multiprocessing — Process-based parallelism
Source code: Lib/multiprocessing/
Introduction
multiprocessing
is a package that supports spawning processes using an API similar to the threading
module. The multiprocessing
package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. Due to this, the multiprocessing
module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows.
The multiprocessing
module also introduces APIs which do not have analogs in the threading
module. A prime example of this is the Pool
object which offers a convenient means of parallelizing the execution of a function across multiple input values, distributing the input data across processes (data parallelism). The following example demonstrates the common practice of defining such functions in a module so that child processes can successfully import that module. This basic example of data parallelism using Pool
,
from multiprocessing import Pool
def f(x):
return x*x
if __name__ == '__main__':
with Pool(5) as p:
print(p.map(f, [1, 2, 3]))
will print to standard output
[1, 4, 9]
The Process
class
In multiprocessing
, processes are spawned by creating a Process
object and then calling its start()
method. Process
follows the API of threading.Thread
. A trivial example of a multiprocess program is
from multiprocessing import Process
def f(name):
print('hello', name)
if __name__ == '__main__':
p = Process(target=f, args=('bob',))
p.start()
p.join()
To show the individual process IDs involved, here is an expanded example:
from multiprocessing import Process
import os
def info(title):
print(title)
print('module name:', __name__)
print('parent process:', os.getppid())
print('process id:', os.getpid())
def f(name):
info('function f')
print('hello', name)
if __name__ == '__main__':
info('main line')
p = Process(target=f, args=('bob',))
p.start()
p.join()
For an explanation of why the if __name__ == '__main__'
part is necessary, see Programming guidelines.
Contexts and start methods
Depending on the platform, multiprocessing
supports three ways to start a process. These start methods are
- spawn
The parent process starts a fresh python interpreter process. The child process will only inherit those resources necessary to run the process objects
run()
method. In particular, unnecessary file descriptors and handles from the parent process will not be inherited. Starting a process using this method is rather slow compared to using fork or forkserver.Available on Unix and Windows. The default on Windows and macOS.
- fork
The parent process uses
os.fork()
to fork the Python interpreter. The child process, when it begins, is effectively identical to the parent process. All resources of the parent are inherited by the child process. Note that safely forking a multithreaded process is problematic.Available on Unix only. The default on Unix.
- forkserver
When the program starts and selects the forkserver start method, a server process is started. From then on, whenever a new process is needed, the parent process connects to the server and requests that it fork a new process. The fork server process is single threaded so it is safe for it to use
os.fork()
. No unnecessary resources are inherited.Available on Unix platforms which support passing file descriptors over Unix pipes.
Changed in version 3.8: On macOS, the spawn start method is now the default. The fork start method should be considered unsafe as it can lead to crashes of the subprocess. See bpo-33725 .
Changed in version 3.4: spawn added on all unix platforms, and forkserver added for some unix platforms. Child processes no longer inherit all of the parents inheritable handles on Windows.
On Unix using the spawn or forkserver start methods will also start a resource tracker process which tracks the unlinked named system resources (such as named semaphores or SharedMemory
objects) created by processes of the program. When all processes have exited the resource tracker unlinks any remaining tracked object. Usually there should be none, but if a process was killed by a signal there may be some “leaked” resources. (Neither leaked semaphores nor shared memory segments will be automatically unlinked until the next reboot. This is problematic for both objects because the system allows only a limited number of named semaphores, and shared memory segments occupy some space in the main memory.)
To select a start method you use the set_start_method()
in the if __name__ == '__main__'
clause of the main module. For example:
import multiprocessing as mp
def foo(q):
q.put('hello')
if __name__ == '__main__':
mp.set_start_method('spawn')
q = mp.Queue()
p = mp.Process(target=foo, args=(q,))
p.start()
print(q.get())
p.join()
set_start_method()
should not be used more than once in the program.
Alternatively, you can use get_context()
to obtain a context object. Context objects have the same API as the multiprocessing module, and allow one to use multiple start methods in the same program.
import multiprocessing as mp
def foo(q):
q.put('hello')
if __name__ == '__main__':
ctx = mp.get_context('spawn')
q = ctx.Queue()
p = ctx.Process(target=foo, args=(q,))
p.start()
print(q.get())
p.join()
Note that objects related to one context may not be compatible with processes for a different context. In particular, locks created using the fork context cannot be passed to processes started using the spawn or forkserver start methods.
