16.6. multiprocessing
— Process-based “threading” interface¶
Novo na versão 2.6.
16.6.1. Introdução¶
multiprocessing
is a package that supports spawning processes using an
API similar to the threading
module. The multiprocessing
package
offers both local and remote concurrency, effectively side-stepping the
Global Interpreter Lock by using subprocesses instead of threads. Due
to this, the multiprocessing
module allows the programmer to fully
leverage multiple processors on a given machine. It runs on both Unix and
Windows.
The multiprocessing
module also introduces APIs which do not have
analogs in the threading
module. A prime example of this is the
Pool
object which offers a convenient means of parallelizing the
execution of a function across multiple input values, distributing the
input data across processes (data parallelism). The following example
demonstrates the common practice of defining such functions in a module so
that child processes can successfully import that module. This basic example
of data parallelism using Pool
,
from multiprocessing import Pool
def f(x):
return x*x
if __name__ == '__main__':
p = Pool(5)
print(p.map(f, [1, 2, 3]))
will print to standard output
[1, 4, 9]
16.6.1.1. A classe Process
¶
In multiprocessing
, processes are spawned by creating a Process
object and then calling its start()
method. Process
follows the API of threading.Thread
. A trivial example of a
multiprocess program is
from multiprocessing import Process
def f(name):
print 'hello', name
if __name__ == '__main__':
p = Process(target=f, args=('bob',))
p.start()
p.join()
To show the individual process IDs involved, here is an expanded example:
from multiprocessing import Process
import os
def info(title):
print title
print 'module name:', __name__
if hasattr(os, 'getppid'): # only available on Unix
print 'parent process:', os.getppid()
print 'process id:', os.getpid()
def f(name):
info('function f')
print 'hello', name
if __name__ == '__main__':
info('main line')
p = Process(target=f, args=('bob',))
p.start()
p.join()
For an explanation of why (on Windows) the if __name__ == '__main__'
part is
necessary, see Programming guidelines.
16.6.1.2. Exchanging objects between processes¶
multiprocessing
supports two types of communication channel between
processes:
Queues
The
Queue
class is a near clone ofQueue.Queue
. For example:from multiprocessing import Process, Queue def f(q): q.put([42, None, 'hello']) if __name__ == '__main__': q = Queue() p = Process(target=f, args=(q,)) p.start() print q.get() # prints "[42, None, 'hello']" p.join()Queues are thread and process safe.
Pipes
The
Pipe()
function returns a pair of connection objects connected by a pipe which by default is duplex (two-way). For example:from multiprocessing import Process, Pipe def f(conn): conn.send([42, None, 'hello']) conn.close() if __name__ == '__main__': parent_conn, child_conn = Pipe() p = Process(target=f, args=(child_conn,)) p.start() print parent_conn.recv() # prints "[42, None, 'hello']" p.join()The two connection objects returned by
Pipe()
represent the two ends of the pipe. Each connection object hassend()
andrecv()
methods (among others). Note that data in a pipe may become corrupted if two processes (or threads) try to read from or write to the same end of the pipe at the same time. Of course there is no risk of corruption from processes using different ends of the pipe at the same time.
16.6.1.3. Sincronização entre processos¶
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()
print 'hello world', i
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.
16.6.1.4. Sharing state between processes¶
As mentioned above, when doing concurrent programming it is usually best to avoid using shared state as far as possible. This is particularly true when using multiple processes.
However, if you really do need to use some shared data then
multiprocessing
provides a couple of ways of doing so.
Shared memory
Data can be stored in a shared memory map using
Value
orArray
. For example, the following codefrom multiprocessing import Process, Value, Array def f(n, a): n.value = 3.1415927 for i in range(len(a)): a[i] = -a[i] if __name__ == '__main__': num = Value('d', 0.0) arr = Array('i', range(10)) p = Process(target=f, args=(num, arr)) p.start() p.join() print num.value print arr[:]será impresso:
3.1415927 [0, -1, -2, -3, -4, -5, -6, -7, -8, -9]The
'd'
and'i'
arguments used when creatingnum
andarr
are typecodes of the kind used by thearray
module:'d'
indicates a double precision float and'i'
indicates a signed integer. These shared objects will be process and thread-safe.For more flexibility in using shared memory one can use the
multiprocessing.sharedctypes
module which supports the creation of arbitrary ctypes objects allocated from shared memory.
Processo do Servidor
A manager object returned by
Manager()
controls a server process which holds Python objects and allows other processes to manipulate them using proxies.A manager returned by
Manager()
will support typeslist
,dict
,Namespace
,Lock
,RLock
,Semaphore
,BoundedSemaphore
,Condition
,Event
,Queue
,Value
andArray
. For example,from multiprocessing import Process, Manager def f(d, l): d[1] = '1' d['2'] = 2 d[0.25] = None l.reverse() if __name__ == '__main__': manager = Manager() d = manager.dict() l = manager.list(range(10)) p = Process(target=f, args=(d, l)) p.start() p.join() print d print lserá impresso:
{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.
16.6.1.5. 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.
Por exemplo:
from multiprocessing import Pool, TimeoutError
import time
import os
def f(x):
return x*x
if __name__ == '__main__':
pool = Pool(processes=4) # start 4 worker processes
# 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"
Note that the methods of a pool should only ever be used by the process which created it.
Nota
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 Pool
examples will not work in the interactive interpreter.
For example:
>>> from multiprocessing import Pool
>>> p = Pool(5)
>>> def f(x):
... return x*x
...
>>> 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 master process somehow.)
16.6.2. Referência¶
The multiprocessing
package mostly replicates the API of the
threading
module.
16.6.2.1. Process
and exceptions¶
-
class
multiprocessing.
Process
(group=None, target=None, name=None, args=(), kwargs={})¶ Process objects represent activity that is run in a separate process. The
Process
class has equivalents of all the methods ofthreading.Thread
.The constructor should always be called with keyword arguments. group should always be
None
; it exists solely for compatibility withthreading.Thread
. target is the callable object to be invoked by therun()
method. It defaults toNone
, meaning nothing is called. name is the process name. By default, a unique name is constructed of the form ‘Process-N1:N2:…:Nk’ where N1,N2,…,Nk is a sequence of integers whose length is determined by the generation of the process. args is the argument tuple for the target invocation. kwargs is a dictionary of keyword arguments for the target invocation. 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.-
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])¶ Block the calling thread until the process whose
join()
method is called terminates or until the optional timeout occurs.If timeout is
None
then there is no timeout.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.
-
is_alive
()¶ Return whether the process is alive.
Roughly, a process object is alive from the moment the
start()
method returns until the child process terminates.
-
daemon
¶ The process’s daemon flag, a Boolean value. This must be set before
start()
is called.The initial value is inherited from the creating process.
When a process exits, it attempts to terminate all of its daemonic child processes.
Note that a daemonic process is not allowed to create child processes. Otherwise a daemonic process would leave its children orphaned if it gets terminated when its parent process exits. Additionally, these are not Unix daemons or services, they are normal processes that will be terminated (and not joined) if non-daemonic processes have exited.
In addition to the
threading.Thread
API,Process
objects also support the following attributes and methods:-
pid
¶ Return the process ID. Before the process is spawned, this will be
None
.
-
exitcode
¶ The child’s exit code. This will be
None
if the process has not yet terminated. A negative value -N indicates that the child was terminated by signal N.
-
authkey
¶ The process’s authentication key (a byte string).
