"multiprocessing" — Process-based parallelism
*********************************************

**Source code:** Lib/multiprocessing/

======================================================================


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

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

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

   from multiprocessing import Pool

   def f(x):
       return x*x

   if __name__ == '__main__':
       with Pool(5) as p:
           print(p.map(f, [1, 2, 3]))

will print to standard output

   [1, 4, 9]


The "Process" class
-------------------

In "multiprocessing", processes are spawned by creating a "Process"
object and then calling its "start()" method.  "Process" follows the
API of "threading.Thread".  A trivial example of a multiprocess
program is

   from multiprocessing import Process

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

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

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

   from multiprocessing import Process
   import os

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

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

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

For an explanation of why the "if __name__ == '__main__'" part is
necessary, see Programming guidelines.


Contexts and start methods
--------------------------

Depending on the platform, "multiprocessing" supports three ways to
start a process.  These *start methods* are

   *spawn*
      The parent process starts a fresh python interpreter process.
      The child process will only inherit those resources necessary to
      run the process object’s "run()" method.  In particular,
      unnecessary file descriptors and handles from the parent process
      will not be inherited.  Starting a process using this method is
      rather slow compared to using *fork* or *forkserver*.

      Available on Unix and Windows.  The default on Windows and
      macOS.

   *fork*
      The parent process uses "os.fork()" to fork the Python
      interpreter.  The child process, when it begins, is effectively
      identical to the parent process.  All resources of the parent
      are inherited by the child process.  Note that safely forking a
      multithreaded process is problematic.

      Available on Unix only.  The default on Unix.

   *forkserver*
      When the program starts and selects the *forkserver* start
      method, a server process is started.  From then on, whenever a
      new process is needed, the parent process connects to the server
      and requests that it fork a new process.  The fork server
      process is single threaded so it is safe for it to use
      "os.fork()".  No unnecessary resources are inherited.

      Available on Unix platforms which support passing file
      descriptors over Unix pipes.

Changed in version 3.8: On macOS, the *spawn* start method is now the
default.  The *fork* start method should be considered unsafe as it
can lead to crashes of the subprocess. See bpo-33725.

Changed in version 3.4: *spawn* added on all unix platforms, and
*forkserver* added for some unix platforms. Child processes no longer
inherit all of the parents inheritable handles on Windows.

On Unix using the *spawn* or *forkserver* start methods will also
start a *resource tracker* process which tracks the unlinked named
system resources (such as named semaphores or "SharedMemory" objects)
created by processes of the program.  When all processes have exited
the resource tracker unlinks any remaining tracked object. Usually
there should be none, but if a process was killed by a signal there
may be some “leaked” resources.  (Neither leaked semaphores nor shared
memory segments will be automatically unlinked until the next reboot.
This is problematic for both objects because the system allows only a
limited number of named semaphores, and shared memory segments occupy
some space in the main memory.)

To select a start method you use the "set_start_method()" in the "if
__name__ == '__main__'" clause of the main module.  For example:

   import multiprocessing as mp

   def foo(q):
       q.put('hello')

   if __name__ == '__main__':
       mp.set_start_method('spawn')
       q = mp.Queue()
       p = mp.Process(target=foo, args=(q,))
       p.start()
       print(q.get())
       p.join()

"set_start_method()" should not be used more than once in the program.

Alternatively, you can use "get_context()" to obtain a context object.
Context objects have the same API as the multiprocessing module, and
allow one to use multiple start methods in the same program.

   import multiprocessing as mp

   def foo(q):
       q.put('hello')

   if __name__ == '__main__':
       ctx = mp.get_context('spawn')
       q = ctx.Queue()
       p = ctx.Process(target=foo, args=(q,))
       p.start()
       print(q.get())
       p.join()

Note that objects related to one context may not be compatible with
processes for a different context.  In particular, locks created using
the *fork* context cannot be passed to processes started using the
*spawn* or *forkserver* start methods.

A library which wants to use a particular start method should probably
use "get_context()" to avoid interfering with the choice of the
library user.

