`multiprocessing` — 基于进程的并行 ¶

可用性 : not Android, not iOS, not WASI.

This module is not supported on mobile platforms or WebAssembly 平台 .

介绍 ¶

multiprocessing 是支持卵生进程的包，所用 API 类似 threading 模块。 multiprocessing 包同时提供本地和远程并发，有效避开全局解释器锁通过使用子进程而不是线程。由于此， multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. It runs on both POSIX and Windows.

The multiprocessing 此外，模块引入的 API 不拥有模拟在 threading 模块。这方面的首要范例是 Pool 对象，它提供了可以跨多个输入值并行执行函数，和跨进程分发输入数据 (数据并行性) 的一种方便手段。以下范例演示在模块中定义这种特征的常见实践，以便子级进程可以成功导入该模块。这个数据并行性的基本范例使用 Pool ,

from multiprocessing import Pool
def f(x):
    return x*x
if __name__ == '__main__':
    with Pool(5) as p:
        print(p.map(f, [1, 2, 3]))

会打印到标准输出

[1, 4, 9]

另请参阅

concurrent.futures.ProcessPoolExecutor 提供更高级接口以将任务压入后台进程不阻塞调用进程的执行。相比使用 Pool 接口直接， concurrent.futures API 更容易允许将工作提交到底层进程池以分开等待结果。

The `Process` class ¶

在 multiprocessing ，卵生进程通过创建 Process 对象然后调用其 start() 方法。 Process 遵循的 API 源自 threading.Thread 。通俗多进程程序范例

from multiprocessing import Process
def f(name):
    print('hello', name)
if __name__ == '__main__':
    p = Process(target=f, args=('bob',))
    p.start()
    p.join()

为展示涉及的单个进程 ID，这里是扩展范例：

from multiprocessing import Process
import os
def info(title):
    print(title)
    print('module name:', __name__)
    print('parent process:', os.getppid())
    print('process id:', os.getpid())
def f(name):
    info('function f')
    print('hello', name)
if __name__ == '__main__':
    info('main line')
    p = Process(target=f, args=('bob',))
    p.start()
    p.join()

为解释为什么 if __name__ == '__main__' 部分是必要，见编程指导方针 .

上下文和启动方法 ¶

从属平台， multiprocessing 支持 3 种进程启动方式。这些 启动方法 are

spawn

The parent process starts a fresh Python interpreter process. The child process will only inherit those resources necessary to run the process object’s run() method. In particular, unnecessary file descriptors and handles from the parent process will not be inherited. Starting a process using this method is rather slow compared to using fork or forkserver .

Available on POSIX and Windows platforms. The default on Windows and macOS.

fork

父级进程使用 os.fork() to fork the Python interpreter. The child process, when it begins, is effectively identical to the parent process. All resources of the parent are inherited by the child process. Note that safely forking a multithreaded process is problematic.

Available on POSIX systems. Currently the default on POSIX except macOS.

注意

The default start method will change away from fork in Python 3.14. Code that requires fork should explicitly specify that via get_context() or set_start_method() .

3.12 版改变： If Python is able to detect that your process has multiple threads, the os.fork() function that this start method calls internally will raise a DeprecationWarning . Use a different start method. See the os.fork() documentation for further explanation.

forkserver

When the program starts and selects the forkserver start method, a server process is spawned. From then on, whenever a new process is needed, the parent process connects to the server and requests that it fork a new process. The fork server process is single threaded unless system libraries or preloaded imports spawn threads as a side-effect so it is generally safe for it to use os.fork() . No unnecessary resources are inherited.

Available on POSIX platforms which support passing file descriptors over Unix pipes such as Linux.

3.4 版改变： spawn added on all POSIX platforms, and forkserver added for some POSIX platforms. Child processes no longer inherit all of the parents inheritable handles on Windows.

3.8 版改变：在 macOS， spawn start method is now the default. The fork start method should be considered unsafe as it can lead to crashes of the subprocess as macOS system libraries may start threads. See bpo-33725 .

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

To select a start method you use the set_start_method() 在 if __name__ == '__main__' clause of the main module. For example:

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() 不应该在程序中使用超过一次。

另外，可以使用 get_context() to obtain a context object. Context objects have the same API as the multiprocessing module, and allow one to use multiple start methods in the same program.

