Doc/library/multiprocessing.rst

:mod:`multiprocessing` --- Process-based "threading" interface
==============================================================

.. module:: multiprocessing
   :synopsis: Process-based "threading" interface.

:mod:`multiprocessing` is a package for the Python language which supports the
spawning of processes using a similar API of the :mod:`threading` module.  It
runs on both Unix and Windows.

The :mod:`multiprocessing` module offers the capability of both local and remote
concurrency effectively side-stepping the Global Interpreter Lock by utilizing
subprocesses for "threads".  Due to this, the :mod:`multiprocessing` module
allows the programmer to fully leverage multiple processors on a given machine.


Introduction
------------


Threads, processes and the GIL
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

To run more than one piece of code at the same time on the same computer one has
the choice of either using multiple processes or multiple threads.

Although a program can be made up of multiple processes, these processes are in
effect completely independent of one another: different processes are not able
to cooperate with one another unless one sets up some means of communication
between them (such as by using sockets).  If a lot of data must be transferred
between processes then this can be inefficient.

On the other hand, multiple threads within a single process are intimately
connected: they share their data but often can interfere badly with one another.
It is often argued that the only way to make multithreaded programming "easy" is
to avoid relying on any shared state and for the threads to only communicate by
passing messages to each other.

CPython has a *Global Interpreter Lock* (GIL) which in many ways makes threading
easier than it is in most languages by making sure that only one thread can
manipulate the interpreter's objects at a time.  As a result, it is often safe
to let multiple threads access data without using any additional locking as one
would need to in a language such as C.

One downside of the GIL is that on multi-processor (or multi-core) systems a
multithreaded Python program can only make use of one processor at a time unless
your application makes heavy use of I/O which effectively side-steps this.  This
is a problem that can be overcome by using multiple processes instead.

This package allows one to write multi-process programs using much the same API
that one uses for writing threaded programs.


Forking and spawning
~~~~~~~~~~~~~~~~~~~~

There are two ways of creating a new process in Python:

* The current process can *fork* a new child process by using the
  :func:`os.fork` function.  This effectively creates an identical copy of the
  current process which is now able to go off and perform some task set by the
  parent process.  This means that the child process inherits *copies* of all
  variables that the parent process had.  However, :func:`os.fork` is not
  available on every platform: in particular Windows does not support it.

* Alternatively, the current process can spawn a completely new Python
  interpreter by using the :mod:`subprocess` module or one of the
  :func:`os.spawn*` functions.  Getting this new interpreter in to a fit state
  to perform the task set for it by its parent process is, however, a bit of a
  challenge.

The :mod:`multiprocessing` module uses :func:`os.fork` if it is available since
it makes life a lot simpler.  Forking the process is also more efficient in
terms of memory usage and the time needed to create the new process.


The :class:`Process` class
~~~~~~~~~~~~~~~~~~~~~~~~~~

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

   from multiprocessing import Process

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

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

Here the function ``f`` is run in a child process.

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



Exchanging objects between processes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

**Queues**

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

      from multiprocessing import Process, Queue

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

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

   Queues are thread and process safe.

**Pipes**

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

      from multiprocessing import Process, Pipe

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

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

   The two connection objects returned by :func:`Pipe` represent the two ends of
   the pipe.  Each connection object has :meth:`send` and :meth:`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
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

   from multiprocessing import Process, Lock

   def f(l, i):
       l.acquire()
       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
:mod:`multiprocessing` provides a couple of ways of doing so.

**Shared memory**

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

      from multiprocessing import Process, Value, Array

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

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

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

          print num.value
          print arr[:]

   will print ::

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

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

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

**Server process**

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

   A manager returned by :func:`Manager` will support types :class:`list`,
   :class:`dict`, :class:`Namespace`, :class:`Lock`, :class:`RLock`,
   :class:`Semaphore`, :class:`BoundedSemaphore`, :class:`Condition`,
   :class:`Event`, :class:`Queue`, :class:`Value` and :class:`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 :class:`multiprocessing.pool.Pool()` class represens 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.applyAsync(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 :mod:`multiprocessing` package mostly replicates the API of the
:mod:`threading` module.


