rogamboa/microservice-containerization

A project demonstrating containerizing a machine learning microservice and deploying it using kubernetes

Microservice Containerization

Project Summary

This project demonstrates the process of containerizing a machine learning microservice using docker and deploying this microservice via kubernetes.

Project Folders and Contents

• application - contains files related to microservice
  • Dockerfile
  • Makefile
  • app.py
  • requirements.txt
  • model_data
• scripts
  • make_prediction.sh - script to test microservice deployment
  • run_docker.sh - script to build docker image and run image on docker locally
  • run_kubernetes.sh - script to deploy docker image on to a kubernetes cluster
  • upload_docker.sh - script to upload newly created docker image
• .circleci
  • config.yml - contains circleci ci/cd configuration to run build tests
• output_txt_files
  • docker_out.txt - example output from running prediction on microservice on local docker
  • kubernetes_out.txt - example output from running prediction on microservice on kubernetes

Usage

Prerequisites

The following steps require the following environment variables to be set. (Alternatively, these variables can be directly substituted within the scripts themselves)

Environment Variables

• DOCKERPATH - path to docker image (ex: repo/name:tag)
• USERNAME - dockerhub username
• PASSWORD - dockerhub password
• IMAGE_ID - image id after building docker image running run_docker.sh

Steps

1. Change working directory to scripts folder
2. Run ./run_docker.sh
3. Run ./upload_docker.sh
4. Optional: Run ./make_prediction.sh to test microservice running locally in docker
5. Run ./run_kubernetes.sh
6. Optional: Run ./make_prediction.sh to test microservice running on kubernetes