From 4ef9318a7d35ae404a87b29802f2c3f922d4e727 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 27 May 2022 17:08:44 -0500
Subject: [PATCH 001/196] initial commit for making a pair of scripts that
 respectively produce a PSI/J spec of a libEnsemble run, and run that spec on
 a system

---
 libensemble/tools/libe-register | 33 +++++++++++++++++++++++++++++++++
 libensemble/tools/libe-submit   |  1 +
 2 files changed, 34 insertions(+)
 create mode 100644 libensemble/tools/libe-register
 create mode 100644 libensemble/tools/libe-submit

diff --git a/libensemble/tools/libe-register b/libensemble/tools/libe-register
new file mode 100644
index 0000000000..bf4251dc33
--- /dev/null
+++ b/libensemble/tools/libe-register
@@ -0,0 +1,33 @@
+#! /usr/bin/python3
+
+import os
+import sys
+import argparse
+from psij import Job, JobSpec
+from psij.resource_spec import ResourceSpecV1
+from psij.serialize import Export
+from libensemble.tools.parse_args import parser
+
+if __name__ == '__main__':
+
+    parser.description = "Produce a serialization for a libEnsemble submission on a resource."
+    parser.prog = "libe-register"
+    parser.add_argument("--nnodes", type=int, nargs="?", help="Number of nodes")
+    script = sys.argv[1]
+    my_args, unknown = parser.parse_known_args(sys.argv[2:])
+
+    assert script.endswith(".py"), "First argument doesn't appear to be a Python script."
+
+    name =  "libE-" + script.split(".py")[0]
+
+    jobspec = JobSpec(name="libe-job",
+                      arguments=[script,
+                                "--comms",
+                                my_args.comms,
+                                "--nworkers",
+                                my_args.nworkers],
+                      directory="/Users/jnavarro/Desktop/libensemble/libensemble/libensemble/tests/regression_tests",
+                      resources=ResourceSpecV1(node_count=4)
+                     )
+
+    # Export().export(obj=jobspec, dest="libe-job.json")
diff --git a/libensemble/tools/libe-submit b/libensemble/tools/libe-submit
new file mode 100644
index 0000000000..dd2027e4be
--- /dev/null
+++ b/libensemble/tools/libe-submit
@@ -0,0 +1 @@
+#! /usr/bin/python3

From 434cb5caa58fd364bcb195fd7548326539b1fc6e Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 31 May 2022 13:57:50 -0500
Subject: [PATCH 002/196] add remaining options, with sensible defaults.

---
 libensemble/tools/libe-register | 92 +++++++++++++++++++++++++++------
 1 file changed, 76 insertions(+), 16 deletions(-)

diff --git a/libensemble/tools/libe-register b/libensemble/tools/libe-register
index bf4251dc33..4df1ba7462 100644
--- a/libensemble/tools/libe-register
+++ b/libensemble/tools/libe-register
@@ -2,32 +2,92 @@
 
 import os
 import sys
+import shutil
+import pathlib
 import argparse
 from psij import Job, JobSpec
 from psij.resource_spec import ResourceSpecV1
+from psij.job_attributes import JobAttributes
 from psij.serialize import Export
-from libensemble.tools.parse_args import parser
+from libensemble.version import __version__
+from libensemble.tools.parse_args import parser as callscript_parser
 
 if __name__ == '__main__':
 
-    parser.description = "Produce a serialization for a libEnsemble submission on a resource."
-    parser.prog = "libe-register"
-    parser.add_argument("--nnodes", type=int, nargs="?", help="Number of nodes")
     script = sys.argv[1]
-    my_args, unknown = parser.parse_known_args(sys.argv[2:])
+    basename = script.split(".py")[0]
 
-    assert script.endswith(".py"), "First argument doesn't appear to be a Python script."
+    if basename == "-h":
+        outfile_default = "libe-job.json"
+        outfile_type=str
+    else:
+        outfile_default = basename + ".json"
+        outfile_type = argparse.FileType('w')
+        assert script.endswith(".py"), "First argument doesn't appear to be a Python script."
 
-    name =  "libE-" + script.split(".py")[0]
+    parser = argparse.ArgumentParser(prog="libe-register",
+        description="Produce a PSI/J representation for a libEnsemble execution.",
+        epilog="Output representations can be passed to `libe-submit`",
+        parents=[callscript_parser],
+        conflict_handler="resolve"
+    )
 
-    jobspec = JobSpec(name="libe-job",
-                      arguments=[script,
-                                "--comms",
-                                my_args.comms,
-                                "--nworkers",
-                                my_args.nworkers],
-                      directory="/Users/jnavarro/Desktop/libensemble/libensemble/libensemble/tests/regression_tests",
-                      resources=ResourceSpecV1(node_count=4)
+    parser.add_argument("-o", "--outfile", type=outfile_type, nargs="?",
+                        help="Output PSI/J representation filename.", default=outfile_default)
+
+    parser.add_argument("-n", "--nnodes", type=int, nargs="?", help="Number of nodes", default=4)
+
+    parser.add_argument("-p", "--python-path", type=pathlib.Path, nargs="?",
+                        help="Which Python to use. Default is current Python.", default=sys.executable)
+
+    parser.add_argument("-m", "--mpirun-path", type=pathlib.Path, nargs="?",
+                        help="Which `mpirun` to use (if using MPI comms). Default is `which mpirun`.", default=shutil.which('mpirun'))
+
+    parser.add_argument("-j", "--jobname", type=str, nargs="?",
+                        help="Scheduler job name.", default="libe-job")
+
+    parser.add_argument("-q", "--queue", type=str, nargs="?",
+                        help="Scheduler queue name.", default=None)
+
+    parser.add_argument("-A", "--project", type=str, nargs="?",
+                        help="Scheduler project name.", default=None)
+
+    parser.add_argument("-t", "--wallclock", type=int, nargs="?",
+                        help="Total wallclock for job.", default=30)
+
+    parser.add_argument("-d", "--directory", type=pathlib.Path, nargs="?",
+                        help="Working directory for job. Default is current directory.", default=os.getcwd())
+
+    parser.add_argument("-s", "--sched", type=str, nargs="?",
+                        help="Target scheduler name", choices = ["cobalt",
+                                        "local",
+                                        "batch-test",
+                                        "flux",
+                                        "lsf",
+                                        "rp",
+                                        "slurm"
+                                        ], default=None)
+
+    jobargs, unknown = parser.parse_known_args(sys.argv[1:])
+
+    if jobargs.comms == "local":
+        executable = jobargs.python_path
+        arguments = [script, "--comms", jobargs.comms, "--nworkers", jobargs.nworkers]
+    else: # jobargs.comms == "mpi":
+        executable = jobargs.mpirun_path
+        arguments = ["-n", jobargs.nworkers+1, str(jobargs.python_path), script]
+
+    jobspec = JobSpec(name=jobargs.jobname,
+                      executable=str(executable),
+                      arguments=arguments,
+                      directory=jobargs.directory,
+                      resources=ResourceSpecV1(node_count=jobargs.nnodes),
+                      attributes=JobAttributes(duration=jobargs.wallclock,
+                                               queue_name=jobargs.queue,
+                                               project_name=jobargs.project),
+                      launcher=jobargs.sched
                      )
 
-    # Export().export(obj=jobspec, dest="libe-job.json")
+    Export().export(obj=jobspec, dest=outfile_default)
+    print(f"libEnsemble {__version__}")
+    print(f"Exported PSI/J serialization: {outfile_default}\nOptionally adjust any fields, or specify job attributes on submission to `libe-submit`.")

From 605fdc681b0b78d890fa8ee7714e0a5dfe0ea9a9 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 31 May 2022 14:32:19 -0500
Subject: [PATCH 003/196] renames, some cleanup, begin submission script

---
 libensemble/tools/libe-submit                 |  1 -
 .../tools/{libe-register => liberegister}     | 27 ++++-------
 libensemble/tools/libesubmit                  | 45 +++++++++++++++++++
 3 files changed, 54 insertions(+), 19 deletions(-)
 delete mode 100644 libensemble/tools/libe-submit
 rename libensemble/tools/{libe-register => liberegister} (75%)
 create mode 100644 libensemble/tools/libesubmit

diff --git a/libensemble/tools/libe-submit b/libensemble/tools/libe-submit
deleted file mode 100644
index dd2027e4be..0000000000
--- a/libensemble/tools/libe-submit
+++ /dev/null
@@ -1 +0,0 @@
-#! /usr/bin/python3
diff --git a/libensemble/tools/libe-register b/libensemble/tools/liberegister
similarity index 75%
rename from libensemble/tools/libe-register
rename to libensemble/tools/liberegister
index 4df1ba7462..9fdd13267e 100644
--- a/libensemble/tools/libe-register
+++ b/libensemble/tools/liberegister
@@ -3,8 +3,8 @@
 import os
 import sys
 import shutil
-import pathlib
 import argparse
+from pathlib import Path
 from psij import Job, JobSpec
 from psij.resource_spec import ResourceSpecV1
 from psij.job_attributes import JobAttributes
@@ -25,7 +25,7 @@ if __name__ == '__main__':
         outfile_type = argparse.FileType('w')
         assert script.endswith(".py"), "First argument doesn't appear to be a Python script."
 
