Local Lab Setup Guide

Installation

Setting up Microk8s

Install Microk8s from the 1.27 stable channel:

sudo snap install microk8s --classic --channel=1.27/stable

Enable required Microk8s add-ons:

microk8s enable ingress
microk8s enable dns
microk8s enable hostpath-storage
microk8s.enable dashboard

Setup kubectl alias and manage Microk8s status:

alias kubectl='microk8s kubectl'
microk8s stop
microk8s start
microk8s status --wait-ready
Local Network Configuration
Update the local network settings via /etc/resolv.conf.

Helm Setup Enable Helm in Microk8s:

microk8s.enable helm

Install Helm:

sudo snap install helm --classic

Deploy using Helm:

helm install 1.0 example-chart

Kubernetes Authentication To configure kubectl to use the Microk8s context:

cat /var/snap/microk8s/current/credentials/client.config > ~/.kube/config

Accessing the Kubernetes Dashboard Retrieve the dashboard token to log in:

kubectl describe secret microk8s-dashboard-token -n kube-system

Setting Up a Local Docker Registry Enable the registry:

microk8s enable registry

View the registry service details:

microk8s kubectl get svc -n container-registry

Build, tag, and push an image to the registry:

docker build -t nginx-https .
docker tag nginx-https localhost:32000/nginx-https:latest
docker push localhost:32000/nginx-https:latest

Confirm the image was pushed:

curl -X GET http://localhost:32000/v2/_catalog

Deploying ArgoCD Enable the ArgoCD community:

microk8s enable community

... [Add any additional steps or configurations for ArgoCD]

Setting Up Postgres Operator Add the Helm repository for the Postgres operator UI:

helm repo add postgres-operator-ui-charts https://opensource.zalando.com/postgres-operator/charts/postgres-operator-ui

Install the Postgres operator UI using Helm:

helm install postgres-operator-ui postgres-operator-ui-charts/postgres-operator-ui

Check the status of the Postgres operator:

kubectl --namespace=default get pods -l "app.kubernetes.io/name=postgres-operator"

Access the Postgres operator UI:

kubectl port-forward svc/postgres-operator-ui 8081:80

Apply the Postgres manifest:

kubectl apply -f postgres/testcluster.yaml

Retrieve the Postgres password:

kubectl get secret postgres.testcluster.credentials.postgresql.acid.zalan.do -o jsonpath='{.data.password}' | base64 --decode

Forward a local port to the Postgres pod:

kubectl port-forward pod/testcluster-0 5432:5432

 kubectl describe postgresqls.acid.zalan.do  testcluster
Name:         testcluster
Namespace:    default
Labels:       team=acid
Annotations:  <none>
API Version:  acid.zalan.do/v1
Kind:         postgresql
Metadata:
  Creation Timestamp:  2023-08-10T12:09:59Z
  Generation:          1
  Resource Version:    130660
  UID:                 9ae585f8-42c6-4351-8d37-b561bebc20d0
Spec:
  Allowed Source Ranges:  <nil>
  Number Of Instances:    1
  Postgresql:
    Version:  15
  Resources:
    Limits:
      Cpu:     500m
      Memory:  500Mi
    Requests:
      Cpu:     100m
      Memory:  100Mi
  Team Id:     acid
  Volume:
    Size:  10Gi
Status:
  Postgres Cluster Status:  Running
Events:                     <none>

Adding a CUDA-enabled Node to a Local Kubernetes Lab

Prerequisites A node with an NVIDIA GPU. Ubuntu operating system. Steps

Install NVIDIA Drivers Install the ubuntu-drivers-common package:

sudo apt install ubuntu-drivers-common

Check available drivers for your NVIDIA device:

ubuntu-drivers devices

Install recommended drivers automatically:

sudo ubuntu-drivers autoinstall

Reboot your node:

sudo reboot

Verify NVIDIA GPU Status Check the status of your NVIDIA GPU:

nvidia-smi

Install CUDA Toolkit, enable gpu addon, check deamonset Install the nvidia-cuda-toolkit:

sudo apt install nvidia-cuda-toolkit
microk8s enable gpu
microk8s kubectl get daemonset,pods -n gpu-operator-resources

Join the Node to the Kubernetes Cluster On your control plane, initiate the node addition:

microk8s add-node

This will display the microk8s join command to be executed on the node you wish to add. On the node you wish to join, run the displayed microk8s join command:

microk8s join <CONTROL_PLANE_IP>:<PORT>/<TOKEN> --worker

Note: The --worker flag ensures the node joins as a worker, without running the control plane components.

check if it worked

 kubectl describe nodes  |  tr -d '\000' | sed -n -e '/^Name/,/Roles/p' -e '/^Capacity/,/Allocatable/p' -e '/^Allocated resources/,/Events/p'  | grep -e Name  -e  nvidia.com  | perl -pe 's/\n//'  |  perl -pe 's/Name:/\n/g' | sed 's/nvidia.com\/gpu:\?//g'  | sed '1s/^/Node Available(GPUs)  Used(GPUs)/' | sed 's/$/ 0 0 0/'  | awk '{print $1, $2, $3}'  | column -t
Node     Available(GPUs)  Used(GPUs)
bastion  1                0
cuda1    1                0

Conclusion By following the steps above, you should have successfully added a CUDA-enabled node to your local Kubernetes lab. You can now deploy GPU-enabled workloads to leverage the computational power of your NVIDIA GPU.

Set up the insecure registry configuration (must be done on both nodes):

sudo mkdir -p /var/snap/microk8s/current/args/certs.d/bastion:32000
sudo touch /var/snap/microk8s/current/args/certs.d/bastion:32000/hosts.toml

Then, edit the hosts.toml file:

sudo vim /var/snap/microk8s/current/args/certs.d/bastion:32000/hosts.toml

And add:

server = "http://bastion:32000"

[host."http://bastion:32000"]
capabilities = ["pull", "resolve"]

Restart MicroK8s on both nodes:

microk8s stop
microk8s start

Minecraft server

helm repo add minecraft https://itzg.github.io/minecraft-server-charts/
helm show values minecraft/minecraft > minecraft.yaml
helm install my-minecraft-release minecraft/minecraft -f minecraft.yaml

Aricg / kubernetes-lab