âčïž Skipped - page is already crawled
| Filter | Status | Condition | Details |
|---|---|---|---|
| HTTP status | PASS | download_http_code = 200 | HTTP 200 |
| Age cutoff | PASS | download_stamp > now() - 6 MONTH | 0.5 months ago |
| History drop | PASS | isNull(history_drop_reason) | No drop reason |
| Spam/ban | PASS | fh_dont_index != 1 AND ml_spam_score = 0 | ml_spam_score=0 |
| Canonical | PASS | meta_canonical IS NULL OR = '' OR = src_unparsed | Not set |
| Property | Value | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| URL | https://docs.python.org/3/library/multiprocessing.html | |||||||||
| Last Crawled | 2026-04-09 17:40:24 (14 days ago) | |||||||||
| First Indexed | 2014-04-12 16:20:38 (12 years ago) | |||||||||
| HTTP Status Code | 200 | |||||||||
| Content | ||||||||||
| Meta Title | multiprocessing â Process-based parallelism â Python 3.14.4 documentation | |||||||||
| Meta Description | Source code: Lib/multiprocessing/ Availability: not Android, not iOS, not WASI. This module is not supported on mobile platforms or WebAssembly platforms. Introduction: multiprocessing is a package... | |||||||||
| Meta Canonical | null | |||||||||
| Boilerpipe Text | 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 POSIX and
Windows.
The
multiprocessing
module also introduces 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
multiprocessing
module also introduces APIs which do not have
analogs in the
threading
module, like the ability to
terminate
,
interrupt
or
kill
a running process.
See also
concurrent.futures.ProcessPoolExecutor
offers a higher level interface
to push tasks to a background process without blocking execution of the
calling process. Compared to using the
Pool
interface directly, the
concurrent.futures
API more readily allows
the submission of work to the underlying process pool to be separated from
waiting for the results.
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
.
The arguments to
Process
usually need to be unpickleable from within
the child process. If you tried typing the above example directly into a REPL it
could lead to an
AttributeError
in the child process trying to locate the
f
function in the
__main__
module.
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 objectâs
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 POSIX and Windows platforms. 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 POSIX systems.
Changed in version 3.14:
This is no longer the default start method on any platform.
Code that requires
fork
must explicitly specify that via
get_context()
or
set_start_method()
.
Changed in version 3.12:
If Python is able to detect that your process has multiple threads, the
os.fork()
function that this start method calls internally will
raise a
DeprecationWarning
. Use a different start method.
See the
os.fork()
documentation for further explanation.
forkserver
When the program starts and selects the
forkserver
start method,
a server process is spawned. 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
unless system libraries or preloaded imports spawn threads as a
side-effect so it is generally safe for it to use
os.fork()
.
No unnecessary resources are inherited.
Available on POSIX platforms which support passing file descriptors over
Unix pipes such as Linux. The default on those.
Changed in version 3.14:
This became the default start method on POSIX platforms.
Changed in version 3.4:
spawn
added on all POSIX platforms, and
forkserver
added for
some POSIX platforms.
Child processes no longer inherit all of the parents inheritable
handles on Windows.
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 as macOS system libraries may start threads. See
bpo-33725
.
Changed in version 3.14:
On POSIX platforms the default start method was changed from
fork
to
forkserver
to retain the performance but avoid common multithreaded
process incompatibilities. See
gh-84559
.
On POSIX 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 âleakedâ 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.
Libraries using
multiprocessing
or
ProcessPoolExecutor
should be designed to allow
their users to provide their own multiprocessing context. Using a specific
context of your own within a library can lead to incompatibilities with the
rest of the library userâs application. Always document if your library
requires a specific start method.
Warning
The
'spawn'
and
'forkserver'
start methods generally cannot
be used with âfrozenâ executables (i.e., binaries produced by
packages like
PyInstaller
and
cx_Freeze
) on POSIX systems.
The
'fork'
start method may work if code does not use threads.
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.
Any object put into a
multiprocessing
queue will be serialized.
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.
The
send()
method serializes the object and
recv()
re-creates the object.
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
,
set
,
Namespace
,
Lock
,
RLock
,
Semaphore
,
BoundedSemaphore
,
Condition
,
Event
,
Barrier
,
Queue
,
Value
and
Array
. For example,
from
multiprocessing
import
Process
,
Manager
def
f
(
d
,
l
,
s
):
d
[
1
]
=
'1'
d
[
'2'
]
=
2
d
[
0.25
]
=
None
l
.
reverse
()
s
.
add
(
'a'
)
s
.
add
(
'b'
)
if
__name__
==
'__main__'
:
with
Manager
()
as
manager
:
d
=
manager
.
dict
()
l
=
manager
.
list
(
range
(
10
))
s
=
manager
.
set
()
p
=
Process
(
target
=
f
,
args
=
(
d
,
l
,
s
))
p
.
start
()
p
.
join
()
print
(
d
)
print
(
l
)
print
(
s
)
will print
{
0.25
:
None
,
1
:
'1'
,
'2'
:
2
}
[
9
,
8
,
7
,
6
,
5
,
4
,
3
,
2
,
1
,
0
]
{
'a'
,
'b'
}
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 seconds
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
:
Can't get attribute 'f' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>
AttributeError: Can't get attribute 'f' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>
AttributeError: Can't get attribute 'f' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>
(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.
Global start method
¶
Python supports several ways to create and initialize a process.
The global start method sets the default mechanism for creating a process.
Several multiprocessing functions and methods that may also instantiate
certain objects will implicitly set the global start method to the systemâs default,
if it hasnât been set already. The global start method can only be set once.
If you need to change the start method from the system default, you must
proactively set the global start method before calling functions or methods,
or creating these objects.
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
. The
args
argument,
which defaults to
()
, can be used to specify a list or tuple of the arguments
to pass to
target
.
If a subclass overrides the constructor, it must make sure it invokes the
base class constructor (
super().__init__()
) before doing anything else
to the process.
Note
In general, all arguments to
Process
must be picklable. This is
frequently observed when trying to create a
Process
or use a
concurrent.futures.ProcessPoolExecutor
from a REPL with a
locally defined
target
function.
Passing a callable object defined in the current REPL session causes the
child process to die via an uncaught
AttributeError
exception when
starting as
target
must have been defined within an importable module
in order to be loaded during unpickling.
Example of this uncatchable error from the child:
>>>
import
multiprocessing
as
mp
>>>
def
knigit
():
...
print
(
"Ni!"
)
...
>>>
process
=
mp
.
Process
(
target
=
knigit
)
>>>
process
.
start
()
>>>
Traceback
(
most
recent
call
last
):
File ".../multiprocessing/spawn.py", line ..., in spawn_main
File ".../multiprocessing/spawn.py", line ..., in _main
AttributeError: module '__main__' has no attribute 'knigit'
>>>
process
<SpawnProcess name='SpawnProcess-1' pid=379473 parent=378707 stopped exitcode=1>
See
The spawn and forkserver start methods
. While this restriction is
not true if using the
"fork"
start method, as of Python
3.14
that
is no longer the default on any platform. See
Contexts and start methods
.
See also
gh-132898
.
Changed in version 3.3:
Added the
daemon
parameter.
run
(
)
¶
Method representing the processâs activity.
You may override this method in a subclass. The standard
run()
method invokes the callable object passed to the objectâs constructor as
the target argument, if any, with sequential and keyword arguments taken
from the
args
and
kwargs
arguments, respectively.
Using a list or tuple as the
args
argument passed to
Process
achieves the same effect.
Example:
>>>
from
multiprocessing
import
Process
>>>
p
=
Process
(
target
=
print
,
args
=
[
1
])
>>>
p
.
run
()
1
>>>
p
=
Process
(
target
=
print
,
args
=
(
1
,))
>>>
p
.
run
()
1
start
(
)
¶
Start the processâs activity.
This must be called at most once per process object. It arranges for the
objectâs
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 processâs
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 processâs 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
âProcess-N
1
:N
2
:âŠ:N
k
â 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 processâs 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 childâs exit code. This will be
None
if the process has not yet
terminated.
If the childâs
run()
method returned normally, the exit code
will be 0. If it terminated via
sys.exit()
with an integer
argument
N
, the exit code will be
N
.
If the child terminated due to an exception not caught within
run()
, the exit code will be 1. If it was terminated by
signal
N
, the exit code will be the negative value
-N
.
authkey
¶
The processâs 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 âreadyâ 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 POSIX, this is
a file descriptor usable with primitives from the
select
module.
Added in version 3.3.
interrupt
(
)
¶
Terminate the process. Works on POSIX using the
SIGINT
signal.
Behavior on Windows is undefined.
By default, this terminates the child process by raising
KeyboardInterrupt
.
This behavior can be altered by setting the respective signal handler in the child
process
signal.signal()
for
SIGINT
.
Note: if the child process catches and discards
KeyboardInterrupt
, the
process will not be terminated.
Note: the default behavior will also set
exitcode
to
1
as if an
uncaught exception was raised in the child process. To have a different
exitcode
you may simply catch
KeyboardInterrupt
and call
exit(your_code)
.
Added in version 3.14.
terminate
(
)
¶
Terminate the process. On POSIX 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 â
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 POSIX.
Added 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
.
Added 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
>>>
mp_context
=
multiprocessing
.
get_context
(
'spawn'
)
>>>
p
=
mp_context
.
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
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 2.5âs
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.
One difference from other Python queue implementations, is that
multiprocessing
queues serializes all objects that are put into them using
pickle
.
The object returned by the get method is a re-created object that does not share
memory with the original object.
Note that one can also create a shared queue by using a manager object â 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 â if they really
bother you then you can instead use a queue created with a
manager
.
After putting an object on an empty queue there may be an
infinitesimal delay before the queueâs
empty()
method returns
False
and
get_nowait()
can
return without raising
queue.Empty
.
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
=
True
)
¶
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.
The
send()
method serializes the object using
pickle
and the
recv()
re-creates the object.
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.
Instantiating this class may set the global start method. See
Global start method
for more details.
The usual
queue.Empty
and
queue.Full
exceptions from the
standard libraryâs
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 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.
May raise an
OSError
on closed queues. (not guaranteed)
full
(
)
¶
Return
True
if the queue is full,
False
otherwise. Because of
multithreading/multiprocessing semantics, this is not reliable.
put
(
obj
[
,
block
[
,
timeout
]
]
)
¶
Put obj into the queue. If the optional argument
block
is
True
(the default) and
timeout
is
None
(the default), block if necessary until
a free slot is available. If
timeout
is a positive number, it blocks at
most
timeout
seconds and raises the
queue.Full
exception if no
free slot was available within that time. Otherwise (
block
is
False
), put an item on the queue if a free slot is immediately
available, else raise the
queue.Full
exception (
timeout
is
ignored in that case).
put_nowait
(
obj
)
¶
Equivalent to
put(obj,
False)
.
get
(
[
block
[
,
timeout
]
]
)
¶
Remove and return an item from the queue. If optional args
block
is
True
(the default) and
timeout
is
None
(the default), block if
necessary until an item is available. If
timeout
is a positive number,
it blocks at most
timeout
seconds and raises the
queue.Empty
exception if no item was available within that time. Otherwise (block is
False
), return an item if one is immediately available, else raise the
queue.Empty
exception (
timeout
is ignored in that case).
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
(
)
¶
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.
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 queueâs 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 â 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 donât care about lost data.
Note
This classâs 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
.
Instantiating this class may set the global start method. See
Global start method
for more details.
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.
Added in version 3.9.
empty
(
)
¶
Return
True
if the queue is empty,
False
otherwise.
Always raises an
OSError
if the SimpleQueue is closed.
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.
Instantiating this class may set the global start method. See
Global start method
for more details.
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 âjoiningâ 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
os.process_cpu_count()
(or
len(os.sched_getaffinity(0))
).
When the number of CPUs cannot be determined a
NotImplementedError
is raised.
Changed in version 3.13:
The return value can also be overridden using the
-X
cpu_count
flag or
PYTHON_CPU_COUNT
as this is
merely a wrapper around the
os
cpu count APIs.
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
.
Added in version 3.8.
multiprocessing.
freeze_support
(
)
¶
Add support for when a program which uses
multiprocessing
has been
frozen to produce an 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 the start method is not
spawn
. In addition, if the module is being run normally by the Python
interpreter (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'
. Not all platforms support all
methods. See
Contexts and start methods
.
Added 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. Note that if
the global start method has not been set, this will set it to the system default
See
Global start method
for more details.
Otherwise
method
should be
'fork'
,
'spawn'
,
'forkserver'
.
ValueError
is raised if the specified
start method is not available. See
Contexts and start methods
.
Added in version 3.4.
multiprocessing.
get_start_method
(
allow_none
=
False
)
¶
Return the name of start method used for starting processes.
If the global start method is not set and
allow_none
is
False
, the global start
method is set to the default, and its name is returned. See
Global start method
for more details.
The return value can be
'fork'
,
'spawn'
,
'forkserver'
or
None
. See
Contexts and start methods
.
Added in version 3.4.
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
.
multiprocessing.
set_executable
(
executable
)
¶
Set 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 something 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 POSIX when the
'spawn'
start method is used.
multiprocessing.
set_forkserver_preload
(
module_names
)
¶
Set a list of module names for the forkserver main process to attempt to
import so that their already imported state is inherited by forked
processes. Any
ImportError
when doing so is silently ignored.
This can be used as a performance enhancement to avoid repeated work
in every process.
For this to work, it must be called before the forkserver process has been
launched (before creating a
Pool
or starting a
Process
).
Only meaningful when using the
'forkserver'
start method.
See
Contexts and start methods
.
Added in version 3.4.
multiprocessing.
set_start_method
(
method
,
force
=
False
)
¶
Set the method which should be used to start child processes.
The
method
argument can be
'fork'
,
'spawn'
or
'forkserver'
.
Raises
RuntimeError
if the start method has already been set and
force
is not
True
. If
method
is
None
and
force
is
True
then the start
method is set to
None
. If
method
is
None
and
force
is
False
then the context is set to the default context.
Note that this should be called at most once, and it should be
protected inside the
if
__name__
==
'__main__'
clause of the
main module.
See
Contexts and start methods
.
Added in version 3.4.
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
â 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()
.
Connection objects also now support the context management protocol â 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 â see
Managers
.
class
multiprocessing.
Barrier
(
parties
[
,
action
[
,
timeout
]
]
)
¶
A barrier object: a clone of
threading.Barrier
.
Instantiating this class may set the global start method. See
Global start method
for more details.
Added in version 3.3.
class
multiprocessing.
BoundedSemaphore
(
[
value
]
)
¶
A bounded semaphore object: a close analog of
threading.BoundedSemaphore
.
Instantiating this class may set the global start method. See
Global start method
for more details.
A solitary difference from its close analog exists: its
acquire
methodâs
first argument is named
block
, as is consistent with
Lock.acquire()
.
locked
(
)
¶
Return a boolean indicating whether this object is locked right now.
Added in version 3.14.
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
.
Instantiating this class may set the global start method. See
Global start method
for more details.
Changed in version 3.3:
The
wait_for()
method was added.
class
multiprocessing.
Event
¶
A clone of
threading.Event
.
Instantiating this class may set the global start method. See
Global start method
for more details.
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.
Instantiating this class may set the global start method. See
Global start method
for more details.
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.
locked
(
)
¶
Return a boolean indicating whether this object is locked right now.
Added in version 3.14.
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.
Instantiating this class may set the global start method. See
Global start method
for more details.
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 argumentâs 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()
.
locked
(
)
¶
Return a boolean indicating whether this object is locked right now.
Added in version 3.14.
class
multiprocessing.
Semaphore
(
[
value
]
)
¶
A semaphore object: a close analog of
threading.Semaphore
.
Instantiating this class may set the global start method. See
Global start method
for more details.
A solitary difference from its close analog exists: its
acquire
methodâs
first argument is named
block
, as is consistent with
Lock.acquire()
.
get_value
(
)
¶
Return the current value of semaphore.
Note that this may raise
NotImplementedError
on platforms like
macOS where
sem_getvalue()
is not implemented.
locked
(
)
¶
Return a boolean indicating whether this object is locked right now.
Added in version 3.14.
Note
On macOS,
sem_timedwait
is unsupported, so calling
acquire()
with
a timeout will emulate that functionâs behavior using a sleeping loop.
Note
Some of this packageâs 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 âprocess-safeâ.
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 with the
exception of
'w'
, which is not supported. In addition, the
'c'
typecode is an alias for
ctypes.c_char
. 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
âprocess-safeâ.
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 â 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 â 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 â see
documentation for
ctypes
.
multiprocessing.sharedctypes.
Array
(
typecode_or_type
,
size_or_initializer
,
*
,
lock
=
True
,
ctx
=
None
)
¶
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
âprocess-safeâ.
ctx
is a context object, or
None
(use the current context). If
None
,
calling this may set the global start method. See
Global start method
for more details.
Note that
lock
and
ctx
are keyword-only parameters.
multiprocessing.sharedctypes.
Value
(
typecode_or_type
,
*
args
,
lock
=
True
,
ctx
=
None
)
¶
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
âprocess-safeâ.
ctx
is a context object, or
None
(use the current context). If
None
,
calling this may set the global start method. See
Global start method
for more details.
Note that
lock
and
ctx
are keyword-only parameters.
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
=
None
,
ctx
=
None
)
¶
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.
ctx
is a context object, or
None
(use the current context). If
None
,
calling this may set the global start method. See
Global start method
for more details.
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(âdâ, 2.4)
MyStruct(4, 6)
RawValue(MyStruct, 4, 6)
(c_short * 7)()
RawArray(c_short, 7)
RawArray(âhâ, 7)
(c_int * 3)(9, 2, 8)
RawArray(c_int, (9, 2, 8))
RawArray(âiâ, (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
=
None
,
authkey
=
None
,
serializer
=
'pickle'
,
ctx
=
None
,
*
,
shutdown_timeout
=
1.0
)
¶
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.
serializer
must be
'pickle'
(use
pickle
serialization) or
'xmlrpclib'
(use
xmlrpc.client
serialization).
ctx
is a context object, or
None
(use the current context). If
None
,
calling this may set the global start method. See
Global start method
for more details.
shutdown_timeout
is a timeout in seconds used to wait until the process
used by the manager completes in the
shutdown()
method. If the
shutdown times out, the process is terminated. If terminating the process
also times out, the process is killed.
Changed in version 3.11:
Added the
shutdown_timeout
parameter.
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 âtype identifierâ 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 âpublic methodsâ of the shared
object will be accessible. (Here a âpublic methodâ 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
methodâs 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 â 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
managerâs 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.
Added 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.
set
(
)
¶
set
(
sequence
)
set
(
mapping
)
Create a shared
set
object and return a proxy for it.
Added in version 3.14:
set
support was added.
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:
>>>
mp_context
=
multiprocessing
.
get_context
(
'spawn'
)
>>>
manager
=
mp_context
.
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 oneâs 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:
>>>
mp_context
=
multiprocessing
.
get_context
(
'spawn'
)
>>>
manager
=
mp_context
.
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 proxyâs 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 managerâs process.
The returned value will be a copy of the result of the call or a proxy to
a new shared object â 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 managerâs
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.process_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. If
None
, calling this function will have the side effect
of setting the current global start method if it has not been set already.
See the
get_context()
function.
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).
Changed in version 3.2:
Added the
maxtasksperchild
parameter.
Changed in version 3.4:
Added the
context
parameter.
Note
Worker processes within a
Pool
typically live for the complete
duration of the Poolâs 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 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 âcorrectâ.)
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)]
.
Added 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.
Added 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()
.
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 âlisteningâ 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 â0.0.0.0â is used, the address will not be a connectable
end point on Windows. If you require a connectable end-point,
you should use â127.0.0.1â.
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
.
Changed in version 3.3:
Listener objects now support the context management protocol â 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 POSIX 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.
POSIX
:
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.)
Added 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:
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 processâs logger â 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.process_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 donât 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 POSIX 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
processâs
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 âfeederâ 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 POSIX 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 âfile like objectâ
multiprocessing
originally unconditionally called:
os
.
close
(
sys
.
stdin
.
fileno
())
in the
multiprocessing.Process._bootstrap()
method â 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 âfile-like objectâ
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 restrictions which donât apply to the
fork
start method.
