Sharing data between processes python
I'm using multiprocessing to create a sub-process to my Python app. I would like to share data between my parent process and the child process. it's important to mention that I need to share this asynchronously, means that the child process and the parent process will update the data during the code running. Show What would be the best way to perform that? asked Feb 2, 2016 at 15:25
2 This is one simple example from python documentation -
You can use pipe as well, Refer for more details - https://docs.python.org/2/library/multiprocessing.html answered Feb 2, 2016 at 15:36
AlokThakurAlokThakur 3,4591 gold badge18 silver badges31 bronze badges 4 Here's an example of multiprocess-multithread and sharing a couple variables:
answered May 5, 2021 at 6:07
grantrgrantr 4947 silver badges10 bronze badges Source code: Lib/multiprocessing/shared_memory.py New in version 3.8. This module provides a class,
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 name is the unique name for the requested shared memory, specified as a string. When creating a new shared memory block, if create
controls whether a new shared memory block is created ( 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 close ()¶Closes access to the shared memory from this instance. In order to ensure proper cleanup of resources, all instances should call unlink ()¶Requests that the underlying shared memory block be destroyed. In order to ensure proper cleanup of
resources, 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
>>> from multiprocessing import shared_memory >>> shm_a = shared_memory.SharedMemory(create=True, size=10) >>> type(shm_a.buf) The following example demonstrates a practical use of the >>> # 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)class multiprocessing.managers. SharedMemoryManager ([address[,
authkey]])¶A subclass of A call to This class provides methods for creating and returning
Refer to SharedMemory (size)¶Create and return a new
ShareableList (sequence)¶Create and return a new
The following example demonstrates the basic mechanisms of a >>> 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 >>> 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 multiprocessing.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 sequence is used
in populating a new name is the unique name for the requested shared memory, as described in the definition for count (value)¶Returns the number of occurrences of index (value)¶Returns first index position of format ¶Read-only attribute containing the shm ¶The
The following example demonstrates basic use of a
>>> from multiprocessing import shared_memory >>> a = shared_memory.ShareableList(['howdy', b'HoWdY', -273.154, 100, None, True, 42]) >>> [ type(entry) for entry in a ] [ The following example depicts how one, two, or many processes may access the same
>>> 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 >>> 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() How do I share data between multiple processes in Python?A simple way to communicate between process with multiprocessing is to use a Queue to pass messages back and forth. Any pickle-able object can pass through a Queue. This short example only passes a single message to a single worker, then the main process waits for the worker to finish.
How do I share data between processes?To share big data: named shared-memory
The fastest way to handle this is to create a file mapping object, map the file object into memory space, notify the parent process of the file mapping handle and data size, and then dump the data to the mapped buffer for the parent process to read.
How do you implement inter process communication in Python?Python library – PyCSP
In the above PyCSP process network, there are two processes – Process1 and Process 2. These processes communicate by passing messages through two channels – channel 1 and channel 2.
How do you synchronize a process in Python?Let us try to understand the above code step by step:. Firstly, a Lock object is created using: lock = multiprocessing.Lock(). Then, lock is passed as target function argument: p1 = multiprocessing.Process(target=withdraw, args=(balance,lock)) p2 = multiprocessing.Process(target=deposit, args=(balance,lock)). |