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Extra reading

Using Your Own Hardware

All code and resources used in this tutorial are available on the tutorial's GitHub repository. If you want to follow along at home or perhaps run your own workshop, you can replicate our cluster setup with:

  • 2-4 Raspberry Pi boards (Pi 3B+ or newer recommended—see Hardware Requirements)
  • MicroSD cards (16GB+) & Power Supplies for each RPi
  • at least one microHDMI-HDMI cable plus external display
  • Network connectivity (WiFi or Ethernet/switches)
  • A laptop or PC with SSH client

To prepare each RPi, we recommend using the Raspberry Pi Imager to install RPi OS Lite 64-Bit for the device. The installer allows you to set the RPi hostname, configure wireless connectivity, enable SSH and create an initial login account.

On first boot, connect the device to an external monitor and log in to check network connectivity and obtain the device IP address. In order to install K3s, you will need to enable memory cgroups by editing /boot/firmware/cmdline.txt and appending to the end of the existing line:

cgroup_memory=1 cgroup_enable=memory

After saving the file, reboot the RPi. You can now disconnect the external monitor and connect via SSH from your own laptop or PC.

Kubernetes Anywhere

While we used Raspberry Pis for the tutorial, K3s can be installed on a wide range of hardware from homelab servers to cloud server instances.

Kubernetes and HPC

While we focus on learning Kubernetes through standard application deployments, those interested in HPC integration should explore the slinky project by SchemMD that enables interoperability between SLURM and Kubernetes. This includes slurm-operator for running SLURM on Kubernetes (as pods) and slurm-bridge for using SLURM to schedule Kubernetes workloads.

Raspberry Pi hardware and OS

Each of our RPi clusters has an RPi4 control node with a quad-core CPU and 4G LPDDR4 RAM, and quad-core RPi4/5 worker nodes with 1G RAM. We installed RPi OS Lite, a lightweight (non-GUI) version of the official Debian-based Raspberry Pi operating system, on 16G SD cards. You can purchase RPi hardware and accessories from UK retailers The PiHut, Rapid and PIMORONI.

Talos Linux and Immutable Infrastructure

For production clusters, Talos Linux is a modern Linux OS designed specifically for running Kubernetes. It is an example of an immutable operating system where the kernel runs from a read-only filesystem and is API-driven, meaning all configuration occurs via API calls. This presents some learning curve and initial setup challenges (there is no shell!), but is worth considering if you want a highly reproducible and secure platform for Kubernetes delivered as IaC.

Cloud Provider Offerings

While we were keen to get you to install on bare metal in this tutorial, Kubernetes is installed regularly on VPSes and in the Cloud. Amazon, Google and Microsoft all provide managed Kuberentes services where they handle the control-plane and patching. You lose some control but may find this convenient if you are already invested in their services. For more information, see Amazon EKS, the Google Kubernetes Engine and the Azure Kubernetes Service.

Kubernetes metrics-server

metrics-server is a lightweight Kubernetes component that collects resource usage information from nodes and pods. It provides metrics such as CPU and memory usage. These are exposed through the Kubernetes Metrics API and are used by commands such as:

kubectl top nodes
kubectl top pods

Unlike Prometheus, metrics-server is intentionally lightweight and only keeps short-term resource metrics. It is designed primarily for quick cluster monitoring, autoscaling and operational visibility.

The pipeline looks roughly like this:

Pod / Node
    ↓
Kubelet
    ↓
metrics-server
    ↓
Kubernetes Metrics API
    ↓
kubectl top

metrics-server periodically queries the kubelets on each node, aggregates the data, and exposes it through the Kubernetes API.

This allows Kubernetes tooling to retrieve live cluster metrics in a consistent way.

For larger production systems, Prometheus is more common because it supports historical storage, alerting, dashboards, and custom metrics.

The official installation is usually done with:

kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml

This deploys:

  • the metrics-server Deployment,
  • RBAC permissions,
  • API services,
  • other required Kubernetes resources.

Verifying the metrics-server deployment with:

kubectl get pods -A | grep metrics-server
kubectl describe deployment metrics-server -n kube-system

kubectl top nodes

and:

kubectl top pods

Or inspect logs:

kubectl logs -n kube-system deploy/metrics-server

This can be useful for troubleshooting connectivity or certificate issues.