More picklability
Ensure that all arguments to
Process
are
picklable. Also, if you subclass
Process.__init__
, you must 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 as 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 âentry pointâ 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 moduleâs
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
(
'
\t
Got ZeroDivisionError as expected from pool.apply()'
)
else
:
raise
AssertionError
(
'expected ZeroDivisionError'
)
try
:
print
(
pool
.
map
(
f
,
list
(
range
(
10
))))
except
ZeroDivisionError
:
print
(
'
\t
Got ZeroDivisionError as expected from pool.map()'
)
else
:
raise
AssertionError
(
'expected ZeroDivisionError'
)
try
:
print
(
list
(
pool
.
imap
(
f
,
list
(
range
(
10
)))))
except
ZeroDivisionError
:
print
(
'
\t
Got 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
(
'
\t
Got 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
() | |||||||||
| Markdown | [](https://www.python.org/)
Theme
### [Table of Contents](https://docs.python.org/3/contents.html)
- [`multiprocessing` â Process-based parallelism](https://docs.python.org/3/library/multiprocessing.html)
- [Introduction](https://docs.python.org/3/library/multiprocessing.html#introduction)
- [The `Process` class](https://docs.python.org/3/library/multiprocessing.html#the-process-class)
- [Contexts and start methods](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods)
- [Exchanging objects between processes](https://docs.python.org/3/library/multiprocessing.html#exchanging-objects-between-processes)
- [Synchronization between processes](https://docs.python.org/3/library/multiprocessing.html#synchronization-between-processes)
- [Sharing state between processes](https://docs.python.org/3/library/multiprocessing.html#sharing-state-between-processes)
- [Using a pool of workers](https://docs.python.org/3/library/multiprocessing.html#using-a-pool-of-workers)
- [Reference](https://docs.python.org/3/library/multiprocessing.html#reference)
- [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method)
- [`Process` and exceptions](https://docs.python.org/3/library/multiprocessing.html#process-and-exceptions)
- [Pipes and Queues](https://docs.python.org/3/library/multiprocessing.html#pipes-and-queues)
- [Miscellaneous](https://docs.python.org/3/library/multiprocessing.html#miscellaneous)
- [Connection Objects](https://docs.python.org/3/library/multiprocessing.html#connection-objects)
- [Synchronization primitives](https://docs.python.org/3/library/multiprocessing.html#synchronization-primitives)
- [Shared `ctypes` Objects](https://docs.python.org/3/library/multiprocessing.html#shared-ctypes-objects)
- [The `multiprocessing.sharedctypes` module](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.sharedctypes)
- [Managers](https://docs.python.org/3/library/multiprocessing.html#managers)
- [Customized managers](https://docs.python.org/3/library/multiprocessing.html#customized-managers)
- [Using a remote manager](https://docs.python.org/3/library/multiprocessing.html#using-a-remote-manager)
- [Proxy Objects](https://docs.python.org/3/library/multiprocessing.html#proxy-objects)
- [Cleanup](https://docs.python.org/3/library/multiprocessing.html#cleanup)
- [Process Pools](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.pool)
- [Listeners and Clients](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.connection)
- [Address Formats](https://docs.python.org/3/library/multiprocessing.html#address-formats)
- [Authentication keys](https://docs.python.org/3/library/multiprocessing.html#authentication-keys)
- [Logging](https://docs.python.org/3/library/multiprocessing.html#logging)
- [The `multiprocessing.dummy` module](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.dummy)
- [Programming guidelines](https://docs.python.org/3/library/multiprocessing.html#programming-guidelines)
- [All start methods](https://docs.python.org/3/library/multiprocessing.html#all-start-methods)
- [The *spawn* and *forkserver* start methods](https://docs.python.org/3/library/multiprocessing.html#the-spawn-and-forkserver-start-methods)
- [Examples](https://docs.python.org/3/library/multiprocessing.html#examples)
#### Previous topic
[`threading` â Thread-based parallelism](https://docs.python.org/3/library/threading.html "previous chapter")
#### Next topic
[`multiprocessing.shared_memory` â Shared memory for direct access across processes](https://docs.python.org/3/library/multiprocessing.shared_memory.html "next chapter")
### This page
- [Report a bug](https://docs.python.org/3/bugs.html)
- [Improve this page](https://docs.python.org/3/improve-page.html?pagetitle=multiprocessing+%E2%80%94+Process-based+parallelism&pageurl=https%3A%2F%2Fdocs.python.org%2F3%2Flibrary%2Fmultiprocessing.html&pagesource=library%2Fmultiprocessing.rst)
- [Show source](https://github.com/python/cpython/blob/main/Doc/library/multiprocessing.rst?plain=1)
### Navigation
- [index](https://docs.python.org/3/genindex.html "General Index")
- [modules](https://docs.python.org/3/py-modindex.html "Python Module Index") \|
- [next](https://docs.python.org/3/library/multiprocessing.shared_memory.html "multiprocessing.shared_memory â Shared memory for direct access across processes") \|
- [previous](https://docs.python.org/3/library/threading.html "threading â Thread-based parallelism") \|
- 
- [Python](https://www.python.org/) »
- [3\.14.4 Documentation](https://docs.python.org/3/index.html) »
- [The Python Standard Library](https://docs.python.org/3/library/index.html) »
- [Concurrent Execution](https://docs.python.org/3/library/concurrency.html) »
- [`multiprocessing` â Process-based parallelism](https://docs.python.org/3/library/multiprocessing.html)
- \|
- Theme
\|
# `multiprocessing` â Process-based parallelism[¶](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing "Link to this heading")
**Source code:** [Lib/multiprocessing/](https://github.com/python/cpython/tree/3.14/Lib/multiprocessing/)
***
[Availability](https://docs.python.org/3/library/intro.html#availability): not Android, not iOS, not WASI.
This module is not supported on [mobile platforms](https://docs.python.org/3/library/intro.html#mobile-availability) or [WebAssembly platforms](https://docs.python.org/3/library/intro.html#wasm-availability).
## Introduction[¶](https://docs.python.org/3/library/multiprocessing.html#introduction "Link to this heading")
`multiprocessing` is a package that supports spawning processes using an API similar to the [`threading`](https://docs.python.org/3/library/threading.html#module-threading "threading: Thread-based parallelism.") module. The `multiprocessing` package offers both local and remote concurrency, effectively side-stepping the [Global Interpreter Lock](https://docs.python.org/3/glossary.html#term-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 POSIX and Windows.
The `multiprocessing` module also introduces the [`Pool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.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`,
Copy
```
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
Copy
```
[1, 4, 9]
```
The `multiprocessing` module also introduces APIs which do not have analogs in the [`threading`](https://docs.python.org/3/library/threading.html#module-threading "threading: Thread-based parallelism.") module, like the ability to [`terminate`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.terminate "multiprocessing.Process.terminate"), [`interrupt`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.interrupt "multiprocessing.Process.interrupt") or [`kill`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.kill "multiprocessing.Process.kill") a running process.
See also
[`concurrent.futures.ProcessPoolExecutor`](https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.ProcessPoolExecutor "concurrent.futures.ProcessPoolExecutor") offers a higher level interface to push tasks to a background process without blocking execution of the calling process. Compared to using the [`Pool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.Pool") interface directly, the [`concurrent.futures`](https://docs.python.org/3/library/concurrent.futures.html#module-concurrent.futures "concurrent.futures: Execute computations concurrently using threads or processes.") API more readily allows the submission of work to the underlying process pool to be separated from waiting for the results.
### The [`Process`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.Process") class[¶](https://docs.python.org/3/library/multiprocessing.html#the-process-class "Link to this heading")
In `multiprocessing`, processes are spawned by creating a [`Process`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.Process") object and then calling its [`start()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.start "multiprocessing.Process.start") method. `Process` follows the API of [`threading.Thread`](https://docs.python.org/3/library/threading.html#threading.Thread "threading.Thread"). A trivial example of a multiprocess program is
Copy
```
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:
Copy
```
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](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-programming).
The arguments to [`Process`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.Process") usually need to be unpickleable from within the child process. If you tried typing the above example directly into a REPL it could lead to an [`AttributeError`](https://docs.python.org/3/library/exceptions.html#AttributeError "AttributeError") in the child process trying to locate the *f* function in the `__main__` module.
### Contexts and start methods[¶](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods "Link to this heading")
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 objectâs [`run()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.run "multiprocessing.Process.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 POSIX and Windows platforms. The default on Windows and macOS.
>
> *fork*
>
> The parent process uses [`os.fork()`](https://docs.python.org/3/library/os.html#os.fork "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 POSIX systems.
>
> Changed in version 3.14: This is no longer the default start method on any platform. Code that requires *fork* must explicitly specify that via [`get_context()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.get_context "multiprocessing.get_context") or [`set_start_method()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.set_start_method "multiprocessing.set_start_method").
>
> Changed in version 3.12: If Python is able to detect that your process has multiple threads, the [`os.fork()`](https://docs.python.org/3/library/os.html#os.fork "os.fork") function that this start method calls internally will raise a [`DeprecationWarning`](https://docs.python.org/3/library/exceptions.html#DeprecationWarning "DeprecationWarning"). Use a different start method. See the `os.fork()` documentation for further explanation.
>
> *forkserver*
>
> When the program starts and selects the *forkserver* start method, a server process is spawned. 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 unless system libraries or preloaded imports spawn threads as a side-effect so it is generally safe for it to use [`os.fork()`](https://docs.python.org/3/library/os.html#os.fork "os.fork"). No unnecessary resources are inherited.
>
> Available on POSIX platforms which support passing file descriptors over Unix pipes such as Linux. The default on those.
>
> Changed in version 3.14: This became the default start method on POSIX platforms.
Changed in version 3.4: *spawn* added on all POSIX platforms, and *forkserver* added for some POSIX platforms. Child processes no longer inherit all of the parents inheritable handles on Windows.
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 as macOS system libraries may start threads. See [bpo-33725](https://bugs.python.org/issue?@action=redirect&bpo=33725).
Changed in version 3.14: On POSIX platforms the default start method was changed from *fork* to *forkserver* to retain the performance but avoid common multithreaded process incompatibilities. See [gh-84559](https://github.com/python/cpython/issues/84559).
On POSIX 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`](https://docs.python.org/3/library/multiprocessing.shared_memory.html#multiprocessing.shared_memory.SharedMemory "multiprocessing.shared_memory.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 âleakedâ 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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.set_start_method "multiprocessing.set_start_method") in the `if __name__ == '__main__'` clause of the main module. For example:
Copy
```
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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.set_start_method "multiprocessing.set_start_method") should not be used more than once in the program.
Alternatively, you can use [`get_context()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.get_context "multiprocessing.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.
Copy
```
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.
Libraries using `multiprocessing` or [`ProcessPoolExecutor`](https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.ProcessPoolExecutor "concurrent.futures.ProcessPoolExecutor") should be designed to allow their users to provide their own multiprocessing context. Using a specific context of your own within a library can lead to incompatibilities with the rest of the library userâs application. Always document if your library requires a specific start method.
Warning
The `'spawn'` and `'forkserver'` start methods generally cannot be used with âfrozenâ executables (i.e., binaries produced by packages like **PyInstaller** and **cx\_Freeze**) on POSIX systems. The `'fork'` start method may work if code does not use threads.
### Exchanging objects between processes[¶](https://docs.python.org/3/library/multiprocessing.html#exchanging-objects-between-processes "Link to this heading")
`multiprocessing` supports two types of communication channel between processes:
**Queues**
> The [`Queue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue "multiprocessing.Queue") class is a near clone of [`queue.Queue`](https://docs.python.org/3/library/queue.html#queue.Queue "queue.Queue"). For example:
>
> Copy
> ```
> 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. Any object put into a `multiprocessing` queue will be serialized.
**Pipes**
> The [`Pipe()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Pipe "multiprocessing.Pipe") function returns a pair of connection objects connected by a pipe which by default is duplex (two-way). For example:
>
> Copy
> ```
> 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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Pipe "multiprocessing.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.
>
> The `send()` method serializes the object and `recv()` re-creates the object.
### Synchronization between processes[¶](https://docs.python.org/3/library/multiprocessing.html#synchronization-between-processes "Link to this heading")
`multiprocessing` contains equivalents of all the synchronization primitives from [`threading`](https://docs.python.org/3/library/threading.html#module-threading "threading: Thread-based parallelism."). For instance one can use a lock to ensure that only one process prints to standard output at a time:
Copy
```
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[¶](https://docs.python.org/3/library/multiprocessing.html#sharing-state-between-processes "Link to this heading")
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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Value "multiprocessing.Value") or [`Array`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Array "multiprocessing.Array"). For example, the following code
>
> Copy
> ```
> 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
>
> Copy
> ```
> 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`](https://docs.python.org/3/library/array.html#module-array "array: Space efficient arrays of uniformly typed numeric values.") 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`](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.sharedctypes "multiprocessing.sharedctypes: Allocate ctypes objects from shared memory.") module which supports the creation of arbitrary ctypes objects allocated from shared memory.
**Server process**
> A manager object returned by [`Manager()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Manager "multiprocessing.Manager") controls a server process which holds Python objects and allows other processes to manipulate them using proxies.
>
> A manager returned by [`Manager()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Manager "multiprocessing.Manager") will support types [`list`](https://docs.python.org/3/library/stdtypes.html#list "list"), [`dict`](https://docs.python.org/3/library/stdtypes.html#dict "dict"), [`set`](https://docs.python.org/3/library/stdtypes.html#set "set"), [`Namespace`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.Namespace "multiprocessing.managers.Namespace"), [`Lock`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock "multiprocessing.Lock"), [`RLock`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock "multiprocessing.RLock"), [`Semaphore`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Semaphore "multiprocessing.Semaphore"), [`BoundedSemaphore`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.BoundedSemaphore "multiprocessing.BoundedSemaphore"), [`Condition`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Condition "multiprocessing.Condition"), [`Event`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Event "multiprocessing.Event"), [`Barrier`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Barrier "multiprocessing.Barrier"), [`Queue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue "multiprocessing.Queue"), [`Value`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Value "multiprocessing.Value") and [`Array`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Array "multiprocessing.Array"). For example,
>
> Copy
> ```
> from multiprocessing import Process, Manager
def f(d, l, s):
d[1] = '1'
d['2'] = 2
d[0.25] = None
l.reverse()
s.add('a')
s.add('b')
if __name__ == '__main__':
with Manager() as manager:
d = manager.dict()
l = manager.list(range(10))
s = manager.set()
p = Process(target=f, args=(d, l, s))
p.start()
p.join()
print(d)
print(l)
print(s)
> ```
>
> will print
>
> Copy
> ```
> {0.25: None, 1: '1', '2': 2}
[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
{'a', 'b'}
> ```
>
> 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[¶](https://docs.python.org/3/library/multiprocessing.html#using-a-pool-of-workers "Link to this heading")
The [`Pool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.Pool") class represents a pool of worker processes. It has methods which allows tasks to be offloaded to the worker processes in a few different ways.
For example:
Copy
```
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 seconds
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](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-programming) however it is worth pointing out here. This means that some examples, such as the [`multiprocessing.pool.Pool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.Pool") examples will not work in the interactive interpreter. For example:
Copy
```
>>> 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: Can't get attribute 'f' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>
AttributeError: Can't get attribute 'f' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>
AttributeError: Can't get attribute 'f' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>
```
(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[¶](https://docs.python.org/3/library/multiprocessing.html#reference "Link to this heading")
The `multiprocessing` package mostly replicates the API of the [`threading`](https://docs.python.org/3/library/threading.html#module-threading "threading: Thread-based parallelism.") module.
### Global start method[¶](https://docs.python.org/3/library/multiprocessing.html#global-start-method "Link to this heading")
Python supports several ways to create and initialize a process. The global start method sets the default mechanism for creating a process.
Several multiprocessing functions and methods that may also instantiate certain objects will implicitly set the global start method to the systemâs default, if it hasnât been set already. The global start method can only be set once. If you need to change the start method from the system default, you must proactively set the global start method before calling functions or methods, or creating these objects.
### [`Process`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.Process") and exceptions[¶](https://docs.python.org/3/library/multiprocessing.html#process-and-exceptions "Link to this heading")
*class* multiprocessing.Process(*group\=None*, *target\=None*, *name\=None*, *args\=()*, *kwargs\={}*, *\**, *daemon\=None*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "Link to this definition")
Process objects represent activity that is run in a separate process. The `Process` class has equivalents of all the methods of [`threading.Thread`](https://docs.python.org/3/library/threading.html#threading.Thread "threading.Thread").
The constructor should always be called with keyword arguments. *group* should always be `None`; it exists solely for compatibility with [`threading.Thread`](https://docs.python.org/3/library/threading.html#threading.Thread "threading.Thread"). *target* is the callable object to be invoked by the [`run()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.run "multiprocessing.Process.run") method. It defaults to `None`, meaning nothing is called. *name* is the process name (see [`name`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.name "multiprocessing.Process.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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.daemon "multiprocessing.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*. The *args* argument, which defaults to `()`, can be used to specify a list or tuple of the arguments to pass to *target*.
If a subclass overrides the constructor, it must make sure it invokes the base class constructor (`super().__init__()`) before doing anything else to the process.
Note
In general, all arguments to `Process` must be picklable. This is frequently observed when trying to create a `Process` or use a [`concurrent.futures.ProcessPoolExecutor`](https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.ProcessPoolExecutor "concurrent.futures.ProcessPoolExecutor") from a REPL with a locally defined *target* function.
Passing a callable object defined in the current REPL session causes the child process to die via an uncaught [`AttributeError`](https://docs.python.org/3/library/exceptions.html#AttributeError "AttributeError") exception when starting as *target* must have been defined within an importable module in order to be loaded during unpickling.
Example of this uncatchable error from the child:
Copy
```
>>> import multiprocessing as mp
>>> def knigit():
... print("Ni!")
...
>>> process = mp.Process(target=knigit)
>>> process.start()
>>> Traceback (most recent call last):
File ".../multiprocessing/spawn.py", line ..., in spawn_main
File ".../multiprocessing/spawn.py", line ..., in _main
AttributeError: module '__main__' has no attribute 'knigit'
>>> process
<SpawnProcess name='SpawnProcess-1' pid=379473 parent=378707 stopped exitcode=1>
```
See [The spawn and forkserver start methods](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-programming-spawn). While this restriction is not true if using the `"fork"` start method, as of Python `3.14` that is no longer the default on any platform. See [Contexts and start methods](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-start-methods). See also [gh-132898](https://github.com/python/cpython/issues/132898).
Changed in version 3.3: Added the *daemon* parameter.
run()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.run "Link to this definition")
Method representing the processâs activity.
You may override this method in a subclass. The standard `run()` method invokes the callable object passed to the objectâs constructor as the target argument, if any, with sequential and keyword arguments taken from the *args* and *kwargs* arguments, respectively.
Using a list or tuple as the *args* argument passed to `Process` achieves the same effect.
Example:
Copy
```
>>> from multiprocessing import Process
>>> p = Process(target=print, args=[1])
>>> p.run()
1
>>> p = Process(target=print, args=(1,))
>>> p.run()
1
```
start()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.start "Link to this definition")
Start the processâs activity.
This must be called at most once per process object. It arranges for the objectâs [`run()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.run "multiprocessing.Process.run") method to be invoked in a separate process.
join(\[*timeout*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.join "Link to this definition")
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 processâs [`exitcode`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.exitcode "multiprocessing.Process.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[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.name "Link to this definition")
The processâs 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 âProcess-N1:N2:âŠ:Nkâ is constructed, where each Nk is the N-th child of its parent.
is\_alive()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.is_alive "Link to this definition")
Return whether the process is alive.
Roughly, a process object is alive from the moment the [`start()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.start "multiprocessing.Process.start") method returns until the child process terminates.
daemon[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.daemon "Link to this definition")
The processâs daemon flag, a Boolean value. This must be set before [`start()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.start "multiprocessing.Process.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`](https://docs.python.org/3/library/threading.html#threading.Thread "threading.Thread") API, `Process` objects also support the following attributes and methods:
pid[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.pid "Link to this definition")
Return the process ID. Before the process is spawned, this will be `None`.
exitcode[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.exitcode "Link to this definition")
The childâs exit code. This will be `None` if the process has not yet terminated.
If the childâs [`run()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.run "multiprocessing.Process.run") method returned normally, the exit code will be 0. If it terminated via [`sys.exit()`](https://docs.python.org/3/library/sys.html#sys.exit "sys.exit") with an integer argument *N*, the exit code will be *N*.
If the child terminated due to an exception not caught within [`run()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.run "multiprocessing.Process.run"), the exit code will be 1. If it was terminated by signal *N*, the exit code will be the negative value *\-N*.
authkey[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.authkey "Link to this definition")
The processâs authentication key (a byte string).
When `multiprocessing` is initialized the main process is assigned a random string using [`os.urandom()`](https://docs.python.org/3/library/os.html#os.urandom "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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.authkey "multiprocessing.Process.authkey") to another byte string.
See [Authentication keys](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-auth-keys).
sentinel[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.sentinel "Link to this definition")
A numeric handle of a system object which will become âreadyâ when the process ends.
You can use this value if you want to wait on several events at once using [`multiprocessing.connection.wait()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.wait "multiprocessing.connection.wait"). Otherwise calling [`join()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.join "multiprocessing.Process.join") is simpler.
On Windows, this is an OS handle usable with the `WaitForSingleObject` and `WaitForMultipleObjects` family of API calls. On POSIX, this is a file descriptor usable with primitives from the [`select`](https://docs.python.org/3/library/select.html#module-select "select: Wait for I/O completion on multiple streams.") module.
Added in version 3.3.
interrupt()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.interrupt "Link to this definition")
Terminate the process. Works on POSIX using the [`SIGINT`](https://docs.python.org/3/library/signal.html#signal.SIGINT "signal.SIGINT") signal. Behavior on Windows is undefined.
By default, this terminates the child process by raising [`KeyboardInterrupt`](https://docs.python.org/3/library/exceptions.html#KeyboardInterrupt "KeyboardInterrupt"). This behavior can be altered by setting the respective signal handler in the child process [`signal.signal()`](https://docs.python.org/3/library/signal.html#signal.signal "signal.signal") for [`SIGINT`](https://docs.python.org/3/library/signal.html#signal.SIGINT "signal.SIGINT").
Note: if the child process catches and discards [`KeyboardInterrupt`](https://docs.python.org/3/library/exceptions.html#KeyboardInterrupt "KeyboardInterrupt"), the process will not be terminated.
Note: the default behavior will also set [`exitcode`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.exitcode "multiprocessing.Process.exitcode") to `1` as if an uncaught exception was raised in the child process. To have a different `exitcode` you may simply catch [`KeyboardInterrupt`](https://docs.python.org/3/library/exceptions.html#KeyboardInterrupt "KeyboardInterrupt") and call `exit(your_code)`.
Added in version 3.14.
terminate()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.terminate "Link to this definition")
Terminate the process. On POSIX this is done using the [`SIGTERM`](https://docs.python.org/3/library/signal.html#signal.SIGTERM "signal.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 â 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()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.kill "Link to this definition")
Same as [`terminate()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.terminate "multiprocessing.Process.terminate") but using the `SIGKILL` signal on POSIX.
Added in version 3.7.
close()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.close "Link to this definition")
Close the `Process` object, releasing all resources associated with it. [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError "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`.
Added in version 3.7.
Note that the [`start()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.start "multiprocessing.Process.start"), [`join()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.join "multiprocessing.Process.join"), [`is_alive()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.is_alive "multiprocessing.Process.is_alive"), [`terminate()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.terminate "multiprocessing.Process.terminate") and [`exitcode`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.exitcode "multiprocessing.Process.exitcode") methods should only be called by the process that created the process object.
Example usage of some of the methods of `Process`:
Copy
```
>>> import multiprocessing, time, signal
>>> mp_context = multiprocessing.get_context('spawn')
>>> p = mp_context.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[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.ProcessError "Link to this definition")
The base class of all `multiprocessing` exceptions.
*exception* multiprocessing.BufferTooShort[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.BufferTooShort "Link to this definition")
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[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.AuthenticationError "Link to this definition")
Raised when there is an authentication error.
*exception* multiprocessing.TimeoutError[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.TimeoutError "Link to this definition")
Raised by methods with a timeout when the timeout expires.
### Pipes and Queues[¶](https://docs.python.org/3/library/multiprocessing.html#pipes-and-queues "Link to this heading")
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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Pipe "multiprocessing.Pipe") (for a connection between two processes) or a queue (which allows multiple producers and consumers).
The [`Queue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue "multiprocessing.Queue"), [`SimpleQueue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.SimpleQueue "multiprocessing.SimpleQueue") and [`JoinableQueue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.JoinableQueue "multiprocessing.JoinableQueue") types are multi-producer, multi-consumer FIFO queues modelled on the [`queue.Queue`](https://docs.python.org/3/library/queue.html#queue.Queue "queue.Queue") class in the standard library. They differ in that `Queue` lacks the [`task_done()`](https://docs.python.org/3/library/queue.html#queue.Queue.task_done "queue.Queue.task_done") and [`join()`](https://docs.python.org/3/library/queue.html#queue.Queue.join "queue.Queue.join") methods introduced into Python 2.5âs `queue.Queue` class.
