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 of queue.Queue
. For
example:
from multiprocessing import Process, Queue
def f(q):
q.put([42, None, 'hello'])
if __name__ == '__main__':
q = Queue()
p = Process(target=f, args=(q,))
p.start()
print(q.get()) # prints "[42, None, 'hello']"
p.join()
Queues are thread and process safe.
Pipes
The Pipe()
function returns a pair of connection objects connected by a
pipe which by default is duplex (two-way). For example:
from multiprocessing import Process, Pipe
def f(conn):
conn.send([42, None, 'hello'])
conn.close()
if __name__ == '__main__':
parent_conn, child_conn = Pipe()
p = Process(target=f, args=(child_conn,))
p.start()
print(parent_conn.recv()) # prints "[42, None, 'hello']"
p.join()
The two connection objects returned by Pipe()
represent the two ends of
the pipe. Each connection object has send()
and
recv()
methods (among others). Note that data in a pipe
may become corrupted if two processes (or threads) try to read from or write
to the same end of the pipe at the same time. Of course there is no risk
of corruption from processes using different ends of the pipe at the same
time.
Synchronization between processes
multiprocessing
contains equivalents of all the synchronization
primitives from threading
. For instance one can use a lock to ensure
that only one process prints to standard output at a time:
from multiprocessing import Process, Lock
def f(l, i):
l.acquire()
try:
print('hello world', i)
finally:
l.release()
if __name__ == '__main__':
lock = Lock()
for num in range(10):
Process(target=f, args=(lock, num)).start()
Without using the lock output from the different processes is liable to get all
mixed up.
Sharing state between processes
As mentioned above, when doing concurrent programming it is usually best to
avoid using shared state as far as possible. This is particularly true when
using multiple processes.
However, if you really do need to use some shared data then
multiprocessing
provides a couple of ways of doing so.
Shared memory
Data can be stored in a shared memory map using Value
or
Array
. For example, the following code
from multiprocessing import Process, Value, Array
def f(n, a):
n.value = 3.1415927
for i in range(len(a)):
a[i] = -a[i]
if __name__ == '__main__':
num = Value('d', 0.0)
arr = Array('i', range(10))
p = Process(target=f, args=(num, arr))
p.start()
p.join()
print(num.value)
print(arr[:])
will print
3.1415927
[0, -1, -2, -3, -4, -5, -6, -7, -8, -9]
The 'd'
and 'i'
arguments used when creating num
and arr
are
typecodes of the kind used by the array
module: 'd'
indicates a
double precision float and 'i'
indicates a signed integer. These shared
objects will be process and thread-safe.
For more flexibility in using shared memory one can use the
multiprocessing.sharedctypes
module which supports the creation of
arbitrary ctypes objects allocated from shared memory.
Server process
A manager object returned by Manager()
controls a server process which
holds Python objects and allows other processes to manipulate them using
proxies.
A manager returned by Manager()
will support types
list
, dict
, Namespace
, Lock
,
RLock
, Semaphore
, BoundedSemaphore
,
Condition
, Event
, Barrier
,
Queue
, Value
and Array
. 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 of
threading.Thread
.
The constructor should always be called with keyword arguments. group
should always be None
; it exists solely for compatibility with
threading.Thread
. target is the callable object to be invoked by
the run()
method. It defaults to None
, meaning nothing is
called. name is the process name (see name
for more details).
args is the argument tuple for the target invocation. kwargs is a
dictionary of keyword arguments for the target invocation. If provided,
the keyword-only daemon argument sets the process daemon
flag
to True
or False
. If None
(the default), this flag will be
inherited from the creating process.
By default, no arguments are passed to target.
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 whose join()
method is called terminates.
If timeout is a positive number, it blocks at most timeout seconds.
Note that the method returns None
if its process terminates or if the
method times out. Check the process’s exitcode
to determine if
it terminated.
A process can be joined many times.
A process cannot join itself because this would cause a deadlock. It is
an error to attempt to join a process before it has been started.
-
name
The 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.
If the child’s run()
method returned normally, the exit code
will be 0. If it terminated via sys.exit()
with an integer
argument N, the exit code will be N.
If the child terminated due to an exception not caught within
run()
, the exit code will be 1. If it was terminated by
signal N, the exit code will be the negative value -N.
-
authkey
The process’s authentication key (a byte string).
When multiprocessing
is initialized the main process is assigned a
random string using os.urandom()
.
When a Process
object is created, it will inherit the
authentication key of its parent process, although this may be changed by
setting authkey
to another byte string.
See Authentication keys.
-
sentinel
A numeric handle of a system object which will become “ready” when
the process ends.
You can use this value if you want to wait on several events at
once using multiprocessing.connection.wait()
. Otherwise
calling join()
is simpler.
On Windows, this is an OS handle usable with the WaitForSingleObject
and WaitForMultipleObjects
family of API calls. On Unix, this is
a file descriptor usable with primitives from the select
module.
-
terminate
()
Terminate the process. On Unix this is done using the SIGTERM
signal;
on Windows TerminateProcess()
is used. Note that exit handlers and
finally clauses, etc., will not be executed.
Note that descendant processes of the process will not be terminated –
they will simply become orphaned.
Warning
If this method is used when the associated process is using a pipe or
queue then the pipe or queue is liable to become corrupted and may
become unusable by other process. Similarly, if the process has
acquired a lock or semaphore etc. then terminating it is liable to
cause other processes to deadlock.
