multiprocessing
— Process-based parallelism¶
Source code:Lib/multiprocessing/
Availability:not Android, not iOS, not WASI.
This module is not supported onmobile platforms orWebAssembly platforms.
Introduction¶
multiprocessing
is a package that supports spawning processes using an
API similar to thethreading
module. Themultiprocessing
package
offers both local and remote concurrency, effectively side-stepping the
Global Interpreter Lockby using
subprocesses instead of threads. Due
to this, themultiprocessing
module allows the programmer to fully
leverage multiple processors on a given machine. It runs on both POSIX and
Windows.
Themultiprocessing
module also introduces APIs which do not have
analogs in thethreading
module. A prime example of this is the
Pool
object which offers a convenient means of
parallelizing the execution of a function across multiple input values,
distributing the input data across processes (data parallelism). The following
example demonstrates the common practice of defining such functions in a module
so that child processes can successfully import that module. This basic example
of data parallelism usingPool
,
frommultiprocessingimportPool
deff(x):
returnx*x
if__name__=='__main__':
withPool(5)asp:
print(p.map(f,[1,2,3]))
will print to standard output
[1,4,9]
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 thePool
interface directly, theconcurrent.futures
API more readily allows
the submission of work to the underlying process pool to be separated from
waiting for the results.
TheProcess
class¶
Inmultiprocessing
,processes are spawned by creating aProcess
object and then calling itsstart()
method.Process
follows the API ofthreading.Thread
.A trivial example of a
multiprocess program is
frommultiprocessingimportProcess
deff(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:
frommultiprocessingimportProcess
importos
definfo(title):
print(title)
print('module name:',__name__)
print('parent process:',os.getppid())
print('process id:',os.getpid())
deff(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 theif__name__=='__main__'
part is
necessary, seeProgramming guidelines.
Contexts and start methods¶
Depending on the platform,multiprocessing
supports three ways
to start a process. Thesestart methodsare
- spawn
The parent process starts a fresh Python interpreter process. The child process will only inherit those resources necessary to run the process object’s
run()
method. In particular, unnecessary file descriptors and handles from the parent process will not be inherited. Starting a process using this method is rather slow compared to usingforkorforkserver.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. Currently the default on POSIX except macOS.
Note
The default start method will change away fromforkin Python 3.14. Code that requiresforkshould explicitly specify that via
get_context()
orset_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 aDeprecationWarning
.Use a different start method. See theos.fork()
documentation for further explanation.- forkserver
When the program starts and selects theforkserverstart 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.
Changed in version 3.4:spawnadded on all POSIX platforms, andforkserveradded for some POSIX platforms. Child processes no longer inherit all of the parents inheritable handles on Windows.
Changed in version 3.8:On macOS, thespawnstart method is now the default. Theforkstart method should be considered unsafe as it can lead to crashes of the subprocess as macOS system libraries may start threads. Seebpo-33725.
On POSIX using thespawnorforkserverstart methods will also
start aresource trackerprocess which tracks the unlinked named
system resources (such as named semaphores or
SharedMemory
objects) created
by processes of the program. When all processes
have exited the resource tracker unlinks any remaining tracked object.
Usually there should be none, but if a process was killed by a signal
there may be some “leaked” resources. (Neither leaked semaphores nor shared
memory segments will be automatically unlinked until the next reboot. This is
problematic for both objects because the system allows only a limited number of
named semaphores, and shared memory segments occupy some space in the main
memory.)
To select a start method you use theset_start_method()
in
theif__name__=='__main__'
clause of the main module. For
example:
importmultiprocessingasmp
deffoo(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 useget_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.
importmultiprocessingasmp
deffoo(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 theforkcontext cannot be passed to processes started using the spawnorforkserverstart methods.
A library which wants to use a particular start method should probably
useget_context()
to avoid interfering with the choice of the
library user.
Warning
The'spawn'
and'forkserver'
start methods generally cannot
be used with “frozen” executables (i.e., binaries produced by
packages likePyInstallerandcx_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 ofqueue.Queue
.For example:frommultiprocessingimportProcess,Queue deff(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:frommultiprocessingimportProcess,Pipe deff(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 hassend()
andrecv()
methods (among others). Note that data in a pipe may become corrupted if two processes (or threads) try to read from or write to thesameend 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 the object andrecv()
re-creates the object.
Synchronization between processes¶
multiprocessing
contains equivalents of all the synchronization
primitives fromthreading
.For instance one can use a lock to ensure
that only one process prints to standard output at a time:
frommultiprocessingimportProcess,Lock
deff(l,i):
l.acquire()
try:
print('hello world',i)
finally:
l.release()
if__name__=='__main__':
lock=Lock()
fornuminrange(10):
Process(target=f,args=(lock,num)).start()
Without using the lock output from the different processes is liable to get all mixed up.
Using a pool of workers¶
ThePool
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:
frommultiprocessingimportPool,TimeoutError
importtime
importos
deff(x):
returnx*x
if__name__=='__main__':
# start 4 worker processes
withPool(processes=4)aspool:
# print "[0, 1, 4,..., 81]"
print(pool.map(f,range(10)))
# print same numbers in arbitrary order
foriinpool.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,())foriinrange(4)]
print([res.get(timeout=1)forresinmultiple_results])
# make a single worker sleep for 10 seconds
res=pool.apply_async(time.sleep,(10,))
try:
print(res.get(timeout=1))
exceptTimeoutError:
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 inProgramming guidelines
however it is worth pointing out here. This means that some examples, such
as themultiprocessing.pool.Pool
examples will not work in the
interactive interpreter. For example:
>>>frommultiprocessingimportPool
>>>p=Pool(5)
>>>deff(x):
...returnx*x
...
>>>withp:
...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¶
Themultiprocessing
package mostly replicates the API of the
threading
module.
