multiprocessing — Process-based parallelism基于进程的并行性

Source code: Lib/multiprocessing/

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Introduction介绍

multiprocessing is a package that supports spawning processes using an API similar to the threading module. 是一个包，它支持使用类似于threading模块的API生成进程。The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. multiprocessing 包提供了本地和远程并发，通过使用子进程而不是线程，有效地避开了全局解释器锁。Due to this, the multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. 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。It runs on both Unix and Windows.它可以在Unix和Windows上运行。

The multiprocessing module also introduces APIs which do not have analogs in the threading module. multiprocessing模块还引入了在threading模块中没有类似物的API。A prime example of this is the Pool object which offers a convenient means of parallelizing the execution of a function across multiple input values, distributing the input data across processes (data parallelism). 这方面的一个主要示例是Pool对象，它提供了一种方便的方法，可以跨多个输入值并行执行函数，跨进程分布输入数据（数据并行）。The following example demonstrates the common practice of defining such functions in a module so that child processes can successfully import that module. 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。This basic example of data parallelism using Pool,这个使用Pool的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]))

will print to standard output将打印到标准输出

[1, 4, 9]

The Process classProcess类

In multiprocessing, processes are spawned by creating a Process object and then calling its start() method. 在multiprocessing中，通过创建Process对象，然后调用其start()方法生成流程。Process follows the API of threading.Thread. Process遵循threading.Thread的API。A trivial example of a multiprocess program is多进程程序的一个简单示例是

from multiprocessing import Process
def f(name):
    print('hello', name)

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

To show the individual process IDs involved, here is an expanded example:为了显示所涉及的各个进程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()

For an explanation of why the if __name__ == '__main__' part is necessary, see Programming guidelines.有关为什么需要if __name__ == '__main__'部分的解释，请参阅编程指南。

Contexts and start methods上下文和启动方法

Depending on the platform, multiprocessing supports three ways to start a process. 根据平台的不同，multiprocessing支持三种启动流程的方法。These start methods are这些“启动方法”是

spawn

The parent process starts a fresh python interpreter process. 父进程启动一个新的python解释器进程。The child process will only inherit those resources necessary to run the process object’s run() method. 子进程将只继承运行进程对象的run()方法所需的资源。In particular, unnecessary file descriptors and handles from the parent process will not be inherited. 特别是，父进程中不必要的文件描述符和句柄将不会被继承。Starting a process using this method is rather slow compared to using fork or forkserver.与使用fork或forkserver相比，使用此方法启动进程相当缓慢。

Available on Unix and Windows. The default on Windows and macOS.在Unix和Windows上可用。Windows和macOS上的默认设置。

fork

The parent process uses os.fork() to fork the Python interpreter. 父进程使用os.fork()派生Python解释器。The child process, when it begins, is effectively identical to the parent process. 子进程在开始时实际上与父进程相同。All resources of the parent are inherited by the child process. 父进程的所有资源都由子进程继承。Note that safely forking a multithreaded process is problematic.注意，安全地分叉多线程进程是有问题的。

Available on Unix only. 仅在Unix上可用。The default on Unix.Unix上的默认值。

forkserver

When the program starts and selects the forkserver start method, a server process is started. 当程序启动并选择forkserver启动方法时，服务器进程启动。From then on, whenever a new process is needed, the parent process connects to the server and requests that it fork a new process. 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派生一个新的进程。The fork server process is single threaded so it is safe for it to use os.fork(). fork服务器进程是单线程的，因此使用os.fork()是安全的。No unnecessary resources are inherited.不会继承不必要的资源。

Available on Unix platforms which support passing file descriptors over Unix pipes.在支持通过Unix管道传递文件描述符的Unix平台上可用。

Changed in version 3.8:版本3.8中更改： On macOS, the spawn start method is now the default. 在macOS上，spawn启动方法现在是默认的。The fork start method should be considered unsafe as it can lead to crashes of the subprocess. fork-start方法应该被认为是不安全的，因为它可能会导致子流程崩溃。See bpo-33725.请参阅bpo-33725。

Changed in version 3.4:版本3.4中更改： spawn added on all unix platforms, and forkserver added for some unix platforms. 在所有unix平台上都添加了spawn，在某些unix平台上添加了forkserver。Child processes no longer inherit all of the parents inheritable handles on Windows.子进程不再继承Windows上的所有父可继承句柄。

