16.6. multiprocessing — 基于进程的 threading 接口 ¶

>>> import multiprocessing, time, signal
>>> p = multiprocessing.Process(target=time.sleep, args=(1000,))
>>> print p, p.is_alive()
<Process(Process-1, initial)> False
>>> p.start()
>>> print p, p.is_alive()
<Process(Process-1, started)> True
>>> p.terminate()
>>> time.sleep(0.1)
>>> print p, p.is_alive()
<Process(Process-1, stopped[SIGTERM])> False
>>> p.exitcode == -signal.SIGTERM
True
						

exception multiprocessing. BufferTooShort ¶

Exception raised by Connection.recv_bytes_into() when the supplied buffer object is too small for the message read.

若 e 是实例化的 BufferTooShort then e.args[0] will give the message as a byte string.

16.6.2.2. 管道和队列 ¶

When using multiple processes, one generally uses message passing for communication between processes and avoids having to use any synchronization primitives like locks.

For passing messages one can use Pipe() (for a connection between two processes) or a queue (which allows multiple producers and consumers).

The Queue , multiprocessing.queues.SimpleQueue and JoinableQueue types are multi-producer, multi-consumer FIFO queues modelled on the Queue.Queue class in the standard library. They differ in that Queue lacks the task_done() and join() methods introduced into Python 2.5’s Queue.Queue 类。

若使用 JoinableQueue 那么 must call JoinableQueue.task_done() for each task removed from the queue or else the semaphore used to count the number of unfinished tasks may eventually overflow, raising an exception.

Note that one can also create a shared queue by using a manager object – see 管理器 .

注意

multiprocessing uses the usual Queue.Empty and Queue.Full exceptions to signal a timeout. They are not available in the multiprocessing namespace so you need to import them from Queue .

注意

When an object is put on a queue, the object is pickled and a background thread later flushes the pickled data to an underlying pipe. This has some consequences which are a little surprising, but should not cause any practical difficulties – if they really bother you then you can instead use a queue created with a manager .

1. After putting an object on an empty queue there may be an infinitesimal delay before the queue’s empty() 方法返回 False and get_nowait() can return without raising Queue.Empty .

2. If multiple processes are enqueuing objects, it is possible for the objects to be received at the other end out-of-order. However, objects enqueued by the same process will always be in the expected order with respect to each other.

警告

If a process is killed using Process.terminate() or os.kill() while it is trying to use a Queue , then the data in the queue is likely to become corrupted. This may cause any other process to get an exception when it tries to use the queue later on.

警告

As mentioned above, if a child process has put items on a queue (and it has not used JoinableQueue.cancel_join_thread ), then that process will not terminate until all buffered items have been flushed to the pipe.

This means that if you try joining that process you may get a deadlock unless you are sure that all items which have been put on the queue have been consumed. Similarly, if the child process is non-daemonic then the parent process may hang on exit when it tries to join all its non-daemonic children.

Note that a queue created using a manager does not have this issue. See 编程指导方针 .

For an example of the usage of queues for interprocess communication see 范例 .

multiprocessing. Pipe ( [ duplex ] ) ¶

Returns a pair (conn1, conn2) of Connection objects representing the ends of a pipe.

若 duplex is True (the default) then the pipe is bidirectional. If duplex is False then the pipe is unidirectional: conn1 can only be used for receiving messages and conn2 can only be used for sending messages.

class multiprocessing. 队列 ( [ maxsize ] ) ¶

Returns a process shared queue implemented using a pipe and a few locks/semaphores. When a process first puts an item on the queue a feeder thread is started which transfers objects from a buffer into the pipe.

The usual Queue.Empty and Queue.Full exceptions from the standard library’s Queue module are raised to signal timeouts.

Queue implements all the methods of Queue.Queue except for task_done() and join() .

qsize ( ) ¶

Return the approximate size of the queue. Because of multithreading/multiprocessing semantics, this number is not reliable.

Note that this may raise NotImplementedError on Unix platforms like Mac OS X where sem_getvalue() is not implemented.

empty ( ) ¶

返回 True 若队列为空， False otherwise. Because of multithreading/multiprocessing semantics, this is not reliable.

full ( ) ¶

返回 True 若队列是满的， False otherwise. Because of multithreading/multiprocessing semantics, this is not reliable.

put ( obj [ , block [ , timeout ] ] ) ¶

Put obj into the queue. If the optional argument block is True (the default) and timeout is None (默认)，阻塞若有必要直到空闲槽可用。若 timeout 是正数，它阻塞最多 timeout 秒并引发 Queue.Full 异常若在该时间内无可用空闲槽。否则 ( block is False ), put an item on the queue if a free slot is immediately available, else raise the Queue.Full 异常 ( timeout 被忽略在这种情况下)。

put_nowait ( obj ) ¶

相当于 put(obj, False) .

get ( [ block [ , timeout ] ] ) ¶

移除并返回项从队列。若可选自变量 block is True (the default) and timeout is None (默认)，阻塞若有必要直到项可用。若 timeout 是正数，它阻塞最多 timeout 秒并引发 Queue.Empty exception if no item was available within that time. Otherwise (block is False ), return an item if one is immediately available, else raise the Queue.Empty 异常 ( timeout 被忽略在这种情况下)。

get_nowait ( ) ¶

相当于 get(False) .

Queue has a few additional methods not found in Queue.Queue . These methods are usually unnecessary for most code:

close ( ) ¶

Indicate that no more data will be put on this queue by the current process. The background thread will quit once it has flushed all buffered data to the pipe. This is called automatically when the queue is garbage collected.

join_thread ( ) ¶

Join the background thread. This can only be used after close() has been called. It blocks until the background thread exits, ensuring that all data in the buffer has been flushed to the pipe.

By default if a process is not the creator of the queue then on exit it will attempt to join the queue’s background thread. The process can call cancel_join_thread() to make join_thread() do nothing.

cancel_join_thread ( ) ¶

Prevent join_thread() from blocking. In particular, this prevents the background thread from being joined automatically when the process exits – see join_thread() .

A better name for this method might be allow_exit_without_flush() . It is likely to cause enqueued data to lost, and you almost certainly will not need to use it. It is really only there if you need the current process to exit immediately without waiting to flush enqueued data to the underlying pipe, and you don’t care about lost data.

注意

This class’s functionality requires a functioning shared semaphore implementation on the host operating system. Without one, the functionality in this class will be disabled, and attempts to instantiate a Queue will result in an ImportError 。见 bpo-3770 for additional information. The same holds true for any of the specialized queue types listed below.

class multiprocessing.queues. SimpleQueue ¶

它是简化的 Queue 类型，很接近锁定 Pipe .

empty ( ) ¶

返回 True 若队列为空， False 否则。

get ( ) ¶

从队列移除并返回项。

put ( item ) ¶

Put item 进队列。

class multiprocessing. JoinableQueue ( [ maxsize ] ) ¶

JoinableQueue ， Queue subclass, is a queue which additionally has task_done() and join() 方法。

task_done ( ) ¶

指示先前排队任务已完成。用于队列消费者线程。对于每个 get() 用于抓取任务，后续调用 task_done() tells the queue that the processing on the task is complete.

若 join() is currently blocking, it will resume when all items have been processed (meaning that a task_done() call was received for every item that had been put() into the queue).

引发 ValueError if called more times than there were items placed in the queue.

join ( ) ¶

Block until all items in the queue have been gotten and processed.

The count of unfinished tasks goes up whenever an item is added to the queue. The count goes down whenever a consumer thread calls task_done() to indicate that the item was retrieved and all work on it is complete. When the count of unfinished tasks drops to zero, join() unblocks.

16.6.2.3. 杂项 ¶

multiprocessing. active_children ( ) ¶

返回当前进程所有存活子级的列表。

Calling this has the side effect of “joining” any processes which have already finished.

multiprocessing. cpu_count ( ) ¶

Return the number of CPUs in the system. May raise NotImplementedError .

multiprocessing. current_process ( ) ¶

返回 Process object corresponding to the current process.

An analogue of threading.current_thread() .

multiprocessing. freeze_support ( ) ¶

Add support for when a program which uses multiprocessing has been frozen to produce a Windows executable. (Has been tested with py2exe , PyInstaller and cx_Freeze )。

One needs to call this function straight after the if __name__ == '__main__' line of the main module. For example:

from multiprocessing import Process, freeze_support
def f():
    print 'hello world!'
if __name__ == '__main__':
    freeze_support()
    Process(target=f).start()
						

若 freeze_support() line is omitted then trying to run the frozen executable will raise RuntimeError .

调用 freeze_support() has no effect when invoked on any operating system other than Windows. In addition, if the module is being run normally by the Python interpreter on Windows (the program has not been frozen), then freeze_support() 不起作用。

multiprocessing. set_executable ( ) ¶

Sets the path of the Python interpreter to use when starting a child process. (By default sys.executable is used). Embedders will probably need to do some thing like

set_executable(os.path.join(sys.exec_prefix, 'pythonw.exe'))
						

before they can create child processes. (Windows only)

注意

multiprocessing contains no analogues of threading.active_count() , threading.enumerate() , threading.settrace() , threading.setprofile() , threading.Timer ，或 threading.local .

16.6.2.4. 连接对象 ¶

Connection objects allow the sending and receiving of picklable objects or strings. They can be thought of as message oriented connected sockets.

通常创建 Connection 对象是使用 Pipe – 另请参阅 Listener 和 Client .

class Connection ¶

send ( obj ) ¶

Send an object to the other end of the connection which should be read using recv() .

The object must be picklable. Very large pickles (approximately 32 MB+, though it depends on the OS) may raise a ValueError 异常。

recv ( ) ¶

Return an object sent from the other end of the connection using send() . Blocks until there is something to receive. Raises EOFError if there is nothing left to receive and the other end was closed.

fileno ( ) ¶

Return the file descriptor or handle used by the connection.

close ( ) ¶

关闭连接。

This is called automatically when the connection is garbage collected.

poll ( [ timeout ] ) ¶

Return whether there is any data available to be read.

若 timeout is not specified then it will return immediately. If timeout is a number then this specifies the maximum time in seconds to block. If timeout is None then an infinite timeout is used.

send_bytes ( buffer [ , offset [ , size ] ] ) ¶

Send byte data from an object supporting the buffer interface as a complete message.

