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CircleCI

Operationalize A Machine Learning Microservice API

Microservice Project [Udacity Cloud DevOps Engineer Nanodegree]

Project Overview

Deploy a containerized Python flask application to serve out predictions (inference) about housing prices through API calls. It uses a a pre-trained, sklearn model that has been trained to predict housing prices in Boston according to several features.

Project Procedure

  • Test project code using linting
  • Complete a Dockerfile to containerize this application
  • Deploy containerized application using Docker and make a prediction
  • 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 the code has been tested

Getting Started

Setup the Environment

  • Create a virtualenv and activate it
python3 -m venv <your_venv>
source <your_venv>/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

Running using docker

  1. Install docker: Check documentations
  2. Run in docker: ./run_docker.sh
  3. RUn prediction: ./run_prediction.sh

Running using kubernetes

  1. Install kubectl : Check the kubernetes documentations to install on your machine
  2. Start minikube: minikube start
  3. Run ./run_kubernetes.sh
  4. Run prediction: ./run_prediction.sh
  5. To stop: minikube delete

Kubernetes Steps

  • Setup and Configure Docker locally
  • Setup and Configure Kubernetes locally
  • Create Flask app in Container
  • Run via kubectl