The :class:`Process` class

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

from multiprocessing import Process

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

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

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

from multiprocessing import Process
import os

def info(title):
    print title
    print 'module name:', __name__
    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 :ref:`multiprocessing-programming`.

Exchanging objects between processes

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

Queues

The :class:`~multiprocessing.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:`~Connection.send` and :meth:`~Connection.recv` methods (among others). Note that data in a pipe may become corrupted if two processes (or threads) try to read from or write to the same end of the pipe at the same time. Of course there is no risk of corruption from processes using different ends of the pipe at the same time.

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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' indicates a signed integer. These shared objects will be process and thread-safe.

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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.

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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.

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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:`~multiprocessing.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 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]"

Note that the methods of a pool should only ever be used by the process which created it.

:class:`Process` and exceptions

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

The constructor should always be called with keyword arguments. group should always be None; it exists solely for compatibility with :class:`threading.Thread`. target is the callable object to be invoked by the :meth:`run()` method. It defaults to None, meaning nothing is called. name is the process name. By default, a unique name is constructed of the form 'Process-N₁:N₂:...:N_k' where N₁,N₂,...,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 (: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.

.. attribute:: name

   The process's name.

   The name is a string used for identification purposes only.  It has no
   semantics.  Multiple processes may be given the same name.  The initial
   name is set by the constructor.

.. method:: is_alive

   Return whether the process is alive.

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

.. attribute:: daemon

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

   The initial value is inherited from the creating process.

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

   Note that a daemonic process is not allowed to create child processes.
   Otherwise a daemonic process would leave its children orphaned if it gets
   terminated when its parent process exits. Additionally, these are **not**
   Unix daemons or services, they are normal processes that will be
   terminated (and not joined) if non-daemonic processes have exited.

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

.. attribute:: pid

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

.. attribute:: exitcode

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

.. attribute:: authkey

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

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

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

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

.. method:: terminate()

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

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

   .. warning::

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

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

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

.. doctest::

    >>> import multiprocessing, time, signal
    >>> 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:: 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:`~multiprocessing.Queue`, :class:`multiprocessing.queues.SimpleQueue` 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:`~multiprocessing.Queue` lacks the :meth:`~Queue.Queue.task_done` and :meth:`~Queue.Queue.join` methods introduced into Python 2.5's :class:`Queue.Queue` class.

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

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.

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

.. method:: qsize()

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

   Note that this may raise :exc:`NotImplementedError` on Unix platforms like
   Mac OS 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(obj[, block[, timeout]])

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

.. method:: put_nowait(obj)

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

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

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

.. method:: get_nowait()

   Equivalent to ``get(False)``.

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

.. method:: close()

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

.. method:: join_thread()

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

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

.. method:: cancel_join_thread()

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

   A better name for this method might be
   ``allow_exit_without_flush()``.  It is likely to cause enqueued
   data to lost, and you almost certainly will not need to use it.
   It is really only there if you need the current process to exit
   immediately without waiting to flush enqueued data to the
   underlying pipe, and you don't care about lost data.

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

.. method:: empty()

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

.. method:: get()

   Remove and return an item from the queue.

.. method:: put(item)

   Put *item* into the queue.

:class:`JoinableQueue`, a :class:`~multiprocessing.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:`~Queue.get` used to fetch a task, a subsequent
   call to :meth:`task_done` tells the queue that the processing on the task
   is complete.

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

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

.. method:: join()

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

   The count of unfinished tasks goes up whenever an item is added to the
   queue.  The count goes down whenever a consumer 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:`~Queue.Queue.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 ``freeze_support()`` line is omitted 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 :data:`sys.executable` is used).  Embedders will probably need to
   do some thing like ::

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

   before they can create child processes.  (Windows only)

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 :func:`Pipe` -- see also :ref:`multiprocessing-listeners-clients`.

.. method:: send(obj)

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

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

.. method:: recv()

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

.. method:: fileno()

   Return the file descriptor or handle used by the connection.

.. method:: close()

   Close the connection.

   This is called automatically when the connection is garbage collected.

.. method:: poll([timeout])

   Return whether there is any data available to be read.

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

.. 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.  Very large
   buffers (approximately 32 MB+, though it depends on the OS) may raise a
   :exc:`ValueError` exception

.. method:: recv_bytes([maxlength])

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

   If *maxlength* is specified and the message is longer than *maxlength*
   then :exc:`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.  Blocks
   until there is something to receive.  Raises
   :exc:`EOFError` if there is nothing left to receive and the other end was
   closed.

