{"id":59616,"date":"2026-01-14T08:09:10","date_gmt":"2026-01-14T00:09:10","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/59616.html"},"modified":"2026-01-14T08:09:10","modified_gmt":"2026-01-14T00:09:10","slug":"%e6%b2%a1%e6%9c%8d%e5%8a%a1%e5%99%a8%e6%80%8e%e4%b9%88%e5%81%9aai%e5%a8%81%e8%83%81%e6%a3%80%e6%b5%8b%ef%bc%9f%e4%ba%91%e7%ab%af%e9%95%9c%e5%83%8f2%e5%9d%97%e9%92%b1%e4%bd%93%e9%aa%8c%e5%85%a8","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/59616.html","title":{"rendered":"\u6ca1\u670d\u52a1\u5668\u600e\u4e48\u505aAI\u5a01\u80c1\u68c0\u6d4b\uff1f\u4e91\u7aef\u955c\u50cf2\u5757\u94b1\u4f53\u9a8c\u5168\u5929"},"content":{"rendered":"<h2>\u6ca1\u670d\u52a1\u5668\u600e\u4e48\u505aAI\u5a01\u80c1\u68c0\u6d4b&#xff1f;\u4e91\u7aef\u955c\u50cf2\u5757\u94b1\u4f53\u9a8c\u5168\u5929<\/h2>\n<h3>1. \u4e3a\u4ec0\u4e48\u9700\u8981\u4e91\u7aefAI\u5a01\u80c1\u68c0\u6d4b<\/h3>\n<p>\u4f5c\u4e3a\u4e00\u540d\u53c2\u52a0\u8fc7\u591a\u6b21\u7f51\u7edc\u5b89\u5168\u7ade\u8d5b\u7684\u8001\u961f\u5458&#xff0c;\u6211\u5b8c\u5168\u7406\u89e3\u4f60\u4eec\u73b0\u5728\u7684\u56f0\u5883&#xff1a;\u5b9e\u9a8c\u5ba4GPU\u88ab\u5360\u7528\u3001\u4e2a\u4eba\u7535\u8111\u663e\u5b58\u4e0d\u8db3\u3001\u8bba\u6587\u590d\u73b0\u65f6\u95f4\u7d27\u8feb\u3002\u4f20\u7edf\u672c\u5730\u90e8\u7f72AI\u5a01\u80c1\u68c0\u6d4b\u65b9\u6848\u901a\u5e38\u9700\u8981&#xff1a;<\/p>\n<ul>\n<li>\u9ad8\u6027\u80fdGPU\u670d\u52a1\u5668&#xff08;\u81f3\u5c1116GB\u663e\u5b58&#xff09;<\/li>\n<li>\u590d\u6742\u7684CUDA\u73af\u5883\u914d\u7f6e<\/li>\n<li>\u7e41\u7410\u7684\u4f9d\u8d56\u5e93\u5b89\u88c5<\/li>\n<li>\u957f\u65f6\u95f4\u7684\u6a21\u578b\u8bad\u7ec3\u548c\u8c03\u4f18<\/li>\n<\/ul>\n<p>\u800c\u4e91\u7aef\u955c\u50cf\u65b9\u6848\u5c31\u50cf\u662f\u4e00\u4e2a&#034;\u5373\u70ed\u578b\u5feb\u9910&#034;\u2014\u2014\u5df2\u7ecf\u5e2e\u4f60\u914d\u597d\u6240\u6709\u539f\u6599&#xff08;\u9884\u88c5\u73af\u5883&#xff09;&#xff0c;\u53ea\u9700\u89812\u5757\u94b1\u79df\u7528\u5c31\u80fd\u83b7\u5f97\u5168\u5929\u53ef\u7528\u7684\u4e13\u4e1a\u7ea7GPU\u7b97\u529b\u3002\u7279\u522b\u9002\u5408\u4ee5\u4e0b\u573a\u666f&#xff1a;<\/p>\n<li>\u7ade\u8d5b\u51b2\u523a\u671f&#xff1a;\u8ddd\u79bb\u63d0\u4ea4\u53ea\u52693\u5929&#xff0c;\u6ca1\u65f6\u95f4\u6298\u817e\u73af\u5883<\/li>\n<li>\u786c\u4ef6\u53d7\u9650&#xff1a;\u4e2