\u6df1\u5ea6\u5b78\u7fd2\u548cAI\u5de5\u4f5c\u8ca0\u8f09\u5206\u6790<\/strong><\/h2>\n

GPU\u52a0\u901f\u4f3a\u670d\u5668\u901a\u904e\u6700\u4f73\u5316\u7684\u5f35\u91cf\u904b\u7b97\u5728\u6df1\u5ea6\u5b78\u7fd2\u4efb\u52d9\u4e2d\u8868\u73fe\u51fa\u8272\u3002\u4ee5\u4e0b\u662f\u5c55\u793aGPU\u52a0\u901f\u9032\u884c\u795e\u7d93\u7db2\u8def\u8a13\u7df4\u7684PyTorch\u7bc4\u4f8b\uff1a<\/p>\n

\r\nimport torch\r\nimport torch.nn as nn\r\n\r\nclass DeepNetwork(nn.Module):\r\n    def __init__(self):\r\n        super(DeepNetwork, self).__init__()\r\n        self.layers = nn.Sequential(\r\n            nn.Linear(784, 512),\r\n            nn.ReLU(),\r\n            nn.Dropout(0.2),\r\n            nn.Linear(512, 256),\r\n            nn.ReLU(),\r\n            nn.Linear(256, 10)\r\n        )\r\n    \r\n    def forward(self, x):\r\n        return self.layers(x)\r\n\r\n# \u5c07\u6a21\u578b\u79fb\u81f3GPU\r\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\r\nmodel = DeepNetwork().to(device)\r\n\r\n# \u8a13\u7df4\u8cc7\u6599\u79fb\u81f3GPU\r\ninputs = inputs.to(device)\r\nlabels = labels.to(device)\r\n<\/code><\/pre>\n
\u79d1\u5b78\u904b\u7b97\u548c\u8cc7\u6599\u5206\u6790\u80fd\u529b<\/strong><\/h2>\n
\u914d\u5099GPU\u7684\u7f8e\u570b\u4f3a\u670d\u5668\u79df\u7528\u8a2d\u65bd\u5728\u79d1\u5b78\u904b\u7b97\u61c9\u7528\u65b9\u9762\u8868\u73fe\u51fa\u8272\u3002\u5e73\u884c\u8655\u7406\u67b6\u69cb\u5141\u8a31\uff1a<\/p>\n
\n
\u5206\u5b50\u52d5\u529b\u5b78\u6a21\u64ec<\/li>\n
\u5929\u6c23\u5efa\u6a21\u904b\u7b97<\/li>\n
\u91cf\u5b50\u5316\u5b78\u904b\u7b97<\/li>\n
\u91d1\u878d\u5e02\u5834\u5206\u6790<\/li>\n<\/ul>\n
\u6548\u80fd\u57fa\u6e96\u6e2c\u8a66\u986f\u793a\uff0cGPU\u52a0\u901f\u7684\u79d1\u5b78\u61c9\u7528\u76f8\u6bd4\u50c5\u4f7f\u7528CPU\u7684\u5be6\u73fe\u53ef\u4ee5\u5be6\u73fe10-100\u500d\u7684\u901f\u5ea6\u63d0\u5347\u3002\u4f8b\u5982\uff0c\u5728NVIDIA V100 GPU\u4e0a\uff0cGROMACS\u5206\u5b50\u52d5\u529b\u5b78\u6a21\u64ec\u53ef\u5be6\u73fe\u9ad8\u905450\u500d\u7684\u52a0\u901f\u3002<\/p>\n
\u7db2\u8def\u57fa\u790e\u8a2d\u65bd\u548c\u8cc7\u6599\u50b3\u8f38\u6700\u4f73\u5316<\/strong><\/h2>\n
