0xdod / project-ml-microservice-kubernetes

udacity cloud devops kubernetes project

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.

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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

Running app.py

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

Kubernetes Steps

Setup and Configure Docker locally

Follow the steps from the official documentation to install and setup docker for your operating system.

Setup and Configure Kubernetes locally

If you use docker desktop, you can enable kubernetes in the settings, or follow the guide here to install minikube and setup a kubernetes cluster on your local system

Create Flask app in Container

Build and push the flask app image to docker hub

./run_docker.sh
./upload_docker.sh

Run via kubectl

run the following commands to get the container running in kubernetes

./run_kubernetes.sh

Open a new terminal to test the deployment and run the following commands

./make_prediction.sh

Description of files

• app.py: main entrypoint for application
• docker_out.txt: output of container's log after making a predicction
• Dockerfile: contains all the commands a user could call on the command line to assemble an image
• kubernetes_out.txt: output of kubernetes deployment pod after making prediction
• make_prediction.sh: contains command to interact with the running app to make a sample prediction request
• Makefile: contains commands for convenience to setup, lint and test our app
• requirements.txt: contains list of external python dependencies
• run_docker.sh: contains commands to run docker image
• run_kubernetes.sh: contains commands to deploy app to a kubernetes cluster
• upload_docker.sh: contains commands to upload docker image to docker hub