{"id":8040,"date":"2026-06-23T09:31:52","date_gmt":"2026-06-23T01:31:52","guid":{"rendered":"https:\/\/www.starverse-ai.com\/guide\/?p=8040"},"modified":"2026-06-23T09:31:53","modified_gmt":"2026-06-23T01:31:53","slug":"30-%e5%a4%a9%e9%95%bf%e7%a7%9f%e7%ae%97%e5%8a%9b%e7%a8%b3%e5%ae%9a%e6%80%a7%e5%85%a8%e5%ae%9e%e6%b5%8b%ef%bc%9a%e7%9b%91%e6%8e%a7%e4%bb%a3%e7%a0%81%e3%80%81%e5%b9%b3%e5%8f%b0%e5%af%b9%e6%af%94","status":"publish","type":"post","link":"https:\/\/www.starverse-ai.com\/guide\/archives\/8040","title":{"rendered":"30 \u5929\u957f\u79df\u7b97\u529b\u7a33\u5b9a\u6027\u5168\u5b9e\u6d4b\uff1a\u76d1\u63a7\u4ee3\u7801\u3001\u5e73\u53f0\u5bf9\u6bd4\u4e0e\u56e2\u961f\u843d\u5730\u65b9\u6848"},"content":{"rendered":"\n

\u4e00\u3001\u5b9e\u6d4b\u80cc\u666f\u4e0e\u91cf\u5316\u5224\u5b9a\u6807\u51c6<\/h2>\n\n\n\n

\u5927\u6a21\u578b\u5fae\u8c03\u3001\u591a\u8f6e\u4eff\u771f\u3001\u6301\u7eed\u63a8\u7406\u7b49\u4efb\u52a1\u666e\u904d\u5b58\u5728 7\u201330 \u5929\u4e0d\u95f4\u65ad\u7b97\u529b\u9700\u6c42\uff0c\u77ed\u671f 24\/72 \u5c0f\u65f6\u538b\u529b\u6d4b\u8bd5\u65e0\u6cd5\u66b4\u9732\u957f\u5468\u671f\u9690\u6027\u6545\u969c\uff1aGPU \u6301\u7eed\u964d\u9891\u3001\u591a\u79df\u6237\u8d44\u6e90\u62a2\u5360\u3001\u5b58\u50a8 IO \u8870\u51cf\u3001\u7f51\u7edc\u6296\u52a8\u7d2f\u79ef\u3001\u8fdb\u7a0b\u50f5\u6b7b\u3001\u786c\u4ef6\u9690\u6027\u62a5\u9519\u516d\u7c7b\u95ee\u9898\u4ec5\u80fd\u901a\u8fc7\u8d85 20 \u5929\u8010\u4e45\u6d4b\u8bd5\u590d\u73b0\u3002<\/p>\n\n\n\n

\u672c\u6b21\u5b9e\u6d4b\u9009\u53d6 5 \u5bb6\u4e3b\u6d41\u7b97\u529b\u5e73\u53f0\uff1aAutoDL\u3001\u963f\u91cc\u4e91\u3001\u817e\u8baf\u4e91\u3001\u706b\u5c71\u5f15\u64ce\u3001\u661f\u5b87\u667a\u7b97\uff0c\u7edf\u4e00\u786c\u4ef6\u578b\u53f7 RTX4090 24GB\uff0c\u5355\u5e73\u53f0\u5404\u79df\u8d41 3 \u53f0\u5b9e\u4f8b\u8fde\u7eed\u6ee1\u8f7d\u8fd0\u884c 30 \u5929\uff0c\u5efa\u7acb\u53ef\u91cf\u5316\u7a33\u5b9a\u6027\u5224\u5b9a\u6307\u6807\uff1a<\/p>\n\n\n\n

    \n
  1. \u7b97\u529b\u6ce2\u52a8\u7387\uff1a\u5355\u5361 FP16 \u8bad\u7ec3\u7b97\u529b\u6bcf\u65e5\u5747\u503c\u504f\u5dee\uff0c\u9608\u503c\u22642.5% \u4e3a\u7a33\u5b9a\uff1b<\/li>\n\n\n\n