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. 在Unix上，使用spawn或forkserver启动方法还将启动资源跟踪进程，该进程跟踪程序进程创建的未链接的命名系统资源（如命名信号量或SharedMemory对象）。When all processes have exited the resource tracker unlinks any remaining tracked object. 当所有进程退出后，资源跟踪器将取消链接任何剩余的跟踪对象。Usually there should be none, but if a process was killed by a signal there may be some “leaked” resources. 通常应该没有，但如果一个进程被一个信号杀死，可能会有一些“泄漏”的资源。(Neither leaked semaphores nor shared memory segments will be automatically unlinked until the next reboot. （在下次重新启动之前，泄漏的信号量和共享内存段都不会自动断开链接。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.)这对于这两个对象都有问题，因为系统只允许有限数量的命名信号量，并且共享内存段在主内存中占用一些空间。）

To select a start method you use the set_start_method() in the if __name__ == '__main__' clause of the main module. 要选择启动方法，请使用主模块的set_start_method()子句中的set_start_method()。For example: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()

set_start_method() should not be used more than once in the program.在程序中不应使用多次。

Alternatively, you can use get_context() to obtain a context object. 或者，您可以使用get_context()获取上下文对象。Context objects have the same API as the multiprocessing module, and allow one to use multiple start methods in the same program.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()

Note that objects related to one context may not be compatible with processes for a different context. 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。In particular, locks created using the fork context cannot be passed to processes started using the spawn or forkserver start methods.特别是，不能将使用fork上下文创建的锁传递给使用spawn或forkserver启动方法启动的进程。

A library which wants to use a particular start method should probably use get_context() to avoid interfering with the choice of the library user.想要使用特定start方法的库可能应该使用get_context()，以避免干扰库用户的选择。

Warning

The 'spawn' and 'forkserver' start methods cannot currently be used with “frozen” executables (i.e., binaries produced by packages like PyInstaller and cx_Freeze) on Unix. 'spawn'和'forkserver'启动方法当前不能用于Unix上的“冻结”可执行文件（即PyInstaller和cx_Freeze等程序包生成的二进制文件）。The 'fork' start method does work.'fork'开始方法确实有效。

Exchanging objects between processes在进程之间交换对象

multiprocessing supports two types of communication channel between processes:支持进程之间的两种通信通道：

Queues

The Queue class is a near clone of queue.Queue. Queue类是queue.Queue的近似克隆。For example: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()

Queues are thread and process safe.队列是线程和进程安全的。

Pipes

The Pipe() function returns a pair of connection objects connected by a pipe which by default is duplex (two-way). Pipe()函数的作用是：返回一对由管道连接的连接对象，默认情况下，管道是双向的。For example: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()

The two connection objects returned by Pipe() represent the two ends of the pipe. Pipe()返回的两个连接对象表示管道的两端。Each connection object has send() and recv() methods (among others). 每个连接对象都有send()和recv()方法（以及其他方法）。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. 请注意，如果两个进程（或线程）试图同时读取或写入管道的同一端，则管道中的数据可能会损坏。Of course there is no risk of corruption from processes using different ends of the pipe at the same time.当然，同时使用管道不同端的进程没有损坏的风险。

Synchronization between processes进程之间的同步

multiprocessing contains equivalents of all the synchronization primitives from threading. 包含threading中所有同步原语的等价物。For instance one can use a lock to ensure that only one process prints to standard output at a time: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()

Without using the lock output from the different processes is liable to get all mixed up.如果不使用来自不同进程的锁输出，则很容易混淆所有进程。

Sharing state between processes在进程之间共享状态

As mentioned above, when doing concurrent programming it is usually best to avoid using shared state as far as possible. 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。This is particularly true when using multiple processes.当使用多个进程时尤其如此。

However, if you really do need to use some shared data then multiprocessing provides a couple of ways of doing so.然而，如果您确实需要使用一些共享数据，那么multiprocessing提供了几种方法。

Shared memory

Data can be stored in a shared memory map using Value or Array. 可以使用Value或Array将数据存储在共享内存映射中。For example, the following code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[:])

will print将打印

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. 创建num和arr时使用的'd'和'i'参数是数组模块使用的类型代码：'d'表示双精度浮点，'i'表示有符号整数。These shared objects will be process and thread-safe.这些共享对象将是进程和线程安全的。

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

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.manager()返回的manager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。

