这里列出了一些可以腌制的东西。特别是，函数只有在模块的顶层定义时才可pickle。

这段代码:

import multiprocessing as mp

class Foo():
    @staticmethod
    def work(self):
        pass

if __name__ == '__main__':   
    pool = mp.Pool()
    foo = Foo()
    pool.apply_async(foo.work)
    pool.close()
    pool.join()

产生一个错误几乎相同的一个你张贴:

Exception in thread Thread-2:
Traceback (most recent call last):
  File "/usr/lib/python2.7/threading.py", line 552, in __bootstrap_inner
    self.run()
  File "/usr/lib/python2.7/threading.py", line 505, in run
    self.__target(*self.__args, **self.__kwargs)
  File "/usr/lib/python2.7/multiprocessing/pool.py", line 315, in _handle_tasks
    put(task)
PicklingError: Can't pickle <type 'function'>: attribute lookup __builtin__.function failed

问题是池方法都使用mp。SimpleQueue将任务传递给工作进程。所有经过mp的东西。SimpleQueue必须是可选的，而foo。工作是不可pickle的，因为它没有在模块的顶层定义。

可以通过在顶层定义一个调用foo.work()的函数来修复:

def work(foo):
    foo.work()

pool.apply_async(work,args=(foo,))

注意，foo是可选的，因为foo是在顶层定义的。__dict__是可腌制的。

我发现，通过尝试对代码段使用分析器，我还可以在一个完美工作的代码段上生成完全相同的错误输出。

注意，这是在Windows上(其中的分叉有点不优雅)。

我在跑:

python -m profile -o output.pstats <script> 

发现删除剖析可以消除错误，而放置剖析可以恢复错误。也快把我逼疯了因为我知道密码以前有用。我正在检查是否有什么东西更新了pool.py…然后有了一种沉沦的感觉，消除了侧写，就是这样。

把它贴在这里存档，以防别人发现。

我会用悲怆。多处理，而不是多处理。感伤。Multiprocessing是Multiprocessing的一个分支，使用莳萝。Dill几乎可以序列化python中的任何东西，因此您可以并行地发送更多内容。正如类方法所需要的那样，pathos fork还能够直接使用多个参数函数。

>>> from pathos.multiprocessing import ProcessingPool as Pool
>>> p = Pool(4)
>>> class Test(object):
...   def plus(self, x, y): 
...     return x+y
... 
>>> t = Test()
>>> p.map(t.plus, x, y)
[4, 6, 8, 10]
>>> 
>>> class Foo(object):
...   @staticmethod
...   def work(self, x):
...     return x+1
... 
>>> f = Foo()
>>> p.apipe(f.work, f, 100)
<processing.pool.ApplyResult object at 0x10504f8d0>
>>> res = _
>>> res.get()
101

在这里获得感伤(如果你喜欢，莳萝): https://github.com/uqfoundation

正如其他人所说，多处理只能将Python对象转移到可以pickle的工作进程。如果您不能像unutbu所描述的那样重新组织代码，您可以使用dills扩展的pickle /unpickling功能来传输数据(特别是代码数据)，如下所示。

这个解决方案只需要安装dill，而不需要像pathos这样的其他库:

import os
from multiprocessing import Pool

import dill


def run_dill_encoded(payload):
    fun, args = dill.loads(payload)
    return fun(*args)


def apply_async(pool, fun, args):
    payload = dill.dumps((fun, args))
    return pool.apply_async(run_dill_encoded, (payload,))


if __name__ == "__main__":

    pool = Pool(processes=5)

    # asyn execution of lambda
    jobs = []
    for i in range(10):
        job = apply_async(pool, lambda a, b: (a, b, a * b), (i, i + 1))
        jobs.append(job)

    for job in jobs:
        print job.get()
    print

    # async execution of static method

    class O(object):

        @staticmethod
        def calc():
            return os.getpid()

    jobs = []
    for i in range(10):
        job = apply_async(pool, O.calc, ())
        jobs.append(job)

    for job in jobs:
        print job.get()

该解决方案只需要安装dill，而不需要像pathos那样安装其他库

def apply_packed_function_for_map((dumped_function, item, args, kwargs),):
    """
    Unpack dumped function as target function and call it with arguments.

