Hướng dẫn python multiprocessing shared list
Source code: Lib/multiprocessing/ Show
Introduction¶
The from multiprocessing import Pool def f(x): return x*x if __name__ == '__main__': with Pool(5) as p: print(p.map(f, [1, 2, 3])) will print to standard output See also
The Process class¶In from multiprocessing import Process def f(name): print('hello', name) if __name__ == '__main__': p = Process(target=f, args=('bob',)) p.start() p.join() To show the individual process IDs involved, here is an expanded example: from multiprocessing import Process import os def info(title): print(title) print('module name:', __name__) print('parent process:', os.getppid()) print('process id:', os.getpid()) def f(name): info('function f') print('hello', name) if __name__ == '__main__': info('main line') p = Process(target=f, args=('bob',)) p.start() p.join() For an
explanation of why the Contexts and start methods¶Depending on the
platform,
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 To select a start method you use the 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()
Alternatively, you can use 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 Warning The
Exchanging objects between processes¶
Queues
Pipes
Synchronization between processes¶
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
Shared memory
Server process
Using a pool of workers¶The 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 >>> 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 Process and exceptions¶classmultiprocessing. Process (group=None, target=None, name=None, args=(),
kwargs={}, *, daemon=None)¶Process objects represent activity that is run in a separate process. The
The constructor should always be called with keyword arguments. group should always be By default, no arguments are passed to target. If a subclass overrides the constructor, it must make sure it invokes the base class constructor ( 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
start ()¶Start the process’s activity. This must be called at most once per process object. It arranges for the object’s join ([timeout])¶If the optional argument timeout is 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
daemon ¶The process’s daemon flag, a Boolean value. This must
be set before 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 pid ¶Return the process ID. Before the process is spawned, this will be
exitcode ¶The child’s exit code. This will be If the child’s
If the child terminated due to an exception not caught within
authkey ¶The process’s authentication key (a byte string). When When a 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
On Windows, this is an OS handle usable with the New in version 3.3. terminate ()¶Terminate
the process. On Unix this is done using the 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 New in version 3.7. close ()¶Close the
New in version 3.7. Note that the Example usage of some of the methods of >>> import multiprocessing, time, signal >>> p = multiprocessing.Process(target=time.sleep, args=(1000,)) >>> print(p, p.is_alive())exception multiprocessing. ProcessError ¶The base class of all multiprocessing. BufferTooShort ¶Exception raised by If multiprocessing. AuthenticationError ¶Raised when there is an authentication error. exceptionmultiprocessing. TimeoutError ¶Raised by methods with a timeout when the timeout expires. Pipes and Queues¶When using multiple processes, one generally uses message passing for communication between processes and avoids having to use any synchronization primitives like locks. For passing messages one can use The
If you use
Note that one can also create a shared queue by using a manager object – see Managers. 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.
Warning If a process is killed using
Warning As mentioned above, if a child process has put items on a queue (and it has not used 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 If duplex is 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
qsize ()¶Return the approximate size of the queue. Because of multithreading/multiprocessing semantics, this number is not reliable. Note that this may raise empty ()¶Return full ()¶Return put (obj[, block[, timeout]])¶Put obj into the queue. If the optional argument block is put_nowait (obj)¶Equivalent to get ([block[, timeout]])¶Remove and return an item from the queue. If optional args block is Changed in version 3.8: If the queue is closed, get_nowait ()¶Equivalent to
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
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 ()¶Prevent A better name for this method might be 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 multiprocessing. SimpleQueue ¶It is a simplified close ()¶Close the queue: release internal resources. A queue must not be used anymore after it is
closed. For example, New in version 3.9. empty ()¶
Return get ()¶Remove and return an item from the queue. put (item)¶Put item into the queue. classmultiprocessing. JoinableQueue ([maxsize])¶
task_done ()¶Indicate that a formerly enqueued task is complete. Used by queue consumers. For each If a Raises a
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 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 When the number of CPUs cannot be determined a
multiprocessing. current_process ()¶Return the
An analogue of multiprocessing. parent_process ()¶Return the New in version 3.8. multiprocessing. freeze_support ()¶Add support for when a program which uses One needs to call this function straight after the from multiprocessing import Process, freeze_support def f(): print('hello world!') if __name__ == '__main__': freeze_support() Process(target=f).start() If the Calling 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 New in version 3.4. multiprocessing. get_context (method=None)¶Return a context object which has the same attributes as the