:class:`Process` and exceptions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. class:: Process([group[, target[, name[, args[, kwargs]]]]])

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

   The constructor should always be called with keyword arguments. *group*
   should always be ``None``; it exists soley for compatibility with
   :class:`threading.Thread`.  *target* is the callable object to be invoked by
   the :meth:`run()` method.  It defaults to None, meaning nothing is
   called. *name* is the process name.  By default, a unique name is constructed
   of the form 'Process-N\ :sub:`1`:N\ :sub:`2`:...:N\ :sub:`k`' where N\
   :sub:`1`,N\ :sub:`2`,...,N\ :sub:`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 (:meth:`Process.__init__`) before doing anything else
   to the process.

   .. method:: run()

      Method representing the process's activity.

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

   .. method:: start()

      Start the process's activity.

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

   .. method:: join([timeout])

      Block the calling thread until the process whose :meth:`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.

   .. method:: get_name()

      Return the process's name.

   .. method:: set_name(name)

      Set 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.

   .. method:: is_alive()

      Return whether the process is alive.

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

   .. method:: is_daemon()

      Return the process's daemon flag.

   .. method:: set_daemon(daemonic)

      Set the process's daemon flag to the Boolean value *daemonic*.  This must
      be called before :meth:`start` is called.

      The initial value is inherited from the creating process.

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

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

   In addition process objects also support the following methods:

   .. method:: get_pid()

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

   .. method:: get_exit_code()

      Return 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*.

   .. method:: get_auth_key()

      Return the process's authentication key (a byte string).

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

      When a :class:`Process` object is created, it will inherit the
      authentication key of its parent process, although this may be changed
      using :meth:`set_auth_key` below.

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

   .. method:: set_auth_key(authkey)

      Set the process's authentication key which must be a byte string.

   .. method:: 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 :meth:`start`, :meth:`join`, :meth:`is_alive` and
   :meth:`get_exit_code` methods should only be called by the process that
   created the process object.

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

       >>> import processing, time, signal
       >>> p = processing.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()
       >>> print p, p.is_alive()
       <Process(Process-1, stopped[SIGTERM])> False
       >>> p.get_exit_code() == -signal.SIGTERM
       True


.. exception:: BufferTooShort

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

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


Pipes and Queues
~~~~~~~~~~~~~~~~

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

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

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

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

.. note::

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


.. warning::

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

.. warning::

   As mentioned above, if a child process has put items on a queue (and it has
   not used :meth:`JoinableQueue.cancel_join_thread`), 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 it non-daemonic children.

   Note that a queue created using a manager does not have this issue.  See
   :ref:`multiprocessing-programming`.

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

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


.. function:: Pipe([duplex])

   Returns a pair ``(conn1, conn2)`` of :class:`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:: 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 :exc:`Queue.Empty` and :exc:`Queue.Full` exceptions from the
   standard library's :mod:`Queue` module are raised to signal timeouts.

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

   .. method:: qsize()

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

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

   .. method:: empty()

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

   .. method:: full()

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

   .. method:: put(item[, block[, timeout]])`

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

   .. method:: put_nowait(item)

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

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

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

   .. method:: get_nowait()
               get_no_wait()

      Equivalent to ``get(False)``.

   :class:`multiprocessing.Queue` has a few additional methods not found in
   :class:`Queue.Queue` which are usually unnecessary:

   .. method:: close()

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

   .. method:: join_thread()

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

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

   .. method:: cancel_join_thread()

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


.. class:: JoinableQueue([maxsize])

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

   .. method:: task_done()

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

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

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


   .. method:: join()

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

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


Miscellaneous
~~~~~~~~~~~~~

.. function:: 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.

.. function:: cpu_count()

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

.. function:: current_process()

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

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

.. function:: freeze_support()

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

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

      from multiprocessing import Process, freeze_support

      def f():
          print 'hello world!'

