sp1thas / airflow-provider-kaggle

Airflow operators and hooks for interacting with the Kaggle API

Airflow Kaggle Provider

Airflow operators and hooks for interacting with the Kaggle API

Overview

This airflow provider allows you to interact with the Kaggle API using the corresponding CLI tool. This provider is implemented in an abstract way in order to provide the maximum backward and forward compatibility, as a result, using this provider you can run any command supported by the kaggle-api.

Installation

Pre-requisites: An environment running apache-airflow >= 2.0

pip install airflow-provider-kaggle

Configuration

In order to use this airflow-provider, you have to create a kaggle connection:

• Conn ID: kaggle_default
• Conn Type: Kaggle
• Extra: <kaggle.json>

NOTE: More details on how to get your Kaggle credentials are available here

Usage

Operators

kaggle_provider.operators.kaggle.KaggleOperator

This is the main operator that can be used to execute any kaggle cli command:

from kaggle_provider.operators.kaggle import KaggleOperator

list_competitions_op = KaggleOperator(task_id='foo', command='competitions_list', op_kwargs={'sort_by': 'prize'})

Hooks

kaggle_provider.hooks.kaggle.KaggleHook

This is the kaggle hook which is used by the operator and can also be used directly in your custom operator too.

from kaggle_provider.hooks.kaggle import KaggleHook

hook = KaggleHook()
hook.run('datasets_list', sort_by="votes", user="sp1thas")

Available commands

• competitions_list
• competition_submit
• competition_submissions
• competition_list_files
• competition_download_file
• competition_download_files
• competition_leaderboard_download
• competition_leaderboard_view
• dataset_list
• dataset_metadata_prep
• dataset_metadata_update
• dataset_metadata
• dataset_list_files
• dataset_status
• dataset_download_file
• dataset_download_files
• dataset_create_version
• dataset_initialize
• dataset_create_new
• download_file
• kernels_list
• kernels_initialize
• kernels_push
• kernels_pull
• kernels_output
• kernels_status
• model_get
• model_list
• model_initialize
• model_create_new
• model_delete
• model_update
• model_instance_get
• model_instance_initialize
• model_instance_create
• model_instance_delete
• model_instance_update
• model_instance_version_create
• model_instance_version_download
• model_instance_version_delete
• download_needed

Details regarding the command arguments can be found in the corresponding method docstring of this module