# `multiprocessing` --- 基于进程的并行

源代码 Lib/multiprocessing/

## 概述

`multiprocessing` 是一个用与 `threading` 模块相似API的支持产生进程的包。 `multiprocessing` 包同时提供本地和远程并发，使用子进程代替线程，有效避免 Global Interpreter Lock 带来的影响。因此， `multiprocessing` 模块允许程序员充分利用机器上的多个核心。Unix 和 Windows 上都可以运行。

`multiprocessing` 模块还引入了在 `threading` 模块中没有类似物的API。这方面的一个主要例子是 `Pool` 对象，它提供了一种方便的方法，可以跨多个输入值并行化函数的执行，跨进程分配输入数据（数据并行）。以下示例演示了在模块中定义此类函数的常见做法，以便子进程可以成功导入该模块。这个数据并行的基本例子使用 `Pool` ，

    from multiprocessing import Pool

    def f(x):
        return x*x

    if __name__ == '__main__':
        with Pool(5) as p:
            print(p.map(f, [1, 2, 3]))

将打印到标准输出

    [1, 4, 9]

### `Process` 类

在 `multiprocessing` 中，通过创建一个 `Process` 对象然后调用它的 `start()` 方法来生成进程。 `Process` 和 `threading.Thread` API 相同。 一个简单的多进程程序示例是:

    from multiprocessing import Process

    def f(name):
        print('hello', name)

    if __name__ == '__main__':
        p = Process(target=f, args=('bob',))
        p.start()
        p.join()

要显示所涉及的各个进程ID，这是一个扩展示例:

    from multiprocessing import Process
    import os

    def info(title):
        print(title)
        print('module name:', __name__)
        print('parent process:', os.getppid())
        print('process id:', os.getpid())

    def f(name):
        info('function f')
        print('hello', name)

    if __name__ == '__main__':
        info('main line')
        p = Process(target=f, args=('bob',))
        p.start()
        p.join()

为了解释为什么 `if __name__ == '__main__'` 部分是必需的，请参见 Programming guidelines。

### 上下文和启动方法

根据不同的平台， `multiprocessing` 支持三种启动进程的方法。这些 启动方法 有

> spawn

>   父进程启动一个新的Python解释器进程。子进程只会继承那些运行进程对象的 `run()` 方法所需的资源。特别是父进程中非必须的文件描述符和句柄不会被继承。相对于使用 fork 或者 forkserver，使用这个方法启动进程相当慢。

>   Available on Unix and Windows. The default on Windows and macOS.

> fork

>   父进程使用 `os.fork()` 来产生 Python 解释器分叉。子进程在开始时实际上与父进程相同。父进程的所有资源都由子进程继承。请注意，安全分叉多线程进程是棘手的。

>   只存在于Unix。Unix中的默认值。

> forkserver

>   程序启动并选择* forkserver * 启动方法时，将启动服务器进程。从那时起，每当需要一个新进程时，父进程就会连接到服务器并请求它分叉一个新进程。分叉服务器进程是单线程的，因此使用 `os.fork()` 是安全的。没有不必要的资源被继承。

>   可在Unix平台上使用，支持通过Unix管道传递文件描述符。

在 3.8 版更改: On macOS, spawn start method is now the default: fork start method is no longer reliable on macOS, see bpo-33725.

在 3.4 版更改: spawn 在所有unix平台上添加，并且为一些unix平台添加了 forkserver 。子进程不再继承Windows上的所有上级进程可继承的句柄。

On Unix using the spawn or forkserver start methods will also start a resource tracker process which tracks the unlinked named system resources (such as named semaphores or `SharedMemory` objects) created by processes of the program. When all processes have exited the resource tracker unlinks any remaining tracked object. Usually there should be none, but if a process was killed by a signal there may be some "leaked" resources. (Neither leaked semaphores nor shared memory segments will be automatically unlinked until the next reboot. This is problematic for both objects because the system allows only a limited number of named semaphores, and shared memory segments occupy some space in the main memory.)

要选择一个启动方法，你应该在主模块的 `if __name__ == '__main__'` 子句中调用 `set_start_method()` 。例如：

    import multiprocessing as mp

    def foo(q):
        q.put('hello')

    if __name__ == '__main__':
        mp.set_start_method('spawn')
        q = mp.Queue()
        p = mp.Process(target=foo, args=(q,))
        p.start()
        print(q.get())
        p.join()

在程序中 `set_start_method()` 不应该被多次调用。

或者，你可以使用 `get_context()` 来获取上下文对象。上下文对象与多处理模块具有相同的API，并允许在同一程序中使用多个启动方法。:

    import multiprocessing as mp

    def foo(q):
        q.put('hello')

    if __name__ == '__main__':
        ctx = mp.get_context('spawn')
        q = ctx.Queue()
        p = ctx.Process(target=foo, args=(q,))
        p.start()
        print(q.get())
        p.join()

请注意，与一个上下文相关的对象可能与不同上下文的进程不兼容。特别是，使用 fork 上下文创建的锁不能传递给使用 spawn 或 forkserver 启动方法启动的进程。

想要使用特定启动方法的库应该使用 `get_context()` 以避免干扰库用户的选择。

警告

`'spawn'` 和 `'forkserver'` 启动方法当前不能在Unix上和“冻结的”可执行内容一同使用（例如，有类似 PyInstaller 和 cx_Freeze 的包产生的二进制文件）。 `'fork'` 启动方法可以使用。

### 在进程之间交换对象

`multiprocessing` 支持进程之间的两种通信通道：

队列

> `Queue` 类是一个近似 `queue.Queue` 的克隆。 例如:

>     from multiprocessing import Process, Queue

>     def f(q):
>         q.put([42, None, 'hello'])

>     if __name__ == '__main__':
>         q = Queue()
>         p = Process(target=f, args=(q,))
>         p.start()
>         print(q.get())    # prints "[42, None, 'hello']"
>         p.join()

> 队列是线程和进程安全的。

管道

> `Pipe()` 函数返回一个由管道连接的连接对象，默认情况下是双工（双向）。例如:

>     from multiprocessing import Process, Pipe

>     def f(conn):
>         conn.send([42, None, 'hello'])
>         conn.close()

>     if __name__ == '__main__':
>         parent_conn, child_conn = Pipe()
>         p = Process(target=f, args=(child_conn,))
>         p.start()
>         print(parent_conn.recv())   # prints "[42, None, 'hello']"
>         p.join()

> 返回的两个连接对象 `Pipe()` 表示管道的两端。每个连接对象都有 `send()` 和 `recv()` 方法（相互之间的）。请注意，如果两个进程（或线程）同时尝试读取或写入管道的 同一 端，则管道中的数据可能会损坏。当然，同时使用管道的不同端的进程不存在损坏的风险。