A manager returned by Manager() will support types list, dict, Namespace, Lock, RLock, Semaphore, BoundedSemaphore, Condition, Event, Barrier, Queue, Value and Array. Manager()返回的管理器将支持类型list、dict、Namespace、Lock、RLock、Semaphore、BoundedSemaphore、Condition、Event、Barrier、Queue、Value和Array。For example,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)

will print将打印

{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. 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。Also, a single manager can be shared by processes on different computers over a network. 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。They are, however, slower than using shared memory.然而，它们比使用共享内存慢。

Using a pool of workers使用工人池

The Pool class represents a pool of worker processes. Pool类表示工作进程池。It has methods which allows tasks to be offloaded to the worker processes in a few different ways.它有一些方法，允许以几种不同的方式将任务卸载到工作进程。

For example: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")

Note that the methods of a pool should only ever be used by the process which created it.请注意，池的方法只能由创建它的进程使用。

Note

Functionality within this package requires that the __main__ module be importable by the children. 此包中的功能要求__main__模块可由子模块导入。This is covered in Programming guidelines however it is worth pointing out here. 这在编程指南中有介绍，但这里值得指出。This means that some examples, such as the multiprocessing.pool.Pool examples will not work in the interactive interpreter. 这意味着某些示例（如multiprocessing.pool.Pool）在交互式解释器中不起作用。For example:例如：

>>> from multiprocessing import Pool
>>> p = Pool(5)
>>> def f(x):
...     return x*x
...
>>> with p:
...   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.)（如果您尝试这样做，它实际上会以半随机方式交错输出三个完整的回溯，然后您可能不得不以某种方式停止父进程。）

Reference参考

The multiprocessing package mostly replicates the API of the threading module.multiprocessing包主要复制threading模块的API。

Process and exceptions和异常

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

Process objects represent activity that is run in a separate process. 流程对象表示在单独的流程中运行的活动。The Process class has equivalents of all the methods of threading.Thread.Process类具有threading.Thread的所有方法的等价物。

The constructor should always be called with keyword arguments. 应始终使用关键字参数调用构造函数。group should always be None; it exists solely for compatibility with threading.Thread. group应始终为None；它的存在只是为了与threading.Thread兼容。target is the callable object to be invoked by the run() method. target是run()方法调用的可调用对象。It defaults to None, meaning nothing is called. 它默认为None，表示不调用任何内容。name is the process name (see name for more details). name是进程名称（有关详细信息，请参阅名称）。args is the argument tuple for the target invocation. args是目标调用的参数元组。kwargs is a dictionary of keyword arguments for the target invocation. kwargs是目标调用的关键字参数字典。If provided, the keyword-only daemon argument sets the process daemon flag to True or False. 如果提供了仅关键字daemon参数，则将进程守护程序标志设置为True或False。If None (the default), this flag will be inherited from the creating process.如果为None（默认值），则此标志将从创建过程中继承。

By default, no arguments are passed to target.默认情况下，不会向target传递任何参数。

If a subclass overrides the constructor, it must make sure it invokes the base class constructor (Process.__init__()) before doing anything else to the process.如果子类重写构造函数，它必须确保在对进程执行任何其他操作之前调用基类构造函数（Process.__init__()）。

Changed in version 3.3:版本3.3中更改： Added the daemon argument.添加了daemon参数。

run()

Method representing the process’s activity.表示流程活动的方法。

You may override this method in a subclass. 您可以在子类中重写此方法。The standard run() method invokes the callable object passed to the object’s constructor as the target argument, if any, with sequential and keyword arguments taken from the args and kwargs arguments, respectively.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），并分别从args和kwargs参数中获取顺序参数和关键字参数。

start()

Start the process’s activity.启动流程的活动。

This must be called at most once per process object. 每个进程对象最多只能调用一次。It arranges for the object’s run() method to be invoked in a separate process.它安排在单独的进程中调用对象的run()方法。

join([timeout])

If the optional argument timeout is None (the default), the method blocks until the process whose join() method is called terminates. 如果可选的参数timeout为None（默认值），则该方法将阻塞，直到调用join()方法的进程终止。If timeout is a positive number, it blocks at most timeout seconds. 如果timeout是正数，它最多阻止超时秒数。Note that the method returns None if its process terminates or if the method times out. 请注意，如果进程终止或方法超时，该方法将返回None。Check the process’s exitcode to determine if it terminated.检查进程的exitcode以确定它是否终止。

A process can be joined many times.一个进程可以多次连接。

A process cannot join itself because this would cause a deadlock. 进程无法加入自身，因为这会导致死锁。It is an error to attempt to join a process before it has been started.在进程启动之前尝试加入它是一个错误。

name

The process’s name. The name is a string used for identification purposes only. 进程的名称。名称是一个字符串，仅用于标识目的。It has no semantics. 它没有语义。Multiple processes may be given the same name.可以为多个进程提供相同的名称。

