"multiprocessing.shared_memory" --- Shared memory for direct access across processes#

Código fuente: Lib/multiprocessing/shared_memory.py

Added in version 3.8.

======================================================================

This module provides a class, "SharedMemory", for the allocation and management of shared memory to be accessed by one or more processes on a multicore or symmetric multiprocessor (SMP) machine. To assist with the life-cycle management of shared memory especially across distinct processes, a "BaseManager" subclass, "SharedMemoryManager", is also provided in the "multiprocessing.managers" module.

In this module, shared memory refers to "POSIX style" shared memory blocks (though is not necessarily implemented explicitly as such) and does not refer to "distributed shared memory". This style of shared memory permits distinct processes to potentially read and write to a common (or shared) region of volatile memory. Processes are conventionally limited to only have access to their own process memory space but shared memory permits the sharing of data between processes, avoiding the need to instead send messages between processes containing that data. Sharing data directly via memory can provide significant performance benefits compared to sharing data via disk or socket or other communications requiring the serialization/deserialization and copying of data.

class multiprocessing.shared_memory.SharedMemory(name=None, create=False, size=0, *, track=True)

Create an instance of the "SharedMemory" class for either creating a new shared memory block or attaching to an existing shared memory block. Each shared memory block is assigned a unique name. In this way, one process can create a shared memory block with a particular name and a different process can attach to that same shared memory block using that same name.

As a resource for sharing data across processes, shared memory blocks may outlive the original process that created them. When one process no longer needs access to a shared memory block that might still be needed by other processes, the "close()" method should be called. When a shared memory block is no longer needed by any process, the "unlink()" method should be called to ensure proper cleanup.

Parámetros: * name (str | None) -- The unique name for the requested shared memory, specified as a string. When creating a new shared memory block, if "None" (the default) is supplied for the name, a novel name will be generated.

  * **create** (*bool*) -- Control whether a new shared memory
    block is created ("True") or an existing shared memory block
    is attached ("False").

  * **size** (*int*) -- The requested number of bytes when
    creating a new shared memory block. Because some platforms
    choose to allocate chunks of memory based upon that platform's
    memory page size, the exact size of the shared memory block
    may be larger or equal to the size requested. When attaching
    to an existing shared memory block, the *size* parameter is
    ignored.

  * **track** (*bool*) -- When "True", register the shared memory
    block with a resource tracker process on platforms where the
    OS does not do this automatically. The resource tracker
    ensures proper cleanup of the shared memory even if all other
    processes with access to the memory exit without doing so.
    Python processes created from a common ancestor using
    "multiprocessing" facilities share a single resource tracker
    process, and the lifetime of shared memory segments is handled
    automatically among these processes. Python processes created
    in any other way will receive their own resource tracker when
    accessing shared memory with *track* enabled. This will cause
    the shared memory to be deleted by the resource tracker of the
    first process that terminates. To avoid this issue, users of
    "subprocess" or standalone Python processes should set *track*
    to "False" when there is already another process in place that
    does the bookkeeping. *track* is ignored on Windows, which has
    its own tracking and automatically deletes shared memory when
    all handles to it have been closed.

Distinto en la versión 3.13: Added the track parameter.

close()

  Close the file descriptor/handle to the shared memory from this
  instance.  "close()" should be called once access to the shared
  memory block from this instance is no longer needed.  Depending
  on operating system, the underlying memory may or may not be
  freed even if all handles to it have been closed.  To ensure
  proper cleanup, use the "unlink()" method.

unlink()

  Delete the underlying shared memory block.  This should be
  called only once per shared memory block regardless of the
  number of handles to it, even in other processes. "unlink()" and
  "close()" can be called in any order, but trying to access data
  inside a shared memory block after "unlink()" may result in
  memory access errors, depending on platform.

  This method has no effect on Windows, where the only way to
  delete a shared memory block is to close all handles.

buf

  Un *memoryview* del contenido del bloque de memoria compartida.

name

  Acceso de solo lectura al nombre único del bloque de memoria
  compartida.

size

  Acceso de solo lectura al tamaño en bytes del bloque de memoria
  compartida.