### 进程之间的同步

`multiprocessing` 包含来自 `threading` 的所有同步基本体的等价物。例如，可以使用锁来确保一次只有一个进程打印到标准输出:

    from multiprocessing import Process, Lock

    def f(l, i):
        l.acquire()
        try:
            print('hello world', i)
        finally:
            l.release()

    if __name__ == '__main__':
        lock = Lock()

        for num in range(10):
            Process(target=f, args=(lock, num)).start()

不使用来自不同进程的锁输出容易产生混淆。

### 在进程之间共享状态

如上所述，在进行并发编程时，通常最好尽量避免使用共享状态。使用多个进程时尤其如此。

但是，如果你真的需要使用一些共享数据，那么 `multiprocessing` 提供了两种方法。

共享内存

> 可以使用 `Value` 或 `Array` 将数据存储在共享内存映射中。例如，以下代码:

>     from multiprocessing import Process, Value, Array

>     def f(n, a):
>         n.value = 3.1415927
>         for i in range(len(a)):
>             a[i] = -a[i]

>     if __name__ == '__main__':
>         num = Value('d', 0.0)
>         arr = Array('i', range(10))

>         p = Process(target=f, args=(num, arr))
>         p.start()
>         p.join()

>         print(num.value)
>         print(arr[:])

> 将打印

>     3.1415927
>     [0, -1, -2, -3, -4, -5, -6, -7, -8, -9]

> 创建 `num` 和 `arr` 时使用的 `'d'` 和 `'i'` 参数是 `array` 模块使用的类型的 typecode ： `'d'` 表示双精度浮点数， `'i'` 表示有符号整数。这些共享对象将是进程和线程安全的。

> 为了更灵活地使用共享内存，可以使用 `multiprocessing.sharedctypes` 模块，该模块支持创建从共享内存分配的任意ctypes对象。

服务器进程

> 由 `Manager()` 返回的管理器对象控制一个服务器进程，该进程保存Python对象并允许其他进程使用代理操作它们。

> `Manager()` 返回的管理器支持类型： `list` 、 `dict` 、 `Namespace` 、 `Lock` 、 `RLock` 、 `Semaphore` 、 `BoundedSemaphore` 、 `Condition` 、 `Event` 、 `Barrier` 、 `Queue` 、 `Value` 和 `Array` 。例如

>     from multiprocessing import Process, Manager

>     def f(d, l):
>         d[1] = '1'
>         d['2'] = 2
>         d[0.25] = None
>         l.reverse()

>     if __name__ == '__main__':
>         with Manager() as manager:
>             d = manager.dict()
>             l = manager.list(range(10))

>             p = Process(target=f, args=(d, l))
>             p.start()
>             p.join()

>             print(d)
>             print(l)

> 将打印

>     {0.25: None, 1: '1', '2': 2}
>     [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]

> 服务器进程管理器比使用共享内存对象更灵活，因为它们可以支持任意对象类型。此外，单个管理器可以通过网络由不同计算机上的进程共享。但是，它们比使用共享内存慢。

### 使用工作进程

`Pool` 类表示一个工作进程池。它具有允许以几种不同方式将任务分配到工作进程的方法。

例如:

    from multiprocessing import Pool, TimeoutError
    import time
    import os

    def f(x):
        return x*x

    if __name__ == '__main__':
        # start 4 worker processes
        with Pool(processes=4) as pool:

            # print "[0, 1, 4,..., 81]"
            print(pool.map(f, range(10)))

            # print same numbers in arbitrary order
            for i in pool.imap_unordered(f, range(10)):
                print(i)

            # evaluate "f(20)" asynchronously
            res = pool.apply_async(f, (20,))      # runs in *only* one process
            print(res.get(timeout=1))             # prints "400"

            # evaluate "os.getpid()" asynchronously
            res = pool.apply_async(os.getpid, ()) # runs in *only* one process
            print(res.get(timeout=1))             # prints the PID of that process

            # launching multiple evaluations asynchronously *may* use more processes
            multiple_results = [pool.apply_async(os.getpid, ()) for i in range(4)]
            print([res.get(timeout=1) for res in multiple_results])

            # make a single worker sleep for 10 secs
            res = pool.apply_async(time.sleep, (10,))
            try:
                print(res.get(timeout=1))
            except TimeoutError:
                print("We lacked patience and got a multiprocessing.TimeoutError")

            print("For the moment, the pool remains available for more work")

        # exiting the 'with'-block has stopped the pool
        print("Now the pool is closed and no longer available")

请注意，池的方法只能由创建它的进程使用。

注解

该软件包中的功能要求子项可以导入 `__main__` 模块。这包含在 Programming guidelines 中，但值得指出。这意味着一些示例，例如 `multiprocessing.pool.Pool` 示例在交互式解释器中不起作用。例如:

    >>> from multiprocessing import Pool
    >>> p = Pool(5)
    >>> def f(x):
    ...     return x*x
    ...
    >>> p.map(f, [1,2,3])
    Process PoolWorker-1:
    Process PoolWorker-2:
    Process PoolWorker-3:
    Traceback (most recent call last):
    AttributeError: 'module' object has no attribute 'f'
    AttributeError: 'module' object has no attribute 'f'
    AttributeError: 'module' object has no attribute 'f'

(If you try this it will actually output three full tracebacks interleaved in a semi-random fashion, and then you may have to stop the parent process somehow.)

## 参考

`multiprocessing` 包大部分复制了 `threading` 模块的API。

### `Process` 和异常

class `multiprocessing.``Process`(group=None, target=None, name=None, args=(), kwargs={}, *, daemon=None)

  进程对象表示在单独进程中运行的活动。 `Process` 类等价于 `threading.Thread` 。

  应始终使用关键字参数调用构造函数。 group 应该始终是 `None` ；它仅用于兼容 `threading.Thread` 。 target 是由 `run()` 方法调用的可调用对象。它默认为 `None` ，意味着什么都没有被调用。 name 是进程名称（有关详细信息，请参阅 `name` ）。 args 是目标调用的参数元组。 kwargs 是目标调用的关键字参数字典。如果提供，则键参数 daemon 将进程 `daemon` 标志设置为 `True` 或 `False` 。如果是 `None` （默认值），则该标志将从创建的进程继承。

  默认情况下，不会将任何参数传递给 target 。

  如果子类重写构造函数，它必须确保它在对进程执行任何其他操作之前调用基类构造函数（ `Process.__init__()` ）。

  在 3.3 版更改: 加入 daemon 参数。

  `run`()

    表示进程活动的方法。

    你可以在子类中重载此方法。标准 `run()` 方法调用传递给对象构造函数的可调用对象作为目标参数（如果有），分别从 args 和 kwargs 参数中获取顺序和关键字参数。

  `start`()

    启动进程活动。

    每个进程对象最多只能调用一次。它安排对象的 `run()` 方法在一个单独的进程中调用。

  `join`([timeout])

    如果可选参数 timeout 是 `None` （默认值），则该方法将阻塞，直到调用 `join()` 方法的进程终止。如果 timeout 是一个正数，它最多会阻塞 timeout 秒。请注意，如果进程终止或方法超时，则该方法返回 `None` 。检查进程的 `exitcode` 以确定它是否终止。

