{
  "id": 198360,
  "title": "Loading in Data from previous notebook?",
  "url": "/competitions/riiid-test-answer-prediction/discussion/198360",
  "author_name": "",
  "post_date": "2020-11-20T20:19:46.164407300Z",
  "votes": null,
  "comment_count": 2,
  "views": 0,
  "content": "<p>Hi there, </p>\n<p>I am relatively new to Kaggle, so I apologize if this is a silly question.</p>\n<p>In this competition there is obviously a large dataset and limited compute power for us to use in the notebook environment. </p>\n<p>Can I compute, then save files, and load them into my notebook in another session to ensure I stay within the compute limits? Is this allowed?</p>\n<p>Thanks!</p>",
  "messages": [
    {
      "id": "1085306",
      "postDate": "11/20/2020 20:19:46",
      "content": "<p>Hi there, </p>\n<p>I am relatively new to Kaggle, so I apologize if this is a silly question.</p>\n<p>In this competition there is obviously a large dataset and limited compute power for us to use in the notebook environment. </p>\n<p>Can I compute, then save files, and load them into my notebook in another session to ensure I stay within the compute limits? Is this allowed?</p>\n<p>Thanks!</p>",
      "rawMarkdown": "Hi there, \n\nI am relatively new to Kaggle, so I apologize if this is a silly question.\n\nIn this competition there is obviously a large dataset and limited compute power for us to use in the notebook environment. \n\nCan I compute, then save files, and load them into my notebook in another session to ensure I stay within the compute limits? Is this allowed?\n\nThanks!",
      "votes": null
    },
    {
      "id": "1086701",
      "postDate": "11/22/2020 00:39:00",
      "content": "<p>YES - lots of examples of shared kernels that use some form of pre-worked data.</p>",
      "rawMarkdown": "YES - lots of examples of shared kernels that use some form of pre-worked data.",
      "votes": null
    },
    {
      "id": "1086714",
      "postDate": "11/22/2020 01:08:17",
      "content": "<p>Thanks! Followed a notebook and load takes under 30secs as a pose to 16mins.</p>\n<p>So much better :)</p>",
      "rawMarkdown": "Thanks! Followed a notebook and load takes under 30secs as a pose to 16mins.\n\nSo much better :)",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1086701,
      "author_name": "pcjimmmy",
      "author_url": "",
      "post_date": "11/22/2020 00:39:00",
      "content": "<p>YES - lots of examples of shared kernels that use some form of pre-worked data.</p>",
      "votes": null,
      "replies": [
        {
          "id": 1086714,
          "author_name": "brendanartley",
          "author_url": "",
          "post_date": "11/22/2020 01:08:17",
          "content": "<p>Thanks! Followed a notebook and load takes under 30secs as a pose to 16mins.</p>\n<p>So much better :)</p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "1085306": "Hi there, \n\nI am relatively new to Kaggle, so I apologize if this is a silly question.\n\nIn this competition there is obviously a large dataset and limited compute power for us to use in the notebook environment. \n\nCan I compute, then save files, and load them into my notebook in another session to ensure I stay within the compute limits? Is this allowed?\n\nThanks!",
    "1086701": "YES - lots of examples of shared kernels that use some form of pre-worked data.",
    "1086714": "Thanks! Followed a notebook and load takes under 30secs as a pose to 16mins.\n\nSo much better :)"
  },
  "source": "meta"
}