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

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

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

--------------

.. include:: ../includes/wasm-mobile-notavail.rst

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 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]

The :mod:`!multiprocessing` module also introduces APIs which do not have
analogs in the :mod:`threading` module, like the ability to :meth:`terminate
<Process.terminate>`, :meth:`interrupt <Process.interrupt>` or :meth:`kill
<Process.kill>` a running process.

.. 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`.

The arguments to :class:`Process` usually need to be unpickleable from within
the child process. If you tried typing the above example directly into a REPL it
could lead to an :exc:`AttributeError` in the child process trying to locate the
*f* function in the ``__main__`` module.


.. _multiprocessing-start-methods:

Contexts and start methods
^^^^^^^^^^^^^^^^^^^^^^^^^^

Depending on the platform, :mod:`!multiprocessing` supports three ways
to start a process.  These *start methods* are

  .. _multiprocessing-start-method-spawn:

  *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.

  .. _multiprocessing-start-method-fork:

  *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.

  .. _multiprocessing-start-method-forkserver:

  *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.

Libraries using :mod:`!multiprocessing` or
:class:`~concurrent.futures.ProcessPoolExecutor` should be designed to allow
their users to provide their own multiprocessing context.  Using a specific
context of your own within a library can lead to incompatibilities with the
rest of the library user's application.  Always document if your library
requires a specific start method.

.. 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.

.. _global-start-method:

Global start method
^^^^^^^^^^^^^^^^^^^

Python supports several ways to create and initialize a process.
The global start method sets the default mechanism for creating a process.

Several multiprocessing functions and methods that may also instantiate
certain objects will implicitly set the global start method to the system's default,
if it hasn’t been set already. The global start method can only be set once.
If you need to change the start method from the system default, you must
proactively set the global start method before calling functions or methods,
or creating these objects.


:class:`Process` and exceptions
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

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

   Process objects represent activity that is run in a separate process. The
   :class:`Process` class has equivalents of all the methods of
   :class:`threading.Thread`.

   The constructor should always be called with keyword arguments. *group*
   should always be ``None``; it exists solely for compatibility with
   :class:`threading.Thread`.  *target* is the callable object to be invoked by
   the :meth:`run` method.  It defaults to ``None``, meaning nothing is
   called. *name* is the process name (see :attr:`name` for more details).
   *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 :attr:`daemon` flag
   to ``True`` or ``False``.  If ``None`` (the default), this flag will be
   inherited from the creating process.

   By default, no arguments are passed to *target*. The *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 (``super().__init__()``) before doing anything else
   to the process.

   .. note::

      In general, all arguments to :class:`Process` must be picklable.  This is
      frequently observed when trying to create a :class:`Process` or use a
      :class:`concurrent.futures.ProcessPoolExecutor` from a REPL with a
      locally defined *target* function.

      Passing a callable object defined in the current REPL session causes the
      child process to die via an uncaught :exc:`AttributeError` exception when
      starting as *target* must have been defined within an importable module
      in order to be loaded during unpickling.

      Example of this uncatchable error from the child::

         >>> import multiprocessing as mp
         >>> def knigit():
         ...     print("Ni!")
         ...
         >>> process = mp.Process(target=knigit)
         >>> process.start()
         >>> Traceback (most recent call last):
           File ".../multiprocessing/spawn.py", line ..., in spawn_main
           File ".../multiprocessing/spawn.py", line ..., in _main
         AttributeError: module '__main__' has no attribute 'knigit'
         >>> process
         <SpawnProcess name='SpawnProcess-1' pid=379473 parent=378707 stopped exitcode=1>

      See :ref:`multiprocessing-programming-spawn`.  While this restriction is
      not true if using the ``"fork"`` start method, as of Python ``3.14`` that
      is no longer the default on any platform.  See
      :ref:`multiprocessing-start-methods`.
      See also :gh:`132898`.

   .. versionchanged:: 3.3
      Added the *daemon* parameter.

   .. method:: run()

      Method representing the process's activity.

      You may override this method in a subclass.  The standard :meth:`run`
      method invokes the callable object passed to the object's constructor as
      the target argument, if any, with sequential and keyword arguments taken
      from the *args* and *kwargs* arguments, respectively.

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

      Example::

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

   .. method:: start()

      Start the process's activity.

      This must be called at most once per process object.  It arranges for the
      object's :meth:`run` method to be invoked in a separate process.

