Prevent erroneous nworkers warning (#1383)

---------

Co-authored-by: Jeffrey Larson <jmlarson@anl.gov>

Author: shuds13

libensemble/ensemble.py

@@ -310,10 +310,12 @@ def __init__(
                 raise ValueError("nworkers must be specified if comms is 'local'")
 
         elif self._known_comms == "mpi" and not parse_args:
-            self.nworkers, self.is_manager = mpi_init(self._libE_specs.mpi_comm)
+            # Set internal _nworkers - not libE_specs (avoid "nworkers will be ignored" warning)
+            self._nworkers, self.is_manager = mpi_init(self._libE_specs.mpi_comm)
 
     def _parse_args(self) -> (int, bool, LibeSpecs):
-        self.nworkers, self.is_manager, libE_specs_parsed, self.extra_args = parse_args_f()
+        # Set internal _nworkers - not libE_specs (avoid "nworkers will be ignored" warning)
+        self._nworkers, self.is_manager, libE_specs_parsed, self.extra_args = parse_args_f()
 
         if not self._libE_specs:
             self._libE_specs = LibeSpecs(**libE_specs_parsed)

libensemble/tests/functionality_tests/test_mpi_warning.py

@@ -0,0 +1,58 @@
+"""
+Runs libEnsemble with Latin hypercube sampling and check no warning.
+
+Execute using MPI (e.g. 3 workers):
+   mpiexec -np 4 python test_mpi_warning.py
+
+The number of concurrent evaluations of the objective function will be 4-1=3.
+"""
+
+# Do not change these lines - they are parsed by run-tests.sh
+# TESTSUITE_COMMS: mpi
+# TESTSUITE_NPROCS: 4
+
+import numpy as np
+import os
+import time
+
+from libensemble import Ensemble
+from libensemble.gen_funcs.sampling import latin_hypercube_sample as gen_f
+
+# Import libEnsemble items for this test
+from libensemble.sim_funcs.simple_sim import norm_eval as sim_f
+from libensemble.specs import ExitCriteria, GenSpecs, SimSpecs
+
+from libensemble import logger
+
+# Main block is necessary only when using local comms with spawn start method (default on macOS and Windows).
+if __name__ == "__main__":
+    log_file = "ensemble_check_warning.log"
+    logger.set_level("MANAGER_WARNING")
+    logger.set_filename(log_file)
+
+    sampling = Ensemble()
+    sampling.libE_specs.save_every_k_sims = 100
+    sampling.sim_specs = SimSpecs(sim_f=sim_f)
+    sampling.gen_specs = GenSpecs(
+        gen_f=gen_f,
+        outputs=[("x", float, 2)],
+        user={
+            "gen_batch_size": 100,
+            "lb": np.array([-3, -2]),
+            "ub": np.array([3, 2]),
+        },
+    )
+
+    sampling.exit_criteria = ExitCriteria(sim_max=100)
+    sampling.add_random_streams()
+
+    if sampling.is_manager:
+        if os.path.exists(log_file):
+            os.remove(log_file)
+
+    sampling.run()
+    if sampling.is_manager:
+        print("len:", len(sampling.H))
+        time.sleep(0.2)
+        assert os.path.exists(log_file)
+        assert os.stat(log_file).st_size == 0, "Unexpected warning"