The initial name is set by the constructor. 初始名称由构造函数设置。If no explicit name is provided to the constructor, a name of the form ‘Process-N₁:N₂:…:N_k’ is constructed, where each N_k is the N-th child of its parent.如果没有向构造函数提供显式名称，则构造一个形式为‘Process-N₁:N₂:…:N_k’的名称，其中每个N_k是其父级的第N个子级。

is_alive()

Return whether the process is alive.返回进程是否处于活动状态。

Roughly, a process object is alive from the moment the start() method returns until the child process terminates.大致上，从start()方法返回的那一刻起，直到子进程终止，一个进程对象都是活动的。

daemon

The process’s daemon flag, a Boolean value. 进程的守护进程标志，一个布尔值。This must be set before start() is called.这必须在调用start()之前设置。

The initial value is inherited from the creating process.初始值继承自创建过程。

When a process exits, it attempts to terminate all of its daemonic child processes.当进程退出时，它会尝试终止其所有守护进程子进程。

Note that a daemonic process is not allowed to create child processes. 注意，守护进程不允许创建子进程。Otherwise a daemonic process would leave its children orphaned if it gets terminated when its parent process exits. 否则，如果守护进程在父进程退出时终止，它的子进程将成为孤儿。Additionally, these are not Unix daemons or services, they are normal processes that will be terminated (and not joined) if non-daemonic processes have exited.此外，这些不是Unix守护进程或服务，它们是正常进程，如果非守护进程已退出，这些进程将被终止（而不是加入）。

In addition to the threading.Thread API, Process objects also support the following attributes and methods:除了threading.ThreadAPI之外，Process对象还支持以下属性和方法：

pid

Return the process ID. Before the process is spawned, this will be None.返回进程ID。在生成进程之前，此值将为None。

exitcode

The child’s exit code. 孩子的退出代码。This will be None if the process has not yet terminated.如果进程尚未终止，则该值将为None。

If the child’s run() method returned normally, the exit code will be 0. 如果子级的run()方法正常返回，则退出代码将为0。If it terminated via sys.exit() with an integer argument N, the exit code will be N.如果它通过sys.exit()以整数参数N终止，则退出代码将为N。

If the child terminated due to an exception not caught within run(), the exit code will be 1. If it was terminated by signal N, the exit code will be the negative value -N.

authkey

The process’s authentication key (a byte string).

When multiprocessing is initialized the main process is assigned a random string using os.urandom().

When a Process object is created, it will inherit the authentication key of its parent process, although this may be changed by setting authkey to another byte string.

See Authentication keys.

sentinel

A numeric handle of a system object which will become “ready” when the process ends.

You can use this value if you want to wait on several events at once using multiprocessing.connection.wait(). Otherwise calling join() is simpler.

On Windows, this is an OS handle usable with the WaitForSingleObject and WaitForMultipleObjects family of API calls. On Unix, this is a file descriptor usable with primitives from the select module.

New in version 3.3.版本3.3中新增。

terminate()

Terminate the process. On Unix this is done using the SIGTERM signal; on Windows TerminateProcess() is used. Note that exit handlers and finally clauses, etc., will not be executed.

Note that descendant processes of the process will not be terminated – they will simply become orphaned.

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. 如果在关联进程使用管道或队列时使用此方法，则管道或队列容易损坏，并可能被其他进程无法使用。Similarly, if the process has acquired a lock or semaphore etc. 类似地，如果进程获取了锁或信号量等。then terminating it is liable to cause other processes to deadlock.然后终止它很可能导致其他进程死锁。

kill()

Same as terminate() but using the SIGKILL signal on Unix.

New in version 3.7.版本3.7中新增。

close()

Close the Process object, releasing all resources associated with it. ValueError is raised if the underlying process is still running. Once close() returns successfully, most other methods and attributes of the Process object will raise ValueError.

New in version 3.7.版本3.7中新增。

Note that the start(), join(), is_alive(), terminate() and exitcode methods should only be called by the process that created the process object.

