Running Image Manipulation in run_in_executor. Adapting to multiprocessing

Question

Hey so I run lots of Image Manipulation on an api built using fastapi async. I would like to be able to run the Image Manipulation asynchronously. As a result I used run_in_executor which I believe runs it in a seperate thread. However I was told that using python multiprocessing is better instead. Does moving have any advantages?.

import asyncio
import functools

from app.exceptions.errors import ManipulationError


def executor(function):
    @functools.wraps(function)
    def decorator(*args, **kwargs):
        try:
            partial = functools.partial(function, *args, **kwargs)
            loop = asyncio.get_event_loop()
            return loop.run_in_executor(None, partial)
        except Exception:
            raise ManipulationError("Uanble To Manipulate Image")

    return decorator

I made this decorator to wrap my blocking funcs as run in executor.

two questions

a) Does moving to multiprocesisng have any advantages

b) How would I do so

Answer 1

a) Does moving to multiprocesisng have any advantages

Yes, it utilizes multiple cores in case of CPU-bound processing.

b) How would I do so

By passing an instance of ProcessPoolExecutor to run_in_executor. (The None value you're passing now means use the default executor provided by asyncio, which is a ThreadPoolExecutor.) For example (untested):

_pool = concurrent.futures.ProcessPoolExecutor()

def executor(function):
    @functools.wraps(function)
    def decorator(*args):
        loop = asyncio.get_event_loop()
        return loop.run_in_executor(_pool, function, *args)

    return decorator

This will also require that all arguments to the function be serializable, so that they can be transferred to the subprocess.