El siguiente ejemplo muestra el uso de bajo nivel de instancias de "SharedMemory":

from multiprocessing import shared_memory shm_a = shared_memory.SharedMemory(create=True, size=10) type(shm_a.buf) buffer = shm_a.buf len(buffer) 10 buffer[:4] = bytearray([22, 33, 44, 55]) # Modify multiple at once buffer[4] = 100 # Modify single byte at a time

Attach to an existing shared memory block#

shm_b = shared_memory.SharedMemory(shm_a.name) import array array.array('b', shm_b.buf[:5]) # Copy the data into a new array.array array('b', [22, 33, 44, 55, 100]) shm_b.buf[:5] = b'howdy' # Modify via shm_b using bytes bytes(shm_a.buf[:5]) # Access via shm_a b'howdy' shm_b.close() # Close each SharedMemory instance shm_a.close() shm_a.unlink() # Call unlink only once to release the shared memory

The following example demonstrates a practical use of the "SharedMemory" class with NumPy arrays, accessing the same "numpy.ndarray" from two distinct Python shells:

In the first Python interactive shell#

import numpy as np a = np.array([1, 1, 2, 3, 5, 8]) # Start with an existing NumPy array from multiprocessing import shared_memory shm = shared_memory.SharedMemory(create=True, size=a.nbytes)

Now create a NumPy array backed by shared memory#

b = np.ndarray(a.shape, dtype=a.dtype, buffer=shm.buf) b[:] = a[:] # Copy the original data into shared memory b array([1, 1, 2, 3, 5, 8]) type(b) type(a) shm.name # We did not specify a name so one was chosen for us 'psm_21467_46075'

In either the same shell or a new Python shell on the same machine#

import numpy as np from multiprocessing import shared_memory

Attach to the existing shared memory block#

existing_shm = shared_memory.SharedMemory(name='psm_21467_46075')

Note that a.shape is (6,) and a.dtype is np.int64 in this example#

c = np.ndarray((6,), dtype=np.int64, buffer=existing_shm.buf) c array([1, 1, 2, 3, 5, 8]) c[-1] = 888 c array([ 1, 1, 2, 3, 5, 888])

Back in the first Python interactive shell, b reflects this change#

b array([ 1, 1, 2, 3, 5, 888])

Clean up from within the second Python shell#

del c # Unnecessary; merely emphasizing the array is no longer used existing_shm.close()

Clean up from within the first Python shell#

del b # Unnecessary; merely emphasizing the array is no longer used shm.close() shm.unlink() # Free and release the shared memory block at the very end

class multiprocessing.managers.SharedMemoryManager([address[, authkey]])

A subclass of "multiprocessing.managers.BaseManager" which can be used for the management of shared memory blocks across processes.

A call to "start()" on a "SharedMemoryManager" instance causes a new process to be started. This new process's sole purpose is to manage the life cycle of all shared memory blocks created through it. To trigger the release of all shared memory blocks managed by that process, call "shutdown()" on the instance. This triggers a "unlink()" call on all of the "SharedMemory" objects managed by that process and then stops the process itself. By creating "SharedMemory" instances through a "SharedMemoryManager", we avoid the need to manually track and trigger the freeing of shared memory resources.

Esta clase proporciona métodos para crear y retornar instancias "SharedMemory" y para crear un objeto de tipo lista ("ShareableList") basados en memoria compartida.

Refer to "BaseManager" for a description of the inherited address and authkey optional input arguments and how they may be used to connect to an existing "SharedMemoryManager" service from other processes.

SharedMemory(size)

  Create and return a new "SharedMemory" object with the specified
  *size* in bytes.

ShareableList(sequence)

  Create and return a new "ShareableList" object, initialized by
  the values from the input *sequence*.

The following example demonstrates the basic mechanisms of a "SharedMemoryManager":

from multiprocessing.managers import SharedMemoryManager smm = SharedMemoryManager() smm.start() # Start the process that manages the shared memory blocks sl = smm.ShareableList(range(4)) sl ShareableList([0, 1, 2, 3], name='psm_6572_7512') raw_shm = smm.SharedMemory(size=128) another_sl = smm.ShareableList('alpha') another_sl ShareableList(['a', 'l', 'p', 'h', 'a'], name='psm_6572_12221') smm.shutdown() # Calls unlink() on sl, raw_shm, and another_sl

The following example depicts a potentially more convenient pattern for using "SharedMemoryManager" objects via the "with" statement to ensure that all shared memory blocks are released after they are no longer needed:

with SharedMemoryManager() as smm: ... sl = smm.ShareableList(range(2000)) ... # Divide the work among two processes, storing partial results in sl ... p1 = Process(target=do_work, args=(sl, 0, 1000)) ... p2 = Process(target=do_work, args=(sl, 1000, 2000)) ... p1.start() ... p2.start() # A multiprocessing.Pool might be more efficient ... p1.join() ... p2.join() # Wait for all work to complete in both processes ... total_result = sum(sl) # Consolidate the partial results now in sl