Warning:

  The "'spawn'" and "'forkserver'" start methods cannot currently be
  used with “frozen” executables (i.e., binaries produced by packages
  like **PyInstaller** and **cx_Freeze**) on Unix. The "'fork'" start
  method does work.


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

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

**Queues**

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

      from multiprocessing import Process, Queue

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

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

   Queues are thread and process safe.

**Pipes**

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

      from multiprocessing import Process, Pipe

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

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

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


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

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

   from multiprocessing import Process, Lock

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

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

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

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


Sharing state between processes
-------------------------------

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

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

**Shared memory**

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

      from multiprocessing import Process, Value, Array

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

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

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

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

   will print

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

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

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

**Server process**

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

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

      from multiprocessing import Process, Manager

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

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

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

              print(d)
              print(l)

   will print

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

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


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

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

For example:

   from multiprocessing import Pool, TimeoutError
   import time
   import os

   def f(x):
       return x*x

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

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

           # print same numbers in arbitrary order
           for i in pool.imap_unordered(f, range(10)):
               print(i)

           # evaluate "f(20)" asynchronously
           res = pool.apply_async(f, (20,))      # runs in *only* one process
           print(res.get(timeout=1))             # prints "400"

           # evaluate "os.getpid()" asynchronously
           res = pool.apply_async(os.getpid, ()) # runs in *only* one process
           print(res.get(timeout=1))             # prints the PID of that process

           # launching multiple evaluations asynchronously *may* use more processes
           multiple_results = [pool.apply_async(os.getpid, ()) for i in range(4)]
           print([res.get(timeout=1) for res in multiple_results])

           # make a single worker sleep for 10 secs
           res = pool.apply_async(time.sleep, (10,))
           try:
               print(res.get(timeout=1))
           except TimeoutError:
               print("We lacked patience and got a multiprocessing.TimeoutError")

           print("For the moment, the pool remains available for more work")

       # exiting the 'with'-block has stopped the pool
       print("Now the pool is closed and no longer available")

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

Note:

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

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

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


Reference
=========

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


"Process" and exceptions
------------------------

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

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

   The constructor should always be called with keyword arguments.
   *group* should always be "None"; it exists solely for compatibility
   with "threading.Thread".  *target* is the callable object to be
   invoked by the "run()" method.  It defaults to "None", meaning
   nothing is called. *name* is the process name (see "name" for more
   details). *args* is the argument tuple for the target invocation.
   *kwargs* is a dictionary of keyword arguments for the target
   invocation.  If provided, the keyword-only *daemon* argument sets
   the process "daemon" flag to "True" or "False".  If "None" (the
   default), this flag will be inherited from the creating process.

   By default, no arguments are passed to *target*.

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

   Changed in version 3.3: Added the *daemon* argument.

   run()

      Method representing the process’s activity.

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

   start()

      Start the process’s activity.

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

   join([timeout])

      If the optional argument *timeout* is "None" (the default), the
      method blocks until the process whose "join()" method is called
      terminates. If *timeout* is a positive number, it blocks at most
      *timeout* seconds. Note that the method returns "None" if its
      process terminates or if the method times out.  Check the
      process’s "exitcode" to determine if it terminated.

      A process can be joined many times.

      A process cannot join itself because this would cause a
      deadlock.  It is an error to attempt to join a process before it
      has been started.

   name

      The process’s name.  The name is a string used for
      identification purposes only.  It has no semantics.  Multiple
      processes may be given the same name.

      The initial name is set by the constructor.  If no explicit name
      is provided to the constructor, a name of the form
      ‘Process-N_1:N_2:…:N_k’ is constructed, where each N_k is the
      N-th child of its parent.

   is_alive()

      Return whether the process is alive.

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

   daemon

      The process’s daemon flag, a Boolean value.  This must be set
      before "start()" is called.

      The initial value is inherited from the creating process.

