{
  "id": 175057,
  "title": "Is anyone planning to add pyro to kaggle?",
  "url": "/competitions/osic-pulmonary-fibrosis-progression/discussion/175057",
  "author_name": "",
  "post_date": "2020-08-17T01:30:37.837929700Z",
  "votes": null,
  "comment_count": 2,
  "views": 0,
  "content": "<ul>\n<li>I recently asked in Kaggle github repositary how to add <a href=\"https://github.com/Kaggle/docker-python/issues/770\" target=\"_blank\">a new package for competition</a>.</li>\n</ul>\n<p>All you need to do is install the package on the image and feel free to submit a PR with a unit test.</p>\n<ul>\n<li>I was planning to send a PR to add pyro in Kaggle notebooks which was requested by <a href=\"https://www.kaggle.com/carlossouza\" target=\"_blank\">@carlossouza</a> in his notebook soon</li>\n</ul>\n<p>If anyone is working on it currently please comment below, to avoid repeated work.</p>\n<p>Between I stumbled upon some cool CFP proposals from Pycon India about Bayesian approaches:</p>\n<ul>\n<li><a href=\"https://in.pycon.org/cfp/workshops-2019/proposals/pyro-demystified-bayesian-deep-learning~en4lb/\" target=\"_blank\">Pyro Demistified</a></li>\n<li><a href=\"https://in.pycon.org/cfp/workshops-2019/proposals/pyro-demystified-bayesian-deep-learning~en4lb/\" target=\"_blank\">Bayes theoreum for beginners</a></li>\n</ul>",
  "messages": [
    {
      "id": "972930",
      "postDate": "08/17/2020 01:30:37",
      "content": "<ul>\n<li>I recently asked in Kaggle github repositary how to add <a href=\"https://github.com/Kaggle/docker-python/issues/770\" target=\"_blank\">a new package for competition</a>.</li>\n</ul>\n<p>All you need to do is install the package on the image and feel free to submit a PR with a unit test.</p>\n<ul>\n<li>I was planning to send a PR to add pyro in Kaggle notebooks which was requested by <a href=\"https://www.kaggle.com/carlossouza\" target=\"_blank\">@carlossouza</a> in his notebook soon</li>\n</ul>\n<p>If anyone is working on it currently please comment below, to avoid repeated work.</p>\n<p>Between I stumbled upon some cool CFP proposals from Pycon India about Bayesian approaches:</p>\n<ul>\n<li><a href=\"https://in.pycon.org/cfp/workshops-2019/proposals/pyro-demystified-bayesian-deep-learning~en4lb/\" target=\"_blank\">Pyro Demistified</a></li>\n<li><a href=\"https://in.pycon.org/cfp/workshops-2019/proposals/pyro-demystified-bayesian-deep-learning~en4lb/\" target=\"_blank\">Bayes theoreum for beginners</a></li>\n</ul>",
      "rawMarkdown": "I recently asked in Kaggle github repositary how to add [a new package for competition](https://github.com/Kaggle/docker-python/issues/770).\n\nAll you need to do is install the package on the image and feel free to submit a PR with a unit test.\n\n- I was planning to send a PR to add pyro in Kaggle notebooks which was requested by @carlossouza in his notebook soon\n\nIf anyone is working on it currently please comment below, to avoid repeated work.\n\nBetween I stumbled upon some cool CFP proposals from Pycon India about Bayesian approaches:\n\n- [Pyro Demistified](https://in.pycon.org/cfp/workshops-2019/proposals/pyro-demystified-bayesian-deep-learning~en4lb/)\n- [Bayes theoreum for beginners](https://in.pycon.org/cfp/workshops-2019/proposals/pyro-demystified-bayesian-deep-learning~en4lb/)",
      "votes": null
    },
    {
      "id": "973299",
      "postDate": "08/17/2020 08:18:47",
      "content": "<p>Yes I've done it here a while back <a href=\"https://www.kaggle.com/jameschapman19/bayesian-nn-pyro-tabular-fixed\" target=\"_blank\">https://www.kaggle.com/jameschapman19/bayesian-nn-pyro-tabular-fixed</a></p>",
      "rawMarkdown": "Yes I've done it here a while back https://www.kaggle.com/jameschapman19/bayesian-nn-pyro-tabular-fixed",
      "votes": null
    },
    {
      "id": "973301",
      "postDate": "08/17/2020 08:19:58",
      "content": "<p>The two datasets should be public now so should be able to copy the datasets and the couple of lines of code or fork the notebook </p>",
      "rawMarkdown": "The two datasets should be public now so should be able to copy the datasets and the couple of lines of code or fork the notebook",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 973299,
      "author_name": "jameschapman19",
      "author_url": "",
      "post_date": "08/17/2020 08:18:47",
      "content": "<p>Yes I've done it here a while back <a href=\"https://www.kaggle.com/jameschapman19/bayesian-nn-pyro-tabular-fixed\" target=\"_blank\">https://www.kaggle.com/jameschapman19/bayesian-nn-pyro-tabular-fixed</a></p>",
      "votes": null,
      "replies": [
        {
          "id": 973301,
          "author_name": "jameschapman19",
          "author_url": "",
          "post_date": "08/17/2020 08:19:58",
          "content": "<p>The two datasets should be public now so should be able to copy the datasets and the couple of lines of code or fork the notebook </p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "972930": "I recently asked in Kaggle github repositary how to add [a new package for competition](https://github.com/Kaggle/docker-python/issues/770).\n\nAll you need to do is install the package on the image and feel free to submit a PR with a unit test.\n\n- I was planning to send a PR to add pyro in Kaggle notebooks which was requested by @carlossouza in his notebook soon\n\nIf anyone is working on it currently please comment below, to avoid repeated work.\n\nBetween I stumbled upon some cool CFP proposals from Pycon India about Bayesian approaches:\n\n- [Pyro Demistified](https://in.pycon.org/cfp/workshops-2019/proposals/pyro-demystified-bayesian-deep-learning~en4lb/)\n- [Bayes theoreum for beginners](https://in.pycon.org/cfp/workshops-2019/proposals/pyro-demystified-bayesian-deep-learning~en4lb/)",
    "973299": "Yes I've done it here a while back https://www.kaggle.com/jameschapman19/bayesian-nn-pyro-tabular-fixed",
    "973301": "The two datasets should be public now so should be able to copy the datasets and the couple of lines of code or fork the notebook"
  },
  "source": "meta"
}