When
multiprocessing
is initialized the main process is assigned a random string usingos.urandom()
.When a
Process
object is created, it will inherit the authentication key of its parent process, although this may be changed by settingauthkey
to another byte string.See Authentication keys.
-
terminate
()¶ Terminate the process. On Unix this is done using the
SIGTERM
signal; on WindowsTerminateProcess()
is used. Note that exit handlers and finally clauses, etc., will not be executed.Note that descendant processes of the process will not be terminated – they will simply become orphaned.
Aviso
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.
Note that the
start()
,join()
,is_alive()
,terminate()
andexitcode
methods should only be called by the process that created the process object.Example usage of some of the methods of
Process
:>>> import multiprocessing, time, signal >>> p = multiprocessing.Process(target=time.sleep, args=(1000,)) >>> print p, p.is_alive() <Process(Process-1, initial)> False >>> p.start() >>> print p, p.is_alive() <Process(Process-1, started)> True >>> p.terminate() >>> time.sleep(0.1) >>> print p, p.is_alive() <Process(Process-1, stopped[SIGTERM])> False >>> p.exitcode == -signal.SIGTERM True
-
-
exception
multiprocessing.
BufferTooShort
¶ Exception raised by
Connection.recv_bytes_into()
when the supplied buffer object is too small for the message read.If
e
is an instance ofBufferTooShort
thene.args[0]
will give the message as a byte string.
16.6.2.2. 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
, multiprocessing.queues.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 Gerenciadores.
Nota
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
.
Nota
When an object is put on a queue, the object is pickled and a background thread later flushes the pickled data to an underlying pipe. This has some consequences which are a little surprising, but should not cause any practical difficulties – if they really bother you then you can instead use a queue created with a manager.
After putting an object on an empty queue there may be an infinitesimal delay before the queue’s
empty()
method returnsFalse
andget_nowait()
can return without raisingQueue.Empty
.If multiple processes are enqueuing objects, it is possible for the objects to be received at the other end out-of-order. However, objects enqueued by the same process will always be in the expected order with respect to each other.
Aviso
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.
Aviso
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 Exemplos.
-
multiprocessing.
Pipe
([duplex])¶ Returns a pair
(conn1, conn2)
ofConnection
objects representing the ends of a pipe.If duplex is
True
(the default) then the pipe is bidirectional. If duplex isFalse
then the pipe is unidirectional:conn1
can only be used for receiving messages andconn2
can only be used for sending messages.
-
class
multiprocessing.
Queue
([maxsize])¶ Returns a process shared queue implemented using a pipe and a few locks/semaphores. When a process first puts an item on the queue a feeder thread is started which transfers objects from a buffer into the pipe.
The usual
Queue.Empty
andQueue.Full
exceptions from the standard library’sQueue
module are raised to signal timeouts.Queue
implements all the methods ofQueue.Queue
except fortask_done()
andjoin()
.-
qsize
()¶ Return the approximate size of the queue. Because of multithreading/multiprocessing semantics, this number is not reliable.
Note that this may raise
NotImplementedError
on Unix platforms like Mac OS X wheresem_getvalue()
is not implemented.
-
empty
()¶ Return
True
if the queue is empty,False
otherwise. Because of multithreading/multiprocessing semantics, this is not reliable.
-
full
()¶ Return
True
if the queue is full,False
otherwise. Because of multithreading/multiprocessing semantics, this is not reliable.
-
put
(obj[, block[, timeout]])¶ Put obj into the queue. If the optional argument block is
True
(the default) and timeout isNone
(the default), block if necessary until a free slot is available. If timeout is a positive number, it blocks at most timeout seconds and raises theQueue.Full
exception if no free slot was available within that time. Otherwise (block isFalse
), put an item on the queue if a free slot is immediately available, else raise theQueue.Full
exception (timeout is ignored in that case).
-
put_nowait
(obj)¶ Equivalent to
put(obj, False)
.
-
get
([block[, timeout]])¶ Remove and return an item from the queue. If optional args block is
True
(the default) and timeout isNone
(the default), block if necessary until an item is available. If timeout is a positive number, it blocks at most timeout seconds and raises theQueue.Empty
exception if no item was available within that time. Otherwise (block isFalse
), return an item if one is immediately available, else raise theQueue.Empty
exception (timeout is ignored in that case).
-
get_nowait
()¶ Equivalent to
get(False)
.
Queue
has a few additional methods not found inQueue.Queue
. These methods are usually unnecessary for most code:-
close
()¶ Indicate that no more data will be put on this queue by the current process. The background thread will quit once it has flushed all buffered data to the pipe. This is called automatically when the queue is garbage collected.
-
join_thread
()¶ Join the background thread. This can only be used after
close()
has been called. It blocks until the background thread exits, ensuring that all data in the buffer has been flushed to the pipe.By default if a process is not the creator of the queue then on exit it will attempt to join the queue’s background thread. The process can call
cancel_join_thread()
to makejoin_thread()
do nothing.
-
cancel_join_thread
()¶ Prevent
join_thread()
from blocking. In particular, this prevents the background thread from being joined automatically when the process exits – seejoin_thread()
.A better name for this method might be
allow_exit_without_flush()
. It is likely to cause enqueued data to lost, and you almost certainly will not need to use it. It is really only there if you need the current process to exit immediately without waiting to flush enqueued data to the underlying pipe, and you don’t care about lost data.
Nota
This class’s functionality requires a functioning shared semaphore implementation on the host operating system. Without one, the functionality in this class will be disabled, and attempts to instantiate a
Queue
will result in anImportError
. See bpo-3770 for additional information. The same holds true for any of the specialized queue types listed below.-
-
class
multiprocessing.queues.
SimpleQueue
¶ It is a simplified
Queue
type, very close to a lockedPipe
.-
empty
()¶ Return
True
if the queue is empty,False
otherwise.
-
get
()¶ Remove and return an item from the queue.
-
put
(item)¶ Put item into the queue.
-
-
class
multiprocessing.
JoinableQueue
([maxsize])¶ JoinableQueue
, aQueue
subclass, is a queue which additionally hastask_done()
andjoin()
methods.-
task_done
()¶ Indicate that a formerly enqueued task is complete. Used by queue consumer threads. For each
get()
used to fetch a task, a subsequent call totask_done()
tells the queue that the processing on the task is complete.If a
join()
is currently blocking, it will resume when all items have been processed (meaning that atask_done()
call was received for every item that had beenput()
into the queue).Raises a
ValueError
if called more times than there were items placed in the queue.
-
join
()¶ Block until all items in the queue have been gotten and processed.
The count of unfinished tasks goes up whenever an item is added to the queue. The count goes down whenever a consumer thread 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.
-
16.6.2.3. Diversos¶
-
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. May raise
NotImplementedError
.
-
multiprocessing.
current_process
()¶ Return the
Process
object corresponding to the current process.An analogue of
threading.current_thread()
.
-
multiprocessing.
freeze_support
()¶ Add support for when a program which uses
multiprocessing
has been frozen to produce a Windows executable. (Has been tested with py2exe, PyInstaller and cx_Freeze.)One needs to call this function straight after the
if __name__ == '__main__'
line of the main module. For example:from multiprocessing import Process, freeze_support def f(): print 'hello world!' if __name__ == '__main__': freeze_support() Process(target=f).start()
If the
freeze_support()
line is omitted then trying to run the frozen executable will raiseRuntimeError
.Calling
freeze_support()
has no effect when invoked on any operating system other than Windows. In addition, if the module is being run normally by the Python interpreter on Windows (the program has not been frozen), thenfreeze_support()
has no effect.
