\u4ee5\u5982\u679c\u4f60\u662f\u7b14\u8bb0\u672c\u7535\u8111\uff0c\u90a3\u4e48\u4ea7\u54c1\u7cfb\u5217\u90a3\u4e2a\u9009\u9879\u5c31\u8981\u9009\u62e9\uff08Notebooks\uff09\u7684\u3002\u8fd8\u6709\u90a3\u4e2a\u4e0b\u8f7d\u7c7b\u578b\u6709\u4e24\u79cd\u4e00\u4e2a\u662fStudio\u7248\u672c\uff0c\u4e00\u4e2a\u662fGame Ready\u7248\u672c\u3002\u5176\u5b9e\u4e24\u4e2a\u7248\u672c\u90fd\u5dee\u4e0d\u591a\uff0c\u4e00\u4e2a\u662f\u504f\u529e\u516c\u7528\uff0c\u4e00\u4e2a\u662f\u504f\u6e38\u620f\u5a31\u4e50\u3002\u6309\u5982\u4e0b\u64cd\u4f5c\u5c06\u9a71\u52a8\u4e0b\u8f7d\u4e0b\u6765\u3002\n  <\/p>\n<\/p>\n<p>\n   1.5\u00a0\u9a71\u52a8\u7a0b\u5e8f\u4e0b\u8f7d\uff0c\u70b9\u51fb\u4e0b\u8f7d\u5373\u53ef\uff1b\n  <\/p>\n<\/p>\n<\/p>\n<p>\n   1.6 \u9ed8\u8ba4\u5b89\u88c5\u4f4d\u7f6e\u5c31\u597d\uff0c\u4e0d\u5efa\u8bae\u5b89\u88c5\u5230\u522b\u7684\u76d8\u91cc\uff1b\n  <\/p>\n<\/p>\n<p>\n   1.7 \u6267\u884c\u4e0b\u4e00\u6b65\u5427\uff1b\n  <\/p>\n<\/p>\n<p>\n   1.8 \u7b2c\u4e00\u6b21\u5b89\u88c5\uff0c\u9ed8\u8ba4\u7cbe\u7b80\u5427\uff1b\n  <\/p>\n<\/p>\n<p>\n   1.9 \u5728\u8fd9\u91cc\u6211\u5c31\u4e0d\u521b\u5efa\u684c\u9762\u5feb\u6377\u65b9\u5f0f\u4e86\uff0c\u6309\u4e2a\u4eba\u6240\u9700\u52fe\u9009\u5427\uff1b\n  <\/p>\n<\/p>\n<h3>\n  <\/h3>\n<h3>\n   2. \u9a8c\u8bc1\u662f\u5426\u6210\u529f\u5b89\u88c5\u597d\u9a71\u52a8<br \/>\n  <\/h3>\n<p>\n   2.1\u00a0\u4f7f\u7528\u5feb\u6377\u952ewindows+r\uff0c\u8f93\u5165cmd\n  <\/p>\n<\/p>\n<p>\n   2.2\u00a0\u8f93\u5165\u4ee5\u4e0b\u4ee3\u7801\n  <\/p>\n<pre><code class=\"language-python\">nvidia-smi<\/code><\/pre>\n<\/p>\n<p>\n   2.3 \u51fa\u73b0\u4e0b\u9762\u7684\u4fe1\u606f\u8bf4\u660e\u5b89\u88c5\u6210\u529f\n  <\/p>\n<p>\n   \u6765\u6765\u6765\uff0c\u8ba9\u6211\u4eec\u505a\u4e2a\u5bf9\u6bd4\n  <\/p>\n<p>\n   2.3.1 \u8fd9\u662f\u6211\u7535\u8111<br \/>\n   <strong><br \/>\n    \u6ca1\u5b89\u88c5<br \/>\n   <\/strong><br \/>\n   \u66f4\u65b0\u7684\u9a71\u52a8\u914d\u7f6e\u56fe\u7247\n  <\/p>\n<\/p>\n<p>\n   2.3.2 \u8fd9\u662f<br \/>\n   <strong><br \/>\n    \u5b89\u88c5\u5b8c<br \/>\n   <\/strong><br \/>\n   \u4e0a\u9762\u7684\u9a71\u52a8\u540e\u7684\u56fe\u7247\n  <\/p>\n<p>\n   \u5982\u4e0b\u56fe\u7684\u4fe1\u606f\u56fe\uff0c\u53ef\u4ee5\u770b\u5230\u9a71\u52a8\u7684\u7248\u672c\u662f552.22\uff1b\u6700\u9ad8\u652f\u6301\u7684CUDA\u7248\u672c\u662f12.4\u7248\u672c\u3002\u77e5\u9053\u4e0b\u9762\u7684\u4fe1\u606f\u5c31\u53ef\u4ee5\u77e5\u9053\u6211\u4eec\u7535\u8111\u6700\u9ad8\u9002\u914d\u7a0b\u5ea6\uff0c\u4f46\u8981<br \/>\n   <strong><br \/>\n    <span style=\"color:#fe2c24\"><br \/>\n     \u6ce8\u610f<br \/>\n    <\/span><br \/>\n   <\/strong><br \/>\n   \u4f4e\u7248\u672c\u7684Pytorch\u662f\u5426\u5411\u4e0a\u652f\u6301\u66f4\u9ad8\u7248\u672c\u7684CUDA\uff0c\u4e5f\u5c31\u662f\u8bf4\u8981\u770b\u6211\u4eec\u9700\u8981\u7684\u9879\u76ee\u652f\u6301\u7684Pytorch\u7248\u672c\u518d\u6765\u5b89\u88c5\u5bf9\u5e94\u7684CUDA\uff0c\u4f8b\u5982\u5728\u4e0b\u9762\u7684\u987a\u5e8f\u91cc 3.\u00a0YOLO\u91cc\u652f\u6301\u7684Pytorch\u7248\u672c\uff01\uff01\uff01\uff01\n  <\/p>\n<\/p>\n<h3>\n   3.\u00a0YOLO\u91cc\u652f\u6301\u7684Pytorch\u7248\u672c<br \/>\n  <\/h3>\n<p>\n   3.1\u00a0 YOLOv5\u652f\u6301\u7684Pytorch\u7248\u672c\n  <\/p>\n<p>\n   \u8fd9\u91cc\u5148\u6d45\u6d45\u770b\u4e00\u4e0b\uff0c\u540e\u9762\u6709\u4e13\u95e8\u7684\u8bb2\u89e3\u5982\u4f55\u4e0b\u8f7dgithub\u9879\u76ee\u5373YOLOv5\u9879\u76ee\u514b\u9686\n  <\/p>\n<\/p>\n<h2>\n   \u56db\u3001Pytorch\u4e0eCUDA\u7248\u672c\u5bf9\u5e94\u5173\u7cfb\u6c47\u603b<br \/>\n  <\/h2>\n<\/p>\n<h3>\n   1.\u00a0pytorch\u4e0ecuda\u7248\u672c\u5173\u7cfb<br \/>\n  <\/h3>\n<\/p>\n<h3>\n   2.\u00a0cuda \u4e0e cudnn\u5173\u7cfb<br \/>\n  <\/h3>\n<\/p>\n<h3>\n   3.\u00a0pytorch \u4e0e python\u5173\u7cfb<br \/>\n  <\/h3>\n<\/p>\n<h2>\n   \u4e94\u3001\u5b89\u88c5CUDA<br \/>\n  <\/h2>\n<h3>\n   1.\u00a0\u4ecenvidia\u5b98\u7f51\u4e0b\u8f7dcuda11.1<br \/>\n  <\/h3>\n<p>\n   \u8fd9\u91cc\u6211\u653e\u767e\u5ea6\u7f51\u76d8\u94fe\u63a5\u5b89\u88c5\u5305\uff0c\u6709\u9700\u8981\u7684\u670b\u53cb\u81ea\u5df1\u53d6\u54e6^_^\n  <\/p>\n<p>\n   \u94fe\u63a5\uff1ahttps:\/\/pan.baidu.com\/s\/1-6Hbfi-pC_f8pSevX65xIQ?pwd=1234<br \/>\n   <br \/>\n   \u63d0\u53d6\u7801\uff1a1234<br \/>\n   <br \/>\n   &#8211;\u6765\u81ea\u767e\u5ea6\u7f51\u76d8\u8d85\u7ea7\u4f1a\u5458V5\u7684\u5206\u4eab\n  <\/p>\n<p>\n   1.0 \u8fd9\u662f\u5b98\u7f51\u94fe\u63a5\uff1a<br \/>\n   <a href=\"https:\/\/developer.nvidia.com\/cuda-toolkit-archive\" rel=\"nofollow\" title=\"CUDA