Python: How to use Value and Array in Multiprocessing pool

Question

For multiprocessing with Process, I can use Value, Array by setting args param.

With multiprocessing with Pool, how can I use Value, Array. There is nothing in the docs on how to do this.

from multiprocessing import Process, Value, Array

def f(n, a):
    n.value = 3.1415927
    for i in range(len(a)):
        a[i] = -a[i]

if __name__ == '__main__':
    num = Value('d', 0.0)
    arr = Array('i', range(10))

    p = Process(target=f, args=(num, arr))
    p.start()
    p.join()

    print(num.value)
    print(arr[:])

I am trying to use Value, Array within the code snippet below.

import multiprocessing


def do_calc(data):
    #  access num or 
    #  work to update arr
    newdata =data * 2
    return newdata

def start_process():
    print 'Starting', multiprocessing.current_process().name

if __name__ == '__main__':
    num             = Value('d', 0.0)
    arr             = Array('i', range(10))  
    inputs          = list(range(10))
    print 'Input   :', inputs

    pool_size       = multiprocessing.cpu_count() * 2
    pool            = multiprocessing.Pool(processes=pool_size,initializer=start_process, )
    pool_outputs    = pool.map(do_calc, inputs)
    pool.close() # no more tasks
    pool.join()  # wrap up current tasks

    print 'Pool    :', pool_outputs

Answer 1

I never knew "the reason" for this, but multiprocessing (mp) uses different pickler/unpickler mechanisms for functions passed to most Pool methods. It's a consequence that objects created by things like mp.Value, mp.Array, mp.Lock, ..., can't be passed as arguments to such methods, although they can be passed as arguments to mp.Process and to the optional initializer function of mp.Pool(). Because of the latter, this works:

import multiprocessing as mp

def init(aa, vv):
    global a, v
    a = aa
    v = vv

def worker(i):
    a[i] = v.value * i

if __name__ == "__main__":
    N = 10
    a = mp.Array('i', [0]*N)
    v = mp.Value('i', 3)
    p = mp.Pool(initializer=init, initargs=(a, v))
    p.map(worker, range(N))
    print(a[:])

and that prints

[0, 3, 6, 9, 12, 15, 18, 21, 24, 27]

That's the only way I know of to get this to work across platforms.

On Linux-y platforms (where mp creates new processes via fork()), you can instead create your mp.Array and mp.Value (etc) objects as module globals any time before you do mp.Pool(). Processes created by fork() inherit whatever is in the module global address space at the time mp.Pool() executes.

But that doesn't work at all on platforms (read "Windows") that don't support fork().