Project Overview

In this project we 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 we operationalize a Python flask app—in a provided file, app.py—that serves out predictions (inference) about housing prices through API calls.

Completed Project Tasks

• Test your project code using linting
• Complete a Dockerfile to containerize this application
• Deploy our 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

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Setup the Environment locally for testing

• Create a virtualenv and activate it
• python3 -m venv ~/.devops
• source ~/.devops/bin/activate
• Run make install to install the necessary dependencies
• While you still have your .devops environment activated, you will still need to install: Docker, Hadolint, Kubernetes (Minikube)
• Run make lint to check final version of Docker file

Running app.py

1. Standalone: python app.py
2. Run in Docker: ./run_docker.sh
3. Run in Kubernetes: ./run_kubernetes.sh
4. Prediction application will be running on http://hostip:8000
5. While it is running execute make_predicion.sh to invoke predicting application in the conainer app.py via API calls

Kubernetes Steps

• Setup and Configure Docker locally
• Setup and Configure Kubernetes locally
• Create Flask app in Container
• Run via kubectl

Project Files Overview

• .circleci/config.yml: Configuration file for CircleCI
• Dockerfile: Spec that isntructs docker how to buil application container
• Makefile: All the required commands to install and lint the applicaiton
• app.py: Boston housing prices predicting application
• requirements.txt: List of required packages to be installed for our application to run
• model_data/boston_housing_prediction.joblib: Pre-trained, sklearn model that has been trained to predict housing prices in Boston, used by app.py
• run_docker.sh: Shell script that builds and runs the docker container
• run_kubernetes.sh: Shell script that runs prediction app as a pod in kubernetes
• make_prediction.sh: Prediction application invokation script
• upload_docker.sh: Shell script that tags and uploads our docker image to dockerhub
• output_txt_files/docker_out.txt: Running run_docker.sh and make_prediction.sh output file
• output_txt_files/kubernetes_out.txt Running run_kubernetes.sh and make_prediction.sh output file