
``multiprocessing`` --- Process-based parallelism
*************************************************


Introduction
============

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

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

Note: Functionality within this package requires that the ``__main__``
  module be importable by the children. This is covered in
  *Programming guidelines* however it is worth pointing out here. This
  means that some examples, such as the ``multiprocessing.Pool``
  examples will not work in the interactive interpreter. For example:

     >>> from multiprocessing import Pool
     >>> p = Pool(5)
     >>> def f(x):
     ...     return x*x
     ...
     >>> p.map(f, [1,2,3])
     Process PoolWorker-1:
     Process PoolWorker-2:
     Process PoolWorker-3:
     Traceback (most recent call last):
     Traceback (most recent call last):
     Traceback (most recent call last):
     AttributeError: 'module' object has no attribute 'f'
     AttributeError: 'module' object has no attribute 'f'
     AttributeError: 'module' object has no attribute 'f'

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


The ``Process`` class
---------------------

In ``multiprocessing``, processes are spawned by creating a
``Process`` object and then calling its ``start()`` method.
``Process`` follows the API of ``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__)
       if hasattr(os, 'getppid'):  # only available on Unix
           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 (on Windows) the ``if __name__ ==
'__main__'`` part is necessary, see *Programming guidelines*.


Exchanging objects between processes
------------------------------------

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

**Queues**

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

      from multiprocessing import Process, Queue

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

      if __name__ == '__main__':
          q = Queue()
          p = Process(target=f, args=(q,))
          p.start()
          print(q.get())    # prints "[42, None, 'hello']"
          p.join()

   Queues are thread and process safe, but note that they must never
   be instantiated as a side effect of importing a module: this can
   lead to a deadlock!  (see *Importing in threaded code*)

**Pipes**

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

      from multiprocessing import Process, Pipe

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

      if __name__ == '__main__':
          parent_conn, child_conn = Pipe()
          p = Process(target=f, args=(child_conn,))
          p.start()
          print(parent_conn.recv())   # prints "[42, None, 'hello']"
          p.join()

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


Synchronization between processes
---------------------------------

``multiprocessing`` contains equivalents of all the synchronization
primitives from ``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()
       print('hello world', i)
       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
``multiprocessing`` provides a couple of ways of doing so.

**Shared memory**

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

      from multiprocessing import Process, Value, Array

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

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

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

          print(num.value)
          print(arr[:])

   will print

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

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

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

**Server process**

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

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

      from multiprocessing import Process, Manager

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

      if __name__ == '__main__':
          manager = Manager()

          d = manager.dict()
          l = manager.list(range(10))

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

          print(d)
          print(l)

   will print

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

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


Using a pool of workers
-----------------------

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

For example:

   from multiprocessing import Pool

   def f(x):
       return x*x

   if __name__ == '__main__':
       pool = Pool(processes=4)               # start 4 worker processes
       result = pool.apply_async(f, [10])     # evaluate "f(10)" asynchronously
       print(result.get(timeout=1))           # prints "100" unless your computer is *very* slow
       print(pool.map(f, range(10)))          # prints "[0, 1, 4,..., 81]"


Reference
=========

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


``Process`` and exceptions
--------------------------

class class multiprocessing.Process(group=None, target=None, name=None, args=(), kwargs={})

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

   The constructor should always be called with keyword arguments.
   *group* should always be ``None``; it exists solely for
   compatibility with ``threading.Thread``.  *target* is the callable
   object to be invoked by the ``run()`` method.  It defaults to
   ``None``, meaning nothing is called. *name* is the process name.
   By default, a unique name is constructed of the form
   'Process-N_1:N_2:...:N_k' where N_1,N_2,...,N_k is a sequence of
   integers whose length is determined by the *generation* of the
   process.  *args* is the argument tuple for the target invocation.
   *kwargs* is a dictionary of keyword arguments for the target
   invocation.  By default, no arguments are passed to *target*.

   If a subclass overrides the constructor, it must make sure it
   invokes the base class constructor (``Process.__init__()``) before
   doing anything else to the process.

   run()

      Method representing the process's activity.

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

   start()

      Start the process's activity.

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

   join([timeout])

      Block the calling thread until the process whose ``join()``
      method is called terminates or until the optional timeout
      occurs.

      If *timeout* is ``None`` then there is no timeout.

      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.

   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.

   is_alive()

      Return whether the process is alive.

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

   daemon

      The process's daemon flag, a Boolean value.  This must be set
      before ``start()`` 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  ``Threading.Thread`` API, ``Process`` objects
   also support the following attributes and methods:

   pid

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

   exitcode

      The child's exit code.  This will be ``None`` if the process has
      not yet terminated.  A negative value *-N* indicates that the
      child was terminated by signal *N*.

   authkey

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

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

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

      See *Authentication keys*.

   terminate()

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

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

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

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

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

      >>> import multiprocessing, time, signal
      >>> p = multiprocessing.Process(target=time.sleep, args=(1000,))
      >>> print(p, p.is_alive())
      <Process(Process-1, initial)> False
      >>> p.start()
      >>> print(p, p.is_alive())
      <Process(Process-1, started)> True
      >>> p.terminate()
      >>> time.sleep(0.1)
      >>> print(p, p.is_alive())
      <Process(Process-1, stopped[SIGTERM])> False
      >>> p.exitcode == -signal.SIGTERM
      True

exception exception multiprocessing.BufferTooShort

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

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


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 ``Pipe()`` (for a connection between
two processes) or a queue (which allows multiple producers and
consumers).

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

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

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

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

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

Warning: As mentioned above, if a child process has put items on a queue (and
  it has not used ``JoinableQueue.cancel_join_thread()``), then that
  process will not terminate until all buffered items have been
  flushed to the pipe.This means that if you try joining that process
  you may get a deadlock unless you are sure that all items which have
  been put on the queue have been consumed.  Similarly, if the child
  process is non-daemonic then the parent process may hang on exit
  when it tries to join all its non-daemonic children.Note that a
  queue created using a manager does not have this issue.  See
  *Programming guidelines*.

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

multiprocessing.Pipe([duplex])

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

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

class class multiprocessing.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.

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

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

   qsize()

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

      Note that this may raise ``NotImplementedError`` on Unix
      platforms like Mac OS X where ``sem_getvalue()`` is not
      implemented.

   empty()

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

   full()

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

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

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

   put_nowait(obj)

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

   get([block[, timeout]])

      Remove and return an item from the queue.  If optional args
      *block* is ``True`` (the default) and *timeout* is ``None`` (the
      default), block if necessary until an item is available.  If
      *timeout* is a positive number, it blocks at most *timeout*
      seconds and raises the ``queue.Empty`` exception if no item was
      available within that time.  Otherwise (block is ``False``),
      return an item if one is immediately available, else raise the
      ``queue.Empty`` exception (*timeout* is ignored in that case).

   get_nowait()

      Equivalent to ``get(False)``.