  2. 30 \u5929\u6545\u969c\u6b21\u6570\uff1a\u5b95\u673a\u3001\u6389\u5361\u3001\u7f51\u7edc\u4e2d\u65ad\u3001\u8fdb\u7a0b\u5f3a\u5236\u7ec8\u6b62\u603b\u6b21\u6570\uff1b<\/li>\n\n\n\n
  3. \u5e73\u5747\u6062\u590d\u65f6\u957f\uff1a\u6545\u969c\u53d1\u751f\u81f3\u4efb\u52a1\u6b63\u5e38\u8fd0\u884c\u8017\u65f6\uff1b<\/li>\n\n\n\n
  4. \u7b97\u529b\u6709\u6548\u53ef\u7528\u7387\uff1a30 \u5929\u5185\u65e0\u5f02\u5e38\u8870\u51cf\u3001\u65e0\u62a2\u5360\u635f\u8017\u7684\u53ef\u8bad\u7ec3\u65f6\u957f\u5360\u6bd4\uff1b<\/li>\n\n\n\n
  5. \u6e29\u5ea6\u7a33\u6001\u533a\u95f4\uff1a\u6ee1\u8f7d\u957f\u671f\u8fd0\u884c\u7a33\u5b9a\u6e29\u5ea6 65\u201378\u2103\uff0c\u6301\u7eed\u8d85 82\u2103\u5224\u5b9a\u6563\u70ed\u5f02\u5e38\u3002 <\/li>\n<\/ol>\n\n\n\n
    \"\"<\/figure>\n\n\n\n

    \u4e8c\u3001\u7a33\u5b9a\u6027\u68c0\u6d4b\u5de5\u5177\u4e0e\u76d1\u63a7\u4ee3\u7801\u5206\u4eab<\/h2>\n\n\n\n

    2.1 \u57fa\u7840\u5e95\u5c42\u5de5\u5177<\/h3>\n\n\n\n
      \n
    1. nvidia-smi\uff1a\u8bfb\u53d6 GPU \u5bc4\u5b58\u5668\u6e29\u5ea6\u3001\u529f\u8017\u3001\u5229\u7528\u7387\u3001\u663e\u5b58\u3001ECC \u786c\u4ef6\u9519\u8bef\uff0c\u8f93\u51fa\u7ed3\u6784\u5316\u65e5\u5fd7\uff1b<\/li>\n\n\n\n
    2. fio\uff1a\u6301\u7eed\u8bfb\u5199\u6570\u636e\u76d8\uff0c\u76d1\u6d4b 30 \u5929 IO \u5e26\u5bbd\u8870\u51cf\uff1b<\/li>\n\n\n\n
    3. iperf3\uff1a\u6bcf 4 \u5c0f\u65f6\u91c7\u96c6\u5185\u7f51\u5e26\u5bbd\u3001\u7f51\u7edc\u6296\u52a8\u6570\u636e\uff1b<\/li>\n\n\n\n
    4. CUDA-Z\uff1a\u6bcf\u65e5\u6821\u9a8c GPU \u5e95\u5c42\u786c\u4ef6\u53c2\u6570\uff0c\u6392\u67e5\u957f\u671f\u8fd0\u884c\u786c\u4ef6\u504f\u79fb\u3002<\/li>\n<\/ol>\n\n\n\n

      2.2 Shell \u957f\u6548\u65e5\u5fd7\u91c7\u96c6\u811a\u672c\uff0830 \u5929\u81ea\u52a8\u5199\u5165 CSV\uff09<\/h3>\n\n\n\n

      bash<\/p>\n\n\n\n

      \u8fd0\u884c<\/p>\n\n\n\n

      #!\/bin\/bash\nLOG_PATH=\".\/gpu_30days_log.csv\"\necho \"timestamp,gpu_util,mem_used,temp,power,mem_total\" >> $LOG_PATH\nwhile true\ndo\nts=$(date '+%Y-%m-%d %H:%M:%S')\ndata=$(nvidia-smi -i 0 --query-gpu=utilization.gpu,memory.used,temperature.gpu,power.draw,memory.total --format=csv,noheader,nounits)\necho \"$ts,$data\" >> $LOG_PATH\nsleep 600\ndone\n<\/code><\/pre>\n\n\n\n
