How to use multiprocessing pool.map with multiple arguments?

Question

In the Python multiprocessing library, is there a variant of pool.map which supports multiple arguments?

text = "test"
def         


        

Answer 1

Using Python 3.3+ with pool.starmap():

from multiprocessing.dummy import Pool as ThreadPool 

def write(i, x):
    print(i, "---", x)

a = ["1","2","3"]
b = ["4","5","6"] 

pool = ThreadPool(2)
pool.starmap(write, zip(a,b)) 
pool.close() 
pool.join()

Result:

1 --- 4
2 --- 5
3 --- 6

You can also zip() more arguments if you like: zip(a,b,c,d,e)

In case you want to have a constant value passed as an argument you have to use import itertools and then zip(itertools.repeat(constant), a) for example.

Answer 2

How to take multiple arguments:

def f1(args):
    a, b, c = args[0] , args[1] , args[2]
    return a+b+c

if __name__ == "__main__":
    import multiprocessing
    pool = multiprocessing.Pool(4) 

    result1 = pool.map(f1, [ [1,2,3] ])
    print(result1)

Answer 3

Another simple alternative is to wrap your function parameters in a tuple and then wrap the parameters that should be passed in tuples as well. This is perhaps not ideal when dealing with large pieces of data. I believe it would make copies for each tuple.

from multiprocessing import Pool

def f((a,b,c,d)):
    print a,b,c,d
    return a + b + c +d

if __name__ == '__main__':
    p = Pool(10)
    data = [(i+0,i+1,i+2,i+3) for i in xrange(10)]
    print(p.map(f, data))
    p.close()
    p.join()

Gives the output in some random order:

0 1 2 3
1 2 3 4
2 3 4 5
3 4 5 6
4 5 6 7
5 6 7 8
7 8 9 10
6 7 8 9
8 9 10 11
9 10 11 12
[6, 10, 14, 18, 22, 26, 30, 34, 38, 42]

Answer 4

From python 3.4.4, you can use multiprocessing.get_context() to obtain a context object to use multiple start methods:

import multiprocessing as mp

def foo(q, h, w):
    q.put(h + ' ' + w)
    print(h + ' ' + w)

if __name__ == '__main__':
    ctx = mp.get_context('spawn')
    q = ctx.Queue()
    p = ctx.Process(target=foo, args=(q,'hello', 'world'))
    p.start()
    print(q.get())
    p.join()

Or you just simply replace

pool.map(harvester(text,case),case, 1)

by:

pool.apply_async(harvester(text,case),case, 1)

Answer 5

A better solution for python2:

from multiprocessing import Pool
def func((i, (a, b))):
    print i, a, b
    return a + b
pool = Pool(3)
pool.map(func, [(0,(1,2)), (1,(2,3)), (2,(3, 4))])

2 3 4

1 2 3

0 1 2

out[]:

[3, 5, 7]

Answer 6

You can use the following two functions so as to avoid writing a wrapper for each new function:

import itertools
from multiprocessing import Pool

def universal_worker(input_pair):
    function, args = input_pair
    return function(*args)

def pool_args(function, *args):
    return zip(itertools.repeat(function), zip(*args))

Use the function function with the lists of arguments arg_0, arg_1 and arg_2 as follows:

pool = Pool(n_core)
list_model = pool.map(universal_worker, pool_args(function, arg_0, arg_1, arg_2)
pool.close()
pool.join()