-    parser = argparse.ArgumentParser(prog="libe-register",
+    parser = argparse.ArgumentParser(prog="liberegister",
         description="Produce a PSI/J representation for a libEnsemble execution.",
         epilog="Output representations can be passed to `libe-submit`",
         parents=[callscript_parser],
@@ -37,11 +37,11 @@ if __name__ == '__main__':
 
     parser.add_argument("-n", "--nnodes", type=int, nargs="?", help="Number of nodes", default=4)
 
-    parser.add_argument("-p", "--python-path", type=pathlib.Path, nargs="?",
+    parser.add_argument("-p", "--python-path", type=Path, nargs="?",
                         help="Which Python to use. Default is current Python.", default=sys.executable)
 
-    parser.add_argument("-m", "--mpirun-path", type=pathlib.Path, nargs="?",
-                        help="Which `mpirun` to use (if using MPI comms). Default is `which mpirun`.", default=shutil.which('mpirun'))
+    parser.add_argument("-m", "--mpirunner", type=Path, nargs="?",
+                        help="Which mpirunner to use (if using MPI comms). Default is `which mpirun`.", default=shutil.which('mpirun'))
 
     parser.add_argument("-j", "--jobname", type=str, nargs="?",
                         help="Scheduler job name.", default="libe-job")
@@ -55,26 +55,18 @@ if __name__ == '__main__':
     parser.add_argument("-t", "--wallclock", type=int, nargs="?",
                         help="Total wallclock for job.", default=30)
 
-    parser.add_argument("-d", "--directory", type=pathlib.Path, nargs="?",
+    parser.add_argument("-d", "--directory", type=Path, nargs="?",
                         help="Working directory for job. Default is current directory.", default=os.getcwd())
 
-    parser.add_argument("-s", "--sched", type=str, nargs="?",
-                        help="Target scheduler name", choices = ["cobalt",
-                                        "local",
-                                        "batch-test",
-                                        "flux",
-                                        "lsf",
-                                        "rp",
-                                        "slurm"
-                                        ], default=None)
-
     jobargs, unknown = parser.parse_known_args(sys.argv[1:])
 
     if jobargs.comms == "local":
         executable = jobargs.python_path
         arguments = [script, "--comms", jobargs.comms, "--nworkers", jobargs.nworkers]
     else: # jobargs.comms == "mpi":
-        executable = jobargs.mpirun_path
+        executable = Path(jobargs.mpirunner)
+        if not executable.exists():
+            executable = shutil.which(jobargs.mpirunner)
         arguments = ["-n", jobargs.nworkers+1, str(jobargs.python_path), script]
 
     jobspec = JobSpec(name=jobargs.jobname,
@@ -85,7 +77,6 @@ if __name__ == '__main__':
                       attributes=JobAttributes(duration=jobargs.wallclock,
                                                queue_name=jobargs.queue,
                                                project_name=jobargs.project),
-                      launcher=jobargs.sched
                      )
 
     Export().export(obj=jobspec, dest=outfile_default)
diff --git a/libensemble/tools/libesubmit b/libensemble/tools/libesubmit
new file mode 100644
index 0000000000..78ddf97d4f
--- /dev/null
+++ b/libensemble/tools/libesubmit
@@ -0,0 +1,45 @@
+#! /usr/bin/python3
+
+import os
+import sys
+import shutil
+import argparse
+from pathlib import Path
+from psij import JobExecutor
+from libensemble.version import __version__
+
+if __name__ == '__main__':
+
+    script = sys.argv[1]
+    basename = script.split(".json")[0]
+
+    assert script.endswith(".json"), "First argument doesn't appear to be a json file."
+
+    parser = argparse.ArgumentParser(prog="libesubmit",
+        description="Submit a libEnsemble PSI/J job representation for execution. Additional options may overwrite the input file.",
+        conflict_handler="resolve"
+    )
+
+    parser.add_argument("-p", "--python-path", type=Path, nargs="?",
+                        help="Which Python to use. Default is current Python.", default=sys.executable)
+
+    parser.add_argument("-m", "--mpirunner", type=Path, nargs="?",
+                        help="Which mpirunner to use (if using MPI comms). Default is `which mpirun`.", default=shutil.which('mpirun'))
+
+    parser.add_argument("-q", "--queue", type=str, nargs="?",
+                        help="Scheduler queue name.", default=None)
+
+    parser.add_argument("-A", "--project", type=str, nargs="?",
+                        help="Scheduler project name.", default=None)
+
+    parser.add_argument("-t", "--wallclock", type=int, nargs="?",
+                        help="Total wallclock for job.", default=30)
+
+    parser.add_argument("-d", "--directory", type=Path, nargs="?",
+                        help="Working directory for job. Default is current directory.", default=os.getcwd())
+
+    jobargs, unknown = parser.parse_known_args(sys.argv[1:])
+
+    print(f"libEnsemble {__version__}")
+    print(f"Exported PSI/J serialization: {outfile_default}\nOptionally adjust any fields, or specify job attributes on submission to `libe-submit`.")
+#! /usr/bin/python3

From 2618db8cb7d79fd68fd592bec511874eecd0796b Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 31 May 2022 14:32:58 -0500
Subject: [PATCH 004/196] black first script

---
 libensemble/tools/liberegister | 125 +++++++++++++++++++++++----------
 1 file changed, 89 insertions(+), 36 deletions(-)

diff --git a/libensemble/tools/liberegister b/libensemble/tools/liberegister
index 9fdd13267e..06879a127d 100644
--- a/libensemble/tools/liberegister
+++ b/libensemble/tools/liberegister
@@ -12,73 +12,126 @@ from psij.serialize import Export
 from libensemble.version import __version__
 from libensemble.tools.parse_args import parser as callscript_parser
 
-if __name__ == '__main__':
+if __name__ == "__main__":
 
     script = sys.argv[1]
     basename = script.split(".py")[0]
 
     if basename == "-h":
         outfile_default = "libe-job.json"
-        outfile_type=str
+        outfile_type = str
     else:
         outfile_default = basename + ".json"
-        outfile_type = argparse.FileType('w')
-        assert script.endswith(".py"), "First argument doesn't appear to be a Python script."
+        outfile_type = argparse.FileType("w")
+        assert script.endswith(
+            ".py"
+        ), "First argument doesn't appear to be a Python script."
 
-    parser = argparse.ArgumentParser(prog="liberegister",
+    parser = argparse.ArgumentParser(
+        prog="liberegister",
         description="Produce a PSI/J representation for a libEnsemble execution.",
         epilog="Output representations can be passed to `libe-submit`",
         parents=[callscript_parser],
-        conflict_handler="resolve"
+        conflict_handler="resolve",
     )
 
-    parser.add_argument("-o", "--outfile", type=outfile_type, nargs="?",
-                        help="Output PSI/J representation filename.", default=outfile_default)
+    parser.add_argument(
+        "-o",
+        "--outfile",
+        type=outfile_type,
+        nargs="?",
+        help="Output PSI/J representation filename.",
+        default=outfile_default,
+    )
 
-    parser.add_argument("-n", "--nnodes", type=int, nargs="?", help="Number of nodes", default=4)
+    parser.add_argument(
+        "-n", "--nnodes", type=int, nargs="?", help="Number of nodes", default=4
+    )
 
-    parser.add_argument("-p", "--python-path", type=Path, nargs="?",
-                        help="Which Python to use. Default is current Python.", default=sys.executable)
+    parser.add_argument(
+        "-p",
+        "--python-path",
+        type=Path,
+        nargs="?",
+        help="Which Python to use. Default is current Python.",
+        default=sys.executable,
+    )
 
-    parser.add_argument("-m", "--mpirunner", type=Path, nargs="?",
-                        help="Which mpirunner to use (if using MPI comms). Default is `which mpirun`.", default=shutil.which('mpirun'))
+    parser.add_argument(
+        "-m",
+        "--mpirunner",
+        type=Path,
+        nargs="?",
+        help="Which mpirunner to use (if using MPI comms). Default is `which mpirun`.",
+        default=shutil.which("mpirun"),
+    )
 
-    parser.add_argument("-j", "--jobname", type=str, nargs="?",
-                        help="Scheduler job name.", default="libe-job")
+    parser.add_argument(
+        "-j",
+        "--jobname",
+        type=str,
+        nargs="?",
+        help="Scheduler job name.",
+        default="libe-job",
+    )
 
-    parser.add_argument("-q", "--queue", type=str, nargs="?",
-                        help="Scheduler queue name.", default=None)
+    parser.add_argument(
+        "-q", "--queue", type=str, nargs="?", help="Scheduler queue name.", default=None
+    )
 
-    parser.add_argument("-A", "--project", type=str, nargs="?",
-                        help="Scheduler project name.", default=None)
+    parser.add_argument(
+        "-A",
+        "--project",
+        type=str,
+        nargs="?",
+        help="Scheduler project name.",
+        default=None,
+    )
 
-    parser.add_argument("-t", "--wallclock", type=int, nargs="?",
-                        help="Total wallclock for job.", default=30)
+    parser.add_argument(
+        "-t",
+        "--wallclock",
+        type=int,
+        nargs="?",
+        help="Total wallclock for job.",
+        default=30,
+    )
 
-    parser.add_argument("-d", "--directory", type=Path, nargs="?",
-                        help="Working directory for job. Default is current directory.", default=os.getcwd())
+    parser.add_argument(
+        "-d",
+        "--directory",
+        type=Path,
+        nargs="?",
+        help="Working directory for job. Default is current directory.",
+        default=os.getcwd(),
+    )
 
     jobargs, unknown = parser.parse_known_args(sys.argv[1:])
 
     if jobargs.comms == "local":
         executable = jobargs.python_path
         arguments = [script, "--comms", jobargs.comms, "--nworkers", jobargs.nworkers]
-    else: # jobargs.comms == "mpi":
+    else:  # jobargs.comms == "mpi":
         executable = Path(jobargs.mpirunner)
         if not executable.exists():
             executable = shutil.which(jobargs.mpirunner)
-        arguments = ["-n", jobargs.nworkers+1, str(jobargs.python_path), script]
-
-    jobspec = JobSpec(name=jobargs.jobname,
-                      executable=str(executable),
-                      arguments=arguments,
-                      directory=jobargs.directory,
-                      resources=ResourceSpecV1(node_count=jobargs.nnodes),
-                      attributes=JobAttributes(duration=jobargs.wallclock,
-                                               queue_name=jobargs.queue,
-                                               project_name=jobargs.project),
-                     )
+        arguments = ["-n", jobargs.nworkers + 1, str(jobargs.python_path), script]
+
+    jobspec = JobSpec(
+        name=jobargs.jobname,
+        executable=str(executable),
+        arguments=arguments,
+        directory=jobargs.directory,
+        resources=ResourceSpecV1(node_count=jobargs.nnodes),
+        attributes=JobAttributes(
+            duration=jobargs.wallclock,
+            queue_name=jobargs.queue,
+            project_name=jobargs.project,
+        ),
+    )
 
     Export().export(obj=jobspec, dest=outfile_default)
     print(f"libEnsemble {__version__}")
-    print(f"Exported PSI/J serialization: {outfile_default}\nOptionally adjust any fields, or specify job attributes on submission to `libe-submit`.")
+    print(
+        f"Exported PSI/J serialization: {outfile_default}\nOptionally adjust any fields, or specify job attributes on submission to `libe-submit`."
+    )

