Dockerfile
links
Supported tags and respective python3.8
,latest
(Dockerfile)python3.7
, (Dockerfile)python3.6
(Dockerfile)python3.8-slim
(Dockerfile)python3.8-alpine3.10
(Dockerfile)python3.7-alpine3.8
(Dockerfile)python3.6-alpine3.8
(Dockerfile)
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:python3.7-2019-10-15
.
uvicorn-gunicorn
Docker image with Uvicorn managed by Gunicorn for high-performance web applications in Python 3.6 and above with performance auto-tuning. Optionally in a slim version or based on Alpine Linux.
GitHub repo: https://github.com/tiangolo/uvicorn-gunicorn-docker
Docker Hub image: https://hub.docker.com/r/tiangolo/uvicorn-gunicorn/
Description
Python web applications running with Uvicorn (using the "ASGI" specification for Python asynchronous web applications) have shown to have some 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
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.
tiangolo/uvicorn-gunicorn
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.
Frameworks
This image was created to be the base image for:
But could be used as the base image to run any Python web application that uses the ASGI specification.
If you are creating a new Starlette web application you should use tiangolo/uvicorn-gunicorn-starlette instead.
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.
Note: Unless you are doing something more technically advanced, you probably should be using Starlette with tiangolo/uvicorn-gunicorn-starlette or FastAPI with tiangolo/uvicorn-gunicorn-fastapi.
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: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 "ASGI" application.
Then you can build your image from the directory that has your Dockerfile
, e.g:
docker build -t myimage ./
- Run a container based on your image:
docker run -d --name mycontainer -p 80:80 myimage
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/ (or equivalent, using your Docker host).
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 and Gunicorn.
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:python3.7
# Install Poetry
RUN curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | 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.
- Because it uses
- 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:
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
ormain
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 ASGI 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 {"message": "Hello world!"}
In this case api
would be the variable with the "ASGI 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
ormain: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
You can use the config file from this image as a starting point for yours.
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:
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 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 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
.
MAX_WORKERS
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.
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 to2
.
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
WORKER_CLASS
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 applications like FastAPI and 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
TIMEOUT
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 FastAPI and 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
KEEP_ALIVE
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
GRACEFUL_TIMEOUT
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
ACCESS_LOG
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
ERROR_LOG
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
GUNICORN_CMD_ARGS
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.
PRE_START_PATH
The path where to find the pre-start script.
By default, set to /app/prestart.sh
.
You can set it like:
docker run -d -p 80:8080 -e PRE_START_PATH="/custom/script.sh" 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
/app/prestart.sh
Custom 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
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 /start.sh
. It does everything described above.
There's also a version for development with live auto-reload at:
/start-reload.sh
Details
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 /start-reload.sh
runs Uvicorn alone (without Gunicorn) and in a single process.
It is ideal for development.
Usage
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 /start-reload.sh
-v $(pwd):/app
: means that the directory$(pwd)
should be mounted as a volume inside of the container at/app
.$(pwd)
: runspwd
("print working directory") and puts it as part of the string.
/start-reload.sh
: adding something (like/start-reload.sh
) at the end of the command, replaces the default "command" with this one. In this case, it replaces the default (/start.sh
) with the development alternative/start-reload.sh
.
Development live reload - Technical Details
As /start-reload.sh
doesn't run with Gunicorn, any of the configurations you put in a gunicorn_conf.py
file won't apply.
But these environment variables will work the same as described above:
MODULE_NAME
VARIABLE_NAME
APP_MODULE
HOST
PORT
LOG_LEVEL
Tests
All the image tags, configurations, environment variables and application options are tested.
Release Notes
Latest Changes
0.6.0
- Add docs about installing and pinning dependencies. PR #41.
- Add
slim
version. PR #40. - Remove leftover unneeded config for tests. PR #39.
- Add extra configs, tests, and docs for:
WORKER_CLASS
TIMEOUT
KEEP_ALIVE
GRACEFUL_TIMEOUT
ACCESS_LOG
ERROR_LOG
GUNICORN_CMD_ARGS
MAX_WORKERS
- PR #38
- Set up CI using GitHub actions, they provide more free instances, so builds finish faster (4 min vs 9 min). PR #37.
- Add support for Python 3.8. PR #36.
- Refactor tests to remove custom testing Dockerfiles, generate them during tests. PR #35.
- Refactor and simplify build process to reduce code duplication. PR #34.
- Disable
pip
cache during installation with--no-cache-dir
. PR #13 by @pmav99. - Migrate local development from Pipenv to Poetry. PR #31.
- Add tests and docs for custom
PRE_START_PATH
env var. PR #30. - Add support for custom
PRE_START_PATH
env var. PR #12 by @mgfinch.
0.5.0
- Refactor tests to use env vars and add image tags for each build date, like
tiangolo/uvicorn-gunicorn:python3.7-2019-10-15
. PR #15. - Update Gunicorn worker heartbeat directory to
/dev/shm
to improve performance. PR #9 by @wshayes. - Upgrade Travis. PR #7.
0.4.0
- Add support for live auto-reload with an additional custom script
/start-reload.sh
, check the updated documentation. PR #6.
0.3.0
- Set
WORKERS_PER_CORE
by default to1
, 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 usingWEB_CONCURRENCY
. This applies for example in the case whereWORKERS_PER_CORE
is set to1
(the default) and the server has only 1 CPU core. PR #5.
0.2.0
- Make
/start.sh
run independently, reading and generating used default environment variables. And remove/entrypoint.sh
as it doesn't modify anything in the system, only reads environment variables. PR #4.
0.1.2
- Whenever this image is built (and each of its tags/versions), trigger a build for the children images (FastAPI and Starlette).
0.1.0
- Add support for
/app/prestart.sh
.
License
This project is licensed under the terms of the MIT license.