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 object’s
run()
method. In particular, unnecessary file descriptors and handles from the parent process will not be inherited. Starting a process using this method is rather slow compared to 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:-
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. 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.
-
exception
multiprocessing.
AuthenticationError
¶ Raised when there is an authentication error.
-
exception
multiprocessing.
TimeoutError
¶ Raised by methods with a timeout when the timeout expires.
Pipes and Queues¶
When using multiple processes, one generally uses message passing for communication between processes and avoids having to use any synchronization primitives like locks.
For passing messages one can use Pipe()
(for a connection between two
processes) or a queue (which allows multiple producers and consumers).
The Queue
, SimpleQueue
and JoinableQueue
types
are multi-producer, multi-consumer FIFO
queues modelled on the queue.Queue
class in the
standard library. They differ in that Queue
lacks the
task_done()
and join()
methods introduced
into Python 2.5’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
.
-
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 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
.
-
get_nowait
()¶ Equivalent to
get(False)
.
multiprocessing.Queue
has a few additional methods not found inqueue.Queue
. These methods are usually unnecessary for most code:-
close
()¶ Indicate that no more data will be put on this queue by the current process. The background thread will quit once it has flushed all buffered data to the pipe. This is called automatically when the queue is garbage collected.
-
join_thread
()¶ Join the background thread. This can only be used after
close()
has been called. It blocks until the background thread exits, ensuring that all data in the buffer has been flushed to the pipe.By default if a process is not the creator of the queue then on exit it will attempt to join the queue’s background thread. The process can call
cancel_join_thread()
to makejoin_thread()
do nothing.
-
cancel_join_thread
()¶ Prevent
join_thread()
from blocking. In particular, this prevents the background thread from being joined automatically when the process exits – seejoin_thread()
.A better name for this method might be
allow_exit_without_flush()
. It is likely to cause enqueued data to 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.
SimpleQueue
¶ It is a simplified
Queue
type, very close to a lockedPipe
.-
empty
()¶ Return
True
if the queue is empty,False
otherwise.
-
get
()¶ Remove and return an item from the queue.
-
put
(item)¶ Put item into the queue.
-
-
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.
-
fileno
()¶ Return the file descriptor or handle used by the connection.
-
close
()¶ Close the connection.
This is called automatically when the connection is garbage collected.
-
poll
([timeout])¶ Return whether there is any data available to be read.
If timeout is not specified then it will return immediately. If timeout is a number then this specifies the maximum time in seconds to block. If timeout is
None
then an infinite timeout is used.Note that multiple connection objects may be polled at once by using
multiprocessing.connection.wait()
.
-
send_bytes
(buffer[, offset[, size]])¶ Send byte data from a bytes-like object as a complete message.
If offset is given then data is read from that position in buffer. If size is given then that many bytes will be read from buffer. Very large buffers (approximately 32 MiB+, though it depends on the OS) may raise a
ValueError
exception
-
recv_bytes
([maxlength])¶ Return a complete message of byte data sent from the other end of the connection as a string. Blocks until there is something to receive. Raises
EOFError
if there is nothing left to receive and the other end has closed.If maxlength is specified and the message is longer than maxlength then
OSError
is raised and the connection will no longer be readable.
-
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
SyncManager
object which can be used for sharing objects between processes. The returned manager object corresponds to a spawned child process and has methods which will create shared objects and return corresponding proxies.
Manager processes will be shutdown as soon as they are garbage collected or
their parent process exits. The manager classes are defined in the
multiprocessing.managers
module:
-
class
multiprocessing.managers.
BaseManager
([address[, authkey]])¶ Create a BaseManager object.
Once created one should call
start()
orget_server().serve_forever()
to ensure that the manager object refers to a started manager process.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
thencurrent_process().authkey
is used. Otherwise authkey is used and it must be a byte string.-
start
([initializer[, initargs]])¶ Start a subprocess to start the manager. If initializer is not
None
then the subprocess will callinitializer(*initargs)
when it starts.
-
get_server
()¶ Returns a
Server
object which represents the actual server under the control of the Manager. TheServer
object supports theserve_forever()
method:>>> from multiprocessing.managers import BaseManager >>> manager = BaseManager(address=('', 50000), authkey=b'abc') >>> server = manager.get_server() >>> server.serve_forever()
Server
additionally has anaddress
attribute.
-
connect
()¶ Connect a local manager object to a remote manager process:
>>> from multiprocessing.managers import BaseManager >>> m = BaseManager(address=('127.0.0.1', 50000), authkey=b'abc') >>> m.connect()
-
shutdown
()¶ Stop the process used by the manager. This is only available if
start()
has been used to start the server process.This can be called multiple times.
-
register
(typeid[, callable[, proxytype[, exposed[, method_to_typeid[, create_method]]]]])¶ A classmethod which can be used for registering a type or callable with the manager class.
typeid is a “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
connect()
method, or if the create_method argument isFalse
then this can be left asNone
.proxytype is a subclass of
BaseProxy
which is used to create proxies for shared objects with this typeid. IfNone
then a proxy class is created automatically.exposed is used to specify a sequence of method names which proxies for this typeid should be allowed to access using
BaseProxy._callmethod()
. (If exposed isNone
thenproxytype._exposed_
is used instead if it exists.) In the case where no exposed list is specified, all “public methods” of the shared object will be accessible. (Here a “public method” means any attribute which has a__call__()
method and whose name does not begin with'_'
.)method_to_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
thenproxytype._method_to_typeid_
is used instead if it exists.) If a method’s name is not a key of this mapping or if the mapping isNone
then the object returned by the method will be copied by value.create_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 Context Manager Types.