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

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

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

   pid

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

   exitcode

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

   authkey

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

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

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

      See Authentication keys.

   sentinel

      A numeric handle of a system object which will become “ready”
      when the process ends.

      You can use this value if you want to wait on several events at
      once using "multiprocessing.connection.wait()".  Otherwise
      calling "join()" is simpler.

      On Windows, this is an OS handle usable with the
      "WaitForSingleObject" and "WaitForMultipleObjects" family of API
      calls.  On Unix, this is a file descriptor usable with
      primitives from the "select" module.

      New in version 3.3.

   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.

   kill()

      Same as "terminate()" but using the "SIGKILL" signal on Unix.

      New in version 3.7.

   close()

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

      New in version 3.7.

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

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

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

exception multiprocessing.ProcessError

   The base class of all "multiprocessing" exceptions.

exception multiprocessing.BufferTooShort

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

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

exception multiprocessing.AuthenticationError

   Raised when there is an authentication error.

exception multiprocessing.TimeoutError

   Raised by methods with a timeout when the timeout expires.


Pipes and Queues
----------------

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

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

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

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

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

Note:

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

Note:

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

  1. After putting an object on an empty queue there may be an
     infinitesimal delay before the queue’s "empty()" method returns
     "False" and "get_nowait()" can return without raising
     "queue.Empty".

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

Warning:

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

Warning:

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

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

multiprocessing.Pipe([duplex])

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

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

class multiprocessing.Queue([maxsize])

   Returns a process shared queue implemented using a pipe and a few
   locks/semaphores.  When a process first puts an item on the queue a
   feeder thread is started which transfers objects from a buffer into
   the pipe.

   The usual "queue.Empty" and "queue.Full" exceptions from the
   standard library’s "queue" module are raised to signal timeouts.

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

   qsize()

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

      Note that this may raise "NotImplementedError" on Unix platforms
      like macOS where "sem_getvalue()" is not implemented.

   empty()

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

   full()

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

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

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

      Changed in version 3.8: If the queue is closed, "ValueError" is
      raised instead of "AssertionError".

   put_nowait(obj)

      Equivalent to "put(obj, False)".

   get([block[, timeout]])

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

      Changed in version 3.8: If the queue is closed, "ValueError" is
      raised instead of "OSError".

   get_nowait()

      Equivalent to "get(False)".

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

   close()

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

   join_thread()

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

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

   cancel_join_thread()

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

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

   Note:

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

class multiprocessing.SimpleQueue

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

   close()

      Close the queue: release internal resources.

      A queue must not be used anymore after it is closed. For
      example, "get()", "put()" and "empty()" methods must no longer
      be called.

      New in version 3.9.

   empty()

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

   get()

      Remove and return an item from the queue.

   put(item)

      Put *item* into the queue.

class multiprocessing.JoinableQueue([maxsize])

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

   task_done()

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

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

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

   join()

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

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


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

multiprocessing.active_children()

   Return list of all live children of the current process.

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

multiprocessing.cpu_count()

   Return the number of CPUs in the system.

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

   When the number of CPUs cannot be determined a
   "NotImplementedError" is raised.

   See also: "os.cpu_count()"

multiprocessing.current_process()

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

   An analogue of "threading.current_thread()".

multiprocessing.parent_process()

   Return the "Process" object corresponding to the parent process of
   the "current_process()". For the main process, "parent_process"
   will be "None".

   New in version 3.8.

multiprocessing.freeze_support()

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

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

      from multiprocessing import Process, freeze_support

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

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

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

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

multiprocessing.get_all_start_methods()

   Returns a list of the supported start methods, the first of which
   is the default.  The possible start methods are "'fork'", "'spawn'"
   and "'forkserver'".  On Windows only "'spawn'" is available.  On
   Unix "'fork'" and "'spawn'" are always supported, with "'fork'"
   being the default.

   New in version 3.4.

multiprocessing.get_context(method=None)

   Return a context object which has the same attributes as the
   "multiprocessing" module.

