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

Python 2的更好解决方案:

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))])

输出

2 3 4

1 2 3

0 1 2

out[]:

[3, 5, 7]

其他回答

将Python 3.3+与pool.starmap()一起使用:

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()

结果:

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

如果您喜欢,还可以zip()更多参数:zip(a,b,c,d,e)

如果希望将常量值作为参数传递:

import itertools

zip(itertools.repeat(constant), a)

如果您的函数应该返回以下内容:

results = pool.starmap(write, zip(a,b))

这将提供一个包含返回值的列表。

从Python 3.4.4中,您可以使用multiprocessing.get_context()获取上下文对象,以使用多个启动方法:

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()

或者你只是简单地替换

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

具有:

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

在J.F.Sebastian的回答中了解了itertools之后,我决定更进一步,编写一个关注并行化的parmap包,在Python 2.7和Python 3.2(以及更高版本)中提供可以接受任意数量位置参数的map和starmap函数。

安装

pip install parmap

如何并行化:

import parmap
# If you want to do:
y = [myfunction(x, argument1, argument2) for x in mylist]
# In parallel:
y = parmap.map(myfunction, mylist, argument1, argument2)

# If you want to do:
z = [myfunction(x, y, argument1, argument2) for (x,y) in mylist]
# In parallel:
z = parmap.starmap(myfunction, mylist, argument1, argument2)

# If you want to do:
listx = [1, 2, 3, 4, 5, 6]
listy = [2, 3, 4, 5, 6, 7]
param = 3.14
param2 = 42
listz = []
for (x, y) in zip(listx, listy):
        listz.append(myfunction(x, y, param1, param2))
# In parallel:
listz = parmap.starmap(myfunction, zip(listx, listy), param1, param2)

我已经将parmap上传到PyPI和GitHub存储库。

例如,问题的答案如下:

import parmap

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

if __name__ == "__main__":
    case = RAW_DATASET  # assuming this is an iterable
    parmap.map(harvester, case, "test", chunksize=1)

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。

另一种方法是将列表列表传递给单参数例程:

import os
from multiprocessing import Pool

def task(args):
    print "PID =", os.getpid(), ", arg1 =", args[0], ", arg2 =", args[1]

pool = Pool()

pool.map(task, [
        [1,2],
        [3,4],
        [5,6],
        [7,8]
    ])

然后可以用自己喜欢的方法构造一个参数列表。