sanic/docs/sanic/deploying.md

Deploying

Deploying Sanic is made simple by the inbuilt webserver. After defining an instance of sanic.Sanic, we can call the run method with the following keyword arguments:

• host (default "127.0.0.1"): Address to host the server on.
• port (default 8000): Port to host the server on.
• debug (default False): Enables debug output (slows server).
• ssl (default None): SSLContext for SSL encryption of worker(s).
• sock (default None): Socket for the server to accept connections from.
• workers (default 1): Number of worker processes to spawn.
• loop (default None): An asyncio-compatible event loop. If none is specified, Sanic creates its own event loop.
• protocol (default HttpProtocol): Subclass of asyncio.protocol.

Workers

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 run arguments.

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 named 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 sanic.worker.GunicornWorker for Gunicorn worker-class argument:

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.

Asynchronous support

This is suitable if you need to share the sanic process with other applications, in particular the loop. 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()