Process
and exceptions¶
- classmultiprocessing.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 ofthreading.Thread
.The constructor should always be called with keyword arguments.group should always be
None
;it exists solely for compatibility withthreading.Thread
.targetis the callable object to be invoked by therun()
method. It defaults toNone
,meaning nothing is called.nameis the process name (seename
for more details). argsis the argument tuple for the target invocation.kwargsis a dictionary of keyword arguments for the target invocation. If provided, the keyword-onlydaemonargument sets the processdaemon
flag toTrue
orFalse
.IfNone
(the default), this flag will be inherited from the creating process.By default, no arguments are passed totarget.Theargsargument, which defaults to
()
,can be used to specify a list or tuple of the arguments to pass totarget.If a subclass overrides the constructor, it must make sure it invokes the base class constructor (
Process.__init__()
) before doing anything else to the process.Changed in version 3.3:Added thedaemonparameter.
- run()¶
Method representing the process’s activity.
You may override this method in a subclass. The standard
run()
method invokes the callable object passed to the object’s constructor as the target argument, if any, with sequential and keyword arguments taken from theargsandkwargsarguments, respectively.Using a list or tuple as theargsargument passed to
Process
achieves the same effect.Example:
>>>frommultiprocessingimportProcess >>>p=Process(target=print,args=[1]) >>>p.run() 1 >>>p=Process(target=print,args=(1,)) >>>p.run() 1
- start()¶
Start the process’s activity.
This must be called at most once per process object. It arranges for the object’s
run()
method to be invoked in a separate process.
- join([timeout])¶
If the optional argumenttimeoutis
None
(the default), the method blocks until the process whosejoin()
method is called terminates. Iftimeoutis a positive number, it blocks at mosttimeoutseconds. Note that the method returnsNone
if its process terminates or if the method times out. Check the process’sexitcode
to determine if it terminated.A process can be joined many times.
A process cannot join itself because this would cause a deadlock. It is an error to attempt to join a process before it has been started.
- name¶
The process’s name. The name is a string used for identification purposes only. It has no semantics. Multiple processes may be given the same name.
The initial name is set by the constructor. If no explicit name is provided to the constructor, a name of the form ‘Process-N1:N2:…:Nk’ is constructed, where each Nkis the N-th child of its parent.
- is_alive()¶
Return whether the process is alive.
Roughly, a process object is alive from the moment the
start()
method returns until the child process terminates.
- daemon¶
The process’s daemon flag, a Boolean value. This must be set before
start()
is called.The initial value is inherited from the creating process.
When a process exits, it attempts to terminate all of its daemonic child processes.
Note that a daemonic process is not allowed to create child processes. Otherwise a daemonic process would leave its children orphaned if it gets terminated when its parent process exits. Additionally, these arenot Unix daemons or services, they are normal processes that will be terminated (and not joined) if non-daemonic processes have exited.
In addition to the
threading.Thread
API,Process
objects also support the following attributes and methods:- pid¶
Return the process ID. Before the process is spawned, this will be
None
.
- exitcode¶
The child’s exit code. This will be
None
if the process has not yet terminated.If the child’s
run()
method returned normally, the exit code will be 0. If it terminated viasys.exit()
with an integer argumentN,the exit code will beN.If the child terminated due to an exception not caught within
run()
,the exit code will be 1. If it was terminated by signalN,the exit code will be the negative value-N.
- authkey¶
The process’s authentication key (a byte string).
When
multiprocessing
is initialized the main process is assigned a random string usingos.urandom()
.When a
Process
object is created, it will inherit the authentication key of its parent process, although this may be changed by settingauthkey
to another byte string.
- sentinel¶
A numeric handle of a system object which will become “ready” when the process ends.
You can use this value if you want to wait on several events at once using
multiprocessing.connection.wait()
.Otherwise callingjoin()
is simpler.On Windows, this is an OS handle usable with the
WaitForSingleObject
andWaitForMultipleObjects
family of API calls. On POSIX, this is a file descriptor usable with primitives from theselect
module.Added in version 3.3.
- terminate()¶
Terminate the process. On POSIX this is done using the
SIGTERM
signal; on WindowsTerminateProcess()
is used. Note that exit handlers and finally clauses, etc., will not be executed.Note that descendant processes of the process willnotbe terminated – they will simply become orphaned.
Warning
If this method is used when the associated process is using a pipe or queue then the pipe or queue is liable to become corrupted and may become unusable by other process. Similarly, if the process has acquired a lock or semaphore etc. then terminating it is liable to cause other processes to deadlock.
- kill()¶
Same as
terminate()
but using theSIGKILL
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. Onceclose()
returns successfully, most other methods and attributes of theProcess
object will raiseValueError
.Added in version 3.7.
Note that the
start()
,join()
,is_alive()
,terminate()
andexitcode
methods should only be called by the process that created the process object.Example usage of some of the methods of
Process
:>>>importmultiprocessing,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
- exceptionmultiprocessing.ProcessError¶
The base class of all
multiprocessing
exceptions.
- exceptionmultiprocessing.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 ofBufferTooShort
thene.args[0]
will give the message as a byte string.
- exceptionmultiprocessing.AuthenticationError¶
Raised when there is an authentication error.
- exceptionmultiprocessing.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 usePipe()
(for a connection between two
processes) or a queue (which allows multiple producers and consumers).
TheQueue
,SimpleQueue
andJoinableQueue
types
are multi-producer, multi-consumerFIFO
queues modelled on thequeue.Queue
class in the
standard library. They differ in thatQueue
lacks the
task_done()
andjoin()
methods introduced
into Python 2.5’squeue.Queue
class.
If you useJoinableQueue
then youmustcall
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 thatmultiprocessing
queues serializes all objects that are put into them usingpickle
.
The object return 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 – see Managers.
Note
multiprocessing
uses the usualqueue.Empty
and
queue.Full
exceptions to signal a timeout. They are not available in
themultiprocessing
namespace so you need to import them from
queue
.
Note
When an object is put on a queue, the object is pickled and a background thread later flushes the pickled data to an underlying pipe. This has some consequences which are a little surprising, but should not cause any practical difficulties – if they really bother you then you can instead use a queue created with a manager.
After putting an object on an empty queue there may be an infinitesimal delay before the queue’s
empty()
method returnsFalse
andget_nowait()
can return without raisingqueue.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 usingProcess.terminate()
oros.kill()
while it is trying to use aQueue
,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 usedJoinableQueue.cancel_join_thread
), then that process will
not terminate until all buffered items have been flushed to the pipe.
This means that if you try joining that process you may get a deadlock unless you are sure that all items which have been put on the queue have been consumed. Similarly, if the child process is non-daemonic then the parent process may hang on exit when it tries to join all its non-daemonic children.