__enter__()
starts the server process (if it has not already started) and then returns the manager object.__exit__()
callsshutdown()
.In previous versions
__enter__()
did not start the manager’s server process if it was not already started.-
-
class
multiprocessing.managers.
SyncManager
¶ A subclass of
BaseManager
which can be used for the synchronization of processes. Objects of this type are returned bymultiprocessing.Manager()
.Its methods create and return Proxy Objects for a number of commonly used data types to be synchronized across processes. This notably includes shared lists and dictionaries.
-
Barrier
(parties[, action[, timeout]])¶ Create a shared
threading.Barrier
object and return a proxy for it.New in version 3.3.
-
BoundedSemaphore
([value])¶ Create a shared
threading.BoundedSemaphore
object and return a proxy for it.
-
Condition
([lock])¶ Create a shared
threading.Condition
object and return a proxy for it.If lock is supplied then it should be a proxy for a
threading.Lock
orthreading.RLock
object.Changed in version 3.3: The
wait_for()
method was added.
-
Event
()¶ Create a shared
threading.Event
object and return a proxy for it.
-
Lock
()¶ Create a shared
threading.Lock
object and return a proxy for it.
-
Queue
([maxsize])¶ Create a shared
queue.Queue
object and return a proxy for it.
-
RLock
()¶ Create a shared
threading.RLock
object and return a proxy for it.
-
Semaphore
([value])¶ Create a shared
threading.Semaphore
object and return a proxy for it.
-
Array
(typecode, sequence)¶ Create an array and return a proxy for it.
-
Value
(typecode, value)¶ Create an object with a writable
value
attribute and return a proxy for it.
Changed in version 3.6: Shared objects are capable of being nested. For example, a shared container object such as a shared list can contain other shared objects which will all be managed and synchronized by the
SyncManager
.-
-
class
multiprocessing.managers.
Namespace
¶ A type that can register with
SyncManager
.A namespace object has no public methods, but does have writable attributes. Its representation shows the values of its attributes.
However, when using a proxy for a namespace object, an attribute beginning with
'_'
will be an attribute of the proxy and not an attribute of the referent:>>> manager = multiprocessing.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 BaseManager
and
uses the register()
classmethod to register new types or
callables with the manager class. For example:
from multiprocessing.managers import BaseManager
class MathsClass:
def add(self, x, y):
return x + y
def mul(self, x, y):
return x * y
class MyManager(BaseManager):
pass
MyManager.register('Maths', MathsClass)
if __name__ == '__main__':
with MyManager() as manager:
maths = manager.Maths()
print(maths.add(4, 3)) # prints 7
print(maths.mul(7, 8)) # prints 56
Using a remote manager¶
It is possible to run a manager server on one machine and have clients use it from other machines (assuming that the firewalls involved allow it).
Running the following commands creates a server for a single shared queue which remote clients can access:
>>> from multiprocessing.managers import BaseManager
>>> from queue import Queue
>>> queue = Queue()
>>> class QueueManager(BaseManager): pass
>>> QueueManager.register('get_queue', callable=lambda:queue)
>>> m = QueueManager(address=('', 50000), authkey=b'abracadabra')
>>> s = m.get_server()
>>> s.serve_forever()
One client can access the server as follows:
>>> from multiprocessing.managers import BaseManager
>>> class QueueManager(BaseManager): pass
>>> QueueManager.register('get_queue')
>>> m = QueueManager(address=('foo.bar.org', 50000), authkey=b'abracadabra')
>>> m.connect()
>>> queue = m.get_queue()
>>> queue.put('hello')
Another client can also use it:
>>> from multiprocessing.managers import BaseManager
>>> class QueueManager(BaseManager): pass
>>> QueueManager.register('get_queue')
>>> m = QueueManager(address=('foo.bar.org', 50000), authkey=b'abracadabra')
>>> m.connect()
>>> queue = m.get_queue()
>>> queue.get()
'hello'
Local processes can also access that queue, using the code from above on the client to access it remotely:
>>> from multiprocessing import Process, Queue
>>> from multiprocessing.managers import BaseManager
>>> class Worker(Process):
... def __init__(self, q):
... self.q = q
... super().__init__()
... def run(self):
... self.q.put('local hello')
...
>>> queue = Queue()
>>> w = Worker(queue)
>>> w.start()
>>> class QueueManager(BaseManager): pass
...
>>> QueueManager.register('get_queue', callable=lambda: queue)
>>> m = QueueManager(address=('', 50000), authkey=b'abracadabra')
>>> s = m.get_server()
>>> s.serve_forever()
Proxy Objects¶
A proxy is an object which refers to a shared object which lives (presumably) in a different process. The shared object is said to be the referent of the proxy. Multiple proxy objects may have the same referent.
