How To Use Docker Tags to Manage Image Versions Effectively

Learn to use Docker tags for managing and versioning docker images, making it easier to handle different application versions.



How To Use Docker Tags to Manage Image Versions Effectively
Image by Editor | Midjourney & Canva

 

Learn how to utilize Docker tags to manage different versions of your Docker images, ensuring consistent and organized development workflows. This guide covers best practices for tagging, updating, and maintaining Docker images.
 

Prerequisites

 

Before you start:

  • You should have Docker installed in your development environment. Get Docker if you haven’t already.
  • A sample application which you want to Dockerize. If you’d like, you can use this example on GitHub.

 

Tagging Docker Images

 

A Docker tag is a label that points to a specific image within a repository. By default, Docker uses the latest tag if no tag is specified. But if you’re developing your app and improving it across versions, you may want to add more explicit tags. These tags are useful for distinguishing between different versions or states of an image.

Say you have a Python project: a Flask app for inventory management with all the required files in the project directory:

project-dir/
├── app.py
├── Dockerfile
├── requirements.txt

 

You can tag an image when you build it like so:

$ docker build -t image_name:tag_name

 

Now let’s build the inventory-app image and tag it:

$ docker build -t inventory-app:1.0.0 .

 

Here:

  • inventory-app is the repository name or the image name.
  • 1.0.0 is the tag for this specific build of the image.

You can run the docker images command to view the newly built image with the specified tag:

$ docker images
REPOSITORY      TAG           IMAGE ID       CREATED        SIZE
inventory-app   1.0.0         32784c60a992   6 minutes ago   146MB

 

You can also tag an existing image as shown:

$ docker tag inventory-app:1.0.0 inventory-app:latest

 

Here, we're tagging an existing image inventory-app:1.0.0 as inventory-app:latest. You’ll see that we have two inventory-app images with different tags and the same image ID:

$ docker images
REPOSITORY      TAG           IMAGE ID       CREATED        SIZE
inventory-app   1.0.0         32784c60a992   6 minutes ago   146MB
inventory-app   latest        32784c60a992   5 minutes ago   146MB

 

Pushing Tagged Images to a Repository

 

To share your Docker images, you can push them to a Docker repository (like DockerHub). You can sign up for a free DockerHub account, login, and push images. You should first log in to DockerHub:

$ docker login

 

You’ll be prompted for your username and password. Upon successful authentication, you can push the tagged image with the docker push command.

Make sure your repository name matches your Docker Hub username or organization. If your Docker Hub username is user and you want to push version 1.0.1 of the image, you tag your image as user/inventory-app:1.0.1:

$ docker tag user/inventory-app:1.0.1
$ docker push user/inventory-app:1.0.1

 

When you need to use a specific version of an image, you can pull it using the tag:

$ docker pull user/inventory-app:1.0.1

 

Best Practices for Tagging Docker Images

 

Here are some best practices to follow when tagging Docker images:

  • Use Semantic Versioning: Follow a versioning scheme like MAJOR.MINOR.PATCH (1.0.0, 1.0.1). This helps in identifying the significance of changes.
  • Avoid Using latest for Production: Use explicit version tags for production deployments.
  • Automate Tagging in CI/CD Pipelines: Integrate Docker tagging into your CI/CD pipelines to ensure consistent and automatic versioning.
  • Include Metadata in Tags: If it makes sense, add build numbers, commit hashes, or dates in tags.

By following these practices when using Docker tags, you can maintain a clean, organized, and versioned set of Docker images.

 

Additional Resources

 

Here are a couple of resources you’ll find helpful:

 
 

Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she's working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.