若 offset is given then data is read from that position in buffer 。若 size is given then that many bytes will be read from buffer. Very large buffers (approximately 32 MB+, though it depends on the OS) may raise a ValueError exception

recv_bytes ( [ maxlength ] ) ¶

Return a complete message of byte data sent from the other end of the connection as a string. Blocks until there is something to receive. Raises EOFError if there is nothing left to receive and the other end has closed.

若 maxlength is specified and the message is longer than maxlength then IOError is raised and the connection will no longer be readable.

recv_bytes_into ( buffer [ , offset ] ) ¶

Read into buffer a complete message of byte data sent from the other end of the connection and return the number of bytes in the message. Blocks until there is something to receive. Raises EOFError if there is nothing left to receive and the other end was closed.

buffer must be an object satisfying the writable buffer interface. If offset is given then the message will be written into the buffer from that position. Offset must be a non-negative integer less than the length of buffer (in bytes).

If the buffer is too short then a BufferTooShort exception is raised and the complete message is available as e.args[0] where e is the exception instance.

例如：

>>> from multiprocessing import Pipe
>>> a, b = Pipe()
>>> a.send([1, 'hello', None])
>>> b.recv()
[1, 'hello', None]
>>> b.send_bytes('thank you')
>>> a.recv_bytes()
'thank you'
>>> import array
>>> arr1 = array.array('i', range(5))
>>> arr2 = array.array('i', [0] * 10)
>>> a.send_bytes(arr1)
>>> count = b.recv_bytes_into(arr2)
>>> assert count == len(arr1) * arr1.itemsize
>>> arr2
array('i', [0, 1, 2, 3, 4, 0, 0, 0, 0, 0])
							

警告

The Connection.recv() method automatically unpickles the data it receives, which can be a security risk unless you can trust the process which sent the message.

Therefore, unless the connection object was produced using Pipe() you should only use the recv() and send() methods after performing some sort of authentication. See 身份验证键 .

警告

If a process is killed while it is trying to read or write to a pipe then the data in the pipe is likely to become corrupted, because it may become impossible to be sure where the message boundaries lie.

16.6.2.5. 同步原语 ¶

Generally synchronization primitives are not as necessary in a multiprocess program as they are in a multithreaded program. See the documentation for threading 模块。

Note that one can also create synchronization primitives by using a manager object – see 管理器 .

class multiprocessing. BoundedSemaphore ( [ 值 ] ) ¶

A bounded semaphore object: a close analog of threading.BoundedSemaphore .

A solitary difference from its close analog exists: its acquire method’s first argument is named block and it supports an optional second argument timeout , as is consistent with Lock.acquire() .

注意

On Mac OS X, this is indistinguishable from Semaphore 因为 sem_getvalue() is not implemented on that platform.

class multiprocessing. 条件 ( [ lock ] ) ¶

A condition variable: a clone of threading.Condition .

若 lock is specified then it should be a Lock or RLock 对象从 multiprocessing .

class multiprocessing. 事件 ¶

克隆自 threading.Event . This method returns the state of the internal semaphore on exit, so it will always return True 除了有给定 timeout (超时)，且操作超时。

2.7 版改变： 以前，方法总是返回 None .

class multiprocessing. 锁 ¶

A non-recursive lock object: a close analog of threading.Lock . Once a process or thread has acquired a lock, subsequent attempts to acquire it from any process or thread will block until it is released; any process or thread may release it. The concepts and behaviors of threading.Lock as it applies to threads are replicated here in multiprocessing.Lock as it applies to either processes or threads, except as noted.

注意， Lock is actually a factory function which returns an instance of multiprocessing.synchronize.Lock initialized with a default context.

Lock 支持 上下文管理器 protocol and thus may be used in with 语句。

acquire ( block=True , timeout=None ) ¶

获得锁，阻塞或非阻塞。

With the block 自变量设为 True (the default), the method call will block until the lock is in an unlocked state, then set it to locked and return True . Note that the name of this first argument differs from that in threading.Lock.acquire() .

With the block 自变量设为 False , the method call does not block. If the lock is currently in a locked state, return False ; otherwise set the lock to a locked state and return True .

When invoked with a positive, floating-point value for timeout , block for at most the number of seconds specified by timeout as long as the lock can not be acquired. Invocations with a negative value for timeout are equivalent to a timeout of zero. Invocations with a timeout value of None (the default) set the timeout period to infinite. The timeout argument has no practical implications if the block argument is set to False and is thus ignored. Returns True if the lock has been acquired or False if the timeout period has elapsed. Note that the timeout argument does not exist in this method’s analog, threading.Lock.acquire() .

release ( ) ¶

Release a lock. This can be called from any process or thread, not only the process or thread which originally acquired the lock.

Behavior is the same as in threading.Lock.release() except that when invoked on an unlocked lock, a ValueError 被引发。

class multiprocessing. RLock ¶

A recursive lock object: a close analog of threading.RLock . A recursive lock must be released by the process or thread that acquired it. Once a process or thread has acquired a recursive lock, the same process or thread may acquire it again without blocking; that process or thread must release it once for each time it has been acquired.

注意， RLock is actually a factory function which returns an instance of multiprocessing.synchronize.RLock initialized with a default context.

RLock 支持 上下文管理器 protocol and thus may be used in with 语句。

acquire ( block=True , timeout=None ) ¶

获得锁，阻塞或非阻塞。

当援引采用 block 自变量设为 True , block until the lock is in an unlocked state (not owned by any process or thread) unless the lock is already owned by the current process or thread. The current process or thread then takes ownership of the lock (if it does not already have ownership) and the recursion level inside the lock increments by one, resulting in a return value of True . Note that there are several differences in this first argument’s behavior compared to the implementation of threading.RLock.acquire() , starting with the name of the argument itself.

当援引采用 block 自变量设为 False , do not block. If the lock has already been acquired (and thus is owned) by another process or thread, the current process or thread does not take ownership and the recursion level within the lock is not changed, resulting in a return value of False . If the lock is in an unlocked state, the current process or thread takes ownership and the recursion level is incremented, resulting in a return value of True .

Use and behaviors of the timeout argument are the same as in Lock.acquire() 。注意， timeout argument does not exist in this method’s analog, threading.RLock.acquire() .

release ( ) ¶

Release a lock, decrementing the recursion level. If after the decrement the recursion level is zero, reset the lock to unlocked (not owned by any process or thread) and if any other processes or threads are blocked waiting for the lock to become unlocked, allow exactly one of them to proceed. If after the decrement the recursion level is still nonzero, the lock remains locked and owned by the calling process or thread.

Only call this method when the calling process or thread owns the lock. An AssertionError is raised if this method is called by a process or thread other than the owner or if the lock is in an unlocked (unowned) state. Note that the type of exception raised in this situation differs from the implemented behavior in threading.RLock.release() .

class multiprocessing. 信号量 ( [ 值 ] ) ¶

A semaphore object: a close analog of threading.Semaphore .

A solitary difference from its close analog exists: its acquire method’s first argument is named block and it supports an optional second argument timeout , as is consistent with Lock.acquire() .

注意

The acquire() 方法为 BoundedSemaphore , Lock , RLock and Semaphore has a timeout parameter not supported by the equivalents in threading . The signature is acquire(block=True, timeout=None) with keyword parameters being acceptable. If block is True and timeout 不是 None then it specifies a timeout in seconds. If block is False then timeout 被忽略。

在 Mac OS X， sem_timedwait 不被支持，所以调用 acquire() with a timeout will emulate that function’s behavior using a sleeping loop.

注意

If the SIGINT signal generated by Ctrl-C arrives while the main thread is blocked by a call to BoundedSemaphore.acquire() , Lock.acquire() , RLock.acquire() , Semaphore.acquire() , Condition.acquire() or Condition.wait() then the call will be immediately interrupted and KeyboardInterrupt 会被引发。

This differs from the behaviour of threading where SIGINT will be ignored while the equivalent blocking calls are in progress.

注意

Some of this package’s functionality requires a functioning shared semaphore implementation on the host operating system. Without one, the multiprocessing.synchronize module will be disabled, and attempts to import it will result in an ImportError 。见 bpo-3770 for additional information.

16.6.2.6. 共享 ctypes 对象 ¶

It is possible to create shared objects using shared memory which can be inherited by child processes.

multiprocessing. 值 ( typecode_or_type , *args [ , lock ] ) ¶

返回 ctypes object allocated from shared memory. By default the return value is actually a synchronized wrapper for the object.

typecode_or_type determines the type of the returned object: it is either a ctypes type or a one character typecode of the kind used by the array 模块。 *args is passed on to the constructor for the type.

若 lock is True (the default) then a new recursive lock object is created to synchronize access to the value. If lock 是 Lock or RLock object then that will be used to synchronize access to the value. If lock is False then access to the returned object will not be automatically protected by a lock, so it will not necessarily be “process-safe”.

Operations like += which involve a read and write are not atomic. So if, for instance, you want to atomically increment a shared value it is insufficient to just do

counter.value += 1
								

Assuming the associated lock is recursive (which it is by default) you can instead do

with counter.get_lock():
    counter.value += 1
								

注意， lock 是仅关键词自变量。

multiprocessing. 数组 ( typecode_or_type , size_or_initializer , * , lock=True ) ¶

Return a ctypes array allocated from shared memory. By default the return value is actually a synchronized wrapper for the array.

typecode_or_type determines the type of the elements of the returned array: it is either a ctypes type or a one character typecode of the kind used by the array module. If size_or_initializer is an integer, then it determines the length of the array, and the array will be initially zeroed. Otherwise, size_or_initializer is a sequence which is used to initialize the array and whose length determines the length of the array.

若 lock is True (the default) then a new lock object is created to synchronize access to the value. If lock 是 Lock or RLock object then that will be used to synchronize access to the value. If lock is False then access to the returned object will not be automatically protected by a lock, so it will not necessarily be “process-safe”.

注意， lock 是仅关键词自变量。

Note that an array of ctypes.c_char has value and raw attributes which allow one to use it to store and retrieve strings.

16.6.2.6.1. The multiprocessing.sharedctypes 模块 ¶

The multiprocessing.sharedctypes module provides functions for allocating ctypes objects from shared memory which can be inherited by child processes.