From e5a5f06626d1996bcc1c0584e2ca3d236ee41433 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 31 May 2022 15:07:23 -0500
Subject: [PATCH 005/196] add launcher choices

---
 libensemble/tools/libesubmit | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/libensemble/tools/libesubmit b/libensemble/tools/libesubmit
index 78ddf97d4f..05de5d3d6b 100644
--- a/libensemble/tools/libesubmit
+++ b/libensemble/tools/libesubmit
@@ -5,14 +5,12 @@ import sys
 import shutil
 import argparse
 from pathlib import Path
-from psij import JobExecutor
+from psij import JobExecutor, Import, JobSpec
 from libensemble.version import __version__
 
 if __name__ == '__main__':
 
     script = sys.argv[1]
-    basename = script.split(".json")[0]
-
     assert script.endswith(".json"), "First argument doesn't appear to be a json file."
 
     parser = argparse.ArgumentParser(prog="libesubmit",
@@ -20,6 +18,11 @@ if __name__ == '__main__':
         conflict_handler="resolve"
     )
 
+        choices = ["cobalt", "local", "flux", "lsf", "pbspro", "rp", "slurm",
+                   "aprun", "single", "multiple", "mpirun", "jsrun", "srun"]
+
+    parser.add_argument("-l", "--launcher", choices=choices)
+
     parser.add_argument("-p", "--python-path", type=Path, nargs="?",
                         help="Which Python to use. Default is current Python.", default=sys.executable)
 
@@ -41,5 +44,8 @@ if __name__ == '__main__':
     jobargs, unknown = parser.parse_known_args(sys.argv[1:])
 
     print(f"libEnsemble {__version__}")
-    print(f"Exported PSI/J serialization: {outfile_default}\nOptionally adjust any fields, or specify job attributes on submission to `libe-submit`.")
-#! /usr/bin/python3
+    print(f"Imported PSI/J serialization: {outfile_default}. Preparing submission.")
+
+    importer = Import()
+    jobspec = importer.load(script)
+    assert isinstance(jobspec, JobSpec), "Invalid input file."

From c5cd5fd18c5dbef8577dc3792974b1ebe20c8682 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 1 Jun 2022 16:55:41 -0500
Subject: [PATCH 006/196] replace some unportable defaults, small fixes,
 instantiate psi/j executor

---
 libensemble/tools/liberegister | 16 +++++++++-------
 libensemble/tools/libesubmit   | 18 ++++++++++++------
 2 files changed, 21 insertions(+), 13 deletions(-)

diff --git a/libensemble/tools/liberegister b/libensemble/tools/liberegister
index 06879a127d..678d64fa36 100644
--- a/libensemble/tools/liberegister
+++ b/libensemble/tools/liberegister
@@ -53,8 +53,8 @@ if __name__ == "__main__":
         "--python-path",
         type=Path,
         nargs="?",
-        help="Which Python to use. Default is current Python.",
-        default=sys.executable,
+        help="Which Python to use.",
+        default="python",
     )
 
     parser.add_argument(
@@ -62,8 +62,8 @@ if __name__ == "__main__":
         "--mpirunner",
         type=Path,
         nargs="?",
-        help="Which mpirunner to use (if using MPI comms). Default is `which mpirun`.",
-        default=shutil.which("mpirun"),
+        help="Which mpirunner to use (if using MPI comms). Default is `mpirun`.",
+        default="mpirun",
     )
 
     parser.add_argument(
@@ -84,7 +84,7 @@ if __name__ == "__main__":
         "--project",
         type=str,
         nargs="?",
-        help="Scheduler project name.",
+        help="Project name for billing hours.",
         default=None,
     )
 
@@ -102,12 +102,14 @@ if __name__ == "__main__":
         "--directory",
         type=Path,
         nargs="?",
-        help="Working directory for job. Default is current directory.",
-        default=os.getcwd(),
+        help="Working directory for job.",
+        default=None,
     )
 
     jobargs, unknown = parser.parse_known_args(sys.argv[1:])
 
+    assert jobargs.nworkers and jobargs.comms, "Must supply the --comms and --nworkers options"
+
     if jobargs.comms == "local":
         executable = jobargs.python_path
         arguments = [script, "--comms", jobargs.comms, "--nworkers", jobargs.nworkers]
diff --git a/libensemble/tools/libesubmit b/libensemble/tools/libesubmit
index 05de5d3d6b..e41cd74735 100644
--- a/libensemble/tools/libesubmit
+++ b/libensemble/tools/libesubmit
@@ -5,21 +5,18 @@ import sys
 import shutil
 import argparse
 from pathlib import Path
-from psij import JobExecutor, Import, JobSpec
+from psij import JobExecutor, Import, JobSpec, Job
 from libensemble.version import __version__
 
 if __name__ == '__main__':
 
-    script = sys.argv[1]
-    assert script.endswith(".json"), "First argument doesn't appear to be a json file."
-
     parser = argparse.ArgumentParser(prog="libesubmit",
         description="Submit a libEnsemble PSI/J job representation for execution. Additional options may overwrite the input file.",
         conflict_handler="resolve"
     )
 
-        choices = ["cobalt", "local", "flux", "lsf", "pbspro", "rp", "slurm",
-                   "aprun", "single", "multiple", "mpirun", "jsrun", "srun"]
+    choices = ["cobalt", "local", "flux", "lsf", "pbspro", "rp", "slurm",
+              "aprun", "single", "multiple", "mpirun", "jsrun", "srun"]
 
     parser.add_argument("-l", "--launcher", choices=choices)
 
@@ -46,6 +43,15 @@ if __name__ == '__main__':
     print(f"libEnsemble {__version__}")
     print(f"Imported PSI/J serialization: {outfile_default}. Preparing submission.")
 
+    script = sys.argv[1]
+    assert script.endswith(".json"), "First argument doesn't appear to be a json file."
+
     importer = Import()
     jobspec = importer.load(script)
     assert isinstance(jobspec, JobSpec), "Invalid input file."
+
+    jex = JobExecutor.get_instance(jobargs.launcher)
+    job = Job()
+    job.spec = jobspec
+
+    jex.submit(job)

From 750d522070089e0caa58b3ad30e1ee9b36e99f08 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 3 Jun 2022 14:46:20 -0500
Subject: [PATCH 007/196] small adjusmtments, auto-specify PYTHONNOUSERSITE,
 add logic to libesubmit to crawl for calling script if matching script not
 found in current directory

---
 libensemble/tools/liberegister | 20 ++++-------
 libensemble/tools/libesubmit   | 61 +++++++++++++++++++++++++++++++---
 2 files changed, 63 insertions(+), 18 deletions(-)

diff --git a/libensemble/tools/liberegister b/libensemble/tools/liberegister
index 678d64fa36..6f1f09bc90 100644
--- a/libensemble/tools/liberegister
+++ b/libensemble/tools/liberegister
@@ -57,14 +57,10 @@ if __name__ == "__main__":
         default="python",
     )
 
-    parser.add_argument(
-        "-m",
-        "--mpirunner",
-        type=Path,
-        nargs="?",
-        help="Which mpirunner to use (if using MPI comms). Default is `mpirun`.",
-        default="mpirun",
-    )
+    choices = ["cobalt", "local", "flux", "lsf", "pbspro", "rp", "slurm",
+              "aprun", "single", "multiple", "mpirun", "jsrun", "srun"]
+
+    parser.add_argument("-l", "--launcher", choices=choices, help="Which MPI or job launcher to use.")
 
     parser.add_argument(
         "-j",
@@ -109,21 +105,19 @@ if __name__ == "__main__":
     jobargs, unknown = parser.parse_known_args(sys.argv[1:])
 
     assert jobargs.nworkers and jobargs.comms, "Must supply the --comms and --nworkers options"
+    executable = jobargs.python_path
 
     if jobargs.comms == "local":
-        executable = jobargs.python_path
         arguments = [script, "--comms", jobargs.comms, "--nworkers", jobargs.nworkers]
     else:  # jobargs.comms == "mpi":
-        executable = Path(jobargs.mpirunner)
-        if not executable.exists():
-            executable = shutil.which(jobargs.mpirunner)
-        arguments = ["-n", jobargs.nworkers + 1, str(jobargs.python_path), script]
+        arguments = [str(jobargs.python_path), script]
 
     jobspec = JobSpec(
         name=jobargs.jobname,
         executable=str(executable),
         arguments=arguments,
         directory=jobargs.directory,
+        environment={"PYTHONNOUSERSITE": 1}
         resources=ResourceSpecV1(node_count=jobargs.nnodes),
         attributes=JobAttributes(
             duration=jobargs.wallclock,
diff --git a/libensemble/tools/libesubmit b/libensemble/tools/libesubmit
index e41cd74735..0315e774e7 100644
--- a/libensemble/tools/libesubmit
+++ b/libensemble/tools/libesubmit
@@ -2,8 +2,10 @@
 
 import os
 import sys
+import time
 import shutil
 import argparse
+from tqdm.auto import tqdm
 from pathlib import Path
 from psij import JobExecutor, Import, JobSpec, Job
 from libensemble.version import __version__
@@ -20,6 +22,8 @@ if __name__ == '__main__':
 
     parser.add_argument("-l", "--launcher", choices=choices)
 
+    parser.add_argument("-w", "--wait", action="store_true", help="Wait for exit_criteria evaluations to complete before exiting.")
+
     parser.add_argument("-p", "--python-path", type=Path, nargs="?",
                         help="Which Python to use. Default is current Python.", default=sys.executable)
 
@@ -33,25 +37,72 @@ if __name__ == '__main__':
                         help="Scheduler project name.", default=None)
 
     parser.add_argument("-t", "--wallclock", type=int, nargs="?",
-                        help="Total wallclock for job.", default=30)
+                        help="Total wallclock for job. Default is 30 minutes.", default=30)
 
     parser.add_argument("-d", "--directory", type=Path, nargs="?",
                         help="Working directory for job. Default is current directory.", default=os.getcwd())
 
     jobargs, unknown = parser.parse_known_args(sys.argv[1:])
 
-    print(f"libEnsemble {__version__}")
-    print(f"Imported PSI/J serialization: {outfile_default}. Preparing submission.")
-
     script = sys.argv[1]
-    assert script.endswith(".json"), "First argument doesn't appear to be a json file."
+    assert script.endswith(".json"), "First argument doesn't appear to be a .json file."
+
+    print(f"*** libEnsemble {__version__} ***")
+    print(f"Imported PSI/J serialization: {script}. Preparing submission...")
 
     importer = Import()
     jobspec = importer.load(script)
     assert isinstance(jobspec, JobSpec), "Invalid input file."
 