If you use [`JoinableQueue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.JoinableQueue "multiprocessing.JoinableQueue") then you **must** call [`JoinableQueue.task_done()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.JoinableQueue.task_done "multiprocessing.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.
One difference from other Python queue implementations, is that `multiprocessing` queues serializes all objects that are put into them using [`pickle`](https://docs.python.org/3/library/pickle.html#module-pickle "pickle: Convert Python objects to streams of bytes and back."). The object returned by the get method is a re-created object that does not share memory with the original object.
Note that one can also create a shared queue by using a manager object â see [Managers](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-managers).
Note
`multiprocessing` uses the usual [`queue.Empty`](https://docs.python.org/3/library/queue.html#queue.Empty "queue.Empty") and [`queue.Full`](https://docs.python.org/3/library/queue.html#queue.Full "queue.Full") exceptions to signal a timeout. They are not available in the `multiprocessing` namespace so you need to import them from [`queue`](https://docs.python.org/3/library/queue.html#module-queue "queue: A synchronized queue class.").
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 â if they really bother you then you can instead use a queue created with a [manager](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-managers).
1. After putting an object on an empty queue there may be an infinitesimal delay before the queueâs [`empty()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.empty "multiprocessing.Queue.empty") method returns [`False`](https://docs.python.org/3/library/constants.html#False "False") and [`get_nowait()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.get_nowait "multiprocessing.Queue.get_nowait") can return without raising [`queue.Empty`](https://docs.python.org/3/library/queue.html#queue.Empty "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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.terminate "multiprocessing.Process.terminate") or [`os.kill()`](https://docs.python.org/3/library/os.html#os.kill "os.kill") while it is trying to use a [`Queue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue "multiprocessing.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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.cancel_join_thread "multiprocessing.Queue.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](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-programming).
For an example of the usage of queues for interprocess communication see [Examples](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-examples).
multiprocessing.Pipe(*duplex\=True*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Pipe "Link to this definition")
Returns a pair `(conn1, conn2)` of [`Connection`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection "multiprocessing.connection.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.
The `send()` method serializes the object using [`pickle`](https://docs.python.org/3/library/pickle.html#module-pickle "pickle: Convert Python objects to streams of bytes and back.") and the `recv()` re-creates the object.
*class* multiprocessing.Queue(\[*maxsize*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue "Link to this definition")
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.
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
The usual [`queue.Empty`](https://docs.python.org/3/library/queue.html#queue.Empty "queue.Empty") and [`queue.Full`](https://docs.python.org/3/library/queue.html#queue.Full "queue.Full") exceptions from the standard libraryâs [`queue`](https://docs.python.org/3/library/queue.html#module-queue "queue: A synchronized queue class.") module are raised to signal timeouts.
`Queue` implements all the methods of [`queue.Queue`](https://docs.python.org/3/library/queue.html#queue.Queue "queue.Queue") except for [`task_done()`](https://docs.python.org/3/library/queue.html#queue.Queue.task_done "queue.Queue.task_done") and [`join()`](https://docs.python.org/3/library/queue.html#queue.Queue.join "queue.Queue.join").
qsize()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.qsize "Link to this definition")
Return the approximate size of the queue. Because of multithreading/multiprocessing semantics, this number is not reliable.
Note that this may raise [`NotImplementedError`](https://docs.python.org/3/library/exceptions.html#NotImplementedError "NotImplementedError") on platforms like macOS where `sem_getvalue()` is not implemented.
empty()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.empty "Link to this definition")
Return `True` if the queue is empty, `False` otherwise. Because of multithreading/multiprocessing semantics, this is not reliable.
May raise an [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError "OSError") on closed queues. (not guaranteed)
full()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.full "Link to this definition")
Return `True` if the queue is full, `False` otherwise. Because of multithreading/multiprocessing semantics, this is not reliable.
put(*obj*\[, *block*\[, *timeout*\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.put "Link to this definition")
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`](https://docs.python.org/3/library/queue.html#queue.Full "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`](https://docs.python.org/3/library/exceptions.html#ValueError "ValueError") is raised instead of [`AssertionError`](https://docs.python.org/3/library/exceptions.html#AssertionError "AssertionError").
put\_nowait(*obj*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.put_nowait "Link to this definition")
Equivalent to `put(obj, False)`.
get(\[*block*\[, *timeout*\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.get "Link to this definition")
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`](https://docs.python.org/3/library/queue.html#queue.Empty "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`](https://docs.python.org/3/library/exceptions.html#ValueError "ValueError") is raised instead of [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError "OSError").
get\_nowait()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.get_nowait "Link to this definition")
Equivalent to `get(False)`.
[`multiprocessing.Queue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue "multiprocessing.Queue") has a few additional methods not found in [`queue.Queue`](https://docs.python.org/3/library/queue.html#queue.Queue "queue.Queue"). These methods are usually unnecessary for most code:
close()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.close "Link to this definition")
Close the queue: release internal resources.
A queue must not be used anymore after it is closed. For example, [`get()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.get "multiprocessing.Queue.get"), [`put()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.put "multiprocessing.Queue.put") and [`empty()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.empty "multiprocessing.Queue.empty") methods must no longer be called.
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()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.join_thread "Link to this definition")
Join the background thread. This can only be used after [`close()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.close "multiprocessing.Queue.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 queueâs background thread. The process can call [`cancel_join_thread()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.cancel_join_thread "multiprocessing.Queue.cancel_join_thread") to make `join_thread()` do nothing.
cancel\_join\_thread()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.cancel_join_thread "Link to this definition")
Prevent [`join_thread()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.join_thread "multiprocessing.Queue.join_thread") from blocking. In particular, this prevents the background thread from being joined automatically when the process exits â 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 donât care about lost data.
Note
This classâs 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`](https://docs.python.org/3/library/exceptions.html#ImportError "ImportError"). See [bpo-3770](https://bugs.python.org/issue?@action=redirect&bpo=3770) for additional information. The same holds true for any of the specialized queue types listed below.
*class* multiprocessing.SimpleQueue[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.SimpleQueue "Link to this definition")
It is a simplified [`Queue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue "multiprocessing.Queue") type, very close to a locked [`Pipe`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Pipe "multiprocessing.Pipe").
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
close()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.SimpleQueue.close "Link to this definition")
Close the queue: release internal resources.
A queue must not be used anymore after it is closed. For example, [`get()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.SimpleQueue.get "multiprocessing.SimpleQueue.get"), [`put()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.SimpleQueue.put "multiprocessing.SimpleQueue.put") and [`empty()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.SimpleQueue.empty "multiprocessing.SimpleQueue.empty") methods must no longer be called.
Added in version 3.9.
empty()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.SimpleQueue.empty "Link to this definition")
Return `True` if the queue is empty, `False` otherwise.
Always raises an [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError "OSError") if the SimpleQueue is closed.
get()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.SimpleQueue.get "Link to this definition")
Remove and return an item from the queue.
put(*item*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.SimpleQueue.put "Link to this definition")
Put *item* into the queue.
*class* multiprocessing.JoinableQueue(\[*maxsize*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.JoinableQueue "Link to this definition")
`JoinableQueue`, a [`Queue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue "multiprocessing.Queue") subclass, is a queue which additionally has [`task_done()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.JoinableQueue.task_done "multiprocessing.JoinableQueue.task_done") and [`join()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.JoinableQueue.join "multiprocessing.JoinableQueue.join") methods.
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
task\_done()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.JoinableQueue.task_done "Link to this definition")
Indicate that a formerly enqueued task is complete. Used by queue consumers. For each [`get()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.get "multiprocessing.Queue.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()`](https://docs.python.org/3/library/queue.html#queue.Queue.join "queue.Queue.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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.put "multiprocessing.Queue.put") into the queue).
Raises a [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError "ValueError") if called more times than there were items placed in the queue.
join()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.JoinableQueue.join "Link to this definition")
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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.JoinableQueue.task_done "multiprocessing.JoinableQueue.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()`](https://docs.python.org/3/library/queue.html#queue.Queue.join "queue.Queue.join") unblocks.
### Miscellaneous[¶](https://docs.python.org/3/library/multiprocessing.html#miscellaneous "Link to this heading")
multiprocessing.active\_children()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.active_children "Link to this definition")
Return list of all live children of the current process.
Calling this has the side effect of âjoiningâ any processes which have already finished.
multiprocessing.cpu\_count()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.cpu_count "Link to this definition")
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 [`os.process_cpu_count()`](https://docs.python.org/3/library/os.html#os.process_cpu_count "os.process_cpu_count") (or `len(os.sched_getaffinity(0))`).
When the number of CPUs cannot be determined a [`NotImplementedError`](https://docs.python.org/3/library/exceptions.html#NotImplementedError "NotImplementedError") is raised.
See also
[`os.cpu_count()`](https://docs.python.org/3/library/os.html#os.cpu_count "os.cpu_count") [`os.process_cpu_count()`](https://docs.python.org/3/library/os.html#os.process_cpu_count "os.process_cpu_count")
Changed in version 3.13: The return value can also be overridden using the [`-X cpu_count`](https://docs.python.org/3/using/cmdline.html#cmdoption-X) flag or [`PYTHON_CPU_COUNT`](https://docs.python.org/3/using/cmdline.html#envvar-PYTHON_CPU_COUNT) as this is merely a wrapper around the [`os`](https://docs.python.org/3/library/os.html#module-os "os: Miscellaneous operating system interfaces.") cpu count APIs.
multiprocessing.current\_process()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.current_process "Link to this definition")
Return the [`Process`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.Process") object corresponding to the current process.
An analogue of [`threading.current_thread()`](https://docs.python.org/3/library/threading.html#threading.current_thread "threading.current_thread").
multiprocessing.parent\_process()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.parent_process "Link to this definition")
Return the [`Process`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.Process") object corresponding to the parent process of the [`current_process()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.current_process "multiprocessing.current_process"). For the main process, `parent_process` will be `None`.
Added in version 3.8.
multiprocessing.freeze\_support()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.freeze_support "Link to this definition")
Add support for when a program which uses `multiprocessing` has been frozen to produce an executable. (Has been tested with **py2exe**, **PyInstaller** and **cx\_Freeze**.)
One needs to call this function straight after the line of the main module. For example:
Copy
```
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`](https://docs.python.org/3/library/exceptions.html#RuntimeError "RuntimeError").
Calling `freeze_support()` has no effect when the start method is not *spawn*. In addition, if the module is being run normally by the Python interpreter (the program has not been frozen), then `freeze_support()` has no effect.
multiprocessing.get\_all\_start\_methods()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.get_all_start_methods "Link to this definition")
Returns a list of the supported start methods, the first of which is the default. The possible start methods are `'fork'`, `'spawn'` and `'forkserver'`. Not all platforms support all methods. See [Contexts and start methods](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-start-methods).
Added in version 3.4.
multiprocessing.get\_context(*method\=None*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.get_context "Link to this definition")
Return a context object which has the same attributes as the `multiprocessing` module.
If *method* is `None` then the default context is returned. Note that if the global start method has not been set, this will set it to the system default See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details. Otherwise *method* should be `'fork'`, `'spawn'`, `'forkserver'`. [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError "ValueError") is raised if the specified start method is not available. See [Contexts and start methods](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-start-methods).
Added in version 3.4.
multiprocessing.get\_start\_method(*allow\_none\=False*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.get_start_method "Link to this definition")
Return the name of start method used for starting processes.
If the global start method is not set and *allow\_none* is `False`, the global start method is set to the default, and its name is returned. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
The return value can be `'fork'`, `'spawn'`, `'forkserver'` or `None`. See [Contexts and start methods](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-start-methods).
Added in version 3.4.
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](https://bugs.python.org/issue?@action=redirect&bpo=33725).
multiprocessing.set\_executable(*executable*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.set_executable "Link to this definition")
Set the path of the Python interpreter to use when starting a child process. (By default [`sys.executable`](https://docs.python.org/3/library/sys.html#sys.executable "sys.executable") is used). Embedders will probably need to do something like
Copy
```
set_executable(os.path.join(sys.exec_prefix, 'pythonw.exe'))
```
before they can create child processes.
Changed in version 3.4: Now supported on POSIX when the `'spawn'` start method is used.
Changed in version 3.11: Accepts a [path-like object](https://docs.python.org/3/glossary.html#term-path-like-object).
multiprocessing.set\_forkserver\_preload(*module\_names*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.set_forkserver_preload "Link to this definition")
Set a list of module names for the forkserver main process to attempt to import so that their already imported state is inherited by forked processes. Any [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError "ImportError") when doing so is silently ignored. This can be used as a performance enhancement to avoid repeated work in every process.
For this to work, it must be called before the forkserver process has been launched (before creating a `Pool` or starting a [`Process`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.Process")).
Only meaningful when using the `'forkserver'` start method. See [Contexts and start methods](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-start-methods).
Added in version 3.4.
multiprocessing.set\_start\_method(*method*, *force\=False*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.set_start_method "Link to this definition")
Set the method which should be used to start child processes. The *method* argument can be `'fork'`, `'spawn'` or `'forkserver'`. Raises [`RuntimeError`](https://docs.python.org/3/library/exceptions.html#RuntimeError "RuntimeError") if the start method has already been set and *force* is not `True`. If *method* is `None` and *force* is `True` then the start method is set to `None`. If *method* is `None` and *force* is `False` then the context is set to the default context.
Note that this should be called at most once, and it should be protected inside the `if __name__ == '__main__'` clause of the main module.
See [Contexts and start methods](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-start-methods).
Added in version 3.4.
Note
`multiprocessing` contains no analogues of [`threading.active_count()`](https://docs.python.org/3/library/threading.html#threading.active_count "threading.active_count"), [`threading.enumerate()`](https://docs.python.org/3/library/threading.html#threading.enumerate "threading.enumerate"), [`threading.settrace()`](https://docs.python.org/3/library/threading.html#threading.settrace "threading.settrace"), [`threading.setprofile()`](https://docs.python.org/3/library/threading.html#threading.setprofile "threading.setprofile"), [`threading.Timer`](https://docs.python.org/3/library/threading.html#threading.Timer "threading.Timer"), or [`threading.local`](https://docs.python.org/3/library/threading.html#threading.local "threading.local").
### Connection Objects[¶](https://docs.python.org/3/library/multiprocessing.html#connection-objects "Link to this heading")
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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Pipe "multiprocessing.Pipe") â see also [Listeners and Clients](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-listeners-clients).
*class* multiprocessing.connection.Connection[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection "Link to this definition")
send(*obj*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.send "Link to this definition")
Send an object to the other end of the connection which should be read using [`recv()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.recv "multiprocessing.connection.Connection.recv").
The object must be picklable. Very large pickles (approximately 32 MiB+, though it depends on the OS) may raise a [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError "ValueError") exception.
recv()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.recv "Link to this definition")
Return an object sent from the other end of the connection using [`send()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.send "multiprocessing.connection.Connection.send"). Blocks until there is something to receive. Raises [`EOFError`](https://docs.python.org/3/library/exceptions.html#EOFError "EOFError") if there is nothing left to receive and the other end was closed.
fileno()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.fileno "Link to this definition")
Return the file descriptor or handle used by the connection.
close()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.close "Link to this definition")
Close the connection.
This is called automatically when the connection is garbage collected.
poll(\[*timeout*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.poll "Link to this definition")
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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.wait "multiprocessing.connection.wait").
send\_bytes(*buffer*\[, *offset*\[, *size*\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.send_bytes "Link to this definition")
Send byte data from a [bytes-like object](https://docs.python.org/3/glossary.html#term-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`](https://docs.python.org/3/library/exceptions.html#ValueError "ValueError") exception
recv\_bytes(\[*maxlength*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.recv_bytes "Link to this definition")
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`](https://docs.python.org/3/library/exceptions.html#EOFError "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`](https://docs.python.org/3/library/exceptions.html#OSError "OSError") is raised and the connection will no longer be readable.
Changed in version 3.3: This function used to raise [`IOError`](https://docs.python.org/3/library/exceptions.html#IOError "IOError"), which is now an alias of [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError "OSError").
recv\_bytes\_into(*buffer*\[, *offset*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.recv_bytes_into "Link to this definition")
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`](https://docs.python.org/3/library/exceptions.html#EOFError "EOFError") if there is nothing left to receive and the other end was closed.
*buffer* must be a writable [bytes-like object](https://docs.python.org/3/glossary.html#term-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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.send "multiprocessing.connection.Connection.send") and [`Connection.recv()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.recv "multiprocessing.connection.Connection.recv").
Connection objects also now support the context management protocol â see [Context Manager Types](https://docs.python.org/3/library/stdtypes.html#typecontextmanager). [`__enter__()`](https://docs.python.org/3/library/stdtypes.html#contextmanager.__enter__ "contextmanager.__enter__") returns the connection object, and [`__exit__()`](https://docs.python.org/3/library/stdtypes.html#contextmanager.__exit__ "contextmanager.__exit__") calls [`close()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.close "multiprocessing.connection.Connection.close").
For example:
Copy
```
>>> 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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.recv "multiprocessing.connection.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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.recv "multiprocessing.connection.Connection.recv") and [`send()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.send "multiprocessing.connection.Connection.send") methods after performing some sort of authentication. See [Authentication keys](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-auth-keys).
Warning
If a process is killed while it is trying to read or write to a pipe then the data in the pipe is likely to become corrupted, because it may become impossible to be sure where the message boundaries lie.
### Synchronization primitives[¶](https://docs.python.org/3/library/multiprocessing.html#synchronization-primitives "Link to this heading")
Generally synchronization primitives are not as necessary in a multiprocess program as they are in a multithreaded program. See the documentation for [`threading`](https://docs.python.org/3/library/threading.html#module-threading "threading: Thread-based parallelism.") module.
Note that one can also create synchronization primitives by using a manager object â see [Managers](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-managers).
*class* multiprocessing.Barrier(*parties*\[, *action*\[, *timeout*\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Barrier "Link to this definition")
A barrier object: a clone of [`threading.Barrier`](https://docs.python.org/3/library/threading.html#threading.Barrier "threading.Barrier").
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
Added in version 3.3.
*class* multiprocessing.BoundedSemaphore(\[*value*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.BoundedSemaphore "Link to this definition")
A bounded semaphore object: a close analog of [`threading.BoundedSemaphore`](https://docs.python.org/3/library/threading.html#threading.BoundedSemaphore "threading.BoundedSemaphore").
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
A solitary difference from its close analog exists: its `acquire` methodâs first argument is named *block*, as is consistent with [`Lock.acquire()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock.acquire "multiprocessing.Lock.acquire").
locked()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.BoundedSemaphore.locked "Link to this definition")
Return a boolean indicating whether this object is locked right now.
Added in version 3.14.
Note
On macOS, this is indistinguishable from [`Semaphore`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Semaphore "multiprocessing.Semaphore") because `sem_getvalue()` is not implemented on that platform.
*class* multiprocessing.Condition(\[*lock*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Condition "Link to this definition")
A condition variable: an alias for [`threading.Condition`](https://docs.python.org/3/library/threading.html#threading.Condition "threading.Condition").
If *lock* is specified then it should be a [`Lock`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock "multiprocessing.Lock") or [`RLock`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock "multiprocessing.RLock") object from `multiprocessing`.
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
Changed in version 3.3: The [`wait_for()`](https://docs.python.org/3/library/threading.html#threading.Condition.wait_for "threading.Condition.wait_for") method was added.
*class* multiprocessing.Event[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Event "Link to this definition")
A clone of [`threading.Event`](https://docs.python.org/3/library/threading.html#threading.Event "threading.Event").
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
*class* multiprocessing.Lock[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock "Link to this definition")
A non-recursive lock object: a close analog of [`threading.Lock`](https://docs.python.org/3/library/threading.html#threading.Lock "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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock "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.
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
`Lock` supports the [context manager](https://docs.python.org/3/glossary.html#term-context-manager) protocol and thus may be used in [`with`](https://docs.python.org/3/reference/compound_stmts.html#with) statements.
acquire(*block\=True*, *timeout\=None*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock.acquire "Link to this definition")
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()`](https://docs.python.org/3/library/threading.html#threading.Lock.acquire "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()`](https://docs.python.org/3/library/threading.html#threading.Lock.acquire "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()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock.release "Link to this definition")
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()`](https://docs.python.org/3/library/threading.html#threading.Lock.release "threading.Lock.release") except that when invoked on an unlocked lock, a [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError "ValueError") is raised.
locked()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock.locked "Link to this definition")
Return a boolean indicating whether this object is locked right now.
Added in version 3.14.
*class* multiprocessing.RLock[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock "Link to this definition")
A recursive lock object: a close analog of [`threading.RLock`](https://docs.python.org/3/library/threading.html#threading.RLock "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.
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
`RLock` supports the [context manager](https://docs.python.org/3/glossary.html#term-context-manager) protocol and thus may be used in [`with`](https://docs.python.org/3/reference/compound_stmts.html#with) statements.
acquire(*block\=True*, *timeout\=None*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock.acquire "Link to this definition")
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 argumentâs behavior compared to the implementation of [`threading.RLock.acquire()`](https://docs.python.org/3/library/threading.html#threading.RLock.acquire "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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock.acquire "multiprocessing.Lock.acquire"). Note that some of these behaviors of *timeout* differ from the implemented behaviors in [`threading.RLock.acquire()`](https://docs.python.org/3/library/threading.html#threading.RLock.acquire "threading.RLock.acquire").
release()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock.release "Link to this definition")
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`](https://docs.python.org/3/library/exceptions.html#AssertionError "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()`](https://docs.python.org/3/library/threading.html#threading.RLock.release "threading.RLock.release").
locked()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock.locked "Link to this definition")
Return a boolean indicating whether this object is locked right now.
Added in version 3.14.
*class* multiprocessing.Semaphore(\[*value*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Semaphore "Link to this definition")
A semaphore object: a close analog of [`threading.Semaphore`](https://docs.python.org/3/library/threading.html#threading.Semaphore "threading.Semaphore").
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
A solitary difference from its close analog exists: its `acquire` methodâs first argument is named *block*, as is consistent with [`Lock.acquire()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock.acquire "multiprocessing.Lock.acquire").
get\_value()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Semaphore.get_value "Link to this definition")
Return the current value of semaphore.
Note that this may raise [`NotImplementedError`](https://docs.python.org/3/library/exceptions.html#NotImplementedError "NotImplementedError") on platforms like macOS where `sem_getvalue()` is not implemented.
locked()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Semaphore.locked "Link to this definition")
Return a boolean indicating whether this object is locked right now.
Added in version 3.14.
Note
On macOS, `sem_timedwait` is unsupported, so calling `acquire()` with a timeout will emulate that functionâs behavior using a sleeping loop.
