{
  "id": 552971,
  "title": "Inconsistent Timeout Errors During Submission with Similar Runtime Logs",
  "url": "/competitions/jane-street-real-time-market-data-forecasting/discussion/552971",
  "author_name": "",
  "post_date": "2024-12-22T19:39:29.328329200Z",
  "votes": null,
  "comment_count": 2,
  "views": 0,
  "content": "<p>I have an issue with my Kaggle submission notebook where I am ensembling 4 similar models. Occasionally, I encounter a timeout error during submission, but other times the same notebook runs successfully and produces results.</p>\n<p>Here’s what I observed in the logs:</p>\n<p>Timeout Error Log: Successfully ran in 17.6s, Runtime 18s · GPU P100<br>\nSuccessful Run Log: Successfully ran in 19.4s, Runtime 19s · GPU P100<br>\nIt doesn’t make sense to me why the notebook with a shorter runtime fails due to a timeout while the one with a slightly longer runtime works fine.</p>\n<p>Does anyone know what could be causing this inconsistency, or have suggestions for avoiding these timeout errors?</p>",
  "messages": [
    {
      "id": "3078777",
      "postDate": "12/22/2024 19:39:29",
      "content": "<p>I have an issue with my Kaggle submission notebook where I am ensembling 4 similar models. Occasionally, I encounter a timeout error during submission, but other times the same notebook runs successfully and produces results.</p>\n<p>Here’s what I observed in the logs:</p>\n<p>Timeout Error Log: Successfully ran in 17.6s, Runtime 18s · GPU P100<br>\nSuccessful Run Log: Successfully ran in 19.4s, Runtime 19s · GPU P100<br>\nIt doesn’t make sense to me why the notebook with a shorter runtime fails due to a timeout while the one with a slightly longer runtime works fine.</p>\n<p>Does anyone know what could be causing this inconsistency, or have suggestions for avoiding these timeout errors?</p>",
      "rawMarkdown": "I have an issue with my Kaggle submission notebook where I am ensembling 4 similar models. Occasionally, I encounter a timeout error during submission, but other times the same notebook runs successfully and produces results.\n\nHere’s what I observed in the logs:\n\nTimeout Error Log: Successfully ran in 17.6s, Runtime 18s · GPU P100\nSuccessful Run Log: Successfully ran in 19.4s, Runtime 19s · GPU P100\nIt doesn’t make sense to me why the notebook with a shorter runtime fails due to a timeout while the one with a slightly longer runtime works fine.\n\nDoes anyone know what could be causing this inconsistency, or have suggestions for avoiding these timeout errors?",
      "votes": null
    },
    {
      "id": "3079892",
      "postDate": "12/24/2024 09:40:53",
      "content": "<p>Hello, first of all, integrating four identical models does not make sense, as they cannot learn new features and are more likely to suffer from overfitting. Secondly, due to the update of the dataset, the memory is no longer sufficient, so you might consider good methods to free up memory, or abandon the ensemble model approach.</p>",
      "rawMarkdown": "Hello, first of all, integrating four identical models does not make sense, as they cannot learn new features and are more likely to suffer from overfitting. Secondly, due to the update of the dataset, the memory is no longer sufficient, so you might consider good methods to free up memory, or abandon the ensemble model approach.",
      "votes": null
    },
    {
      "id": "3080271",
      "postDate": "12/25/2024 00:07:26",
      "content": "<p>Thank you! They are not identical models per se! </p>",
      "rawMarkdown": "Thank you! They are not identical models per se!",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 3079892,
      "author_name": "feiwenxuan",
      "author_url": "",
      "post_date": "12/24/2024 09:40:53",
      "content": "<p>Hello, first of all, integrating four identical models does not make sense, as they cannot learn new features and are more likely to suffer from overfitting. Secondly, due to the update of the dataset, the memory is no longer sufficient, so you might consider good methods to free up memory, or abandon the ensemble model approach.</p>",
      "votes": null,
      "replies": [
        {
          "id": 3080271,
          "author_name": "arashab",
          "author_url": "",
          "post_date": "12/25/2024 00:07:26",
          "content": "<p>Thank you! They are not identical models per se! </p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "3078777": "I have an issue with my Kaggle submission notebook where I am ensembling 4 similar models. Occasionally, I encounter a timeout error during submission, but other times the same notebook runs successfully and produces results.\n\nHere’s what I observed in the logs:\n\nTimeout Error Log: Successfully ran in 17.6s, Runtime 18s · GPU P100\nSuccessful Run Log: Successfully ran in 19.4s, Runtime 19s · GPU P100\nIt doesn’t make sense to me why the notebook with a shorter runtime fails due to a timeout while the one with a slightly longer runtime works fine.\n\nDoes anyone know what could be causing this inconsistency, or have suggestions for avoiding these timeout errors?",
    "3079892": "Hello, first of all, integrating four identical models does not make sense, as they cannot learn new features and are more likely to suffer from overfitting. Secondly, due to the update of the dataset, the memory is no longer sufficient, so you might consider good methods to free up memory, or abandon the ensemble model approach.",
    "3080271": "Thank you! They are not identical models per se!"
  },
  "source": "meta"
}