Merge pull request #1535 from Libensemble/examples/abbrev_local

Examples/abbrev local

Author: shuds13

docs/FAQ.rst

@@ -90,7 +90,7 @@ Common Errors
 
 .. dropdown:: **PETSc and MPI errors with "[unset]: write_line error; fd=-1 buf=:cmd=abort exitcode=59"**
 
-  with ``python [test with PETSc].py --comms local --nworkers 4``
+  with ``python [test with PETSc].py --nworkers 4``
 
   This error occurs on some platforms when using PETSc with libEnsemble
   in ``local`` (multiprocessing) mode. We believe this is due to PETSc initializing MPI

docs/platforms/bebop.rst

@@ -69,7 +69,7 @@ Once in the interactive session, you may need to reload your modules::
 
 Now run your script with four workers (one for generator and three for simulations)::
 
-    python my_libe_script.py --comms local --nworkers 4
+    python my_libe_script.py --nworkers 4
 
 ``mpirun`` should also work. This line launches libEnsemble with a manager and
 **three** workers to one allocated compute node, with three nodes available for

docs/platforms/frontier.rst

@@ -64,7 +64,7 @@ Now grab an interactive session on one node::
 
 Then in the session run::
 
-    python run_libe_forces.py --comms local --nworkers 9
+    python run_libe_forces.py --nworkers 9
 
 This places the generator on the first worker and runs simulations on the
 others (each simulation using one GPU).

docs/platforms/improv.rst

@@ -56,7 +56,7 @@ Once in the interactive session, you may need to reload the modules::
 
 Now run forces with five workers (one for generator and four for simulations)::
 
-    python run_libe_forces.py --comms local --nworkers 5
+    python run_libe_forces.py --nworkers 5
 
 mpi4py comms
 ============

docs/platforms/platforms_index.rst

@@ -59,7 +59,7 @@ of the allocation::
 
 or::
 
-    python myscript.py --comms local --nworkers 4
+    python myscript.py --nworkers 4
 
 Either of these will run libEnsemble (inc. manager and 4 workers) on the first node. The remaining
 4 nodes will be divided among the workers for submitted applications. If the same run was

docs/platforms/polaris.rst

@@ -64,7 +64,7 @@ A simple example batch script for a libEnsemble use case that runs 5 workers
 
     cd $PBS_O_WORKDIR
 
-    python run_libe_forces.py --comms local --nworkers 5
+    python run_libe_forces.py --nworkers 5
 
 The script can be run with::
 

docs/resource_manager/overview.rst

@@ -276,7 +276,7 @@ Also, this can be set on the command line as a convenience.
 
 .. code-block:: bash
 
-    python run_ensemble.py --comms local --nworkers 5 --nresource_sets 8
+    python run_ensemble.py --nworkers 5 --nresource_sets 8
 
 .. _persistent_sampling_var_resources.py: https://github.com/Libensemble/libensemble/blob/develop/libensemble/gen_funcs/persistent_sampling_var_resources.py
 .. _test_GPU_variable_resources.py: https://github.com/Libensemble/libensemble/blob/develop/libensemble/tests/regression_tests/test_GPU_variable_resources.py

docs/resource_manager/zero_resource_workers.rst

@@ -9,7 +9,7 @@ only run ``gen_f`` functions in-place (i.e., they do not use the Executor
 to submit applications to allocated nodes). Suppose the user is using the
 :meth:`parse_args()<tools.parse_args>` function and runs::
 
-    python run_ensemble_persistent_gen.py --comms local --nworkers 3
+    python run_ensemble_persistent_gen.py --nworkers 3
 
 If three nodes are available in the node allocation, the result may look like the
 following.
@@ -21,7 +21,7 @@ following.
 
 To avoid the the wasted node above, add an extra worker::
 
-    python run_ensemble_persistent_gen.py --comms local --nworkers 4
+    python run_ensemble_persistent_gen.py --nworkers 4
 
 and in the calling script (*run_ensemble_persistent_gen.py*), explicitly set the
 number of resource sets to the number of workers that will be running simulations.

docs/running_libE.rst

@@ -53,7 +53,7 @@ supercomputers.
         or an :class:`Ensemble<libensemble.ensemble.Ensemble>` object with ``Ensemble(parse_args=True)``,
         you can specify these on the command line::
 
-            python myscript.py --comms local --nworkers N
+            python myscript.py --nworkers N
 
         This will launch one manager and ``N`` workers.
 

docs/tutorials/aposmm_tutorial.rst

@@ -117,7 +117,7 @@ busy.
 In practice, since a single worker becomes "persistent" for APOSMM, users
 should initiate one more worker than the number of parallel simulations::
 
-    python my_aposmm_routine.py --comms local --nworkers 4
+    python my_aposmm_routine.py --nworkers 4
 
 results in three workers running simulations and one running APSOMM.
 
@@ -265,7 +265,7 @@ optimization method::
 
 Finally, run this libEnsemble / APOSMM optimization routine with the following::
 
-    python my_first_aposmm.py --comms local --nworkers 4
+    python my_first_aposmm.py --nworkers 4
 
 Please note that one worker will be "persistent" for APOSMM for the duration of
 the routine.