Project Overview

In this project, I applied the skills I have acquired in the course to operationalize a Machine Learning Microservice API.

I was 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 the 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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Directory & Files

Below are critical files for the project and the purpose they serve

• app.py : Main app entry for python
• model_data : Model files
• output_txt_files : Docker and Kubernetes logs output files
• Dockerfile : Docker configuration file
• dockerignore : Just like .gitignore for git this file helps docker to ignore some files and repositories during some comands like copy
• Makefile : Contains commands for installing linting and testing. Might need to pre-install some depencies such as hadolint.
• requirements.txt : Python dependencies
• run_docker.sh : builds and runs the docker image
• upload_docker.sh : Uploads docker image to my docker hub
• run_kubernetes.sh : Fetches the uploaded docker image and runs it locally with minukube. Requires the installation of minikube and start it with minikube start
• make_predictions.sh : Calls the API for testing

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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 Locally app.py

1. Standalone: `python app.py`
2. Run in Docker:  `./run_docker.sh`
3. Run in Kubernetes:  `./run_kubernetes.sh`

Steps

• Setup and Configure Docker locally (Ubuntu). Check more at https://docs.docker.com/engine/install/ubuntu/

  • sudo apt-get remove docker docker-engine docker.io containerd runc
  • sudo apt-get update
  • sudo chmod a+r /etc/apt/keyrings/docker.gpg
  • sudo apt-get update
  • sudo apt-get install docker-ce docker-ce-cli containerd.io docker-compose-plugin
    
    

• Install kubectl binary with curl on Linux

  • curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl"
  • sudo install -o root -g root -m 0755 kubectl /usr/local/bin/kubectl
  • Check its version: kubectl version --client
    
    

• Setup and Configure Minicube locally (Ubuntu). Check https://minikube.sigs.k8s.io/docs/start for more.

  • curl -LO https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64
  • sudo install minikube-linux-amd64 /usr/local/bin/minikube
  • Start your cluster by: minikube start
    
    

• Create Flask app in Container

  • ./run_docker.sh
    
    

• Run via kubectl

  • ./run_kubernetes.sh
  • Step 4: Will need to be run manually and will need to replace pod/flask-housing-api-64fd7f4598-s2rfq with the pod id (use kubectl get pods)

• Stop and Delete local clusters

  • minikube stop
  • minikube delete