• About
• Scenario
• Project Goal
• Tasks
• Submission Info
• Running the Project
  • Running the app
    • Locally
    • With Docker
    • With Kubernetes

This is the final project for Udacity's Cloud DevOps Engineer Nanodegree. The project tests one's ability to operationalize a Python flask app, that serves out predictions (inference) about housing prices through API calls.

You're 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. The data was initially taken from Kaggle

The 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.

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

This submission satisfies the mentioned requirements. Here's a brief overview of some files:

• app.py: Flask api server, exposes and endpoint /predict that will predict housing prices in Boston using a pre-trained model.

• Dockerfile: Container building instructions for flask api.

• run_docker.sh: A script that builds a Docker image of the app.

• upload_docker.sh: A script that uploads the built Docker image to DockerHub.

• run_kubernetes.sh A script to deploy the containerized app to a k8s cluster.

This project depends on python3.7, Docker, Make, and Kubernetes (or Minikube, locally.)

# Create and activate a virtual python environment
python3.7 -m venv ~/.venv
source ~/.venv/bin/activate

# Install dependencies
make install

# Run app.py
python app.py

# When the api is running and ready to listen for connections
# run a simple test
./make_predictions.sh

./run_docker.sh

./run_kubernetes.sh