This repo´s only purpose is to demonstrate how to containerize a simple project with docker
or kubernetes
.
It is made as part of the curriculum of Udacity's Cloud DevOps Engineer Nanodegree Program, project Operationalize a Machine Learning Microservice API.
This work is based on Udacity's starter code.
In this repo a pre-trained, sklearn
model is included 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.
- Create a virtualenv and activate it
- Run
make install
to install the necessary dependencies
Linux, Mac and Windows 10 WSL:
$ python -m venv venv
$ source venv/bin/activate
(venv)$ make install
$ ./run_docker.sh
$ ./run_kubernetes.sh
Either run make_prediction.sh
or send a http POST
request to the the respective endpoint, http://localhost:80/predict
. Study the make_prediction.sh
file as to how the payload should look like.