python3.9
,latest
(*)python3.9-slim
(*)python3.9-alpine
(*)python3.8
python3.8-slim
python3.8-alpine
python3.7
python3.7-slim
python3.7-alpine
Docker image with Hypercorn for FastAPI application in Python 3.7+. With slim and alpine options.
Hypercorn is an HTTP2 ready ASGI web server based on the sans-io hyper, h11, h2, and wsproto libraries and inspired by Gunicorn.
Hypercorn supports HTTP/1, HTTP/2, WebSockets (over HTTP/1 and HTTP/2), ASGI/2, and ASGI/3 specifications. Hypercorn can utilise asyncio, uvloop, or trio worker types.
FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+.
The key features are:
- Fast: Very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic).
- Fast to code: Increase the speed to develop features by about 300% to 500% *.
- Less bugs: Reduce about 40% of human (developer) induced errors. *
- Intuitive: Great editor support. Completion everywhere. Less time debugging.
- Easy: Designed to be easy to use and learn. Less time reading docs.
- Short: Minimize code duplication. Multiple features from each parameter declaration. Less bugs.
- Robust: Get production-ready code. With automatic interactive documentation.
- Standards-based: Based on (and fully compatible with) the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema.
* estimation based on tests on an internal development team, building production applications.
- You can use this image as a base image for other images, using this in your Dockerfile:
FROM bynect/hypercorn-fastapi:python3.8-slim
COPY ./app /app
It will expect a file either at /app/app/main.py
and /app/main
containing the variable app
containing your FastAPI application.
Then you can build you Dockerfile, e.g:
$ docker build -t myimage ./
These are the environment variables that you can set in the container to configure it and their default values. You can set alternative values for them either from shell or from Dockerfile, e.g:
#from shell
$ docker run -d -p 80:80 -e MODULE_NAME="custom_app.custom_main" myimage
#from Dockerile
FROM bynect/hypercorn-fastapi:python3.8-slim
ENV MODULE_NAME="custom_app.custom_main"
COPY ./app /app
The Python "module" (file) to be imported by Hypercorn, 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
The variable inside of the Python module that contains the FastAPI 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 {"Hello": "World"}
In this case api
would be the variable with the FastAPI 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 Hypercorn.
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
The path to a Hypercorn Python configuration file.
By default:
/app/app/hypercorn_conf.py
if file exists/app/hypercorn_conf.py
if file exists/hypercorn_conf.py
included file
* ordered by priority.
You can set it like:
$ docker run -d -p 80:80 -e GUNICORN_CONF="/app/custom_gunicorn_conf.py" myimage
Note: that HYPERCORN_CONF
needs the prefix file:
for Python file, python:
for Python module and no prefix for TOML file.
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 FastAPI 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.
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 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.
The "host" used by Hypercorn, the IP where Hypercorn 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
The tcp port the container should listen on when USE_TCP
is set to true.
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 TCP_PORT="8080" myimage
If Hypercorn will use ssl-related options. When false ssl-related options are not used.
By default is set to:
false
Depends on
CA_CERTS
-CERTFILE
-KEYFILE
At least one ofUSE_SSL
andUSE_TCP
MUST be set to true.
If Hypercorn will use tcp-related options. When false tcp-related options are not used.
By default is set to:
true
At least one of
USE_SSL
andUSE_TCP
MUST be set to true.
The ssl port the container should listen on when USE_SSL
is set to true.
If you are running your container in a restrictive environment that forces you to use some specific port (like 8000
) you can set it with this variable.
By default:
443
You can set it like:
$ docker run -d -p 443:8000 -e SSL_PORT="8000" myimage
Depens on
USE_SSL
The actual host and port passed to Hypercorn.
If USE_SSL
is set to true the default value will be based on HOST
and SSL_PORT
.
So, if you didn't change anything, it will be set by default to:
0.0.0.0:443
Otherwise, if USE_SSL
is not set to true, the value will be based on HOST
and TCP_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
The host and port passed to Hypercorn as fallback in HTTPS connections.
If USE_SSL
and USE_TCP
are both true the default value is based on the variables HOST
and TCP_PORT
.
So, if you didn't change anything, it will be set by default to:
0.0.0.0:80
Otherwise, if USE_SSL
is not set to true or USE_TCP
is set to false, the value will be set to None
.
You can manually set only when the aforementioned conditions are true.
Depens on
USE_SSL
andUSE_TCP
Quic bind to be used instead of bind. By default it's not set.
You can set it like:
$ docker run -d -p 80:8080 -e QUIC_BIND="0.0.0.0:8080" myimage
The log level for Hypercorn.
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 worker class to be used by Hypercorn.
By default, set to asyncio
.
The three avaible values are:
asyncio
uvloop
trio
You can set it like:
$ docker run -d -p 80:8080 -e WORKER_CLASS="uvloop" myimage
Path to CA certificate file. By default it's not set.
Depends on
USE_SSL
Path to CA certificate file. By default it's not set.
Depends on
USE_SSL
Path to CA certificate file. By default it's not set.
Depends on
USE_SSL
Ciphers used by ssl connection. By default:
"ECDHE+AESGCM"
Depends on
USE_SSL
The number of seconds to wait for requests on a Keep-Alive connection.
By default, set to 5
.
You can set it like:
$ docker run -d -p 80:8080 -e KEEP_ALIVE="20" myimage
Timeout for graceful workers restart.
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
The maximum number of pending connections. By default set to 100
.
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
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.
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.
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/app/hypercorn_conf.py
/app/hypercorn_conf.py
/hypercorn_conf.py
* ordered by priority.
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
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 Hypercorn with 1 asyncio
worker.
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 /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
.
As /start-reload.sh
runs Hypercorn for debug/development purpose it doesn't use hypercorn_config file.
But these environment variables will work the same as described above:
MODULE_NAME
VARIABLE_NAME
APP_MODULE
HOST
TCP_PORT
(only tcp avaible)LOG_LEVEL
The included /hypercorn_conf.py
has some options that accepts boolean value.
These are the valid values. Invalid values will raise an exception.
Falsy values (compared after lowered):
"no"
"n"
"0"
"false"
Truly values (compared after lowered):
"yes"
"y"
"1"
"true"
Python 3.9 is now supported, but some optional packages are not installed due to incompatible Python version.
Incompatible packages:
trio
(hypercorn[trio]
)
Licensed under MIT License.
Based on tiangolo/uvicorn-gunicorn-docker