Supported tags and respective Dockerfile links

• python3.11, latest (Dockerfile)
• python3.10 (Dockerfile)
• python3.9, (Dockerfile)
• python3.8, (Dockerfile)
• python3.7, (Dockerfile)

Deprecated tags

🚨 These tags are no longer supported or maintained, they are removed from the GitHub repository, but the last versions pushed might still be available in Docker Hub if anyone has been pulling them:

• python3.8-alpine
• python3.6
• python2.7

The last date tags for these versions are:

• python3.8-alpine-2024-03-11
• python3.6-2022-11-25
• python2.7-2022-11-25

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. tiangolo/uwsgi-nginx-flask:python3.7-2019-10-14.

uwsgi-nginx-flask

Docker image with uWSGI and Nginx for Flask web applications in Python running in a single container.

Description

This Docker image allows you to create Flask web applications in Python that run with uWSGI and Nginx in a single container.

The combination of uWSGI with Nginx is a common way to deploy Python Flask web applications. It is widely used in the industry and would give you decent performance. (*)

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

* Note on performance and features

If you are starting a new project, you might benefit from a newer and faster framework based on ASGI instead of WSGI (Flask and Django are WSGI-based).

You could use an ASGI framework like:

• FastAPI (which is based on Starlette) with this Docker image: tiangolo/uvicorn-gunicorn-fastapi.
• Starlette directly, with this Docker image: tiangolo/uvicorn-gunicorn-starlette.

FastAPI, or Starlette, would give you about 800% (8x) the performance achievable with Flask using this image (tiangolo/uwsgi-nginx-flask). You can see the third-party benchmarks here.

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

If you need Flask

If you need to use Flask (instead of something based on ASGI) and you need to have the best performance possible, you can use the alternative image: tiangolo/meinheld-gunicorn-flask.

tiangolo/meinheld-gunicorn-flask will give you about 400% (4x) the performance of this image (tiangolo/uwsgi-nginx-flask).

It is very similar to tiangolo/uwsgi-nginx-flask, so you can still use many of the ideas described here.

GitHub repo: https://github.com/tiangolo/uwsgi-nginx-flask-docker

Docker Hub image: https://hub.docker.com/r/tiangolo/uwsgi-nginx-flask/

🚨 WARNING: You Probably Don't Need this Docker Image

You are probably using Kubernetes or similar tools. In that case, you probably don't need this image (or any other similar base image). You are probably better off building a Docker image from scratch.

If you have a cluster of machines with Kubernetes, Docker Swarm Mode, Nomad, or other similar complex system to manage distributed containers on multiple machines, then you will probably want to handle replication at the cluster level instead of using a process manager in each container that starts multiple worker processes, which is what this Docker image does.

In those cases (e.g. using Kubernetes) you would probably want to build a Docker image from scratch, installing your dependencies, and running a single process instead of this image.

For example, using Gunicorn you could have a file app/gunicorn_conf.py with:

# Gunicorn config variables
loglevel = "info"
errorlog = "-"  # stderr
accesslog = "-"  # stdout
worker_tmp_dir = "/dev/shm"
graceful_timeout = 120
timeout = 120
keepalive = 5
threads = 3

And then you could have a Dockerfile with:

FROM python:3.9

WORKDIR /code

COPY ./requirements.txt /code/requirements.txt

RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt

COPY ./app /code/app

CMD ["gunicorn", "--conf", "app/gunicorn_conf.py", "--bind", "0.0.0.0:80", "app.main:app"]

You can read more about these ideas in the FastAPI documentation about: FastAPI in Containers - Docker as the same ideas would apply to other web applications in containers.

When to Use this Docker Image

A Simple App

You could want a process manager running multiple worker processes in the container if your application is simple enough that you don't need (at least not yet) to fine-tune the number of processes too much, and you can just use an automated default, and you are running it on a single server, not a cluster.

Docker Compose

You could be deploying to a single server (not a cluster) with Docker Compose, so you wouldn't have an easy way to manage replication of containers (with Docker Compose) while preserving the shared network and load balancing.

Then you could want to have a single container with a process manager starting several worker processes inside, as this Docker image does.

Prometheus and Other Reasons

You could also have other reasons that would make it easier to have a single container with multiple processes instead of having multiple containers with a single process in each of them.

For example (depending on your setup) you could have some tool like a Prometheus exporter in the same container that should have access to each of the requests that come.

In this case, if you had multiple containers, by default, when Prometheus came to read the metrics, it would get the ones for a single container each time (for the container that handled that particular request), instead of getting the accumulated metrics for all the replicated containers.

Then, in that case, it could be simpler to have one container with multiple processes, and a local tool (e.g. a Prometheus exporter) on the same container collecting Prometheus metrics for all the internal processes and exposing those metrics on that single container.

Read more about it all in the FastAPI documentation about: FastAPI in Containers - Docker, as the same concepts apply to other web applications in containers.