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

   close()

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

   join_thread()

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

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

   cancel_join_thread()

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

class class multiprocessing.queues.SimpleQueue

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

   empty()

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

   get()

      Remove and return an item from the queue.

   put(item)

      Put *item* into the queue.

class class multiprocessing.JoinableQueue([maxsize])

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

   task_done()

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

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

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

   join()

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

      The count of unfinished tasks goes up whenever an item is added
      to the queue.  The count goes down whenever a consumer thread
      calls ``task_done()`` to indicate that the item was retrieved
      and all work on it is complete.  When the count of unfinished
      tasks drops to zero, ``join()`` unblocks.


Miscellaneous
-------------

multiprocessing.active_children()

   Return list of all live children of the current process.

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

multiprocessing.cpu_count()

   Return the number of CPUs in the system.  May raise
   ``NotImplementedError``.

multiprocessing.current_process()

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

   An analogue of ``threading.current_thread()``.

multiprocessing.freeze_support()

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

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

      from multiprocessing import Process, freeze_support

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

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

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

   If the module is being run normally by the Python interpreter then
   ``freeze_support()`` has no effect.

multiprocessing.set_executable()

   Sets the path of the Python interpreter to use when starting a
   child process. (By default ``sys.executable`` is used).  Embedders
   will probably need to do some thing like

      set_executable(os.path.join(sys.exec_prefix, 'pythonw.exe'))

   before they can create child processes.  (Windows only)

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


Connection Objects
------------------

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

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

class class multiprocessing.Connection

   send(obj)

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

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

   recv()

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

   fileno()

      Return the file descriptor or handle used by the connection.

   close()

      Close the connection.

      This is called automatically when the connection is garbage
      collected.

   poll([timeout])

      Return whether there is any data available to be read.

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

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

      Send byte data from an object supporting the buffer interface as
      a complete message.

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

   recv_bytes([maxlength])

      Return a complete message of byte data sent from the other end
      of the connection as a string.  Blocks until there is something
      to receive. Raises ``EOFError`` if there is nothing left to
      receive and the other end has closed.

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

   recv_bytes_into(buffer[, offset])

      Read into *buffer* a complete message of byte data sent from the
      other end of the connection and return the number of bytes in
      the message.  Blocks until there is something to receive.
      Raises ``EOFError`` if there is nothing left to receive and the
      other end was closed.

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

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

For example:

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

Warning: The ``Connection.recv()`` method automatically unpickles the data it
  receives, which can be a security risk unless you can trust the
  process which sent the message.Therefore, unless the connection
  object was produced using ``Pipe()`` you should only use the
  ``recv()`` and ``send()`` methods after performing some sort of
  authentication.  See *Authentication keys*.

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


Synchronization primitives
--------------------------

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

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

class class multiprocessing.BoundedSemaphore([value])

   A bounded semaphore object: a clone of
   ``threading.BoundedSemaphore``.

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

class class multiprocessing.Condition([lock])

   A condition variable: a clone of ``threading.Condition``.

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

class class multiprocessing.Event

   A clone of ``threading.Event``. This method returns the state of
   the internal semaphore on exit, so it will always return ``True``
   except if a timeout is given and the operation times out.

   Changed in version 3.1: Previously, the method always returned
   ``None``.

class class multiprocessing.Lock

   A non-recursive lock object: a clone of ``threading.Lock``.

class class multiprocessing.RLock

   A recursive lock object: a clone of ``threading.RLock``.

class class multiprocessing.Semaphore([value])

   A semaphore object: a clone of ``threading.Semaphore``.

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

Note: If the SIGINT signal generated by Ctrl-C arrives while the main
  thread is blocked by a call to ``BoundedSemaphore.acquire()``,
  ``Lock.acquire()``, ``RLock.acquire()``, ``Semaphore.acquire()``,
  ``Condition.acquire()`` or ``Condition.wait()`` then the call will
  be immediately interrupted and ``KeyboardInterrupt`` will be
  raised.This differs from the behaviour of ``threading`` where SIGINT
  will be ignored while the equivalent blocking calls are in progress.


Shared ``ctypes`` Objects
-------------------------

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

multiprocessing.Value(typecode_or_type, *args[, lock])

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

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

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

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

multiprocessing.Array(typecode_or_type, size_or_initializer, *, lock=True)

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

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

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

   Note that *lock* is a keyword only argument.

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


The ``multiprocessing.sharedctypes`` module
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

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

multiprocessing.sharedctypes.RawArray(typecode_or_type, size_or_initializer)

   Return a ctypes array allocated from shared memory.

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

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

multiprocessing.sharedctypes.RawValue(typecode_or_type, *args)

   Return a ctypes object allocated from shared memory.

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

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

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

multiprocessing.sharedctypes.Array(typecode_or_type, size_or_initializer, *args[, lock])

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

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

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

multiprocessing.sharedctypes.Value(typecode_or_type, *args[, lock])

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

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

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

multiprocessing.sharedctypes.copy(obj)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The results printed are

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


Managers
--------

Managers provide a way to create data which can be shared between
different processes. A manager object controls a server process which
manages *shared objects*.  Other processes can access the shared
objects by using proxies.

multiprocessing.Manager()

   Returns a started ``SyncManager`` object which can be used for
   sharing objects between processes.  The returned manager object
   corresponds to a spawned child process and has methods which will
   create shared objects and return corresponding proxies.

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

class class multiprocessing.managers.BaseManager([address[, authkey]])

   Create a BaseManager object.

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

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

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

   start([initializer[, initargs]])

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

   get_server()

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

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

      ``Server`` additionally has an ``address`` attribute.

   connect()

      Connect a local manager object to a remote manager process:

         >>> from multiprocessing.managers import BaseManager
         >>> m = BaseManager(address=('127.0.0.1', 5000), authkey=b'abc')
         >>> m.connect()

   shutdown()

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

      This can be called multiple times.

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

      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 created using the
      ``from_address()`` classmethod or if the *create_method*
      argument is ``False`` then this can be left as ``None``.

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

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

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

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

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

   address

      The address used by the manager.

class class multiprocessing.managers.SyncManager

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

   It also supports creation of shared lists and dictionaries.

