{"id":18431,"date":"2024-11-05T13:47:35","date_gmt":"2024-11-05T05:47:35","guid":{"rendered":"https:\/\/www.simcentric.com\/uncategorized-sc\/types-of-computing-power-servers-hong-kong-hosting-guide\/"},"modified":"2024-11-05T13:49:57","modified_gmt":"2024-11-05T05:49:57","slug":"types-of-computing-power-servers-hong-kong-hosting-guide","status":"publish","type":"post","link":"https:\/\/www.simcentric.com\/sc\/hong-kong-dedicated-server-sc\/types-of-computing-power-servers-hong-kong-hosting-guide\/","title":{"rendered":"\u7b97\u529b\u670d\u52a1\u5668\u7c7b\u578b\uff1a\u9999\u6e2f\u670d\u52a1\u5668\u79df\u7528\u6307\u5357"},"content":{"rendered":"

[vc_row][vc_column][vc_column_text css=””]<\/p>\n

\u7b97\u529b\u670d\u52a1\u5668<\/a>\u5df2\u7ecf\u5f7b\u5e95\u6539\u53d8\u4e86\u9999\u6e2f\u670d\u52a1\u5668\u79df\u7528<\/a>\u9886\u57df\u7684\u6570\u636e\u5904\u7406\u80fd\u529b\u3002\u4ece\u4eba\u5de5\u667a\u80fd\u5f00\u53d1\u5230\u79d1\u5b66\u7814\u7a76\uff0c\u8fd9\u4e9b\u4e13\u4e1a\u8bbe\u5907\u6784\u6210\u4e86\u73b0\u4ee3\u8ba1\u7b97\u4efb\u52a1\u7684\u6838\u5fc3\u57fa\u7840\u3002\u672c\u6280\u672f\u6307\u5357\u5c06\u63a2\u8ba8\u5404\u79cd\u7b97\u529b\u670d\u52a1\u5668\u7c7b\u578b\u53ca\u5176\u5728\u9999\u6e2f\u7279\u6b8a\u57fa\u7840\u8bbe\u65bd\u73af\u5883\u4e0b\u7684\u6700\u4f18\u914d\u7f6e\u3002<\/p>\n

\u4e86\u89e3\u7b97\u529b\u670d\u52a1\u5668<\/strong><\/h2>\n

\u4e0e\u4f20\u7edf\u7684\u7f51\u7ad9\u6258\u7ba1\u670d\u52a1\u5668\u4e0d\u540c\uff0c\u7b97\u529b\u670d\u52a1\u5668\u4e13\u95e8\u4e3a\u5bc6\u96c6\u8ba1\u7b97\u4efb\u52a1\u800c\u8bbe\u8ba1\u3002\u5b83\u4eec\u7684\u67b6\u6784\u4e3b\u8981\u7740\u91cd\u4e8e\u5e76\u884c\u5904\u7406\u80fd\u529b\u548c\u4e13\u4e1a\u786c\u4ef6\u52a0\u901f\u3002\u4ee5\u4e0b\u662f\u5176\u6838\u5fc3\u7ec4\u4ef6\u7684\u6280\u672f\u7ec6\u8282\uff1a<\/p>\n

\r\n# Python\u811a\u672c\u7528\u4e8e\u68c0\u67e5GPU\u517c\u5bb9\u6027\r\nimport torch\r\n\r\ndef check_gpu_capability():\r\n    if torch.cuda.is_available():\r\n        for i in range(torch.cuda.device_count()):\r\n            props = torch.cuda.get_device_properties(i)\r\n            print(f\"GPU:{i} {props.name}\")\r\n            print(f\"Memory: {props.total_memory \/ 1024**3:.2f}GB\")\r\n            print(f\"Compute Capability: {props.major}.{props.minor}\")\r\n    else:\r\n        print(\"No CUDA-capable GPU found\")<\/code><\/pre>\n
