CODEWITHSUNDEEP
pythonmultiprocessing
Hooked
Hooked

Reputation: 88128

How can I restrict the scope of a multiprocessing process?

Using python's multiprocessing module, the following contrived example runs with minimal memory requirements:

import multiprocessing 
# completely_unrelated_array = range(2**25)

def foo(x):
    for x in xrange(2**28):pass
    print x**2

P = multiprocessing.Pool()

for x in range(8):
    multiprocessing.Process(target=foo, args=(x,)).start()

Uncomment the creation of the completely_unrelated_array and you'll find that each spawned process allocates the memory for a copy of the completely_unrelated_array! This is a minimal example of a much larger project that I can't figure out how to workaround; multiprocessing seems to make a copy of everything that is global. I don't need a shared memory object, I simply need to pass in x, and process it without the memory overhead of the entire program.

Side observation: What's interesting is that print id(completely_unrelated_array) inside foo gives the same value, suggesting that somehow that might not be copies...

Upvotes: 19

Views: 7576

Answers (2)

dano
dano

Reputation: 94881

Because of the nature of os.fork(), any variables in the global namespace of your __main__ module will be inherited by the child processes (assuming you're on a Posix platform), so you'll see the memory usage in the children reflect that as soon as they're created. I'm not sure if all that memory is really being allocated though, as far as I know that memory is shared until you actually try to change it in the child, at which point a new copy is made. Windows, on the other hand, doesn't use os.fork() - it re-imports the main module in each child, and pickles any local variables you want sent to the children. So, using Windows you can actually avoid the large global ending up copied in the child by only defining it inside an if __name__ == "__main__": guard, because everything inside that guard will only run in the parent process:

import time
import multiprocessing 


def foo(x):
    for x in range(2**28):pass
    print(x**2)

if __name__ == "__main__":
    completely_unrelated_array = list(range(2**25)) # This will only be defined in the parent on Windows
    P = multiprocessing.Pool()

    for x in range(8):
        multiprocessing.Process(target=foo, args=(x,)).start()

Now, in Python 2.x, you can only create new multiprocessing.Process objects by forking if you're using a Posix platform. But on Python 3.4, you can specify how the new processes are created, by using contexts. So, we can specify the "spawn" context, which is the one Windows uses, to create our new processes, and use the same trick:

# Note that this is Python 3.4+ only
import time
import multiprocessing 

def foo(x):
    for x in range(2**28):pass
    print(x**2)


if __name__ == "__main__":
    completely_unrelated_array = list(range(2**23))  # Again, this only exists in the parent
    ctx = multiprocessing.get_context("spawn") # Use process spawning instead of fork
    P = ctx.Pool()

    for x in range(8):
        ctx.Process(target=foo, args=(x,)).start()

If you need 2.x support, or want to stick with using os.fork() to create new Process objects, I think the best you can do to get the reported memory usage down is immediately delete the offending object in the child:

import time
import multiprocessing 
import gc

def foo(x):
    init()
    for x in range(2**28):pass
    print(x**2)

def init():
    global completely_unrelated_array
    completely_unrelated_array = None
    del completely_unrelated_array
    gc.collect()

if __name__ == "__main__":
    completely_unrelated_array = list(range(2**23))
    P = multiprocessing.Pool(initializer=init)

    for x in range(8):
        multiprocessing.Process(target=foo, args=(x,)).start()
    time.sleep(100)

Upvotes: 13

jtaylor
jtaylor

Reputation: 2424

What is important here is which platform you are targeting. Unix systems processes are created by using Copy-On-Write (cow) memory. So even though each process gets a copy of the full memory of the parent process, that memory is only actually allocated on a per page bases (4KiB)when it is modified. So if you are only targeting these platforms you don't have to change anything.

If you are targeting platforms without cow forks you may want to use python 3.4 and its new forking contexts spawn and forkserver, see the documentation These methods will create new processes which share nothing or limited state with the parent and all memory passing is explicit.

But not that that the spawned process will import your module so all global data will be explicitly copied and no copy-on-write is possible. To prevent this you have to reduce the scope of the data.

import multiprocessing  as mp
import numpy as np

def foo(x):
    import time
    time.sleep(60)

if __name__ == "__main__":
    mp.set_start_method('spawn')
    # not global so forks will not have this allocated due to the spawn method
    # if the method would be fork the children would still have this memory allocated
    # but it could be copy-on-write
    completely_unrelated_array = np.ones((5000, 10000))
    P = mp.Pool()
    for x in range(3):
        mp.Process(target=foo, args=(x,)).start()

e.g top output with spawn:

%MEM     TIME+ COMMAND
29.2   0:00.52 python3                                                
0.5   0:00.00 python3    
0.5   0:00.00 python3    
0.5   0:00.00 python3

and with fork:

%MEM     TIME+ COMMAND
29.2   0:00.52 python3                                                
29.1   0:00.00 python3    
29.1   0:00.00 python3                                                
29.1   0:00.00 python3

note how its more than 100%, due to copy-on-write

Upvotes: 5

Related Questions