使用带有 multiprocessing.Pool 的锁时遇到问题:酸洗错误

• To avoid this, you can make your lock variable a global variable. Then you will be able to reference it within your pool process function directly as a global variable, and will not have to pass it as an argument to the pool process function. This works because Python uses the OS fork mechanism when creating the pool processes and hence copies the entire contents of the process that creates the pool processes to them. This is the only way of passing a lock to a Python process created with the multiprocessing package. Incidentally, it is not necessary to use the Manager class just for this lock. With this change your code would look like this:

import multiprocessing
from functools import partial

lock = None  # Global definition of lock
pool = None  # Global definition of pool


def make_network(initial_tag, max_tags=2, max_iter=3):
    global lock
    global pool
    lock = multiprocessing.Lock()
    pool = multiprocessing.Pool(8)


def get_more_tags():
    global lock
    pass


# this is a very expensive function that I would like to parallelize
# over a list of tags. It involves a (relatively cheap) call to an external
# database, which needs a lock to avoid simultaneous queries. It takes a
# list of strings (tags) as its sole argument, and returns a list of sets
# with entries corresponding to the input list.
f = partial(get_more_tags, max_tags=max_tags) 

def _recursively_find_more_tags(tags, level):
    global pool
    if level >= max_iter:
        raise StopIteration
    new_tags = pool.map(f, tags)
    to_search = []
    for i, s in zip(tags, new_tags):
        for t in s:
            joined = ' '.join(t)
            print(i + "|" + joined)
            to_search.append(joined)
    try:
        return _recursively_find_more_tags(to_search, level + 1)
    except StopIteration:
        return None

_recursively_find_more_tags([initial_tag], 0)

在您的真实代码中，锁和池变量可能是类实例变量.