-
kill
()
Same as terminate()
but using the SIGKILL
signal on Unix.
-
close
()
Close the Process
object, releasing all resources associated
with it. ValueError
is raised if the underlying process
is still running. Once close()
returns successfully, most
other methods and attributes of the Process
object will
raise ValueError
.
Note that the start()
, join()
, is_alive()
,
terminate()
and exitcode
methods should only be called by
the process that created the process object.
Example usage of some of the methods of Process
:
>>> import multiprocessing, time, signal
>>> 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 of BufferTooShort
then e.args[0]
will give
the message as a byte string.
-
exception
multiprocessing.
AuthenticationError
Raised when there is an authentication error.
-
exception
multiprocessing.
TimeoutError
Raised by methods with a timeout when the timeout expires.
Pipes and Queues
When using multiple processes, one generally uses message passing for
communication between processes and avoids having to use any synchronization
primitives like locks.
For passing messages one can use Pipe()
(for a connection between two
processes) or a queue (which allows multiple producers and consumers).
The Queue
, SimpleQueue
and JoinableQueue
types
are multi-producer, multi-consumer FIFO
queues modelled on the queue.Queue
class in the
standard library. They differ in that Queue
lacks the
task_done()
and join()
methods introduced
into Python 2.5’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 returns False
and get_nowait()
can
return without raising queue.Empty
.
If multiple processes are enqueuing objects, it is possible for
the objects to be received at the other end out-of-order.
However, objects enqueued by the same process will always be in
the expected order with respect to each other.
Warning
If a process is killed using Process.terminate()
or os.kill()
while it is trying to use a Queue
, then the data in the queue is
likely to become corrupted. This may cause any other process to get an
exception when it tries to use the queue later on.
Warning
As mentioned above, if a child process has put items on a queue (and it has
not used JoinableQueue.cancel_join_thread
), then that process will
not terminate until all buffered items have been flushed to the pipe.
This means that if you try joining that process you may get a deadlock unless
you are sure that all items which have been put on the queue have been
consumed. Similarly, if the child process is non-daemonic then the parent
process may hang on exit when it tries to join all its non-daemonic children.
Note that a queue created using a manager does not have this issue. See
Programming guidelines.
For an example of the usage of queues for interprocess communication see
Examples.
-
multiprocessing.
Pipe
([duplex])
Returns a pair (conn1, conn2)
of
Connection
objects representing the
ends of a pipe.
If duplex is True
(the default) then the pipe is bidirectional. If
duplex is False
then the pipe is unidirectional: conn1
can only be
used for receiving messages and conn2
can only be used for sending
messages.
-
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
and queue.Full
exceptions from the
standard library’s queue
module are raised to signal timeouts.
Queue
implements all the methods of queue.Queue
except for
task_done()
and join()
.
-
qsize
()
Return the approximate size of the queue. Because of
multithreading/multiprocessing semantics, this number is not reliable.
Note that this may raise NotImplementedError
on Unix platforms like
macOS where sem_getvalue()
is not implemented.
-
empty
()
Return True
if the queue is empty, False
otherwise. Because of
multithreading/multiprocessing semantics, this is not reliable.
-
full
()
Return True
if the queue is full, False
otherwise. Because of
multithreading/multiprocessing semantics, this is not reliable.
-
put
(obj[, block[, timeout]])
Put obj into the queue. If the optional argument block is True
(the default) and timeout is None
(the default), block if necessary until
a free slot is available. If timeout is a positive number, it blocks at
most timeout seconds and raises the queue.Full
exception if no
free slot was available within that time. Otherwise (block is
False
), put an item on the queue if a free slot is immediately
available, else raise the queue.Full
exception (timeout is
ignored in that case).
-
put_nowait
(obj)
Equivalent to put(obj, False)
.
-
get
([block[, timeout]])
Remove and return an item from the queue. If optional args block is
True
(the default) and timeout is None
(the default), block if
necessary until an item is available. If timeout is a positive number,
it blocks at most timeout seconds and raises the queue.Empty
exception if no item was available within that time. Otherwise (block is
False
), return an item if one is immediately available, else raise the
queue.Empty
exception (timeout is ignored in that case).
Changed in version 3.8: If the queue is closed, ValueError
is raised instead of
OSError
.
-
get_nowait
()
Equivalent to get(False)
.
multiprocessing.Queue
has a few additional methods not found in
queue.Queue
. These methods are usually unnecessary for most
code:
-
close
()
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 make join_thread()
do nothing.
-
cancel_join_thread
()
Prevent join_thread()
from blocking. In particular, this prevents
the background thread from being joined automatically when the process
exits – see join_thread()
.
A better name for this method might be
allow_exit_without_flush()
. It is likely to cause enqueued
data to be lost, and you almost certainly will not need to use it.
It is really only there if you need the current process to exit
immediately without waiting to flush enqueued data to the
underlying pipe, and you don’t care about lost data.
Note
This class’s functionality requires a functioning shared semaphore
implementation on the host operating system. Without one, the
functionality in this class will be disabled, and attempts to
instantiate a Queue
will result in an ImportError
. See
bpo-3770 for additional information. The same holds true for any
of the specialized queue types listed below.
-
class
multiprocessing.
SimpleQueue
It is a simplified Queue
type, very close to a locked Pipe
.