Toolkit Archive | NVIDIA Developer\"><br \/>\n    CUDA Toolkit Archive | NVIDIA Developer<br \/>\n   <\/a>\n  <\/p>\n<\/p>\n<p>\n   1.1\u00a0\u6309\u7167\u4e0b\u9762\u7ea2\u8272\u6846\u6846\u4f9d\u6b21\u70b9\u51fb\uff0c\u6700\u540e\u70b9\u51fb\u4e0b\u8f7d\uff1b\n  <\/p>\n<\/p>\n<p>\n   1.2 \u4e0b\u8f7d\u5b8c\u540e\uff0c\u53cc\u51fb\u8fd0\u884c\uff0c\u6700\u597d\u9ed8\u8ba4\u5b89\u88c5\u4f4d\u7f6e\uff1b\n  <\/p>\n<\/p>\n<p>\n   1.3 \u70b9\u51fb\u201c\u7ee7\u7eed\u201d\uff1b\n  <\/p>\n<\/p>\n<p>\n   1.4 \u8fd8\u662f\u201c\u540c\u610f\u201d\uff1b\n  <\/p>\n<\/p>\n<p>\n   1.5 \u8fd9\u4e00\u6b65\u9009\u62e9\u201c\u81ea\u5b9a\u4e49\u201d\uff0c\u518d\u70b9\u51fb\u201c\u4e0b\u4e00\u6b65\u201d\uff1b\n  <\/p>\n<\/p>\n<p>\n   1.6 \u53d6\u6d88\u52fe\u9009\u201cVisual Studio Integration\u201d\u540e\uff0c\u70b9\u51fb\u201c\u4e0b\u4e00\u6b65\u201d\uff1b\n  <\/p>\n<\/p>\n<p>\n   1.7 \u70b9\u51fb\u201c\u4e0b\u4e00\u6b65\u201d\uff1b\n  <\/p>\n<\/p>\n<p>\n   1.8 \u70b9\u51fb \u201c\u4e0b\u4e00\u6b65\u201d\n  <\/p>\n<\/p>\n<p>\n   1.9 \u70b9\u51fb\u201c\u5173\u95ed\u201d\uff0c \u5c31\u5b89\u88c5\u5b8c\u6210\u4e86\uff01\n  <\/p>\n<\/p>\n<h2>\n   \u516d\u3001\u5b89\u88c5cuDNN<br \/>\n  <\/h2>\n<h3>\n   1.\u00a0\u4ecenvidia\u5b98\u7f51\u4e0b\u8f7dcuDNN v8.0.4<br \/>\n  <\/h3>\n<p>\n   \u8fd9\u91cc\u6211\u653e\u767e\u5ea6\u7f51\u76d8\u94fe\u63a5\u5b89\u88c5\u5305\uff0c\u6709\u9700\u8981\u7684\u670b\u53cb\u81ea\u5df1\u53d6\u54e6^_^\n  <\/p>\n<p>\n   \u94fe\u63a5\uff1ahttps:\/\/pan.baidu.com\/s\/1bnBmgncp9Ha659bT9ThaxA?pwd=1234<br \/>\n   <br \/>\n   \u63d0\u53d6\u7801\uff1a1234<br \/>\n   <br \/>\n   &#8211;\u6765\u81ea\u767e\u5ea6\u7f51\u76d8\u8d85\u7ea7\u4f1a\u5458V5\u7684\u5206\u4eab\n  <\/p>\n<p>\n   1.1 \u8fd9\u91cc\u662f\u5b98\u7f51\u5730\u5740\uff1a<br \/>\n   <a href=\"https:\/\/developer.nvidia.com\/rdp\/cudnn-archive\" rel=\"nofollow\" title=\"cuDNN Archive | NVIDIA Developer\"><br \/>\n    cuDNN Archive | NVIDIA Developer<br \/>\n   <\/a>\n  <\/p>\n<\/p>\n<p>\n   1.2\u00a0\u70b9\u51fb\u5b83\u4e0b\u8f7d\u662f\u4e00\u4e2a\u538b\u7f29\u5305\uff0c\u5982\u679c\u7b2c\u4e00\u6b21\u4e0b\u8f7d\u7684\u670b\u53cb\u9700\u8981\u8f93\u5165\u90ae\u7bb1\u767b\u5f55\uff0c\u767b\u5f55\u5373\u53ef\uff0c\u767b\u5f55\u540e\u8fd8\u662f\u4e0b\u8f7d\u4e0d\u4e86\uff0c\u4e0d\u8981\u614c~\uff0c\u518d\u91cd\u65b0\u8fdb\u4e00\u4e0b\u7f51\u7ad9\uff0c\u518d\u70b9\u51fb\u4e0b\u8f7d\u5373\u53ef\uff01\n  <\/p>\n<\/p>\n<h3>\n  <\/h3>\n<h3>\n   2. \u7b2c\u4e00\u6b65\u5148\u89e3\u5f00\u538b\u7f29\u5305\uff0c\u518d\u66ff\u6362CUDA\u91cc\u7684\u6587\u4ef6<br \/>\n  <\/h3>\n<p>\n   2.1 C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.1\uff0c\u8fd9\u662f\u6211\u7684\u9ed8\u8ba4CUDA\u5b89\u88c5\u4f4d\u7f6e\u8def\u5f84\u3002\u5982\u679c\u4e5f\u662f\u9ed8\u8ba4\u5b89\u88c5\uff0c\u90a3\u4e48\u6211\u4eec\u7684\u4f4d\u7f6e\u5b58\u653e\u90fd\u5dee\u4e0d\u591a\uff0c\u53ef\u4ee5\u6309\u6211\u7684\u8def\u5f84\u627e\u4e00\u4e0b\u6587\u4ef6\u5939\u3002\n  <\/p>\n<\/p>\n<p>\n   2.2\u00a0\u52fe\u9009\u4e0b\u9762\u7684\u201c\u4e3a\u6240\u6709\u5f53\u524d\u9879\u76ee\u6267\u884c\u6b64\u64cd\u4f5c\u201d\uff0c\u518d\u70b9\u51fb\u201c\u7ee7\u7eed\u201d\uff0c\u5373\u53ef\uff1b\n  <\/p>\n<\/p>\n<h3>\n   3. \u914d\u7f6e\u7cfb\u7edf\u73af\u5883\u53d8\u91cf<br \/>\n  <\/h3>\n<p>\n   3.1 \u5728\u684c\u9762\u201c\u6b64\u7535\u8111\u201d\uff0c\u9f20\u6807\u53f3\u952e\uff0c\u9009\u62e9\u201c\u5c5e\u6027\u201d\uff1b\n  <\/p>\n<\/p>\n<p>\n   3.2 \u6253\u5f00\u540e\uff0c\u9f20\u6807\u6eda\u8f6e\u76f4\u63a5\u5411\u4e0b\u5212\u5230\u5e95\uff0c\u70b9\u51fb\u201c\u9ad8\u7ea7\u7cfb\u7edf\u8bbe\u7f6e\u201d\uff1b\n  <\/p>\n<\/p>\n<p>\n   3.3 \u9009\u62e9\u201c\u73af\u5883\u53d8\u91cf\u201d\uff0c \u5c31\u53ef\u4ee5\u5728\u7cfb\u7edf\u53d8\u91cf\u90a3\u91cc\u770b\u5230\u6211\u4eec\u5b89\u88c5\u597d\u7684CUDA\u4e86\uff1b\n  <\/p>\n<\/p>\n<p>\n   3.4 \u5728\u7cfb\u7edf\u73af\u5883\u53d8\u91cf\u4e0b\u9762\u9f20\u6807\u4e0b\u6ed1\u627e\u5230\u201cPath\u201d\uff0c\u53cc\u51fb\u6253\u5f00\uff1b\n  <\/p>\n<p>\n   \u8bb0\u4f4f\u81ea\u5df1\u7684CUDA\u5b58\u653e\u4f4d\u7f6e\uff1aC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.1\n  <\/p>\n<\/p>\n<p>\n   3.5 \u70b9\u51fb\u201c\u65b0\u5efa\u201d\uff0c\u6dfb\u52a0\u73af\u5883\u53d8\u91cf\uff0c\u4f9d\u6b21\u65b0\u5efa\u4e94\u6b21\uff0c\u6dfb\u52a0\u4e94\u6b21\uff1b\n  <\/p>\n<pre><code