{
  "id": 545958,
  "title": "A little computation problems when running the training datasets ",
  "url": "/competitions/jane-street-real-time-market-data-forecasting/discussion/545958",
  "author_name": "",
  "post_date": "2024-11-13T01:47:50.995735400Z",
  "votes": 1,
  "comment_count": 1,
  "views": 0,
  "content": "<p>I'm facing a computation problem when running the datasets , therefore , I run just a chunk of the datasets , however I see others running the entire training sets (See screenshot). <br>\nwhen I use the google cloud AI , I get a problem reading the root path folder <br>\nimport os<br>\nimport pandas as pd<br>\nimport polars as pl<br>\nimport kaggle_evaluation.jane_street_inference_server</p>\n<h2>Results:</h2>\n<p>ModuleNotFoundError                       Traceback (most recent call last)<br>\nCell In[25], line 5<br>\n      3 import pandas as pd<br>\n      4 import polars as pl<br>\n----&gt; 5 import kaggle_evaluation.jane_street_inference_server</p>\n<p>ModuleNotFoundError: No module named 'kaggle_evaluation<br>\nthen I check the root path on juypter lab <br>\nimport os</p>\n<h1>Define the dataset path</h1>\n<p>dataset_path = \"/\"</p>\n<p>os.path.exists('dataset_path') <br>\nresult:False</p>",
  "messages": [
    {
      "id": "3044064",
      "postDate": "11/13/2024 01:47:50",
      "content": "<p>I'm facing a computation problem when running the datasets , therefore , I run just a chunk of the datasets , however I see others running the entire training sets (See screenshot). <br>\nwhen I use the google cloud AI , I get a problem reading the root path folder <br>\nimport os<br>\nimport pandas as pd<br>\nimport polars as pl<br>\nimport kaggle_evaluation.jane_street_inference_server</p>\n<h2>Results:</h2>\n<p>ModuleNotFoundError                       Traceback (most recent call last)<br>\nCell In[25], line 5<br>\n      3 import pandas as pd<br>\n      4 import polars as pl<br>\n----&gt; 5 import kaggle_evaluation.jane_street_inference_server</p>\n<p>ModuleNotFoundError: No module named 'kaggle_evaluation<br>\nthen I check the root path on juypter lab <br>\nimport os</p>\n<h1>Define the dataset path</h1>\n<p>dataset_path = \"/\"</p>\n<p>os.path.exists('dataset_path') <br>\nresult:False</p>",
      "rawMarkdown": "I'm facing a computation problem when running the datasets , therefore , I run just a chunk of the datasets , however I see others running the entire training sets (See screenshot). \nwhen I use the google cloud AI , I get a problem reading the root path folder \nimport os\nimport pandas as pd\nimport polars as pl\nimport kaggle_evaluation.jane_street_inference_server\nResults:\n---------------------------------------------------------------------------\nModuleNotFoundError                       Traceback (most recent call last)\nCell In[25], line 5\n      3 import pandas as pd\n      4 import polars as pl\n----> 5 import kaggle_evaluation.jane_street_inference_server\n\nModuleNotFoundError: No module named 'kaggle_evaluation\nthen I check the root path on juypter lab \nimport os\n\n# Define the dataset path\ndataset_path = \"/\"\n\nos.path.exists('dataset_path') \nresult:False",
      "votes": null
    },
    {
      "id": "3044144",
      "postDate": "11/13/2024 05:09:22",
      "content": "<p>You may train the model outside of kaggle and then submit on a kaggle kernel by saving and loading the fitted models. Pickle files, joblib or any equivalent choice is good for you <a href=\"https://www.kaggle.com/ahatia\" target=\"_blank\">@ahatia</a> </p>",
      "rawMarkdown": "You may train the model outside of kaggle and then submit on a kaggle kernel by saving and loading the fitted models. Pickle files, joblib or any equivalent choice is good for you @ahatia",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 3044144,
      "author_name": "ravi20076",
      "author_url": "",
      "post_date": "11/13/2024 05:09:22",
      "content": "<p>You may train the model outside of kaggle and then submit on a kaggle kernel by saving and loading the fitted models. Pickle files, joblib or any equivalent choice is good for you <a href=\"https://www.kaggle.com/ahatia\" target=\"_blank\">@ahatia</a> </p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "3044064": "I'm facing a computation problem when running the datasets , therefore , I run just a chunk of the datasets , however I see others running the entire training sets (See screenshot). \nwhen I use the google cloud AI , I get a problem reading the root path folder \nimport os\nimport pandas as pd\nimport polars as pl\nimport kaggle_evaluation.jane_street_inference_server\nResults:\n---------------------------------------------------------------------------\nModuleNotFoundError                       Traceback (most recent call last)\nCell In[25], line 5\n      3 import pandas as pd\n      4 import polars as pl\n----> 5 import kaggle_evaluation.jane_street_inference_server\n\nModuleNotFoundError: No module named 'kaggle_evaluation\nthen I check the root path on juypter lab \nimport os\n\n# Define the dataset path\ndataset_path = \"/\"\n\nos.path.exists('dataset_path') \nresult:False",
    "3044144": "You may train the model outside of kaggle and then submit on a kaggle kernel by saving and loading the fitted models. Pickle files, joblib or any equivalent choice is good for you @ahatia"
  },
  "source": "meta"
}