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. In particular, locks created using the fork context cannot be passed to processes started using the spawn or forkserver start methods.

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.

警告

The 'spawn' and 'forkserver' start methods generally cannot be used with “frozen” executables (i.e., binaries produced by packages like PyInstaller and cx_Freeze ) on POSIX systems. The 'fork' start method may work if code does not use threads.

在进程间交换对象 ¶

multiprocessing 支持 2 种类型进程之间的通信通道：

队列

The Queue 类几乎克隆 queue.Queue 。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. Any object put into a multiprocessing queue will be serialized.

Pipes

The Pipe() 函数返回一对通过管道 (默认情况下，为双工双向) 连接的 Connection 对象。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()
2 Connection 对象的返回通过 Pipe() 表示管道的 2 端。每个连接对象都有 send() and recv() methods (among others). Note that data in a pipe may become corrupted if two processes (or threads) try to read from or write to the same end of the pipe at the same time. Of course there is no risk of corruption from processes using different ends of the pipe at the same time.

The send() method serializes the object and recv() re-creates the object.

在进程间同步 ¶

multiprocessing 包含的所有同步原语的等价物来自 threading 。例如，可以使用锁来确保每次仅一进程打印到标准输出：

from multiprocessing import Process, Lock
def f(l, i):
    l.acquire()
    try:
        print('hello world', i)
    finally:
        l.release()
if __name__ == '__main__':
    lock = Lock()
    for num in range(10):
        Process(target=f, args=(lock, num)).start()

不使用锁，来自不同进程的输出很容易搞混。

使用工作者池 ¶

The Pool class represents a pool of worker processes. It has methods which allows tasks to be offloaded to the worker processes in a few different ways.

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 seconds
        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.

注意

Functionality within this package requires that the __main__ module be importable by the children. This is covered in 编程指导方针 however it is worth pointing out here. This means that some examples, such as the multiprocessing.pool.Pool examples will not work in the interactive interpreter. For example:

>>> 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):
Traceback (most recent call last):
Traceback (most recent call last):
AttributeError: Can't get attribute 'f' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>
AttributeError: Can't get attribute 'f' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>
AttributeError: Can't get attribute 'f' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>

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

参考 ¶

The multiprocessing 包主要复现的 API 源自 threading 模块。

`Process` 和异常 ¶

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

进程对象表示在单独进程中运行的活动。 Process 类拥有的相当于所有方法为 threading.Thread .

The constructor should always be called with keyword arguments. group should always be None ; it exists solely for compatibility with threading.Thread . target 是要被援引的可调用对象通过 run() method. It defaults to None ，意味着什么都不调用。 name is the process name (see name 了解更多细节)。 args is the argument tuple for the target invocation. kwargs is a dictionary of keyword arguments for the target invocation. If provided, the keyword-only daemon argument sets the process daemon flag to True or False 。若 None (the default), this flag will be inherited from the creating process.

默认情况下，没有自变量被传递给 target 。 args argument, which defaults to () , can be used to specify a list or tuple of the arguments to pass to target .

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.

3.3 版改变：添加 daemon 参数。

run ( ) ¶

表示进程活动的方法。

可以在子类中覆盖此方法。标准 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 自变量，分别。

Using a list or tuple as the args argument passed to Process achieves the same effect.

范例：

>>> from multiprocessing import Process
>>> p = Process(target=print, args=[1])
>>> p.run()
1
>>> p = Process(target=print, args=(1,))
>>> p.run()
1

start ( ) ¶

启动进程的活动。

This must be called at most once per process object. It arranges for the object’s run() method to be invoked in a separate process.

join ( [ timeout ] ) ¶

If the optional argument timeout is None (the default), the method blocks until the process whose join() method is called terminates. If timeout 是正数，它阻塞最多 timeout seconds. Note that the method returns None if its process terminates or if the method times out. Check the process’s exitcode to determine if it terminated.

A process can be joined many times.

A process cannot join itself because this would cause a deadlock. It is an error to attempt to join a process before it has been started.

名称 ¶

The process’s name. The name is a string used for identification purposes only. It has no semantics. Multiple processes may be given the same name.