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

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

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

.. function:: 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 ::

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

    before they can create child processes.  (Windows only)


.. note::

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


Connection Objects
~~~~~~~~~~~~~~~~~~

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

Connection objects usually created using :func:`Pipe()` -- see also
:ref:`multiprocessing-listeners-clients`.

.. class:: Connection

   .. method:: send(obj)

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

      The object must be picklable.

   .. method:: recv()

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

   .. method:: fileno()

      Returns the file descriptor or handle used by the connection.

   .. method:: close()

      Close the connection.

      This is called automatically when the connection is garbage collected.

   .. method:: poll([timeout])

      Return whether there is any data available to be read.

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

   .. method:: 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.

   .. method:: recv_bytes([maxlength])

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

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

   .. method:: recv_bytes_into(buffer[, offset])

      Read into *buffer* a complete message of byte data sent from the other end
      of the connection and return the number of bytes in the message.  Raises
      :exc:`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 :exc:`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('thank you')
    >>> a.recv_bytes()
    'thank you'
    >>> import array
    >>> arr1 = array.array('i', range(5))
    >>> arr2 = array.array('i', [0] * 10)
    >>> a.send_bytes(arr1)
    >>> count = b.recv_bytes_into(arr2)
    >>> assert count == len(arr1) * arr1.itemsize
    >>> arr2
    array('i', [0, 1, 2, 3, 4, 0, 0, 0, 0, 0])


.. warning::

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

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

.. warning::

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


Synchronization primitives
~~~~~~~~~~~~~~~~~~~~~~~~~~

Generally synchronization primitives are not as necessary in a multiprocess
program as they are in a mulithreaded program.  See the documentation for the
standard library's :mod:`threading` module.

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

.. class:: BoundedSemaphore([value])

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

   (On Mac OSX this is indistiguishable from :class:`Semaphore` because
   ``sem_getvalue()`` is not implemented on that platform).

.. class:: Condition([lock])

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

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

.. class:: Event()

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

.. class:: Lock()

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

.. class:: RLock()

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

.. class:: Semaphore([value])

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

.. note::

   The :meth:`acquire()` method of :class:`BoundedSemaphore`, :class:`Lock`,
   :class:`RLock` and :class:`Semaphore` has a timeout parameter not supported
   by the equivalents in :mod:`threading`.  The signature is
   ``acquire(block=True, timeout=None)`` with keyword parameters being
   acceptable.  If *block* is ``True`` and *timeout* is not ``None`` then it
   specifies a timeout in seconds.  If *block* is ``False`` then *timeout* is
   ignored.

.. note::

   If the SIGINT signal generated by Ctrl-C arrives while the main thread is
   blocked by a call to :meth:`BoundedSemaphore.acquire`, :meth:`Lock.acquire`,
   :meth:`RLock.acquire`, :meth:`Semaphore.acquire`, :meth:`Condition.acquire`
   or :meth:`Condition.wait` then the call will be immediately interrupted and
   :exc:`KeyboardInterrupt` will be raised.

   This differs from the behaviour of :mod:`threading` where SIGINT will be
   ignored while the equivalent blocking calls are in progress.


Shared :mod:`ctypes` Objects
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

.. function:: Value(typecode_or_type[, lock[, *args]])

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

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

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

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

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

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

   Note that *lock* is a keyword only argument.

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


The :mod:`multiprocessing.sharedctypes` module
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

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

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

.. note::

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

.. function:: RawArray(typecode_or_type, size_or_initializer)

   Return a ctypes array allocated from shared memory.

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

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

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

   Return a ctypes object allocated from shared memory.

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

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

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

.. function:: Array(typecode_or_type, size_or_initializer[, lock[, *args]])

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

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

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

.. function:: Value(typecode_or_type, *args[, lock])

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

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

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

.. function:: copy(obj)

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

.. function:: synchronized(obj[, lock])

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

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

   Note that accessing the ctypes object through the wrapper can be a lot slower
   han 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 :class:`ctypes.Structure`.)

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


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

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

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

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

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

       n = Value('i', 7)
       x = Value(ctypes.c_double, 1.0/3.0, lock=False)
       s = Array('c', '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]


.. highlightlang:: none

The results printed are ::

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

.. highlightlang:: python


.. _multiprocessing-managers:

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.

.. function:: multiprocessing.Manager()

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

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

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

.. class:: BaseManager([address[, authkey]])

   Create a BaseManager object.

   Once created one should call :meth:`start` or :meth:`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().get_auth_key()``.  Otherwise *authkey* is used and it
   must be a string.

   .. method:: start()

      Start a subprocess to start the manager.

   .. method:: server_forever()

      Run the server in the current process.

   .. method:: from_address(address, authkey)

      A class method which creates a manager object referring to a pre-existing
      server process which is using the given address and authentication key.

   .. method:: shutdown()

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

      This can be called multiple times.