    一个进程可以合并多次。

    进程无法并入自身，因为这会导致死锁。尝试在启动进程之前合并进程是错误的。

  `name`

    进程的名称。该名称是一个字符串，仅用于识别目的。它没有语义。可以为多个进程指定相同的名称。

    初始名称由构造器设定。 如果没有为构造器提供显式名称，则会构造一个形式为 'Process-N1:N2:...:Nk' 的名称，其中每个 Nk 是其父亲的第 N 个孩子。

  `is_alive`()

    返回进程是否还活着。

    粗略地说，从 `start()` 方法返回到子进程终止之前，进程对象仍处于活动状态。

  `daemon`

    进程的守护标志，一个布尔值。这必须在 `start()` 被调用之前设置。

    初始值继承自创建进程。

    当进程退出时，它会尝试终止其所有守护进程子进程。

    请注意，不允许守护进程创建子进程。否则，守护进程会在子进程退出时终止其子进程。 另外，这些 不是 Unix守护进程或服务，它们是正常进程，如果非守护进程已经退出，它们将被终止（并且不被合并）。

  除了 `threading.Thread` API ，`Process` 对象还支持以下属性和方法：

  `pid`

    返回进程ID。在生成该进程之前，这将是 `None` 。

  `exitcode`

    的退子进程出代码。如果进程尚未终止，这将是 `None` 。负值 -N 表示孩子被信号 N 终止。

  `authkey`

    进程的身份验证密钥（字节字符串）。

    当 `multiprocessing` 初始化时，主进程使用 `os.urandom()` 分配一个随机字符串。

    当创建 `Process` 对象时，它将继承其父进程的身份验证密钥，尽管可以通过将 `authkey` 设置为另一个字节字符串来更改。

    参见 Authentication keys 。

  `sentinel`

    系统对象的数字句柄，当进程结束时将变为 "ready" 。

    如果要使用 `multiprocessing.connection.wait()` 一次等待多个事件，可以使用此值。否则调用 `join()` 更简单。

    在Windows上，这是一个操作系统句柄，可以与 `WaitForSingleObject` 和 `WaitForMultipleObjects` 系列API调用一起使用。在Unix上，这是一个文件描述符，可以使用来自 `select` 模块的原语。

    3.3 新版功能.

  `terminate`()

    终止进程。 在Unix上，这是使用 `SIGTERM` 信号完成的；在Windows上使用 `TerminateProcess()` 。 请注意，不会执行退出处理程序和finally子句等。

    请注意，进程的后代进程将不会被终止 —— 它们将简单地变成孤立的。

    警告

    如果在关联进程使用管道或队列时使用此方法，则管道或队列可能会损坏，并可能无法被其他进程使用。类似地，如果进程已获得锁或信号量等，则终止它可能导致其他进程死锁。

  `kill`()

    与 `terminate()` 相同，但在Unix上使用 `SIGKILL` 信号。

    3.7 新版功能.

  `close`()

    关闭 `Process` 对象，释放与之关联的所有资源。如果底层进程仍在运行，则会引发 `ValueError` 。一旦 `close()` 成功返回， `Process` 对象的大多数其他方法和属性将引发 `ValueError` 。

    3.7 新版功能.

  注意 `start()` 、 `join()` 、 `is_alive()` 、 `terminate()` 和 `exitcode` 方法只能由创建进程对象的进程调用。

  `Process` 一些方法的示例用法：

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

exception `multiprocessing.``ProcessError`

  所有 `multiprocessing` 异常的基类。

exception `multiprocessing.``BufferTooShort`

  当提供的缓冲区对象太小而无法读取消息时， `Connection.recv_bytes_into()` 引发的异常。

  如果 `e` 是一个 `BufferTooShort` 实例，那么 `e.args[0]` 将把消息作为字节字符串给出。

exception `multiprocessing.``AuthenticationError`

  出现身份验证错误时引发。

exception `multiprocessing.``TimeoutError`

  有超时的方法超时时引发。

### 管道和队列

使用多进程时，一般使用消息机制实现进程间通信，尽可能避免使用同步原语，例如锁。

消息机制包含： `Pipe()` (可以用于在两个进程间传递消息)，以及队列(能够在多个生产者和消费者之间通信)。

The `Queue`, `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` class.

如果你使用了 `JoinableQueue` ，那么你**必须**对每个已经移出队列的任务调用 `JoinableQueue.task_done()` 。不然的话用于统计未完成任务的信号量最终会溢出并抛出异常。

另外还可以通过使用一个管理器对象创建一个共享队列，详见 Managers 。

注解

`multiprocessing` 使用了普通的 `queue.Empty` 和 `queue.Full` 异常去表示超时。 你需要从 `queue` 中导入它们，因为它们并不在 `multiprocessing` 的命名空间中。

注解

当一个对象被放入一个队列中时，这个对象首先会被一个后台线程用pickle序列化，并将序列化后的数据通过一个底层管道的管道传递到队列中。这种做法会有点让人惊讶，但一般不会出现什么问题。如果它们确实妨碍了你，你可以使用一个由管理器 manager 创建的队列替换它。

1. 将一个对象放入一个空队列后，可能需要极小的延迟，队列的方法 `empty()` 才会返回 `False` 。而 `get_nowait()` 可以不抛出 `queue.Empty` 直接返回。

2. 如果有多个进程同时将对象放入队列，那么在队列的另一端接受到的对象可能是无序的。但是由同一个进程放入的多个对象的顺序在另一端输出时总是一样的。

警告

如果一个进程通过调用 `Process.terminate()` 或 `os.kill()` 在尝试使用 `Queue` 期间被终止了，那么队列中的数据很可能被破坏。 这可能导致其他进程在尝试使用该队列时遇到异常。

警告

正如刚才提到的，如果一个子进程将一些对象放进队列中 (并且它没有用 `JoinableQueue.cancel_join_thread` 方法)，那么这个进程在所有缓冲区的对象被刷新进管道之前，是不会终止的。

这意味着，除非你确定所有放入队列中的对象都已经被消费了，否则如果你试图等待这个进程，你可能会陷入死锁中。相似地，如果该子进程不是后台进程，那么父进程可能在试图等待所有非后台进程退出时挂起。

注意用管理器创建的队列不存在这个问题，详见 Programming guidelines 。

该 示例 展示了如何使用队列实现进程间通信。

`multiprocessing.``Pipe`([duplex])

  返回一对 `Connection`对象 ``(conn1, conn2)`` ， 分别表示管道的两端。

  如果 duplex 被置为 `True` (默认值)，那么该管道是双向的。如果 duplex 被置为 `False` ，那么该管道是单向的，即 `conn1` 只能用于接收消息，而 `conn2` 仅能用于发送消息。

class `multiprocessing.``Queue`([maxsize])

