Project Overview

This project using sklearn model to predict housing prices in Boston according to several features, such as average rooms in a home and data about highway access. It's using CircleCI to deploy a Python flask app to prodives predictions through API calls.

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 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

You should be in your virtual machine first

1. Setup and Configure Docker locally

   • You should have wsl2 first: wsl --install wsl --update wsl --set-default-version 2

   • You can douwnload Docker Desktop for Windows here: https://docs.docker.com/desktop/install/windows-install/

2. Setup and Configure Minikube locally

   • Download and setup New-Item -Path 'c:' -Name 'minikube' -ItemType Directory -Force Invoke-WebRequest -OutFile 'c:\minikube\minikube.exe' -Uri 'https://github.com/kubernetes/minikube/releases/latest/download/minikube-windows-amd64.exe' -UseBasicParsing

   • Add Minikube to PATH: $oldPath = [Environment]::GetEnvironmentVariable('Path', [EnvironmentVariableTarget]::Machine) if ($oldPath.Split(';') -inotcontains 'C:\minikube') { [Environment]::SetEnvironmentVariable('Path', $('{0};C:\minikube' -f $oldPath), [EnvironmentVariableTarget]::Machine) }

   • Minikube start

3. Install dependencies using make

   • npm install make -g
   • make all

4. Setup and Configure Kubernetes locally

   • You can download it using curl: curl.exe -LO "https://dl.k8s.io/release/v1.28.2/bin/windows/amd64/kubectl.exe"

5. Running script files

   • ./run_docker.sh
   • ./run_kubernetes.sh
   • ./upload_docker.sh
   • ./make_prediction.sh