Sharing your docker image

Last updated on 2024-07-05 | Edit this page

Overview

Questions

  • How to share a container image in Docker Hub?
  • How to build and share a container image through GitHub

Objectives

  • Learn how to push a container image to Docker Hub
  • Learn how to trigger the container image build through GitHub actions and share it through a GitHub Package.

Overview

In the previous episode you learnt how to build a container image.

In this episode, we will go through two alternatives for sharing the image.

Share an image through Docker Hub

Create an account in the Docker image registry: go to Docker Hub and click on “Sign up”. This is a separate account from GitHub or anything else.

On the terminal, log in to Docker Hub:

BASH 

docker login --username=<yourdockerhubname>

Tag the image with your username:

BASH 

docker tag ml-python:v0.1 <yourdockerhubname>/ml-python:v0.1

Note that the ML image is big, the ML packages made it double the size of the base image!

If you want a quick exercise, use your emoji image instead:

BASH 

docker tag python-emoji:v0.1 <yourdockerhubname>/python-emoji:v0.1

Push the image to Docker Hub:

BASH 

docker push <yourdockerhubname>/ml-python:v0.1

or, for a quick check

BASH 

docker push <yourdockerhubname>/python-emoji:v0.1

Once done, if your Docker Hub repository is public, anyone see your image in https://hub.docker.com/u/<yourdockerhubname> and can pull it from it.

Document your image

Add a description and an overview.

Find your image in https://hub.docker.com/u/<yourdockerhubname>. Add a brief descripion. In “Overview”, write usage instructions.

Exchange images

Try someone else’s image.

Are the instructions clear?

Build and share an image through GitHub

You can also use GitHub actions to build the image and share it through GitHub packages.

Your repository should contain a Dockerfile and all the files needed in it.

Create a .github/workflows/ directory (with that exact name) in your repository:

BASH 

mkdir .github
mkdir .github/workflows

and save a file docker-build-deploy.yaml in it with the following content:

name: Create and publish a Docker image

on:
  push:
    branches:
      - main

env:
  REGISTRY: ghcr.io
  IMAGE_NAME: ${{ github.repository }}

jobs:
  build-and-push-image:
    runs-on: ubuntu-latest

    permissions:
      contents: read
      packages: write

    steps:
      - name: Checkout repository
        uses: actions/checkout@v4

      - name: Log in to the Container registry
        uses: docker/login-action@v3
        with:
          registry: ${{ env.REGISTRY }}
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}

      - name: Docker Metadata
        id: meta
        uses: docker/metadata-action@v5
        with:
          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}

      - name: Build and push Docker image
        uses: docker/build-push-action@v5
        with:
          context: .
          push: true
          tags: ${{ steps.meta.outputs.tags }}
          labels: ${{ steps.meta.outputs.labels }}

Before pushing to the GitHub repository, exclude files you do not want to push in a .gitignore file.

Then check the status to make sure that you commit and push what you want

BASH 

git status

The add and commit

BASH 

git add .
git commit -m "Adding a worflow to build the container"

Check the remote to see that the push will go where you want

BASH 

git remote -v

and push the changes

BASH 

git push origin main

Once done, click on “Actions” in the GitHub Web UI and you will see the build ongoing:

If all goes well, after some minutes, you will see a green tick mark and the container image will be available in Packages

https://github.com/<yourgithubname>/<repository>/pkgs/container/<repository>

The image name is the same as the repository name because we have chosen so on line 10 of the docker-build-deploy.yaml file.

Learn more

Learn more about Dockerfile instructions in the Dockefile reference.

Learn more about writing Dockerfiles in the HSF Docker tutorial.

Learn more about using Docker containers in the analysis work in the Analysis pipelines training.

Key Points

  • It is easy to build a new container image on top of an existing image.
  • You can install packages that you need if they are not present in an existing image.
  • You can add code and eventually compile it in the image so that it is ready to use when the container starts.