{"id":13424,"date":"2024-07-10T15:58:40","date_gmt":"2024-07-10T07:58:40","guid":{"rendered":"https:\/\/www.simcentric.com\/uncategorized-tc\/what-is-a-gpu-dedicated-server-and-what-are-its-uses\/"},"modified":"2024-07-10T16:00:32","modified_gmt":"2024-07-10T08:00:32","slug":"what-is-a-gpu-dedicated-server-and-what-are-its-uses","status":"publish","type":"post","link":"https:\/\/www.simcentric.com\/tc\/hong-kong-dedicated-server-tc\/what-is-a-gpu-dedicated-server-and-what-are-its-uses\/","title":{"rendered":"\u4ec0\u9ebd\u662fGPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\uff1f\u5b83\u6709\u54ea\u4e9b\u7528\u9014\uff1f"},"content":{"rendered":"\n

\n \u5728\u4e0d\u65b7\u767c\u5c55\u7684\u6280\u8853\u9818\u57df\uff0cGPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668<\/a>\u4f5c\u70ba\u4e00\u7a2e\u5f37\u5927\u7684\u5de5\u5177\uff0c\u6b63\u5728\u5404\u500b\u8a08\u7b97\u9818\u57df\u5f15\u767c\u8b8a\u9769\u3002\u90a3\u9ebc\uff0c\u4ec0\u9ebc\u662fGPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\uff1f\u70ba\u4ec0\u9ebc\u5b83\u5728\u5f9e\u4eba\u5de5\u667a\u6167\u5230\u904a\u6232\u7684\u5404\u500b\u884c\u696d\u4e2d\u8b8a\u5f97\u81f3\u95dc\u91cd\u8981\uff1f\u672c\u6587\u6df1\u5165\u63a2\u8a0e\u4e86GPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\u7684\u7d30\u7bc0\uff0c\u7a81\u986f\u5176\u512a\u52e2\u3001\u61c9\u7528\u4ee5\u53ca\u70ba\u4ec0\u9ebc\u9078\u64c7\u9999\u6e2f\u4f3a\u670d\u5668<\/a>\u53ef\u80fd\u662f\u6eff\u8db3\u696d\u52d9\u9700\u6c42\u7684\u6700\u4f73\u6c7a\u7b56\u3002\n <\/p>\n

\u4ec0\u9ebc\u662fGPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\uff1f<\/strong><\/h2>\n

\n GPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\u662f\u4e00\u7a2e\u5305\u542b\u4e00\u500b\u6216\u591a\u500b\u5716\u5f62\u8655\u7406\u55ae\u5143\uff08GPU\uff09\u4ee5\u53ca\u50b3\u7d71\u4e2d\u592e\u8655\u7406\u55ae\u5143\uff08CPU\uff09\u7684\u4f3a\u670d\u5668\u3002\u8207\u4e3b\u8981\u7528\u65bc\u901a\u7528\u8655\u7406\u7684CPU\u4e0d\u540c\uff0cGPU\u5c08\u9580\u7528\u65bc\u8655\u7406\u8907\u96dc\u7684\u6578\u5b78\u8a08\u7b97\uff0c\u7279\u5225\u662f\u90a3\u4e9b\u53ef\u4ee5\u4e26\u884c\u5316\u7684\u8a08\u7b97\u3002\u9019\u4f7f\u5f97\u5b83\u5011\u5728\u9700\u8981\u5927\u91cf\u8a08\u7b97\u80fd\u529b\u7684\u4efb\u52d9\u4e2d\u975e\u5e38\u9ad8\u6548\u3002\n <\/p>\n

