慕田峪7331174
这一问题的答案取决于版本和情况。对于Python的最新版本(自3.3以来)，最普遍的答案是：塞巴斯蒂安.1它使用Pool.starmap方法，它接受一个参数元组序列。然后自动从每个元组中解压缩参数，并将它们传递给定的函数：import multiprocessingfrom itertools import productdef 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，您需要编写一个帮助函数来显式地解压参数。如果你想用with，您还需要编写一个包装器来转换Pool变成上下文管理器。(多亏了μ子)指出这一点。)import multiprocessingfrom itertools import productfrom contextlib import contextmanagerdef merge_names(a, b):
return '{} & {}'.format(a, b)def merge_names_unpack(args):
return merge_names(*args)@contextmanagerdef 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，您还可以使用partial，但仅限于Python2.7+。import multiprocessingfrom functools import partialfrom contextlib import contextmanager@contextmanagerdef 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.在很大程度上，这是他的回答所启发的，而他的回答可能反而应该被接受。但是，由于这一个被困在顶端，它似乎是最好的改进，为未来的读者。
红糖糍粑
是否有池.map的变体支持多个参数？Python 3.3包括pool.starmap()方法:#!/usr/bin/env python3from functools import partialfrom itertools import repeatfrom multiprocessing import Pool, freeze_supportdef func(a, b):
return a + bdef 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 == Nif __name__=="__main__":
freeze_support()
main()对于旧版本：#!/usr/bin/env python2import itertoolsfrom multiprocessing import Pool, freeze_supportdef func(a, b):
print a, bdef 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()输出量1 1
2 1
3 1注意itertools.izip()和itertools.repeat()用在这里。由于@unutbu提到的bug你不能用functools.partial()或者类似于Python2.6的功能，所以简单的包装函数func_star()应该明确定义。另见解决办法 建议uptimebox.
慕娘9325324
我想下面的情况会更好def multi_run_wrapper(args):
return add(*args)def add(x,y):
return x+yif __name__ == "__main__":
from multiprocessing import Pool
pool = Pool(4)
results = pool.map(multi_run_wrapper,[(1,2),(2,3),(3,4)])
print results输出量[3, 5, 7]