Control the number of subprocesses using to call external commands in python

Question

I understand using subprocess is the preferred way of calling external command.

But what if I want to run several commands in parall, but limit the number of processes being spawned? What bothers me is that I can't block the subprocesses. For example, if I call

subprocess.Popen(cmd, stderr=outputfile, stdout=outputfile)

Then the process will continue, without waiting for cmd to finish. Therefore, I can't wrap it up in a worker of multiprocessing library.

For example, if I do:

def worker(cmd): 
    subprocess.Popen(cmd, stderr=outputfile, stdout=outputfile);

pool = Pool( processes = 10 );
results =[pool.apply_async(worker, [cmd]) for cmd in cmd_list];
ans = [res.get() for res in results];

then each worker will finish and return after spawning a subprocess. So I can't really limit the number of processes generated by subprocess by using Pool.

What's the proper way of limiting the number of subprocesses?

Answer 1

You can use subprocess.call if you want to wait for the command to complete. See pydoc subprocess for more information.

You could also call the Popen.wait method in your worker:

def worker(cmd): 
    p = subprocess.Popen(cmd, stderr=outputfile, stdout=outputfile);
    p.wait()

---

Because there seems to be some confusion about this answer, here's a complete example:

import concurrent.futures
import multiprocessing
import random
import subprocess


def worker(workerid):
    print(f"start {workerid}")
    p = subprocess.Popen(["sleep", f"{random.randint(1,30)}"])
    p.wait()
    print(f"stop {workerid}")
    return workerid


def main():
    tasks = []
    with concurrent.futures.ThreadPoolExecutor(max_workers=20) as pool:
        for i in range(20):
            tasks.append(pool.submit(worker, i))

        print("waiting for tasks...", flush=True)
        for task in concurrent.futures.as_completed(tasks):
            print(f"completed {task.result()}", flush=True)
        print("done.")


if __name__ == "__main__":
    main()

If you run the above code, you will see that all of the worker processes start in parallel and that we are able to gather values as they are completed.