Note that a queue created using a manager does not have this issue. See Programming guidelines.
For an example of the usage of queues for interprocess communication see Examples.
- multiprocessing.Pipe([duplex])¶
Returns a pair
(conn1,conn2)
ofConnection
objects representing the ends of a pipe.Ifduplexis
True
(the default) then the pipe is bidirectional. If duplexisFalse
then the pipe is unidirectional:conn1
can only be used for receiving messages andconn2
can only be used for sending messages.The
send()
method serializes the the object usingpickle
and therecv()
re-creates the object.
- classmultiprocessing.Queue([maxsize])¶
Returns a process shared queue implemented using a pipe and a few locks/semaphores. When a process first puts an item on the queue a feeder thread is started which transfers objects from a buffer into the pipe.
The usual
queue.Empty
andqueue.Full
exceptions from the standard library’squeue
module are raised to signal timeouts.Queue
implements all the methods ofqueue.Queue
except fortask_done()
andjoin()
.- 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 wheresem_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 argumentblockis
True
(the default) andtimeoutisNone
(the default), block if necessary until a free slot is available. Iftimeoutis a positive number, it blocks at mosttimeoutseconds and raises thequeue.Full
exception if no free slot was available within that time. Otherwise (blockisFalse
), put an item on the queue if a free slot is immediately available, else raise thequeue.Full
exception (timeoutis ignored in that case).Changed in version 3.8:If the queue is closed,
ValueError
is raised instead ofAssertionError
.
- put_nowait(obj)¶
Equivalent to
put(obj,False)
.
- get([block[,timeout]])¶
Remove and return an item from the queue. If optional argsblockis
True
(the default) andtimeoutisNone
(the default), block if necessary until an item is available. Iftimeoutis a positive number, it blocks at mosttimeoutseconds and raises thequeue.Empty
exception if no item was available within that time. Otherwise (block isFalse
), return an item if one is immediately available, else raise thequeue.Empty
exception (timeoutis ignored in that case).Changed in version 3.8:If the queue is closed,
ValueError
is raised instead ofOSError
.
- get_nowait()¶
Equivalent to
get(False)
.
multiprocessing.Queue
has a few additional methods not found inqueue.Queue
.These methods are usually unnecessary for most code:- close()¶
Indicate that no more data will be put on this queue by the current process. The background thread will quit once it has flushed all buffered data to the pipe. This is called automatically when the queue is garbage collected.
- join_thread()¶
Join the background thread. This can only be used after
close()
has been called. It blocks until the background thread exits, ensuring that all data in the buffer has been flushed to the pipe.By default if a process is not the creator of the queue then on exit it will attempt to join the queue’s background thread. The process can call
cancel_join_thread()
to makejoin_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 – seejoin_thread()
.A better name for this method might be
allow_exit_without_flush()
.It is likely to cause enqueued data to be lost, and you almost certainly will not need to use it. It is really only there if you need the current process to exit immediately without waiting to flush enqueued data to the underlying pipe, and you don’t care about lost data.
Note
This class’s functionality requires a functioning shared semaphore implementation on the host operating system. Without one, the functionality in this class will be disabled, and attempts to instantiate a
Queue
will result in anImportError
.See bpo-3770for additional information. The same holds true for any of the specialized queue types listed below.
- classmultiprocessing.SimpleQueue¶
It is a simplified
Queue
type, very close to a lockedPipe
.- close()¶
Close the queue: release internal resources.
A queue must not be used anymore after it is closed. For example,
get()
,put()
andempty()
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)¶
Putiteminto the queue.
- classmultiprocessing.JoinableQueue([maxsize])¶
JoinableQueue
,aQueue
subclass, is a queue which additionally hastask_done()
andjoin()
methods.- task_done()¶
Indicate that a formerly enqueued task is complete. Used by queue consumers. For each
get()
used to fetch a task, a subsequent call totask_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 atask_done()
call was received for every item that had beenput()
into the queue).Raises a
ValueError
if called more times than there were items placed in the queue.
- join()¶
Block until all items in the queue have been gotten and processed.
The count of unfinished tasks goes up whenever an item is added to the queue. The count goes down whenever a consumer calls
task_done()
to indicate that the item was retrieved and all work on it is complete. When the count of unfinished tasks drops to zero,join()
unblocks.
Miscellaneous¶
- multiprocessing.active_children()¶
Return list of all live children of the current process.
Calling this has the side effect of “joining” any processes which have already finished.
- multiprocessing.cpu_count()¶
Return the number of CPUs in the system.
This number is not equivalent to the number of CPUs the current process can use. The number of usable CPUs can be obtained with
os.process_cpu_count()
(orlen(os.sched_getaffinity(0))
).When the number of CPUs cannot be determined a
NotImplementedError
is raised.See also
Changed in version 3.13:The return value can also be overridden using the
-Xcpu_count
flag orPYTHON_CPU_COUNT
as this is merely a wrapper around theos
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 thecurrent_process()
.For the main process,parent_process
will beNone
.Added in version 3.8.
- multiprocessing.freeze_support()¶
Add support for when a program which uses
multiprocessing
has been frozen to produce a Windows executable. (Has been tested withpy2exe, PyInstallerandcx_Freeze.)One needs to call this function straight after the
if__name__== '__main__'
line of the main module. For example:frommultiprocessingimportProcess,freeze_support deff(): 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 raiseRuntimeError
.Calling
freeze_support()
has no effect when invoked on any operating system other than Windows. In addition, if the module is being run normally by the Python interpreter on Windows (the program has not been frozen), thenfreeze_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. SeeContexts 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.Ifmethodis
None
then the default context is returned. Otherwisemethodshould be'fork'
,'spawn'
,'forkserver'
.ValueError
is raised if the specified start method is not available. SeeContexts 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 start method has not been fixed andallow_noneis false, then the start method is fixed to the default and the name is returned. If the start method has not been fixed andallow_none is true then
None
is returned.The return value can be
'fork'
,'spawn'
,'forkserver'
orNone
.SeeContexts and start methods.Added in version 3.4.
