Source code:
———
Introduction
multiprocessing is a package that supports spawning processes using an API similar to the
module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the
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
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,
frommultiprocessingimportPooldeff(x):returnx*xif__name__=='__main__':withPool(5)asp: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
module, like the ability to
,
or
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
interface directly, the
API more readily allows the submission of work to the underlying process pool to be separated from waiting for the results.
The
class
In multiprocessing, processes are spawned by creating a
object and then calling its
method. Process follows the API of
. A trivial example of a multiprocess program is
frommultiprocessingimportProcessdeff(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:
frommultiprocessingimportProcessimportosdefinfo(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 the if__name__=='__main__' part is necessary, see
.
The arguments to
usually need to be picklable so they can be passed to the child process. If you tried typing the above example directly into a REPL it could lead to an
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
spawnThe parent process starts a fresh Python interpreter process. The child process will only inherit those resources necessary to run the process object’s
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.
forkThe parent process uses
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
or
.
Changed in version 3.12: If Python is able to detect that your process has multiple threads, the
function that this start method calls internally will raise a
. Use a different start method. See the os.fork() documentation for further explanation.
forkserverWhen 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
. 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
.
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
.
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
objects) created by processes of the program. When all processes have exited the resource tracker unlinks any remaining tracked object. Usually there should be none, but if a process was killed by a signal there may be some “leaked” resources. (Neither leaked semaphores nor shared memory segments will be automatically unlinked until the next reboot. This is problematic for both objects because the system allows only a limited number of named semaphores, and shared memory segments occupy some space in the main memory.)
To select a start method you use the
in the if__name__=='__main__' clause of the main module. For example:
importmultiprocessingasmpdeffoo(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()
should not be used more than once in the program.
Alternatively, you can use
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.
importmultiprocessingasmpdeffoo(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
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 user’s application. Always document if your library requires a specific start method.
Warning
The 'spawn' and 'forkserver' start methods generally cannot be used with “frozen” 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
class is a near clone of
. For example:
frommultiprocessingimportProcess,Queuedeff(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
function returns a pair of connection objects connected by a pipe which by default is duplex (two-way). For example:
frommultiprocessingimportProcess,Pipedeff(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
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
. For instance one can use a lock to ensure that only one process prints to standard output at a time:
frommultiprocessingimportProcess,Lockdeff(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.
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
or
. For example, the following code
frommultiprocessingimportProcess,Value,Arraydeff(n,a):n.value=3.1415927foriinrange(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
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
module which supports the creation of arbitrary ctypes objects allocated from shared memory.
Server process
A manager object returned by
controls a server process which holds Python objects and allows other processes to manipulate them using proxies.
A manager returned by
will support types
,
,
,
,
,
,
,
,
,
,
,
,
and
. For example,
frommultiprocessingimportProcess,Managerdeff(d,l,s):d[1]='1'd['2']=2d[0.25]=Nonel.reverse()s.add('a')s.add('b')if__name__=='__main__':withManager()asmanager: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
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,TimeoutErrorimporttimeimportosdeff(x):returnx*xif__name__=='__main__':# start 4 worker processeswithPool(processes=4)aspool:# print "[0, 1, 4,..., 81]"print(pool.map(f,range(10)))# print same numbers in arbitrary orderforiinpool.imap_unordered(f,range(10)):print(i)# evaluate "f(20)" asynchronouslyres=pool.apply_async(f,(20,))# runs in *only* one processprint(res.get(timeout=1))# prints "400"# evaluate "os.getpid()" asynchronouslyres=pool.apply_async(os.getpid,())# runs in *only* one processprint(res.get(timeout=1))# prints the PID of that process# launching multiple evaluations asynchronously *may* use more processesmultiple_results=[pool.apply_async(os.getpid,())foriinrange(4)]print([res.get(timeout=1)forresinmultiple_results])# make a single worker sleep for 10 secondsres=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 poolprint("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
however it is worth pointing out here. This means that some examples, such as the
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
The multiprocessing package mostly replicates the API of the
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 system’s default, if it hasn’t 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.