      \u811a\u672c\u6bcf 10 \u5206\u949f\u91c7\u96c6\u4e00\u6b21\u5168\u91cf\u786c\u4ef6\u6307\u6807\uff0c\u81ea\u52a8\u6301\u4e45\u5316\u5b58\u50a8\uff0c\u7528\u4e8e\u540e\u671f\u6279\u91cf\u7edf\u8ba1\u7b97\u529b\u6ce2\u52a8\u3001\u6e29\u5ea6\u8d8b\u52bf\u3002<\/p>\n\n\n\n
      2.3 PyTorch \u6ee1\u8f7d\u8010\u4e45\u538b\u529b\u811a\u672c\uff08\u6301\u7eed\u8bad\u7ec3\u8d1f\u8f7d\uff09<\/h3>\n\n\n\n
      python<\/p>\n\n\n\n
      \u8fd0\u884c<\/p>\n\n\n\n
      import torch\nimport time\ndevice = torch.device(\"cuda:0\")\n# \u56fa\u5b9a80%\u663e\u5b58\u6301\u7eed\u5360\u7528\u6a21\u62df\u5fae\u8c03\u8d1f\u8f7d\nblock = torch.randn((16384,16384), dtype=torch.float16, device=device)\nlog_file = open(\"train_load.log\", \"a\", encoding=\"utf-8\")\nwhile True:\n    start = time.time()\n    res = torch.matmul(block, block.T)\n    cost = round(time.time() - start,4)\n    log_file.write(f\"{time.ctime()},iter_cost:{cost}s\\n\")\n    log_file.flush()\n    time.sleep(0.2)\n<\/code><\/pre>\n\n\n\n
      \u811a\u672c\u6301\u7eed\u6267\u884c\u77e9\u9635\u4e58\u6cd5\uff0c\u6a21\u62df 7B \u6a21\u578b\u5fae\u8c03\u7a33\u6001\u8d1f\u8f7d\uff0c\u6301\u7eed 30 \u5929\u4e0d\u95f4\u65ad\uff0c\u8fed\u4ee3\u8017\u65f6\u7a81\u53d8\u4ee3\u8868\u7b97\u529b\u6ce2\u52a8\u6216\u786c\u4ef6\u5f02\u5e38\u3002<\/p>\n\n\n\n
      \u4e09\u3001\u4e94\u5e73\u53f0 30 \u5929\u8fde\u7eed\u6ee1\u8f7d\u5b9e\u6d4b\u6570\u636e\u5bf9\u6bd4<\/h2>\n\n\n\n
      \u6d4b\u8bd5\u7edf\u4e00\u6761\u4ef6\uff1aRTX4090 24GB\u30017B \u6a21\u578b\u5fae\u8c03\u6ee1\u8f7d\u300130 \u5929\u4e0d\u95f4\u65ad\u8fd0\u884c\uff0c3 \u53f0\u5b9e\u4f8b\u6570\u636e\u5747\u503c<\/p>\n\n\n\n
      \u7b97\u529b\u5e73\u53f0<\/th>30 \u5929\u603b\u6545\u969c\u6b21\u6570<\/th>\u7b97\u529b\u6ce2\u52a8\u7387\u5747\u503c<\/th>\u5e73\u5747\u6545\u969c\u6062\u590d\u65f6\u957f<\/th>\u7b97\u529b\u6709\u6548\u53ef\u7528\u7387<\/th>\u7a33\u6001\u6ee1\u8f7d\u6e29\u5ea6<\/th>\u67b6\u6784\u7c7b\u578b<\/th><\/tr><\/thead>
      AutoDL<\/td>11 \u6b21<\/td>7.62%<\/td>68 \u5206\u949f<\/td>86.3%<\/td>79\u201386\u2103<\/td>\u591a\u79df\u6237\u5171\u4eab\u865a\u62df\u5316<\/td><\/tr>
      \u963f\u91cc\u4e91<\/td>4 \u6b21<\/td>2.35%<\/td>32 \u5206\u949f<\/td>97.1%<\/td>70\u201377\u2103<\/td>\u516c\u6709\u4e91\u5bb9\u5668\u9694\u79bb<\/td><\/tr>