注意

Although it is possible to store a pointer in shared memory remember that this will refer to a location in the address space of a specific process. However, the pointer is quite likely to be invalid in the context of a second process and trying to dereference the pointer from the second process may cause a crash.

multiprocessing.sharedctypes. RawArray ( typecode_or_type , size_or_initializer ) ¶

Return a ctypes array allocated from shared memory.

typecode_or_type determines the type of the elements of the returned array: it is either a ctypes type or a one character typecode of the kind used by the array module. If size_or_initializer is an integer then it determines the length of the array, and the array will be initially zeroed. Otherwise size_or_initializer is a sequence which is used to initialize the array and whose length determines the length of the array.

Note that setting and getting an element is potentially non-atomic – use Array() instead to make sure that access is automatically synchronized using a lock.

multiprocessing.sharedctypes. RawValue ( typecode_or_type , *args ) ¶

Return a ctypes object allocated from shared memory.

typecode_or_type determines the type of the returned object: it is either a ctypes type or a one character typecode of the kind used by the array 模块。 *args is passed on to the constructor for the type.

Note that setting and getting the value is potentially non-atomic – use Value() instead to make sure that access is automatically synchronized using a lock.

Note that an array of ctypes.c_char has value and raw attributes which allow one to use it to store and retrieve strings – see documentation for ctypes .

multiprocessing.sharedctypes. 数组 ( typecode_or_type , size_or_initializer , *args [ , lock ] ) ¶

如同 RawArray() except that depending on the value of lock a process-safe synchronization wrapper may be returned instead of a raw ctypes array.

若 lock is True (the default) then a new lock object is created to synchronize access to the value. If lock 是 Lock or RLock object then that will be used to synchronize access to the value. If lock is False then access to the returned object will not be automatically protected by a lock, so it will not necessarily be “process-safe”.

注意， lock 是仅关键词自变量。

multiprocessing.sharedctypes. 值 ( typecode_or_type , *args [ , lock ] ) ¶

如同 RawValue() except that depending on the value of lock a process-safe synchronization wrapper may be returned instead of a raw ctypes object.

若 lock is True (the default) then a new lock object is created to synchronize access to the value. If lock 是 Lock or RLock object then that will be used to synchronize access to the value. If lock is False then access to the returned object will not be automatically protected by a lock, so it will not necessarily be “process-safe”.

注意， lock 是仅关键词自变量。

multiprocessing.sharedctypes. copy ( obj ) ¶

Return a ctypes object allocated from shared memory which is a copy of the ctypes object obj .

multiprocessing.sharedctypes. synchronized ( obj [ , lock ] ) ¶

Return a process-safe wrapper object for a ctypes object which uses lock to synchronize access. If lock is None (the default) then a multiprocessing.RLock object is created automatically.

A synchronized wrapper will have two methods in addition to those of the object it wraps: get_obj() returns the wrapped object and get_lock() returns the lock object used for synchronization.

Note that accessing the ctypes object through the wrapper can be a lot slower than accessing the raw ctypes object.

The table below compares the syntax for creating shared ctypes objects from shared memory with the normal ctypes syntax. (In the table MyStruct is some subclass of ctypes.Structure )。

ctypes	sharedctypes 使用类型	sharedctypes 使用 typecode
c_double(2.4)	RawValue(c_double, 2.4)	RawValue(‘d’, 2.4)
MyStruct(4, 6)	RawValue(MyStruct, 4, 6)
(c_short * 7)()	RawArray(c_short, 7)	RawArray(‘h’, 7)
(c_int * 3)(9, 2, 8)	RawArray(c_int, (9, 2, 8))	RawArray(‘i’, (9, 2, 8))

Below is an example where a number of ctypes objects are modified by a child process:

from multiprocessing import Process, Lock
from multiprocessing.sharedctypes import Value, Array
from ctypes import Structure, c_double
class Point(Structure):
    _fields_ = [('x', c_double), ('y', c_double)]
def modify(n, x, s, A):
    n.value **= 2
    x.value **= 2
    s.value = s.value.upper()
    for a in A:
        a.x **= 2
        a.y **= 2
if __name__ == '__main__':
    lock = Lock()
    n = Value('i', 7)
    x = Value(c_double, 1.0/3.0, lock=False)
    s = Array('c', 'hello world', lock=lock)
    A = Array(Point, [(1.875,-6.25), (-5.75,2.0), (2.375,9.5)], lock=lock)
    p = Process(target=modify, args=(n, x, s, A))
    p.start()
    p.join()
    print n.value
    print x.value
    print s.value
    print [(a.x, a.y) for a in A]
						

打印的结果为

49
0.1111111111111111
HELLO WORLD
[(3.515625, 39.0625), (33.0625, 4.0), (5.640625, 90.25)]
						

16.6.2.7. 管理器 ¶

Managers provide a way to create data which can be shared between different processes. A manager object controls a server process which manages shared objects . Other processes can access the shared objects by using proxies.

multiprocessing. Manager ( ) ¶

返回已启动的 SyncManager object which can be used for sharing objects between processes. The returned manager object corresponds to a spawned child process and has methods which will create shared objects and return corresponding proxies.

Manager processes will be shutdown as soon as they are garbage collected or their parent process exits. The manager classes are defined in the multiprocessing.managers 模块：

class multiprocessing.managers. BaseManager ( [ address [ , authkey ] ] ) ¶

创建 BaseManager 对象。

一旦创建一个应该调用 start() or get_server().serve_forever() 确保管理器对象引用已启动的管理器进程。

address 是管理器进程监听新连接的地址。若 address is None 那么选取任意一个。

authkey is the authentication key which will be used to check the validity of incoming connections to the server process. If authkey is None then current_process().authkey . Otherwise authkey is used and it must be a string.

start ( [ initializer [ , initargs ] ] ) ¶

启动子进程以启动管理器。若 initializer 不是 None 那么子进程将调用 initializer(*initargs) 当它开始时。

get_server ( ) ¶

返回 Server object which represents the actual server under the control of the Manager. The Server 对象支持 serve_forever() 方法：

>>> from multiprocessing.managers import BaseManager
>>> manager = BaseManager(address=('', 50000), authkey='abc')
>>> server = manager.get_server()
>>> server.serve_forever()
										

Server 此外，还拥有 address 属性。

connect ( ) ¶

连接本地管理器对象到远程管理器进程：

>>> from multiprocessing.managers import BaseManager
>>> m = BaseManager(address=('127.0.0.1', 5000), authkey='abc')
>>> m.connect()
										

shutdown ( ) ¶

停止用于管理器的进程。这才可用若 start() 已用于启动服务器进程。

这可以被多次调用。

register ( typeid [ , callable [ , proxytype [ , exposed [ , method_to_typeid [ , create_method ] ] ] ] ] ) ¶

A classmethod which can be used for registering a type or callable with the manager class.

typeid is a “type identifier” which is used to identify a particular type of shared object. This must be a string.

callable is a callable used for creating objects for this type identifier. If a manager instance will be created using the from_address() classmethod or if the create_method 自变量为 False then this can be left as None .

proxytype 是子类化的 BaseProxy which is used to create proxies for shared objects with this typeid 。若 None then a proxy class is created automatically.

exposed is used to specify a sequence of method names which proxies for this typeid should be allowed to access using BaseProxy._callmethod() . (If exposed is None then proxytype._exposed_ is used instead if it exists.) In the case where no exposed list is specified, all “public methods” of the shared object will be accessible. (Here a “public method” means any attribute which has a __call__() method and whose name does not begin with '_' )。

method_to_typeid is a mapping used to specify the return type of those exposed methods which should return a proxy. It maps method names to typeid strings. (If method_to_typeid is None then proxytype._method_to_typeid_ is used instead if it exists.) If a method’s name is not a key of this mapping or if the mapping is None then the object returned by the method will be copied by value.

create_method determines whether a method should be created with name typeid which can be used to tell the server process to create a new shared object and return a proxy for it. By default it is True .

BaseManager 实例还有 1 只读特性：

address ¶

用于管理器的地址。

class multiprocessing.managers. SyncManager ¶

子类化的 BaseManager which can be used for the synchronization of processes. Objects of this type are returned by multiprocessing.Manager() .

It also supports creation of shared lists and dictionaries.

BoundedSemaphore ( [ 值 ] ) ¶

创建共享 threading.BoundedSemaphore 对象并返回它的代理。

条件 ( [ lock ] ) ¶

创建共享 threading.Condition 对象并返回它的代理。

若 lock 被供给，则它应该是代理对于 threading.Lock or threading.RLock 对象。

事件 ( ) ¶

创建共享 threading.Event 对象并返回它的代理。

锁 ( ) ¶

创建共享 threading.Lock 对象并返回它的代理。

Namespace ( ) ¶

创建共享 Namespace 对象并返回它的代理。

队列 ( [ maxsize ] ) ¶

创建共享 Queue.Queue 对象并返回它的代理。

RLock ( ) ¶

创建共享 threading.RLock 对象并返回它的代理。

信号量 ( [ 值 ] ) ¶

创建共享 threading.Semaphore 对象并返回它的代理。

数组 ( typecode , sequence ) ¶

创建数组并返回其代理。

值 ( typecode , 值 ) ¶

创建对象具有可写 value 属性并为它返回代理。

dict ( ) ¶

dict ( 映射 )

dict ( sequence )

创建共享 dict 对象并返回它的代理。

list ( ) ¶

list ( sequence )

创建共享 list 对象并返回它的代理。

注意

Modifications to mutable values or items in dict and list proxies will not be propagated through the manager, because the proxy has no way of knowing when its values or items are modified. To modify such an item, you can re-assign the modified object to the container proxy:

# create a list proxy and append a mutable object (a dictionary)
lproxy = manager.list()
lproxy.append({})
# now mutate the dictionary
d = lproxy[0]
d['a'] = 1
d['b'] = 2
# at this point, the changes to d are not yet synced, but by
# reassigning the dictionary, the proxy is notified of the change
lproxy[0] = d
								

class multiprocessing.managers. Namespace ¶

A type that can register with SyncManager .

A namespace object has no public methods, but does have writable attributes. Its representation shows the values of its attributes.

However, when using a proxy for a namespace object, an attribute beginning with '_' will be an attribute of the proxy and not an attribute of the referent:

>>> manager = multiprocessing.Manager()
>>> Global = manager.Namespace()
>>> Global.x = 10
>>> Global.y = 'hello'
>>> Global._z = 12.3    # this is an attribute of the proxy
>>> print Global
Namespace(x=10, y='hello')
									

16.6.2.7.1. 定制管理器 ¶

To create one’s own manager, one creates a subclass of BaseManager and uses the register() classmethod to register new types or callables with the manager class. For example:

from multiprocessing.managers import BaseManager
class MathsClass(object):
    def add(self, x, y):
        return x + y
    def mul(self, x, y):
        return x * y
class MyManager(BaseManager):
    pass
MyManager.register('Maths', MathsClass)
if __name__ == '__main__':
    manager = MyManager()
    manager.start()
    maths = manager.Maths()
    print maths.add(4, 3)         # prints 7
    print maths.mul(7, 8)         # prints 56
								

16.6.2.7.2. 使用远程管理器 ¶

It is possible to run a manager server on one machine and have clients use it from other machines (assuming that the firewalls involved allow it).