Example usage of some of the methods of Process:

 >>> 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

exceptionmultiprocessing.ProcessError

The base class of all multiprocessing exceptions.所有multiprocessing异常的基类。

exceptionmultiprocessing.BufferTooShort

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

If e is an instance of BufferTooShort then e.args[0] will give the message as a byte string.

exceptionmultiprocessing.AuthenticationError

Raised when there is an authentication error.出现身份验证错误时引发。

exceptionmultiprocessing.TimeoutError

Raised by methods with a timeout when the timeout expires.由超时过期时超时的方法引发。

Pipes and Queues管道和队列

When using multiple processes, one generally uses message passing for communication between processes and avoids having to use any synchronization primitives like locks.当使用多个进程时，通常使用消息传递在进程之间进行通信，并避免使用任何同步原语（如锁）。

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.

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

Note that one can also create a shared queue by using a manager object – see Managers.请注意，还可以使用管理器对象创建共享队列-请参阅管理器。

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. 当一个对象被放入队列时，该对象被pickle，后台线程随后将pickle数据刷新到底层管道。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. 如果多个进程是排队对象，则对象可能会在另一端无序接收。However, objects enqueued by the same process will always be in the expected order with respect to each other.然而，由同一进程排队的对象将始终按预期顺序相互排列。

Warning警告

If a process is killed using Process.terminate() or os.kill() while it is trying to use a Queue, then the data in the queue is likely to become corrupted. This may cause any other process to get an exception when it tries to use the queue later on.这可能会导致任何其他进程在稍后尝试使用队列时发生异常。

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.如上所述，如果子进程已将项放入队列（并且未使用JoinableQueue.cancel_join_thread），则该进程将不会终止，直到所有缓冲项都已刷新到管道。

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. 这意味着，如果您尝试加入该进程，则可能会出现死锁，除非您确定已放入队列的所有项目都已被消耗。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.类似地，如果子进程是非守护进程，则父进程在尝试连接其所有非守护子进程时可能会挂起退出。

Note that a queue created using a manager does not have this issue. 请注意，使用管理器创建的队列没有此问题。See Programming guidelines.请参阅编程指南。

For an example of the usage of queues for interprocess communication see Examples.有关使用队列进行进程间通信的示例，请参阅示例。

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.

classmultiprocessing.Queue([maxsize])

Returns a process shared queue implemented using a pipe and a few locks/semaphores. 返回使用管道和几个锁/信号量实现的进程共享队列。When a process first puts an item on the queue a feeder thread is started which transfers objects from a buffer into the pipe.当一个进程首次将一个项目放入队列时，就会启动一个馈线线程，该线程将对象从缓冲区转移到管道中。

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. 返回队列的近似大小。Because of multithreading/multiprocessing semantics, this number is not reliable.由于多线程/多处理语义，这个数字不可靠。

Note that this may raise NotImplementedError on Unix platforms like macOS 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).

Changed in version 3.8:版本3.8中更改： If the queue is closed, ValueError is raised instead of AssertionError.

put_nowait(obj)

Equivalent to put(obj, False).

get([block[, timeout]])

Remove and return an item from the queue. 从队列中删除并返回项目。If optional args block is True (the default) and timeout is None (the default), block if necessary until an item is available. 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).

Changed in version 3.8:版本3.8中更改： If the queue is closed, ValueError is raised instead of OSError.如果队列已关闭，则会引发ValueError而不是OSError。

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. 指示当前进程不会在此队列上放置更多数据。The background thread will quit once it has flushed all buffered data to the pipe. 后台线程将在将所有缓冲数据刷新到管道后退出。This is called automatically when the queue is garbage collected.当队列被垃圾回收时，会自动调用该函数。

join_thread()

Join the background thread. 加入背景线程。This can only be used after close() has been called. 这只能在调用close()后使用。It blocks until the background thread exits, ensuring that all data in the buffer has been flushed to the pipe.它会一直阻塞，直到后台线程退出，以确保缓冲区中的所有数据都已刷新到管道中。

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. 默认情况下，如果进程不是队列的创建者，则在退出时它将尝试加入队列的后台线程。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 be 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.只有当您需要立即退出当前进程，而不需要等待将排队的数据刷新到底层管道，并且您不关心数据丢失时，它才真正存在。

Note

This class’s functionality requires a functioning shared semaphore implementation on the host operating system. 该类的功能需要在主机操作系统上实现功能正常的共享信号量。Without one, the functionality in this class will be disabled, and attempts to instantiate a Queue will result in an ImportError. See bpo-3770 for additional information. The same holds true for any of the specialized queue types listed below.下面列出的任何专用队列类型也是如此。

classmultiprocessing.SimpleQueue

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

close()

Close the queue: release internal resources.关闭队列：释放内部资源。

A queue must not be used anymore after it is closed. 队列关闭后不得再使用。For example, get(), put() and empty() methods must no longer be called.