+    jobspec.attributes.project_name = jobargs.project
+    jobspec.attributes.queue_name = jobargs.queue
+
+    # we enforced passing a python script in liberegister
+    callscript = [i for i in jobspec.arguments if str(i).endswith('.py')][0]
+    print(f"Calling script: {callscript}. Checking in: {jobargs.directory}")
+
+    if callscript not in os.listdir(jobargs.directory):
+        print('... not found!')
+        exit = input("Check elsewhere, starting from your home directory? (Y/N) ")
+        if exit.upper() != 'Y':
+            print("Exiting")
+            sys.exit()
+
+        def libe_walkdir(folder):
+            """Walk through every file in a directory"""
+            for dirpath, dirs, files in os.walk(folder):
+                for filename in files:
+                    yield os.path.abspath(os.path.join(dirpath, filename))
+
+        print("preparing... ctrl+c to abort.")
+        filescount = 0
+        for _ in tqdm(libe_walkdir(os.path.expanduser('~'))):
+            filescount += 1
+
+        print("detecting... ctrl+c to abort.")
+        candidate_script_paths = []
+        try:
+            for filepath in tqdm(libe_walkdir(os.path.expanduser('~')), total=filescount):
+                if callscript in filepath.split('/'):
+                    candidate_script_paths.append(filepath)
+                    tqdm.write(f"{len(candidate_script_paths)}. {filepath}")
+
+            exit = input("Specify a detected script: ")
+            new_callscript = candidate_script_paths[int(exit)-1]
+
+        except KeyboardInterrupt:
+            exit = input("detection interrupted. ctrl+c again to exit, or specify a detected script: ")
+            new_callscript = candidate_script_paths[int(exit)-1]
+
+        jobspec.arguments[jobspec.arugments.index(callscript)] = new_callscript
+
+    else:
+        print('...found! Proceding.')
+
     jex = JobExecutor.get_instance(jobargs.launcher)
     job = Job()
     job.spec = jobspec
 
     jex.submit(job)
+    if jobargs.wait:
+        job.wait()

From 076972ebdf55ab3cbcf3bfef0b2aaea3d33dd46d Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 3 Jun 2022 15:28:42 -0500
Subject: [PATCH 008/196] small fixes

---
 libensemble/tools/liberegister | 2 +-
 libensemble/tools/libesubmit   | 3 ---
 2 files changed, 1 insertion(+), 4 deletions(-)

diff --git a/libensemble/tools/liberegister b/libensemble/tools/liberegister
index 6f1f09bc90..5451c6d769 100644
--- a/libensemble/tools/liberegister
+++ b/libensemble/tools/liberegister
@@ -117,7 +117,7 @@ if __name__ == "__main__":
         executable=str(executable),
         arguments=arguments,
         directory=jobargs.directory,
-        environment={"PYTHONNOUSERSITE": 1}
+        environment={"PYTHONNOUSERSITE": 1},
         resources=ResourceSpecV1(node_count=jobargs.nnodes),
         attributes=JobAttributes(
             duration=jobargs.wallclock,
diff --git a/libensemble/tools/libesubmit b/libensemble/tools/libesubmit
index 0315e774e7..41ce44b97c 100644
--- a/libensemble/tools/libesubmit
+++ b/libensemble/tools/libesubmit
@@ -27,9 +27,6 @@ if __name__ == '__main__':
     parser.add_argument("-p", "--python-path", type=Path, nargs="?",
                         help="Which Python to use. Default is current Python.", default=sys.executable)
 
-    parser.add_argument("-m", "--mpirunner", type=Path, nargs="?",
-                        help="Which mpirunner to use (if using MPI comms). Default is `which mpirun`.", default=shutil.which('mpirun'))
-
     parser.add_argument("-q", "--queue", type=str, nargs="?",
                         help="Scheduler queue name.", default=None)
 

From 0d18e26e9283015b49a0c09ab4416a238feec748 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 6 Jun 2022 16:49:18 -0500
Subject: [PATCH 009/196] small adjustments

---
 libensemble/tools/liberegister | 2 +-
 libensemble/tools/libesubmit   | 5 ++---
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/libensemble/tools/liberegister b/libensemble/tools/liberegister
index 5451c6d769..32d31a03b5 100644
--- a/libensemble/tools/liberegister
+++ b/libensemble/tools/liberegister
@@ -108,7 +108,7 @@ if __name__ == "__main__":
     executable = jobargs.python_path
 
     if jobargs.comms == "local":
-        arguments = [script, "--comms", jobargs.comms, "--nworkers", jobargs.nworkers]
+        arguments = [script, "--comms", jobargs.comms, "--nworkers", str(jobargs.nworkers)]
     else:  # jobargs.comms == "mpi":
         arguments = [str(jobargs.python_path), script]
 
diff --git a/libensemble/tools/libesubmit b/libensemble/tools/libesubmit
index 41ce44b97c..ad8c323c0c 100644
--- a/libensemble/tools/libesubmit
+++ b/libensemble/tools/libesubmit
@@ -17,8 +17,7 @@ if __name__ == '__main__':
         conflict_handler="resolve"
     )
 
-    choices = ["cobalt", "local", "flux", "lsf", "pbspro", "rp", "slurm",
-              "aprun", "single", "multiple", "mpirun", "jsrun", "srun"]
+    choices = ["cobalt", "local", "flux", "lsf", "pbspro", "rp", "slurm"]
 
     parser.add_argument("-l", "--launcher", choices=choices)
 
@@ -91,7 +90,7 @@ if __name__ == '__main__':
             exit = input("detection interrupted. ctrl+c again to exit, or specify a detected script: ")
             new_callscript = candidate_script_paths[int(exit)-1]
 
-        jobspec.arguments[jobspec.arugments.index(callscript)] = new_callscript
+        jobspec.arguments[jobspec.arguments.index(callscript)] = new_callscript
 
     else:
         print('...found! Proceding.')

From a9991a5d76a310b8302ec8132d825d60825bb7e0 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 8 Jun 2022 11:07:15 -0500
Subject: [PATCH 010/196] various small fixes, plus allow user to narrow down
 search directory for calling script

---
 libensemble/tools/liberegister |  4 +--
 libensemble/tools/libesubmit   | 54 +++++++++++++++++++++++++++-------
 2 files changed, 45 insertions(+), 13 deletions(-)

diff --git a/libensemble/tools/liberegister b/libensemble/tools/liberegister
index 32d31a03b5..4dd8af6184 100644
--- a/libensemble/tools/liberegister
+++ b/libensemble/tools/liberegister
@@ -117,7 +117,7 @@ if __name__ == "__main__":
         executable=str(executable),
         arguments=arguments,
         directory=jobargs.directory,
-        environment={"PYTHONNOUSERSITE": 1},
+        environment={"PYTHONNOUSERSITE": "1"},
         resources=ResourceSpecV1(node_count=jobargs.nnodes),
         attributes=JobAttributes(
             duration=jobargs.wallclock,
@@ -127,7 +127,7 @@ if __name__ == "__main__":
     )
 
     Export().export(obj=jobspec, dest=outfile_default)
-    print(f"libEnsemble {__version__}")
+    print(f"*** libEnsemble {__version__} ***")
     print(
         f"Exported PSI/J serialization: {outfile_default}\nOptionally adjust any fields, or specify job attributes on submission to `libe-submit`."
     )
diff --git a/libensemble/tools/libesubmit b/libensemble/tools/libesubmit
index ad8c323c0c..2994614d3b 100644
--- a/libensemble/tools/libesubmit
+++ b/libensemble/tools/libesubmit
@@ -4,10 +4,13 @@ import os
 import sys
 import time
 import shutil
+import socket
 import argparse
 from tqdm.auto import tqdm
 from pathlib import Path
-from psij import JobExecutor, Import, JobSpec, Job
+from psij import JobExecutor, Import, Export, JobSpec, Job
+from psij.resource_spec import ResourceSpecV1
+from psij.job_attributes import JobAttributes
 from libensemble.version import __version__
 
 if __name__ == '__main__':
@@ -19,7 +22,7 @@ if __name__ == '__main__':
 
     choices = ["cobalt", "local", "flux", "lsf", "pbspro", "rp", "slurm"]
 
-    parser.add_argument("-l", "--launcher", choices=choices)
+    parser.add_argument("-l", "--launcher", choices=choices, required=True)
 
     parser.add_argument("-w", "--wait", action="store_true", help="Wait for exit_criteria evaluations to complete before exiting.")
 
@@ -50,38 +53,56 @@ if __name__ == '__main__':
     jobspec = importer.load(script)
     assert isinstance(jobspec, JobSpec), "Invalid input file."
 