Note
Some of this packageâs 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`](https://docs.python.org/3/library/exceptions.html#ImportError "ImportError"). See [bpo-3770](https://bugs.python.org/issue?@action=redirect&bpo=3770) for additional information.
### Shared [`ctypes`](https://docs.python.org/3/library/ctypes.html#module-ctypes "ctypes: A foreign function library for Python.") Objects[¶](https://docs.python.org/3/library/multiprocessing.html#shared-ctypes-objects "Link to this heading")
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*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Value "Link to this definition")
Return a [`ctypes`](https://docs.python.org/3/library/ctypes.html#module-ctypes "ctypes: A foreign function library for Python.") 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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Value "multiprocessing.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`](https://docs.python.org/3/library/array.html#module-array "array: Space efficient arrays of uniformly typed numeric values.") 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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock "multiprocessing.Lock") or [`RLock`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock "multiprocessing.RLock") object then that will be used to synchronize access to the value. If *lock* is `False` then access to the returned object will not be automatically protected by a lock, so it will not necessarily be âprocess-safeâ.
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
Copy
```
counter.value += 1
```
Assuming the associated lock is recursive (which it is by default) you can instead do
Copy
```
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*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Array "Link to this definition")
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](https://docs.python.org/3/library/ctypes.html#ctypes-fundamental-data-types) or a one character typecode of the kind used by the [`array`](https://docs.python.org/3/library/array.html#module-array "array: Space efficient arrays of uniformly typed numeric values.") module with the exception of `'w'`, which is not supported. In addition, the `'c'` typecode is an alias for [`ctypes.c_char`](https://docs.python.org/3/library/ctypes.html#ctypes.c_char "ctypes.c_char"). 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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock "multiprocessing.Lock") or [`RLock`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock "multiprocessing.RLock") object then that will be used to synchronize access to the value. If *lock* is `False` then access to the returned object will not be automatically protected by a lock, so it will not necessarily be âprocess-safeâ.
Note that *lock* is a keyword only argument.
Note that an array of [`ctypes.c_char`](https://docs.python.org/3/library/ctypes.html#ctypes.c_char "ctypes.c_char") has *value* and *raw* attributes which allow one to use it to store and retrieve strings.
#### The `multiprocessing.sharedctypes` module[¶](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.sharedctypes "Link to this heading")
The `multiprocessing.sharedctypes` module provides functions for allocating [`ctypes`](https://docs.python.org/3/library/ctypes.html#module-ctypes "ctypes: A foreign function library for Python.") 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*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.RawArray "Link to this definition")
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`](https://docs.python.org/3/library/array.html#module-array "array: Space efficient arrays of uniformly typed numeric values.") 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 â use [`Array()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.Array "multiprocessing.sharedctypes.Array") instead to make sure that access is automatically synchronized using a lock.
multiprocessing.sharedctypes.RawValue(*typecode\_or\_type*, *\*args*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.RawValue "Link to this definition")
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`](https://docs.python.org/3/library/array.html#module-array "array: Space efficient arrays of uniformly typed numeric values.") module. *\*args* is passed on to the constructor for the type.
Note that setting and getting the value is potentially non-atomic â use [`Value()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.Value "multiprocessing.sharedctypes.Value") instead to make sure that access is automatically synchronized using a lock.
Note that an array of [`ctypes.c_char`](https://docs.python.org/3/library/ctypes.html#ctypes.c_char "ctypes.c_char") has `value` and `raw` attributes which allow one to use it to store and retrieve strings â see documentation for [`ctypes`](https://docs.python.org/3/library/ctypes.html#module-ctypes "ctypes: A foreign function library for Python.").
multiprocessing.sharedctypes.Array(*typecode\_or\_type*, *size\_or\_initializer*, *\**, *lock\=True*, *ctx\=None*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.Array "Link to this definition")
The same as [`RawArray()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.RawArray "multiprocessing.sharedctypes.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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock "multiprocessing.Lock") or [`RLock`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock "multiprocessing.RLock") object then that will be used to synchronize access to the value. If *lock* is `False` then access to the returned object will not be automatically protected by a lock, so it will not necessarily be âprocess-safeâ.
*ctx* is a context object, or `None` (use the current context). If `None`, calling this may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
Note that *lock* and *ctx* are keyword-only parameters.
multiprocessing.sharedctypes.Value(*typecode\_or\_type*, *\*args*, *lock\=True*, *ctx\=None*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.Value "Link to this definition")
The same as [`RawValue()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.RawValue "multiprocessing.sharedctypes.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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock "multiprocessing.Lock") or [`RLock`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock "multiprocessing.RLock") object then that will be used to synchronize access to the value. If *lock* is `False` then access to the returned object will not be automatically protected by a lock, so it will not necessarily be âprocess-safeâ.
*ctx* is a context object, or `None` (use the current context). If `None`, calling this may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
Note that *lock* and *ctx* are keyword-only parameters.
multiprocessing.sharedctypes.copy(*obj*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.copy "Link to this definition")
Return a ctypes object allocated from shared memory which is a copy of the ctypes object *obj*.
multiprocessing.sharedctypes.synchronized(*obj*, *lock\=None*, *ctx\=None*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.synchronized "Link to this definition")
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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock "multiprocessing.RLock") object is created automatically.
*ctx* is a context object, or `None` (use the current context). If `None`, calling this may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
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](https://docs.python.org/3/glossary.html#term-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`](https://docs.python.org/3/library/ctypes.html#ctypes.Structure "ctypes.Structure").)
| ctypes | sharedctypes using type | sharedctypes using typecode |
|---|---|---|
| c\_double(2.4) | RawValue(c\_double, 2.4) | RawValue(âdâ, 2.4) |
| MyStruct(4, 6) | RawValue(MyStruct, 4, 6) | |
| (c\_short \* 7)() | RawArray(c\_short, 7) | RawArray(âhâ, 7) |
| (c\_int \* 3)(9, 2, 8) | RawArray(c\_int, (9, 2, 8)) | RawArray(âiâ, (9, 2, 8)) |
Below is an example where a number of ctypes objects are modified by a child process:
Copy
```
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[¶](https://docs.python.org/3/library/multiprocessing.html#managers "Link to this heading")
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()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Manager "Link to this definition")
Returns a started [`SyncManager`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager "multiprocessing.managers.SyncManager") object which can be used for sharing objects between processes. The returned manager object corresponds to a spawned child process and has methods which will create shared objects and return corresponding proxies.
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`](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.managers "multiprocessing.managers: Share data between process with shared objects.") module:
*class* multiprocessing.managers.BaseManager(*address\=None*, *authkey\=None*, *serializer\='pickle'*, *ctx\=None*, *\**, *shutdown\_timeout\=1\.0*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager "Link to this definition")
Create a BaseManager object.
Once created one should call [`start()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.start "multiprocessing.managers.BaseManager.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.
*serializer* must be `'pickle'` (use [`pickle`](https://docs.python.org/3/library/pickle.html#module-pickle "pickle: Convert Python objects to streams of bytes and back.") serialization) or `'xmlrpclib'` (use [`xmlrpc.client`](https://docs.python.org/3/library/xmlrpc.client.html#module-xmlrpc.client "xmlrpc.client: XML-RPC client access.") serialization).
*ctx* is a context object, or `None` (use the current context). If `None`, calling this may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
*shutdown\_timeout* is a timeout in seconds used to wait until the process used by the manager completes in the [`shutdown()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.shutdown "multiprocessing.managers.BaseManager.shutdown") method. If the shutdown times out, the process is terminated. If terminating the process also times out, the process is killed.
Changed in version 3.11: Added the *shutdown\_timeout* parameter.
start(\[*initializer*\[, *initargs*\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.start "Link to this definition")
Start a subprocess to start the manager. If *initializer* is not `None` then the subprocess will call `initializer(*initargs)` when it starts.
get\_server()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.get_server "Link to this definition")
Returns a `Server` object which represents the actual server under the control of the Manager. The `Server` object supports the `serve_forever()` method:
Copy
```
>>> from multiprocessing.managers import BaseManager
>>> manager = BaseManager(address=('', 50000), authkey=b'abc')
>>> server = manager.get_server()
>>> server.serve_forever()
```
`Server` additionally has an [`address`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.address "multiprocessing.managers.BaseManager.address") attribute.
connect()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.connect "Link to this definition")
Connect a local manager object to a remote manager process:
Copy
```
>>> from multiprocessing.managers import BaseManager
>>> m = BaseManager(address=('127.0.0.1', 50000), authkey=b'abc')
>>> m.connect()
```
shutdown()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.shutdown "Link to this definition")
Stop the process used by the manager. This is only available if [`start()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.start "multiprocessing.managers.BaseManager.start") has been used to start the server process.
This can be called multiple times.
register(*typeid*\[, *callable*\[, *proxytype*\[, *exposed*\[, *method\_to\_typeid*\[, *create\_method*\]\]\]\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.register "Link to this definition")
A classmethod which can be used for registering a type or callable with the manager class.
*typeid* is a âtype identifierâ 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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.connect "multiprocessing.managers.BaseManager.connect") method, or if the *create\_method* argument is `False` then this can be left as `None`.
*proxytype* is a subclass of [`BaseProxy`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseProxy "multiprocessing.managers.BaseProxy") which is used to create proxies for shared objects with this *typeid*. If `None` then a proxy class is created automatically.
*exposed* is used to specify a sequence of method names which proxies for this typeid should be allowed to access using [`BaseProxy._callmethod()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseProxy._callmethod "multiprocessing.managers.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 âpublic methodsâ of the shared object will be accessible. (Here a âpublic methodâ means any attribute which has a [`__call__()`](https://docs.python.org/3/reference/datamodel.html#object.__call__ "object.__call__") method and whose name does not begin with `'_'`.)
*method\_to\_typeid* is a mapping used to specify the return type of those exposed methods which should return a proxy. It maps method names to typeid strings. (If *method\_to\_typeid* is `None` then `proxytype._method_to_typeid_` is used instead if it exists.) If a methodâs 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[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.address "Link to this definition")
The address used by the manager.
Changed in version 3.3: Manager objects support the context management protocol â see [Context Manager Types](https://docs.python.org/3/library/stdtypes.html#typecontextmanager). [`__enter__()`](https://docs.python.org/3/library/stdtypes.html#contextmanager.__enter__ "contextmanager.__enter__") starts the server process (if it has not already started) and then returns the manager object. [`__exit__()`](https://docs.python.org/3/library/stdtypes.html#contextmanager.__exit__ "contextmanager.__exit__") calls [`shutdown()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.shutdown "multiprocessing.managers.BaseManager.shutdown").
In previous versions [`__enter__()`](https://docs.python.org/3/library/stdtypes.html#contextmanager.__enter__ "contextmanager.__enter__") did not start the managerâs server process if it was not already started.
*class* multiprocessing.managers.SyncManager[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager "Link to this definition")
A subclass of [`BaseManager`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager "multiprocessing.managers.BaseManager") which can be used for the synchronization of processes. Objects of this type are returned by [`multiprocessing.Manager()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Manager "multiprocessing.Manager").
Its methods create and return [Proxy Objects](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-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*\[, *timeout*\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.Barrier "Link to this definition")
Create a shared [`threading.Barrier`](https://docs.python.org/3/library/threading.html#threading.Barrier "threading.Barrier") object and return a proxy for it.
Added in version 3.3.
BoundedSemaphore(\[*value*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.BoundedSemaphore "Link to this definition")
Create a shared [`threading.BoundedSemaphore`](https://docs.python.org/3/library/threading.html#threading.BoundedSemaphore "threading.BoundedSemaphore") object and return a proxy for it.
Condition(\[*lock*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.Condition "Link to this definition")
Create a shared [`threading.Condition`](https://docs.python.org/3/library/threading.html#threading.Condition "threading.Condition") object and return a proxy for it.
If *lock* is supplied then it should be a proxy for a [`threading.Lock`](https://docs.python.org/3/library/threading.html#threading.Lock "threading.Lock") or [`threading.RLock`](https://docs.python.org/3/library/threading.html#threading.RLock "threading.RLock") object.
Changed in version 3.3: The [`wait_for()`](https://docs.python.org/3/library/threading.html#threading.Condition.wait_for "threading.Condition.wait_for") method was added.
Event()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.Event "Link to this definition")
Create a shared [`threading.Event`](https://docs.python.org/3/library/threading.html#threading.Event "threading.Event") object and return a proxy for it.
Lock()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.Lock "Link to this definition")
Create a shared [`threading.Lock`](https://docs.python.org/3/library/threading.html#threading.Lock "threading.Lock") object and return a proxy for it.
Namespace()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.Namespace "Link to this definition")
Create a shared [`Namespace`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.Namespace "multiprocessing.managers.Namespace") object and return a proxy for it.
Queue(\[*maxsize*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.Queue "Link to this definition")
Create a shared [`queue.Queue`](https://docs.python.org/3/library/queue.html#queue.Queue "queue.Queue") object and return a proxy for it.
RLock()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.RLock "Link to this definition")
Create a shared [`threading.RLock`](https://docs.python.org/3/library/threading.html#threading.RLock "threading.RLock") object and return a proxy for it.
Semaphore(\[*value*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.Semaphore "Link to this definition")
Create a shared [`threading.Semaphore`](https://docs.python.org/3/library/threading.html#threading.Semaphore "threading.Semaphore") object and return a proxy for it.
Array(*typecode*, *sequence*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.Array "Link to this definition")
Create an array and return a proxy for it.
Value(*typecode*, *value*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.Value "Link to this definition")
Create an object with a writable `value` attribute and return a proxy for it.
dict()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.dict "Link to this definition")
dict(*mapping*)
dict(*sequence*)
Create a shared [`dict`](https://docs.python.org/3/library/stdtypes.html#dict "dict") object and return a proxy for it.
list()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.list "Link to this definition")
list(*sequence*)
Create a shared [`list`](https://docs.python.org/3/library/stdtypes.html#list "list") object and return a proxy for it.
set()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.set "Link to this definition")
set(*sequence*)
set(*mapping*)
Create a shared [`set`](https://docs.python.org/3/library/stdtypes.html#set "set") object and return a proxy for it.
Added in version 3.14: [`set`](https://docs.python.org/3/library/stdtypes.html#set "set") support was added.
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[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.Namespace "Link to this definition")
A type that can register with [`SyncManager`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager "multiprocessing.managers.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:
Copy
```
>>> mp_context = multiprocessing.get_context('spawn')
>>> manager = mp_context.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[¶](https://docs.python.org/3/library/multiprocessing.html#customized-managers "Link to this heading")
To create oneâs own manager, one creates a subclass of [`BaseManager`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager "multiprocessing.managers.BaseManager") and uses the [`register()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.register "multiprocessing.managers.BaseManager.register") classmethod to register new types or callables with the manager class. For example:
Copy
```
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[¶](https://docs.python.org/3/library/multiprocessing.html#using-a-remote-manager "Link to this heading")
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:
Copy
```
>>> 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:
Copy
```
>>> 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:
Copy
```
>>> 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:
Copy
```
>>> 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[¶](https://docs.python.org/3/library/multiprocessing.html#proxy-objects "Link to this heading")
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:
Copy
```
>>> mp_context = multiprocessing.get_context('spawn')
>>> manager = mp_context.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()`](https://docs.python.org/3/library/stdtypes.html#str "str") to a proxy will return the representation of the referent, whereas applying [`repr()`](https://docs.python.org/3/library/functions.html#repr "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](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-proxy-objects). This permits nesting of these managed lists, dicts, and other [Proxy Objects](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-proxy-objects):
Copy
```
>>> 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:
Copy
```
>>> 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`](https://docs.python.org/3/library/stdtypes.html#list "list") or [`dict`](https://docs.python.org/3/library/stdtypes.html#dict "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:
Copy
```
# 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](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-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:
Copy
```
>>> 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[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseProxy "Link to this definition")
Proxy objects are instances of subclasses of `BaseProxy`.
\_callmethod(*methodname*\[, *args*\[, *kwds*\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseProxy._callmethod "Link to this definition")
Call and return the result of a method of the proxyâs referent.
If `proxy` is a proxy whose referent is `obj` then the expression
Copy
```
proxy._callmethod(methodname, args, kwds)
```
will evaluate the expression
Copy
```
getattr(obj, methodname)(*args, **kwds)
```
in the managerâs process.
The returned value will be a copy of the result of the call or a proxy to a new shared object â see documentation for the *method\_to\_typeid* argument of [`BaseManager.register()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.register "multiprocessing.managers.BaseManager.register").
If an exception is raised by the call, then is re-raised by `_callmethod()`. If some other exception is raised in the managerâs 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()`:
Copy
```
>>> 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()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseProxy._getvalue "Link to this definition")
Return a copy of the referent.
If the referent is unpicklable then this will raise an exception.
\_\_repr\_\_()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseProxy.__repr__ "Link to this definition")
Return a representation of the proxy object.
\_\_str\_\_()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseProxy.__str__ "Link to this definition")
Return the representation of the referent.
#### Cleanup[¶](https://docs.python.org/3/library/multiprocessing.html#cleanup "Link to this heading")
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[¶](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.pool "Link to this heading")
One can create a pool of processes which will carry out tasks submitted to it with the [`Pool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.Pool") class.
*class* multiprocessing.pool.Pool(\[*processes*\[, *initializer*\[, *initargs*\[, *maxtasksperchild*\[, *context*\]\]\]\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "Link to this definition")
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.process_cpu_count()`](https://docs.python.org/3/library/os.html#os.process_cpu_count "os.process_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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.Pool") method of a context object. In both cases *context* is set appropriately. If `None`, calling this function will have the side effect of setting the current global start method if it has not been set already. See the `get_context()` function.
Note that the methods of the pool object should only be called by the process which created the pool.
Warning
[`multiprocessing.pool`](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.pool "multiprocessing.pool: Create pools of processes.") 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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.close "multiprocessing.pool.Pool.close") and [`terminate()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.terminate "multiprocessing.pool.Pool.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__()`](https://docs.python.org/3/reference/datamodel.html#object.__del__ "object.__del__") for more information).
Changed in version 3.2: Added the *maxtasksperchild* parameter.
Changed in version 3.4: Added the *context* parameter.
Changed in version 3.13: *processes* uses [`os.process_cpu_count()`](https://docs.python.org/3/library/os.html#os.process_cpu_count "os.process_cpu_count") by default, instead of [`os.cpu_count()`](https://docs.python.org/3/library/os.html#os.cpu_count "os.cpu_count").
Note
Worker processes within a `Pool` typically live for the complete duration of the Poolâs 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 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*\[, *kwds*\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.apply "Link to this definition")
Call *func* with arguments *args* and keyword arguments *kwds*. It blocks until the result is ready. Given this blocks, [`apply_async()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.apply_async "multiprocessing.pool.Pool.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*\[, *error\_callback*\]\]\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.apply_async "Link to this definition")
A variant of the [`apply()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.apply "multiprocessing.pool.Pool.apply") method which returns a [`AsyncResult`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.AsyncResult "multiprocessing.pool.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*\[, *chunksize*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map "Link to this definition")
A parallel equivalent of the [`map()`](https://docs.python.org/3/library/functions.html#map "map") built-in function (it supports only one *iterable* argument though, for multiple iterables see [`starmap()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.starmap "multiprocessing.pool.Pool.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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.imap "multiprocessing.pool.Pool.imap") or [`imap_unordered()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.imap_unordered "multiprocessing.pool.Pool.imap_unordered") with explicit *chunksize* option for better efficiency.
map\_async(*func*, *iterable*\[, *chunksize*\[, *callback*\[, *error\_callback*\]\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map_async "Link to this definition")
A variant of the [`map()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map "multiprocessing.pool.Pool.map") method which returns a [`AsyncResult`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.AsyncResult "multiprocessing.pool.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*\[, *chunksize*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.imap "Link to this definition")
A lazier version of [`map()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map "multiprocessing.pool.Pool.map").
The *chunksize* argument is the same as the one used by the [`map()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map "multiprocessing.pool.Pool.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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.TimeoutError "multiprocessing.TimeoutError") if the result cannot be returned within *timeout* seconds.
imap\_unordered(*func*, *iterable*\[, *chunksize*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.imap_unordered "Link to this definition")
The same as [`imap()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.imap "multiprocessing.pool.Pool.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 âcorrectâ.)
starmap(*func*, *iterable*\[, *chunksize*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.starmap "Link to this definition")
Like [`map()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map "multiprocessing.pool.Pool.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 .
Added in version 3.3.
starmap\_async(*func*, *iterable*\[, *chunksize*\[, *callback*\[, *error\_callback*\]\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.starmap_async "Link to this definition")
A combination of [`starmap()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.starmap "multiprocessing.pool.Pool.starmap") and [`map_async()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map_async "multiprocessing.pool.Pool.map_async") that iterates over *iterable* of iterables and calls *func* with the iterables unpacked. Returns a result object.
Added in version 3.3.
close()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.close "Link to this definition")
Prevents any more tasks from being submitted to the pool. Once all the tasks have been completed the worker processes will exit.
terminate()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.terminate "Link to this definition")
Stops the worker processes immediately without completing outstanding work. When the pool object is garbage collected `terminate()` will be called immediately.
join()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.join "Link to this definition")
Wait for the worker processes to exit. One must call [`close()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.close "multiprocessing.pool.Pool.close") or [`terminate()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.terminate "multiprocessing.pool.Pool.terminate") before using `join()`.
Changed in version 3.3: Pool objects now support the context management protocol â see [Context Manager Types](https://docs.python.org/3/library/stdtypes.html#typecontextmanager). [`__enter__()`](https://docs.python.org/3/library/stdtypes.html#contextmanager.__enter__ "contextmanager.__enter__") returns the pool object, and [`__exit__()`](https://docs.python.org/3/library/stdtypes.html#contextmanager.__exit__ "contextmanager.__exit__") calls [`terminate()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.terminate "multiprocessing.pool.Pool.terminate").
*class* multiprocessing.pool.AsyncResult[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.AsyncResult "Link to this definition")
The class of the result returned by [`Pool.apply_async()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.apply_async "multiprocessing.pool.Pool.apply_async") and [`Pool.map_async()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map_async "multiprocessing.pool.Pool.map_async").
get(\[*timeout*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.AsyncResult.get "Link to this definition")
Return the result when it arrives. If *timeout* is not `None` and the result does not arrive within *timeout* seconds then [`multiprocessing.TimeoutError`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.TimeoutError "multiprocessing.TimeoutError") is raised. If the remote call raised an exception then that exception will be reraised by `get()`.
wait(\[*timeout*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.AsyncResult.wait "Link to this definition")
Wait until the result is available or until *timeout* seconds pass.
ready()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.AsyncResult.ready "Link to this definition")
Return whether the call has completed.
successful()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.AsyncResult.successful "Link to this definition")
Return whether the call completed without raising an exception. Will raise [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError "ValueError") if the result is not ready.