   BoundedSemaphore([value])

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

   Condition([lock])

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

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

   Event()

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

   Lock()

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

   Namespace()

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

   Queue([maxsize])

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

   RLock()

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

   Semaphore([value])

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

   Array(typecode, sequence)

      Create an array and return a proxy for it.

   Value(typecode, value)

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

   dict()
   dict(mapping)
   dict(sequence)

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

   list()
   list(sequence)

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

   Note: Modifications to mutable values or items in dict and list proxies
     will not be propagated through the manager, because the proxy has
     no way of knowing when its values or items are modified.  To
     modify such an item, you can re-assign the modified object to the
     container proxy:

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


Namespace objects
~~~~~~~~~~~~~~~~~

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

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

   >>> manager = multiprocessing.Manager()
   >>> Global = manager.Namespace()
   >>> Global.x = 10
   >>> Global.y = 'hello'
   >>> Global._z = 12.3    # this is an attribute of the proxy
   >>> print(Global)
   Namespace(x=10, y='hello')


Customized managers
~~~~~~~~~~~~~~~~~~~

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

   from multiprocessing.managers import BaseManager

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

   class MyManager(BaseManager):
       pass

   MyManager.register('Maths', MathsClass)

   if __name__ == '__main__':
       manager = MyManager()
       manager.start()
       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
   >>> import queue
   >>> queue = queue.Queue()
   >>> class QueueManager(BaseManager): pass
   >>> QueueManager.register('get_queue', callable=lambda:queue)
   >>> m = QueueManager(address=('', 50000), authkey=b'abracadabra')
   >>> s = m.get_server()
   >>> s.serve_forever()

One client can access the server as follows:

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

Another client can also use it:

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

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

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


Proxy Objects
-------------

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

A proxy object has methods which invoke corresponding methods of its
referent (although not every method of the referent will necessarily
be available through the proxy).  A proxy can usually be used in most
of the same ways that its referent can:

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

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

An important feature of proxy objects is that they are picklable so
they can be passed between processes.  Note, however, that if a proxy
is sent to the corresponding manager's process then unpickling it will
produce the referent itself.  This means, for example, that one shared
object can contain a second:

   >>> a = manager.list()
   >>> b = manager.list()
   >>> a.append(b)         # referent of a now contains referent of b
   >>> print(a, b)
   [[]] []
   >>> b.append('hello')
   >>> print(a, b)
   [['hello']] ['hello']

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

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

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

class class multiprocessing.managers.BaseProxy

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

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

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

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

         proxy._callmethod(methodname, args, kwds)

      will evaluate the expression

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

      in the manager's process.

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

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

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

      An example of the usage of ``_callmethod()``:

         >>> l = manager.list(range(10))
         >>> l._callmethod('__len__')
         10
         >>> l._callmethod('__getslice__', (2, 7))   # equiv to `l[2:7]`
         [2, 3, 4, 5, 6]
         >>> l._callmethod('__getitem__', (20,))     # equiv to `l[20]`
         Traceback (most recent call last):
         ...
         IndexError: list index out of range

   _getvalue()

      Return a copy of the referent.

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

   __repr__()

      Return a representation of the proxy object.

   __str__()

      Return the representation of the referent.


Cleanup
~~~~~~~

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

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

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

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

   New in version 3.2: *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.

   Note: Worker processes within a ``Pool`` typically live for the
     complete duration of the Pool's work queue. A frequent pattern
     found in other systems (such as Apache, mod_wsgi, etc) to free
     resources held by workers is to allow a worker within a pool to
     complete only a set amount of work before being exiting, being
     cleaned up and a new process spawned to replace the old one. The
     *maxtasksperchild* argument to the ``Pool`` exposes this ability
     to the end user.

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

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

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

      A variant of the ``apply()`` method which returns a result
      object.

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

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

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

   map(func, iterable[, chunksize])

      A parallel equivalent of the ``map()`` built-in function (it
      supports only one *iterable* argument though).  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.

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

      A variant of the ``map()`` method which returns a result object.

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

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

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

   imap(func, iterable[, chunksize])

      A lazier version of ``map()``.

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

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

   imap_unordered(func, iterable[, chunksize])

      The same as ``imap()`` except that the ordering of the results
      from the returned iterator should be considered arbitrary.
      (Only when there is only one worker process is the order
      guaranteed to be "correct".)

   close()

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

   terminate()

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

   join()

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

class class multiprocessing.pool.AsyncResult

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

   get([timeout])

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

   wait([timeout])

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

   ready()

      Return whether the call has completed.

   successful()

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

The following example demonstrates the use of a pool:

   from multiprocessing import Pool

   def f(x):
       return x*x

   if __name__ == '__main__':
       pool = Pool(processes=4)              # start 4 worker processes

       result = pool.apply_async(f, (10,))   # evaluate "f(10)" asynchronously
       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

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


Listeners and Clients
---------------------

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

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

multiprocessing.connection.deliver_challenge(connection, authkey)

   Send a randomly generated message to the other end of the
   connection and wait for a reply.

   If the reply matches the digest of the message using *authkey* as
   the key then a welcome message is sent to the other end of the
   connection.  Otherwise ``AuthenticationError`` is raised.

multiprocessing.connection.answerChallenge(connection, authkey)

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

   If a welcome message is not received, then ``AuthenticationError``
   is raised.

multiprocessing.connection.Client(address[, family[, authenticate[, authkey]]])

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

   The type of the connection is determined by *family* argument, but
   this can generally be omitted since it can usually be inferred from
   the format of *address*. (See *Address Formats*)

   If *authenticate* is ``True`` or *authkey* is a byte string then
   digest authentication is used.  The key used for authentication
   will be either *authkey* or ``current_process().authkey`` if
   *authkey* is ``None``. If authentication fails then
   ``AuthenticationError`` is raised.  See *Authentication keys*.

class class multiprocessing.connection.Listener([address[, family[, backlog[, authenticate[, authkey]]]]])

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

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

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

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

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

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

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

   If *authkey* is ``None`` and *authenticate* is ``True`` then
   ``current_process().authkey`` is used as the authentication key.
   If *authkey* is ``None`` and *authenticate* is ``False`` then no
   authentication is done.  If authentication fails then
   ``AuthenticationError`` is raised.  See *Authentication keys*.

   accept()

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

   close()

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

   Listener objects have the following read-only properties:

   address

      The address which is being used by the Listener object.

   last_accepted

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

The module defines two exceptions:

exception exception multiprocessing.connection.AuthenticationError

   Exception raised when there is an authentication error.

**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'
   listener = Listener(address, authkey=b'secret password')

   conn = listener.accept()
   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]))

   conn.close()
   listener.close()

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)
   conn = Client(address, authkey=b'secret password')

   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])

   conn.close()


Address Formats
~~~~~~~~~~~~~~~

* An ``'AF_INET'`` address is a tuple of the form ``(hostname, port)``
  where *hostname* is a string and *port* is an integer.

