# 17.2. `multiprocessing` — 基于进程的并行

源代码： Lib/multiprocessing /

## 17.2.1. 引言

`multiprocessing`是一个支持使用和`threading`模块类似的API生成进程的模块。`multiprocessing`包提供本地和远程并发，通过使用子进程而不是线程有效地转移全局解释器锁。因此，`multiprocessing`模块允许程序员充分利用给定机器上的多个处理器。它在Unix和Windows上都可以运行。

`multiprocessing`模块还引入了`threading`模块中没有的API。一个主要的例子是`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。下面是使用`Pool`进行数据并行的基本示例，

    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]

### 17.2.1.1. `Process`类

在`multiprocessing`中，通过创建`Process`对象，然后调用其`start()`方法来生成进程。`Process`遵循`threading.Thread`的API。多进程程序的一个简单例子是

    from multiprocessing import Process

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

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

要显示涉及的单个进程ID，下面是一个扩展示例：

    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()

关于为什么`if __name__ == '__main__'`是必要的，请参阅编程指南。

### 17.2.1.2. 上下文和启动方法

根据平台，`multiprocessing`支持三种方式来启动进程。这些启动方法是

> spawn

>   父进程启动一个新的python解释器进程。子进程只会继承运行进程对象`run()`方法所需的那些资源。特别是，不会继承父进程中不必要的文件描述符和句柄。与使用fork或forkserver相比，使用此方法启动进程相当慢。

>   在Unix和Windows上可用。 Windows上的默认值。

> fork

>   父进程使用`os.fork()`来派生Python解释器。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有问题的。

>   仅在Unix上可用。 Unix上的默认值。

> forkserver

>   当程序启动并选择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。 fork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。

>   在Unix平台上可用，它支持通过Unix管道传递文件描述符。

版本3.4更新：所有Unix平台上添加spawn，以及为某些Unix平台添加forkserver。在Windows上，子进程不再继承父进程所有可继承句柄。

在Unix上使用spawn或forkserver启动方法还将启动一个信号量跟踪器进程，该进程跟踪由程序的进程创建的未链接的命名信号量。当所有进程退出后，信号量跟踪器取消任何剩余信号量的链接。通常应该没有剩余信号量，但如果一个进程被一个信号杀死，可能有一些“泄漏”的信号量。（取消命名信号量的链接是一件重要的事情，因为系统只允许有限的数量，并且它们将不会自动取消链接，直到下次重新启动。）

要选择启动的方法，请在main模块的`if __name__ == '__main__'`子句中使用`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()`应最多只使用一次。

或者，你可以使用`get_context()`来获取context对象。Context对象具有与multiprocessing模块相同的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()

注意，与context相关的对象可能与用于不同context的进程不兼容。特别地，使用fork context创建的锁不能传递到使用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。

### 17.2.1.3. 在进程之间交换对象

`multiprocessing` 支持进程之间的两种通信通道：

Queues

> The `Queue` class is a near clone of `queue.Queue`. For example:

>     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()

> Queues are thread and process safe.

Pipes

> The `Pipe()` function returns a pair of connection objects connected by a pipe which by default is duplex (two-way). For example:

>     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()

> The two connection objects returned by `Pipe()` represent the two ends of the pipe. Each connection object has `send()` and `recv()` methods (among others). Note that data in a pipe may become corrupted if two processes (or threads) try to read from or write to the same end of the pipe at the same time. Of course there is no risk of corruption from processes using different ends of the pipe at the same time.

### 17.2.1.4. 进程之间的同步

`multiprocessing`包含了全部和`threading`相同的同步原语 。例如，可以使用锁来保证同一时间只有一个进程在使用标准输出：

    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()

如果不使用锁，来自不同进程的输出很容易都混淆在一起。

### 17.2.1.5. 进程之间的状态共享

如上所述，当进行并发编程时，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，如果你确实需要使用某些共享数据，那么`multiprocessing`提供了两种方法。

共享内存

> Data can be stored in a shared memory map using `Value` or `Array`. For example, the following code

>     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[:])

> will print

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

> The `'d'` and `'i'` arguments used when creating `num` and `arr` are typecodes of the kind used by the `array` module: `'d'` indicates a double precision float and `'i'` indicates a signed integer. These shared objects will be process and thread-safe.

