Docker image with Uvicorn managed by Gunicorn for high-performance FastAPI web applications in Python 3.7 and 3.6 with performance auto-tuning. Optionally with Alpine Linux.
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uvicorn-gunicorn-fastapi

Docker image with Uvicorn managed by Gunicorn for high-performance FastAPI web applications in Python 3.7 and 3.6 with performance auto-tuning. Optionally with Alpine Linux.

GitHub repo: https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker

Docker Hub image: https://hub.docker.com/r/tiangolo/uvicorn-fastapi-gunicorn/

Description

FastAPI has shown to be a Python web framework with one of the best performances, as measured by third-party benchmarks, thanks to being based on and powered by Starlette.

The achievable performance is on par with (and in many cases superior to) Go and Node.js frameworks.

This image has an "auto-tuning" mechanism included, so that you can just add your code and get that same high performance automatically. And without making sacrifices.

Technical Details

Uvicorn

Uvicorn is a lightning-fast "ASGI" server.

It runs asynchronous Python web code in a single process.

Gunicorn

You can use Gunicorn to manage Uvicorn and run multiple of these concurrent processes.

That way, you get the best of concurrency and parallelism.

FastAPI

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+.

The key features are:

  • Fast: Very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic).
  • Fast to code: Increase the speed to develop features by about 300% to 500% *.
  • Less bugs: Reduce about 40% of human (developer) induced errors. *
  • Intuitive: Great editor support. Completion everywhere. Less time debugging.
  • Easy: Designed to be easy to use and learn. Less time reading docs.
  • Short: Minimize code duplication. Multiple features from each parameter declaration. Less bugs.
  • Robust: Get production-ready code. With automatic interactive documentation.
  • Standards-based: Based on (and fully compatible with) the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema.

* estimation based on tests on an internal development team, building production applications.

tiangolo/uvicorn-gunicorn-fastapi

This image will set a sensible configuration based on the server it is running on (the amount of CPU cores available) without making sacrifices.

It has sensible defaults, but you can configure it with environment variables or override the configuration files.

There is also an Alpine version. If you want it, use one of the Alpine tags from above.

tiangolo/uvicorn-gunicorn

This image (tiangolo/uvicorn-gunicorn-fastapi) is based on tiangolo/uvicorn-gunicorn.

That image is what actually does all the work.

This image just installs FastAPI and has the documentation specifically targeted at FastAPI.

If you feel confident about your knowledge of Uvicorn, Gunicorn and ASGI, you can use that image directly.

tiangolo/uvicorn-gunicorn-starlette

There is a sibling Docker image: tiangolo/uvicorn-gunicorn-starlette

If you are creating a new Starlette web application and you want to discard all the additional features from FastAPI you should use tiangolo/uvicorn-gunicorn-starlette instead.

Note: FastAPI is based on Starlette and adds several features on top of it. Useful for APIs and other cases: data validation, data conversion, documentation with OpenAPI, dependency injection, security/authentication and others.

How to use

  • You don't need to clone the GitHub repo. You can use this image as a base image for other images, using this in your Dockerfile:
FROM tiangolo/uvicorn-gunicorn-fastapi:python3.7

COPY ./app /app

It will expect a file at /app/app/main.py.

Or otherwise a file at /app/main.py.

And will expect it to contain a variable app with your FastAPI application.

Then you can build your image from the directory that has your Dockerfile, e.g:

docker build -t myimage ./

Quick Start

Build your Image

  • Go to your project directory.
  • Create a Dockerfile with:
FROM tiangolo/uvicorn-gunicorn-fastapi:python3.7

COPY ./app /app
  • Create an app directory and enter in it.
  • Create a main.py file with:
from fastapi import FastAPI

app = FastAPI()


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: str = None):
    return {"item_id": item_id, "q": q}
  • You should now have a directory structure like:
.
├── app
│   └── main.py
└── Dockerfile
  • Go to the project directory (in where your Dockerfile is, containing your app directory).
  • Build your FastAPI image:
docker build -t myimage .
  • Run a container based on your image:
docker run -d --name mycontainer -p 80:80 myimage

Now you have an optimized FastAPI server in a Docker container. Auto-tuned for your current server (and number of CPU cores).

Check it

You should be able to check it in your Docker container's URL, for example: http://192.168.99.100/items/5?q=somequery or http://127.0.0.1/items/5?q=somequery (or equivalent, using your Docker host).

You will see something like:

{"item_id": 5, "q": "somequery"}

Interactive API docs

Now you can go to http://192.168.99.100/docs or http://127.0.0.1/docs (or equivalent, using your Docker host).

You will see the automatic interactive API documentation (provided by Swagger UI):

Swagger UI

Alternative API docs

And you can also go to http://192.168.99.100/redoc or http://127.0.0.1/redoc(or equivalent, using your Docker host).

You will see the alternative automatic documentation (provided by ReDoc):

ReDoc

Advanced usage

Environment variables

These are the environment variables that you can set in the container to configure it and their default values:

MODULE_NAME

The Python "module" (file) to be imported by Gunicorn, this module would contain the actual application in a variable.