If method is New in version 3.4. multiprocessing. get_start_method (allow_none=False)¶Return the name of start method used for starting processes. If the start method has not been fixed and
allow_none is false, then the start method is fixed to the default and the name is returned. If the start method has not been fixed and allow_none is true then The return value can be 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. New in version 3.4. multiprocessing. set_executable (executable)¶Set the path of the Python interpreter to use when starting a child process. (By default 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 multiprocessing. set_start_method (method)¶Set the method which should be used to start child processes. method can be Note that this should be called at most once, and it should be protected inside the New in version 3.4. Connection Objects¶Connection objects allow the sending and receiving of picklable objects or strings. They can be thought of as message oriented connected sockets. Connection objects are usually created using
multiprocessing.connection. Connection ¶ send (obj)¶Send an object to the other end of the connection which should be read using The object must be picklable. Very large pickles (approximately 32 MiB+, though it depends on the OS) may raise a
recv ()¶Return an object sent from the other end of the connection using
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 Note that multiple connection objects may be polled at once by using 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 recv_bytes ([maxlength])¶Return a complete message of byte data sent from the other end of the connection as a string. Blocks until there is something to receive. Raises
If maxlength is specified and the message is longer than maxlength then Changed in version 3.3: This
function used to raise 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
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 Changed in version 3.3: Connection objects themselves can now be transferred between processes using
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
Therefore, unless the connection object was produced using 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 Note that one can also create synchronization primitives by using a manager object – see Managers. classmultiprocessing. Barrier (parties[, action[,
timeout]])¶A barrier object: a clone of New in version 3.3. classmultiprocessing. BoundedSemaphore ([value])¶A bounded semaphore object: a close analog of A solitary
difference from its close analog exists: its Note On macOS, this is indistinguishable from
multiprocessing. Condition ([lock])¶A condition variable: an alias for If lock is specified then
it should be a Changed
in version 3.3: The multiprocessing. Event ¶A clone of
multiprocessing. Lock ¶A non-recursive lock object: a close analog of
Note that
acquire (block=True, timeout=None)¶Acquire a lock, blocking or non-blocking. With the block argument set to With the block argument set to 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 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
multiprocessing. RLock ¶A recursive lock object: a close analog of Note that
acquire (block=True, timeout=None)¶Acquire a lock, blocking or non-blocking. When invoked with the block argument set to When invoked with the block argument set to Use and behaviors of the timeout argument are the same as in release ()¶Release a lock, decrementing the recursion level. If after the decrement the recursion level is zero, reset the lock to unlocked (not owned by any process or thread) and if any other processes or threads are blocked waiting for the lock to become unlocked, allow exactly one of them to proceed. If after the decrement the recursion level is still nonzero, the lock remains locked and owned by the calling process or thread. Only call this method when the calling process or thread owns the lock. An
multiprocessing. Semaphore ([value])¶A semaphore object: a close analog of A solitary difference from its close analog exists: its Note On macOS, Note If the
SIGINT signal generated by Ctrl-C arrives while the main thread is blocked by a call to This differs from the behaviour of Note Some of this
package’s functionality requires a functioning shared semaphore implementation on the host operating system. Without one, the 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 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 If lock is Operations like 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 If lock is Note that lock is a keyword only argument. Note that an array of The |
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:
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.
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.
New in version 3.3.
BoundedSemaphore
([value])¶Create a shared threading.BoundedSemaphore
object
and return a proxy for it.
Condition
([lock])¶Create a shared
threading.Condition
object and return a proxy for it.
If lock is supplied then it should be a proxy for a threading.Lock
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
.
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))>>> 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) [] [] >>> 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.
classmultiprocessing.managers.
BaseProxy
¶Proxy objects are instances of subclasses of BaseProxy
.
_callmethod
(methodname[, args[, kwds]])¶Call and return the result of a method of the proxy’s referent.
If proxy
is a proxy whose referent is obj
then
the expression
proxy._callmethod(methodname, args, kwds)
will evaluate the expression
getattr(obj, methodname)(*args, **kwds)
in the manager’s process.
