Example:

Project Overview

This project contain a 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. This project tests your ability to operationalize a Python flask app—in a provided file, app.py—that 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.

Instructions

You will find below how to configure the environment and run the app locally.

Setup the Environment

• Create a virtualenv using make setup
• Run source ~/.devops to activate the virtualenv
• Run make install to install the necessary dependencies

Running app.py

1. Standalone: python app.py
2. Run in Docker: ./run_docker.sh
3. Push image to dockerHub: ./upload_docker.sh
4. Run in Kubernetes: ./run_kubernetes.sh

Files description

• .circleci/config.yml - CircleCI integration code to checks the project code for errors.
• app.py - Application source code.
• Dockerfile - Instructions for docker image build.
• make_prediction.sh - Shell script used for sending data to the containerized application via the appropriate port for testing purposes.
• Makefile - This file defines a set of tasks to be executed to setup the environment.
• requirements.txt - List all the dependencies needed to run the application.
• run_docker.sh - Shell script used to run and build a docker image.
• upload_docker.sh - Shell script used to upload the image to docker hub.
• run_kubernetes.sh - Shell script used to run the app in kubernetes using the published docker image.