\u6b64\u811a\u672c\u53ef\u5e2e\u52a9\u8bc6\u522b\u5bf9\u7b97\u529b\u670d\u52a1\u5668\u9009\u62e9\u81f3\u5173\u91cd\u8981\u7684GPU\u529f\u80fd\u3002\u5bf9\u4e8e\u9999\u6e2f\u670d\u52a1\u5668\u79df\u7528\u73af\u5883\uff0c\u6211\u4eec\u5efa\u8bae\u5728\u90e8\u7f72\u524d\u8fdb\u884c\u5168\u9762\u7684\u786c\u4ef6\u8bca\u65ad\u3002<\/p>\n
\u7b97\u529b\u670d\u52a1\u5668\u7c7b\u578b\u8be6\u89e3<\/strong><\/h2>\n
\u73b0\u4ee3\u8ba1\u7b97\u57fa\u7840\u8bbe\u65bd\u9700\u8981\u4e13\u4e1a\u7684\u670d\u52a1\u5668\u914d\u7f6e\u3002\u8ba9\u6211\u4eec\u901a\u8fc7\u5b9e\u9645\u5e94\u7528\u548c\u6027\u80fd\u6307\u6807\u6765\u5206\u6790\u6bcf\u79cd\u7c7b\u578b\u3002<\/p>\n
1. GPU\u8ba1\u7b97\u670d\u52a1\u5668<\/h3>\n
GPU\u670d\u52a1\u5668\u5728\u5e76\u884c\u5904\u7406\u4efb\u52a1\u65b9\u9762\u8868\u73b0\u51fa\u8272\u3002\u9999\u6e2f\u6bd7\u90bb\u4e3b\u8981\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u4e2d\u5fc3\u7684\u4f18\u52bf\u4f7f\u8fd9\u4e9b\u670d\u52a1\u5668\u7279\u522b\u6709\u4ef7\u503c\u3002\u4ee5\u4e0b\u662f\u5178\u578b\u7684\u9ad8\u6027\u80fd\u914d\u7f6e\uff1a<\/p>\n
\r\n# \u670d\u52a1\u5668\u914d\u7f6e\u793a\u4f8b\r\n{\r\n    \"gpu\": {\r\n        \"model\": \"NVIDIA A100\",\r\n        \"count\": 8,\r\n        \"memory_per_gpu\": \"80GB\",\r\n        \"nvlink_enabled\": true\r\n    },\r\n    \"cpu\": {\r\n        \"type\": \"AMD EPYC 7763\",\r\n        \"cores\": 64,\r\n        \"threads\": 128\r\n    },\r\n    \"memory\": {\r\n        \"total\": \"2TB\",\r\n        \"type\": \"DDR4-3200\",\r\n        \"ecc\": true\r\n    }\r\n}<\/code><\/pre>\n
2. AI\u8bad\u7ec3\u670d\u52a1\u5668<\/h3>\n
AI\u8bad\u7ec3\u670d\u52a1\u5668\u9700\u8981\u9488\u5bf9\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u8fdb\u884c\u7279\u5b9a\u4f18\u5316\u3002\u8003\u8651\u4f7f\u7528\u6b64\u57fa\u51c6\u6d4b\u8bd5\u811a\u672c\u8fdb\u884c\u6027\u80fd\u8bc4\u4f30\uff1a<\/p>\n
\r\nimport tensorflow as tf\r\nfrom tensorflow.keras import mixed_precision\r\n\r\ndef configure_training_server():\r\n    # \u542f\u7528\u6df7\u5408\u7cbe\u5ea6\u4ee5\u63d0\u9ad8\u6027\u80fd\r\n    policy = mixed_precision.Policy('mixed_float16')\r\n    mixed_precision.set_global_policy(policy)\r\n    \r\n    # \u914d\u7f6e\u5185\u5b58\u589e\u957f\r\n    gpus = tf.config.list_physical_devices('GPU')\r\n    for gpu in gpus:\r\n        tf.config.experimental.set_memory_growth(gpu, True)\r\n        \r\n    # \u8bbe\u7f6eXLA\u4f18\u5316\r\n    tf.config.optimizer.set_jit(True)\r\n    \r\n    return tf.config.list_physical_devices()<\/code><\/pre>\n
3. \u9ad8\u6027\u80fd\u8ba1\u7b97(HPC)\u670d\u52a1\u5668<\/h3>\n