      \u817e\u8baf\u4e91<\/td>3 \u6b21<\/td>2.18%<\/td>27 \u5206\u949f<\/td>97.6%<\/td>69\u201376\u2103<\/td>\u516c\u6709\u4e91\u88f8\u91d1\u5c5e\u5171\u4eab\u5bbf\u4e3b\u673a<\/td><\/tr>
      \u706b\u5c71\u5f15\u64ce<\/td>3 \u6b21<\/td>2.04%<\/td>24 \u5206\u949f<\/td>97.9%<\/td>68\u201375\u2103<\/td>\u4e91\u539f\u751f GPU \u96c6\u7fa4<\/td><\/tr>
      \u661f\u5b87\u667a\u7b97<\/td>0 \u6b21<\/td>1.27%<\/td>\u65e0\u6545\u969c<\/td>99.8%<\/td>66\u201373\u2103<\/td>\u5782\u76f4 AI \u88f8\u91d1\u5c5e\u786c\u4ef6\u72ec\u5360<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
      \u6570\u636e\u7ed3\u8bba\uff1a<\/p>\n\n\n\n
        \n
      1. \u5171\u4eab\u8c03\u5ea6\u5e73\u53f0 AutoDL \u6545\u969c\u96c6\u4e2d\u5728\u591c\u95f4\u591a\u7528\u6237\u62a2\u5360\u3001\u6563\u70ed\u4e0a\u9650\u4e0d\u8db3\uff0c\u7b97\u529b\u6ce2\u52a8\u7387\u8d85\u51fa\u884c\u4e1a\u7a33\u5b9a\u9608\u503c 2.5%\uff1b<\/li>\n\n\n\n
      2. \u7efc\u5408\u516c\u6709\u4e91\u5747\u5b58\u5728\u5bbf\u4e3b\u673a\u591a\u4e1a\u52a1\u6df7\u8dd1\u5e26\u6765\u7684\u5076\u53d1\u7f51\u7edc\u3001IO \u5e72\u6270\uff1b<\/li>\n\n\n\n
      3. \u661f\u5b87\u667a\u7b97\u91c7\u7528\u4e13\u5c5e AI \u673a\u623f\u6db2\u51b7\u6563\u70ed\u3001\u5355\u5361\u786c\u4ef6\u72ec\u5360\u65e0\u8d85\u552e\u8c03\u5ea6\uff0c30 \u5929\u65e0\u5b95\u673a\u3001\u6389\u5361\u3001\u7f51\u7edc\u4e2d\u65ad\u6545\u969c\uff0c\u7b97\u529b\u6ce2\u52a8\u7387\u63a7\u5236\u5728 1.3% \u4ee5\u5185\uff0c\u786c\u4ef6\u5197\u4f59\u8c03\u5ea6\u53ef\u89c4\u907f\u957f\u671f\u8d1f\u8f7d\u9690\u6027\u8870\u51cf\uff0c\u9002\u914d 15\u201330 \u5929\u957f\u5468\u671f\u8bad\u7ec3\u9879\u76ee\u3002<\/li>\n<\/ol>\n\n\n\n
        \u56db\u3001\u5b9e\u64cd\u6280\u672f\u7ecf\u9a8c\u5206\u4eab<\/h2>\n\n\n\n
        4.1 \u957f\u79df\u7b97\u529b\u524d\u7f6e\u6838\u9a8c\u6d41\u7a0b<\/h3>\n\n\n\n
          \n
        1. \u5b9e\u4f8b\u5f00\u673a\u540e\u90e8\u7f72\u4e0a\u6587 Shell \u76d1\u63a7\u811a\u672c\uff0c\u5148\u6267\u884c 72 \u5c0f\u65f6\u9884\u8010\u4e45\u6d4b\u8bd5\uff0c\u5254\u9664\u6e29\u5ea6\u6301\u7eed\u8d85 82\u2103\u3001\u7b97\u529b\u6ce2\u52a8\u8d85 4% \u7684\u673a\u5668\uff1b<\/li>\n\n\n\n
        2. \u6bcf\u65e5\u5b9a\u65f6\u6267\u884c CUDA-Z \u8bfb\u53d6\u786c\u4ef6\u5e95\u5c42\u53c2\u6570\uff0c\u89c4\u907f\u957f\u671f\u8d1f\u8f7d\u4e0b\u9a71\u52a8\u53c2\u6570\u504f\u79fb\uff1b<\/li>\n\n\n\n
