{"id":64113,"date":"2026-01-22T19:32:48","date_gmt":"2026-01-22T11:32:48","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/64113.html"},"modified":"2026-01-22T19:32:48","modified_gmt":"2026-01-22T11:32:48","slug":"ai%e7%b3%bb%e7%bb%9f%e6%95%85%e9%9a%9c%e8%af%8a%e6%96%ad%ef%bc%9a%e6%a8%a1%e5%9e%8b%e5%b4%a9%e6%ba%83%e3%80%81%e7%ae%97%e5%8a%9b%e7%93%b6%e9%a2%88%e4%b8%8e%e6%95%b0%e6%8d%ae%e6%bc%82%e7%a7%bb%e7%9a%84","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/64113.html","title":{"rendered":"AI\u7cfb\u7edf\u6545\u969c\u8bca\u65ad\uff1a\u6a21\u578b\u5d29\u6e83\u3001\u7b97\u529b\u74f6\u9888\u4e0e\u6570\u636e\u6f02\u79fb\u7684\u8bc6\u522b\u4e0e\u89e3\u51b3\u7b56\u7565"},"content":{"rendered":"<h2>AI\u7cfb\u7edf\u6545\u969c\u8bca\u65ad&#xff1a;\u6a21\u578b\u5d29\u6e83\u3001\u7b97\u529b\u74f6\u9888\u4e0e\u6570\u636e\u6f02\u79fb\u7684\u8bc6\u522b\u4e0e\u89e3\u51b3\u7b56\u7565<\/h2>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260122113246-69720adece7fc.gif\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<h2>\u4e00\u3001\u5f15\u8a00<\/h2>\n<p>\u5728AI\u7cfb\u7edf\u89c4\u6a21\u5316\u843d\u5730\u751f\u4ea7\u73af\u5883\u7684\u8fc7\u7a0b\u4e2d&#xff0c;\u7a33\u5b9a\u6027\u662f\u51b3\u5b9a\u5176\u5546\u4e1a\u4ef7\u503c\u7684\u6838\u5fc3\u6307\u6807\u4e4b\u4e00\u3002\u76f8\u8f83\u4e8e\u5b9e\u9a8c\u5ba4\u573a\u666f\u7684\u53ef\u63a7\u6027&#xff0c;\u751f\u4ea7\u73af\u5883\u4e2d\u7684\u590d\u6742\u6570\u636e\u5206\u5e03\u3001\u6ce2\u52a8\u7684\u8ba1\u7b97\u8d1f\u8f7d\u53ca\u52a8\u6001\u4e1a\u52a1\u9700\u6c42&#xff0c;\u6613\u5f15\u53d1\u5404\u7c7b\u6545\u969c&#xff0c;\u5176\u4e2d\u6a21\u578b\u5d29\u6e83\u3001\u7b97\u529b\u74f6\u9888\u4e0e\u6570\u636e\u6f02\u79fb\u662f\u4e09\u7c7b\u9ad8\u9891\u4e14\u5f71\u54cd\u6df1\u8fdc\u7684\u95ee\u9898\u3002\u6a21\u578b\u5d29\u6e83\u53ef\u80fd\u5bfc\u81f4\u63a8\u7406\u7ed3\u679c\u5931\u771f\u3001\u8bad\u7ec3\u4efb\u52a1\u4e2d\u65ad&#xff0c;\u5982\u795e\u7ecf\u7f51\u7edc\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u68af\u5ea6\u7206\u70b8\u5f15\u53d1\u7684\u53d1\u6563\u7684&#xff0c;\u6216\u63a8\u7406\u65f6\u8f93\u51fa\u5f02\u5e38\u503c&#xff1b;\u7b97\u529b\u74f6\u9888\u4f1a\u9020\u6210\u63a8\u7406\u5ef6\u8fdf\u6fc0\u589e\u3001\u541e\u5410\u91cf\u4e0b\u964d&#xff0c;\u6781\u7aef\u60c5\u51b5\u4e0b\u51fa\u73b0GPU\u5185\u5b58\u6ea2\u51fa&#xff08;OOM&#xff09;&#xff0c;\u76f4\u63a5\u963b\u65ad\u670d\u52a1\u54cd\u5e94&#xff1b;\u6570\u636e\u6f02\u79fb\u5219\u56e0\u771f\u5b9e\u573a\u666f\u6570\u636e\u5206\u5e03\u504f\u79bb\u8bad\u7ec3\u6570\u636e&#xff0c;\u5bfc\u81f4\u6a21\u578b\u6027\u80fd\u6301\u7eed\u8870\u51cf&#xff0c;\u5374\u96be\u4ee5\u901a\u8fc7\u5e38\u89c4\u76d1\u63a7\u5feb\u901f\u5b9a\u4f4d\u3002\u8fd9\u7c7b\u6545\u969c\u4e0d\u4ec5\u4f1a\u5f71\u54cd\u4e1a\u52a1\u6d41\u7a0b\u7684\u8fde\u7eed\u6027&#xff0c;\u8fd8\u53ef\u80fd\u5f15\u53d1\u51b3\u7b56\u5931\u8bef\u3001\u7528\u6237\u4f53\u9a8c\u6076\u5316\u7b49\u8fde\u9501\u53cd\u5e94&#xff0c;\u56e0\u6b64\u5efa\u7acb\u79d1\u5b66\u7684\u6545\u969c\u8bc6\u522b\u3001\u8bca\u65ad\u4e0e\u89e3\u51b3\u4f53\u7cfb&#xff0c;\u5bf9\u4fdd\u969cAI\u7cfb\u7edf\u7a33\u5b9a\u8fd0\u884c\u81f3\u5173\u91cd\u8981\u3002<\/p>\n<h2>\u4e8c\u3001\u95ee\u9898\u8bca\u65ad\u65b9\u6cd5<\/h2>\n<p>\u9488\u5bf9\u6a21\u578b\u5d29\u6e83\u3001\u7b97\u529b\u74f6\u9888\u4e0e\u6570\u636e\u6f02\u79fb\u4e09\u7c7b\u6545\u969c&#xff0c;\u9700\u5efa\u7acb\u91cf\u5316\u68c0\u6d4b\u6307\u6807\u4e0e\u5b9e\u65f6\u76d1\u63a7\u673a\u5236&#xff0c;\u5b9e\u73b0\u6545\u969c\u7684\u7cbe\u51c6\u8bc6\u522b\u4e0e\u65e9\u671f\u9884\u8b66\u3002<\/p>\n<h3>2.1 \u6a21\u578b\u5d29\u6e83\u7684\u8bca\u65ad\u6307\u6807\u4e0e\u65b9\u6cd5<\/h3>\n<p>\u6a21\u578b\u5d29\u6e83\u4e3b\u8981\u5206\u4e3a\u8bad\u7ec3\u9636\u6bb5\u5d29\u6e83\u4e0e\u63a8\u7406\u9636\u6bb5\u5f02\u5e38&#xff0c;\u6838\u5fc3\u8bca\u65ad\u6307\u6807\u56f4\u7ed5\u6a21\u578b\u53c2\u6570\u66f4\u65b0\u3001\u635f\u5931\u53d8\u5316\u53ca\u8f93\u51fa\u5408\u7406\u6027\u5c55\u5f00&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u8bad\u7ec3\u9636\u6bb5&#xff1a;\u635f\u5931\u51fd\u6570\u503c\u7a81\u53d8&#xff08;\u5982\u9aa4\u5347\u3001\u9aa4\u964d\u81f3\u8d8b\u4e8e\u6052\u5b9a&#xff09;\u3001\u68af\u5ea6\u8303\u6570\u5f02\u5e38&#xff08;\u8fc7\u5927\u6216\u8fc7\u5c0f&#xff09;\u3001\u53c2\u6570\u66f4\u65b0\u5e45\u5ea6\u5f02\u5e38&#xff08;\u5982\u53c2\u6570\u503c\u8d85\u51fa\u5408\u7406\u8303\u56f4&#xff09;&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u63a8\u7406\u9636\u6bb5&#xff1a;\u8f93\u51fa\u7ed3\u679c\u5206\u5e03\u5f02\u5e38&#xff08;\u5982\u5206\u7c7b\u4efb\u52a1\u4e2d\u67d0\u4e00\u7c7b\u522b\u5360\u6bd4\u9aa4\u5347\u81f3100%&#xff09;\u3001\u63a8\u7406\u8017\u65f6\u7a81\u53d8\u3001\u8f93\u51fa\u7a7a\u503c\u6216\u6781\u503c\u3002<\/p>\n<\/li>\n<\/ul>\n<p>\u53ef\u901a\u8fc7\u5b9e\u65f6\u76d1\u63a7\u635f\u5931\u66f2\u7ebf\u3001\u68af\u5ea6\u8303\u6570\u53ca\u8f93\u51fa\u7edf\u8ba1\u7279\u5f81&#xff0c;\u5feb\u901f\u5b9a\u4f4d\u6a21\u578b\u5d29\u6e83\u95ee\u9898\u3002<\/p>\n<h3>2.2 \u7b97\u529b\u74f6\u9888\u7684\u8bca\u65ad\u6307\u6807\u4e0e\u65b9\u6cd5<\/h3>\n<p>\u7b97\u529b\u74f6\u9888\u96c6\u4e2d\u4f53\u73b0\u4e3a\u8ba1\u7b97\u8d44\u6e90\u4f9b\u7ed9\u4e0d\u8db3\u4e0e\u8d44\u6e90\u5229\u7528\u7387\u5931\u8861&#xff0c;\u6838\u5fc3\u68c0\u6d4b\u6307\u6807\u5305\u62ec&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u786c\u4ef6\u8d44\u6e90\u6307\u6807&#xff1a;GPU\u5229\u7528\u7387&#xff08;\u6301\u7eed\u4f4e\u4e8e30%\u53ef\u80fd\u5b58\u5728\u8d44\u6e90\u6d6a\u8d39&#xff0c;\u6301\u7eed\u9ad8\u4e8e95%\u6613\u5f15\u53d1\u74f6\u9888&#xff09;\u3001GPU\u663e\u5b58\u5360\u7528\u7387&#xff08;\u63a5\u8fd1100%\u65f6\u6613\u89e6\u53d1OOM&#xff09;\u3001CPU\u5229\u7528\u7387\u53ca\u5185\u5b58\u5360\u7528&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u670d\u52a1\u6027\u80fd\u6307\u6807&#xff1a;\u63a8\u7406\u5ef6\u8fdf&#xff08;P95\/P99\u5ef6\u8fdf\u6fc0\u589e\u662f\u74f6\u9888\u6838\u5fc3\u4fe1\u53f7&#xff09;\u3001\u541e\u5410\u91cf&#xff08;\u5355\u4f4d\u65f6\u95f4\u5185\u5904\u7406\u8bf7\u6c42\u6570\u4e0b\u964d&#xff09;\u3001\u8bf7\u6c42\u6392\u961f\u957f\u5ea6&#xff08;\u6301\u7eed\u589e\u957f\u8bf4\u660e\u5904\u7406\u80fd\u529b\u4e0d\u8db3&#xff09;\u3002<\/p>\n<\/li>\n<\/ul>\n<h3>2.3 \u6570\u636e\u6f02\u79fb\u7684\u8bca\u65ad\u6307\u6807\u4e0e\u65b9\u6cd5<\/h3>\n<p>\u6570\u636e\u6f02\u79fb\u5206\u4e3a\u7279\u5f81\u6f02\u79fb&#xff08;\u8f93\u5165\u7279\u5f81\u5206\u5e03\u504f\u79fb&#xff09;\u4e0e\u6807\u7b7e\u6f02\u79fb&#xff08;\u8f93\u51fa\u6807\u7b7e\u5206\u5e03\u504f\u79fb&#xff09;&#xff0c;\u5e38\u7528\u91cf\u5316\u6307\u6807\u5305\u62ec&#xff1a;<\/p>\n<ul>\n<li>\n<p>Population Stability Index&#xff08;PSI&#xff09;&#xff1a;\u8861\u91cf\u7279\u5f81\u5206\u5e03\u7684\u7a33\u5b9a\u6027&#xff0c;PSI&lt;0.1\u8868\u793a\u65e0\u663e\u8457\u6f02\u79fb&#xff0c;0.1\u2264PSI&lt;0.25\u8868\u793a\u8f7b\u5fae\u6f02\u79fb&#xff0c;PSI\u22650.25\u8868\u793a\u4e25\u91cd\u6f02\u79fb&#xff1b;<\/p>\n<\/li>\n<li>\n<p>Kullback-Leibler&#xff08;KL&#xff09;\u6563\u5ea6&#xff1a;\u91cf\u5316\u4e24\u4e2a\u5206\u5e03\u7684\u5dee\u5f02\u7a0b\u5ea6&#xff0c;\u503c\u8d8a\u5927\u8bf4\u660e\u6f02\u79fb\u8d8a\u660e\u663e&#xff1b;<\/p>\n<\/li>\n<li>\n<p>Jensen-Shannon&#xff08;JS&#xff09;\u6563\u5ea6&#xff1a;KL\u6563\u5ea6\u7684\u5bf9\u79f0\u5f62\u5f0f&#xff0c;\u53d6\u503c\u8303\u56f4[0,1]&#xff0c;\u66f4\u9002\u5408\u8de8\u5206\u5e03\u5bf9\u6bd4\u3002<\/p>\n<\/li>\n<\/ul>\n<h4>\u4ee3\u7801\u793a\u4f8b&#xff1a;\u57fa\u4e8ePSI\u7684\u7279\u5f81\u6f02\u79fb\u5b9e\u65f6\u68c0\u6d4b<\/h4>\n<p>\u4ee5\u4e0b\u4ee3\u7801\u4f7f\u7528scikit-learn\u5b9e\u73b0PSI\u8ba1\u7b97&#xff0c;\u53ef\u5d4c\u5165\u751f\u4ea7\u73af\u5883\u76d1\u63a7\u6d41\u7a0b&#xff0c;\u5b9e\u65f6\u68c0\u6d4b\u7279\u5f81\u5206\u5e03\u504f\u79fb\u3002<\/p>\n<p><span class=\"token keyword\">import<\/span> numpy <span class=\"token keyword\">as<\/span> np<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>preprocessing <span class=\"token keyword\">import<\/span> KBinsDiscretizer<\/p>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">calculate_psi<\/span><span class=\"token punctuation\">(<\/span>expected<span class=\"token punctuation\">:<\/span> np<span class=\"token punctuation\">.<\/span>ndarray<span class=\"token punctuation\">,<\/span> actual<span class=\"token punctuation\">:<\/span> np<span class=\"token punctuation\">.