\u9999\u6e2f\u670d\u52a1\u5668\u79df\u7528\u8bbe\u65bd\u4e2d\u7684HPC\u670d\u52a1\u5668\u901a\u5e38\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u9700\u6c42\u3002\u8fd9\u4e9b\u914d\u7f6e\u901a\u5e38\u5305\u62ecInfiniBand\u7f51\u7edc\u548c\u5e76\u884c\u6587\u4ef6\u7cfb\u7edf\u3002\u53ef\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u5b9e\u73b0\u57fa\u672c\u76d1\u63a7\uff1a<\/p>\n
\r\n#!\/bin\/bash\r\n# HPC\u6027\u80fd\u76d1\u63a7\r\n\r\nmonitor_cluster_health() {\r\n    echo \"=== HPC\u96c6\u7fa4\u5065\u5eb7\u68c0\u67e5 ===\"\r\n    date\r\n    \r\n    # \u68c0\u67e5InfiniBand\u8fde\u63a5\u6027\r\n    ibstat | grep \"State:\"\r\n    \r\n    # \u76d1\u63a7Lustre\u6587\u4ef6\u7cfb\u7edf\r\n    lfs df -h\r\n    \r\n    # \u68c0\u67e5MPI\u8fde\u63a5\u6027\r\n    mpirun --version\r\n    \r\n    # \u76d1\u63a7GPU\u4f7f\u7528\u7387\r\n    nvidia-smi --query-gpu=utilization.gpu,memory.used,temperature.gpu --format=csv\r\n}<\/code><\/pre>\n
\u9009\u62e9\u5408\u9002\u7684\u7b97\u529b\u670d\u52a1\u5668<\/strong><\/h2>\n
\u9009\u62e9\u6807\u51c6\u5fc5\u987b\u5728\u6027\u80fd\u9700\u6c42\u4e0e\u9999\u6e2f\u7279\u6b8a\u670d\u52a1\u5668\u79df\u7528\u73af\u5883\u4e4b\u95f4\u53d6\u5f97\u5e73\u8861\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528\u81ea\u5b9a\u4e49\u8bc4\u4f30\u6846\u67b6\u7684\u7cfb\u7edf\u65b9\u6cd5\uff1a<\/p>\n
\r\nclass ServerEvaluator:\r\n    def __init__(self, requirements):\r\n        self.requirements = requirements\r\n        self.scores = {}\r\n        \r\n    def evaluate_network_performance(self, location):\r\n        latency_tests = {\r\n            'hongkong_local': '2ms',\r\n            'mainland_china': '20-30ms',\r\n            'southeast_asia': '50-70ms',\r\n            'global_routes': {\r\n                'us_west': '120-140ms',\r\n                'europe': '180-200ms'\r\n            }\r\n        }\r\n        return latency_tests\r\n        \r\n    def calculate_tco(self, specs):\r\n        \"\"\"\u603b\u62e5\u6709\u6210\u672c\u8ba1\u7b97\u5668\"\"\"\r\n        power_cost_hk = 1.2  # \u6e2f\u5e01\/\u5343\u74e6\u65f6\r\n        cooling_overhead = 1.4\r\n        \r\n        annual_power_cost = (\r\n            specs['power_draw'] * \r\n            24 * 365 * \r\n            power_cost_hk * \r\n            cooling_overhead\r\n        )\r\n        return annual_power_cost<\/code><\/pre>\n
\u9999\u6e2f\u670d\u52a1\u5668\u79df\u7528\u4f18\u52bf<\/strong><\/h2>\n
