Deploying Sanic is made simple by the inbuilt webserver. After defining an
sanic.Sanic, we can call the
run method with the following
"127.0.0.1"): Address to host the server on.
8000): Port to host the server on.
False): Enables debug output (slows server).
SSLContextfor SSL encryption of worker(s).
None): Socket for the server to accept connections from.
1): Number of worker processes to spawn.
asyncio-compatible event loop. If none is specified, Sanic creates its own event loop.
HttpProtocol): Subclass of asyncio.protocol.
By default, Sanic listens in the main process using only one CPU core. To crank
up the juice, just specify the number of workers in the
app.run(host='0.0.0.0', port=1337, workers=4)
Sanic will automatically spin up multiple processes and route traffic between them. We recommend as many workers as you have available cores.
Running via command
If you like using command line arguments, you can launch a Sanic server by
executing the module. For example, if you initialized Sanic as
app in a file
server.py, you could run the server like so:
python -m sanic server.app --host=0.0.0.0 --port=1337 --workers=4
With this way of running sanic, it is not necessary to invoke
app.run in your
Python file. If you do, make sure you wrap it so that it only executes when
directly run by the interpreter.
if __name__ == '__main__': app.run(host='0.0.0.0', port=1337, workers=4)
Running via Gunicorn
Gunicorn ‘Green Unicorn’ is a WSGI HTTP Server for UNIX. It’s a pre-fork worker model ported from Ruby’s Unicorn project.
In order to run Sanic application with Gunicorn, you need to use the special
gunicorn myapp:app --bind 0.0.0.0:1337 --worker-class sanic.worker.GunicornWorker
If your application suffers from memory leaks, you can configure Gunicorn to gracefully restart a worker after it has processed a given number of requests. This can be a convenient way to help limit the effects of the memory leak.
See the Gunicorn Docs for more information.
This is suitable if you need to share the sanic process with other applications, in particular the
However be advised that this method does not support using multiple processes, and is not the preferred way
to run the app in general.
Here is an incomplete example (please see
run_async.py in examples for something more practical):
server = app.create_server(host="0.0.0.0", port=8000) loop = asyncio.get_event_loop() task = asyncio.ensure_future(server) loop.run_forever()