  返回一个使用一个管道和少量锁和信号量实现的共享队列实例。当一个进程将一个对象放进队列中时，一个写入线程会启动并将对象从缓冲区写入管道中。

  一旦超时，将抛出标准库 `queue` 模块中常见的异常 `queue.Empty` 和 `queue.Full`。

  除了 `task_done()` 和 `join()` 之外，`Queue` 实现了标准库类 `queue.Queue` 中所有的方法。

  `qsize`()

    返回队列的大致长度。由于多线程或者多进程的上下文，这个数字是不可靠的。

    注意，在 Unix 平台上，例如 Mac OS X ，这个方法可能会抛出 `NotImplementedError` 异常，因为该平台没有实现 `sem_getvalue()` 。

  `empty`()

    如果队列是空的，返回 `True` ，反之返回 `False` 。 由于多线程或多进程的环境，该状态是不可靠的。

  `full`()

    如果队列是满的，返回 `True` ，反之返回 `False` 。 由于多线程或多进程的环境，该状态是不可靠的。

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

    将 obj 放入队列。如果可选参数 block 是 `True` (默认值) 而且 timeout 是 `None` (默认值), 将会阻塞当前进程，直到有空的缓冲槽。如果 timeout 是正数，将会在阻塞了最多 timeout 秒之后还是没有可用的缓冲槽时抛出 `queue.Full` 异常。反之 (block 是 `False` 时)，仅当有可用缓冲槽时才放入对象，否则抛出 `queue.Full` 异常 (在这种情形下 timeout 参数会被忽略)。

    在 3.8 版更改: If the queue is closed, `ValueError` is raised instead of `AssertionError`.

  `put_nowait`(obj)

    相当于 `put(obj, False)`。

  `get`([block[, timeout]])

    从队列中取出并返回对象。如果可选参数 block 是 `True` (默认值) 而且 timeout 是 `None` (默认值), 将会阻塞当前进程，直到队列中出现可用的对象。如果 timeout 是正数，将会在阻塞了最多 timeout 秒之后还是没有可用的对象时抛出 `queue.Empty` 异常。反之 (block 是 `False` 时)，仅当有可用对象能够取出时返回，否则抛出 `queue.Empty` 异常 (在这种情形下 timeout 参数会被忽略)。

    在 3.8 版更改: If the queue is closed, `ValueError` is raised instead of `OSError`.

  `get_nowait`()

    相当于 `get(False)`。

  `multiprocessing.Queue` 类有一些在 `queue.Queue` 类中没有出现的方法。这些方法在大多数情形下并不是必须的。

  `close`()

    指示当前进程将不会再往队列中放入对象。一旦所有缓冲区中的数据被写入管道之后，后台的线程会退出。这个方法在队列被gc回收时会自动调用。

  `join_thread`()

    等待后台线程。这个方法仅在调用了 `close()` 方法之后可用。这会阻塞当前进程，直到后台线程退出，确保所有缓冲区中的数据都被写入管道中。

    默认情况下，如果一个不是队列创建者的进程试图退出，它会尝试等待这个队列的后台线程。这个进程可以使用 `cancel_join_thread()` 让 `join_thread()` 方法什么都不做直接跳过。

  `cancel_join_thread`()

    防止 `join_thread()` 方法阻塞当前进程。具体而言，这防止进程退出时自动等待后台线程退出。详见 `join_thread()`。

    可能这个方法称为”`allow_exit_without_flush()`“ 会更好。这有可能会导致正在排队进入队列的数据丢失，大多数情况下你不需要用到这个方法，仅当你不关心底层管道中可能丢失的数据，只是希望进程能够马上退出时使用。

  注解

  该类的功能依赖于宿主操作系统具有可用的共享信号量实现。否则该类将被禁用，任何试图实例化一个 `Queue` 对象的操作都会抛出 `ImportError` 异常，更多信息详见 bpo-3770 。后续说明的任何专用队列对象亦如此。

class `multiprocessing.``SimpleQueue`

  这是一个简化的 `Queue` 类的实现，很像带锁的 `Pipe` 。

  `empty`()

    如果队列为空返回 `True` ，否则返回 `False` 。

  `get`()

    从队列中移出并返回一个对象。

  `put`(item)

    将 item 放入队列。

class `multiprocessing.``JoinableQueue`([maxsize])

  `JoinableQueue` 类是 `Queue` 的子类，额外添加了 `task_done()` 和 `join()` 方法。

  `task_done`()

    指出之前进入队列的任务已经完成。由队列的消费者进程使用。对于每次调用 `get()` 获取的任务，执行完成后调用 `task_done()` 告诉队列该任务已经处理完成。

    如果 `join()` 方法正在阻塞之中，该方法会在所有对象都被处理完的时候返回 (即对之前使用 `put()` 放进队列中的所有对象都已经返回了对应的 `task_done()` ) 。

    如果被调用的次数多于放入队列中的项目数量，将引发 `ValueError` 异常 。

  `join`()

    阻塞至队列中所有的元素都被接收和处理完毕。

    当条目添加到队列的时候，未完成任务的计数就会增加。每当消费者进程调用 `task_done()` 表示这个条目已经被回收，该条目所有工作已经完成，未完成计数就会减少。当未完成计数降到零的时候， `join()` 阻塞被解除。

### 杂项

`multiprocessing.``active_children`()

  返回当前进程存活的子进程的列表。

  调用该方法有“等待”已经结束的进程的副作用。

`multiprocessing.``cpu_count`()

  返回系统的CPU数量。

  该数量不同于当前进程可以使用的CPU数量。可用的CPU数量可以由 `len(os.sched_getaffinity(0))` 方法获得。

  可能引发 `NotImplementedError` 。

  参见

  `os.cpu_count()`

`multiprocessing.``current_process`()

  返回与当前进程相对应的 `Process` 对象。

  和 `threading.current_thread()` 相同。

`multiprocessing.``parent_process`()

  Return the `Process` object corresponding to the parent process of the `current_process()`. For the main process, `parent_process` will be `None`.

  3.8 新版功能.

`multiprocessing.``freeze_support`()

  为使用了 `multiprocessing` 的程序，提供冻结以产生 Windows 可执行文件的支持。(在 py2exe, PyInstaller 和 cx_Freeze 上测试通过)

  需要在 main 模块的 `if __name__ == '__main__'` 该行之后马上调用该函数。例如：

      from multiprocessing import Process, freeze_support

      def f():
          print('hello world!')

      if __name__ == '__main__':
          freeze_support()
          Process(target=f).start()

  如果没有调用 `freeze_support()` 在尝试运行被冻结的可执行文件时会抛出 `RuntimeError` 异常。

  对 `freeze_support()` 的调用在非 Windows 平台上是无效的。如果该模块在 Windows 平台的 Python 解释器中正常运行 (该程序没有被冻结)， 调用``freeze_support()`` 也是无效的。

`multiprocessing.``get_all_start_methods`()

  返回支持的启动方法的列表，该列表的首项即为默认选项。可能的启动方法有 `'fork'`, `'spawn'` 和``'forkserver'`。在 Windows 中，只有 ``'spawn'` 是可用的。Unix平台总是支持``'fork'`` 和``'spawn'`，且 ``'fork'` 是默认值。

  3.4 新版功能.

