Project Overview

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

You are given a pre-trained, 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.

Project Tasks

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

• Test your project code using linting
• Complete a Dockerfile to containerize this application
• Deploy your 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 your code has been tested

You can find a detailed project rubric, here.

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

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Setup the Environment

• Create a virtualenv and activate it
• 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
4. Make prediction(Docker): ./make_prediction.sh

Kubernetes Steps on Linux

Install Minikuber single-node cluster:

• Check if virtualization is supported:

grep -E --color 'vmx|svm' /proc/cpuinfo

• Install VirtualBox

• Install Minikube via direct download:

curl -Lo minikube https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64 \
  && chmod +x minikube

• Add the Minikube executable to your path:

sudo mkdir -p /usr/local/bin/
sudo install minikube /usr/local/bin/

• Start up a local Kubernetes cluster:

minikube start --driver=virtualbox
minikube status

Expected output:

host: Running
kubelet: Running
apiserver: Running
kubeconfig: Configured

Running and expose Deployment in Kubernetes

1. Run the deployment: kubectl apply -f prediction-deployment.yaml
2. Run the service: kubectl apply -f prediction-service.yaml
3. Make prediction(Kubernetes): ./make_prediction_k8s.sh

Scale up and scale down the deployment:

• Scale up to 6 replicas: kubectl scale deployment prediction --replicas=6
• Scale down to 3 replicas: kubectl scale deployment prediction --replicas=3
• Video file scale-up-down.flv shows the scale up and scale down characteristics of the kubernetes application.

Delete or stop minikube cluster

• minikube delete
• minikube stop

File description

• Dockerfile - instructions for building Docker image
• Makefile - File that organize the code compilation
• app.py - File with the application logic
• make_prediction.sh - File for making a prediction for application running in Docker
• make_prediction_k8s.sh - File for making a prediction for application running in Minikube Kubernetes cluster
• prediction-deployment.yaml - Run the application as a Kubernetes deployment
• prediction-service.yaml - Expose the deployment running in Kubernetes
• requirements.txt - List of all of the Python packages that the prediction app depends on
• run_docker.sh - This script builds a container from Dockerfiles, lists images and runs the app in Docker
• run_kubernetes.sh - This script runs a Pod with an image from Dockerhub repo and forwards the container port to the host
• scale-up-down.flv - Demo video showing Kubernetes scale up/down capabilities
• upload_docker.sh - This file tags and uploads an image to Docker Hub