{
  "id": 360281,
  "title": "Help me how to load the datsets",
  "url": "/competitions/rsna-2022-cervical-spine-fracture-detection/discussion/360281",
  "author_name": "Seong Ahn",
  "post_date": "2022-10-16T02:03:08.217000",
  "votes": 8,
  "comment_count": 4,
  "views": 0,
  "content": "<p>Hello, I am newbie in Kaggle.<br>\nIt might be stupid question but I'm struggling on loading the datasets.<br>\nSince it is really big, so I cannot handle on the local like colab.</p>\n<p>I tried codes in <a href=\"https://www.kaggle.com/competitions/rsna-2022-cervical-spine-fracture-detection/discussion/356025\" target=\"_blank\">https://www.kaggle.com/competitions/rsna-2022-cervical-spine-fracture-detection/discussion/356025</a> here but I couldn't make it. </p>\n<p>Is there anybody who can help me?</p>",
  "messages": [
    {
      "id": 1989451,
      "postDate": "2022-10-16T02:03:08.217Z",
      "content": "<p>Hello, I am newbie in Kaggle.<br>\nIt might be stupid question but I'm struggling on loading the datasets.<br>\nSince it is really big, so I cannot handle on the local like colab.</p>\n<p>I tried codes in <a href=\"https://www.kaggle.com/competitions/rsna-2022-cervical-spine-fracture-detection/discussion/356025\" target=\"_blank\">https://www.kaggle.com/competitions/rsna-2022-cervical-spine-fracture-detection/discussion/356025</a> here but I couldn't make it. </p>\n<p>Is there anybody who can help me?</p>",
      "rawMarkdown": "Hello, I am newbie in Kaggle.\nIt might be stupid question but I'm struggling on loading the datasets.\nSince it is really big, so I cannot handle on the local like colab.\n\nI tried codes in https://www.kaggle.com/competitions/rsna-2022-cervical-spine-fracture-detection/discussion/356025 here but I couldn't make it. \n\nIs there anybody who can help me?",
      "votes": 8
    },
    {
      "id": 1996815,
      "postDate": "2022-10-20T12:44:42.213Z",
      "content": "<p>The best work around is:</p>\n<ol>\n<li>download original data to local PC (400GB)</li>\n<li>preprocess data. e.g. resize to smaller resolution (e.g. 50GB)</li>\n<li>upload to google drive</li>\n<li>colab</li>\n</ol>",
      "rawMarkdown": "The best work around is:\n1. download original data to local PC (400GB)\n2. preprocess data. e.g. resize to smaller resolution (e.g. 50GB)\n3. upload to google drive\n4. colab",
      "votes": 2
    },
    {
      "id": 2002094,
      "postDate": "2022-10-24T14:02:44.177Z",
      "content": "<p>Hello, another possibility would be to preprocess the data first on a Kaggle notebook and then download the output dataset which would be much lighter.</p>\n<p>An example of preprocessed dataset I made : <a href=\"https://www.kaggle.com/datasets/morganmb/3dconv64x64x64interpolorder3widthandheight\" target=\"_blank\">https://www.kaggle.com/datasets/morganmb/3dconv64x64x64interpolorder3widthandheight</a></p>",
      "rawMarkdown": "Hello, another possibility would be to preprocess the data first on a Kaggle notebook and then download the output dataset which would be much lighter.\n\nAn example of preprocessed dataset I made : https://www.kaggle.com/datasets/morganmb/3dconv64x64x64interpolorder3widthandheight\n"
    },
    {
      "id": 1991504,
      "postDate": "2022-10-17T07:57:25.487Z",
      "content": "<p>Hi there. Have you tried reading the dcm files and converting them to png already?</p>",
      "rawMarkdown": "Hi there. Have you tried reading the dcm files and converting them to png already?"
    },
    {
      "id": 1991891,
      "postDate": "2022-10-17T12:39:38.227Z",
      "rawMarkdown": "",
      "isDeleted": true
    }
  ],
  "comments": [
    {
      "id": 1996815,
      "author_name": "Qishen Ha",
      "author_url": "",
      "post_date": "2022-10-20T12:44:42.213000",
      "content": "<p>The best work around is:</p>\n<ol>\n<li>download original data to local PC (400GB)</li>\n<li>preprocess data. e.g. resize to smaller resolution (e.g. 50GB)</li>\n<li>upload to google drive</li>\n<li>colab</li>\n</ol>",
      "votes": 2,
      "replies": []
    },
    {
      "id": 2002094,
      "author_name": "MorganMB",
      "author_url": "",
      "post_date": "2022-10-24T14:02:44.177000",
      "content": "<p>Hello, another possibility would be to preprocess the data first on a Kaggle notebook and then download the output dataset which would be much lighter.</p>\n<p>An example of preprocessed dataset I made : <a href=\"https://www.kaggle.com/datasets/morganmb/3dconv64x64x64interpolorder3widthandheight\" target=\"_blank\">https://www.kaggle.com/datasets/morganmb/3dconv64x64x64interpolorder3widthandheight</a></p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 1991504,
      "author_name": "Bob de Graaf",
      "author_url": "",
      "post_date": "2022-10-17T07:57:25.487000",
      "content": "<p>Hi there. Have you tried reading the dcm files and converting them to png already?</p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 1991891,
      "author_name": "",
      "author_url": "",
      "post_date": "2022-10-17T12:39:38.227000",
      "content": "",
      "votes": 0,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1989451": "Hello, I am newbie in Kaggle.\nIt might be stupid question but I'm struggling on loading the datasets.\nSince it is really big, so I cannot handle on the local like colab.\n\nI tried codes in https://www.kaggle.com/competitions/rsna-2022-cervical-spine-fracture-detection/discussion/356025 here but I couldn't make it. \n\nIs there anybody who can help me?",
    "1996815": "The best work around is:\n1. download original data to local PC (400GB)\n2. preprocess data. e.g. resize to smaller resolution (e.g. 50GB)\n3. upload to google drive\n4. colab",
    "2002094": "Hello, another possibility would be to preprocess the data first on a Kaggle notebook and then download the output dataset which would be much lighter.\n\nAn example of preprocessed dataset I made : https://www.kaggle.com/datasets/morganmb/3dconv64x64x64interpolorder3widthandheight\n",
    "1991504": "Hi there. Have you tried reading the dcm files and converting them to png already?",
    "1991891": ""
  }
}