\u7f8e\u570b\u57fa\u5730\u7684GPU\u4f3a\u670d\u5668\u53d7\u76ca\u65bc\u5148\u9032\u7684\u7db2\u8def\u57fa\u790e\u8a2d\u65bd\uff1a<\/p>\n
\n
\u9ad8\u983b\u5bec\u9023\u63a5\uff08100 Gbps+\uff09<\/li>\n
\u8207\u4e3b\u8981\u7db2\u969b\u7db2\u8def\u4ea4\u63db\u4e2d\u5fc3\u7684\u76f4\u63a5\u9023\u63a5<\/li>\n
\u5230\u95dc\u9375\u96f2\u7aef\u4f9b\u61c9\u5546\u7684\u4f4e\u5ef6\u9072\u8def\u7531<\/li>\n
\u5148\u9032\u7684DDoS\u9632\u8b77\u7cfb\u7d71<\/li>\n<\/ul>\n
GPU\u5de5\u4f5c\u8ca0\u8f09\u7684\u7db2\u8def\u6700\u4f73\u5316\u6280\u8853\u5305\u62ec\uff1a<\/p>\n
\r\n# \u4f7f\u7528CUDA\u6d41\u9032\u884cGPU\u8a18\u61b6\u9ad4\u7ba1\u7406\u7684\u7bc4\u4f8b\r\nstream1 = torch.cuda.Stream()\r\nstream2 = torch.cuda.Stream()\r\n\r\nwith torch.cuda.stream(stream1):\r\n    # \u975e\u540c\u6b65\u8cc7\u6599\u50b3\u8f38\r\n    data_gpu = data_cpu.cuda(non_blocking=True)\r\n    # \u904b\u7b97\r\n    result1 = model(data_gpu)\r\n\r\nwith torch.cuda.stream(stream2):\r\n    # \u5e73\u884c\u8655\u7406\r\n    result2 = another_operation()\r\n<\/code><\/pre>\n
\u6210\u672c\u6548\u76ca\u5206\u6790\u548c\u6295\u8cc7\u56de\u5831\u8003\u91cf<\/strong><\/h2>\n
\u5728\u8a55\u4f30\u7f8e\u570b\u4f3a\u670d\u5668\u79df\u7528\u74b0\u5883\u4e2d\u7684GPU\u6574\u5408\u6642\uff0c\u7e3d\u64c1\u6709\u6210\u672c\uff08TCO\uff09\u8a08\u7b97\u5fc5\u9808\u8003\u616e\u5e7e\u500b\u95dc\u9375\u7d44\u6210\u90e8\u5206\u3002\u4e3b\u8981\u8003\u91cf\u5305\u62ec\uff1a<\/p>\n
\n
\u521d\u59cb\u786c\u9ad4\u6295\u8cc7\n
\n
\u4f01\u696d\u7d1aGPU\uff08A100\u3001V100\u7cfb\u5217\uff09<\/li>\n
\u6563\u71b1\u57fa\u790e\u8a2d\u65bd\u8981\u6c42<\/li>\n
\u4f9b\u96fb\u7cfb\u7d71<\/li>\n
\u914d\u5957\u786c\u9ad4\u7d44\u4ef6<\/li>\n<\/ul>\n<\/li>\n
\u71df\u904b\u6210\u672c\n
\n
\u96fb\u529b\u6d88\u8017\u6700\u4f73\u5316<\/li>\n
\u51b7\u537b\u7cfb\u7d71\u6548\u7387<\/li>\n
\u7dad\u8b77\u8981\u6c42<\/li>\n
\u6280\u8853\u652f\u63f4\u8cc7\u6e90<\/li>\n<\/ul>\n<\/li>\n
\u6548\u80fd\u512a\u52e2\n
\n
\u5de5\u4f5c\u8ca0\u8f09\u52a0\u901f\u6307\u6a19<\/li>\n
\u8655\u7406\u6642\u9593\u6e1b\u5c11<\/li>\n
\u8cc7\u6e90\u5229\u7528\u7387\u6539\u5584<\/li>\n
\u53ef\u64f4\u5c55\u6027\u6f5b\u529b<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
\u6548\u80fd\u76e3\u63a7\u548c\u6700\u4f73\u5316\u5de5\u5177<\/strong><\/h2>\n