\n GPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\u8207\u50b3\u7d71\u4f3a\u670d\u5668\u7684\u4e3b\u8981\u5340\u5225\u5728\u65bc\u5176\u67b6\u69cb\u3002\u50b3\u7d71\u4f3a\u670d\u5668\u5b8c\u5168\u4f9d\u8cf4CPU\u9032\u884c\u8655\u7406\uff0c\u800cGPU\u4f3a\u670d\u5668\u5247\u5229\u7528GPU\u8655\u7406\u773e\u591a\u4e26\u767c\u64cd\u4f5c\u7684\u80fd\u529b\uff0c\u5f9e\u800c\u986f\u8457\u63d0\u5347\u7279\u5b9a\u4efb\u52d9\u7684\u6027\u80fd\u3002\u9019\u7a2e\u4e26\u884c\u8655\u7406\u80fd\u529b\u5c0d\u65bc\u6578\u64da\u5bc6\u96c6\u578b\u61c9\u7528\u7279\u5225\u6709\u5229\uff0c\u4e0b\u9762\u6211\u5011\u5c07\u8a73\u7d30\u63a2\u8a0e\u9019\u4e9b\u61c9\u7528\u3002\n <\/p>\n

GPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\u7684\u512a\u52e2<\/strong><\/h2>\n

1. \u6027\u80fd\u512a\u52e2<\/h2>\n

\n \u7531\u65bc\u9ad8\u4e26\u884c\u8655\u7406\u80fd\u529b\uff0cGPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\u5728\u6027\u80fd\u4e0a\u8868\u73fe\u51fa\u8272\u3002\u5b83\u5011\u80fd\u5920\u540c\u6642\u8655\u7406\u6578\u5343\u500b\u57f7\u884c\u7dd2\uff0c\u9069\u7528\u65bc\u9700\u8981\u5927\u91cf\u8a08\u7b97\u7684\u4efb\u52d9\uff0c\u5982\u8a13\u7df4\u6df1\u5ea6\u5b78\u7fd2\u6a21\u578b\u6216\u6e32\u67d3\u9ad8\u89e3\u6790\u5ea6\u5716\u5f62\u3002\n <\/p>\n

# \u4f7f\u7528CUDA\u5728Python\u4e2d\u9032\u884c\u4e26\u884c\u8655\u7406\u4efb\u52d9\u7684\u7bc4\u4f8b\r\nimport numpy as np\r\nfrom numba import cuda\r\n\r\n@cuda.jit\r\ndef add_kernel(a, b, c):\r\n    idx = cuda.grid(1)\r\n    if idx < a.size:\r\n        c[idx] = a[idx] + b[idx]\r\n\r\nN = 1000000\r\na = np.ones(N, dtype=np.float32)\r\nb = np.ones(N, dtype=np.float32)\r\nc = np.zeros(N, dtype=np.float32)\r\n\r\nthreads_per_block = 256\r\nblocks_per_grid = (a.size + (threads_per_block - 1)) \/\/ threads_per_block\r\n\r\nadd_kernel[blocks_per_grid, threads_per_block](a, b, c)\r\n\r\nprint(c[:10])  # \u8f38\u51fa\u61c9\u70ba\u4e00\u500b\u75312\u7d44\u6210\u7684\u9663\u5217\r\n    <\/code><\/pre>\n
2. \u9748\u6d3b\u6027<\/h2>\n
\n        GPU\u4f3a\u670d\u5668\u975e\u5e38\u9748\u6d3b\uff0c\u53ef\u4ee5\u6839\u64da\u5404\u7a2e\u61c9\u7528\u9032\u884c\u5b9a\u5236\u3002\u7121\u8ad6\u662f\u904b\u884c\u8907\u96dc\u7684\u6a21\u64ec\u3001\u8655\u7406\u5927\u6578\u64da\u96c6\uff0c\u9084\u662f\u958b\u767c\u8907\u96dc\u7684\u5716\u5f62\uff0cGPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\u90fd\u80fd\u63d0\u4f9b\u9069\u61c9\u9019\u4e9b\u591a\u6a23\u5316\u9700\u6c42\u7684\u9748\u6d3b\u6027\u3002\n    <\/p>\n