`multiprocessing.``get_context`(method=None)

  返回一个 Context 对象。该对象具有和 `multiprocessing` 模块相同的API。

  如果 method 设置成 `None` 那么将返回默认上下文对象。否则 method 应该是 `'fork'`, `'spawn'`, `'forkserver'` 。 如果指定的启动方法不存在，将抛出 `ValueError` 异常。

  3.4 新版功能.

`multiprocessing.``get_start_method`(allow_none=False)

  返回启动进程时使用的启动方法名。

  如果启动方法已经固定，并且 allow_none 被设置成 False ，那么启动方法将被固定为默认的启动方法，并且返回其方法名。如果启动方法没有设定，并且 allow_none 被设置成 True ，那么将返回 `None` 。

  返回值将为 `'fork'` , `'spawn'` , `'forkserver'` 或者 `None` 。 `'fork'``是 Unix 的默认值， ``'spawn'` 是 Windows 的默认值。

  3.4 新版功能.

`multiprocessing.``set_executable`()

  设置在启动子进程时使用的 Python 解释器路径。 ( 默认使用 `sys.executable` ) 嵌入式编程人员可能需要这样做：

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

  以使他们可以创建子进程。

  在 3.4 版更改: 现在在 Unix 平台上使用 `'spawn'` 启动方法时支持调用该方法。

`multiprocessing.``set_start_method`(method)

  设置启动子进程的方法。 method 可以是 `'fork'` , `'spawn'` 或者 `'forkserver'` 。

  注意这最多只能调用一次，并且需要藏在 main 模块中，由 `if __name__ == '__main__'` 保护着。

  3.4 新版功能.

注解

`multiprocessing` 并没有包含类似 `threading.active_count()` , `threading.enumerate()` , `threading.settrace()` , `threading.setprofile()`, `threading.Timer` , 或者 `threading.local` 的方法和类。

### 连接（Connection）对象

Connection 对象允许收发可以序列化的对象或字符串。它们可以看作面向消息的连接套接字。

通常使用 `Pipe` 创建 Connection 对象。详见 ： Listeners and Clients.

class `multiprocessing.connection.``Connection`
  `send`(obj)

    将一个对象发送到连接的另一端，可以用 `recv()` 读取。

    发送的对象必须是可以序列化的，过大的对象 ( 接近 32MiB+ ，这个值取决于操作系统 ) 有可能引发 `ValueError` 异常。

  `recv`()

    返回一个由另一端使用 `send()` 发送的对象。该方法会一直阻塞直到接收到对象。 如果对端关闭了连接或者没有东西可接收，将抛出 `EOFError` 异常。

  `fileno`()

    返回由连接对象使用的描述符或者句柄。

  `close`()

    关闭连接对象。

    当连接对象被垃圾回收时会自动调用。

  `poll`([timeout])

    返回连接对象中是否有可以读取的数据。

    如果未指定 timeout ，此方法会马上返回。如果 timeout 是一个数字，则指定了最大阻塞的秒数。如果 timeout 是 `None` ，那么将一直等待，不会超时。

    注意通过使用 `multiprocessing.connection.wait()` 可以一次轮询多个连接对象。

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

    从一个 bytes-like object （字节类对象）对象中取出字节数组并作为一条完整消息发送。

    如果由 offset 给定了在 buffer 中读取数据的位置。 如果给定了 size ，那么将会从缓冲区中读取多个字节。 过大的缓冲区 ( 接近 32MiB+ ，此值依赖于操作系统 ) 有可能引发 `ValueError` 异常。

  `recv_bytes`([maxlength])

    以字符串形式返回一条从连接对象另一端发送过来的字节数据。此方法在接收到数据前将一直阻塞。 如果连接对象被对端关闭或者没有数据可读取，将抛出 `EOFError` 异常。

    如果给定了 maxlength 并且消息短于 maxlength 那么将抛出 `OSError` 并且该连接对象将不再可读。

    在 3.3 版更改: 曾经该函数抛出 `IOError` ，现在这是 `OSError` 的别名。

  `recv_bytes_into`(buffer[, offset])

    将一条完整的字节数据消息读入 buffer 中并返回消息的字节数。 此方法在接收到数据前将一直阻塞。 如果连接对象被对端关闭或者没有数据可读取，将抛出 `EOFError` 异常。

    buffer must be a writable bytes-like object. 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).

    如果缓冲区太小，则将引发 `BufferTooShort` 异常，并且完整的消息将会存放在异常实例 `e` 的 `e.args[0]` 中。

  在 3.3 版更改: 现在连接对象自身可以通过 `Connection.send()` 和 `Connection.recv()` 在进程之间传递。

  3.3 新版功能: 连接对象现已支持上下文管理协议 -- 参见 see 上下文管理器类型 。 `__enter__()` 返回连接对象， `__exit__()` 会调用 `close()` 。

例如:

    >>> from multiprocessing import Pipe
    >>> a, b = Pipe()
    >>> a.send([1, 'hello', None])
    >>> b.recv()
    [1, 'hello', None]
    >>> b.send_bytes(b'thank you')
    >>> a.recv_bytes()
    b'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.

因此， 除非连接对象是由 `Pipe()` 产生的，在通过一些认证手段之前你应该只使用 `recv()` 和 `send()` 方法。参考 Authentication keys 。

警告

如果一个进程在试图读写管道时被终止了，那么管道中的数据很可能是不完整的，因为此时可能无法确定消息的边界。

### 同步原语

通常来说同步愿意在多进程环境中并不像它们在多线程环境中那么必要。参考 `threading` 模块的文档。

注意可以使用管理器对象创建同步原语，参考 Managers 。

class `multiprocessing.``Barrier`(parties[, action[, timeout]])

  类似 `threading.Barrier` 的栅栏对象。

  3.3 新版功能.

class `multiprocessing.``BoundedSemaphore`([value])

  非常类似 `threading.BoundedSemaphore` 的有界信号量对象。

  一个小小的不同在于，它的 `acquire` 方法的第一个参数名是和 `Lock.acquire()` 一样的 block 。

  注解

  在 Mac OS X 平台上， 该对象于 `Semaphore` 不同在于 `sem_getvalue()` 方法并没有在该平台上实现。

class `multiprocessing.``Condition`([lock])

  条件变量： `threading.Condition` 的别名。

  指定的 lock 参数应该是 `multiprocessing` 模块中的 `Lock` 或者 `RLock` 对象。

  在 3.3 版更改: 新增了 `wait_for()` 方法。

class `multiprocessing.``Event`

  A clone of `threading.Event`.

class `multiprocessing.``Lock`

  原始锁（非递归锁）对象，类似于 `threading.Lock` 。一旦一个进程或者线程拿到了锁，后续的任何其他进程或线程的其他请求都会被阻塞直到锁被释放。任何进程或线程都可以释放锁。除非另有说明，否则 `multiprocessing.Lock` 用于进程或者线程的概念和行为都和 `threading.Lock` 一致。

  注意 `Lock` 实际上是一个工厂函数。它返回由默认上下文初始化的 `multiprocessing.synchronize.Lock` 对象。

  `Lock` supports the context manager protocol and thus may be used in `with` statements.