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 of
.
The constructor should always be called with keyword arguments. group should always be None; it exists solely for compatibility with
. target is the callable object to be invoked by the
method. It defaults to None, meaning nothing is called. name is the process name (see
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
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
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:
>>> importmultiprocessingasmp>>> defknigit():... print("Ni!")...>>> process=mp.Process(target=knigit)>>> process.start()>>> Traceback(mostrecentcalllast): File ".../multiprocessing/spawn.py", line ..., in spawn_main File ".../multiprocessing/spawn.py", line ..., in _mainAttributeError: 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
. See also
.
Changed in version 3.3: Added the daemon parameter.
run()
Method representing the process’s activity.
You may override this method in a subclass. The standard run() method invokes the callable object passed to the object’s constructor as the target argument, if any, with sequential and keyword arguments taken from the args and kwargs arguments, respectively.
Using a list or tuple as the args argument 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()1start()
Start the process’s activity.
This must be called at most once per process object. It arranges for the object’s
method to be invoked in a separate process.
join([timeout])
If the optional argument timeout is None (the default), the method blocks until the process whose join() method is called terminates. If timeout is a positive number, it blocks at most timeout seconds. Note that the method returns None if its process terminates or if the method times out. Check the process’s
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 Nk 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
method returns until the child process terminates.
daemon
The process’s daemon flag, a Boolean value. This must be set before
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
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
method returned normally, the exit code will be 0. If it terminated via
with an integer argument N, the exit code will be N.
If the child terminated due to an exception not caught within
, the exit code will be 1. If it was terminated by signal N, 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 using
.
When a Process object is created, it will inherit the authentication key of its parent process, although this may be changed by setting
to another byte string.
See
.
sentinel
A numeric handle of a system object which will become “ready” when the process ends.
You can use this value if you want to wait on several events at once using
multiprocessing.connection.wait()
. Otherwise calling
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
module.
Added in version 3.3.
interrupt()
Terminate the process. Works on POSIX using the
signal. Behavior on Windows is undefined.
By default, this terminates the child process by raising
. This behavior can be altered by setting the respective signal handler in the child process
for
.
Note: if the child process catches and discards
, the process will not be terminated.
Note: the default behavior will also set
to 1 as if an uncaught exception was raised in the child process. To have a different exitcode you may simply catch
and call exit(your_code).
Added in version 3.14.
terminate()
Terminate the process. On POSIX this is done using the
signal; on Windows TerminateProcess() is used. Note that exit handlers and finally clauses, etc., will not be executed.
Note that descendant processes of the process will not be terminated – they will simply become orphaned.
Warning
If this method is used when the associated process is using a pipe or queue then the pipe or queue is liable to become corrupted and may become unusable by other process. Similarly, if the process has acquired a lock or semaphore etc. then terminating it is liable to cause other processes to deadlock.
kill()
Same as
but using the SIGKILL signal on POSIX.
Added in version 3.7.
close()
Close the Process object, releasing all resources associated with it.
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
,
,
,
and
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.SIGTERMTrueexceptionmultiprocessing.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 of BufferTooShort then e.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 use
(for a connection between two processes) or a queue (which allows multiple producers and consumers).
The
,
and
types are multi-producer, multi-consumer FIFO queues modelled on the
class in the standard library. They differ in that Queue lacks the
and
methods introduced into Python 2.5’s queue.Queue class.
If you use
then you must call
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
. 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 – see
.
Note
multiprocessing uses the usual
and
exceptions to signal a timeout. They are not available in the multiprocessing namespace so you need to import them from
.
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
.
After putting an object on an empty queue there may be an infinitesimal delay before the queue’s
method returns
and
can return without raising
.
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
or
while it is trying to use a
, 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
.
For an example of the usage of queues for interprocess communication see
.
multiprocessing.Pipe(duplex=True)
Returns a pair (conn1,conn2) of
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
and the recv() 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.
Instantiating this class may set the global start method. See
for more details.
The usual
and
exceptions from the standard library’s
module are raised to signal timeouts.