\u9999\u6e2f\u7684\u6218\u7565\u4f4d\u7f6e\u4e3a\u7b97\u529b\u670d\u52a1\u5668\u90e8\u7f72\u63d0\u4f9b\u4e86\u72ec\u7279\u4f18\u52bf\u3002\u7f51\u7edc\u6027\u80fd\u6307\u6807\u5c55\u793a\u4e86\u8fd9\u4e00\u4f18\u52bf\uff1a<\/p>\n
\r\n# \u7f51\u7edc\u6027\u80fd\u5206\u6790\r\n{\r\n    \"connectivity\": {\r\n        \"tier1_providers\": 8,\r\n        \"internet_exchanges\": 4,\r\n        \"average_bandwidth\": \"10Tbps\"\r\n    },\r\n    \"latency_matrix\": {\r\n        \"tokyo\": \"45ms\",\r\n        \"singapore\": \"35ms\",\r\n        \"shanghai\": \"25ms\",\r\n        \"silicon_valley\": \"140ms\"\r\n    },\r\n    \"redundancy\": {\r\n        \"submarine_cables\": 12,\r\n        \"terrestrial_routes\": 5,\r\n        \"backup_paths\": \"n+2\"\r\n    }\r\n}<\/code><\/pre>\n
\u8fd9\u4e9b\u6307\u6807\u5c55\u793a\u4e86\u9999\u6e2f\u4f18\u8d8a\u7684\u670d\u52a1\u5668\u79df\u7528\u57fa\u7840\u8bbe\u65bd\uff0c\u7279\u522b\u6709\u5229\u4e8e\u5206\u5e03\u5f0f\u8ba1\u7b97\u5de5\u4f5c\u8d1f\u8f7d\u3002\u8be5\u57ce\u5e02\u5148\u8fdb\u7684\u5149\u7ea4\u7f51\u7edc\u786e\u4fdd\u4e86\u8de8\u5883\u64cd\u4f5c\u7684\u6700\u4f4e\u5ef6\u8fdf\u3002<\/p>\n
\u4f18\u5316\u548c\u7ef4\u62a4\u6307\u5357<\/strong><\/h2>\n
\u5728\u9999\u6e2f\u670d\u52a1\u5668\u79df\u7528\u73af\u5883\u4e2d\uff0c\u6709\u6548\u7684\u7b97\u529b\u670d\u52a1\u5668\u7ba1\u7406\u9700\u8981\u590d\u6742\u7684\u76d1\u63a7\u548c\u4f18\u5316\u3002\u4ee5\u4e0b\u662f\u5168\u9762\u7684\u7ba1\u7406\u6846\u67b6\uff1a<\/p>\n
\r\n#!\/bin\/bash\r\n# \u9ad8\u7ea7\u670d\u52a1\u5668\u76d1\u63a7\u5957\u4ef6\r\n\r\nTHRESHOLD_CPU=85\r\nTHRESHOLD_MEMORY=90\r\nTHRESHOLD_TEMP=75\r\n\r\nmonitor_critical_metrics() {\r\n    # \u6e29\u5ea6\u76d1\u63a7\u4e0e\u5730\u533a\u7279\u5b9a\u8c03\u6574\r\n    # \u9999\u6e2f\u7684\u6e7f\u5ea6\u9700\u8981\u66f4\u4e25\u683c\u7684\u5236\u51b7\u53c2\u6570\r\n    gpu_temp=$(nvidia-smi --query-gpu=temperature.gpu --format=csv,noheader)\r\n    \r\n    if [ $gpu_temp -gt $THRESHOLD_TEMP ]; then\r\n        trigger_cooling_protocol\r\n    fi\r\n    \r\n    # \u5185\u5b58\u4f7f\u7528\u4f18\u5316\r\n    memory_usage=$(free | grep Mem | awk '{print $3\/$2 * 100.0}')\r\n    \r\n    if (( $(echo \"$memory_usage > $THRESHOLD_MEMORY\" | bc -l) )); then\r\n        optimize_memory_allocation\r\n    fi\r\n}\r\n\r\noptimize_memory_allocation() {\r\n    echo \"\u5f00\u59cb\u5185\u5b58\u4f18\u5316...\"\r\n    sync\r\n    echo 3 > \/proc\/sys\/vm\/drop_caches\r\n    echo \"\u5185\u5b58\u7f13\u5b58\u5df2\u6e05\u9664\"\r\n}<\/code><\/pre>\n
