Hello,

I came across with what seems like a bug (or at least a disagreement with the current documentation).

Discussion:

I expected that after creating a numpy-array (on terminal 1), which is backed by shared memory, I would be able to use it in other terminals until I would call `shm.unlink()` (on terminal 1), at which point, the memory block would be released and no longer accessible.

What happened is that after accessing the numpy-array from terminal 2, I called 'close()' on the local 'existing_shm' instance and exited the interpreter, which displayed the `warning` seen below. 

After, I tried to access the same shared memory block from terminal 3, and a FileNotFoundError was raised. (The same error was also raised when I tried to call 'shm.unlink()' on terminal 1, after calling 'close()' on terminal 2.)

It seems that calling `close()` on an instance, destroys further access to the shared memory block from any point, while what I expected was to be able to access the array (i.e. on terminal 2), modify it, "close" my access to it, and after be able to access the modified array on i.e. terminal 3.

If the error is on my side I apologize for raising this issue and I would appreciate for clarification on what I am doing wrong.

Thank you.
Diogo


Please check below for the commands issued:

## Terminal 1

>>> from multiprocessing import shared_memory
>>> import numpy as np
>>> 
>>> a = np.array([x for x in range(10)])
>>> shm = shared_memory.SharedMemory(create=True, size=a.nbytes)
>>> b = np.ndarray(a.shape, dtype=a.dtype, buffer=shm.buf)
>>> b[:] = a[:]
>>> 
>>> shm.name
'psm_592ec635'


## Terminal 2

>>> from multiprocessing import shared_memory
>>> import numpy as np
>>> 
>>> existing_shm = shared_memory.SharedMemory('psm_592ec635')
>>> c = np.ndarray((10,), dtype=np.int64, buffer=existing_shm.buf)
>>> 
>>> c
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> 
>>> del c
>>> existing_shm.close()
>>> 
>>> exit()
~: /usr/lib/python3.8/multiprocessing/resource_tracker.py:203: UserWarning: resource_tracker: There appear to be 1 leaked shared_memory objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '


## Finally, on terminal 3

>>> from multiprocessing import shared_memory
>>> import numpy as np
>>> 
>>> existing_shm = shared_memory.SharedMemory('psm_592ec635')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib/python3.8/multiprocessing/shared_memory.py", line 100, in __init__
    self._fd = _posixshmem.shm_open(
FileNotFoundError: [Errno 2] No such file or directory: '/psm_592ec635'