Articles
NodeJS app dockerization
NodeJS apps can be containerized using the <code>docker build</code> command. This article is based on <a href="https://nodejs.org/de/docs/guides/nodejs-docker-webapp/" rel="noopener noreferrer nofollow">this guide</a>.
Self hosted Docker registry
When using the <code>docker pull</code> command, container images are by default downloaded from docker hub, the official public registry for container images. However, for some projects, images are better stored on a private platform. This can be achieved by hosting one's own docker registry.
Docker HTTP (insecure) registry
By default Docker refuses to push/pull from registries that are not served using HTTPS.
GitLab CI
GitLab CI is a feature of GitLab that allows users to have actions triggered upon pushing a repository to it's remote. For example, it can be used to execute all tests defined in the code, containerize the application and deploy it. A popular alternative to GitLab CI is Jenkins.
Passing variables to Kubernetes manifest
When using kubectl apply, environment variables in Kubernetes manifests are not parsed. For this to happen, the envsubst command can be used.
Generic Kubernetes manifest for web application deployment
Deployment name, container registry and service port are externalized, making this manifest general-purpose
Creating a private docker registry for Kubernetes
A docker registry can be run easily using as a docker container using docker itself.
Application containerization
Let's imagine a developer building an application on his computer and that this application is meant to be deployed on a different machine (production environment). In order to execute properly, this application requires multiple libraries, binaries and packages. For example, a Python program requires the Python interpreter as well as all the imported Python modules.
Node.js DevOps example
In this article, we’ll build a simple Node.js application that uses Express to respond to HTTP requests. In order to deploy this application to production, we’ll also configure a GitLab CI/CD pipeline so as to dockerize it and deploy its container to a Kubernetes cluster.
Deployment of a TensorFlow model to Kubernetes
Let’s imagine that you’ve just finished training your new TensorFlow model and want to start using it in your application(s). One obvious way to do so is to simply import it in the source code of every application that uses it. However, it might be more versatile to keep your model in one place as standalone and simply have applications exchange data with it through API calls. This article will go through the steps of building such a system and deploy the result to Kubernetes.