The initial name is set by the constructor. If no explicit name is provided to the constructor, a name of the form ‘Process-N ₁ :N ₂ :…:N _k ’ is constructed, where each N _k is the N-th child of its parent.

is_alive ( ) ¶

返回进程是否存活。

Roughly, a process object is alive from the moment the start() method returns until the child process terminates.

daemon ¶

The process’s daemon flag, a Boolean value. This must be set before start() 被调用。

The initial value is inherited from the creating process.

When a process exits, it attempts to terminate all of its daemonic child processes.

Note that a daemonic process is not allowed to create child processes. Otherwise a daemonic process would leave its children orphaned if it gets terminated when its parent process exits. 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.

除了 threading.Thread API, Process objects also support the following attributes and methods:

pid ¶: 返回进程 ID。在卵生进程前，这将是 None .

exitcode ¶

子级退出代码。这将是 None if the process has not yet terminated.

If the child’s run() method returned normally, the exit code will be 0. If it terminated via sys.exit() with an integer argument N , the exit code will be 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 ¶

进程的身份验证密钥 (字节字符串)。

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

当 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.

见身份验证键 .

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 POSIX, this is a file descriptor usable with primitives from the select 模块。

Added in version 3.3.

terminate ( ) ¶

Terminate the process. On POSIX 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.

警告

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

kill ( ) ¶

如同 terminate() but using the SIGKILL signal on POSIX.

3.7 版添加。

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 .

3.7 版添加。

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

exception multiprocessing. ProcessError ¶: The base class of all multiprocessing 异常。

exception multiprocessing. BufferTooShort ¶

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

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

exception multiprocessing. AuthenticationError ¶: 引发当存在身份验证错误时。

exception multiprocessing. TimeoutError ¶: Raised by methods with a timeout when the timeout expires.

管道和队列 ¶

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

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

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

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

One difference from other Python queue implementations, is that multiprocessing queues serializes all objects that are put into them using pickle . The object return by the get method is a re-created object that does not share memory with the original object.

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

注意

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

注意

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

After putting an object on an empty queue there may be an infinitesimal delay before the queue’s empty() 方法返回 False and get_nowait() can return without raising queue.Empty .
If multiple processes are enqueuing objects, it is possible for the objects to be received at the other end out-of-order. However, objects enqueued by the same process will always be in the expected order with respect to each other.

警告

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

警告

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

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

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

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

multiprocessing. Pipe ( [ duplex ] ) ¶

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

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

The send() method serializes the object using pickle 和 recv() re-creates the object.

class multiprocessing. 队列 ( [ maxsize ] ) ¶

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

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

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

qsize ( ) ¶

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

Note that this may raise NotImplementedError on platforms like macOS where sem_getvalue() is not implemented.

empty ( ) ¶

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

May raise an OSError on closed queues. (not guaranteed)

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

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

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

3.8 版改变： If the queue is closed, ValueError is raised instead of AssertionError .

put_nowait ( obj ) ¶: 相当于 put(obj, False) .

get ( [ block [ , timeout ] ] ) ¶

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

3.8 版改变： If the queue is closed, ValueError is raised instead of OSError .

get_nowait ( ) ¶: 相当于 get(False) .

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

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

join_thread ( ) ¶

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

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

cancel_join_thread ( ) ¶

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

A better name for this method might be allow_exit_without_flush() . It is likely to cause enqueued data to 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.

注意

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

class multiprocessing. SimpleQueue ¶

它是简化的 Queue 类型，很接近锁定 Pipe .

close ( ) ¶

Close the queue: release internal resources.

A queue must not be used anymore after it is closed. For example, get() , put() and empty() methods must no longer be called.

Added in version 3.9.

empty ( ) ¶

返回 True 若队列为空， False 否则。

Always raises an OSError if the SimpleQueue is closed.

get ( ) ¶: 从队列移除并返回项。

put ( item ) ¶: Put item 进队列。

class multiprocessing. JoinableQueue ( [ maxsize ] ) ¶

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

task_done ( ) ¶

Indicate that a formerly enqueued task is complete. Used by queue consumers. For each get() 用于抓取任务，后续调用 task_done() tells the queue that the processing on the task is complete.

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

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

join ( ) ¶

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

The count of unfinished tasks goes up whenever an item is added to the queue. The count goes down whenever a consumer 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.