-
close
()
Close the queue: release internal resources.
A queue must not be used anymore after it is closed. For example,
get()
, put()
and empty()
methods must no longer be
called.
-
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
, a Queue
subclass, is a queue which
additionally has task_done()
and join()
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 to task_done()
tells the queue that the processing on the task
is complete.
If a join()
is currently blocking, it will resume when all
items have been processed (meaning that a task_done()
call was
received for every item that had been put()
into the queue).
Raises a ValueError
if called more times than there were items
placed in the queue.
-
join
()
Block until all items in the queue have been gotten and processed.
The count of unfinished tasks goes up whenever an item is added to the
queue. The count goes down whenever a consumer calls
task_done()
to indicate that the item was retrieved and all work on
it is complete. When the count of unfinished tasks drops to zero,
join()
unblocks.
Miscellaneous
-
multiprocessing.
active_children
()
Return list of all live children of the current process.
Calling this has the side effect of “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))
When the number of CPUs cannot be determined a NotImplementedError
is raised.
See also
os.cpu_count()
-
multiprocessing.
current_process
()
Return the Process
object corresponding to the current process.
An analogue of threading.current_thread()
.
-
multiprocessing.
parent_process
()
Return the Process
object corresponding to the parent process of
the current_process()
. For the main process, parent_process
will
be None
.
-
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 raise RuntimeError
.
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), then freeze_support()
has no effect.
-
multiprocessing.
get_all_start_methods
()
Returns a list of the supported start methods, the first of which
is the default. The possible start methods are 'fork'
,
'spawn'
and 'forkserver'
. On Windows only 'spawn'
is
available. On Unix 'fork'
and 'spawn'
are always
supported, with 'fork'
being the default.
-
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.
-
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'
or None
. 'fork'
is the default on Unix, while 'spawn'
is
the default on Windows and macOS.
Changed in version 3.8: On macOS, the spawn start method is now the default. The fork start
method should be considered unsafe as it can lead to crashes of the
subprocess. See bpo-33725.
-
multiprocessing.
set_executable
(executable)
Set the path of the Python interpreter to use when starting a child process.
(By default sys.executable
is used). Embedders will probably need to
do some thing like
set_executable(os.path.join(sys.exec_prefix, 'pythonw.exe'))
before they can create child processes.
Changed in version 3.4: Now supported on 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.
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. Raises
EOFError
if there is nothing left to receive
and the other end was closed.
-
fileno
()
Return the file descriptor or handle used by the connection.
-
close
()
Close the connection.
This is called automatically when the connection is garbage collected.
-
poll
([timeout])
Return whether there is any data available to be read.
If timeout is not specified then it will return immediately. If
timeout is a number then this specifies the maximum time in seconds to
block. If timeout is None
then an infinite timeout is used.
Note that multiple connection objects may be polled at once by
using multiprocessing.connection.wait()
.
-
send_bytes
(buffer[, offset[, size]])
Send byte data from a bytes-like object as a complete message.
If offset is given then data is read from that position in buffer. If
size is given then that many bytes will be read from buffer. Very large
buffers (approximately 32 MiB+, though it depends on the OS) may raise a
ValueError
exception
-
recv_bytes
([maxlength])
Return a complete message of byte data sent from the other end of the
connection as a string. Blocks until there is something to receive.
Raises EOFError
if there is nothing left
to receive and the other end has closed.
If maxlength is specified and the message is longer than maxlength
then OSError
is raised and the connection will no longer be
readable.
Changed in version 3.3: This function used to raise IOError
, which is now an
alias of OSError
.
-
recv_bytes_into
(buffer[, offset])
Read into buffer a complete message of byte data sent from the other end
of the connection and return the number of bytes in the message. Blocks
until there is something to receive. Raises
EOFError
if there is nothing left to receive and the other end was
closed.
buffer must be a writable bytes-like object. If
offset is given then the message will be written into the buffer from
that position. Offset must be a non-negative integer less than the
length of buffer (in bytes).
If the buffer is too short then a BufferTooShort
exception is
raised and the complete message is available as e.args[0]
where e
is the exception instance.
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
.
-
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 with Lock.acquire()
.
Note
On macOS, this is indistinguishable from Semaphore
because
sem_getvalue()
is not implemented on that platform.
-
class
multiprocessing.
Condition
([lock])
A condition variable: an alias for threading.Condition
.
If lock is specified then it should be a Lock
or RLock
object from multiprocessing
.
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 of
threading.Lock
as it applies to threads are replicated here in
multiprocessing.Lock
as it applies to either processes or threads,
except as noted.
Note that Lock
is actually a factory function which returns an
instance of multiprocessing.synchronize.Lock
initialized with a
default context.
Lock
supports the context manager protocol and thus may be
used in with
statements.
-
acquire
(block=True, timeout=None)
Acquire a lock, blocking or non-blocking.
With the block argument set to True
(the default), the method call
will block until the lock is in an unlocked state, then set it to locked
and return True
. Note that the name of this first argument differs
from that in threading.Lock.acquire()
.
With the block argument set to False
, the method call does not
block. If the lock is currently in a locked state, return False
;
otherwise set the lock to a locked state and return True
.
