Create an action, test it offline, and publish it in the GitHub Action Marketplace. Automation, complexity reduction, reproducibility, and maintainability are all advantages that can be realized by a continuous integration (CI) pipeline. With GitHub Actions, you can build these CI pipelines.
Automation, complexity reduction, reproducibility, and maintainability are all advantages that can be realized by a continuous integration (CI) pipeline. With GitHub Actions, you can build these CI pipelines.
“You can create workflows using actions defined in your repository, open-source Actions in a public repository on GitHub, or a published Docker container image.” — GitHub Docs
I recently started a new project at work where I had to implement a new CI pipeline. In this process, I had to call an API, validate the result, and pass it on. I ended up with an inline script of 20 lines within the
run section. That was anything but simple, maintainable, and reusable.
After this miserable failure, I looked up how to create custom GitHub Actions. I was pleasantly surprised that it is very easy to write, test, and publish your own custom GitHub Action. It took me around one hour to research, implement, test, deploy, and release my action. You can check it out on GitHub.
In the following tutorial, we are going to create a custom GitHub Action in four steps. Our Action will execute a simple bash script. This bash script will call the pokeapi.co API-endpoint with a PokeDex ID as a parameter. Then we will parse the result and return the name of the Pokemon. After that, we will
echo the result of our bash script in our GitHub Actions workflow.
What are we going to do:
action.yamlfile with inputs and outputs.
You can find everything we will do in this GitHub repository.
In this article I’d be going through the process of deploying Node application on AWS Elastic BeanStalk using Github Actions.
A common challenge that cloud native application developers face is manually testing against inconsistent environments. GitHub Actions can be triggered based on nearly any GitHub event making it possible to build in accountability for updating tests and fixing bugs.
How to publish a custom GitHub Action on Marketplace for building Machine Learning Applications.
In this article we are going to compare three most popular machine learning projects for you.
DevOps and Cloud computing are joined at the hip, now that fact is well appreciated by the organizations that engaged in SaaS cloud and developed applications in the Cloud. During the COVID crisis period, most of the organizations have started using cloud computing services and implementing a cloud-first strategy to establish their remote operations. Similarly, the extended DevOps strategy will make the development process more agile with automated test cases.