-
multiprocessing.
set_executable
()¶ Sets the path of the Python interpreter to use when starting a child process. (By default
sys.executable
is used). Embedders will probably need to do some thing likeset_executable(os.path.join(sys.exec_prefix, 'pythonw.exe'))
before they can create child processes. (Windows only)
Nota
multiprocessing
contains no analogues of
threading.active_count()
, threading.enumerate()
,
threading.settrace()
, threading.setprofile()
,
threading.Timer
, or threading.local
.
16.6.2.4. Connection Objects¶
Connection objects allow the sending and receiving of picklable objects or strings. They can be thought of as message oriented connected sockets.
Connection objects are usually created using
Pipe
– see also
Listeners and Clients.
-
class
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 MB+, though it depends on the OS) may raise a
ValueError
exception.
-
recv
()¶ Return an object sent from the other end of the connection using
send()
. Blocks until there is something to receive. RaisesEOFError
if there is nothing left to receive and the other end was closed.
-
fileno
()¶ Return the file descriptor or handle used by the connection.
-
close
()¶ Close the connection.
This is called automatically when the connection is garbage collected.
-
poll
([timeout])¶ Return whether there is any data available to be read.
If timeout is not specified then it will return immediately. If timeout is a number then this specifies the maximum time in seconds to block. If timeout is
None
then an infinite timeout is used.
-
send_bytes
(buffer[, offset[, size]])¶ Send byte data from an object supporting the buffer interface 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 MB+, 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
IOError
is raised and the connection will no longer be readable.
-
recv_bytes_into
(buffer[, offset])¶ Read into buffer a complete message of byte data sent from the other end of the connection and return the number of bytes in the message. Blocks until there is something to receive. Raises
EOFError
if there is nothing left to receive and the other end was closed.buffer must be an object satisfying the writable buffer interface. If offset is given then the message will be written into the buffer from that position. Offset must be a non-negative integer less than the length of buffer (in bytes).
If the buffer is too short then a
BufferTooShort
exception is raised and the complete message is available ase.args[0]
wheree
is the exception instance.
-
Por exemplo:
>>> from multiprocessing import Pipe
>>> a, b = Pipe()
>>> a.send([1, 'hello', None])
>>> b.recv()
[1, 'hello', None]
>>> b.send_bytes('thank you')
>>> a.recv_bytes()
'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])
Aviso
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.
Aviso
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.
16.6.2.5. 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 Gerenciadores.
-
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 and it supports an optional second argument timeout, as is consistent withLock.acquire()
.Nota
On Mac OS X, this is indistinguishable from
Semaphore
becausesem_getvalue()
is not implemented on that platform.
-
class
multiprocessing.
Condition
([lock])¶ A condition variable: a clone of
threading.Condition
.If lock is specified then it should be a
Lock
orRLock
object frommultiprocessing
.
-
class
multiprocessing.
Event
¶ A clone of
threading.Event
. This method returns the state of the internal semaphore on exit, so it will always returnTrue
except if a timeout is given and the operation times out.Alterado na versão 2.7: Previously, the method always returned
None
.
-
class
multiprocessing.
Lock
¶ A non-recursive lock object: a close analog of
threading.Lock
. Once a process or thread has acquired a lock, subsequent attempts to acquire it from any process or thread will block until it is released; any process or thread may release it. The concepts and behaviors ofthreading.Lock
as it applies to threads are replicated here inmultiprocessing.Lock
as it applies to either processes or threads, except as noted.Note that
Lock
is actually a factory function which returns an instance ofmultiprocessing.synchronize.Lock
initialized with a default context.Lock
supports the context manager protocol and thus may be used inwith
statements.-
acquire
(block=True, timeout=None)¶ Acquire a lock, blocking or non-blocking.
With the block argument set to
True
(the default), the method call will block until the lock is in an unlocked state, then set it to locked and returnTrue
. Note that the name of this first argument differs from that inthreading.Lock.acquire()
.With the block argument set to
False
, the method call does not block. If the lock is currently in a locked state, returnFalse
; otherwise set the lock to a locked state and returnTrue
.When invoked with a positive, floating-point value for timeout, block for at most the number of seconds specified by timeout as long as the lock can not be acquired. Invocations with a negative value for timeout are equivalent to a timeout of zero. Invocations with a timeout value of
None
(the default) set the timeout period to infinite. The timeout argument has no practical implications if the block argument is set toFalse
and is thus ignored. ReturnsTrue
if the lock has been acquired orFalse
if the timeout period has elapsed. Note that the timeout argument does not exist in this method’s analog,threading.Lock.acquire()
.
-
release
()¶ Release a lock. This can be called from any process or thread, not only the process or thread which originally acquired the lock.
Behavior is the same as in
threading.Lock.release()
except that when invoked on an unlocked lock, aValueError
is raised.
-
-
class
multiprocessing.
RLock
¶ A recursive lock object: a close analog of
threading.RLock
. A recursive lock must be released by the process or thread that acquired it. Once a process or thread has acquired a recursive lock, the same process or thread may acquire it again without blocking; that process or thread must release it once for each time it has been acquired.Note that
RLock
is actually a factory function which returns an instance ofmultiprocessing.synchronize.RLock
initialized with a default context.RLock
supports the context manager protocol and thus may be used inwith
statements.-
acquire
(block=True, timeout=None)¶ Acquire a lock, blocking or non-blocking.
When invoked with the block argument set to
True
, block until the lock is in an unlocked state (not owned by any process or thread) unless the lock is already owned by the current process or thread. The current process or thread then takes ownership of the lock (if it does not already have ownership) and the recursion level inside the lock increments by one, resulting in a return value ofTrue
. Note that there are several differences in this first argument’s behavior compared to the implementation ofthreading.RLock.acquire()
, starting with the name of the argument itself.When invoked with the block argument set to
False
, do not block. If the lock has already been acquired (and thus is owned) by another process or thread, the current process or thread does not take ownership and the recursion level within the lock is not changed, resulting in a return value ofFalse
. If the lock is in an unlocked state, the current process or thread takes ownership and the recursion level is incremented, resulting in a return value ofTrue
.Use and behaviors of the timeout argument are the same as in
Lock.acquire()
. Note that the timeout argument does not exist in this method’s analog,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 inthreading.RLock.release()
.
-
-
class
multiprocessing.
Semaphore
([value])¶ A semaphore object: a close analog of
threading.Semaphore
.A solitary difference from its close analog exists: its
acquire
method’s first argument is named block and it supports an optional second argument timeout, as is consistent withLock.acquire()
.
Nota
The acquire()
method of BoundedSemaphore
, Lock
,
RLock
and Semaphore
has a timeout parameter not supported
by the equivalents in threading
. The signature is
acquire(block=True, timeout=None)
with keyword parameters being
acceptable. If block is True
and timeout is not None
then it
specifies a timeout in seconds. If block is False
then timeout is
ignored.
On Mac OS X, sem_timedwait
is unsupported, so calling acquire()
with
a timeout will emulate that function’s behavior using a sleeping loop.
Nota
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.
Nota
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.
16.6.2.7. Gerenciadores¶
Managers provide a way to create data which can be shared between different processes. A manager object controls a server process which manages shared objects. Other processes can access the shared objects by using proxies.