杂项 ¶

multiprocessing. active_children ( ) ¶

返回当前进程所有存活子级的列表。

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

multiprocessing. cpu_count ( ) ¶

返回系统中的 CPU 数。

此数字不等效于当前进程可以使用的 CPU 数。可用 CPU 数可以获得采用 os.process_cpu_count() (或 len(os.sched_getaffinity(0)) ).

当 CPU 数无法确定时 NotImplementedError 被引发。

另请参阅

os.cpu_count() os.process_cpu_count()

Changed in version 3.13: The return value can also be overridden using the -X cpu_count flag or PYTHON_CPU_COUNT as this is merely a wrapper around the os cpu count APIs.

multiprocessing. current_process ( ) ¶

返回 Process object corresponding to the current process.

An analogue of threading.current_thread() .

multiprocessing. parent_process ( ) ¶

返回 Process object corresponding to the parent process of the current_process() . For the main process, parent_process 将是 None .

Added in version 3.8.

multiprocessing. freeze_support ( ) ¶

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

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

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

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

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

multiprocessing. 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' . Not all platforms support all methods. See 上下文和启动方法 .

Added in version 3.4.

multiprocessing. get_context ( 方法 = None ) ¶

Return a context object which has the same attributes as the multiprocessing 模块。

若方法 is None then the default context is returned. Otherwise 方法应该为 'fork' , 'spawn' , 'forkserver' . ValueError is raised if the specified start method is not available. See 上下文和启动方法 .

Added in version 3.4.

multiprocessing. get_start_method ( allow_none = False ) ¶

Return the name of start method used for starting processes.

If the start method has not been fixed and allow_none is false, then the start method is fixed to the default and the name is returned. If the start method has not been fixed and allow_none 为 True 则 None 被返回。

The return value can be 'fork' , 'spawn' , 'forkserver' or None 。见上下文和启动方法 .

Added in version 3.4.

3.8 版改变：在 macOS， 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 .

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.

3.4 版改变： Now supported on POSIX when the 'spawn' start method is used.

3.11 版改变：接受像路径对象 .

multiprocessing. set_forkserver_preload ( module_names ) ¶

Set a list of module names for the forkserver main process to attempt to import so that their already imported state is inherited by forked processes. Any ImportError when doing so is silently ignored. This can be used as a performance enhancement to avoid repeated work in every process.

For this to work, it must be called before the forkserver process has been launched (before creating a Pool or starting a Process ).

Only meaningful when using the 'forkserver' start method. See 上下文和启动方法 .

Added in version 3.4.

multiprocessing. set_start_method ( 方法 , force = False ) ¶

Set the method which should be used to start child processes. The 方法自变量可以是 'fork' , 'spawn' or 'forkserver' 。引发 RuntimeError if the start method has already been set and force 不是 True 。若方法 is None and force is True then the start method is set to None 。若方法 is None and force is False then the context is set to the default context.

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

见上下文和启动方法 .

Added in version 3.4.

注意

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

连接对象 ¶

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

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

class multiprocessing.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 异常。

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

fileno ( ) ¶: Return the file descriptor or handle used by the connection.

close ( ) ¶

关闭连接。

This is called automatically when the connection is garbage collected.

poll ( [ timeout ] ) ¶

Return whether there is any data available to be read.

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

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

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

发送字节数据从像字节对象作为完整消息。

若 offset is given then data is read from that position in buffer 。若 size is given then that many bytes will be read from buffer. Very large buffers (approximately 32 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.

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

3.3 版改变：此函数用于引发 IOError ，现在是别名化的 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 必须是可写像字节对象。若 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.

3.3 版改变： Connection objects themselves can now be transferred between processes using Connection.send() and Connection.recv() .

Connection objects also now support the context management protocol – see 上下文管理器类型 . __enter__() returns the connection object, and __exit__() 调用 close() .

例如：

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

警告

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

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

警告

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

同步原语 ¶

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

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

class multiprocessing. 屏障 ( parties [ , action [ , timeout ] ] ) ¶

屏障对象：克隆自 threading.Barrier .

Added in version 3.3.

class multiprocessing. BoundedSemaphore ( [ 值 ] ) ¶

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

A solitary difference from its close analog exists: its acquire method’s first argument is named block , as is consistent with Lock.acquire() .

注意

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

class multiprocessing. 条件 ( [ lock ] ) ¶

条件变量：别名化的 threading.Condition .

