在Python多处理库中,是否有支持多个参数的pool.map变体?

import multiprocessing

text = "test"

def harvester(text, case):
    X = case[0]
    text + str(X)

if __name__ == '__main__':
    pool = multiprocessing.Pool(processes=6)
    case = RAW_DATASET
    pool.map(harvester(text, case), case, 1)
    pool.close()
    pool.join()

当前回答

pool.map是否有支持多个参数的变体?

Python 3.3包含pool.starmap()方法:

#!/usr/bin/env python3
from functools import partial
from itertools import repeat
from multiprocessing import Pool, freeze_support

def func(a, b):
    return a + b

def main():
    a_args = [1,2,3]
    second_arg = 1
    with Pool() as pool:
        L = pool.starmap(func, [(1, 1), (2, 1), (3, 1)])
        M = pool.starmap(func, zip(a_args, repeat(second_arg)))
        N = pool.map(partial(func, b=second_arg), a_args)
        assert L == M == N

if __name__=="__main__":
    freeze_support()
    main()

对于旧版本:

#!/usr/bin/env python2
import itertools
from multiprocessing import Pool, freeze_support

def func(a, b):
    print a, b

def func_star(a_b):
    """Convert `f([1,2])` to `f(1,2)` call."""
    return func(*a_b)

def main():
    pool = Pool()
    a_args = [1,2,3]
    second_arg = 1
    pool.map(func_star, itertools.izip(a_args, itertools.repeat(second_arg)))

if __name__=="__main__":
    freeze_support()
    main()

输出

1 1
2 1
3 1

注意这里是如何使用itertools.izip()和itertools.crepeat()的。

由于@unsubu提到的错误,您不能在Python 2.6上使用functools.partial()或类似功能,因此应该显式定义简单包装函数func_tar()。另请参阅uptimebox建议的解决方法。

其他回答

答案取决于版本和情况。最近版本的Python(从3.3开始)的最一般的答案首先由J.F.Sebastian在下面描述。1它使用Pool.starmap方法,接受一系列参数元组。然后,它会自动将每个元组中的参数解包,并将它们传递给给定的函数:

import multiprocessing
from itertools import product

def merge_names(a, b):
    return '{} & {}'.format(a, b)

if __name__ == '__main__':
    names = ['Brown', 'Wilson', 'Bartlett', 'Rivera', 'Molloy', 'Opie']
    with multiprocessing.Pool(processes=3) as pool:
        results = pool.starmap(merge_names, product(names, repeat=2))
    print(results)

# Output: ['Brown & Brown', 'Brown & Wilson', 'Brown & Bartlett', ...

对于早期版本的Python,您需要编写一个助手函数来显式地解包参数。如果要与一起使用,还需要编写一个包装器,将Pool转换为上下文管理器。(感谢穆恩指出了这一点。)

import multiprocessing
from itertools import product
from contextlib import contextmanager

def merge_names(a, b):
    return '{} & {}'.format(a, b)

def merge_names_unpack(args):
    return merge_names(*args)

@contextmanager
def poolcontext(*args, **kwargs):
    pool = multiprocessing.Pool(*args, **kwargs)
    yield pool
    pool.terminate()

if __name__ == '__main__':
    names = ['Brown', 'Wilson', 'Bartlett', 'Rivera', 'Molloy', 'Opie']
    with poolcontext(processes=3) as pool:
        results = pool.map(merge_names_unpack, product(names, repeat=2))
    print(results)

# Output: ['Brown & Brown', 'Brown & Wilson', 'Brown & Bartlett', ...

在更简单的情况下,使用固定的第二个参数,也可以使用partial,但仅在Python 2.7+中使用。

import multiprocessing
from functools import partial
from contextlib import contextmanager

@contextmanager
def poolcontext(*args, **kwargs):
    pool = multiprocessing.Pool(*args, **kwargs)
    yield pool
    pool.terminate()

def merge_names(a, b):
    return '{} & {}'.format(a, b)

if __name__ == '__main__':
    names = ['Brown', 'Wilson', 'Bartlett', 'Rivera', 'Molloy', 'Opie']
    with poolcontext(processes=3) as pool:
        results = pool.map(partial(merge_names, b='Sons'), names)
    print(results)

# Output: ['Brown & Sons', 'Wilson & Sons', 'Bartlett & Sons', ...

1.这大部分都是由他的答案激发的,而他的答案很可能应该被接受。但由于这本书一直停留在顶端,似乎最好为未来读者改进它。

pool.map是否有支持多个参数的变体?

Python 3.3包含pool.starmap()方法:

#!/usr/bin/env python3
from functools import partial
from itertools import repeat
from multiprocessing import Pool, freeze_support

def func(a, b):
    return a + b

def main():
    a_args = [1,2,3]
    second_arg = 1
    with Pool() as pool:
        L = pool.starmap(func, [(1, 1), (2, 1), (3, 1)])
        M = pool.starmap(func, zip(a_args, repeat(second_arg)))
        N = pool.map(partial(func, b=second_arg), a_args)
        assert L == M == N

if __name__=="__main__":
    freeze_support()
    main()

对于旧版本:

#!/usr/bin/env python2
import itertools
from multiprocessing import Pool, freeze_support

def func(a, b):
    print a, b

def func_star(a_b):
    """Convert `f([1,2])` to `f(1,2)` call."""
    return func(*a_b)

def main():
    pool = Pool()
    a_args = [1,2,3]
    second_arg = 1
    pool.map(func_star, itertools.izip(a_args, itertools.repeat(second_arg)))

if __name__=="__main__":
    freeze_support()
    main()

输出

1 1
2 1
3 1

注意这里是如何使用itertools.izip()和itertools.crepeat()的。

由于@unsubu提到的错误,您不能在Python 2.6上使用functools.partial()或类似功能,因此应该显式定义简单包装函数func_tar()。另请参阅uptimebox建议的解决方法。

text = "test"

def unpack(args):
    return args[0](*args[1:])

def harvester(text, case):
    X = case[0]
    text+ str(X)

if __name__ == '__main__':
    pool = multiprocessing.Pool(processes=6)
    case = RAW_DATASET
    # args is a list of tuples 
    # with the function to execute as the first item in each tuple
    args = [(harvester, text, c) for c in case]
    # doing it this way, we can pass any function
    # and we don't need to define a wrapper for each different function
    # if we need to use more than one
    pool.map(unpack, args)
    pool.close()
    pool.join()

对于Python 2,可以使用此技巧

def fun(a, b):
    return a + b

pool = multiprocessing.Pool(processes=6)
b = 233
pool.map(lambda x:fun(x, b), range(1000))

这里有另一种方法,IMHO比提供的任何其他答案都更简单和优雅。

该程序有一个函数,它获取两个参数,打印它们并打印总和:

import multiprocessing

def main():

    with multiprocessing.Pool(10) as pool:
        params = [ (2, 2), (3, 3), (4, 4) ]
        pool.starmap(printSum, params)
    # end with

# end function

def printSum(num1, num2):
    mySum = num1 + num2
    print('num1 = ' + str(num1) + ', num2 = ' + str(num2) + ', sum = ' + str(mySum))
# end function

if __name__ == '__main__':
    main()

输出为:

num1 = 2, num2 = 2, sum = 4
num1 = 3, num2 = 3, sum = 6
num1 = 4, num2 = 4, sum = 8

有关更多信息,请参阅python文档:

https://docs.python.org/3/library/multiprocessing.html#module-多处理工具

特别是要检查星图功能。

我使用的是Python 3.6,我不确定这是否适用于较旧的Python版本

为什么在文档中没有这样一个非常直接的例子,我不确定。