+    jobspec.directory = str(jobargs.directory)
     jobspec.attributes.project_name = jobargs.project
     jobspec.attributes.queue_name = jobargs.queue
+    if jobspec.executable == 'python':
+        jobspec.executable = str(jobargs.python_path)
+    jobspec.attributes.duration = jobargs.wallclock
 
     # we enforced passing a python script in liberegister
     callscript = [i for i in jobspec.arguments if str(i).endswith('.py')][0]
-    print(f"Calling script: {callscript}. Checking in: {jobargs.directory}")
+    print(f"Calling script: {callscript}")
 
-    if callscript not in os.listdir(jobargs.directory):
-        print('... not found!')
-        exit = input("Check elsewhere, starting from your home directory? (Y/N) ")
+    if callscript not in os.listdir(jobargs.directory) and not os.path.isfile(callscript):
+        print('... not found in Job working directory!')
+        exit = input("Check somewhere else? (Y/N): ")
         if exit.upper() != 'Y':
             print("Exiting")
             sys.exit()
 
-        def libe_walkdir(folder):
+        home = os.path.expanduser('~')
+        check_dirs = []
+        for i in os.listdir(home):
+            if os.path.isdir(os.path.join(home, i)) and '.' not in i:
+                check_dirs.append(i)
+
+        print(home + ":")
+        for i in enumerate(check_dirs):
+            print(f"{i[0]}. {i[1]}")
+
+        inchoice = input("Specify a starting directory: ")
+        choice = home + "/" + check_dirs[int(inchoice)]
+
+        def walkdir(folder):
             """Walk through every file in a directory"""
-            for dirpath, dirs, files in os.walk(folder):
+            for dirpath, dirs, files in os.walk(folder, topdown=True):
                 for filename in files:
                     yield os.path.abspath(os.path.join(dirpath, filename))
 
         print("preparing... ctrl+c to abort.")
         filescount = 0
-        for _ in tqdm(libe_walkdir(os.path.expanduser('~'))):
+        for _ in tqdm(walkdir(choice)):
             filescount += 1
 
         print("detecting... ctrl+c to abort.")
+        print(home + ":")
         candidate_script_paths = []
         try:
-            for filepath in tqdm(libe_walkdir(os.path.expanduser('~')), total=filescount):
+            for filepath in tqdm(walkdir(choice), total=filescount):
                 if callscript in filepath.split('/'):
                     candidate_script_paths.append(filepath)
-                    tqdm.write(f"{len(candidate_script_paths)}. {filepath}")
+                    tqdm.write(f"{len(candidate_script_paths)}. {'...' + filepath.split(choice)[1]}")
 
             exit = input("Specify a detected script: ")
             new_callscript = candidate_script_paths[int(exit)-1]
@@ -95,10 +116,21 @@ if __name__ == '__main__':
     else:
         print('...found! Proceding.')
 
+    # Little bit strange I have to re-initialize this class to re-serialize
+    jobspec.resources = ResourceSpecV1(node_count=jobspec.resources['node_count'])
+
+    if socket.gethostname() in script:
+        reserialdest = script
+    else:
+        reserialdest = script.replace(".", "." + socket.gethostname() + ".")
+
+    Export().export(obj=jobspec, dest=reserialdest)
+
     jex = JobExecutor.get_instance(jobargs.launcher)
     job = Job()
     job.spec = jobspec
 
+    print('Submitting Job!:', job)
     jex.submit(job)
     if jobargs.wait:
         job.wait()

From 6eef8f052502f3befc7af26d79d1d5c35a27191d Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 8 Jun 2022 12:15:42 -0500
Subject: [PATCH 011/196] black, and create stdout/stderr files

---
 libensemble/tools/liberegister |  22 +++++--
 libensemble/tools/libesubmit   | 113 +++++++++++++++++++++++----------
 2 files changed, 94 insertions(+), 41 deletions(-)

diff --git a/libensemble/tools/liberegister b/libensemble/tools/liberegister
index 4dd8af6184..2e8504f9b2 100644
--- a/libensemble/tools/liberegister
+++ b/libensemble/tools/liberegister
@@ -57,10 +57,12 @@ if __name__ == "__main__":
         default="python",
     )
 
-    choices = ["cobalt", "local", "flux", "lsf", "pbspro", "rp", "slurm",
-              "aprun", "single", "multiple", "mpirun", "jsrun", "srun"]
+    choices = ["cobalt", "local", "flux", "lsf", "pbspro", "rp", "slurm"]
 
-    parser.add_argument("-l", "--launcher", choices=choices, help="Which MPI or job launcher to use.")
+    parser.add_argument(
+        "-s", "--scheduler", choices=choices, help="Which scheduler to use.",
+        default=None
+    )
 
     parser.add_argument(
         "-j",
@@ -104,13 +106,21 @@ if __name__ == "__main__":
 
     jobargs, unknown = parser.parse_known_args(sys.argv[1:])
 
-    assert jobargs.nworkers and jobargs.comms, "Must supply the --comms and --nworkers options"
+    assert (
+        jobargs.nworkers and jobargs.comms
+    ), "Must supply the --comms and --nworkers options"
     executable = jobargs.python_path
 
     if jobargs.comms == "local":
-        arguments = [script, "--comms", jobargs.comms, "--nworkers", str(jobargs.nworkers)]
+        arguments = [
+            script,
+            "--comms",
+            jobargs.comms,
+            "--nworkers",
+            str(jobargs.nworkers),
+        ]
     else:  # jobargs.comms == "mpi":
-        arguments = [str(jobargs.python_path), script]
+        arguments = [script]
 
     jobspec = JobSpec(
         name=jobargs.jobname,
diff --git a/libensemble/tools/libesubmit b/libensemble/tools/libesubmit
index 2994614d3b..4100570e6a 100644
--- a/libensemble/tools/libesubmit
+++ b/libensemble/tools/libesubmit
@@ -4,7 +4,6 @@ import os
 import sys
 import time
 import shutil
-import socket
 import argparse
 from tqdm.auto import tqdm
 from pathlib import Path
@@ -13,33 +12,64 @@ from psij.resource_spec import ResourceSpecV1
 from psij.job_attributes import JobAttributes
 from libensemble.version import __version__
 
-if __name__ == '__main__':
+if __name__ == "__main__":
 
-    parser = argparse.ArgumentParser(prog="libesubmit",
+    parser = argparse.ArgumentParser(
+        prog="libesubmit",
         description="Submit a libEnsemble PSI/J job representation for execution. Additional options may overwrite the input file.",
-        conflict_handler="resolve"
+        conflict_handler="resolve",
     )
 
     choices = ["cobalt", "local", "flux", "lsf", "pbspro", "rp", "slurm"]
 
-    parser.add_argument("-l", "--launcher", choices=choices, required=True)
+    parser.add_argument("-s", "--scheduler", choices=choices, required=True)
 
-    parser.add_argument("-w", "--wait", action="store_true", help="Wait for exit_criteria evaluations to complete before exiting.")
+    parser.add_argument(
+        "-w",
+        "--wait",
+        action="store_true",
+        help="Wait for exit_criteria evaluations to complete before exiting.",
+    )
 
-    parser.add_argument("-p", "--python-path", type=Path, nargs="?",
-                        help="Which Python to use. Default is current Python.", default=sys.executable)
+    parser.add_argument(
+        "-p",
+        "--python-path",
+        type=Path,
+        nargs="?",
+        help="Which Python to use. Default is current Python.",
+        default=sys.executable,
+    )
 
-    parser.add_argument("-q", "--queue", type=str, nargs="?",
-                        help="Scheduler queue name.", default=None)
+    parser.add_argument(
+        "-q", "--queue", type=str, nargs="?", help="Scheduler queue name.", default=None
+    )
 
-    parser.add_argument("-A", "--project", type=str, nargs="?",
-                        help="Scheduler project name.", default=None)
+    parser.add_argument(
+        "-A",
+        "--project",
+        type=str,
+        nargs="?",
+        help="Scheduler project name.",
+        default=None,
+    )
 
-    parser.add_argument("-t", "--wallclock", type=int, nargs="?",
-                        help="Total wallclock for job. Default is 30 minutes.", default=30)
+    parser.add_argument(
+        "-t",
+        "--wallclock",
+        type=int,
+        nargs="?",
+        help="Total wallclock for job. Default is 30 minutes.",
+        default=30,
+    )
 
-    parser.add_argument("-d", "--directory", type=Path, nargs="?",
-                        help="Working directory for job. Default is current directory.", default=os.getcwd())
+    parser.add_argument(
+        "-d",
+        "--directory",
+        type=Path,
+        nargs="?",
+        help="Working directory for job. Default is current directory.",
+        default=os.getcwd(),
+    )
 
     jobargs, unknown = parser.parse_known_args(sys.argv[1:])
 
@@ -56,25 +86,27 @@ if __name__ == '__main__':
     jobspec.directory = str(jobargs.directory)
     jobspec.attributes.project_name = jobargs.project
     jobspec.attributes.queue_name = jobargs.queue
-    if jobspec.executable == 'python':
+    if jobspec.executable == "python":
         jobspec.executable = str(jobargs.python_path)
     jobspec.attributes.duration = jobargs.wallclock
 
     # we enforced passing a python script in liberegister
-    callscript = [i for i in jobspec.arguments if str(i).endswith('.py')][0]
+    callscript = [i for i in jobspec.arguments if str(i).endswith(".py")][0]
     print(f"Calling script: {callscript}")
 
-    if callscript not in os.listdir(jobargs.directory) and not os.path.isfile(callscript):
-        print('... not found in Job working directory!')
+    if callscript not in os.listdir(jobargs.directory) and not os.path.isfile(
+        callscript
+    ):
+        print("... not found in Job working directory!")
         exit = input("Check somewhere else? (Y/N): ")
-        if exit.upper() != 'Y':
+        if exit.upper() != "Y":
             print("Exiting")
             sys.exit()
 
-        home = os.path.expanduser('~')
+        home = os.path.expanduser("~")
         check_dirs = []
         for i in os.listdir(home):
-            if os.path.isdir(os.path.join(home, i)) and '.' not in i:
+            if os.path.isdir(os.path.join(home, i)) and "." not in i:
                 check_dirs.append(i)
 
         print(home + ":")
@@ -100,37 +132,48 @@ if __name__ == '__main__':
         candidate_script_paths = []
         try:
             for filepath in tqdm(walkdir(choice), total=filescount):
-                if callscript in filepath.split('/'):
+                if callscript in filepath.split("/"):
                     candidate_script_paths.append(filepath)
-                    tqdm.write(f"{len(candidate_script_paths)}. {'...' + filepath.split(choice)[1]}")
+                    tqdm.write(
+                        f"{len(candidate_script_paths)}. {'...' + filepath.split(choice)[1]}"
+                    )
 
             exit = input("Specify a detected script: ")
-            new_callscript = candidate_script_paths[int(exit)-1]
+            new_callscript = candidate_script_paths[int(exit) - 1]
 
         except KeyboardInterrupt:
-            exit = input("detection interrupted. ctrl+c again to exit, or specify a detected script: ")
-            new_callscript = candidate_script_paths[int(exit)-1]
+            exit = input(
+                "detection interrupted. ctrl+c again to exit, or specify a detected script: "
+            )
+            new_callscript = candidate_script_paths[int(exit) - 1]
 
         jobspec.arguments[jobspec.arguments.index(callscript)] = new_callscript
 
     else:
-        print('...found! Proceding.')
+        print("...found! Proceding.")
 
