Repair parallelized population sampling

RELEASE-NOTES.md

@@ -11,6 +11,7 @@
 
 ### Maintenance
 - Moved math operations out of `Rice`, `TruncatedNormal`, `Triangular` and `ZeroInflatedNegativeBinomial` `random` methods. Math operations on values returned by `draw_values` might not broadcast well, and all the `size` aware broadcasting is left to `generate_samples`. Fixes [#3481](https://github.com/pymc-devs/pymc3/issues/3481) and [#3508](https://github.com/pymc-devs/pymc3/issues/3508)
+- Parallelization of population steppers (`DEMetropolis`) is now set via the `cores` argument. ([#3559](https://github.com/pymc-devs/pymc3/pull/3559))
 
 ## PyMC3 3.7 (May 29 2019)
 

pymc3/sampling.py

@@ -452,7 +452,7 @@ def sample(draws=500, step=None, init='auto', n_init=200000, start=None, trace=N
             if has_population_samplers:
                 _log.info('Population sampling ({} chains)'.format(chains))
                 _print_step_hierarchy(step)
-                trace = _sample_population(**sample_args)
+                trace = _sample_population(**sample_args, parallelize=cores > 1)
             else:
                 _log.info('Sequential sampling ({} chains in 1 job)'.format(chains))
                 _print_step_hierarchy(step)
@@ -689,7 +689,7 @@ def __init__(self, steppers, parallelize):
         if parallelize:
             try:
                 # configure a child process for each stepper
-                _log.info('Attempting to parallelize chains.')
+                _log.info('Attempting to parallelize chains to all cores. You can turn this off with `pm.sample(cores=1)`.')
                 import multiprocessing
                 for c, stepper in enumerate(tqdm(steppers)):
                     slave_end, master_end = multiprocessing.Pipe()
@@ -714,7 +714,7 @@ def __init__(self, steppers, parallelize):
                 _log.debug('Error was: ', exec_info=True)
         else:
             _log.info('Chains are not parallelized. You can enable this by passing '
-                      'pm.sample(parallelize=True).')
+                      '`pm.sample(cores=n)`, where n > 1.')
         return super().__init__()
 
     def __enter__(self):

pymc3/tests/test_step.py

@@ -915,12 +915,25 @@ def test_checks_population_size(self):
                 trace = sample(draws=100, chains=4, step=step)
         pass
 
+    def test_nonparallelized_chains_are_random(self):
+        with Model() as model:
+            x = Normal("x", 0, 1)
+            for stepper in TestPopulationSamplers.steppers:
+                step = stepper()
+                trace = sample(chains=4, cores=1, draws=20, tune=0, step=DEMetropolis())
+                samples = np.array(trace.get_values("x", combine=False))[:, 5]
+
+                assert len(set(samples)) == 4, "Parallelized {} " "chains are identical.".format(
+                    stepper
+                )
+        pass
+
     def test_parallelized_chains_are_random(self):
         with Model() as model:
             x = Normal("x", 0, 1)
             for stepper in TestPopulationSamplers.steppers:
                 step = stepper()
-                trace = sample(chains=4, draws=20, tune=0, step=DEMetropolis())
+                trace = sample(chains=4, cores=4, draws=20, tune=0, step=DEMetropolis())
                 samples = np.array(trace.get_values("x", combine=False))[:, 5]
 
                 assert len(set(samples)) == 4, "Parallelized {} " "chains are identical.".format(