When invoked with a positive, floating-point value for timeout, block
for at most the number of seconds specified by timeout as long as
the lock can not be acquired. Invocations with a negative value for
timeout are equivalent to a timeout of zero. Invocations with a
timeout value of None
(the default) set the timeout period to
infinite. Note that the treatment of negative or None
values for
timeout differs from the implemented behavior in
threading.Lock.acquire()
. The timeout argument has no practical
implications if the block argument is set to False
and is thus
ignored. Returns True
if the lock has been acquired or False
if
the timeout period has elapsed.
-
release
()
Release a lock. This can be called from any process or thread, not only
the process or thread which originally acquired the lock.
Behavior is the same as in threading.Lock.release()
except that
when invoked on an unlocked lock, a ValueError
is raised.
-
class
multiprocessing.
RLock
A recursive lock object: a close analog of threading.RLock
. A
recursive lock must be released by the process or thread that acquired it.
Once a process or thread has acquired a recursive lock, the same process
or thread may acquire it again without blocking; that process or thread
must release it once for each time it has been acquired.
Note that RLock
is actually a factory function which returns an
instance of multiprocessing.synchronize.RLock
initialized with a
default context.
RLock
supports the context manager protocol and thus may be
used in with
statements.
-
acquire
(block=True, timeout=None)
Acquire a lock, blocking or non-blocking.
When invoked with the block argument set to True
, block until the
lock is in an unlocked state (not owned by any process or thread) unless
the lock is already owned by the current process or thread. The current
process or thread then takes ownership of the lock (if it does not
already have ownership) and the recursion level inside the lock increments
by one, resulting in a return value of True
. Note that there are
several differences in this first argument’s behavior compared to the
implementation of threading.RLock.acquire()
, starting with the name
of the argument itself.
When invoked with the block argument set to False
, do not block.
If the lock has already been acquired (and thus is owned) by another
process or thread, the current process or thread does not take ownership
and the recursion level within the lock is not changed, resulting in
a return value of False
. If the lock is in an unlocked state, the
current process or thread takes ownership and the recursion level is
incremented, resulting in a return value of True
.
Use and behaviors of the timeout argument are the same as in
Lock.acquire()
. Note that some of these behaviors of timeout
differ from the implemented behaviors in threading.RLock.acquire()
.
-
release
()
Release a lock, decrementing the recursion level. If after the
decrement the recursion level is zero, reset the lock to unlocked (not
owned by any process or thread) and if any other processes or threads
are blocked waiting for the lock to become unlocked, allow exactly one
of them to proceed. If after the decrement the recursion level is still
nonzero, the lock remains locked and owned by the calling process or
thread.
Only call this method when the calling process or thread owns the lock.
An AssertionError
is raised if this method is called by a process
or thread other than the owner or if the lock is in an unlocked (unowned)
state. Note that the type of exception raised in this situation
differs from the implemented behavior in threading.RLock.release()
.
-
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 with Lock.acquire()
.
Note
On macOS, 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.
Shared ctypes
Objects
It is possible to create shared objects using shared memory which can be
inherited by child processes.
-
multiprocessing.
Value
(typecode_or_type, *args, lock=True)
Return a ctypes
object allocated from shared memory. By default the
return value is actually a synchronized wrapper for the object. The object
itself can be accessed via the value attribute of a Value
.
typecode_or_type determines the type of the returned object: it is either a
ctypes type or a one character typecode of the kind used by the array
module. *args is passed on to the constructor for the type.
If lock is True
(the default) then a new recursive lock
object is created to synchronize access to the value. If lock is
a Lock
or RLock
object then that will be used to
synchronize access to the value. If lock is False
then
access to the returned object will not be automatically protected
by a lock, so it will not necessarily be “process-safe”.
Operations like +=
which involve a read and write are not
atomic. So if, for instance, you want to atomically increment a
shared value it is insufficient to just do
Assuming the associated lock is recursive (which it is by default)
you can instead do
with counter.get_lock():
counter.value += 1
Note that lock is a keyword-only argument.
-
multiprocessing.
Array
(typecode_or_type, size_or_initializer, *, lock=True)
Return a ctypes array allocated from shared memory. By default the return
value is actually a synchronized wrapper for the array.
typecode_or_type determines the type of the elements of the returned array:
it is either a ctypes type or a one character typecode of the kind used by
the array
module. If size_or_initializer is an integer, then it
determines the length of the array, and the array will be initially zeroed.
Otherwise, size_or_initializer is a sequence which is used to initialize
the array and whose length determines the length of the array.
If lock is True
(the default) then a new lock object is created to
synchronize access to the value. If lock is a Lock
or
RLock
object then that will be used to synchronize access to the
value. If lock is False
then access to the returned object will not be
automatically protected by a lock, so it will not necessarily be
“process-safe”.
Note that lock is a keyword only argument.
Note that an array of ctypes.c_char
has value and raw
attributes which allow one to use it to store and retrieve strings.
The multiprocessing.sharedctypes
module provides functions for allocating
ctypes
objects from shared memory which can be inherited by child
processes.
Note
Although it is possible to store a pointer in shared memory remember that
this will refer to a location in the address space of a specific process.
However, the pointer is quite likely to be invalid in the context of a second
process and trying to dereference the pointer from the second process may
cause a crash.
-
multiprocessing.sharedctypes.
RawArray
(typecode_or_type, size_or_initializer)
Return a ctypes array allocated from shared memory.
typecode_or_type determines the type of the elements of the returned array:
it is either a ctypes type or a one character typecode of the kind used by
the array
module. If size_or_initializer is an integer then it
determines the length of the array, and the array will be initially zeroed.