> For more flexibility in using shared memory one can use the `multiprocessing.sharedctypes` module which supports the creation of arbitrary ctypes objects allocated from shared memory.

Server process

> A manager object returned by `Manager()` controls a server process which holds Python objects and allows other processes to manipulate them using proxies.

> A manager returned by `Manager()` will support types `list`, `dict`, `Namespace`, `Lock`, `RLock`, `Semaphore`, `BoundedSemaphore`, `Condition`, `Event`, `Barrier`, `Queue`, `Value` and `Array`. For example,

>     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)

> will print

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

> Server process managers are more flexible than using shared memory objects because they can be made to support arbitrary object types. Also, a single manager can be shared by processes on different computers over a network. They are, however, slower than using shared memory.

### 17.2.1.6. 使用工作进程的进程池

`Pool`类表示工作进程的进程池。它具有允许将任务以几种不同的方式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")

注意，进程池的方法只应该被创建它的进程使用。

注意

此包中的功能要求`__main__`模块可由子进程导入。这在编程指南中有所说明，但值得在这里指出。这意味着一些示例，如`multiprocessing.pool.Pool`示例在交互式解释器中不能工作。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'

（如果你尝试这样，它实际上会输出以半随机方式交错的三个完整的traceback，然后你可能不得不停止主进程。）

## 17.2.2. Reference

The `multiprocessing` package mostly replicates the API of the `threading` module.

### 17.2.2.1. `进程`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的进程中运行的活动。进程(`Process`)类有类似`threading.Thread`的所有方法。

  应始终使用关键字参数调用构造函数。group应始终为`None`；它仅用于与`threading.Thread`兼容。target是`run()`方法调用的可调用对象。它默认为`None`，这意味着什么都不会被调用。name是进程名称（有关更多详细信息，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。如果给形参赋值，则daemon参数将进程`daemon`设置为`True`或`False`。如果为`None`（默认），该标志将从创建过程中继承。

  默认情况下，没有参数传递给target。

  如果一个子类重写构造函数，它必须确保它在执行任何其他操作之前调用基类构造函数（`Process .__ init __()`）。

  在版本3.3中更改：添加了daemon参数。

  `run`()

    表示进程行为的方法。

    您可以在子类中重写此方法。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，如果有的话，从args和kwargs(元组和字典)传参。

  `start`()

    开启进程。

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

  `join`([timeout])

    如果可选参数timeout为`None`（缺省值），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，它会阻止至多timeout秒。

    一个过程可以连接多次。

    进程无法自行加入，因为这会导致死锁。尝试在启动之前加入进程是错误的。

  `name`

    该进程的名称。 该名称是仅用于识别目的的字符串。它没有语义。多个进程可以被赋予相同的名称。

    初始名称由构造函数设置。如果没有明确的名字被提供给构造函数，则构造一个名为“Process-N1：N2：...：Nk”的表单，其中每个Nk是其父进程的第N个子进程。

  `is_alive`()

    返回进程是否存在。

    粗略地说，一个进程对象在`start()`方法返回之前一直存在，直到子进程终止。

  `daemon`

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

    初始值是从创建过程继承的。

    当进程退出时，它将尝试终止其所有守护进程的子进程。

    注意，守护进程不允许创建子进程。因为如果容许这样做，当守护进程的父进程退出时，该父进程终止守护进程，守护进程的子进程就会被孤立另外，这些是不是 Unix守护进程或服务，它们是正常的进程，如果非守护进程退出，它们将被终止（而不是加入）。

