了解多处理：Python中的共享内存管理，锁和队列

Question

多重处理是python中的强大工具，我想更深入地了解它。我想知道何时使用 常规的
锁和队列，何时使用多处理管理器在所有进程之间共享它们。

我提出了以下测试方案，其中包含四种不同的条件进行多处理：

1. 使用池和 NO Manager

2. 使用池和管理器

3. 使用单个流程和 NO Manager

4. 使用单个流程和一个经理

工作

所有条件都执行作业功能the_job。the_job包括一些通过锁固定的打印件。此外，该函数的输入只是放入队列中（以查看是否可以从队列中恢复它）。该输入是一个简单的索引idx从range(10)在称为主脚本创建start_scenario（在底部示出）。

def the_job(args):
    """The job for multiprocessing.

    Prints some stuff secured by a lock and 
    finally puts the input into a queue.

    """
    idx = args[0]
    lock = args[1]
    queue=args[2]

    lock.acquire()
    print 'I'
    print 'was '
    print 'here '
    print '!!!!'
    print '1111'
    print 'einhundertelfzigelf\n'
    who= ' By run %d \n' % idx
    print who
    lock.release()

    queue.put(idx)

条件的成功定义为从队列中完全调用输入，请参见read_queue底部的功能。

条件

条件1和2是不言自明的。条件1涉及创建锁和队列，并将它们传递给进程池：

def scenario_1_pool_no_manager(jobfunc, args, ncores):
    """Runs a pool of processes WITHOUT a Manager for the lock and queue.

    FAILS!

    """
    mypool = mp.Pool(ncores)
    lock = mp.Lock()
    queue = mp.Queue()

    iterator = make_iterator(args, lock, queue)

    mypool.imap(jobfunc, iterator)

    mypool.close()
    mypool.join()

    return read_queue(queue)

（辅助功能make_iterator在本文的底部给出。）条件1失败，显示为RuntimeError: Lock objects should only be shared between processes through inheritance。

条件2相当相似，但是现在锁定和队列在管理者的监督下：

def scenario_2_pool_manager(jobfunc, args, ncores):
    """Runs a pool of processes WITH a Manager for the lock and queue.

    SUCCESSFUL!

    """
    mypool = mp.Pool(ncores)
    lock = mp.Manager().Lock()
    queue = mp.Manager().Queue()

    iterator = make_iterator(args, lock, queue)
    mypool.imap(jobfunc, iterator)
    mypool.close()
    mypool.join()

    return read_queue(queue)

在条件3中，手动启动新进程，并且在没有管理器的情况下创建锁和队列：

def scenario_3_single_processes_no_manager(jobfunc, args, ncores):
    """Runs an individual process for every task WITHOUT a Manager,

    SUCCESSFUL!

    """
    lock = mp.Lock()
    queue = mp.Queue()

    iterator = make_iterator(args, lock, queue)

    do_job_single_processes(jobfunc, iterator, ncores)

    return read_queue(queue)

条件4相似，但再次使用管理器：

def scenario_4_single_processes_manager(jobfunc, args, ncores):
    """Runs an individual process for every task WITH a Manager,

    SUCCESSFUL!

    """
    lock = mp.Manager().Lock()
    queue = mp.Manager().Queue()

    iterator = make_iterator(args, lock, queue)

    do_job_single_processes(jobfunc, iterator, ncores)

    return read_queue(queue)

在3和4两种情况下，我针对10个任务中的每一个都启动一个新进程the_job，最多 同时 运行 ncores个
进程。这是通过以下辅助功能实现的：

def do_job_single_processes(jobfunc, iterator, ncores):
    """Runs a job function by starting individual processes for every task.