A proxy object has methods which invoke corresponding methods of its referent (although not every method of the referent will necessarily be available through the proxy). In this way, a proxy can be used just like its referent can:
>>> from multiprocessing import Manager
>>> manager = Manager()
>>> l = manager.list([i*i for i in range(10)])
>>> print(l)
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
>>> print(repr(l))
<ListProxy object, typeid 'list' at 0x...>
>>> l[4]
16
>>> l[2:5]
[4, 9, 16]
Notice that applying str()
to a proxy will return the representation of
the referent, whereas applying repr()
will return the representation of
the proxy.
An important feature of proxy objects is that they are picklable so they can be passed between processes. As such, a referent can contain Proxy Objects. This permits nesting of these managed lists, dicts, and other Proxy Objects:
>>> a = manager.list()
>>> b = manager.list()
>>> a.append(b) # referent of a now contains referent of b
>>> print(a, b)
[<ListProxy object, typeid 'list' at ...>] []
>>> b.append('hello')
>>> print(a[0], b)
['hello'] ['hello']
Similarly, dict and list proxies may be nested inside one another:
>>> l_outer = manager.list([ manager.dict() for i in range(2) ])
>>> d_first_inner = l_outer[0]
>>> d_first_inner['a'] = 1
>>> d_first_inner['b'] = 2
>>> l_outer[1]['c'] = 3
>>> l_outer[1]['z'] = 26
>>> print(l_outer[0])
{'a': 1, 'b': 2}
>>> print(l_outer[1])
{'c': 3, 'z': 26}
If standard (non-proxy) list
or dict
objects are contained
in a referent, modifications to those mutable values will not be propagated
through the manager because the proxy has no way of knowing when the values
contained within are modified. However, storing a value in a container proxy
(which triggers a __setitem__
on the proxy object) does propagate through
the manager and so to effectively modify such an item, one could re-assign the
modified value to the container proxy:
# create a list proxy and append a mutable object (a dictionary)
lproxy = manager.list()
lproxy.append({})
# now mutate the dictionary
d = lproxy[0]
d['a'] = 1
d['b'] = 2
# at this point, the changes to d are not yet synced, but by
# updating the dictionary, the proxy is notified of the change
lproxy[0] = d
This approach is perhaps less convenient than employing nested Proxy Objects for most use cases but also demonstrates a level of control over the synchronization.
Note
The proxy types in multiprocessing
do nothing to support comparisons
by value. So, for instance, we have:
>>> manager.list([1,2,3]) == [1,2,3]
False
One should just use a copy of the referent instead when making comparisons.
-
class
multiprocessing.managers.
BaseProxy
¶ Proxy objects are instances of subclasses of
BaseProxy
.-
_callmethod
(methodname[, args[, kwds]])¶ Call and return the result of a method of the proxy’s referent.
If
proxy
is a proxy whose referent isobj
then the expressionproxy._callmethod(methodname, args, kwds)
will evaluate the expression
getattr(obj, methodname)(*args, **kwds)
in the manager’s process.
The returned value will be a copy of the result of the call or a proxy to a new shared object – see documentation for the method_to_typeid argument of
BaseManager.register()
.If an exception is raised by the call, then is re-raised by
_callmethod()
. If some other exception is raised in the manager’s process then this is converted into aRemoteError
exception and is raised by_callmethod()
.Note in particular that an exception will be raised if methodname has not been exposed.
An example of the usage of
_callmethod()
:>>> l = manager.list(range(10)) >>> l._callmethod('__len__') 10 >>> l._callmethod('__getitem__', (slice(2, 7),)) # equivalent to l[2:7] [2, 3, 4, 5, 6] >>> l._callmethod('__getitem__', (20,)) # equivalent to l[20] Traceback (most recent call last): ... IndexError: list index out of range
-
_getvalue
()¶ Return a copy of the referent.
If the referent is unpicklable then this will raise an exception.
-
__repr__
()¶ Return a representation of the proxy object.
-
__str__
()¶ Return the representation of the referent.
-
Cleanup¶
A proxy object uses a weakref callback so that when it gets garbage collected it deregisters itself from the manager which owns its referent.
A shared object gets deleted from the manager process when there are no longer any proxies referring to it.
Process Pools¶
One can create a pool of processes which will carry out tasks submitted to it
with the Pool
class.
-
class
multiprocessing.pool.
Pool
([processes[, initializer[, initargs[, maxtasksperchild[, context]]]]])¶ A process pool object which controls a pool of worker processes to which jobs can be submitted. It supports asynchronous results with timeouts and callbacks and has a parallel map implementation.
processes is the number of worker processes to use. If processes is
None
then the number returned byos.cpu_count()
is used.If initializer is not
None
then each worker process will callinitializer(*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 thePool()
method of a context object. In both cases context is set appropriately.Note that the methods of the pool object should only be called by the process which created the pool.
Warning
multiprocessing.pool
objects have internal resources that need to be properly managed (like any other resource) by using the pool as a context manager or by callingclose()
andterminate()
manually. Failure to do this can lead to the process hanging on finalization.Note that is not correct to rely on the garbage colletor to destroy the pool as CPython does not assure that the finalizer of the pool will be called (see
object.__del__()
for more information).New in version 3.2: maxtasksperchild
New in version 3.4: context
Note
Worker processes within a
Pool
typically live for the complete duration of the Pool’s work queue. A frequent pattern found in other systems (such as Apache, mod_wsgi, etc) to free resources held by workers is to allow a worker within a pool to complete only a set amount of work before being exiting, being cleaned up and a new process spawned to replace the old one. The maxtasksperchild argument to thePool
exposes this ability to the end user.-
apply
(func[, args[, kwds]])¶ Call func with arguments args and keyword arguments kwds. It blocks until the result is ready. Given this blocks,
apply_async()
is better suited for performing work in parallel. Additionally, func is only executed in one of the workers of the pool.