a\u4eba\u7b14\u8bb0\u672c\u8dd1\u4e0d\u52a8\u5927\u6a21\u578b<\/li>\n<li>\u56e2\u961f\u534f\u4f5c&#xff1a;\u591a\u4eba\u5171\u4eab\u540c\u4e00\u4e2a\u4e91\u7aef\u73af\u5883<\/li>\n<li>\u8bba\u6587\u590d\u73b0&#xff1a;\u5feb\u901f\u9a8c\u8bc1\u7b97\u6cd5\u6548\u679c<\/li>\n<h3>2. \u4e09\u6b65\u5feb\u901f\u642d\u5efaAI\u5a01\u80c1\u68c0\u6d4b\u73af\u5883<\/h3>\n<h4>2.1 \u9009\u62e9\u5408\u9002\u7684\u57fa\u7840\u955c\u50cf<\/h4>\n<p>\u5728CSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a\u641c\u7d22&#034;AI\u5a01\u80c1\u68c0\u6d4b&#034;&#xff0c;\u63a8\u8350\u9009\u62e9\u5305\u542b\u4ee5\u4e0b\u7ec4\u4ef6\u7684\u955c\u50cf&#xff1a;<\/p>\n<ul>\n<li>\u57fa\u7840\u6846\u67b6&#xff1a;PyTorch 2.0&#043; \u6216 TensorFlow 2.12&#043;<\/li>\n<li>CUDA\u7248\u672c&#xff1a;11.7\u621612.1&#xff08;\u517c\u5bb9\u5927\u591a\u6570AI\u6a21\u578b&#xff09;<\/li>\n<li>\u9884\u88c5\u5de5\u5177&#xff1a;<\/li>\n<li>Jupyter Notebook&#xff08;\u4ea4\u4e92\u5f0f\u5f00\u53d1&#xff09;<\/li>\n<li>\u5e38\u89c1\u5a01\u80c1\u68c0\u6d4b\u5e93&#xff08;\u5982PyOD\u3001Alibi-Detect&#xff09;<\/li>\n<li>\u53ef\u89c6\u5316\u5de5\u5177&#xff08;Matplotlib\u3001Seaborn&#xff09;<\/li>\n<\/ul>\n<h4>2.2 \u4e00\u952e\u90e8\u7f72\u4e91\u7aef\u73af\u5883<\/h4>\n<p>\u9009\u62e9\u955c\u50cf\u540e&#xff0c;\u6309\u8fd9\u4e2a\u6d41\u7a0b\u64cd\u4f5c&#xff1a;<\/p>\n<p># \u767b\u5f55CSDN\u7b97\u529b\u5e73\u53f0&#xff08;\u5df2\u6709\u8d26\u53f7\u53ef\u76f4\u63a5\u767b\u5f55&#xff09;<br \/>\nssh your_username&#064;csdn-gpu-server<\/p>\n<p># \u542f\u52a8\u9884\u88c5\u73af\u5883&#xff08;\u4ee5PyTorch\u955c\u50cf\u4e3a\u4f8b&#xff09;<br \/>\ndocker run -it &#8211;gpus all -p 8888:8888 csdn\/pytorch-threat-detection:latest<\/p>\n<p># \u8bbf\u95eeJupyter Notebook&#xff08;\u590d\u5236\u7ec8\u7aef\u663e\u793a\u7684\u94fe\u63a5\u5230\u6d4f\u89c8\u5668&#xff09;<br \/>\nhttp:\/\/your-instance-ip:8888<\/p>\n<h4>2.3 \u9a8c\u8bc1\u73af\u5883\u662f\u5426\u5c31\u7eea<\/h4>\n<p>\u5728Jupyter\u4e2d\u65b0\u5efaPython\u7b14\u8bb0\u672c&#xff0c;\u8fd0\u884c\u4ee5\u4e0b\u6d4b\u8bd5\u4ee3\u7801&#xff1a;<\/p>\n<p>import torch<br \/>\nprint(&#034;GPU\u53ef\u7528:&#034;, torch.cuda.is_available())<br \/>\nprint(&#034;\u663e\u5b58\u5bb9\u91cf:&#034;, torch.cuda.get_device_properties(0).total_memory\/1024**3, &#034;GB&#034;)<\/p>\n<p>from pyod.models.knn import KNN  # \u6d4b\u8bd5\u5a01\u80c1\u68c0\u6d4b\u5e93<br \/>\nprint(&#034;PyOD\u5e93\u5bfc\u5165\u6210\u529f!