Changed in version 3.8:On macOS, thespawnstart method is now the default. Theforkstart method should be considered unsafe as it can lead to crashes of the subprocess. Seebpo-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 some thing likeset_executable(os.path.join(sys.exec_prefix,' Python w.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 apath-like object.
- 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 aProcess
).Only meaningful when using the
'forkserver'
start method. SeeContexts 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. Themethodargument can be
'fork'
,'spawn'
or'forkserver'
. RaisesRuntimeError
if the start method has already been set andforce is notTrue
.IfmethodisNone
andforceisTrue
then the start method is set toNone
.IfmethodisNone
andforceisFalse
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.SeeContexts and start methods.
Added in version 3.4.
Note
multiprocessing
contains no analogues of
threading.active_count()
,threading.enumerate()
,
threading.settrace()
,threading.setprofile()
,
threading.Timer
,orthreading.local
.
Connection Objects¶
Connection objects allow the sending and receiving of picklable objects or strings. They can be thought of as message oriented connected sockets.
Connection objects are usually created using
Pipe
– see also
Listeners and Clients.
- classmultiprocessing.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. RaisesEOFError
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.
Iftimeoutis not specified then it will return immediately. If timeoutis a number then this specifies the maximum time in seconds to block. Iftimeoutis
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 abytes-like objectas a complete message.
Ifoffsetis given then data is read from that position inbuffer.If sizeis 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.Ifmaxlengthis specified and the message is longer thanmaxlength then
OSError
is raised and the connection will no longer be readable.
- recv_bytes_into(buffer[,offset])¶
Read intobuffera 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.buffermust be a writablebytes-like object.If offsetis given then the message will be written into the buffer from that position. Offset must be a non-negative integer less than the length ofbuffer(in bytes).
If the buffer is too short then a
BufferTooShort
exception is raised and the complete message is available ase.args[0]
wheree
is the exception instance.
Changed in version 3.3:Connection objects themselves can now be transferred between processes using
Connection.send()
andConnection.recv()
.Connection objects also now support the context management protocol – see Context Manager Types.
__enter__()
returns the connection object, and__exit__()
callsclose()
.
For example:
>>>frommultiprocessingimportPipe
>>>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'
>>>importarray
>>>arr1=array.array('i',range(5))
>>>arr2=array.array('i',[0]*10)
>>>a.send_bytes(arr1)
>>>count=b.recv_bytes_into(arr2)
>>>assertcount==len(arr1)*arr1.itemsize
>>>arr2
array('i', [0, 1, 2, 3, 4, 0, 0, 0, 0, 0])
Warning
TheConnection.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 usingPipe()
you
should only use therecv()
andsend()
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 – seeManagers.
- classmultiprocessing.Barrier(parties[,action[,timeout]])¶
A barrier object: a clone of
threading.Barrier
.Added in version 3.3.
- classmultiprocessing.BoundedSemaphore([value])¶
A bounded semaphore object: a close analog of
threading.BoundedSemaphore
.A solitary difference from its close analog exists: its
acquire
method’s first argument is namedblock,as is consistent withLock.acquire()
.Note
On macOS, this is indistinguishable from
Semaphore
becausesem_getvalue()
is not implemented on that platform.
- classmultiprocessing.Condition([lock])¶
A condition variable: an alias for
threading.Condition
.Iflockis specified then it should be a
Lock
orRLock
object frommultiprocessing
.Changed in version 3.3:The
wait_for()
method was added.
- classmultiprocessing.Event¶
A clone of
threading.Event
.
- classmultiprocessing.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 ofthreading.Lock
as it applies to threads are replicated here inmultiprocessing.Lock
as it applies to either processes or threads, except as noted.Note that
Lock
is actually a factory function which returns an instance ofmultiprocessing.synchronize.Lock
initialized with a default context.Lock
supports thecontext managerprotocol and thus may be used inwith
statements.- acquire(block=True,timeout=None)¶
Acquire a lock, blocking or non-blocking.
With theblockargument set to
True
(the default), the method call will block until the lock is in an unlocked state, then set it to locked and returnTrue
.Note that the name of this first argument differs from that inthreading.Lock.acquire()
.With theblockargument set to
False
,the method call does not block. If the lock is currently in a locked state, returnFalse
; otherwise set the lock to a locked state and returnTrue
.When invoked with a positive, floating-point value fortimeout,block for at most the number of seconds specified bytimeoutas long as the lock can not be acquired. Invocations with a negative value for timeoutare equivalent to atimeoutof zero. Invocations with a timeoutvalue of
None
(the default) set the timeout period to infinite. Note that the treatment of negative orNone
values for timeoutdiffers from the implemented behavior inthreading.Lock.acquire()
.Thetimeoutargument has no practical implications if theblockargument is set toFalse
and is thus ignored. ReturnsTrue
if the lock has been acquired orFalse
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, aValueError
is raised.
- classmultiprocessing.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 ofmultiprocessing.synchronize.RLock
initialized with a default context.RLock
supports thecontext managerprotocol and thus may be used inwith
statements.- acquire(block=True,timeout=None)¶
Acquire a lock, blocking or non-blocking.
When invoked with theblockargument 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 ofTrue
.Note that there are several differences in this first argument’s behavior compared to the implementation ofthreading.RLock.acquire()
,starting with the name of the argument itself.When invoked with theblockargument 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 ofFalse
.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 ofTrue
.Use and behaviors of thetimeoutargument are the same as in
Lock.acquire()
.Note that some of these behaviors oftimeout differ from the implemented behaviors inthreading.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 inthreading.RLock.release()
.
- classmultiprocessing.Semaphore([value])¶
A semaphore object: a close analog of
threading.Semaphore
.A solitary difference from its close analog exists: its
acquire
method’s first argument is namedblock,as is consistent withLock.acquire()
.
Note
On macOS,sem_timedwait
is unsupported, so callingacquire()
with
a timeout will emulate that function’s behavior using a sleeping loop.
Note
Some of this package’s functionality requires a functioning shared semaphore
implementation on the host operating system. Without one, the
multiprocessing.synchronize
module will be disabled, and attempts to
import it will result in anImportError
.See
bpo-3770for additional information.
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:
- classmultiprocessing.managers.BaseManager(address=None,authkey=None,serializer='pickle',ctx=None,*,shutdown_timeout=1.0)¶
Create a BaseManager object.