    :param (dumped_function, item, args, kwargs):
        a tuple of dumped function and its arguments
    :return:
        result of target function
    """
    target_function = dill.loads(dumped_function)
    res = target_function(item, *args, **kwargs)
    return res


def pack_function_for_map(target_function, items, *args, **kwargs):
    """
    Pack function and arguments to object that can be sent from one
    multiprocessing.Process to another. The main problem is:
        «multiprocessing.Pool.map*» or «apply*»
        cannot use class methods or closures.
    It solves this problem with «dill».
    It works with target function as argument, dumps it («with dill»)
    and returns dumped function with arguments of target function.
    For more performance we dump only target function itself
    and don't dump its arguments.
    How to use (pseudo-code):

        ~>>> import multiprocessing
        ~>>> images = [...]
        ~>>> pool = multiprocessing.Pool(100500)
        ~>>> features = pool.map(
        ~...     *pack_function_for_map(
        ~...         super(Extractor, self).extract_features,
        ~...         images,
        ~...         type='png'
        ~...         **options,
        ~...     )
        ~... )
        ~>>>

    :param target_function:
        function, that you want to execute like  target_function(item, *args, **kwargs).
    :param items:
        list of items for map
    :param args:
        positional arguments for target_function(item, *args, **kwargs)
    :param kwargs:
        named arguments for target_function(item, *args, **kwargs)
    :return: tuple(function_wrapper, dumped_items)
        It returs a tuple with
            * function wrapper, that unpack and call target function;
            * list of packed target function and its' arguments.
    """
    dumped_function = dill.dumps(target_function)
    dumped_items = [(dumped_function, item, args, kwargs) for item in items]
    return apply_packed_function_for_map, dumped_items

它也适用于numpy数组。

不能pickle <type 'function'>:属性查找__builtin__。函数失败

如果传递给异步作业的模型对象中有任何内置函数，也会出现此错误。

因此，确保检查传递的模型对象没有内置函数。(在我们的例子中，我们在模型中使用django-model-utils的FieldTracker()函数来跟踪某个字段)。这里是相关GitHub问题的链接。

基于@rocksportrocker解决方案， 在发送和RECVing结果时使用dill是有意义的。

import dill
import itertools
def run_dill_encoded(payload):
    fun, args = dill.loads(payload)
    res = fun(*args)
    res = dill.dumps(res)
    return res

def dill_map_async(pool, fun, args_list,
                   as_tuple=True,
                   **kw):
    if as_tuple:
        args_list = ((x,) for x in args_list)

    it = itertools.izip(
        itertools.cycle([fun]),
        args_list)
    it = itertools.imap(dill.dumps, it)
    return pool.map_async(run_dill_encoded, it, **kw)

if __name__ == '__main__':
    import multiprocessing as mp
    import sys,os
    p = mp.Pool(4)
    res = dill_map_async(p, lambda x:[sys.stdout.write('%s\n'%os.getpid()),x][-1],
                  [lambda x:x+1]*10,)
    res = res.get(timeout=100)
    res = map(dill.loads,res)
    print(res)

当多处理出现这个问题时，一个简单的解决方案是从Pool切换到ThreadPool。除了import-，无需更改其他代码即可完成此操作

from multiprocessing.pool import ThreadPool as Pool

这是因为ThreadPool与主线程共享内存，而不是创建一个新进程——这意味着不需要pickle。

The downside to this method is that python isn't the greatest language with handling threads- it uses something called the Global Interpreter Lock to stay thread safe, which can slow down some use cases here. However, if you're primarily interacting with other systems (running HTTP commands, talking with a database, writing to filesystems) then your code is likely not bound by CPU and won't take much of a hit. In fact I've found when writing HTTP/HTTPS benchmarks that the threaded model used here has less overhead and delays, as the overhead from creating new processes is much higher than the overhead for creating new threads and the program was otherwise just waiting for HTTP responses.

所以如果你在python用户空间中处理大量的东西，这可能不是最好的方法。

正如@penky Suresh在这个回答中建议的那样，不要使用内置关键字。

显然，args是处理多处理时的内置关键字

class TTS:
    def __init__(self):
        pass

    def process_and_render_items(self):
        multiprocessing_args = [{"a": "b", "c": "d"}, {"e": "f", "g": "h"}]

        with ProcessPoolExecutor(max_workers=10) as executor:
          # Using args here is fine. 
            future_processes = {
              executor.submit(TTS.process_and_render_item, args)
                for args in multiprocessing_args
            }

            for future in as_completed(future_processes):
                try:
                    data = future.result()
                except Exception as exc:
                    print(f"Generated an exception: {exc}")
                else:
                   print(f"Generated data for comment process: {future}")
 

    # Dont use 'args' here. It seems to be a built-in keyword.
    # Changing 'args' to 'arg' worked for me.
    def process_and_render_item(arg):
        print(arg)
      # This will print {"a": "b", "c": "d"} for the first process
      # and {"e": "f", "g": "h"} for the second process.

PS:制表符/空格可能有点不对。

一个快速的解决方法是使函数全局化

from multiprocessing import Pool


class Test:
    def __init__(self, x):
        self.x = x
    
    @staticmethod
    def test(x):
        return x**2


    def test_apply(self, list_):
        global r
        def r(x):
            return Test.test(x + self.x)

        with Pool() as p:
            l = p.map(r, list_)

        return l



if __name__ == '__main__':
    o = Test(2)
    print(o.test_apply(range(10)))