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CircleCI

Project Overview

This project containerizes a machine learning Python flask app using Docker and deploys it on a Kubernetes cluster. The app predicts 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. The data was initially taken from Kaggle.

Running app.py

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

Making predictions

In a separate terminal, run: ./make_prediction.sh

Project Structure

The project includes 4 directories:

  • model_data, containing the machine learning models
  • output_txt_files, containing the output of running a prediction on Docker and on Kubernetes
  • .circleci, containing the configuration file to implement CI/CD through CircleCI
  • .git, containing the git files

It also includes the following files:

  • app.py, the main Python flask app
  • Dockerfile, to define a Docker container
  • LICENSE, containing info on the license
  • Makefile, to build the project
  • make_prediction.sh, to run predictions
  • README.md, this file
  • requirements.txt, containing the dependencies that are installed through the Makefile
  • run_docker.sh, to launch a Docker container
  • run_kubernetes.sh, to deploy containers on a Kubernetes cluster
  • upload_docker.sh, to upload a Docker image to Docker Hub
  • .gitignore, to list files that Git should ignore

Authors

License

This project is licensed under the MIT License - see the LICENSE file for details.