  `acquire`(block=True, timeout=None)

    获得锁，阻塞或非阻塞的。

    如果 block 参数被设为 `True` ( 默认值 ) ， 对该方法的调用在锁处于释放状态之前都会阻塞，然后将锁设置为锁住状态并返回 `True` 。需要注意的是第一个参数名与 `threading.Lock.acquire()` 的不同。

    如果 block 参数被设置成 `False` ，方法的调用将不会阻塞。 如果锁当前处于锁住状态，将返回 `False` ； 否则将锁设置成锁住状态，并返回 `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. Note that the treatment of negative or `None` values for timeout differs from the implemented behavior in `threading.Lock.acquire()`. 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.

  `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` is raised.

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.

  Note that `RLock` is actually a factory function which returns an instance of `multiprocessing.synchronize.RLock` initialized with a default context.

  `RLock` supports the context manager protocol and thus may be used in `with` statements.

  `acquire`(block=True, timeout=None)

    获得锁，阻塞或非阻塞的。

    When invoked with the block argument set to `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.

    When invoked with the block argument set to `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()`. Note that some of these behaviors of timeout differ from the implemented behaviors in `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.``Semaphore`([value])

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

  一个小小的不同在于，它的 `acquire` 方法的第一个参数名是和 `Lock.acquire()` 一样的 block 。

注解

On Mac OS X, `sem_timedwait` is unsupported, so calling `acquire()` with a timeout will emulate that function's behavior using a sleeping loop.

注解

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` will be raised.

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

注解

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`. See bpo-3770 for additional information.

### Shared `ctypes` Objects

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

`multiprocessing.``Value`(typecode_or_type, *args, lock=True)

  Return a `ctypes` object allocated from shared memory. By default the return value is actually a synchronized wrapper for the object. The object itself can be accessed via the value attribute of a `Value`.

  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` module. *args is passed on to the constructor for the type.

  If lock is `True` (the default) then a new recursive lock object is created to synchronize access to the value. If lock is a `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

  Note that lock is a keyword-only argument.

`multiprocessing.``Array`(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.

  If lock is `True` (the default) then a new lock object is created to synchronize access to the value. If lock is a `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".

  Note that lock is a keyword only argument.

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

#### The `multiprocessing.sharedctypes` module

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

注解

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` module. *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.``Array`(typecode_or_type, size_or_initializer, *, lock=True)

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

  If lock is `True` (the default) then a new lock object is created to synchronize access to the value. If lock is a `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".

  Note that lock is a keyword-only argument.

`multiprocessing.sharedctypes.``Value`(typecode_or_type, *args, lock=True)

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

  If lock is `True` (the default) then a new lock object is created to synchronize access to the value. If lock is a `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".

  Note that lock is a keyword-only argument.

`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.

  在 3.5 版更改: Synchronized objects support the context manager protocol.

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 | 使用类型的共享ctypes | 使用 typecode 的共享 ctypes
--- | --- | ---
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', b'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])

The results printed are

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

### Managers

Managers provide a way to create data which can be shared between different processes, including sharing over a network between processes running on different machines. A manager object controls a server process which manages shared objects. Other processes can access the shared objects by using proxies.

`multiprocessing.``Manager`()

  Returns a started `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` module:

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

  Create a BaseManager object.

  Once created one should call `start()` or `get_server().serve_forever()` to ensure that the manager object refers to a started manager process.

  address is the address on which the manager process listens for new connections. If address is `None` then an arbitrary one is chosen.

  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` is used. Otherwise authkey is used and it must be a byte string.

  `start`([initializer[, initargs]])

    Start a subprocess to start the manager. If initializer is not `None` then the subprocess will call `initializer(*initargs)` when it starts.

  `get_server`()

    Returns a `Server` object which represents the actual server under the control of the Manager. The `Server` object supports the `serve_forever()` method:

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

    `Server` additionally has an `address` attribute.

  `connect`()

    Connect a local manager object to a remote manager process:

        >>> from multiprocessing.managers import BaseManager
        >>> m = BaseManager(address=('127.0.0.1', 50000), authkey=b'abc')
        >>> m.connect()

  `shutdown`()

    Stop the process used by the manager. This is only available if `start()` has been used to start the server process.

    它可以被多次调用。

  `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 connected to the server using the `connect()` method, or if the create_method argument is `False` then this can be left as `None`.

    proxytype is a subclass of `BaseProxy` which is used to create proxies for shared objects with this typeid. If `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` instances also have one read-only property:

  `address`

    管理器所用的地址。

  在 3.3 版更改: Manager objects support the context management protocol -- see 上下文管理器类型. `__enter__()` starts the server process (if it has not already started) and then returns the manager object. `__exit__()` calls `shutdown()`.

  In previous versions `__enter__()` did not start the manager's server process if it was not already started.

class `multiprocessing.managers.``SyncManager`

  A subclass of `BaseManager` which can be used for the synchronization of processes. Objects of this type are returned by `multiprocessing.Manager()`.

  Its methods create and return 代理对象 for a number of commonly used data types to be synchronized across processes. This notably includes shared lists and dictionaries.

  `Barrier`(parties[, action[, timeout]])

    Create a shared `threading.Barrier` object and return a proxy for it.

    3.3 新版功能.

  `BoundedSemaphore`([value])

    Create a shared `threading.BoundedSemaphore` object and return a proxy for it.

  `Condition`([lock])

    Create a shared `threading.Condition` object and return a proxy for it.

    If lock is supplied then it should be a proxy for a `threading.Lock` or `threading.RLock` object.

    在 3.3 版更改: 新增了 `wait_for()` 方法。

  `Event`()

    Create a shared `threading.Event` object and return a proxy for it.

  `Lock`()

    Create a shared `threading.Lock` object and return a proxy for it.

  `Namespace`()

    Create a shared `Namespace` object and return a proxy for it.

  `Queue`([maxsize])

    Create a shared `queue.Queue` object and return a proxy for it.

  `RLock`()

    Create a shared `threading.RLock` object and return a proxy for it.

  `Semaphore`([value])

    Create a shared `threading.Semaphore` object and return a proxy for it.

  `Array`(typecode, sequence)

    Create an array and return a proxy for it.

  `Value`(typecode, value)

    Create an object with a writable `value` attribute and return a proxy for it.

  `dict`()
  `dict`(mapping)
  `dict`(sequence)

    Create a shared `dict` object and return a proxy for it.

  `list`()
  `list`(sequence)

    Create a shared `list` object and return a proxy for it.

