container.training/slides/k8s/setup-devel.md

Running a local development cluster

• Let's review some options to run Kubernetes locally

• There is no "best option", it depends what you value:

  • ability to run on all platforms (Linux, Mac, Windows, other?)

  • ability to run clusters with multiple nodes

  • ability to run multiple clusters side by side

  • ability to run recent (or even, unreleased) versions of Kubernetes

  • availability of plugins

  • etc.

---

CoLiMa

• Container runtimes for LiMa

  (LiMa = Linux on macOS)

• For macOS only (Intel and ARM architectures)

• CLI-driven (no GUI like Docker/Rancher Desktop)

• Supports containerd, Docker, Kubernetes

• Installable with brew, nix, or ports

• More info: https://github.com/abiosoft/colima

---

Docker Desktop

• Available on Linux, Mac, and Windows

• Free for personal use and small businesses

  (less than 250 employees and less than $10 millions in annual revenue)

• Gives you one cluster with one node

• Streamlined installation and user experience

• Great integration with various network stacks and e.g. corporate VPNs

• Ideal for Docker users who need good integration between both platforms

---

k3d

• Based on K3s by Rancher Labs

• Requires Docker

• Runs Kubernetes nodes in Docker containers

• Can deploy multiple clusters, with multiple nodes

• Runs the control plane on Kubernetes nodes

• Control plane can also run on multiple nodes

---

k3d in action

• Install k3d (e.g. get the binary from https://github.com/rancher/k3d/releases)

• Create a simple cluster:

  k3d cluster create petitcluster
  

• Create a more complex cluster with a custom version:

  k3d cluster create groscluster \
        --image rancher/k3s:v1.18.9-k3s1 --servers 3 --agents 5
  

  (3 nodes for the control plane + 5 worker nodes)

• Clusters are automatically added to .kube/config file

---

KinD

• Kubernetes-in-Docker

• Requires Docker (obviously!)

• Should also work with Podman and Rootless Docker

• Deploying a single node cluster using the latest version is simple:

  kind create cluster
  

• More advanced scenarios require writing a short config file

  (to define multiple nodes, multiple control plane nodes, set Kubernetes versions ...)

• Can deploy multiple clusters

---

MicroK8s

• Available on Linux, and since recently, on Mac and Windows as well

• The Linux version is installed through Snap

  (which is pre-installed on all recent versions of Ubuntu)

• Also supports clustering (as in, multiple machines running MicroK8s)

• DNS is not enabled by default; enable it with microk8s enable dns

---

Minikube

• The "legacy" option!

  (note: this is not a bad thing, it means that it's very stable, has lots of plugins, etc.)

• Supports many drivers

  (HyperKit, Hyper-V, KVM, VirtualBox, but also Docker and many others)

• Can deploy a single cluster; recent versions can deploy multiple nodes

• Great option if you want a "Kubernetes first" experience

  (i.e. if you don't already have Docker and/or don't want/need it)

---

Orbstack

• Mac only

• Runs Docker containers, Kubernetes, and Linux virtual machines

• Emphasis on speed and energy usage (battery life)

• Great support for ClusterIP and LoadBalancer services

• Free for personal use; paid product otherwise

---

Podman Desktop

• Available on Linux, Mac, and Windows

• Free and open-source

• Doesn't support Kubernetes directly, but supports KinD

---

Rancher Desktop

• Available on Linux, Mac, and Windows

• Free and open-source

• Runs a single cluster with a single node

• Lets you pick the Kubernetes version that you want to use

  (and change it any time you like)

• Emphasis on ease of use (like Docker Desktop)

• Based on k3s and other proven components

---

VM with custom install

• Choose your own adventure!

• Pick any Linux distribution!

• Build your cluster from scratch or use a Kubernetes installer!

• Discover exotic CNI plugins and container runtimes!

• The only limit is yourself, and the time you are willing to sink in!

---

GPU support

Some solutions can expose your GPU to your containers.

This can be useful for machine learning inference and training.

It only works for some combinations of hardware and operating system.

For example:

• WSL2 + NVIDIA is supported by Docker Desktop and Podman Desktop

• Linux + NVIDIA is supported by Podman Desktop

• MacOS + Apple silicon is supported by Podman Desktop

See Docker Deskop and Podman Desktop documentations for more details.

???

:EN:- Kubernetes options for local development :FR:- Installation de Kubernetes pour travailler en local