simon-ejilogo / microserv4

Deploying a python machine learning model on kubernetes cluster using docker, circleci and AWS

Project Overview

This project is to operationalize a Machine Learning Microservice API.

We were 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.

With this project we can show our understanding on how 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

Our 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

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Setup the Environment

I was able to execute this project on AWS EC2 instance running ubuntu 18.04.6 LTS

You can check how to create one from this article : https://www.cloudbooklet.com/create-an-ec2-instance-on-aws-with-ubuntu-18-04/

• Clone the repository

git clone https://github.com/CFCIfe/udapeople-microsvc.git
cd uda_project4_

• Create a virtualenv with Python 3.7 and activate it.

python3 -m pip install virtualenv --user
python3 -m virtualenv ~/.devops
source ~/.devops/bin/activate

• Run Lint checks

###### Install [hadolint](https://github.com/hadolint/hadolint)
wget -O /bin/hadolint https://github.com/hadolint/hadolint/releases/download/v1.16.3/hadolint-Linux-x86_64 &&\
    chmod +x /bin/hadolint
make lint

• Run make install to install the necessary dependencies

Running app.py

1. Run in Docker: ./run_docker.sh
2. Run in Kubernetes: ./run_kubernetes.sh

Details of ./run_docker.sh

• Setup and Configure Docker locally using this link: https://www.docker.com/products/docker-desktop
• Builds an image of the flash app (app.py) and tags it.
• Exposes port 80 for the container and maps to port 8000 of the host

Kubernetes Steps

• Setup and Configure Kubernetes locally here: https://kubernetes.io/docs/tasks/tools/install-kubectl-linux/
• Install Minikube here to run K8slocally: https://minikube.sigs.k8s.io/docs/start/
• Run via kubectl: ./run_kubernetes.sh

SHOUTOUT

Special thanks to Udacity for providing a starter project template to build on : https://github.com/udacity/DevOps_Microservices.git