   .. method:: join([timeout])

      If the optional argument *timeout* is ``None`` (the default), the method
      blocks until the process whose :meth:`join` method is called terminates.
      If *timeout* is a positive number, it blocks at most *timeout* seconds.
      Note that the method returns ``None`` if its process terminates or if the
      method times out.  Check the process's :attr:`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.

   .. attribute:: name

      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\ :sub:`1`:N\ :sub:`2`:...:N\ :sub:`k`' is constructed, where
      each N\ :sub:`k` is the N-th child of its parent.

   .. method:: is_alive

      Return whether the process is alive.

      Roughly, a process object is alive from the moment the :meth:`start`
      method returns until the child process terminates.

   .. attribute:: daemon

      The process's daemon flag, a Boolean value.  This must be set before
      :meth:`start` is called.

      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.

   In addition to the  :class:`threading.Thread` API, :class:`Process` objects
   also support the following attributes and methods:

   .. attribute:: pid

      Return the process ID.  Before the process is spawned, this will be
      ``None``.

   .. attribute:: exitcode

      The child's exit code.  This will be ``None`` if the process has not yet
      terminated.

      If the child's :meth:`run` method returned normally, the exit code
      will be 0.  If it terminated via :func:`sys.exit` with an integer
      argument *N*, the exit code will be *N*.

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

   .. attribute:: authkey

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

      When :mod:`!multiprocessing` is initialized the main process is assigned a
      random string using :func:`os.urandom`.

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

      See :ref:`multiprocessing-auth-keys`.

   .. attribute:: 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 :func:`multiprocessing.connection.wait`.  Otherwise
      calling :meth:`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 :mod:`select` module.

      .. versionadded:: 3.3

   .. method:: interrupt()

      Terminate the process. Works on POSIX using the :py:const:`~signal.SIGINT` signal.
      Behavior on Windows is undefined.

      By default, this terminates the child process by raising :exc:`KeyboardInterrupt`.
      This behavior can be altered by setting the respective signal handler in the child
      process :func:`signal.signal` for :py:const:`~signal.SIGINT`.

      Note: if the child process catches and discards :exc:`KeyboardInterrupt`, the
      process will not be terminated.

      Note: the default behavior will also set :attr:`exitcode` to ``1`` as if an
      uncaught exception was raised in the child process. To have a different
      :attr:`exitcode` you may simply catch :exc:`KeyboardInterrupt` and call
      ``exit(your_code)``.

      .. versionadded:: 3.14

   .. method:: terminate()

      Terminate the process.  On POSIX this is done using the :py:const:`~signal.SIGTERM` signal;
      on Windows :c:func:`!TerminateProcess` is used.  Note that exit handlers and
      finally clauses, etc., will not be executed.

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

      .. warning::

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

   .. method:: kill()

      Same as :meth:`terminate` but using the ``SIGKILL`` signal on POSIX.

      .. versionadded:: 3.7

   .. method:: close()

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

      .. versionadded:: 3.7

   Note that the :meth:`start`, :meth:`join`, :meth:`is_alive`,
   :meth:`terminate` and :attr:`exitcode` methods should only be called by
   the process that created the process object.

   Example usage of some of the methods of :class:`Process`:

   .. doctest::

       >>> 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:: 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 returned 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
: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>`.

   (1) 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`.

   (2) If multiple processes are enqueuing objects, it is possible for
       the objects to be received at the other end out-of-order.
       However, objects enqueued by the same process will always be in
       the expected order with respect to each other.

.. warning::

   If a process is killed using :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=True)

   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.

.. class:: Queue([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.

   Instantiating this class may set the global start method. See
   :ref:`global-start-method` for more details.

   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()

      Close the queue: release internal resources.

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

      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.

.. class:: SimpleQueue()

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

   Instantiating this class may set the global start method. See
   :ref:`global-start-method` for more details.

   .. 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([maxsize])

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

   Instantiating this class may set the global start method. See
   :ref:`global-start-method` for more details.

   .. 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 an 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 the start method is not
   *spawn*. In addition, if the module is being run normally by the Python
   interpreter (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. Note that if
   the global start method has not been set, this will set it to the system default
   See :ref:`global-start-method` for more details.
   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 global start method is not set and *allow_none* is ``False``, the global start
   method is set to the default, and its name is returned. See
   :ref:`global-start-method` for more details.

   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 something 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`.

