Question

多进程工作池的大多数示例在不同的进程中执行单个函数，例如

def foo(args):
   pass

if __name__ == '__main__':
   pool = multiprocessing.Pool(processes=30)
   res=pool.map_async(foo,args)

有没有办法在池中处理两个不同的独立功能？这样你就可以分配f.e. foo（）的15个进程和bar（）的15个进程或者是一个绑定到单个函数的池？或者你必须使用

手动为不同的功能创建不同的过程

 p = Process(target=foo, args=(whatever,))
 q = Process(target=bar, args=(whatever,))
 q.start()
 p.start()

忘了工作人员？

Answer 1

要传递不同的功能，您只需多次拨打map_async。

这是一个例子来说明，

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))

结果将是：

>>> 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]

Answer 2

他们不并行运行。请参阅以下代码：

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()

以上代码产生以下打印输出：

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

Answer 3

以下是@Rayamon所分享想法的有效示例：

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)

Answer 4

为进一步说明上述其他答案，下面是一个示例：

使用Pool（平方函数）并行运行具有多个输入的单个函数有趣的一面，请注意“ 5 981 25”行上的错误操作
使用不同的输入（args和kwargs）运行多个函数，并使用Pool（pf1，pf2，pf3函数）收集其结果

import datetime
import multiprocessing
import time
import random

from multiprocessing import Pool

def square(x):
    # calculate the square of the value of x
    print(x, x*x)
    return x*x

def pf1(*args, **kwargs):
    sleep_time = random.randint(3, 6)
    print("Process : %s\tFunction : %s\tArgs: %s\tsleeping for %d\tTime : %s\n" % (multiprocessing.current_process().name, "pf1", args, sleep_time, datetime.datetime.now()))
    print("Keyword Args from pf1: %s" % kwargs)
    time.sleep(sleep_time)
    print(multiprocessing.current_process().name, "\tpf1 done at %s\n" % datetime.datetime.now())
    return (sum(*args), kwargs)

def pf2(*args):
    sleep_time = random.randint(7, 10)
    print("Process : %s\tFunction : %s\tArgs: %s\tsleeping for %d\tTime : %s\n" % (multiprocessing.current_process().name, "pf2", args, sleep_time, datetime.datetime.now()))
    time.sleep(sleep_time)
    print(multiprocessing.current_process().name, "\tpf2 done at %s\n" % datetime.datetime.now())
    return sum(*args)

def pf3(*args):
    sleep_time = random.randint(0, 3)
    print("Process : %s\tFunction : %s\tArgs: %s\tsleeping for %d\tTime : %s\n" % (multiprocessing.current_process().name, "pf3", args, sleep_time, datetime.datetime.now()))
    time.sleep(sleep_time)
    print(multiprocessing.current_process().name, "\tpf3 done at %s\n" % datetime.datetime.now())
    return sum(*args)

def smap(f, *arg):
    if len(arg) == 2:
        args, kwargs = arg
        return f(list(args), **kwargs)
    elif len(arg) == 1:
        args = arg
        return f(*args)


if __name__ == '__main__':

    # Define the dataset
    dataset = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]

    # Output the dataset
    print ('Dataset: ' + str(dataset))

    # Run this with a pool of 5 agents having a chunksize of 3 until finished
    agents = 5
    chunksize = 3
    with Pool(processes=agents) as pool:
        result = pool.map(square, dataset)
    print("Result of Squares : %s\n\n" % result)
    with Pool(processes=3) as pool:
        result = pool.starmap(smap, [(pf1, [1,2,3], {'a':123, 'b':456}), (pf2, [11,22,33]), (pf3, [111,222,333])])

    # Output the result
    print ('Result: %s ' % result)


Output:
*******

Dataset: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
1 1
2 4
3 9
4 16
6 36
7 49
8 64
59 81
 25
10 100
11 121
12 144
13 169
14 196
Result of Squares : [1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196]


Process : ForkPoolWorker-6  Function : pf1  Args: ([1, 2, 3],)  sleeping for 3  Time : 2020-07-20 00:51:56.477299

Keyword Args from pf1: {'a': 123, 'b': 456}
Process : ForkPoolWorker-7  Function : pf2  Args: ([11, 22, 33],)   sleeping for 8  Time : 2020-07-20 00:51:56.477371

Process : ForkPoolWorker-8  Function : pf3  Args: ([111, 222, 333],)    sleeping for 1  Time : 2020-07-20 00:51:56.477918

ForkPoolWorker-8    pf3 done at 2020-07-20 00:51:57.478808

ForkPoolWorker-6    pf1 done at 2020-07-20 00:51:59.478877

ForkPoolWorker-7    pf2 done at 2020-07-20 00:52:04.478016

Result: [(6, {'a': 123, 'b': 456}), 66, 666] 

Process finished with exit code 0

Answer 5

您可以使用 map 或某些lambda函数（编辑：实际上您不能使用lambda函数）。您可以使用一个简单的地图功能：

def smap(f, *args):
    return f(*args)

pool = multiprocessing.Pool(processes=30)
res=pool.map(smap, function_list, args_list1, args_list2,...)

普通的 map 函数将可迭代对象作为输入，这很不方便。

Answer 6

多种功能

以下示例展示了如何在一个池中运行多个函数。

from multiprocessing import Pool
import functools

def inc(x):
    return x + 1

def dec(x):
    return x - 1

def add(x, y):
    return x + y

def smap(f):
    return f()

def main():

    f_inc = functools.partial(inc, 4)
    f_dec = functools.partial(dec, 2)
    f_add = functools.partial(add, 3, 4)

    with Pool() as pool:
        res = pool.map(smap, [f_inc, f_dec, f_add])
        print(res)

if __name__ == '__main__':
    main()

我们有三个函数，它们在一个池中独立运行。我们使用 functools.partial 在执行之前准备函数及其参数。

来源：https://zetcode.com/python/multiprocessing/

具有不同功能的多处理池

6 个答案:

多种功能