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

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

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

      *callable* is a callable used for creating objects for this type
      identifier.  If a manager instance will be created using the
      :meth:`from_address()` classmethod or if the *create_method* argument is
      ``False`` then this can be left as ``None``.

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

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

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

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

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

   .. attribute:: address

      The address used by the manager.


.. class:: SyncManager

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

   It also supports creation of shared lists and dictionaries.

   .. method:: BoundedSemaphore([value])

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

   .. method:: Condition([lock])

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

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

   .. method:: Event()

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

   .. method:: Lock()

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

   .. method:: Namespace()

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

   .. method:: Queue([maxsize])

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

   .. method:: RLock()

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

   .. method:: Semaphore([value])

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

   .. method:: Array(typecode, sequence)

      Create an array and return a proxy for it.  (*format* is ignored.)

   .. method:: Value(typecode, value)

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

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

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

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

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


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 :class:`BaseManager` and
use the :meth:`resgister()` classmethod to register new types or callables with
the manager class.  For example::

   from multiprocessing.managers import BaseManager

   class MathsClass(object):
       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('getQueue', callable=lambda:queue)
   >>> m = QueueManager(address=('', 50000), authkey='abracadabra')
   >>> m.serveForever()

One client can access the server as follows::

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

Another client can also use it::

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


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 0xb799974c>
   >>> l[4]
   16
   >>> l[2:5]
   [4, 9, 16]

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

An important feature of proxy objects is that they are picklable so they can be
passed between processes.  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 :mod:`multiprocessing` do nothing to support comparisons
   by value.  So, for instance, ::

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

   will return ``False``.  One should just use a copy of the referent instead
   when making comparisons.

.. class:: BaseProxy

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

   .. method:: _call_method(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._call_method(methodname, args, kwds)

      will evaluate the expression ::

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

      in the manager's process.

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

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

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

      An example of the usage of :meth:`_call_method()`::

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

   .. method:: _get_value()

      Return a copy of the referent.

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

   .. method:: __repr__

      Return a representation of the proxy object.

   .. method:: __str__

      Return the representation of the referent.


Cleanup
>>>>>>>

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

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


Process Pools
~~~~~~~~~~~~~

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

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

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

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

   *processes* is the number of worker processes to use.  If *processes* is
   ``None`` then the number returned by :func:`cpu_count` is used.  If
   *initializer* is not ``None`` then each worker process will call
   ``initializer(*initargs)`` when it starts.

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

      Equivalent of the :func:`apply` builtin function.  It blocks till the
      result is ready.

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

      A variant of the :meth:`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 (unless the call failed).  *callback* should complete immediately since
      otherwise the thread which handles the results will get blocked.

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

      A parallel equivalent of the :func:`map` builtin function.  It blocks till
      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.

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

      A variant of the :meth:`.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 (unless the call failed).  *callback* should complete immediately since
      otherwise the thread which handles the results will get blocked.

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

      An equivalent of :func:`itertools.imap`.

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

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

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

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

   .. method:: close()

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

   .. method:: terminate()

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

   .. method:: join()

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


.. class:: AsyncResult

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

   .. method:: get([timeout)

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

   .. method:: wait([timeout])

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

   .. method:: ready()

      Return whether the call has completed.

   .. method:: successful()

      Return whether the call completed without raising an exception.  Will
      raise :exc:`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.applyAsync(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 it.next()                       # prints "0"
       print it.next()                       # prints "1"
       print it.next(timeout=1)              # prints "4" unless your computer is *very* slow

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


.. _multiprocessing-listeners-clients:

Listeners and Clients
~~~~~~~~~~~~~~~~~~~~~

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

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

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


.. function:: deliver_challenge(connection, authkey)

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

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

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

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

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

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

   If *authentication* is ``True`` or *authkey* is a string then digest
   authentication is used.  The key used for authentication will be either
   *authkey* or ``current_process().get_auth_key()`` if *authkey* is ``None``.
   If authentication fails then :exc:`AuthenticationError` is raised.  See
   :ref:`multiprocessing-auth-keys`.

.. class:: 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.

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

   If the listener object uses a socket then *backlog* (1 by default) is passed
   to the :meth:`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 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().get_auth_key()`` is used as the authentication key.  If
   *authkey* is ``None`` and *authentication* is ``False`` then no
   authentication is done.  If authentication fails then
   :exc:`AuthenticationError` is raised.  See :ref:`multiprocessing-auth-keys`.

