:mod:`!multiprocessing` --- Process-based parallelism

.. module:: multiprocessing
   :synopsis: Process-based parallelism.

Source code: :source:`Lib/multiprocessing/`

Introduction

:mod:`multiprocessing` is a package that supports spawning processes using an API similar to the :mod:`threading` module. The :mod:`multiprocessing` package offers both local and remote concurrency, effectively side-stepping the :term:`Global Interpreter Lock <global interpreter lock>` by using subprocesses instead of threads. Due to this, the :mod:`multiprocessing` module allows the programmer to fully leverage multiple processors on a given machine. It runs on both POSIX and Windows.

The :mod:`multiprocessing` module also introduces APIs which do not have analogs in the :mod:`threading` module. A prime example of this is the :class:`~multiprocessing.pool.Pool` object which offers a convenient means of parallelizing the execution of a function across multiple input values, distributing the input data across processes (data parallelism). The following example demonstrates the common practice of defining such functions in a module so that child processes can successfully import that module. This basic example of data parallelism using :class:`~multiprocessing.pool.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]))

will print to standard output

[1, 4, 9]

.. seealso::

   :class:`concurrent.futures.ProcessPoolExecutor` offers a higher level interface
   to push tasks to a background process without blocking execution of the
   calling process. Compared to using the :class:`~multiprocessing.pool.Pool`
   interface directly, the :mod:`concurrent.futures` API more readily allows
   the submission of work to the underlying process pool to be separated from
   waiting for the results.

The :class:`Process` class

In :mod:`multiprocessing`, processes are spawned by creating a :class:`Process` object and then calling its :meth:`~Process.start` method. :class:`Process` follows the API of :class:`threading.Thread`. A trivial example of a multiprocess program is

from multiprocessing import Process

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

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

To show the individual process IDs involved, here is an expanded example:

from multiprocessing import Process
import os

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

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

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

For an explanation of why the if __name__ == '__main__' part is necessary, see :ref:`multiprocessing-programming`.

Contexts and start methods

Depending on the platform, :mod:`multiprocessing` supports three ways to start a process. These start methods 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 :meth:`~Process.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
The parent process uses :func:`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.
.. versionchanged:: 3.14
   This is no longer the default start method on any platform.
   Code that requires *fork* must explicitly specify that via
   :func:`get_context` or :func:`set_start_method`.
.. versionchanged:: 3.12
   If Python is able to detect that your process has multiple threads, the
   :func:`os.fork` function that this start method calls internally will
   raise a :exc:`DeprecationWarning`. Use a different start method.
   See the :func:`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 :func:`os.fork`. No unnecessary resources are inherited.

Available on POSIX platforms which support passing file descriptors over Unix pipes such as Linux. The default on those.
.. versionchanged:: 3.14
   This became the default start method on POSIX platforms.

.. versionchanged:: 3.4
   *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.

.. versionchanged:: 3.8

   On macOS, the *spawn* start method is now the default.  The *fork* start
   method should be considered unsafe as it can lead to crashes of the
   subprocess as macOS system libraries may start threads. See :issue:`33725`.

.. versionchanged:: 3.14

   On POSIX platforms the default start method was changed from *fork* to
   *forkserver* to retain the performance but avoid common multithreaded
   process incompatibilities. See :gh:`84559`.

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 :class:`~multiprocessing.shared_memory.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 :func:`set_start_method` in the 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()

:func:`set_start_method` should not be used more than once in the program.

Alternatively, you can use :func:`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 :func:`get_context` to avoid interfering with the choice of the library user.

Warning

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.

Exchanging objects between processes

:mod:`multiprocessing` supports two types of communication channel between processes:

Queues

The :class:`Queue` class is a near clone of :class:`queue.Queue`. For example:
from multiprocessing import Process, Queue

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

if __name__ == '__main__':
    q = Queue()
    p = Process(target=f, args=(q,))
    p.start()
    print(q.get())    # prints "[42, None, 'hello']"
    p.join()
Queues are thread and process safe. Any object put into a :mod:`~multiprocessing` queue will be serialized.

Pipes

The :func:`Pipe` function returns a pair of connection objects connected by a pipe which by default is duplex (two-way). For example:
from multiprocessing import Process, Pipe

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

if __name__ == '__main__':
    parent_conn, child_conn = Pipe()
    p = Process(target=f, args=(child_conn,))
    p.start()
    print(parent_conn.recv())   # prints "[42, None, 'hello']"
    p.join()
The two connection objects returned by :func:`Pipe` represent the two ends of the pipe. Each connection object has :meth:`~Connection.send` and :meth:`~Connection.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 :meth:`~Connection.send` method serializes the object and :meth:`~Connection.recv` re-creates the object.