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

  `pid`

    返回进程ID。在生成过程之前，这将是`None`。

  `exitcode`

    子进程的退出代码。如果进程尚未终止，则这将是`None`。负值-N表示子进程被信号N终止。

  `authkey`

    进程的身份验证密钥（一个字节字符串）。

    当多进程(`multiprocessing`)被初始化时，主进程会使用`os.urandom()`分配一个随机字符串。

    创建一个进程(`Process`)对象时，它将继承其父进程的身份验证密钥，但可以通过将`authkey`设置为另一个字节字符串来更改。

    请参阅身份验证密钥。

  `sentinel`

    系统对象的数字句柄，当过程结束时它将变为“就绪”。

    如果您想使用`multiprocessing.connection.wait()`一次等待几个事件，则可以使用此值。否则调用`join()`更简单。

    在Windows上，这是一个可与`WaitForSingleObject`和`WaitForMultipleObjects`系列API调用一起使用的操作系统句柄。在Unix上，这是一个可用于`select`模块的基元的文件描述符。

    3.3版本中的新功能

  `terminate`()

    终止该过程。在Unix上使用`SIGTERM`信号完成；在Windows上使用`TerminateProcess()`。请注意，退出处理程序执行后,最后的子句等将不会执行。

    请注意，进程的后代进程将不终止 - 它们将简单地变为孤立。

    Warning

    If this method is used when the associated process is using a pipe or queue then the pipe or queue is liable to become corrupted and may become unusable by other process. Similarly, if the process has acquired a lock or semaphore etc. then terminating it is liable to cause other processes to deadlock.

  注意`start()`，`join()`，`is_alive()`，`terminate()`和`exitcode`方法只应由创建过程对象的进程调用。

  进程(`Process`)的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(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.``ProcessError`

  所有`多线程`异常的基类。

exception `multiprocessing.``BufferTooShort`

  当提供的缓冲区对象对于消息读取而言太小时，由`Connection.recv_bytes_into()`引发异常。

  如果`e`是`BufferTooShort`的实例，则`e.a​​rgs [0]`将以字节字符串形式给出消息。

exception `multiprocessing.``AuthenticationError`

  出现身份验证错误时引发。

exception `multiprocessing.``TimeoutError`

  当超时过期时由超时方法提出。

### 17.2.2.2. Pipes 和 Queues

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`, `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.

如果您使用`JoinableQueue`，那么您必须为每一个从队列中移除的任务去调用`JoinableQueue.task_done()`。否则的话，那些用于记录未完成的任务数量的信号量或许最终会溢出，从而引发异常。

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

Note

`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`.

Note

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()` method returns `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.

Warning

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.

Warning

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 Programming guidelines.

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

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

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

  If 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.``Queue`([maxsize])

  返回进程共享的队列，底层使用管道和锁来实现。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`()

    Return `True` if the queue is empty, `False` otherwise. Because of multithreading/multiprocessing semantics, this is not reliable.

  `full`()

    Return `True` if the queue is full, `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` (the default), block if necessary until a free slot is available. If timeout is a positive number, it blocks at most timeout seconds and raises the `queue.Full` exception if no free slot was available within that time. Otherwise (block is `False`), put an item on the queue if a free slot is immediately available, else raise the `queue.Full` exception (timeout is ignored in that case).

  `put_nowait`(obj)

    Equivalent to `put(obj, False)`.

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

    Remove and return an item from the queue. 如果可选的参数 block为`True`（默认值）和超时为`None`（默认值）将阻塞，直到队列里有项目可用。If timeout is a positive number, it blocks at most timeout seconds and raises the `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` exception (timeout is ignored in that case).

  `get_nowait`()

    Equivalent to `get(False)`.

  `multiprocessing.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.

  Note

  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`. See issue 3770 for additional information. The same holds true for any of the specialized queue types listed below.

class `multiprocessing.``SimpleQueue`

  It is a simplified `Queue` type, very close to a locked `Pipe`.

  `empty`()

    Return `True` if the queue is empty, `False` otherwise.

  `get`()

    Remove and return an item from the queue.

  `put`(item)

    Put item into the queue.