The returned value will be a copy of the result of the call or a proxy to a new shared object – see documentation for the method_to_typeid argument of 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.
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)]
.
New in version 3.3.
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.
New in version 3.3.
close
()¶Prevents any more tasks from being submitted to the pool. Once all the tasks have been completed the worker processes will exit.
terminate
()¶Stops the worker processes immediately without completing outstanding work. When the pool object is garbage collected
terminate()
will be called immediately.
join
()¶Wait for
the worker processes to exit. One must call close()
or terminate()
before using
join()
.
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.
Changed in version 3.7: If
the result is not ready, ValueError
is raised instead of AssertionError
.
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.
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 readable
Connection
object;a connected and readable
socket.socket
object; orthe
sentinel
attribute of aProcess
object.
A connection or socket object is ready when there is data available to be read from it, or the other end has been closed.
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.)
New in version 3.3.
Examples
The following server code creates a listener which uses 'secret password'
as an authentication key. It then waits for a connection and sends some data to the client:
from multiprocessing.connection import Listener from array import array address = ('localhost', 6000) # family is deduced to be 'AF_INET' with Listener(address, authkey=b'secret password') as listener: with listener.accept() as conn: print('connection accepted from', listener.last_accepted) conn.send([2.25, None, 'junk', float]) conn.send_bytes(b'hello') conn.send_bytes(array('i', [42, 1729]))
The following code connects to the server and receives some data from the server:
from multiprocessing.connection import Client from array import array address = ('localhost', 6000) with Client(address, authkey=b'secret password') as conn: print(conn.recv()) # => [2.25, None, 'junk', float] print(conn.recv_bytes()) # => 'hello' arr = array('i', [0, 0, 0, 0, 0]) print(conn.recv_bytes_into(arr)) # => 8 print(arr) # => array('i', [42, 1729, 0, 0, 0])
The following code uses
wait()
to wait for messages from multiple processes at once:
import time, random from multiprocessing import Process, Pipe, current_process from multiprocessing.connection import wait def foo(w): for i in range(10): w.send((i, current_process().name)) w.close() if __name__ == '__main__': readers = [] for i in range(4): r, w = Pipe(duplex=False) readers.append(r) p = Process(target=foo, args=(w,)) p.start() # We close the writable end of the pipe now to be sure that # p is the only process which owns a handle for it. This # ensures that when p closes its handle for the writable end, # wait() will promptly report the readable end as being ready. w.close() while readers: for r in wait(readers): try: msg = r.recv() except EOFError: readers.remove(r) else: print(msg)
Address Formats¶
An
'AF_INET'
address is a tuple of the form(hostname, port)
where hostname is a string and port is an integer.An
'AF_UNIX'
address is a string representing a filename on the filesystem.An
'AF_PIPE'
address is a string of the formr'\.\pipe{PipeName}'
. To useClient()
to connect to a named pipe on a remote computer called ServerName one should use an address of the formr'\ServerName\pipe{PipeName}'
instead.
Note that any string beginning with two backslashes is assumed by default to be an 'AF_PIPE'
address rather than an 'AF_UNIX'
address.
Authentication keys¶
When one uses Connection.recv
, the data received is automatically unpickled. Unfortunately unpickling data from an untrusted source is a security risk. Therefore
Listener
and Client()
use the hmac
module to provide digest authentication.
An authentication key is a byte string which can be thought of as a password: once a connection is established both ends will demand proof that the other knows the authentication key. (Demonstrating that both ends are using the same key does not involve sending the key over the connection.)
If authentication is requested but no authentication key is specified then the return value of current_process().authkey
is
used (see Process
). This value will be automatically inherited by any Process
object that the current process creates. This means that (by default) all processes of a multi-process program will share a single
authentication key which can be used when setting up connections between themselves.
Suitable authentication keys can also be generated by using os.urandom()
.
Logging¶
Some support for logging is available. Note, however,
that the logging
package does not use process shared locks so it is possible (depending on the handler type) for messages from different processes to get mixed up.
multiprocessing.
get_logger
()¶Returns the logger used by multiprocessing
. If necessary, a new one will be created.
