thanhtung249 / DevOps_Microservices

Supporting material and projects for a course on Cloud DevOps: Microservices.

Project Overview

This project deploys 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 Files

• config.yml: CircleCI configuration file for running the tests
• app.py: Python flask app that serves out predictions (inference) about housing prices through API calls
• Dockerfile: Dockerfile for building the image
• make_prediction.sh: Sends a request to the Python flask app to get a prediction, for localhost
• Makefile: Instructions on environment setup and lint tests
• run_docker.sh: run Docker locally
• run_kubernetes.sh: run the app in kubernetes
• upload_docker.sh: upload the image to docker

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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

Kubernetes Steps

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