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Kernels Commands

Commands for interacting with Kaggle Kernels (notebooks and scripts).

kaggle kernels list

Lists available kernels.

Usage:

kaggle kernels list [options]

Options:

  • -m, --mine: Display only your kernels.
  • -p, --page <PAGE>: Page number for results (default: 1).
  • --page-size <SIZE>: Number of items per page (default: 20).
  • -s, --search <SEARCH_TERM>: Search term.
  • -v, --csv: Print results in CSV format.
  • --parent <PARENT_KERNEL>: Filter by parent kernel (format: owner/kernel-slug).
  • --competition <COMPETITION_SLUG>: Filter by competition.
  • --dataset <DATASET_SLUG>: Filter by dataset (format: owner/dataset-slug).
  • --user <USER>: Filter by a specific user.
  • --language <LANGUAGE>: Filter by language (all, python, r, sqlite, julia).
  • --kernel-type <TYPE>: Filter by kernel type (all, script, notebook).
  • --output-type <TYPE>: Filter by output type (all, visualizations, data).
  • --sort-by <SORT_BY>: Sort results (hotness, commentCount, dateCreated, dateRun, relevance, scoreAscending, scoreDescending, viewCount, voteCount). Default: hotness.

Examples:

  1. List your own kernels containing "Exercise" in the title, page 2, 5 items per page, in CSV format, sorted by run date:

    kaggle kernels list -m -s Exercise --page-size 5 -p 2 -v --sort-by dateRun

  2. List kernels that are children of $KAGGLE_DEVELOPER/exercise-lists (replace $KAGGLE_DEVELOPER with your username):

    kaggle kernels list --parent $KAGGLE_DEVELOPER/exercise-lists

  3. List the first 5 kernels for the "house-prices-advanced-regression-techniques" competition:

    kaggle kernels list --competition house-prices-advanced-regression-techniques --page-size 5

  4. List the first 5 kernels associated with the dataset dansbecker/home-data-for-ml-course:

    kaggle kernels list --dataset dansbecker/home-data-for-ml-course --page-size 5

  5. List Python notebooks by user $KAGGLE_DEVELOPER that output data:

    kaggle kernels list --user $KAGGLE_DEVELOPER --language python --kernel-type notebook --output-type data

Purpose:

This command allows you to find kernels based on various filters like ownership, associated competition/dataset, language, or type.

kaggle kernels files

Lists output files for a specific kernel.

Usage:

kaggle kernels files <KERNEL> [options]

Arguments:

  • <KERNEL>: Kernel URL suffix (format: owner/kernel-slug, e.g., kerneler/sqlite-global-default).

Options:

  • -v, --csv: Print results in CSV format.
  • --page-token <PAGE_TOKEN>: Page token for results paging.
  • --page-size <PAGE_SIZE>: Number of items to show on a page (default: 20, max: 200).

Example:

List the first output file for the kernel kerneler/sqlite-global-default in CSV format:

kaggle kernels files kerneler/sqlite-global-default -v --page-size=1

Purpose:

Use this command to view the files generated by a kernel run.

kaggle kernels init

Initializes a metadata file (kernel-metadata.json) for a new or existing kernel. See metadata file format.

Usage:

kaggle kernels init -p <FOLDER_PATH>

Options:

  • -p, --path <FOLDER_PATH>: The path to the folder where the kernel-metadata.json file will be created (defaults to the current directory).

Example:

Initialize a kernel metadata file in the tests/kernel folder:

kaggle kernels init -p tests/kernel

Purpose:

This command creates a template kernel-metadata.json file. You need to edit this file with details like the kernel's title, ID (slug), language, kernel type, and data sources before pushing it to Kaggle.

kaggle kernels push

Pushes new code/notebook and metadata to a kernel, then runs the kernel.

Usage:

kaggle kernels push -p <FOLDER_PATH> [options]

Options:

  • --accelerator <ACCELERATOR_ID>: ID name of the accelerator to use during the run. E.g. "NvidiaTeslaP100" (aka default GPU), "NvidiaTeslaT4", "TpuV6E8".
  • -p, --path <FOLDER_PATH>: Path to the folder containing the kernel file (e.g., .ipynb, .Rmd, .py) and the kernel-metadata.json file (defaults to the current directory).
  • -t, --timeout <SECONDS>: Maximum run time in seconds.

