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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.

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

  1. Setup and Configure Docker

  2. Setup and Configure Kubernetes

    • For Windows users, the recommended way is to use Docker Desktop. Open Docker Desktop, go to Settings, navigate to Kubernetes, and check "Enable Kubernetes."
    • Verify the Kubernetes configuration by running: kubectl version --output json.

  3. Create Flask App in a Container

    • Build the Docker image for the Flask app using the following command: docker build --tag udacity-pj4:v1.0.0 .
    • Run the container with the created image: docker run -d --rm -p 8000:80 udacity-pj4:v1.0.0

  4. Deploy Flask App via Kubernetes

    • Create an environment file .env and set variable DOCKER_PASSWORD=<your-docker-hub-pw>.
    • run: source .env.
    • Export your Docker Hub ID using: export docker_path=<your-docker-hub-id>.
    • Log in to Docker Hub to push the image: echo "$DOCKER_PASSWORD" | docker login --username $docker_path --password-stdin.
    • Tag and push the Docker image to Docker Hub: docker image tag udacity-pj4:v1.0.0 $docker_path/udacity-pj4:v1.0.0 && docker image push $docker_path/udacity-pj4:v1.0.0.
    • Create a Kubernetes deployment: kubectl create deploy udacity-pj4 --image="$docker_path/udacity-pj4:v1.0.0".
    • Check whether the pod is in the READY state: kubectl get pods.
    • Wait pods ready, forward the port to access the Flask app locally: kubectl port-forward deployment.apps/udacity-pj4 8000:80.