Running the following commands creates a server for a single shared queue which remote clients can access:

>>> from multiprocessing.managers import BaseManager
>>> import Queue
>>> queue = Queue.Queue()
>>> class QueueManager(BaseManager): pass
>>> QueueManager.register('get_queue', callable=lambda:queue)
>>> m = QueueManager(address=('', 50000), authkey='abracadabra')
>>> s = m.get_server()
>>> s.serve_forever()
								

One client can access the server as follows:

>>> from multiprocessing.managers import BaseManager
>>> class QueueManager(BaseManager): pass
>>> QueueManager.register('get_queue')
>>> m = QueueManager(address=('foo.bar.org', 50000), authkey='abracadabra')
>>> m.connect()
>>> queue = m.get_queue()
>>> queue.put('hello')
								

Another client can also use it:

>>> from multiprocessing.managers import BaseManager
>>> class QueueManager(BaseManager): pass
>>> QueueManager.register('get_queue')
>>> m = QueueManager(address=('foo.bar.org', 50000), authkey='abracadabra')
>>> m.connect()
>>> queue = m.get_queue()
>>> queue.get()
'hello'
								

Local processes can also access that queue, using the code from above on the client to access it remotely:

>>> from multiprocessing import Process, Queue
>>> from multiprocessing.managers import BaseManager
>>> class Worker(Process):
...     def __init__(self, q):
...         self.q = q
...         super(Worker, self).__init__()
...     def run(self):
...         self.q.put('local hello')
...
>>> queue = Queue()
>>> w = Worker(queue)
>>> w.start()
>>> class QueueManager(BaseManager): pass
...
>>> QueueManager.register('get_queue', callable=lambda: queue)
>>> m = QueueManager(address=('', 50000), authkey='abracadabra')
>>> s = m.get_server()
>>> s.serve_forever()
								

16.6.2.8. 代理对象 ¶

A proxy is an object which refers to a shared object which lives (presumably) in a different process. The shared object is said to be the referent of the proxy. Multiple proxy objects may have the same referent.

A proxy object has methods which invoke corresponding methods of its referent (although not every method of the referent will necessarily be available through the proxy). A proxy can usually be used in most of the same ways that its referent can:

>>> from multiprocessing import Manager
>>> manager = Manager()
>>> l = manager.list([i*i for i in range(10)])
>>> print l
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
>>> print repr(l)
<ListProxy object, typeid 'list' at 0x...>
>>> l[4]
16
>>> l[2:5]
[4, 9, 16]
									

Notice that applying str() to a proxy will return the representation of the referent, whereas applying repr() will return the representation of the proxy.

An important feature of proxy objects is that they are picklable so they can be passed between processes. Note, however, that if a proxy is sent to the corresponding manager’s process then unpickling it will produce the referent itself. This means, for example, that one shared object can contain a second:

>>> a = manager.list()
>>> b = manager.list()
>>> a.append(b)         # referent of a now contains referent of b
>>> print a, b
[[]] []
>>> b.append('hello')
>>> print a, b
[['hello']] ['hello']
										

注意

The proxy types in multiprocessing do nothing to support comparisons by value. So, for instance, we have:

>>> manager.list([1,2,3]) == [1,2,3]
False
											

One should just use a copy of the referent instead when making comparisons.

class multiprocessing.managers. BaseProxy ¶

Proxy objects are instances of subclasses of BaseProxy .

_callmethod ( methodname [ , args [ , kwds ] ] ) ¶

Call and return the result of a method of the proxy’s referent.

若 proxy is a proxy whose referent is obj then the expression

proxy._callmethod(methodname, args, kwds)
															

will evaluate the expression

getattr(obj, methodname)(*args, **kwds)
															

in the manager’s process.

The returned value will be a copy of the result of the call or a proxy to a new shared object – see documentation for the method_to_typeid 自变量 BaseManager.register() .

If an exception is raised by the call, then is re-raised by _callmethod() . If some other exception is raised in the manager’s process then this is converted into a RemoteError exception and is raised by _callmethod() .

Note in particular that an exception will be raised if methodname has not been exposed .

An example of the usage of _callmethod() :

>>> l = manager.list(range(10))
>>> l._callmethod('__len__')
10
>>> l._callmethod('__getslice__', (2, 7))   # equiv to `l[2:7]`
[2, 3, 4, 5, 6]
>>> l._callmethod('__getitem__', (20,))     # equiv to `l[20]`
Traceback (most recent call last):
...
IndexError: list index out of range
																

_getvalue ( ) ¶

Return a copy of the referent.

If the referent is unpicklable then this will raise an exception.

__repr__ ( ) ¶

返回代理对象的表示。

__str__ ( ) ¶

返回引用的表示。

16.6.2.8.1. 清理 ¶

A proxy object uses a weakref callback so that when it gets garbage collected it deregisters itself from the manager which owns its referent.

A shared object gets deleted from the manager process when there are no longer any proxies referring to it.

16.6.2.9. 进程池 ¶

可以创建用于履行提交给它的任务的一个进程池，采用 Pool 类。

class multiprocessing. Pool ( [ processes [ , initializer [ , initargs [ , maxtasksperchild ] ] ] ] ) ¶

进程池对象，可以控制向其提交作业的工作者进程池。它支持带有超时和回调的异步结果，并拥有并行映射实现。

processes 是要使用的工作进程数。若 processes is None 那么，返回数通过 cpu_count() 被使用。若 initializer 不是 None 那么，各工作者进程将调用 initializer(*initargs) 当它开始时。

注意：池对象的方法仅应由创建池的进程调用。

New in version 2.7: maxtasksperchild 是工作进程在退出之前可以完成的任务数，然后以刷新工作者进程替换，释放未使用的资源。默认 maxtasksperchild is None ，这意味着工作者进程将活得与池一样长。

注意

工作者进程在 Pool typically live for the complete duration of the Pool’s work queue. A frequent pattern found in other systems (such as Apache, mod_wsgi, etc) to free resources held by workers is to allow a worker within a pool to complete only a set amount of work before being exiting, being cleaned up and a new process spawned to replace the old one. The maxtasksperchild 自变量到 Pool exposes this ability to the end user.

apply ( func [ , args [ , kwds ] ] ) ¶

Equivalent of the apply() built-in function. It blocks until the result is ready, so apply_async() 更适合并行履行工作。此外， func 是池的唯一执行工作者。

apply_async ( func [ , args [ , kwds [ , callback ] ] ] ) ¶

变体 apply() 方法返回结果对象。

若 callback is specified then it should be a callable which accepts a single argument. When the result becomes ready callback is applied to it (unless the call failed). callback should complete immediately since otherwise the thread which handles the results will get blocked.

map ( func , iterable [ , chunksize ] ) ¶

并行等效于 map() built-in function (it supports only one iterable argument though). It blocks until the result is ready.

This method chops the iterable into a number of chunks which it submits to the process pool as separate tasks. The (approximate) size of these chunks can be specified by setting chunksize to a positive integer.

map_async ( func , iterable [ , chunksize [ , callback ] ] ) ¶

变体 map() 方法返回结果对象。

若 callback is specified then it should be a callable which accepts a single argument. When the result becomes ready callback is applied to it (unless the call failed). callback should complete immediately since otherwise the thread which handles the results will get blocked.

imap ( func , iterable [ , chunksize ] ) ¶

An equivalent of itertools.imap() .

The chunksize argument is the same as the one used by the map() method. For very long iterables using a large value for chunksize can make the job complete much faster than using the default value of 1 .

Also if chunksize is 1 那么 next() method of the iterator returned by the imap() method has an optional timeout parameter: next(timeout) 会引发 multiprocessing.TimeoutError if the result cannot be returned within timeout 秒。

imap_unordered ( func , iterable [ , chunksize ] ) ¶

如同 imap() except that the ordering of the results from the returned iterator should be considered arbitrary. (Only when there is only one worker process is the order guaranteed to be “correct”.)

close ( ) ¶

Prevents any more tasks from being submitted to the pool. Once all the tasks have been completed the worker processes will exit.

terminate ( ) ¶

Stops the worker processes immediately without completing outstanding work. When the pool object is garbage collected terminate() will be called immediately.

join ( ) ¶

等待工作者进程退出。必须调用 close() or terminate() before using join() .

class multiprocessing.pool. AsyncResult ¶

The class of the result returned by Pool.apply_async() and Pool.map_async() .

get ( [ timeout ] ) ¶

返回结果当它到达时。若 timeout 不是 None and the result does not arrive within timeout seconds then multiprocessing.TimeoutError is raised. If the remote call raised an exception then that exception will be reraised by get() .

wait ( [ timeout ] ) ¶

等待直到结果可用或直到 timeout 秒过去。

ready ( ) ¶

返回调用是否已完成。

successful ( ) ¶

Return whether the call completed without raising an exception. Will raise AssertionError if the result is not ready.

下列范例演示池的使用：

from multiprocessing import Pool
import time
def f(x):
    return x*x
if __name__ == '__main__':
    pool = Pool(processes=4)              # start 4 worker processes
    result = pool.apply_async(f, (10,))   # evaluate "f(10)" asynchronously in a single process
    print result.get(timeout=1)           # prints "100" unless your computer is *very* slow
    print pool.map(f, range(10))          # prints "[0, 1, 4,..., 81]"
    it = pool.imap(f, range(10))
    print it.next()                       # prints "0"
    print it.next()                       # prints "1"
    print it.next(timeout=1)              # prints "4" unless your computer is *very* slow
    result = pool.apply_async(time.sleep, (10,))
    print result.get(timeout=1)           # raises multiprocessing.TimeoutError
													

16.6.2.10. Listener 和 Client ¶

Usually message passing between processes is done using queues or by using Connection objects returned by Pipe() .