Otherwise size_or_initializer is a sequence which is used to initialize the
array and whose length determines the length of the array.
Note that setting and getting an element is potentially non-atomic – use
Array()
instead to make sure that access is automatically synchronized
using a lock.
-
multiprocessing.sharedctypes.
RawValue
(typecode_or_type, *args)
Return a ctypes object allocated from shared memory.
typecode_or_type determines the type of the returned object: it is either a
ctypes type or a one character typecode of the kind used by the array
module. *args is passed on to the constructor for the type.
Note that setting and getting the value is potentially non-atomic – use
Value()
instead to make sure that access is automatically synchronized
using a lock.
Note that an array of ctypes.c_char
has value
and raw
attributes which allow one to use it to store and retrieve strings – see
documentation for ctypes
.
-
multiprocessing.sharedctypes.
Array
(typecode_or_type, size_or_initializer, *, lock=True)
The same as RawArray()
except that depending on the value of lock a
process-safe synchronization wrapper may be returned instead of a raw ctypes
array.
If lock is True
(the default) then a new lock object is created to
synchronize access to the value. If lock is a
Lock
or RLock
object
then that will be used to synchronize access to the
value. If lock is False
then access to the returned object will not be
automatically protected by a lock, so it will not necessarily be
“process-safe”.
Note that lock is a keyword-only argument.
-
multiprocessing.sharedctypes.
Value
(typecode_or_type, *args, lock=True)
The same as RawValue()
except that depending on the value of lock a
process-safe synchronization wrapper may be returned instead of a raw ctypes
object.
If lock is True
(the default) then a new lock object is created to
synchronize access to the value. If lock is a Lock
or
RLock
object then that will be used to synchronize access to the
value. If lock is False
then access to the returned object will not be
automatically protected by a lock, so it will not necessarily be
“process-safe”.
Note that lock is a keyword-only argument.
-
multiprocessing.sharedctypes.
copy
(obj)
Return a ctypes object allocated from shared memory which is a copy of the
ctypes object obj.
-
multiprocessing.sharedctypes.
synchronized
(obj[, lock])
Return a process-safe wrapper object for a ctypes object which uses lock to
synchronize access. If lock is None
(the default) then a
multiprocessing.RLock
object is created automatically.
A synchronized wrapper will have two methods in addition to those of the
object it wraps: get_obj()
returns the wrapped object and
get_lock()
returns the lock object used for synchronization.
Note that accessing the ctypes object through the wrapper can be a lot slower
than accessing the raw ctypes object.
Changed in version 3.5: Synchronized objects support the context manager protocol.
The table below compares the syntax for creating shared ctypes objects from
shared memory with the normal ctypes syntax. (In the table MyStruct
is some
subclass of ctypes.Structure
.)
ctypes |
sharedctypes using type |
sharedctypes using typecode |
c_double(2.4) |
RawValue(c_double, 2.4) |
RawValue(‘d’, 2.4) |
MyStruct(4, 6) |
RawValue(MyStruct, 4, 6) |
|
(c_short * 7)() |
RawArray(c_short, 7) |
RawArray(‘h’, 7) |
(c_int * 3)(9, 2, 8) |
RawArray(c_int, (9, 2, 8)) |
RawArray(‘i’, (9, 2, 8)) |
Below is an example where a number of ctypes objects are modified by a child
process:
from multiprocessing import Process, Lock
from multiprocessing.sharedctypes import Value, Array
from ctypes import Structure, c_double
class Point(Structure):
_fields_ = [('x', c_double), ('y', c_double)]
def modify(n, x, s, A):
n.value **= 2
x.value **= 2
s.value = s.value.upper()
for a in A:
a.x **= 2
a.y **= 2
if __name__ == '__main__':
lock = Lock()
n = Value('i', 7)
x = Value(c_double, 1.0/3.0, lock=False)
s = Array('c', b'hello world', lock=lock)
A = Array(Point, [(1.875,-6.25), (-5.75,2.0), (2.375,9.5)], lock=lock)
p = Process(target=modify, args=(n, x, s, A))
p.start()
p.join()
print(n.value)
print(x.value)
print(s.value)
print([(a.x, a.y) for a in A])
The results printed are
49
0.1111111111111111
HELLO WORLD
[(3.515625, 39.0625), (33.0625, 4.0), (5.640625, 90.25)]
Managers
Managers provide a way to create data which can be shared between different
processes, including sharing over a network between processes running on
different machines. A manager object controls a server process which manages
shared objects. Other processes can access the shared objects by using
proxies.
-
multiprocessing.
Manager
()
Returns a started SyncManager
object which
can be used for sharing objects between processes. The returned manager
object corresponds to a spawned child process and has methods which will
create shared objects and return corresponding proxies.
Manager processes will be shutdown as soon as they are garbage collected or
their parent process exits. The manager classes are defined in the
multiprocessing.managers
module:
-
class
multiprocessing.managers.
BaseManager
([address[, authkey]])
Create a BaseManager object.
Once created one should call start()
or get_server().serve_forever()
to ensure
that the manager object refers to a started manager process.
address is the address on which the manager process listens for new
connections. If address is None
then an arbitrary one is chosen.
authkey is the authentication key which will be used to check the
validity of incoming connections to the server process. If
authkey is None
then current_process().authkey
is used.