Once created one should call
start()
orget_server().serve_forever()
to ensure that the manager object refers to a started manager process.addressis the address on which the manager process listens for new connections. Ifaddressis
None
then an arbitrary one is chosen.authkeyis the authentication key which will be used to check the validity of incoming connections to the server process. If authkeyis
None
thencurrent_process().authkey
is used. Otherwiseauthkeyis used and it must be a byte string.serializermust be
'pickle'
(usepickle
serialization) or'xmlrpclib'
(usexmlrpc.client
serialization).ctxis a context object, or
None
(use the current context). See theget_context()
function.shutdown_timeoutis 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 theshutdown_timeoutparameter.
- start([initializer[,initargs]])¶
Start a subprocess to start the manager. Ifinitializeris not
None
then the subprocess will callinitializer(*initargs)
when it starts.
- get_server()¶
Returns a
Server
object which represents the actual server under the control of the Manager. TheServer
object supports theserve_forever()
method:>>>frommultiprocessing.managersimportBaseManager >>>manager=BaseManager(address=('',50000),authkey=b'abc') >>>server=manager.get_server() >>>server.serve_forever()
Server
additionally has anaddress
attribute.
- connect()¶
Connect a local manager object to a remote manager process:
>>>frommultiprocessing.managersimportBaseManager >>>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.
typeidis a “type identifier” which is used to identify a particular type of shared object. This must be a string.
callableis 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_methodargument isFalse
then this can be left asNone
.proxytypeis a subclass of
BaseProxy
which is used to create proxies for shared objects with thistypeid.IfNone
then a proxy class is created automatically.exposedis used to specify a sequence of method names which proxies for this typeid should be allowed to access using
BaseProxy._callmethod()
.(IfexposedisNone
thenproxytype._exposed_
is used instead if it exists.) In the case where no exposed list is specified, all “public methods” of the shared object will be accessible. (Here a “public method” means any attribute which has a__call__()
method and whose name does not begin with'_'
.)method_to_typeidis a mapping used to specify the return type of those exposed methods which should return a proxy. It maps method names to typeid strings. (Ifmethod_to_typeidis
None
thenproxytype._method_to_typeid_
is used instead if it exists.) If a method’s name is not a key of this mapping or if the mapping isNone
then the object returned by the method will be copied by value.create_methoddetermines whether a method should be created with name typeidwhich can be used to tell the server process to create a new shared object and return a proxy for it. By default it is
True
.
BaseManager
instances also have one read-only property:- address¶
The address used by the manager.
Changed in version 3.3:Manager objects support the context management protocol – see Context Manager Types.
__enter__()
starts the server process (if it has not already started) and then returns the manager object.__exit__()
callsshutdown()
.In previous versions
__enter__()
did not start the manager’s server process if it was not already started.
- classmultiprocessing.managers.SyncManager¶
A subclass of
BaseManager
which can be used for the synchronization of processes. Objects of this type are returned bymultiprocessing.Manager()
.Its methods create and returnProxy Objectsfor 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.Iflockis supplied then it should be a proxy for a
threading.Lock
orthreading.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.
- 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.
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
.
- classmultiprocessing.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 one’s own manager, one creates a subclass ofBaseManager
and
uses theregister()
classmethod to register new types or
callables with the manager class. For example:
frommultiprocessing.managersimportBaseManager
classMathsClass:
defadd(self,x,y):
returnx+y
defmul(self,x,y):
returnx*y
classMyManager(BaseManager):
pass
MyManager.register('Maths',MathsClass)
if__name__=='__main__':
withMyManager()asmanager:
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:
>>>frommultiprocessing.managersimportBaseManager
>>>fromqueueimportQueue
>>>queue=Queue()
>>>classQueueManager(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:
>>>frommultiprocessing.managersimportBaseManager
>>>classQueueManager(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:
>>>frommultiprocessing.managersimportBaseManager
>>>classQueueManager(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:
>>>frommultiprocessingimportProcess,Queue
>>>frommultiprocessing.managersimportBaseManager
>>>classWorker(Process):
...def__init__(self,q):
...self.q=q
...super().__init__()
...defrun(self):
...self.q.put('local hello')
...
>>>queue=Queue()
>>>w=Worker(queue)
>>>w.start()
>>>classQueueManager(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 whichrefersto a shared object which lives (presumably) in a different process. The shared object is said to be thereferentof 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*iforiinrange(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 applyingstr()
to a proxy will return the representation of
the referent, whereas applyingrepr()
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 otherProxy 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()foriinrange(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
ordict
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 Objectsfor most use cases but also demonstrates a level of control over the synchronization.
Note
The proxy types inmultiprocessing
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.
- classmultiprocessing.managers.BaseProxy¶
Proxy objects are instances of subclasses of
BaseProxy
.- _callmethod(methodname[,args[,kwds]])¶
Call and return the result of a method of the proxy’s referent.
If
proxy
is a proxy whose referent isobj
then the expressionproxy._callmethod(methodname,args,kwds)
will evaluate the expression
getattr(obj,methodname)(*args,**kwds)
in the manager’s process.
The returned value will be a copy of the result of the call or a proxy to a new shared object – see documentation for themethod_to_typeid argument of
BaseManager.register()
.If an exception is raised by the call, then is re-raised by
_callmethod()
.If some other exception is raised in the manager’s process then this is converted into aRemoteError
exception and is raised by_callmethod()
.Note in particular that an exception will be raised ifmethodnamehas not beenexposed.
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 thePool
class.
- classmultiprocessing.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.
processesis the number of worker processes to use. Ifprocessesis
None
then the number returned byos.process_cpu_count()
is used.Ifinitializeris not
None
then each worker process will callinitializer(*initargs)
when it starts.maxtasksperchildis 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 defaultmaxtasksperchildis
None
,which means worker processes will live as long as the pool.contextcan be used to specify the context used for starting the worker processes. Usually a pool is created using the function
multiprocessing.Pool()
or thePool()
method of a context object. In both casescontextis set appropriately.Note that the methods of the pool object should only be called by the process which created the pool.
Warning
multiprocessing.pool
objects have internal resources that need to be properly managed (like any other resource) by using the pool as a context manager or by callingclose()
andterminate()
manually. Failure to do this can lead to the process hanging on finalization.Note that it isnot correctto 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 themaxtasksperchildparameter.