When first
created the logger has level logging.NOTSET
and no default handler. Messages sent to this logger will not by default propagate to the root logger.
Note that on Windows child processes will only inherit the level of the parent process’s logger – any other customization of the logger will not be inherited.
multiprocessing.
log_to_stderr
(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.
The multiprocessing.dummy module¶
multiprocessing.dummy
replicates the API of
multiprocessing
but is no more than a wrapper around the threading
module.
In particular, the Pool
function provided by
multiprocessing.dummy
returns an instance of ThreadPool
, which is a subclass of
Pool
that supports all the same method calls but uses a pool of worker threads rather than worker processes.
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 asPool
, which is designed around a pool of processes and predates the introduction of theconcurrent.futures
module. As such, it inherits some operations that don’t make sense for a pool backed by threads, and it has its own type for representing the status of asynchronous jobs,AsyncResult
, that is not understood by any other libraries.Users should generally prefer to use
concurrent.futures.ThreadPoolExecutor
, which has a simpler interface that was designed around threads from the start, and which returnsconcurrent.futures.Future
instances that are compatible with many other libraries, includingasyncio
.
Programming guidelines¶
There are certain guidelines and idioms
which should be adhered to when using multiprocessing
.
All start methods¶
The following applies to all start methods.
Avoid shared state
As far as possible one should try to avoid shifting large amounts of data between processes.
It is probably best to stick to using queues or pipes for communication between processes rather than using the lower level synchronization primitives.
Picklability
Ensure that the arguments to the methods of proxies are picklable.
Thread safety of proxies
Do not use a proxy object from more than one thread unless you protect it with a lock.
(There is never a problem with different processes using the same proxy.)
Joining zombie processes
On Unix when a process finishes but has not been joined it becomes a zombie. There should never be very many because each time a new process starts (or
active_children()
is called) all completed processes which have not yet been joined will be joined. Also calling a finished process’sProcess.is_alive
will join the process. Even so it is probably good practice to explicitly join all the processes that you start.
Better to inherit than pickle/unpickle
When using the spawn or forkserver start methods many types from
multiprocessing
need to be picklable so that child processes can use them. However, one should generally avoid sending shared objects to other processes using pipes or queues. Instead you should arrange the program so that a process which needs access to a shared resource created elsewhere can inherit it from an ancestor process.
Avoid terminating processes
Using the
Process.terminate
method to stop a process is liable to cause any shared resources (such as locks, semaphores, pipes and queues) currently being used by the process to become broken or unavailable to other processes.Therefore it is probably best to only consider using
Process.terminate
on processes which never use any shared resources.
Joining processes that use queues
Bear in mind that a process that has put items in a queue will wait before terminating until all the buffered items are fed by the “feeder” thread to the underlying pipe. (The child process can call the
Queue.cancel_join_thread
method of the queue to avoid this behaviour.)This means that whenever you use a queue you need to make sure that all items which have been put on the queue will eventually be removed before the process is joined. Otherwise you cannot be sure that processes which have put items on the queue will terminate. Remember also that non-daemonic processes will be joined automatically.
An example which will deadlock is the following:
from multiprocessing import Process, Queue def f(q): q.put('X' * 1000000) if __name__ == '__main__': queue = Queue() p = Process(target=f, args=(queue,)) p.start() p.join() # this deadlocks obj = queue.get()A fix here would be to swap the last two lines (or simply remove the
p.join()
line).
Explicitly pass resources to child processes
On Unix using the fork start method, a child process can make use of a shared resource created in a parent process using a global resource. However, it is better to pass the object as an argument to the constructor for the child process.
Apart from making the code (potentially) compatible with Windows and the other start methods this also ensures that as long as the child process is still alive the object will not be garbage collected in the parent process. This might be important if some resource is freed when the object is garbage collected in the parent process.