A library which wants to use a particular start method should probably use get_context()
to avoid interfering with the choice of the library user.
Warning
The 'spawn'
and 'forkserver'
start methods cannot currently be used with “frozen” executables (i.e., binaries produced by packages like PyInstaller and cx_Freeze) on Unix. The 'fork'
start method does work.
Exchanging objects between processes
multiprocessing
supports two types of communication channel between processes:
Queues
The
Queue
class is a near clone ofqueue.Queue
. For example:from multiprocessing import Process, Queue def f(q): q.put([42, None, 'hello']) if __name__ == '__main__': q = Queue() p = Process(target=f, args=(q,)) p.start() print(q.get()) # prints "[42, None, 'hello']" p.join()Queues are thread and process safe.
Pipes
The
Pipe()
function returns a pair of connection objects connected by a pipe which by default is duplex (two-way). For example:from multiprocessing import Process, Pipe def f(conn): conn.send([42, None, 'hello']) conn.close() if __name__ == '__main__': parent_conn, child_conn = Pipe() p = Process(target=f, args=(child_conn,)) p.start() print(parent_conn.recv()) # prints "[42, None, 'hello']" p.join()The two connection objects returned by
Pipe()
represent the two ends of the pipe. Each connection object hassend()
andrecv()
methods (among others). Note that data in a pipe may become corrupted if two processes (or threads) try to read from or write to the same end of the pipe at the same time. Of course there is no risk of corruption from processes using different ends of the pipe at the same time.
Synchronization between processes
multiprocessing
contains equivalents of all the synchronization primitives from threading
. For instance one can use a lock to ensure that only one process prints to standard output at a time:
from multiprocessing import Process, Lock
def f(l, i):
l.acquire()
try:
print('hello world', i)
finally:
l.release()
if __name__ == '__main__':
lock = Lock()
for num in range(10):
Process(target=f, args=(lock, num)).start()
Without using the lock output from the different processes is liable to get all mixed up.
Sharing state between processes
As mentioned above, when doing concurrent programming it is usually best to avoid using shared state as far as possible. This is particularly true when using multiple processes.
However, if you really do need to use some shared data then multiprocessing
provides a couple of ways of doing so.
Shared memory
Data can be stored in a shared memory map using
Value
orArray
. For example, the following codefrom multiprocessing import Process, Value, Array def f(n, a): n.value = 3.1415927 for i in range(len(a)): a[i] = -a[i] if __name__ == '__main__': num = Value('d', 0.0) arr = Array('i', range(10)) p = Process(target=f, args=(num, arr)) p.start() p.join() print(num.value) print(arr[:])will print
3.1415927 [0, -1, -2, -3, -4, -5, -6, -7, -8, -9]The
'd'
and'i'
arguments used when creatingnum
andarr
are typecodes of the kind used by thearray
module:'d'
indicates a double precision float and'i'
indicates a signed integer. These shared objects will be process and thread-safe.For more flexibility in using shared memory one can use the
multiprocessing.sharedctypes
module which supports the creation of arbitrary ctypes objects allocated from shared memory.
Server process
A manager object returned by
Manager()
controls a server process which holds Python objects and allows other processes to manipulate them using proxies.A manager returned by
Manager()
will support typeslist
,dict
,Namespace
,Lock
,RLock
,Semaphore
,BoundedSemaphore
,Condition
,Event
,Barrier
,Queue
,Value
andArray
. For example,from multiprocessing import Process, Manager def f(d, l): d[1] = '1' d['2'] = 2 d[0.25] = None l.reverse() if __name__ == '__main__': with Manager() as manager: d = manager.dict() l = manager.list(range(10)) p = Process(target=f, args=(d, l)) p.start() p.join() print(d) print(l)will print
{0.25: None, 1: '1', '2': 2} [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]Server process managers are more flexible than using shared memory objects because they can be made to support arbitrary object types. Also, a single manager can be shared by processes on different computers over a network. They are, however, slower than using shared memory.
Using a pool of workers
The Pool
class represents a pool of worker processes. It has methods which allows tasks to be offloaded to the worker processes in a few different ways.
