[vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n
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\u6839\u64da\u60a8\u7684\u8981\u6c42,\u60a8\u53ef\u80fd\u6703\u9078\u64c7:<\/p>\n \u4f8b\u5982,\u4e00\u500b\u5c0f\u578bAI\u65b0\u5275\u516c\u53f8\u7684\u4e2d\u7aef\u8a2d\u7f6e\u53ef\u80fd\u5982\u4e0b\u6240\u793a:<\/p>\n [\/vc_column_text][\/vc_column][\/vc_row] [vc_row el_class=”blog-detail-section”][vc_column][vc_column_text] <\/p>\n \u9999\u6e2f\u7684\u6230\u7565\u4f4d\u7f6e\u4f7f\u5176\u6210\u70baGPU\u4f3a\u670d\u5668\u79df\u7528\u548c\u4f3a\u670d\u5668\u8a17\u7ba1\u7684\u7406\u60f3\u4e2d\u5fc3\u3002\u5e02\u5834\u4e0a\u7684\u4e3b\u8981\u53c3\u8207\u8005\u63d0\u4f9b\u5404\u7a2e\u9078\u64c7,\u50f9\u683c\u6839\u64da\u914d\u7f6e\u548c\u670d\u52d9\u7d1a\u5225\u800c\u6709\u6240\u4e0d\u540c\u3002\u5728\u9078\u64c7\u63d0\u4f9b\u5546\u6642,\u8acb\u8003\u616e\u6b63\u5e38\u904b\u884c\u6642\u9593\u4fdd\u8b49\u3001\u652f\u63f4\u54c1\u8cea\u548c\u53ef\u64f4\u5c55\u6027\u9078\u9805\u7b49\u56e0\u7d20\u3002<\/p>\n [\/vc_column_text][\/vc_column][\/vc_row] [vc_row el_class=”blog-detail-section”][vc_column][vc_column_text] <\/p>\n \u8981\u5f9eGPU\u4f3a\u670d\u5668\u4e2d\u69a8\u53d6\u6bcf\u4e00\u5206\u6548\u80fd:<\/p>\n \u4ee5\u4e0b\u662f\u76e3\u63a7GPU\u4f7f\u7528\u60c5\u6cc1\u7684bash\u8173\u672c:<\/p>\n \u6b64\u8173\u672c\u63d0\u4f9bGPU\u5229\u7528\u7387\u548c\u8a18\u61b6\u9ad4\u4f7f\u7528\u60c5\u6cc1\u7684\u5373\u6642\u66f4\u65b0,\u5e6b\u52a9\u60a8\u512a\u5316\u5de5\u4f5c\u8ca0\u8f09\u3002<\/p>\n [\/vc_column_text][\/vc_column][\/vc_row] [vc_row el_class=”blog-detail-section”][vc_column][vc_column_text] <\/p>\n \u8acb\u95dc\u6ce8\u9019\u4e9b\u65b0\u8208\u8da8\u52e2:<\/p>\n \u96a8\u8457\u5c0dGPU\u904b\u7b97\u80fd\u529b\u9700\u6c42\u7684\u6301\u7e8c\u589e\u9577,\u9999\u6e2f\u7684\u4f3a\u670d\u5668\u5e02\u5834\u6709\u671b\u63d0\u4f9b\u5c16\u7aef\u89e3\u6c7a\u65b9\u6848\u4f86\u6eff\u8db3\u9019\u4e9b\u4e0d\u65b7\u767c\u5c55\u7684\u9700\u6c42\u3002<\/p>\n [\/vc_column_text][\/vc_column][\/vc_row] [vc_row el_class=”blog-detail-section”][vc_column][vc_column_text] <\/p>\n \u9078\u64c7\u5408\u9069\u7684GPU\u4f3a\u670d\u5668\u914d\u7f6e\u9700\u8981\u5728\u6548\u80fd\u9700\u6c42\u3001\u9810\u7b97\u9650\u5236\u548c\u672a\u4f86\u53ef\u64f4\u5c55\u6027\u9700\u6c42\u4e4b\u9593\u9032\u884c\u6b0a\u8861\u3002\u901a\u904e\u4e86\u89e3\u95dc\u9375\u7d44\u4ef6\u4e26\u8003\u616e\u9999\u6e2f\u4f3a\u670d\u5668\u5e02\u5834\u7684\u7368\u7279\u512a\u52e2,\u60a8\u53ef\u4ee5\u505a\u51fa\u8207\u904b\u7b97\u76ee\u6a19\u76f8\u7b26\u7684\u660e\u667a\u6c7a\u7b56\u3002\u7121\u8ad6\u60a8\u662f\u6df1\u5165\u7814\u7a76\u6df1\u5ea6\u5b78\u7fd2\u3001\u8655\u7406\u5927\u6578\u64da\u9084\u662f\u6e32\u67d3\u8907\u96dc\u76843D\u6a21\u578b,\u5408\u9069\u7684GPU\u4f3a\u670d\u5668\u8a2d\u7f6e\u90fd\u53ef\u80fd\u6210\u70ba\u60a8\u5c08\u6848\u7684\u904a\u6232\u898f\u5247\u6539\u8b8a\u8005\u3002<\/p>\n [\/vc_column_text][\/vc_column][\/vc_row] [vc_row el_class=”blog-detail-section”][vc_column][vc_column_text] <\/p>\n \u901a\u904e\u8003\u616e\u9019\u4e9b\u56e0\u7d20\u4e26\u5229\u7528\u9999\u6e2f\u5e02\u5834\u4e0a\u53ef\u7528\u7684\u5f37\u5927GPU\u4f3a\u670d\u5668\u9078\u9805,\u60a8\u53ef\u4ee5\u69cb\u5efa\u4e00\u500b\u91dd\u5c0d\u7279\u5b9a\u9700\u6c42\u91cf\u8eab\u5b9a\u88fd\u7684\u5f37\u5927\u904b\u7b97\u8a2d\u7f6e\u3002\u7121\u8ad6\u60a8\u662f\u7d93\u71dfAI\u65b0\u5275\u516c\u53f8\u9084\u662f\u9032\u884c\u8907\u96dc\u7684\u8cc7\u6599\u5206\u6790,\u6b63\u78ba\u7684GPU\u914d\u7f6e\u90fd\u53ef\u4ee5\u5c07\u60a8\u7684\u5c08\u6848\u63a8\u5411\u65b0\u7684\u6548\u80fd\u548c\u6548\u7387\u9ad8\u5ea6\u3002<\/p>\n <\/body> [vc_row el_class=”blog-detail-section”][vc_ […]<\/p>\n\u4e86\u89e3GPU\u4f3a\u670d\u5668\u914d\u7f6e\u8981\u9ede<\/strong><\/h2>\n
\n
GPU\u4f3a\u670d\u5668\u61c9\u7528:\u5f9e\u4eba\u5de5\u667a\u6167\u5230\u6e32\u67d3<\/strong><\/h2>\n
\n
import torch\r\nimport torch.nn as nn\r\nimport torch.optim as optim\r\n\r\n# Define a simple neural network\r\nclass Net(nn.Module):\r\n def __init__(self):\r\n super(Net, self).__init__()\r\n self.fc1 = nn.Linear(784, 128)\r\n self.fc2 = nn.Linear(128, 10)\r\n\r\n def forward(self, x):\r\n x = torch.relu(self.fc1(x))\r\n x = self.fc2(x)\r\n return x\r\n\r\n# Initialize the model\r\nmodel = Net().cuda() # Move the model to GPU\r\n\r\n# Define loss function and optimizer\r\ncriterion = nn.CrossEntropyLoss()\r\noptimizer = optim.SGD(model.parameters(), lr=0.01)\r\n\r\n# Training