* An ``'AF_UNIX'`` address is a string representing a filename on the
  filesystem.

* An ``'AF_PIPE'`` address is a string of the form
     ``r'\\.\pipe\*PipeName*'``.  To use ``Client()`` to connect to a
     named pipe on a remote computer called *ServerName* one should
     use an address of the form ``r'\\*ServerName*\pipe\*PipeName*'``
     instead.

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


Authentication keys
-------------------

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

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

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

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


Logging
-------

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

multiprocessing.get_logger()

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

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

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

multiprocessing.log_to_stderr()

   This function performs a call to ``get_logger()`` but in addition
   to returning the logger created by get_logger, it adds a handler
   which sends output to ``sys.stderr`` using format
   ``'[%(levelname)s/%(processName)s] %(message)s'``.

Below is an example session with logging turned on:

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

In addition to having these two logging functions, the multiprocessing
also exposes two additional logging level attributes. These are
``SUBWARNING`` and ``SUBDEBUG``. The table below illustrates where
theses fit in the normal level hierarchy.

+------------------+------------------+
| Level            | Numeric value    |
+==================+==================+
| ``SUBWARNING``   | 25               |
+------------------+------------------+
| ``SUBDEBUG``     | 5                |
+------------------+------------------+

For a full table of logging levels, see the ``logging`` module.

These additional logging levels are used primarily for certain debug
messages within the multiprocessing module. Below is the same example
as above, except with ``SUBDEBUG`` enabled:

   >>> import multiprocessing, logging
   >>> logger = multiprocessing.log_to_stderr()
   >>> logger.setLevel(multiprocessing.SUBDEBUG)
   >>> 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 '/.../pymp-djGBXN/listener-...'
   >>> del m
   [SUBDEBUG/MainProcess] finalizer calling ...
   [INFO/MainProcess] sending shutdown message to manager
   [DEBUG/SyncManager-...] manager received shutdown message
   [SUBDEBUG/SyncManager-...] calling <Finalize object, callback=unlink, ...
   [SUBDEBUG/SyncManager-...] finalizer calling <built-in function unlink> ...
   [SUBDEBUG/SyncManager-...] calling <Finalize object, dead>
   [SUBDEBUG/SyncManager-...] finalizer calling <function rmtree at 0x5aa730> ...
   [INFO/SyncManager-...] manager exiting with exitcode 0


The ``multiprocessing.dummy`` module
------------------------------------

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


Programming guidelines
======================

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


All platforms
-------------

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 from the ``threading`` module.

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

Better to inherit than pickle/unpickle

   On Windows many types from ``multiprocessing`` need to be picklable
   so that child processes can use them.  However, one should
   generally avoid sending shared objects to other processes using
   pipes or queues.  Instead you should arrange the program so that a
   process which needs access to a shared resource created elsewhere
   can inherit it from an ancestor process.

Avoid terminating processes

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

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

Joining processes that use queues

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

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

   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 round (or simply
   remove the ``p.join()`` line).

Explicitly pass resources to child processes

   On Unix 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
   this also ensures that as long as the child process is still alive
   the object will not be garbage collected in the parent process.
   This might be important if some resource is freed when the object
   is garbage collected in the parent process.

   So for instance

      from multiprocessing import Process, Lock

      def f():
          ... do something using "lock" ...

      if __name__ == '__main__':
         lock = Lock()
         for i in range(10):
              Process(target=f).start()

   should be rewritten as

      from multiprocessing import Process, Lock

      def f(l):
          ... do something using "l" ...

      if __name__ == '__main__':
         lock = Lock()
         for i in range(10):
              Process(target=f, args=(lock,)).start()

Beware of replacing ``sys.stdin`` with a "file like object"

   ``multiprocessing`` originally unconditionally called:

      os.close(sys.stdin.fileno())

   in the ``multiprocessing.Process._bootstrap()`` method --- this
   resulted in issues with processes-in-processes. This has been
   changed to:

      sys.stdin.close()
      sys.stdin = open(os.devnull)

   Which solves the fundamental issue of processes colliding with each
   other resulting in a bad file descriptor error, but introduces a
   potential danger to applications which replace ``sys.stdin()`` with
   a "file-like object" with output buffering.  This danger is that if
   multiple processes call ``close()`` on this file-like object, it
   could result in the same data being flushed to the object multiple
   times, resulting in corruption.

   If you write a file-like object and implement your own caching, you
   can make it fork-safe by storing the pid whenever you append to the
   cache, and discarding the cache when the pid changes. For example:

      @property
      def cache(self):
          pid = os.getpid()
          if pid != self._pid:
              self._pid = pid
              self._cache = []
          return self._cache

   For more information, see issue 5155, issue 5313 and issue 5331


Windows
-------

Since Windows lacks ``os.fork()`` it has a few extra restrictions:

More picklability

   Ensure that all arguments to ``Process.__init__()`` are picklable.
   This means, in particular, that bound or unbound methods cannot be
   used directly as the ``target`` argument on Windows --- just define
   a function and use that instead.

   Also, if you subclass ``Process`` then make sure that instances
   will be picklable when the ``Process.start()`` method is called.

Global variables

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

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

Safe importing of main module

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

   For example, under Windows running the following module would fail
   with a ``RuntimeError``:

      from multiprocessing import Process

      def foo():
          print('hello')

      p = Process(target=foo)
      p.start()

   Instead one should protect the "entry point" of the program by
   using ``if __name__ == '__main__':`` as follows:

      from multiprocessing import Process, freeze_support

      def foo():
          print('hello')

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

   (The ``freeze_support()`` line can be omitted if the program will
   be run normally instead of frozen.)