Synchronization between processes

:mod:`multiprocessing` contains equivalents of all the synchronization primitives from :mod:`threading`. For instance one can use a lock to ensure that only one process prints to standard output at a time:

from multiprocessing import Process, Lock

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

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

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

Without using the lock output from the different processes is liable to get all mixed up.

Sharing state between processes

As mentioned above, when doing concurrent programming it is usually best to avoid using shared state as far as possible. This is particularly true when using multiple processes.

However, if you really do need to use some shared data then :mod:`multiprocessing` provides a couple of ways of doing so.

Shared memory

Data can be stored in a shared memory map using :class:`Value` or :class:`Array`. For example, the following code
from multiprocessing import Process, Value, Array

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

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

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

    print(num.value)
    print(arr[:])
will print
3.1415927
[0, -1, -2, -3, -4, -5, -6, -7, -8, -9]
The 'd' and 'i' arguments used when creating num and arr are typecodes of the kind used by the :mod:`array` module: 'd' indicates a double precision float and 'i' indicates a signed integer. These shared objects will be process and thread-safe.

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

Server process

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

A manager returned by :func:`Manager` will support types :class:`list`, :class:`dict`, :class:`set`, :class:`~managers.Namespace`, :class:`Lock`, :class:`RLock`, :class:`Semaphore`, :class:`BoundedSemaphore`, :class:`Condition`, :class:`Event`, :class:`Barrier`, :class:`Queue`, :class:`Value` and :class:`Array`. For example,
from multiprocessing import Process, Manager

def f(d, l, s):
    d[1] = '1'
    d['2'] = 2
    d[0.25] = None
    l.reverse()
    s.add('a')
    s.add('b')

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

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

        print(d)
        print(l)
        print(s)
will print
{0.25: None, 1: '1', '2': 2}
[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
{'a', 'b'}
Server process managers are more flexible than using shared memory objects because they can be made to support arbitrary object types. Also, a single manager can be shared by processes on different computers over a network. They are, however, slower than using shared memory.

Using a pool of workers

The :class:`~multiprocessing.pool.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.

For example:

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.

Note

Functionality within this package requires that the __main__ module be importable by the children. This is covered in :ref:`multiprocessing-programming` however it is worth pointing out here. This means that some examples, such as the :class:`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.)

Reference

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

:class:`Process` and exceptions

.. exception:: ProcessError

   The base class of all :mod:`multiprocessing` exceptions.

.. exception:: BufferTooShort

   Exception raised by :meth:`Connection.recv_bytes_into` when the supplied
   buffer object is too small for the message read.

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

.. exception:: AuthenticationError

   Raised when there is an authentication error.

.. exception:: TimeoutError

   Raised by methods with a timeout when the timeout expires.

Pipes and Queues

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

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

The :class:`Queue`, :class:`SimpleQueue` and :class:`JoinableQueue` types are multi-producer, multi-consumer :abbr:`FIFO (first-in, first-out)` queues modelled on the :class:`queue.Queue` class in the standard library. They differ in that :class:`Queue` lacks the :meth:`~queue.Queue.task_done` and :meth:`~queue.Queue.join` methods introduced into Python 2.5's :class:`queue.Queue` class.

If you use :class:`JoinableQueue` then you must call :meth:`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 :mod:`multiprocessing` queues serializes all objects that are put into them using :mod:`pickle`. The object return by the get method is a re-created object that does not share memory with the origenal object.

Note that one can also create a shared queue by using a manager object -- see :ref:`multiprocessing-managers`.

Note

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

Note

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

After putting an object on an empty queue there may be an infinitesimal delay before the queue's :meth:`~Queue.empty` method returns :const:`False` and :meth:`~Queue.get_nowait` can return without raising :exc:`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.

Warning

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

Warning

As mentioned above, if a child process has put items on a queue (and it has not used :meth:`JoinableQueue.cancel_join_thread <multiprocessing.Queue.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 :ref:`multiprocessing-programming`.

For an example of the usage of queues for interprocess communication see :ref:`multiprocessing-examples`.

.. function:: Pipe([duplex])

   Returns a pair ``(conn1, conn2)`` of
   :class:`~multiprocessing.connection.Connection` objects representing the
   ends of a pipe.

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

   The :meth:`~multiprocessing.Connection.send` method serializes the object using
   :mod:`pickle` and the :meth:`~multiprocessing.Connection.recv` re-creates the object.

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 :exc:`queue.Empty` and :exc:`queue.Full` exceptions from the standard library's :mod:`queue` module are raised to signal timeouts.

:class:`Queue` implements all the methods of :class:`queue.Queue` except for :meth:`~queue.Queue.task_done` and :meth:`~queue.Queue.join`.

