The project contains a pre-trained, sklearn 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. It serves out predictions (inference) about housing prices through API calls. This project could be extended to any pre-trained machine learning model, such as those for image recognition and data labeling.

The project uses Docker for containerization and Kubernetes for container orchestration

Setting Up the Environment

• Standalone

  • Ensure python 3.7 is available in the host machine
  • Install venv
  • Create the virtual environment for the project
  • Activate the environment
  • Install the app's dependencies by running:

  make install

• Docker

  • Set up and configure Docker
  • (Optional) Install hadolint to lint the Dockerfile

• Kubernetes

  • Set up and configure Docker
  • Set up and configure Kubernetes (minikube and Kubectl used in this project)
  • Create a containerized Flask application

Commands

• Standalone:

python app.py

• Run in Docker:

./run_docker.sh

• Run in Kubernetes:

./run_kubernetes.sh

• app.py - flask application that returns the predictions
• requirements.txt - contains the app's dependencies
• Makefile - contains commands to easily setup a virtual environment, install dependencies, and lint files
• Dockerfile - contains instructions on how to build a docker image
• run_docker.sh - builds the docker image, tags it, and runs the container
• upload_docker.sh - tags the docker image and pushes it to a repository
• run_kubernetes.sh - runs the application in kubernetes
• make_prediciton.sh - sends a request to the app for prediction
• docker_out.txt - contains sample output of a successful response after running make_prediciton.sh with a successfully running container application
• kubernetes_out.txt - contains sample output of successful response after running make_prediciton.sh with a successfully running application in kubernetes
• .circleci/config.yml - CircleCI config file to build the app