Returns a started
SyncManager
object which can be used for sharing objects between processes. The returned manager object corresponds to a spawned child process and has methods which will create shared objects and return corresponding proxies.
Manager processes will be shutdown as soon as they are garbage collected or
their parent process exits. The manager classes are defined in the
multiprocessing.managers
module:
-
class
multiprocessing.managers.
BaseManager
([address[, authkey]])¶ Criando um objeto BaseManager.
Once created one should call
start()
orget_server().serve_forever()
to ensure that the manager object refers to a started manager process.address is the address on which the manager process listens for new connections. If address is
None
then an arbitrary one is chosen.authkey is the authentication key which will be used to check the validity of incoming connections to the server process. If authkey is
None
thencurrent_process().authkey
. Otherwise authkey is used and it must be a string.-
start
([initializer[, initargs]])¶ Start a subprocess to start the manager. If initializer is not
None
then the subprocess will callinitializer(*initargs)
when it starts.
-
get_server
()¶ Returns a
Server
object which represents the actual server under the control of the Manager. TheServer
object supports theserve_forever()
method:>>> from multiprocessing.managers import BaseManager >>> manager = BaseManager(address=('', 50000), authkey='abc') >>> server = manager.get_server() >>> server.serve_forever()
Server
additionally has anaddress
attribute.
-
connect
()¶ Connect a local manager object to a remote manager process:
>>> from multiprocessing.managers import BaseManager >>> m = BaseManager(address=('127.0.0.1', 5000), authkey='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 created using the
from_address()
classmethod or if the create_method argument isFalse
then this can be left asNone
.proxytype is a subclass of
BaseProxy
which is used to create proxies for shared objects with this typeid. IfNone
then a proxy class is created automatically.exposed is used to specify a sequence of method names which proxies for this typeid should be allowed to access using
BaseProxy._callmethod()
. (If exposed isNone
thenproxytype._exposed_
is used instead if it exists.) In the case where no exposed list is specified, all “public methods” of the shared object will be accessible. (Here a “public method” means any attribute which has a__call__()
method and whose name does not begin with'_'
.)method_to_typeid is a mapping used to specify the return type of those exposed methods which should return a proxy. It maps method names to typeid strings. (If method_to_typeid is
None
thenproxytype._method_to_typeid_
is used instead if it exists.) If a method’s name is not a key of this mapping or if the mapping isNone
then the object returned by the method will be copied by value.create_method determines whether a method should be created with name typeid which can be used to tell the server process to create a new shared object and return a proxy for it. By default it is
True
.
BaseManager
instances also have one read-only property:-
address
¶ The address used by the manager.
-
-
class
multiprocessing.managers.
SyncManager
¶ A subclass of
BaseManager
which can be used for the synchronization of processes. Objects of this type are returned bymultiprocessing.Manager()
.It also supports creation of shared lists and dictionaries.
-
BoundedSemaphore
([value])¶ Create a shared
threading.BoundedSemaphore
object and return a proxy for it.
-
Condition
([lock])¶ Create a shared
threading.Condition
object and return a proxy for it.If lock is supplied then it should be a proxy for a
threading.Lock
orthreading.RLock
object.
-
Event
()¶ Create a shared
threading.Event
object and return a proxy for it.
-
Lock
()¶ Create a shared
threading.Lock
object and return a proxy for it.
-
Queue
([maxsize])¶ Create a shared
Queue.Queue
object and return a proxy for it.
-
RLock
()¶ Create a shared
threading.RLock
object and return a proxy for it.
-
Semaphore
([value])¶ Create a shared
threading.Semaphore
object and return a proxy for it.
-
Array
(typecode, sequence)¶ Create an array and return a proxy for it.
-
Value
(typecode, value)¶ Create an object with a writable
value
attribute and return a proxy for it.
-
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.
Nota
Modifications to mutable values or items in dict and list proxies will not be propagated through the manager, because the proxy has no way of knowing when its values or items are modified. To modify such an item, you can re-assign the modified object 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 # reassigning the dictionary, the proxy is notified of the change lproxy[0] = d
-
-
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')
16.6.2.7.1. 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(object):
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__':
manager = MyManager()
manager.start()
maths = manager.Maths()
print maths.add(4, 3) # prints 7
print maths.mul(7, 8) # prints 56
16.6.2.7.2. 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
>>> import Queue
>>> queue = Queue.Queue()
>>> class QueueManager(BaseManager): pass
>>> QueueManager.register('get_queue', callable=lambda:queue)
>>> m = QueueManager(address=('', 50000), authkey='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='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='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(Worker, self).__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='abracadabra')
>>> s = m.get_server()
>>> s.serve_forever()
16.6.2.8. 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). A proxy can usually be used in most of the same ways that 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. Note, however, that if a proxy is sent to the corresponding manager’s process then unpickling it will produce the referent itself. This means, for example, that one shared object can contain a second:
>>> a = manager.list()
>>> b = manager.list()
>>> a.append(b) # referent of a now contains referent of b
>>> print a, b
[[]] []
>>> b.append('hello')
>>> print a, b
[['hello']] ['hello']
Nota
The proxy types in multiprocessing
do nothing to support comparisons
by value. So, for instance, we have:
>>> manager.list([1,2,3]) == [1,2,3]
False
One should just use a copy of the referent instead when making comparisons.
-
class
multiprocessing.managers.
BaseProxy
¶ Proxy objects are instances of subclasses of
BaseProxy
.-
_callmethod
(methodname[, args[, kwds]])¶ Call and return the result of a method of the proxy’s referent.
If
proxy
is a proxy whose referent isobj
then the expressionproxy._callmethod(methodname, args, kwds)
will evaluate the expression
getattr(obj, methodname)(*args, **kwds)
in the manager’s process.
The returned value will be a copy of the result of the call or a proxy to a new shared object – see documentation for the method_to_typeid argument of
BaseManager.register()
.If an exception is raised by the call, then is re-raised by
_callmethod()
. If some other exception is raised in the manager’s process then this is converted into aRemoteError
exception and is raised by_callmethod()
.Note in particular that an exception will be raised if methodname has not been exposed.
An example of the usage of
_callmethod()
:>>> l = manager.list(range(10)) >>> l._callmethod('__len__') 10 >>> l._callmethod('__getslice__', (2, 7)) # equiv to `l[2:7]` [2, 3, 4, 5, 6] >>> l._callmethod('__getitem__', (20,)) # equiv 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.
-
16.6.2.8.1. 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.
16.6.2.9. Process Pools¶
One can create a pool of processes which will carry out tasks submitted to it
with the Pool
class.
-
class
multiprocessing.
Pool
([processes[, initializer[, initargs[, maxtasksperchild]]]])¶ 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 bycpu_count()
is used. If initializer is notNone
then each worker process will callinitializer(*initargs)
when it starts.Note that the methods of the pool object should only be called by the process which created the pool.
Novo na versão 2.7: 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.Nota
Worker processes within a
Pool
typically live for the complete duration of the Pool’s work queue. A frequent pattern found in other systems (such as Apache, mod_wsgi, etc) to free resources held by workers is to allow a worker within a pool to complete only a set amount of work before being exiting, being cleaned up and a new process spawned to replace the old one. The maxtasksperchild argument to thePool
exposes this ability to the end user.-
apply
(func[, args[, kwds]])¶ Equivalent of the
apply()
built-in function. It blocks until the result is ready, soapply_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]]])¶ A variant of the
apply()
method which returns a result 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 (unless the call failed). callback 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). 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.
