The official CLI to interact with Kaggle.

User documentation

Key Features

Some of the key features are:

• List competitions, download competition data, submit to a competition.
• List, create, update, download or delete datasets.
• List, create, update, download or delete models & model variations.
• List, update & run, download code & output or delete kernels (notebooks).

Installation

Install the kaggle package with pip:

Additional installation instructions can be found here.

Quick start

Explore the available commands by running:

See the User documentation for more examples & tutorials.

Development

Prerequisites

We use hatch to manage this project.

Follow these instructions to install it.

Run kaggle from source

Option 1: Execute a one-liner of code from the command line

hatch run kaggle datasets list

Option 2: Run many commands in a shell

hatch shell

# Inside the shell, you can run many commands
kaggle datasets list
kaggle competitions list
...

Lint / Format

# Lint check
hatch run lint:style
hatch run lint:typing
hatch run lint:all     # for both

# Format
hatch run lint:fmt

Tests

Note: These tests are not true unit tests and are calling the Kaggle web server.

# Run against kaggle.com
hatch run test:prod

# Run against a local web server (Kaggle engineers only)
hatch run test:local

Integration Tests

To run integration tests on your local machine, you need to set up your Kaggle credentials. You can do this by following the authentication instructions.

After setting up your credentials, you can run the integration tests as follows:

hatch run test:integration

Running hatch commands inside Docker

This is useful to run in a consistent environment and easily switch between Python versions.

The following shows how to run hatch run lint:all but this also works for any other hatch commands:

# Use default Python version
./docker-hatch run lint:all

Changelog

See CHANGELOG.

Contributing

See CONTRIBUTING.md.

License

The Kaggle CLI is released under the Apache 2.0 license.