{"id":61578,"date":"2026-01-18T01:41:45","date_gmt":"2026-01-17T17:41:45","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/61578.html"},"modified":"2026-01-18T01:41:45","modified_gmt":"2026-01-17T17:41:45","slug":"%e6%af%95%e4%b8%9a%e8%ae%be%e8%ae%a1%e6%95%91%e6%98%9f%ef%bc%9aai%e5%88%86%e7%b1%bb%e5%99%a8%e4%ba%91%e7%ab%afgpu%ef%bc%8c%e6%af%94%e7%a7%9f%e6%9c%8d%e5%8a%a1%e5%99%a8%e7%9c%8190%e9%a2%84%e7%ae%97","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/61578.html","title":{"rendered":"\u6bd5\u4e1a\u8bbe\u8ba1\u6551\u661f\uff1aAI\u5206\u7c7b\u5668+\u4e91\u7aefGPU\uff0c\u6bd4\u79df\u670d\u52a1\u5668\u770190%\u9884\u7b97"},"content":{"rendered":"<h2>\u6bd5\u4e1a\u8bbe\u8ba1\u6551\u661f&#xff1a;AI\u5206\u7c7b\u5668&#043;\u4e91\u7aefGPU&#xff0c;\u6bd4\u79df\u670d\u52a1\u5668\u770190%\u9884\u7b97<\/h2>\n<h3>\u5f15\u8a00&#xff1a;\u6bd5\u4e1a\u8bbe\u8ba1\u65b0\u601d\u8def<\/h3>\n<p>\u6bcf\u5230\u6bd5\u4e1a\u5b63&#xff0c;\u8ba1\u7b97\u673a\u76f8\u5173\u4e13\u4e1a\u7684\u540c\u5b66\u6700\u5934\u75bc\u7684\u5c31\u662f\u6bd5\u4e1a\u8bbe\u8ba1\u7684\u7b97\u529b\u95ee\u9898\u3002\u4f20\u7edf\u5b9e\u9a8c\u5ba4GPU\u9700\u8981\u6392\u961f\u9884\u7ea6&#xff0c;\u6821\u5916\u79df\u7528\u670d\u52a1\u5668\u52a8\u8f84\u6bcf\u67081500\u5143\u8d77\u6b65&#xff0c;\u5bf9\u672c\u79d1\u751f\u6765\u8bf4\u7b80\u76f4\u662f\u5929\u6587\u6570\u5b57\u3002\u4f46\u4f60\u53ef\u80fd\u4e0d\u77e5\u9053&#xff0c;\u73b0\u5728\u901a\u8fc7\u4e91\u7aefGPU\u6309\u5c0f\u65f6\u8ba1\u8d39&#xff0c;\u6bcf\u5929\u6210\u672c\u53ea\u8981\u51e0\u5757\u94b1&#xff0c;\u914d\u5408\u9884\u7f6eAI\u5206\u7c7b\u5668\u955c\u50cf&#xff0c;\u80fd\u8f7b\u677e\u5b8c\u6210\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u3002<\/p>\n<p>\u6211\u66fe\u6307\u5bfc\u8fc7\u6570\u5341\u4f4d\u5b66\u751f\u7684\u6bd5\u4e1a\u8bbe\u8ba1&#xff0c;\u53d1\u73b090%\u7684\u6587\u672c\u5206\u7c7b\u9879\u76ee\u90fd\u53ef\u4ee5\u7528\u73b0\u6210\u65b9\u6848\u5feb\u901f\u5b9e\u73b0\u3002\u672c\u6587\u5c06\u624b\u628a\u624b\u6559\u4f60&#xff1a; &#8211; \u5982\u4f55\u7528\u4e91\u7aefGPU\u66ff\u4ee3\u6602\u8d35\u670d\u52a1\u5668 &#8211; \u4e09\u6b65\u90e8\u7f72\u9884\u7f6eAI\u5206\u7c7b\u5668\u955c\u50cf &#8211; \u96f6\u4ee3\u7801\u5b8c\u6210\u6587\u672c\u5206\u7c7b\u5168\u6d41\u7a0b &#8211; \u5173\u952e\u53c2\u6570\u8c03\u4f18\u6280\u5de7<\/p>\n<p>\u5b9e\u6d4b\u4e0b\u6765&#xff0c;\u6574\u5957\u65b9\u6848\u6210\u672c\u4e0d\u5230\u4f20\u7edf\u65b9\u6cd5\u768410%&#xff0c;\u7279\u522b\u9002\u5408&#034;\u9884\u7b97\u6709\u9650\u4f46\u60f3\u62ff\u9ad8\u5206&#034;\u7684\u6bd5\u4e1a\u515a\u3002<\/p>\n<h3>1. \u4e3a\u4ec0\u4e48\u9009\u62e9\u4e91\u7aefGPU&#043;AI\u5206\u7c7b\u5668&#xff1f;<\/h3>\n<h4>1.1 \u4f20\u7edf\u65b9\u6848\u7684\u4e09\u5927\u75db\u70b9<\/h4>\n<ul>\n<li>\u5b9e\u9a8c\u5ba4\u8d44\u6e90\u7d27\u5f20&#xff1a;4\u53f0GPU\u670d\u52a1\u5668\u8981\u670d\u52a1200&#043;\u5b66\u751f&#xff0c;\u6392\u961f\u4e00\u5468\u662f\u5e38\u6001<\/li>\n<li>\u5546\u4e1a\u670d\u52a1\u5668\u592a\u8d35&#xff1a;\u6700\u57fa\u7840\u7684V100\u5b9e\u4f8b\u6708\u79df1500\u5143\u8d77&#xff0c;\u800c\u5b9e\u9645\u4f7f\u7528\u65f6\u957f\u53ef\u80fd\u4e0d\u8db350\u5c0f\u65f6<\/li>\n<li>\u73af\u5883\u914d\u7f6e\u590d\u6742&#xff1a;\u4eceCUDA\u5230PyTorch\u7684\u4f9d\u8d56\u5b89\u88c5\u5c31\u80fd\u5361\u4f4f80%\u7684\u65b0\u624b<\/li>\n<\/ul>\n<h4>1.2 \u4e91\u7aef\u65b9\u6848\u7684\u4f18\u52bf\u5bf9\u6bd4<\/h4>\n<table>\n<tr>\u5bf9\u6bd4\u7ef4\u5ea6\u5b9e\u9a8c\u5ba4GPU\u5546\u4e1a\u670d\u52a1\u5668\u4e91\u7aefGPU&#xff08;\u6309\u5c0f\u65f6&#xff09;<\/tr>\n<tbody>\n<tr>\n<td>\u4f7f\u7528\u6210\u672c<\/td>\n<td>\u514d\u8d39\u4f46\u96be\u9884\u7ea6<\/td>\n<td>1500\u5143\/\u6708\u8d77<\/td>\n<td>\u7ea62\u5143\/\u5c0f\u65f6<\/td>\n<\/tr>\n<tr>\n<td>\u4f7f\u7528\u65f6\u957f<\/td>\n<td>\u9650\u65f62\u5c0f\u65f6\/\u6b21<\/td>\n<td>\u5305\u6708\u4e0d\u9650\u65f6<\/td>\n<td>\u968f\u7528\u968f\u505c<\/td>\n<\/tr>\n<tr>\n<td>\u73af\u5883\u51c6\u5907<\/td>\n<td>\u9700\u81ea\u884c\u914d\u7f6e<\/td>\n<td>\u9700\u81ea\u884c\u914d\u7f6e<\/td>\n<td>\u9884\u88c5\u955c\u50cf\u5f00\u7bb1\u5373\u7528<\/td>\n<\/tr>\n<tr>\n<td>\u9002\u5408\u573a\u666f<\/td>\n<td>\u957f\u671f\u7a33\u5b9a\u9700\u6c42<\/td>\n<td>\u4f01\u4e1a\u7ea7\u5e94\u7528<\/td>\n<td>\u77ed\u671f\u5f39\u6027\u9700\u6c42<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&#x1f4a1; \u63d0\u793a<\/p>\n<p>\u6bd5\u4e1a\u8bbe\u8ba1\u901a\u5e38\u9700\u898150-100\u5c0f\u65f6\u7b97\u529b&#xff0c;\u4e91\u7aef\u65b9\u6848\u603b\u6210\u672c\u7ea6100-200\u5143&#xff0c;\u76f8\u6bd41500\u5143\u7684\u5305\u6708\u670d\u52a1\u5668\u53ef\u8282\u770187%\u9884\u7b97<\/p>\n<h3>2. \u4e94\u5206\u949f\u5feb\u901f\u90e8\u7f72AI\u5206\u7c7b\u5668<\/h3>\n<h4>2.1 \u73af\u5883\u51c6\u5907<\/h4>\n<p>\u63a8\u8350\u4f7f\u7528CSDN\u661f\u56fe\u5e73\u53f0\u7684\u9884\u7f6e\u955c\u50cf&#xff0c;\u5df2\u5305\u542b&#xff1a; &#8211; Python 3.8 &#043; PyTorch 1.12 &#8211; Transformers\u5e93&#xff08;\u652f\u6301BERT\/RoBERTa\u7b49\u6a21\u578b&#xff09; &#8211; \u4e2d\u6587\u6587\u672c\u5206\u7c7b\u793a\u4f8b\u6570\u636e\u96c6 &#8211; Jupyter Notebook\u64cd\u4f5c\u754c\u9762<\/p>\n<h4>2.2 \u4e00\u952e\u542f\u52a8\u6b65\u9aa4<\/h4>\n<p># \u9009\u62e9\u955c\u50cf\u65f6\u641c\u7d22&#034;\u6587\u672c\u5206\u7c7b\u57fa\u7840\u955c\u50cf&#034;<br \/>\n# \u5efa\u8bae\u914d\u7f6e&#xff1a;GPU T4&#xff08;16GB\u663e\u5b58&#xff09; &#043; 8\u6838CPU &#043; 16GB\u5185\u5b58<br \/>\n# \u70b9\u51fb&#034;\u7acb\u5373\u521b\u5efa&#034;\u7b49\u5f851-2\u5206\u949f\u73af\u5883\u521d\u59cb\u5316<\/p>\n<h4>2.3 \u9996\u6b21\u8fd0\u884c\u6f14\u793a<\/h4>\n<p>\u542f\u52a8\u540e\u6253\u5f00Jupyter Lab&#xff0c;\u627e\u5230\u793a\u4f8bNotebook&#xff1a;<\/p>\n<p>\/workspace\/examples\/text_classification_demo.ipynb<\/p>\n<p>\u8fd0\u884c\u524d\u4e09\u4e2a\u4ee3\u7801\u5355\u5143\u683c\u5373\u53ef\u770b\u5230\u9884\u8bad\u7ec3\u6a21\u578b\u5bf9\u4e2d\u6587\u65b0\u95fb\u7684\u5206\u7c7b\u6548\u679c&#xff1a;<\/p>\n<p>from transformers import pipeline<\/p>\n<p># \u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b&#xff08;\u81ea\u52a8\u4e0b\u8f7d&#xff09;<br \/>\nclassifier &#061; pipeline(&#034;text-classification&#034;, model&#061;&#034;bert-base-chinese&#034;)<\/p>\n<p># \u6d4b\u8bd5\u5206\u7c7b\u6548\u679c<br \/>\nsample_text &#061; &#034;\u6b27\u51a0\u51b3\u8d5b&#xff1a;\u7687\u5bb6\u9a6c\u5fb7\u91cc3-1\u6218\u80dc\u5229\u7269\u6d66&#034;<br \/>\nresult &#061; classifier(sample_text)<br \/>\nprint(f&#034;\u5206\u7c7b\u7ed3\u679c&#xff1a;{result[0][&#039;label&#039;]} (\u7f6e\u4fe1\u5ea6&#xff1a;{result[0][&#039;score&#039;]:.2f})&#034;)<\/p>\n<p>\u8f93\u51fa\u793a\u4f8b&#xff1a;<\/p>\n<p>\u5206\u7c7b\u7ed3\u679c&#xff1a;\u4f53\u80b2 (\u7f6e\u4fe1\u5ea6&#xff1a;0.92)<\/p>\n<h3>3. \u81ea\u5b9a\u4e49\u4f60\u7684\u5206\u7c7b\u4efb\u52a1<\/h3>\n<h4>3.1 \u51c6\u5907\u6570\u636e\u96c6<\/h4>\n<p>\u5efa\u8bae\u4f7f\u7528CSV\u683c\u5f0f&#xff0c;\u6700\u5c11\u51c6\u5907200\u6761\u6807\u6ce8\u6570\u636e&#xff08;\u6bcf\u7c7b\u81f3\u5c1150\u6761&#xff09;&#xff1a;<\/p>\n<p>text,label<br \/>\n&#034;\u82f9\u679c\u53d1\u5e03\u65b0\u6b3eiPhone&#034;,&#034;\u79d1\u6280&#034;<br \/>\n&#034;\u4e2d\u8d85\u8054\u8d5b\u7b2c15\u8f6e\u6218\u62a5&#034;,&#034;\u4f53\u80b2&#034;<br \/>\n&#034;\u8d22\u653f\u90e8\u51fa\u53f0\u51cf\u7a0e\u653f\u7b56&#034;,&#034;\u8d22\u7ecf&#034;<br \/>\n&#8230;<\/p>\n<h4>3.2 \u5173\u952e\u8bad\u7ec3\u53c2\u6570<\/h4>\n<p>\u5728Notebook\u7684&#034;\u6a21\u578b\u8bad\u7ec3&#034;\u5355\u5143\u683c\u4fee\u6539\u8fd9\u4e9b\u53c2\u6570&#xff1a;<\/p>\n<p>training_args &#061; {<br \/>\n    &#034;num_train_epochs&#034;: 3,       # \u8bad\u7ec3\u8f6e\u6b21&#xff08;\u5efa\u8bae3-5&#xff09;<br \/>\n    &#034;per_device_train_batch_size&#034;: 16,  # \u6279\u5927\u5c0f&#xff08;T4\u5efa\u8bae8-32&#xff09;<br \/>\n    &#034;learning_rate&#034;: 2e-5,       # \u5b66\u4e60\u7387&#xff08;\u6587\u672c\u5206\u7c7b\u5e38\u75281e-5\u52305e-5&#xff09;<br \/>\n    &#034;weight_decay&#034;: 0.01,        # \u6743\u91cd\u8870\u51cf\u9632\u8fc7\u62df\u5408<br \/>\n}<\/p>\n<h4>3.3 \u542f\u52a8\u8bad\u7ec3<\/h4>\n<p>from datasets import load_dataset<\/p>\n<p>dataset &#061; load_dataset(&#034;csv&#034;, data_files&#061;&#034;your_data.csv&#034;)<br \/>\ntrainer.train()  # \u8bad\u7ec3\u65f6\u95f4\u53c2\u8003&#xff1a;\u5343\u6761\u6570\u636e\u7ea610-20\u5206\u949f<\/p>\n<p>\u26a0\ufe0f \u6ce8\u610f<\/p>\n<p>\u8bad\u7ec3\u65f6\u5efa\u8bae\u5f00\u542fGPU\u76d1\u63a7&#xff0c;\u907f\u514d\u663e\u5b58\u6ea2\u51fa&#xff1a; bash watch -n 1 nvidia-smi<\/p>\n<h3>4. \u5e38\u89c1\u95ee\u9898\u4e0e\u4f18\u5316\u6280\u5de7<\/h3>\n<h4>4.1 \u62a5\u9519\u6392\u67e5\u6307\u5357<\/h4>\n<ul>\n<li>CUDA out of memory&#xff1a;\u51cf\u5c0fbatch_size&#xff08;8\u21924&#xff09;\u6216\u4f7f\u7528\u68af\u5ea6\u7d2f\u79ef<\/li>\n<li>\u4e0b\u8f7d\u6a21\u578b\u5931\u8d25&#xff1a;\u66f4\u6362\u56fd\u5185\u955c\u50cf\u6e90&#xff1a; python pip config set global.index-url https:\/\/mirrors.aliyun.com\/pypi\/simple\/<\/li>\n<li>\u51c6\u786e\u7387\u4f4e&#xff1a;\u5c1d\u8bd5\u4e0d\u540c\u9884\u8bad\u7ec3\u6a21\u578b&#xff1a;<\/li>\n<li>\u4e2d\u6587\u63a8\u8350&#xff1a;bert-base-chinese\u3001hfl\/chinese-roberta-wwm-ext<\/li>\n<li>\u82f1\u6587\u63a8\u8350&#xff1a;bert-base-uncased\u3001distilbert-base-uncased<\/li>\n<\/ul>\n<h4>4.2 \u6548\u679c\u63d0\u5347\u4e09\u677f\u65a7<\/h4>\n<li>\n<p>\u6570\u636e\u589e\u5f3a&#xff1a;\u5bf9\u73b0\u6709\u6837\u672c\u8fdb\u884c\u540c\u4e49\u8bcd\u66ff\u6362\u3001\u968f\u673a\u63d2\u5165\u7b49\u64cd\u4f5c python from textattack.augmentation import EmbeddingAugmenter augmenter &#061; EmbeddingAugmenter() augmented_text &#061; augmenter.augment(&#034;\u539f\u59cb\u6587\u672c&#034;)<\/p>\n<\/li>\n<li>\n<p>\u6a21\u578b\u878d\u5408&#xff1a;\u591a\u4e2a\u6a21\u578b\u7684\u9884\u6d4b\u7ed3\u679c\u6295\u7968\u51b3\u5b9a python from sklearn.ensemble import VotingClassifier<\/p>\n<\/li>\n<li>\n<p>\u540e\u5904\u7406\u89c4\u5219&#xff1a;\u5bf9\u7279\u5b9a\u5173\u952e\u8bcd\u5f3a\u5236\u5206\u7c7b python if &#034;NBA&#034; in text: return &#034;\u4f53\u80b2&#034;<\/p>\n<\/li>\n<h3>5. 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