   .. method:: accept()

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

   .. method:: close()

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

   Listener objects have the following read-only properties:

   .. attribute:: address

      The address which is being used by the Listener object.

   .. attribute:: last_accepted

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


The module defines two exceptions:

.. exception:: AuthenticationError

   Exception raised when there is an authentication error.

.. exception:: BufferTooShort

   Exception raise by the :meth:`Connection.recv_bytes_into` method of a
   connection object when the supplied buffer object is too small for the
   message read.

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


**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='secret password')

   conn = listener.accept()
   print 'connection accepted from', listener.last_accepted

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

   conn.send_bytes('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='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()


.. _multiprocessing-address-formats:

Address Formats
>>>>>>>>>>>>>>>

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

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

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

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


.. _multiprocessing-auth-keys:

Authentication keys
~~~~~~~~~~~~~~~~~~~

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

An authentication key is a 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 do authentication key is specified then the
return value of ``current_process().get_auth_key`` is used (see
:class:`Process`).  This value will automatically inherited by any
:class:`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 the
themselves.

Suitable authentication keys can also be generated by using :func:`os.urandom`.


Logging
~~~~~~~

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

.. currentmodule:: multiprocessing
.. function:: get_logger()

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

   When first created the logger has level :data:`logging.NOTSET` and has a
   handler which sends output to :data:`sys.stderr` using format
   ``'[%(levelname)s/%(processName)s] %(message)s'``.  (The logger allows use of
   the non-standard ``'%(processName)s'`` format.)  Message sent to this logger
   will not by default propogate 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.

Below is an example session with logging turned on::

    >>> import processing, logging
    >>> logger = processing.getLogger()
    >>> logger.setLevel(logging.INFO)
    >>> logger.warning('doomed')
    [WARNING/MainProcess] doomed
    >>> m = processing.Manager()
    [INFO/SyncManager-1] child process calling self.run()
    [INFO/SyncManager-1] manager bound to '\\\\.\\pipe\\pyc-2776-0-lj0tfa'
    >>> del m
    [INFO/MainProcess] sending shutdown message to manager
    [INFO/SyncManager-1] manager exiting with exitcode 0


The :mod:`multiprocessing.dummy` module
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

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


.. _multiprocessing-programming:

Programming guidelines
----------------------

There are certain guidelines and idioms which should be adhered to when using
:mod:`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 :mod:`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
    :func:`active_children` is called) all completed processes which have not
    yet been joined will be joined.  Also calling a finished process's
    :meth:`Process.is_alive` 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 of types from :mod:`multiprocessing` need to be picklable so
    that child processes can use them.  However, one should generally avoid
    sending shared objects to other processes using pipes or queues.  Instead
    you should arrange the program so that a process which need access to a
    shared resource created elsewhere can inherit it from an ancestor process.

Avoid terminating processes

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

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

Joining processes that use queues

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

Explicity 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()


Windows
~~~~~~~

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

More picklability

    Ensure that all arguments to :meth:`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 :class:`Process` then make sure that instances will be
    picklable when the :meth:`Process.start` method is called.

Global variables

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

        from multiprocessing import Process

        def foo():
            print 'hello'

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

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

       from multiprocessing import Process, freeze_support

       def foo():
           print 'hello'

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

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

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

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


.. _multiprocessing-examples:

Examples
--------

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

.. literalinclude:: ../includes/mp_newtype.py


Using :class:`Pool`:

.. literalinclude:: ../includes/mp_pool.py


Synchronization types like locks, conditions and queues:

.. literalinclude:: ../includes/mp_synchronize.py


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

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


An example of how a pool of worker processes can each run a
:class:`SimpleHTTPServer.HttpServer` instance while sharing a single listening
socket.

.. literalinclude:: ../includes/mp_webserver.py


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

.. literalinclude:: ../includes/mp_benchmarks.py

An example/demo of how to use the :class:`managers.SyncManager`, :class:`Process`
and others to build a system which can distribute processes and work via a 
distributed queue to a "cluster" of machines on a network, accessible via SSH.
You will need to have private key authentication for all hosts configured for
this to work.

.. literalinclude:: ../includes/mp_distributing.py