This project operationalises a Python flask app that serves out predictions (inference) about housing prices through API calls.

It contains the following files/folders:

1. .circleci folder - CircleCI configuration
2. output_txt_files folder - Two .txt files with terminal results from running run_docker.sh and run_kubernetes.sh
3. templates folder - frontend templates for Flask app
4. app.py - Flask app
5. run_docker.sh - run Docker container locally
6. run_kubernetes.sh - run Docker container with Kubernetes (minikube)
7. upload_docker - upload Docker image to repository (DockerHub)
8. make_prediction.sh - run prediction POST requests
9. Others - Dockerfile, Makefile, requirements.txt

1. Setup python virtual environment - run these commands:

python3 -m venv ~/.[name] # create python VE
source ~/.[name]/bin/activate #activate

(Replace name with preferred environment name)

2. Setup other requirements - Docker, Hadolint, Minikube
3. Install project dependencies and run lint checks for errors in Dockerfile

make install
make lint

4. Run a local container and test if app is successfully launched - ./run_docker.sh
5. Run prediction - ./make_prediction.sh
6. Upload Docker image to remote repository (DockerHub) - run script ./upload_docker.sh
7. Start minikube cluster - run minikube start
8. Deploy container with Minikube - run script ./run_kubernetes.sh
9. Run prediction - ./make_prediction.sh
10. (Extras) Run puthon script alone - python app.py

All steps as above, modify ./run_kubernetes.sh script to match installed minikube configuration on your machine.

• Amazon AWS - Cloud services
• AWS CLI - AWS Command-line tool
• Cloud9 - Cloud-based IDE
• Circle CI - Cloud-based CI/CD service
• Docker - Containerisation tool
• Kubernetes - Container Orchestration tool
• Minikube - Run Kubernetes clusters locally