Queue implements all the methods of
except for
,
, and
.
qsize()
Return the approximate size of the queue. Because of multithreading/multiprocessing semantics, this number is not reliable.
Note that this may raise
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
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
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
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,
is raised instead of
.
get_nowait()
Equivalent to get(False).
multiprocessing.Queue has a few additional methods not found in
. 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,
,
and
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
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
to make join_thread() do nothing.
cancel_join_thread()
Prevent
from blocking. In particular, this prevents the background thread from being joined automatically when the process exits – see join_thread().
A better name for this method might be allow_exit_without_flush(). It is likely to cause enqueued data to be lost, and you almost certainly will not need to use it. It is really only there if you need the current process to exit immediately without waiting to flush enqueued data to the underlying pipe, and you don’t care about lost data.
Note
This class’s functionality requires a functioning shared semaphore implementation on the host operating system. Without one, the functionality in this class will be disabled, and attempts to instantiate a Queue will result in an
. See
for additional information. The same holds true for any of the specialized queue types listed below.
classmultiprocessing.SimpleQueue
It is a simplified
type, very close to a locked
.
Instantiating this class may set the global start method. See
for more details.
close()
Close the queue: release internal resources.
A queue must not be used anymore after it is closed. For example,
,
and
methods must no longer be called.
Added in version 3.9.
empty()
Return True if the queue is empty, False otherwise.
Always raises an
if the SimpleQueue is closed.
get()
Remove and return an item from the queue.
put(item)
Put item into the queue.
classmultiprocessing.JoinableQueue([maxsize])
JoinableQueue, a
subclass, is a queue which additionally has
and
methods.
Instantiating this class may set the global start method. See
for more details.
task_done()
Indicate that a formerly enqueued task is complete. Used by queue consumers. For each
used to fetch a task, a subsequent call to task_done() tells the queue that the processing on the task is complete.
If a
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
into the queue).
Raises a
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
to indicate that the item was retrieved and all work on it is complete. When the count of unfinished tasks drops to zero,
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
(or len(os.sched_getaffinity(0))).
When the number of CPUs cannot be determined a
is raised.
Changed in version 3.13: The return value can also be overridden using the
flag or
as this is merely a wrapper around the
cpu count APIs.
multiprocessing.current_process()
Return the
object corresponding to the current process.
An analogue of
.
multiprocessing.parent_process()
Return the
object corresponding to the parent process of the
. 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:
frommultiprocessingimportProcess,freeze_supportdeff():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
.
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
.
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
for more details. Otherwise method should be 'fork', 'spawn', 'forkserver'.
is raised if the specified start method is not available. See
.
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
for more details.
The return value can be 'fork', 'spawn', 'forkserver' or None. See
.
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
.
multiprocessing.set_executable(executable)
Set the path of the Python interpreter to use when starting a child process. (By default
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, *, on_error='ignore')
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. 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
).
The on_error parameter controls how
exceptions during module preloading are handled: "ignore" (default) silently ignores failures, "warn" causes the forkserver subprocess to emit an
to stderr, and "fail" causes the forkserver subprocess to exit with the exception traceback on stderr, making subsequent process creation fail with
or
.
Only meaningful when using the 'forkserver' start method. See
.
Added in version 3.4.
Changed in version 3.15: Added the on_error parameter.
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
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
.
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
– see also
.
classmultiprocessing.connection.Connection
send(obj)
Send an object to the other end of the connection which should be read using
.
The object must be picklable. Very large pickles (approximately 32 MiB+, though it depends on the OS) may raise a
exception.
recv()
Return an object sent from the other end of the connection using
. Blocks until there is something to receive. Raises
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(buf[, offset[, size]])
Send byte data from a
as a complete message.
If offset is given then data is read from that position in buf. If size is given then that many bytes will be read from buf. Very large buffers (approximately 32 MiB+, though it depends on the OS) may raise a
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
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
is raised and the connection will no longer be readable.