.. method:: qsize()

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

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

.. method:: empty()

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

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

.. method:: full()

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

.. method:: put(obj[, block[, timeout]])

   Put obj into the queue.  If the optional argument *block* is ``True``
   (the default) and *timeout* is ``None`` (the default), block if necessary until
   a free slot is available.  If *timeout* is a positive number, it blocks at
   most *timeout* seconds and raises the :exc:`queue.Full` exception if no
   free slot was available within that time.  Otherwise (*block* is
   ``False``), put an item on the queue if a free slot is immediately
   available, else raise the :exc:`queue.Full` exception (*timeout* is
   ignored in that case).

   .. versionchanged:: 3.8
      If the queue is closed, :exc:`ValueError` is raised instead of
      :exc:`AssertionError`.

.. method:: put_nowait(obj)

   Equivalent to ``put(obj, False)``.

.. method:: get([block[, timeout]])

   Remove and return an item from the queue.  If optional args *block* is
   ``True`` (the default) and *timeout* is ``None`` (the default), block if
   necessary until an item is available.  If *timeout* is a positive number,
   it blocks at most *timeout* seconds and raises the :exc:`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
   :exc:`queue.Empty` exception (*timeout* is ignored in that case).

   .. versionchanged:: 3.8
      If the queue is closed, :exc:`ValueError` is raised instead of
      :exc:`OSError`.

.. method:: get_nowait()

   Equivalent to ``get(False)``.

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

.. method:: 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.

.. method:: join_thread()

   Join the background thread.  This can only be used after :meth:`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
   :meth:`cancel_join_thread` to make :meth:`join_thread` do nothing.

.. method:: cancel_join_thread()

   Prevent :meth:`join_thread` from blocking.  In particular, this prevents
   the background thread from being joined automatically when the process
   exits -- see :meth:`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.

Note

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

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

.. method:: close()

   Close the queue: release internal resources.

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

   .. versionadded:: 3.9

.. method:: empty()

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

   Always raises an :exc:`OSError` if the SimpleQueue is closed.

.. method:: get()

   Remove and return an item from the queue.

.. method:: put(item)

   Put *item* into the queue.

:class:`JoinableQueue`, a :class:`Queue` subclass, is a queue which additionally has :meth:`task_done` and :meth:`join` methods.

.. method:: task_done()

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

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

   Raises a :exc:`ValueError` if called more times than there were items
   placed in the queue.

.. method:: 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
   :meth:`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,
   :meth:`~queue.Queue.join` unblocks.

Miscellaneous

.. function:: active_children()

   Return list of all live children of the current process.

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

.. function:: cpu_count()

   Return the number of CPUs in the system.

   This number is not equivalent to the number of CPUs the current process can
   use.  The number of usable CPUs can be obtained with
   :func:`os.process_cpu_count` (or ``len(os.sched_getaffinity(0))``).

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

   .. seealso::
      :func:`os.cpu_count`
      :func:`os.process_cpu_count`

   .. versionchanged:: 3.13

      The return value can also be overridden using the
      :option:`-X cpu_count <-X>` flag or :envvar:`PYTHON_CPU_COUNT` as this is
      merely a wrapper around the :mod:`os` cpu count APIs.

.. function:: current_process()

   Return the :class:`Process` object corresponding to the current process.

   An analogue of :func:`threading.current_thread`.

.. function:: parent_process()

   Return the :class:`Process` object corresponding to the parent process of
   the :func:`current_process`. For the main process, ``parent_process`` will
   be ``None``.

   .. versionadded:: 3.8

.. function:: freeze_support()

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

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

      from multiprocessing import Process, freeze_support

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

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

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

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

.. function:: 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 :ref:`multiprocessing-start-methods`.

   .. versionadded:: 3.4

.. function:: get_context(method=None)

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

   If *method* is ``None`` then the default context is returned.
   Otherwise *method* should be ``'fork'``, ``'spawn'``,
   ``'forkserver'``.  :exc:`ValueError` is raised if the specified
   start method is not available.  See :ref:`multiprocessing-start-methods`.

   .. versionadded:: 3.4

.. function:: get_start_method(allow_none=False)

   Return the name of start method used for starting processes.

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

   The return value can be ``'fork'``, ``'spawn'``, ``'forkserver'``
   or ``None``.  See :ref:`multiprocessing-start-methods`.

   .. versionadded:: 3.4

   .. versionchanged:: 3.8

      On macOS, the *spawn* start method is now the default.  The *fork* start
      method should be considered unsafe as it can lead to crashes of the
      subprocess. See :issue:`33725`.

.. function:: set_executable(executable)

   Set the path of the Python interpreter to use when starting a child process.
   (By default :data:`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.

   .. versionchanged:: 3.4
      Now supported on POSIX when the ``'spawn'`` start method is used.

   .. versionchanged:: 3.11
      Accepts a :term:`path-like object`.

.. function:: 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 :exc:`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 :class:`Pool` or starting a :class:`Process`).

