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For CPU-bound Python work, let asyncio coordinate tasks on its event-loop thread and send the computation to a ProcessPoolExecutor. On Linux, check your Python version and multiprocessing start method: Python 3.14 changed the POSIX default from fork to forkserver. For external programs, use asyncio’s subprocess APIs instead; they solve a different problem.
Choose the right meaning of “async multiprocessing”
The phrase can describe two different patterns. In one, an asyncio application submits Python functions to separate worker processes. In the other, asyncio launches and monitors external programs. Choose based on what is doing the work:
| 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 | Awaiting the submitted work’s result; the callable runs in a worker process, not as an asyncio coroutine |
asyncio.create_subprocess_exec |
Running a known executable with separate arguments | Asynchronous communication with the child and waiting for its completion |
asyncio.create_subprocess_shell |
Commands that genuinely need shell syntax | Asynchronous communication with a shell-launched process; the shell adds parsing and quoting risks |
Do not call a CPU-heavy synchronous function directly from an event-loop task: while that function runs, it prevents the loop from promptly handling other tasks and I/O. Python’s asyncio development guide says blocking CPU-bound code should not be called directly and recommends an executor for this work.
Check Linux’s multiprocessing start method
The start method determines how worker processes are created and what they inherit. On Linux, do not assume that fork is the default: Python 3.14 changed the default on POSIX systems, including Linux, to forkserver. Python 3.14 states that fork is no longer the default on any platform. Review the Python 3.14.8 multiprocessing documentation and check the actual version and context in your deployment.
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| Method | Practical implications |
|---|---|
spawn |
Starts a fresh interpreter and inherits fewer resources from the parent, but has startup overhead. Worker functions and arguments must meet importability and pickling requirements. |
fork |
Creates a child from the parent process and inherits its resources. Python warns that safely forking a multithreaded process is problematic. Since Python 3.12, Python may emit a DeprecationWarning when it can detect multiple threads and fork is selected. |
forkserver |
Delegates process creation to a server process. It is the POSIX default beginning in Python 3.14, including on Linux, and has importability and pickling requirements similar to spawn. |
Choose a context deliberately if your supported Python versions, deployment mode, or dependencies require a particular behavior. Avoid changing a global start-method setting casually: multiprocessing 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 also notes that spawn and forkserver generally cannot be used with frozen executables on POSIX. They use a resource tracker for named resources such as semaphores and shared memory, so abrupt signal termination can leave resources that need attention.
Run Python CPU work in a process pool
Keep the worker function at module scope, use an import-safe entry point, and submit data the selected start method can serialize. Create and manage the executor in the application’s main coroutine or another explicit application scope. This minimal pattern returns the square of 12:
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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 entry-point guard matters because spawn and forkserver need to import the module without rerunning application startup code. Keep submitted functions and their inputs importable and picklable under the chosen context. Pass resources explicitly rather than assuming a worker can safely use parent-process globals.
The example leaves the executor’s context at its default. For an application that needs to pin a context, ProcessPoolExecutor accepts a mp_context argument; use a multiprocessing context appropriate to the application’s supported versions and deployment. If you are writing a library that uses multiprocessing internally, accept a caller-provided context rather than imposing one, as the multiprocessing documentation recommends.
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Shut down workers as part of application cleanup
Do not leave pool lifetime to interpreter finalization. The context manager in the example closes the executor when its block exits. With the lower-level multiprocessing APIs, use a context manager or explicitly manage the pool with close() and, when appropriate, terminate(). Python warns that unmanaged pools can hang during finalization. In a long-running asyncio service, put executor creation and shutdown in the application’s explicit startup and shutdown lifecycle, rather than creating a new pool for every small task.
Launch external programs asynchronously
For a program such as an image converter, compiler, or command-line utility, prefer asyncio.create_subprocess_exec with the executable and its arguments supplied separately. That avoids asking a shell to parse a constructed command string. Retain the returned Process object while the program runs, then use its asynchronous communication or waiting methods:
Rank #4
import asyncio
async def run_program() -> None:
proc = await asyncio.create_subprocess_exec(
"python3", "-c", "print('done')",
stdout=asyncio.subprocess.PIPE,
stderr=asyncio.subprocess.PIPE,
)
stdout, stderr = await proc.communicate()
print(stdout.decode().strip())
if proc.returncode != 0:
raise RuntimeError(stderr.decode())communicate() reads the configured output streams and waits for completion. If you do not need to collect output, you can await proc.wait(). Keep a reference to the process object until it finishes: the Python 3.14.8 asyncio subprocess documentation warns that garbage collection of a still-running process object kills the child.
Use a shell only when shell syntax is necessary
asyncio.create_subprocess_shell runs a shell command string. Whitespace, quotes, substitutions, and other special characters are interpreted by the shell, so never interpolate untrusted input into that string unsafely. Python places responsibility for appropriate quoting on the application and identifies shlex.quote() as a way to quote constructed shell strings. Prefer the argument-list interface when shell features are not needed.
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Check deployment constraints before choosing
- Python version: Python 3.14 uses
forkserveras the POSIX default; older Linux deployments may select a different default. Verify the deployed interpreter and context. - Threads in the parent: Treat
forkcautiously in a multithreaded application; Python explicitly warns that safely forking one is problematic. - Packaging: If distributing a frozen POSIX executable, check the documented limitations of
spawnandforkserver. - Shared resources: Ensure locks and other multiprocessing objects come from a context compatible with the children that receive them.
- Failure cleanup: Plan how workers and named resources are cleaned up if the application or a child is terminated abruptly.
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