fastapi-ai-template

2 min readOct 25, 2022

FastAPI is a great choice in order to deploy RESTApi with python. Its fast, easy and powerful, and something to check if you work deploying AI models for real-time inferencing in kubernetes.

I made a “production ready” template for FastAPI that you can use to deploy your own AI models.

Whats included?

With this template you will find a very easy way to create a full template that covers the following:

Telemetry: ApplicationInsights ready
Security: Security through API key
Webserver: gunicorn configuration to fine tune threads and memory
Docker support: Dockerfile and docker-compose setup
Debug: visual studio code debug configuration setted up

Cookiecutter template

You don´t need to fork this project. This project is a template for cookiecutter that can be used directly from your command line without forking it. The output of this project is a fully configured FastAPI application ready for you to start coding and deploy.

Setup

Since this is a template for cookiecutter, you need first to install cookiecutter.

Once installed, you can run the following command to create a new project:

# go to the directory where you want to create the project
cd ~
mkdir your-project
cd your-project
# create your project using this template
cookiecutter https://github.com/enriquecatala/fastapi-ai-template.git \
             -v \
             --directory="base-template"

This will create a base ApiREST project ready for you to test and deploy. It will include a test method that you can use to test your code.

docker compose build
docker compose up

Now navigate to http://127.0.0.1:5000/docs to see the documentation.

Configuration

your-project/
├── fastapi-ai-template/  <--------- Project template 
    └── .vs-code/         <--------- visual studio debug config 
        ├── launch.json        
        └── tasks.json
    └── app/              <--------- Project folder 
        ├── api/          <--------- api routing
        ├── core/         <--------- event handling and security
        ├── models/       <--------- Model classes definition
        ├── services/     <--------- API main methods (this is where you have the main logic)
        └── main.py
    ├── Dockerfile 
    ├── docker-compose.yml 
    ├── gunicorn_conf.py  <--------- web server config
    ├── requirements.txt  <--------- default libraries
    └── ...

Help!

Please if you think this worth the effort, consider ❤️ Sponsor this project and help me increase functionality