class=\"language-python\">C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.1\\lib\\x64\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.1\\include\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.1\\extras\\CUPTI\\lib64\nC:\\ProgramData\\NVIDIA Corporation\\CUDA Samples\\v11.1\\bin\\win64\nC:\\ProgramData\\NVIDIA Corporation\\CUDA Samples\\v11.1\\common\\lib\\x64<\/code><\/pre>\n<\/p>\n<p>\n   3.6 \u4e0b\u56fe\u662f\u6dfb\u52a0\u5b8c\u540e\u7684\u6837\u5b50\uff0c\u70b9\u51fb\u201c\u786e\u5b9a\u201d\uff1b\n  <\/p>\n<p>\n   \u5982\u679c\u51fa\u73b0\u3010Path\u73af\u5883\u53d8\u91cf\u201c\u6b64\u73af\u5883\u53d8\u91cf\u592a\u5927, \u6b64\u5bf9\u8bdd\u6846\u5141\u8bb8\u5c06\u503c\u8bbe\u7f6e\u4e3a\u6700\u957f2047\u4e2a\u5b57\u7b26\u201d\u95ee\u9898\u3011\u90a3\u4e48\u4e5f\u53ef\u4ee5\u89e3\u51b3\uff0c\u76f4\u63a5\u5728\u6ce8\u518c\u8868\u4fee\u6539\uff0c\u4e0d\u4f1a\u7684\u53ef\u4ee5\u53c2\u8003\u4e00\u4e0b\u6211\u63a8\u8350\u7684\n  <\/p>\n<p>\n   \u94fe\u63a5\uff1a<br \/>\n   <a href=\"https:\/\/developer.aliyun.com\/article\/1371210\" rel=\"nofollow\" title=\"\u6b64\u73af\u5883\u53d8\u91cf\u592a\u5927, \u6b64\u5bf9\u8bdd\u6846\u5141\u8bb8\u5c06\u503c\u8bbe\u7f6e\u4e3a\u6700\u957f2047\u4e2a\u5b57\u7b26\uff08\u4e00\u62db\u5e2e\u4f60\u89e3\u51b3\uff0c\u4eb2\u6d4b\u6709\u6548\uff09-\u963f\u91cc\u4e91\u5f00\u53d1\u8005\u793e\u533a (aliyun.com)\"><br \/>\n    \u6b64\u73af\u5883\u53d8\u91cf\u592a\u5927, \u6b64\u5bf9\u8bdd\u6846\u5141\u8bb8\u5c06\u503c\u8bbe\u7f6e\u4e3a\u6700\u957f2047\u4e2a\u5b57\u7b26\uff08\u4e00\u62db\u5e2e\u4f60\u89e3\u51b3\uff0c\u4eb2\u6d4b\u6709\u6548\uff09-\u963f\u91cc\u4e91\u5f00\u53d1\u8005\u793e\u533a (aliyun.com)<br \/>\n   <\/a>\n  <\/p>\n<\/p>\n<h3>\n  <\/h3>\n<h3>\n   4. \u91cd\u542f\u7535\u8111\uff0c\u4f7f\u73af\u5883\u53d8\u91cf\u914d\u7f6e\u751f\u6548\uff01<br \/>\n  <\/h3>\n<\/p>\n<h3>\n   5. \u9a8c\u8bc1\u662f\u5426\u5b89\u88c5\u6210\u529fCUDA\u548ccuDNN<br \/>\n  <\/h3>\n<p>\n   5.1 \u4f7f\u7528\u7cfb\u7edf\u5feb\u6377\u952eWindows+r\uff0c\u8f93\u5165cmd\uff0c\u8f93\u5165\u5207\u6362\u8def\u5f84\uff1acd C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.1\\extras\\demo_suite\n  <\/p>\n<pre><code class=\"language-python\"># \u5207\u6362\u8def\u5f84\ncd C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.1\\extras\\demo_suite\n\n# \u4f9d\u6b21\u8f93\u5165\nbandwidthTest.exe\n\ndeviceQuery.exe<\/code><\/pre>\n<p>\n   