Mulitprocess Pools with different functions
Here is a working example of the idea shared by @Rayamon:
import functools
from multiprocessing import Pool
def a(param1, param2, param3):
return param1 + param2 + param3
def b(param1, param2):
return param1 + param2
def smap(f):
return f()
func1 = functools.partial(a, 1, 2, 3)
func2 = functools.partial(b, 1, 2)
pool = Pool(processes=2)
res = pool.map(smap, [func1, func2])
pool.close()
pool.join()
print(res)
To pass different functions, you can simply call map_async
multiple times.
Here is an example to illustrate that,
from multiprocessing import Pool
from time import sleep
def square(x):
return x * x
def cube(y):
return y * y * y
pool = Pool(processes=20)
result_squares = pool.map_async(f, range(10))
result_cubes = pool.map_async(g, range(10))
The result will be:
>>> print result_squares.get(timeout=1)
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
>>> print result_cubes.get(timeout=1)
[0, 1, 8, 27, 64, 125, 216, 343, 512, 729]
They will not run in parallel. See following code:
def updater1(q,i):
print "UPDATER 1:", i
return
def updater2(q,i):
print "UPDATER2:", i
return
if __name__=='__main__':
a = range(10)
b=["abc","def","ghi","jkl","mno","pqr","vas","dqfq","grea","qfwqa","qwfsa","qdqs"]
pool = multiprocessing.Pool()
func1 = partial(updater1,q)
func2 = partial(updater2,q)
pool.map_async(func1, a)
pool.map_async(func2, b)
pool.close()
pool.join()
The above code yields the following printout:
UPDATER 1: 1
UPDATER 1: 0
UPDATER 1: 2
UPDATER 1: 3
UPDATER 1: 4
UPDATER 1: 5
UPDATER 1: 6
UPDATER 1: 7
UPDATER 1: 8
UPDATER 1: 9
UPDATER2: abc
UPDATER2: def
UPDATER2: ghi
UPDATER2: jkl
UPDATER2: mno
UPDATER2: pqr
UPDATER2: vas
UPDATER2: dqfq
UPDATER2: grea
UPDATER2: qfwqa
UPDATER2: qwfsa
UPDATER2: qdqs
You can use map or some lambda function (edit: actually you can't use a lambda function). You can use a simple map function:
def smap(f, *args):
return f(*args)
pool = multiprocessing.Pool(processes=30)
res=pool.map(smap, function_list, args_list1, args_list2,...)
The normal map function takes iterables as inputs, which is inconvenient.