For example:
from multiprocessing import Pool, TimeoutError
import time
import os
def f(x):
return x*x
if __name__ == '__main__':
# start 4 worker processes
with Pool(processes=4) as pool:
# print "[0, 1, 4,..., 81]"
print(pool.map(f, range(10)))
# print same numbers in arbitrary order
for i in pool.imap_unordered(f, range(10)):
print(i)
# evaluate "f(20)" asynchronously
res = pool.apply_async(f, (20,)) # runs in *only* one process
print(res.get(timeout=1)) # prints "400"
# evaluate "os.getpid()" asynchronously
res = pool.apply_async(os.getpid, ()) # runs in *only* one process
print(res.get(timeout=1)) # prints the PID of that process
# launching multiple evaluations asynchronously *may* use more processes
multiple_results = [pool.apply_async(os.getpid, ()) for i in range(4)]
print([res.get(timeout=1) for res in multiple_results])
# make a single worker sleep for 10 secs
res = pool.apply_async(time.sleep, (10,))
try:
print(res.get(timeout=1))
except TimeoutError:
print("We lacked patience and got a multiprocessing.TimeoutError")
print("For the moment, the pool remains available for more work")
# exiting the 'with'-block has stopped the pool
print("Now the pool is closed and no longer available")
Note that the methods of a pool should only ever be used by the process which created it.
Note
Functionality within this package requires that the __main__
module be importable by the children. This is covered in Programming guidelines however it is worth pointing out here. This means that some examples, such as the multiprocessing.pool.Pool
examples will not work in the interactive interpreter. For example:
>>> from multiprocessing import Pool
>>> p = Pool(5)
>>> def f(x):
... return x*x
...
>>> with p:
... p.map(f, [1,2,3])
Process PoolWorker-1:
Process PoolWorker-2:
Process PoolWorker-3:
Traceback (most recent call last):
AttributeError: 'module' object has no attribute 'f'
AttributeError: 'module' object has no attribute 'f'
AttributeError: 'module' object has no attribute 'f'
(If you try this it will actually output three full tracebacks interleaved in a semi-random fashion, and then you may have to stop the parent process somehow.)
Reference
The multiprocessing
package mostly replicates the API of the threading
module.
Process
and exceptions
-
class
multiprocessing.
Process
( group=None, target=None, name=None, args=(), kwargs={}, *, daemon=None ) -
Process objects represent activity that is run in a separate process. The
Process
class has equivalents of all the methods ofthreading.Thread
.The constructor should always be called with keyword arguments. group should always be
None
; it exists solely for compatibility withthreading.Thread
. target is the callable object to be invoked by therun()
method. It defaults toNone
, meaning nothing is called. name is the process name (seename
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 processdaemon
flag toTrue
orFalse
. IfNone
(the default), this flag will be inherited from the creating process.By default, no arguments are passed to target.
If a subclass overrides the constructor, it must make sure it invokes the base class constructor (
Process.__init__()
) before doing anything else to the process.Changed in version 3.3: Added the daemon argument.
-
run
( ) -
Method representing the process’s activity.
You may override this method in a subclass. The standard
run()
method invokes the callable object passed to the object’s constructor as the target argument, if any, with sequential and keyword arguments taken from the args and kwargs arguments, respectively.
-
start
( ) -
Start the process’s activity.
This must be called at most once per process object. It arranges for the object’s
run()
method to be invoked in a separate process.
-
join
( [ timeout ] ) -
If the optional argument timeout is
None
(the default), the method blocks until the process whosejoin()
method is called terminates. If timeout is a positive number, it blocks at most timeout seconds. Note that the method returnsNone
if its process terminates or if the method times out. Check the process’sexitcode
to determine if it terminated.A process can be joined many times.
A process cannot join itself because this would cause a deadlock. It is an error to attempt to join a process before it has been started.
-
name
-
The process’s name. The name is a string used for identification purposes only. It has no semantics. Multiple processes may be given the same name.
The initial name is set by the constructor. If no explicit name is provided to the constructor, a name of the form ‘Process-N1:N2:…:Nk’ is constructed, where each 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
start()
method returns until the child process terminates.
-
daemon
-
The process’s daemon flag, a Boolean value. This must be set before
start()
is called.The initial value is inherited from the creating process.
When a process exits, it attempts to terminate all of its daemonic child processes.
