Python sharing a lock between processes

Question

I am attempting to use a partial function so that pool.map() can target a function that has more than one parameter (in this case a Lock() object).

Here is example code (taken from an answer to a previous question of mine):

from functools import partial  def target(lock, iterable_item):     for item in items:         # Do cool stuff         if (... some condition here ...):             lock.acquire()             # Write to stdout or logfile, etc.             lock.release()  def main():     iterable = [1, 2, 3, 4, 5]     pool = multiprocessing.Pool()     l = multiprocessing.Lock()     func = partial(target, l)     pool.map(func, iterable)     pool.close()     pool.join() 

However when I run this code, I get the error:

Runtime Error: Lock objects should only be shared between processes through inheritance. 

What am I missing here? How can I share the lock between my subprocesses?

Answer 1

You can't pass normal multiprocessing.Lock objects to Pool methods, because they can't be pickled. There are two ways to get around this. One is to create Manager() and pass a Manager.Lock():

def main():     iterable = [1, 2, 3, 4, 5]     pool = multiprocessing.Pool()     m = multiprocessing.Manager()     l = m.Lock()     func = partial(target, l)     pool.map(func, iterable)     pool.close()     pool.join() 

This is a little bit heavyweight, though; using a Manager requires spawning another process to host the Manager server. And all calls to acquire/release the lock have to be sent to that server via IPC.

The other option is to pass the regular multiprocessing.Lock() at Pool creation time, using the initializer kwarg. This will make your lock instance global in all the child workers:

def target(iterable_item):     for item in items:         # Do cool stuff         if (... some condition here ...):             lock.acquire()             # Write to stdout or logfile, etc.             lock.release() def init(l):     global lock     lock = l  def main():     iterable = [1, 2, 3, 4, 5]     l = multiprocessing.Lock()     pool = multiprocessing.Pool(initializer=init, initargs=(l,))     pool.map(target, iterable)     pool.close()     pool.join() 

The second solution has the side-effect of no longer requiring partial.