不管怎样， multiprocessing.connection module allows some extra flexibility. It basically gives a high level message oriented API for dealing with sockets or Windows named pipes, and also has support for digest authentication 使用 hmac 模块。

multiprocessing.connection. deliver_challenge ( connection , authkey ) ¶

Send a randomly generated message to the other end of the connection and wait for a reply.

If the reply matches the digest of the message using authkey as the key then a welcome message is sent to the other end of the connection. Otherwise AuthenticationError 被引发。

multiprocessing.connection. answer_challenge ( connection , authkey ) ¶

Receive a message, calculate the digest of the message using authkey as the key, and then send the digest back.

If a welcome message is not received, then AuthenticationError 被引发。

multiprocessing.connection. Client ( address [ , 系列 [ , authenticate [ , authkey ] ] ] ) ¶

Attempt to set up a connection to the listener which is using address address ，返回 Connection .

The type of the connection is determined by 系列 argument, but this can generally be omitted since it can usually be inferred from the format of address 。(见 地址格式 )

若 authenticate is True or authkey is a string then digest authentication is used. The key used for authentication will be either authkey or current_process().authkey) if authkey is None . If authentication fails then AuthenticationError 被引发。见 身份验证键 .

class multiprocessing.connection. Listener ( [ address [ , 系列 [ , backlog [ , authenticate [ , authkey ] ] ] ] ] ) ¶

A wrapper for a bound socket or Windows named pipe which is ‘listening’ for connections.

address is the address to be used by the bound socket or named pipe of the listener object.

注意

If an address of ‘0.0.0.0’ is used, the address will not be a connectable end point on Windows. If you require a connectable end-point, you should use ‘127.0.0.1’.

系列 is the type of socket (or named pipe) to use. This can be one of the strings 'AF_INET' (for a TCP socket), 'AF_UNIX' (for a Unix domain socket) or 'AF_PIPE' (for a Windows named pipe). Of these only the first is guaranteed to be available. If 系列 is None then the family is inferred from the format of address 。若 address is also None then a default is chosen. This default is the family which is assumed to be the fastest available. See 地址格式 。注意，若 系列 is 'AF_UNIX' and address is None then the socket will be created in a private temporary directory created using tempfile.mkstemp() .

If the listener object uses a socket then backlog (1 by default) is passed to the listen() method of the socket once it has been bound.

若 authenticate is True ( False by default) or authkey 不是 None then digest authentication is used.

若 authkey is a string then it will be used as the authentication key; otherwise it must be None .

若 authkey is None and authenticate is True then current_process().authkey is used as the authentication key. If authkey is None and authenticate is False then no authentication is done. If authentication fails then AuthenticationError 被引发。见 身份验证键 .

accept ( ) ¶

Accept a connection on the bound socket or named pipe of the listener object and return a Connection object. If authentication is attempted and fails, then AuthenticationError 被引发。

close ( ) ¶

Close the bound socket or named pipe of the listener object. This is called automatically when the listener is garbage collected. However it is advisable to call it explicitly.

Listener 对象拥有以下只读特性：

address ¶

Listener 对象正在使用的地址。

last_accepted ¶

The address from which the last accepted connection came. If this is unavailable then it is None .

The module defines the following exceptions:

exception multiprocessing.connection. ProcessError ¶

The base class of all multiprocessing 异常。

exception multiprocessing.connection. BufferTooShort ¶

Exception raised by Connection.recv_bytes_into() when the supplied buffer object is too small for the message read.

exception multiprocessing.connection. AuthenticationError ¶

引发当存在身份验证错误时。

exception multiprocessing.connection. TimeoutError ¶

Raised by methods with a timeout when the timeout expires.

范例

The following server code creates a listener which uses 'secret password' as an authentication key. It then waits for a connection and sends some data to the client:

from multiprocessing.connection import Listener
from array import array
address = ('localhost', 6000)     # family is deduced to be 'AF_INET'
listener = Listener(address, authkey='secret password')
conn = listener.accept()
print 'connection accepted from', listener.last_accepted
conn.send([2.25, None, 'junk', float])
conn.send_bytes('hello')
conn.send_bytes(array('i', [42, 1729]))
conn.close()
listener.close()
													

The following code connects to the server and receives some data from the server:

from multiprocessing.connection import Client
from array import array
address = ('localhost', 6000)
conn = Client(address, authkey='secret password')
print conn.recv()                 # => [2.25, None, 'junk', float]
print conn.recv_bytes()            # => 'hello'
arr = array('i', [0, 0, 0, 0, 0])
print conn.recv_bytes_into(arr)     # => 8
print arr                         # => array('i', [42, 1729, 0, 0, 0])
conn.close()
													

16.6.2.10.1. 地址格式 ¶

• An 'AF_INET' 地址是元组采用形式 (hostname, port) where hostname 是字符串和 port 是整数。

• An 'AF_UNIX' 地址是表示文件系统中文件名的字符串。

• An 'AF_PIPE' 地址是字符串采用形式

  r'\.\pipe{PipeName}' 。要使用 Client() 连接到远程计算机的命名管道称为 ServerName ，应使用地址形式 r'\ServerName\pipe{PipeName}' 代替。

注意：默认情况下，任何以 2 反斜杠开头的字符串均假定为 'AF_PIPE' 地址而不是 'AF_UNIX' 地址。

16.6.2.11. 身份验证键 ¶

When one uses Connection.recv() , the data received is automatically unpickled. Unfortunately unpickling data from an untrusted source is a security risk. Therefore Listener and Client() 使用 hmac module to provide digest authentication.

An authentication key is a string which can be thought of as a password: once a connection is established both ends will demand proof that the other knows the authentication key. (Demonstrating that both ends are using the same key does not involve sending the key over the connection.)

If authentication is requested but no authentication key is specified then the return value of current_process().authkey 被使用 (见 Process ). This value will be automatically inherited by any Process object that the current process creates. This means that (by default) all processes of a multi-process program will share a single authentication key which can be used when setting up connections between themselves.

Suitable authentication keys can also be generated by using os.urandom() .

16.6.2.12. 日志 ¶

Some support for logging is available. Note, however, that the logging package does not use process shared locks so it is possible (depending on the handler type) for messages from different processes to get mixed up.

multiprocessing. get_logger ( ) ¶

Returns the logger used by multiprocessing . If necessary, a new one will be created.

When first created the logger has level logging.NOTSET and no default handler. Messages sent to this logger will not by default propagate to the root logger.

Note that on Windows child processes will only inherit the level of the parent process’s logger – any other customization of the logger will not be inherited.

multiprocessing. log_to_stderr ( ) ¶

This function performs a call to get_logger() but in addition to returning the logger created by get_logger, it adds a handler which sends output to sys.stderr using format '[%(levelname)s/%(processName)s] %(message)s' .

Below is an example session with logging turned on:

>>> import multiprocessing, logging
>>> logger = multiprocessing.log_to_stderr()
>>> logger.setLevel(logging.INFO)
>>> logger.warning('doomed')
[WARNING/MainProcess] doomed
>>> m = multiprocessing.Manager()
[INFO/SyncManager-...] child process calling self.run()
[INFO/SyncManager-...] created temp directory /.../pymp-...
[INFO/SyncManager-...] manager serving at '/.../listener-...'
>>> del m
[INFO/MainProcess] sending shutdown message to manager
[INFO/SyncManager-...] manager exiting with exitcode 0
													

In addition to having these two logging functions, the multiprocessing also exposes two additional logging level attributes. These are SUBWARNING and SUBDEBUG . The table below illustrates where theses fit in the normal level hierarchy.

级别	数值
SUBWARNING	25
SUBDEBUG	5

For a full table of logging levels, see the logging 模块。

These additional logging levels are used primarily for certain debug messages within the multiprocessing module. Below is the same example as above, except with SUBDEBUG enabled:

>>> import multiprocessing, logging
>>> logger = multiprocessing.log_to_stderr()
>>> logger.setLevel(multiprocessing.SUBDEBUG)
>>> logger.warning('doomed')
[WARNING/MainProcess] doomed
>>> m = multiprocessing.Manager()
[INFO/SyncManager-...] child process calling self.run()
[INFO/SyncManager-...] created temp directory /.../pymp-...
[INFO/SyncManager-...] manager serving at '/.../pymp-djGBXN/listener-...'
>>> del m
[SUBDEBUG/MainProcess] finalizer calling ...
[INFO/MainProcess] sending shutdown message to manager
[DEBUG/SyncManager-...] manager received shutdown message
[SUBDEBUG/SyncManager-...] calling <Finalize object, callback=unlink, ...
[SUBDEBUG/SyncManager-...] finalizer calling <built-in function unlink> ...
[SUBDEBUG/SyncManager-...] calling <Finalize object, dead>
[SUBDEBUG/SyncManager-...] finalizer calling <function rmtree at 0x5aa730> ...
[INFO/SyncManager-...] manager exiting with exitcode 0
													

16.6.2.13. The multiprocessing.dummy 模块 ¶

multiprocessing.dummy 复现的 API 源自 multiprocessing 但不超过包裹器围绕 threading 模块。

16.6.3. 编程指导方针 ¶

应遵循某些指导方针和习语，当使用 multiprocessing .

16.6.3.1. 所有平台 ¶

避免共享状态

As far as possible one should try to avoid shifting large amounts of data between processes.

It is probably best to stick to using queues or pipes for communication between processes rather than using the lower level synchronization primitives from the threading 模块。

Picklability

Ensure that the arguments to the methods of proxies are picklable.

代理的线程安全

Do not use a proxy object from more than one thread unless you protect it with a lock.

(There is never a problem with different processes using the same proxy.)

Joining zombie processes

On Unix when a process finishes but has not been joined it becomes a zombie. There should never be very many because each time a new process starts (or active_children() is called) all completed processes which have not yet been joined will be joined. Also calling a finished process’s Process.is_alive will join the process. Even so it is probably good practice to explicitly join all the processes that you start.

Better to inherit than pickle/unpickle

On Windows many types from multiprocessing need to be picklable so that child processes can use them. However, one should generally avoid sending shared objects to other processes using pipes or queues. Instead you should arrange the program so that a process which needs access to a shared resource created elsewhere can inherit it from an ancestor process.

避免终止进程

使用 Process.terminate method to stop a process is liable to cause any shared resources (such as locks, semaphores, pipes and queues) currently being used by the process to become broken or unavailable to other processes.

Therefore it is probably best to only consider using Process.terminate on processes which never use any shared resources.