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

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


Examples
========

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

   #
   # This module shows how to use arbitrary callables with a subclass of
   # `BaseManager`.
   #
   # Copyright (c) 2006-2008, R Oudkerk
   # All rights reserved.
   #

   from multiprocessing import freeze_support
   from multiprocessing.managers import BaseManager, BaseProxy
   import operator

   ##

   class Foo:
       def f(self):
           print('you called Foo.f()')
       def g(self):
           print('you called Foo.g()')
       def _h(self):
           print('you called Foo._h()')

   # A simple generator function
   def baz():
       for i in range(10):
           yield i*i

   # Proxy type for generator objects
   class GeneratorProxy(BaseProxy):
       _exposed_ = ('next', '__next__')
       def __iter__(self):
           return self
       def __next__(self):
           return self._callmethod('next')
       def __next__(self):
           return self._callmethod('__next__')

   # Function to return the operator module
   def get_operator_module():
       return operator

   ##

   class MyManager(BaseManager):
       pass

   # register the Foo class; make `f()` and `g()` accessible via proxy
   MyManager.register('Foo1', Foo)

   # register the Foo class; make `g()` and `_h()` accessible via proxy
   MyManager.register('Foo2', Foo, exposed=('g', '_h'))

   # register the generator function baz; use `GeneratorProxy` to make proxies
   MyManager.register('baz', baz, proxytype=GeneratorProxy)

   # register get_operator_module(); make public functions accessible via proxy
   MyManager.register('operator', get_operator_module)

   ##

   def test():
       manager = MyManager()
       manager.start()

       print('-' * 20)

       f1 = manager.Foo1()
       f1.f()
       f1.g()
       assert not hasattr(f1, '_h')
       assert sorted(f1._exposed_) == sorted(['f', 'g'])

       print('-' * 20)

       f2 = manager.Foo2()
       f2.g()
       f2._h()
       assert not hasattr(f2, 'f')
       assert sorted(f2._exposed_) == sorted(['g', '_h'])

       print('-' * 20)

       it = manager.baz()
       for i in it:
           print('<%d>' % i, end=' ')
       print()

       print('-' * 20)

       op = manager.operator()
       print('op.add(23, 45) =', op.add(23, 45))
       print('op.pow(2, 94) =', op.pow(2, 94))
       print('op.getslice(range(10), 2, 6) =', op.getslice(list(range(10)), 2, 6))
       print('op.repeat(range(5), 3) =', op.repeat(list(range(5)), 3))
       print('op._exposed_ =', op._exposed_)

   ##

   if __name__ == '__main__':
       freeze_support()
       test()

Using ``Pool``:

   #
   # A test of `multiprocessing.Pool` class
   #
   # Copyright (c) 2006-2008, R Oudkerk
   # All rights reserved.
   #

   import multiprocessing
   import time
   import random
   import sys

   #
   # Functions used by test code
   #

   def calculate(func, args):
       result = func(*args)
       return '%s says that %s%s = %s' % (
           multiprocessing.current_process().name,
           func.__name__, args, result
           )

   def calculatestar(args):
       return calculate(*args)

   def mul(a, b):
       time.sleep(0.5 * random.random())
       return a * b

   def plus(a, b):
       time.sleep(0.5 * random.random())
       return a + b

   def f(x):
       return 1.0 / (x - 5.0)

   def pow3(x):
       return x ** 3

   def noop(x):
       pass

   #
   # Test code
   #

   def test():
       print('cpu_count() = %d\n' % multiprocessing.cpu_count())

       #
       # Create pool
       #

       PROCESSES = 4
       print('Creating pool with %d processes\n' % PROCESSES)
       pool = multiprocessing.Pool(PROCESSES)
       print('pool = %s' % pool)
       print()

       #
       # Tests
       #

       TASKS = [(mul, (i, 7)) for i in range(10)] + \
               [(plus, (i, 8)) for i in range(10)]

       results = [pool.apply_async(calculate, t) for t in TASKS]
       imap_it = pool.imap(calculatestar, TASKS)
       imap_unordered_it = pool.imap_unordered(calculatestar, TASKS)

       print('Ordered results using pool.apply_async():')
       for r in results:
           print('\t', r.get())
       print()

       print('Ordered results using pool.imap():')
       for x in imap_it:
           print('\t', x)
       print()

       print('Unordered results using pool.imap_unordered():')
       for x in imap_unordered_it:
           print('\t', x)
       print()

       print('Ordered results using pool.map() --- will block till complete:')
       for x in pool.map(calculatestar, TASKS):
           print('\t', x)
       print()

       #
       # Simple benchmarks
       #

       N = 100000
       print('def pow3(x): return x**3')

       t = time.time()
       A = list(map(pow3, range(N)))
       print('\tmap(pow3, range(%d)):\n\t\t%s seconds' % \
             (N, time.time() - t))

       t = time.time()
       B = pool.map(pow3, range(N))
       print('\tpool.map(pow3, range(%d)):\n\t\t%s seconds' % \
             (N, time.time() - t))

       t = time.time()
       C = list(pool.imap(pow3, range(N), chunksize=N//8))
       print('\tlist(pool.imap(pow3, range(%d), chunksize=%d)):\n\t\t%s' \
             ' seconds' % (N, N//8, time.time() - t))

       assert A == B == C, (len(A), len(B), len(C))
       print()

       L = [None] * 1000000
       print('def noop(x): pass')
       print('L = [None] * 1000000')

       t = time.time()
       A = list(map(noop, L))
       print('\tmap(noop, L):\n\t\t%s seconds' % \
             (time.time() - t))

       t = time.time()
       B = pool.map(noop, L)
       print('\tpool.map(noop, L):\n\t\t%s seconds' % \
             (time.time() - t))

       t = time.time()
       C = list(pool.imap(noop, L, chunksize=len(L)//8))
       print('\tlist(pool.imap(noop, L, chunksize=%d)):\n\t\t%s seconds' % \
             (len(L)//8, time.time() - t))

       assert A == B == C, (len(A), len(B), len(C))
       print()

       del A, B, C, L

       #
       # Test error handling
       #

       print('Testing error handling:')

       try:
           print(pool.apply(f, (5,)))
       except ZeroDivisionError:
           print('\tGot ZeroDivisionError as expected from pool.apply()')
       else:
           raise AssertionError('expected ZeroDivisionError')

       try:
           print(pool.map(f, list(range(10))))
       except ZeroDivisionError:
           print('\tGot ZeroDivisionError as expected from pool.map()')
       else:
           raise AssertionError('expected ZeroDivisionError')

       try:
           print(list(pool.imap(f, list(range(10)))))
       except ZeroDivisionError:
           print('\tGot ZeroDivisionError as expected from list(pool.imap())')
       else:
           raise AssertionError('expected ZeroDivisionError')

       it = pool.imap(f, list(range(10)))
       for i in range(10):
           try:
               x = next(it)
           except ZeroDivisionError:
               if i == 5:
                   pass
           except StopIteration:
               break
           else:
               if i == 5:
                   raise AssertionError('expected ZeroDivisionError')

       assert i == 9
       print('\tGot ZeroDivisionError as expected from IMapIterator.next()')
       print()