     # Little bit strange I have to re-initialize this class to re-serialize
-    jobspec.resources = ResourceSpecV1(node_count=jobspec.resources['node_count'])
+    jobspec.resources = ResourceSpecV1(node_count=jobspec.resources["node_count"])
 
-    if socket.gethostname() in script:
+    jex = JobExecutor.get_instance(jobargs.scheduler)
+    job = Job()
+
+    if job.id.split('-')[0] in script:
         reserialdest = script
     else:
-        reserialdest = script.replace(".", "." + socket.gethostname() + ".")
+        reserialdest = job.id.split('-')[0] + '.' + script
+
+    stdout_path = job.id.split('-')[0] + '.' + script.replace('json', 'out')
+    stderr_path = job.id.split('-')[0] + '.' + script.replace('json', 'err')
+    jobspec.stdout_path = stdout_path
+    jobspec.stderr_path = stderr_path
 
     Export().export(obj=jobspec, dest=reserialdest)
 
-    jex = JobExecutor.get_instance(jobargs.launcher)
-    job = Job()
     job.spec = jobspec
 
-    print('Submitting Job!:', job)
+    print("Submitting Job!:", job)
     jex.submit(job)
+
     if jobargs.wait:
         job.wait()

From cabb0190f8bdce31765b341f90c8ed992437397c Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 8 Jun 2022 12:26:54 -0500
Subject: [PATCH 012/196] proper python headers?

---
 libensemble/tools/liberegister | 2 +-
 libensemble/tools/libesubmit   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/libensemble/tools/liberegister b/libensemble/tools/liberegister
index 2e8504f9b2..4583cb21e1 100644
--- a/libensemble/tools/liberegister
+++ b/libensemble/tools/liberegister
@@ -1,4 +1,4 @@
-#! /usr/bin/python3
+#! /usr/bin/env python
 
 import os
 import sys
diff --git a/libensemble/tools/libesubmit b/libensemble/tools/libesubmit
index 4100570e6a..70977d485c 100644
--- a/libensemble/tools/libesubmit
+++ b/libensemble/tools/libesubmit
@@ -1,4 +1,4 @@
-#! /usr/bin/python3
+#! /usr/bin/env python
 
 import os
 import sys

From f8670307c935e62f13d1a744bda18218d90ec626 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 8 Jun 2022 14:41:28 -0500
Subject: [PATCH 013/196] small reformattings, set new script as installable in
 environment's bin

---
 libensemble/tools/liberegister | 37 ++++++++++++++++------------------
 setup.py                       |  4 ++++
 2 files changed, 21 insertions(+), 20 deletions(-)

diff --git a/libensemble/tools/liberegister b/libensemble/tools/liberegister
index 4583cb21e1..bf9287f764 100644
--- a/libensemble/tools/liberegister
+++ b/libensemble/tools/liberegister
@@ -14,19 +14,6 @@ from libensemble.tools.parse_args import parser as callscript_parser
 
 if __name__ == "__main__":
 
-    script = sys.argv[1]
-    basename = script.split(".py")[0]
-
-    if basename == "-h":
-        outfile_default = "libe-job.json"
-        outfile_type = str
-    else:
-        outfile_default = basename + ".json"
-        outfile_type = argparse.FileType("w")
-        assert script.endswith(
-            ".py"
-        ), "First argument doesn't appear to be a Python script."
-
     parser = argparse.ArgumentParser(
         prog="liberegister",
         description="Produce a PSI/J representation for a libEnsemble execution.",
@@ -35,13 +22,15 @@ if __name__ == "__main__":
         conflict_handler="resolve",
     )
 
+    parser.add_argument('calling_script', nargs='?')
+
     parser.add_argument(
         "-o",
         "--outfile",
-        type=outfile_type,
+        type=str,
         nargs="?",
         help="Output PSI/J representation filename.",
-        default=outfile_default,
+        default='libe-job.json',
     )
 
     parser.add_argument(
@@ -106,21 +95,29 @@ if __name__ == "__main__":
 
     jobargs, unknown = parser.parse_known_args(sys.argv[1:])
 
-    assert (
-        jobargs.nworkers and jobargs.comms
-    ), "Must supply the --comms and --nworkers options"
+    if not jobargs.calling_script:
+        parser.print_help()
+        sys.exit("\nMust supply a calling script, with the --comms and --nworkers options")
+
+    if not jobargs.calling_script.endswith('.py'):
+        parser.print_help()
+        sys.exit("\nFirst argument doesn't appear to be a Python script.")
+
+    basename = jobargs.calling_script.split(".py")[0]
+    outfile_default = basename + ".json"
+
     executable = jobargs.python_path
 
     if jobargs.comms == "local":
         arguments = [
-            script,
+            jobargs.calling_script,
             "--comms",
             jobargs.comms,
             "--nworkers",
             str(jobargs.nworkers),
         ]
     else:  # jobargs.comms == "mpi":
-        arguments = [script]
+        arguments = [jobargs.calling_script]
 
     jobspec = JobSpec(
         name=jobargs.jobname,
diff --git a/setup.py b/setup.py
index ad2411aa62..e741de693e 100644
--- a/setup.py
+++ b/setup.py
@@ -98,6 +98,10 @@ def run_tests(self):
             "sphinx_rtd_theme",
         ],
     },
+
+    scripts=['libensemble/tools/liberegister',
+             'libensemble/tools/libesubmit'],
+
     classifiers=[
         "Development Status :: 4 - Beta",
         "Intended Audience :: Developers",

From 8a1a84749a9b265b6eb41e0f92b09262c7980c72 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 8 Jun 2022 15:45:28 -0500
Subject: [PATCH 014/196] make default nnodes 1

---
 libensemble/tools/liberegister | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libensemble/tools/liberegister b/libensemble/tools/liberegister
index bf9287f764..b06ce96f02 100644
--- a/libensemble/tools/liberegister
+++ b/libensemble/tools/liberegister
@@ -34,7 +34,7 @@ if __name__ == "__main__":
     )
 
     parser.add_argument(
-        "-n", "--nnodes", type=int, nargs="?", help="Number of nodes", default=4
+        "-n", "--nnodes", type=int, nargs="?", help="Number of nodes", default=1
     )
 
     parser.add_argument(

From dca292361be30f3a04d9cb97b5bcca2a65a17010 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 13 Jun 2022 11:31:06 -0500
Subject: [PATCH 015/196] probably giving up on evaluating and waiting for exit
 criteria for now...

---
 libensemble/tools/libesubmit | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/libensemble/tools/libesubmit b/libensemble/tools/libesubmit
index 70977d485c..b67a621451 100644
--- a/libensemble/tools/libesubmit
+++ b/libensemble/tools/libesubmit
@@ -28,7 +28,7 @@ if __name__ == "__main__":
         "-w",
         "--wait",
         action="store_true",
-        help="Wait for exit_criteria evaluations to complete before exiting.",
+        help="Wait for Job to complete before exiting.",
     )
 
     parser.add_argument(
@@ -176,4 +176,5 @@ if __name__ == "__main__":
     jex.submit(job)
 
     if jobargs.wait:
+        print("Waiting on Job completion...")
         job.wait()

From 66a5cf6073d5dc36d0016979f2a61f119e74ff4d Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 13 Jun 2022 11:31:32 -0500
Subject: [PATCH 016/196] black

---
 libensemble/tools/liberegister | 17 +++++++++++------
 libensemble/tools/libesubmit   |  8 ++++----
 2 files changed, 15 insertions(+), 10 deletions(-)

diff --git a/libensemble/tools/liberegister b/libensemble/tools/liberegister
index b06ce96f02..71eeeeffbe 100644
--- a/libensemble/tools/liberegister
+++ b/libensemble/tools/liberegister
@@ -22,7 +22,7 @@ if __name__ == "__main__":
         conflict_handler="resolve",
     )
 
-    parser.add_argument('calling_script', nargs='?')
+    parser.add_argument("calling_script", nargs="?")
 
     parser.add_argument(
         "-o",
@@ -30,7 +30,7 @@ if __name__ == "__main__":
         type=str,
         nargs="?",
         help="Output PSI/J representation filename.",
-        default='libe-job.json',
+        default="libe-job.json",
     )
 
     parser.add_argument(
@@ -49,8 +49,11 @@ if __name__ == "__main__":
     choices = ["cobalt", "local", "flux", "lsf", "pbspro", "rp", "slurm"]
 
     parser.add_argument(
-        "-s", "--scheduler", choices=choices, help="Which scheduler to use.",
-        default=None
+        "-s",
+        "--scheduler",
+        choices=choices,
+        help="Which scheduler to use.",
+        default=None,
     )
 
     parser.add_argument(
@@ -97,9 +100,11 @@ if __name__ == "__main__":
 
     if not jobargs.calling_script:
         parser.print_help()
-        sys.exit("\nMust supply a calling script, with the --comms and --nworkers options")
+        sys.exit(
+            "\nMust supply a calling script, with the --comms and --nworkers options"
+        )
 
-    if not jobargs.calling_script.endswith('.py'):
+    if not jobargs.calling_script.endswith(".py"):
         parser.print_help()
         sys.exit("\nFirst argument doesn't appear to be a Python script.")
 
diff --git a/libensemble/tools/libesubmit b/libensemble/tools/libesubmit
index b67a621451..2138c69c14 100644
--- a/libensemble/tools/libesubmit
+++ b/libensemble/tools/libesubmit
@@ -158,13 +158,13 @@ if __name__ == "__main__":
     jex = JobExecutor.get_instance(jobargs.scheduler)
     job = Job()
 
-    if job.id.split('-')[0] in script:
+    if job.id.split("-")[0] in script:
         reserialdest = script
     else:
-        reserialdest = job.id.split('-')[0] + '.' + script
+        reserialdest = job.id.split("-")[0] + "." + script
 
-    stdout_path = job.id.split('-')[0] + '.' + script.replace('json', 'out')
-    stderr_path = job.id.split('-')[0] + '.' + script.replace('json', 'err')
+    stdout_path = job.id.split("-")[0] + "." + script.replace("json", "out")
+    stderr_path = job.id.split("-")[0] + "." + script.replace("json", "err")
     jobspec.stdout_path = stdout_path
     jobspec.stderr_path = stderr_path
 

