multiprocess package documentation

multiprocess: better multiprocessing and multithreading in Python

About Multiprocess

multiprocess is a fork of multiprocessing. multiprocess extends multiprocessing to provide enhanced serialization, using dill. multiprocess leverages multiprocessing to support the spawning of processes using the API of the Python standard library’s threading module. multiprocessing has been distributed as part of the standard library since Python 2.6.

multiprocess is part of pathos, a Python framework for heterogeneous computing. multiprocess is in active development, so any user feedback, bug reports, comments, or suggestions are highly appreciated. A list of issues is located at, with a legacy list maintained at

Major Features

multiprocess enables:

  • objects to be transferred between processes using pipes or multi-producer/multi-consumer queues

  • objects to be shared between processes using a server process or (for simple data) shared memory

multiprocess provides:

  • equivalents of all the synchronization primitives in threading

  • a Pool class to facilitate submitting tasks to worker processes

  • enhanced serialization, using dill

Current Release

The latest released version of multiprocess is available from:

multiprocess is distributed under a 3-clause BSD license, and is a fork of multiprocessing.

Development Version

You can get the latest development version with all the shiny new features at:

If you have a new contribution, please submit a pull request.


multiprocess can be installed with pip:

$ pip install multiprocess

For Python 2, a C compiler is required to build the included extension module from source. Python 3 and binary installs do not require a C compiler.


multiprocess requires:

  • python (or pypy), >=3.8

  • setuptools, >=42

  • dill, >=0.3.8

Basic Usage

The multiprocess.Process class follows the API of threading.Thread. For example

from multiprocess import Process, Queue

def f(q):
    q.put('hello world')

if __name__ == '__main__':
    q = Queue()
    p = Process(target=f, args=[q])
    print (q.get())

Synchronization primitives like locks, semaphores and conditions are available, for example

>>> from multiprocess import Condition
>>> c = Condition()
>>> print (c)
<Condition(<RLock(None, 0)>), 0>
>>> c.acquire()
>>> print (c)
<Condition(<RLock(MainProcess, 1)>), 0>

One can also use a manager to create shared objects either in shared memory or in a server process, for example

>>> from multiprocess import Manager
>>> manager = Manager()
>>> l = manager.list(range(10))
>>> l.reverse()
>>> print (l)
[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
>>> print (repr(l))
<Proxy[list] object at 0x00E1B3B0>

Tasks can be offloaded to a pool of worker processes in various ways, for example

>>> from multiprocess import Pool
>>> def f(x): return x*x
>>> p = Pool(4)
>>> result = p.map_async(f, range(10))
>>> print (result.get(timeout=1))
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

When dill is installed, serialization is extended to most objects, for example

>>> from multiprocess import Pool
>>> p = Pool(4)
>>> print ( x: (lambda y:y**2)(x) + x, xrange(10)))
[0, 2, 6, 12, 20, 30, 42, 56, 72, 90]

More Information

Probably the best way to get started is to look at the documentation at Also see multiprocess.tests for scripts that demonstrate how multiprocess can be used to leverge multiple processes to execute Python in parallel. You can run the test suite with python -m multiprocess.tests. As multiprocess conforms to the multiprocessing interface, the examples and documentation found at also apply to multiprocess if one will import multiprocessing as multiprocess. See for a set of examples that demonstrate some basic use cases and benchmarking for running Python code in parallel. Please feel free to submit a ticket on github, or ask a question on stackoverflow (@Mike McKerns). If you would like to share how you use multiprocess in your work, please send an email (to mmckerns at uqfoundation dot org).


If you use multiprocess to do research that leads to publication, we ask that you acknowledge use of multiprocess by citing the following in your publication:

M.M. McKerns, L. Strand, T. Sullivan, A. Fang, M.A.G. Aivazis,
"Building a framework for predictive science", Proceedings of
the 10th Python in Science Conference, 2011;

Michael McKerns and Michael Aivazis,
"pathos: a framework for heterogeneous computing", 2010- ;

Please see or for further information.

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

Returns a synchronized shared array

exception AuthenticationError

Bases: ProcessError

Barrier(parties, action=None, timeout=None)

Returns a barrier object


Returns a bounded semaphore object

exception BufferTooShort

Bases: ProcessError


Returns a condition object


Returns an event object


Returns a queue object


Returns a non-recursive lock object


Returns a manager associated with a running server process

The managers methods such as Lock(), Condition() and Queue() can be used to create shared objects.


Returns two connection object connected by a pipe

Pool(processes=None, initializer=None, initargs=(), maxtasksperchild=None)

Returns a process pool object

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

Bases: BaseProcess

static _Popen(process_obj)
static _after_fork()
_start_method = None
exception ProcessError

Bases: Exception


Returns a queue object


Returns a recursive lock object

RawArray(typecode_or_type, size_or_initializer)

Returns a shared array

RawValue(typecode_or_type, *args)

Returns a shared object


Returns a semaphore object


Returns a queue object

exception TimeoutError

Bases: ProcessError

Value(typecode_or_type, *args, lock=True)

Returns a synchronized shared object


Return list of process objects corresponding to live child processes


Install support for sending connections and sockets between processes


Returns the number of CPUs in the system


Return process object representing the current process


Check whether this is a fake forked process in a frozen executable. If so then run code specified by commandline and exit.


Return package logger – if it does not already exist then it is created.


Turn on logging and add a handler which prints to stderr


Return process object representing the parent process


Sets the path to a python.exe or pythonw.exe binary used to run child processes instead of sys.executable when using the ‘spawn’ start method. Useful for people embedding Python.


Set list of module names to try to load in forkserver process. This is really just a hint.

set_start_method(method, force=False)

Indices and tables