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

3.3 版改变： The wait_for() 方法被添加。

class multiprocessing. 事件 ¶: 克隆自 threading.Event .

class multiprocessing. 锁 ¶

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

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

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

acquire ( block = True , timeout = None ) ¶

获得锁，阻塞或非阻塞。

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

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

When invoked with a positive, floating-point value for timeout , block for at most the number of seconds specified by timeout as long as the lock can not be acquired. Invocations with a negative value for timeout are equivalent to a timeout of zero. Invocations with a timeout value of None (the default) set the timeout period to infinite. Note that the treatment of negative or None values for timeout differs from the implemented behavior in threading.Lock.acquire() 。 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 被引发。

class multiprocessing. RLock ¶

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

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

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

acquire ( block = True , timeout = None ) ¶

获得锁，阻塞或非阻塞。

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

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

Use and behaviors of the timeout argument are the same as in Lock.acquire() . Note that some of these behaviors of timeout differ from the implemented behaviors in threading.RLock.acquire() .

release ( ) ¶

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

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

class multiprocessing. 信号量 ( [ 值 ] ) ¶

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

A solitary difference from its close analog exists: its acquire method’s first argument is named block , as is consistent with Lock.acquire() .

注意

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

注意

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

共享 `ctypes` 对象 ¶

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

multiprocessing. 值 ( typecode_or_type , * args , lock = True ) ¶

返回 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 模块。 *args is passed on to the constructor for the type.

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

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

counter.value += 1

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

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

注意， lock 是仅关键词自变量。

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

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

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

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

注意， lock 是仅关键词自变量。

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

The `multiprocessing.sharedctypes` 模块 ¶

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

注意

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

multiprocessing.sharedctypes. RawArray ( typecode_or_type , size_or_initializer ) ¶

Return a ctypes array allocated from shared memory.

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

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

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

Return a ctypes object allocated from shared memory.

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

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

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

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

注意， lock 是仅关键词自变量。

multiprocessing.sharedctypes. 值 ( typecode_or_type , * args , lock = True ) ¶

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

注意， lock 是仅关键词自变量。

multiprocessing.sharedctypes. copy ( obj ) ¶: Return a ctypes object allocated from shared memory which is a copy of the ctypes object obj .

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

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

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

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

3.5 版改变：同步对象支持上下文管理器协议。

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

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

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

from multiprocessing import Process, Lock
from multiprocessing.sharedctypes import Value, Array
from ctypes import Structure, c_double
class Point(Structure):
    _fields_ = [('x', c_double), ('y', c_double)]
def modify(n, x, s, A):
    n.value **= 2
    x.value **= 2
    s.value = s.value.upper()
    for a in A:
        a.x **= 2
        a.y **= 2
if __name__ == '__main__':
    lock = Lock()
    n = Value('i', 7)
    x = Value(c_double, 1.0/3.0, lock=False)
    s = Array('c', 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])

打印的结果为

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

管理器 ¶

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

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

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

class multiprocessing.managers. BaseManager ( address = None , authkey = None , serializer = 'pickle' , ctx = None , * , shutdown_timeout = 1.0 ) ¶

创建 BaseManager 对象。

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

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

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

serializer 必须为 'pickle' (使用 pickle serialization) or 'xmlrpclib' (使用 xmlrpc.client serialization).

ctx is a context object, or None (use the current context). See the get_context() 函数。

shutdown_timeout is a timeout in seconds used to wait until the process used by the manager completes in the shutdown() method. If the shutdown times out, the process is terminated. If terminating the process also times out, the process is killed.

3.11 版改变：添加 shutdown_timeout 参数。

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

get_server ( ) ¶

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

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

Server 此外，还拥有 address 属性。

connect ( ) ¶

连接本地管理器对象到远程管理器进程：

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

shutdown ( ) ¶

停止用于管理器的进程。这才可用若 start() 已用于启动服务器进程。

这可以被多次调用。

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

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

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

callable is a callable used for creating objects for this type identifier. If a manager instance will be connected to the server using the connect() method, or if the create_method 自变量为 False then this can be left as None .

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

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

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

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

BaseManager 实例还有 1 只读特性：

address ¶: 用于管理器的地址。

3.3 版改变：管理器对象支持上下文管理协议 – 见上下文管理器类型 . __enter__() starts the server process (if it has not already started) and then returns the manager object. __exit__() 调用 shutdown() .