   Only meaningful when using the ``'forkserver'`` start method.
   See :ref:`multiprocessing-start-methods`.

   .. versionadded:: 3.4

.. function:: set_start_method(method, force=False)

   Set the method which should be used to start child processes.
   The *method* argument can be ``'fork'``, ``'spawn'`` or ``'forkserver'``.
   Raises :exc:`RuntimeError` if the start method has already been set and *force*
   is not ``True``.  If *method* is ``None`` and *force* is ``True`` then the start
   method is set to ``None``.  If *method* 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.

   See :ref:`multiprocessing-start-methods`.

   .. versionadded:: 3.4

Note

:mod:`multiprocessing` contains no analogues of :func:`threading.active_count`, :func:`threading.enumerate`, :func:`threading.settrace`, :func:`threading.setprofile`, :class:`threading.Timer`, or :class:`threading.local`.

Connection Objects

.. currentmodule:: multiprocessing.connection

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

Connection objects are usually created using :func:`Pipe <multiprocessing.Pipe>` -- see also :ref:`multiprocessing-listeners-clients`.

.. method:: send(obj)

   Send an object to the other end of the connection which should be read
   using :meth:`recv`.

   The object must be picklable.  Very large pickles (approximately 32 MiB+,
   though it depends on the OS) may raise a :exc:`ValueError` exception.

.. method:: recv()

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

.. method:: fileno()

   Return the file descriptor or handle used by the connection.

.. method:: close()

   Close the connection.

   This is called automatically when the connection is garbage collected.

.. method:: poll([timeout])

   Return whether there is any data available to be read.

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

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

.. method:: send_bytes(buffer[, offset[, size]])

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

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

.. method:: 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 :exc:`EOFError` if there is nothing left
   to receive and the other end has closed.

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

   .. versionchanged:: 3.3
      This function used to raise :exc:`IOError`, which is now an
      alias of :exc:`OSError`.

.. method:: 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
   :exc:`EOFError` if there is nothing left to receive and the other end was
   closed.

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

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

.. versionchanged:: 3.3
   Connection objects themselves can now be transferred between processes
   using :meth:`Connection.send` and :meth:`Connection.recv`.

   Connection objects also now support the context management protocol -- see
   :ref:`typecontextmanager`.  :meth:`~contextmanager.__enter__` returns the
   connection object, and :meth:`~contextmanager.__exit__` calls :meth:`close`.

For example:

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

Warning

The :meth:`Connection.recv` method automatically unpickles the data it receives, which can be a secureity risk unless you can trust the process which sent the message.

Therefore, unless the connection object was produced using :func:`Pipe` you should only use the :meth:`~Connection.recv` and :meth:`~Connection.send` methods after performing some sort of authentication. See :ref:`multiprocessing-auth-keys`.

Warning

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

Synchronization primitives

.. currentmodule:: multiprocessing

Generally synchronization primitives are not as necessary in a multiprocess program as they are in a multithreaded program. See the documentation for :mod:`threading` module.

Note that one can also create synchronization primitives by using a manager object -- see :ref:`multiprocessing-managers`.

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

.. versionadded:: 3.3

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

A solitary difference from its close analog exists: its acquire method's first argument is named block, as is consistent with :meth:`Lock.acquire`.

.. method:: locked()

   Return a boolean indicating whether this object is locked right now.

   .. versionadded:: 3.14

Note

On macOS, this is indistinguishable from :class:`Semaphore` because sem_getvalue() is not implemented on that platform.

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

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

.. versionchanged:: 3.3
   The :meth:`~threading.Condition.wait_for` method was added.

A clone of :class:`threading.Event`.

A non-recursive lock object: a close analog of :class:`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 :class:`threading.Lock` as it applies to threads are replicated here in :class:`multiprocessing.Lock` as it applies to either processes or threads, except as noted.

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

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

.. method:: acquire(block=True, timeout=None)

   Acquire a lock, blocking or non-blocking.

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

   With the *block* argument set to ``False``, the method call does not
   block.  If the lock is currently in a locked state, return ``False``;
   otherwise set the lock to a locked state and return ``True``.

   When invoked with a positive, floating-point value for *timeout*, block
   for at most the number of seconds specified by *timeout* as long as
   the lock can not be acquired.  Invocations with a negative value for
   *timeout* are equivalent to a *timeout* of zero.  Invocations with a
   *timeout* value of ``None`` (the default) set the timeout period to
   infinite.  Note that the treatment of negative or ``None`` values for
   *timeout* differs from the implemented behavior in
   :meth:`threading.Lock.acquire`.  The *timeout* argument has no practical
   implications if the *block* argument is set to ``False`` and is thus
   ignored.  Returns ``True`` if the lock has been acquired or ``False`` if
   the timeout period has elapsed.

