UDACITY PROJECT 4: ML Microservice

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.

Summary

This a ML preidction app that is written in python and can be run using docker and kubernetes. The project involves creating a dockerfile to containerize the app, then running the image and then pushing it to docker hub. Then we use kubernetes as container orchestrator to deploy the apps. Then test it on both environments.

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
The makefile contains all other neccessary commands.

Running `app.py`

Standalone: python app.py
Run in Docker: ./run_docker.sh
Run in Kubernetes: ./run_kubernetes.sh
Run test against docker and kubernetes: ./make_prediction.sh
Upload docker image to Docker Hub: ./upload_docker.sh

Kubernetes Steps

Setup and Configure Docker locally

install docker as described in the link.
create a virtual environment by running make setup
then run make install to install the required things
run make lint to check Dockerfile
run make build-app to containerize the app
run ./run_docker.sh to run the docker image
finally run ./make_prediction.sh to query the app in the container

Setup and Configure Kubernetes locally

You can install k3s from rancher or use minikube
The steps are provided in the run_kubernetes.sh script

Create Flask app in Container

run make build-app to containerize the app
run ./run_docker.sh to run the docker image

Run via kubectl

do chmod +x run_kubernetes.sh to make it executable
then ./run_kubernetes.sh to run the script
finally run ./make_prediction.sh to query the app in the kubernetes environment

Files Explained

*.sh - the shell files are the scripts for running the project
Makefile: the reusable commands are stored here
requirements.txt: the list of dependencies is stored here
output_txt_files: the output data is stored here for the docker and kubernetes logs
config.yml: the configuration for the circleci
model_data: the ML model data
Dockerfile: the Dockerfile for the container

billmal071/ml-app