When using a "SharedMemoryManager" in a "with" statement, the shared memory blocks created using that manager are all released when the "with" statement's code block finishes execution.

class multiprocessing.shared_memory.ShareableList(sequence=None, *, name=None)

Provide a mutable list-like object where all values stored within are stored in a shared memory block. This constrains storable values to the following built-in data types:

• "int" (signed 64-bit)

• "float"

• "bool"

• "str" (less than 10M bytes each when encoded as UTF-8)

• "bytes" (less than 10M bytes each)

• "None"

It also notably differs from the built-in "list" type in that these lists can not change their overall length (i.e. no "append()", "insert()", etc.) and do not support the dynamic creation of new "ShareableList" instances via slicing.

sequence is used in populating a new "ShareableList" full of values. Set to "None" to instead attach to an already existing "ShareableList" by its unique shared memory name.

name is the unique name for the requested shared memory, as described in the definition for "SharedMemory". When attaching to an existing "ShareableList", specify its shared memory block's unique name while leaving sequence set to "None".

Nota:

 A known issue exists for "bytes" and "str" values. If they end
 with "\x00" nul bytes or characters, those may be *silently
 stripped* when fetching them by index from the "ShareableList".
 This ".rstrip(b'\x00')" behavior is considered a bug and may go
 away in the future. See gh-106939.

Para aplicaciones donde eliminar espacios nulos finales es un problema, solucionarlo añadiendo siempre incondicionalmente un byte extra que no sea 0 al final de dichos valores al almacenar y eliminándolo incondicionalmente al recuperarlos.

  >>> from multiprocessing import shared_memory
  >>> nul_bug_demo = shared_memory.ShareableList(['?\x00', b'\x03\x02\x01\x00\x00\x00'])
  >>> nul_bug_demo[0]
  '?'
  >>> nul_bug_demo[1]
  b'\x03\x02\x01'
  >>> nul_bug_demo.shm.unlink()
  >>> padded = shared_memory.ShareableList(['?\x00\x07', b'\x03\x02\x01\x00\x00\x00\x07'])
  >>> padded[0][:-1]
  '?\x00'
  >>> padded[1][:-1]
  b'\x03\x02\x01\x00\x00\x00'
  >>> padded.shm.unlink()

count(value)

  Return the number of occurrences of *value*.

index(value)

  Return first index position of *value*. Raise "ValueError" if
  *value* is not present.

format

  Atributo de solo lectura que contiene el formato de empaquetado
  "struct" utilizado por todos los valores almacenados
  actualmente.

shm

  La instancia de "SharedMemory" donde se almacenan los valores.

El siguiente ejemplo muestra el uso básico de una instancia "ShareableList":

from multiprocessing import shared_memory a = shared_memory.ShareableList(['howdy', b'HoWdY', -273.154, 100, None, True, 42]) [ type(entry) for entry in a ] [, , , , , , ] a[2] -273.154 a[2] = -78.5 a[2] -78.5 a[2] = 'dry ice' # Changing data types is supported as well a[2] 'dry ice' a[2] = 'larger than previously allocated storage space' Traceback (most recent call last): ... ValueError: exceeds available storage for existing str a[2] 'dry ice' len(a) 7 a.index(42) 6 a.count(b'howdy') 0 a.count(b'HoWdY') 1 a.shm.close() a.shm.unlink() del a # Use of a ShareableList after call to unlink() is unsupported

El siguiente ejemplo muestra cómo uno, dos o muchos procesos pueden acceder al mismo "ShareableList" al proporcionar el nombre del bloque de memoria compartida detrás de él:

b = shared_memory.ShareableList(range(5)) # In a first process c = shared_memory.ShareableList(name=b.shm.name) # In a second process c ShareableList([0, 1, 2, 3, 4], name='...') c[-1] = -999 b[-1] -999 b.shm.close() c.shm.close() c.shm.unlink()

The following examples demonstrates that "ShareableList" (and underlying "SharedMemory") objects can be pickled and unpickled if needed. Note, that it will still be the same shared object. This happens, because the deserialized object has the same unique name and is just attached to an existing object with the same name (if the object is still alive):

import pickle from multiprocessing import shared_memory sl = shared_memory.ShareableList(range(10)) list(sl) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

deserialized_sl = pickle.loads(pickle.dumps(sl)) list(deserialized_sl) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

sl[0] = -1 deserialized_sl[1] = -2 list(sl) [-1, -2, 2, 3, 4, 5, 6, 7, 8, 9] list(deserialized_sl) [-1, -2, 2, 3, 4, 5, 6, 7, 8, 9]

sl.shm.close() sl.shm.unlink()