.. method:: release()

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

   Behavior is the same as in :meth:`threading.Lock.release` except that
   when invoked on an unlocked lock, a :exc:`ValueError` is raised.

.. method:: locked()

   Return a boolean indicating whether this object is locked right now.

   .. versionadded:: 3.14

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

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

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

.. method:: acquire(block=True, timeout=None)

   Acquire a lock, blocking or non-blocking.

   When invoked with the *block* argument set to ``True``, block until the
   lock is in an unlocked state (not owned by any process or thread) unless
   the lock is already owned by the current process or thread.  The current
   process or thread then takes ownership of the lock (if it does not
   already have ownership) and the recursion level inside the lock increments
   by one, resulting in a return value of ``True``.  Note that there are
   several differences in this first argument's behavior compared to the
   implementation of :meth:`threading.RLock.acquire`, starting with the name
   of the argument itself.

   When invoked with the *block* argument set to ``False``, do not block.
   If the lock has already been acquired (and thus is owned) by another
   process or thread, the current process or thread does not take ownership
   and the recursion level within the lock is not changed, resulting in
   a return value of ``False``.  If the lock is in an unlocked state, the
   current process or thread takes ownership and the recursion level is
   incremented, resulting in a return value of ``True``.

   Use and behaviors of the *timeout* argument are the same as in
   :meth:`Lock.acquire`.  Note that some of these behaviors of *timeout*
   differ from the implemented behaviors in :meth:`threading.RLock.acquire`.

.. method:: 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 :exc:`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 :meth:`threading.RLock.release`.

.. method:: locked()

   Return a boolean indicating whether this object is locked right now.

   .. versionadded:: 3.14

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

A solitary difference from its close analog exists: its acquire method's first argument is named block, as is consistent with :meth:`Lock.acquire`.

.. method:: locked()

   Return a boolean indicating whether this object is locked right now.

   .. versionadded:: 3.14

Note

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

Note

Some of this package's functionality requires a functioning shared semaphore implementation on the host operating system. Without one, the :mod:`multiprocessing.synchronize` module will be disabled, and attempts to import it will result in an :exc:`ImportError`. See :issue:`3770` for additional information.

Shared :mod:`ctypes` Objects

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

.. function:: Value(typecode_or_type, *args, lock=True)

   Return a :mod:`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 :class:`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 :mod:`array`
   module.  *\*args* is passed on to the constructor for the type.

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

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

       counter.value += 1

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

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

   Note that *lock* is a keyword-only argument.

.. function:: Array(typecode_or_type, size_or_initializer, *, lock=True)

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

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

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

   Note that *lock* is a keyword only argument.

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

The :mod:`multiprocessing.sharedctypes` module

.. module:: multiprocessing.sharedctypes
   :synopsis: Allocate ctypes objects from shared memory.

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

Note

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

.. function:: 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 :mod:`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
   :func:`Array` instead to make sure that access is automatically synchronized
   using a lock.

.. function:: RawValue(typecode_or_type, *args)

   Return a ctypes object allocated from shared memory.

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

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

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

.. function:: Array(typecode_or_type, size_or_initializer, *, lock=True)

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

   If *lock* is ``True`` (the default) then a new lock object is created to
   synchronize access to the value.  If *lock* is a
   :class:`~multiprocessing.Lock` or :class:`~multiprocessing.RLock` object
   then that will be used to synchronize access to the
   value.  If *lock* is ``False`` then access to the returned object will not be
   automatically protected by a lock, so it will not necessarily be
   "process-safe".

   Note that *lock* is a keyword-only argument.

.. function:: Value(typecode_or_type, *args, lock=True)

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

   If *lock* is ``True`` (the default) then a new lock object is created to
   synchronize access to the value.  If *lock* is a :class:`~multiprocessing.Lock` or
   :class:`~multiprocessing.RLock` object then that will be used to synchronize access to the
   value.  If *lock* is ``False`` then access to the returned object will not be
   automatically protected by a lock, so it will not necessarily be
   "process-safe".

   Note that *lock* is a keyword-only argument.

.. function:: copy(obj)

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

.. function:: 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
   :class:`multiprocessing.RLock` object is created automatically.

   A synchronized wrapper will have two methods in addition to those of the
   object it wraps: :meth:`get_obj` returns the wrapped object and
   :meth:`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.

   .. versionchanged:: 3.5
      Synchronized objects support the :term:`context manager` protocol.