3. \u9069\u61c9\u6027<\/h2>\n
\n        \u96a8\u8457\u5c0d\u5927\u6578\u64da\u5206\u6790\u548cAI\u9a45\u52d5\u61c9\u7528\u9700\u6c42\u7684\u589e\u52a0\uff0cGPU\u4f3a\u670d\u5668\u7684\u9069\u61c9\u6027\u4f7f\u5176\u8b8a\u5f97\u4e0d\u53ef\u6216\u7f3a\u3002\u5b83\u5011\u80fd\u5920\u8655\u7406\u8a13\u7df4\u6a5f\u5668\u5b78\u7fd2\u6a21\u578b\u548c\u8655\u7406\u5927\u898f\u6a21\u6578\u64da\u6240\u9700\u7684\u5de8\u5927\u8a08\u7b97\u8ca0\u8377\uff0c\u9019\u5728\u7576\u4eca\u6578\u64da\u9a45\u52d5\u7684\u884c\u696d\u4e2d\u975e\u5e38\u5e38\u898b\u3002\n    <\/p>\n
GPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\u7684\u4e3b\u8981\u61c9\u7528<\/strong><\/h2>\n
1. \u4eba\u5de5\u667a\u6167\u548c\u6a5f\u5668\u5b78\u7fd2<\/h2>\n
\n        GPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\u6700\u986f\u8457\u7684\u61c9\u7528\u4e4b\u4e00\u662f\u5728\u4eba\u5de5\u667a\u6167\uff08AI\uff09\u548c\u6a5f\u5668\u5b78\u7fd2\u9818\u57df\u3002GPU\u7684\u4e26\u884c\u8655\u7406\u80fd\u529b\u986f\u8457\u52a0\u901f\u4e86\u6df1\u5ea6\u5b78\u7fd2\u6a21\u578b\u7684\u8a13\u7df4\uff0c\u4f7fAI\u6f14\u7b97\u6cd5\u7684\u958b\u767c\u66f4\u52a0\u5feb\u901f\u548c\u9ad8\u6548\u3002\n    <\/p>\n
# \u4f7f\u7528TensorFlow\u8a13\u7df4\u6df1\u5ea6\u5b78\u7fd2\u6a21\u578b\u7684\u7bc4\u4f8b\r\nimport tensorflow as tf\r\nfrom tensorflow.keras.models import Sequential\r\nfrom tensorflow.keras.layers import Dense\r\n\r\n# \u5275\u5efa\u4e00\u500b\u7c21\u55ae\u7684\u6a21\u578b\r\nmodel = Sequential([\r\n    Dense(64, activation='relu', input_shape=(784,)),\r\n    Dense(64, activation='relu'),\r\n    Dense(10, activation='softmax')\r\n])\r\n\r\n# \u7de8\u8b6f\u6a21\u578b\r\nmodel.compile(optimizer='adam',\r\n              loss='sparse_categorical_crossentropy',\r\n              metrics=['accuracy'])\r\n\r\n# \u5728GPU\u4e0a\u8a13\u7df4\u6a21\u578b\r\ngpu_devices = tf.config.experimental.list_physical_devices('GPU')\r\nif gpu_devices:\r\n    tf.config.experimental.set_memory_growth(gpu_devices[0], True)\r\n\r\n(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\r\nx_train = x_train.reshape(-1, 784).astype('float32') \/ 255\r\nx_test = x_test.reshape(-1, 784).astype('float32') \/ 255\r\n\r\nmodel.fit(x_train, y_train, epochs=5, batch_size=32, validation_data=(x_test, y_test))\r\n    <\/code><\/pre>\n
2. \u5716\u50cf\u548c\u5f71\u7247\u8655\u7406<\/h2>\n
