Data Science

Machine Learning

Docker

Kubernetes

Python

Scaling our Docker Container using Kubernetes: End-to-End Series (Part — 6)

By Hemanka Sarmah

In our previous article, we used Docker to containerize our web app. Now, we will look at how to deploy docker containers in Kubernetes.

Also, Check out our Article on:

1. Data Abstraction: End-to-End Series (Part — 1)
2. Data Preprocessing and EDA: End-to-End Series (Part — 2)
3. Model Building and Experimentation: End-to-End Series (Part — 3)
4. Creating a WebApp using Flask+Gunicorn on Ubuntu Server: End-to-End Series (Part — 4)
5. Containerizing the WebApp using Docker: End-to-End Series (Part — 5)
6. Automating building and deployment using Jenkins: End-to-End Series (Part — 7)

Pushing docker image to docker hub

• We will push our docker image to the docker hub so that the Kubernetes cluster could pull the image from the hub and add the container to the pod.

→ Tag the image with the docker hub username

docker tag <imagename> <Dockerhub_username>/<imagename>:latest

→ Push to docker hub

• Once the newly tagged image is created, we will then push that image to the hub.

docker push <Dockerhub_username>/<imagename>:latest

Enabling the Kubernetes in docker desktop

• Open your docker desktop

• go to settings > Kubernetes

• Enable Kubernetes
• Apply and Restart

Creating YAML file

• We will create a flask.YAML file.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: flask-demo
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      bb: web
  template:
    metadata:
      labels:
        bb: web
    spec:
      containers:
      - name: flask
        image: abhigyan97/flask-app:1.0
---
apiVersion: v1
kind: Service
metadata:
  name: flask-entrypoint
  namespace: default
spec:
  type: NodePort
  selector:
    bb: web
  ports:
  - port: 8000
    targetPort: 8000
    nodePort: 30001

Deploying to Kubernetes

• Open CLI and navigate to the folder location containing all the files and type the below command.

kubectl apply -f flask.yaml

• To confirm if your pod is running or not, run the command

kubectl get deployments

Ready indicates that 1 out of 1 pods are running, if it’s 0/1 that means your pod has crashed.

• Now, go to your browser and in the search bar type:

localhost:<nodeport>

The node port will the one mentioned in our YAML file, which is 30001 in our case.

Deleting pod

After deployment, if you want to delete your pod.

• Run the command to look at all the pods running.

kubectl get pods --all-namespaces

the name that we gave to our pod in the flask.yaml file was flask-demo

• Now, select the pod you want to delete and run the command.

kubectl delete deployment <pod_name>

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