       #
       # Testing timeouts
       #

       print('Testing ApplyResult.get() with timeout:', end=' ')
       res = pool.apply_async(calculate, TASKS[0])
       while 1:
           sys.stdout.flush()
           try:
               sys.stdout.write('\n\t%s' % res.get(0.02))
               break
           except multiprocessing.TimeoutError:
               sys.stdout.write('.')
       print()
       print()

       print('Testing IMapIterator.next() with timeout:', end=' ')
       it = pool.imap(calculatestar, TASKS)
       while 1:
           sys.stdout.flush()
           try:
               sys.stdout.write('\n\t%s' % it.next(0.02))
           except StopIteration:
               break
           except multiprocessing.TimeoutError:
               sys.stdout.write('.')
       print()
       print()

       #
       # Testing callback
       #

       print('Testing callback:')

       A = []
       B = [56, 0, 1, 8, 27, 64, 125, 216, 343, 512, 729]

       r = pool.apply_async(mul, (7, 8), callback=A.append)
       r.wait()

       r = pool.map_async(pow3, list(range(10)), callback=A.extend)
       r.wait()

       if A == B:
           print('\tcallbacks succeeded\n')
       else:
           print('\t*** callbacks failed\n\t\t%s != %s\n' % (A, B))

       #
       # Check there are no outstanding tasks
       #

       assert not pool._cache, 'cache = %r' % pool._cache

       #
       # Check close() methods
       #

       print('Testing close():')

       for worker in pool._pool:
           assert worker.is_alive()

       result = pool.apply_async(time.sleep, [0.5])
       pool.close()
       pool.join()

       assert result.get() is None

       for worker in pool._pool:
           assert not worker.is_alive()

       print('\tclose() succeeded\n')

       #
       # Check terminate() method
       #

       print('Testing terminate():')

       pool = multiprocessing.Pool(2)
       DELTA = 0.1
       ignore = pool.apply(pow3, [2])
       results = [pool.apply_async(time.sleep, [DELTA]) for i in range(100)]
       pool.terminate()
       pool.join()

       for worker in pool._pool:
           assert not worker.is_alive()

       print('\tterminate() succeeded\n')

       #
       # Check garbage collection
       #

       print('Testing garbage collection:')

       pool = multiprocessing.Pool(2)
       DELTA = 0.1
       processes = pool._pool
       ignore = pool.apply(pow3, [2])
       results = [pool.apply_async(time.sleep, [DELTA]) for i in range(100)]

       results = pool = None

       time.sleep(DELTA * 2)

       for worker in processes:
           assert not worker.is_alive()

       print('\tgarbage collection succeeded\n')


   if __name__ == '__main__':
       multiprocessing.freeze_support()

       assert len(sys.argv) in (1, 2)

       if len(sys.argv) == 1 or sys.argv[1] == 'processes':
           print(' Using processes '.center(79, '-'))
       elif sys.argv[1] == 'threads':
           print(' Using threads '.center(79, '-'))
           import multiprocessing.dummy as multiprocessing
       else:
           print('Usage:\n\t%s [processes | threads]' % sys.argv[0])
           raise SystemExit(2)

       test()

Synchronization types like locks, conditions and queues:

   #
   # A test file for the `multiprocessing` package
   #
   # Copyright (c) 2006-2008, R Oudkerk
   # All rights reserved.
   #

   import time
   import sys
   import random
   from queue import Empty

   import multiprocessing               # may get overwritten


   #### TEST_VALUE

   def value_func(running, mutex):
       random.seed()
       time.sleep(random.random()*4)

       mutex.acquire()
       print('\n\t\t\t' + str(multiprocessing.current_process()) + ' has finished')
       running.value -= 1
       mutex.release()

   def test_value():
       TASKS = 10
       running = multiprocessing.Value('i', TASKS)
       mutex = multiprocessing.Lock()

       for i in range(TASKS):
           p = multiprocessing.Process(target=value_func, args=(running, mutex))
           p.start()

       while running.value > 0:
           time.sleep(0.08)
           mutex.acquire()
           print(running.value, end=' ')
           sys.stdout.flush()
           mutex.release()

       print()
       print('No more running processes')


   #### TEST_QUEUE

   def queue_func(queue):
       for i in range(30):
           time.sleep(0.5 * random.random())
           queue.put(i*i)
       queue.put('STOP')

   def test_queue():
       q = multiprocessing.Queue()

       p = multiprocessing.Process(target=queue_func, args=(q,))
       p.start()

       o = None
       while o != 'STOP':
           try:
               o = q.get(timeout=0.3)
               print(o, end=' ')
               sys.stdout.flush()
           except Empty:
               print('TIMEOUT')

       print()


   #### TEST_CONDITION

   def condition_func(cond):
       cond.acquire()
       print('\t' + str(cond))
       time.sleep(2)
       print('\tchild is notifying')
       print('\t' + str(cond))
       cond.notify()
       cond.release()

   def test_condition():
       cond = multiprocessing.Condition()

       p = multiprocessing.Process(target=condition_func, args=(cond,))
       print(cond)

       cond.acquire()
       print(cond)
       cond.acquire()
       print(cond)

       p.start()

       print('main is waiting')
       cond.wait()
       print('main has woken up')

       print(cond)
       cond.release()
       print(cond)
       cond.release()

       p.join()
       print(cond)


   #### TEST_SEMAPHORE

   def semaphore_func(sema, mutex, running):
       sema.acquire()

       mutex.acquire()
       running.value += 1
       print(running.value, 'tasks are running')
       mutex.release()

       random.seed()
       time.sleep(random.random()*2)

       mutex.acquire()
       running.value -= 1
       print('%s has finished' % multiprocessing.current_process())
       mutex.release()

       sema.release()

   def test_semaphore():
       sema = multiprocessing.Semaphore(3)
       mutex = multiprocessing.RLock()
       running = multiprocessing.Value('i', 0)

       processes = [
           multiprocessing.Process(target=semaphore_func,
                                   args=(sema, mutex, running))
           for i in range(10)
           ]

       for p in processes:
           p.start()

       for p in processes:
           p.join()


   #### TEST_JOIN_TIMEOUT

   def join_timeout_func():
       print('\tchild sleeping')
       time.sleep(5.5)
       print('\n\tchild terminating')

   def test_join_timeout():
       p = multiprocessing.Process(target=join_timeout_func)
       p.start()

       print('waiting for process to finish')

       while 1:
           p.join(timeout=1)
           if not p.is_alive():
               break
           print('.', end=' ')
           sys.stdout.flush()