Changed in version 3.7: If the result is not ready, [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError "ValueError") is raised instead of [`AssertionError`](https://docs.python.org/3/library/exceptions.html#AssertionError "AssertionError").
The following example demonstrates the use of a pool:
Copy
```
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[¶](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.connection "Link to this heading")
Usually message passing between processes is done using queues or by using [`Connection`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection "multiprocessing.connection.Connection") objects returned by [`Pipe()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Pipe "multiprocessing.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`](https://docs.python.org/3/library/hmac.html#module-hmac "hmac: Keyed-Hashing for Message Authentication (HMAC) implementation") module, and for polling multiple connections at the same time.
multiprocessing.connection.deliver\_challenge(*connection*, *authkey*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.deliver_challenge "Link to this definition")
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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.AuthenticationError "multiprocessing.AuthenticationError") is raised.
multiprocessing.connection.answer\_challenge(*connection*, *authkey*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.answer_challenge "Link to this definition")
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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.AuthenticationError "multiprocessing.AuthenticationError") is raised.
multiprocessing.connection.Client(*address*\[, *family*\[, *authkey*\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Client "Link to this definition")
Attempt to set up a connection to the listener which is using address *address*, returning a [`Connection`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection "multiprocessing.connection.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](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.AuthenticationError "multiprocessing.AuthenticationError") is raised if authentication fails. See [Authentication keys](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-auth-keys).
*class* multiprocessing.connection.Listener(\[*address*\[, *family*\[, *backlog*\[, *authkey*\]\]\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Listener "Link to this definition")
A wrapper for a bound socket or Windows named pipe which is âlisteningâ 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 â0.0.0.0â is used, the address will not be a connectable end point on Windows. If you require a connectable end-point, you should use â127.0.0.1â.
*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](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-address-formats). Note that if *family* is `'AF_UNIX'` and address is `None` then the socket will be created in a private temporary directory created using [`tempfile.mkstemp()`](https://docs.python.org/3/library/tempfile.html#tempfile.mkstemp "tempfile.mkstemp").
If the listener object uses a socket then *backlog* (1 by default) is passed to the [`listen()`](https://docs.python.org/3/library/socket.html#socket.socket.listen "socket.socket.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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.AuthenticationError "multiprocessing.AuthenticationError") is raised if authentication fails. See [Authentication keys](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-auth-keys).
accept()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Listener.accept "Link to this definition")
Accept a connection on the bound socket or named pipe of the listener object and return a [`Connection`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection "multiprocessing.connection.Connection") object. If authentication is attempted and fails, then [`AuthenticationError`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.AuthenticationError "multiprocessing.AuthenticationError") is raised.
close()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Listener.close "Link to this definition")
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[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Listener.address "Link to this definition")
The address which is being used by the Listener object.
last\_accepted[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Listener.last_accepted "Link to this definition")
The address from which the last accepted connection came. If this is unavailable then it is `None`.
Changed in version 3.3: Listener objects now support the context management protocol â see [Context Manager Types](https://docs.python.org/3/library/stdtypes.html#typecontextmanager). [`__enter__()`](https://docs.python.org/3/library/stdtypes.html#contextmanager.__enter__ "contextmanager.__enter__") returns the listener object, and [`__exit__()`](https://docs.python.org/3/library/stdtypes.html#contextmanager.__exit__ "contextmanager.__exit__") calls [`close()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Listener.close "multiprocessing.connection.Listener.close").
multiprocessing.connection.wait(*object\_list*, *timeout\=None*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.wait "Link to this definition")
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 POSIX and Windows, an object can appear in *object\_list* if it is
- a readable [`Connection`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection "multiprocessing.connection.Connection") object;
- a connected and readable [`socket.socket`](https://docs.python.org/3/library/socket.html#socket.socket "socket.socket") object; or
- the [`sentinel`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.sentinel "multiprocessing.Process.sentinel") attribute of a [`Process`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.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.
**POSIX**: `wait(object_list, timeout)` almost equivalent `select.select(object_list, [], [], timeout)`. The difference is that, if [`select.select()`](https://docs.python.org/3/library/select.html#select.select "select.select") is interrupted by a signal, it can raise [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError "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()`](https://docs.python.org/3/library/io.html#io.IOBase.fileno "io.IOBase.fileno") method which returns a socket handle or pipe handle. (Note that pipe handles and socket handles are **not** waitable handles.)
Added 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:
Copy
```
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:
Copy
```
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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.wait "multiprocessing.connection.wait") to wait for messages from multiple processes at once:
Copy
```
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[¶](https://docs.python.org/3/library/multiprocessing.html#address-formats "Link to this heading")
- 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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Client "multiprocessing.connection.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[¶](https://docs.python.org/3/library/multiprocessing.html#authentication-keys "Link to this heading")
When one uses [`Connection.recv`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.recv "multiprocessing.connection.Connection.recv"), the data received is automatically unpickled. Unfortunately unpickling data from an untrusted source is a security risk. Therefore [`Listener`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Listener "multiprocessing.connection.Listener") and [`Client()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Client "multiprocessing.connection.Client") use the [`hmac`](https://docs.python.org/3/library/hmac.html#module-hmac "hmac: Keyed-Hashing for Message Authentication (HMAC) implementation") 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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.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()`](https://docs.python.org/3/library/os.html#os.urandom "os.urandom").
### Logging[¶](https://docs.python.org/3/library/multiprocessing.html#logging "Link to this heading")
Some support for logging is available. Note, however, that the [`logging`](https://docs.python.org/3/library/logging.html#module-logging "logging: Flexible event logging system for applications.") 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()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.get_logger "Link to this definition")
Returns the logger used by `multiprocessing`. If necessary, a new one will be created.
When first created the logger has level [`logging.NOTSET`](https://docs.python.org/3/library/logging.html#logging.NOTSET "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 processâs logger â any other customization of the logger will not be inherited.
multiprocessing.log\_to\_stderr(*level\=None*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.log_to_stderr "Link to this definition")
This function performs a call to [`get_logger()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.get_logger "multiprocessing.get_logger") but in addition to returning the logger created by get\_logger, it adds a handler which sends output to [`sys.stderr`](https://docs.python.org/3/library/sys.html#sys.stderr "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:
Copy
```
>>> 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`](https://docs.python.org/3/library/logging.html#module-logging "logging: Flexible event logging system for applications.") module.
### The `multiprocessing.dummy` module[¶](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.dummy "Link to this heading")
`multiprocessing.dummy` replicates the API of `multiprocessing` but is no more than a wrapper around the [`threading`](https://docs.python.org/3/library/threading.html#module-threading "threading: Thread-based parallelism.") module.
In particular, the `Pool` function provided by `multiprocessing.dummy` returns an instance of [`ThreadPool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.ThreadPool "multiprocessing.pool.ThreadPool"), which is a subclass of [`Pool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.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*\[, *initargs*\]\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.ThreadPool "Link to this definition")
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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.Pool") instances, and their resources must also be properly managed, either by using the pool as a context manager or by calling [`close()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.close "multiprocessing.pool.Pool.close") and [`terminate()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.terminate "multiprocessing.pool.Pool.terminate") manually.
*processes* is the number of worker threads to use. If *processes* is `None` then the number returned by [`os.process_cpu_count()`](https://docs.python.org/3/library/os.html#os.process_cpu_count "os.process_cpu_count") is used.
If *initializer* is not `None` then each worker process will call `initializer(*initargs)` when it starts.
Unlike [`Pool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.Pool"), *maxtasksperchild* and *context* cannot be provided.
Note
A `ThreadPool` shares the same interface as [`Pool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.Pool"), which is designed around a pool of processes and predates the introduction of the [`concurrent.futures`](https://docs.python.org/3/library/concurrent.futures.html#module-concurrent.futures "concurrent.futures: Execute computations concurrently using threads or processes.") module. As such, it inherits some operations that donât make sense for a pool backed by threads, and it has its own type for representing the status of asynchronous jobs, [`AsyncResult`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.AsyncResult "multiprocessing.pool.AsyncResult"), that is not understood by any other libraries.
Users should generally prefer to use [`concurrent.futures.ThreadPoolExecutor`](https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.ThreadPoolExecutor "concurrent.futures.ThreadPoolExecutor"), which has a simpler interface that was designed around threads from the start, and which returns [`concurrent.futures.Future`](https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.Future "concurrent.futures.Future") instances that are compatible with many other libraries, including [`asyncio`](https://docs.python.org/3/library/asyncio.html#module-asyncio "asyncio: Asynchronous I/O.").
## Programming guidelines[¶](https://docs.python.org/3/library/multiprocessing.html#programming-guidelines "Link to this heading")
There are certain guidelines and idioms which should be adhered to when using `multiprocessing`.
### All start methods[¶](https://docs.python.org/3/library/multiprocessing.html#all-start-methods "Link to this heading")
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 POSIX 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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.active_children "multiprocessing.active_children") is called) all completed processes which have not yet been joined will be joined. Also calling a finished processâs [`Process.is_alive`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.is_alive "multiprocessing.Process.is_alive") will join the process. Even so it is probably good practice to explicitly join all the processes that you start.
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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.terminate "multiprocessing.Process.terminate") method to stop a process is liable to cause any shared resources (such as locks, semaphores, pipes and queues) currently being used by the process to become broken or unavailable to other processes.
>
> Therefore it is probably best to only consider using [`Process.terminate`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.terminate "multiprocessing.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 âfeederâ thread to the underlying pipe. (The child process can call the [`Queue.cancel_join_thread`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.cancel_join_thread "multiprocessing.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:
>
> Copy
> ```
> 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 POSIX 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
>
> Copy
> ```
> 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
>
> Copy
> ```
> 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`](https://docs.python.org/3/library/sys.html#sys.stdin "sys.stdin") with a âfile like objectâ
> `multiprocessing` originally unconditionally called:
>
> Copy
> ```
> os.close(sys.stdin.fileno())
> ```
>
> in the `multiprocessing.Process._bootstrap()` method â this resulted in issues with processes-in-processes. This has been changed to:
>
> Copy
> ```
> 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()`](https://docs.python.org/3/library/sys.html#sys.stdin "sys.stdin") with a âfile-like objectâ with output buffering. This danger is that if multiple processes call [`close()`](https://docs.python.org/3/library/io.html#io.IOBase.close "io.IOBase.close") on this file-like object, it could result in the same data being flushed to the object multiple times, resulting in corruption.
>
> 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:
>
> Copy
> ```
> @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](https://bugs.python.org/issue?@action=redirect&bpo=5155), [bpo-5313](https://bugs.python.org/issue?@action=redirect&bpo=5313) and [bpo-5331](https://bugs.python.org/issue?@action=redirect&bpo=5331)
### The *spawn* and *forkserver* start methods[¶](https://docs.python.org/3/library/multiprocessing.html#the-spawn-and-forkserver-start-methods "Link to this heading")
There are a few extra restrictions which donât apply to the *fork* start method.
More picklability
> Ensure that all arguments to [`Process`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.Process") are picklable. Also, if you subclass `Process.__init__`, you must make sure that instances will be picklable when the [`Process.start`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.start "multiprocessing.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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.start "multiprocessing.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 as starting a new process).
>
> For example, using the *spawn* or *forkserver* start method running the following module would fail with a [`RuntimeError`](https://docs.python.org/3/library/exceptions.html#RuntimeError "RuntimeError"):
>
> Copy
> ```
> from multiprocessing import Process
def foo():
print('hello')
p = Process(target=foo)
p.start()
> ```
>
> Instead one should protect the âentry pointâ of the program by using as follows:
>
> Copy
> ```
> 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 moduleâs `foo()` function.
>
> Similar restrictions apply if a pool or manager is created in the main module.
## Examples[¶](https://docs.python.org/3/library/multiprocessing.html#examples "Link to this heading")
Demonstration of how to create and use customized managers and proxies:
Copy
```
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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.Pool"):
Copy
```
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:
Copy
```
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()
```
### [Table of Contents](https://docs.python.org/3/contents.html)
- [`multiprocessing` â Process-based parallelism](https://docs.python.org/3/library/multiprocessing.html)
- [Introduction](https://docs.python.org/3/library/multiprocessing.html#introduction)
- [The `Process` class](https://docs.python.org/3/library/multiprocessing.html#the-process-class)
- [Contexts and start methods](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods)
- [Exchanging objects between processes](https://docs.python.org/3/library/multiprocessing.html#exchanging-objects-between-processes)
- [Synchronization between processes](https://docs.python.org/3/library/multiprocessing.html#synchronization-between-processes)
- [Sharing state between processes](https://docs.python.org/3/library/multiprocessing.html#sharing-state-between-processes)
- [Using a pool of workers](https://docs.python.org/3/library/multiprocessing.html#using-a-pool-of-workers)
- [Reference](https://docs.python.org/3/library/multiprocessing.html#reference)
- [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method)
- [`Process` and exceptions](https://docs.python.org/3/library/multiprocessing.html#process-and-exceptions)
- [Pipes and Queues](https://docs.python.org/3/library/multiprocessing.html#pipes-and-queues)
- [Miscellaneous](https://docs.python.org/3/library/multiprocessing.html#miscellaneous)
- [Connection Objects](https://docs.python.org/3/library/multiprocessing.html#connection-objects)
- [Synchronization primitives](https://docs.python.org/3/library/multiprocessing.html#synchronization-primitives)
- [Shared `ctypes` Objects](https://docs.python.org/3/library/multiprocessing.html#shared-ctypes-objects)
- [The `multiprocessing.sharedctypes` module](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.sharedctypes)
- [Managers](https://docs.python.org/3/library/multiprocessing.html#managers)
- [Customized managers](https://docs.python.org/3/library/multiprocessing.html#customized-managers)
- [Using a remote manager](https://docs.python.org/3/library/multiprocessing.html#using-a-remote-manager)
- [Proxy Objects](https://docs.python.org/3/library/multiprocessing.html#proxy-objects)
- [Cleanup](https://docs.python.org/3/library/multiprocessing.html#cleanup)
- [Process Pools](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.pool)
- [Listeners and Clients](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.connection)
- [Address Formats](https://docs.python.org/3/library/multiprocessing.html#address-formats)
- [Authentication keys](https://docs.python.org/3/library/multiprocessing.html#authentication-keys)
- [Logging](https://docs.python.org/3/library/multiprocessing.html#logging)
- [The `multiprocessing.dummy` module](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.dummy)
- [Programming guidelines](https://docs.python.org/3/library/multiprocessing.html#programming-guidelines)
- [All start methods](https://docs.python.org/3/library/multiprocessing.html#all-start-methods)
- [The *spawn* and *forkserver* start methods](https://docs.python.org/3/library/multiprocessing.html#the-spawn-and-forkserver-start-methods)
- [Examples](https://docs.python.org/3/library/multiprocessing.html#examples)
#### Previous topic
[`threading` â Thread-based parallelism](https://docs.python.org/3/library/threading.html "previous chapter")
#### Next topic
[`multiprocessing.shared_memory` â Shared memory for direct access across processes](https://docs.python.org/3/library/multiprocessing.shared_memory.html "next chapter")
### This page
- [Report a bug](https://docs.python.org/3/bugs.html)
- [Improve this page](https://docs.python.org/3/improve-page.html?pagetitle=multiprocessing+%E2%80%94+Process-based+parallelism&pageurl=https%3A%2F%2Fdocs.python.org%2F3%2Flibrary%2Fmultiprocessing.html&pagesource=library%2Fmultiprocessing.rst)
- [Show source](https://github.com/python/cpython/blob/main/Doc/library/multiprocessing.rst?plain=1)
«
### Navigation
- [index](https://docs.python.org/3/genindex.html "General Index")
- [modules](https://docs.python.org/3/py-modindex.html "Python Module Index") \|
- [next](https://docs.python.org/3/library/multiprocessing.shared_memory.html "multiprocessing.shared_memory â Shared memory for direct access across processes") \|
- [previous](https://docs.python.org/3/library/threading.html "threading â Thread-based parallelism") \|
- 
- [Python](https://www.python.org/) »
- [3\.14.4 Documentation](https://docs.python.org/3/index.html) »
- [The Python Standard Library](https://docs.python.org/3/library/index.html) »
- [Concurrent Execution](https://docs.python.org/3/library/concurrency.html) »
- [`multiprocessing` â Process-based parallelism](https://docs.python.org/3/library/multiprocessing.html)
- \|
- Theme
\|
© [Copyright](https://docs.python.org/3/copyright.html) 2001 Python Software Foundation.
This page is licensed under the Python Software Foundation License Version 2.
Examples, recipes, and other code in the documentation are additionally licensed under the Zero Clause BSD License.
See [History and License](https://docs.python.org/license.html) for more information.
The Python Software Foundation is a non-profit corporation. [Please donate.](https://www.python.org/psf/donations/)
Last updated on Apr 09, 2026 (15:27 UTC). [Found a bug](https://docs.python.org/bugs.html)?
Created using [Sphinx](https://www.sphinx-doc.org/) 8.2.3. | |||||||||
| Readable Markdown | **Source code:** [Lib/multiprocessing/](https://github.com/python/cpython/tree/3.14/Lib/multiprocessing/)
***
## Introduction[¶](https://docs.python.org/3/library/multiprocessing.html#introduction "Link to this heading")
`multiprocessing` is a package that supports spawning processes using an API similar to the [`threading`](https://docs.python.org/3/library/threading.html#module-threading "threading: Thread-based parallelism.") module. The `multiprocessing` package offers both local and remote concurrency, effectively side-stepping the [Global Interpreter Lock](https://docs.python.org/3/glossary.html#term-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 POSIX and Windows.
The `multiprocessing` module also introduces the [`Pool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.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 `multiprocessing` module also introduces APIs which do not have analogs in the [`threading`](https://docs.python.org/3/library/threading.html#module-threading "threading: Thread-based parallelism.") module, like the ability to [`terminate`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.terminate "multiprocessing.Process.terminate"), [`interrupt`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.interrupt "multiprocessing.Process.interrupt") or [`kill`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.kill "multiprocessing.Process.kill") a running process.
See also
[`concurrent.futures.ProcessPoolExecutor`](https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.ProcessPoolExecutor "concurrent.futures.ProcessPoolExecutor") offers a higher level interface to push tasks to a background process without blocking execution of the calling process. Compared to using the [`Pool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.Pool") interface directly, the [`concurrent.futures`](https://docs.python.org/3/library/concurrent.futures.html#module-concurrent.futures "concurrent.futures: Execute computations concurrently using threads or processes.") API more readily allows the submission of work to the underlying process pool to be separated from waiting for the results.
### The [`Process`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.Process") class[¶](https://docs.python.org/3/library/multiprocessing.html#the-process-class "Link to this heading")
In `multiprocessing`, processes are spawned by creating a [`Process`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.Process") object and then calling its [`start()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.start "multiprocessing.Process.start") method. `Process` follows the API of [`threading.Thread`](https://docs.python.org/3/library/threading.html#threading.Thread "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](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-programming).
The arguments to [`Process`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.Process") usually need to be unpickleable from within the child process. If you tried typing the above example directly into a REPL it could lead to an [`AttributeError`](https://docs.python.org/3/library/exceptions.html#AttributeError "AttributeError") in the child process trying to locate the *f* function in the `__main__` module.
### Contexts and start methods[¶](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods "Link to this heading")
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 objectâs [`run()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.run "multiprocessing.Process.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 POSIX and Windows platforms. The default on Windows and macOS.
>
> *fork*
>
> The parent process uses [`os.fork()`](https://docs.python.org/3/library/os.html#os.fork "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 POSIX systems.
>
> Changed in version 3.14: This is no longer the default start method on any platform. Code that requires *fork* must explicitly specify that via [`get_context()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.get_context "multiprocessing.get_context") or [`set_start_method()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.set_start_method "multiprocessing.set_start_method").
>
> Changed in version 3.12: If Python is able to detect that your process has multiple threads, the [`os.fork()`](https://docs.python.org/3/library/os.html#os.fork "os.fork") function that this start method calls internally will raise a [`DeprecationWarning`](https://docs.python.org/3/library/exceptions.html#DeprecationWarning "DeprecationWarning"). Use a different start method. See the `os.fork()` documentation for further explanation.
>
> *forkserver*
>
> When the program starts and selects the *forkserver* start method, a server process is spawned. 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 unless system libraries or preloaded imports spawn threads as a side-effect so it is generally safe for it to use [`os.fork()`](https://docs.python.org/3/library/os.html#os.fork "os.fork"). No unnecessary resources are inherited.
>
> Available on POSIX platforms which support passing file descriptors over Unix pipes such as Linux. The default on those.
>
> Changed in version 3.14: This became the default start method on POSIX platforms.
Changed in version 3.4: *spawn* added on all POSIX platforms, and *forkserver* added for some POSIX platforms. Child processes no longer inherit all of the parents inheritable handles on Windows.
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 as macOS system libraries may start threads. See [bpo-33725](https://bugs.python.org/issue?@action=redirect&bpo=33725).
Changed in version 3.14: On POSIX platforms the default start method was changed from *fork* to *forkserver* to retain the performance but avoid common multithreaded process incompatibilities. See [gh-84559](https://github.com/python/cpython/issues/84559).
On POSIX 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`](https://docs.python.org/3/library/multiprocessing.shared_memory.html#multiprocessing.shared_memory.SharedMemory "multiprocessing.shared_memory.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 âleakedâ 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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.set_start_method "multiprocessing.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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.set_start_method "multiprocessing.set_start_method") should not be used more than once in the program.
Alternatively, you can use [`get_context()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.get_context "multiprocessing.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.
Libraries using `multiprocessing` or [`ProcessPoolExecutor`](https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.ProcessPoolExecutor "concurrent.futures.ProcessPoolExecutor") should be designed to allow their users to provide their own multiprocessing context. Using a specific context of your own within a library can lead to incompatibilities with the rest of the library userâs application. Always document if your library requires a specific start method.
Warning
The `'spawn'` and `'forkserver'` start methods generally cannot be used with âfrozenâ executables (i.e., binaries produced by packages like **PyInstaller** and **cx\_Freeze**) on POSIX systems. The `'fork'` start method may work if code does not use threads.
### Exchanging objects between processes[¶](https://docs.python.org/3/library/multiprocessing.html#exchanging-objects-between-processes "Link to this heading")
`multiprocessing` supports two types of communication channel between processes:
**Queues**
> The [`Queue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue "multiprocessing.Queue") class is a near clone of [`queue.Queue`](https://docs.python.org/3/library/queue.html#queue.Queue "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. Any object put into a `multiprocessing` queue will be serialized.
