Multiprocessing是python中的一个强大工具，我想更深入地理解它。 我想知道何时使用常规 Locks和Queues以及何时使用多处理Manager在所有进程中共享这些内容。
我提出了以下测试方案，其中包含四种不同的多处理条件：
使用游泳池和否经理
使用游泳池和经理
使用单个流程和否经理
使用个别流程和经理
所有条件都执行工作职能the_job。 the_job由一些由锁定保护的打印组成。此外，函数的输入只是放入队列（以查看它是否可以从队列中恢复）。此输入只是在主脚本idx中创建的range(10)索引start_scenario（显示在底部）。
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)
条件的成功被定义为完全回忆输入
从队列中，查看底部的函数read_queue。
条件1和2是相当不言自明的。 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)
（帮助函数make_iterator在本文的底部给出。）
条件1因RuntimeError: Lock objects should only be shared between processes through inheritance而失败。
条件2非常相似，但现在锁和队列都在经理的监督下：
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类似，但现在再次使用经理：
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)
在这两个条件中 - 3和4 - 我开始新的
the_job的10个任务中的每个任务的进程，最多只有 ncores 进程
在同一时间运作。这是通过以下辅助函数实现的：
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)
只有条件1失败（RuntimeError: Lock objects should only be shared between processes through inheritance），而其他3个条件成功。我试着围绕这个结果。
为什么池需要在所有进程之间共享锁和队列，但条件3中的各个进程不需要？
我所知道的是，对于池条件（1和2），来自迭代器的所有数据都通过pickle传递，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主进程的继承传递的（我正在使用 Linux ）。
我想直到从子进程内部更改内存，访问父进程使用的相同内存（写时复制）。但是只要有人说lock.acquire()，就应该改变它，并且子进程确实使用放在内存中其他位置的不同锁，不是吗？一个子进程如何知道兄弟已经激活了一个不通过管理员共享的锁？
最后，有些相关的是我的问题，有多少不同的条件3和4。两者都有单独的流程，但它们在经理的使用上有所不同。两者都被认为是有效的代码吗？或者如果实际上不需要经理，应该避免使用经理吗？
对于那些只想复制并粘贴所有内容来执行代码的人，这里是完整的脚本：
__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()
答案 0 :(得分：30)
multiprocessing.Lock是使用操作系统提供的信号量对象实现的。在Linux上，子进程通过os.fork从父进程继承了信号量的句柄。这不是信号量的副本;它实际上是继承父项所具有的相同句柄，与继承文件描述符的方式相同。另一方面，Windows不支持os.fork，因此必须挑选Lock。它通过使用Windows DuplicateHandle API创建multiprocessing.Lock对象在内部使用的Windows信号量的重复句柄来实现此目的，该API声明：
重复句柄指的是与原始句柄相同的对象。 因此，对象的任何更改都会通过两者进行反映 手柄
DuplicateHandle API允许您将重复句柄的所有权授予子进程，以便子进程在取消对其进行实际使用后可以使用它。通过创建子项拥有的重复句柄，您可以有效地“共享”锁定对象。
这是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_spawning中的__getstate__调用，该调用会在挑选对象时调用。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__
)
通过调用Lock，该功能可以确保您“继承”thread_is_spawning。在Linux上，该方法只返回False：
@staticmethod
def thread_is_spawning():
return False
这是因为Linux不需要pickle来继承Lock，所以如果在Linux上实际调用__getstate__，我们就不能继承。在Windows上，还有更多内容：
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
如果thread_is_spawning对象具有True属性，Popen._tls会返回process_handle。我们可以看到process_handle属性在__init__中创建，然后我们想要继承的数据使用dump从父级传递给子级，然后删除该属性。因此，在thread_is_spawning期间True只会__init__。根据{{3}}，这实际上是一个人为限制，用于模拟与Linux上os.fork相同的行为。 Windows实际上可以支持随时传递Lock，因为DuplicateHandle可以随时运行。
以上所有内容均适用于Queue对象，因为它在内部使用Lock。
我会说继承Lock个对象比使用Manager.Lock()更可取，因为当您使用Manager.Lock时，您对Lock的每次调用都必须是通过IPC发送到Manager进程，这比使用驻留在调用进程内的共享Lock要慢得多。但是，这两种方法都是完全有效的。
最后，可以使用Lock / Pool关键字参数，在不使用Manager的情况下将initializer传递给initargs的所有成员：
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)
这是有效的，因为传递给initargs的参数传递给在__init__内运行的Process对象的Pool方法，因此它们最终被继承，而不是比腌制。