I\'m breaking my teeth on multiprocessing within Python but I\'m not having any luck wrapping my head around the subject. Basically I have a procedure that is time consuming
Maybe something like this is what you're looking for? Keep in mind I'm writing for Python 3. Your print statement above is Python 2, in which case a side note would be to use xrange instead of range.
from argparse import ArgumentParser
from random import random
from subprocess import Popen
from sys import exit
from time import sleep
def add_something(i):
# Sleep to simulate the long calculation
sleep(random() * 30)
return i + 1
def run_my_process():
# Start up all of the processes, pass i as command line argument
# since you have your function in the same file, we'll have to handle that
# inside 'main' below
processes = []
for i in range(100):
processes.append(Popen(['python', 'thisfile.py', str(i)]))
# Wait for your desired process result
# Might want to add a short sleep to the loop
done = False
while not done:
for proc in processes:
returncode = proc.poll()
if returncode == 90:
done = True
break
# Kill any process that are still running
for proc in processes:
if proc.returncode is None:
# Might run into a race condition here,
# so might want to wrap with try block
proc.kill()
if __name__ == '__main__':
# Look for optional i argument here
parser = ArgumentParser()
parser.add_argument('i', type=int, nargs='?')
i = parser.parse_args().i
# If there isn't an i, then run the whole thing
if i is None:
run_my_process()
else:
# Otherwise, run your expensive calculation and return the result
returncode = add_something(i)
print(returncode)
exit(returncode)
EDIT:
Here's a somewhat cleaner version that uses the multiprocessing module instead of subprocess:
from random import random
from multiprocessing import Process
from sys import exit
from time import sleep
def add_something(i):
# Sleep to simulate the long calculation
sleep(random() * 30)
exitcode = i + 1
print(exitcode)
exit(exitcode)
def run_my_process():
# Start up all of the processes
processes = []
for i in range(100):
proc = Process(target=add_something, args=[i])
processes.append(proc)
proc.start()
# Wait for the desired process result
done = False
while not done:
for proc in processes:
if proc.exitcode == 90:
done = True
break
# Kill any processes that are still running
for proc in processes:
if proc.is_alive():
proc.terminate()
if __name__ == '__main__':
run_my_process()
EDIT 2:
Here's one last version, which I think is much better than the other two:
from random import random
from multiprocessing import Pool
from time import sleep
def add_something(i):
# Sleep to simulate the long calculation
sleep(random() * 30)
return i + 1
def run_my_process():
# Create a process pool
pool = Pool(100)
# Callback function that checks results and kills the pool
def check_result(result):
print(result)
if result == 90:
pool.terminate()
# Start up all of the processes
for i in range(100):
pool.apply_async(add_something, args=[i], callback=check_result)
pool.close()
pool.join()
if __name__ == '__main__':
run_my_process()