Changed in version 3.3: This function used to raise
, which is now an alias of
.
recv_bytes_into(buf[, offset])
Read into buf 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
if there is nothing left to receive and the other end was closed.
buf must be a writable
. 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 buf (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
and
.
Connection objects also now support the context management protocol – see
.
returns the connection object, and
calls
.
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>>> arr2array('i', [0, 1, 2, 3, 4, 0, 0, 0, 0, 0])Warning
The
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
and
methods after performing some sort of authentication. See
.
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
module.
Note that one can also create synchronization primitives by using a manager object – see
.
classmultiprocessing.Barrier(parties[, action[, timeout]])
A barrier object: a clone of
.
Instantiating this class may set the global start method. See
for more details.
Added in version 3.3.
classmultiprocessing.BoundedSemaphore([value])
A bounded semaphore object: a close analog of
.
Instantiating this class may set the global start method. See
for more details.
A solitary difference from its close analog exists: its acquire method’s first argument is named block, as is consistent with
.
locked()
Return a boolean indicating whether this object is locked right now.
Added in version 3.14.
Note
On macOS, this is indistinguishable from
because sem_getvalue() is not implemented on that platform.
classmultiprocessing.Condition([lock])
A condition variable: an alias for
.
If lock is specified then it should be a
or
object from multiprocessing.
Instantiating this class may set the global start method. See
for more details.
Changed in version 3.3: The
method was added.
classmultiprocessing.Event
A clone of
.
Instantiating this class may set the global start method. See
for more details.
classmultiprocessing.Lock
A non-recursive lock object: a close analog of
. 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
for more details.
Lock supports the
protocol and thus may be used in
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
.
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
. 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
except that when invoked on an unlocked lock, a
is raised.
locked()
Return a boolean indicating whether this object is locked right now.
Added in version 3.14.
classmultiprocessing.RLock
A recursive lock object: a close analog of
. 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
for more details.
RLock supports the
protocol and thus may be used in
statements.
acquire(block=True, timeout=None)
Acquire a lock, blocking or non-blocking.
When invoked with the block argument set to True, block until the lock is in an unlocked state (not owned by any process or thread) unless the lock is already owned by the current process or thread. The current process or thread then takes ownership of the lock (if it does not already have ownership) and the recursion level inside the lock increments by one, resulting in a return value of True. Note that there are several differences in this first argument’s behavior compared to the implementation of
, 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
. Note that some of these behaviors of timeout differ from the implemented behaviors in
.
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
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
.
locked()
Return a boolean indicating whether this object is locked right now.
Added in version 3.14.
classmultiprocessing.Semaphore([value])
A semaphore object: a close analog of
.
Instantiating this class may set the global start method. See
for more details.
A solitary difference from its close analog exists: its acquire method’s first argument is named block, as is consistent with
.
get_value()
Return the current value of semaphore.
Note that this may raise
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 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 an
. See
for additional information.
Shared
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
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
.
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
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
or
object then that will be used to synchronize access to the value. If lock is False then access to the returned object will not be automatically protected by a lock, so it will not necessarily be “process-safe”.
Operations like += which involve a read and write are not atomic. So if, for instance, you want to atomically increment a shared value it is insufficient to just do
counter.value+=1Assuming the associated lock is recursive (which it is by default) you can instead do
withcounter.get_lock():counter.value+=1Note 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
or a one character typecode of the kind used by the
module with the exception of 'w', which is not supported. In addition, the 'c' typecode is an alias for
. 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
or
object then that will be used to synchronize access to the value. If lock is False then access to the returned object will not be automatically protected by a lock, so it will not necessarily be “process-safe”.
Note that lock is a keyword only argument.
Note that an array of
has value and raw attributes which can both be used to store and retrieve byte strings. While raw allows interaction with a
object the full size of the array, reading value will terminate after a null byte, like most programming languages handle strings.
The multiprocessing.sharedctypes module
The multiprocessing.sharedctypes module provides functions for allocating
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
module. If size_or_initializer is an integer then it determines the length of the array, and the array will be initially zeroed. Otherwise size_or_initializer is a sequence which is used to initialize the array and whose length determines the length of the array.