\u6027\u80fd\u8c03\u4f18\u6700\u4f73\u5b9e\u8df5<\/strong><\/h2>\n
\u5728\u9999\u6e2f\u670d\u52a1\u5668\u79df\u7528\u73af\u5883\u4e2d\u5b9e\u73b0\u6700\u4f73\u6027\u80fd\u9700\u8981\u7279\u5b9a\u7684\u914d\u7f6e\u8c03\u6574\u3002\u53c2\u8003\u4ee5\u4e0b\u7f51\u7edc\u4f18\u5316\u793a\u4f8b\uff1a<\/p>\n
\r\n# \/etc\/sysctl.conf \u9488\u5bf9\u9999\u6e2f\u670d\u52a1\u5668\u79df\u7528\u7684\u4f18\u5316\r\n\r\n# \u4e3a\u9ad8\u541e\u5410\u91cf\u573a\u666f\u4f18\u5316\u7f51\u7edc\u6808\r\nnet.core.rmem_max = 16777216\r\nnet.core.wmem_max = 16777216\r\nnet.ipv4.tcp_rmem = 4096 87380 16777216\r\nnet.ipv4.tcp_wmem = 4096 65536 16777216\r\n\r\n# BBR\u62e5\u585e\u63a7\u5236\u4ee5\u83b7\u5f97\u66f4\u597d\u7684\u6027\u80fd\r\nnet.core.default_qdisc = fq\r\nnet.ipv4.tcp_congestion_control = bbr\r\n\r\n# \u4f18\u5316\u9ad8\u5e76\u53d1\u8fde\u63a5\r\nnet.ipv4.tcp_max_syn_backlog = 8192\r\nnet.core.somaxconn = 65535<\/code><\/pre>\n
\u7ed3\u8bba\u548c\u672a\u6765\u5c55\u671b<\/strong><\/h2>\n
\u7b97\u529b\u670d\u52a1\u5668\u7684\u683c\u5c40\u4e0d\u65ad\u53d1\u5c55\uff0c\u7279\u522b\u662f\u5728\u9999\u6e2f\u5148\u8fdb\u7684\u670d\u52a1\u5668\u79df\u7528\u57fa\u7840\u8bbe\u65bd\u4e2d\u3002\u4f7f\u7528\u8fd9\u4e9b\u89e3\u51b3\u65b9\u6848\u7684\u7ec4\u7ec7\u5fc5\u987b\u5728\u6027\u80fd\u9700\u6c42\u4e0e\u6210\u672c\u6548\u76ca\u4e4b\u95f4\u53d6\u5f97\u5e73\u8861\uff0c\u540c\u65f6\u8003\u8651\u9999\u6e2f\u6218\u7565\u4f4d\u7f6e\u548c\u5148\u8fdb\u7f51\u7edc\u57fa\u7840\u8bbe\u65bd\u5e26\u6765\u7684\u72ec\u7279\u4f18\u52bf\u3002<\/p>\n
\u4e3a\u4e86\u5728\u7b97\u529b\u670d\u52a1\u5668\u90e8\u7f72\u4e2d\u83b7\u5f97\u6700\u4f73\u6548\u679c\uff0c\u9700\u8981\u8003\u8651\u9999\u6e2f\u670d\u52a1\u5668\u79df\u7528\u7684\u4f18\u52bf\uff1a\u4f18\u8d8a\u7684\u8fde\u63a5\u6027\u3001\u5148\u8fdb\u7684\u5236\u51b7\u57fa\u7840\u8bbe\u65bd\u4ee5\u53ca\u6bd7\u90bb\u4e3b\u8981\u4e9a\u6d32\u5e02\u573a\u7684\u6218\u7565\u4f4d\u7f6e\u3002\u5b9a\u671f\u6027\u80fd\u76d1\u63a7\u548c\u4e3b\u52a8\u7ef4\u62a4\u5bf9\u4fdd\u6301\u6700\u4f73\u8fd0\u8425\u6548\u7387\u4ecd\u7136\u81f3\u5173\u91cd\u8981\u3002<\/p>\n
[\/vc_column_text][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"