So for instance
from multiprocessing import Process, Lock def f(): ... do something using "lock" ... if __name__ == '__main__': lock = Lock() for i in range(10): Process(target=f).start()should be rewritten as
from multiprocessing import Process, Lock def f(l): ... do something using "l" ... if __name__ == '__main__': lock = Lock() for i in range(10): Process(target=f, args=(lock,)).start()
Beware of replacing sys.stdin
with a “file like object”
multiprocessing
originally unconditionally called:os.close(sys.stdin.fileno())in the
multiprocessing.Process._bootstrap()
method — this resulted in issues with processes-in-processes. This has been changed to:sys.stdin.close() sys.stdin = open(os.open(os.devnull, os.O_RDONLY), closefd=False)Which solves the fundamental issue of processes colliding with each other resulting in a bad file descriptor error, but introduces a potential danger to applications which replace
sys.stdin()
with a “file-like object” with output buffering. This danger is that if multiple processes callclose()
on this file-like object, it could result in the same data being flushed to the object multiple times, resulting in corruption.If you write a file-like object and implement your own caching, you can make it fork-safe by storing the pid whenever you append to the cache, and discarding the cache when the pid changes. For example:
@property def cache(self): pid = os.getpid() if pid != self._pid: self._pid = pid self._cache = [] return self._cacheFor 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 subclassProcess
then make sure that instances will be picklable when theProcess.start
method is called.
Global variables
Bear in mind that if code run in a child process tries to access a global variable, then the value it sees (if any) may not be the same as the value in the parent process at the time that
Process.start
was called.However, global variables which are just module level constants cause no problems.
Safe importing of main module
Make sure that the main module can be safely imported by a new Python interpreter without causing unintended side effects (such a starting a new process).
For example, using the spawn or forkserver start method running the following module would fail with a
RuntimeError
:from multiprocessing import Process def foo(): print('hello') p = Process(target=foo) p.start()Instead one should protect the “entry point” of the program by using
if __name__ == '__main__':
as follows:from multiprocessing import Process, freeze_support, set_start_method def foo(): print('hello') if __name__ == '__main__': freeze_support() set_start_method('spawn') p = Process(target=foo) p.start()(The
freeze_support()
line can be omitted if the program will be run normally instead of frozen.)This allows the newly spawned Python interpreter to safely import the module and then run the module’s
foo()
function.Similar restrictions apply if a pool or manager is created in the main module.
Examples¶
Demonstration of how to create and use customized managers and proxies:
from multiprocessing import freeze_support from multiprocessing.managers import BaseManager, BaseProxy import operator ## class Foo: def f(self): print('you called Foo.f()') def g(self): print('you called Foo.g()') def _h(self): print('you called Foo._h()') # A simple generator function def baz(): for i in range(10): yield i*i # Proxy type for generator objects class GeneratorProxy(BaseProxy): _exposed_ = ['__next__'] def __iter__(self): return self def __next__(self): return self._callmethod('__next__') # Function to return the operator module def get_operator_module(): return operator ## class MyManager(BaseManager): pass # register the Foo class; make `f()` and `g()` accessible via proxy MyManager.register('Foo1', Foo) # register the Foo class; make `g()` and `_h()` accessible via proxy MyManager.register('Foo2', Foo, exposed=('g', '_h')) # register the generator function baz; use `GeneratorProxy` to make proxies MyManager.register('baz', baz, proxytype=GeneratorProxy) # register get_operator_module(); make public functions accessible via proxy MyManager.register('operator', get_operator_module) ## def test(): manager = MyManager() manager.start() print('-' * 20) f1 = manager.Foo1() f1.f() f1.g() assert not hasattr(f1, '_h') assert sorted(f1._exposed_) == sorted(['f', 'g']) print('-' * 20) f2 = manager.Foo2() f2.g() f2._h() assert not hasattr(f2, 'f') assert sorted(f2._exposed_) == sorted(['g', '_h']) print('-' * 20) it = manager.baz() for i in it: print('<%d>' % i, end=' ') print() print('-' * 20) op = manager.operator() print('op.add(23, 45) =', op.add(23, 45)) print('op.pow(2, 94) =', op.pow(2, 94)) print('op._exposed_ =', op._exposed_) ## if __name__ == '__main__': freeze_support() test()