Note that setting and getting an element is potentially non-atomic – use
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
module. *args is passed on to the constructor for the type.
Note that setting and getting the value is potentially non-atomic – use
instead to make sure that access is automatically synchronized using a lock.
Note that an array of
has value and raw attributes which allow one to use it to store and retrieve strings – see documentation for
.
multiprocessing.sharedctypes.Array(typecode_or_type, size_or_initializer, *, lock=True, ctx=None)
The same as
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
or
object then that will be used to synchronize access to the value. If lock is False then access to the returned object will not be automatically protected by a lock, so it will not necessarily be “process-safe”.
ctx is a context object, or None (use the current context). If None, calling this may set the global start method. See
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
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
or
object then that will be used to synchronize access to the value. If lock is False then access to the returned object will not be automatically protected by a lock, so it will not necessarily be “process-safe”.
ctx is a context object, or None (use the current context). If None, calling this may set the global start method. See
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
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
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
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
sharedctypes using type
sharedctypes using typecode
c_double(2.4)
RawValue(c_double, 2.4)
RawValue(‘d’, 2.4)
MyStruct(4, 6)
RawValue(MyStruct, 4, 6)
(c_short * 7)()
RawArray(c_short, 7)
RawArray(‘h’, 7)
(c_int * 3)(9, 2, 8)
RawArray(c_int, (9, 2, 8))
RawArray(‘i’, (9, 2, 8))
Below is an example where a number of ctypes objects are modified by a child process:
frommultiprocessingimportProcess,Lockfrommultiprocessing.sharedctypesimportValue,ArrayfromctypesimportStructure,c_doubleclassPoint(Structure):_fields_=[('x',c_double),('y',c_double)]defmodify(n,x,s,A):n.value**=2x.value**=2s.value=s.value.upper()forainA:a.x**=2a.y**=2if__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)forainA])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
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
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
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
serialization) or 'xmlrpclib' (use
serialization).
ctx is a context object, or None (use the current context). If None, calling this may set the global start method. See
for more details.
shutdown_timeout is a timeout in seconds used to wait until the process used by the manager completes in the
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:
>>> frommultiprocessing.managersimportBaseManager>>> manager=BaseManager(address=('',50000),authkey=b'abc')>>> server=manager.get_server()>>> server.serve_forever()Server additionally has an
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
has been used to start the server process.
This can be called multiple times.
register(typeid[, callable[, proxytype[, exposed[, method_to_typeid[, create_method]]]]])
A classmethod which can be used for registering a type or callable with the manager class.
typeid is a “type identifier” which is used to identify a particular type of shared object. This must be a string.
callable is a callable used for creating objects for this type identifier. If a manager instance will be connected to the server using the
method, or if the create_method argument is False then this can be left as None.
proxytype is a subclass of
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
. (If exposed is None then proxytype._exposed_ is used instead if it exists.) In the case where no exposed list is specified, all “public methods” of the shared object will be accessible. (Here a “public method” means any attribute which has a
method and whose name does not begin with '_'.)
method_to_typeid is a mapping used to specify the return type of those exposed methods which should return a proxy. It maps method names to typeid strings. (If method_to_typeid is None then proxytype._method_to_typeid_ is used instead if it exists.) If a method’s name is not a key of this mapping or if the mapping is None then the object returned by the method will be copied by value.
create_method determines whether a method should be created with name typeid which can be used to tell the server process to create a new shared object and return a proxy for it. By default it is True.
BaseManager instances also have one read-only property:
address
The address used by the manager.
Changed in version 3.3: Manager objects support the context management protocol – see
.
starts the server process (if it has not already started) and then returns the manager object.
calls
.
In previous versions
did not start the manager’s server process if it was not already started.
classmultiprocessing.managers.SyncManager
A subclass of
which can be used for the synchronization of processes. Objects of this type are returned by
.
Its methods create and return
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
object and return a proxy for it.
Added in version 3.3.
BoundedSemaphore([value])
Create a shared
object and return a proxy for it.