New in version 3.9.版本3.9中新增。

empty()

Return True if the queue is empty, False otherwise.如果队列为空，则返回True，否则返回False。

get()

Remove and return an item from the queue.

put(item)

Put item into the queue.

classmultiprocessing.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.

Miscellaneous混杂的

multiprocessing.active_children()

Return list of all live children of the current process.返回当前进程的所有活动子进程的列表。

Calling this has the side effect of “joining” any processes which have already finished.调用此函数的副作用是“加入”任何已完成的进程。

multiprocessing.cpu_count()

Return the number of CPUs in the system.返回系统中CPU的数量。

This number is not equivalent to the number of CPUs the current process can use. 这个数字不等于当前进程可以使用的CPU数量。The number of usable CPUs can be obtained with len(os.sched_getaffinity(0))

When the number of CPUs cannot be determined a NotImplementedError is raised.

See also

os.cpu_count()

multiprocessing.current_process()

Return the Process object corresponding to the current process.

An analogue of threading.current_thread().

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.

New in version 3.8.版本3.8中新增。

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.版本3.4中新增。

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.版本3.4中新增。

multiprocessing.get_start_method(allow_none=False)

Return the name of start method used for starting processes.返回用于启动进程的启动方法的名称。

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 and macOS.

Changed in version 3.8:版本3.8中更改： On macOS, the spawn start method is now the default. The fork start method should be considered unsafe as it can lead to crashes of the subprocess. See bpo-33725.

New in version 3.4.版本3.4中新增。

multiprocessing.set_executable(executable)

Set 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:版本3.4中更改： Now supported on Unix when the 'spawn' start method is used.当使用'spawn'启动方法时，现在在Unix上受支持。

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.版本3.4中新增。

Note

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

Connection Objects连接对象

Connection objects allow the sending and receiving of picklable objects or strings. 连接对象允许发送和接收可拾取的对象或字符串。They can be thought of as message oriented connected sockets.它们可以被视为面向消息的连接套接字。

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

classmultiprocessing.connection.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 MiB+, 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 is something to receive. Raises EOFError if there is nothing left to receive and the other end was closed.

fileno()

Return the file descriptor or handle used by the connection.返回连接使用的文件描述符或句柄。

close()

Close the connection.

This is called automatically when the connection is garbage collected.当连接被垃圾回收时，会自动调用该函数。

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 MiB+, 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:版本3.3中更改： 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:版本3.3中更改： Connection objects themselves can now be transferred between processes using Connection.send() and Connection.recv().

New in version 3.3.版本3.3中新增。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.如果进程在尝试读取或写入管道时被终止，那么管道中的数据可能会损坏，因为可能无法确定消息边界在哪里。

Synchronization primitives同步原语

Generally synchronization primitives are not as necessary in a multiprocess program as they are in a multithreaded program. 通常，同步原语在多进程程序中不像在多线程程序中那样必要。See the documentation for threading module.

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

classmultiprocessing.Barrier(parties[, action[, timeout]])

A barrier object: a clone of threading.Barrier.

New in version 3.3.版本3.3中新增。

classmultiprocessing.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 macOS, this is indistinguishable from Semaphore because sem_getvalue() is not implemented on that platform.

classmultiprocessing.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:版本3.3中更改： The wait_for() method was added.

classmultiprocessing.Event

A clone of threading.Event.

classmultiprocessing.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.

classmultiprocessing.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.请注意，RLock实际上是一个工厂函数，它返回使用默认上下文初始化的multiprocessingsynchronizeRLock的实例。

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.获取锁，阻塞或非阻塞。

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.释放锁，降低递归级别。如果递减后递归级别为零，则将锁重置为未锁定（不属于任何进程或线程），如果任何其他进程或线程被阻止，等待锁解锁，则只允许其中一个进程或线程继续。如果减量后递归级别仍然非零，则锁仍被锁定并由调用进程或线程拥有。

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().

classmultiprocessing.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 macOS, 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 bpo-3770 for additional information.