-
apply_async
(func[, args[, kwds[, callback[, error_callback]]]])¶ A variant of the
apply()
method which returns aAsyncResult
object.If callback is specified then it should be a callable which accepts a single argument. When the result becomes ready callback is applied to it, that is unless the call failed, in which case the error_callback is applied instead.
If error_callback is specified then it should be a callable which accepts a single argument. If the target function fails, then the error_callback is called with the exception instance.
Callbacks should complete immediately since otherwise the thread which handles the results will get blocked.
-
map
(func, iterable[, chunksize])¶ A parallel equivalent of the
map()
built-in function (it supports only one iterable argument though, for multiple iterables seestarmap()
). It blocks until the result is ready.This method chops the iterable into a number of chunks which it submits to the process pool as separate tasks. The (approximate) size of these chunks can be specified by setting chunksize to a positive integer.
Note that it may cause high memory usage for very long iterables. Consider using
imap()
orimap_unordered()
with explicit chunksize option for better efficiency.
-
map_async
(func, iterable[, chunksize[, callback[, error_callback]]])¶ A variant of the
map()
method which returns aAsyncResult
object.If callback is specified then it should be a callable which accepts a single argument. When the result becomes ready callback is applied to it, that is unless the call failed, in which case the error_callback is applied instead.
If error_callback is specified then it should be a callable which accepts a single argument. If the target function fails, then the error_callback is called with the exception instance.
Callbacks should complete immediately since otherwise the thread which handles the results will get blocked.
-
imap
(func, iterable[, chunksize])¶ A lazier version of
map()
.The chunksize argument is the same as the one used by the
map()
method. For very long iterables using a large value for chunksize can make the job complete much faster than using the default value of1
.Also if chunksize is
1
then thenext()
method of the iterator returned by theimap()
method has an optional timeout parameter:next(timeout)
will raisemultiprocessing.TimeoutError
if the result cannot be returned within timeout seconds.
-
imap_unordered
(func, iterable[, chunksize])¶ The same as
imap()
except that the ordering of the results from the returned iterator should be considered arbitrary. (Only when there is only one worker process is the order guaranteed to be “correct”.)
-
starmap
(func, iterable[, chunksize])¶ Like
map()
except that the elements of the iterable are expected to be iterables that are unpacked as arguments.Hence an iterable of
[(1,2), (3, 4)]
results in[func(1,2), func(3,4)]
.New in version 3.3.
-
starmap_async
(func, iterable[, chunksize[, callback[, error_callback]]])¶ A combination of
starmap()
andmap_async()
that iterates over iterable of iterables and calls func with the iterables unpacked. Returns a result object.New in version 3.3.
-
close
()¶ Prevents any more tasks from being submitted to the pool. Once all the tasks have been completed the worker processes will exit.
-
terminate
()¶ Stops the worker processes immediately without completing outstanding work. When the pool object is garbage collected
terminate()
will be called immediately.
-
join
()¶ Wait for the worker processes to exit. One must call
close()
orterminate()
before usingjoin()
.
New in version 3.3: Pool objects now support the context management protocol – see Context Manager Types.
__enter__()
returns the pool object, and__exit__()
callsterminate()
.-
-
class
multiprocessing.pool.
AsyncResult
¶ The class of the result returned by
Pool.apply_async()
andPool.map_async()
.-
get
([timeout])¶ Return the result when it arrives. If timeout is not
None
and the result does not arrive within timeout seconds thenmultiprocessing.TimeoutError
is raised. If the remote call raised an exception then that exception will be reraised byget()
.
-
wait
([timeout])¶ Wait until the result is available or until timeout seconds pass.
-
ready
()¶ Return whether the call has completed.
-
successful
()¶ Return whether the call completed without raising an exception. Will raise
ValueError
if the result is not ready.Changed in version 3.7: If the result is not ready,
ValueError
is raised instead ofAssertionError
.
-
The following example demonstrates the use of a pool:
from multiprocessing import Pool
import time
def f(x):
return x*x
if __name__ == '__main__':
with Pool(processes=4) as pool: # start 4 worker processes
result = pool.apply_async(f, (10,)) # evaluate "f(10)" asynchronously in a single process
print(result.get(timeout=1)) # prints "100" unless your computer is *very* slow
print(pool.map(f, range(10))) # prints "[0, 1, 4,..., 81]"
it = pool.imap(f, range(10))
print(next(it)) # prints "0"
print(next(it)) # prints "1"
print(it.next(timeout=1)) # prints "4" unless your computer is *very* slow
result = pool.apply_async(time.sleep, (10,))
print(result.get(timeout=1)) # raises multiprocessing.TimeoutError
Listeners and Clients¶
Usually message passing between processes is done using queues or by using
Connection
objects returned by
Pipe()
.
However, the multiprocessing.connection
module allows some extra
flexibility. It basically gives a high level message oriented API for dealing
with sockets or Windows named pipes. It also has support for digest
authentication using the hmac
module, and for polling
multiple connections at the same time.