loop (pseudo-code)\r\nfor epoch in range(num_epochs):\r\n for data, target in train_loader:\r\n data, target = data.cuda(), target.cuda() # Move data to GPU\r\n optimizer.zero_grad()\r\n output = model(data)\r\n loss = criterion(output, target)\r\n loss.backward()\r\n optimizer.step()\r\n<\/code><\/pre>\n\u6839\u64da\u9700\u6c42\u5b9a\u88fdGPU\u914d\u7f6e<\/strong><\/h2>\n
\n
\n
\u9999\u6e2fGPU\u4f3a\u670d\u5668\u5e02\u5834:\u4e00\u89bd<\/strong><\/h2>\n
\u512a\u5316GPU\u4f3a\u670d\u5668\u6548\u80fd<\/strong><\/h2>\n
\n
nvidia-smi<\/code>\u7b49\u5de5\u5177\u76e3\u63a7GPU\u5229\u7528\u7387<\/li>\n<\/ul>\n#!\/bin\/bash\r\nwhile true; do\r\n nvidia-smi --query-gpu=timestamp,name,utilization.gpu,memory.used,memory.total --format=csv\r\n sleep 5\r\ndone\r\n<\/code><\/pre>\nGPU\u4f3a\u670d\u5668\u6280\u8853\u7684\u672a\u4f86\u8da8\u52e2<\/strong><\/h2>\n
\n
\u7d50\u8ad6:\u9078\u64c7\u7406\u60f3\u7684GPU\u4f3a\u670d\u5668\u914d\u7f6e<\/strong><\/h2>\n
\u5e38\u898b\u554f\u984c<\/strong><\/h2>\n
\n
\n \u7b54:\u9019\u53d6\u6c7a\u65bc\u60a8\u7684\u6a21\u578b\u5927\u5c0f\u548c\u8a13\u7df4\u8cc7\u6599\u3002\u5f9e1-2\u500bGPU\u958b\u59cb,\u5982\u679c\u9700\u8981\u53ef\u4ee5\u64f4\u5c55\u3002<\/li>\n
\n \u7b54:\u5c0d\u65bc\u53ef\u4e26\u884c\u5316\u7684\u4efb\u52d9,\u591a\u500bGPU\u901a\u5e38\u63d0\u4f9b\u66f4\u597d\u7684\u6548\u80fd\u3002\u5c0d\u65bc\u5927\u578b\u8907\u96dc\u6a21\u578b,\u8f03\u5c11\u4f46\u66f4\u5f37\u5927\u7684GPU\u53ef\u80fd\u66f4\u53ef\u53d6\u3002<\/li>\n
\n \u7b54:\u96d6\u7136GPU\u8655\u7406\u7e41\u91cd\u7684\u904b\u7b97,\u4f46\u5f37\u5927\u7684CPU\u5c0d\u65bc\u8cc7\u6599\u9810\u8655\u7406\u548c\u7ba1\u7406\u6574\u9ad4\u5de5\u4f5c\u8ca0\u8f09\u81f3\u95dc\u91cd\u8981\u3002<\/li>\n
\n \u7b54:\u9999\u6e2f\u70ba\u4e9e\u6d32\u548c\u897f\u65b9\u5e02\u5834\u63d0\u4f9b\u4e86\u51fa\u8272\u7684\u9023\u63a5\u6027\u3001\u53ef\u9760\u7684\u57fa\u790e\u8a2d\u65bd\u548c\u53cb\u597d\u7684\u5546\u696d\u74b0\u5883\u3002<\/li>\n
\n \u7b54:\u4f7f\u7528NVIDIA\u7684\u529f\u7387\u8a08\u7b97\u5668\u7b49\u5de5\u5177,\u6216\u67e5\u95b1\u6bcf\u500b\u7d44\u4ef6\u7684TDP(\u71b1\u8a2d\u8a08\u529f\u8017)\u4f86\u7372\u5f97\u4f30\u7b97\u3002<\/li>\n<\/ol>\n
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