vSivarajah/Docker-project

This is a docker project workflow

Project Overview

This project involves operationalization of a machine learning microservice api.

We have been 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.

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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

Included files in the git repository

• Makefile - contains steps to setup, install, lint our application.
• Dockerfile - file that builds a container image that contains the prediction application
• app.py - The prediction application itself, written in python.
• deployment.yaml - Kubernetes deployment spec of our application.
• make_prediction.sh - file containing a CURL command to execute a prediction.
• output_txt_files/** - log files for docker and k8s
• requirements.txt - list of necessary packages.
• run_docker.sh - tags and builds a docker image
• upload_docker.sh - pushes the built image to centralized registry such as docker hub.
• run_kubernetes.sh - contains steps that runs a k8s deployment, lists the pod and port-forward the application to localhost.