{
  "id": 121486,
  "title": "Code 137",
  "url": "/competitions/nfl-playing-surface-analytics/discussion/121486",
  "author_name": "",
  "post_date": "2019-12-13T16:26:30.974974100Z",
  "votes": null,
  "comment_count": 1,
  "views": 0,
  "content": "<p>Hello,</p>\n\n<p>I am committing my kernel, and it gets about 80% committed and then fails due to error code 137 (too much memory). I'm not sure how to get around this since our code has to be run online for this competition. </p>\n\n<p>I know the datasets are quite large, so I wasn't sure if there is a way to remove a dataset in the code after I was done doing the analysis on that part? </p>\n\n<p>Any help or other suggestions would be greatly appreciated!!</p>",
  "messages": [
    {
      "id": "694455",
      "postDate": "12/13/2019 16:26:30",
      "content": "<p>Hello,</p>\n\n<p>I am committing my kernel, and it gets about 80% committed and then fails due to error code 137 (too much memory). I'm not sure how to get around this since our code has to be run online for this competition. </p>\n\n<p>I know the datasets are quite large, so I wasn't sure if there is a way to remove a dataset in the code after I was done doing the analysis on that part? </p>\n\n<p>Any help or other suggestions would be greatly appreciated!!</p>",
      "rawMarkdown": "Hello,\n\nI am committing my kernel, and it gets about 80% committed and then fails due to error code 137 (too much memory). I'm not sure how to get around this since our code has to be run online for this competition. \n\nI know the datasets are quite large, so I wasn't sure if there is a way to remove a dataset in the code after I was done doing the analysis on that part? \n\nAny help or other suggestions would be greatly appreciated!!",
      "votes": null
    },
    {
      "id": "695757",
      "postDate": "12/15/2019 14:22:08",
      "content": "<p>Check this <a href=\"https://www.kaggle.com/jpmiller/using-track-data-on-a-memory-budget\">kernel</a> by <a href=\"/jpmiller\">@jpmiller</a> \nMight be helpful</p>",
      "rawMarkdown": "Check this [kernel](https://www.kaggle.com/jpmiller/using-track-data-on-a-memory-budget) by @jpmiller \nMight be helpful",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 695757,
      "author_name": "rasyidstat",
      "author_url": "",
      "post_date": "12/15/2019 14:22:08",
      "content": "<p>Check this <a href=\"https://www.kaggle.com/jpmiller/using-track-data-on-a-memory-budget\">kernel</a> by <a href=\"/jpmiller\">@jpmiller</a> \nMight be helpful</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "694455": "Hello,\n\nI am committing my kernel, and it gets about 80% committed and then fails due to error code 137 (too much memory). I'm not sure how to get around this since our code has to be run online for this competition. \n\nI know the datasets are quite large, so I wasn't sure if there is a way to remove a dataset in the code after I was done doing the analysis on that part? \n\nAny help or other suggestions would be greatly appreciated!!",
    "695757": "Check this [kernel](https://www.kaggle.com/jpmiller/using-track-data-on-a-memory-budget) by @jpmiller \nMight be helpful"
  },
  "source": "meta"
}