Note that a daemonic process is not allowed to create child processes. Otherwise a daemonic process would leave its children orphaned if it gets terminated when its parent process exits. Additionally, these are not Unix daemons or services, they are normal processes that will be terminated (and not joined) if non-daemonic processes have exited.
In addition to the
threading.Thread
API,Process
objects also support the following attributes and methods:-
exitcode
-
The child’s exit code. This will be
None
if the process has not yet terminated. A negative value -N indicates that the child was terminated by signal N.
-
authkey
-
The process’s authentication key (a byte string).
When
multiprocessing
is initialized the main process is assigned a random string usingos.urandom()
.When a
Process
object is created, it will inherit the authentication key of its parent process, although this may be changed by settingauthkey
to another byte string.See Authentication keys.
-
sentinel
-
A numeric handle of a system object which will become “ready” when the process ends.
You can use this value if you want to wait on several events at once using
multiprocessing.connection.wait()
. Otherwise callingjoin()
is simpler.On Windows, this is an OS handle usable with the
WaitForSingleObject
andWaitForMultipleObjects
family of API calls. On Unix, this is a file descriptor usable with primitives from theselect
module.New in version 3.3.
-
terminate
( ) -
Terminate the process. On Unix this is done using the
SIGTERM
signal; on WindowsTerminateProcess()
is used. Note that exit handlers and finally clauses, etc., will not be executed.Note that descendant processes of the process will not be terminated – they will simply become orphaned.
Warning
If this method is used when the associated process is using a pipe or queue then the pipe or queue is liable to become corrupted and may become unusable by other process. Similarly, if the process has acquired a lock or semaphore etc. then terminating it is liable to cause other processes to deadlock.
-
kill
( ) -
Same as
terminate()
but using theSIGKILL
signal on Unix.New in version 3.7.
-
close
( ) -
Close the
Process
object, releasing all resources associated with it.ValueError
is raised if the underlying process is still running. Onceclose()
returns successfully, most other methods and attributes of theProcess
object will raiseValueError
.New in version 3.7.
Note that the
start()
,join()
,is_alive()
,terminate()
andexitcode
methods should only be called by the process that created the process object.Example usage of some of the methods of
Process
:>>> import multiprocessing, time, signal >>> p = multiprocessing.Process(target=time.sleep, args=(1000,)) >>> print(p, p.is_alive()) <Process ... initial> False >>> p.start() >>> print(p, p.is_alive()) <Process ... started> True >>> p.terminate() >>> time.sleep(0.1) >>> print(p, p.is_alive()) <Process ... stopped exitcode=-SIGTERM> False >>> p.exitcode == -signal.SIGTERM True
-
-
exception
multiprocessing.
ProcessError
-
The base class of all
multiprocessing
exceptions.
-
exception
multiprocessing.
BufferTooShort
-
Exception raised by
Connection.recv_bytes_into()
when the supplied buffer object is too small for the message read.If
e
is an instance ofBufferTooShort
thene.args[0]
will give the message as a byte string.
Pipes and Queues
When using multiple processes, one generally uses message passing for communication between processes and avoids having to use any synchronization primitives like locks.
For passing messages one can use Pipe()
(for a connection between two processes) or a queue (which allows multiple producers and consumers).
The Queue
, SimpleQueue
and JoinableQueue
types are multi-producer, multi-consumer FIFO queues modelled on the queue.Queue
class in the standard library. They differ in that Queue
lacks the task_done()
and join()
methods introduced into Python 2.5’s queue.Queue
class.
If you use JoinableQueue
then you must call JoinableQueue.task_done()
for each task removed from the queue or else the semaphore used to count the number of unfinished tasks may eventually overflow, raising an exception.
Note that one can also create a shared queue by using a manager object – see Managers.
Note
multiprocessing
uses the usual queue.Empty
and queue.Full
exceptions to signal a timeout. They are not available in the multiprocessing
namespace so you need to import them from queue
.
Note
When an object is put on a queue, the object is pickled and a background thread later flushes the pickled data to an underlying pipe. This has some consequences which are a little surprising, but should not cause any practical difficulties – if they really bother you then you can instead use a queue created with a manager.
After putting an object on an empty queue there may be an infinitesimal delay before the queue’s
empty()
method returnsFalse
andget_nowait()
can return without raisingqueue.Empty
.If multiple processes are enqueuing objects, it is possible for the objects to be received at the other end out-of-order. However, objects enqueued by the same process will always be in the expected order with respect to each other.