  在 3.6 版更改: Shared objects are capable of being nested. For example, a shared container object such as a shared list can contain other shared objects which will all be managed and synchronized by the `SyncManager`.

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')

#### Customized managers

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:
        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__':
        with MyManager() as manager:
            maths = manager.Maths()
            print(maths.add(4, 3))         # prints 7
            print(maths.mul(7, 8))         # prints 56

#### Using a remote manager

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

### 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). In this way, a proxy can be used just like 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. As such, a referent can contain 代理对象. This permits nesting of these managed lists, dicts, and other 代理对象:

    >>> a = manager.list()
    >>> b = manager.list()
    >>> a.append(b)         # referent of a now contains referent of b
    >>> print(a, b)
    [<ListProxy object, typeid 'list' at ...>] []
    >>> b.append('hello')
    >>> print(a[0], b)
    ['hello'] ['hello']

Similarly, dict and list proxies may be nested inside one another:

    >>> l_outer = manager.list([ manager.dict() for i in range(2) ])
    >>> d_first_inner = l_outer[0]
    >>> d_first_inner['a'] = 1
    >>> d_first_inner['b'] = 2
    >>> l_outer[1]['c'] = 3
    >>> l_outer[1]['z'] = 26
    >>> print(l_outer[0])
    {'a': 1, 'b': 2}
    >>> print(l_outer[1])
    {'c': 3, 'z': 26}

If standard (non-proxy) `list` or `dict` objects are contained in a referent, modifications to those mutable values will not be propagated through the manager because the proxy has no way of knowing when the values contained within are modified. However, storing a value in a container proxy (which triggers a `__setitem__` on the proxy object) does propagate through the manager and so to effectively modify such an item, one could re-assign the modified value 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
    # updating the dictionary, the proxy is notified of the change
    lproxy[0] = d

This approach is perhaps less convenient than employing nested 代理对象 for most use cases but also demonstrates a level of control over the synchronization.

注解

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.

    If `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 argument of `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('__getitem__', (slice(2, 7),)) # equivalent to l[2:7]
        [2, 3, 4, 5, 6]
        >>> l._callmethod('__getitem__', (20,))          # equivalent 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__`()

    Return a representation of the proxy object.

  `__str__`()

    Return the representation of the referent.

#### Cleanup

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.

### 进程池

One can create a pool of processes which will carry out tasks submitted to it with the `Pool` class.

class `multiprocessing.pool.``Pool`([processes[, initializer[, initargs[, maxtasksperchild[, context]]]]])

  A process pool object which controls a pool of worker processes to which jobs can be submitted. It supports asynchronous results with timeouts and callbacks and has a parallel map implementation.

  processes is the number of worker processes to use. If processes is `None` then the number returned by `os.cpu_count()` is used.

  If initializer is not `None` then each worker process will call `initializer(*initargs)` when it starts.

  maxtasksperchild is the number of tasks a worker process can complete before it will exit and be replaced with a fresh worker process, to enable unused resources to be freed. The default maxtasksperchild is `None`, which means worker processes will live as long as the pool.

  context can be used to specify the context used for starting the worker processes. Usually a pool is created using the function `multiprocessing.Pool()` or the `Pool()` method of a context object. In both cases context is set appropriately.

  Note that the methods of the pool object should only be called by the process which created the pool.

  3.2 新版功能: maxtasksperchild

  3.4 新版功能: context

  注解

  Worker processes within a `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 argument to the `Pool` exposes this ability to the end user.

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

    Call func with arguments args and keyword arguments kwds. It blocks until the result is ready. Given this blocks, `apply_async()` is better suited for performing work in parallel. Additionally, func is only executed in one of the workers of the pool.

  `apply_async`(func[, args[, kwds[, callback[, error_callback]]]])

    A variant of the `apply()` method which returns a result object.

    If callback is specified then it should be a callable which accepts a single argument. When the result becomes ready callback is applied to it, that is unless the call failed, in which case the error_callback is applied instead.

    If error_callback is specified then it should be a callable which accepts a single argument. If the target function fails, then the error_callback is called with the exception instance.

    Callbacks should complete immediately since otherwise the thread which handles the results will get blocked.

  `map`(func, iterable[, chunksize])

    A parallel equivalent of the `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.

    Note that it may cause high memory usage for very long iterables. Consider using `imap()` or `imap_unordered()` with explicit chunksize option for better efficiency.

  `map_async`(func, iterable[, chunksize[, callback[, error_callback]]])

    A variant of the `map()` method which returns a result object.

    If callback is specified then it should be a callable which accepts a single argument. When the result becomes ready callback is applied to it, that is unless the call failed, in which case the error_callback is applied instead.

    If error_callback is specified then it should be a callable which accepts a single argument. If the target function fails, then the error_callback is called with the exception instance.

    Callbacks should complete immediately since otherwise the thread which handles the results will get blocked.

  `imap`(func, iterable[, chunksize])

    A lazier version of `map()`.

    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` then the `next()` method of the iterator returned by the `imap()` method has an optional timeout parameter: `next(timeout)` will raise `multiprocessing.TimeoutError` if the result cannot be returned within timeout seconds.

  `imap_unordered`(func, iterable[, chunksize])

    The same as `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".)

  `starmap`(func, iterable[, chunksize])

    Like `map()` except that the elements of the iterable are expected to be iterables that are unpacked as arguments.

    Hence an iterable of `[(1,2), (3, 4)]` results in `[func(1,2), func(3,4)]`.

    3.3 新版功能.

  `starmap_async`(func, iterable[, chunksize[, callback[, error_callback]]])

    A combination of `starmap()` and `map_async()` that iterates over iterable of iterables and calls func with the iterables unpacked. Returns a result object.

    3.3 新版功能.

  `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`()

    Wait for the worker processes to exit. One must call `close()` or `terminate()` before using `join()`.

  3.3 新版功能: Pool objects now support the context management protocol -- see 上下文管理器类型. `__enter__()` returns the pool object, and `__exit__()` calls `terminate()`.

class `multiprocessing.pool.``AsyncResult`

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

  `get`([timeout])

    Return the result when it arrives. If timeout is not `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])

    Wait until the result is available or until timeout seconds pass.

  `ready`()

    Return whether the call has completed.

  `successful`()

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

The following example demonstrates the use of a pool:

    from multiprocessing import Pool
    import time

    def f(x):
        return x*x

    if __name__ == '__main__':
        with Pool(processes=4) as pool:         # 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(next(it))                     # prints "0"
            print(next(it))                     # 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

### Listeners and Clients

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

However, the `multiprocessing.connection` module allows some extra flexibility. It basically gives a high level message oriented API for dealing with sockets or Windows named pipes. It also has support for digest authentication using the `hmac` module, and for polling multiple connections at the same time.

`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` is raised.

`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` is raised.