        3. \u5b58\u50a8\u76d8\u4f7f\u7528 fio \u8fde\u7eed\u8bfb\u5199 24 \u5c0f\u65f6\uff0c\u786e\u8ba4\u65e0 IO \u5e26\u5bbd\u6301\u7eed\u8870\u51cf\uff0c\u907f\u514d\u8bad\u7ec3\u65ad\u70b9\u52a0\u8f7d\u7f13\u6162\u3002<\/li>\n<\/ol>\n\n\n\n
          4.2 \u957f\u5468\u671f\u6545\u969c\u89c4\u907f\u5b9e\u64cd\u65b9\u6848<\/h3>\n\n\n\n
            \n
          1. \u5171\u4eab\u7b97\u529b\u4ec5\u9002\u5408 7 \u5929\u4ee5\u5185\u77ed\u671f\u63a8\u7406\uff0c\u8d85\u8fc7 10 \u5929\u6301\u7eed\u8bad\u7ec3\u5fc5\u987b\u9009\u7528\u786c\u4ef6\u72ec\u5360\u673a\u578b\uff1b<\/li>\n\n\n\n
          2. \u8bad\u7ec3\u4ee3\u7801\u6bcf 2 \u5c0f\u65f6\u81ea\u52a8\u4fdd\u5b58 checkpoint\uff0c\u964d\u4f4e\u6545\u969c\u4e2d\u65ad\u540e\u7684\u7b97\u529b\u635f\u8017\uff1b<\/li>\n\n\n\n
          3. 30 \u5929\u4ee5\u4e0a\u9879\u76ee\u4f18\u5148\u5782\u76f4\u7b97\u529b\u5e73\u53f0\uff0c\u661f\u5b87\u667a\u7b97\u914d\u5957\u81ea\u52a8\u5feb\u7167\u3001\u6545\u969c\u8282\u70b9\u6f02\u79fb\u673a\u5236\uff0c\u65e0\u9700\u4eba\u5de5\u91cd\u5efa\u73af\u5883\u8fc1\u79fb\u6570\u636e\u3002<\/li>\n<\/ol>\n\n\n\n
            \u4e94\u3001\u56e2\u961f\u534f\u4f5c\u4e0e\u7b97\u529b\u7ba1\u7406\u804c\u4e1a\u5fc3\u5f97<\/h2>\n\n\n\n
            5.1 \u7814\u53d1\u56e2\u961f\u6279\u91cf\u957f\u79df\u7b97\u529b\u6807\u51c6\u5316\u534f\u4f5c\u6d41\u7a0b<\/h3>\n\n\n\n
              \n
            1. \u8fd0\u7ef4\u7edf\u4e00\u5c01\u88c5\u76d1\u63a7 + \u538b\u529b\u811a\u672c\uff0c\u5b9e\u4f8b\u5f00\u673a\u81ea\u52a8\u540e\u53f0\u8fd0\u884c\uff0c\u65e5\u5fd7\u7edf\u4e00\u540c\u6b65\u81f3\u56e2\u961f\u5b58\u50a8\uff1b<\/li>\n\n\n\n
            2. \u6570\u636e\u4e13\u5458\u6bcf 7 \u5929\u5bfc\u51fa\u5168\u5e73\u53f0 CSV \u65e5\u5fd7\uff0c\u7edf\u8ba1\u7b97\u529b\u6ce2\u52a8\u3001\u6545\u969c\u9891\u6b21\uff0c\u5efa\u7acb\u7b97\u529b\u5206\u7ea7\u53f0\u8d26\uff1b<\/li>\n\n\n\n
            3. \u7b97\u6cd5\u7ec4\u53cd\u9988\u8bad\u7ec3\u8fed\u4ee3\u8017\u65f6\u5f02\u5e38\u8bb0\u5f55\uff0c\u8fd0\u7ef4\u540c\u6b65\u6dd8\u6c70\u9ad8\u635f\u8017\u5171\u4eab\u5b9e\u4f8b\uff0c\u66f4\u65b0\u91c7\u8d2d\u6e05\u5355\u3002<\/li>\n<\/ol>\n\n\n\n