Warning
If a process is killed using Process.terminate()
or os.kill()
while it is trying to use a Queue
, then the data in the queue is likely to become corrupted. This may cause any other process to get an exception when it tries to use the queue later on.
Warning
As mentioned above, if a child process has put items on a queue (and it has not used JoinableQueue.cancel_join_thread
), then that process will not terminate until all buffered items have been flushed to the pipe.
This means that if you try joining that process you may get a deadlock unless you are sure that all items which have been put on the queue have been consumed. Similarly, if the child process is non-daemonic then the parent process may hang on exit when it tries to join all its non-daemonic children.
Note that a queue created using a manager does not have this issue. See Programming guidelines.
For an example of the usage of queues for interprocess communication see Examples.
-
multiprocessing.
Pipe
( [ duplex ] ) -
Returns a pair
(conn1, conn2)
ofConnection
objects representing the ends of a pipe.If duplex is
True
(the default) then the pipe is bidirectional. If duplex isFalse
then the pipe is unidirectional:conn1
can only be used for receiving messages andconn2
can only be used for sending messages.
-
class
multiprocessing.
Queue
( [ maxsize ] ) -
Returns a process shared queue implemented using a pipe and a few locks/semaphores. When a process first puts an item on the queue a feeder thread is started which transfers objects from a buffer into the pipe.
The usual
queue.Empty
andqueue.Full
exceptions from the standard library’squeue
module are raised to signal timeouts.Queue
implements all the methods ofqueue.Queue
except fortask_done()
andjoin()
.-
qsize
( ) -
Return the approximate size of the queue. Because of multithreading/multiprocessing semantics, this number is not reliable.
Note that this may raise
NotImplementedError
on Unix platforms like Mac OS X wheresem_getvalue()
is not implemented.
-
empty
( ) -
Return
True
if the queue is empty,False
otherwise. Because of multithreading/multiprocessing semantics, this is not reliable.
-
full
( ) -
Return
True
if the queue is full,False
otherwise. Because of multithreading/multiprocessing semantics, this is not reliable.
-
put
( obj [, block [, timeout ] ] ) -
Put obj into the queue. If the optional argument block is
True
(the default) and timeout isNone
(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 thequeue.Full
exception if no free slot was available within that time. Otherwise (block isFalse
), put an item on the queue if a free slot is immediately available, else raise thequeue.Full
exception (timeout is ignored in that case).Changed in version 3.8: If the queue is closed,
ValueError
is raised instead ofAssertionError
.
-
get
( [ block [, timeout ] ] ) -
Remove and return an item from the queue. If optional args block is
True
(the default) and timeout isNone
(the default), block if necessary until an item is available. If timeout is a positive number, it blocks at most timeout seconds and raises thequeue.Empty
exception if no item was available within that time. Otherwise (block isFalse
), return an item if one is immediately available, else raise thequeue.Empty
exception (timeout is ignored in that case).Changed in version 3.8: If the queue is closed,
ValueError
is raised instead ofOSError
.
multiprocessing.Queue
has a few additional methods not found inqueue.Queue
. These methods are usually unnecessary for most code:-
close
( ) -
Indicate that no more data will be put on this queue by the current process. The background thread will quit once it has flushed all buffered data to the pipe. This is called automatically when the queue is garbage collected.
-
join_thread
( ) -
Join the background thread. This can only be used after
close()
has been called. It blocks until the background thread exits, ensuring that all data in the buffer has been flushed to the pipe.By default if a process is not the creator of the queue then on exit it will attempt to join the queue’s background thread. The process can call
cancel_join_thread()
to makejoin_thread()
do nothing.
-
cancel_join_thread
( ) -
Prevent
join_thread()
from blocking. In particular, this prevents the background thread from being joined automatically when the process exits – seejoin_thread()
.A better name for this method might be
allow_exit_without_flush()
. It is likely to cause enqueued data to lost, and you almost certainly will not need to use it. It is really only there if you need the current process to exit immediately without waiting to flush enqueued data to the underlying pipe, and you don’t care about lost data.
Note
This class’s functionality requires a functioning shared semaphore implementation on the host operating system. Without one, the functionality in this class will be disabled, and attempts to instantiate a
Queue
will result in anImportError
. See bpo-3770 for additional information. The same holds true for any of the specialized queue types listed below. -
-
class
multiprocessing.
