Python Multiprocessing Numpy Random [duplicate]

Answer 1

To seed a random array this post was most useful. The following g(core) function succeeded in generating a random permutation for each core.

def g(core):
    pid = mp.current_process()._identity[0]
    randst = np.random.mtrand.RandomState(pid)
    randarray = randst.randint(0,100, size=(1,100)

Answer 2

Calling g(core) and permuting a copy of the array returns 4 identically 'shuffled' arrays. This seems to indicate that the working copy is not local the child process.

What it likely indicates is that the random number generator is initialized identically in each child process, producing the same sequence. You need to seed each child's generator (perhaps throwing the child's process id into the mix).