   #### TEST_EVENT

   def event_func(event):
       print('\t%r is waiting' % multiprocessing.current_process())
       event.wait()
       print('\t%r has woken up' % multiprocessing.current_process())

   def test_event():
       event = multiprocessing.Event()

       processes = [multiprocessing.Process(target=event_func, args=(event,))
                    for i in range(5)]

       for p in processes:
           p.start()

       print('main is sleeping')
       time.sleep(2)

       print('main is setting event')
       event.set()

       for p in processes:
           p.join()


   #### TEST_SHAREDVALUES

   def sharedvalues_func(values, arrays, shared_values, shared_arrays):
       for i in range(len(values)):
           v = values[i][1]
           sv = shared_values[i].value
           assert v == sv

       for i in range(len(values)):
           a = arrays[i][1]
           sa = list(shared_arrays[i][:])
           assert a == sa

       print('Tests passed')

   def test_sharedvalues():
       values = [
           ('i', 10),
           ('h', -2),
           ('d', 1.25)
           ]
       arrays = [
           ('i', list(range(100))),
           ('d', [0.25 * i for i in range(100)]),
           ('H', list(range(1000)))
           ]

       shared_values = [multiprocessing.Value(id, v) for id, v in values]
       shared_arrays = [multiprocessing.Array(id, a) for id, a in arrays]

       p = multiprocessing.Process(
           target=sharedvalues_func,
           args=(values, arrays, shared_values, shared_arrays)
           )
       p.start()
       p.join()

       assert p.exitcode == 0


   ####

   def test(namespace=multiprocessing):
       global multiprocessing

       multiprocessing = namespace

       for func in [test_value, test_queue, test_condition,
                    test_semaphore, test_join_timeout, test_event,
                    test_sharedvalues]:

           print('\n\t######## %s\n' % func.__name__)
           func()

       ignore = multiprocessing.active_children()      # cleanup any old processes
       if hasattr(multiprocessing, '_debug_info'):
           info = multiprocessing._debug_info()
           if info:
               print(info)
               raise ValueError('there should be no positive refcounts left')


   if __name__ == '__main__':
       multiprocessing.freeze_support()

       assert len(sys.argv) in (1, 2)

       if len(sys.argv) == 1 or sys.argv[1] == 'processes':
           print(' Using processes '.center(79, '-'))
           namespace = multiprocessing
       elif sys.argv[1] == 'manager':
           print(' Using processes and a manager '.center(79, '-'))
           namespace = multiprocessing.Manager()
           namespace.Process = multiprocessing.Process
           namespace.current_process = multiprocessing.current_process
           namespace.active_children = multiprocessing.active_children
       elif sys.argv[1] == 'threads':
           print(' Using threads '.center(79, '-'))
           import multiprocessing.dummy as namespace
       else:
           print('Usage:\n\t%s [processes | manager | threads]' % sys.argv[0])
           raise SystemExit(2)

       test(namespace)

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

   #
   # Simple example which uses a pool of workers to carry out some tasks.
   #
   # Notice that the results will probably not come out of the output
   # queue in the same in the same order as the corresponding tasks were
   # put on the input queue.  If it is important to get the results back
   # in the original order then consider using `Pool.map()` or
   # `Pool.imap()` (which will save on the amount of code needed anyway).
   #
   # Copyright (c) 2006-2008, R Oudkerk
   # All rights reserved.
   #

   import time
   import random

   from multiprocessing import Process, Queue, current_process, freeze_support

   #
   # Function run by worker processes
   #

   def worker(input, output):
       for func, args in iter(input.get, 'STOP'):
           result = calculate(func, args)
           output.put(result)

   #
   # Function used to calculate result
   #

   def calculate(func, args):
       result = func(*args)
       return '%s says that %s%s = %s' % \
           (current_process().name, func.__name__, args, result)

   #
   # Functions referenced by tasks
   #

   def mul(a, b):
       time.sleep(0.5*random.random())
       return a * b

   def plus(a, b):
       time.sleep(0.5*random.random())
       return a + b

   #
   #
   #

   def test():
       NUMBER_OF_PROCESSES = 4
       TASKS1 = [(mul, (i, 7)) for i in range(20)]
       TASKS2 = [(plus, (i, 8)) for i in range(10)]

       # Create queues
       task_queue = Queue()
       done_queue = Queue()

       # Submit tasks
       for task in TASKS1:
           task_queue.put(task)

       # Start worker processes
       for i in range(NUMBER_OF_PROCESSES):
           Process(target=worker, args=(task_queue, done_queue)).start()

       # Get and print results
       print('Unordered results:')
       for i in range(len(TASKS1)):
           print('\t', done_queue.get())

       # Add more tasks using `put()`
       for task in TASKS2:
           task_queue.put(task)

       # Get and print some more results
       for i in range(len(TASKS2)):
           print('\t', done_queue.get())

       # Tell child processes to stop
       for i in range(NUMBER_OF_PROCESSES):
           task_queue.put('STOP')


   if __name__ == '__main__':
       freeze_support()
       test()

An example of how a pool of worker processes can each run a
``SimpleHTTPRequestHandler`` instance while sharing a single listening
socket.

   #
   # Example where a pool of http servers share a single listening socket
   #
   # On Windows this module depends on the ability to pickle a socket
   # object so that the worker processes can inherit a copy of the server
   # object.  (We import `multiprocessing.reduction` to enable this pickling.)
   #
   # Not sure if we should synchronize access to `socket.accept()` method by
   # using a process-shared lock -- does not seem to be necessary.
   #
   # Copyright (c) 2006-2008, R Oudkerk
   # All rights reserved.
   #

   import os
   import sys

   from multiprocessing import Process, current_process, freeze_support
   from http.server import HTTPServer
   from http.server import SimpleHTTPRequestHandler

   if sys.platform == 'win32':
       import multiprocessing.reduction    # make sockets pickable/inheritable


   def note(format, *args):
       sys.stderr.write('[%s]\t%s\n' % (current_process().name, format % args))


   class RequestHandler(SimpleHTTPRequestHandler):
       # we override log_message() to show which process is handling the request
       def log_message(self, format, *args):
           note(format, *args)

   def serve_forever(server):
       note('starting server')
       try:
           server.serve_forever()
       except KeyboardInterrupt:
           pass


   def runpool(address, number_of_processes):
       # create a single server object -- children will each inherit a copy
       server = HTTPServer(address, RequestHandler)