**Pipes**
> The [`Pipe()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Pipe "multiprocessing.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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Pipe "multiprocessing.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.
>
> The `send()` method serializes the object and `recv()` re-creates the object.
### Synchronization between processes[¶](https://docs.python.org/3/library/multiprocessing.html#synchronization-between-processes "Link to this heading")
`multiprocessing` contains equivalents of all the synchronization primitives from [`threading`](https://docs.python.org/3/library/threading.html#module-threading "threading: Thread-based parallelism."). 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[¶](https://docs.python.org/3/library/multiprocessing.html#sharing-state-between-processes "Link to this heading")
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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Value "multiprocessing.Value") or [`Array`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Array "multiprocessing.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`](https://docs.python.org/3/library/array.html#module-array "array: Space efficient arrays of uniformly typed numeric values.") 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`](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.sharedctypes "multiprocessing.sharedctypes: Allocate ctypes objects from shared memory.") module which supports the creation of arbitrary ctypes objects allocated from shared memory.
**Server process**
> A manager object returned by [`Manager()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Manager "multiprocessing.Manager") controls a server process which holds Python objects and allows other processes to manipulate them using proxies.
>
> A manager returned by [`Manager()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Manager "multiprocessing.Manager") will support types [`list`](https://docs.python.org/3/library/stdtypes.html#list "list"), [`dict`](https://docs.python.org/3/library/stdtypes.html#dict "dict"), [`set`](https://docs.python.org/3/library/stdtypes.html#set "set"), [`Namespace`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.Namespace "multiprocessing.managers.Namespace"), [`Lock`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock "multiprocessing.Lock"), [`RLock`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock "multiprocessing.RLock"), [`Semaphore`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Semaphore "multiprocessing.Semaphore"), [`BoundedSemaphore`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.BoundedSemaphore "multiprocessing.BoundedSemaphore"), [`Condition`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Condition "multiprocessing.Condition"), [`Event`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Event "multiprocessing.Event"), [`Barrier`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Barrier "multiprocessing.Barrier"), [`Queue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue "multiprocessing.Queue"), [`Value`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Value "multiprocessing.Value") and [`Array`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Array "multiprocessing.Array"). For example,
> ```
> from multiprocessing import Process, Manager
def f(d, l, s):
d[1] = '1'
d['2'] = 2
d[0.25] = None
l.reverse()
s.add('a')
s.add('b')
if __name__ == '__main__':
with Manager() as manager:
d = manager.dict()
l = manager.list(range(10))
s = manager.set()
p = Process(target=f, args=(d, l, s))
p.start()
p.join()
print(d)
print(l)
print(s)
> ```
> will print
> ```
> {0.25: None, 1: '1', '2': 2}
[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
{'a', 'b'}
> ```
> 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[¶](https://docs.python.org/3/library/multiprocessing.html#using-a-pool-of-workers "Link to this heading")
The [`Pool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.Pool") class represents a pool of worker processes. It has methods which allows tasks to be offloaded to the worker processes in a few different ways.
For example:
```
from multiprocessing import Pool, 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 seconds
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](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-programming) however it is worth pointing out here. This means that some examples, such as the [`multiprocessing.pool.Pool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "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: Can't get attribute 'f' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>
AttributeError: Can't get attribute 'f' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>
AttributeError: Can't get attribute 'f' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>
```
(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[¶](https://docs.python.org/3/library/multiprocessing.html#reference "Link to this heading")
The `multiprocessing` package mostly replicates the API of the [`threading`](https://docs.python.org/3/library/threading.html#module-threading "threading: Thread-based parallelism.") module.
### Global start method[¶](https://docs.python.org/3/library/multiprocessing.html#global-start-method "Link to this heading")
Python supports several ways to create and initialize a process. The global start method sets the default mechanism for creating a process.
Several multiprocessing functions and methods that may also instantiate certain objects will implicitly set the global start method to the systemâs default, if it hasnât been set already. The global start method can only be set once. If you need to change the start method from the system default, you must proactively set the global start method before calling functions or methods, or creating these objects.
### [`Process`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.Process") and exceptions[¶](https://docs.python.org/3/library/multiprocessing.html#process-and-exceptions "Link to this heading")
*class* multiprocessing.Process(*group\=None*, *target\=None*, *name\=None*, *args\=()*, *kwargs\={}*, *\**, *daemon\=None*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "Link to this definition")
Process objects represent activity that is run in a separate process. The `Process` class has equivalents of all the methods of [`threading.Thread`](https://docs.python.org/3/library/threading.html#threading.Thread "threading.Thread").
The constructor should always be called with keyword arguments. *group* should always be `None`; it exists solely for compatibility with [`threading.Thread`](https://docs.python.org/3/library/threading.html#threading.Thread "threading.Thread"). *target* is the callable object to be invoked by the [`run()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.run "multiprocessing.Process.run") method. It defaults to `None`, meaning nothing is called. *name* is the process name (see [`name`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.name "multiprocessing.Process.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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.daemon "multiprocessing.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*. The *args* argument, which defaults to `()`, can be used to specify a list or tuple of the arguments to pass to *target*.
If a subclass overrides the constructor, it must make sure it invokes the base class constructor (`super().__init__()`) before doing anything else to the process.
Note
In general, all arguments to `Process` must be picklable. This is frequently observed when trying to create a `Process` or use a [`concurrent.futures.ProcessPoolExecutor`](https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.ProcessPoolExecutor "concurrent.futures.ProcessPoolExecutor") from a REPL with a locally defined *target* function.
Passing a callable object defined in the current REPL session causes the child process to die via an uncaught [`AttributeError`](https://docs.python.org/3/library/exceptions.html#AttributeError "AttributeError") exception when starting as *target* must have been defined within an importable module in order to be loaded during unpickling.
Example of this uncatchable error from the child:
```
>>> import multiprocessing as mp
>>> def knigit():
... print("Ni!")
...
>>> process = mp.Process(target=knigit)
>>> process.start()
>>> Traceback (most recent call last):
File ".../multiprocessing/spawn.py", line ..., in spawn_main
File ".../multiprocessing/spawn.py", line ..., in _main
AttributeError: module '__main__' has no attribute 'knigit'
>>> process
<SpawnProcess name='SpawnProcess-1' pid=379473 parent=378707 stopped exitcode=1>
```
See [The spawn and forkserver start methods](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-programming-spawn). While this restriction is not true if using the `"fork"` start method, as of Python `3.14` that is no longer the default on any platform. See [Contexts and start methods](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-start-methods). See also [gh-132898](https://github.com/python/cpython/issues/132898).
Changed in version 3.3: Added the *daemon* parameter.
run()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.run "Link to this definition")
Method representing the processâs activity.
You may override this method in a subclass. The standard `run()` method invokes the callable object passed to the objectâs constructor as the target argument, if any, with sequential and keyword arguments taken from the *args* and *kwargs* arguments, respectively.
Using a list or tuple as the *args* argument passed to `Process` achieves the same effect.
Example:
```
>>> from multiprocessing import Process
>>> p = Process(target=print, args=[1])
>>> p.run()
1
>>> p = Process(target=print, args=(1,))
>>> p.run()
1
```
start()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.start "Link to this definition")
Start the processâs activity.
This must be called at most once per process object. It arranges for the objectâs [`run()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.run "multiprocessing.Process.run") method to be invoked in a separate process.
join(\[*timeout*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.join "Link to this definition")
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 processâs [`exitcode`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.exitcode "multiprocessing.Process.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[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.name "Link to this definition")
The processâs 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 âProcess-N1:N2:âŠ:Nkâ is constructed, where each Nk is the N-th child of its parent.
is\_alive()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.is_alive "Link to this definition")
Return whether the process is alive.
Roughly, a process object is alive from the moment the [`start()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.start "multiprocessing.Process.start") method returns until the child process terminates.
daemon[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.daemon "Link to this definition")
The processâs daemon flag, a Boolean value. This must be set before [`start()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.start "multiprocessing.Process.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`](https://docs.python.org/3/library/threading.html#threading.Thread "threading.Thread") API, `Process` objects also support the following attributes and methods:
pid[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.pid "Link to this definition")
Return the process ID. Before the process is spawned, this will be `None`.
exitcode[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.exitcode "Link to this definition")
The childâs exit code. This will be `None` if the process has not yet terminated.
If the childâs [`run()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.run "multiprocessing.Process.run") method returned normally, the exit code will be 0. If it terminated via [`sys.exit()`](https://docs.python.org/3/library/sys.html#sys.exit "sys.exit") with an integer argument *N*, the exit code will be *N*.
If the child terminated due to an exception not caught within [`run()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.run "multiprocessing.Process.run"), the exit code will be 1. If it was terminated by signal *N*, the exit code will be the negative value *\-N*.
authkey[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.authkey "Link to this definition")
The processâs authentication key (a byte string).
When `multiprocessing` is initialized the main process is assigned a random string using [`os.urandom()`](https://docs.python.org/3/library/os.html#os.urandom "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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.authkey "multiprocessing.Process.authkey") to another byte string.
See [Authentication keys](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-auth-keys).
sentinel[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.sentinel "Link to this definition")
A numeric handle of a system object which will become âreadyâ when the process ends.
You can use this value if you want to wait on several events at once using [`multiprocessing.connection.wait()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.wait "multiprocessing.connection.wait"). Otherwise calling [`join()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.join "multiprocessing.Process.join") is simpler.
On Windows, this is an OS handle usable with the `WaitForSingleObject` and `WaitForMultipleObjects` family of API calls. On POSIX, this is a file descriptor usable with primitives from the [`select`](https://docs.python.org/3/library/select.html#module-select "select: Wait for I/O completion on multiple streams.") module.
Added in version 3.3.
interrupt()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.interrupt "Link to this definition")
Terminate the process. Works on POSIX using the [`SIGINT`](https://docs.python.org/3/library/signal.html#signal.SIGINT "signal.SIGINT") signal. Behavior on Windows is undefined.
By default, this terminates the child process by raising [`KeyboardInterrupt`](https://docs.python.org/3/library/exceptions.html#KeyboardInterrupt "KeyboardInterrupt"). This behavior can be altered by setting the respective signal handler in the child process [`signal.signal()`](https://docs.python.org/3/library/signal.html#signal.signal "signal.signal") for [`SIGINT`](https://docs.python.org/3/library/signal.html#signal.SIGINT "signal.SIGINT").
Note: if the child process catches and discards [`KeyboardInterrupt`](https://docs.python.org/3/library/exceptions.html#KeyboardInterrupt "KeyboardInterrupt"), the process will not be terminated.
Note: the default behavior will also set [`exitcode`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.exitcode "multiprocessing.Process.exitcode") to `1` as if an uncaught exception was raised in the child process. To have a different `exitcode` you may simply catch [`KeyboardInterrupt`](https://docs.python.org/3/library/exceptions.html#KeyboardInterrupt "KeyboardInterrupt") and call `exit(your_code)`.
Added in version 3.14.
terminate()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.terminate "Link to this definition")
Terminate the process. On POSIX this is done using the [`SIGTERM`](https://docs.python.org/3/library/signal.html#signal.SIGTERM "signal.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 â 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()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.kill "Link to this definition")
Same as [`terminate()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.terminate "multiprocessing.Process.terminate") but using the `SIGKILL` signal on POSIX.
Added in version 3.7.
close()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.close "Link to this definition")
Close the `Process` object, releasing all resources associated with it. [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError "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`.
Added in version 3.7.
Note that the [`start()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.start "multiprocessing.Process.start"), [`join()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.join "multiprocessing.Process.join"), [`is_alive()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.is_alive "multiprocessing.Process.is_alive"), [`terminate()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.terminate "multiprocessing.Process.terminate") and [`exitcode`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.exitcode "multiprocessing.Process.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
>>> mp_context = multiprocessing.get_context('spawn')
>>> p = mp_context.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[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.ProcessError "Link to this definition")
The base class of all `multiprocessing` exceptions.
*exception* multiprocessing.BufferTooShort[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.BufferTooShort "Link to this definition")
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[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.AuthenticationError "Link to this definition")
Raised when there is an authentication error.
*exception* multiprocessing.TimeoutError[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.TimeoutError "Link to this definition")
Raised by methods with a timeout when the timeout expires.
### Pipes and Queues[¶](https://docs.python.org/3/library/multiprocessing.html#pipes-and-queues "Link to this heading")
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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Pipe "multiprocessing.Pipe") (for a connection between two processes) or a queue (which allows multiple producers and consumers).
The [`Queue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue "multiprocessing.Queue"), [`SimpleQueue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.SimpleQueue "multiprocessing.SimpleQueue") and [`JoinableQueue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.JoinableQueue "multiprocessing.JoinableQueue") types are multi-producer, multi-consumer FIFO queues modelled on the [`queue.Queue`](https://docs.python.org/3/library/queue.html#queue.Queue "queue.Queue") class in the standard library. They differ in that `Queue` lacks the [`task_done()`](https://docs.python.org/3/library/queue.html#queue.Queue.task_done "queue.Queue.task_done") and [`join()`](https://docs.python.org/3/library/queue.html#queue.Queue.join "queue.Queue.join") methods introduced into Python 2.5âs `queue.Queue` class.
If you use [`JoinableQueue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.JoinableQueue "multiprocessing.JoinableQueue") then you **must** call [`JoinableQueue.task_done()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.JoinableQueue.task_done "multiprocessing.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.
One difference from other Python queue implementations, is that `multiprocessing` queues serializes all objects that are put into them using [`pickle`](https://docs.python.org/3/library/pickle.html#module-pickle "pickle: Convert Python objects to streams of bytes and back."). The object returned by the get method is a re-created object that does not share memory with the original object.
Note that one can also create a shared queue by using a manager object â see [Managers](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-managers).
Note
`multiprocessing` uses the usual [`queue.Empty`](https://docs.python.org/3/library/queue.html#queue.Empty "queue.Empty") and [`queue.Full`](https://docs.python.org/3/library/queue.html#queue.Full "queue.Full") exceptions to signal a timeout. They are not available in the `multiprocessing` namespace so you need to import them from [`queue`](https://docs.python.org/3/library/queue.html#module-queue "queue: A synchronized queue class.").
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 â if they really bother you then you can instead use a queue created with a [manager](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-managers).
1. After putting an object on an empty queue there may be an infinitesimal delay before the queueâs [`empty()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.empty "multiprocessing.Queue.empty") method returns [`False`](https://docs.python.org/3/library/constants.html#False "False") and [`get_nowait()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.get_nowait "multiprocessing.Queue.get_nowait") can return without raising [`queue.Empty`](https://docs.python.org/3/library/queue.html#queue.Empty "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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.terminate "multiprocessing.Process.terminate") or [`os.kill()`](https://docs.python.org/3/library/os.html#os.kill "os.kill") while it is trying to use a [`Queue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue "multiprocessing.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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.cancel_join_thread "multiprocessing.Queue.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](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-programming).
For an example of the usage of queues for interprocess communication see [Examples](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-examples).
multiprocessing.Pipe(*duplex\=True*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Pipe "Link to this definition")
Returns a pair `(conn1, conn2)` of [`Connection`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection "multiprocessing.connection.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.
The `send()` method serializes the object using [`pickle`](https://docs.python.org/3/library/pickle.html#module-pickle "pickle: Convert Python objects to streams of bytes and back.") and the `recv()` re-creates the object.
*class* multiprocessing.Queue(\[*maxsize*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue "Link to this definition")
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.
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
The usual [`queue.Empty`](https://docs.python.org/3/library/queue.html#queue.Empty "queue.Empty") and [`queue.Full`](https://docs.python.org/3/library/queue.html#queue.Full "queue.Full") exceptions from the standard libraryâs [`queue`](https://docs.python.org/3/library/queue.html#module-queue "queue: A synchronized queue class.") module are raised to signal timeouts.
`Queue` implements all the methods of [`queue.Queue`](https://docs.python.org/3/library/queue.html#queue.Queue "queue.Queue") except for [`task_done()`](https://docs.python.org/3/library/queue.html#queue.Queue.task_done "queue.Queue.task_done") and [`join()`](https://docs.python.org/3/library/queue.html#queue.Queue.join "queue.Queue.join").
qsize()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.qsize "Link to this definition")
Return the approximate size of the queue. Because of multithreading/multiprocessing semantics, this number is not reliable.
Note that this may raise [`NotImplementedError`](https://docs.python.org/3/library/exceptions.html#NotImplementedError "NotImplementedError") on platforms like macOS where `sem_getvalue()` is not implemented.
empty()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.empty "Link to this definition")
Return `True` if the queue is empty, `False` otherwise. Because of multithreading/multiprocessing semantics, this is not reliable.
May raise an [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError "OSError") on closed queues. (not guaranteed)
full()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.full "Link to this definition")
Return `True` if the queue is full, `False` otherwise. Because of multithreading/multiprocessing semantics, this is not reliable.
put(*obj*\[, *block*\[, *timeout*\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.put "Link to this definition")
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`](https://docs.python.org/3/library/queue.html#queue.Full "queue.Full") exception if no free slot was available within that time. Otherwise (*block* is `False`), put an item on the queue if a free slot is immediately available, else raise the `queue.Full` exception (*timeout* is ignored in that case).
put\_nowait(*obj*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.put_nowait "Link to this definition")
Equivalent to `put(obj, False)`.
get(\[*block*\[, *timeout*\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.get "Link to this definition")
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`](https://docs.python.org/3/library/queue.html#queue.Empty "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`](https://docs.python.org/3/library/exceptions.html#ValueError "ValueError") is raised instead of [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError "OSError").
get\_nowait()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.get_nowait "Link to this definition")
Equivalent to `get(False)`.
[`multiprocessing.Queue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue "multiprocessing.Queue") has a few additional methods not found in [`queue.Queue`](https://docs.python.org/3/library/queue.html#queue.Queue "queue.Queue"). These methods are usually unnecessary for most code:
close()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.close "Link to this definition")
Close the queue: release internal resources.
A queue must not be used anymore after it is closed. For example, [`get()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.get "multiprocessing.Queue.get"), [`put()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.put "multiprocessing.Queue.put") and [`empty()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.empty "multiprocessing.Queue.empty") methods must no longer be called.
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()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.join_thread "Link to this definition")
Join the background thread. This can only be used after [`close()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.close "multiprocessing.Queue.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 queueâs background thread. The process can call [`cancel_join_thread()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.cancel_join_thread "multiprocessing.Queue.cancel_join_thread") to make `join_thread()` do nothing.
cancel\_join\_thread()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.cancel_join_thread "Link to this definition")
Prevent [`join_thread()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.join_thread "multiprocessing.Queue.join_thread") from blocking. In particular, this prevents the background thread from being joined automatically when the process exits â 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 donât care about lost data.
Note
This classâs 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`](https://docs.python.org/3/library/exceptions.html#ImportError "ImportError"). See [bpo-3770](https://bugs.python.org/issue?@action=redirect&bpo=3770) for additional information. The same holds true for any of the specialized queue types listed below.
*class* multiprocessing.SimpleQueue[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.SimpleQueue "Link to this definition")
It is a simplified [`Queue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue "multiprocessing.Queue") type, very close to a locked [`Pipe`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Pipe "multiprocessing.Pipe").
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
close()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.SimpleQueue.close "Link to this definition")
Close the queue: release internal resources.
A queue must not be used anymore after it is closed. For example, [`get()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.SimpleQueue.get "multiprocessing.SimpleQueue.get"), [`put()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.SimpleQueue.put "multiprocessing.SimpleQueue.put") and [`empty()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.SimpleQueue.empty "multiprocessing.SimpleQueue.empty") methods must no longer be called.
Added in version 3.9.
empty()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.SimpleQueue.empty "Link to this definition")
Return `True` if the queue is empty, `False` otherwise.
Always raises an [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError "OSError") if the SimpleQueue is closed.
get()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.SimpleQueue.get "Link to this definition")
Remove and return an item from the queue.
put(*item*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.SimpleQueue.put "Link to this definition")
Put *item* into the queue.
*class* multiprocessing.JoinableQueue(\[*maxsize*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.JoinableQueue "Link to this definition")
`JoinableQueue`, a [`Queue`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue "multiprocessing.Queue") subclass, is a queue which additionally has [`task_done()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.JoinableQueue.task_done "multiprocessing.JoinableQueue.task_done") and [`join()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.JoinableQueue.join "multiprocessing.JoinableQueue.join") methods.
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
task\_done()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.JoinableQueue.task_done "Link to this definition")
Indicate that a formerly enqueued task is complete. Used by queue consumers. For each [`get()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.get "multiprocessing.Queue.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()`](https://docs.python.org/3/library/queue.html#queue.Queue.join "queue.Queue.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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.put "multiprocessing.Queue.put") into the queue).
Raises a [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError "ValueError") if called more times than there were items placed in the queue.
join()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.JoinableQueue.join "Link to this definition")
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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.JoinableQueue.task_done "multiprocessing.JoinableQueue.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()`](https://docs.python.org/3/library/queue.html#queue.Queue.join "queue.Queue.join") unblocks.
### Miscellaneous[¶](https://docs.python.org/3/library/multiprocessing.html#miscellaneous "Link to this heading")
multiprocessing.active\_children()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.active_children "Link to this definition")
Return list of all live children of the current process.
Calling this has the side effect of âjoiningâ any processes which have already finished.
multiprocessing.cpu\_count()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.cpu_count "Link to this definition")
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 [`os.process_cpu_count()`](https://docs.python.org/3/library/os.html#os.process_cpu_count "os.process_cpu_count") (or `len(os.sched_getaffinity(0))`).
When the number of CPUs cannot be determined a [`NotImplementedError`](https://docs.python.org/3/library/exceptions.html#NotImplementedError "NotImplementedError") is raised.
Changed in version 3.13: The return value can also be overridden using the [`-X cpu_count`](https://docs.python.org/3/using/cmdline.html#cmdoption-X) flag or [`PYTHON_CPU_COUNT`](https://docs.python.org/3/using/cmdline.html#envvar-PYTHON_CPU_COUNT) as this is merely a wrapper around the [`os`](https://docs.python.org/3/library/os.html#module-os "os: Miscellaneous operating system interfaces.") cpu count APIs.
multiprocessing.current\_process()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.current_process "Link to this definition")
Return the [`Process`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.Process") object corresponding to the current process.
An analogue of [`threading.current_thread()`](https://docs.python.org/3/library/threading.html#threading.current_thread "threading.current_thread").
multiprocessing.parent\_process()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.parent_process "Link to this definition")
Return the [`Process`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.Process") object corresponding to the parent process of the [`current_process()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.current_process "multiprocessing.current_process"). For the main process, `parent_process` will be `None`.
Added in version 3.8.
multiprocessing.freeze\_support()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.freeze_support "Link to this definition")
Add support for when a program which uses `multiprocessing` has been frozen to produce an executable. (Has been tested with **py2exe**, **PyInstaller** and **cx\_Freeze**.)
