Github action to push Kaggle kernels from github and also for submitting them to their respective competitions.

This is a GitHub Action which will push your kernels to Kaggle And also submit them to your competitions.

Usage

name: Kaggle kernel actions

on:
  push:
    branches:
      - master

jobs:
  build:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        os: [ ubuntu-latest, macos-latest ]
        python-version: [ '2.7.17', '3.6.10' ]
    name: Python ${{ matrix.python-version }} ${{ matrix.os }} 

    steps:
    - uses: actions/checkout@v2

    - name: Push kernel to kaggle
      uses: joelhanson/kaggle-kernel-actions
      with:
        kaggle_username: ${{ secrets.kaggle_username }}
        kaggle_key: ${{ secrets.kaggle_key }}
        kaggle_make_new_kernel: true
        kaggle_metadata_path: kernel-metadata.json
        kernel_id: <username>/<kernel_slug>
        competition: titanic

Getting Started ✈️

You can include the action in your workflow to trigger on any event that GitHub actions supports. If the remote branch that you wish to deploy to doesn't already exist the action will create it for you. Your workflow will also need to include the actions/checkout step before this workflow runs in order for the deployment to work.

If you'd like to make it so the workflow only triggers on push events to specific branches then you can modify the on section.

on:
  push:
    branches:
      - master

Configuration 📁

The with portion of the workflow must be configured before the action will work. You can add these in the with section found in the examples above. Any secrets must be referenced using the bracket syntax and stored in the GitHub repositories Settings/Secrets menu. You can learn more about setting environment variables with GitHub actions here.

Required Setup

One of the following deployment options must be configured.

Key	Value Information	Type	Required	Default
kaggle_username	You should provide kaggle username.'	secrets	Yes
kaggle_key	To provide you kaggle access key to learn for visit.	secrets	YES
kernel_id	The id for the kaggle kernel example: username/kernel-slug.	with	Yes	false
kaggle_make_new_kernel	This is make new kernel when the kernel id does not exist the current existing ones.	with	No	false
kaggle_metadata_path	The metadata path for the kaggle kernel. The metadata details can be found here. To create one you can run kaggle kernel init. Either the metadata file or the metadata details should be provided for this github action to work.	with	No	.
kernel_id_no	This is the kernel number which is got when pull an already existing kernel.	with	No
kernel_title	The title of the kernel. Required for new kernels - optional for existing ones. Please be aware that kernel titles and slugs are linked to each other. A kernel slug is always the title lowercased with dashes (-) replacing spaces. (If you wish to rename your kernel, you may change the title within the metadata. However, you will need to update the id as well AFTER the rename is complete.)	with	No
code_file_path	The path to the code file. This should be provided in the metadata file or as input.	with	Yes	.
language	The language your kernel is written in. Valid options are python, r, and rmarkdown. Required.This should be provided in the metadata file or as input	with	No	python
kernel_type	The type of kernel. Valid options are script and notebook. Required.	with	No	notebook
is_private	Whether or not the kernel should be private. If not specified, will be true.	with	No	true
enable_gpu	Whether or not the kernel should run on a GPU. If not specified, will be false.	with	No	false
enable_internet	Whether or not the kernel should be able to access the internet. If not specified, will be false.	with	No	false
competition	The name of the competition that you are working on. Providing this will automatically get the datasets from the competition to the kernel inputs. A competition, specified as "competition-slug"	with	No
dataset_sources	A list of dataset sources, specified as "username/dataset-slug".	with	No
competition_sources	A list of competition sources, specified as "competition-slug".	with	No
kernel_sources	A list of kernel sources, specified as "username/kernel-slug".	with	No
deploy_kernel	This will tell the action to push the kernel to kaggle or not.	with	No	true
submit_to_competition	This is a boolean flag to submit the kernel if specified true.(still in progress)	with	No	false
submition_message	This will override the default submission message ( ) with this message specified here.	with	No
collect_output	This is to tell the action to save the output of the kernel run as zip. GitHub will save your cache but will begin evicting caches until the total size is less than 2 GB.	with	No	false

---

Artifacts 📁

You can get the outputs as artifacts, for example the output accuracy and the models can be saved. Hence can get this output from the workflow artifacts. learn more about artifacts.

The following is example of how to download the artifacts.

name: Push notebook to kaggle

on: push

jobs:
  deploy:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        os: [ ubuntu-latest, macos-latest ]
        os: [ ubuntu-latest ]
        python-version: [ '2.7.17', '3.6.10' ]
    name: Python ${{ matrix.python-version }} ${{ matrix.os }}

    - name: Kaggle deploy as new
      uses: joelhanson/kaggle-kernel-actions
      with:
        kaggle_username: ${{ secrets.kaggle_username }}
        kaggle_key: ${{ secrets.kaggle_key }}
        kaggle_make_new_kernel: true
        kernel_id: <username>/<kernel_slug>
        code_file_path: <folder>/<file_path_to.ipynb>
        kernel_title: <kernel_title>
        language: python
        kernel_type: notebook
        is_private: true
        enable_gpu: false
        enable_internet: false
        competition: titanic
        submit_to_competition: false
        collect_output: true

    - name: Download outputs
      uses: actions/upload-artifact@v1
      with:
        name: outputs
        path: outputs/outputs.zip

---

Submit kernels (In progress)

You can submit kernels by adding the following inputs for the jobs

    - name: Kaggle deploy from existing metadata file
      uses: ./
      with:
        kaggle_username: ${{ secrets.kaggle_username }}
        kaggle_key: ${{ secrets.kaggle_key }}
        kaggle_make_new_kernel: true
        kaggle_metadata_path: test/kernel-metadata.json
        kernel_id: joelhanson/github-action-test
        competition: titanic
        submit_to_competition: true  # This needs to be true

Further Actions

You send notifications to you telegram or to slack by using some of the other actions provided in the github marketplace

License 👨🏻‍💻

The Dockerfile and associated scripts and documentation in this project are released under the MIT License.

Container images built with this project include third party materials. As with all Docker images, these likely also contain other software which may be under other licenses. It is the image user's responsibility to ensure that any use of this image complies with any relevant licenses for all software contained within.

FAQ

1. Where to find the kernel_id?

You can find the kernel_id by going to the top menu of the kernel and clicking copy api command.