<\/span>ndarray<span class=\"token punctuation\">,<\/span> bins<span class=\"token punctuation\">:<\/span> <span class=\"token builtin\">int<\/span> <span class=\"token operator\">&#061;<\/span> <span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">&#8211;<\/span><span class=\"token operator\">&gt;<\/span> <span class=\"token builtin\">float<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token triple-quoted-string string\">&#034;&#034;&#034;<br \/>\n    \u8ba1\u7b97Population Stability Index&#xff08;PSI&#xff09;<br \/>\n    \u53c2\u6570&#xff1a;<br \/>\n        expected: \u8bad\u7ec3\u96c6\u7279\u5f81\u5206\u5e03&#xff08;\u57fa\u51c6\u5206\u5e03&#xff09;<br \/>\n        actual: \u751f\u4ea7\u73af\u5883\u5b9e\u65f6\u7279\u5f81\u5206\u5e03&#xff08;\u5f85\u68c0\u6d4b\u5206\u5e03&#xff09;<br \/>\n        bins: \u79bb\u6563\u5316\u5206\u7bb1\u6570&#xff0c;\u9ed8\u8ba410<br \/>\n    \u8fd4\u56de&#xff1a;<br \/>\n        psi: PSI\u503c<br \/>\n    &#034;&#034;&#034;<\/span><br \/>\n    <span class=\"token comment\"># \u79fb\u9664\u7f3a\u5931\u503c<\/span><br \/>\n    expected <span class=\"token operator\">&#061;<\/span> expected<span class=\"token punctuation\">[<\/span><span class=\"token operator\">~<\/span>np<span class=\"token punctuation\">.<\/span>isnan<span class=\"token punctuation\">(<\/span>expected<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">]<\/span><br \/>\n    actual <span class=\"token operator\">&#061;<\/span> actual<span class=\"token punctuation\">[<\/span><span class=\"token operator\">~<\/span>np<span class=\"token punctuation\">.<\/span>isnan<span class=\"token punctuation\">(<\/span>actual<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">]<\/span><\/p>\n<p>    <span class=\"token comment\"># \u79bb\u6563\u5316\u5904\u7406&#xff08;\u907f\u514d\u56e0\u8fde\u7eed\u503c\u5bfc\u81f4\u5206\u5e03\u5bf9\u6bd4\u5931\u771f&#xff09;<\/span><br \/>\n    discretizer <span class=\"token operator\">&#061;<\/span> KBinsDiscretizer<span class=\"token punctuation\">(<\/span>n_bins<span class=\"token operator\">&#061;<\/span>bins<span class=\"token punctuation\">,<\/span> encode<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;ordinal&#039;<\/span><span class=\"token punctuation\">,<\/span> strategy<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;quantile&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    discretizer<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>expected<span class=\"token punctuation\">.<\/span>reshape<span class=\"token punctuation\">(<\/span><span class=\"token operator\">&#8211;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token comment\"># \u8ba1\u7b97\u4e24\u4e2a\u5206\u5e03\u5728\u5404\u5206\u7bb1\u4e2d\u7684\u5360\u6bd4<\/span><br \/>\n    exp_counts <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>bincount<span class=\"token punctuation\">(<\/span>discretizer<span class=\"token punctuation\">.<\/span>transform<span class=\"token punctuation\">(<\/span>expected<span class=\"token punctuation\">.<\/span>reshape<span class=\"token punctuation\">(<\/span><span class=\"token operator\">&#8211;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>flatten<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    act_counts <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>bincount<span class=\"token punctuation\">(<\/span>discretizer<span class=\"token punctuation\">.<\/span>transform<span class=\"token punctuation\">(<\/span>actual<span class=\"token punctuation\">.<\/span>reshape<span class=\"token punctuation\">(<\/span><span class=\"token operator\">&#8211;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>flatten<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token comment\"># \u5f52\u4e00\u5316\u4e3a\u6982\u7387&#xff0c;\u907f\u514d\u96f6\u503c\u5bfc\u81f4\u8ba1\u7b97\u9519\u8bef<\/span><br \/>\n    exp_prob <span class=\"token operator\">&#061;<\/span> exp_counts <span class=\"token operator\">\/<\/span> exp_counts<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">sum<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">&#043;<\/span> <span class=\"token number\">1e-10<\/span>  <span class=\"token comment\"># \u52a0\u5e73\u6ed1\u9879<\/span><br \/>\n    act_prob <span class=\"token operator\">&#061;<\/span> act_counts <span class=\"token operator\">\/<\/span> act_counts<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">sum<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">&#043;<\/span> <span class=\"token number\">1e-10<\/span><\/p>\n<p>    <span class=\"token comment\"># \u8ba1\u7b97PSI<\/span><br \/>\n    psi <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">sum<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">(<\/span>exp_prob <span class=\"token operator\">&#8211;<\/span> act_prob<span class=\"token punctuation\">)<\/span> <span class=\"token operator\">*<\/span> np<span class=\"token punctuation\">.<\/span>log<span class=\"token punctuation\">(<\/span>exp_prob <span class=\"token operator\">\/<\/span> act_prob<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">return<\/span> psi<\/p>\n<p><span class=\"token comment\"># \u793a\u4f8b&#xff1a;\u6a21\u62df\u8bad\u7ec3\u96c6\u4e0e\u751f\u4ea7\u73af\u5883\u7279\u5f81\u5206\u5e03<\/span><br \/>\nnp<span class=\"token punctuation\">.<\/span>random<span class=\"token punctuation\">.<\/span>seed<span class=\"token punctuation\">(<\/span><span class=\"token number\">42<\/span><span class=\"token punctuation\">)<\/span><br \/>\ntrain_feature <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>random<span class=\"token punctuation\">.<\/span>normal<span class=\"token punctuation\">(<\/span>loc<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">,<\/span> scale<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">10000<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u57fa\u51c6\u5206\u5e03&#xff08;\u6b63\u6001\u5206\u5e03&#xff09;<\/span><br \/>\nprod_feature <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>random<span class=\"token punctuation\">.<\/span>normal<span class=\"token punctuation\">(<\/span>loc<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.5<\/span><span class=\"token punctuation\">,<\/span> scale<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1.2<\/span><span class=\"token punctuation\">,<\/span> size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">5000<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u5b58\u5728\u8f7b\u5fae\u6f02\u79fb\u7684\u5206\u5e03<\/span><\/p>\n<p>psi_value <span class=\"token operator\">&#061;<\/span> calculate_psi<span class=\"token punctuation\">(<\/span>train_feature<span class=\"token punctuation\">,<\/span> prod_feature<span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u7279\u5f81PSI\u503c&#xff1a;<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>psi_value<span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.4f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">if<\/span> psi_value <span class=\"token operator\">&lt;<\/span> <span class=\"token number\">0.1<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;\u65e0\u663e\u8457\u7279\u5f81\u6f02\u79fb&#034;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">elif<\/span> <span class=\"token number\">0.1<\/span> <span class=\"token operator\">&lt;&#061;<\/span> psi_value <span class=\"token operator\">&lt;<\/span> <span class=\"token number\">0.25<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;\u5b58\u5728\u8f7b\u5fae\u7279\u5f81\u6f02\u79fb&#xff0c;\u5efa\u8bae\u6301\u7eed\u76d1\u63a7&#034;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">else<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;\u5b58\u5728\u4e25\u91cd\u7279\u5f81\u6f02\u79fb&#xff0c;\u9700\u7acb\u5373\u5904\u7406&#034;<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>\u4e0a\u8ff0\u4ee3\u7801\u901a\u8fc7\u5206\u7bb1\u79bb\u6563\u5316\u5904\u7406\u8fde\u7eed\u7279\u5f81&#xff0c;\u907f\u514d\u4e86\u56e0\u5355\u503c\u9891\u7387\u8fc7\u4f4e\u5bfc\u81f4\u7684\u5206\u5e03\u5bf9\u6bd4\u8bef\u5dee&#xff0c;\u540c\u65f6\u52a0\u5165\u5e73\u6ed1\u9879\u9632\u6b62\u5bf9\u6570\u8ba1\u7b97\u4e2d\u51fa\u73b0\u65e0\u7a77\u5927\u3002\u5b9e\u9645\u5e94\u7528\u4e2d\u53ef\u5c06\u5176\u5c01\u88c5\u4e3a\u76d1\u63a7\u7ec4\u4ef6&#xff0c;\u5bf9\u6838\u5fc3\u7279\u5f81\u5b9a\u65f6\u8ba1\u7b97PSI&#xff0c;\u89e6\u53d1\u9608\u503c\u544a\u8b66\u3002<\/p>\n<h2>\u4e09\u3001\u89e3\u51b3\u7b56\u7565<\/h2>\n<h3>3.1 \u6a21\u578b\u5d29\u6e83\u7684\u7f13\u89e3\u63aa\u65bd\u4e0e\u5b9e\u73b0<\/h3>\n<p>\u9488\u5bf9\u6a21\u578b\u5d29\u6e83\u7684\u4e0d\u540c\u573a\u666f&#xff0c;\u9700\u4ece\u53c2\u6570\u7ea6\u675f\u3001\u8bad\u7ec3\u7b56\u7565\u53ca\u63a8\u7406\u9632\u62a4\u4e09\u4e2a\u7ef4\u5ea6\u5236\u5b9a\u63aa\u65bd&#xff1a;<\/p>\n<li>\u68af\u5ea6\u88c1\u526a\u6291\u5236\u68af\u5ea6\u7206\u70b8&#xff1a;\u8bad\u7ec3\u6df1\u5c42\u795e\u7ecf\u7f51\u7edc\u65f6&#xff0c;\u901a\u8fc7\u5bf9\u68af\u5ea6\u8303\u6570\u8bbe\u7f6e\u9608\u503c&#xff0c;\u907f\u514d\u68af\u5ea6\u7d2f\u79ef\u5bfc\u81f4\u53c2\u6570\u66f4\u65b0\u5e45\u5ea6\u8fc7\u5927\u3002\u4ee3\u7801\u793a\u4f8b&#xff08;PyTorch&#xff09;&#xff1a;<br \/>\n&#096;import torch<br \/>\nimport torch.nn as nn<br \/>\nimport torch.optim as optim<\/li>\n<h2>\u5b9a\u4e49\u7b80\u5355\u6a21\u578b<\/h2>\n<p>model &#061; nn.Sequential(nn.Linear(100, 200), nn.ReLU(), nn.Linear(200, 10))<br \/>\ncriterion &#061; nn.CrossEntropyLoss()<br \/>\noptimizer &#061; optim.Adam(model.parameters(), lr&#061;1e-3)<\/p>\n<h2>\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u52a0\u5165\u68af\u5ea6\u88c1\u526a<\/h2>\n<p>for epoch in range(10):<br \/>\nfor batch_x, batch_y in dataloader:<br \/>\noptimizer.zero_grad()<br \/>\noutput &#061; model(batch_x)<br \/>\nloss &#061; criterion(output, batch_y)<br \/>\nloss.backward()<\/p>\n<p>    # \u68af\u5ea6\u88c1\u526a&#xff0c;\u8303\u6570\u9608\u503c\u8bbe\u4e3a1.0<br \/>\n    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm&#061;1.0)<br \/>\n    optimizer.step()&#096;<\/p>\n<li>\n<p>\u52a0\u5165\u6b63\u5219\u5316\u9632\u6b62\u8fc7\u62df\u5408\u4e0e\u8bad\u7ec3\u53d1\u6563&#xff1a;\u901a\u8fc7L2\u6b63\u5219\u5316\u7ea6\u675f\u53c2\u6570\u89c4\u6a21&#xff0c;\u6216Dropout\u968f\u673a\u5931\u6d3b\u795e\u7ecf\u5143&#xff0c;\u63d0\u5347\u6a21\u578b\u6cdb\u5316\u80fd\u529b&#xff0c;\u51cf\u5c11\u8bad\u7ec3\u5d29\u6e83\u98ce\u9669\u3002\u5728PyTorch\u4e2d\u53ef\u901a\u8fc7\u5728\u4f18\u5316\u5668\u4e2d\u8bbe\u7f6eweight_decay\u5b9e\u73b0L2\u6b63\u5219\u5316\u3002<\/p>\n<\/li>\n<li>\n<p>\u63a8\u7406\u9636\u6bb5\u5f02\u5e38\u68c0\u6d4b\u4e0e\u964d\u7ea7&#xff1a;\u5728\u63a8\u7406\u670d\u52a1\u4e2d\u52a0\u5165\u8f93\u51fa\u6821\u9a8c\u903b\u8f91&#xff0c;\u5f53\u68c0\u6d4b\u5230\u5f02\u5e38\u8f93\u51fa&#xff08;\u5982\u6781\u503c\u3001\u6982\u7387\u5206\u5e03\u5f02\u5e38&#xff09;\u65f6&#xff0c;\u81ea\u52a8\u5207\u6362\u81f3\u5907\u7528\u6a21\u578b\u6216\u8fd4\u56de\u9ed8\u8ba4\u7ed3\u679c&#xff0c;\u907f\u514d\u5f71\u54cd\u4e1a\u52a1\u3002<\/p>\n<\/li>\n<h3>3.2 \u7b97\u529b\u74f6\u9888\u7684\u7f13\u89e3\u63aa\u65bd\u4e0e\u5b9e\u73b0<\/h3>\n<p>\u7b97\u529b\u74f6\u9888\u7684\u89e3\u51b3\u9700\u517c\u987e\u8d44\u6e90\u5229\u7528\u7387\u4f18\u5316\u4e0e\u6a21\u578b\u8f7b\u91cf\u5316&#xff0c;\u6838\u5fc3\u63aa\u65bd\u5982\u4e0b&#xff1a;<\/p>\n<li>\u6a21\u578b\u91cf\u5316\u51cf\u5c11\u663e\u5b58\u5360\u7528\u4e0e\u8ba1\u7b97\u91cf&#xff1a;\u5c06\u6a21\u578b\u53c2\u6570\u4eceFP32&#xff08;\u5355\u7cbe\u5ea6&#xff09;\u91cf\u5316\u4e3aFP16&#xff08;\u534a\u7cbe\u5ea6&#xff09;\u6216INT8&#xff08;\u6574\u578b&#xff09;&#xff0c;\u53ef\u663e\u8457\u964d\u4f4e\u663e\u5b58\u5360\u7528&#xff0c;\u63d0\u5347\u63a8\u7406\u901f\u5ea6\u3002\u4ee3\u7801\u793a\u4f8b&#xff08;PyTorch FP16\u91cf\u5316&#xff09;&#xff1a;<br \/>\n&#096;import torch<\/li>\n<h2>\u6a21\u578b\u4e0e\u6570\u636e\u79fb\u81f3GPU<\/h2>\n<p>device &#061; torch.device(\u201ccuda\u201d if torch.cuda.is_available() else \u201ccpu\u201d)<br \/>\nmodel &#061; model.to(device)<\/p>\n<h2>\u542f\u7528FP16\u6df7\u5408\u7cbe\u5ea6\u8bad\u7ec3\/\u63a8\u7406<\/h2>\n<p>scaler &#061; torch.cuda.amp.GradScaler()<\/p>\n<h2>\u63a8\u7406\u8fc7\u7a0b<\/h2>\n<p>with torch.cuda.amp.autocast():<br \/>\nfor batch_x in inference_dataloader:<br \/>\nbatch_x &#061; batch_x.to(device)<br \/>\noutput &#061; model(batch_x)<br \/>\n# \u540e\u7eed\u5904\u7406\u903b\u8f91&#096;\u91cf\u5316\u540e\u53ef\u51cf\u5c11\u7ea650%\u663e\u5b58\u5360\u7528&#xff0c;\u63a8\u7406\u901f\u5ea6\u63d0\u534730%-50%&#xff0c;\u9002\u5408\u5bf9\u7cbe\u5ea6\u8981\u6c42\u4e0d\u6781\u81f4\u7684\u4e1a\u52a1\u573a\u666f\u3002<\/p>\n<li>checkpoint\u673a\u5236\u8282\u7701\u663e\u5b58&#xff1a;\u9488\u5bf9\u6df1\u5c42\u6a21\u578b&#xff0c;\u901a\u8fc7\u68af\u5ea6\u68c0\u67e5\u70b9&#xff08;Gradient Checkpointing&#xff09;\u7b56\u7565&#xff0c;\u5728\u8bad\u7ec3\u65f6\u4ec5\u4fdd\u5b58\u90e8\u5206\u4e2d\u95f4\u6fc0\u6d3b\u503c&#xff0c;\u901a\u8fc7\u53cd\u5411\u4f20\u64ad\u65f6\u91cd\u65b0\u8ba1\u7b97\u5176\u4f59\u6fc0\u6d3b\u503c&#xff0c;\u6362\u53d6\u663e\u5b58\u5360\u7528\u964d\u4f4e\u3002\u4ee3\u7801\u793a\u4f8b&#xff08;PyTorch&#xff09;&#xff1a;<br \/>\n&#096;import torch<br \/>\nfrom torch.utils.checkpoint import checkpoint<\/li>\n<p>class DeepModel(nn.Module):<br \/>\ndef init(self):<br \/>\nsuper().init()<br \/>\nself.layer1 &#061; nn.Linear(100, 1024)<br \/>\nself.layer2 &#061; nn.Linear(1024, 2048)<br \/>\nself.layer3 &#061; nn.Linear(2048, 10)<\/p>\n<p>def forward(self, x):<br \/>\n    # \u5bf9\u8ba1\u7b97\u5bc6\u96c6\u5c42\u542f\u7528checkpoint<br \/>\n    x &#061; checkpoint(self.layer1, x)<br \/>\n    x &#061; torch.relu(x)<br \/>\n    x &#061; checkpoint(self.layer2, x)<br \/>\n    x &#061; torch.relu(x)<br \/>\n    x &#061; self.layer3(x)<br \/>\n    return x<\/p>\n<p>model &#061; DeepModel().to(device)<\/p>\n<h2>\u8bad\u7ec3\u903b\u8f91\u4e0e\u5e38\u89c4\u6a21\u578b\u4e00\u81f4&#096;<\/h2>\n<p>    \u8be5\u65b9\u6cd5\u4f1a\u589e\u52a0\u5c11\u91cf\u8ba1\u7b97\u5f00\u9500&#xff08;\u7ea610%-20%&#xff09;&#xff0c;\u4f46\u53ef\u5c06\u663e\u5b58\u5360\u7528\u964d\u4f4e40%-60%\u3002<\/p>\n<li>\u8d44\u6e90\u8c03\u5ea6\u4f18\u5316&#xff1a;\u901a\u8fc7\u52a8\u6001\u6279\u5904\u7406&#xff08;\u6839\u636e\u663e\u5b58\u5269\u4f59\u91cf\u8c03\u6574batch size&#xff09;\u3001\u8bf7\u6c42\u961f\u5217\u9650\u6d41\u3001GPU\u96c6\u7fa4\u8d1f\u8f7d\u5747\u8861\u7b49\u5de5\u7a0b\u624b\u6bb5&#xff0c;\u63d0\u5347\u8d44\u6e90\u5229\u7528\u7387\u3002\u4f8b\u5982\u4f7f\u7528Kubernetes\u8c03\u5ea6GPU\u8d44\u6e90&#xff0c;\u907f\u514d\u5355\u5361\u8fc7\u8f7d\u3002<\/li>\n<h3>3.3 \u6570\u636e\u6f02\u79fb\u7684\u7f13\u89e3\u63aa\u65bd\u4e0e\u5b9e\u73b0<\/h3>\n<p>\u5e94\u5bf9\u6570\u636e\u6f02\u79fb\u9700\u5efa\u7acb\u201c\u68c0\u6d4b-\u9002\u5e94-\u66f4\u65b0\u201d\u7684\u95ed\u73af\u673a\u5236&#xff0c;\u6838\u5fc3\u63aa\u65bd\u5982\u4e0b&#xff1a;<\/p>\n<li>\u5728\u7ebf\u589e\u91cf\u91cd\u8bad\u7ec3&#xff1a;\u5f53\u68c0\u6d4b\u5230\u8f7b\u5fae\u6f02\u79fb\u65f6&#xff0c;\u4f7f\u7528\u751f\u4ea7\u73af\u5883\u65b0\u6570\u636e\u589e\u91cf\u66f4\u65b0\u6a21\u578b&#xff0c;\u907f\u514d\u5168\u91cf\u91cd\u8bad\u7ec3\u7684\u9ad8\u6210\u672c\u3002\u4ee3\u7801\u793a\u4f8b&#xff08;\u57fa\u4e8eEvidently AI\u68c0\u6d4b\u6f02\u79fb&#043;\u589e\u91cf\u8bad\u7ec3&#xff09;&#xff1a;<br \/>\n&#096;import pandas as pd<br \/>\nfrom evidently.report import Report<br \/>\nfrom evidently.metrics import DataDriftMetric<br \/>\nimport torch<br \/>\nfrom torch.utils.data import DataLoader, TensorDataset<\/li>\n<h2>1. \u6f02\u79fb\u68c0\u6d4b&#xff08;\u4f7f\u7528Evidently AI&#xff09;<\/h2>\n<h2>\u51c6\u5907\u57fa\u51c6\u6570\u636e&#xff08;\u8bad\u7ec3\u96c6&#xff09;\u4e0e\u5b9e\u65f6\u6570\u636e&#xff08;\u751f\u4ea7\u6570\u636e&#xff09;<\/h2>\n<p>reference_data &#061; pd.read_csv(\u201ctrain_data.csv\u201d)<br \/>\ncurrent_data &#061; pd.read_csv(\u201cprod_data.csv\u201d)<\/p>\n<h2>\u5b9a\u4e49\u6f02\u79fb\u68c0\u6d4b\u62a5\u544a<\/h2>\n<p>drift_report &#061; Report(metrics&#061;[DataDriftMetric(column_name&#061;\u201ccore_feature\u201d)])<br \/>\ndrift_report.run(reference_data&#061;reference_data, current_data&#061;current_data)<br \/>\ndrift_result &#061; drift_report.as_dict()<\/p>\n<h2>2. \u82e5\u68c0\u6d4b\u5230\u6f02\u79fb&#xff0c;\u6267\u884c\u589e\u91cf\u8bad\u7ec3<\/h2>\n<p>if drift_result[\u201cmetrics\u201d][0][\u201cresult\u201d][\u201cdrift_detected\u201d]:<br \/>\n# \u63d0\u53d6\u65b0\u6570\u636e\u5e76\u9884\u5904\u7406<br \/>\nX_new &#061; torch.tensor(current_data[[\u201ccore_feature\u201d]].values, dtype&#061;torch.float32)<br \/>\ny_new &#061; torch.tensor(current_data[\u201clabel\u201d].values, dtype&#061;torch.long)<br \/>\nnew_dataset &#061; TensorDataset(X_new, y_new)<br \/>\nnew_dataloader &#061; DataLoader(new_dataset, batch_size&#061;32)<\/p>\n<p># \u589e\u91cf\u8bad\u7ec3&#xff08;\u51bb\u7ed3\u90e8\u5206\u5c42&#xff0c;\u4ec5\u66f4\u65b0\u9876\u5c42&#xff09;<br \/>\nfor param in model.parameters():<br \/>\n    param.requires_grad &#061; False<br \/>\nmodel.fc &#061; nn.Linear(model.fc.in_features, 10)  # \u66ff\u6362\u9876\u5c42\u5206\u7c7b\u5668<br \/>\nmodel.fc.requires_grad &#061; True<\/p>\n<p>optimizer &#061; optim.Adam(model.fc.parameters(), lr&#061;5e-4)<br \/>\ncriterion &#061; nn.CrossEntropyLoss()<\/p>\n<p>for epoch in range(3):  # \u5c11\u91cfepoch\u589e\u91cf\u66f4\u65b0<br \/>\n    for batch_x, batch_y in new_dataloader:<br \/>\n        optimizer.zero_grad()<br \/>\n        output &#061; model(batch_x)<br \/>\n        loss &#061; criterion(output, batch_y)<br \/>\n        loss.backward()<br \/>\n        optimizer.step()<br \/>\nprint(&#034;\u589e\u91cf\u8bad\u7ec3\u5b8c\u6210&#xff0c;\u6a21\u578b\u5df2\u9002\u914d\u65b0\u6570\u636e\u5206\u5e03&#034;)&#096;<\/p>\n<li>\n<p>\u7279\u5f81\u5de5\u7a0b\u81ea\u9002\u5e94\u8c03\u6574&#xff1a;\u9488\u5bf9\u7279\u5f81\u6f02\u79fb&#xff0c;\u901a\u8fc7\u5728\u7ebf\u7279\u5f81\u6807\u51c6\u5316\u3001\u5f52\u4e00\u5316&#xff08;\u4f7f\u7528\u751f\u4ea7\u6570\u636e\u91cd\u65b0\u8ba1\u7b97\u5747\u503c\/\u65b9\u5dee&#xff09;&#xff0c;\u6216\u52a8\u6001\u7b5b\u9009\u7a33\u5b9a\u7279\u5f81&#xff0c;\u51cf\u5c11\u6f02\u79fb\u5bf9\u6a21\u578b\u7684\u5f71\u54cd\u3002<\/p>\n<\/li>\n<li>\n<p>\u6a21\u578b\u878d\u5408\u4e0e\u964d\u7ea7\u7b56\u7565&#xff1a;\u8bad\u7ec3\u591a\u4e2a\u9002\u5e94\u4e0d\u540c\u6570\u636e\u5206\u5e03\u7684\u6a21\u578b&#xff0c;\u5f53\u68c0\u6d4b\u5230\u6f02\u79fb\u65f6&#xff0c;\u81ea\u52a8\u5207\u6362\u81f3\u5bf9\u5f53\u524d\u5206\u5e03\u9002\u5e94\u6027\u66f4\u5f3a\u7684\u6a21\u578b&#xff1b;\u82e5\u6f02\u79fb\u4e25\u91cd&#xff0c;\u6682\u65f6\u964d\u7ea7\u4e3a\u89c4\u5219\u5f15\u64ce&#xff0c;\u786e\u4fdd\u4e1a\u52a1\u8fde\u7eed\u6027\u3002<\/p>\n<\/li>\n<h2>\u56db\u3001\u7cfb\u7edf\u5316\u6545\u969c\u5904\u7406\u6d41\u7a0b\u56fe<\/h2>\n<p>\u4ee5\u4e0b\u4e3a\u57fa\u4e8eMermaid\u8bed\u6cd5\u7684AI\u7cfb\u7edf\u6545\u969c\u5904\u7406\u6d41\u7a0b\u56fe&#xff0c;\u4ece\u5f02\u5e38\u544a\u8b66\u51fa\u53d1&#xff0c;\u901a\u8fc7\u5206\u5c42\u5224\u65ad\u5b9a\u4f4d\u6545\u969c\u7c7b\u578b&#xff0c;\u5e76\u6267\u884c\u5bf9\u5e94\u5904\u7406\u7b56\u7565&#xff0c;\u5f62\u6210\u95ed\u73af\u7ba1\u7406\u3002<\/p>\n<p>#mermaid-svg-RhY1KtufMstuebKE{font-family:\\&#8221;trebuchet ms\\&#8221;,verdana,arial,sans-serif;font-size:16px;fill:#333;}@keyframes edge-animation-frame{from{stroke-dashoffset:0;}}@keyframes dash{to{stroke-dashoffset:0;}}#mermaid-svg-RhY1KtufMstuebKE .edge-animation-slow{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 50s linear infinite;stroke-linecap:round;}#mermaid-svg-RhY1KtufMstuebKE .edge-animation-fast{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 20s linear infinite;stroke-linecap:round;}#mermaid-svg-RhY1KtufMstuebKE .error-icon{fill:#552222;}#mermaid-svg-RhY1KtufMstuebKE .error-text{fill:#552222;stroke:#552222;}#mermaid-svg-RhY1KtufMstuebKE .edge-thickness-normal{stroke-width:1px;}#mermaid-svg-RhY1KtufMstuebKE .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg-RhY1KtufMstuebKE .edge-pattern-solid{stroke-dasharray:0;}#mermaid-svg-RhY1KtufMstuebKE .edge-thickness-invisible{stroke-width:0;fill:none;}#mermaid-svg-RhY1KtufMstuebKE 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polygon,#mermaid-svg-RhY1KtufMstuebKE .node path{fill:#ECECFF;stroke:#9370DB;stroke-width:1px;}#mermaid-svg-RhY1KtufMstuebKE .rough-node .label text,#mermaid-svg-RhY1KtufMstuebKE .node .label text,#mermaid-svg-RhY1KtufMstuebKE .image-shape .label,#mermaid-svg-RhY1KtufMstuebKE .icon-shape .label{text-anchor:middle;}#mermaid-svg-RhY1KtufMstuebKE .node .katex path{fill:#000;stroke:#000;stroke-width:1px;}#mermaid-svg-RhY1KtufMstuebKE .rough-node .label,#mermaid-svg-RhY1KtufMstuebKE .node .label,#mermaid-svg-RhY1KtufMstuebKE .image-shape .label,#mermaid-svg-RhY1KtufMstuebKE .icon-shape .label{text-align:center;}#mermaid-svg-RhY1KtufMstuebKE .node.clickable{cursor:pointer;}#mermaid-svg-RhY1KtufMstuebKE .root .anchor path{fill:#333333!important;stroke-width:0;stroke:#333333;}#mermaid-svg-RhY1KtufMstuebKE .arrowheadPath{fill:#333333;}#mermaid-svg-RhY1KtufMstuebKE .edgePath .path{stroke:#333333;stroke-width:2.0px;}#mermaid-svg-RhY1KtufMstuebKE .flowchart-link{stroke:#333333;fill:none;}#mermaid-svg-RhY1KtufMstuebKE .edgeLabel{background-color:rgba(232,232,232, 0.8);text-align:center;}#mermaid-svg-RhY1KtufMstuebKE .edgeLabel p{background-color:rgba(232,232,232, 0.8);}#mermaid-svg-RhY1KtufMstuebKE .edgeLabel rect{opacity:0.5;background-color:rgba(232,232,232, 0.8);fill:rgba(232,232,232, 0.8);}#mermaid-svg-RhY1KtufMstuebKE .labelBkg{background-color:rgba(232, 232, 232, 0.5);}#mermaid-svg-RhY1KtufMstuebKE .cluster rect{fill:#ffffde;stroke:#aaaa33;stroke-width:1px;}#mermaid-svg-RhY1KtufMstuebKE .cluster text{fill:#333;}#mermaid-svg-RhY1KtufMstuebKE .cluster span{color:#333;}#mermaid-svg-RhY1KtufMstuebKE div.mermaidTooltip{position:absolute;text-align:center;max-width:200px;padding:2px;font-family:\\&#8221;trebuchet ms\\&#8221;,verdana,arial,sans-serif;font-size:12px;background:hsl(80, 100%, 96.2745098039%);border:1px solid #aaaa33;border-radius:2px;pointer-events:none;z-index:100;}#mermaid-svg-RhY1KtufMstuebKE .flowchartTitleText{text-anchor:middle;font-size:18px;fill:#333;}#mermaid-svg-RhY1KtufMstuebKE rect.text{fill:none;stroke-width:0;}#mermaid-svg-RhY1KtufMstuebKE .icon-shape,#mermaid-svg-RhY1KtufMstuebKE .image-shape{background-color:rgba(232,232,232, 0.8);text-align:center;}#mermaid-svg-RhY1KtufMstuebKE .icon-shape p,#mermaid-svg-RhY1KtufMstuebKE .image-shape p{background-color:rgba(232,232,232, 0.8);padding:2px;}#mermaid-svg-RhY1KtufMstuebKE .icon-shape rect,#mermaid-svg-RhY1KtufMstuebKE .image-shape rect{opacity:0.5;background-color:rgba(232,232,232, 0.8);fill:rgba(232,232,232, 0.8);}#mermaid-svg-RhY1KtufMstuebKE .label-icon{display:inline-block;height:1em;overflow:visible;vertical-align:-0.125em;}#mermaid-svg-RhY1KtufMstuebKE .node .label-icon path{fill:currentColor;stroke:revert;stroke-width:revert;}#mermaid-svg-RhY1KtufMstuebKE :root{&#8211;mermaid-font-family:\\&#8221;trebuchet ms\\&#8221;,verdana,arial,sans-serif;}<span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/p>\n<p>\u662f<\/p>\n<p><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/p>\n<p>\u662f<\/p>\n<p><\/span><span class=\"edgeLabel\"><\/p>\n<p>\u5426<\/p>\n<p><\/span><span class=\"edgeLabel\"><\/p>\n<p>\u5426<\/p>\n<p><\/span><span class=\"edgeLabel\"><\/p>\n<p>\u662f<\/p>\n<p><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/p>\n<p>\u5426<\/p>\n<p><\/span><span class=\"edgeLabel\"><\/p>\n<p>\u662f<\/p>\n<p><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/p>\n<p>\u5426<\/p>\n<p><\/span><span class=\"edgeLabel\"><\/span><span class=\"edgeLabel\"><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u7cfb\u7edf\u5f02\u5e38\u544a\u8b66<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u6307\u6807\u68c0\u6d4b&#xff1a;\u635f\u5931\/\u68af\u5ea6\/\u8f93\u51fa\u662f\u5426\u5f02\u5e38<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u6a21\u578b\u5d29\u6e83\u6545\u969c<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u6267\u884c\u7f13\u89e3\u63aa\u65bd&#xff1a;\u68af\u5ea6\u88c1\u526a\/\u6b63\u5219\u5316\/\u63a8\u7406\u964d\u7ea7<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u9a8c\u8bc1\u6545\u969c\u662f\u5426\u89e3\u51b3<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u6062\u590d\u6b63\u5e38\u670d\u52a1<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u89e6\u53d1\u4eba\u5de5\u5e72\u9884<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u6307\u6807\u68c0\u6d4b&#xff1a;GPU\/\u663e\u5b58\/\u5ef6\u8fdf\u662f\u5426\u5f02\u5e38<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u7b97\u529b\u74f6\u9888\u6545\u969c<\/p>\n<p><\/span><span class=\"nodeLabel\"><\/p>\n<p>\u6267\u884c\u7f13\u89e3\u63aa\u65bd&#xff1a;\u6a21\u578b\u91cf\u5316\/checkpoint\/\u8d44\u6e90\u8c03\u5ea6<\/p>\n<p><\/span><span 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