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CircleCI

Project Overview

In this project, I applied the skills which I have acquired in this course to operationalize a Machine Learning Microservice API. 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.

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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

This project aim 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.

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
  • Run make lint to run linting tests on both Dockerfile and app.py or run make all to both install dependencies and run linting

Running the application app.py

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

Make predictions

  1. First ensure the app is running in one terminal using either of the above methods.

  2. Run ./make_prediction.sh in a separate terminal window

Docker Steps:

  • Setup and Configure Docker locally
    1. Install docker on your system. Follow this link instrauctions on installation
    2. Run the app in docker(Dockerfile): ./run_docker.sh
    3. Check if the image is created: docker images
    4. make pridictions: ./make_prediction.sh
    5. uplaod docker image to docker hub: ./upload_docker.sh
    6. Save Output logs to docker_out.txt file

Kubernetes Steps

  • Setup and Configure Kubernetes locally
    1. Install kubernetes. If you installed docker desktop it comes with kubernetes you don't need to do another installation
    2. Install minikube. Follow instructions here for installation and setup
    3. Start minickube: minikube start
    4. deploy with kubernetes(image uploaded dockerhub): ./run_kubernetes.sh
    5. make predictions: ./make_prediction.sh
  • Run via kubectl
  • Save Output logs to kubernetes.out.txt file

Project files description

  • app.py: Contains code that run the prediction model
  • Dockerfile: Contains instructions to contenarize the app.
  • docker_out.txt: Contains sample output after making predictions with dockerised app
  • kubernetes_out.txt: Contains sample output for predictions of the app deployed with kubernetes
  • Makefile: Defines a set of instructions to run using make. two of the instructions used are: lint and install
  • make_prediction.sh: This file queries the model with some input data to make predicions
  • requirements.txt: includes project dependencies
  • run_docker.sh: script that runs the dockerised app
  • run_kubernetes.sh: script that setups and runs the app with kubernetes
  • uplaod_docker.sh: script that uplaods local app docker image to docker hub