CI, for example

Posted by Andy Campbell, June 19, 2025

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Hello everyone, it has been a long time! I am excited to resurrect the conversation a bit here because we have a number of topics that are in the pipeline. Actually, there have always been plenty of topics in the ol' blog pipeline, but the good news now is that we actually have been able to sit down and write some of them.

Over the next little while Ajay Puvvala will be publishing a series of posts related to some exciting features we have in MATLAB around test selection. Super exciting stuff so be sure to keep an eye out for these.

While you wait, I also wanted to quickly plug a couple of GitHub repositories that we've published to help with your CI workflows.

The simplest config that works
(Slightly) more advanced configurations
Multi-platform Toolbox Creation
Multi-release deployment to integrate with Python®

The simplest config that works

First, if you want to just get up and going on your CI platform with the simplest of configs, take a look at the CI Configuration Examples repository. This shows a basic "Hello World!" example for GitHub® Actions, GitLab® CI/CD, Jenkins™, Azure® DevOps, and CircleCI®. You can go there right now, fork it, and immediately use cloud hosted runners on GitHub, Azure DevOps, or CircleCI to see the CI process in action. Note, GitHub makes it particularly easy because Actions are just built into GitHub itself.

(Slightly) more advanced configurations

Additionally, for those looking for more advanced configurations, check out the Advanced CI Configuration Examples repository. This includes more complex setups and best practices for integrating MATLAB with these CI/CD tools. In this repository you will currently find two workflows that are demonstrated on 4 CI platforms: GitHub Actions, Azure DevOps, Jenkins, and CircleCI.

Multi-platform Toolbox Creation

The first example goes through the build of a MATLAB toolbox across 3 platforms to produce and release a single .mltbx file. This workflow currently shows:

How to use a matrix build across platforms (Linux, Windows, and Mac)
Building platform specific mex files
Uploading these artifacts on each
Retrieving the built mex files for each platform and packaging the toolbox
Publishing the toolbox to a GitHub release for distribution

Multi-release deployment to integrate with Python®

The second example shows how you can leverage the MATLAB Compiler SDK™ to create a python package from this algorithm. It does this across releases and operating systems. Some highlights of this workflow:

A double matrix build (across MATLAB releases and operating systems)
Creation and deployment of a python package from the MATLAB code using Compiler SDK
Equivalence testing of the deployed algorithm using MATLAB Test
Using batch tokens for licensing of MATLAB Compiler SDK on cloud hosted runners. While licensing of many MathWorks products is provided for public projects, batch tokens allow you to leverage other products not supported (like Coders and Compilers). They also enable you to leverage CI for private projects.

It's great to be back! Stay tuned for more and we'd love to hear how you are doing and what you'd like to learn more about!

Published with MATLAB® R2025a