Shared ctypes Objects共享ctypes对象

It is possible to create shared objects using shared memory which can be inherited by child processes.可以使用可由子进程继承的共享内存创建共享对象。

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假设关联的锁是递归的（默认情况下是递归的），您可以改为这样做

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 modulemultiprocessing.sharedctypes模块

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. 尽管可以在共享内存中存储指针，但请记住，这将引用特定进程地址空间中的位置。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.但是，在第二个进程的上下文中，指针很可能无效，尝试从第二个进程取消对指针的引用可能会导致崩溃。

multiprocessing.sharedctypes.RawArray(typecode_or_type, size_or_initializer)

Return a ctypes array allocated from shared memory.返回从共享内存分配的ctypes数组。

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.返回从共享内存分配的ctypes对象。

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.注意，lock是一个只包含关键字的参数。

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”.

Note that lock is a keyword-only argument.注意，lock是一个只包含关键字的参数。

multiprocessing.sharedctypes.copy(obj)

Return a ctypes object allocated from shared memory which is a copy of the ctypes object obj.返回从共享内存中分配的ctypes对象，该对象是ctypes对象obj的副本。

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.注意，通过包装器访问ctypes对象可能比访问原始ctypes对象慢得多。

Changed in version 3.5:版本3.5中更改： 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. 下表比较了从共享内存创建共享ctypes对象的语法与普通ctypes语法。(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)]

Managers经理

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. 管理器提供了一种创建可在不同进程之间共享的数据的方法，包括在不同机器上运行的进程之间通过网络共享。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:

classmultiprocessing.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. 启动子流程以启动管理器。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:将本地管理器对象连接到远程管理器进程：

>>> 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.

This can be called multiple times.这可以多次调用。

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

The address used by the manager.

Changed in version 3.3:版本3.3中更改： 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.

classmultiprocessing.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 Proxy Objects 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.

New in version 3.3.版本3.3中新增。

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:版本3.3中更改： 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.

Changed in version 3.6:版本3.6中更改： 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.

classmultiprocessing.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定制经理

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使用远程管理器

It is possible to run a manager server on one machine and have clients use it from other machines (assuming that the firewalls involved allow it).可以在一台机器上运行manager服务器，并让客户端从其他机器上使用它（假设相关防火墙允许）。

Running the following commands creates a server for a single shared queue which remote clients can access:运行以下命令将为远程客户端可以访问的单个共享队列创建服务器：

>>> 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:一个客户端可以按如下方式访问服务器：

>>> 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:其他客户端也可以使用它：

>>> 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:本地进程也可以访问该队列，使用客户端上面的代码远程访问该队列：

>>> from multiprocessing import Process, Queue
>>> from multiprocessing.managers import BaseManager
>>> class Worker(Process):
...     def __init__(self, q):
...         self.q = q
...         super().__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()

Proxy Objects对象

A proxy is an object which refers to a shared object which lives (presumably) in a different process. 代理是指（可能）生活在不同进程中的共享对象。The shared object is said to be the referent of the proxy. 共享对象被称为代理的引用对象。Multiple proxy objects may have the same referent.多个代理对象可能具有相同的引用。

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). 代理对象具有调用其引用对象的相应方法的方法（尽管并非引用对象的每个方法都必须通过代理可用）。In this way, a proxy can be used just like its referent can:这样，代理可以像其引用一样使用：

>>> 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 Proxy Objects. This permits nesting of these managed lists, dicts, and other Proxy Objects:

>>> 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 Proxy Objects for most use cases but also demonstrates a level of control over the synchronization.

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.

classmultiprocessing.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清理

A proxy object uses a weakref callback so that when it gets garbage collected it deregisters itself from the manager which owns its referent.代理对象使用weakref回调，因此当它被垃圾回收时，它会从拥有其引用的管理器中注销自己。

A shared object gets deleted from the manager process when there are no longer any proxies referring to it.当不再有任何代理引用共享对象时，该对象将从管理器进程中删除。

Process Pools进程池

One can create a pool of processes which will carry out tasks submitted to it with the Pool class.可以创建一个进程池，这些进程将执行使用Pool类提交给它的任务。

classmultiprocessing.pool.Pool([processes[, initializer[, initargs[, maxtasksperchild[, context]]]]])

A process pool object which controls a pool of worker processes to which jobs can be submitted. 一个进程池对象，用于控制作业可以提交到的工作进程池。It supports asynchronous results with timeouts and callbacks and has a parallel map implementation.它支持带有超时和回调的异步结果，并具有并行映射实现。

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.请注意，池对象的方法只能由创建池的进程调用。

Warning

multiprocessing.pool objects have internal resources that need to be properly managed (like any other resource) by using the pool as a context manager or by calling close() and terminate() manually. Failure to do this can lead to the process hanging on finalization.

Note that it is not correct to rely on the garbage collector to destroy the pool as CPython does not assure that the finalizer of the pool will be called (see object.__del__() for more information).

