python multiprocessing 共享内存_multiprocessing.shared_memory

3.8 新版功能.

该模块提供了一个 SharedMemory 类，用于分配和管理多核或对称多处理器(SMP)机器上进程间的共享内存。为了协助管理不同进程间的共享内存生命周期，multiprocessing.managers 模块也提供了一个 BaseManager 的子类： SharedMemoryManager。

In this module, shared memory refers to "System V 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.

classmultiprocessing.shared_memory.SharedMemory(name=None, create=False, size=0)¶

Creates a new shared memory block or attaches 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.

name is 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 controls whether a new shared memory block is created (True)

or an existing shared memory block is attached (False).

size specifies 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.

close()¶

Closes access to the shared memory from this instance. In order to

ensure proper cleanup of resources, all instances should call

close() once the instance is no longer needed. Note that calling

close() does not cause the shared memory block itself to be

destroyed.

unlink()¶

Requests that the underlying shared memory block be destroyed. In

order to ensure proper cleanup of resources, unlink() should be

called once (and only once) across all processes which have need

for the shared memory block. After requesting its destruction, a

shared memory block may or may not be immediately destroyed and

this behavior may differ across platforms. Attempts to access data

inside the shared memory block after unlink() has been called may

result in memory access errors. Note: the last process relinquishing

its hold on a shared memory block may call unlink() and

close() in either order.

buf¶

A memoryview of contents of the shared memory block.

name¶

Read-only access to the unique name of the shared memory block.

size¶

Read-only access to size in bytes of the shared memory block.

The following example demonstrates low-level use of SharedMemory

instances:

>>>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

classmultiprocessing.managers.SharedMemoryManager([address[, authkey]])¶

A subclass of 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 SharedMemory.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.

This class provides methods for creating and returning SharedMemory

instances and for creating a list-like object (ShareableList)

backed by shared memory.

Refer to multiprocessing.managers.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.

classmultiprocessing.shared_memory.ShareableList(sequence=None, *, name=None)¶

Provides a mutable list-like object where all values stored within are

stored in a shared memory block. This constrains storable values to

only the int, float, bool, str (less than 10M bytes each),

bytes (less than 10M bytes each), and None built-in data types.

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.

count(value)¶

Returns the number of occurrences of value.

index(value)¶

Returns first index position of value. Raises ValueError if

value is not present.

format¶

Read-only attribute containing the struct packing format used by

all currently stored values.

shm¶

The SharedMemory instance where the values are stored.

The following example demonstrates basic use of a ShareableList

instance:

>>>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

The following example depicts how one, two, or many processes may access the

same ShareableList by supplying the name of the shared memory block

behind it:

>>>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()