    At most `ncores` processes operate at the same time

    :param jobfunc: Job to do

    :param iterator:

        Iterator over different parameter settings,
        contains a lock and a queue

    :param ncores:

        Number of processes operating at the same time

    """
    keep_running=True
    process_dict = {} # Dict containing all subprocees

    while len(process_dict)>0 or keep_running:

        terminated_procs_pids = []
        # First check if some processes did finish their job
        for pid, proc in process_dict.iteritems():

            # Remember the terminated processes
            if not proc.is_alive():
                terminated_procs_pids.append(pid)

        # And delete these from the process dict
        for terminated_proc in terminated_procs_pids:
            process_dict.pop(terminated_proc)

        # If we have less active processes than ncores and there is still
        # a job to do, add another process
        if len(process_dict) < ncores and keep_running:
            try:
                task = iterator.next()
                proc = mp.Process(target=jobfunc,
                                                   args=(task,))
                proc.start()
                process_dict[proc.pid]=proc
            except StopIteration:
                # All tasks have been started
                keep_running=False

        time.sleep(0.1)

结果

仅条件1失败（RuntimeError: Lock objects should only be shared between processes through inheritance），而其他3个条件成功。我尽力解决这个问题。

为什么池需要在所有进程之间共享锁和队列，而条件3中的单个进程却不需要？

我知道的是，对于池条件（1和2），来自迭代器的所有数据都是通过酸洗传递的，而在单进程条件（3和4）中，来自迭代器的所有数据都是通过对主进程的继承来传递的（我是使用
Linux
）。我猜直到在子进程中更改内存之前，都会访问父进程使用的相同内存（写时复制）。但是，只要一说完lock.acquire()，就应该更改它，子进程确实使用放在内存中其他位置的不同锁，不是吗？一个孩子进程如何知道一个兄弟激活了一个不能通过管理员共享的锁？

最后，我的问题有点相关：条件3和4有多少不同。两者都有各自的流程，但在管理人员的用法上有所不同。两者都被视为 有效
代码吗？或者，如果实际上不需要经理，应该避免使用经理吗？

完整剧本

对于那些只想复制并粘贴所有内容以执行代码的人，下面是完整的脚本：

__author__ = 'Me and myself'

import multiprocessing as mp
import time

def the_job(args):
    """The job for multiprocessing.

    Prints some stuff secured by a lock and 
    finally puts the input into a queue.

    """
    idx = args[0]
    lock = args[1]
    queue=args[2]

    lock.acquire()
    print 'I'
    print 'was '
    print 'here '
    print '!!!!'
    print '1111'
    print 'einhundertelfzigelf\n'
    who= ' By run %d \n' % idx
    print who
    lock.release()

    queue.put(idx)


def read_queue(queue):
    """Turns a qeue into a normal python list."""
    results = []
    while not queue.empty():
        result = queue.get()
        results.append(result)
    return results


def make_iterator(args, lock, queue):
    """Makes an iterator over args and passes the lock an queue to each element."""
    return ((arg, lock, queue) for arg in args)


def start_scenario(scenario_number = 1):
    """Starts one of four multiprocessing scenarios.

    :param scenario_number: Index of scenario, 1 to 4

    """
    args = range(10)
    ncores = 3
    if scenario_number==1:
        result =  scenario_1_pool_no_manager(the_job, args, ncores)

    elif scenario_number==2:
        result =  scenario_2_pool_manager(the_job, args, ncores)

    elif scenario_number==3:
        result =  scenario_3_single_processes_no_manager(the_job, args, ncores)

    elif scenario_number==4:
        result =  scenario_4_single_processes_manager(the_job, args, ncores)

    if result != args:
        print 'Scenario %d fails: %s != %s' % (scenario_number, args, result)
    else:
        print 'Scenario %d successful!' % scenario_number


def scenario_1_pool_no_manager(jobfunc, args, ncores):
    """Runs a pool of processes WITHOUT a Manager for the lock and queue.

    FAILS!