Joining processes that use queues

Bear in mind that a process that has put items in a queue will wait before terminating until all the buffered items are fed by the “feeder” thread to the underlying pipe. (The child process can call the cancel_join_thread() method of the queue to avoid this behaviour.)

This means that whenever you use a queue you need to make sure that all items which have been put on the queue will eventually be removed before the process is joined. Otherwise you cannot be sure that processes which have put items on the queue will terminate. Remember also that non-daemonic processes will be joined automatically.

An example which will deadlock is the following:

from multiprocessing import Process, Queue
def f(q):
    q.put('X' * 1000000)
if __name__ == '__main__':
    queue = Queue()
    p = Process(target=f, args=(queue,))
    p.start()
    p.join()                    # this deadlocks
    obj = queue.get()
													

A fix here would be to swap the last two lines (or simply remove the p.join() line).

把资源明确传递给子级进程

On Unix a child process can make use of a shared resource created in a parent process using a global resource. However, it is better to pass the object as an argument to the constructor for the child process.

Apart from making the code (potentially) compatible with Windows this also ensures that as long as the child process is still alive the object will not be garbage collected in the parent process. This might be important if some resource is freed when the object is garbage collected in the parent process.

So for instance

from multiprocessing import Process, Lock
def f():
    ... do something using "lock" ...
if __name__ == '__main__':
    lock = Lock()
    for i in range(10):
        Process(target=f).start()
													

should be rewritten as

from multiprocessing import Process, Lock
def f(l):
    ... do something using "l" ...
if __name__ == '__main__':
    lock = Lock()
    for i in range(10):
        Process(target=f, args=(lock,)).start()
													

Beware of replacing sys.stdin with a “file like object”

multiprocessing originally unconditionally called:

os.close(sys.stdin.fileno())
													

在 multiprocessing.Process._bootstrap() method — this resulted in issues with processes-in-processes. This has been changed to:

sys.stdin.close()
sys.stdin = open(os.devnull)
													

Which solves the fundamental issue of processes colliding with each other resulting in a bad file descriptor error, but introduces a potential danger to applications which replace sys.stdin() with a “file-like object” with output buffering. This danger is that if multiple processes call close() on this file-like object, it could result in the same data being flushed to the object multiple times, resulting in corruption.

If you write a file-like object and implement your own caching, you can make it fork-safe by storing the pid whenever you append to the cache, and discarding the cache when the pid changes. For example:

@property
def cache(self):
    pid = os.getpid()
    if pid != self._pid:
        self._pid = pid
        self._cache = []
    return self._cache
													

更多信息，见 bpo-5155 , bpo-5313 and bpo-5331

16.6.3.2. Windows ¶

Since Windows lacks os.fork() it has a few extra restrictions:

More picklability

Ensure that all arguments to Process.__init__() are picklable. This means, in particular, that bound or unbound methods cannot be used directly as the target argument on Windows — just define a function and use that instead.

Also, if you subclass Process then make sure that instances will be picklable when the Process.start 方法被调用。

全局变量

Bear in mind that if code run in a child process tries to access a global variable, then the value it sees (if any) may not be the same as the value in the parent process at the time that Process.start was called.

However, global variables which are just module level constants cause no problems.

Safe importing of main module

Make sure that the main module can be safely imported by a new Python interpreter without causing unintended side effects (such a starting a new process).

For example, under Windows running the following module would fail with a RuntimeError :

from multiprocessing import Process
def foo():
    print 'hello'
p = Process(target=foo)
p.start()
													

Instead one should protect the “entry point” of the program by using if __name__ == '__main__': 如下：

from multiprocessing import Process, freeze_support
def foo():
    print 'hello'
if __name__ == '__main__':
    freeze_support()
    p = Process(target=foo)
    p.start()
													

( freeze_support() line can be omitted if the program will be run normally instead of frozen.)

This allows the newly spawned Python interpreter to safely import the module and then run the module’s foo() 函数。

Similar restrictions apply if a pool or manager is created in the main module.

16.6.4. 范例 ¶

Demonstration of how to create and use customized managers and proxies:

#
# This module shows how to use arbitrary callables with a subclass of
# `BaseManager`.
#
# Copyright (c) 2006-2008, R Oudkerk
# All rights reserved.
#
from multiprocessing import freeze_support
from multiprocessing.managers import BaseManager, BaseProxy
import operator
##
class Foo(object):
    def f(self):
        print 'you called Foo.f()'
    def g(self):
        print 'you called Foo.g()'
    def _h(self):
        print 'you called Foo._h()'
# A simple generator function
def baz():
    for i in xrange(10):
        yield i*i
# Proxy type for generator objects
class GeneratorProxy(BaseProxy):
    _exposed_ = ('next', '__next__')
    def __iter__(self):
        return self
    def next(self):
        return self._callmethod('next')
    def __next__(self):
        return self._callmethod('__next__')
# Function to return the operator module
def get_operator_module():
    return operator
##
class MyManager(BaseManager):
    pass
# register the Foo class; make `f()` and `g()` accessible via proxy
MyManager.register('Foo1', Foo)
# register the Foo class; make `g()` and `_h()` accessible via proxy
MyManager.register('Foo2', Foo, exposed=('g', '_h'))
# register the generator function baz; use `GeneratorProxy` to make proxies
MyManager.register('baz', baz, proxytype=GeneratorProxy)
# register get_operator_module(); make public functions accessible via proxy
MyManager.register('operator', get_operator_module)
##
def test():
    manager = MyManager()
    manager.start()
    print '-' * 20
    f1 = manager.Foo1()
    f1.f()
    f1.g()
    assert not hasattr(f1, '_h')
    assert sorted(f1._exposed_) == sorted(['f', 'g'])
    print '-' * 20
    f2 = manager.Foo2()
    f2.g()
    f2._h()
    assert not hasattr(f2, 'f')
    assert sorted(f2._exposed_) == sorted(['g', '_h'])
    print '-' * 20
    it = manager.baz()
    for i in it:
        print '<%d>' % i,
    print
    print '-' * 20
    op = manager.operator()
    print 'op.add(23, 45) =', op.add(23, 45)
    print 'op.pow(2, 94) =', op.pow(2, 94)
    print 'op.getslice(range(10), 2, 6) =', op.getslice(range(10), 2, 6)
    print 'op.repeat(range(5), 3) =', op.repeat(range(5), 3)
    print 'op._exposed_ =', op._exposed_
##
if __name__ == '__main__':
    freeze_support()
    test()
													

使用 Pool :

#
# A test of `multiprocessing.Pool` class
#
# Copyright (c) 2006-2008, R Oudkerk
# All rights reserved.
#
import multiprocessing
import time
import random
import sys
#
# Functions used by test code
#
def calculate(func, args):
    result = func(*args)
    return '%s says that %s%s = %s' % (
        multiprocessing.current_process().name,
        func.__name__, args, result
        )
def calculatestar(args):
    return calculate(*args)
def mul(a, b):
    time.sleep(0.5*random.random())
    return a * b
def plus(a, b):
    time.sleep(0.5*random.random())
    return a + b
def f(x):
    return 1.0 / (x-5.0)
def pow3(x):
    return x**3
def noop(x):
    pass
#
# Test code
#
def test():
    print 'cpu_count() = %d\n' % multiprocessing.cpu_count()
    #
    # Create pool
    #
    PROCESSES = 4
    print 'Creating pool with %d processes\n' % PROCESSES
    pool = multiprocessing.Pool(PROCESSES)
    print 'pool = %s' % pool
    print
    #
    # Tests
    #
    TASKS = [(mul, (i, 7)) for i in range(10)] + \
            [(plus, (i, 8)) for i in range(10)]
    results = [pool.apply_async(calculate, t) for t in TASKS]
    imap_it = pool.imap(calculatestar, TASKS)
    imap_unordered_it = pool.imap_unordered(calculatestar, TASKS)
    print 'Ordered results using pool.apply_async():'
    for r in results:
        print '\t', r.get()
    print
    print 'Ordered results using pool.imap():'
    for x in imap_it:
        print '\t', x
    print
    print 'Unordered results using pool.imap_unordered():'
    for x in imap_unordered_it:
        print '\t', x
    print
    print 'Ordered results using pool.map() --- will block till complete:'
    for x in pool.map(calculatestar, TASKS):
        print '\t', x
    print
    #
    # Simple benchmarks
    #
    N = 100000
    print 'def pow3(x): return x**3'
    t = time.time()
    A = map(pow3, xrange(N))
    print '\tmap(pow3, xrange(%d)):\n\t\t%s seconds' % \
          (N, time.time() - t)
    t = time.time()
    B = pool.map(pow3, xrange(N))
    print '\tpool.map(pow3, xrange(%d)):\n\t\t%s seconds' % \
          (N, time.time() - t)
    t = time.time()
    C = list(pool.imap(pow3, xrange(N), chunksize=N//8))
    print '\tlist(pool.imap(pow3, xrange(%d), chunksize=%d)):\n\t\t%s' \
          ' seconds' % (N, N//8, time.time() - t)
    assert A == B == C, (len(A), len(B), len(C))
    print
    L = [None] * 1000000
    print 'def noop(x): pass'
    print 'L = [None] * 1000000'
    t = time.time()
    A = map(noop, L)
    print '\tmap(noop, L):\n\t\t%s seconds' % \
          (time.time() - t)
    t = time.time()
    B = pool.map(noop, L)
    print '\tpool.map(noop, L):\n\t\t%s seconds' % \
          (time.time() - t)
    t = time.time()
    C = list(pool.imap(noop, L, chunksize=len(L)//8))
    print '\tlist(pool.imap(noop, L, chunksize=%d)):\n\t\t%s seconds' % \
          (len(L)//8, time.time() - t)
    assert A == B == C, (len(A), len(B), len(C))
    print
    del A, B, C, L
    #
    # Test error handling
    #
    print 'Testing error handling:'
    try:
        print pool.apply(f, (5,))
    except ZeroDivisionError:
        print '\tGot ZeroDivisionError as expected from pool.apply()'
    else:
        raise AssertionError('expected ZeroDivisionError')
    try:
        print pool.map(f, range(10))
    except ZeroDivisionError:
        print '\tGot ZeroDivisionError as expected from pool.map()'
    else:
        raise AssertionError('expected ZeroDivisionError')
    try:
        print list(pool.imap(f, range(10)))
    except ZeroDivisionError:
        print '\tGot ZeroDivisionError as expected from list(pool.imap())'
    else:
        raise AssertionError('expected ZeroDivisionError')
    it = pool.imap(f, range(10))
    for i in range(10):
        try:
            x = it.next()
        except ZeroDivisionError:
            if i == 5:
                pass
        except StopIteration:
            break
        else:
            if i == 5:
                raise AssertionError('expected ZeroDivisionError')
    assert i == 9
    print '\tGot ZeroDivisionError as expected from IMapIterator.next()'
    print
    #
    # Testing timeouts
    #
    print 'Testing ApplyResult.get() with timeout:',
    res = pool.apply_async(calculate, TASKS[0])
    while 1:
        sys.stdout.flush()
        try:
            sys.stdout.write('\n\t%s' % res.get(0.02))
            break
        except multiprocessing.TimeoutError:
            sys.stdout.write('.')
    print
    print
    print 'Testing IMapIterator.next() with timeout:',
    it = pool.imap(calculatestar, TASKS)
    while 1:
        sys.stdout.flush()
        try:
            sys.stdout.write('\n\t%s' % it.next(0.02))
        except StopIteration:
            break
        except multiprocessing.TimeoutError:
            sys.stdout.write('.')
    print
    print
    #
    # Testing callback
    #
    print 'Testing callback:'
    A = []
    B = [56, 0, 1, 8, 27, 64, 125, 216, 343, 512, 729]
    r = pool.apply_async(mul, (7, 8), callback=A.append)
    r.wait()
    r = pool.map_async(pow3, range(10), callback=A.extend)
    r.wait()
    if A == B:
        print '\tcallbacks succeeded\n'
    else:
        print '\t*** callbacks failed\n\t\t%s != %s\n' % (A, B)
    #
    # Check there are no outstanding tasks
    #
    assert not pool._cache, 'cache = %r' % pool._cache
    #
    # Check close() methods
    #
    print 'Testing close():'
    for worker in pool._pool:
        assert worker.is_alive()
    result = pool.apply_async(time.sleep, [0.5])
    pool.close()
    pool.join()
    assert result.get() is None
    for worker in pool._pool:
        assert not worker.is_alive()
    print '\tclose() succeeded\n'
    #
    # Check terminate() method
    #
    print 'Testing terminate():'
    pool = multiprocessing.Pool(2)
    DELTA = 0.1
    ignore = pool.apply(pow3, [2])
    results = [pool.apply_async(time.sleep, [DELTA]) for i in range(100)]
    pool.terminate()
    pool.join()
    for worker in pool._pool:
        assert not worker.is_alive()
    print '\tterminate() succeeded\n'
    #
    # Check garbage collection
    #
    print 'Testing garbage collection:'
    pool = multiprocessing.Pool(2)
    DELTA = 0.1
    processes = pool._pool
    ignore = pool.apply(pow3, [2])
    results = [pool.apply_async(time.sleep, [DELTA]) for i in range(100)]
    results = pool = None
    time.sleep(DELTA * 2)
    for worker in processes:
        assert not worker.is_alive()
    print '\tgarbage collection succeeded\n'
if __name__ == '__main__':
    multiprocessing.freeze_support()
    assert len(sys.argv) in (1, 2)
    if len(sys.argv) == 1 or sys.argv[1] == 'processes':
        print ' Using processes '.center(79, '-')
    elif sys.argv[1] == 'threads':
        print ' Using threads '.center(79, '-')
        import multiprocessing.dummy as multiprocessing
    else:
        print 'Usage:\n\t%s [processes | threads]' % sys.argv[0]
        raise SystemExit(2)
    test()
													