\n        \u5728\u591a\u5a92\u9ad4\u9818\u57df\uff0cGPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\u5728\u5716\u50cf\u548c\u5f71\u7247\u8655\u7406\u4efb\u52d9\u4e2d\u975e\u5e38\u6709\u50f9\u503c\u3002\u5b83\u5011\u5ee3\u6cdb\u7528\u65bc\u5f71\u7247\u7de8\u78bc\u3001\u6e32\u67d3\u548c\u5373\u6642\u5716\u50cf\u5206\u6790\uff0c\u6191\u85c9\u5176\u8655\u7406\u4e26\u884c\u64cd\u4f5c\u7684\u80fd\u529b\u78ba\u4fdd\u9ad8\u6548\u548c\u5feb\u901f\u3002\n    <\/p>\n
3. \u79d1\u5b78\u8a08\u7b97<\/h2>\n
\n        \u79d1\u5b78\u8a08\u7b97\u901a\u5e38\u6d89\u53ca\u9700\u8981\u5927\u91cf\u8a08\u7b97\u8cc7\u6e90\u7684\u6a21\u64ec\u548c\u5efa\u6a21\u3002GPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\u63d0\u4f9b\u4e86\u57f7\u884c\u9019\u4e9b\u8907\u96dc\u8a08\u7b97\u6240\u9700\u7684\u5f37\u5927\u80fd\u529b\uff0c\u4f7f\u5176\u6210\u70ba\u7269\u7406\u3001\u5316\u5b78\u548c\u751f\u7269\u8cc7\u8a0a\u5b78\u7b49\u9818\u57df\u7684\u5fc5\u5099\u5de5\u5177\u3002\n    <\/p>\n
4. \u5340\u584a\u93c8\u548c\u52a0\u5bc6\u8ca8\u5e63\u6316\u7926<\/h2>\n
\n        \u5340\u584a\u93c8\u884c\u696d\u4e5f\u63a5\u53d7\u4e86GPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\uff0c\u56e0\u70ba\u5b83\u5011\u5728\u8655\u7406\u52a0\u5bc6\u8a08\u7b97\u65b9\u9762\u7684\u6548\u7387\u3002\u5c0d\u65bc\u52a0\u5bc6\u8ca8\u5e63\u6316\u7926\uff0cGPU\u76f8\u5c0d\u65bcCPU\u63d0\u4f9b\u4e86\u986f\u8457\u7684\u6027\u80fd\u512a\u52e2\uff0c\u63d0\u9ad8\u4e86\u6316\u7926\u64cd\u4f5c\u7684\u76c8\u5229\u6027\u3002\n    <\/p>\n
5. \u904a\u6232\u958b\u767c\u548c\u5716\u5f62\u6e32\u67d3<\/h2>\n
\n        \u5728\u904a\u6232\u884c\u696d\uff0cGPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\u5c0d\u65bc\u904a\u6232\u958b\u767c\u548c\u5716\u5f62\u6e32\u67d3\u81f3\u95dc\u91cd\u8981\u3002\u5b83\u5011\u80fd\u5920\u5275\u5efa\u8a73\u7d30\u7684\u9ad8\u54c1\u8cea\u5716\u5f62\u4e26\u652f\u63f4\u5373\u6642\u6e32\u67d3\uff0c\u9019\u5c0d\u65bc\u6c89\u6d78\u5f0f\u904a\u6232\u9ad4\u9a57\u548c\u865b\u64ec\u5be6\u5883\uff08VR\uff09\u61c9\u7528\u81f3\u95dc\u91cd\u8981\u3002\n    <\/p>\n
\u70ba\u4ec0\u9ebc\u9078\u64c7\u9999\u6e2fGPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\uff1f<\/strong><\/h2>\n