Changed in version 3.4:Added thecontextparameter.
Changed in version 3.13:processesuses
os.process_cpu_count()
by default, instead ofos.cpu_count()
.Note
Worker processes within a
Pool
typically live for the complete duration of the Pool’s work queue. A frequent pattern found in other systems (such as Apache, mod_wsgi, etc) to free resources held by workers is to allow a worker within a pool to complete only a set amount of work before being exiting, being cleaned up and a new process spawned to replace the old one. Themaxtasksperchild argument to thePool
exposes this ability to the end user.- apply(func[,args[,kwds]])¶
Callfuncwith argumentsargsand keyword argumentskwds.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 aAsyncResult
object.Ifcallbackis specified then it should be a callable which accepts a single argument. When the result becomes readycallbackis applied to it, that is unless the call failed, in which case theerror_callback is applied instead.
Iferror_callbackis specified then it should be a callable which accepts a single argument. If the target function fails, then theerror_callbackis 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 oneiterableargument though, for multiple iterables seestarmap()
). 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 settingchunksizeto a positive integer.
Note that it may cause high memory usage for very long iterables. Consider using
imap()
orimap_unordered()
with explicitchunksize option for better efficiency.
- map_async(func,iterable[,chunksize[,callback[,error_callback]]])¶
A variant of the
map()
method which returns aAsyncResult
object.Ifcallbackis specified then it should be a callable which accepts a single argument. When the result becomes readycallbackis applied to it, that is unless the call failed, in which case theerror_callback is applied instead.
Iferror_callbackis specified then it should be a callable which accepts a single argument. If the target function fails, then theerror_callbackis 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()
.Thechunksizeargument is the same as the one used by the
map()
method. For very long iterables using a large value forchunksizecan make the job completemuchfaster than using the default value of1
.Also ifchunksizeis
1
then thenext()
method of the iterator returned by theimap()
method has an optionaltimeoutparameter:next(timeout)
will raisemultiprocessing.TimeoutError
if the result cannot be returned withintimeoutseconds.
- imap_unordered(func,iterable[,chunksize])¶
The same as
imap()
except that the ordering of the results from the returned iterator should be considered arbitrary. (Only when there is only one worker process is the order guaranteed to be “correct”.)
- starmap(func,iterable[,chunksize])¶
Like
map()
except that the elements of theiterableare expected to be iterables that are unpacked as arguments.Hence aniterableof
[(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()
andmap_async()
that iterates over iterableof iterables and callsfuncwith 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()
orterminate()
before usingjoin()
.
Changed in version 3.3:Pool objects now support the context management protocol – see Context Manager Types.
__enter__()
returns the pool object, and__exit__()
callsterminate()
.
- classmultiprocessing.pool.AsyncResult¶
The class of the result returned by
Pool.apply_async()
andPool.map_async()
.- get([timeout])¶
Return the result when it arrives. Iftimeoutis not
None
and the result does not arrive withintimeoutseconds thenmultiprocessing.TimeoutError
is raised. If the remote call raised an exception then that exception will be reraised byget()
.
- wait([timeout])¶
Wait until the result is available or untiltimeoutseconds 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 ofAssertionError
.
The following example demonstrates the use of a pool:
frommultiprocessingimportPool
importtime
deff(x):
returnx*x
if__name__=='__main__':
withPool(processes=4)aspool:# 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, themultiprocessing.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 fordigest
authenticationusing thehmac
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 usingauthkeyas 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 usingauthkeyas 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 byfamilyargument, but this can generally be omitted since it can usually be inferred from the format of address.(SeeAddress Formats)
Ifauthkeyis 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 ifauthkeyisNone
.AuthenticationError
is raised if authentication fails. SeeAuthentication keys.
- classmultiprocessing.connection.Listener([address[,family[,backlog[,authkey]]]])¶
A wrapper for a bound socket or Windows named pipe which is ‘listening’ for connections.
addressis the address to be used by the bound socket or named pipe of the listener object.
Note
If an address of ‘0.0.0.0’ is used, the address will not be a connectable end point on Windows. If you require a connectable end-point, you should use ‘127.0.0.1’.
familyis 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. IffamilyisNone
then the family is inferred from the format ofaddress.Ifaddressis alsoNone
then a default is chosen. This default is the family which is assumed to be the fastest available. See Address Formats.Note that iffamilyis'AF_UNIX'
and address isNone
then the socket will be created in a private temporary directory created usingtempfile.mkstemp()
.If the listener object uses a socket thenbacklog(1 by default) is passed to the
listen()
method of the socket once it has been bound.Ifauthkeyis 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 ifauthkeyisNone
.AuthenticationError
is raised if authentication fails. SeeAuthentication 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, thenAuthenticationError
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 – see Context Manager Types.
__enter__()
returns the listener object, and__exit__()
callsclose()
.
- multiprocessing.connection.wait(object_list,timeout=None)¶
Wait till an object inobject_listis ready. Returns the list of those objects inobject_listwhich are ready. Iftimeoutis a float then the call blocks for at most that many seconds. If timeoutis
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 inobject_listif it is
a readable
Connection
object;a connected and readable
socket.socket
object; or
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 equivalentselect.select(object_list,[],[],timeout)
.The difference is that, ifselect.select()
is interrupted by a signal, it can raiseOSError
with an error number ofEINTR
,whereaswait()
will not.Windows:An item inobject_listmust 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 afileno()
method which returns a socket handle or pipe handle. (Note that pipe handles and socket handles arenotwaitable handles.)Added in version 3.3.