JoinableQueue
( [ maxsize ] ) -
JoinableQueue
, aQueue
subclass, is a queue which additionally hastask_done()
andjoin()
methods.-
task_done
( ) -
Indicate that a formerly enqueued task is complete. Used by queue consumers. For each
get()
used to fetch a task, a subsequent call totask_done()
tells the queue that the processing on the task is complete.If a
join()
is currently blocking, it will resume when all items have been processed (meaning that atask_done()
call was received for every item that had beenput()
into the queue).Raises a
ValueError
if called more times than there were items placed in the queue.
-
join
( ) -
Block until all items in the queue have been gotten and processed.
The count of unfinished tasks goes up whenever an item is added to the queue. The count goes down whenever a consumer calls
task_done()
to indicate that the item was retrieved and all work on it is complete. When the count of unfinished tasks drops to zero,join()
unblocks.
-
Miscellaneous
-
multiprocessing.
active_children
( ) -
Return list of all live children of the current process.
Calling this has the side effect of “joining” any processes which have already finished.
-
multiprocessing.
cpu_count
( ) -
Return the number of CPUs in the system.
This number is not equivalent to the number of CPUs the current process can use. The number of usable CPUs can be obtained with
len(os.sched_getaffinity(0))
May raise
NotImplementedError
.See also
-
multiprocessing.
current_process
( ) -
Return the
Process
object corresponding to the current process.An analogue of
threading.current_thread()
.
-
multiprocessing.
parent_process
( ) -
Return the
Process
object corresponding to the parent process of thecurrent_process()
. For the main process,parent_process
will beNone
.New in version 3.8.
-
multiprocessing.
freeze_support
( ) -
Add support for when a program which uses
multiprocessing
has been frozen to produce a Windows executable. (Has been tested with py2exe, PyInstaller and cx_Freeze.)One needs to call this function straight after the
if __name__ == '__main__'
line of the main module. For example:from multiprocessing import Process, freeze_support def f(): print('hello world!') if __name__ == '__main__': freeze_support() Process(target=f).start()
If the
freeze_support()
line is omitted then trying to run the frozen executable will raiseRuntimeError
.Calling
freeze_support()
has no effect when invoked on any operating system other than Windows. In addition, if the module is being run normally by the Python interpreter on Windows (the program has not been frozen), thenfreeze_support()
has no effect.
-
multiprocessing.
get_all_start_methods
( ) -
Returns a list of the supported start methods, the first of which is the default. The possible start methods are
'fork'
,'spawn'
and'forkserver'
. On Windows only'spawn'
is available. On Unix'fork'
and'spawn'
are always supported, with'fork'
being the default.New in version 3.4.
-
multiprocessing.
get_context
( method=None ) -
Return a context object which has the same attributes as the
multiprocessing
module.If method is
None
then the default context is returned. Otherwise method should be'fork'
,'spawn'
,'forkserver'
.ValueError
is raised if the specified start method is not available.New in version 3.4.
-
multiprocessing.
get_start_method
( allow_none=False ) -
Return the name of start method used for starting processes.
If the start method has not been fixed and allow_none is false, then the start method is fixed to the default and the name is returned. If the start method has not been fixed and allow_none is true then
None
is returned.The return value can be
'fork'
,'spawn'
,'forkserver'
orNone
.'fork'
is the default on Unix, while'spawn'
is the default on Windows.New in version 3.4.
-
multiprocessing.
set_executable
( ) -
Sets the path of the Python interpreter to use when starting a child process. (By default
sys.executable
is used). Embedders will probably need to do some thing likeset_executable(os.path.join(sys.exec_prefix, 'pythonw.exe'))
before they can create child processes.
Changed in version 3.4: Now supported on Unix when the
'spawn'
start method is used.
-
multiprocessing.
set_start_method
( method ) -
Set the method which should be used to start child processes. method can be
'fork'
,'spawn'
or'forkserver'
.Note that this should be called at most once, and it should be protected inside the
if __name__ == '__main__'
clause of the main module.New in version 3.4.
Note
multiprocessing
contains no analogues of threading.active_count()
, threading.enumerate()
, threading.settrace()
, threading.setprofile()
, threading.Timer
, or threading.local
.