One needs to call this function straight after the 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`](https://docs.python.org/3/library/exceptions.html#RuntimeError "RuntimeError").
Calling `freeze_support()` has no effect when the start method is not *spawn*. In addition, if the module is being run normally by the Python interpreter (the program has not been frozen), then `freeze_support()` has no effect.
multiprocessing.get\_all\_start\_methods()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.get_all_start_methods "Link to this definition")
Returns a list of the supported start methods, the first of which is the default. The possible start methods are `'fork'`, `'spawn'` and `'forkserver'`. Not all platforms support all methods. See [Contexts and start methods](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-start-methods).
Added in version 3.4.
multiprocessing.get\_context(*method\=None*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.get_context "Link to this definition")
Return a context object which has the same attributes as the `multiprocessing` module.
If *method* is `None` then the default context is returned. Note that if the global start method has not been set, this will set it to the system default See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details. Otherwise *method* should be `'fork'`, `'spawn'`, `'forkserver'`. [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError "ValueError") is raised if the specified start method is not available. See [Contexts and start methods](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-start-methods).
Added in version 3.4.
multiprocessing.get\_start\_method(*allow\_none\=False*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.get_start_method "Link to this definition")
Return the name of start method used for starting processes.
If the global start method is not set and *allow\_none* is `False`, the global start method is set to the default, and its name is returned. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
The return value can be `'fork'`, `'spawn'`, `'forkserver'` or `None`. See [Contexts and start methods](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-start-methods).
Added in version 3.4.
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](https://bugs.python.org/issue?@action=redirect&bpo=33725).
multiprocessing.set\_executable(*executable*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.set_executable "Link to this definition")
Set the path of the Python interpreter to use when starting a child process. (By default [`sys.executable`](https://docs.python.org/3/library/sys.html#sys.executable "sys.executable") is used). Embedders will probably need to do something 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 POSIX when the `'spawn'` start method is used.
multiprocessing.set\_forkserver\_preload(*module\_names*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.set_forkserver_preload "Link to this definition")
Set a list of module names for the forkserver main process to attempt to import so that their already imported state is inherited by forked processes. Any [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError "ImportError") when doing so is silently ignored. This can be used as a performance enhancement to avoid repeated work in every process.
For this to work, it must be called before the forkserver process has been launched (before creating a `Pool` or starting a [`Process`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.Process")).
Only meaningful when using the `'forkserver'` start method. See [Contexts and start methods](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-start-methods).
Added in version 3.4.
multiprocessing.set\_start\_method(*method*, *force\=False*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.set_start_method "Link to this definition")
Set the method which should be used to start child processes. The *method* argument can be `'fork'`, `'spawn'` or `'forkserver'`. Raises [`RuntimeError`](https://docs.python.org/3/library/exceptions.html#RuntimeError "RuntimeError") if the start method has already been set and *force* is not `True`. If *method* is `None` and *force* is `True` then the start method is set to `None`. If *method* is `None` and *force* is `False` then the context is set to the default context.
Note that this should be called at most once, and it should be protected inside the `if __name__ == '__main__'` clause of the main module.
See [Contexts and start methods](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-start-methods).
Added in version 3.4.
### Connection Objects[¶](https://docs.python.org/3/library/multiprocessing.html#connection-objects "Link to this heading")
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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Pipe "multiprocessing.Pipe") â see also [Listeners and Clients](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-listeners-clients).
*class* multiprocessing.connection.Connection[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection "Link to this definition")
send(*obj*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.send "Link to this definition")
Send an object to the other end of the connection which should be read using [`recv()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.recv "multiprocessing.connection.Connection.recv").
The object must be picklable. Very large pickles (approximately 32 MiB+, though it depends on the OS) may raise a [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError "ValueError") exception.
recv()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.recv "Link to this definition")
Return an object sent from the other end of the connection using [`send()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.send "multiprocessing.connection.Connection.send"). Blocks until there is something to receive. Raises [`EOFError`](https://docs.python.org/3/library/exceptions.html#EOFError "EOFError") if there is nothing left to receive and the other end was closed.
fileno()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.fileno "Link to this definition")
Return the file descriptor or handle used by the connection.
close()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.close "Link to this definition")
Close the connection.
This is called automatically when the connection is garbage collected.
poll(\[*timeout*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.poll "Link to this definition")
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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.wait "multiprocessing.connection.wait").
send\_bytes(*buffer*\[, *offset*\[, *size*\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.send_bytes "Link to this definition")
Send byte data from a [bytes-like object](https://docs.python.org/3/glossary.html#term-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`](https://docs.python.org/3/library/exceptions.html#ValueError "ValueError") exception
recv\_bytes(\[*maxlength*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.recv_bytes "Link to this definition")
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`](https://docs.python.org/3/library/exceptions.html#EOFError "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`](https://docs.python.org/3/library/exceptions.html#OSError "OSError") is raised and the connection will no longer be readable.
Changed in version 3.3: This function used to raise [`IOError`](https://docs.python.org/3/library/exceptions.html#IOError "IOError"), which is now an alias of [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError "OSError").
recv\_bytes\_into(*buffer*\[, *offset*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.recv_bytes_into "Link to this definition")
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`](https://docs.python.org/3/library/exceptions.html#EOFError "EOFError") if there is nothing left to receive and the other end was closed.
*buffer* must be a writable [bytes-like object](https://docs.python.org/3/glossary.html#term-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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.send "multiprocessing.connection.Connection.send") and [`Connection.recv()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.recv "multiprocessing.connection.Connection.recv").
Connection objects also now support the context management protocol â see [Context Manager Types](https://docs.python.org/3/library/stdtypes.html#typecontextmanager). [`__enter__()`](https://docs.python.org/3/library/stdtypes.html#contextmanager.__enter__ "contextmanager.__enter__") returns the connection object, and [`__exit__()`](https://docs.python.org/3/library/stdtypes.html#contextmanager.__exit__ "contextmanager.__exit__") calls [`close()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.close "multiprocessing.connection.Connection.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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.recv "multiprocessing.connection.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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.recv "multiprocessing.connection.Connection.recv") and [`send()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.send "multiprocessing.connection.Connection.send") methods after performing some sort of authentication. See [Authentication keys](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-auth-keys).
Warning
If a process is killed while it is trying to read or write to a pipe then the data in the pipe is likely to become corrupted, because it may become impossible to be sure where the message boundaries lie.
### Synchronization primitives[¶](https://docs.python.org/3/library/multiprocessing.html#synchronization-primitives "Link to this heading")
Generally synchronization primitives are not as necessary in a multiprocess program as they are in a multithreaded program. See the documentation for [`threading`](https://docs.python.org/3/library/threading.html#module-threading "threading: Thread-based parallelism.") module.
Note that one can also create synchronization primitives by using a manager object â see [Managers](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-managers).
*class* multiprocessing.Barrier(*parties*\[, *action*\[, *timeout*\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Barrier "Link to this definition")
A barrier object: a clone of [`threading.Barrier`](https://docs.python.org/3/library/threading.html#threading.Barrier "threading.Barrier").
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
Added in version 3.3.
*class* multiprocessing.BoundedSemaphore(\[*value*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.BoundedSemaphore "Link to this definition")
A bounded semaphore object: a close analog of [`threading.BoundedSemaphore`](https://docs.python.org/3/library/threading.html#threading.BoundedSemaphore "threading.BoundedSemaphore").
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
A solitary difference from its close analog exists: its `acquire` methodâs first argument is named *block*, as is consistent with [`Lock.acquire()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock.acquire "multiprocessing.Lock.acquire").
locked()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.BoundedSemaphore.locked "Link to this definition")
Return a boolean indicating whether this object is locked right now.
Added in version 3.14.
Note
On macOS, this is indistinguishable from [`Semaphore`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Semaphore "multiprocessing.Semaphore") because `sem_getvalue()` is not implemented on that platform.
*class* multiprocessing.Condition(\[*lock*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Condition "Link to this definition")
A condition variable: an alias for [`threading.Condition`](https://docs.python.org/3/library/threading.html#threading.Condition "threading.Condition").
If *lock* is specified then it should be a [`Lock`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock "multiprocessing.Lock") or [`RLock`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock "multiprocessing.RLock") object from `multiprocessing`.
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
Changed in version 3.3: The [`wait_for()`](https://docs.python.org/3/library/threading.html#threading.Condition.wait_for "threading.Condition.wait_for") method was added.
*class* multiprocessing.Event[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Event "Link to this definition")
A clone of [`threading.Event`](https://docs.python.org/3/library/threading.html#threading.Event "threading.Event").
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
*class* multiprocessing.Lock[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock "Link to this definition")
A non-recursive lock object: a close analog of [`threading.Lock`](https://docs.python.org/3/library/threading.html#threading.Lock "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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock "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.
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
`Lock` supports the [context manager](https://docs.python.org/3/glossary.html#term-context-manager) protocol and thus may be used in [`with`](https://docs.python.org/3/reference/compound_stmts.html#with) statements.
acquire(*block\=True*, *timeout\=None*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock.acquire "Link to this definition")
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()`](https://docs.python.org/3/library/threading.html#threading.Lock.acquire "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()`](https://docs.python.org/3/library/threading.html#threading.Lock.acquire "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()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock.release "Link to this definition")
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()`](https://docs.python.org/3/library/threading.html#threading.Lock.release "threading.Lock.release") except that when invoked on an unlocked lock, a [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError "ValueError") is raised.
locked()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock.locked "Link to this definition")
Return a boolean indicating whether this object is locked right now.
Added in version 3.14.
*class* multiprocessing.RLock[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock "Link to this definition")
A recursive lock object: a close analog of [`threading.RLock`](https://docs.python.org/3/library/threading.html#threading.RLock "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.
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
`RLock` supports the [context manager](https://docs.python.org/3/glossary.html#term-context-manager) protocol and thus may be used in [`with`](https://docs.python.org/3/reference/compound_stmts.html#with) statements.
acquire(*block\=True*, *timeout\=None*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock.acquire "Link to this definition")
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 argumentâs behavior compared to the implementation of [`threading.RLock.acquire()`](https://docs.python.org/3/library/threading.html#threading.RLock.acquire "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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock.acquire "multiprocessing.Lock.acquire"). Note that some of these behaviors of *timeout* differ from the implemented behaviors in [`threading.RLock.acquire()`](https://docs.python.org/3/library/threading.html#threading.RLock.acquire "threading.RLock.acquire").
release()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock.release "Link to this definition")
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`](https://docs.python.org/3/library/exceptions.html#AssertionError "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()`](https://docs.python.org/3/library/threading.html#threading.RLock.release "threading.RLock.release").
locked()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock.locked "Link to this definition")
Return a boolean indicating whether this object is locked right now.
Added in version 3.14.
*class* multiprocessing.Semaphore(\[*value*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Semaphore "Link to this definition")
A semaphore object: a close analog of [`threading.Semaphore`](https://docs.python.org/3/library/threading.html#threading.Semaphore "threading.Semaphore").
Instantiating this class may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
A solitary difference from its close analog exists: its `acquire` methodâs first argument is named *block*, as is consistent with [`Lock.acquire()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock.acquire "multiprocessing.Lock.acquire").
get\_value()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Semaphore.get_value "Link to this definition")
Return the current value of semaphore.
Note that this may raise [`NotImplementedError`](https://docs.python.org/3/library/exceptions.html#NotImplementedError "NotImplementedError") on platforms like macOS where `sem_getvalue()` is not implemented.
locked()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Semaphore.locked "Link to this definition")
Return a boolean indicating whether this object is locked right now.
Added in version 3.14.
Note
On macOS, `sem_timedwait` is unsupported, so calling `acquire()` with a timeout will emulate that functionâs behavior using a sleeping loop.
Note
Some of this packageâs 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`](https://docs.python.org/3/library/exceptions.html#ImportError "ImportError"). See [bpo-3770](https://bugs.python.org/issue?@action=redirect&bpo=3770) for additional information.
### Shared [`ctypes`](https://docs.python.org/3/library/ctypes.html#module-ctypes "ctypes: A foreign function library for Python.") Objects[¶](https://docs.python.org/3/library/multiprocessing.html#shared-ctypes-objects "Link to this heading")
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*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Value "Link to this definition")
Return a [`ctypes`](https://docs.python.org/3/library/ctypes.html#module-ctypes "ctypes: A foreign function library for Python.") 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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Value "multiprocessing.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`](https://docs.python.org/3/library/array.html#module-array "array: Space efficient arrays of uniformly typed numeric values.") 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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock "multiprocessing.Lock") or [`RLock`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock "multiprocessing.RLock") object then that will be used to synchronize access to the value. If *lock* is `False` then access to the returned object will not be automatically protected by a lock, so it will not necessarily be âprocess-safeâ.
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*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Array "Link to this definition")
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](https://docs.python.org/3/library/ctypes.html#ctypes-fundamental-data-types) or a one character typecode of the kind used by the [`array`](https://docs.python.org/3/library/array.html#module-array "array: Space efficient arrays of uniformly typed numeric values.") module with the exception of `'w'`, which is not supported. In addition, the `'c'` typecode is an alias for [`ctypes.c_char`](https://docs.python.org/3/library/ctypes.html#ctypes.c_char "ctypes.c_char"). 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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock "multiprocessing.Lock") or [`RLock`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock "multiprocessing.RLock") object then that will be used to synchronize access to the value. If *lock* is `False` then access to the returned object will not be automatically protected by a lock, so it will not necessarily be âprocess-safeâ.
Note that *lock* is a keyword only argument.
Note that an array of [`ctypes.c_char`](https://docs.python.org/3/library/ctypes.html#ctypes.c_char "ctypes.c_char") has *value* and *raw* attributes which allow one to use it to store and retrieve strings.
#### The `multiprocessing.sharedctypes` module[¶](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.sharedctypes "Link to this heading")
The `multiprocessing.sharedctypes` module provides functions for allocating [`ctypes`](https://docs.python.org/3/library/ctypes.html#module-ctypes "ctypes: A foreign function library for Python.") 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*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.RawArray "Link to this definition")
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`](https://docs.python.org/3/library/array.html#module-array "array: Space efficient arrays of uniformly typed numeric values.") 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 â use [`Array()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.Array "multiprocessing.sharedctypes.Array") instead to make sure that access is automatically synchronized using a lock.
multiprocessing.sharedctypes.RawValue(*typecode\_or\_type*, *\*args*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.RawValue "Link to this definition")
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`](https://docs.python.org/3/library/array.html#module-array "array: Space efficient arrays of uniformly typed numeric values.") module. *\*args* is passed on to the constructor for the type.
Note that setting and getting the value is potentially non-atomic â use [`Value()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.Value "multiprocessing.sharedctypes.Value") instead to make sure that access is automatically synchronized using a lock.
Note that an array of [`ctypes.c_char`](https://docs.python.org/3/library/ctypes.html#ctypes.c_char "ctypes.c_char") has `value` and `raw` attributes which allow one to use it to store and retrieve strings â see documentation for [`ctypes`](https://docs.python.org/3/library/ctypes.html#module-ctypes "ctypes: A foreign function library for Python.").
multiprocessing.sharedctypes.Array(*typecode\_or\_type*, *size\_or\_initializer*, *\**, *lock\=True*, *ctx\=None*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.Array "Link to this definition")
The same as [`RawArray()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.RawArray "multiprocessing.sharedctypes.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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock "multiprocessing.Lock") or [`RLock`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock "multiprocessing.RLock") object then that will be used to synchronize access to the value. If *lock* is `False` then access to the returned object will not be automatically protected by a lock, so it will not necessarily be âprocess-safeâ.
*ctx* is a context object, or `None` (use the current context). If `None`, calling this may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
Note that *lock* and *ctx* are keyword-only parameters.
multiprocessing.sharedctypes.Value(*typecode\_or\_type*, *\*args*, *lock\=True*, *ctx\=None*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.Value "Link to this definition")
The same as [`RawValue()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.RawValue "multiprocessing.sharedctypes.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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Lock "multiprocessing.Lock") or [`RLock`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock "multiprocessing.RLock") object then that will be used to synchronize access to the value. If *lock* is `False` then access to the returned object will not be automatically protected by a lock, so it will not necessarily be âprocess-safeâ.
*ctx* is a context object, or `None` (use the current context). If `None`, calling this may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
Note that *lock* and *ctx* are keyword-only parameters.
multiprocessing.sharedctypes.copy(*obj*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.copy "Link to this definition")
Return a ctypes object allocated from shared memory which is a copy of the ctypes object *obj*.
multiprocessing.sharedctypes.synchronized(*obj*, *lock\=None*, *ctx\=None*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.sharedctypes.synchronized "Link to this definition")
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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.RLock "multiprocessing.RLock") object is created automatically.
*ctx* is a context object, or `None` (use the current context). If `None`, calling this may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
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](https://docs.python.org/3/glossary.html#term-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`](https://docs.python.org/3/library/ctypes.html#ctypes.Structure "ctypes.Structure").)
| ctypes | sharedctypes using type | sharedctypes using typecode |
|---|---|---|
| c\_double(2.4) | RawValue(c\_double, 2.4) | RawValue(âdâ, 2.4) |
| MyStruct(4, 6) | RawValue(MyStruct, 4, 6) | |
| (c\_short \* 7)() | RawArray(c\_short, 7) | RawArray(âhâ, 7) |
| (c\_int \* 3)(9, 2, 8) | RawArray(c\_int, (9, 2, 8)) | RawArray(âiâ, (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[¶](https://docs.python.org/3/library/multiprocessing.html#managers "Link to this heading")
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()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Manager "Link to this definition")
Returns a started [`SyncManager`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager "multiprocessing.managers.SyncManager") object which can be used for sharing objects between processes. The returned manager object corresponds to a spawned child process and has methods which will create shared objects and return corresponding proxies.
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`](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.managers "multiprocessing.managers: Share data between process with shared objects.") module:
*class* multiprocessing.managers.BaseManager(*address\=None*, *authkey\=None*, *serializer\='pickle'*, *ctx\=None*, *\**, *shutdown\_timeout\=1\.0*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager "Link to this definition")
Create a BaseManager object.
Once created one should call [`start()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.start "multiprocessing.managers.BaseManager.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.
*serializer* must be `'pickle'` (use [`pickle`](https://docs.python.org/3/library/pickle.html#module-pickle "pickle: Convert Python objects to streams of bytes and back.") serialization) or `'xmlrpclib'` (use [`xmlrpc.client`](https://docs.python.org/3/library/xmlrpc.client.html#module-xmlrpc.client "xmlrpc.client: XML-RPC client access.") serialization).
*ctx* is a context object, or `None` (use the current context). If `None`, calling this may set the global start method. See [Global start method](https://docs.python.org/3/library/multiprocessing.html#global-start-method) for more details.
*shutdown\_timeout* is a timeout in seconds used to wait until the process used by the manager completes in the [`shutdown()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.shutdown "multiprocessing.managers.BaseManager.shutdown") method. If the shutdown times out, the process is terminated. If terminating the process also times out, the process is killed.
Changed in version 3.11: Added the *shutdown\_timeout* parameter.
start(\[*initializer*\[, *initargs*\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.start "Link to this definition")
Start a subprocess to start the manager. If *initializer* is not `None` then the subprocess will call `initializer(*initargs)` when it starts.
get\_server()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.get_server "Link to this definition")
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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.address "multiprocessing.managers.BaseManager.address") attribute.
connect()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.connect "Link to this definition")
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()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.shutdown "Link to this definition")
Stop the process used by the manager. This is only available if [`start()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.start "multiprocessing.managers.BaseManager.start") has been used to start the server process.
This can be called multiple times.
register(*typeid*\[, *callable*\[, *proxytype*\[, *exposed*\[, *method\_to\_typeid*\[, *create\_method*\]\]\]\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.register "Link to this definition")
A classmethod which can be used for registering a type or callable with the manager class.
*typeid* is a âtype identifierâ 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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.connect "multiprocessing.managers.BaseManager.connect") method, or if the *create\_method* argument is `False` then this can be left as `None`.
*proxytype* is a subclass of [`BaseProxy`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseProxy "multiprocessing.managers.BaseProxy") which is used to create proxies for shared objects with this *typeid*. If `None` then a proxy class is created automatically.
*exposed* is used to specify a sequence of method names which proxies for this typeid should be allowed to access using [`BaseProxy._callmethod()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseProxy._callmethod "multiprocessing.managers.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 âpublic methodsâ of the shared object will be accessible. (Here a âpublic methodâ means any attribute which has a [`__call__()`](https://docs.python.org/3/reference/datamodel.html#object.__call__ "object.__call__") method and whose name does not begin with `'_'`.)
*method\_to\_typeid* is a mapping used to specify the return type of those exposed methods which should return a proxy. It maps method names to typeid strings. (If *method\_to\_typeid* is `None` then `proxytype._method_to_typeid_` is used instead if it exists.) If a methodâs 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[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.address "Link to this definition")
The address used by the manager.
Changed in version 3.3: Manager objects support the context management protocol â see [Context Manager Types](https://docs.python.org/3/library/stdtypes.html#typecontextmanager). [`__enter__()`](https://docs.python.org/3/library/stdtypes.html#contextmanager.__enter__ "contextmanager.__enter__") starts the server process (if it has not already started) and then returns the manager object. [`__exit__()`](https://docs.python.org/3/library/stdtypes.html#contextmanager.__exit__ "contextmanager.__exit__") calls [`shutdown()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.shutdown "multiprocessing.managers.BaseManager.shutdown").
In previous versions [`__enter__()`](https://docs.python.org/3/library/stdtypes.html#contextmanager.__enter__ "contextmanager.__enter__") did not start the managerâs server process if it was not already started.
*class* multiprocessing.managers.SyncManager[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager "Link to this definition")
A subclass of [`BaseManager`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager "multiprocessing.managers.BaseManager") which can be used for the synchronization of processes. Objects of this type are returned by [`multiprocessing.Manager()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Manager "multiprocessing.Manager").
Its methods create and return [Proxy Objects](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-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*\[, *timeout*\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.Barrier "Link to this definition")
Create a shared [`threading.Barrier`](https://docs.python.org/3/library/threading.html#threading.Barrier "threading.Barrier") object and return a proxy for it.
Added in version 3.3.
BoundedSemaphore(\[*value*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.BoundedSemaphore "Link to this definition")
Create a shared [`threading.BoundedSemaphore`](https://docs.python.org/3/library/threading.html#threading.BoundedSemaphore "threading.BoundedSemaphore") object and return a proxy for it.
Condition(\[*lock*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.Condition "Link to this definition")
Create a shared [`threading.Condition`](https://docs.python.org/3/library/threading.html#threading.Condition "threading.Condition") object and return a proxy for it.
