There are several processes which possibly try to call
open_file.write(data) open_file.flush()
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.,A subclass of BaseManager which can be used for the management of shared memory blocks across processes.,This class provides methods for creating and returning SharedMemory instances and for creating a list-like object (ShareableList) backed by shared memory.,multiprocessing.shared_memory — Shared memory for direct access across processes
>>> from multiprocessing import shared_memory
>>> shm_a = shared_memory.SharedMemory(create=True, size=10)
>>> type(shm_a.buf)
<class 'memoryview'>
>>> 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>>> # 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 'numpy.ndarray'>
>>> type(a)
<class 'numpy.ndarray'>
>>> 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>>> 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>>> 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>>> from multiprocessing import shared_memory
>>> a = shared_memory.ShareableList(['howdy', b'HoWdY', -273.154, 100, None, True, 42])
>>> [ type(entry) for entry in a ]
[<class 'str'>, <class 'bytes'>, <class 'float'>, <class 'int'>, <class 'NoneType'>, <class 'bool'>, <class 'int'>]
>>> 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>>> 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()
Last Updated : 18 Oct, 2021,GATE CS 2021 Syllabus
Result(in process p1): [1, 4, 9, 16]
Result(in main program): []Result(in process p1): [1, 4, 9, 16]
Sum of squares(in process p1): 30
Result(in main program): [1, 4, 9, 16]
Sum of squares(in main program): 30 result = multiprocessing.Array('i', 4)Here, we only need to specify data type. The value can be given an initial value(say 10) like this:
square_sum = multiprocessing.Value('i', 10)p1 = multiprocessing.Process(target = square_list, args = (mylist, result, square_sum))
Result(in process p1): [1, 4, 9, 16]
Sum of squares(in process p1): 30
Result(in main program): [1, 4, 9, 16]
Sum of squares(in main program): 30 result = multiprocessing.Array('i', 4)Similarly, we create a Value square_sum like this:
square_sum = multiprocessing.Value('i')p1 = multiprocessing.Process(target = square_list, args = (mylist, result, square_sum))
square_sum is given a value by using its value attribute:
square_sum.value = sum(result)
result = multiprocessing.Array('i', 4)Similarly, we create a Value square_sum like this:
square_sum = multiprocessing.Value('i')Here, we only need to specify data type. The value can be given an initial value(say 10) like this:
square_sum = multiprocessing.Value('i', 10)p1 = multiprocessing.Process(target = square_list, args = (mylist, result, square_sum))
for idx, num in enumerate(mylist):
result[idx] = num * numsquare_sum is given a value by using its value attribute:
square_sum.value = sum(result)
last modified July 29, 2022
#!/usr/bin/python
from multiprocessing
import Process
def fun(name):
print(f 'hello {name}')
def main():
p = Process(target = fun, args = ('Peter', ))
p.start()
if __name__ == '__main__':
main()We create a new process and pass a value to it.
def fun(name):
print(f 'hello {name}')The function prints the passed parameter.
def main():
p = Process(target = fun, args = ('Peter', ))
p.start()#!/usr/bin/python
from multiprocessing
import Process
import time
def fun():
print('starting fun')
time.sleep(2)
print('finishing fun')
def main():
p = Process(target = fun)
p.start()
p.join()
if __name__ == '__main__':
print('starting main')
main()
print('finishing main')
The example calls the join on the newly created process.
$. / joining.py
starting main
starting fun
finishing fun
finishing mainSharing CUDA tensors between processes is supported only in Python 3, using a spawn or forkserver start methods.,The API is 100% compatible with the original module - it’s enough to change import multiprocessing to import torch.multiprocessing to have all the tensors sent through the queues or shared via other mechanisms, moved to shared memory.,If the consumer process dies abnormally to a fatal signal, the shared tensor could be forever kept in memory as long as the sending process is running.,Multiprocessing package - torch.multiprocessing Strategy management Sharing CUDA tensors Sharing strategies File descriptor - file_descriptor File system - file_system Spawning subprocesses
# # Good x = queue.get() # do somethings with x del x
# # Bad x = queue.get() # do somethings with x # do everything else(producer have to keep x in memory)
# # producer # send tensors, do something event.wait()
# # consumer # receive tensors and use them event.set()
# not going to work x = queue.get() queue_2.put(x)
# you need to create a process - local copy x = queue.get() x_clone = x.clone() queue_2.put(x_clone)