       # create child processes to act as workers
       for i in range(number_of_processes - 1):
           Process(target=serve_forever, args=(server,)).start()

       # main process also acts as a worker
       serve_forever(server)


   def test():
       DIR = os.path.join(os.path.dirname(__file__), '..')
       ADDRESS = ('localhost', 8000)
       NUMBER_OF_PROCESSES = 4

       print('Serving at http://%s:%d using %d worker processes' % \
             (ADDRESS[0], ADDRESS[1], NUMBER_OF_PROCESSES))
       print('To exit press Ctrl-' + ['C', 'Break'][sys.platform=='win32'])

       os.chdir(DIR)
       runpool(ADDRESS, NUMBER_OF_PROCESSES)


   if __name__ == '__main__':
       freeze_support()
       test()

Some simple benchmarks comparing ``multiprocessing`` with
``threading``:

   #
   # Simple benchmarks for the multiprocessing package
   #
   # Copyright (c) 2006-2008, R Oudkerk
   # All rights reserved.
   #

   import time
   import sys
   import multiprocessing
   import threading
   import queue
   import gc

   if sys.platform == 'win32':
       _timer = time.clock
   else:
       _timer = time.time

   delta = 1


   #### TEST_QUEUESPEED

   def queuespeed_func(q, c, iterations):
       a = '0' * 256
       c.acquire()
       c.notify()
       c.release()

       for i in range(iterations):
           q.put(a)

       q.put('STOP')

   def test_queuespeed(Process, q, c):
       elapsed = 0
       iterations = 1

       while elapsed < delta:
           iterations *= 2

           p = Process(target=queuespeed_func, args=(q, c, iterations))
           c.acquire()
           p.start()
           c.wait()
           c.release()

           result = None
           t = _timer()

           while result != 'STOP':
               result = q.get()

           elapsed = _timer() - t

           p.join()

       print(iterations, 'objects passed through the queue in', elapsed, 'seconds')
       print('average number/sec:', iterations/elapsed)


   #### TEST_PIPESPEED

   def pipe_func(c, cond, iterations):
       a = '0' * 256
       cond.acquire()
       cond.notify()
       cond.release()

       for i in range(iterations):
           c.send(a)

       c.send('STOP')

   def test_pipespeed():
       c, d = multiprocessing.Pipe()
       cond = multiprocessing.Condition()
       elapsed = 0
       iterations = 1

       while elapsed < delta:
           iterations *= 2

           p = multiprocessing.Process(target=pipe_func,
                                       args=(d, cond, iterations))
           cond.acquire()
           p.start()
           cond.wait()
           cond.release()

           result = None
           t = _timer()

           while result != 'STOP':
               result = c.recv()

           elapsed = _timer() - t
           p.join()

       print(iterations, 'objects passed through connection in',elapsed,'seconds')
       print('average number/sec:', iterations/elapsed)


   #### TEST_SEQSPEED

   def test_seqspeed(seq):
       elapsed = 0
       iterations = 1

       while elapsed < delta:
           iterations *= 2

           t = _timer()

           for i in range(iterations):
               a = seq[5]

           elapsed = _timer() - t

       print(iterations, 'iterations in', elapsed, 'seconds')
       print('average number/sec:', iterations/elapsed)


   #### TEST_LOCK

   def test_lockspeed(l):
       elapsed = 0
       iterations = 1

       while elapsed < delta:
           iterations *= 2

           t = _timer()

           for i in range(iterations):
               l.acquire()
               l.release()

           elapsed = _timer() - t

       print(iterations, 'iterations in', elapsed, 'seconds')
       print('average number/sec:', iterations/elapsed)


   #### TEST_CONDITION

   def conditionspeed_func(c, N):
       c.acquire()
       c.notify()

       for i in range(N):
           c.wait()
           c.notify()

       c.release()

   def test_conditionspeed(Process, c):
       elapsed = 0
       iterations = 1

       while elapsed < delta:
           iterations *= 2

           c.acquire()
           p = Process(target=conditionspeed_func, args=(c, iterations))
           p.start()

           c.wait()

           t = _timer()

           for i in range(iterations):
               c.notify()
               c.wait()

           elapsed = _timer() - t

           c.release()
           p.join()

       print(iterations * 2, 'waits in', elapsed, 'seconds')
       print('average number/sec:', iterations * 2 / elapsed)

   ####

   def test():
       manager = multiprocessing.Manager()

       gc.disable()

       print('\n\t######## testing Queue.Queue\n')
       test_queuespeed(threading.Thread, queue.Queue(),
                       threading.Condition())
       print('\n\t######## testing multiprocessing.Queue\n')
       test_queuespeed(multiprocessing.Process, multiprocessing.Queue(),
                       multiprocessing.Condition())
       print('\n\t######## testing Queue managed by server process\n')
       test_queuespeed(multiprocessing.Process, manager.Queue(),
                       manager.Condition())
       print('\n\t######## testing multiprocessing.Pipe\n')
       test_pipespeed()

       print()

       print('\n\t######## testing list\n')
       test_seqspeed(list(range(10)))
       print('\n\t######## testing list managed by server process\n')
       test_seqspeed(manager.list(list(range(10))))
       print('\n\t######## testing Array("i", ..., lock=False)\n')
       test_seqspeed(multiprocessing.Array('i', list(range(10)), lock=False))
       print('\n\t######## testing Array("i", ..., lock=True)\n')
       test_seqspeed(multiprocessing.Array('i', list(range(10)), lock=True))

       print()

       print('\n\t######## testing threading.Lock\n')
       test_lockspeed(threading.Lock())
       print('\n\t######## testing threading.RLock\n')
       test_lockspeed(threading.RLock())
       print('\n\t######## testing multiprocessing.Lock\n')
       test_lockspeed(multiprocessing.Lock())
       print('\n\t######## testing multiprocessing.RLock\n')
       test_lockspeed(multiprocessing.RLock())
       print('\n\t######## testing lock managed by server process\n')
       test_lockspeed(manager.Lock())
       print('\n\t######## testing rlock managed by server process\n')
       test_lockspeed(manager.RLock())

       print()

       print('\n\t######## testing threading.Condition\n')
       test_conditionspeed(threading.Thread, threading.Condition())
       print('\n\t######## testing multiprocessing.Condition\n')
       test_conditionspeed(multiprocessing.Process, multiprocessing.Condition())
       print('\n\t######## testing condition managed by a server process\n')
       test_conditionspeed(multiprocessing.Process, manager.Condition())

       gc.enable()

   if __name__ == '__main__':
       multiprocessing.freeze_support()
       test()
