FastAPI Users

Ready-to-use and customizable users management for FastAPI

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Documentation: https://fastapi-users.github.io/fastapi-users/

Source Code: https://github.com/fastapi-users/fastapi-users

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Add quickly a registration and authentication system to your FastAPI project. FastAPI Users is designed to be as customizable and adaptable as possible.

Features

• Extensible base user model

• Ready-to-use register, login, reset password and verify e-mail routes

• Ready-to-use social OAuth2 login flow

• Dependency callables to inject current user in route

• Pluggable password validation

• Customizable database backend

  • SQLAlchemy async backend included thanks to encode/databases
  • MongoDB async backend included thanks to mongodb/motor
  • Tortoise ORM backend included
  • ormar backend included

• Multiple customizable authentication backends

  • JWT authentication backend included
  • Cookie authentication backend included

• Full OpenAPI schema support, even with several authentication backends

📚 Discover my book: Building Data Science Applications with FastAPI

Develop, manage, and deploy efficient machine learning applications with Python

What is this book about?

This book covers the following exciting features:

• Explore the basics of modern Python and async I/O programming
• Get to grips with basic and advanced concepts of the FastAPI framework
• Implement a FastAPI dependency to efficiently run a machine learning model
• Integrate a simple face detection algorithm in a FastAPI backend
• Integrate common Python data science libraries in a web backend
• Deploy a performant and reliable web backend for a data science application

If you feel this book is for you, get your copy today!

Contributors and sponsors ✨☕️

Thanks goes to these wonderful people (emoji key):

François Voron 🚧	Paolo Dina 💵 💻	Dmytro Ohorodnik 🐛	Matthew D. Scholefield 🐛	roywes 🐛 💻	Satwik Kansal 📖	Edd Salkield 💻 📖
mark-todd 💻 📖	lill74 🐛 💻 📖	SelfhostedPro 🛡️ 💻	Oskar Gmerek 📖	Martin Collado 🐛 💻	Eric Lopes 📖 🛡️	Beau Breon 💻
Niyas Mohammed 📖	prostomarkeloff 📖 💻	Marius Mézerette 🐛 🤔	Nickolas Grigoriadis 🐛	Open Data Coder 🤔	Mohammed Alshehri 🤔	Tyler Renelle 🤔
collerek 💻	Robert Bracco 💵	Augusto Herrmann 📖	Smithybrewer 🐛	silllli 📖	alexferrari88 💵	sandalwoodbox 🐛
Vlad Hoi 📖	Joe Nudell 🐛	Ben 💻	BoYanZh 📖	David Brochart 📖	Daan Beverdam 💻	Stéphane Raimbault ⚠️ 🐛
Sondre Lillebø Gundersen 📖	Maxim 📖 🐛	scottdavort 💵	John Dukewich 📖	Yasser Tahiri 💻	Brandon H. Goding 💻	PovilasK 💻
Just van den Broecke 💵	jakemanger 🐛 💻	Ikko Ashimine 💻	Matyáš Richter 💻

This project follows the all-contributors specification. Contributions of any kind welcome!

Development

Setup environment

You should create a virtual environment and activate it:

python -m venv venv/

source venv/bin/activate

And then install the development dependencies:

pip install -r requirements.dev.txt

Run unit tests

You can run all the tests with:

make test

The command will start a MongoDB container for the related unit tests. So you should have Docker installed.

Alternatively, you can run pytest yourself. The MongoDB unit tests will be skipped if no server is available on your local machine:

pytest

There are quite a few unit tests, so you might run into ulimit issues where there are too many open file descriptors. You may be able to set a new, higher limit temporarily with:

ulimit -n 2048

Format the code

Execute the following command to apply isort and black formatting:

make format

License

This project is licensed under the terms of the MIT license.