   If *method* is "None" then the default context is returned.
   Otherwise *method* should be "'fork'", "'spawn'", "'forkserver'".
   "ValueError" is raised if the specified start method is not
   available.

   New in version 3.4.

multiprocessing.get_start_method(allow_none=False)

   Return the name of start method used for starting processes.

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

   The return value can be "'fork'", "'spawn'", "'forkserver'" or
   "None".  "'fork'" is the default on Unix, while "'spawn'" is the
   default on Windows and macOS.

Changed in version 3.8: On macOS, the *spawn* start method is now the
default.  The *fork* start method should be considered unsafe as it
can lead to crashes of the subprocess. See bpo-33725.

New in version 3.4.

multiprocessing.set_executable()

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

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

   before they can create child processes.

   Changed in version 3.4: Now supported on Unix when the "'spawn'"
   start method is used.

multiprocessing.set_start_method(method)

   Set the method which should be used to start child processes.
   *method* can be "'fork'", "'spawn'" or "'forkserver'".

   Note that this should be called at most once, and it should be
   protected inside the "if __name__ == '__main__'" clause of the main
   module.

   New in version 3.4.

Note:

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


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

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

Connection objects are usually created using "Pipe" – see also
Listeners and Clients.

class multiprocessing.connection.Connection

   send(obj)

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

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

   recv()

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

   fileno()

      Return the file descriptor or handle used by the connection.

   close()

      Close the connection.

      This is called automatically when the connection is garbage
      collected.

   poll([timeout])

      Return whether there is any data available to be read.

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

      Note that multiple connection objects may be polled at once by
      using "multiprocessing.connection.wait()".

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

      Send byte data from a *bytes-like object* as a complete message.

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

   recv_bytes([maxlength])

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

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

      Changed in version 3.3: This function used to raise "IOError",
      which is now an alias of "OSError".

   recv_bytes_into(buffer[, offset])

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

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

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

   Changed in version 3.3: Connection objects themselves can now be
   transferred between processes using "Connection.send()" and
   "Connection.recv()".

   New in version 3.3: Connection objects now support the context
   management protocol – see Context Manager Types.  "__enter__()"
   returns the connection object, and "__exit__()" calls "close()".

For example:

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

Warning:

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

Warning:

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


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

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

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

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

   A barrier object: a clone of "threading.Barrier".

   New in version 3.3.

class multiprocessing.BoundedSemaphore([value])

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

   A solitary difference from its close analog exists: its "acquire"
   method’s first argument is named *block*, as is consistent with
   "Lock.acquire()".

   Note:

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

class multiprocessing.Condition([lock])

   A condition variable: an alias for "threading.Condition".

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

   Changed in version 3.3: The "wait_for()" method was added.

class multiprocessing.Event

   A clone of "threading.Event".

class multiprocessing.Lock

   A non-recursive lock object: a close analog of "threading.Lock".
   Once a process or thread has acquired a lock, subsequent attempts
   to acquire it from any process or thread will block until it is
   released; any process or thread may release it.  The concepts and
   behaviors of "threading.Lock" as it applies to threads are
   replicated here in "multiprocessing.Lock" as it applies to either
   processes or threads, except as noted.

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

   "Lock" supports the *context manager* protocol and thus may be used
   in "with" statements.

   acquire(block=True, timeout=None)

      Acquire a lock, blocking or non-blocking.

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

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

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

   release()

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

      Behavior is the same as in "threading.Lock.release()" except
      that when invoked on an unlocked lock, a "ValueError" is raised.

class multiprocessing.RLock

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

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

   "RLock" supports the *context manager* protocol and thus may be
   used in "with" statements.

   acquire(block=True, timeout=None)

      Acquire a lock, blocking or non-blocking.

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

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

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

   release()

      Release a lock, decrementing the recursion level.  If after the
      decrement the recursion level is zero, reset the lock to
      unlocked (not owned by any process or thread) and if any other
      processes or threads are blocked waiting for the lock to become
      unlocked, allow exactly one of them to proceed.  If after the
      decrement the recursion level is still nonzero, the lock remains
      locked and owned by the calling process or thread.

