by tiangolo

Docker image with Meinheld and Gunicorn for Flask applications in Python. Optionally with Alpine Lin...

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Supported tags and respective

Note: Note: There are tags for each build date. If you need to "pin" the Docker image version you use, you can select one of those tags. E.g.



Docker image with Meinheld managed by Gunicorn for high-performance web applications in Flask using Python 3.6 and above and Python 2.7, with performance auto-tuning. Optionally with Alpine Linux.

GitHub repo:

Docker Hub image:


Python Flask web applications running with Meinheld controlled by Gunicorn have some of the best performances achievable by Flask (*).

If you have an already existing application in Flask or are building a new one, this image will give you the best performance possible (or close to that).

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

* Note on performance and features

If you are starting a new project, you might benefit from a newer and faster framework like FastAPI (based on ASGI instead of WSGI like Flask and Django), and a Docker image like tiangolo/uvicorn-gunicorn-fastapi.

It would give you about 200% the performance achievable with Flask, even when using this image.

Also, if you want to use new technologies like WebSockets it would be easier with a newer framework based on ASGI, like FastAPI. As the standard ASGI was designed to be able to handle asynchronous code like the one needed for WebSockets.

Technical Details


Meinheld is a high-performance WSGI-compliant web server.


You can use Gunicorn to manage Meinheld and run multiple processes of it.


Flask is a microframework for Python based on Werkzeug, Jinja 2 and good intentions.


This image was created to be an alternative to tiangolo/uwsgi-nginx-flask, providing about 400% the performance of that image.

It is based on the more generic image tiangolo/meinheld-gunicorn. That's the one you would use for other WSGI frameworks, like Django.

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
FROM tiangolo/meinheld-gunicorn-flask:python3.7

COPY ./app /app

It will expect a file at


Or otherwise a file at


And will expect it to contain a variable

with your "WSGI" application.

Then you can build your image from the directory that has your

, e.g:
docker build -t myimage ./

Advanced usage

Environment variables

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


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

By default:

  • app.main
    if there's a file
  • main
    if there's a file

For example, if your main file was at

, you could set it like:
docker run -d -p 80:80 -e MODULE_NAME="custom_app.custom_main" myimage


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

By default:

  • app

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

from flask import Flask
api = Flask(__name__)

@api.route("/") def hello(): return "Hello World from Flask"

In this case

would be the variable with the "Flask application". You could set it like:
docker run -d -p 80:80 -e VARIABLE_NAME="api" myimage


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

By default, set based on the variables

  • app.main:app
  • main:app

You can set it like:

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


The path to a Gunicorn Python configuration file.

By default:

  • /app/
    if it exists
  • /app/app/
    if it exists
  • /
    (the included default)

You can set it like:

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


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

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 an ASGI application that you know won't need high performance. And you don't want to waste server resources. You could make it use

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.


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
    . So, in a server with 2 cores, by default it will be set to

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.


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
, 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:



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

) 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


The actual host and port passed to Gunicorn.

By default, set based on the variables


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


You can set it like:

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


The log level for Gunicorn.

One of:

  • debug
  • info
  • warning
  • error
  • critical

By default, set to


If you need to squeeze more performance sacrificing logging, set it to

, 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


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

You can override it by including a file in:

  • /app/
  • /app/app/
  • /


If you need to run anything before starting the app, you can add a file
to the directory
. 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

file in your code directory (that will be copied by your
) 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


If you need to run a Python script before starting the app, you could make the

file run your Python script, with something like:
#! /usr/bin/env bash

Run custom Python script before starting

python /app/


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

Release Notes

Latest Changes

  • Add Python 3.8 with Alpine 3.11. PR #28.
  • Fix typo in README. PR #18 by @tahmid-choyon.
  • Add support for Python 3.8. PR #27.
  • Refactor build setup:
    • Use GitHub actions for CI.
    • Simplify, centralize, and deduplicate code and configs.
    • Update tests.
    • Move from Pipenv to Poetry.
    • PR #26.


  • Refactor tests to use env vars and add image tags for each build date, like
    . PR #17.


  • Add support for Python 2.7 (you should use Python 3.7 or Python 3.6). PR #11.

  • Update Travis CI configuration. PR #10 by @cclauss.


  • Add support for


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

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