.. class:: Connection

   .. 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:

.. doctest::

    >>> 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])

.. _multiprocessing-recv-pickle-security:

.. warning::

    The :meth:`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 :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`.

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

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

   Instantiating this class may set the global start method. See
   :ref:`global-start-method` for more details.

   .. versionadded:: 3.3

.. class:: BoundedSemaphore([value])

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

   Instantiating this class may set the global start method. See
   :ref:`global-start-method` for more details.

   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.

.. class:: Condition([lock])

   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`.

   Instantiating this class may set the global start method. See
   :ref:`global-start-method` for more details.

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

.. class:: Event()

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

   Instantiating this class may set the global start method. See
   :ref:`global-start-method` for more details.

.. class:: Lock()

   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.

   Instantiating this class may set the global start method. See
   :ref:`global-start-method` for more details.

   :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 originally 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


.. class:: RLock()

   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.

   Instantiating this class may set the global start method. See
   :ref:`global-start-method` for more details.

   :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


.. class:: Semaphore([value])

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

   Instantiating this class may set the global start method. See
   :ref:`global-start-method` for more details.

   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:: get_value()

      Return the current value of semaphore.

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


   .. 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 :ref:`ctypes type <ctypes-fundamental-data-types>` or a one
   character typecode of the kind used by the :mod:`array` module with the
   exception of ``'w'``, which is not supported.  In addition, the ``'c'``
   typecode is an alias for :class:`ctypes.c_char`.  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, ctx=None)

   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".

   *ctx* is a context object, or ``None`` (use the current context). If ``None``,
   calling this may set the global start method. See
   :ref:`global-start-method` for more details.

   Note that *lock* and *ctx* are keyword-only parameters.

.. function:: Value(typecode_or_type, *args, lock=True, ctx=None)

   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".

   *ctx* is a context object, or ``None`` (use the current context). If ``None``,
   calling this may set the global start method. See
   :ref:`global-start-method` for more details.

   Note that *lock* and *ctx* are keyword-only parameters.

.. function:: copy(obj)

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

.. function:: synchronized(obj, lock=None, ctx=None)

   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.

   *ctx* is a context object, or ``None`` (use the current context). If ``None``,
   calling this may set the global start method. See
   :ref:`global-start-method` for more details.

   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])


.. highlight:: none

The results printed are ::

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

.. highlight:: python3


.. _multiprocessing-managers:

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:

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

   Create a BaseManager object.

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

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

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

   *serializer* must be ``'pickle'`` (use :mod:`pickle` serialization) or
   ``'xmlrpclib'`` (use :mod:`xmlrpc.client` serialization).

   *ctx* is a context object, or ``None`` (use the current context). If ``None``,
   calling this may set the global start method. See
   :ref:`global-start-method` for more details.

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

   .. versionchanged:: 3.11
      Added the *shutdown_timeout* parameter.

   .. method:: start([initializer[, initargs]])

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

   .. method:: get_server()

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

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

      :class:`Server` additionally has an :attr:`address` attribute.

   .. method:: connect()

      Connect a local manager object to a remote manager process::

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

   .. method:: shutdown()

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

      This can be called multiple times.

   .. method:: 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 :meth:`connect` method, or if the
      *create_method* argument is ``False`` then this can be left as
      ``None``.

      *proxytype* is a subclass of :class:`BaseProxy` which is used to create
      proxies for shared objects with this *typeid*.  If ``None`` then a proxy
      class is created automatically.

      *exposed* is used to specify a sequence of method names which proxies for
      this typeid should be allowed to access using
      :meth:`BaseProxy._callmethod`.  (If *exposed* is ``None`` then
      :attr:`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 :meth:`~object.__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
      :attr:`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``.

   :class:`BaseManager` instances also have one read-only property:

   .. attribute:: address

      The address used by the manager.

   .. versionchanged:: 3.3
      Manager objects support the context management protocol -- see
      :ref:`typecontextmanager`.  :meth:`~contextmanager.__enter__` starts the
      server process (if it has not already started) and then returns the
      manager object.  :meth:`~contextmanager.__exit__` calls :meth:`shutdown`.

      In previous versions :meth:`~contextmanager.__enter__` did not start the
      manager's server process if it was not already started.

.. class:: SyncManager

   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`.

.. class:: Namespace

   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:

   .. doctest::

    >>> 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()

.. _multiprocessing-proxy_objects:

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:

.. doctest::

   >>> 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`:

.. doctest::

   >>> 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:

   .. doctest::

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

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

.. class:: BaseProxy

   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.