      Only call this method when the calling process or thread owns
      the lock. An "AssertionError" is raised if this method is called
      by a process or thread other than the owner or if the lock is in
      an unlocked (unowned) state.  Note that the type of exception
      raised in this situation differs from the implemented behavior
      in "threading.RLock.release()".

class multiprocessing.Semaphore([value])

   A semaphore object: a close analog of "threading.Semaphore".

   A solitary difference from its close analog exists: its "acquire"
   method’s first argument is named *block*, as is consistent with
   "Lock.acquire()".

Note:

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

Note:

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

Note:

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


Shared "ctypes" Objects
-----------------------

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

multiprocessing.Value(typecode_or_type, *args, lock=True)

   Return a "ctypes" object allocated from shared memory.  By default
   the return value is actually a synchronized wrapper for the object.
   The object itself can be accessed via the *value* attribute of a
   "Value".

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

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

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

      counter.value += 1

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

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

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

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

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

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

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

   Note that *lock* is a keyword only argument.

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


The "multiprocessing.sharedctypes" module
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

Note:

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

multiprocessing.sharedctypes.RawArray(typecode_or_type, size_or_initializer)

   Return a ctypes array allocated from shared memory.

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

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

multiprocessing.sharedctypes.RawValue(typecode_or_type, *args)

   Return a ctypes object allocated from shared memory.

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

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

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

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

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

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

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

multiprocessing.sharedctypes.Value(typecode_or_type, *args, lock=True)

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

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

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

multiprocessing.sharedctypes.copy(obj)

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

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

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

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

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

   Changed in version 3.5: Synchronized objects support the *context
   manager* protocol.

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

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

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

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

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

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

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

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

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

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

The results printed are

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


Managers
--------

Managers provide a way to create data which can be shared between
different processes, including sharing over a network between
processes running on different machines. A manager object controls a
server process which manages *shared objects*.  Other processes can
access the shared objects by using proxies.

multiprocessing.Manager()

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

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

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

   Create a BaseManager object.

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

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

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

   start([initializer[, initargs]])

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

   get_server()

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

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

      "Server" additionally has an "address" attribute.

   connect()

      Connect a local manager object to a remote manager process:

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

   shutdown()

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

      This can be called multiple times.

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

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

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

      *callable* is a callable used for creating objects for this type
      identifier.  If a manager instance will be connected to the
      server using the "connect()" method, or if the *create_method*
      argument is "False" then this can be left as "None".

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

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

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

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

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

   address

      The address used by the manager.

   Changed in version 3.3: Manager objects support the context
   management protocol – see Context Manager Types.  "__enter__()"
   starts the server process (if it has not already started) and then
   returns the manager object.  "__exit__()" calls "shutdown()".In
   previous versions "__enter__()" did not start the manager’s server
   process if it was not already started.

class multiprocessing.managers.SyncManager

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

   Its methods create and return Proxy Objects for a number of
   commonly used data types to be synchronized across processes. This
   notably includes shared lists and dictionaries.

   Barrier(parties[, action[, timeout]])

      Create a shared "threading.Barrier" object and return a proxy
      for it.

      New in version 3.3.

   BoundedSemaphore([value])

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

   Condition([lock])

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

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

      Changed in version 3.3: The "wait_for()" method was added.

   Event()

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

   Lock()

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

   Namespace()

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

   Queue([maxsize])

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

   RLock()

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

   Semaphore([value])

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

   Array(typecode, sequence)

      Create an array and return a proxy for it.

   Value(typecode, value)

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

   dict()
   dict(mapping)
   dict(sequence)

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

   list()
   list(sequence)

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

   Changed in version 3.6: Shared objects are capable of being nested.
   For example, a shared container object such as a shared list can
   contain other shared objects which will all be managed and
   synchronized by the "SyncManager".

class multiprocessing.managers.Namespace

   A type that can register with "SyncManager".