Examples
The following server code creates a listener which uses'secretpassword'
as
an authentication key. It then waits for a connection and sends some data to
the client:
frommultiprocessing.connectionimportListener
fromarrayimportarray
address=('localhost',6000)# family is deduced to be 'AF_INET'
withListener(address,authkey=b'secret password')aslistener:
withlistener.accept()asconn:
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:
frommultiprocessing.connectionimportClient
fromarrayimportarray
address=('localhost',6000)
withClient(address,authkey=b'secret password')asconn:
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 useswait()
to
wait for messages from multiple processes at once:
frommultiprocessingimportProcess,Pipe,current_process
frommultiprocessing.connectionimportwait
deffoo(w):
foriinrange(10):
w.send((i,current_process().name))
w.close()
if__name__=='__main__':
readers=[]
foriinrange(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()
whilereaders:
forrinwait(readers):
try:
msg=r.recv()
exceptEOFError:
readers.remove(r)
else:
print(msg)
Address Formats¶
An
'AF_INET'
address is a tuple of the form(hostname,port)
where hostnameis a string andportis an integer.An
'AF_UNIX'
address is a string representing a filename on the filesystem.An
'AF_PIPE'
address is a string of the formr'\\.\pipe\PipeName'
.To useClient()
to connect to a named pipe on a remote computer calledServerNameone should use an address of the formr'\\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 usesConnection.recv
,the
data received is automatically
unpickled. Unfortunately unpickling data from an untrusted source is a security
risk. ThereforeListener
andClient()
use thehmac
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 doesnotinvolve sending the key over the connection.)
If authentication is requested but no authentication key is specified then the
return value ofcurrent_process().authkey
is used (see
Process
). This value will be automatically inherited by
anyProcess
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 usingos.urandom()
.
Logging¶
Some support for logging is available. Note, however, that thelogging
package does not use process shared locks so it is possible (depending on the
handler type) for messages from different processes to get mixed up.
- multiprocessing.get_logger()¶
Returns the logger used by
multiprocessing
.If necessary, a new one will be created.When first created the logger has level
logging.NOTSET
and no default handler. Messages sent to this logger will not by default propagate to the root logger.Note that on Windows child processes will only inherit the level of the parent process’s logger – any other customization of the logger will not be inherited.
- multiprocessing.log_to_stderr(level=None)¶
This function performs a call to
get_logger()
but in addition to returning the logger created by get_logger, it adds a handler which sends output tosys.stderr
using format'[%(levelname)s/%(processName)s]%(message)s'
. You can modifylevelname
of the logger by passing alevel
argument.
Below is an example session with logging turned on:
>>>importmultiprocessing,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-...'
>>>delm
[INFO/MainProcess] sending shutdown message to manager
[INFO/SyncManager-...] manager exiting with exitcode 0
For a full table of logging levels, see thelogging
module.
Themultiprocessing.dummy
module¶
multiprocessing.dummy
replicates the API ofmultiprocessing
but is
no more than a wrapper around thethreading
module.
In particular, thePool
function provided bymultiprocessing.dummy
returns an instance ofThreadPool
,which is a subclass of
Pool
that supports all the same method calls but uses a pool of
worker threads rather than worker processes.
- classmultiprocessing.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 withPool
instances, and their resources must also be properly managed, either by using the pool as a context manager or by callingclose()
andterminate()
manually.processesis the number of worker threads to use. Ifprocessesis
None
then the number returned byos.process_cpu_count()
is used.Ifinitializeris not
None
then each worker process will callinitializer(*initargs)
when it starts.Unlike
Pool
,maxtasksperchildandcontextcannot be provided.Note
A
ThreadPool
shares the same interface asPool
,which is designed around a pool of processes and predates the introduction of theconcurrent.futures
module. As such, it inherits some operations that don’t make sense for a pool backed by threads, and it has its own type for representing the status of asynchronous jobs,AsyncResult
,that is not understood by any other libraries.Users should generally prefer to use
concurrent.futures.ThreadPoolExecutor
,which has a simpler interface that was designed around threads from the start, and which returnsconcurrent.futures.Future
instances that are compatible with many other libraries, includingasyncio
.
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 thesameproxy.)
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 process’sProcess.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 thespawnorforkserverstart methods many types from
multiprocessing
need to be picklable so that child processes can use them. However, one should generally avoid sending shared objects to other processes using pipes or queues. Instead you should arrange the program so that a process which needs access to a shared resource created elsewhere can inherit it from an ancestor process.
Avoid terminating processes
Using the
Process.terminate
method to stop a process is liable to cause any shared resources (such as locks, semaphores, pipes and queues) currently being used by the process to become broken or unavailable to other processes.Therefore it is probably best to only consider using
Process.terminate
on processes which never use any shared resources.
Joining processes that use queues
Bear in mind that a process that has put items in a queue will wait before terminating until all the buffered items are fed by the “feeder” thread to the underlying pipe. (The child process can call the
Queue.cancel_join_thread
method of the queue to avoid this behaviour.)This means that whenever you use a queue you need to make sure that all items which have been put on the queue will eventually be removed before the process is joined. Otherwise you cannot be sure that processes which have put items on the queue will terminate. Remember also that non-daemonic processes will be joined automatically.
An example which will deadlock is the following:
frommultiprocessingimportProcess,Queue deff(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 theforkstart 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
frommultiprocessingimportProcess,Lock deff(): ...dosomethingusing"lock"... if__name__=='__main__': lock=Lock() foriinrange(10): Process(target=f).start()should be rewritten as
frommultiprocessingimportProcess,Lock deff(l): ...dosomethingusing"l"... if__name__=='__main__': lock=Lock() foriinrange(10): Process(target=f,args=(lock,)).start()
Beware of replacingsys.stdin
with a “file like object”
multiprocessing
originally unconditionally called:os.close(sys.stdin.fileno())in the
multiprocessing.Process._bootstrap()
method — this resulted in issues with processes-in-processes. This has been changed to:sys.stdin.close() sys.stdin=open(os.open(os.devnull,os.O_RDONLY),closefd=False)Which solves the fundamental issue of processes colliding with each other resulting in a bad file descriptor error, but introduces a potential danger to applications which replace
sys.stdin()
with a “file-like object” with output buffering. This danger is that if multiple processes callclose()
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 defcache(self): pid=os.getpid() ifpid!=self._pid: self._pid=pid self._cache=[] returnself._cache
Thespawnandforkserverstart methods¶
There are a few extra restrictions which don’t apply to thefork start method.
