Kubernetes on Docker Desktop in 2 Minutes

Docker Desktop is the easiest way to run Kubernetes on your local machine – it gives you a fully certified Kubernetes cluster and manages all the components for you.

In this tutorial you’ll learn how to set up Kubernetes on Docker Desktop and run a simple demo app. You’ll gain experience of working with Kubernetes and comparing the app definition syntax to Docker Compose.

1. Install Docker Desktop

2. Enable Kubernetes

Kubernetes itself runs in containers. When you deploy a Kubenetes cluster you first install Docker (or another container runtime like containerd) and then use tools like kubeadm which starts all the Kubernetes components in containers. Docker Desktop does all that for you.

Make sure you have Docker Desktop running – in the taskbar in Windows and the menu bar on the Mac you’ll see Docker’s whale logo. Click the whale and select Settings:

3. The Docker Desktop menu

A new screen opens with all of Docker Desktop’s configuration options. Click on Kubernetes and check the Enable Kubernetes checkbox:

4. Enabling Kubernetes in Docker Desktop

That’s it! Docker Desktop will download all the Kubernetes images in the background and get everything started up. When it’s ready you’ll see two green lights in the bottom of the settings screen saying Docker running and Kubernetes running.

The star in the screenshot shows the Reset Kubernetes Cluster button, which is one of the reasons why Docker Desktop is the best of the local Kubernetes options. Click that and it will reset your cluster back to a fresh install of Kubernetes.

5. Verify your Kubernetes cluster

If you’ve worked with Docker before, you’re used to managing containers with the docker and docker-compose command lines. Kubernetes uses a different tool called kubectl to manage apps – Docker Desktop installs kubectl for you too.

6. Test driving Kubeview

Kubeview is a Kubernetes cluster visualization tool that offers a graphical representation of your cluster’s resources, including pods, deployments, services, and more. It provides a visual interface to explore and navigate through the various components of your cluster, helping you gain insights into resource allocation, dependencies, and performance.

i. Using Helm

Assuming that you have already installed Git and Helm on your laptop, follow the below steps

git clone https://github.com/benc-uk/kubeview
cd kubeview/charts/
helm install kubeview kubeview

Testing it locally

kubectl port-forward svc/kubeview -n default 80:80

ii. Using Kubectl

To start using Kubeview, you need to install it in your Kubernetes cluster. Follow these steps:

Step 1: Deploy the Kubeview application

Kubeview can be deployed as a Kubernetes application using a YAML manifest file. Simply apply the manifest to your cluster using the following command:

kubectl apply -f https://raw.githubusercontent.com/benc-uk/kubeview/main/deploy/kubeview.yaml

Step 2: Verify the installation:

After applying the manifest, check that Kubeview is up and running by executing the following command:

kubectl get pods -n kubeview

Exploring Your Cluster with Kubeview

Once Kubeview is installed, you can access its web-based interface to explore your Kubernetes cluster visually. Here’s how to get started:

Step 1: Access the Kubeview dashboard

Retrieve the URL to access the Kubeview dashboard by executing the following command:

kubectl --namespace kubeview port-forward service/kubeview 8080:80

This command sets up a port forward to access Kubeview on your local machine.

Step 2: Open the Kubeview dashboard

Open a web browser and visit http://localhost:8080 to access the Kubeview dashboard. You will be greeted with an intuitive interface that displays an overview of your cluster’s resources.

Step 3: Navigate through your cluster

Explore your cluster by clicking on different resources in the Kubeview interface. You can drill down into pods, deployments, services, and other components to visualize their relationships and connections. Kubeview provides clear visual representations, such as node graphs and dependency diagrams, to help you understand the structure of your cluster.

Step 4: Analyze resource details

Clicking on individual resources reveals detailed information about them, including resource usage, health status, and labels. You can also view logs and events associated with each resource, allowing for efficient troubleshooting and monitoring.

Leveraging Kubeview’s Advanced Features

Kubeview offers additional advanced features that enhance your cluster exploration and monitoring experience. These include:

• Search and filter capabilities: Use Kubeview’s search and filter functionalities to quickly locate specific resources or filter them based on labels, namespaces, or other criteria.
• Real-time updates: Kubeview automatically updates the visual representation of your cluster as changes occur, providing you with near real-time insights into the state of your resources.
• Support for multiple clusters: If you manage multiple Kubernetes clusters, Kubeview can handle them all. You can easily switch between clusters within the Kubeview interface.

Visualizing K8s cluster using kubectl ai

Using Kubernetes via AI

Getting Started

• Docker Desktop
• Install Kubectl-ai

brew tap sozercan/kubectl-ai https://github.com/sozercan/kubectl-ai
brew install kubectl-ai

• Get OpenAI Keys via https://platform.openai.com/account/api-keys

kubectl-ai requires an OpenAI API key or an Azure OpenAI Service API key and endpoint, and a valid Kubernetes configuration.

Creating your first Nginx Pod

kubectl ai "create an nginx pod"
✨ Attempting to apply the following manifest:

apiVersion: v1
kind: Pod
metadata:
  name: nginx-pod
spec:
  containers:
  - name: nginx
    image: nginx
    ports:
    - containerPort: 80
Use the arrow keys to navigate: ↓ ↑ → ← 
? Would you like to apply this? [Reprompt/Apply/Don't Apply]: 
+   Reprompt
  ▸ Apply
    Don't Apply

Deployment

Select “Reprompt” and type “make this into deployment”

Reprompt: make this into deployment
✨ Attempting to apply the following manifest:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  replicas: 1
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.7.9
        ports:
        - containerPort: 80

ReplicaSet

Reprompt: Scale to 3 replicas

Reprompt: Scale to 3 replicas
✨ Attempting to apply the following manifest:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx
        ports:
        - containerPort: 80

kubectl ai "Create Nginx Pod running on port 82 with 3 replicasets labeled web"

Services

kubectl ai "create a service for the nginx deployment with load balancer that uses nginx selector"
✨ Attempting to apply the following manifest:

apiVersion: v1
kind: Service
metadata:
  name: nginx-service
spec:
  selector:
    app: nginx
  ports:
  - port: 80
    targetPort: 80
  type: LoadBalancer
✔ Apply

Here’s the final view of the Kubeview:

Conclusion

If you’re new to Kubernetes and looking for a hassle-free setup, Docker Desktop is your gateway to a single node Kubernetes cluster. With Docker Desktop, you can quickly get started and effortlessly install essential tools like kubeview and kubectl-ai, further simplifying your Kubernetes experience.

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