[vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n
\u968f\u7740AI\u548c\u6df1\u5ea6\u5b66\u4e60\u5de5\u4f5c\u8d1f\u8f7d\u53d8\u5f97\u8d8a\u6765\u8d8ademanding\uff0c\u6d4b\u8bd5GPU\u670d\u52a1\u5668<\/a>\u6027\u80fd\u5bf9\u4e8e\u90e8\u7f72\u673a\u5668\u5b66\u4e60\u57fa\u7840\u8bbe\u65bd\u7684\u7ec4\u7ec7\u6765\u8bf4\u53d8\u5f97\u81f3\u5173\u91cd\u8981\u3002\u672c\u7efc\u5408\u6307\u5357\u63a2\u8ba8\u4e86GPU\u670d\u52a1\u5668\u6d4b\u8bd5\u7684\u57fa\u672c\u8981\u7d20\uff0c\u91cd\u70b9\u5173\u6ce8\u9999\u6e2f\u6570\u636e\u4e2d\u5fc3<\/a>\u7684\u7279\u5b9a\u57fa\u51c6\u6d4b\u8bd5\u65b9\u6cd5\u3002<\/p>\n [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n \u5728\u8bc4\u4f30GPU\u670d\u52a1\u5668\u6027\u80fd\u65f6\uff0c\u9700\u8981\u5173\u6ce8\u4ee5\u4e0b\u51e0\u4e2a\u5173\u952e\u6307\u6807\uff1a<\/p>\n – FLOPS\uff08\u6bcf\u79d2\u6d6e\u70b9\u8fd0\u7b97\u6b21\u6570\uff09<\/p>\n – \u5185\u5b58\u5e26\u5bbd\u548c\u5ef6\u8fdf<\/p>\n – \u80fd\u6e90\u6548\u7387<\/p>\n – \u6e29\u5ea6\u9608\u503c<\/p>\n – \u7f51\u7edc\u6027\u80fd<\/p>\n [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n \u8ba9\u6211\u4eec\u6df1\u5165\u4e86\u89e3GPU\u6027\u80fd\u6d4b\u8bd5\u7684\u5b9e\u7528\u5de5\u5177\u3002\u4ee5\u4e0b\u662f\u68c0\u67e5\u57fa\u672cGPU\u4fe1\u606f\u7684\u547d\u4ee4\uff1a<\/p>\n \u5bf9\u4e8e\u5168\u9762\u6d4b\u8bd5\uff0c\u6211\u4eec\u63a8\u8350\uff1a<\/p>\n 1. MLPerf – \u673a\u5668\u5b66\u4e60\u57fa\u51c6\u6d4b\u8bd5\u7684\u884c\u4e1a\u6807\u51c6<\/p>\n 2. GPU-Z – \u8be6\u7ec6\u7684\u786c\u4ef6\u76d1\u63a7<\/p>\n 3. TensorFlow\u7684\u5185\u7f6e\u57fa\u51c6\u6d4b\u8bd5<\/p>\n 4. CUDA\u6837\u4f8b<\/p>\n [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n \u4ee5\u4e0b\u662f\u6267\u884c\u57fa\u7840\u6df1\u5ea6\u5b66\u4e60\u57fa\u51c6\u6d4b\u8bd5\u7684Python\u811a\u672c\uff1a<\/p>\n [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n \u5bf9\u4e8e\u9999\u6e2f\u7684GPU\u670d\u52a1\u5668\uff0c\u7f51\u7edc\u6027\u80fd\u81f3\u5173\u91cd\u8981\u3002\u4ee5\u4e0b\u662f\u6d4b\u8bd5\u7f51\u7edc\u5ef6\u8fdf\u7684bash\u811a\u672c\uff1a<\/p>\n [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n \u4e3a\u6700\u5927\u5316GPU\u670d\u52a1\u5668\u6027\u80fd\uff1a<\/p>\n 1. \u542f\u7528CUDA\u591a\u8fdb\u7a0b\u670d\u52a1\uff08MPS\uff09<\/p>\n 2. \u4f18\u5316CUDA\u914d\u7f6e<\/p>\n 3. \u76d1\u63a7\u548c\u8c03\u6574\u529f\u7387\u9650\u5236<\/p>\n 4. \u5b9e\u65bd\u9002\u5f53\u7684\u6563\u70ed\u89e3\u51b3\u65b9\u6848<\/p>\n CUDA\u914d\u7f6e\u793a\u4f8b\uff1a<\/p>\n [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n \u5728\u6d4b\u8bd5\u9999\u6e2f\u6570\u636e\u4e2d\u5fc3\u7684GPU\u670d\u52a1\u5668\u65f6\uff0c\u9700\u8981\u8003\u8651\uff1a<\/p>\n – \u672c\u5730\u7f51\u7edc\u72b6\u51b5<\/p>\n – \u8de8\u5883\u5e26\u5bbd\u9650\u5236<\/p>\n – \u7535\u529b\u7a33\u5b9a\u6027<\/p>\n – \u6563\u70ed\u6548\u7387<\/p>\n [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n \u76d1\u63a7\u8fd9\u4e9b\u6f5c\u5728\u7684\u74f6\u9888\uff1a<\/p>\n 1. PCIe\u5e26\u5bbd\u9650\u5236<\/p>\n 2. CPU\u74f6\u9888<\/p>\n 3. \u5185\u5b58\u9650\u5236<\/p>\n 4. \u6563\u70ed\u8282\u6d41<\/p>\n [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n \u6709\u6548\u7684GPU\u670d\u52a1\u5668\u6d4b\u8bd5\u9700\u8981\u7cfb\u7edf\u5730\u7ed3\u5408\u786c\u4ef6\u548c\u8f6f\u4ef6\u57fa\u51c6\u6d4b\u8bd5\u7684\u65b9\u6cd5\u3002\u5bf9\u4e8e\u9999\u6e2f\u5730\u533a\u7684\u90e8\u7f72\uff0c\u8003\u8651\u672c\u5730\u57fa\u7840\u8bbe\u65bd\u7279\u5f81\u5bf9\u4e8e\u5b9e\u73b0\u6700\u4f73\u6027\u80fd\u81f3\u5173\u91cd\u8981\u3002\u5b9a\u671f\u6d4b\u8bd5\u548c\u76d1\u63a7\u53ef\u786e\u4fdd\u60a8\u7684GPU\u670d\u52a1\u5668\u5728AI\u548c\u6df1\u5ea6\u5b66\u4e60\u5de5\u4f5c\u8d1f\u8f7d\u4e2d\u4fdd\u6301\u6700\u4f73\u6027\u80fd\u3002<\/p>\n [\/vc_column_text][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":" [vc_row el_class=”blog-detail-section”][vc_column][vc_column_text] \u968f\u7740AI\u548c\u6df1\u5ea6\u5b66\u4e60\u5de5\u4f5c\u8d1f\u8f7d\u53d8\u5f97\u8d8a\u6765\u8d8ademanding\uff0c\u6d4b\u8bd5GPU\u670d\u52a1\u5668\u6027\u80fd\u5bf9\u4e8e\u90e8\u7f72\u673a\u5668\u5b66\u4e60\u57fa\u7840\u8bbe\u65bd\u7684\u7ec4\u7ec7\u6765\u8bf4\u53d8\u5f97\u81f3\u5173\u91cd\u8981\u3002\u672c\u7efc\u5408\u6307\u5357\u63a2\u8ba8\u4e86GPU\u670d\u52a1\u5668\u6d4b\u8bd5\u7684\u57fa\u672c\u8981\u7d20\uff0c\u91cd\u70b9\u5173\u6ce8\u9999\u6e2f\u6570\u636e\u4e2d\u5fc3\u7684\u7279\u5b9a\u57fa\u51c6\u6d4b\u8bd5\u65b9\u6cd5\u3002 [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text] GPU\u670d\u52a1\u5668\u7684\u5173\u952e\u6027\u80fd\u6307\u6807 \u5728\u8bc4\u4f30GPU\u670d\u52a1\u5668\u6027\u80fd\u65f6\uff0c\u9700\u8981\u5173\u6ce8\u4ee5\u4e0b\u51e0\u4e2a\u5173\u952e\u6307\u6807\uff1a – FLOPS\uff08\u6bcf\u79d2\u6d6e\u70b9\u8fd0\u7b97\u6b21\u6570\uff09 – \u5185\u5b58\u5e26\u5bbd\u548c\u5ef6\u8fdf – \u80fd\u6e90\u6548\u7387 – \u6e29\u5ea6\u9608\u503c – \u7f51\u7edc\u6027\u80fd [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text] \u57fa\u672c\u6d4b\u8bd5\u5de5\u5177 \u8ba9\u6211\u4eec\u6df1\u5165\u4e86\u89e3GPU\u6027\u80fd\u6d4b\u8bd5\u7684\u5b9e\u7528\u5de5\u5177\u3002\u4ee5\u4e0b\u662f\u68c0\u67e5\u57fa\u672cGPU\u4fe1\u606f\u7684\u547d\u4ee4\uff1a