-
map_async
(func, iterable[, chunksize[, callback]])¶ A variant of the
map()
method which returns a result 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 (unless the call failed). callback should complete immediately since otherwise the thread which handles the results will get blocked.
-
imap
(func, iterable[, chunksize])¶ An equivalent of
itertools.imap()
.The chunksize argument is the same as the one used by the
map()
method. For very long iterables using a large value for chunksize can make the job complete much faster than using the default value of1
.Also if chunksize is
1
then thenext()
method of the iterator returned by theimap()
method has an optional timeout parameter:next(timeout)
will raisemultiprocessing.TimeoutError
if the result cannot be returned within timeout seconds.
-
imap_unordered
(func, iterable[, chunksize])¶ The same as
imap()
except that the ordering of the results from the returned iterator should be considered arbitrary. (Only when there is only one worker process is the order guaranteed to be “correct”.)
-
close
()¶ Prevents any more tasks from being submitted to the pool. Once all the tasks have been completed the worker processes will exit.
-
terminate
()¶ Stops the worker processes immediately without completing outstanding work. When the pool object is garbage collected
terminate()
will be called immediately.
-
join
()¶ Wait for the worker processes to exit. One must call
close()
orterminate()
before usingjoin()
.
-
-
class
multiprocessing.pool.
AsyncResult
¶ The class of the result returned by
Pool.apply_async()
andPool.map_async()
.-
get
([timeout])¶ Return the result when it arrives. If timeout is not
None
and the result does not arrive within timeout seconds thenmultiprocessing.TimeoutError
is raised. If the remote call raised an exception then that exception will be reraised byget()
.
-
wait
([timeout])¶ Wait until the result is available or until timeout seconds pass.
-
ready
()¶ Return whether the call has completed.
-
successful
()¶ Return whether the call completed without raising an exception. Will raise
AssertionError
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__':
pool = Pool(processes=4) # 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 it.next() # prints "0"
print it.next() # 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
16.6.2.10. 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, and also has support for digest
authentication using the hmac
module.
-
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[, authenticate[, 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 authenticate is
True
or authkey is a string then digest authentication is used. The key used for authentication will be either authkey orcurrent_process().authkey)
if authkey isNone
. If authentication fails thenAuthenticationError
is raised. See Authentication keys.
-
class
multiprocessing.connection.
Listener
([address[, family[, backlog[, authenticate[, 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.
Nota
If an address of ‘0.0.0.0’ is used, the address will not be a connectable end point on Windows. If you require a connectable end-point, you should use ‘127.0.0.1’.
family is the type of socket (or named pipe) to use. This can be one of the strings
'AF_INET'
(for a TCP socket),'AF_UNIX'
(for a Unix domain socket) or'AF_PIPE'
(for a Windows named pipe). Of these only the first is guaranteed to be available. If family isNone
then the family is inferred from the format of address. If address is alsoNone
then a default is chosen. This default is the family which is assumed to be the fastest available. See Address Formats. Note that if family is'AF_UNIX'
and address isNone
then the socket will be created in a private temporary directory created usingtempfile.mkstemp()
.If the listener object uses a socket then backlog (1 by default) is passed to the
listen()
method of the socket once it has been bound.If authenticate is
True
(False
by default) or authkey is notNone
then digest authentication is used.If authkey is a string then it will be used as the authentication key; otherwise it must be
None
.If authkey is
None
and authenticate isTrue
thencurrent_process().authkey
is used as the authentication key. If authkey isNone
and authenticate isFalse
then no authentication is done. If authentication fails thenAuthenticationError
is raised. See Authentication keys.-
accept
()¶ Accept a connection on the bound socket or named pipe of the listener object and return a
Connection
object. If authentication is attempted and fails, thenAuthenticationError
is raised.
-
close
()¶ Close the bound socket or named pipe of the listener object. This is called automatically when the listener is garbage collected. However it is advisable to call it explicitly.
Listener objects have the following read-only properties:
-
address
¶ The address which is being used by the Listener object.
-
last_accepted
¶ The address from which the last accepted connection came. If this is unavailable then it is
None
.
-
The module defines the following exceptions:
-
exception
multiprocessing.connection.
ProcessError
¶ The base class of all
multiprocessing
exceptions.
-
exception
multiprocessing.connection.
BufferTooShort
¶ Exception raised by
Connection.recv_bytes_into()
when the supplied buffer object is too small for the message read.
-
exception
multiprocessing.connection.
AuthenticationError
¶ Raised when there is an authentication error.
-
exception
multiprocessing.connection.
TimeoutError
¶ Raised by methods with a timeout when the timeout expires.
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'
listener = Listener(address, authkey='secret password')
conn = listener.accept()
print 'connection accepted from', listener.last_accepted
conn.send([2.25, None, 'junk', float])
conn.send_bytes('hello')
conn.send_bytes(array('i', [42, 1729]))
conn.close()
listener.close()
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)
conn = Client(address, authkey='secret password')
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])
conn.close()
16.6.2.10.1. Address Formats¶
Um endereço
'AF_INET'
é uma tupla na forma de(hostname, port)
sendo hostname uma string e port um inteiro.An
'AF_UNIX'
address is a string representing a filename on the filesystem.- An
'AF_PIPE'
address is a string of the form r'\.\pipe{PipeName}'
. To useClient()
to connect to a named pipe on a remote computer called ServerName one should use an address of the formr'\ServerName\pipe{PipeName}'
instead.
- An
Note that any string beginning with two backslashes is assumed by default to be
an 'AF_PIPE'
address rather than an 'AF_UNIX'
address.
16.6.2.11. 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 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()
.
16.6.2.12. Gerando logs¶
Some support for logging is available. Note, however, that the logging
package does not use process shared locks so it is possible (depending on the
handler type) for messages from different processes to get mixed up.
-
multiprocessing.
get_logger
()¶ Returns the logger used by
multiprocessing
. If necessary, a new one will be created.When first created the logger has level
logging.NOTSET
and no default handler. Messages sent to this logger will not by default propagate to the root logger.Note that on Windows child processes will only inherit the level of the parent process’s logger – any other customization of the logger will not be inherited.
-
multiprocessing.
log_to_stderr
()¶ This function performs a call to
get_logger()
but in addition to returning the logger created by get_logger, it adds a handler which sends output tosys.stderr
using format'[%(levelname)s/%(processName)s] %(message)s'
.
Below is an example session with logging turned on:
>>> import multiprocessing, logging
>>> logger = multiprocessing.log_to_stderr()
>>> logger.setLevel(logging.INFO)
>>> logger.warning('doomed')
[WARNING/MainProcess] doomed
>>> m = multiprocessing.Manager()
[INFO/SyncManager-...] child process calling self.run()
[INFO/SyncManager-...] created temp directory /.../pymp-...
[INFO/SyncManager-...] manager serving at '/.../listener-...'
>>> del m
[INFO/MainProcess] sending shutdown message to manager
[INFO/SyncManager-...] manager exiting with exitcode 0
In addition to having these two logging functions, the multiprocessing also
exposes two additional logging level attributes. These are SUBWARNING
and SUBDEBUG
. The table below illustrates where theses fit in the
normal level hierarchy.
Nível |
Valor numérico |
---|---|
|
25 |
|
5 |
For a full table of logging levels, see the logging
module.