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

  `JoinableQueue`, a `Queue` subclass, is a queue which additionally has `task_done()` and `join()` methods.

  `task_done`()

    Indicate that a formerly enqueued task is complete. Used by queue consumers. For each `get()` used to fetch a task, a subsequent call to `task_done()` tells the queue that the processing on the task is complete.

    If a `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).

    Raises a `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 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.

### 17.2.2.3. Miscellaneous

`multiprocessing.``active_children`()

  Return list of all live children of the current process.

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

`multiprocessing.``cpu_count`()

  返回系统中的CPU数量。可能会引发`NotImplementedError`。

  See also

  `os.cpu_count()`

`multiprocessing.``current_process`()

  Return the `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()

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

  Calling `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()` has no effect.

`multiprocessing.``get_all_start_methods`()

  Returns a list of the supported start methods, the first of which is the default. The possible start methods are `'fork'`, `'spawn'` and `'forkserver'`. On Windows only `'spawn'` is available. On Unix `'fork'` and `'spawn'` are always supported, with `'fork'` being the default.

  New in version 3.4.

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

  Return a context object which has the same attributes as the `multiprocessing` module.

  If method is None then the default context is returned. Otherwise method should be `'fork'`, `'spawn'`, `'forkserver'`. `ValueError` is raised if the specified start method is not available.

  New in version 3.4.

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

  Return the name of start method used for starting processes.

  If the start method has not been fixed and allow_none is false, then the start method is fixed to the default and the name is returned. If the start method has not been fixed and allow_none is true then None is returned.

  The return value can be `'fork'`, `'spawn'`, `'forkserver'` or None. `'fork'` is the default on Unix, while `'spawn'` is the default on Windows.

  New in version 3.4.

`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.

  Changed in version 3.4: Now supported on Unix when the `'spawn'` start method is used.

`multiprocessing.``set_start_method`(method)

  Set the method which should be used to start child processes. method can be `'fork'`, `'spawn'` or `'forkserver'`.

  Note that this should be called at most once, and it should be protected inside the `if __name__ == '__main__'` clause of the main module.

  New in version 3.4.

Note

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

### 17.2.2.4. Connection Objects

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

Connection objects are usually created using `Pipe()` – see also Listeners and Clients.

class `multiprocessing.``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` exception.

  `recv`()

    Return an object sent from the other end of the connection using `send()`. Blocks until there its 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`()

    Close the connection.

    This is called automatically when the connection is garbage collected.

  `poll`([timeout])

    Return whether there is any data available to be read.

    If 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.

    Note that multiple connection objects may be polled at once by using `multiprocessing.connection.wait()`.

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

    Send byte data from a bytes-like object as a complete message.

    If offset is given then data is read from that position in buffer. If 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.

    If maxlength is specified and the message is longer than maxlength then `OSError` is raised and the connection will no longer be readable.

    Changed in version 3.3: This function used to raise `IOError`, which is now an alias of `OSError`.

  `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 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).

    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.

  Changed in version 3.3: Connection objects themselves can now be transferred between processes using `Connection.send()` and `Connection.recv()`.

  New in version 3.3: Connection objects now support the context management protocol – see Context Manager Types. `__enter__()` returns the connection object, and `__exit__()` calls `close()`.

For example:

    >>> 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])

Warning

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 Authentication keys.

Warning

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.

### 17.2.2.5. 同步原语

通常，同步原语在多进程程序中并不像在多线程程序中那样必要。参阅`threading`模块的文档。

注意，也可以使用Manager对象创建同步原语 - 请参阅Managers。

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

  A barrier object: a clone of `threading.Barrier`.

  New in version 3.3.

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

  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, as is consistent with `Lock.acquire()`.

  Note

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

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

  A condition variable: an alias for `threading.Condition`.

  If lock is specified then it should be a `Lock` or `RLock` object from `multiprocessing`.

