peninahnaserian / docker-kubernetes-microservice

Operationalize a ML Microservice API

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

Kubernetes Steps

• Setup and Configure Docker locally

1. Create a free docker account, where you'll choose a unique username and link your email to a docker account. Your username is your unique docker ID.
2. To install the latest version of docker, choose the Community Edition for your operating system, on docker's installation site.
3. After installation, you can verify that you've successfully installed docker by printing its version in your terminal: docker --version

• Setup and Configure Kubernetes locally

1. Install a virtual machine like VirtualBox: For Mac: brew cask install virtualbox For Windows: recommend to use a Windows host
2. Install minikube : For Mac : brew cask install minikube For Windows: recommend using Windows installer
3. Run minikube start
4. Check if cluster is running kubectl config view

• Create Flask app in Container Run the docker sript file: ./run_docker.sh Upload the built image: ./upload_docker.sh

• Run via kubectl Run the kubernetes script file: ./run_kubernetes.sh