\u4f01\u696d\u7d1aGPU\u4f3a\u670d\u5668\u9700\u8981\u5168\u9762\u7684\u76e3\u63a7\u89e3\u6c7a\u65b9\u6848\u3002\u4ee5\u4e0b\u662f\u57fa\u672c\u76e3\u63a7\u5be6\u73fe\u7684\u6982\u8ff0\uff1a<\/p>\n
\r\n# NVIDIA\u7cfb\u7d71\u7ba1\u7406\u4ecb\u9762\u7bc4\u4f8b\r\nnvidia-smi --query-gpu=timestamp,name,pci.bus_id,driver_version,pstate,pcie.link.gen.max,\\\r\npcie.link.gen.current,temperature.gpu,utilization.gpu,utilization.memory,\\\r\nmemory.total,memory.free,memory.used --format=csv -l 5\r\n\r\n# GPU\u76e3\u63a7\u8173\u672c\r\ndef monitor_gpu():\r\n    import pynvml\r\n    pynvml.nvmlInit()\r\n    deviceCount = pynvml.nvmlDeviceGetCount()\r\n    for i in range(deviceCount):\r\n        handle = pynvml.nvmlDeviceGetHandleByIndex(i)\r\n        info = pynvml.nvmlDeviceGetMemoryInfo(handle)\r\n        print(f\"GPU:{i} \u8a18\u61b6\u9ad4\u4f7f\u7528: {info.used\/1024**2:.2f}MB\")\r\n<\/code><\/pre>\n
GPU\u4f3a\u670d\u5668\u7684\u5b89\u5168\u8003\u91cf<\/strong><\/h2>\n
\u7531\u65bcGPU\u4f3a\u670d\u5668\u5728\u8655\u7406\u654f\u611f\u5de5\u4f5c\u8ca0\u8f09\u65b9\u9762\u7684\u95dc\u9375\u4f5c\u7528\uff0c\u56e0\u6b64\u9700\u8981\u5f37\u5927\u7684\u5b89\u5168\u5354\u5b9a\u3002\u4e3b\u8981\u5b89\u5168\u5be6\u65bd\u5305\u62ec\uff1a<\/p>\n
\n
\u57fa\u790e\u8a2d\u65bd\u5b89\u5168\n
\n
\u5be6\u9ad4\u5b58\u53d6\u63a7\u5236\u7cfb\u7d71<\/li>\n
\u74b0\u5883\u76e3\u63a7<\/li>\n
\u96fb\u529b\u5197\u9918<\/li>\n<\/ul>\n<\/li>\n
\u7db2\u8def\u5b89\u5168\n
\n
\u5c08\u7528VLAN\u914d\u7f6e<\/li>\n
\u591a\u5c64\u9632\u706b\u7246\u4fdd\u8b77<\/li>\n
\u6d41\u91cf\u9694\u96e2\u63aa\u65bd<\/li>\n<\/ul>\n<\/li>\n
\u8cc7\u6599\u5b89\u5168\n
\n
\u786c\u9ad4\u7d1a\u52a0\u5bc6<\/li>\n
\u5b89\u5168\u958b\u6a5f\u6a5f\u5236<\/li>\n
\u8a18\u61b6\u9ad4\u4fdd\u8b77\u529f\u80fd<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
\u672a\u4f86\u8da8\u52e2\u548c\u6280\u8853\u85cd\u5716<\/strong><\/h2>\n
GPU\u4f3a\u670d\u5668\u79df\u7528\u9818\u57df\u96a8\u8457\u65b0\u8208\u6280\u8853\u548c\u529f\u80fd\u4e0d\u65b7\u767c\u5c55\uff1a<\/p>\n
\n
\u67b6\u69cb\u9032\u6b65\n
\n
\u4e0b\u4e00\u4ee3GPU\u67b6\u69cb<\/li>\n
\u589e\u5f37\u7684\u8a18\u61b6\u9ad4\u5b50\u7cfb\u7d71<\/li>\n