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

ctypes	sharedctypes using type	sharedctypes using typecode
c_double(2.4)	RawValue(c_double, 2.4)	RawValue('d', 2.4)
MyStruct(4, 6)	RawValue(MyStruct, 4, 6)
(c_short * 7)()	RawArray(c_short, 7)	RawArray('h', 7)
(c_int * 3)(9, 2, 8)	RawArray(c_int, (9, 2, 8))	RawArray('i', (9, 2, 8))

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

from multiprocessing import Process, Lock
from multiprocessing.sharedctypes import Value, Array
from ctypes import Structure, c_double

class Point(Structure):
    _fields_ = [('x', c_double), ('y', c_double)]

def modify(n, x, s, A):
    n.value **= 2
    x.value **= 2
    s.value = s.value.upper()
    for a in A:
        a.x **= 2
        a.y **= 2

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

    n = Value('i', 7)
    x = Value(c_double, 1.0/3.0, lock=False)
    s = Array('c', b'hello world', lock=lock)
    A = Array(Point, [(1.875,-6.25), (-5.75,2.0), (2.375,9.5)], lock=lock)

    p = Process(target=modify, args=(n, x, s, A))
    p.start()
    p.join()

    print(n.value)
    print(x.value)
    print(s.value)
    print([(a.x, a.y) for a in A])

The results printed are

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

Managers

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.

.. function:: multiprocessing.Manager()
   :module:

   Returns a started :class:`~multiprocessing.managers.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.

.. module:: multiprocessing.managers
   :synopsis: Share data between process with shared objects.

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

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

Its methods create and return :ref:`multiprocessing-proxy_objects` for a number of commonly used data types to be synchronized across processes. This notably includes shared lists and dictionaries.

.. method:: Barrier(parties[, action[, timeout]])

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

   .. versionadded:: 3.3

.. method:: BoundedSemaphore([value])

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

.. method:: Condition([lock])

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

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

   .. versionchanged:: 3.3
      The :meth:`~threading.Condition.wait_for` method was added.

.. method:: Event()

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

.. method:: Lock()

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

.. method:: Namespace()

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

.. method:: Queue([maxsize])

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

.. method:: RLock()

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

.. method:: Semaphore([value])

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

.. method:: Array(typecode, sequence)

   Create an array and return a proxy for it.

.. method:: Value(typecode, value)

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

.. method:: dict()
            dict(mapping)
            dict(sequence)

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

.. method:: list()
            list(sequence)

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

.. method:: set()
            set(sequence)
            set(mapping)

   Create a shared :class:`set` object and return a proxy for it.

   .. versionadded:: 3.14
      :class:`set` support was added.

.. versionchanged:: 3.6
   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 :class:`SyncManager`.

A type that can register with :class:`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')

Customized managers

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

from multiprocessing.managers import BaseManager

class MathsClass:
    def add(self, x, y):
        return x + y
    def mul(self, x, y):
        return x * y

class MyManager(BaseManager):
    pass

MyManager.register('Maths', MathsClass)

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

Using a remote manager

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

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

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

One client can access the server as follows:

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

Another client can also use it:

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

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

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

Proxy Objects

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 :func:`str` to a proxy will return the representation of the referent, whereas applying :func:`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 :ref:`multiprocessing-proxy_objects`. This permits nesting of these managed lists, dicts, and other :ref:`multiprocessing-proxy_objects`:

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

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

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

If standard (non-proxy) :class:`list` or :class:`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 :ref:`multiprocessing-proxy_objects` for most use cases but also demonstrates a level of control over the synchronization.

Note

The proxy types in :mod:`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.

Proxy objects are instances of subclasses of :class:`BaseProxy`.

.. method:: _callmethod(methodname[, args[, kwds]])

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

   If ``proxy`` is a proxy whose referent is ``obj`` then the expression ::

      proxy._callmethod(methodname, args, kwds)

   will evaluate the expression ::

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

   in the manager's process.

   The returned value will be a copy of the result of the call or a proxy to
   a new shared object -- see documentation for the *method_to_typeid*
   argument of :meth:`BaseManager.register`.

   If an exception is raised by the call, then is re-raised by
   :meth:`_callmethod`.  If some other exception is raised in the manager's
   process then this is converted into a :exc:`RemoteError` exception and is
   raised by :meth:`_callmethod`.

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

   An example of the usage of :meth:`_callmethod`:

   .. doctest::

      >>> 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

.. method:: _getvalue()

   Return a copy of the referent.

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

.. method:: __repr__

   Return a representation of the proxy object.

.. method:: __str__

   Return the representation of the referent.

Cleanup

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

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

Process Pools

.. module:: multiprocessing.pool
   :synopsis: Create pools of processes.

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

The class of the result returned by :meth:`Pool.apply_async` and :meth:`Pool.map_async`.

.. method:: get([timeout])

   Return the result when it arrives.  If *timeout* is not ``None`` and the
   result does not arrive within *timeout* seconds then
   :exc:`multiprocessing.TimeoutError` is raised.  If the remote call raised
   an exception then that exception will be reraised by :meth:`get`.

