Python-Docker-WebApp-Deploy-AWS-Kubernetes ## First Deploy

CI/CD Deployment Workflow

This project demonstrates a complete CI/CD pipeline for deploying a Python Flask application to an AWS-hosted Kubernetes (kubeadm) cluster using GitHub Actions, Docker, and Kubernetes.

Architecture

Developer
    │
    │ Git Push
    ▼
GitHub Repository
    │
    ▼
GitHub Actions
    │
    ├── Checkout source code
    ├── Build Docker image
    ├── Push image to Docker Hub
    ├── Copy Kubernetes manifests to Kubernetes Master
    ├── SSH into Kubernetes Master
    ├── Apply Kubernetes manifests
    ├── Update Deployment with the latest Docker image
    └── Wait for successful rollout
    │
    ▼
AWS kubeadm Kubernetes Cluster
    │
    ▼
Python Flask Application

Deployment Workflow

1. Source Code Commit

The deployment process starts when code is pushed to the main branch or when the workflow is manually triggered using workflow_dispatch.

git add .
git commit -m "Deploy new version"
git push origin main

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2. Build Docker Image

GitHub Actions checks out the latest source code and builds a Docker image using the project's Dockerfile.

Example:

docker build \
-t dockerhub-user/python-app:<commit-sha> \
-t dockerhub-user/python-app:latest .

Two tags are created:

• latest – Latest application version
• Commit SHA – Immutable version for deployments and rollback

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3. Push Image to Docker Hub

The newly built image is pushed to Docker Hub.

docker push dockerhub-user/python-app:<commit-sha>
docker push dockerhub-user/python-app:latest

This makes the application image available for the Kubernetes cluster.

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4. Copy Kubernetes Manifests

The workflow securely copies the Kubernetes manifests (deployment.yaml and service.yaml) to the Kubernetes master node using SCP.

GitHub Actions
        │
        ▼
AWS EC2 Kubernetes Master

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5. Connect to Kubernetes Master

GitHub Actions establishes an SSH connection to the Kubernetes master node.

This approach is useful for private kubeadm clusters because kubectl is already configured on the master node and the Kubernetes API server does not need to be exposed publicly.

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6. Deploy to Kubernetes

The workflow applies the Kubernetes manifests.

kubectl apply -f k8s/deployment.yaml
kubectl apply -f k8s/service.yaml

If the resources already exist, Kubernetes updates them instead of recreating them.

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7. Update Deployment Image

The deployment is updated to use the newly built Docker image.

kubectl set image deployment/python-app \
python-app=dockerhub-user/python-app:<commit-sha>

Using the commit SHA ensures every deployment references an immutable image version.

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8. Rolling Update

Kubernetes performs a rolling update.

During the rollout:

• New Pods are created.
• Health checks are performed.
• Old Pods are terminated only after new Pods become healthy.
• The application remains available with minimal or no downtime.

The workflow waits until the rollout completes successfully.

kubectl rollout status deployment/python-app

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9. Verify Deployment

After deployment, the workflow verifies the cluster state.

kubectl get deployment
kubectl get pods
kubectl get svc

This confirms that the Deployment, Pods, and Service are running successfully.

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Technologies Used

• Python (Flask)
• Docker
• GitHub Actions
• Docker Hub
• Kubernetes (kubeadm)
• AWS EC2
• SSH
• SCP

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Benefits of This Approach

• Fully automated CI/CD pipeline
• Versioned Docker images using Git commit SHA
• Rolling updates with minimal downtime
• Repeatable and consistent deployments
• Secure deployment using SSH instead of exposing the Kubernetes API
• Suitable for self-managed Kubernetes (kubeadm) clusters

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Deployment Flow Summary

Developer
    │
    ▼
Git Push
    │
    ▼
GitHub Actions
    │
    ├── Checkout Code
    ├── Build Docker Image
    ├── Push Image to Docker Hub
    ├── Copy Kubernetes YAML Files
    ├── SSH to Kubernetes Master
    ├── kubectl apply
    ├── kubectl set image
    ├── Rolling Update
    └── Verify Deployment
    │
    ▼
AWS kubeadm Kubernetes Cluster
    │
    ▼
Python Flask Application