\u6539\u9032\u7684\u80fd\u6548\u8a2d\u8a08<\/li>\n<\/ul>\n<\/li>\n
\u8edf\u9ad4\u751f\u614b\u7cfb\u7d71\n
\n
\u5148\u9032\u7684AI\u6846\u67b6<\/li>\n
\u6700\u4f73\u5316\u7684\u958b\u767c\u5de5\u5177<\/li>\n
\u589e\u5f37\u7684\u76e3\u63a7\u89e3\u6c7a\u65b9\u6848<\/li>\n<\/ul>\n<\/li>\n
\u57fa\u790e\u8a2d\u65bd\u6f14\u9032\n
\n
\u667a\u6167\u51b7\u537b\u6280\u8853<\/li>\n
\u52d5\u614b\u96fb\u6e90\u7ba1\u7406<\/li>\n
\u81ea\u52d5\u5316\u8cc7\u6e90\u64f4\u5c55<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
\u7d50\u8ad6<\/strong><\/h2>\n
\u5728\u7f8e\u570b\u4f3a\u670d\u5668\u79df\u7528\u74b0\u5883\u4e2d\u6574\u5408GPU\u4ee3\u8868\u4e86\u904b\u7b97\u57fa\u790e\u8a2d\u65bd\u7684\u8b8a\u9769\u6027\u9032\u6b65\u3002\u900f\u904e\u7b56\u7565\u6027\u786c\u9ad4\u9078\u64c7\u3001\u6700\u4f73\u5316\u7684\u51b7\u537b\u7cfb\u7d71\u548c\u9ad8\u6548\u7684\u5de5\u4f5c\u8ca0\u8f09\u7ba1\u7406\uff0c\u7d44\u7e54\u53ef\u4ee5\u5229\u7528GPU\u52a0\u901f\u5728AI\u3001\u79d1\u5b78\u904b\u7b97\u548c\u8cc7\u6599\u5206\u6790\u61c9\u7528\u65b9\u9762\u5be6\u73fe\u986f\u8457\u7684\u6548\u80fd\u63d0\u5347\u3002\u5c55\u671bGPU\u6280\u8853\u7684\u672a\u4f86\u767c\u5c55\uff0cGPU\u52a0\u901f\u4f3a\u670d\u5668\u5728\u7f8e\u570b\u4f3a\u670d\u5668\u79df\u7528\u8a2d\u65bd\u4e2d\u7684\u89d2\u8272\u5c07\u6301\u7e8c\u64f4\u5927\uff0c\u63a8\u52d5\u591a\u500b\u6280\u8853\u9818\u57df\u7684\u5275\u65b0\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
\u5728\u7f8e\u570b\u4f3a\u670d\u5668\u79df\u7528\u74b0\u5883\u4e2d\u6574\u5408\u5c08\u7528\u5716\u5f62\u8655\u7406\u55ae\u5143(GPU)\u5df2\u7d93\u5fb9\u5e95\u6539\u8b8a\u4e86\u591a\u500b\u9818\u57df\u7684\u904b\u7b97\u80fd\u529b\u3002\u5f9e\u52a0\u901fAI\u5de5\u4f5c\u8ca0\u8f09\u5230\u5be6\u73fe […]<\/p>\n
Read More…<\/a><\/p>\n","protected":false},"author":3,"featured_media":22489,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[4815],"tags":[6621,6622,6068,4609,1892],"class_list":["post-22492","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-america-dedicated-server-tc","tag-us-server-gpu-tc","tag-deep-learning-servers-tc","tag-ai-training-hardware-tc","tag-gpu","tag-server-hosting-tc"],"acf":[],"yoast_head":"\n