Connection Objects
Connection objects allow the sending and receiving of picklable objects or strings. They can be thought of as message oriented connected sockets.
Connection objects are usually created using Pipe
– see also Listeners and Clients.
-
class
multiprocessing.connection.
Connection
-
-
send
( obj ) -
Send an object to the other end of the connection which should be read using
recv()
.The object must be picklable. Very large pickles (approximately 32 MiB+, though it depends on the OS) may raise a
ValueError
exception.
-
recv
( ) -
Return an object sent from the other end of the connection using
send()
. Blocks until there is something to receive. RaisesEOFError
if there is nothing left to receive and the other end was closed.
-
close
( ) -
Close the connection.
This is called automatically when the connection is garbage collected.
-
poll
( [ timeout ] ) -
Return whether there is any data available to be read.
If timeout is not specified then it will return immediately. If timeout is a number then this specifies the maximum time in seconds to block. If timeout is
None
then an infinite timeout is used.Note that multiple connection objects may be polled at once by using
multiprocessing.connection.wait()
.
-
send_bytes
( buffer [, offset [, size ] ] ) -
Send byte data from a bytes-like object as a complete message.
If offset is given then data is read from that position in buffer. If size is given then that many bytes will be read from buffer. Very large buffers (approximately 32 MiB+, though it depends on the OS) may raise a
ValueError
exception
-
recv_bytes
( [ maxlength ] ) -
Return a complete message of byte data sent from the other end of the connection as a string. Blocks until there is something to receive. Raises
EOFError
if there is nothing left to receive and the other end has closed.If maxlength is specified and the message is longer than maxlength then
OSError
is raised and the connection will no longer be readable.
-
recv_bytes_into
( buffer [, offset ] ) -
Read into buffer a complete message of byte data sent from the other end of the connection and return the number of bytes in the message. Blocks until there is something to receive. Raises
EOFError
if there is nothing left to receive and the other end was closed.buffer must be a writable bytes-like object. If offset is given then the message will be written into the buffer from that position. Offset must be a non-negative integer less than the length of buffer (in bytes).
If the buffer is too short then a
BufferTooShort
exception is raised and the complete message is available ase.args[0]
wheree
is the exception instance.
Changed in version 3.3: Connection objects themselves can now be transferred between processes using
Connection.send()
andConnection.recv()
.New in version 3.3: Connection objects now support the context management protocol – see Context Manager Types.
__enter__()
returns the connection object, and__exit__()
callsclose()
. -
For example:
>>> from multiprocessing import Pipe
>>> a, b = Pipe()
>>> a.send([1, 'hello', None])
>>> b.recv()
[1, 'hello', None]
>>> b.send_bytes(b'thank you')
>>> a.recv_bytes()
b'thank you'
>>> import array
>>> arr1 = array.array('i', range(5))
>>> arr2 = array.array('i', [0] * 10)
>>> a.send_bytes(arr1)
>>> count = b.recv_bytes_into(arr2)
>>> assert count == len(arr1) * arr1.itemsize
>>> arr2
array('i', [0, 1, 2, 3, 4, 0, 0, 0, 0, 0])
Warning
The Connection.recv()
method automatically unpickles the data it receives, which can be a security risk unless you can trust the process which sent the message.
Therefore, unless the connection object was produced using Pipe()
you should only use the recv()
and send()
methods after performing some sort of authentication. See Authentication keys.
Warning
If a process is killed while it is trying to read or write to a pipe then the data in the pipe is likely to become corrupted, because it may become impossible to be sure where the message boundaries lie.
Synchronization primitives
Generally synchronization primitives are not as necessary in a multiprocess program as they are in a multithreaded program. See the documentation for threading
module.
Note that one can also create synchronization primitives by using a manager object – see Managers.
-
class
multiprocessing.
Barrier
( parties [, action [, timeout ] ] ) -
A barrier object: a clone of
threading.Barrier
.New in version 3.3.
-
class
multiprocessing.
BoundedSemaphore
( [ value ] ) -
A bounded semaphore object: a close analog of
threading.BoundedSemaphore
.A solitary difference from its close analog exists: its
acquire
method’s first argument is named block, as is consistent withLock.acquire()
.Note
On Mac OS X, this is indistinguishable from
Semaphore
becausesem_getvalue()
is not implemented on that platform.
-
class
multiprocessing.
