microk8s VS kubernetes-the-hard-way

If you want hands-on practice you should have a running Kubernetes cluster (I used MicroK8s for this tutorial) and Helm (see how to install on Installing Helm tutorial). It is important that you understand the basics of these tools to fully understand.

And install microk8s:

MicroK8s is a lightweight, batteries included Kubernetes distribution by Canonical designed for running edge workloads which also happens to be developer-friendly and a great choice for building your own homelab. The following lab covers how to install and run MicroK8s on your own edge node running Ubuntu 22.04 LTS, deploy the NGINX web service and exposing your NGINX website to the Internet with SSL/TLS enabled using AWS resources included within the Free Tier.

One quick and easy win I can recommend, is microk8s.

I had the same problem (and there's a github issue about this: https://github.com/canonical/microk8s/issues/2186). I swapped to k3s and the usage was half of what microk8s used.

microk8s currently has a showstopping issue that makes it guaranteed to have an irrecoverable failure in HA mode. see https://github.com/canonical/microk8s/issues/3227

k0s is better but also has a lot of bugs. it's the closest to vanilla kubernetes among all the distributions.

> like the simplest GPU support

linux users should be ready to install the nvidia device plugin. if they can't do that, they're never going to succeed in running a gpu accelerated application on their cluster anyway.

> like bootstrapping

in my experience, writing all the bootstrap scripts is painful. but now that there's chatgpt, so much of the drudgery as gone away.

MicroK8S to spin up a Kubernetes cluster

They now power my microk8s/x86 cluster (in addition to my 8-node Raspberry Pi4 ARM64 microk8s cluster), microceph cluster and my LXD cluster, and all are configured with WOL, so I can bring up the cluster from any machine in the homelab, on demand.

As I delved into the world of Kubernetes, I opted for what seemed like the scenic route—until reality hit. With determination and a sprinkle of naivety, I plunged into crafting a Kubernetes cluster from scratch. Little did I know, I was in for a wild ride of commands, configurations, and complexities. But fear not! With the guiding light of a course based on the notorious "Kubernetes The Hard Way" GitHub repository, albeit customized for AWS instead of Google Cloud, I embarked on my journey to build my digital fortress, one step at a time.

Another resource that I found pretty helpful was "Kubernetes the Hard Way" by Kelsey Hightower despite its complexity.

https://github.com/kelseyhightower/kubernetes-the-hard-way

I recently published Kubernetes the Harder Way, a guide loosely based on Kelsey Hightower's Kubernetes the Hard Way, but lenghtier, more explanatory, broader in scope, and - most importantly - harder, by targeting a local machine instead of Google Cloud Platform.

How about this? It will setup a kubernetes cluster on GCP with 3 worker nodes?

https://github.com/kelseyhightower/kubernetes-the-hard-way

Some costs here but click on the google cloud calc as it seems to have gone up since he wrote this:

If you wanna go all in: https://github.com/kelseyhightower/kubernetes-the-hard-way

Oh, and both: "Kubernetes up and Running" and "Production Kubernetes" are great books.

Using Kubernetes The Hard Way is a great way to learn it deeply and quickly.

If you wanna learn it deeply and quickly, Kelsey Hightower's Kubernetes the hard way is fantastic!