prediction-microservice-demo

AWS Playground: Containerized python flask app for predicting housing prices

Build Status:

This project contains a Python Flash app with a machine learning model that has been trained to predict housing prices in Boston according to several features, such as average rooms in a home and data about highway access, teacher-to-pupil ratios, and so on.

You can read more about the data, which was initially taken from Kaggle, on the data source site: https://www.kaggle.com/c/boston-housing

The repository contains configuration and scripts to:

• build a Dockerfile
• upload this to the docker repo
• deploy the app using kubernetes
• ...and continuously test changes to this setup with CircleCI.

Deploying the app

Setup the Environment

• Create a virtualenv and activate it:

python3 -m venv ~/.predictionapp
source ~/.predictionapp/bin/activate

• Run make install to install the necessary dependencies

Running app.py

1. Standalone: python app.py
2. Run in Docker: ./run_docker.sh
3. Run in Kubernetes: ./run_kubernetes.sh

Kubernetes Steps

• Setup and Configure Docker locally
• Setup and Configure Kubernetes locally
• Create Flask app in Container
• Run via kubectl

Files

.
├── model_data/           # Prediction Model Data
├── output_txt_files/     # Output from running app in docker and kubernetes
├── Makefile              # Makefile configuration for building/testing the app
├── Dockerfile            # Docker configuration for building image
├── app.py                # Prediction application (Python Flask app)
├── make_prediction.sh    # Script that retrieves a prediction from running app instance
├── README.md             # This document :)
├── requirements.txt      # Environment requirements
├── run_docker.sh         # Script to build and run docker image
├── run_kubernetes.sh     # Script to run app on kubernetes cluster
└── upload_docker.sh      # Script to upload built docker image to Docker repo