From 85cb75a955d6793c08a0ff76c34914232c912cc0 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 13 Jun 2022 11:44:28 -0500
Subject: [PATCH 017/196] fix reference to other psi-j script

---
 libensemble/tools/liberegister | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/libensemble/tools/liberegister b/libensemble/tools/liberegister
index 71eeeeffbe..d75f345861 100644
--- a/libensemble/tools/liberegister
+++ b/libensemble/tools/liberegister
@@ -17,7 +17,7 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser(
         prog="liberegister",
         description="Produce a PSI/J representation for a libEnsemble execution.",
-        epilog="Output representations can be passed to `libe-submit`",
+        epilog="Output representations can be passed to `libesubmit`",
         parents=[callscript_parser],
         conflict_handler="resolve",
     )
@@ -141,5 +141,5 @@ if __name__ == "__main__":
     Export().export(obj=jobspec, dest=outfile_default)
     print(f"*** libEnsemble {__version__} ***")
     print(
-        f"Exported PSI/J serialization: {outfile_default}\nOptionally adjust any fields, or specify job attributes on submission to `libe-submit`."
+        f"Exported PSI/J serialization: {outfile_default}\nOptionally adjust any fields, or specify job attributes on submission to `libesubmit`."
     )

From f54c7865b4db02ad6e613e8acbb8d98c2ee56bd7 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 13 Jun 2022 14:41:17 -0500
Subject: [PATCH 018/196] first attempts to define MPI resources for an MPI run

---
 libensemble/tools/liberegister |  4 +++-
 libensemble/tools/libesubmit   | 17 ++++++++++++++---
 2 files changed, 17 insertions(+), 4 deletions(-)

diff --git a/libensemble/tools/liberegister b/libensemble/tools/liberegister
index d75f345861..7de7693331 100644
--- a/libensemble/tools/liberegister
+++ b/libensemble/tools/liberegister
@@ -121,8 +121,10 @@ if __name__ == "__main__":
             "--nworkers",
             str(jobargs.nworkers),
         ]
+        resources = ResourceSpecV1(node_count=jobargs.nnodes)
     else:  # jobargs.comms == "mpi":
         arguments = [jobargs.calling_script]
+        resources = ResourceSpecV1(process_count=jobargs.nworkers+1, processes_per_node=1)
 
     jobspec = JobSpec(
         name=jobargs.jobname,
@@ -130,7 +132,7 @@ if __name__ == "__main__":
         arguments=arguments,
         directory=jobargs.directory,
         environment={"PYTHONNOUSERSITE": "1"},
-        resources=ResourceSpecV1(node_count=jobargs.nnodes),
+        resources=resources,
         attributes=JobAttributes(
             duration=jobargs.wallclock,
             queue_name=jobargs.queue,
diff --git a/libensemble/tools/libesubmit b/libensemble/tools/libesubmit
index 2138c69c14..3198625ab2 100644
--- a/libensemble/tools/libesubmit
+++ b/libensemble/tools/libesubmit
@@ -20,9 +20,15 @@ if __name__ == "__main__":
         conflict_handler="resolve",
     )
 
-    choices = ["cobalt", "local", "flux", "lsf", "pbspro", "rp", "slurm"]
+    choices = {"cobalt": "aprun",
+               "local": "mpirun",
+               "flux": "mpirun",
+               "lsf": "jsrun",
+               "pbspro": "mpirun",
+               "rp": "mpirun",
+               "slurm": "srun"}
 
-    parser.add_argument("-s", "--scheduler", choices=choices, required=True)
+    parser.add_argument("-s", "--scheduler", choices=choices.keys(), required=True)
 
     parser.add_argument(
         "-w",
@@ -153,7 +159,12 @@ if __name__ == "__main__":
         print("...found! Proceding.")
 
     # Little bit strange I have to re-initialize this class to re-serialize
-    jobspec.resources = ResourceSpecV1(node_count=jobspec.resources["node_count"])
+    if not jobspec.resources["node_count"]: # running with MPI - need corresponding executor
+        jobspec.resources = ResourceSpecV1(process_count=jobspec.resources["process_count"])
+        jobspec.launcher = choices[jobargs.scheduler]
+    else:
+        jobspec.resources = ResourceSpecV1(node_count=jobspec.resources["node_count"])
+
 
     jex = JobExecutor.get_instance(jobargs.scheduler)
     job = Job()

From 6d94da013079b741ef93f5987af9bd380906b078 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 13 Jun 2022 14:41:35 -0500
Subject: [PATCH 019/196] black

---
 libensemble/tools/liberegister |  4 +++-
 libensemble/tools/libesubmit   | 25 +++++++++++++++----------
 2 files changed, 18 insertions(+), 11 deletions(-)

diff --git a/libensemble/tools/liberegister b/libensemble/tools/liberegister
index 7de7693331..62bd90a49e 100644
--- a/libensemble/tools/liberegister
+++ b/libensemble/tools/liberegister
@@ -124,7 +124,9 @@ if __name__ == "__main__":
         resources = ResourceSpecV1(node_count=jobargs.nnodes)
     else:  # jobargs.comms == "mpi":
         arguments = [jobargs.calling_script]
-        resources = ResourceSpecV1(process_count=jobargs.nworkers+1, processes_per_node=1)
+        resources = ResourceSpecV1(
+            process_count=jobargs.nworkers + 1, processes_per_node=1
+        )
 
     jobspec = JobSpec(
         name=jobargs.jobname,
diff --git a/libensemble/tools/libesubmit b/libensemble/tools/libesubmit
index 3198625ab2..912fb1f856 100644
--- a/libensemble/tools/libesubmit
+++ b/libensemble/tools/libesubmit
@@ -20,13 +20,15 @@ if __name__ == "__main__":
         conflict_handler="resolve",
     )
 
-    choices = {"cobalt": "aprun",
-               "local": "mpirun",
-               "flux": "mpirun",
-               "lsf": "jsrun",
-               "pbspro": "mpirun",
-               "rp": "mpirun",
-               "slurm": "srun"}
+    choices = {
+        "cobalt": "aprun",
+        "local": "mpirun",
+        "flux": "mpirun",
+        "lsf": "jsrun",
+        "pbspro": "mpirun",
+        "rp": "mpirun",
+        "slurm": "srun",
+    }
 
     parser.add_argument("-s", "--scheduler", choices=choices.keys(), required=True)
 
@@ -159,13 +161,16 @@ if __name__ == "__main__":
         print("...found! Proceding.")
 
     # Little bit strange I have to re-initialize this class to re-serialize
-    if not jobspec.resources["node_count"]: # running with MPI - need corresponding executor
-        jobspec.resources = ResourceSpecV1(process_count=jobspec.resources["process_count"])
+    if not jobspec.resources[
+        "node_count"
+    ]:  # running with MPI - need corresponding executor
+        jobspec.resources = ResourceSpecV1(
+            process_count=jobspec.resources["process_count"]
+        )
         jobspec.launcher = choices[jobargs.scheduler]
     else:
         jobspec.resources = ResourceSpecV1(node_count=jobspec.resources["node_count"])
 
-
     jex = JobExecutor.get_instance(jobargs.scheduler)
     job = Job()
 

From 6cb26bb4ee53f25248b2ea96173366df980e2efc Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 13 Jun 2022 15:05:50 -0500
Subject: [PATCH 020/196] specify processes per node

---
 libensemble/tools/libesubmit | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/libensemble/tools/libesubmit b/libensemble/tools/libesubmit
index 912fb1f856..06fa74b332 100644
--- a/libensemble/tools/libesubmit
+++ b/libensemble/tools/libesubmit
@@ -165,7 +165,8 @@ if __name__ == "__main__":
         "node_count"
     ]:  # running with MPI - need corresponding executor
         jobspec.resources = ResourceSpecV1(
-            process_count=jobspec.resources["process_count"]
+            process_count=jobspec.resources["process_count"],
+            processes_per_node=jobspec.resources["processes_per_node"]
         )
         jobspec.launcher = choices[jobargs.scheduler]
     else:

From 111375fb6a3b0abea2707397295e014a6f45d203 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 13 Jun 2022 15:08:30 -0500
Subject: [PATCH 021/196] try libesubmit with ppn=1

---
 libensemble/tools/libesubmit | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libensemble/tools/libesubmit b/libensemble/tools/libesubmit
index 06fa74b332..060067918d 100644
--- a/libensemble/tools/libesubmit
+++ b/libensemble/tools/libesubmit
@@ -166,7 +166,7 @@ if __name__ == "__main__":
     ]:  # running with MPI - need corresponding executor
         jobspec.resources = ResourceSpecV1(
             process_count=jobspec.resources["process_count"],
-            processes_per_node=jobspec.resources["processes_per_node"]
+            processes_per_node=1
         )
         jobspec.launcher = choices[jobargs.scheduler]
     else:

From be55f7364d6f627898ed012dd2ab12bccb77750d Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 13 Jun 2022 15:35:59 -0500
Subject: [PATCH 022/196] just experimenting until some arrangement of
 resources for MPI runs works

---
 libensemble/tools/libesubmit | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/libensemble/tools/libesubmit b/libensemble/tools/libesubmit
index 060067918d..83118567df 100644
--- a/libensemble/tools/libesubmit
+++ b/libensemble/tools/libesubmit
@@ -11,6 +11,7 @@ from psij import JobExecutor, Import, Export, JobSpec, Job
 from psij.resource_spec import ResourceSpecV1
 from psij.job_attributes import JobAttributes
 from libensemble.version import __version__
+from libensemble.resources import node_resources
 
 if __name__ == "__main__":
 
@@ -166,7 +167,8 @@ if __name__ == "__main__":
     ]:  # running with MPI - need corresponding executor
         jobspec.resources = ResourceSpecV1(
             process_count=jobspec.resources["process_count"],
-            processes_per_node=1
+            processes_per_node=1,
+            cpu_cores_per_process=64
         )
         jobspec.launcher = choices[jobargs.scheduler]
     else:

From 613c8ba3597e61bc8894ea608cf67f2a5df2d752 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 14 Jun 2022 10:29:09 -0500
Subject: [PATCH 023/196] trying out dry-run feature and adding nnodes option
 to libesubmit

