{
  "id": 567354,
  "title": "How do I submit a notebook?",
  "url": "/competitions/stanford-rna-3d-folding/discussion/567354",
  "author_name": "",
  "post_date": "2025-03-09T21:16:38.668994300Z",
  "votes": -1,
  "comment_count": 2,
  "views": 0,
  "content": "<p>I have worked with kaggle competitions previously but all those required me to upload a .csv file for evaluation. I see that this is a code competition.</p>\n<p>What is it? How do I make a submission? In the 9 hour limit, does my model need to retrain or can I load a saved model?</p>\n<p>A detailed step by step guide on how to make a submission to a code file would be greatly appreciated.</p>\n<p>Thanks.</p>",
  "messages": [
    {
      "id": "3145480",
      "postDate": "03/09/2025 21:16:38",
      "content": "<p>I have worked with kaggle competitions previously but all those required me to upload a .csv file for evaluation. I see that this is a code competition.</p>\n<p>What is it? How do I make a submission? In the 9 hour limit, does my model need to retrain or can I load a saved model?</p>\n<p>A detailed step by step guide on how to make a submission to a code file would be greatly appreciated.</p>\n<p>Thanks.</p>",
      "rawMarkdown": "I have worked with kaggle competitions previously but all those required me to upload a .csv file for evaluation. I see that this is a code competition.\n\nWhat is it? How do I make a submission? In the 9 hour limit, does my model need to retrain or can I load a saved model?\n\nA detailed step by step guide on how to make a submission to a code file would be greatly appreciated.\n\nThanks.",
      "votes": null
    },
    {
      "id": "3145532",
      "postDate": "03/10/2025 00:13:08",
      "content": "<p>Most of your questions are already answered in the <a href=\"https://www.kaggle.com/competitions/stanford-rna-3d-folding/overview\" target=\"_blank\"><strong>Overview</strong></a> section, specifically in <code>Code requirements</code>. It is a good idea to read that whole page before joining a competition. Yet another good idea is to browse the existing discussions, where in many cases questions like yours have already been answered.</p>\n<p>It is up to you whether to perform the training on Kaggle or not. I never do. The prediction, however, has to be done on Kaggle. This means that you create a Kaggle dataset with saved model weights, load it into your notebook, and do the inference.</p>\n<p>General guidelines about types of Kaggle competitions are <a href=\"https://www.kaggle.com/docs/competitions\" target=\"_blank\"><strong>here</strong></a>.</p>",
      "rawMarkdown": "Most of your questions are already answered in the [**Overview**](https://www.kaggle.com/competitions/stanford-rna-3d-folding/overview) section, specifically in `Code requirements`. It is a good idea to read that whole page before joining a competition. Yet another good idea is to browse the existing discussions, where in many cases questions like yours have already been answered.\n\nIt is up to you whether to perform the training on Kaggle or not. I never do. The prediction, however, has to be done on Kaggle. This means that you create a Kaggle dataset with saved model weights, load it into your notebook, and do the inference.\n\nGeneral guidelines about types of Kaggle competitions are [**here**](https://www.kaggle.com/docs/competitions).",
      "votes": null
    },
    {
      "id": "3145897",
      "postDate": "03/10/2025 10:30:38",
      "content": "<p>Thanks a lot.<br>\nI'll ensure to read the said sections of the competitions.</p>",
      "rawMarkdown": "Thanks a lot.\nI'll ensure to read the said sections of the competitions.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 3145532,
      "author_name": "tilii7",
      "author_url": "",
      "post_date": "03/10/2025 00:13:08",
      "content": "<p>Most of your questions are already answered in the <a href=\"https://www.kaggle.com/competitions/stanford-rna-3d-folding/overview\" target=\"_blank\"><strong>Overview</strong></a> section, specifically in <code>Code requirements</code>. It is a good idea to read that whole page before joining a competition. Yet another good idea is to browse the existing discussions, where in many cases questions like yours have already been answered.</p>\n<p>It is up to you whether to perform the training on Kaggle or not. I never do. The prediction, however, has to be done on Kaggle. This means that you create a Kaggle dataset with saved model weights, load it into your notebook, and do the inference.</p>\n<p>General guidelines about types of Kaggle competitions are <a href=\"https://www.kaggle.com/docs/competitions\" target=\"_blank\"><strong>here</strong></a>.</p>",
      "votes": null,
      "replies": [
        {
          "id": 3145897,
          "author_name": "utkarshchaudharycse",
          "author_url": "",
          "post_date": "03/10/2025 10:30:38",
          "content": "<p>Thanks a lot.<br>\nI'll ensure to read the said sections of the competitions.</p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "3145480": "I have worked with kaggle competitions previously but all those required me to upload a .csv file for evaluation. I see that this is a code competition.\n\nWhat is it? How do I make a submission? In the 9 hour limit, does my model need to retrain or can I load a saved model?\n\nA detailed step by step guide on how to make a submission to a code file would be greatly appreciated.\n\nThanks.",
    "3145532": "Most of your questions are already answered in the [**Overview**](https://www.kaggle.com/competitions/stanford-rna-3d-folding/overview) section, specifically in `Code requirements`. It is a good idea to read that whole page before joining a competition. Yet another good idea is to browse the existing discussions, where in many cases questions like yours have already been answered.\n\nIt is up to you whether to perform the training on Kaggle or not. I never do. The prediction, however, has to be done on Kaggle. This means that you create a Kaggle dataset with saved model weights, load it into your notebook, and do the inference.\n\nGeneral guidelines about types of Kaggle competitions are [**here**](https://www.kaggle.com/docs/competitions).",
    "3145897": "Thanks a lot.\nI'll ensure to read the said sections of the competitions."
  },
  "source": "meta"
}