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

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

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


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

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

   from multiprocessing.managers import BaseManager

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

   class MyManager(BaseManager):
       pass

   MyManager.register('Maths', MathsClass)

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


Using a remote manager
~~~~~~~~~~~~~~~~~~~~~~

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

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

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

One client can access the server as follows:

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

Another client can also use it:

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

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

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


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

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

A proxy object has methods which invoke corresponding methods of its
referent (although not every method of the referent will necessarily
be available through the proxy).  In this way, a proxy can be used
just like its referent can:

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

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

An important feature of proxy objects is that they are picklable so
they can be passed between processes.  As such, a referent can contain
Proxy Objects.  This permits nesting of these managed lists, dicts,
and other Proxy Objects:

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

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

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

If standard (non-proxy) "list" or "dict" objects are contained in a
referent, modifications to those mutable values will not be propagated
through the manager because the proxy has no way of knowing when the
values contained within are modified.  However, storing a value in a
container proxy (which triggers a "__setitem__" on the proxy object)
does propagate through the manager and so to effectively modify such
an item, one could re-assign the modified value to the container
proxy:

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

This approach is perhaps less convenient than employing nested Proxy
Objects for most use cases but also demonstrates a level of control
over the synchronization.

Note:

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

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

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

class multiprocessing.managers.BaseProxy

   Proxy objects are instances of subclasses of "BaseProxy".

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

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

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

         proxy._callmethod(methodname, args, kwds)

      will evaluate the expression

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

      in the manager’s process.

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

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

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

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

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

   _getvalue()

      Return a copy of the referent.

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

   __repr__()

      Return a representation of the proxy object.

   __str__()

      Return the representation of the referent.


Cleanup
~~~~~~~

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

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


Process Pools
-------------

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

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

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

   *processes* is the number of worker processes to use.  If
   *processes* is "None" then the number returned by "os.cpu_count()"
   is used.

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

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

   *context* can be used to specify the context used for starting the
   worker processes.  Usually a pool is created using the function
   "multiprocessing.Pool()" or the "Pool()" method of a context
   object.  In both cases *context* is set appropriately.

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

   Warning:

     "multiprocessing.pool" objects have internal resources that need
     to be properly managed (like any other resource) by using the
     pool as a context manager or by calling "close()" and
     "terminate()" manually. Failure to do this can lead to the
     process hanging on finalization.Note that it is **not correct**
     to rely on the garbage collector to destroy the pool as CPython
     does not assure that the finalizer of the pool will be called
     (see "object.__del__()" for more information).

   New in version 3.2: *maxtasksperchild*

   New in version 3.4: *context*

   Note:

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

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

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

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

      A variant of the "apply()" method which returns a "AsyncResult"
      object.

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

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

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

   map(func, iterable[, chunksize])

      A parallel equivalent of the "map()" built-in function (it
      supports only one *iterable* argument though, for multiple
      iterables see "starmap()"). It blocks until the result is ready.

      This method chops the iterable into a number of chunks which it
      submits to the process pool as separate tasks.  The
      (approximate) size of these chunks can be specified by setting
      *chunksize* to a positive integer.

      Note that it may cause high memory usage for very long
      iterables. Consider using "imap()" or "imap_unordered()" with
      explicit *chunksize* option for better efficiency.

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

      A variant of the "map()" method which returns a "AsyncResult"
      object.

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

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

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

   imap(func, iterable[, chunksize])

      A lazier version of "map()".

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

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

   imap_unordered(func, iterable[, chunksize])

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

   starmap(func, iterable[, chunksize])

      Like "map()" except that the elements of the *iterable* are
      expected to be iterables that are unpacked as arguments.

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

      New in version 3.3.

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

      A combination of "starmap()" and "map_async()" that iterates
      over *iterable* of iterables and calls *func* with the iterables
      unpacked. Returns a result object.