\n        \u5c0d\u65bc\u5c0b\u6c42GPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\u7684\u4f01\u696d\u548c\u958b\u767c\u8005\u4f86\u8aaa\uff0c\u9999\u6e2f\u4f3a\u670d\u5668\u63d0\u4f9b\u4e86\u591a\u7a2e\u512a\u52e2\u3002\u9999\u6e2f\u7684\u6230\u7565\u5730\u7406\u4f4d\u7f6e\u70ba\u4e9e\u592a\u5730\u5340\u63d0\u4f9b\u4e86\u512a\u79c0\u7684\u9023\u63a5\u6027\u548c\u4f4e\u5ef6\u9072\uff0c\u4f7f\u5176\u6210\u70ba\u570b\u969b\u904b\u71df\u7684\u7406\u60f3\u6a1e\u7d10\u3002\n    <\/p>\n
\n        \u6b64\u5916\uff0c\u9999\u6e2f\u5065\u5168\u7684\u6578\u64da\u96b1\u79c1\u6cd5\u5f8b\u548c\u9ad8\u901f\u7db2\u969b\u7db2\u8def\u57fa\u790e\u8a2d\u65bd\u78ba\u4fdd\u4e86\u53ef\u9760\u548c\u5b89\u5168\u7684\u6578\u64da\u8655\u7406\uff0c\u9019\u5c0d\u5546\u696d\u548c\u7814\u7a76\u61c9\u7528\u90fd\u81f3\u95dc\u91cd\u8981\u3002\u9078\u64c7\u9999\u6e2f\u4f3a\u670d\u5668\u53ef\u4ee5\u63d0\u9ad8\u904b\u71df\u6548\u7387\uff0c\u4e26\u5728\u5168\u7403\u5e02\u5834\u4e2d\u63d0\u4f9b\u7af6\u722d\u512a\u52e2\u3002\n    <\/p>\n
\u5982\u4f55\u9078\u64c7\u5408\u9069\u7684GPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668<\/strong><\/h2>\n
1. \u78ba\u5b9a\u60a8\u7684\u696d\u52d9\u9700\u6c42<\/h2>\n
\n        \u9078\u64c7GPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\u7684\u7b2c\u4e00\u6b65\u662f\u5206\u6790\u60a8\u7684\u5177\u9ad4\u696d\u52d9\u9700\u6c42\u3002\u8003\u616e\u60a8\u9700\u8981\u57f7\u884c\u7684\u8a08\u7b97\u4efb\u52d9\u3001\u5c07\u8981\u8655\u7406\u7684\u6578\u64da\u91cf\u4ee5\u53ca\u5c0d\u60a8\u7684\u64cd\u4f5c\u81f3\u95dc\u91cd\u8981\u7684\u6027\u80fd\u6307\u6a19\u3002\n    <\/p>\n
2. \u9078\u64c7\u5408\u9069\u7684GPU\u985e\u578b<\/h2>\n
\n        \u4e0d\u540c\u7684GPU\u63d0\u4f9b\u4e0d\u540c\u6c34\u5e73\u7684\u6027\u80fd\uff0c\u4e26\u9069\u7528\u65bc\u4e0d\u540c\u7684\u4efb\u52d9\u3002\u4f8b\u5982\uff0cNVIDIA\u7684Tesla\u548cQuadro\u7cfb\u5217\u5206\u5225\u8a2d\u8a08\u7528\u65bc\u9ad8\u6027\u80fd\u8a08\u7b97\u548c\u5c08\u696d\u5716\u5f62\u3002\u8a55\u4f30\u4e0d\u540cGPU\u578b\u865f\u7684\u898f\u683c\u548c\u529f\u80fd\u53ef\u4ee5\u5e6b\u52a9\u60a8\u505a\u51fa\u660e\u667a\u7684\u6c7a\u5b9a\u3002\n    <\/p>\n
3. \u9078\u64c7\u53ef\u9760\u7684\u670d\u52d9\u63d0\u4f9b\u5546<\/h2>\n
\n        \u6700\u5f8c\uff0c\u9078\u64c7\u4e00\u500b\u4fe1\u8b7d\u826f\u597d\u7684\u670d\u52d9\u63d0\u4f9b\u5546\u5c0d\u65bc\u78ba\u4fdd\u60a8\u7684GPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\u7684\u53ef\u9760\u6027\u548c\u8cea\u91cf\u81f3\u95dc\u91cd\u8981\u3002\u8003\u616e\u63d0\u4f9b\u5546\u7684\u5f80\u7e3e\u3001\u5ba2\u6236\u652f\u6301\u4ee5\u53ca\u63d0\u4f9b\u7684\u670d\u52d9\u7bc4\u570d\uff0c\u4ee5\u78ba\u4fdd\u60a8\u7684\u4f3a\u670d\u5668\u6eff\u8db3\u60a8\u7684\u9700\u6c42\u4e26\u63d0\u4f9b\u7121\u7e2b\u7684\u7528\u6236\u9ad4\u9a57\u3002\n    <\/p>\n
