This blog post is about to deploy the IBM Watson Speech to Text Library for Embed to an IBM Cloud Kubernetes cluster. IBM Cloud Kubernetes cluster is a “certified, managed Kubernetes solution, built for creating a cluster of compute hosts to deploy and manage containerized apps on IBM Cloud“.
This blog post is about to run Watson NLP for Embed example in a KServe ModelMesh Serving environment on an IBM Cloud Kubernetes cluster in a Virtual Private Cloud environment and reuses parts of the IBM Watson Libraries for Embed documentation.
This blog post is about to deploy the IBM Watson Natural Language Processing Library for Embed to an IBM Cloud Kubernetes cluster in a Virtual Private Cloud (VPC) environment and is related to my blog post Run Watson NLP for Embed on IBM Cloud Code Engine. IBM Cloud Kubernetes cluster is a “certified, managed Kubernetes solution, built for creating a cluster of compute hosts to deploy and manage containerized apps on IBM Cloud“.
Sometimes we need to ensure that resources in Kubernetes are fully deleted before we setup other resources. In Kubernetes the timing and the synchronization can be very import and relevant. In that blog post we see a function of a bash script, that exactly does that job for namespaces. We are using a “for loop” combined with a nested “while loops” and other functionalities in bash to address that topic.
That blog post is about an easy example to get your custom logs of your operator, when the operator is running on a Kubernetes cluster. That blog post does reference an example GitHub project called Example Tenancy Frontend Operator you can use to verify the steps. (branch monitor-grafana-operator) In this project I wrote a short custom logging that... Continue Reading →
That blog post does focus on a basic installation of the Grafana operator to get an understanding how that operator basically works in the context to the two blog posts I made before
hat blog post does focus on a customized monitoring with Prometheus for a custom operator implementation build with the golang Operator SDK. For the monitoring we will use the Prometheus operator. Alain Arom and I inspected that topic and here we show you one example hands-on journey how to get the technical job done. There are a lot of materials out there, but in that blog post we follow an end-to-end scenario for a beginner to intermediate level (without any stop in the middle 😉 of the road). We will only focus on:how it basically works and not why or what we should do in monitoring.
In that blog post we will add a webhook to our existing operator project Multi Tenancy Frontend Operator in the branch update-operator were we created the v2alpha2 API version for the operator in the last blog post "Add a new API version to an existing operator". The final implementation for the current blog post you find in the webhook-gen-operator branch. (details about conversion webhook) Yes, that... Continue Reading →
This is my next blog post related to operators. That blog post is about adding a new API version to our existing example Multi Tenancy Frontend Operator. When we have added the new API version we will deploy the changed operator to a Kubernetes cluster using the Operator Lifecycle Manager (OLM).
That is the next blog post related to operators. Now it’s about deploy an operator without the Operator SDK. In the last blog post we used the operator-sdk run bundle command which created for us all needed specifications and images to run the bundle. Therefor we need to take a closer look into the Operator Lifecycle Manager (OLM).