      New in version 3.3.

   close()

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

   terminate()

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

   join()

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

   New in version 3.3: Pool objects now support the context management
   protocol – see Context Manager Types.  "__enter__()" returns the
   pool object, and "__exit__()" calls "terminate()".

class multiprocessing.pool.AsyncResult

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

   get([timeout])

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

   wait([timeout])

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

   ready()

      Return whether the call has completed.

   successful()

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

      Changed in version 3.7: If the result is not ready, "ValueError"
      is raised instead of "AssertionError".

The following example demonstrates the use of a pool:

   from multiprocessing import Pool
   import time

   def f(x):
       return x*x

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

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

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

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


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

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

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

multiprocessing.connection.deliver_challenge(connection, authkey)

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

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

multiprocessing.connection.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 "AuthenticationError" is
   raised.

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

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

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

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

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

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

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

   Note:

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

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

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

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

   accept()

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

   close()

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

   Listener objects have the following read-only properties:

   address

      The address which is being used by the Listener object.

   last_accepted

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

   New in version 3.3: Listener objects now support the context
   management protocol – see Context Manager Types.  "__enter__()"
   returns the listener object, and "__exit__()" calls "close()".

multiprocessing.connection.wait(object_list, timeout=None)

   Wait till an object in *object_list* is ready.  Returns the list of
   those objects in *object_list* which are ready.  If *timeout* is a
   float then the call blocks for at most that many seconds.  If
   *timeout* is "None" then it will block for an unlimited period. A
   negative timeout is equivalent to a zero timeout.

   For both Unix and Windows, an object can appear in *object_list* if
   it is

   * a readable "Connection" object;

   * a connected and readable "socket.socket" object; or

   * the "sentinel" attribute of a "Process" object.

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

   **Unix**: "wait(object_list, timeout)" almost equivalent
   "select.select(object_list, [], [], timeout)".  The difference is
   that, if "select.select()" is interrupted by a signal, it can raise
   "OSError" with an error number of "EINTR", whereas "wait()" will
   not.

   **Windows**: An item in *object_list* must either be an integer
   handle which is waitable (according to the definition used by the
   documentation of the Win32 function "WaitForMultipleObjects()") or
   it can be an object with a "fileno()" method which returns a socket
   handle or pipe handle.  (Note that pipe handles and socket handles
   are **not** waitable handles.)

   New in version 3.3.

**Examples**

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

   from multiprocessing.connection import Listener
   from array import array

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

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

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

           conn.send_bytes(b'hello')

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

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

   from multiprocessing.connection import Client
   from array import array

   address = ('localhost', 6000)

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

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

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

The following code uses "wait()" to wait for messages from multiple
processes at once:

   import time, random
   from multiprocessing import Process, Pipe, current_process
   from multiprocessing.connection import wait

   def foo(w):
       for i in range(10):
           w.send((i, current_process().name))
       w.close()

   if __name__ == '__main__':
       readers = []

       for i in range(4):
           r, w = Pipe(duplex=False)
           readers.append(r)
           p = Process(target=foo, args=(w,))
           p.start()
           # We close the writable end of the pipe now to be sure that
           # p is the only process which owns a handle for it.  This
           # ensures that when p closes its handle for the writable end,
           # wait() will promptly report the readable end as being ready.
           w.close()

       while readers:
           for r in wait(readers):
               try:
                   msg = r.recv()
               except EOFError:
                   readers.remove(r)
               else:
                   print(msg)


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

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

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

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

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


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

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

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

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

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


Logging
-------

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

multiprocessing.get_logger()

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

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

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

multiprocessing.log_to_stderr(level=None)

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

Below is an example session with logging turned on:

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

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


The "multiprocessing.dummy" module
----------------------------------

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

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

class multiprocessing.pool.ThreadPool([processes[, initializer[, initargs]]])

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

   *processes* is the number of worker threads to use.  If *processes*
   is "None" then the number returned by "os.cpu_count()" is used.

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

   Unlike "Pool", *maxtasksperchild* and *context* cannot be provided.

