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Docker image with Uvicorn managed by Gunicorn for high-performance Starlette web applications in Python 3.7 and 3.6 with performance auto-tuning. Optionally with Alpine Linux.
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python3.6 🎉 Add Starlette images for Python 3.6, 3.7. Alpine, docs and tests. Jan 4, 2019
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README.md

Build Status

Supported tags and respective Dockerfile links

uvicorn-gunicorn-starlette

Docker image with Uvicorn managed by Gunicorn for high-performance Starlette 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-starlette-docker

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

Description

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

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.

Starlette

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

tiangolo/uvicorn-gunicorn-starlette

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-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.

tiangolo/uvicorn-gunicorn-fastapi

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:python3.7

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 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:python3.7

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

app = Starlette()


@app.route("/")
async def homepage(request):
    return JSONResponse({"message": "Hello World!"})
  • 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 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: http://192.168.99.100/ or http://127.0.0.1/ (or equivalent, using your Docker host).

You will see something like:

{"message": "Hello World!"}

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 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()


@api.route("/")
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

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:

  • 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.

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 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.

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

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): runs pwd ("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.

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

0.4.0

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

0.3.0

  • 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.

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 in parent image.

0.1.0

  • Add support for /app/prestart.sh.

License

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

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