  Changed in version 3.3: The `wait_for()` method was added.

class `multiprocessing.``Event`

  A clone of `threading.Event`.

class `multiprocessing.``Lock`

  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.

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

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

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

    Acquire a lock, blocking or non-blocking.

    With the block argument set to `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 argument set to `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. 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)

    Acquire a lock, blocking or non-blocking.

    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`()

    释放一个锁，同时递归级别减一。如果递归级别递减到零，则将锁重置为unlocked状态（即不属于任何进程或线程），如果有任何其他进程或线程被阻塞，等待解锁，则准许其中一个进程继续。如果递归级别仍然不为零，则锁保持锁定并上交给调用进程或线程。

    只有在调用进程或线程拥有锁时才能调用此方法。如果此方法被所有者以外的进程或线程调用，或者锁处于解锁状态（无主），则会引发`AssertionError`。请注意，在这种情况下引发的异常类型与`threading.RLock.release()`中实现的方式有所区别。

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

  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, as is consistent with `Lock.acquire()`.

Note

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

Note

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.

Note

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 issue 3770 for additional information.

### 17.2.2.6. 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)

  返回从共享内存分配的`ctypes`对象。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.

#### 17.2.2.6.1. The `multiprocessing.sharedctypes` module

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

Note

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”.

  请注意，lock是一个强制关键字参数。

`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”.

  请注意，lock是一个强制关键字参数。

`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.

  Changed in version 3.5: 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 | sharedctypes using type | sharedctypes using 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', 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)]

### 17.2.2.7. Managers

Managers提供了一种方法来创建可以在不同进程之间共享的数据，包括通过网络在不同机器上运行的进程之间共享。manager对象控制着管理共享对象的服务器进程。其他进程可以通过使用代理来访问共享对象。

`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是管理器进程侦听新连接的地址。如果address为`None`，则选择任意一个。

  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` 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', 5000), 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.

    This can be called multiple times.

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

    类方法，用于注册类型或者可调用的manager类

    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`

    管理器使用的地址。

  Changed in version 3.3: Manager objects support the context management protocol – see Context Manager Types. `__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()`.

  It also supports creation of shared lists and dictionaries.

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

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

    New in version 3.3.

  `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.

    Changed in version 3.3: The `wait_for()` method was added.

  `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.

  Note

  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')

#### 17.2.2.7.1. 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

#### 17.2.2.7.2. 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
    >>> import queue
    >>> 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()

### 17.2.2.8. Proxy Objects

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']

Note

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.

#### 17.2.2.8.1. 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.

### 17.2.2.9. 进程池

可以用`Pool`类创建一个进程池来执行提交给它的任务。

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

  一个进程池对象，控制一个包含工作进程的进程池，作业可以提交给它。它支持具有超时和回调的异步结果，并具有并行映射实现。

  processes是要使用的工作进程数。如果processes为`None`，则使用`os.cpu_count()`返回的数字。

  如果initializer不是`None`，则每个工作进程在启动时将调用`initializer(*initargs)`。

  maxtasksperchild是工作进程在退出并由新工作进程替换之前可以完成的任务数，以使未使用的资源可以释放。默认的maxtasksperchild是None，这意味着工作进程的生存时间将与进程池一样长。

  context可用于指定用于启动工作进程的上下文。通常使用`multiprocessing.Pool()`或上下文对象的`Pool()`方法创建进程池。在这两种情况下，都会正确地设置context。

  注意，进程池对象的方法只能由创建进程池的进程调用。

  版本3.2中的新功能： maxtasksperchild

  版本3.4中的新功能： context

  注

  `Pool`内的工作进程通常在进程池的工作队列的整个持续时间内生效。在其他系统（例如Apache、mod_wsgi等）中，释放工作进程持有的资源的常见模式是允许进程池内的工作进程只完成一定数量的作业，然后再退出、清理并生成一个新的进程来替换旧的进程。`Pool`的maxtasksperchild参数就是向用户提供这个功能。