Condition
( [ lock ] ) -
A condition variable: an alias for
threading.Condition
.If lock is specified then it should be a
Lock
orRLock
object frommultiprocessing
.Changed in version 3.3: The
wait_for()
method was added.
-
class
multiprocessing.
Event
-
A clone of
threading.Event
.
-
class
multiprocessing.
Lock
-
A non-recursive lock object: a close analog of
threading.Lock
. Once a process or thread has acquired a lock, subsequent attempts to acquire it from any process or thread will block until it is released; any process or thread may release it. The concepts and behaviors ofthreading.Lock
as it applies to threads are replicated here inmultiprocessing.Lock
as it applies to either processes or threads, except as noted.Note that
Lock
is actually a factory function which returns an instance ofmultiprocessing.synchronize.Lock
initialized with a default context.Lock
supports the context manager protocol and thus may be used inwith
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 returnTrue
. Note that the name of this first argument differs from that inthreading.Lock.acquire()
.With the block argument set to
False
, the method call does not block. If the lock is currently in a locked state, returnFalse
; otherwise set the lock to a locked state and returnTrue
.When invoked with a positive, floating-point value 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 orNone
values for timeout differs from the implemented behavior inthreading.Lock.acquire()
. The timeout argument has no practical implications if the block argument is set toFalse
and is thus ignored. ReturnsTrue
if the lock has been acquired orFalse
if the timeout period has elapsed.
-
release
( ) -
Release a lock. This can be called from any process or thread, not only the process or thread which originally acquired the lock.
Behavior is the same as in
threading.Lock.release()
except that when invoked on an unlocked lock, aValueError
is raised.
-
-
class
multiprocessing.
RLock
-
A recursive lock object: a close analog of
threading.RLock
. A recursive lock must be released by the process or thread that acquired it. Once a process or thread has acquired a recursive lock, the same process or thread may acquire it again without blocking; that process or thread must release it once for each time it has been acquired.Note that
RLock
is actually a factory function which returns an instance ofmultiprocessing.synchronize.RLock
initialized with a default context.RLock
supports the context manager protocol and thus may be used inwith
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 ofTrue
. Note that there are several differences in this first argument’s behavior compared to the implementation ofthreading.RLock.acquire()
, starting with the name of the argument itself.When invoked with 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 ofFalse
. If the lock is in an unlocked state, the current process or thread takes ownership and the recursion level is incremented, resulting in a return value ofTrue
.Use and behaviors of the timeout argument are the same as in
Lock.acquire()
. Note that some of these behaviors of timeout differ from the implemented behaviors inthreading.RLock.acquire()
.
-
release
( ) -
Release a lock, decrementing the recursion level. If after the decrement the recursion level is zero, reset the lock to unlocked (not owned by any process or thread) and if any other processes or threads are blocked waiting for the lock to become unlocked, allow exactly one of them to proceed. If after the decrement the recursion level is still nonzero, the lock remains locked and owned by the calling process or thread.
Only call this method when the calling process or thread owns the lock. An
AssertionError
is raised if this method is called by a process or thread other than the owner or if the lock is in an unlocked (unowned) state. Note that the type of exception raised in this situation differs from the implemented behavior inthreading.RLock.release()
.
-
-
class
multiprocessing.
Semaphore
( [ value ] ) -
A semaphore object: a close analog of
threading.Semaphore
.A solitary difference from its close analog exists: its
acquire
method’s first argument is named block, as is consistent withLock.acquire()
.
Note
On Mac OS X, sem_timedwait
is unsupported, so calling acquire()
with a timeout will emulate that function’s behavior using a sleeping loop.
Note
If the SIGINT signal generated by Ctrl-C arrives while the main thread is blocked by a call to BoundedSemaphore.acquire()
, Lock.acquire()
, RLock.acquire()
, Semaphore.acquire()
, Condition.acquire()
or Condition.wait()
then the call will be immediately interrupted and KeyboardInterrupt
will be raised.
This differs from the behaviour of threading
where SIGINT will be ignored while the equivalent blocking calls are in progress.
Note
Some of this package’s functionality requires a functioning shared semaphore implementation on the host operating system. Without one, the multiprocessing.synchronize
module will be disabled, and attempts to import it will result in an ImportError
. See bpo-3770 for additional information.
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.
-
Returns a started