Condition([lock])
Create a shared
object and return a proxy for it.
If lock is supplied then it should be a proxy for a
or
object.
Changed in version 3.3: The
method was added.
Event()
Create a shared
object and return a proxy for it.
Lock()
Create a shared
object and return a proxy for it.
Namespace()
Create a shared
object and return a proxy for it.
Queue([maxsize])
Create a shared
object and return a proxy for it.
RLock()
Create a shared
object and return a proxy for it.
Semaphore([value])
Create a shared
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
object and return a proxy for it.
list()
list(sequence)Create a shared
object and return a proxy for it.
set()
set(sequence)set(mapping)Create a shared
object and return a proxy for it.
Added in version 3.14:
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.
classmultiprocessing.managers.Namespace
A type that can register with
.
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 of
and uses the
classmethod to register new types or callables with the manager class. For example:
frommultiprocessing.managersimportBaseManagerclassMathsClass:defadd(self,x,y):returnx+ydefmul(self,x,y):returnx*yclassMyManager(BaseManager):passMyManager.register('Maths',MathsClass)if__name__=='__main__':withMyManager()asmanager:maths=manager.Maths()print(maths.add(4,3))# prints 7print(maths.mul(7,8))# prints 56Using 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 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*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 applying
to a proxy will return the representation of the referent, whereas applying
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
. This permits nesting of these managed lists, dicts, and other
:
>>> 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)
or
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 dictionaryd=lproxy[0]d['a']=1d['b']=2# at this point, the changes to d are not yet synced, but by# updating the dictionary, the proxy is notified of the changelproxy[0]=dThis approach is perhaps less convenient than employing nested
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]FalseOne 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 is obj then the expression
proxy._callmethod(methodname,args,kwds)will evaluate the expression
getattr(obj,methodname)(*args,**kwds)in the manager’s process.
The returned value will be a copy of the result of the call or a proxy to a new shared object – see documentation for the method_to_typeid argument of
.
If an exception is raised by the call, then is re-raised by _callmethod(). If some other exception is raised in the manager’s process then this is converted into a RemoteError exception and is raised by _callmethod().
Note in particular that an exception will be raised if methodname has not been exposed.
An example of the usage of _callmethod():
>>> l=manager.list(range(10))>>> l._callmethod('__len__')10>>> l._callmethod('__getitem__',(slice(2,7),))# equivalent to l[2:7][2, 3, 4, 5, 6]>>> l._callmethod('__getitem__',(20,))# equivalent to l[20]Traceback (most recent call last):...IndexError: list index out of range_getvalue()
Return a copy of the referent.
If the referent is unpicklable then this will raise an exception.
__repr__()
Return a representation of the proxy object.
__str__()
Return the representation of the referent.
Cleanup
A proxy object uses a weakref callback so that when it gets garbage collected it deregisters itself from the manager which owns its referent.
A shared object gets deleted from the manager process when there are no longer any proxies referring to it.
Process Pools
One can create a pool of processes which will carry out tasks submitted to it with the
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.
processes is the number of worker processes to use. If processes is None then the number returned by
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
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
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
and
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
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 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 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,
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
method which returns a
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
built-in function (it supports only one iterable argument though, for multiple iterables see
). 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
or
with explicit chunksize option for better efficiency.
map_async(func, iterable[, chunksize[, callback[, error_callback]]])
A variant of the
method which returns a
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=1, *, buffersize=None)
A lazier version of
.
The chunksize argument is the same as the one used by the
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
if the result cannot be returned within timeout seconds.
The iterable is collected immediately rather than lazily, unless a buffersize is specified to limit the number of submitted tasks whose results have not yet been yielded. If the buffer is full, iteration over the iterables pauses until a result is yielded from the buffer. To fully utilize pool’s capacity when using this feature, set buffersize at least to the number of processes in pool (to consume iterable as you go), or even higher (to prefetch the next N=buffersize-processes arguments).
Changed in version 3.16.0a0 (unreleased): Added the buffersize parameter.
imap_unordered(func, iterable, chunksize=1, *, buffersize=None)
The same as
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”.)
