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Supported tags and respective Dockerfile links

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. tiangolo/uvicorn-gunicorn-starlette:python3.7-2019-10-15.


Docker image with Uvicorn managed by Gunicorn for high-performance Starlette web applications in Python 3.6 and above with performance auto-tuning. Optionally in a slim version or based on Alpine Linux.

GitHub repo:

Docker Hub image:


Starlette has shown to be a Python web framework with one of the best performances, as measured by third-party benchmarks.

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 is a lightning-fast "ASGI" server.

It runs asynchronous Python web code in a single process.


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

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


Starlette is a lightweight ASGI framework/toolkit, which is ideal for building high performance asyncio services.


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 a slim version and another one based on Alpine Linux. If you want one of those, use one of the tags from above.


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

That image is what actually does all the work.

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

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


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

If you are creating a new FastAPI web application you should use tiangolo/uvicorn-gunicorn-fastapi 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-starlette:python3.9

COPY ./app /app

It will expect a file at /app/app/

Or otherwise a file at /app/

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

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

docker build -t myimage ./

Quick Start

  • Go to your project directory.
  • Create a Dockerfile with:
FROM tiangolo/uvicorn-gunicorn-starlette:python3.9

COPY ./app /app
  • Create an app directory and enter in it.
  • Create a file with:
from starlette.applications import Starlette
from starlette.responses import JSONResponse

app = Starlette()

async def homepage(request):
    return JSONResponse({"message": "Hello World!"})
  • You should now have a directory structure like:
├── app
│   └──
└── Dockerfile
  • Go to the project directory (in where your Dockerfile is, containing your app directory).
  • Build your Starlette 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 Starlette server in a Docker container. Auto-tuned for your current server (and number of CPU cores).

You should be able to check it in your Docker container's URL, for example: or (or equivalent, using your Docker host).

You will see something like:

{"message": "Hello World!"}

Dependencies and packages

You will probably also want to add any dependencies for your app and pin them to a specific version, probably including Uvicorn, Gunicorn, and Starlette.

This way you can make sure your app always works as expected.

You could install packages with pip commands in your Dockerfile, using a requirements.txt, or even using Poetry.

And then you can upgrade those dependencies in a controlled way, running your tests, making sure that everything works, but without breaking your production application if some new version is not compatible.

Using Poetry

Here's a small example of one of the ways you could install your dependencies making sure you have a pinned version for each package.

Let's say you have a project managed with Poetry, so, you have your package dependencies in a file pyproject.toml. And possibly a file poetry.lock.

Then you could have a Dockerfile like:

FROM tiangolo/uvicorn-gunicorn-starlette:python3.9

# Install Poetry
RUN curl -sSL | POETRY_HOME=/opt/poetry python && \
    cd /usr/local/bin && \
    ln -s /opt/poetry/bin/poetry && \
    poetry config virtualenvs.create false

# Copy using poetry.lock* in case it doesn't exist yet
COPY ./app/pyproject.toml ./app/poetry.lock* /app/

RUN poetry install --no-root --no-dev

COPY ./app /app

That will:

  • Install poetry and configure it for running inside of the Docker container.
  • Copy your application requirements.
    • Because it uses ./app/poetry.lock* (ending with a *), it won't crash if that file is not available yet.
  • Install the dependencies.
  • Then copy your app code.

It's important to copy the app code after installing the dependencies, that way you can take advantage of Docker's cache. That way it won't have to install everything from scratch every time you update your application files, only when you add new dependencies.

This also applies for any other way you use to install your dependencies. If you use a requirements.txt, copy it alone and install all the dependencies on the top of the Dockerfile, and add your app code after it.

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 application in a variable.

By default:

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

For example, if your main file was at /app/custom_app/, 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 Starlette application.

By default:

  • app

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

from starlette.applications import Starlette
from starlette.responses import JSONResponse

api = Starlette()

async def homepage(request):
    return JSONResponse({"message": "Hello World!"})

In this case api would be the variable with the Starlette 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 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


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

You can use the config file from the base image as a starting point for yours.


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:

  • 1

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 Starlette 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.

Note: By default, if WORKERS_PER_CORE is 1 and the server has only 1 CPU core, instead of starting 1 single worker, it will start 2. This is to avoid bad performance and blocking applications (server application) on small machines (server machine/cloud/etc). This can be overridden using WEB_CONCURRENCY.


Set the maximum number of workers to use.

You can use it to let the image compute the number of workers automatically but making sure it's limited to a maximum.

This can be useful, for example, if each worker uses a database connection and your database has a maximum limit of open connections.

By default it's not set, meaning that it's unlimited.

You can set it like:

docker run -d -p 80:80 -e MAX_WORKERS="24" myimage

This would make the image start at most 24 workers, independent of how many CPU cores are available in the server.


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 2.

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 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


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:


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 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


The class to be used by Gunicorn for the workers.

By default, set to uvicorn.workers.UvicornWorker.

The fact that it uses Uvicorn is what allows using ASGI frameworks like Starlette, and that is also what provides the maximum performance.

You probably shouldn't change it.

But if for some reason you need to use the alternative Uvicorn worker: uvicorn.workers.UvicornH11Worker you can set it with this environment variable.

You can set it like:

docker run -d -p 80:8080 -e WORKER_CLASS="uvicorn.workers.UvicornH11Worker" myimage


Workers silent for more than this many seconds are killed and restarted.

Read more about it in the Gunicorn docs: timeout.

By default, set to 120.