Examples (simple project templates)

• python3.8 tag: general Flask web application:

example-flask-python3.8.zip

• python3.8 tag: general Flask web application, structured as a package, for bigger Flask projects, with different submodules. Use it only as an example of how to import your modules and how to structure your own project:

example-flask-package-python3.8.zip

• python3.8 tag: static/index.html served directly in /, e.g. for Vue, React, Angular, or any other Single-Page Application that uses a static index.html, not modified by Python:

example-flask-python3.8-index.zip

General Instructions

You don't have to clone this repo.

You can use this image as a base image for other images.

Assuming you have a file requirements.txt, you could have a Dockerfile like this:

FROM tiangolo/uwsgi-nginx-flask:python3.11

COPY ./requirements.txt /app/requirements.txt

RUN pip install --no-cache-dir --upgrade -r /app/requirements.txt

COPY ./app /app

There are several image tags available but for new projects you should use the latest version available.

There are several template projects that you can download (as a .zip file) to bootstrap your project in the section "Examples (project templates)" above.

This Docker image is based on tiangolo/uwsgi-nginx. That Docker image has uWSGI and Nginx installed in the same container and was made to be the base of this image.

Quick Start

Note: You can download the example-flask-python3.8.zip project example and use it as the template for your project from the section Examples above.

Or you may follow the instructions to build your project from scratch:

• Go to your project directory
• Create a Dockerfile with:

FROM tiangolo/uwsgi-nginx-flask:python3.11

COPY ./app /app

• Create an app directory and enter in it
• Create a main.py file (it should be named like that and should be in your app directory) with:

from flask import Flask
app = Flask(__name__)

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

if __name__ == "__main__":
    # Only for debugging while developing
    app.run(host='0.0.0.0', debug=True, port=80)

the main application object should be named app (in the code) as in this example.

Note: The section with the main() function is for debugging purposes. To learn more, read the Advanced instructions below.

• 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 Flask image:

docker build -t myimage .

• Run a container based on your image:

docker run -d --name mycontainer -p 80:80 myimage

...and you have an optimized Flask server in a Docker container.

You should be able to check it in your Docker container's URL, for example: http://192.168.99.100 or http://127.0.0.1

Project Generators

There are several project generators that you can use to start your project, with everything already configured.

Server set up

All these project generators include automatic and free HTTPS certificates generation provided by:

• Traefik and
• Let's Encrypt

...using the ideas from DockerSwarm.rocks.

It would take about 20 minutes to read that guide and have a Docker cluster (of one or more servers) up and running ready for your projects.

You can have several projects in the same cluster, all with automatic HTTPS, even if they have different domains or sub-domains.

Generate a project

Then you can use one of the following project generators.

It would take about 5 extra minutes to generate one of these projects.

Deploy

And it would take about 3 more minutes to deploy them in your cluster.

In total, about 28 minutes to start from scratch and get an HTTPS Docker cluster with your full application(s) ready.

These are the project generators:

flask-frontend-docker

Project link: https://github.com/tiangolo/flask-frontend-docker

Minimal project generator with a Flask backend, a modern frontend (Vue, React or Angular) using Docker multi-stage building and Nginx, a Traefik load balancer with HTTPS, Docker Compose (and Docker Swarm mode) etc.

full-stack

Project Link: https://github.com/tiangolo/full-stack

Full stack project generator with Flask backend, PostgreSQL DB, PGAdmin, SQLAlchemy, Alembic migrations, Celery asynchronous jobs, API testing, CI integration, Docker Compose (and Docker Swarm mode), Swagger, automatic HTTPS, Vue.js, etc.

full-stack-flask-couchbase

Project Link: https://github.com/tiangolo/full-stack-flask-couchbase

Full stack project generator with Flask backend, Couchbase, Couchbase Sync Gateway, Celery asynchronous jobs, API testing, CI integration, Docker Compose (and Docker Swarm mode), Swagger, automatic HTTPS, Vue.js, etc.

Similar to the one above (full-stack), but with Couchbase instead of PostgreSQL, and some more features.

full-stack-flask-couchdb

Project Link: https://github.com/tiangolo/full-stack-flask-couchdb

Full stack project generator with Flask backend, CouchDB, Celery asynchronous jobs, API testing, CI integration, Docker Compose (and Docker Swarm mode), Swagger, automatic HTTPS, Vue.js, etc.

Similar to full-stack-flask-couchbase, but with CouchDB instead of Couchbase (or PostgreSQL).

Quick Start for SPAs *

Modern Single Page Applications

If you are building modern frontend applications (e.g. Vue, React, Angular) you would most probably be compiling a modern version of JavaScript (ES2015, TypeScript, etc) to a less modern, more compatible version.

If you want to serve your (compiled) frontend code by the same backend (Flask) Docker container, you would have to copy the code to the container after compiling it.

That means that you would need to have all the frontend tools installed on the building machine (it might be your computer, a remote server, etc).

That also means that you would have to, somehow, always remember to compile the frontend code right before building the Docker image.

And it might also mean that you could then have to add your compiled frontend code to your git repository (hopefully you are using Git already, or learning how to use git).

Adding your compiled code to Git is a very bad idea for several reasons, some of those are:

• You don't have a single, ultimate source of truth (the source code).
• The compiled code might be stale, even when your source code is new, which might make you spend a lot of time debugging.
• You might run into a lot of code conflicts when