By default:

  • app.main if there's a file /app/app/main.py or
  • main if there's a file /app/main.py

For example, if your main file was at /app/custom_app/custom_main.py, you could set it like:

docker run -d -p 80:80 -e MODULE_NAME="custom_app.custom_main" myimage

VARIABLE_NAME

The variable inside of the Python module that contains the FastAPI application.

By default:

  • app

For example, if your main Python file has something like:

from fastapi import FastAPI

api = FastAPI()


@api.get("/")
def read_root():
    return {"Hello": "World"}

In this case api would be the variable with the FastAPI application. You could set it like:

docker run -d -p 80:80 -e VARIABLE_NAME="api" myimage

APP_MODULE

The string with the Python module and the variable name passed to Gunicorn.

By default, set based on the variables MODULE_NAME and VARIABLE_NAME:

  • app.main:app or
  • main:app

You can set it like:

docker run -d -p 80:80 -e APP_MODULE="custom_app.custom_main:api" myimage

GUNICORN_CONF

The path to a Gunicorn Python configuration file.

By default:

  • /app/gunicorn_conf.py if it exists
  • /app/app/gunicorn_conf.py if it exists
  • /gunicorn_conf.py (the included default)

You can set it like:

docker run -d -p 80:80 -e GUNICORN_CONF="/app/custom_gunicorn_conf.py" myimage

WORKERS_PER_CORE

This image will check how many CPU cores are available in the current server running your container.

It will set the number of workers to the number of CPU cores multiplied by this value.

By default:

  • 2

You can set it like:

docker run -d -p 80:80 -e WORKERS_PER_CORE="3" myimage

If you used the value 3 in a server with 2 CPU cores, it would run 6 worker processes.

You can use floating point values too.

So, for example, if you have a big server (let's say, with 8 CPU cores) running several applications, and you have a FastAPI application that you know won't need high performance. And you don't want to waste server resources. You could make it use 0.5 workers per CPU core. For example:

docker run -d -p 80:80 -e WORKERS_PER_CORE="0.5" myimage

In a server with 8 CPU cores, this would make it start only 4 worker processes.

WEB_CONCURRENCY

Override the automatic definition of number of workers.

By default:

  • Set to the number of CPU cores in the current server multiplied by the environment variable WORKERS_PER_CORE. So, in a server with 2 cores, by default it will be set to 4.

You can set it like:

docker run -d -p 80:80 -e WEB_CONCURRENCY="2" myimage

This would make the image start 2 worker processes, independent of how many CPU cores are available in the server.

HOST

The "host" used by Gunicorn, the IP where Gunicorn will listen for requests.

It is the host inside of the container.

So, for example, if you set this variable to 127.0.0.1, it will only be available inside the container, not in the host running it.

It's is provided for completeness, but you probably shouldn't change it.

By default:

  • 0.0.0.0

PORT

The port the container should listen on.

If you are running your container in a restrictive environment that forces you to use some specific port (like 8080) you can set it with this variable.

By default:

  • 80

You can set it like:

docker run -d -p 80:8080 -e PORT="8080" myimage

BIND

The actual host and port passed to Gunicorn.

By default, set based on the variables HOST and PORT.

So, if you didn't change anything, it will be set by default to:

  • 0.0.0.0:80

You can set it like:

docker run -d -p 80:8080 -e BIND="0.0.0.0:8080" myimage

LOG_LEVEL

The log level for Gunicorn.

One of:

  • debug
  • info
  • warning
  • error
  • critical

By default, set to info.

If you need to squeeze more performance sacrificing logging, set it to warning, for example:

You can set it like:

docker run -d -p 80:8080 -e LOG_LEVEL="warning" myimage

Custom Gunicorn configuration file

The image includes a default Gunicorn Python config file at /gunicorn_conf.py.

It uses the environment variables declared above to set all the configurations.

You can override it by including a file in:

  • /app/gunicorn_conf.py
  • /app/app/gunicorn_conf.py
  • /gunicorn_conf.py

Custom /app/prestart.sh

If you need to run anything before starting the app, you can add a file prestart.sh to the directory /app. The image will automatically detect and run it before starting everything.

For example, if you want to add Alembic SQL migrations (with SQLALchemy), you could create a ./app/prestart.sh file in your code directory (that will be copied by your Dockerfile) with:

#! /usr/bin/env bash

# Let the DB start
sleep 10;
# Run migrations
alembic upgrade head

and it would wait 10 seconds to give the database some time to start and then run that alembic command.

If you need to run a Python script before starting the app, you could make the /app/prestart.sh file run your Python script, with something like:

#! /usr/bin/env bash

# Run custom Python script before starting
python /app/my_custom_prestart_script.py

Tests

All the image tags, configurations, environment variables and application options are tested.

Release Notes

0.1.0

  • Add support for /app/prestart.sh.

License

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