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

class multiprocessing.managers. SyncManager ¶

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

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

屏障 ( parties [ , action [ , timeout ] ] ) ¶

创建共享 threading.Barrier 对象并返回它的代理。

Added in version 3.3.

BoundedSemaphore ( [ 值 ] ) ¶: 创建共享 threading.BoundedSemaphore 对象并返回它的代理。

条件 ( [ lock ] ) ¶

创建共享 threading.Condition 对象并返回它的代理。

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

3.3 版改变： The wait_for() 方法被添加。

事件 ( ) ¶: 创建共享 threading.Event 对象并返回它的代理。

锁 ( ) ¶: 创建共享 threading.Lock 对象并返回它的代理。

Namespace ( ) ¶: 创建共享 Namespace 对象并返回它的代理。

队列 ( [ maxsize ] ) ¶: 创建共享 queue.Queue 对象并返回它的代理。

RLock ( ) ¶: 创建共享 threading.RLock 对象并返回它的代理。

信号量 ( [ 值 ] ) ¶: 创建共享 threading.Semaphore 对象并返回它的代理。

数组 ( typecode , sequence ) ¶: 创建数组并返回其代理。

值 ( typecode , 值 ) ¶: 创建对象具有可写 value 属性并为它返回代理。

dict ( ) ¶
dict ( 映射 )
dict ( sequence ): 创建共享 dict 对象并返回它的代理。

list ( ) ¶
list ( sequence ): 创建共享 list 对象并返回它的代理。

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

class multiprocessing.managers. Namespace ¶

A type that can register with SyncManager .

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

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

>>> mp_context = multiprocessing.get_context('spawn')
>>> manager = mp_context.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')

定制管理器 ¶

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

from multiprocessing.managers import BaseManager
class MathsClass:
    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

使用远程管理器 ¶

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

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

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

One client can access the server as follows:

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

Another client can also use it:

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

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

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

代理对象 ¶

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

A proxy object has methods which invoke corresponding methods of its referent (although not every method of the referent will necessarily be available through the proxy). In this way, a proxy can be used just like its referent can:

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

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

An important feature of proxy objects is that they are picklable so they can be passed between processes. As such, a referent can contain 代理对象 . This permits nesting of these managed lists, dicts, and other 代理对象 :

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

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

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

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

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

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

注意

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

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

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

class multiprocessing.managers. BaseProxy ¶

Proxy objects are instances of subclasses of BaseProxy .

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

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

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

proxy._callmethod(methodname, args, kwds)

will evaluate the expression

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

in the manager’s process.

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

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

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

An example of the usage of _callmethod() :

>>> l = manager.list(range(10))
>>> l._callmethod('__len__')
10
>>> l._callmethod('__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__ ( ) ¶: 返回代理对象的表示。

__str__ ( ) ¶: 返回引用的表示。

清理 ¶

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

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

进程池 ¶

可以创建用于履行提交给它的任务的一个进程池，采用 Pool 类。

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

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

processes 是要使用的工作进程数。若 processes is None 那么，返回数通过 os.process_cpu_count() 被使用。

若 initializer 不是 None 那么，各工作者进程将调用 initializer(*initargs) 当它开始时。

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

context 可用于指定为启动工作者进程的上下文。通常，创建池是使用函数 multiprocessing.Pool() 或 Pool() 方法为上下文对象。在这两种情况下， context 可适当设置。

注意：池对象的方法仅应由创建池的进程调用。

警告

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__() 了解更多信息)。

3.2 版改变：添加 maxtasksperchild 参数。

3.4 版改变：添加 context 参数。

Changed in version 3.13: processes 使用 os.process_cpu_count() by default, instead of os.cpu_count() .

注意

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

apply ( func [ , args [ , kwds ] ] ) ¶: 调用 func 采用自变量 args 和关键词自变量 kwds 。它会阻塞，直到结果就绪为止。提供这种阻塞, apply_async() 更适合并行履行工作。此外， func 是池的唯一执行工作者。

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

变体 apply() 方法返回 AsyncResult 对象。

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

若 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 ] ) ¶

并行等效于 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 ] ] ] ) ¶

变体 map() 方法返回 AsyncResult 对象。

若 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 ] ) ¶

惰性版本的 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 那么 next() method of the iterator returned by the imap() method has an optional timeout parameter: next(timeout) 会引发 multiprocessing.TimeoutError if the result cannot be returned within timeout 秒。

imap_unordered ( func , iterable [ , chunksize ] ) ¶: 如同 imap() except that the ordering of the results from the returned iterator should be considered arbitrary. (Only when there is only one worker process is the order guaranteed to be “correct”.)

starmap ( func , iterable [ , chunksize ] ) ¶

像 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)] 产生 [func(1,2), func(3,4)] .

