FastAPI Model Server Skeleton

Serving machine learning models production-ready, fast, easy and secure powered by the great FastAPI by Sebastián Ramírez](https://github.com/tiangolo).

This repository contains a skeleton app which can be used to speed-up your next machine learning project. The code is fully tested and provides a preconfigured tox to quickly expand this sample code.

To experiment and get a feeling on how to use this skeleton, a sample regression model for house price prediction is included in this project. Follow the installation and setup instructions to run the sample model and serve it aso RESTful API.

Requirements

Docker
Docker Compose

Installation

Start the stack with Docker Compose:

make build

Setup

Duplicate the .env.example file and rename it to .env
In the .env file configure the API_KEY entry. The key is used for authenticating our API.
A sample API key can be generated using Python REPL:

import uuid
print(str(uuid.uuid4()))

Run It

Start your app with:

make up

Go to http://localhost:8888/docs.
Click Authorize and enter the API key as created in the Setup step.
You can use the sample payload from the docs/sample_payload.json file when trying out the house price prediction model using the API.

Run Tests

If you're not using tox, please install with:

pip install tox

Run your tests with:

tox