New in version 3.2.版本3.2中新增。maxtasksperchild

New in version 3.4.版本3.4中新增。context

Note

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 AsyncResult 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, for multiple iterables see starmap()). 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 AsyncResult 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)].

New in version 3.3.版本3.3中新增。

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.

New in version 3.3.版本3.3中新增。

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().

New in version 3.3.版本3.3中新增。Pool objects now support the context management protocol – see Context Manager Types. __enter__() returns the pool object, and __exit__() calls terminate().

classmultiprocessing.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 ValueError if the result is not ready.

Changed in version 3.7:版本3.7中更改： If the result is not ready, ValueError is raised instead of AssertionError.

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监听器和客户端

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.

classmultiprocessing.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.

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 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.

New in version 3.3.版本3.3中新增。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.版本3.3中新增。

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地址格式

• 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身份验证密钥

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. 身份验证密钥是一个字节字符串，可以将其视为密码：一旦建立了连接，两端将要求证明另一端知道身份验证密钥。(Demonstrating that both ends are using the same key does not involve sending the key over the connection.)（证明两端使用相同的密钥并不涉及通过连接发送密钥。）

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().

Logging登录中

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(level=None)

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'. You can modify levelname of the logger by passing a level argument.

Below is an example session with logging turned on:下面是打开日志记录的示例会话：

>>> 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 modulemultiprocessing.dummy（多处理虚拟）模块

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

In particular, the Pool function provided by multiprocessing.dummy returns an instance of ThreadPool, which is a subclass of Pool that supports all the same method calls but uses a pool of worker threads rather than worker processes.

classmultiprocessing.pool.ThreadPool([processes[, initializer[, initargs]]])

A thread pool object which controls a pool of worker threads to which jobs can be submitted. ThreadPool instances are fully interface compatible with Pool instances, and their resources must also be properly managed, either by using the pool as a context manager or by calling close() and terminate() manually.

processes is the number of worker threads 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.

Unlike Pool, maxtasksperchild and context cannot be provided.

Note

A ThreadPool shares the same interface as Pool, which is designed around a pool of processes and predates the introduction of the concurrent.futures module. As such, it inherits some operations that don’t make sense for a pool backed by threads, and it has its own type for representing the status of asynchronous jobs, AsyncResult, that is not understood by any other libraries.

Users should generally prefer to use concurrent.futures.ThreadPoolExecutor, which has a simpler interface that was designed around threads from the start, and which returns concurrent.futures.Future instances that are compatible with many other libraries, including asyncio.

Programming guidelines编程指南

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

All start methods所有启动方法

The following applies to all start methods.以下适用于所有启动方法。

Avoid shared state避免共享状态

As far as possible one should try to avoid shifting large amounts of data between processes.应尽可能避免在进程之间转移大量数据。

It is probably best to stick to using queues or pipes for communication between processes rather than using the lower level synchronization primitives.最好坚持使用队列或管道在进程之间进行通信，而不是使用较低级别的同步原语。

Picklability

Ensure that the arguments to the methods of proxies are picklable.确保代理方法的参数是可选取的。

Thread safety of proxies代理的线程安全

Do not use a proxy object from more than one thread unless you protect it with a lock.不要使用来自多个线程的代理对象，除非用锁保护它。

(There is never a problem with different processes using the same proxy.)（使用同一代理的不同进程从来没有问题。）

Joining zombie processes加入僵尸进程

On Unix when a process finishes but has not been joined it becomes a zombie. 在Unix上，当进程完成但尚未加入时，它将变成僵尸。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避免终止进程

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加入使用队列的进程

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. 这意味着，无论何时使用队列，都需要确保在加入流程之前，已放入队列的所有项目最终都将被删除。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:将死锁的示例如下：

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显式地将资源传递给子进程

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. 如果编写类似文件的对象并实现自己的缓存，则可以在每次附加到缓存时存储pid，并在pid更改时丢弃缓存，从而实现分叉安全。For example:例如：

@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 methodsspawn和forkserver启动方法

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.然而，仅仅是模块级常数的全局变量不会引起任何问题。

Safe importing of main module安全导入主模块

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).确保新的Python解释器可以安全地导入主模块，而不会产生意外的副作用（例如启动新进程）。

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.如果在主模块中创建了池或管理器，则适用类似的限制。

Examples示例

Demonstration of how to create and use customized managers and proxies:演示如何创建和使用定制的经理和代理：

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:使用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()