   *buffer* must be 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:

.. doctest::

    >>> 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])

Synchronization primitives

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

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

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

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

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

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

A clone of :class:`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.

.. versionchanged:: 2.7
   Previously, the method always returned ``None``.

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

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

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

Shared :mod:`ctypes` Objects

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

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

   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 *raw*
   attributes which allow one to use it to store and retrieve strings.

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

.. function:: RawArray(typecode_or_type, size_or_initializer)

   Return a ctypes array allocated from shared memory.

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

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

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

   Return a ctypes object allocated from shared memory.

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

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

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

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

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

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

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

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

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

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

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

.. function:: copy(obj)

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

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

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

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

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

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', '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

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

.. highlightlang:: python

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

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

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

Create a BaseManager object.

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

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

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

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

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

.. method:: get_server()

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

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

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

.. method:: connect()

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

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

.. method:: shutdown()

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

   This can be called multiple times.

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

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

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

   *callable* is a callable used for creating objects for this type
   identifier.  If a manager instance will be 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:`BaseProxy` which is used to create
   proxies for shared objects with this *typeid*.  If ``None`` then a proxy
   class is created automatically.

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

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

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

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

.. attribute:: address

   The address used by the manager.

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

.. method:: RLock()

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

.. method:: Semaphore([value])

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

.. method:: Array(typecode, sequence)

   Create an array and return a proxy for it.

.. method:: Value(typecode, value)

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

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

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

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

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

# 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

.. doctest::

   >>> 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')

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

>>> 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='abracadabra')
>>> s = m.get_server()
>>> s.serve_forever()

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

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

>>> 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='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:

.. doctest::

   >>> 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 :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:

.. doctest::

   >>> 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']

.. doctest::

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

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

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

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

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

      proxy._callmethod(methodname, args, kwds)

   will evaluate the expression ::

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

   in the manager's process.

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

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

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

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

   .. doctest::

      >>> l = manager.list(range(10))
      >>> l._callmethod('__len__')
      10
      >>> l._callmethod('__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

.. method:: _getvalue()

   Return a copy of the referent.

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

.. method:: __repr__

   Return a representation of the proxy object.

.. method:: __str__

   Return the representation of the referent.

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.

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.

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

.. versionadded:: 2.7
   *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.

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

   Equivalent of the :func:`apply` built-in function.  It blocks until the
   result is ready, so :meth:`apply_async` is better suited for performing
   work in parallel. Additionally, *func* is only executed in one of the
   workers of the pool.

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

   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` 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.

.. 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 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`.

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

Listeners and Clients

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

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

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

.. 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:: answer_challenge(connection, authkey)

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

   If a welcome message is not received, then :exc:`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:`~multiprocessing.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 *authenticate* is ``True`` or *authkey* is a 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 :exc:`AuthenticationError` is raised.  See
   :ref:`multiprocessing-auth-keys`.

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

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

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

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

If 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().authkey is used as the authentication key. If authkey is None and authenticate 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:`~multiprocessing.Connection` object.  If
   authentication is attempted and fails, then
   :exc:`~multiprocessing.AuthenticationError` is raised.

.. method:: close()

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

Listener objects have the following read-only properties:

.. attribute:: address

   The address which is being used by the Listener object.

.. attribute:: last_accepted

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

The module defines two exceptions:

.. exception:: 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='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()

Authentication keys

When one uses :meth:`Connection.recv <multiprocessing.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().authkey is used (see :class:`~multiprocessing.Process`). This value will automatically inherited by any :class:`~multiprocessing.Process` object that the current process creates. This means that (by default) all processes of a multi-process program will share a single authentication key which can be used when setting up connections between themselves.

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

Logging

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

.. currentmodule:: multiprocessing

.. function:: get_logger()

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

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

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

.. currentmodule:: multiprocessing

.. function:: log_to_stderr()

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

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 :const:`SUBWARNING` and :const:`SUBDEBUG`. The table below illustrates where theses fit in the normal level hierarchy.

For a full table of logging levels, see the :mod:`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 :const:`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 :mod:`multiprocessing.dummy` module

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

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

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.

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

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Better to inherit than pickle/unpickle

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

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Avoid terminating processes

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

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

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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:`~multiprocessing.Queue.cancel_join_thread` method of the queue to avoid this behaviour.)

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

:mod:`multiprocessing` originally unconditionally called:

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

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

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

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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`

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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.

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

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Global variables

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

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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`:

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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.