README.md

Part 1: Deploying the Flask application locally

Step 1: Clone the code Clone the code from the repository:

git clone <repository_url> Step 2: Install dependencies The application uses the psutil and Flask, Plotly, boto3 libraries. Install them using pip:

pip3 install -r requirements.txt Step 3: Run the application To run the application, navigate to the root directory of the project and execute the following command:

python3 app.py This will start the Flask server on localhost:5000. Navigate to http://localhost:5000/ on your browser to access the application.

Part 2: Dockerizing the Flask application Step 1: Create a Dockerfile Create a Dockerfile in the root directory of the project with the following contents:

Use the official Python image as the base image

FROM python:3.9-slim-buster

Set the working directory in the container

WORKDIR /app

Copy the requirements file to the working directory

COPY requirements.txt .

RUN pip3 install --no-cache-dir -r requirements.txt

Copy the application code to the working directory

COPY . .

Set the environment variables for the Flask app

ENV FLASK_RUN_HOST=0.0.0.0

Expose the port on which the Flask app will run

EXPOSE 5000

Start the Flask app when the container is run

CMD ["flask", "run"] Step 2: Build the Docker image To build the Docker image, execute the following command:

docker build -t <image_name> . Step 3: Run the Docker container To run the Docker container, execute the following command:

docker run -p 5000:5000 <image_name> This will start the Flask server in a Docker container on localhost:5000. Navigate to http://localhost:5000/ on your browser to access the application.

Part 3: Pushing the Docker image to ECR Step 1: Create an ECR repository Create an ECR repository using Python:

import boto3

Create an ECR client

ecr_client = boto3.client('ecr')

Create a new ECR repository

repository_name = 'my-ecr-repo' response = ecr_client.create_repository(repositoryName=repository_name)

Print the repository URI

repository_uri = response['repository']['repositoryUri'] print(repository_uri) Step 2: Push the Docker image to ECR Push the Docker image to ECR using the push commands on the console:

docker push <ecr_repo_uri>: Part 4: Creating an EKS cluster and deploying the app using Python Step 1: Create an EKS cluster Create an EKS cluster and add node group

Step 2: Create a node group Create a node group in the EKS cluster.

Step 3: Create deployment and service from kubernetes import client, config

Load Kubernetes configuration

config.load_kube_config()

Create a Kubernetes API client

api_client = client.ApiClient()

Define the deployment

deployment = client.V1Deployment( metadata=client.V1ObjectMeta(name="my-flask-app"), spec=client.V1DeploymentSpec( replicas=1, selector=client.V1LabelSelector( match_labels={"app": "my-flask-app"} ), template=client.V1PodTemplateSpec( metadata=client.V1ObjectMeta( labels={"app": "my-flask-app"} ), spec=client.V1PodSpec( containers=[ client.V1Container( name="my-flask-container", image="568373317874.dkr.ecr.us-east-1.amazonaws.com/my-cloud-native-repo:latest", ports=[client.V1ContainerPort(container_port=5000)] ) ] ) ) ) )

Create the deployment

api_instance = client.AppsV1Api(api_client) api_instance.create_namespaced_deployment( namespace="default", body=deployment )

Define the service

service = client.V1Service( metadata=client.V1ObjectMeta(name="my-flask-service"), spec=client.V1ServiceSpec( selector={"app": "my-flask-app"}, ports=[client.V1ServicePort(port=5000)] ) )

Create the service

api_instance = client.CoreV1Api(api_client) api_instance.create_namespaced_service( namespace="default", body=service ) make sure to edit the name of the image on line 25 with your image Uri.

Once you run this file by running “python3 eks.py” deployment and service will be created. Check by running following commands: kubectl get deployment -n default (check deployments) kubectl get service -n default (check service) kubectl get pods -n default (to check the pods) Once your pod is up and running, run the port-forward to expose the service

kubectl port-forward service/<service_name> 5000:5000 About Cloud Native app on K8S to monitor system resources using python

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