Notice that Uvicorn and ASGI frameworks like Starlette are async, not sync. So it's probably safe to have higher timeouts than for sync workers.

You can set it like:

docker run -d -p 80:8080 -e TIMEOUT="20" myimage


The number of seconds to wait for requests on a Keep-Alive connection.

Read more about it in the Gunicorn docs: keepalive.

By default, set to 2.

You can set it like:

docker run -d -p 80:8080 -e KEEP_ALIVE="20" myimage


Timeout for graceful workers restart.

Read more about it in the Gunicorn docs: graceful-timeout.

By default, set to 120.

You can set it like:

docker run -d -p 80:8080 -e GRACEFUL_TIMEOUT="20" myimage


The access log file to write to.

By default "-", which means stdout (print in the Docker logs).

If you want to disable ACCESS_LOG, set it to an empty value.

For example, you could disable it with:

docker run -d -p 80:8080 -e ACCESS_LOG= myimage


The error log file to write to.

By default "-", which means stderr (print in the Docker logs).

If you want to disable ERROR_LOG, set it to an empty value.

For example, you could disable it with:

docker run -d -p 80:8080 -e ERROR_LOG= myimage


Any additional command line settings for Gunicorn can be passed in the GUNICORN_CMD_ARGS environment variable.

Read more about it in the Gunicorn docs: Settings.

These settings will have precedence over the other environment variables and any Gunicorn config file.

For example, if you have a custom TLS/SSL certificate that you want to use, you could copy them to the Docker image or mount them in the container, and set --keyfile and --certfile to the location of the files, for example:

docker run -d -p 80:8080 -e GUNICORN_CMD_ARGS="--keyfile=/secrets/key.pem --certfile=/secrets/cert.pem" -e PORT=443 myimage

Note: instead of handling TLS/SSL yourself and configuring it in the container, it's recommended to use a "TLS Termination Proxy" like Traefik. You can read more about it in the FastAPI documentation about HTTPS.


The path where to find the pre-start script.

By default, set to /app/

You can set it like:

docker run -d -p 80:8080 -e PRE_START_PATH="/custom/" 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/
  • /

Custom /app/

If you need to run anything before starting the app, you can add a file 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/ 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/ file run your Python script, with something like:

#! /usr/bin/env bash

# Run custom Python script before starting
python /app/

You can customize the location of the prestart script with the environment variable PRE_START_PATH described above.

Development live reload

The default program that is run is at / It does everything described above.

There's also a version for development with live auto-reload at:



For development, it's useful to be able to mount the contents of the application code inside of the container as a Docker "host volume", to be able to change the code and test it live, without having to build the image every time.

In that case, it's also useful to run the server with live auto-reload, so that it re-starts automatically at every code change.

The additional script / runs Uvicorn alone (without Gunicorn) and in a single process.

It is ideal for development.


For example, instead of running:

docker run -d -p 80:80 myimage

You could run:

docker run -d -p 80:80 -v $(pwd):/app myimage /
  • -v $(pwd):/app: means that the directory $(pwd) should be mounted as a volume inside of the container at /app.
    • $(pwd): runs pwd ("print working directory") and puts it as part of the string.
  • / adding something (like / at the end of the command, replaces the default "command" with this one. In this case, it replaces the default (/ with the development alternative /

Development live reload - Technical Details

As / doesn't run with Gunicorn, any of the configurations you put in a file won't apply.

But these environment variables will work the same as described above:

  • HOST
  • PORT


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

Release Notes

Latest Changes

  • 👷 Add support for Python 3.9 and Python 3.9 Alpine. PR #36 by @tiangolo.
  • 👷 Update Latest Changes GitHub Action. PR #26 by @tiangolo.
  • 📌 Add external dependencies to get automatic Dependabot upgrade PRs. PR #27 by @tiangolo.
  • 👷 Add GitHub Action latest-changes, update issue-manager, add funding. PR #22 by @tiangolo.


  • Add docs about installing and pinning dependencies. PR #19.
  • Add slim version. PR #18.
  • Fix testing race condition by sending the request first PR #17.
  • Update and refactor bringing all the new features from the base image. Includes:
    • Centralize, simplify, and deduplicate code and setup
    • Move CI to GitHub actions
    • Add Python 3.8 (and Alpine)
    • Add new configs and docs:
      • TIMEOUT
      • KEEP_ALIVE
      • ACCESS_LOG
      • ERROR_LOG
    • PR #16.
  • Disable pip cache during installation. PR #15.
  • Migrate local development from Pipenv to Poetry. PR #14.
  • Add docs for custom PRE_START_PATH env var. PR #13.


  • Refactor tests to use env vars and add image tags for each build date, like tiangolo/uvicorn-gunicorn-starlette:python3.7-2019-10-15. PR #8.
  • Upgrade Travis. PR #5.


  • Add support for live auto-reload with an additional custom script /, check the updated documentation. PR #6 in parent image.


  • Set WORKERS_PER_CORE by default to 1, as it shows to have the best performance on benchmarks.
  • Make the default web concurrency, when WEB_CONCURRENCY is not set, to a minimum of 2 workers. This is to avoid bad performance and blocking applications (server application) on small machines (server machine/cloud/etc). This can be overridden using WEB_CONCURRENCY. This applies for example in the case where WORKERS_PER_CORE is set to 1 (the default) and the server has only 1 CPU core. PR #4 and PR #5 in parent image.


  • Make / run independently, reading and generating used default environment variables. And remove / as it doesn't modify anything in the system, only reads environment variables. PR #4 in parent image.


  • Add support for /app/


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

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