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

    使用参数args和关键字参数kwds调用func。它阻塞直到结果完成。相比这个代码块，`apply_async()`更适合并行执行工作。此外，func仅在进程池的其中一个工作进程中执行。

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

    `apply()`方法的一个变体，它返回一个结果对象。

    如果指定callback，那么它应该是一个可接受单个参数的可调用对象。当结果完成时就对它应用callback，在调用失败的情况下则应用error_callback。

    如果指定error_callback，那么它应该是一个接受单个参数的可调用对象。如果目标函数失败，则以该异常实例调用error_callback。

    回调应该立即完成，否则处理结果的线程将被阻塞。

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

    与`map()`内置函数（虽然它只支持一个iterable参数）等价的并行方式。它阻塞直到结果准备好。

    该方法将iterable分成多个块，然后将这些块作为单独的任务提交到进程池。这些块的（近似）大小可以通过将chunksize设置为正整数来指定。

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

    返回结果对象的`map()`方法的变体。

    如果指定callback，那么它应该是一个可接受单个参数的可调用对象。当结果完成时就对它应用callback，在调用失败的情况下则应用error_callback。

    如果指定error_callback，那么它应该是一个接受单个参数的可调用对象。如果目标函数失败，则以该异常实例调用error_callback。

    回调应该立即完成，否则处理结果的线程将被阻塞。

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

    `map()`的惰性版本。

    chunksize参数与`map()`方法使用的参数相同。对于非常长的iterable，chunksize使用一个较大值的可以使得作业比使用默认值`1` 更快地完成。

    此外，如果chunksize是`1`，那么`imap()`方法返回的迭代器的`next()`方法有一个可选的参数timeout：如果在timeout秒内无法返回结果，那么`next(timeout)`将引发`multiprocessing.TimeoutError`。

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

    与`imap()`相同，除了返回的迭代器的结果的顺序应该被认为是任意的。（只有当只有一个工作进程时，其顺序才保证是“正确的”。）

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

    类似`map()`，除了iterable的元素应该是可迭代对象，它们将分拆为参数。

    因此，`[(1,2), (3, 4)]`这个iterable将导致`[func(1,2), func(3,4)]`。

    版本3.3中的新功能。

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

    `starmap()`和`map_async()`的组合，在可迭代对象组成的iterable上迭代并以分拆后的可迭代对象调用func。返回一个结果对象。

    版本3.3中的新功能。

  `close`()

    阻止任何更多的任务提交到进程池。一旦所有任务完成，工作进程将退出。

  `terminate`()

    立即停止工作进程，而不需要完成未完成的工作。当进程池对象被垃圾收集，`terminate()`将被立即调用。

  `join`()

    等待工作进程退出。在使用`join()`之前，必须调用`close()`或`terminate()`。

  版本3.3中的新功能：进程池对象现在支持上下文管理协议 —— 请参阅上下文管理器类型。`__enter__()`返回进程池对象，`__exit__()`调用`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

### 17.2.2.10. 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[, authenticate[, 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 authenticate is `True` or authkey is a byte 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` is raised. See Authentication keys.

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

  Note

  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 authenticate is `True` (`False` by default) or authkey is not `None` then digest authentication is used.

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

  If 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` is raised. 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`.

  New in version 3.3: Listener objects now support the context management protocol – see Context Manager Types. `__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.)

  New in version 3.3.

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)

#### 17.2.2.10.1. 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.

### 17.2.2.11. 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()`.

### 17.2.2.12. Logging

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.

### 17.2.2.13. The `multiprocessing.dummy` module

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

## 17.2.3. 编程指南

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

### 17.2.3.1. 所有启动方法

The following applies to all start methods.

Avoid shared state

> 尽可能避免在进程之间移动大量数据。

> 最好坚持使用队列或管道进行进程之间的通信，而不是使用较低的级别同步原语。

Picklability

> 确保代理方法的参数是可选的。

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.

> 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())

> 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 issue 5155, issue 5313 and issue 5331

### 17.2.3.2. 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.

## 17.2.4. Examples

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()