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Use asyncio to coordinate asynchronous I/O, a process pool to run CPU-bound Python functions, and asyncio subprocess APIs to manage external programs. On Linux, check your Python version and multiprocessing start method: Python 3.14 changed the POSIX default from fork to forkserver. The safe choice depends on how your application starts, what it passes to workers, and whether it needs to launch Python functions or other programs.
First, choose the kind of work you need to run
“Async multiprocessing” can describe two different patterns. In the first, an asyncio event loop submits Python functions to a process pool and awaits their results. In the second, asyncio launches and monitors external subprocesses. These approaches solve different problems; neither means that CPU-heavy synchronous work should run on the event-loop thread.
| Approach | Use it for | What asyncio manages |
|---|---|---|
ProcessPoolExecutor with loop.run_in_executor |
CPU-bound Python callables that can be imported and whose arguments and results can be serialized as required by the selected start method. | The future representing the submitted function’s result. The worker runs in another process; it does not run an asyncio coroutine for you. |
asyncio.create_subprocess_exec |
Running a known executable with an argument list. | Asynchronous communication with the child and waiting for it to finish. |
asyncio.create_subprocess_shell |
Commands that genuinely need shell syntax. | Asynchronous management of the shell process; your application must also handle shell quoting safely. |
For CPU-bound Python code, running a synchronous function directly in the event loop blocks that thread and delays other tasks and I/O. Python’s asyncio development guide advises that blocking CPU-bound code should not be called directly and recommends using an executor, including a process pool when appropriate.
Check the multiprocessing start method before relying on defaults
On Linux, the default changed with Python 3.14. The Python 3.14.8 multiprocessing documentation says forkserver is now the default on POSIX, which includes Linux; fork is no longer the default on any platform. Older instructions that assume Linux always defaults to fork are therefore version-dependent.
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The start method determines how worker processes are created and affects startup, inherited resources, and code requirements:
spawn: Starts a fresh Python interpreter and inherits fewer resources from the parent. It has more startup overhead thanfork. Worker functions and arguments must meet the method’s importability and pickling requirements.fork: Creates a child that initially resembles its parent and inherits its resources. Python warns, “Note that safely forking a multithreaded process is problematic.” Since an asyncio application may coexist with other threads, do not treatforkas automatically safe.forkserver: Uses a server process to create workers and is the Python 3.14 POSIX default. It also requires code and arguments suitable for its process-creation and serialization rules.
Python may issue a DeprecationWarning when it can detect multiple threads and fork is selected, starting in Python 3.12. Check the Python version and the context your application actually uses; choose a method deliberately when compatibility or deployment requirements make the default unsuitable.
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Run CPU-bound Python functions through a process pool
Define workers at module scope, keep process creation behind the main-module guard, and create the executor in an application scope with a clear shutdown path. This illustrative pattern uses ProcessPoolExecutor with its default context; adapt the context and lifecycle to the Python versions and deployment modes you support.
import asyncio
from concurrent.futures import ProcessPoolExecutor
def cpu_work(value: int) -> int:
return value * value
async def main() -> None:
loop = asyncio.get_running_loop()
with ProcessPoolExecutor() as pool:
result = await loop.run_in_executor(pool, cpu_work, 12)
print(result)
if __name__ == "__main__":
asyncio.run(main())The awaited call returns 144 for this worker. The function runs in a worker process rather than blocking the event-loop thread while it computes. The example does not specify an explicit multiprocessing context, so the selected default can vary by Python version and platform.
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Make worker inputs safe to transfer
With spawn and forkserver, submitted functions need to be importable by the child, and arguments must be picklable. Keep worker functions at module scope rather than defining them inside a coroutine or another function. Pass required data explicitly instead of relying on parent-process globals or inherited resources.
If an application or library uses multiprocessing internally, context choices should be compatible across the components that share multiprocessing objects. Python notes that objects from different contexts may not be compatible; for example, a lock created in a fork context cannot be passed to a spawn or forkserver child. Python’s context guidance recommends that libraries allow users to supply a context rather than imposing one.
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Make executor shutdown part of application shutdown
The example’s with block scopes the executor and shuts it down after the awaited task completes. In a longer-running application, manage the executor in an explicit application lifecycle and shut it down as part of orderly shutdown; do not create unmanaged pools and leave cleanup to interpreter finalization. If using the lower-level multiprocessing.Pool API, use its context manager or explicitly close or terminate it. The multiprocessing documentation warns that unmanaged pools can hang during finalization.
Launch external programs with asyncio subprocess APIs
If the work is an external command rather than a Python function, use asyncio’s subprocess interface instead of submitting it to a process pool. Prefer create_subprocess_exec when you can name the executable and pass arguments separately:
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import asyncio
async def run_command() -> None:
process = await asyncio.create_subprocess_exec(
"python3", "--version",
stdout=asyncio.subprocess.PIPE,
stderr=asyncio.subprocess.PIPE,
)
stdout, stderr = await process.communicate()
print("exit status:", process.returncode)
print(stdout.decode().strip())
asyncio.run(run_command())communicate() reads the configured output streams and waits for the process to finish; wait() is also asynchronous. Keep a reference to the process object while the child is running. The asyncio subprocess documentation warns that garbage collection of a still-running process object kills the child.
Use a shell only when the command needs one
create_subprocess_shell adds shell parsing, which creates an input-quoting and injection risk. Python assigns the application responsibility for quoting whitespace and special characters to avoid shell injection vulnerabilities. Do not interpolate untrusted input into a shell command. If shell syntax is genuinely needed, construct the command carefully and use shlex.quote() for values that must be inserted into a shell string.
Account for deployment and resource cleanup
- Frozen executables on POSIX: The multiprocessing documentation says
spawnandforkservergenerally cannot be used with frozen executables on POSIX. Packaging can therefore affect which start method is practical. - Named shared resources:
spawnandforkserveruse a resource tracker for named resources such as semaphores and shared memory. Abrupt signal termination can leave resources that need attention. - Performance expectations: A process pool has startup and data-transfer costs. The official guidance does not establish a universal throughput advantage or benchmark; performance depends on the workload and deployment.
Use the current concurrent.futures reference alongside the multiprocessing and asyncio documentation when choosing executor behavior. For most applications, the practical decision is: submit serializable CPU-bound Python functions to a managed process pool, launch external tools with create_subprocess_exec, and reserve shell execution for commands that truly require shell syntax.
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