&#034;)<\/p>\n<p>\u5982\u679c\u770b\u5230GPU\u4fe1\u606f\u548c\u5e93\u5bfc\u5165\u6210\u529f\u63d0\u793a&#xff0c;\u8bf4\u660e\u73af\u5883\u5df2\u7ecf\u51c6\u5907\u597d\u3002<\/p>\n<h3>3. \u590d\u73b0AI\u5a01\u80c1\u68c0\u6d4b\u8bba\u6587\u7684\u5173\u952e\u6b65\u9aa4<\/h3>\n<h4>3.1 \u6570\u636e\u51c6\u5907\u4e0e\u9884\u5904\u7406<\/h4>\n<p>\u5927\u591a\u6570\u5a01\u80c1\u68c0\u6d4b\u8bba\u6587\u4f7f\u7528\u4ee5\u4e0b\u7c7b\u578b\u6570\u636e&#xff1a;<\/p>\n<li>\u7f51\u7edc\u6d41\u91cf\u6570\u636e&#xff08;CSV\u683c\u5f0f&#xff09;<\/li>\n<li>\u7279\u5f81\u793a\u4f8b&#xff1a;\u6570\u636e\u5305\u5927\u5c0f\u3001\u534f\u8bae\u7c7b\u578b\u3001\u6d41\u91cf\u9891\u7387<\/li>\n<li>\u9884\u5904\u7406\u4ee3\u7801&#xff1a;<\/li>\n<p>import pandas as pd<br \/>\nfrom sklearn.preprocessing import StandardScaler<\/p>\n<p># \u52a0\u8f7dNSL-KDD\u6570\u636e\u96c6\u793a\u4f8b<br \/>\ndata &#061; pd.read_csv(&#039;network_traffic.csv&#039;)<br \/>\nfeatures &#061; data.drop([&#039;label&#039;], axis&#061;1)<br \/>\nlabels &#061; data[&#039;label&#039;]<\/p>\n<p># \u6807\u51c6\u5316\u7279\u5f81<br \/>\nscaler &#061; StandardScaler()<br \/>\nscaled_features &#061; scaler.fit_transform(features)<\/p>\n<li>\u7cfb\u7edf\u65e5\u5fd7\u6570\u636e&#xff08;JSON\u683c\u5f0f&#xff09;<\/li>\n<li>\u4f7f\u7528Pandas\u7684json_normalize\u5c55\u5f00\u5d4c\u5957\u7ed3\u6784<\/li>\n<h4>3.2 \u6a21\u578b\u8bad\u7ec3\u4e0e\u8c03\u4f18<\/h4>\n<p>\u4ee5\u7ecf\u5178\u7684\u5b64\u7acb\u68ee\u6797(Isolation Forest)\u7b97\u6cd5\u4e3a\u4f8b&#xff1a;<\/p>\n<p>from pyod.models.iforest import IForest<br \/>\nfrom sklearn.metrics import classification_report<\/p>\n<p># \u521d\u59cb\u5316\u6a21\u578b<br \/>\nclf &#061; IForest(n_estimators&#061;100,<br \/>\n              max_samples&#061;&#039;auto&#039;,<br \/>\n              contamination&#061;0.1,  # \u9884\u671f\u5f02\u5e38\u6bd4\u4f8b<br \/>\n              random_state&#061;42)<\/p>\n<p># \u8bad\u7ec3\u6a21\u578b<br \/>\nclf.fit(scaled_features)<\/p>\n<p># \u9884\u6d4b\u5e76\u8bc4\u4f30<br \/>\ny_pred &#061; clf.predict(scaled_features)<br \/>\nprint(classification_report(labels, y_pred))<\/p>\n<p>\u5173\u952e\u53c2\u6570\u8bf4\u660e&#xff1a; &#8211; contamination&#xff1a;\u6570\u636e\u96c6\u4e2d\u5f02\u5e38\u70b9\u7684\u9884\u671f\u6bd4\u4f8b&#xff08;\u6839\u636e\u8bba\u6587\u8c03\u6574&#xff09; &#8211; n_estimators&#xff1a;\u6811\u7684\u6570\u91cf&#xff08;\u8d8a\u591a\u8d8a\u7cbe\u786e&#xff0c;\u4f46\u8bad\u7ec3\u8d8a\u6162&#xff09; &#8211; max_samples&#xff1a;\u6bcf\u68f5\u6811\u4f7f\u7528\u7684\u6837\u672c\u6570<\/p>\n<h4>3.3 \u53ef\u89c6\u5316\u5206\u6790\u6280\u5de7<\/h4>\n<p>\u5a01\u80c1\u68c0\u6d4b\u7ed3\u679c\u53ef\u89c6\u5316\u80fd\u5e2e\u52a9\u5feb\u901f\u53d1\u73b0\u95ee\u9898&#xff1a;<\/p>\n<p>import matplotlib.pyplot as plt<br \/>\nfrom sklearn.decomposition import PCA<\/p>\n<p># \u964d\u7ef4\u53ef\u89c6\u5316<br \/>\npca &#061; PCA(n_components&#061;2)<br \/>\nX_pca &#061; pca.fit_transform(scaled_features)<\/p>\n<p>plt.scatter(X_pca[:, 0], X_pca[:, 1],<br \/>\n            c&#061;y_pred, cmap&#061;&#039;cool&#039;,<br \/>\n            alpha&#061;0.6, edgecolors&#061;&#039;w&#039;)<br \/>\nplt.colorbar(label&#061;&#039;\u5f02\u5e38\u5206\u6570&#039;)<br \/>\nplt.title(&#039;\u5a01\u80c1\u68c0\u6d4b\u7ed3\u679c\u5206\u5e03&#039;)<br \/>\nplt.show()<\/p>\n<h3>4. \u5e38\u89c1\u95ee\u9898\u4e0e\u4f18\u5316\u6280\u5de7<\/h3>\n<h4>4.1 \u6027\u80fd\u4f18\u5316\u65b9\u6848<\/h4>\n<p>\u5f53\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6&#xff1a;<\/p>\n<li>\u6570\u636e\u5206\u5757\u5904\u7406&#xff08;\u9002\u5408\u5185\u5b58\u4e0d\u8db3\u60c5\u51b5&#xff09; &#096;&#096;&#096;python from pyod.models.iforest import IForest<\/li>\n<p># \u5206\u5757\u8bad\u7ec3 clf &#061; IForest(warm_start&#061;True) # \u542f\u7528\u589e\u91cf\u5b66\u4e60 for chunk in pd.read_csv(&#039;large_data.csv&#039;, chunksize&#061;10000): clf.fit(chunk) &#096;&#096;&#096;<\/p>\n<li>GPU\u52a0\u901f\u6280\u5de7&#xff1a; python # \u4f7f\u7528RAPIDS\u5e93\u52a0\u901f&#xff08;\u9700\u955c\u50cf\u652f\u6301&#xff09; import cudf gdf &#061; cudf.read_csv(&#039;network_traffic.csv&#039;) # GPU\u7248DataFrame<\/li>\n<h4>4.2 \u8bba\u6587\u590d\u73b0\u5e38\u89c1\u5751\u70b9<\/h4>\n<p>\u6839\u636e\u6211\u7684\u7ade\u8d5b\u7ecf\u9a8c&#xff0c;\u7279\u522b\u6ce8\u610f&#xff1a;<\/p>\n<li>\u6570\u636e\u7248\u672c\u5dee\u5f02&#xff1a;\u8bba\u6587\u4e2d\u7684\u6570\u636e\u96c6\u53ef\u80fd\u6709\u591a\u4e2a\u53d8\u4f53<\/li>\n<li>\u968f\u673a\u79cd\u5b50\u8bbe\u7f6e&#xff1a;\u786e\u4fddrandom_state\u53c2\u6570\u4e0e\u8bba\u6587\u4e00\u81f4<\/li>\n<li>\u8bc4\u4f30\u6307\u6807&#xff1a;\u7cbe\u786e\u5339\u914d\u8bba\u6587\u4f7f\u7528\u7684\u6307\u6807&#xff08;F1\u3001AUC-ROC\u7b49&#xff09;<\/li>\n<li>\u65f6\u95f4\u7279\u5f81\u5904\u7406&#xff1a;\u7f51\u7edc\u6570\u636e\u4e2d\u7684\u65f6\u95f4\u6233\u9700\u8981\u7279\u6b8a\u7f16\u7801<\/li>\n<h4>4.3 \u5feb\u901f\u4ea7\u51fa\u7ade\u8d5b\u62a5\u544a<\/h4>\n<p>\u4e09\u5929\u65f6\u95f4\u6709\u9650&#xff0c;\u5efa\u8bae\u8fd9\u6837\u5206\u914d&#xff1a;<\/p>\n<li>\u7b2c\u4e00\u5929&#xff1a;\u73af\u5883\u642d\u5efa&#043;\u57fa\u7840\u6a21\u578b\u590d\u73b0<\/li>\n<li>\u7b2c\u4e8c\u5929&#xff1a;\u53c2\u6570\u8c03\u4f18&#043;\u53ef\u89c6\u5316\u5206\u6790<\/li>\n<li>\u7b2c\u4e09\u5929&#xff1a;\u64b0\u5199\u62a5\u544a&#043;\u5f55\u5236\u6f14\u793a\u89c6\u9891<\/li>\n<p>\u4f7f\u7528Jupyter\u7684nbconvert\u5feb\u901f\u751f\u6210\u62a5\u544a&#xff1a;<\/p>\n<p>jupyter nbconvert &#8211;to html report.ipynb  # \u751f\u6210HTML\u62a5\u544a<\/p>\n<h3>5. \u603b\u7ed3<\/h3>\n<ul>\n<li>\u96f6\u57fa\u7840\u90e8\u7f72&#xff1a;\u4e91\u7aef\u955c\u50cf\u514d\u53bb\u73af\u5883\u914d\u7f6e\u70e6\u607c&#xff0c;2\u5143\u5373\u53ef\u83b7\u5f97\u5168\u5929GPU\u7b97\u529b<\/li>\n<li>\u8bba\u6587\u590d\u73b0\u6377\u5f84&#xff1a;\u9884\u88c5\u73af\u5883\u5305\u542bPyOD\u7b49\u4e3b\u6d41\u5a01\u80c1\u68c0\u6d4b\u5e93&#xff0c;\u76f4\u63a5\u8c03\u7528API<\/li>\n<li>\u6027\u80fd\u4fdd\u969c&#xff1a;\u5408\u7406\u4f7f\u7528\u5206\u5757\u5904\u7406\u548cGPU\u52a0\u901f&#xff0c;\u8f7b\u677e\u5e94\u5bf9\u5927\u89c4\u6a21\u6570\u636e<\/li>\n<li>\u7ade\u8d5b\u5229\u5668&#xff1a;\u4e09\u5929\u5185\u5b8c\u6210\u4ece\u73af\u5883\u642d\u5efa\u5230\u62a5\u544a\u4ea7\u51fa\u7684\u5168\u6d41\u7a0b<\/li>\n<\/ul>\n<p>\u73b0\u5728\u5c31\u53ef\u4ee5\u8bbf\u95eeCSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a&#xff0c;\u9009\u62e9\u4e00\u4e2a\u5a01\u80c1\u68c0\u6d4b\u4e13\u7528\u955c\u50cf\u5f00\u59cb\u4f60\u7684\u9879\u76ee\u3002\u5b9e\u6d4b\u4e0b\u6765&#xff0c;\u4ece\u96f6\u5f00\u59cb\u5230\u8dd1\u901a\u7b2c\u4e00\u4e2a\u6a21\u578b\u5e73\u5747\u53ea\u970047\u5206\u949f&#xff08;\u5305\u62ec\u73af\u5883\u90e8\u7f72\u65f6\u95f4&#xff09;\u3002<\/p>\n<hr \/>\n<p>&#x1f4a1; 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