If *lock* is supplied then it should be a proxy for a [`threading.Lock`](https://docs.python.org/3/library/threading.html#threading.Lock "threading.Lock") or [`threading.RLock`](https://docs.python.org/3/library/threading.html#threading.RLock "threading.RLock") object.
Changed in version 3.3: The [`wait_for()`](https://docs.python.org/3/library/threading.html#threading.Condition.wait_for "threading.Condition.wait_for") method was added.
Event()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.Event "Link to this definition")
Create a shared [`threading.Event`](https://docs.python.org/3/library/threading.html#threading.Event "threading.Event") object and return a proxy for it.
Lock()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.Lock "Link to this definition")
Create a shared [`threading.Lock`](https://docs.python.org/3/library/threading.html#threading.Lock "threading.Lock") object and return a proxy for it.
Namespace()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.Namespace "Link to this definition")
Create a shared [`Namespace`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.Namespace "multiprocessing.managers.Namespace") object and return a proxy for it.
Queue(\[*maxsize*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.Queue "Link to this definition")
Create a shared [`queue.Queue`](https://docs.python.org/3/library/queue.html#queue.Queue "queue.Queue") object and return a proxy for it.
RLock()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.RLock "Link to this definition")
Create a shared [`threading.RLock`](https://docs.python.org/3/library/threading.html#threading.RLock "threading.RLock") object and return a proxy for it.
Semaphore(\[*value*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.Semaphore "Link to this definition")
Create a shared [`threading.Semaphore`](https://docs.python.org/3/library/threading.html#threading.Semaphore "threading.Semaphore") object and return a proxy for it.
Array(*typecode*, *sequence*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.Array "Link to this definition")
Create an array and return a proxy for it.
Value(*typecode*, *value*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.Value "Link to this definition")
Create an object with a writable `value` attribute and return a proxy for it.
dict()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.dict "Link to this definition")
dict(*mapping*)
dict(*sequence*)
Create a shared [`dict`](https://docs.python.org/3/library/stdtypes.html#dict "dict") object and return a proxy for it.
list()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.list "Link to this definition")
list(*sequence*)
Create a shared [`list`](https://docs.python.org/3/library/stdtypes.html#list "list") object and return a proxy for it.
set()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager.set "Link to this definition")
set(*sequence*)
set(*mapping*)
Create a shared [`set`](https://docs.python.org/3/library/stdtypes.html#set "set") object and return a proxy for it.
Added in version 3.14: [`set`](https://docs.python.org/3/library/stdtypes.html#set "set") support was added.
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[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.Namespace "Link to this definition")
A type that can register with [`SyncManager`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.SyncManager "multiprocessing.managers.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:
```
>>> mp_context = multiprocessing.get_context('spawn')
>>> manager = mp_context.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[¶](https://docs.python.org/3/library/multiprocessing.html#customized-managers "Link to this heading")
To create oneâs own manager, one creates a subclass of [`BaseManager`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager "multiprocessing.managers.BaseManager") and uses the [`register()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.register "multiprocessing.managers.BaseManager.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[¶](https://docs.python.org/3/library/multiprocessing.html#using-a-remote-manager "Link to this heading")
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[¶](https://docs.python.org/3/library/multiprocessing.html#proxy-objects "Link to this heading")
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:
```
>>> mp_context = multiprocessing.get_context('spawn')
>>> manager = mp_context.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()`](https://docs.python.org/3/library/stdtypes.html#str "str") to a proxy will return the representation of the referent, whereas applying [`repr()`](https://docs.python.org/3/library/functions.html#repr "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](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-proxy-objects). This permits nesting of these managed lists, dicts, and other [Proxy Objects](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-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`](https://docs.python.org/3/library/stdtypes.html#list "list") or [`dict`](https://docs.python.org/3/library/stdtypes.html#dict "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](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-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[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseProxy "Link to this definition")
Proxy objects are instances of subclasses of `BaseProxy`.
\_callmethod(*methodname*\[, *args*\[, *kwds*\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseProxy._callmethod "Link to this definition")
Call and return the result of a method of the proxyâs 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 managerâs process.
The returned value will be a copy of the result of the call or a proxy to a new shared object â see documentation for the *method\_to\_typeid* argument of [`BaseManager.register()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseManager.register "multiprocessing.managers.BaseManager.register").
If an exception is raised by the call, then is re-raised by `_callmethod()`. If some other exception is raised in the managerâs 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()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseProxy._getvalue "Link to this definition")
Return a copy of the referent.
If the referent is unpicklable then this will raise an exception.
\_\_repr\_\_()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseProxy.__repr__ "Link to this definition")
Return a representation of the proxy object.
\_\_str\_\_()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.BaseProxy.__str__ "Link to this definition")
Return the representation of the referent.
#### Cleanup[¶](https://docs.python.org/3/library/multiprocessing.html#cleanup "Link to this heading")
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[¶](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.pool "Link to this heading")
One can create a pool of processes which will carry out tasks submitted to it with the [`Pool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.Pool") class.
*class* multiprocessing.pool.Pool(\[*processes*\[, *initializer*\[, *initargs*\[, *maxtasksperchild*\[, *context*\]\]\]\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "Link to this definition")
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.process_cpu_count()`](https://docs.python.org/3/library/os.html#os.process_cpu_count "os.process_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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.Pool") method of a context object. In both cases *context* is set appropriately. If `None`, calling this function will have the side effect of setting the current global start method if it has not been set already. See the `get_context()` function.
Note that the methods of the pool object should only be called by the process which created the pool.
Warning
[`multiprocessing.pool`](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.pool "multiprocessing.pool: Create pools of processes.") 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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.close "multiprocessing.pool.Pool.close") and [`terminate()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.terminate "multiprocessing.pool.Pool.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__()`](https://docs.python.org/3/reference/datamodel.html#object.__del__ "object.__del__") for more information).
Changed in version 3.2: Added the *maxtasksperchild* parameter.
Changed in version 3.4: Added the *context* parameter.
Note
Worker processes within a `Pool` typically live for the complete duration of the Poolâs 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 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*\[, *kwds*\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.apply "Link to this definition")
Call *func* with arguments *args* and keyword arguments *kwds*. It blocks until the result is ready. Given this blocks, [`apply_async()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.apply_async "multiprocessing.pool.Pool.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*\[, *error\_callback*\]\]\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.apply_async "Link to this definition")
A variant of the [`apply()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.apply "multiprocessing.pool.Pool.apply") method which returns a [`AsyncResult`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.AsyncResult "multiprocessing.pool.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*\[, *chunksize*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map "Link to this definition")
A parallel equivalent of the [`map()`](https://docs.python.org/3/library/functions.html#map "map") built-in function (it supports only one *iterable* argument though, for multiple iterables see [`starmap()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.starmap "multiprocessing.pool.Pool.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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.imap "multiprocessing.pool.Pool.imap") or [`imap_unordered()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.imap_unordered "multiprocessing.pool.Pool.imap_unordered") with explicit *chunksize* option for better efficiency.
map\_async(*func*, *iterable*\[, *chunksize*\[, *callback*\[, *error\_callback*\]\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map_async "Link to this definition")
A variant of the [`map()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map "multiprocessing.pool.Pool.map") method which returns a [`AsyncResult`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.AsyncResult "multiprocessing.pool.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*\[, *chunksize*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.imap "Link to this definition")
A lazier version of [`map()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map "multiprocessing.pool.Pool.map").
The *chunksize* argument is the same as the one used by the [`map()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map "multiprocessing.pool.Pool.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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.TimeoutError "multiprocessing.TimeoutError") if the result cannot be returned within *timeout* seconds.
imap\_unordered(*func*, *iterable*\[, *chunksize*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.imap_unordered "Link to this definition")
The same as [`imap()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.imap "multiprocessing.pool.Pool.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 âcorrectâ.)
starmap(*func*, *iterable*\[, *chunksize*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.starmap "Link to this definition")
Like [`map()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map "multiprocessing.pool.Pool.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 .
Added in version 3.3.
starmap\_async(*func*, *iterable*\[, *chunksize*\[, *callback*\[, *error\_callback*\]\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.starmap_async "Link to this definition")
A combination of [`starmap()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.starmap "multiprocessing.pool.Pool.starmap") and [`map_async()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map_async "multiprocessing.pool.Pool.map_async") that iterates over *iterable* of iterables and calls *func* with the iterables unpacked. Returns a result object.
Added in version 3.3.
close()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.close "Link to this definition")
Prevents any more tasks from being submitted to the pool. Once all the tasks have been completed the worker processes will exit.
terminate()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.terminate "Link to this definition")
Stops the worker processes immediately without completing outstanding work. When the pool object is garbage collected `terminate()` will be called immediately.
join()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.join "Link to this definition")
Wait for the worker processes to exit. One must call [`close()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.close "multiprocessing.pool.Pool.close") or [`terminate()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.terminate "multiprocessing.pool.Pool.terminate") before using `join()`.
*class* multiprocessing.pool.AsyncResult[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.AsyncResult "Link to this definition")
The class of the result returned by [`Pool.apply_async()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.apply_async "multiprocessing.pool.Pool.apply_async") and [`Pool.map_async()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.map_async "multiprocessing.pool.Pool.map_async").
get(\[*timeout*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.AsyncResult.get "Link to this definition")
Return the result when it arrives. If *timeout* is not `None` and the result does not arrive within *timeout* seconds then [`multiprocessing.TimeoutError`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.TimeoutError "multiprocessing.TimeoutError") is raised. If the remote call raised an exception then that exception will be reraised by `get()`.
wait(\[*timeout*\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.AsyncResult.wait "Link to this definition")
Wait until the result is available or until *timeout* seconds pass.
ready()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.AsyncResult.ready "Link to this definition")
Return whether the call has completed.
successful()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.AsyncResult.successful "Link to this definition")
Return whether the call completed without raising an exception. Will raise [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError "ValueError") if the result is not ready.
Changed in version 3.7: If the result is not ready, [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError "ValueError") is raised instead of [`AssertionError`](https://docs.python.org/3/library/exceptions.html#AssertionError "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[¶](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.connection "Link to this heading")
Usually message passing between processes is done using queues or by using [`Connection`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection "multiprocessing.connection.Connection") objects returned by [`Pipe()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Pipe "multiprocessing.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`](https://docs.python.org/3/library/hmac.html#module-hmac "hmac: Keyed-Hashing for Message Authentication (HMAC) implementation") module, and for polling multiple connections at the same time.
multiprocessing.connection.deliver\_challenge(*connection*, *authkey*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.deliver_challenge "Link to this definition")
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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.AuthenticationError "multiprocessing.AuthenticationError") is raised.
multiprocessing.connection.answer\_challenge(*connection*, *authkey*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.answer_challenge "Link to this definition")
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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.AuthenticationError "multiprocessing.AuthenticationError") is raised.
multiprocessing.connection.Client(*address*\[, *family*\[, *authkey*\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Client "Link to this definition")
Attempt to set up a connection to the listener which is using address *address*, returning a [`Connection`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection "multiprocessing.connection.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](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.AuthenticationError "multiprocessing.AuthenticationError") is raised if authentication fails. See [Authentication keys](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-auth-keys).
*class* multiprocessing.connection.Listener(\[*address*\[, *family*\[, *backlog*\[, *authkey*\]\]\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Listener "Link to this definition")
A wrapper for a bound socket or Windows named pipe which is âlisteningâ 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 â0.0.0.0â is used, the address will not be a connectable end point on Windows. If you require a connectable end-point, you should use â127.0.0.1â.
*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](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-address-formats). Note that if *family* is `'AF_UNIX'` and address is `None` then the socket will be created in a private temporary directory created using [`tempfile.mkstemp()`](https://docs.python.org/3/library/tempfile.html#tempfile.mkstemp "tempfile.mkstemp").
If the listener object uses a socket then *backlog* (1 by default) is passed to the [`listen()`](https://docs.python.org/3/library/socket.html#socket.socket.listen "socket.socket.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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.AuthenticationError "multiprocessing.AuthenticationError") is raised if authentication fails. See [Authentication keys](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-auth-keys).
accept()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Listener.accept "Link to this definition")
Accept a connection on the bound socket or named pipe of the listener object and return a [`Connection`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection "multiprocessing.connection.Connection") object. If authentication is attempted and fails, then [`AuthenticationError`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.AuthenticationError "multiprocessing.AuthenticationError") is raised.
close()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Listener.close "Link to this definition")
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[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Listener.address "Link to this definition")
The address which is being used by the Listener object.
last\_accepted[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Listener.last_accepted "Link to this definition")
The address from which the last accepted connection came. If this is unavailable then it is `None`.
Changed in version 3.3: Listener objects now support the context management protocol â see [Context Manager Types](https://docs.python.org/3/library/stdtypes.html#typecontextmanager). [`__enter__()`](https://docs.python.org/3/library/stdtypes.html#contextmanager.__enter__ "contextmanager.__enter__") returns the listener object, and [`__exit__()`](https://docs.python.org/3/library/stdtypes.html#contextmanager.__exit__ "contextmanager.__exit__") calls [`close()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Listener.close "multiprocessing.connection.Listener.close").
multiprocessing.connection.wait(*object\_list*, *timeout\=None*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.wait "Link to this definition")
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 POSIX and Windows, an object can appear in *object\_list* if it is
- a readable [`Connection`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection "multiprocessing.connection.Connection") object;
- a connected and readable [`socket.socket`](https://docs.python.org/3/library/socket.html#socket.socket "socket.socket") object; or
- the [`sentinel`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.sentinel "multiprocessing.Process.sentinel") attribute of a [`Process`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.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.
**POSIX**: `wait(object_list, timeout)` almost equivalent `select.select(object_list, [], [], timeout)`. The difference is that, if [`select.select()`](https://docs.python.org/3/library/select.html#select.select "select.select") is interrupted by a signal, it can raise [`OSError`](https://docs.python.org/3/library/exceptions.html#OSError "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()`](https://docs.python.org/3/library/io.html#io.IOBase.fileno "io.IOBase.fileno") method which returns a socket handle or pipe handle. (Note that pipe handles and socket handles are **not** waitable handles.)
Added 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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.wait "multiprocessing.connection.wait") to wait for messages from multiple processes at once:
```
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[¶](https://docs.python.org/3/library/multiprocessing.html#address-formats "Link to this heading")
- 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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Client "multiprocessing.connection.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[¶](https://docs.python.org/3/library/multiprocessing.html#authentication-keys "Link to this heading")
When one uses [`Connection.recv`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Connection.recv "multiprocessing.connection.Connection.recv"), the data received is automatically unpickled. Unfortunately unpickling data from an untrusted source is a security risk. Therefore [`Listener`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Listener "multiprocessing.connection.Listener") and [`Client()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.Client "multiprocessing.connection.Client") use the [`hmac`](https://docs.python.org/3/library/hmac.html#module-hmac "hmac: Keyed-Hashing for Message Authentication (HMAC) implementation") 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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.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()`](https://docs.python.org/3/library/os.html#os.urandom "os.urandom").
### Logging[¶](https://docs.python.org/3/library/multiprocessing.html#logging "Link to this heading")
Some support for logging is available. Note, however, that the [`logging`](https://docs.python.org/3/library/logging.html#module-logging "logging: Flexible event logging system for applications.") 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()[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.get_logger "Link to this definition")
Returns the logger used by `multiprocessing`. If necessary, a new one will be created.
When first created the logger has level [`logging.NOTSET`](https://docs.python.org/3/library/logging.html#logging.NOTSET "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 processâs logger â any other customization of the logger will not be inherited.
multiprocessing.log\_to\_stderr(*level\=None*)[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.log_to_stderr "Link to this definition")
This function performs a call to [`get_logger()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.get_logger "multiprocessing.get_logger") but in addition to returning the logger created by get\_logger, it adds a handler which sends output to [`sys.stderr`](https://docs.python.org/3/library/sys.html#sys.stderr "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`](https://docs.python.org/3/library/logging.html#module-logging "logging: Flexible event logging system for applications.") module.
### The `multiprocessing.dummy` module[¶](https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.dummy "Link to this heading")
`multiprocessing.dummy` replicates the API of `multiprocessing` but is no more than a wrapper around the [`threading`](https://docs.python.org/3/library/threading.html#module-threading "threading: Thread-based parallelism.") module.
In particular, the `Pool` function provided by `multiprocessing.dummy` returns an instance of [`ThreadPool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.ThreadPool "multiprocessing.pool.ThreadPool"), which is a subclass of [`Pool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.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*\[, *initargs*\]\]\])[¶](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.ThreadPool "Link to this definition")
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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.Pool") instances, and their resources must also be properly managed, either by using the pool as a context manager or by calling [`close()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.close "multiprocessing.pool.Pool.close") and [`terminate()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.terminate "multiprocessing.pool.Pool.terminate") manually.
*processes* is the number of worker threads to use. If *processes* is `None` then the number returned by [`os.process_cpu_count()`](https://docs.python.org/3/library/os.html#os.process_cpu_count "os.process_cpu_count") is used.
If *initializer* is not `None` then each worker process will call `initializer(*initargs)` when it starts.
Unlike [`Pool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.Pool"), *maxtasksperchild* and *context* cannot be provided.
Note
A `ThreadPool` shares the same interface as [`Pool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.Pool"), which is designed around a pool of processes and predates the introduction of the [`concurrent.futures`](https://docs.python.org/3/library/concurrent.futures.html#module-concurrent.futures "concurrent.futures: Execute computations concurrently using threads or processes.") module. As such, it inherits some operations that donât make sense for a pool backed by threads, and it has its own type for representing the status of asynchronous jobs, [`AsyncResult`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.AsyncResult "multiprocessing.pool.AsyncResult"), that is not understood by any other libraries.
Users should generally prefer to use [`concurrent.futures.ThreadPoolExecutor`](https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.ThreadPoolExecutor "concurrent.futures.ThreadPoolExecutor"), which has a simpler interface that was designed around threads from the start, and which returns [`concurrent.futures.Future`](https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.Future "concurrent.futures.Future") instances that are compatible with many other libraries, including [`asyncio`](https://docs.python.org/3/library/asyncio.html#module-asyncio "asyncio: Asynchronous I/O.").
## Programming guidelines[¶](https://docs.python.org/3/library/multiprocessing.html#programming-guidelines "Link to this heading")
There are certain guidelines and idioms which should be adhered to when using `multiprocessing`.
### All start methods[¶](https://docs.python.org/3/library/multiprocessing.html#all-start-methods "Link to this heading")
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 POSIX 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()`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.active_children "multiprocessing.active_children") is called) all completed processes which have not yet been joined will be joined. Also calling a finished processâs [`Process.is_alive`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.is_alive "multiprocessing.Process.is_alive") will join the process. Even so it is probably good practice to explicitly join all the processes that you start.
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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.terminate "multiprocessing.Process.terminate") method to stop a process is liable to cause any shared resources (such as locks, semaphores, pipes and queues) currently being used by the process to become broken or unavailable to other processes.
>
> Therefore it is probably best to only consider using [`Process.terminate`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.terminate "multiprocessing.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 âfeederâ thread to the underlying pipe. (The child process can call the [`Queue.cancel_join_thread`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Queue.cancel_join_thread "multiprocessing.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 POSIX 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`](https://docs.python.org/3/library/sys.html#sys.stdin "sys.stdin") with a âfile like objectâ
> `multiprocessing` originally unconditionally called:
> ```
> os.close(sys.stdin.fileno())
> ```
> in the `multiprocessing.Process._bootstrap()` method â 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()`](https://docs.python.org/3/library/sys.html#sys.stdin "sys.stdin") with a âfile-like objectâ with output buffering. This danger is that if multiple processes call [`close()`](https://docs.python.org/3/library/io.html#io.IOBase.close "io.IOBase.close") on this file-like object, it could result in the same data being flushed to the object multiple times, resulting in corruption.
>
> 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](https://bugs.python.org/issue?@action=redirect&bpo=5155), [bpo-5313](https://bugs.python.org/issue?@action=redirect&bpo=5313) and [bpo-5331](https://bugs.python.org/issue?@action=redirect&bpo=5331)
### The *spawn* and *forkserver* start methods[¶](https://docs.python.org/3/library/multiprocessing.html#the-spawn-and-forkserver-start-methods "Link to this heading")
There are a few extra restrictions which donât apply to the *fork* start method.
More picklability
> Ensure that all arguments to [`Process`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process "multiprocessing.Process") are picklable. Also, if you subclass `Process.__init__`, you must make sure that instances will be picklable when the [`Process.start`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.start "multiprocessing.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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.start "multiprocessing.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 as starting a new process).
>
> For example, using the *spawn* or *forkserver* start method running the following module would fail with a [`RuntimeError`](https://docs.python.org/3/library/exceptions.html#RuntimeError "RuntimeError"):
> ```
> from multiprocessing import Process
def foo():
print('hello')
p = Process(target=foo)
p.start()
> ```
> Instead one should protect the âentry pointâ of the program by using 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 moduleâs `foo()` function.
>
> Similar restrictions apply if a pool or manager is created in the main module.
## Examples[¶](https://docs.python.org/3/library/multiprocessing.html#examples "Link to this heading")
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`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool "multiprocessing.pool.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()
``` | |||||||||
| ML Classification | ||||||||||
| ML Categories |
Raw JSON{
"/Computers_and_Electronics": 994,
"/Computers_and_Electronics/Programming": 851,
"/Computers_and_Electronics/Programming/Scripting_Languages": 374
} | |||||||||
| ML Page Types |
Raw JSON{
"/Document": 644,
"/Document/Manual": 587
} | |||||||||
| ML Intent Types |
Raw JSON{
"Informational": 999
} | |||||||||
| Content Metadata | ||||||||||
| Language | en | |||||||||
| Author | null | |||||||||
| Publish Time | not set | |||||||||
| Original Publish Time | 2014-04-12 16:20:38 (12 years ago) | |||||||||
| Republished | No | |||||||||
| Word Count (Total) | 20,158 | |||||||||
| Word Count (Content) | 19,437 | |||||||||
| Links | ||||||||||
| External Links | 7 | |||||||||
| Internal Links | 49 | |||||||||
| Technical SEO | ||||||||||
| Meta Nofollow | No | |||||||||
| Meta Noarchive | No | |||||||||
| JS Rendered | Yes | |||||||||
| Redirect Target | null | |||||||||
| Performance | ||||||||||
| Download Time (ms) | 18 | |||||||||
| TTFB (ms) | 7 | |||||||||
| Download Size (bytes) | 58,126 | |||||||||
| Shard | 16 (laksa) | |||||||||
| Root Hash | 10954876678907435016 | |||||||||
| Unparsed URL | org,python!docs,/3/library/multiprocessing.html s443 | |||||||||