Project Summary

This project deploys a Machine Learning Microservice API using Docker and Kubernetes. The Machine Learning Model is exposed by a python flask app via Web API. The model is 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.

Files Overview

1. .circleci: It holds all CICD configurations for CircleCI build server.
2. model_data: It holds the prediction model library and the prediction model data used to train the model.
3. output_txt_files: It holds the output logs for the deployed and running application for both docker and kubernetes.
4. app.py: The index file for starting the python flask application server.
5. Dockerfile: Docker configurations for building and running the docker image based of the project.
6. requirements.txt: The package dependencies for the python flask application.
7. run_docker.sh: Script to build and run docker image.
8. run_kubernetes.sh: Script to run docker image using kubernetes.
9. upload_docker.sh: Script to upload docker image to dockerhub.

Setup the Environment

• Create a virtualenv with Python 3.7 and activate it. Refer to this link for help on specifying the Python version in the virtualenv.

python3 -m pip install --user virtualenv
# You should have Python 3.7 available in your host. 
# Check the Python path using `which python3`
# Use a command similar to this one:
python3 -m virtualenv --python=<path-to-Python3.7> .devops
source .devops/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