Changed in version 3.16.0a0 (unreleased): Added the buffersize parameter.
starmap(func, iterable[, chunksize])
Like
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
and
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
or
before using join().
classmultiprocessing.pool.AsyncResult
The class of the result returned by
and
.
get([timeout])
Return the result when it arrives. If timeout is not None and the result does not arrive within timeout seconds then
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
if the result is not ready.
Changed in version 3.7: If the result is not ready,
is raised instead of
.
The following example demonstrates the use of a pool:
frommultiprocessingimportPoolimporttimedeff(x):returnx*xif__name__=='__main__':withPool(processes=4)aspool:# start 4 worker processesresult=pool.apply_async(f,(10,))# evaluate "f(10)" asynchronously in a single processprint(result.get(timeout=1))# prints "100" unless your computer is *very* slowprint(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* slowresult=pool.apply_async(time.sleep,(10,))print(result.get(timeout=1))# raises multiprocessing.TimeoutErrorListeners and Clients
Usually message passing between processes is done using queues or by using
objects returned by
.
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
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
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
is raised.
multiprocessing.connection.Client(address[, family[, authkey]])
Attempt to set up a connection to the listener which is using address address, returning a
.
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
)
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.
is raised if authentication fails. See
.
classmultiprocessing.connection.Listener([address[, family[, backlog[, authkey]]]])
A wrapper for a bound socket or Windows named pipe which is ‘listening’ for connections.
address is the address to be used by the bound socket or named pipe of the listener object.
Note
If an address of ‘0.0.0.0’ is used, the address will not be a connectable end point on Windows. If you require a connectable end-point, you should use ‘127.0.0.1’.
family is the type of socket (or named pipe) to use. This can be one of the strings 'AF_INET' (for a TCP socket), 'AF_UNIX' (for a Unix domain socket) or 'AF_PIPE' (for a Windows named pipe). Of these only the first is guaranteed to be available. If family is None then the family is inferred from the format of address. If address is also None then a default is chosen. This default is the family which is assumed to be the fastest available. See
. Note that if family is 'AF_UNIX' and address is None then the socket will be created in a private temporary directory created using
.
If the listener object uses a socket then backlog (1 by default) is passed to the
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.
is raised if authentication fails. See
.
accept()
Accept a connection on the bound socket or named pipe of the listener object and return a
object. If authentication is attempted and fails, then
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
.
returns the listener object, and
calls
.
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
object;
a connected and readable
object; or
the
attribute of a
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
is interrupted by a signal, it can raise
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
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 'secretpassword' as an authentication key. It then waits for a connection and sends some data to the client:
frommultiprocessing.connectionimportListenerfromarrayimportarrayaddress=('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.connectionimportClientfromarrayimportarrayaddress=('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))# => 8print(arr)# => array('i', [42, 1729, 0, 0, 0])The following code uses
to wait for messages from multiple processes at once:
frommultiprocessingimportProcess,Pipe,current_processfrommultiprocessing.connectionimportwaitdeffoo(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 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
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
, the data received is automatically unpickled. Unfortunately unpickling data from an untrusted source is a security risk. Therefore
and
use the
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
). 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
.
This authentication protects
and
connections, which are reachable by address. It is not applied to the anonymous pipes created by
or used internally by
. multiprocessing treats all local processes running as the same user as trusted; on most operating systems such processes can access each other’s pipe file descriptors regardless. Applications that require isolation between processes of the same user must arrange it at the operating-system level – for example, by running workers under a different user account or in a sandbox.
Logging
Some support for logging is available. Note, however, that the
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
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
but in addition to returning the logger created by get_logger, it adds a handler which sends output to
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:
>>> 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 0For a full table of logging levels, see the
module.
The multiprocessing.dummy module
multiprocessing.dummy replicates the API of multiprocessing but is no more than a wrapper around the
module.
In particular, the Pool function provided by multiprocessing.dummy returns an instance of
, which is a subclass of
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 with
instances, and their resources must also be properly managed, either by using the pool as a context manager or by calling
and
manually.
processes is the number of worker threads to use. If processes is None then the number returned by
is used.