More picklability
Ensure that all arguments to
Process.__init__()
are picklable. Also, if you subclassProcess
then make sure that instances will be picklable when theProcess.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 thespawnorforkserverstart method running the following module would fail with a
RuntimeError
:frommultiprocessingimportProcess deffoo(): print('hello') p=Process(target=foo) p.start()Instead one should protect the “entry point” of the program by using
if __name__=='__main__':
as follows:frommultiprocessingimportProcess,freeze_support,set_start_method deffoo(): print('hello') if__name__=='__main__': freeze_support() set_start_method('spawn') p=Process(target=foo) p.start()(The
freeze_support()
line can be omitted if the program will be run normally instead of frozen.)This allows the newly spawned Python interpreter to safely import the module and then run the module’s
foo()
function.Similar restrictions apply if a pool or manager is created in the main module.
Examples¶
Demonstration of how to create and use customized managers and proxies:
frommultiprocessingimportfreeze_support
frommultiprocessing.managersimportBaseManager,BaseProxy
importoperator
##
classFoo:
deff(self):
print('you called Foo.f()')
defg(self):
print('you called Foo.g()')
def_h(self):
print('you called Foo._h()')
# A simple generator function
defbaz():
foriinrange(10):
yieldi*i
# Proxy type for generator objects
classGeneratorProxy(BaseProxy):
_exposed_=['__next__']
def__iter__(self):
returnself
def__next__(self):
returnself._callmethod('__next__')
# Function to return the operator module
defget_operator_module():
returnoperator
##
classMyManager(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)
##
deftest():
manager=MyManager()
manager.start()
print('-'*20)
f1=manager.Foo1()
f1.f()
f1.g()
assertnothasattr(f1,'_h')
assertsorted(f1._exposed_)==sorted(['f','g'])
print('-'*20)
f2=manager.Foo2()
f2.g()
f2._h()
assertnothasattr(f2,'f')
assertsorted(f2._exposed_)==sorted(['g','_h'])
print('-'*20)
it=manager.baz()
foriinit:
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()
UsingPool
:
importmultiprocessing
importtime
importrandom
importsys
#
# Functions used by test code
#
defcalculate(func,args):
result=func(*args)
return'%ssays that%s%s=%s'%(
multiprocessing.current_process().name,
func.__name__,args,result
)
defcalculatestar(args):
returncalculate(*args)
defmul(a,b):
time.sleep(0.5*random.random())
returna*b
defplus(a,b):
time.sleep(0.5*random.random())
returna+b
deff(x):
return1.0/(x-5.0)
defpow3(x):
returnx**3
defnoop(x):
pass
#
# Test code
#
deftest():
PROCESSES=4
print('Creating pool with%dprocesses\n'%PROCESSES)
withmultiprocessing.Pool(PROCESSES)aspool:
#
# Tests
#
TASKS=[(mul,(i,7))foriinrange(10)]+\
[(plus,(i,8))foriinrange(10)]
results=[pool.apply_async(calculate,t)fortinTASKS]
imap_it=pool.imap(calculatestar,TASKS)
imap_unordered_it=pool.imap_unordered(calculatestar,TASKS)
print('Ordered results using pool.apply_async():')
forrinresults:
print('\t',r.get())
print()
print('Ordered results using pool.imap():')
forxinimap_it:
print('\t',x)
print()
print('Unordered results using pool.imap_unordered():')
forxinimap_unordered_it:
print('\t',x)
print()
print('Ordered results using pool.map() --- will block till complete:')
forxinpool.map(calculatestar,TASKS):
print('\t',x)
print()
#
# Test error handling
#
print('Testing error handling:')
try:
print(pool.apply(f,(5,)))
exceptZeroDivisionError:
print('\tGot ZeroDivisionError as expected from pool.apply()')
else:
raiseAssertionError('expected ZeroDivisionError')
try:
print(pool.map(f,list(range(10))))
exceptZeroDivisionError:
print('\tGot ZeroDivisionError as expected from pool.map()')
else:
raiseAssertionError('expected ZeroDivisionError')
try:
print(list(pool.imap(f,list(range(10)))))
exceptZeroDivisionError:
print('\tGot ZeroDivisionError as expected from list(pool.imap())')
else:
raiseAssertionError('expected ZeroDivisionError')
it=pool.imap(f,list(range(10)))
foriinrange(10):
try:
x=next(it)
exceptZeroDivisionError:
ifi==5:
pass
exceptStopIteration:
break
else:
ifi==5:
raiseAssertionError('expected ZeroDivisionError')
asserti==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])
while1:
sys.stdout.flush()
try:
sys.stdout.write('\n\t%s'%res.get(0.02))
break
exceptmultiprocessing.TimeoutError:
sys.stdout.write('.')
print()
print()
print('Testing IMapIterator.next() with timeout:',end=' ')
it=pool.imap(calculatestar,TASKS)
while1:
sys.stdout.flush()
try:
sys.stdout.write('\n\t%s'%it.next(0.02))
exceptStopIteration:
break
exceptmultiprocessing.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:
importtime
importrandom
frommultiprocessingimportProcess,Queue,current_process,freeze_support
#
# Function run by worker processes
#
defworker(input,output):
forfunc,argsiniter(input.get,'STOP'):
result=calculate(func,args)
output.put(result)
#
# Function used to calculate result
#
defcalculate(func,args):
result=func(*args)
return'%ssays that%s%s=%s'%\
(current_process().name,func.__name__,args,result)
#
# Functions referenced by tasks
#
defmul(a,b):
time.sleep(0.5*random.random())
returna*b
defplus(a,b):
time.sleep(0.5*random.random())
returna+b
#
#
#
deftest():
NUMBER_OF_PROCESSES=4
TASKS1=[(mul,(i,7))foriinrange(20)]
TASKS2=[(plus,(i,8))foriinrange(10)]
# Create queues
task_queue=Queue()
done_queue=Queue()
# Submit tasks
fortaskinTASKS1:
task_queue.put(task)
# Start worker processes
foriinrange(NUMBER_OF_PROCESSES):
Process(target=worker,args=(task_queue,done_queue)).start()
# Get and print results
print('Unordered results:')
foriinrange(len(TASKS1)):
print('\t',done_queue.get())
# Add more tasks using `put()`
fortaskinTASKS2:
task_queue.put(task)
# Get and print some more results
foriinrange(len(TASKS2)):
print('\t',done_queue.get())
# Tell child processes to stop
foriinrange(NUMBER_OF_PROCESSES):
task_queue.put('STOP')
if__name__=='__main__':
freeze_support()
test()