Example:

Push the kernel from the tests/kernel folder (assuming it contains the kernel file and kernel-metadata.json):

kaggle kernels push -p tests/kernel

Purpose:

This command uploads your local kernel file and its metadata to Kaggle. If the kernel specified in the metadata exists under your account, it will be updated. Otherwise, a new kernel will be created. After uploading, Kaggle will attempt to run the kernel.

Accelerators available as of Feb 2026:

  • NvidiaTeslaP100
  • TpuV38
  • NvidiaTeslaT4
  • NvidiaTeslaT4Highmem
  • Tpu1VmV38
  • NvidiaTeslaA100
  • NvidiaL4
  • TpuV5E8
  • NvidiaL4X1
  • TpuV6E8
  • NvidiaH100
  • NvidiaRtxPro6000

Some of these are only available to participants of specific competitions, and some are only available to Kaggle admins.

kaggle kernels pull

Pulls down the code/notebook and metadata for a kernel.

Usage:

kaggle kernels pull <KERNEL> [options]

Arguments:

  • <KERNEL>: Kernel URL suffix (format: owner/kernel-slug, e.g., $KAGGLE_DEVELOPER/exercise-as-with).

Options:

  • -p, --path <PATH>: Folder to download files to (defaults to current directory).
  • -w, --wp: Download files to the current working path.
  • -m, --metadata: Generate a kernel-metadata.json file along with the kernel code.

Examples:

  1. Pull the kernel $KAGGLE_DEVELOPER/exercise-as-with and its metadata into the tests/kernel folder:

    kaggle kernels pull -p tests/kernel $KAGGLE_DEVELOPER/exercise-as-with -m

  2. Pull the kernel $KAGGLE_DEVELOPER/exercise-as-with into the current working directory:

    kaggle kernels pull --wp $KAGGLE_DEVELOPER/exercise-as-with

Purpose:

This command allows you to download the source code and optionally the metadata of a kernel from Kaggle to your local machine.

kaggle kernels output

Gets the data output from the latest run of a kernel.

Usage:

kaggle kernels output <KERNEL> [options]

Arguments:

  • <KERNEL>: Kernel URL suffix (e.g., kerneler/using-google-bird-vocalization-model).

Options:

  • -p, --path <PATH>: Folder to download output files to (defaults to current directory).
  • -w, --wp: Download files to the current working path.
  • -o, --force: Force download, overwriting existing files.
  • -q, --quiet: Suppress verbose output.
  • --file-pattern <REGEX>: Regex pattern to match against filenames. Only files matching the pattern will be downloaded.

Example:

Download the output of the kernel kerneler/using-google-bird-vocalization-model, forcing overwrite:

kaggle kernels output kerneler/sqlite-global-default -o

Download PNG files only:

kaggle kernels output <kernel> --file-pattern ".*\.png$"  # Only PNG files

Purpose:

Use this command to retrieve the files generated by a kernel run, such as submission files, processed data, or visualizations.

kaggle kernels status

Displays the status of the latest run of a kernel.

Usage:

kaggle kernels status <KERNEL>

Arguments:

  • <KERNEL>: Kernel URL suffix (e.g., kerneler/sqlite-global-default).

Example:

Get the status of the kernel kerneler/sqlite-global-default:

kaggle kernels status kerneler/sqlite-global-default

Purpose:

This command tells you whether the latest run of your kernel is still running, completed successfully, or failed.

kaggle kernels delete

Deletes a kernel from Kaggle.

Usage:

kaggle kernels delete <KERNEL> [options]

Arguments:

  • <KERNEL>: Kernel URL suffix (format: owner/kernel-slug, e.g., $KAGGLE_DEVELOPER/exercise-delete).

Options:

  • -y, --yes: Automatically confirm deletion without prompting.

Example:

Delete the kernel $KAGGLE_DEVELOPER/exercise-delete and automatically confirm:

kaggle kernels delete $KAGGLE_DEVELOPER/exercise-delete --yes

Purpose:

This command permanently removes one of your kernels from Kaggle. Use with caution.
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