`multiprocessing.connection.``Client`(address[, family[, authkey]])

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

  The type of the connection is determined by family argument, but this can generally be omitted since it can usually be inferred from the format of address. (See Address Formats)

  If authkey is given and not None, it should be a byte string and will be used as the secret key for an HMAC-based authentication challenge. No authentication is done if authkey is None. `AuthenticationError` is raised if authentication fails. See Authentication keys.

class `multiprocessing.connection.``Listener`([address[, family[, backlog[, 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.

  注解

  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'.

  family 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 family is `None` then the family is inferred from the format of address. If address is also `None` then a default is chosen. This default is the family which is assumed to be the fastest available. See Address Formats. Note that if family 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.

  If authkey is given and not None, it should be a byte string and will be used as the secret key for an HMAC-based authentication challenge. No authentication is done if authkey is None. `AuthenticationError` is raised if authentication fails. See Authentication keys.

  `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` is raised.

  `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 objects have the following read-only properties:

  `address`

    The address which is being used by the Listener object.

  `last_accepted`

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

  3.3 新版功能: Listener objects now support the context management protocol -- see 上下文管理器类型. `__enter__()` returns the listener object, and `__exit__()` calls `close()`.

`multiprocessing.connection.``wait`(object_list, timeout=None)

  Wait till an object in object_list is ready. Returns the list of those objects in object_list which are ready. If timeout is a float then the call blocks for at most that many seconds. If timeout is `None` then it will block for an unlimited period. A negative timeout is equivalent to a zero timeout.

  For both Unix and Windows, an object can appear in object_list if it is

  - a readable `Connection` object;

  - a connected and readable `socket.socket` object; or

  - the `sentinel` attribute of a `Process` object.

  A connection or socket object is ready when there is data available to be read from it, or the other end has been closed.

  Unix: `wait(object_list, timeout)` almost equivalent `select.select(object_list, [], [], timeout)`. The difference is that, if `select.select()` is interrupted by a signal, it can raise `OSError` with an error number of `EINTR`, whereas `wait()` will not.

  Windows: An item in object_list must either be an integer handle which is waitable (according to the definition used by the documentation of the Win32 function `WaitForMultipleObjects()`) or it can be an object with a `fileno()` method which returns a socket handle or pipe handle. (Note that pipe handles and socket handles are not waitable handles.)

  3.3 新版功能.

Examples

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'

    with Listener(address, authkey=b'secret password') as listener:
        with listener.accept() as conn:
            print('connection accepted from', listener.last_accepted)

            conn.send([2.25, None, 'junk', float])

            conn.send_bytes(b'hello')

            conn.send_bytes(array('i', [42, 1729]))

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)

    with Client(address, authkey=b'secret password') as conn:
        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])

The following code uses `wait()` to wait for messages from multiple processes at once:

    import time, random
    from multiprocessing import Process, Pipe, current_process
    from multiprocessing.connection import wait

    def foo(w):
        for i in range(10):
            w.send((i, current_process().name))
        w.close()

    if __name__ == '__main__':
        readers = []

        for i in range(4):
            r, w = Pipe(duplex=False)
            readers.append(r)
            p = Process(target=foo, args=(w,))
            p.start()
            # We close the writable end of the pipe now to be sure that
            # p is the only process which owns a handle for it.  This
            # ensures that when p closes its handle for the writable end,
            # wait() will promptly report the readable end as being ready.
            w.close()

        while readers:
            for r in wait(readers):
                try:
                    msg = r.recv()
                except EOFError:
                    readers.remove(r)
                else:
                    print(msg)

#### Address Formats

- An `'AF_INET'` address is a tuple of the form `(hostname, port)` where hostname is a string and port is an integer.

- An `'AF_UNIX'` address is a string representing a filename on the filesystem.

- An `'AF_PIPE'` address is a string of the form

    `r'\.\pipe{PipeName}'`. To use `Client()` to connect to a named pipe on a remote computer called ServerName one should use an address of the form `r'\ServerName\pipe{PipeName}'` instead.

Note that any string beginning with two backslashes is assumed by default to be an `'AF_PIPE'` address rather than an `'AF_UNIX'` address.

### Authentication keys

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()` use the `hmac` module to provide digest authentication.

An authentication key is a byte 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` is used (see `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()`.

### 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

For a full table of logging levels, see the `logging` module.

### The `multiprocessing.dummy` module

`multiprocessing.dummy` replicates the API of `multiprocessing` but is no more than a wrapper around the `threading` module.

## Programming guidelines

There are certain guidelines and idioms which should be adhered to when using `multiprocessing`.

### All start methods

The following applies to all start methods.

Avoid shared state

> 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.

Picklability

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

Thread safety of proxies

> 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

> When using the spawn or forkserver start methods 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.

Avoid terminating processes

> Using the `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 `Queue.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).

Explicitly pass resources to child processes

> On Unix using the fork start method, 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 and the other start methods 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.

> 所以对于实例：

>     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()

> 应当重写成这样：

>     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())

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

>     sys.stdin.close()
>     sys.stdin = open(os.open(os.devnull, os.O_RDONLY), closefd=False)

> 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

> For more information, see bpo-5155, bpo-5313 and bpo-5331

### The spawn and forkserver start methods

There are a few extra restriction which don't apply to the fork start method.

More picklability

> Ensure that all arguments to `Process.__init__()` are picklable. Also, if you subclass `Process` then make sure that instances will be picklable when the `Process.start` method is called.

Global variables

> 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, using the spawn or forkserver start method 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__':` as follows:

>     from multiprocessing import Process, freeze_support, set_start_method

>     def foo():
>         print('hello')

>     if __name__ == '__main__':
>         freeze_support()
>         set_start_method('spawn')
>         p = Process(target=foo)
>         p.start()

> (The `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()` function.

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

## 示例

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

    from multiprocessing import freeze_support
    from multiprocessing.managers import BaseManager, BaseProxy
    import operator

    ##

    class Foo:
        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 range(10):
            yield i*i

    # Proxy type for generator objects
    class GeneratorProxy(BaseProxy):
        _exposed_ = ['__next__']
        def __iter__(self):
            return self
        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, end=' ')
        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._exposed_ =', op._exposed_)

    ##

    if __name__ == '__main__':
        freeze_support()
        test()

Using `Pool`:

    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():
        PROCESSES = 4
        print('Creating pool with %d processes\n' % PROCESSES)

        with multiprocessing.Pool(PROCESSES) as pool:
            #
            # 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()

            #
            # 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, list(range(10))))
            except ZeroDivisionError:
                print('\tGot ZeroDivisionError as expected from pool.map()')
            else:
                raise AssertionError('expected ZeroDivisionError')

            try:
                print(list(pool.imap(f, list(range(10)))))
            except ZeroDivisionError:
                print('\tGot ZeroDivisionError as expected from list(pool.imap())')
            else:
                raise AssertionError('expected ZeroDivisionError')

            it = pool.imap(f, list(range(10)))
            for i in range(10):
                try:
                    x = next(it)
                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:', end=' ')
            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:', end=' ')
            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()

    if __name__ == '__main__':
        multiprocessing.freeze_support()
        test()

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

    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()