Otherwise authkey is used and it must be a byte string.
-
start
([initializer[, initargs]])
Start a subprocess to start the manager. If initializer is not None
then the subprocess will call initializer(*initargs)
when it starts.
-
get_server
()
Returns a Server
object which represents the actual server under
the control of the Manager. The Server
object supports the
serve_forever()
method:
>>> from multiprocessing.managers import BaseManager
>>> manager = BaseManager(address=('', 50000), authkey=b'abc')
>>> server = manager.get_server()
>>> server.serve_forever()
Server
additionally has an address
attribute.
-
connect
()
Connect a local manager object to a remote manager process:
>>> from multiprocessing.managers import BaseManager
>>> m = BaseManager(address=('127.0.0.1', 50000), authkey=b'abc')
>>> m.connect()
-
shutdown
()
Stop the process used by the manager. This is only available if
start()
has been used to start the server process.
This can be called multiple times.
-
register
(typeid[, callable[, proxytype[, exposed[, method_to_typeid[, create_method]]]]])
A classmethod which can be used for registering a type or callable with
the manager class.
typeid is a “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 is False
then this can be left as
None
.
proxytype is a subclass of BaseProxy
which is used to create
proxies for shared objects with this typeid. If None
then a proxy
class is created automatically.
exposed is used to specify a sequence of method names which proxies for
this typeid should be allowed to access using
BaseProxy._callmethod()
. (If exposed is None
then
proxytype._exposed_
is used instead if it exists.) In the case
where no exposed list is specified, all “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
then
proxytype._method_to_typeid_
is used instead if it exists.) If a
method’s name is not a key of this mapping or if the mapping is None
then the object returned by the method will be copied by value.
create_method determines whether a method should be created with name
typeid which can be used to tell the server process to create a new
shared object and return a proxy for it. By default it is True
.
BaseManager
instances also have one read-only property:
-
address
The address used by the manager.
Changed in version 3.3: Manager objects support the context management protocol – see
Context Manager Types. __enter__()
starts the
server process (if it has not already started) and then returns the
manager object. __exit__()
calls shutdown()
.
In previous versions __enter__()
did not start the
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 by
multiprocessing.Manager()
.
Its methods create and return Proxy Objects for a
number of commonly used data types to be synchronized across processes.
This notably includes shared lists and dictionaries.
-
Barrier
(parties[, action[, timeout]])
Create a shared threading.Barrier
object and return a
proxy for it.
-
BoundedSemaphore
([value])
Create a shared threading.BoundedSemaphore
object and return a
proxy for it.
-
Condition
([lock])
Create a shared threading.Condition
object and return a proxy for
it.
If lock is supplied then it should be a proxy for a
threading.Lock
or threading.RLock
object.
Changed in version 3.3: The wait_for()
method was added.
-
Event
()
Create a shared threading.Event
object and return a proxy for it.
-
Lock
()
Create a shared threading.Lock
object and return a proxy for it.
-
Namespace
()
Create a shared Namespace
object and return a proxy for it.
-
Queue
([maxsize])
Create a shared queue.Queue
object and return a proxy for it.
-
RLock
()
Create a shared threading.RLock
object and return a proxy for it.
-
Semaphore
([value])
Create a shared threading.Semaphore
object and return a proxy for
it.
-
Array
(typecode, sequence)
Create an array and return a proxy for it.
-
Value
(typecode, value)
Create an object with a writable value
attribute and return a proxy
for it.
-
dict
()
-
dict
(mapping)
-
dict
(sequence)
Create a shared dict
object and return a proxy for it.
-
list
()
-
list
(sequence)
Create a shared list
object and return a proxy for it.
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 is obj
then the expression
proxy._callmethod(methodname, args, kwds)
will evaluate the expression
getattr(obj, methodname)(*args, **kwds)
in the manager’s process.
The returned value will be a copy of the result of the call or a proxy to
a new shared object – see documentation for the method_to_typeid
argument of 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 a RemoteError
exception and is
raised by _callmethod()
.
Note in particular that an exception will be raised if methodname has
not been exposed.
An example of the usage of _callmethod()
:
>>> l = manager.list(range(10))
>>> l._callmethod('__len__')
10
>>> l._callmethod('__getitem__', (slice(2, 7),)) # equivalent to l[2:7]
[2, 3, 4, 5, 6]
>>> l._callmethod('__getitem__', (20,)) # equivalent to l[20]
Traceback (most recent call last):
...
IndexError: list index out of range
-
_getvalue
()
Return a copy of the referent.
If the referent is unpicklable then this will raise an exception.
-
__repr__
()
Return a representation of the proxy object.
-
__str__
()
Return the representation of the referent.
Cleanup
A proxy object uses a weakref callback so that when it gets garbage collected it
deregisters itself from the manager which owns its referent.
A shared object gets deleted from the manager process when there are no longer
any proxies referring to it.
Process Pools
One can create a pool of processes which will carry out tasks submitted to it
with the Pool
class.
-
class
multiprocessing.pool.
Pool
([processes[, initializer[, initargs[, maxtasksperchild[, context]]]]])
A process pool object which controls a pool of worker processes to which jobs
can be submitted. It supports asynchronous results with timeouts and
callbacks and has a parallel map implementation.
processes is the number of worker processes to use. If processes is
None
then the number returned by os.cpu_count()
is used.