Synchronization types like locks, conditions and queues:

#
# A test file for the `multiprocessing` package
#
# Copyright (c) 2006-2008, R Oudkerk
# All rights reserved.
#
import time, sys, random
from Queue import Empty
import multiprocessing               # may get overwritten
#### TEST_VALUE
def value_func(running, mutex):
    random.seed()
    time.sleep(random.random()*4)
    mutex.acquire()
    print '\n\t\t\t' + str(multiprocessing.current_process()) + ' has finished'
    running.value -= 1
    mutex.release()
def test_value():
    TASKS = 10
    running = multiprocessing.Value('i', TASKS)
    mutex = multiprocessing.Lock()
    for i in range(TASKS):
        p = multiprocessing.Process(target=value_func, args=(running, mutex))
        p.start()
    while running.value > 0:
        time.sleep(0.08)
        mutex.acquire()
        print running.value,
        sys.stdout.flush()
        mutex.release()
    print
    print 'No more running processes'
#### TEST_QUEUE
def queue_func(queue):
    for i in range(30):
        time.sleep(0.5 * random.random())
        queue.put(i*i)
    queue.put('STOP')
def test_queue():
    q = multiprocessing.Queue()
    p = multiprocessing.Process(target=queue_func, args=(q,))
    p.start()
    o = None
    while o != 'STOP':
        try:
            o = q.get(timeout=0.3)
            print o,
            sys.stdout.flush()
        except Empty:
            print 'TIMEOUT'
    print
#### TEST_CONDITION
def condition_func(cond):
    cond.acquire()
    print '\t' + str(cond)
    time.sleep(2)
    print '\tchild is notifying'
    print '\t' + str(cond)
    cond.notify()
    cond.release()
def test_condition():
    cond = multiprocessing.Condition()
    p = multiprocessing.Process(target=condition_func, args=(cond,))
    print cond
    cond.acquire()
    print cond
    cond.acquire()
    print cond
    p.start()
    print 'main is waiting'
    cond.wait()
    print 'main has woken up'
    print cond
    cond.release()
    print cond
    cond.release()
    p.join()
    print cond
#### TEST_SEMAPHORE
def semaphore_func(sema, mutex, running):
    sema.acquire()
    mutex.acquire()
    running.value += 1
    print running.value, 'tasks are running'
    mutex.release()
    random.seed()
    time.sleep(random.random()*2)
    mutex.acquire()
    running.value -= 1
    print '%s has finished' % multiprocessing.current_process()
    mutex.release()
    sema.release()
def test_semaphore():
    sema = multiprocessing.Semaphore(3)
    mutex = multiprocessing.RLock()
    running = multiprocessing.Value('i', 0)
    processes = [
        multiprocessing.Process(target=semaphore_func,
                                args=(sema, mutex, running))
        for i in range(10)
        ]
    for p in processes:
        p.start()
    for p in processes:
        p.join()
#### TEST_JOIN_TIMEOUT
def join_timeout_func():
    print '\tchild sleeping'
    time.sleep(5.5)
    print '\n\tchild terminating'
def test_join_timeout():
    p = multiprocessing.Process(target=join_timeout_func)
    p.start()
    print 'waiting for process to finish'
    while 1:
        p.join(timeout=1)
        if not p.is_alive():
            break
        print '.',
        sys.stdout.flush()
#### TEST_EVENT
def event_func(event):
    print '\t%r is waiting' % multiprocessing.current_process()
    event.wait()
    print '\t%r has woken up' % multiprocessing.current_process()
def test_event():
    event = multiprocessing.Event()
    processes = [multiprocessing.Process(target=event_func, args=(event,))
                 for i in range(5)]
    for p in processes:
        p.start()
    print 'main is sleeping'
    time.sleep(2)
    print 'main is setting event'
    event.set()
    for p in processes:
        p.join()
#### TEST_SHAREDVALUES
def sharedvalues_func(values, arrays, shared_values, shared_arrays):
    for i in range(len(values)):
        v = values[i][1]
        sv = shared_values[i].value
        assert v == sv
    for i in range(len(values)):
        a = arrays[i][1]
        sa = list(shared_arrays[i][:])
        assert a == sa
    print 'Tests passed'
def test_sharedvalues():
    values = [
        ('i', 10),
        ('h', -2),
        ('d', 1.25)
        ]
    arrays = [
        ('i', range(100)),
        ('d', [0.25 * i for i in range(100)]),
        ('H', range(1000))
        ]
    shared_values = [multiprocessing.Value(id, v) for id, v in values]
    shared_arrays = [multiprocessing.Array(id, a) for id, a in arrays]
    p = multiprocessing.Process(
        target=sharedvalues_func,
        args=(values, arrays, shared_values, shared_arrays)
        )
    p.start()
    p.join()
    assert p.exitcode == 0
####
def test(namespace=multiprocessing):
    global multiprocessing
    multiprocessing = namespace
    for func in [ test_value, test_queue, test_condition,
                  test_semaphore, test_join_timeout, test_event,
                  test_sharedvalues ]:
        print '\n\t######## %s\n' % func.__name__
        func()
    ignore = multiprocessing.active_children()      # cleanup any old processes
    if hasattr(multiprocessing, '_debug_info'):
        info = multiprocessing._debug_info()
        if info:
            print info
            raise ValueError('there should be no positive refcounts left')
if __name__ == '__main__':
    multiprocessing.freeze_support()
    assert len(sys.argv) in (1, 2)
    if len(sys.argv) == 1 or sys.argv[1] == 'processes':
        print ' Using processes '.center(79, '-')
        namespace = multiprocessing
    elif sys.argv[1] == 'manager':
        print ' Using processes and a manager '.center(79, '-')
        namespace = multiprocessing.Manager()
        namespace.Process = multiprocessing.Process
        namespace.current_process = multiprocessing.current_process
        namespace.active_children = multiprocessing.active_children
    elif sys.argv[1] == 'threads':
        print ' Using threads '.center(79, '-')
        import multiprocessing.dummy as namespace
    else:
        print 'Usage:\n\t%s [processes | manager | threads]' % sys.argv[0]
        raise SystemExit(2)
    test(namespace)
													

An example showing how to use queues to feed tasks to a collection of worker processes and collect the results:

#
# Simple example which uses a pool of workers to carry out some tasks.
#
# Notice that the results will probably not come out of the output
# queue in the same in the same order as the corresponding tasks were
# put on the input queue.  If it is important to get the results back
# in the original order then consider using `Pool.map()` or
# `Pool.imap()` (which will save on the amount of code needed anyway).
#
# Copyright (c) 2006-2008, R Oudkerk
# All rights reserved.
#
import time
import random
from multiprocessing import Process, Queue, current_process, freeze_support
#
# Function run by worker processes
#
def worker(input, output):
    for func, args in iter(input.get, 'STOP'):
        result = calculate(func, args)
        output.put(result)
#
# Function used to calculate result
#
def calculate(func, args):
    result = func(*args)
    return '%s says that %s%s = %s' % \
        (current_process().name, func.__name__, args, result)
#
# Functions referenced by tasks
#
def mul(a, b):
    time.sleep(0.5*random.random())
    return a * b
def plus(a, b):
    time.sleep(0.5*random.random())
    return a + b
#
#
#
def test():
    NUMBER_OF_PROCESSES = 4
    TASKS1 = [(mul, (i, 7)) for i in range(20)]
    TASKS2 = [(plus, (i, 8)) for i in range(10)]
    # Create queues
    task_queue = Queue()
    done_queue = Queue()
    # Submit tasks
    for task in TASKS1:
        task_queue.put(task)
    # Start worker processes
    for i in range(NUMBER_OF_PROCESSES):
        Process(target=worker, args=(task_queue, done_queue)).start()
    # Get and print results
    print 'Unordered results:'
    for i in range(len(TASKS1)):
        print '\t', done_queue.get()
    # Add more tasks using `put()`
    for task in TASKS2:
        task_queue.put(task)
    # Get and print some more results
    for i in range(len(TASKS2)):
        print '\t', done_queue.get()
    # Tell child processes to stop
    for i in range(NUMBER_OF_PROCESSES):
        task_queue.put('STOP')
if __name__ == '__main__':
    freeze_support()
    test()
													

An example of how a pool of worker processes can each run a SimpleHTTPServer.HttpServer instance while sharing a single listening socket.

