How to Deploy an Application on Kubernetes

Kubernetes can be intimidating for beginners, but following a step-by-step deployment process can simplify the task. This guide will help make the process even easier.

We'll go through the deployment step by step for clarity.

Step 1: Preparation

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Assume you already have a program written in Python named program.py. You should have a cloud server with Linux installed, and apart from Kubernetes, you'll need Docker, which you likely already know how to use. Nonetheless, it's worth revisiting how to build containers in Docker, which is where we'll start.

Before stepping into production, validate cluster stability with Kubernetes Cluster Health Checks tutorial—run kubectl get componentstatuses, inspect livez/readyz endpoints, and review kubectl get events to confirm all control-plane and node components are healthy prior to your application rollout.

Step 2: Building the Container Image

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There are various ways to build a Docker container image. One of the most convenient tools is buildah. After installing it, create a directory for building the image and specify dependencies in a requirements.txt file. Here’s an example of such a file; you should replace the dependencies with your own.

Next, open and examine the Dockerfile, the configuration file for Docker that contains instructions for building the container image. Pay attention to the following lines:

• FROM: Specifies the interpreter. For example: FROM python:3.8.

• RUN mkdir: Creates a directory inside the image, e.g., RUN mkdir /my_project.

• WORKDIR: Sets the working directory path, e.g., WORKDIR /my_project.

• ADD: Necessary for creating a container for Kubernetes, e.g., ADD . /my_project/.

• RUN pip install -r: Runs pip to install dependencies from the requirements file, e.g., RUN pip install -r requirements.txt.

• EXPOSE: Opens a port, e.g., EXPOSE 8000.

• CMD: Specifies the command to run the application, e.g., CMD ["python", "/my_project/program.py"].

With this setup, you'll have a my_project directory containing program.py, the dependencies file, and the Dockerfile. Now, build the image using buildah:

buildah bud -f ./Dockerfile

Copy the generated hash and use it in the following command:

buildah push <hash> docker-daemon:program:v0

Then, check the created container image:

docker image ls

And verify its functionality:

docker run --rm -d -v `pwd`:/my_project -p 8000:8000 program:v0

If you see a "Hello" message, the image is ready. The next step is to push the container to a repository.

Step 3: Deploying the Application

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To deploy a Kubernetes application, start by creating a deployment.yaml file, which will also be used to maintain the desired number of replicas. Here’s a basic example of deployment.yaml:

kind: Deployment
metadata:
  name: program
  labels:
    app: program
spec:
  replicas: 3
  selector:
    matchLabels:
      app: program
  template:
    metadata:
      labels:
        app: program
    spec:
      containers:
      - name: program
        image: <your login>/<your repository>:program
        ports:
        - containerPort: 8000
          protocol: TCP
        resources:
          limits:
            memory: 840Mi
            cpu: 1
          requests:
            memory: 420Mi
            cpu: 500m

After saving the file, deploy the container with kubectl and verify its status:

kubectl create -f deployment.yaml
kubectl get pod -o wide

Troubleshooting

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If issues arise with pod availability due to IP changes when pods move between nodes, resolve this using a service.yaml file:

kind: Service
metadata:
  labels:
    app: program
  name: program
spec:
  ports:
  - port: 8080
    protocol: TCP
    targetPort: 8000
  selector:
    app: program
  type: ClusterIP

Deploy the service to fix pod availability:

kubectl create -f service.yaml

From Theory to Practice

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Let's say we have an application written in the latest version of Python, and we’ll call it newgenAI. Our task is to deploy this application in an already created Kubernetes cluster.

Step 1: Prepare the Container Image Using Dockerfile

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In the Dockerfile, we perform the following steps:

• Specify the correct interpreter: 

FROM python:3.11

• Create a directory:

RUN mkdir /newgenAI

• Set the path: 

WORKDIR /newgenAI

• Add the path for Kubernetes: 

ADD . /newgenAI

• Install dependencies: 

RUN pip install -r requirements.txt

• Open the port: 

EXPOSE 9000

• Start the application: 

CMD ["python", "/newgenAI/newgenAI.py"]

Step 2: Build the Image and Check Its Functionality

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Now, we have a directory named newgenAI containing newgenAI.py, the dependencies file, and the Dockerfile. Let's build the image:

buildah bud -f ./Dockerfile

buildah will output a hash in response, which you need to insert into the following command:

buildah push <insert hash here> docker-daemon:newgenAI:v0

Check the image's functionality:

docker run --rm -d -v pwd:/newgenAI -p 9000:9000 newgenAI:v0

After getting a "Hello" response, push the ready container image to the repository with the command docker push repo/ (replace repo/ with the name of the directory you created).

Step 3: Deploy the Image in Kubernetes

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First, create and configure the deployment.yaml file based on the template provided earlier, paying attention to the allocated resources. Since we are working on a generative AI project, 1 CPU and 840 MB of RAM might not be sufficient, so make sure to set appropriate values and create a reserve.

Now, deploy the created image using kubectl:

kubectl create -f deployment.yaml

Finally, check if it’s running:

kubectl get pod -o wide

The response should show a status of "Running."

Now you know how to deploy an application on Kubernetes and can tackle more complex tasks with K8s.