-
multiprocessing.connection.
deliver_challenge
(connection, authkey)¶ Send a randomly generated message to the other end of the connection and wait for a reply.
If the reply matches the digest of the message using authkey as the key then a welcome message is sent to the other end of the connection. Otherwise
AuthenticationError
is raised.
-
multiprocessing.connection.
answer_challenge
(connection, authkey)¶ Receive a message, calculate the digest of the message using authkey as the key, and then send the digest back.
If a welcome message is not received, then
AuthenticationError
is raised.
-
multiprocessing.connection.
Client
(address[, family[, authkey]])¶ Attempt to set up a connection to the listener which is using address address, returning a
Connection
.The type of the connection is determined by family argument, but this can generally be omitted since it can usually be inferred from the format of address. (See Address Formats)
If authkey is given and not None, it should be a byte string and will be used as the secret key for an HMAC-based authentication challenge. No authentication is done if authkey is None.
AuthenticationError
is raised if authentication fails. See Authentication keys.
-
class
multiprocessing.connection.
Listener
([address[, family[, backlog[, authkey]]]])¶ A wrapper for a bound socket or Windows named pipe which is ‘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 isNone
then the family is inferred from the format of address. If address is alsoNone
then a default is chosen. This default is the family which is assumed to be the fastest available. See Address Formats. Note that if family is'AF_UNIX'
and address isNone
then the socket will be created in a private temporary directory created usingtempfile.mkstemp()
.If the listener object uses a socket then backlog (1 by default) is passed to the
listen()
method of the socket once it has been bound.If authkey is given and not None, it should be a byte string and will be used as the secret key for an HMAC-based authentication challenge. No authentication is done if authkey is None.
AuthenticationError
is raised if authentication fails. See Authentication keys.-
accept
()¶ Accept a connection on the bound socket or named pipe of the listener object and return a
Connection
object. If authentication is attempted and fails, thenAuthenticationError
is raised.
-
close
()¶ Close the bound socket or named pipe of the listener object. This is called automatically when the listener is garbage collected. However it is advisable to call it explicitly.
Listener objects have the following read-only properties:
-
address
¶ The address which is being used by the Listener object.
-
last_accepted
¶ The address from which the last accepted connection came. If this is unavailable then it is
None
.
New in version 3.3: Listener objects now support the context management protocol – see Context Manager Types.
__enter__()
returns the listener object, and__exit__()
callsclose()
.-
-
multiprocessing.connection.
wait
(object_list, timeout=None)¶ Wait till an object 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 Unix and Windows, an object can appear in object_list if it is
a readable
Connection
object;a connected and readable
socket.socket
object; or
A connection or socket object is ready when there is data available to be read from it, or the other end has been closed.
Unix:
wait(object_list, timeout)
almost equivalentselect.select(object_list, [], [], timeout)
. The difference is that, ifselect.select()
is interrupted by a signal, it can raiseOSError
with an error number ofEINTR
, whereaswait()
will not.Windows: An item 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 afileno()
method which returns a socket handle or pipe handle. (Note that pipe handles and socket handles are not waitable handles.)New in version 3.3.
Examples
The following server code creates a listener which uses 'secret password'
as
an authentication key. It then waits for a connection and sends some data to
the client:
from multiprocessing.connection import Listener
from array import array
address = ('localhost', 6000) # family is deduced to be 'AF_INET'
with Listener(address, authkey=b'secret password') as listener:
with listener.accept() as conn:
print('connection accepted from', listener.last_accepted)
conn.send([2.25, None, 'junk', float])
conn.send_bytes(b'hello')
conn.send_bytes(array('i', [42, 1729]))
The following code connects to the server and receives some data from the server:
from multiprocessing.connection import Client
from array import array
address = ('localhost', 6000)
with Client(address, authkey=b'secret password') as conn:
print(conn.recv()) # => [2.25, None, 'junk', float]
print(conn.recv_bytes()) # => 'hello'
arr = array('i', [0, 0, 0, 0, 0])
print(conn.recv_bytes_into(arr)) # => 8
print(arr) # => array('i', [42, 1729, 0, 0, 0])
The following code uses wait()
to
wait for messages from multiple processes at once:
import time, random
from multiprocessing import Process, Pipe, current_process
from multiprocessing.connection import wait
def foo(w):
for i in range(10):
w.send((i, current_process().name))
w.close()
if __name__ == '__main__':
readers = []
for i in range(4):
r, w = Pipe(duplex=False)
readers.append(r)
p = Process(target=foo, args=(w,))
p.start()
# We close the writable end of the pipe now to be sure that
# p is the only process which owns a handle for it. This
# ensures that when p closes its handle for the writable end,
# wait() will promptly report the readable end as being ready.
w.close()
while readers:
for r in wait(readers):
try:
msg = r.recv()
except EOFError:
readers.remove(r)
else:
print(msg)
Address Formats¶
An
'AF_INET'
address is a tuple of the form(hostname, port)
where hostname is a string and port is an integer.An
'AF_UNIX'
address is a string representing a filename on the filesystem.An
'AF_PIPE'
address is a string of the formr'\.\pipe{PipeName}'
. To useClient()
to connect to a named pipe on a remote computer called ServerName one should use an address of the formr'\ServerName\pipe{PipeName}'
instead.