Added in version 3.3.

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.

Added in version 3.3.

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

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

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

3.3 版改变： Pool objects now support the context management protocol – see 上下文管理器类型 . __enter__() returns the pool object, and __exit__() 调用 terminate() .

class multiprocessing.pool. AsyncResult ¶

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

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

wait ( [ timeout ] ) ¶: 等待直到结果可用或直到 timeout 秒过去。

ready ( ) ¶: 返回调用是否已完成。

successful ( ) ¶

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

3.7 版改变： If the result is not ready, ValueError is raised instead of AssertionError .

下列范例演示池的使用：

from multiprocessing import Pool
import time
def f(x):
    return x*x
if __name__ == '__main__':
    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

Listener 和 Client ¶

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

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

multiprocessing.connection. answer_challenge ( connection , authkey ) ¶

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

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

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

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

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

若 authkey 有给定且不是 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 身份验证键 .

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

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

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

注意

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

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

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

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

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

Listener 对象拥有以下只读特性：

address ¶: Listener 对象正在使用的地址。

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

3.3 版改变： Listener objects now support the context management protocol – see 上下文管理器类型 . __enter__() returns the listener object, and __exit__() 调用 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 POSIX 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 对象。

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

POSIX : 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 ，而 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.)

Added in version 3.3.

范例

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

from multiprocessing.connection import Listener
from array import array
address = ('localhost', 6000)     # family is deduced to be 'AF_INET'
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:

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)

地址格式 ¶

An 'AF_INET' 地址是元组采用形式 (hostname, port) where hostname 是字符串和 port 是整数。
An 'AF_UNIX' 地址是表示文件系统中文件名的字符串。
An 'AF_PIPE' 地址是字符串采用形式 r'\\.\pipe\PipeName' 。要使用 Client() 连接到远程计算机的命名管道称为 ServerName ，应使用地址形式 r'\\ServerName\pipe\PipeName' 代替。

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

身份验证键 ¶

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

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

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

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

日志 ¶

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

multiprocessing. get_logger ( ) ¶

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

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

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

multiprocessing. log_to_stderr ( 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 自变量。

Below is an example session with logging turned on:

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

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

The `multiprocessing.dummy` 模块 ¶

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

In particular, the Pool function provided by multiprocessing.dummy 返回实例化的 ThreadPool ，这是子类对于 Pool that supports all the same method calls but uses a pool of worker threads rather than worker processes.

class multiprocessing.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 那么，返回数通过 os.process_cpu_count() 被使用。

若 initializer 不是 None 那么，各工作者进程将调用 initializer(*initargs) 当它开始时。

不像 Pool , maxtasksperchild and context cannot be provided.

注意

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 .

编程指导方针 ¶

应遵循某些指导方针和习语，当使用 multiprocessing .

所有启动方法 ¶

以下可应用于所有启动方法。

避免共享状态

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

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

Picklability

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

代理的线程安全

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

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

Joining zombie processes

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

Better to inherit than pickle/unpickle

当使用 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.

避免终止进程

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

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

Joining processes that use queues

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

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

An example which will deadlock is the following:
from multiprocessing import Process, Queue
def f(q):
    q.put('X' * 1000000)
if __name__ == '__main__':
    queue = Queue()
    p = Process(target=f, args=(queue,))
    p.start()
    p.join()                    # this deadlocks
    obj = queue.get()
A fix here would be to swap the last two lines (or simply remove the p.join() line).

把资源明确传递给子级进程

On POSIX 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())
在 multiprocessing.Process._bootstrap() method — this resulted in issues with processes-in-processes. This has been changed to:
sys.stdin.close()
sys.stdin = open(os.open(os.devnull, os.O_RDONLY), closefd=False)
Which solves the fundamental issue of processes colliding with each other resulting in a bad file descriptor error, but introduces a potential danger to applications which replace sys.stdin() with a “file-like object” with output buffering. This danger is that if multiple processes call close() on this file-like object, it could result in the same data being flushed to the object multiple times, resulting in corruption.