If initializer is not None then each worker process will call initializer(*initargs) when it starts.
Unlike
, maxtasksperchild and context cannot be provided.
Note
A ThreadPool shares the same interface as
, which is designed around a pool of processes and predates the introduction of the
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,
, 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
instances that are compatible with many other libraries, including
.
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
is called) all completed processes which have not yet been joined will be joined. Also calling a finished process’s
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
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
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
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,Queuedeff(q):q.put('X'*1000000)if__name__=='__main__':queue=Queue()p=Process(target=f,args=(queue,))p.start()p.join()# this deadlocksobj=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
frommultiprocessingimportProcess,Lockdeff():...dosomethingusing"lock"...if__name__=='__main__':lock=Lock()foriinrange(10):Process(target=f).start()should be rewritten as
frommultiprocessingimportProcess,Lockdeff(l):...dosomethingusing"l"...if__name__=='__main__':lock=Lock()foriinrange(10):Process(target=f,args=(lock,)).start()
Beware of replacing
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
with a “file-like object” with output buffering. This danger is that if multiple processes call
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:
@propertydefcache(self):pid=os.getpid()ifpid!=self._pid:self._pid=pidself._cache=[]returnself._cacheFor more information, see
,
and
The spawn and forkserver start methods
There are a few extra restrictions which don’t apply to the fork start method.
More picklability
Ensure that all arguments to
are picklable. Also, if you subclass Process.__init__, you must make sure that instances will be picklable when the
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
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
:
frommultiprocessingimportProcessdeffoo():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_methoddeffoo():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_supportfrommultiprocessing.managersimportBaseManager,BaseProxyimportoperator##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 functiondefbaz():foriinrange(10):yieldi*i# Proxy type for generator objectsclassGeneratorProxy(BaseProxy):_exposed_=['__next__']def__iter__(self):returnselfdef__next__(self):returnself._callmethod('__next__')# Function to return the operator moduledefget_operator_module():returnoperator##classMyManager(BaseManager):pass# register the Foo class; make `f()` and `g()` accessible via proxyMyManager.register('Foo1',Foo)# register the Foo class; make `g()` and `_h()` accessible via proxyMyManager.register('Foo2',Foo,exposed=('g','_h'))# register the generator function baz; use `GeneratorProxy` to make proxiesMyManager.register('baz',baz,proxytype=GeneratorProxy)# register get_operator_module(); make public functions accessible via proxyMyManager.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()Using
:
importmultiprocessingimporttimeimportrandomimportsys## Functions used by test code#defcalculate(func,args):result=func(*args)return'%s says 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*bdefplus(a,b):time.sleep(0.5*random.random())returna+bdeff(x):return1.0/(x-5.0)defpow3(x):returnx**3defnoop(x):pass## Test code#deftest():PROCESSES=4print('Creating pool with %d processes\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:passexceptStopIteration:breakelse:ifi==5:raiseAssertionError('expected ZeroDivisionError')asserti==9print('\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))breakexceptmultiprocessing.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:breakexceptmultiprocessing.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:
importtimeimportrandomfrommultiprocessingimportProcess,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'%s says 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*bdefplus(a,b):time.sleep(0.5*random.random())returna+b###deftest():NUMBER_OF_PROCESSES=4TASKS1=[(mul,(i,7))foriinrange(20)]TASKS2=[(plus,(i,8))foriinrange(10)]# Create queuestask_queue=Queue()done_queue=Queue()# Submit tasksfortaskinTASKS1:task_queue.put(task)# Start worker processesforiinrange(NUMBER_OF_PROCESSES):Process(target=worker,args=(task_queue,done_queue)).start()# Get and print resultsprint('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 resultsforiinrange(len(TASKS2)):print('\t',done_queue.get())# Tell child processes to stopforiinrange(NUMBER_OF_PROCESSES):task_queue.put('STOP')if__name__=='__main__':freeze_support()test()