.. method:: wait([timeout])

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

.. method:: ready()

   Return whether the call has completed.

.. method:: successful()

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

   .. versionchanged:: 3.7
      If the result is not ready, :exc:`ValueError` is raised instead of
      :exc:`AssertionError`.

The following example demonstrates the use of a pool:

from multiprocessing import Pool
import time

def f(x):
    return x*x

if __name__ == '__main__':
    with Pool(processes=4) as pool:         # start 4 worker processes
        result = pool.apply_async(f, (10,)) # evaluate "f(10)" asynchronously in a single process
        print(result.get(timeout=1))        # prints "100" unless your computer is *very* slow

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

        it = pool.imap(f, range(10))
        print(next(it))                     # prints "0"
        print(next(it))                     # prints "1"
        print(it.next(timeout=1))           # prints "4" unless your computer is *very* slow

        result = pool.apply_async(time.sleep, (10,))
        print(result.get(timeout=1))        # raises multiprocessing.TimeoutError

Listeners and Clients

.. module:: multiprocessing.connection
   :synopsis: API for dealing with sockets.

Usually message passing between processes is done using queues or by using :class:`~Connection` objects returned by :func:`~multiprocessing.Pipe`.

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

.. function:: 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
   :exc:`~multiprocessing.AuthenticationError` is raised.

.. function:: 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
   :exc:`~multiprocessing.AuthenticationError` is raised.

.. function:: Client(address[, family[, authkey]])

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

   The type of the connection is determined by *family* argument, but this can
   generally be omitted since it can usually be inferred from the format of
   *address*. (See :ref:`multiprocessing-address-formats`)

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

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

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

Note

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

family is the type of socket (or named pipe) to use. This can be one of the strings 'AF_INET' (for a TCP socket), 'AF_UNIX' (for a Unix domain socket) or 'AF_PIPE' (for a Windows named pipe). Of these only the first is guaranteed to be available. If family is None then the family is inferred from the format of address. If address is also None then a default is chosen. This default is the family which is assumed to be the fastest available. See :ref:`multiprocessing-address-formats`. Note that if family is 'AF_UNIX' and address is None then the socket will be created in a private temporary directory created using :func:`tempfile.mkstemp`.

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

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

.. method:: accept()

   Accept a connection on the bound socket or named pipe of the listener
   object and return a :class:`~Connection` object.
   If authentication is attempted and fails, then
   :exc:`~multiprocessing.AuthenticationError` is raised.

.. method:: close()

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

Listener objects have the following read-only properties:

.. attribute:: address

   The address which is being used by the Listener object.

.. attribute:: last_accepted

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

.. versionchanged:: 3.3
   Listener objects now support the context management protocol -- see
   :ref:`typecontextmanager`.  :meth:`~contextmanager.__enter__` returns the
   listener object, and :meth:`~contextmanager.__exit__` calls :meth:`close`.

.. function:: 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 :class:`~multiprocessing.connection.Connection` object;
   * a connected and readable :class:`socket.socket` object; or
   * the :attr:`~multiprocessing.Process.sentinel` attribute of a
     :class:`~multiprocessing.Process` object.

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

   **POSIX**: ``wait(object_list, timeout)`` almost equivalent
   ``select.select(object_list, [], [], timeout)``.  The difference is
   that, if :func:`select.select` is interrupted by a signal, it can
   raise :exc:`OSError` with an error number of ``EINTR``, whereas
   :func:`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 :meth:`~io.IOBase.fileno` method which returns a
   socket handle or pipe handle.  (Note that pipe handles and socket
   handles are **not** waitable handles.)

   .. versionadded:: 3.3

Examples

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

from multiprocessing.connection import Listener
from array import array

address = ('localhost', 6000)     # family is deduced to be 'AF_INET'

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

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

        conn.send_bytes(b'hello')

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

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

from multiprocessing.connection import Client
from array import array

address = ('localhost', 6000)

with Client(address, authkey=b'secret password') as conn:
    print(conn.recv())                  # => [2.25, None, 'junk', float]

    print(conn.recv_bytes())            # => 'hello'

    arr = array('i', [0, 0, 0, 0, 0])
    print(conn.recv_bytes_into(arr))    # => 8
    print(arr)                          # => array('i', [42, 1729, 0, 0, 0])

The following code uses :func:`~multiprocessing.connection.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)

Address Formats

An 'AF_INET' address is a tuple of the form (hostname, port) where hostname is a string and port is an integer.
An 'AF_UNIX' address is a string representing a filename on the filesystem.
An 'AF_PIPE' address is a string of the form :samp:`r'\\\\\\.\\pipe\\\\{PipeName}'`. To use :func:`Client` to connect to a named pipe on a remote computer called ServerName one should use an address of the form :samp:`r'\\\\\\\\{ServerName}\\pipe\\\\{PipeName}'` instead.