nvidia-smi –query-gpu=gpu_name,memory.total,memory.free,memory.used,temperature.gpu,utilization.gpu,utilization.memory –format=csv \u5bf9\u4e8e\u5168\u9762\u6d4b\u8bd5\uff0c\u6211\u4eec\u63a8\u8350\uff1a 1. MLPerf – \u673a\u5668\u5b66\u4e60\u57fa\u51c6\u6d4b\u8bd5\u7684\u884c\u4e1a\u6807\u51c6 2. GPU-Z – \u8be6\u7ec6\u7684\u786c\u4ef6\u76d1\u63a7 3. TensorFlow\u7684\u5185\u7f6e\u57fa\u51c6\u6d4b\u8bd5 4. CUDA\u6837\u4f8b [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text] \u6df1\u5ea6\u5b66\u4e60\u57fa\u51c6\u6d4b\u8bd5\u8bbe\u7f6e \u4ee5\u4e0b\u662f\u6267\u884c\u57fa\u7840\u6df1\u5ea6\u5b66\u4e60\u57fa\u51c6\u6d4b\u8bd5\u7684Python\u811a\u672c\uff1a import tensorflow as tf import time def benchmark_model(): model = tf.keras.applications.ResNet50(weights=None) data = tf.random.normal([64, […]<\/p>\nGPU\u670d\u52a1\u5668\u7684\u5173\u952e\u6027\u80fd\u6307\u6807<\/strong><\/h2>\n
\u57fa\u672c\u6d4b\u8bd5\u5de5\u5177<\/strong><\/h2>\n
nvidia-smi --query-gpu=gpu_name,memory.total,memory.free,memory.used,temperature.gpu,utilization.gpu,utilization.memory --format=csv\r\n<\/code><\/pre>\n\u6df1\u5ea6\u5b66\u4e60\u57fa\u51c6\u6d4b\u8bd5\u8bbe\u7f6e<\/strong><\/h2>\n
import tensorflow as tf\r\nimport time\r\n\r\ndef benchmark_model():\r\n model = tf.keras.applications.ResNet50(weights=None)\r\n data = tf.random.normal([64, 224, 224, 3])\r\n \r\n # Warm-up run\r\n model(data)\r\n \r\n # Benchmark\r\n times = []\r\n for _ in range(100):\r\n start_time = time.time()\r\n model(data)\r\n times.append(time.time() - start_time)\r\n \r\n return np.mean(times)\r\n\r\naverage_inference_time = benchmark_model()\r\nprint(f\"Average inference time: {average_inference_time:.4f} seconds\")\r\n<\/code><\/pre>\n\u7f51\u7edc\u6027\u80fd\u6d4b\u8bd5<\/strong><\/h2>\n
#!\/bin\/bash\r\n# Test latency to key Asian regions\r\nlocations=(\"tokyo.server.com\" \"singapore.server.com\" \"hongkong.server.com\")\r\n\r\nfor location in \"${locations[@]}\"\r\ndo\r\n echo \"Testing latency to $location\"\r\n ping -c 10 $location | tail -1 | awk '{print $4}' | cut -d '\/' -f 2\r\ndone\r\n<\/code><\/pre>\n\u6027\u80fd\u4f18\u5316\u5efa\u8bae<\/strong><\/h2>\n
export CUDA_VISIBLE_DEVICES=0,1\r\nexport CUDA_CACHE_PATH=\/tmp\/cuda-cache\r\nexport CUDA_MPS_PIPE_DIRECTORY=\/tmp\/nvidia-mps\r\n<\/code><\/pre>\n\u5b9e\u9645\u6027\u80fd\u5206\u6790<\/strong><\/h2>\n
\u5e38\u89c1\u95ee\u9898\u6545\u969c\u6392\u9664<\/strong><\/h2>\n
\u7ed3\u8bba<\/strong><\/h2>\n