If you write a file-like object and implement your own caching, you can make it fork-safe by storing the pid whenever you append to the cache, and discarding the cache when the pid changes. For example:
@property
def cache(self):
    pid = os.getpid()
    if pid != self._pid:
        self._pid = pid
        self._cache = []
    return self._cache
更多信息，见 bpo-5155 , bpo-5313 and bpo-5331

The spawn and forkserver 启动方法 ¶

There are a few extra restrictions 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 方法被调用。

全局变量

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

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

Safe importing of main module

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

For example, using the spawn or forkserver start method running the following module would fail with a RuntimeError :
from multiprocessing import Process
def foo():
    print('hello')
p = Process(target=foo)
p.start()
Instead one should protect the “entry point” of the program by using if __name__ == '__main__': 如下：
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()
( freeze_support() line can be omitted if the program will be run normally instead of frozen.)

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

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

范例 ¶

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

from multiprocessing import freeze_support
from multiprocessing.managers import BaseManager, BaseProxy
import operator
##
class Foo:
    def f(self):
        print('you called Foo.f()')
    def g(self):
        print('you called Foo.g()')
    def _h(self):
        print('you called Foo._h()')
# A simple generator function
def baz():
    for i in range(10):
        yield i*i
# Proxy type for generator objects
class GeneratorProxy(BaseProxy):
    _exposed_ = ['__next__']
    def __iter__(self):
        return self
    def __next__(self):
        return self._callmethod('__next__')
# Function to return the operator module
def get_operator_module():
    return operator
##
class MyManager(BaseManager):
    pass
# register the Foo class; make `f()` and `g()` accessible via proxy
MyManager.register('Foo1', Foo)
# register the Foo class; make `g()` and `_h()` accessible via proxy
MyManager.register('Foo2', Foo, exposed=('g', '_h'))
# register the generator function baz; use `GeneratorProxy` to make proxies
MyManager.register('baz', baz, proxytype=GeneratorProxy)
# register get_operator_module(); make public functions accessible via proxy
MyManager.register('operator', get_operator_module)
##
def test():
    manager = MyManager()
    manager.start()
    print('-' * 20)
    f1 = manager.Foo1()
    f1.f()
    f1.g()
    assert not hasattr(f1, '_h')
    assert sorted(f1._exposed_) == sorted(['f', 'g'])
    print('-' * 20)
    f2 = manager.Foo2()
    f2.g()
    f2._h()
    assert not hasattr(f2, 'f')
    assert sorted(f2._exposed_) == sorted(['g', '_h'])
    print('-' * 20)
    it = manager.baz()
    for i in it:
        print('<%d>' % i, end=' ')
    print()
    print('-' * 20)
    op = manager.operator()
    print('op.add(23, 45) =', op.add(23, 45))
    print('op.pow(2, 94) =', op.pow(2, 94))
    print('op._exposed_ =', op._exposed_)
##
if __name__ == '__main__':
    freeze_support()
    test()

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()

内容表

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multiprocessing — 基于进程的并行 ¶

介绍 ¶

The Process class ¶

上下文和启动方法 ¶

在进程间交换对象 ¶

在进程间同步 ¶

在进程间共享状态 ¶

使用工作者池 ¶

参考 ¶

Process 和异常 ¶

管道和队列 ¶

杂项 ¶

连接对象 ¶

同步原语 ¶

共享 ctypes 对象 ¶

The multiprocessing.sharedctypes 模块 ¶

管理器 ¶

定制管理器 ¶

使用远程管理器 ¶

代理对象 ¶

清理 ¶

进程池 ¶

Listener 和 Client ¶

地址格式 ¶

身份验证键 ¶

日志 ¶

The multiprocessing.dummy 模块 ¶

编程指导方针 ¶

所有启动方法 ¶

The spawn and forkserver 启动方法 ¶

范例 ¶

内容表

上一话题

下一话题

本页

`multiprocessing` — 基于进程的并行 ¶

The `Process` class ¶

`Process` 和异常 ¶

共享 `ctypes` 对象 ¶

The `multiprocessing.sharedctypes` 模块 ¶

The `multiprocessing.dummy` 模块 ¶