    """
    mypool = mp.Pool(ncores)
    lock = mp.Lock()
    queue = mp.Queue()

    iterator = make_iterator(args, lock, queue)

    mypool.map(jobfunc, iterator)

    mypool.close()
    mypool.join()

    return read_queue(queue)


def scenario_2_pool_manager(jobfunc, args, ncores):
    """Runs a pool of processes WITH a Manager for the lock and queue.

    SUCCESSFUL!

    """
    mypool = mp.Pool(ncores)
    lock = mp.Manager().Lock()
    queue = mp.Manager().Queue()

    iterator = make_iterator(args, lock, queue)
    mypool.map(jobfunc, iterator)
    mypool.close()
    mypool.join()

    return read_queue(queue)


def scenario_3_single_processes_no_manager(jobfunc, args, ncores):
    """Runs an individual process for every task WITHOUT a Manager,

    SUCCESSFUL!

    """
    lock = mp.Lock()
    queue = mp.Queue()

    iterator = make_iterator(args, lock, queue)

    do_job_single_processes(jobfunc, iterator, ncores)

    return read_queue(queue)


def scenario_4_single_processes_manager(jobfunc, args, ncores):
    """Runs an individual process for every task WITH a Manager,

    SUCCESSFUL!

    """
    lock = mp.Manager().Lock()
    queue = mp.Manager().Queue()

    iterator = make_iterator(args, lock, queue)

    do_job_single_processes(jobfunc, iterator, ncores)

    return read_queue(queue)


def do_job_single_processes(jobfunc, iterator, ncores):
    """Runs a job function by starting individual processes for every task.

    At most `ncores` processes operate at the same time

    :param jobfunc: Job to do

    :param iterator:

        Iterator over different parameter settings,
        contains a lock and a queue

    :param ncores:

        Number of processes operating at the same time

    """
    keep_running=True
    process_dict = {} # Dict containing all subprocees

    while len(process_dict)>0 or keep_running:

        terminated_procs_pids = []
        # First check if some processes did finish their job
        for pid, proc in process_dict.iteritems():

            # Remember the terminated processes
            if not proc.is_alive():
                terminated_procs_pids.append(pid)

        # And delete these from the process dict
        for terminated_proc in terminated_procs_pids:
            process_dict.pop(terminated_proc)

        # If we have less active processes than ncores and there is still
        # a job to do, add another process
        if len(process_dict) < ncores and keep_running:
            try:
                task = iterator.next()
                proc = mp.Process(target=jobfunc,
                                                   args=(task,))
                proc.start()
                process_dict[proc.pid]=proc
            except StopIteration:
                # All tasks have been started
                keep_running=False

        time.sleep(0.1)


def main():
    """Runs 1 out of 4 different multiprocessing scenarios"""
    start_scenario(1)


if __name__ == '__main__':
    main()

Answer 1

multiprocessing.Lock使用操作系统提供的信号量对象实现。在Linux上，子级只是通过继承了父级的信号量句柄os.fork。这不是信号量的副本。它实际上继承了父级具有的相同句柄，可以继承文件描述符的相同方式。另一方面，Windows不支持os.fork，因此必须使Windows处于“腌制”状态Lock。它multiprocessing.Lock使用Windows
DuplicateHandleAPI通过创建对象内部使用的Windows信号灯的重复句柄来实现此目的，该API指出：

重复的句柄引用与原始句柄相同的对象。因此，对对象的任何更改都会通过两个手柄反映出来

该DuplicateHandleAPI允许您将重复句柄的所有权交给子进程，以便子进程在取消选择之后实际上可以使用它。通过创建由孩子拥有的重复句柄，您可以有效地“共享”锁对象。

