/Deploy-ML-Microservices-K8S-Udacity

Udacity project 4: Deploy Machine Learning Microservices using Kubernetes and Docker

Primary LanguagePython

CircleCI

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.

Setup the Environment

  • Create a virtualenv and activate it
  • Run make install to install the necessary dependencies

Running app.py

  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
  • Create Flask app in Container
  • Run via kubectl

Application Overview

The Python application is a pre-trained sklearn model that can be used to predict housing prices in Boston. A sample script to use the application is included as make_prediction.sh.

$ ./make_prediction.sh

Run the application locally

A Makefile is included that will set up a virtual environment with all the dependencies necessary for the project. To setup the venv:

$ make setup

This will create a virtual environment in ~/.devops and source it to activate it. You can deactivate by running the command:

$ deactivate

All that is left is to install the dependencies and start the application:

$ make install
$ python app.py

Run the application in a Docker container

The included run_docker.sh script is all that is needed to get a Docker container running with the application. It will create an image (if one does not exist already) and start a container.

$ ./run_docker.sh

It will start the container and attach the current terminal to it to be able to see the log output. In a separate terminal, run the make_prediction script to see a prediction and the log output.

$ ./make_prediction.sh

Running the application in a Kubernetes cluster

To start a Kubernetes cluster of the application, run the following command and use the included run_kubernetes.sh script:

$ minikube start
$ ./run_kubernetes.sh

The script will attempt to start port-forwarding automatically; however, the pod takes time to load. After the pod has loaded, you can run the script again to start port-forwarding and interact with the application as above (in another terminal, run make_prediction.sh).

After you are finished testing the cluster, use CTRL+C to send a SIGINT to the port forwarding, stop the minikube and (optionally) delete the resources.

$ minikube stop
$ minikube delete