Using
Pool
:
import multiprocessing import time import random import sys # # Functions used by test code # def calculate(func, args): result = func(*args) return '%s says that %s%s = %s' % ( multiprocessing.current_process().name, func.__name__, args, result ) def calculatestar(args): return calculate(*args) def mul(a, b): time.sleep(0.5 * random.random()) return a * b def plus(a, b): time.sleep(0.5 * random.random()) return a + b def f(x): return 1.0 / (x - 5.0) def pow3(x): return x ** 3 def noop(x): pass # # Test code # def test(): PROCESSES = 4 print('Creating pool with %d processes\n' % PROCESSES) with multiprocessing.Pool(PROCESSES) as pool: # # Tests # TASKS = [(mul, (i, 7)) for i in range(10)] + \ [(plus, (i, 8)) for i in range(10)] results = [pool.apply_async(calculate, t) for t in TASKS] imap_it = pool.imap(calculatestar, TASKS) imap_unordered_it = pool.imap_unordered(calculatestar, TASKS) print('Ordered results using pool.apply_async():') for r in results: print('\t', r.get()) print() print('Ordered results using pool.imap():') for x in imap_it: print('\t', x) print() print('Unordered results using pool.imap_unordered():') for x in imap_unordered_it: print('\t', x) print() print('Ordered results using pool.map() --- will block till complete:') for x in pool.map(calculatestar, TASKS): print('\t', x) print() # # Test error handling # print('Testing error handling:') try: print(pool.apply(f, (5,))) except ZeroDivisionError: print('\tGot ZeroDivisionError as expected from pool.apply()') else: raise AssertionError('expected ZeroDivisionError') try: print(pool.map(f, list(range(10)))) except ZeroDivisionError: print('\tGot ZeroDivisionError as expected from pool.map()') else: raise AssertionError('expected ZeroDivisionError') try: print(list(pool.imap(f, list(range(10))))) except ZeroDivisionError: print('\tGot ZeroDivisionError as expected from list(pool.imap())') else: raise AssertionError('expected ZeroDivisionError') it = pool.imap(f, list(range(10))) for i in range(10): try: x = next(it) except ZeroDivisionError: if i == 5: pass except StopIteration: break else: if i == 5: raise AssertionError('expected ZeroDivisionError') assert i == 9 print('\tGot ZeroDivisionError as expected from IMapIterator.next()') print() # # Testing timeouts # print('Testing ApplyResult.get() with timeout:', end=' ') res = pool.apply_async(calculate, TASKS[0]) while 1: sys.stdout.flush() try: sys.stdout.write('\n\t%s' % res.get(0.02)) break except multiprocessing.TimeoutError: sys.stdout.write('.') print() print() print('Testing IMapIterator.next() with timeout:', end=' ') it = pool.imap(calculatestar, TASKS) while 1: sys.stdout.flush() try: sys.stdout.write('\n\t%s' % it.next(0.02)) except StopIteration: break except multiprocessing.TimeoutError: sys.stdout.write('.') print() print() if __name__ == '__main__': multiprocessing.freeze_support() test()
An example showing how to use queues to feed tasks to a collection of worker processes and collect the results:
import time import random from multiprocessing import Process, Queue, current_process, freeze_support # # Function run by worker processes # def worker(input, output): for func, args in iter(input.get, 'STOP'): result = calculate(func, args) output.put(result) # # Function used to calculate result # def calculate(func, args): result = func(*args) return '%s says that %s%s = %s' % \ (current_process().name, func.__name__, args, result) # # Functions referenced by tasks # def mul(a, b): time.sleep(0.5*random.random()) return a * b def plus(a, b): time.sleep(0.5*random.random()) return a + b # # # def test(): NUMBER_OF_PROCESSES = 4 TASKS1 = [(mul, (i, 7)) for i in range(20)] TASKS2 = [(plus, (i, 8)) for i in range(10)] # Create queues task_queue = Queue() done_queue = Queue() # Submit tasks for task in TASKS1: task_queue.put(task) # Start worker processes for i in range(NUMBER_OF_PROCESSES): Process(target=worker, args=(task_queue, done_queue)).start() # Get and print results print('Unordered results:') for i in range(len(TASKS1)): print('\t', done_queue.get()) # Add more tasks using `put()` for task in TASKS2: task_queue.put(task) # Get and print some more results for i in range(len(TASKS2)): print('\t', done_queue.get()) # Tell child processes to stop for i in range(NUMBER_OF_PROCESSES): task_queue.put('STOP') if __name__ == '__main__': freeze_support() test()