Note that any string beginning with two backslashes is assumed by default to be
an 'AF_PIPE'
address rather than an 'AF_UNIX'
address.
Authentication keys¶
When one uses Connection.recv
, the
data received is automatically
unpickled. Unfortunately unpickling data from an untrusted source is a security
risk. Therefore Listener
and Client()
use the hmac
module
to provide digest authentication.
An authentication key is a byte string which can be thought of as a password: once a connection is established both ends will demand proof that the other knows the authentication key. (Demonstrating that both ends are using the same key does not involve sending the key over the connection.)
If authentication is requested but no authentication key is specified then the
return value of current_process().authkey
is used (see
Process
). This value will be automatically inherited by
any Process
object that the current process creates.
This means that (by default) all processes of a multi-process program will share
a single authentication key which can be used when setting up connections
between themselves.
Suitable authentication keys can also be generated by using os.urandom()
.
Logging¶
Some support for logging is available. Note, however, that the logging
package does not use process shared locks so it is possible (depending on the
handler type) for messages from different processes to get mixed up.
-
multiprocessing.
get_logger
()¶ Returns the logger used by
multiprocessing
. If necessary, a new one will be created.When first created the logger has level
logging.NOTSET
and no default handler. Messages sent to this logger will not by default propagate to the root logger.Note that on Windows child processes will only inherit the level of the parent process’s logger – any other customization of the logger will not be inherited.
-
multiprocessing.
log_to_stderr
()¶ This function performs a call to
get_logger()
but in addition to returning the logger created by get_logger, it adds a handler which sends output tosys.stderr
using format'[%(levelname)s/%(processName)s] %(message)s'
.
Below is an example session with logging turned on:
>>> import multiprocessing, logging
>>> logger = multiprocessing.log_to_stderr()
>>> logger.setLevel(logging.INFO)
>>> logger.warning('doomed')
[WARNING/MainProcess] doomed
>>> m = multiprocessing.Manager()
[INFO/SyncManager-...] child process calling self.run()
[INFO/SyncManager-...] created temp directory /.../pymp-...
[INFO/SyncManager-...] manager serving at '/.../listener-...'
>>> del m
[INFO/MainProcess] sending shutdown message to manager
[INFO/SyncManager-...] manager exiting with exitcode 0
For a full table of logging levels, see the logging
module.
The multiprocessing.dummy
module¶
multiprocessing.dummy
replicates the API of multiprocessing
but is
no more than a wrapper around the threading
module.
In particular, the Pool
function provided by multiprocessing.dummy
returns an instance of ThreadPool
, which is a subclass of
Pool
that supports all the same method calls but uses a pool of
worker threads rather than worker processes.
-
class
multiprocessing.pool.
ThreadPool
([processes[, initializer[, initargs]]])¶ A thread pool object which controls a pool of worker threads to which jobs can be submitted.
ThreadPool
instances are fully interface compatible withPool
instances, and their resources must also be properly managed, either by using the pool as a context manager or by callingclose()
andterminate()
manually.processes is the number of worker threads to use. If processes is
None
then the number returned byos.cpu_count()
is used.If initializer is not
None
then each worker process will callinitializer(*initargs)
when it starts.Unlike
Pool
, maxtasksperchild and context cannot be provided.Note
A
ThreadPool
shares the same interface asPool
, which is designed around a pool of processes and predates the introduction of theconcurrent.futures
module. As such, it inherits some operations that don’t make sense for a pool backed by threads, and it has its own type for representing the status of asynchronous jobs,AsyncResult
, that is not understood by any other libraries.Users should generally prefer to use
concurrent.futures.ThreadPoolExecutor
, which has a simpler interface that was designed around threads from the start, and which returnsconcurrent.futures.Future
instances that are compatible with many other libraries, includingasyncio
.
Programming guidelines¶
There are certain guidelines and idioms which should be adhered to when using
multiprocessing
.
All start methods¶
The following applies to all start methods.
Avoid shared state
As far as possible one should try to avoid shifting large amounts of data between processes.
It is probably best to stick to using queues or pipes for communication between processes rather than using the lower level synchronization primitives.
Picklability
Ensure that the arguments to the methods of proxies are picklable.
Thread safety of proxies
Do not use a proxy object from more than one thread unless you protect it with a lock.
(There is never a problem with different processes using the same proxy.)
Joining zombie processes
On Unix when a process finishes but has not been joined it becomes a zombie. There should never be very many because each time a new process starts (or
active_children()
is called) all completed processes which have not yet been joined will be joined. Also calling a finished process’sProcess.is_alive
will join the process. Even so it is probably good practice to explicitly join all the processes that you start.
Better to inherit than pickle/unpickle
When using the spawn or forkserver start methods many types from
multiprocessing
need to be picklable so that child processes can use them. However, one should generally avoid sending shared objects to other processes using pipes or queues. Instead you should arrange the program so that a process which needs access to a shared resource created elsewhere can inherit it from an ancestor process.
Avoid terminating processes
Using the
Process.terminate
method to stop a process is liable to cause any shared resources (such as locks, semaphores, pipes and queues) currently being used by the process to become broken or unavailable to other processes.Therefore it is probably best to only consider using
Process.terminate
on processes which never use any shared resources.