\n       GPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\u4ee3\u8868\u4e86\u8a08\u7b97\u6280\u8853\u7684\u91cd\u5927\u9032\u6b65\uff0c\u70ba\u5ee3\u6cdb\u7684\u61c9\u7528\u63d0\u4f9b\u4e86\u7121\u8207\u502b\u6bd4\u7684\u6027\u80fd\u548c\u9748\u6d3b\u6027\u3002\u7121\u8ad6\u60a8\u5f9e\u4e8bAI\u958b\u767c\u3001\u5f71\u7247\u8655\u7406\u3001\u79d1\u5b78\u7814\u7a76\u3001\u5340\u584a\u93c8\u9084\u662f\u904a\u6232\u958b\u767c\uff0cGPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\u90fd\u53ef\u4ee5\u63d0\u4f9b\u5be6\u73fe\u76ee\u6a19\u6240\u9700\u7684\u5f37\u5927\u80fd\u529b\u548c\u9748\u6d3b\u6027\u3002\u9078\u64c7\u9999\u6e2f\u4f3a\u670d\u5668\uff0c\u60a8\u53ef\u4ee5\u5229\u7528\u8a72\u5730\u5340\u7684\u6230\u7565\u512a\u52e2\u4f86\u589e\u5f37\u904b\u71df\u6548\u7387\uff0c\u4e26\u5728\u7af6\u722d\u6fc0\u70c8\u7684\u5168\u7403\u5e02\u5834\u4e2d\u812b\u7a4e\u800c\u51fa\u3002\n    <\/p>\n","protected":false},"excerpt":{"rendered":"
\u5728\u4e0d\u65b7\u767c\u5c55\u7684\u6280\u8853\u9818\u57df\uff0cGPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\u4f5c\u70ba\u4e00\u7a2e\u5f37\u5927\u7684\u5de5\u5177\uff0c\u6b63\u5728\u5404\u500b\u8a08\u7b97\u9818\u57df\u5f15\u767c\u8b8a\u9769\u3002\u90a3\u9ebc\uff0c\u4ec0\u9ebc\u662fGPU\u7368\u7acb […]<\/p>\n
Read More…<\/a><\/p>\n","protected":false},"author":3,"featured_media":13382,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[61],"tags":[2322,2321,259],"class_list":["post-13424","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-hong-kong-dedicated-server-tc","tag-the-use-of-gpu-dedicated-server-tc","tag-gpu-dedicated-server-tc","tag-259"],"acf":[],"yoast_head":"\n\u4ec0\u9ebd\u662fGPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\uff1f\u5b83\u6709\u54ea\u4e9b\u7528\u9014\uff1f<\/title>\n<meta name=\"description\" content=\"\u6df1\u5165\u4e86\u89e3GPU\u7368\u7acb\u986f\u5361\u4f3a\u670d\u5668\u662f\u4ec0\u9ebc\uff0c\u5b83\u5011\u7684\u512a\u52e2\u4ee5\u53ca\u5b83\u5011\u5728AI\u3001\u5f71\u7247\u8655\u7406\u3001\u79d1\u5b78\u8a08\u7b97\u3001\u5340\u584a\u93c8\u548c\u904a\u6232\u4e2d\u7684\u61c9\u7528\u3002\" \/>\n<meta name=\"robots\" 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