/Operationalize-a-ML-Microservice-API

Operationalize Machine Learning Microservcie to predict House Prices in Boston using Docker and Kubernete

Primary LanguageShell

CircleCI CircleCI

Operationalize-a-ML-Microservice-API

Project Overview | Installation | Documentation

Project Overview

In this project, we will apply the skills you have acquired in this course to operationalize a Machine Learning Microservice API.

We have 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. Read more about the data, which was initially taken from Kaggle, on the data source site. This project tests our 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.

Project Tasks

project goal is to operationalize machine learning microservice using kubernetes, which is an open-source system for automating the management of containerized applications. In this project we will:

  • Test project code using linting
  • Complete a Dockerfile to containerize this application
  • Deploy containerized application using Docker and make a prediction
  • Improve the log statements in the source code for this application
  • Configure Kubernetes and create a Kubernetes cluster
  • Deploy a container using Kubernetes and make a prediction
  • Upload a complete Github repo with CircleCI to indicate that code has been tested

The final implementation of the project will showcase your abilities to operationalize production microservices.

File Information

  • Dockerfile
    • file that contains all commands, in order, needed to build a image.
  • Makefile
    • makefile to install dependencies, calls make with the install build target and lint test for Dockerfile and python calls make with lint build target
  • app.py
    • flask ML app to predict the house price
  • make_prediction.sh
    • Shell Script which CURL POST request with payload to ML Microservice API endpoint
  • requirements.txt
    • A file contains all dependencies for app.py
  • run_docker.sh
    • Shell Script to run docker container and exposed container port to host.
  • upload_docker.sh
    • Shell Script to push image to docker registery with tag
  • run_kubernetes.sh
    • Shell Script to run kubernetes Pod with docker container and forward port from container to host
  • .circleci/config.yml
    • YAML file for CircleCI configuration to build and test the Microservice API
  • output_txt_files/docker_out.txt
    • Docker container ouput logs information about accessing ML Microservice API endpoint
  • output_txt_files/kubernetes_out.txt
    • Kubernete pod logs about accessing the ML Microservices API endpoint

Setup the Environment

  • Create a virtualenv and activate it
python3 -m venv ~/.devops
  • Run make install to install the necessary dependencies
  • Install Hadolint for Linting test
sudo  wget -O /bin/hadolint https://github.com/hadolint/hadolint/releases/download/v1.16.3/hadolint-Linux-x86_64
sudo chmod +x /bin/hadolint
  • Run make lint for linting Dockerfile and app.py
------------------------------------
Your code has been rated at 10.00/10
  • Install and configure Docker
  • Install and configure Kubernete Cluster, Minikube.

Running app.py

  1. Standalone: python app.py
  2. Run in Docker: ./run_docker.sh
  3. Push Docker Image: ./upload_docker.sh
  4. 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
  • check Kubernetes pod logs kubectl logs pod-name

Make Prediction

  • Later container started successfully, execute ./make_prediction.sh
{
 "prediction": [
   20.35373177134412
 ]
}

REFERENCE