If initializer is not None
then each worker process will call
initializer(*initargs)
when it starts.
maxtasksperchild is the number of tasks a worker process can complete
before it will exit and be replaced with a fresh worker process, to enable
unused resources to be freed. The default maxtasksperchild is None
, which
means worker processes will live as long as the pool.
context can be used to specify the context used for starting
the worker processes. Usually a pool is created using the
function multiprocessing.Pool()
or the Pool()
method
of a context object. In both cases context is set
appropriately.
Note that the methods of the pool object should only be called by
the process which created the pool.
Warning
multiprocessing.pool
objects have internal resources that need to be
properly managed (like any other resource) by using the pool as a context manager
or by calling close()
and terminate()
manually. Failure to do this
can lead to the process hanging on finalization.
Note that it is not correct to rely on the garbage collector to destroy the pool
as CPython does not assure that the finalizer of the pool will be called
(see object.__del__()
for more information).
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 the Pool
exposes this ability to the end user.
-
apply
(func[, args[, kwds]])
Call func with arguments args and keyword arguments kwds. It blocks
until the result is ready. Given this blocks, apply_async()
is
better suited for performing work in parallel. Additionally, func
is only executed in one of the workers of the pool.
-
apply_async
(func[, args[, kwds[, callback[, error_callback]]]])
A variant of the apply()
method which returns a
AsyncResult
object.
If callback is specified then it should be a callable which accepts a
single argument. When the result becomes ready callback is applied to
it, that is unless the call failed, in which case the error_callback
is applied instead.
If error_callback is specified then it should be a callable which
accepts a single argument. If the target function fails, then
the error_callback is called with the exception instance.
Callbacks should complete immediately since otherwise the thread which
handles the results will get blocked.
-
map
(func, iterable[, chunksize])
A parallel equivalent of the map()
built-in function (it supports only
one iterable argument though, for multiple iterables see starmap()
).
It blocks until the result is ready.
This method chops the iterable into a number of chunks which it submits to
the process pool as separate tasks. The (approximate) size of these
chunks can be specified by setting chunksize to a positive integer.
Note that it may cause high memory usage for very long iterables. Consider
using imap()
or imap_unordered()
with explicit chunksize
option for better efficiency.
-
map_async
(func, iterable[, chunksize[, callback[, error_callback]]])
A variant of the map()
method which returns a
AsyncResult
object.
If callback is specified then it should be a callable which accepts a
single argument. When the result becomes ready callback is applied to
it, that is unless the call failed, in which case the error_callback
is applied instead.
If error_callback is specified then it should be a callable which
accepts a single argument. If the target function fails, then
the error_callback is called with the exception instance.
Callbacks should complete immediately since otherwise the thread which
handles the results will get blocked.
-
imap
(func, iterable[, chunksize])
A lazier version of map()
.
The chunksize argument is the same as the one used by the map()
method. For very long iterables using a large value for chunksize can
make the job complete much faster than using the default value of
1
.
Also if chunksize is 1
then the next()
method of the iterator
returned by the imap()
method has an optional timeout parameter:
next(timeout)
will raise multiprocessing.TimeoutError
if the
result cannot be returned within timeout seconds.
-
imap_unordered
(func, iterable[, chunksize])
The same as imap()
except that the ordering of the results from the
returned iterator should be considered arbitrary. (Only when there is
only one worker process is the order guaranteed to be “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)]
.
-
starmap_async
(func, iterable[, chunksize[, callback[, error_callback]]])
A combination of starmap()
and map_async()
that iterates over
iterable of iterables and calls func with the iterables unpacked.
Returns a result object.
-
close
()
Prevents any more tasks from being submitted to the pool. Once all the
tasks have been completed the worker processes will exit.
-
terminate
()
Stops the worker processes immediately without completing outstanding
work. When the pool object is garbage collected terminate()
will be
called immediately.
-
join
()
Wait for the worker processes to exit. One must call close()
or
terminate()
before using join()
.
-
class
multiprocessing.pool.
AsyncResult
The class of the result returned by Pool.apply_async()
and
Pool.map_async()
.
-
get
([timeout])
Return the result when it arrives. If timeout is not None
and the
result does not arrive within timeout seconds then
multiprocessing.TimeoutError
is raised. If the remote call raised
an exception then that exception will be reraised by get()
.
-
wait
([timeout])
Wait until the result is available or until timeout seconds pass.
-
ready
()
Return whether the call has completed.
-
successful
()
Return whether the call completed without raising an exception. Will
raise ValueError
if the result is not ready.
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 is None
then the
family is inferred from the format of address. If address is also
None
then a default is chosen. This default is the family which is
assumed to be the fastest available. See
Address Formats. Note that if family is
'AF_UNIX'
and address is None
then the socket will be created in a
private temporary directory created using tempfile.mkstemp()
.
If the listener object uses a socket then backlog (1 by default) is passed
to the listen()
method of the socket once it has been
bound.
If authkey is given and not None, it should be a byte string and will be
used as the secret key for an HMAC-based authentication challenge. No
authentication is done if authkey is None.
AuthenticationError
is raised if authentication fails.
See Authentication keys.
-
accept
()
Accept a connection on the bound socket or named pipe of the listener
object and return a Connection
object.
If authentication is attempted and fails, then
AuthenticationError
is raised.