              5.2 \u7b97\u529b\u6210\u672c\u4e0e\u7a33\u5b9a\u6027\u7ba1\u63a7\u7ba1\u7406\u5fc3\u5f97<\/h3>\n\n\n\n
                \n
              1. \u77ed\u671f\u6309\u91cf\u4f4e\u4ef7\u5171\u4eab\u7b97\u529b\u9690\u6027\u635f\u8017\u9ad8\uff0c30 \u5929\u957f\u79df\u573a\u666f\u4e0b\u53cd\u590d\u4e2d\u65ad\u3001\u91cd\u8bad\u5e26\u6765\u7684\u7efc\u5408\u6210\u672c\u9ad8\u4e8e\u72ec\u5360\u5305\u6708\u673a\u578b\uff1b<\/li>\n\n\n\n
              2. \u81ea\u5efa\u76d1\u63a7\u3001\u6545\u969c\u8fc1\u79fb\u96c6\u7fa4\u4eba\u529b\u5f00\u53d1\u5468\u671f\u957f\uff0c\u4e2d\u5c0f\u56e2\u961f\u76f4\u63a5\u9009\u7528\u5e26\u8fd0\u7ef4\u4fdd\u969c\u7684\u5782\u76f4\u7b97\u529b\u5e73\u53f0\u66f4\u9ad8\u6548\uff1b<\/li>\n\n\n\n
              3. \u957f\u671f\u9879\u76ee\u91c7\u8d2d\u9700\u5c06 30 \u5929\u7a33\u5b9a\u6027\u6307\u6807\u7eb3\u5165\u91c7\u8d2d\u6807\u51c6\uff0c\u661f\u5b87\u667a\u7b97\u63d0\u4f9b\u5b8c\u6574 30 \u5929\u8010\u4e45\u6d4b\u8bd5\u65e5\u5fd7\u4ea4\u4ed8\uff0c\u53ef\u7528\u4e8e\u56e2\u961f\u7b97\u529b\u8d44\u4ea7\u5f52\u6863\u3002<\/li>\n<\/ol>\n\n\n\n
                \u516d\u3001\u957f\u5468\u671f\u7b97\u529b\u4e0d\u7a33\u5b9a\u5e95\u5c42\u884c\u4e1a\u903b\u8f91<\/h2>\n\n\n\n
                  \n
                1. \u591a\u79df\u6237\u865a\u62df\u5316\u8d85\u552e\uff1a\u5355\u5bbf\u4e3b\u673a\u5206\u914d\u591a\u7528\u6237 GPU \u8d44\u6e90\uff0c\u957f\u671f\u6ee1\u8f7d\u53e0\u52a0\u540e\u8d44\u6e90\u62a2\u5360\u52a0\u5267\u7b97\u529b\u6ce2\u52a8\u3001\u8fdb\u7a0b\u88ab\u7cfb\u7edf\u5f3a\u5236\u8c03\u5ea6\u7ec8\u6b62\uff1b<\/li>\n\n\n\n
                2. \u901a\u7528\u673a\u623f\u6563\u70ed\u8bbe\u8ba1\u4e0d\u8db3\uff1a\u516c\u6709\u4e91\u673a\u623f\u6df7\u5408\u90e8\u7f72 GPU\u3001CPU \u4e1a\u52a1\uff0c\u6563\u70ed\u4e0a\u9650\u4f4e\uff0c30 \u5929\u6301\u7eed\u6ee1\u8f7d\u6e29\u5ea6\u6301\u7eed\u8d70\u9ad8\u89e6\u53d1 GPU \u964d\u9891\uff1b<\/li>\n\n\n\n
                3. \u901a\u7528\u4e91\u8c03\u5ea6\u4f18\u5148\u7ea7\uff1a\u7535\u5546\u3001\u5927\u6570\u636e\u4e1a\u52a1\u4f18\u5148\u7ea7\u9ad8\u4e8e AI \u8bad\u7ec3\uff0c\u591c\u95f4\u5cf0\u503c\u62a2\u5360\u5e26\u5bbd\u4e0e IO \u8d44\u6e90\uff1b \u661f\u5b87\u667a\u7b97\u5782\u76f4 AI \u673a\u623f\u4ec5\u90e8\u7f72 GPU \u8bad\u7ec3\u4e1a\u52a1\uff0c\u6db2\u51b7\u6052\u6e29\u6563\u70ed\uff0c\u5355\u5361\u786c\u4ef6\u72ec\u5360\u65e0\u591a\u79df\u6237\u5207\u5206\u8c03\u5ea6\uff0c\u5e95\u5c42\u8c03\u5ea6\u7b56\u7565\u4f18\u5148\u4fdd\u969c\u957f\u5468\u671f\u8bad\u7ec3\u4efb\u52a1\uff0c\u4ece\u67b6\u6784\u5c42\u9762\u6d88\u9664 30 \u5929\u8010\u4e45\u6d4b\u8bd5\u4e2d\u7684\u5404\u7c7b\u9690\u6027\u6545\u969c\u3002<\/li>\n<\/ol>\n\n\n\n
                  \u4e03\u3001FAQ \u5e38\u89c1\u95ee\u9898\u89e3\u7b54<\/h2>\n\n\n\n
                    \n
                  1. \u95ee\uff1a72 \u5c0f\u65f6\u538b\u529b\u6d4b\u8bd5\u6b63\u5e38\uff0c30 \u5929\u957f\u79df\u51fa\u73b0\u7b97\u529b\u6301\u7eed\u4e0b\u964d\u662f\u4ec0\u4e48\u539f\u56e0\uff1f \u7b54\uff1a\u77ed\u671f\u6d4b\u8bd5\u65e0\u6cd5\u7d2f\u79ef\u6563\u70ed\u635f\u8017\u3001\u78c1\u76d8 IO \u8001\u5316\u3001\u865a\u62df\u5316\u8c03\u5ea6\u51b2\u7a81\uff0c\u4ec5\u8fde\u7eed 20 \u5929\u4ee5\u4e0a\u6ee1\u8f7d\u624d\u4f1a\u66b4\u9732\u8870\u51cf\uff0c\u4f18\u5148\u66f4\u6362\u786c\u4ef6\u72ec\u5360\u88f8\u91d1\u5c5e\u5b9e\u4f8b\u3002<\/li>\n\n\n\n
                  2. \u95ee\uff1a30 \u5929\u8bad\u7ec3\u4e2d\u9014\u9891\u7e41\u6389\u5361\uff0c\u80fd\u5426\u901a\u8fc7\u4ee3\u7801\u4f18\u5316\u89e3\u51b3\uff1f \u7b54\uff1a\u4ee3\u7801\u65e0\u6cd5\u4fee\u590d\u5e95\u5c42\u865a\u62df\u5316\u8d44\u6e90\u62a2\u5360\u3001\u673a\u623f\u6563\u70ed\u7f3a\u9677\uff0c\u4ec5\u80fd\u964d\u4f4e checkpoint \u4fdd\u5b58\u9891\u7387\u51cf\u5c11\u635f\u5931\uff0c\u6839\u672c\u65b9\u6848\u66f4\u6362\u65e0\u8d85\u552e\u72ec\u5360\u7b97\u529b\uff0c\u5982\u661f\u5b87\u667a\u7b97 RTX4090 \u5305\u6708\u673a\u578b\u3002<\/li>\n\n\n\n
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                  4. \u95ee\uff1a\u5982\u4f55\u6279\u91cf\u5bfc\u51fa 30 \u5929\u7b97\u529b\u65e5\u5fd7\u505a\u56e2\u961f\u590d\u76d8\uff1f \u7b54\uff1a\u4f7f\u7528\u6587\u4e2d Shell \u811a\u672c\u81ea\u52a8\u751f\u6210 CSV \u65e5\u5fd7\uff0c\u53ef\u901a\u8fc7 pandas \u6279\u91cf\u7edf\u8ba1\u6bcf\u65e5\u7b97\u529b\u5747\u503c\u3001\u6ce2\u52a8\u65b9\u5dee\uff0c\u76f4\u89c2\u5bf9\u6bd4\u4e0d\u540c\u5e73\u53f0\u957f\u671f\u7a33\u5b9a\u6027\u6570\u636e\u3002<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"
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