Joining processes that use queues
Bear in mind that a process that has put items in a queue will wait before terminating until all the buffered items are fed by the “feeder” thread to the underlying pipe. (The child process can call the
Queue.cancel_join_thread
method of the queue to avoid this behaviour.)This means that whenever you use a queue you need to make sure that all items which have been put on the queue will eventually be removed before the process is joined. Otherwise you cannot be sure that processes which have put items on the queue will terminate. Remember also that non-daemonic processes will be joined automatically.
An example which will deadlock is the following:
from multiprocessing import Process, Queue def f(q): q.put('X' * 1000000) if __name__ == '__main__': queue = Queue() p = Process(target=f, args=(queue,)) p.start() p.join() # this deadlocks obj = queue.get()A fix here would be to swap the last two lines (or simply remove the
p.join()
line).
Explicitly pass resources to child processes
On Unix using the fork start method, a child process can make use of a shared resource created in a parent process using a global resource. However, it is better to pass the object as an argument to the constructor for the child process.
Apart from making the code (potentially) compatible with Windows and the other start methods this also ensures that as long as the child process is still alive the object will not be garbage collected in the parent process. This might be important if some resource is freed when the object is garbage collected in the parent process.
So for instance
from multiprocessing import Process, Lock def f(): ... do something using "lock" ... if __name__ == '__main__': lock = Lock() for i in range(10): Process(target=f).start()should be rewritten as
from multiprocessing import Process, Lock def f(l): ... do something using "l" ... if __name__ == '__main__': lock = Lock() for i in range(10): Process(target=f, args=(lock,)).start()
Beware of replacing sys.stdin
with a “file like object”
multiprocessing
originally unconditionally called:os.close(sys.stdin.fileno())in the
multiprocessing.Process._bootstrap()
method — this resulted in issues with processes-in-processes. This has been changed to:sys.stdin.close() sys.stdin = open(os.open(os.devnull, os.O_RDONLY), closefd=False)Which solves the fundamental issue of processes colliding with each other resulting in a bad file descriptor error, but introduces a potential danger to applications which replace
sys.stdin()
with a “file-like object” with output buffering. This danger is that if multiple processes callclose()
on this file-like object, it could result in the same data being flushed to the object multiple times, resulting in corruption.If you write a file-like object and implement your own caching, you can make it fork-safe by storing the pid whenever you append to the cache, and discarding the cache when the pid changes. For example:
@property def cache(self): pid = os.getpid() if pid != self._pid: self._pid = pid self._cache = [] return self._cache
The spawn and forkserver start methods¶
There are a few extra restriction which don’t apply to the fork start method.
More picklability
Ensure that all arguments to
Process.__init__()
are picklable. Also, if you subclassProcess
then make sure that instances will be picklable when theProcess.start
method is called.
Global variables
Bear in mind that if code run in a child process tries to access a global variable, then the value it sees (if any) may not be the same as the value in the parent process at the time that
Process.start
was called.However, global variables which are just module level constants cause no problems.
Safe importing of main module
Make sure that the main module can be safely imported by a new Python interpreter without causing unintended side effects (such a starting a new process).
For example, using the spawn or forkserver start method running the following module would fail with a
RuntimeError
:from multiprocessing import Process def foo(): print('hello') p = Process(target=foo) p.start()Instead one should protect the “entry point” of the program by using
if __name__ == '__main__':
as follows:from multiprocessing import Process, freeze_support, set_start_method def foo(): print('hello') if __name__ == '__main__': freeze_support() set_start_method('spawn') p = Process(target=foo) p.start()(The
freeze_support()
line can be omitted if the program will be run normally instead of frozen.)This allows the newly spawned Python interpreter to safely import the module and then run the 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:
from multiprocessing import freeze_support
from multiprocessing.managers import BaseManager, BaseProxy
import operator
##
class Foo:
def f(self):
print('you called Foo.f()')
def g(self):
print('you called Foo.g()')
def _h(self):
print('you called Foo._h()')
# A simple generator function
def baz():
for i in range(10):
yield i*i
# Proxy type for generator objects
class GeneratorProxy(BaseProxy):
_exposed_ = ['__next__']
def __iter__(self):
return self
def __next__(self):
return self._callmethod('__next__')
# Function to return the operator module
def get_operator_module():
return operator
##
class MyManager(BaseManager):
pass
# register the Foo class; make `f()` and `g()` accessible via proxy
MyManager.register('Foo1', Foo)