      Note:

        A "ThreadPool" shares the same interface as "Pool", which is
        designed around a pool of processes and predates the
        introduction of the "concurrent.futures" module.  As such, it
        inherits some operations that don’t make sense for a pool
        backed by threads, and it has its own type for representing
        the status of asynchronous jobs, "AsyncResult", that is not
        understood by any other libraries.Users should generally
        prefer to use "concurrent.futures.ThreadPoolExecutor", which
        has a simpler interface that was designed around threads from
        the start, and which returns "concurrent.futures.Future"
        instances that are compatible with many other libraries,
        including "asyncio".


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

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


All start methods
-----------------

The following applies to all start methods.

Avoid shared state

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

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

Picklability

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

Thread safety of proxies

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

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

Joining zombie processes

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

Better to inherit than pickle/unpickle

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

Avoid terminating processes

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

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

Joining processes that use queues

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

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

   An example which will deadlock is the following:

      from multiprocessing import Process, Queue

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

      if __name__ == '__main__':
          queue = Queue()
          p = Process(target=f, args=(queue,))
          p.start()
          p.join()                    # this deadlocks
          obj = queue.get()

   A fix here would be to swap the last two lines (or simply remove
   the "p.join()" line).

Explicitly pass resources to child processes

   On Unix using the *fork* start method, a child process can make use
   of a shared resource created in a parent process using a global
   resource.  However, it is better to pass the object as an argument
   to the constructor for the child process.

   Apart from making the code (potentially) compatible with Windows
   and the other start methods this also ensures that as long as the
   child process is still alive the object will not be garbage
   collected in the parent process.  This might be important if some
   resource is freed when the object is garbage collected in the
   parent process.

   So for instance

      from multiprocessing import Process, Lock

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

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

   should be rewritten as

      from multiprocessing import Process, Lock

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

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

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

   "multiprocessing" originally unconditionally called:

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

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

      sys.stdin.close()
      sys.stdin = open(os.open(os.devnull, os.O_RDONLY), closefd=False)

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

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

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

   For more information, see bpo-5155, bpo-5313 and bpo-5331


The *spawn* and *forkserver* start methods
------------------------------------------

There are a few extra restriction which don’t apply to the *fork*
start method.

More picklability

   Ensure that all arguments to "Process.__init__()" are picklable.
   Also, if you subclass "Process" then make sure that instances will
   be picklable when the "Process.start" method is called.

Global variables

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

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

Safe importing of main module

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

   For example, using the *spawn* or *forkserver* start method running
   the following module would fail with a "RuntimeError":

      from multiprocessing import Process

      def foo():
          print('hello')

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

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

      from multiprocessing import Process, freeze_support, set_start_method

      def foo():
          print('hello')

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

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

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

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


Examples
========

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

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

   ##

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

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

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

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

   ##

   class MyManager(BaseManager):
       pass

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

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

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

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

   ##

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

       print('-' * 20)

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

       print('-' * 20)

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

       print('-' * 20)

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

       print('-' * 20)

       op = manager.operator()
       print('op.add(23, 45) =', op.add(23, 45))
       print('op.pow(2, 94) =', op.pow(2, 94))
       print('op._exposed_ =', op._exposed_)

   ##

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

Using "Pool":

   import multiprocessing
   import time
   import random
   import sys

   #
   # Functions used by test code
   #

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

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

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

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

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

   def pow3(x):
       return x ** 3

   def noop(x):
       pass

   #
   # Test code
   #

   def test():
       PROCESSES = 4
       print('Creating pool with %d processes\n' % PROCESSES)

       with multiprocessing.Pool(PROCESSES) as pool:
           #
           # Tests
           #

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

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

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

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

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

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

           #
           # Test error handling
           #

           print('Testing error handling:')

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

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

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

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

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

           #
           # Testing timeouts
           #

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

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


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

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

   import time
   import random

   from multiprocessing import Process, Queue, current_process, freeze_support

   #
   # Function run by worker processes
   #

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

   #
   # Function used to calculate result
   #

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

   #
   # Functions referenced by tasks
   #

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

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

   #
   #
   #

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

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

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

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

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

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

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

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


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