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Getting Started with Kaggle

Setting Up Kaggle

  1. Create an Account: Register for an account on Kaggle.
  2. Identity Verification:
    • Go to Profile > Settings.
    • Complete Phone Verification by adding and verifying your phone number.
    • Complete Identity Verification by scanning your face.

Creating a New Notebook

  • Create your first notebook by selecting Create > New Notebook.
  • Kaggle notebooks support Jupyter-style shortcuts and magic commands.
  • Notebooks save automatically, and you can upload your own .ipynb files.

Upload notebook

Enabling GPU in Kaggle

To enable GPU in your Kaggle notebook, follow these steps:

  1. Verify Identity: Ensure you have completed identity verification as described above.

  2. Enable GPU: You have two options to activate the GPU in your notebook:

    • Option 1:
      • Open Settings in your notebook.
      • Under Accelerator, select GPU.

Enable GPU in settings

  • Option 2:
    • Click the button in the lower right corner of your notebook interface.
    • In Session Options, go to Accelerator and select GPU.
view version version history

Available Resources

  • Accelerator Options:
    • 2x GPU T4 (15GB each), GPU P100 (16GB), TPU VM v3-8
    • 30 hours per week, reset every Saturday
  • System Specs:
    • Disk: HDD 58GB
    • RAM: 29GB
    • CPU: 4 cores
  • Session Limit: 12 hours per session

Resource usage can be monitored in the top-right corner of the notebook.
Resource view

Working with Datasets

  • To use a dataset in your notebook, you first need to upload it to Kaggle.

  • Go to Create > New Dataset and upload your dataset. For large files, consider zipping them before uploading.

  • In your notebook, select Add Input to access your uploaded dataset.

    Add input

  • Check Your Work and then Datasets to view your uploaded datasets.

    Select dataset

  • Click the plus sign to add your dataset (ensure it has been successfully uploaded).

  • Once added, your dataset will be available in the /kaggle/input directory by default.

Saving and Accessing Outputs

  • By default, any files or folders you download or create in the notebook are stored in /kaggle/working under Output.
    Output tab

  • To save your output, click Save Version.
    Save version

  • Enter a version name, set the version type to Quick Save, and configure the save output setting to Always save output when creating Quick Save.

    Quick Save

  • Wait for the save process to complete.
    Saving queue

  • Once completed, you can go to the Output tab, locate your files, and download them.
    Download output files

  • Note: You can directly download smaller output files from the output tab in your notebook.

Run All + Recurrent Workflow (MUST READ)

Problem: When using Colab, you need to download the output files (weights, logs, etc.) after each run, then reupload for the next run/training/inference. This can be time-consuming and inconvenient.

Solution: Data (output) workflow in Kaggle is more efficient than Colab!!!

Details: If you want to use the output of your notebook as the input for the same notebook, follow these steps:

  1. Save and Run All: Ensure your notebook runs successfully by using the Save and Run All (Commit) option. This will execute the notebook and save the outputs in one step. Remember to turn on the GPU before running the notebook.

a

  1. Add Input: After the notebook has run successfully, click Add Input and select Your Work > Notebooks. Choose the notebook you just ran. Here is an example of how the notebook will look after running and adding the input:

a

  1. Access Output Files: You can access the output files directly without creating a new dataset each time. However, this method defaults to the latest version of the output files.

  2. Save as Dataset (Alternative): You can also save the output files as a dataset by clicking New Dataset in the Output tab. This will create a reusable dataset with the saved outputs.

NOTE: This method is especially useful for recurrent workflows, such as training a model over multiple epochs or running multiple experiments with the same data. The time for saving outputs and notebooks is significantly reduced compared to Colab.