Setting random seed in python disturbs multiprocessing

I've observed that setting a random seed before using multiprocessing in python causes strange behaviour.

In python 3.5.2, only 2 or 3 cores are used with a low percentage of used CPU. In python 2.7.13, all requested cores are used at 100%, but the code seems to never finish. When I remove the initialization of the random seed, the parallelization works fine.

This happens even though there are not an explicit use of random in the parallelized function. I now assume the seed is shared among processes and that prevents the smooth running of multiprocessing, but can someone provide the correct answer?

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I've run the code on Linux and here is a minimal code example :

  from multiprocessing import Pool
  import numpy as np
  import random

  random.seed = 2018

  NB_CPUS = 4

  def test(x):
      return x**2

  pool = Pool(NB_CPUS)
  args = [np.random.rand() for _ in range(100000)]

  results = pool.map(test, args)

  pool.terminate()
  results[-5:]