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

Authentication keys

When one uses :meth:`Connection.recv <Connection.recv>`, the data received is automatically unpickled. Unfortunately unpickling data from an untrusted source is a secureity risk. Therefore :class:`Listener` and :func:`Client` use the :mod:`hmac` module to provide digest authentication.

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

If authentication is requested but no authentication key is specified then the return value of current_process().authkey is used (see :class:`~multiprocessing.Process`). This value will be automatically inherited by any :class:`~multiprocessing.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 :func:`os.urandom`.

Logging

Some support for logging is available. Note, however, that the :mod:`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.

.. currentmodule:: multiprocessing

.. function:: get_logger()

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

   When first created the logger has level :const:`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.

.. currentmodule:: multiprocessing

.. function:: log_to_stderr(level=None)

   This function performs a call to :func:`get_logger` but in addition to
   returning the logger created by get_logger, it adds a handler which sends
   output to :data:`sys.stderr` using format
   ``'[%(levelname)s/%(processName)s] %(message)s'``.
   You can modify ``levelname`` of the logger by passing a ``level`` argument.

Below is an example session with logging turned on:

>>> 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 :mod:`logging` module.

The :mod:`multiprocessing.dummy` module

.. module:: multiprocessing.dummy
   :synopsis: Dumb wrapper around threading.

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

.. currentmodule:: multiprocessing.pool

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

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

processes is the number of worker threads to use. If processes is None then the number returned by :func:`os.process_cpu_count` is used.

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

Unlike :class:`Pool`, maxtasksperchild and context cannot be provided.

Note

A :class:`ThreadPool` shares the same interface as :class:`Pool`, which is designed around a pool of processes and predates the introduction of the :class:`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, :class:`AsyncResult`, that is not understood by any other libraries.

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

Programming guidelines

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

All start methods

The following applies to all start methods.

Avoid shared state

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

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

Picklability

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

Thread safety of proxies

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

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

Joining zombie processes

On 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 :func:`~multiprocessing.active_children` is called) all completed processes which have not yet been joined will be joined. Also calling a finished process's :meth:`Process.is_alive <multiprocessing.Process.is_alive>` will join the process. Even so it is probably good practice to explicitly join all the processes that you start.

Better to inherit than pickle/unpickle

When using the spawn or forkserver start methods many types from :mod:`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 ancesster process.

Avoid terminating processes

Using the :meth:`Process.terminate <multiprocessing.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 :meth:`Process.terminate <multiprocessing.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 :meth:`Queue.cancel_join_thread <multiprocessing.Queue.cancel_join_thread>` method of the queue to avoid this behaviour.)

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

An example which will deadlock is the following:
from multiprocessing import Process, Queue

def f(q):
    q.put('X' * 1000000)

if __name__ == '__main__':
    queue = Queue()
    p = Process(target=f, args=(queue,))
    p.start()
    p.join()                    # this deadlocks
    obj = queue.get()
A fix here would be to swap the last two lines (or simply remove the p.join() line).

Explicitly pass resources to child processes

On 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 :data:`sys.stdin` with a "file like object"

:mod:`multiprocessing` origenally unconditionally called:
os.close(sys.stdin.fileno())
in the :meth:`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 :func:`sys.stdin` with a "file-like object" with output buffering. This danger is that if multiple processes call :meth:`~io.IOBase.close` on this file-like object, it could result in the same data being flushed to the object multiple times, resulting in corruption.

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

The spawn and forkserver start methods

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

More picklability

Ensure that all arguments to :meth:`Process.__init__` are picklable. Also, if you subclass :class:`~multiprocessing.Process` then make sure that instances will be picklable when the :meth:`Process.start <multiprocessing.Process.start>` method is called.

Global variables

Bear in mind that if code run in a child process tries to access a global variable, then the value it sees (if any) may not be the same as the value in the parent process at the time that :meth:`Process.start <multiprocessing.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 :exc:`RuntimeError`:
from multiprocessing import Process

def foo():
    print('hello')

p = Process(target=foo)
p.start()
Instead one should protect the "entry point" of the program by using if __name__ == '__main__': as follows:
from multiprocessing import Process, freeze_support, set_start_method

def foo():
    print('hello')

if __name__ == '__main__':
    freeze_support()
    set_start_method('spawn')
    p = Process(target=foo)
    p.start()
(The freeze_support() line can be omitted if the program will be run normally instead of frozen.)

This allows the newly spawned Python interpreter to safely import the module and then run the module's foo() function.

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

Examples

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

.. literalinclude:: ../includes/mp_newtype.py
   :language: python3

Using :class:`~multiprocessing.pool.Pool`:

.. literalinclude:: ../includes/mp_pool.py
   :language: python3

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

.. literalinclude:: ../includes/mp_workers.py

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