These additional logging levels are used primarily for certain debug messages
within the multiprocessing module. Below is the same example as above, except
with SUBDEBUG
enabled:
>>> import multiprocessing, logging
>>> logger = multiprocessing.log_to_stderr()
>>> logger.setLevel(multiprocessing.SUBDEBUG)
>>> 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 '/.../pymp-djGBXN/listener-...'
>>> del m
[SUBDEBUG/MainProcess] finalizer calling ...
[INFO/MainProcess] sending shutdown message to manager
[DEBUG/SyncManager-...] manager received shutdown message
[SUBDEBUG/SyncManager-...] calling <Finalize object, callback=unlink, ...
[SUBDEBUG/SyncManager-...] finalizer calling <built-in function unlink> ...
[SUBDEBUG/SyncManager-...] calling <Finalize object, dead>
[SUBDEBUG/SyncManager-...] finalizer calling <function rmtree at 0x5aa730> ...
[INFO/SyncManager-...] manager exiting with exitcode 0
16.6.2.13. The multiprocessing.dummy
module¶
multiprocessing.dummy
replicates the API of multiprocessing
but is
no more than a wrapper around the threading
module.
16.6.3. Programming guidelines¶
There are certain guidelines and idioms which should be adhered to when using
multiprocessing
.
16.6.3.1. All platforms¶
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 from the
threading
module.
Picklability
Ensure that the arguments to the methods of proxies are picklable.
Thread safety of proxies
Do not use a proxy object from more than one thread unless you protect it with a lock.
(There is never a problem with different processes using the same proxy.)
Joining zombie processes
On Unix when a process finishes but has not been joined it becomes a zombie. There should never be very many because each time a new process starts (or
active_children()
is called) all completed processes which have not yet been joined will be joined. Also calling a finished process’sProcess.is_alive
will join the process. Even so it is probably good practice to explicitly join all the processes that you start.
Better to inherit than pickle/unpickle
On Windows 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
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 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 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.devnull)Which solves the fundamental issue of processes colliding with each other resulting in a bad file descriptor error, but introduces a potential danger to applications which replace
sys.stdin()
with a “file-like object” with output buffering. This danger is that if multiple processes callclose()
on this file-like object, it could result in the same data being flushed to the object multiple times, resulting in corruption.If you write a file-like object and implement your own caching, you can make it fork-safe by storing the pid whenever you append to the cache, and discarding the cache when the pid changes. For example:
@property def cache(self): pid = os.getpid() if pid != self._pid: self._pid = pid self._cache = [] return self._cache
16.6.3.2. Windows¶
Since Windows lacks os.fork()
it has a few extra restrictions:
More picklability
Ensure that all arguments to
Process.__init__()
are picklable. This means, in particular, that bound or unbound methods cannot be used directly as thetarget
argument on Windows — just define a function and use that instead.Also, if you subclass
Process
then make sure that instances will be picklable when theProcess.start
method is called.
Global variables
Bear in mind that if code run in a child process tries to access a global variable, then the value it sees (if any) may not be the same as the value in the parent process at the time that
Process.start
was called.However, global variables which are just module level constants cause no problems.
Safe importing of main module
Make sure that the main module can be safely imported by a new Python interpreter without causing unintended side effects (such a starting a new process).
For example, under Windows 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 def foo(): print 'hello' if __name__ == '__main__': freeze_support() 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.
16.6.4. Exemplos¶
Demonstration of how to create and use customized managers and proxies:
#
# This module shows how to use arbitrary callables with a subclass of
# `BaseManager`.
#
# Copyright (c) 2006-2008, R Oudkerk
# All rights reserved.
#
from multiprocessing import freeze_support
from multiprocessing.managers import BaseManager, BaseProxy
import operator
##
class Foo(object):
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 xrange(10):
yield i*i
# Proxy type for generator objects
class GeneratorProxy(BaseProxy):
_exposed_ = ('next', '__next__')
def __iter__(self):
return self
def next(self):
return self._callmethod('next')
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,
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.getslice(range(10), 2, 6) =', op.getslice(range(10), 2, 6)
print 'op.repeat(range(5), 3) =', op.repeat(range(5), 3)
print 'op._exposed_ =', op._exposed_
##
if __name__ == '__main__':
freeze_support()
test()
Using Pool
:
#
# A test of `multiprocessing.Pool` class
#
# Copyright (c) 2006-2008, R Oudkerk
# All rights reserved.
#
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():
print 'cpu_count() = %d\n' % multiprocessing.cpu_count()
#
# Create pool
#
PROCESSES = 4
print 'Creating pool with %d processes\n' % PROCESSES
pool = multiprocessing.Pool(PROCESSES)
print 'pool = %s' % pool
print
#
# 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
#
# Simple benchmarks
#
N = 100000
print 'def pow3(x): return x**3'
t = time.time()
A = map(pow3, xrange(N))
print '\tmap(pow3, xrange(%d)):\n\t\t%s seconds' % \
(N, time.time() - t)
t = time.time()
B = pool.map(pow3, xrange(N))
print '\tpool.map(pow3, xrange(%d)):\n\t\t%s seconds' % \
(N, time.time() - t)
t = time.time()
C = list(pool.imap(pow3, xrange(N), chunksize=N//8))
print '\tlist(pool.imap(pow3, xrange(%d), chunksize=%d)):\n\t\t%s' \
' seconds' % (N, N//8, time.time() - t)
assert A == B == C, (len(A), len(B), len(C))
print
L = [None] * 1000000
print 'def noop(x): pass'
print 'L = [None] * 1000000'
t = time.time()
A = map(noop, L)
print '\tmap(noop, L):\n\t\t%s seconds' % \
(time.time() - t)
t = time.time()
B = pool.map(noop, L)
print '\tpool.map(noop, L):\n\t\t%s seconds' % \
(time.time() - t)
t = time.time()
C = list(pool.imap(noop, L, chunksize=len(L)//8))
print '\tlist(pool.imap(noop, L, chunksize=%d)):\n\t\t%s seconds' % \
(len(L)//8, time.time() - t)
assert A == B == C, (len(A), len(B), len(C))
print
del A, B, C, L
#
# 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, range(10))
except ZeroDivisionError:
print '\tGot ZeroDivisionError as expected from pool.map()'
else:
raise AssertionError('expected ZeroDivisionError')
try:
print list(pool.imap(f, range(10)))
except ZeroDivisionError:
print '\tGot ZeroDivisionError as expected from list(pool.imap())'
else:
raise AssertionError('expected ZeroDivisionError')
it = pool.imap(f, range(10))
for i in range(10):
try:
x = it.next()
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:',
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:',
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
#
# Testing callback
#
print 'Testing callback:'
A = []
B = [56, 0, 1, 8, 27, 64, 125, 216, 343, 512, 729]
r = pool.apply_async(mul, (7, 8), callback=A.append)
r.wait()
r = pool.map_async(pow3, range(10), callback=A.extend)
r.wait()
if A == B:
print '\tcallbacks succeeded\n'
else:
print '\t*** callbacks failed\n\t\t%s != %s\n' % (A, B)
#
# Check there are no outstanding tasks
#
assert not pool._cache, 'cache = %r' % pool._cache
#
# Check close() methods
#
print 'Testing close():'
for worker in pool._pool:
assert worker.is_alive()
result = pool.apply_async(time.sleep, [0.5])
pool.close()
pool.join()
assert result.get() is None
for worker in pool._pool:
assert not worker.is_alive()
print '\tclose() succeeded\n'
#
# Check terminate() method
#
print 'Testing terminate():'
pool = multiprocessing.Pool(2)
DELTA = 0.1
ignore = pool.apply(pow3, [2])
results = [pool.apply_async(time.sleep, [DELTA]) for i in range(100)]
pool.terminate()
pool.join()
for worker in pool._pool:
assert not worker.is_alive()
print '\tterminate() succeeded\n'
#
# Check garbage collection
#
print 'Testing garbage collection:'
pool = multiprocessing.Pool(2)
DELTA = 0.1
processes = pool._pool
ignore = pool.apply(pow3, [2])
results = [pool.apply_async(time.sleep, [DELTA]) for i in range(100)]
results = pool = None
time.sleep(DELTA * 2)
for worker in processes:
assert not worker.is_alive()
print '\tgarbage collection succeeded\n'
if __name__ == '__main__':
multiprocessing.freeze_support()
assert len(sys.argv) in (1, 2)
if len(sys.argv) == 1 or sys.argv[1] == 'processes':
print ' Using processes '.center(79, '-')
elif sys.argv[1] == 'threads':
print ' Using threads '.center(79, '-')
import multiprocessing.dummy as multiprocessing
else:
print 'Usage:\n\t%s [processes | threads]' % sys.argv[0]
raise SystemExit(2)
test()
Synchronization types like locks, conditions and queues:
#
# A test file for the `multiprocessing` package
#
# Copyright (c) 2006-2008, R Oudkerk
# All rights reserved.
#
import time, sys, random
from Queue import Empty
import multiprocessing # may get overwritten
#### TEST_VALUE
def value_func(running, mutex):
random.seed()
time.sleep(random.random()*4)
mutex.acquire()
print '\n\t\t\t' + str(multiprocessing.current_process()) + ' has finished'
running.value -= 1
mutex.release()
def test_value():
TASKS = 10
running = multiprocessing.Value('i', TASKS)
mutex = multiprocessing.Lock()
for i in range(TASKS):
p = multiprocessing.Process(target=value_func, args=(running, mutex))
p.start()
while running.value > 0:
time.sleep(0.08)
mutex.acquire()
print running.value,
sys.stdout.flush()
mutex.release()
print
print 'No more running processes'
#### TEST_QUEUE
def queue_func(queue):
for i in range(30):
time.sleep(0.5 * random.random())
queue.put(i*i)
queue.put('STOP')
def test_queue():
q = multiprocessing.Queue()
p = multiprocessing.Process(target=queue_func, args=(q,))
p.start()
o = None
while o != 'STOP':
try:
o = q.get(timeout=0.3)
print o,
sys.stdout.flush()
except Empty:
print 'TIMEOUT'
print
#### TEST_CONDITION
def condition_func(cond):
cond.acquire()
print '\t' + str(cond)
time.sleep(2)
print '\tchild is notifying'
print '\t' + str(cond)
cond.notify()
cond.release()
def test_condition():
cond = multiprocessing.Condition()
p = multiprocessing.Process(target=condition_func, args=(cond,))
print cond
cond.acquire()
print cond
cond.acquire()
print cond
p.start()
print 'main is waiting'
cond.wait()
print 'main has woken up'
print cond
cond.release()
print cond
cond.release()
p.join()
print cond
#### TEST_SEMAPHORE
def semaphore_func(sema, mutex, running):
sema.acquire()
mutex.acquire()
running.value += 1
print running.value, 'tasks are running'
mutex.release()
random.seed()
time.sleep(random.random()*2)
mutex.acquire()
running.value -= 1
print '%s has finished' % multiprocessing.current_process()
mutex.release()
sema.release()
def test_semaphore():
sema = multiprocessing.Semaphore(3)
mutex = multiprocessing.RLock()
running = multiprocessing.Value('i', 0)
processes = [
multiprocessing.Process(target=semaphore_func,
args=(sema, mutex, running))
for i in range(10)
]
for p in processes:
p.start()
for p in processes:
p.join()
#### TEST_JOIN_TIMEOUT
def join_timeout_func():
print '\tchild sleeping'
time.sleep(5.5)
print '\n\tchild terminating'
def test_join_timeout():
p = multiprocessing.Process(target=join_timeout_func)
p.start()
print 'waiting for process to finish'
while 1:
p.join(timeout=1)
if not p.is_alive():
break
print '.',
sys.stdout.flush()
#### TEST_EVENT
def event_func(event):
print '\t%r is waiting' % multiprocessing.current_process()
event.wait()
print '\t%r has woken up' % multiprocessing.current_process()
def test_event():
event = multiprocessing.Event()
processes = [multiprocessing.Process(target=event_func, args=(event,))
for i in range(5)]
for p in processes:
p.start()
print 'main is sleeping'
time.sleep(2)
print 'main is setting event'
event.set()
for p in processes:
p.join()
#### TEST_SHAREDVALUES
def sharedvalues_func(values, arrays, shared_values, shared_arrays):
for i in range(len(values)):
v = values[i][1]
sv = shared_values[i].value
assert v == sv
for i in range(len(values)):
a = arrays[i][1]
sa = list(shared_arrays[i][:])
assert a == sa
print 'Tests passed'
def test_sharedvalues():
values = [
('i', 10),
('h', -2),
('d', 1.25)
]
arrays = [
('i', range(100)),
('d', [0.25 * i for i in range(100)]),
('H', range(1000))
]
shared_values = [multiprocessing.Value(id, v) for id, v in values]
shared_arrays = [multiprocessing.Array(id, a) for id, a in arrays]
p = multiprocessing.Process(
target=sharedvalues_func,
args=(values, arrays, shared_values, shared_arrays)
)
p.start()
p.join()
assert p.exitcode == 0
####
def test(namespace=multiprocessing):
global multiprocessing
multiprocessing = namespace
for func in [ test_value, test_queue, test_condition,
test_semaphore, test_join_timeout, test_event,
test_sharedvalues ]:
print '\n\t######## %s\n' % func.__name__
func()
ignore = multiprocessing.active_children() # cleanup any old processes
if hasattr(multiprocessing, '_debug_info'):
info = multiprocessing._debug_info()
if info:
print info
raise ValueError('there should be no positive refcounts left')
if __name__ == '__main__':
multiprocessing.freeze_support()
assert len(sys.argv) in (1, 2)
if len(sys.argv) == 1 or sys.argv[1] == 'processes':
print ' Using processes '.center(79, '-')
namespace = multiprocessing
elif sys.argv[1] == 'manager':
print ' Using processes and a manager '.center(79, '-')
namespace = multiprocessing.Manager()
namespace.Process = multiprocessing.Process
namespace.current_process = multiprocessing.current_process
namespace.active_children = multiprocessing.active_children
elif sys.argv[1] == 'threads':
print ' Using threads '.center(79, '-')
import multiprocessing.dummy as namespace
else:
print 'Usage:\n\t%s [processes | manager | threads]' % sys.argv[0]
raise SystemExit(2)
test(namespace)
An example showing how to use queues to feed tasks to a collection of worker processes and collect the results:
#
# Simple example which uses a pool of workers to carry out some tasks.
#
# Notice that the results will probably not come out of the output
# queue in the same in the same order as the corresponding tasks were
# put on the input queue. If it is important to get the results back
# in the original order then consider using `Pool.map()` or
# `Pool.imap()` (which will save on the amount of code needed anyway).
#
# Copyright (c) 2006-2008, R Oudkerk
# All rights reserved.
#
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()
An example of how a pool of worker processes can each run a
SimpleHTTPServer.HttpServer
instance while sharing a single listening
socket.