5.2 \u518d\u4f9d\u6b21\u8f93\u5165bandwidthTest.exe\u3001deviceQuery.exe\uff0c\u6bcf\u6b21\u90fd\u51fa\u73b0\u2018Pass\u2019\u5219\u6210\u529f\u3002\n  <\/p>\n<\/p>\n<h2>\n   \u4e03\u3001\u5b89\u88c5Pytorch\uff08GPU\u7248\u672c\uff09<br \/>\n  <\/h2>\n<h3>\n   1. \u5b98\u7f51\u67e5\u8be2<br \/>\n  <\/h3>\n<p>\n   \u627e\u5230\u5bf9\u5e94\u81ea\u5df1\u9700\u8981\u7684\u7248\u672c\n  <\/p>\n<p>\n   \u7f51\u5740\uff1a<br \/>\n   <a href=\"https:\/\/pytorch.org\/get-started\/previous-versions\/\" rel=\"nofollow\" title=\"Previous PyTorch Versions | PyTorch\"><br \/>\n    Previous PyTorch Versions | PyTorch<br \/>\n   <\/a>\n  <\/p>\n<\/p>\n<h3>\n   2. \u7531\u4e8e\u9879\u76ee\u9700\u8981\uff0c\u6211\u5b89\u88c5\u7684\u662ftorch\u00a01.8\u7248\u672c<br \/>\n  <\/h3>\n<pre><code class=\"language-python\">pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https:\/\/download.pytorch.org\/whl\/torch_stable.html<\/code><\/pre>\n<p>\n   2.1 \u6253\u5f00\u201cAnaconda Prompt (Anaconda3)\u201d\uff0c\u9996\u5148\u5207\u6362\u4e0a\u9762\u6211\u4eec\u521b\u5efa\u597d\u7684\u73af\u5883\uff0c\u5728\u8f93\u5165\u6211\u4eec\u4e0a\u9762\u67e5\u8be2\u7684\uff1b\n  <\/p>\n<\/p>\n<p>\n   2.2 \u8fd9\u662f\u5b89\u88c5\u597d\u4e4b\u540e\u7684\u6a21\u6837~\n  <\/p>\n<\/p>\n<h2>\n   \u516b\u3001\u5728\u00a0pycharm\u9a8c\u8bc1\u914d\u7f6e\u662f\u5426\u6210\u529f<br \/>\n  <\/h2>\n<p>\n   \u6211\u6bd4\u8f83\u559c\u6b22\u8dd1\u4ee3\u7801\u9a8c\u8bc1\uff0c\u6240\u4ee5\u53ef\u4ee5\u76f4\u63a5\u5728pycharm\u91cc\u9762\u9a8c\u8bc1\uff08\u63d0\u793a\uff1a\u4e0a\u9762\u6709\u6211\u5b89\u88c5pycharm\u7684\u6559\u7a0b\uff09\n  <\/p>\n<h3>\n   1. \u65b0\u5efa\u6587\u4ef6\u5939\uff0c\u9f20\u6807\u53f3\u952e\u7528\u201cpycharm\u201d\u6253\u5f00<br \/>\n  <\/h3>\n<p>\n   \u6216\u8005\u76f4\u63a5\u5728pycharm\u65b0\u5efa\u9879\u76ee\u90fd\u53ef\u4ee5\uff1b\n  <\/p>\n<\/p>\n<h3>\n   2. \u6253\u5f00\u540e\uff0c\u9996\u5148\u5728pycharm\u91cc\u914d\u7f6e\u6211\u4eec\u7684\u6df1\u5ea6\u5b66\u4e60\u73af\u5883<br \/>\n  <\/h3>\n<p>\n   2.1 \u201csetting\u201d\n  <\/p>\n<\/p>\n<p>\n   2.2\u00a0\u6309\u7167\u4e0b\u9762\u7684\u6b65\u9aa4\uff0c\u5728\u7b2c\u4e09\u6b65\u90a3\u91cc\u70b9\u51fb\u5c0f\u9f7f\u8f6e\u5c31\u51fa\u73b0\u201cAdd\u201d\u5566 ^_^\n  <\/p>\n<\/p>\n<p>\n   2.3\u00a0\u7d27\u8ddf\u4e0b\u9762\u6b65\u9aa4\u8d70\u54e6\uff1b\n  <\/p>\n<\/p>\n<p>\n   2.4\u00a0\u5148\u70b9\u51fb\u201capply\u201d \uff0c\u518d\u201c\u786e\u5b9a\u201d\uff0c\u8fd9\u6837\u6211\u4eec\u5c31\u914d\u7f6e\u540e\u6211\u4eec\u9700\u8981\u7684\u73af\u5883\u5566\n  <\/p>\n<\/p>\n<p>\n   2.5 \u521b\u5efa\u4e00\u4e2apython\u6587\u4ef6\uff1b\n  <\/p>\n<\/p>\n<p>\n   2.6 \u4efb\u610f\u53d6\u540d~\n  <\/p>\n<\/p>\n<h3>\n   3. \u8f93\u5165\u4ee3\u7801<br \/>\n  <\/h3>\n<pre><code class=\"language-python\"># \u5bfc\u5165PyTorch\u5e93\nimport torch\n\n# \u6253\u5370\u53ef\u7528\u7684GPU\u8bbe\u5907\u6570\u91cf\nprint(torch.cuda.device_count())\n\n# \u6253\u5370\u662f\u5426\u53ef\u4ee5\u4f7f\u7528CUDA\uff0c\u5373\u662f\u5426\u53ef\u4ee5\u5728GPU\u4e0a\u8fd0\u884c\u8ba1\u7b97\nprint(torch.cuda.is_available())\n\n# \u6253\u5370torch\u7684\u7248\u672c\nprint(torch.__version__)\n\n# \u6253\u5370\u662f\u5426\u53ef\u4ee5\u4f7f\u7528cuDNN\uff0c\u8fd9\u662f\u4e00\u4e2a\u7528\u4e8e\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u7684\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u4f18\u5316\u7684\u8ba1\u7b97\u548c\u5185\u5b58\u8bbf\u95ee\u6a21\u5f0f\nprint(torch.backends.cudnn.is_available)\n\n# \u6253\u5370CUDA\u7684\u7248\u672c\u53f7\nprint(torch.cuda_version)\n\n# \u6253\u5370cuDNN\u7684\u7248\u672c\u53f7\nprint(torch.backends.cudnn.version())\n<\/code><\/pre>\n<h3>\n   4. \u6210\u529f\u754c\u9762 ^_^<br \/>\n  <\/h3>\n<\/p>\n<h2>\n   \u4e5d\u3001\u4e0b\u8f7dgithub\u9879\u76ee-YOLOv5\u9879\u76ee\u514b\u9686<br \/>\n  <\/h2>\n<p>\n   1. \u5185\u5bb9\u592a\u591a\u4e86\uff0c\u653e\u5230\u53e6\u5916\u7684CSDN\u91cc\u5566 ^_^\n  <\/p>\n<p>\n   \u94fe\u63a5\uff1a<br \/>\n   <a href=\"https:\/\/blog.csdn.net\/2302_76846184\/article\/details\/138392538?spm=1001.2014.3001.5502\" title=\"\u90e8\u7f72YOLOv5\u9879\u76ee\u7684\u5de5\u4f5c\u7a7a\u95f4----\u4ee5\u53ca\u5982\u4f55\u4e0b\u8f7dgithub\u9879\u76ee-CSDN\u535a\u5ba2\"><br \/>\n    \u90e8\u7f72YOLOv5\u9879\u76ee\u7684\u5de5\u4f5c\u7a7a\u95f4&#8212;-\u4ee5\u53ca\u5982\u4f55\u4e0b\u8f7dgithub\u9879\u76ee-CSDN\u535a\u5ba2<br \/>\n   <\/a>\n  <\/p>\n<\/p>\n<blockquote>\n<p>\n    \u611f\u8c22\u60a8\u8ba4\u771f\u89c2\u770b\u5b8c\u6bd5\u6b64\u6587\u7ae0\uff0c\u5982\u679c\u6b64\u6587\u7ae0\u5bf9\u60a8\u6709\u5e2e\u52a9\u7684\u8bdd\uff0c\u8fd8\u8bf7\u60a8<br \/>\n    <strong><br \/>\n     \u70b9\u8d5e<br \/>\n    <\/strong><br \/>\n    \u3001<br \/>\n    <strong><br \/>\n     \u6536\u85cf<br \/>\n    <\/strong><br \/>\n    \u3001<br \/>\n    <strong><br \/>\n     \u8bc4\u8bba<br \/>\n    <\/strong><br \/>\n    \uff0c\u8fd9\u5bf9\u6211\u6709\u5f88\u5927\u7684\u5e2e\u52a9\u3002\n   <\/p>\n<\/blockquote>\n<\/p>\n<\/p><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>\u4e0d\u8981\u76f2\u76ee\u4e00\u4e0b\u5b50\u5c31\u8ddf\u7740\u505a\u54e6\uff0c\u5148\u4ece\u5934\u5230\u5c3e\u770b\u4e00\u904d\u54e6\uff0c\u5408\u9002\u518d\u6765\uff01\u6216\u8005\u591a\u53c2\u8003\u4e00\u4e0b\u5f88\u591a\u5927\u4f6c\u7684\uff01 \u524d\u8a00 Anaconda+P [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":215,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[18],"tags":[],"class_list":["post-3532","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-18"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ 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property=\"og:description\" content=\"\u4e0d\u8981\u76f2\u76ee\u4e00\u4e0b\u5b50\u5c31\u8ddf\u7740\u505a\u54e6\uff0c\u5148\u4ece\u5934\u5230\u5c3e\u770b\u4e00\u904d\u54e6\uff0c\u5408\u9002\u518d\u6765\uff01\u6216\u8005\u591a\u53c2\u8003\u4e00\u4e0b\u5f88\u591a\u5927\u4f6c\u7684\uff01 \u524d\u8a00 Anaconda+P [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"http:\/\/www.wunen.com\/index.php\/2025\/06\/09\/\u8d85\u8be6\u7ec6\u642d\u5efa\u6df1\u5ea6\u5b66\u4e60\u73af\u5883\u3010anacondapycharmnvidia-\u663e\u5361\u9a71\u52a8pytorch\u542bgpu\u7248\/\" \/>\n<meta property=\"og:site_name\" content=\"\u7269\u5ae9\u8f6f\u4ef6\u8d44\u8baf\u7f51\" \/>\n<meta property=\"article:published_time\" content=\"2025-06-09T10:00:10+00:00\" \/>\n<meta property=\"og:image\" content=\"http:\/\/www.wunen.com\/wp-content\/uploads\/2025\/03\/\u8d44\u8baf.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"260\" \/>\n\t<meta property=\"og:image:height\" content=\"180\" \/>\n\t<meta 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