-
close
()
Close the bound socket or named pipe of the listener object. This is
called automatically when the listener is garbage collected. However it
is advisable to call it explicitly.
Listener objects have the following read-only properties:
-
address
The address which is being used by the Listener object.
-
last_accepted
The address from which the last accepted connection came. If this is
unavailable then it is None
.
-
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 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 equivalent
select.select(object_list, [], [], timeout)
. The difference is
that, if select.select()
is interrupted by a signal, it can
raise OSError
with an error number of EINTR
, whereas
wait()
will not.
Windows: An item in object_list must either be an integer
handle which is waitable (according to the definition used by the
documentation of the Win32 function WaitForMultipleObjects()
)
or it can be an object with a fileno()
method which returns a
socket handle or pipe handle. (Note that pipe handles and socket
handles are not waitable handles.)
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)
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
(level=None)
This function performs a call to get_logger()
but in addition to
returning the logger created by get_logger, it adds a handler which sends
output to sys.stderr
using format
'[%(levelname)s/%(processName)s] %(message)s'
.
You can modify levelname
of the logger by passing a level
argument.
Below is an example session with logging turned on:
>>> import multiprocessing, logging
>>> logger = multiprocessing.log_to_stderr()
>>> logger.setLevel(logging.INFO)
>>> logger.warning('doomed')
[WARNING/MainProcess] doomed
>>> m = multiprocessing.Manager()
[INFO/SyncManager-...] child process calling self.run()
[INFO/SyncManager-...] created temp directory /.../pymp-...
[INFO/SyncManager-...] manager serving at '/.../listener-...'
>>> del m
[INFO/MainProcess] sending shutdown message to manager
[INFO/SyncManager-...] manager exiting with exitcode 0
For a full table of logging levels, see the logging
module.
multiprocessing.dummy
replicates the API of multiprocessing
but is
no more than a wrapper around the threading
module.
In particular, the Pool
function provided by multiprocessing.dummy
returns an instance of ThreadPool
, which is a subclass of
Pool
that supports all the same method calls but uses a pool of
worker threads rather than worker processes.
-
class
multiprocessing.pool.
ThreadPool
([processes[, initializer[, initargs]]])
A thread pool object which controls a pool of worker threads to which jobs
can be submitted. ThreadPool
instances are fully interface
compatible with Pool
instances, and their resources must also be
properly managed, either by using the pool as a context manager or by
calling close()
and
terminate()
manually.
processes is the number of worker threads to use. If processes is
None
then the number returned by os.cpu_count()
is used.
If initializer is not None
then each worker process will call
initializer(*initargs)
when it starts.
Unlike Pool
, maxtasksperchild and context cannot be provided.
Note
A ThreadPool
shares the same interface as Pool
, which
is designed around a pool of processes and predates the introduction of
the concurrent.futures
module. As such, it inherits some
operations that 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
returns concurrent.futures.Future
instances that are
compatible with many other libraries, including asyncio
.
Programming guidelines
There are certain guidelines and idioms which should be adhered to when using
multiprocessing
.
All start methods
The following applies to all start methods.
Avoid shared state
As far as possible one should try to avoid shifting large amounts of data
between processes.
It is probably best to stick to using queues or pipes for communication
between processes rather than using the lower level synchronization
primitives.
Picklability
Ensure that the arguments to the methods of proxies are picklable.
Thread safety of proxies
Do not use a proxy object from more than one thread unless you protect it
with a lock.
(There is never a problem with different processes using the same proxy.)
Joining zombie processes
On 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’s Process.is_alive
will
join the process. Even so it is probably good
practice to explicitly join all the processes that you start.
Better to inherit than pickle/unpickle
When using the spawn or forkserver start methods many types
from multiprocessing
need to be picklable so that child
processes can use them. However, one should generally avoid
sending shared objects to other processes using pipes or queues.
Instead you should arrange the program so that a process which
needs access to a shared resource created elsewhere can inherit it
from an ancestor process.
Avoid terminating processes
Using the Process.terminate
method to stop a process is liable to
cause any shared resources (such as locks, semaphores, pipes and queues)
currently being used by the process to become broken or unavailable to other
processes.
Therefore it is probably best to only consider using
Process.terminate
on processes
which never use any shared resources.
Joining processes that use queues
Bear in mind that a process that has put items in a queue will wait before
terminating until all the buffered items are fed by the “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 call
close()
on this file-like object, it could result in the same
data being flushed to the object multiple times, resulting in corruption.
If you write a file-like object and implement your own caching, you can
make it fork-safe by storing the pid whenever you append to the cache,
and discarding the cache when the pid changes. For example:
@property
def cache(self):
pid = os.getpid()
if pid != self._pid:
self._pid = pid
self._cache = []
return self._cache
For more information, see bpo-5155, bpo-5313 and bpo-5331
The spawn and forkserver start methods
There are a few extra restriction which don’t apply to the fork
start method.
More picklability
Ensure that all arguments to Process.__init__()
are picklable.
Also, if you subclass Process
then make sure that
instances will be picklable when the Process.start
method is called.
Global variables
Bear in mind that if code run in a child process tries to access a global
variable, then the value it sees (if any) may not be the same as the value
in the parent process at the time that Process.start
was called.
However, global variables which are just module level constants cause no
problems.
Safe importing of main module
Make sure that the main module can be safely imported by a new Python
interpreter without causing unintended side effects (such 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()