这是中的信号量对象 multiprocessing/synchronize.py

class SemLock(object):

    def __init__(self, kind, value, maxvalue):
        sl = self._semlock = _multiprocessing.SemLock(kind, value, maxvalue)
        debug('created semlock with handle %s' % sl.handle)
        self._make_methods()

        if sys.platform != 'win32':
            def _after_fork(obj):
                obj._semlock._after_fork()
            register_after_fork(self, _after_fork)

    def _make_methods(self):
        self.acquire = self._semlock.acquire
        self.release = self._semlock.release
        self.__enter__ = self._semlock.__enter__
        self.__exit__ = self._semlock.__exit__

    def __getstate__(self):  # This is called when you try to pickle the `Lock`.
        assert_spawning(self)
        sl = self._semlock
        return (Popen.duplicate_for_child(sl.handle), sl.kind, sl.maxvalue)

    def __setstate__(self, state): # This is called when unpickling a `Lock`
        self._semlock = _multiprocessing.SemLock._rebuild(*state)
        debug('recreated blocker with handle %r' % state[0])
        self._make_methods()

请注意assert_spawningin中的调用__getstate__，该调用在腌制对象时被调用。这是如何实现的：

#
# Check that the current thread is spawning a child process
#

def assert_spawning(self):
    if not Popen.thread_is_spawning():
        raise RuntimeError(
            '%s objects should only be shared between processes'
            ' through inheritance' % type(self).__name__
            )

该函数可以确保您Lock通过调用来“继承” the函数thread_is_spawning。在Linux上，该方法仅返回False：

@staticmethod
def thread_is_spawning():
    return False

这是因为Linux不需要腌制即可继承Lock，因此，如果__getstate__实际上是在Linux上调用它，则我们一定不能继承。在Windows上，还有更多操作：

def dump(obj, file, protocol=None):
    ForkingPickler(file, protocol).dump(obj)

class Popen(object):
    '''
    Start a subprocess to run the code of a process object
    '''
    _tls = thread._local()

    def __init__(self, process_obj):
        ...
        # send information to child
        prep_data = get_preparation_data(process_obj._name)
        to_child = os.fdopen(wfd, 'wb')
        Popen._tls.process_handle = int(hp)
        try:
            dump(prep_data, to_child, HIGHEST_PROTOCOL)
            dump(process_obj, to_child, HIGHEST_PROTOCOL)
        finally:
            del Popen._tls.process_handle
            to_child.close()


    @staticmethod
    def thread_is_spawning():
        return getattr(Popen._tls, 'process_handle', None) is not None

在这里，thread_is_spawning返回True如果Popen._tls对象具有process_handle的属性。我们可以看到process_handle在中创建了属性__init__，然后使用将要继承的数据从父级传递给子级dump，然后删除了该属性。所以thread_is_spawning只会在True期间__init__。根据这个python-
ideas邮件列表线程，实际上这是一个人为限制，它被添加来模拟与os.forkLinux上相同的行为。Windows实际上 可以
支持Lock随时通过传递，因为DuplicateHandle可以随时运行。

以上所有内容均适用于该Queue对象，因为它在Lock内部使用。

我想说，继承Lock对象比使用a更可取Manager.Lock()，因为当使用a时Manager.Lock，Lock必须通过IPC将每个对的调用都通过IPC发送到Manager进程，这比使用Lock调用内部的共享要慢得多。处理。不过，这两种方法都是完全有效的。

最后，可以使用/关键字参数将a传递给aLock的所有成员，Pool而无需使用a ：Manager``initializer``initargs

lock = None
def initialize_lock(l):
   global lock
   lock = l

def scenario_1_pool_no_manager(jobfunc, args, ncores):
    """Runs a pool of processes WITHOUT a Manager for the lock and queue.

    """
    lock = mp.Lock()
    mypool = mp.Pool(ncores, initializer=initialize_lock, initargs=(lock,))
    queue = mp.Queue()

    iterator = make_iterator(args, queue)

    mypool.imap(jobfunc, iterator) # Don't pass lock. It has to be used as a global in the child. (This means `jobfunc` would need to be re-written slightly.

    mypool.close()
    mypool.join()

return read_queue(queue)

之所以可行，是因为传递的参数传递给在中运行的对象initargs的__init__方法，因此它们最终被继承而不是被腌制。Process``Pool