# register the Foo class; make `g()` and `_h()` accessible via proxy
MyManager.register('Foo2', Foo, exposed=('g', '_h'))
# register the generator function baz; use `GeneratorProxy` to make proxies
MyManager.register('baz', baz, proxytype=GeneratorProxy)
# register get_operator_module(); make public functions accessible via proxy
MyManager.register('operator', get_operator_module)
##
def test():
manager = MyManager()
manager.start()
print('-' * 20)
f1 = manager.Foo1()
f1.f()
f1.g()
assert not hasattr(f1, '_h')
assert sorted(f1._exposed_) == sorted(['f', 'g'])
print('-' * 20)
f2 = manager.Foo2()
f2.g()
f2._h()
assert not hasattr(f2, 'f')
assert sorted(f2._exposed_) == sorted(['g', '_h'])
print('-' * 20)
it = manager.baz()
for i in it:
print('<%d>' % i, end=' ')
print()
print('-' * 20)
op = manager.operator()
print('op.add(23, 45) =', op.add(23, 45))
print('op.pow(2, 94) =', op.pow(2, 94))
print('op._exposed_ =', op._exposed_)
##
if __name__ == '__main__':
freeze_support()
test()
Using Pool
:
import multiprocessing
import time
import random
import sys
#
# Functions used by test code
#
def calculate(func, args):
result = func(*args)
return '%s says that %s%s = %s' % (
multiprocessing.current_process().name,
func.__name__, args, result
)
def calculatestar(args):
return calculate(*args)
def mul(a, b):
time.sleep(0.5 * random.random())
return a * b
def plus(a, b):
time.sleep(0.5 * random.random())
return a + b
def f(x):
return 1.0 / (x - 5.0)
def pow3(x):
return x ** 3
def noop(x):
pass
#
# Test code
#
def test():
PROCESSES = 4
print('Creating pool with %d processes\n' % PROCESSES)
with multiprocessing.Pool(PROCESSES) as pool:
#
# Tests
#
TASKS = [(mul, (i, 7)) for i in range(10)] + \
[(plus, (i, 8)) for i in range(10)]
results = [pool.apply_async(calculate, t) for t in TASKS]
imap_it = pool.imap(calculatestar, TASKS)
imap_unordered_it = pool.imap_unordered(calculatestar, TASKS)
print('Ordered results using pool.apply_async():')
for r in results:
print('\t', r.get())
print()
print('Ordered results using pool.imap():')
for x in imap_it:
print('\t', x)
print()
print('Unordered results using pool.imap_unordered():')
for x in imap_unordered_it:
print('\t', x)
print()
print('Ordered results using pool.map() --- will block till complete:')
for x in pool.map(calculatestar, TASKS):
print('\t', x)
print()
#
# Test error handling
#
print('Testing error handling:')
try:
print(pool.apply(f, (5,)))
except ZeroDivisionError:
print('\tGot ZeroDivisionError as expected from pool.apply()')
else:
raise AssertionError('expected ZeroDivisionError')
try:
print(pool.map(f, list(range(10))))
except ZeroDivisionError:
print('\tGot ZeroDivisionError as expected from pool.map()')
else:
raise AssertionError('expected ZeroDivisionError')
try:
print(list(pool.imap(f, list(range(10)))))
except ZeroDivisionError:
print('\tGot ZeroDivisionError as expected from list(pool.imap())')
else:
raise AssertionError('expected ZeroDivisionError')
it = pool.imap(f, list(range(10)))
for i in range(10):
try:
x = next(it)
except ZeroDivisionError:
if i == 5:
pass
except StopIteration:
break
else:
if i == 5:
raise AssertionError('expected ZeroDivisionError')
assert i == 9
print('\tGot ZeroDivisionError as expected from IMapIterator.next()')
print()
#
# Testing timeouts
#
print('Testing ApplyResult.get() with timeout:', end=' ')
res = pool.apply_async(calculate, TASKS[0])
while 1:
sys.stdout.flush()
try:
sys.stdout.write('\n\t%s' % res.get(0.02))
break
except multiprocessing.TimeoutError:
sys.stdout.write('.')
print()
print()
print('Testing IMapIterator.next() with timeout:', end=' ')
it = pool.imap(calculatestar, TASKS)
while 1:
sys.stdout.flush()
try:
sys.stdout.write('\n\t%s' % it.next(0.02))
except StopIteration:
break
except multiprocessing.TimeoutError:
sys.stdout.write('.')
print()
print()
if __name__ == '__main__':
multiprocessing.freeze_support()
test()
An example showing how to use queues to feed tasks to a collection of worker processes and collect the results:
import time
import random
from multiprocessing import Process, Queue, current_process, freeze_support
#
# Function run by worker processes
#
def worker(input, output):
for func, args in iter(input.get, 'STOP'):
result = calculate(func, args)
output.put(result)
#
# Function used to calculate result
#
def calculate(func, args):
result = func(*args)
return '%s says that %s%s = %s' % \
(current_process().name, func.__name__, args, result)
#
# Functions referenced by tasks
#
def mul(a, b):
time.sleep(0.5*random.random())
return a * b
def plus(a, b):
time.sleep(0.5*random.random())
return a + b
#
#
#
def test():
NUMBER_OF_PROCESSES = 4
TASKS1 = [(mul, (i, 7)) for i in range(20)]
TASKS2 = [(plus, (i, 8)) for i in range(10)]
# Create queues
task_queue = Queue()
done_queue = Queue()
# Submit tasks
for task in TASKS1:
task_queue.put(task)
# Start worker processes
for i in range(NUMBER_OF_PROCESSES):
Process(target=worker, args=(task_queue, done_queue)).start()
# Get and print results
print('Unordered results:')
for i in range(len(TASKS1)):
print('\t', done_queue.get())
# Add more tasks using `put()`
for task in TASKS2:
task_queue.put(task)
# Get and print some more results
for i in range(len(TASKS2)):
print('\t', done_queue.get())
# Tell child processes to stop
for i in range(NUMBER_OF_PROCESSES):
task_queue.put('STOP')
if __name__ == '__main__':
freeze_support()
test()