[vc_row][vc_column][vc_column_text css=””] \u7b97\u529b\u670d\u52a1\u5668\u5df2\u7ecf\u5f7b\u5e95\u6539\u53d8\u4e86\u9999\u6e2f\u670d\u52a1\u5668\u79df\u7528\u9886\u57df\u7684\u6570\u636e\u5904\u7406\u80fd\u529b\u3002\u4ece\u4eba\u5de5\u667a\u80fd\u5f00\u53d1\u5230\u79d1\u5b66\u7814\u7a76\uff0c\u8fd9\u4e9b\u4e13\u4e1a\u8bbe\u5907\u6784\u6210\u4e86\u73b0\u4ee3\u8ba1\u7b97\u4efb\u52a1\u7684\u6838\u5fc3\u57fa\u7840\u3002\u672c\u6280\u672f\u6307\u5357\u5c06\u63a2\u8ba8\u5404\u79cd\u7b97\u529b\u670d\u52a1\u5668\u7c7b\u578b\u53ca\u5176\u5728\u9999\u6e2f\u7279\u6b8a\u57fa\u7840\u8bbe\u65bd\u73af\u5883\u4e0b\u7684\u6700\u4f18\u914d\u7f6e\u3002 \u4e86\u89e3\u7b97\u529b\u670d\u52a1\u5668 \u4e0e\u4f20\u7edf\u7684\u7f51\u7ad9\u6258\u7ba1\u670d\u52a1\u5668\u4e0d\u540c\uff0c\u7b97\u529b\u670d\u52a1\u5668\u4e13\u95e8\u4e3a\u5bc6\u96c6\u8ba1\u7b97\u4efb\u52a1\u800c\u8bbe\u8ba1\u3002\u5b83\u4eec\u7684\u67b6\u6784\u4e3b\u8981\u7740\u91cd\u4e8e\u5e76\u884c\u5904\u7406\u80fd\u529b\u548c\u4e13\u4e1a\u786c\u4ef6\u52a0\u901f\u3002\u4ee5\u4e0b\u662f\u5176\u6838\u5fc3\u7ec4\u4ef6\u7684\u6280\u672f\u7ec6\u8282\uff1a # Python\u811a\u672c\u7528\u4e8e\u68c0\u67e5GPU\u517c\u5bb9\u6027 import torch def check_gpu_capability(): if torch.cuda.is_available(): for i in range(torch.cuda.device_count()): props = torch.cuda.get_device_properties(i) print(f”GPU:{i} {props.name}”) print(f”Memory: {props.total_memory \/ 1024**3:.2f}GB”) print(f”Compute Capability: {props.major}.{props.minor}”) else: print(“No CUDA-capable GPU found”) \u6b64\u811a\u672c\u53ef\u5e2e\u52a9\u8bc6\u522b\u5bf9\u7b97\u529b\u670d\u52a1\u5668\u9009\u62e9\u81f3\u5173\u91cd\u8981\u7684GPU\u529f\u80fd\u3002\u5bf9\u4e8e\u9999\u6e2f\u670d\u52a1\u5668\u79df\u7528\u73af\u5883\uff0c\u6211\u4eec\u5efa\u8bae\u5728\u90e8\u7f72\u524d\u8fdb\u884c\u5168\u9762\u7684\u786c\u4ef6\u8bca\u65ad\u3002 \u7b97\u529b\u670d\u52a1\u5668\u7c7b\u578b\u8be6\u89e3 \u73b0\u4ee3\u8ba1\u7b97\u57fa\u7840\u8bbe\u65bd\u9700\u8981\u4e13\u4e1a\u7684\u670d\u52a1\u5668\u914d\u7f6e\u3002\u8ba9\u6211\u4eec\u901a\u8fc7\u5b9e\u9645\u5e94\u7528\u548c\u6027\u80fd\u6307\u6807\u6765\u5206\u6790\u6bcf\u79cd\u7c7b\u578b\u3002 1. GPU\u8ba1\u7b97\u670d\u52a1\u5668 GPU\u670d\u52a1\u5668\u5728\u5e76\u884c\u5904\u7406\u4efb\u52a1\u65b9\u9762\u8868\u73b0\u51fa\u8272\u3002\u9999\u6e2f\u6bd7\u90bb\u4e3b\u8981\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u4e2d\u5fc3\u7684\u4f18\u52bf\u4f7f\u8fd9\u4e9b\u670d\u52a1\u5668\u7279\u522b\u6709\u4ef7\u503c\u3002\u4ee5\u4e0b\u662f\u5178\u578b\u7684\u9ad8\u6027\u80fd\u914d\u7f6e\uff1a # \u670d\u52a1\u5668\u914d\u7f6e\u793a\u4f8b { “gpu”: { “model”: “NVIDIA A100”, “count”: 8, “memory_per_gpu”: “80GB”, “nvlink_enabled”: true }, […]<\/p>\n
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