#
# Example where a pool of http servers share a single listening socket
#
# On Windows this module depends on the ability to pickle a socket
# object so that the worker processes can inherit a copy of the server
# object. (We import `multiprocessing.reduction` to enable this pickling.)
#
# Not sure if we should synchronize access to `socket.accept()` method by
# using a process-shared lock -- does not seem to be necessary.
#
# Copyright (c) 2006-2008, R Oudkerk
# All rights reserved.
#
import os
import sys
from multiprocessing import Process, current_process, freeze_support
from BaseHTTPServer import HTTPServer
from SimpleHTTPServer import SimpleHTTPRequestHandler
if sys.platform == 'win32':
import multiprocessing.reduction # make sockets pickable/inheritable
def note(format, *args):
sys.stderr.write('[%s]\t%s\n' % (current_process().name, format%args))
class RequestHandler(SimpleHTTPRequestHandler):
# we override log_message() to show which process is handling the request
def log_message(self, format, *args):
note(format, *args)
def serve_forever(server):
note('starting server')
try:
server.serve_forever()
except KeyboardInterrupt:
pass
def runpool(address, number_of_processes):
# create a single server object -- children will each inherit a copy
server = HTTPServer(address, RequestHandler)
# create child processes to act as workers
for i in range(number_of_processes-1):
Process(target=serve_forever, args=(server,)).start()
# main process also acts as a worker
serve_forever(server)
def test():
DIR = os.path.join(os.path.dirname(__file__), '..')
ADDRESS = ('localhost', 8000)
NUMBER_OF_PROCESSES = 4
print 'Serving at http://%s:%d using %d worker processes' % \
(ADDRESS[0], ADDRESS[1], NUMBER_OF_PROCESSES)
print 'To exit press Ctrl-' + ['C', 'Break'][sys.platform=='win32']
os.chdir(DIR)
runpool(ADDRESS, NUMBER_OF_PROCESSES)
if __name__ == '__main__':
freeze_support()
test()
Some simple benchmarks comparing multiprocessing
with threading
:
#
# Simple benchmarks for the multiprocessing package
#
# Copyright (c) 2006-2008, R Oudkerk
# All rights reserved.
#
import time, sys, multiprocessing, threading, Queue, gc
if sys.platform == 'win32':
_timer = time.clock
else:
_timer = time.time
delta = 1
#### TEST_QUEUESPEED
def queuespeed_func(q, c, iterations):
a = '0' * 256
c.acquire()
c.notify()
c.release()
for i in xrange(iterations):
q.put(a)
q.put('STOP')
def test_queuespeed(Process, q, c):
elapsed = 0
iterations = 1
while elapsed < delta:
iterations *= 2
p = Process(target=queuespeed_func, args=(q, c, iterations))
c.acquire()
p.start()
c.wait()
c.release()
result = None
t = _timer()
while result != 'STOP':
result = q.get()
elapsed = _timer() - t
p.join()
print iterations, 'objects passed through the queue in', elapsed, 'seconds'
print 'average number/sec:', iterations/elapsed
#### TEST_PIPESPEED
def pipe_func(c, cond, iterations):
a = '0' * 256
cond.acquire()
cond.notify()
cond.release()
for i in xrange(iterations):
c.send(a)
c.send('STOP')
def test_pipespeed():
c, d = multiprocessing.Pipe()
cond = multiprocessing.Condition()
elapsed = 0
iterations = 1
while elapsed < delta:
iterations *= 2
p = multiprocessing.Process(target=pipe_func,
args=(d, cond, iterations))
cond.acquire()
p.start()
cond.wait()
cond.release()
result = None
t = _timer()
while result != 'STOP':
result = c.recv()
elapsed = _timer() - t
p.join()
print iterations, 'objects passed through connection in',elapsed,'seconds'
print 'average number/sec:', iterations/elapsed
#### TEST_SEQSPEED
def test_seqspeed(seq):
elapsed = 0
iterations = 1
while elapsed < delta:
iterations *= 2
t = _timer()
for i in xrange(iterations):
a = seq[5]
elapsed = _timer()-t
print iterations, 'iterations in', elapsed, 'seconds'
print 'average number/sec:', iterations/elapsed
#### TEST_LOCK
def test_lockspeed(l):
elapsed = 0
iterations = 1
while elapsed < delta:
iterations *= 2
t = _timer()
for i in xrange(iterations):
l.acquire()
l.release()
elapsed = _timer()-t
print iterations, 'iterations in', elapsed, 'seconds'
print 'average number/sec:', iterations/elapsed
#### TEST_CONDITION
def conditionspeed_func(c, N):
c.acquire()
c.notify()
for i in xrange(N):
c.wait()
c.notify()
c.release()
def test_conditionspeed(Process, c):
elapsed = 0
iterations = 1
while elapsed < delta:
iterations *= 2
c.acquire()
p = Process(target=conditionspeed_func, args=(c, iterations))
p.start()
c.wait()
t = _timer()
for i in xrange(iterations):
c.notify()
c.wait()
elapsed = _timer()-t
c.release()
p.join()
print iterations * 2, 'waits in', elapsed, 'seconds'
print 'average number/sec:', iterations * 2 / elapsed
####
def test():
manager = multiprocessing.Manager()
gc.disable()
print '\n\t######## testing Queue.Queue\n'
test_queuespeed(threading.Thread, Queue.Queue(),
threading.Condition())
print '\n\t######## testing multiprocessing.Queue\n'
test_queuespeed(multiprocessing.Process, multiprocessing.Queue(),
multiprocessing.Condition())
print '\n\t######## testing Queue managed by server process\n'
test_queuespeed(multiprocessing.Process, manager.Queue(),
manager.Condition())
print '\n\t######## testing multiprocessing.Pipe\n'
test_pipespeed()
print
print '\n\t######## testing list\n'
test_seqspeed(range(10))
print '\n\t######## testing list managed by server process\n'
test_seqspeed(manager.list(range(10)))
print '\n\t######## testing Array("i", ..., lock=False)\n'
test_seqspeed(multiprocessing.Array('i', range(10), lock=False))
print '\n\t######## testing Array("i", ..., lock=True)\n'
test_seqspeed(multiprocessing.Array('i', range(10), lock=True))
print
print '\n\t######## testing threading.Lock\n'
test_lockspeed(threading.Lock())
print '\n\t######## testing threading.RLock\n'
test_lockspeed(threading.RLock())
print '\n\t######## testing multiprocessing.Lock\n'
test_lockspeed(multiprocessing.Lock())
print '\n\t######## testing multiprocessing.RLock\n'
test_lockspeed(multiprocessing.RLock())
print '\n\t######## testing lock managed by server process\n'
test_lockspeed(manager.Lock())
print '\n\t######## testing rlock managed by server process\n'
test_lockspeed(manager.RLock())
print
print '\n\t######## testing threading.Condition\n'
test_conditionspeed(threading.Thread, threading.Condition())
print '\n\t######## testing multiprocessing.Condition\n'
test_conditionspeed(multiprocessing.Process, multiprocessing.Condition())
print '\n\t######## testing condition managed by a server process\n'
test_conditionspeed(multiprocessing.Process, manager.Condition())
gc.enable()
if __name__ == '__main__':
multiprocessing.freeze_support()
test()