#
# Example where a pool of http servers share a single listening socket
#
# On Windows this module depends on the ability to pickle a socket
# object so that the worker processes can inherit a copy of the server
# object.  (We import `multiprocessing.reduction` to enable this pickling.)
#
# Not sure if we should synchronize access to `socket.accept()` method by
# using a process-shared lock -- does not seem to be necessary.
#
# Copyright (c) 2006-2008, R Oudkerk
# All rights reserved.
#
import os
import sys
from multiprocessing import Process, current_process, freeze_support
from BaseHTTPServer import HTTPServer
from SimpleHTTPServer import SimpleHTTPRequestHandler
if sys.platform == 'win32':
    import multiprocessing.reduction    # make sockets pickable/inheritable
def note(format, *args):
    sys.stderr.write('[%s]\t%s\n' % (current_process().name, format%args))
class RequestHandler(SimpleHTTPRequestHandler):
    # we override log_message() to show which process is handling the request
    def log_message(self, format, *args):
        note(format, *args)
def serve_forever(server):
    note('starting server')
    try:
        server.serve_forever()
    except KeyboardInterrupt:
        pass
def runpool(address, number_of_processes):
    # create a single server object -- children will each inherit a copy
    server = HTTPServer(address, RequestHandler)
    # create child processes to act as workers
    for i in range(number_of_processes-1):
        Process(target=serve_forever, args=(server,)).start()
    # main process also acts as a worker
    serve_forever(server)
def test():
    DIR = os.path.join(os.path.dirname(__file__), '..')
    ADDRESS = ('localhost', 8000)
    NUMBER_OF_PROCESSES = 4
    print 'Serving at http://%s:%d using %d worker processes' % \
          (ADDRESS[0], ADDRESS[1], NUMBER_OF_PROCESSES)
    print 'To exit press Ctrl-' + ['C', 'Break'][sys.platform=='win32']
    os.chdir(DIR)
    runpool(ADDRESS, NUMBER_OF_PROCESSES)
if __name__ == '__main__':
    freeze_support()
    test()
													

Some simple benchmarks comparing multiprocessing with threading :

#
# Simple benchmarks for the multiprocessing package
#
# Copyright (c) 2006-2008, R Oudkerk
# All rights reserved.
#
import time, sys, multiprocessing, threading, Queue, gc
if sys.platform == 'win32':
    _timer = time.clock
else:
    _timer = time.time
delta = 1
#### TEST_QUEUESPEED
def queuespeed_func(q, c, iterations):
    a = '0' * 256
    c.acquire()
    c.notify()
    c.release()
    for i in xrange(iterations):
        q.put(a)
    q.put('STOP')
def test_queuespeed(Process, q, c):
    elapsed = 0
    iterations = 1
    while elapsed < delta:
        iterations *= 2
        p = Process(target=queuespeed_func, args=(q, c, iterations))
        c.acquire()
        p.start()
        c.wait()
        c.release()
        result = None
        t = _timer()
        while result != 'STOP':
            result = q.get()
        elapsed = _timer() - t
        p.join()
    print iterations, 'objects passed through the queue in', elapsed, 'seconds'
    print 'average number/sec:', iterations/elapsed
#### TEST_PIPESPEED
def pipe_func(c, cond, iterations):
    a = '0' * 256
    cond.acquire()
    cond.notify()
    cond.release()
    for i in xrange(iterations):
        c.send(a)
    c.send('STOP')
def test_pipespeed():
    c, d = multiprocessing.Pipe()
    cond = multiprocessing.Condition()
    elapsed = 0
    iterations = 1
    while elapsed < delta:
        iterations *= 2
        p = multiprocessing.Process(target=pipe_func,
                                    args=(d, cond, iterations))
        cond.acquire()
        p.start()
        cond.wait()
        cond.release()
        result = None
        t = _timer()
        while result != 'STOP':
            result = c.recv()
        elapsed = _timer() - t
        p.join()
    print iterations, 'objects passed through connection in',elapsed,'seconds'
    print 'average number/sec:', iterations/elapsed
#### TEST_SEQSPEED
def test_seqspeed(seq):
    elapsed = 0
    iterations = 1
    while elapsed < delta:
        iterations *= 2
        t = _timer()
        for i in xrange(iterations):
            a = seq[5]
        elapsed = _timer()-t
    print iterations, 'iterations in', elapsed, 'seconds'
    print 'average number/sec:', iterations/elapsed
#### TEST_LOCK
def test_lockspeed(l):
    elapsed = 0
    iterations = 1
    while elapsed < delta:
        iterations *= 2
        t = _timer()
        for i in xrange(iterations):
            l.acquire()
            l.release()
        elapsed = _timer()-t
    print iterations, 'iterations in', elapsed, 'seconds'
    print 'average number/sec:', iterations/elapsed
#### TEST_CONDITION
def conditionspeed_func(c, N):
    c.acquire()
    c.notify()
    for i in xrange(N):
        c.wait()
        c.notify()
    c.release()
def test_conditionspeed(Process, c):
    elapsed = 0
    iterations = 1
    while elapsed < delta:
        iterations *= 2
        c.acquire()
        p = Process(target=conditionspeed_func, args=(c, iterations))
        p.start()
        c.wait()
        t = _timer()
        for i in xrange(iterations):
            c.notify()
            c.wait()
        elapsed = _timer()-t
        c.release()
        p.join()
    print iterations * 2, 'waits in', elapsed, 'seconds'
    print 'average number/sec:', iterations * 2 / elapsed
####
def test():
    manager = multiprocessing.Manager()
    gc.disable()
    print '\n\t######## testing Queue.Queue\n'
    test_queuespeed(threading.Thread, Queue.Queue(),
                    threading.Condition())
    print '\n\t######## testing multiprocessing.Queue\n'
    test_queuespeed(multiprocessing.Process, multiprocessing.Queue(),
                    multiprocessing.Condition())
    print '\n\t######## testing Queue managed by server process\n'
    test_queuespeed(multiprocessing.Process, manager.Queue(),
                    manager.Condition())
    print '\n\t######## testing multiprocessing.Pipe\n'
    test_pipespeed()
    print
    print '\n\t######## testing list\n'
    test_seqspeed(range(10))
    print '\n\t######## testing list managed by server process\n'
    test_seqspeed(manager.list(range(10)))
    print '\n\t######## testing Array("i", ..., lock=False)\n'
    test_seqspeed(multiprocessing.Array('i', range(10), lock=False))
    print '\n\t######## testing Array("i", ..., lock=True)\n'
    test_seqspeed(multiprocessing.Array('i', range(10), lock=True))
    print
    print '\n\t######## testing threading.Lock\n'
    test_lockspeed(threading.Lock())
    print '\n\t######## testing threading.RLock\n'
    test_lockspeed(threading.RLock())
    print '\n\t######## testing multiprocessing.Lock\n'
    test_lockspeed(multiprocessing.Lock())
    print '\n\t######## testing multiprocessing.RLock\n'
    test_lockspeed(multiprocessing.RLock())
    print '\n\t######## testing lock managed by server process\n'
    test_lockspeed(manager.Lock())
    print '\n\t######## testing rlock managed by server process\n'
    test_lockspeed(manager.RLock())
    print
    print '\n\t######## testing threading.Condition\n'
    test_conditionspeed(threading.Thread, threading.Condition())
    print '\n\t######## testing multiprocessing.Condition\n'
    test_conditionspeed(multiprocessing.Process, multiprocessing.Condition())
    print '\n\t######## testing condition managed by a server process\n'
    test_conditionspeed(multiprocessing.Process, manager.Condition())
    gc.enable()
if __name__ == '__main__':
    multiprocessing.freeze_support()
    test()
													

内容表

• 16.6. multiprocessing — 基于进程的 threading 接口
  • 16.6.1. Introduction
    • 16.6.1.1. The Process class
    • 16.6.1.2. Exchanging objects between processes
    • 16.6.1.3. Synchronization between processes
    • 16.6.1.4. Sharing state between processes
    • 16.6.1.5. Using a pool of workers
  • 16.6.2. Reference
    • 16.6.2.1. Process 和异常
    • 16.6.2.2. Pipes and Queues
    • 16.6.2.3. Miscellaneous
    • 16.6.2.4. Connection Objects
    • 16.6.2.5. Synchronization primitives
    • 16.6.2.6. Shared ctypes 对象
      • 16.6.2.6.1. The multiprocessing.sharedctypes 模块
    • 16.6.2.7. Managers
      • 16.6.2.7.1. Customized managers
      • 16.6.2.7.2. Using a remote manager
    • 16.6.2.8. Proxy Objects
      • 16.6.2.8.1. Cleanup
    • 16.6.2.9. Process Pools
    • 16.6.2.10. Listeners and Clients
      • 16.6.2.10.1. Address Formats
    • 16.6.2.11. Authentication keys
    • 16.6.2.12. Logging
    • 16.6.2.13. The multiprocessing.dummy 模块
  • 16.6.3. Programming guidelines
    • 16.6.3.1. All platforms
    • 16.6.3.2. Windows
  • 16.6.4. Examples

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16.5. dummy_thread — 直接置换 thread 模块

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