Project Overview

In this project, I have operationalized a Machine Learning Microservice API using Docker, Kubernetes, CircleCI and AWS.

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 my 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.

Setup the Environment

• Create a virtualenv with Python 3.7 and activate it.

python3 -m pip install --user virtualenv

• You should have Python 3.7 available in your host.
• Check the Python path using which python3
• Create and activate a virtual environment
  • you need to install pip and virtualenv

virtualenv .devops

*Then cd into project directory and activate the virtual environment.

.devops/Scripts/activate

• Run make install to install the necessary dependencies

Running the Application

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

Kubernetes Steps

• Setup and Configure Docker locally
• Setup and Configure Kubernetes locally You'll need to install Docker, Minikube and VirtialBox to run K8s locally. The following commands spin up a Kubernetes cluster and configure it to run the Docker image you've built.

    minikube start
    kubectl get pod

• Create Flask app in Container

    docker run -p 8080:80 mosesmbadi/machine-learning-api

• Run via kubectl

    kubectl run machine-learning-api --image=$dockerpath --port=80
    kubectl port-forward mosesmbadi/machine-learning-api 8080:80

File directories

.circleci/config.yml - circleci configuration

model_data - housing prices in Boston area

output_files/docker_out.txt - docker log outputs

output_files/kubernetes_out.txt - kubernetes log outputs

app.py - flask app API endpoint with routes to get house prices in Boston

Dockerfile - Docker configuration file

make_prediction.sh - script to log predictions endpoint output

Makefile - The Makefile includes instructions on environment setup and lint tests

requirements.txt - python dependencies for this project

run_docker.sh - shell script to build docker image and run it

run_kubernetes.sh - shell script to run the Docker Hub container with kubernetes

upload_docker.sh - shell script to upload local docker build image to docker hub (online repository)

N/B: In some cases you may need to manually run individual instructions in the bash files.