.. class:: Pool([processes[, initializer[, initargs[, maxtasksperchild [, context]]]]])

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

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

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

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

   *context* can be used to specify the context used for starting
   the worker processes.  Usually a pool is created using the
   function :func:`multiprocessing.Pool` or the :meth:`Pool` method
   of a context object.  In both cases *context* is set
   appropriately. If ``None``, calling this function will have the side effect
   of setting the current global start method if it has not been set already.
   See the :func:`get_context` function.

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

   .. warning::
      :class:`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 :meth:`close` and :meth:`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 :meth:`object.__del__` for more information).

   .. versionchanged:: 3.2
      Added the *maxtasksperchild* parameter.

   .. versionchanged:: 3.4
      Added the *context* parameter.

   .. versionchanged:: 3.13
      *processes* uses :func:`os.process_cpu_count` by default, instead of
      :func:`os.cpu_count`.

   .. note::

      Worker processes within a :class:`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 exiting, being cleaned up and a new
      process spawned to replace the old one. The *maxtasksperchild*
      argument to the :class:`Pool` exposes this ability to the end user.

   .. method:: apply(func[, args[, kwds]])

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

   .. method:: apply_async(func[, args[, kwds[, callback[, error_callback]]]])

      A variant of the :meth:`apply` method which returns a
      :class:`~multiprocessing.pool.AsyncResult` object.

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

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

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

   .. method:: map(func, iterable[, chunksize])

      A parallel equivalent of the :func:`map` built-in function (it supports only
      one *iterable* argument though, for multiple iterables see :meth:`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 :meth:`imap` or :meth:`imap_unordered` with explicit *chunksize*
      option for better efficiency.

   .. method:: map_async(func, iterable[, chunksize[, callback[, error_callback]]])

      A variant of the :meth:`.map` method which returns a
      :class:`~multiprocessing.pool.AsyncResult` object.

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

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

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

   .. method:: imap(func, iterable[, chunksize])

      A lazier version of :meth:`.map`.

      The *chunksize* argument is the same as the one used by the :meth:`.map`
      method.  For very long iterables using a large value for *chunksize* can
      make the job complete **much** faster than using the default value of
      ``1``.

      Also if *chunksize* is ``1`` then the :meth:`!next` method of the iterator
      returned by the :meth:`imap` method has an optional *timeout* parameter:
      ``next(timeout)`` will raise :exc:`multiprocessing.TimeoutError` if the
      result cannot be returned within *timeout* seconds.

   .. method:: imap_unordered(func, iterable[, chunksize])

      The same as :meth:`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".)

   .. method:: starmap(func, iterable[, chunksize])

      Like :meth:`~multiprocessing.pool.Pool.map` except that the
      elements of the *iterable* are expected to be iterables that are
      unpacked as arguments.

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

      .. versionadded:: 3.3

   .. method:: starmap_async(func, iterable[, chunksize[, callback[, error_callback]]])

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

      .. versionadded:: 3.3

   .. method:: close()

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

   .. method:: terminate()

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

   .. method:: join()

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

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


.. class:: AsyncResult

   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


.. _multiprocessing-listeners-clients:

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`.

.. class:: Listener([address[, family[, backlog[, authkey]]]])

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

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

   .. note::

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

   *family* is the type of socket (or named pipe) to use.  This can be one of
   the strings ``'AF_INET'`` (for a TCP socket), ``'AF_UNIX'`` (for a Unix
   domain socket) or ``'AF_PIPE'`` (for a Windows named pipe).  Of these only
   the first is guaranteed to be available.  If *family* is ``None`` then the
   family is inferred from the format of *address*.  If *address* is also
   ``None`` then a default is chosen.  This default is the family which is
   assumed to be the fastest available.  See
   :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)


.. _multiprocessing-address-formats:

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.


.. _multiprocessing-auth-keys:

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 security
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.


.. class:: ThreadPool([processes[, initializer[, initargs]]])

   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`.


.. _multiprocessing-programming:

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 ancestor 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` originally 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`

.. _multiprocessing-programming-spawn:
.. _multiprocessing-programming-forkserver:

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 :class:`~multiprocessing.Process` are
    picklable.  Also, if you subclass ``Process.__init__``, you must 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.

.. _multiprocessing-safe-main-import:

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.


.. _multiprocessing-examples:

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