---
 libensemble/tools/libesubmit | 17 +++++++++++++++--
 1 file changed, 15 insertions(+), 2 deletions(-)

diff --git a/libensemble/tools/libesubmit b/libensemble/tools/libesubmit
index 83118567df..b4b5480f6f 100644
--- a/libensemble/tools/libesubmit
+++ b/libensemble/tools/libesubmit
@@ -40,6 +40,16 @@ if __name__ == "__main__":
         help="Wait for Job to complete before exiting.",
     )
 
+    parser.add_argument(
+        "--dry",
+        action="store_true",
+        help="Parameterize and re-serialize a Job, without submitting.",
+    )
+
+    parser.add_argument(
+        "-n", "--nnodes", type=int, nargs="?", help="Number of nodes", default=1
+    )
+
     parser.add_argument(
         "-p",
         "--python-path",
@@ -98,6 +108,8 @@ if __name__ == "__main__":
     if jobspec.executable == "python":
         jobspec.executable = str(jobargs.python_path)
     jobspec.attributes.duration = jobargs.wallclock
+    if jobspec.resources.node_count == 1:
+        jobspec.resources.node_count = jobargs.nnodes
 
     # we enforced passing a python script in liberegister
     callscript = [i for i in jobspec.arguments if str(i).endswith(".py")][0]
@@ -191,8 +203,9 @@ if __name__ == "__main__":
 
     job.spec = jobspec
 
-    print("Submitting Job!:", job)
-    jex.submit(job)
+    if not jobargs.dry:
+        print("Submitting Job!:", job)
+        jex.submit(job)
 
     if jobargs.wait:
         print("Waiting on Job completion...")

From 9d9d1e306bb4faa30a5cd90dc10d0d34aa78db1b Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 15 Jun 2022 10:35:45 -0500
Subject: [PATCH 024/196] print help and exit if libesubmit didn't take in a
 json file

---
 libensemble/tools/libesubmit | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/libensemble/tools/libesubmit b/libensemble/tools/libesubmit
index b4b5480f6f..7f2cd09498 100644
--- a/libensemble/tools/libesubmit
+++ b/libensemble/tools/libesubmit
@@ -93,7 +93,9 @@ if __name__ == "__main__":
     jobargs, unknown = parser.parse_known_args(sys.argv[1:])
 
     script = sys.argv[1]
-    assert script.endswith(".json"), "First argument doesn't appear to be a .json file."
+    if not jobargs.script.endswith(".json"):
+        parser.print_help()
+        sys.exit("First argument doesn't appear to be a .json file.")
 
     print(f"*** libEnsemble {__version__} ***")
     print(f"Imported PSI/J serialization: {script}. Preparing submission...")

From 8d95433cdad8ddaf8c7ab84c4378fdc5a34201a2 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 30 Jun 2022 15:34:37 -0500
Subject: [PATCH 025/196] move psij scripts into base scripts directory

---
 {libensemble/tools => scripts}/liberegister | 0
 {libensemble/tools => scripts}/libesubmit   | 0
 setup.py                                    | 4 ++--
 3 files changed, 2 insertions(+), 2 deletions(-)
 rename {libensemble/tools => scripts}/liberegister (100%)
 rename {libensemble/tools => scripts}/libesubmit (100%)

diff --git a/libensemble/tools/liberegister b/scripts/liberegister
similarity index 100%
rename from libensemble/tools/liberegister
rename to scripts/liberegister
diff --git a/libensemble/tools/libesubmit b/scripts/libesubmit
similarity index 100%
rename from libensemble/tools/libesubmit
rename to scripts/libesubmit
diff --git a/setup.py b/setup.py
index e741de693e..a34cdd3fca 100644
--- a/setup.py
+++ b/setup.py
@@ -99,8 +99,8 @@ def run_tests(self):
         ],
     },
 
-    scripts=['libensemble/tools/liberegister',
-             'libensemble/tools/libesubmit'],
+    scripts=['scripts/liberegister',
+             'scripts/libesubmit'],
 
     classifiers=[
         "Development Status :: 4 - Beta",

From c1aea4db2edcdce670ed56e2e883ecf5618c675c Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 30 Jun 2022 15:49:39 -0500
Subject: [PATCH 026/196] fix symlink

---
 examples/gen_funcs/persistent_uniform_sampling.py | 1 -
 persistent_sampling.py                            | 1 +
 2 files changed, 1 insertion(+), 1 deletion(-)
 delete mode 120000 examples/gen_funcs/persistent_uniform_sampling.py
 create mode 120000 persistent_sampling.py

diff --git a/examples/gen_funcs/persistent_uniform_sampling.py b/examples/gen_funcs/persistent_uniform_sampling.py
deleted file mode 120000
index c55d2017a6..0000000000
--- a/examples/gen_funcs/persistent_uniform_sampling.py
+++ /dev/null
@@ -1 +0,0 @@
-../../libensemble/gen_funcs/persistent_uniform_sampling.py
\ No newline at end of file
diff --git a/persistent_sampling.py b/persistent_sampling.py
new file mode 120000
index 0000000000..539123e8be
--- /dev/null
+++ b/persistent_sampling.py
@@ -0,0 +1 @@
+../../libensemble/gen_funcs/persistent_sampling.py
\ No newline at end of file

From eff8244fe1093de37ee2b45a432e620a2881dcf6 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 7 Jul 2022 15:30:04 -0500
Subject: [PATCH 027/196] adds +dev to version number on develop

---
 README.rst             | 2 +-
 libensemble/version.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.rst b/README.rst
index c09becb1be..59da5a4f26 100644
--- a/README.rst
+++ b/README.rst
@@ -243,7 +243,7 @@ Resources
     author  = {Stephen Hudson and Jeffrey Larson and Stefan M. Wild and
                David Bindel and John-Luke Navarro},
     institution = {Argonne National Laboratory},
-    number  = {Revision 0.9.2},
+    number  = {Revision 0.9.2+dev},
     year    = {2022},
     url     = {https://buildmedia.readthedocs.org/media/pdf/libensemble/latest/libensemble.pdf}
   }
diff --git a/libensemble/version.py b/libensemble/version.py
index a2fecb4576..ddb97c066f 100644
--- a/libensemble/version.py
+++ b/libensemble/version.py
@@ -1 +1 @@
-__version__ = "0.9.2"
+__version__ = "0.9.2+dev"

From 95befdd07d4927eb553eea7ccb41b47c1659dc8f Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 7 Jul 2022 16:01:26 -0500
Subject: [PATCH 028/196] mentioning ssh interface, other fixes

---
 CHANGELOG.rst         |  6 +++---
 docs/conf.py          |  1 +
 docs/running_libE.rst | 12 ++++++++++++
 libensemble/libE.py   |  2 +-
 4 files changed, 17 insertions(+), 4 deletions(-)

diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 815baa34bc..de3e46c31b 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -19,9 +19,9 @@ New capabilities:
 * Added configuration options for `libE_stats.txt` file. #743
 * Support for `spawn` and `forkserver` multiprocessing start methods. #797
 
- * Note that macOS no longer switches to using `fork`. macOS (since Python 3.8) and Windows
- default to using `spawn`. When using `spawn`, we recommend placing calling script code in
- an ``if __name__ == "__main__":`` block. The multiprocessing interface can be used to switch methods (https://docs.python.org/3/library/multiprocessing.html#multiprocessing.set_start_method).
+ * Note that macOS no longer switches to using `fork`. macOS (since Python 3.8) and Windows default to
+   using `spawn`. When using `spawn`, we recommend placing calling script code in an ``if __name__ == "__main__":`` block.
+   The multiprocessing interface can be used to switch methods (https://docs.python.org/3/library/multiprocessing.html#multiprocessing.set_start_method).
 
 Updates to example functions:
 
diff --git a/docs/conf.py b/docs/conf.py
index 76c7028e92..6972705401 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -79,6 +79,7 @@ def __getattr__(cls, name):
 sys.path.append(os.path.abspath("../libensemble/utils"))
 sys.path.append(os.path.abspath("../libensemble/tools"))
 sys.path.append(os.path.abspath("../libensemble/executors"))
+sys.path.append(os.path.abspath("../libensemble/executors/balsam_executors"))
 sys.path.append(os.path.abspath("../libensemble/resources"))
 # print(sys.path)
 
diff --git a/docs/running_libE.rst b/docs/running_libE.rst
index 5a04b2a08e..61cfd08ee8 100644
--- a/docs/running_libE.rst
+++ b/docs/running_libE.rst
@@ -106,6 +106,18 @@ The ``libE_specs`` options for TCP are::
     'authkey' [String]:
         Authkey.
 
+Reverse-ssh interface
+^^^^^^^^^^^^^^^^^^^^^
+
+Via specifying ``--comms ssh`` on the command line, libEnsemble workers can
+be launched to remote ssh-accessible systems without needing to specify ``'port'`` or ``'authkey'``.
+
+The working directory and Python to run on the remote system need to be specified. Running a calling script may resemble::
+
+    python myscript.py --comms ssh --workers machine1 machine2 --worker_pwd /home/workers --worker_python /home/.conda/.../python
+
+Note that currently, the interface assumes that all remote machines share a filesystem. We'll be adjusting this in the future.
+
 Limitations of TCP mode
 ^^^^^^^^^^^^^^^^^^^^^^^
 
diff --git a/libensemble/libE.py b/libensemble/libE.py
index 69afedec36..2eedd10f83 100644
--- a/libensemble/libE.py
+++ b/libensemble/libE.py
@@ -102,7 +102,7 @@
 
 On macOS (since Python 3.8) and Windows, the default multiprocessing start method is ``'spawn'``
 and you must place most calling script code (or just ``libE()`` / ``Ensemble().run()`` at a minimum) in
-an ``if __name__ == "__main__:" block.
+an ``if __name__ == "__main__:"`` block.
 
 Therefore a calling script that is universal across
 all platforms and comms-types may resemble:

From e60aff2073ca81391fb7decc494060393e3ffab5 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 7 Jul 2022 16:34:33 -0500
Subject: [PATCH 029/196] typo

---
 libensemble/libE.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libensemble/libE.py b/libensemble/libE.py
index 2eedd10f83..fa8de62929 100644
--- a/libensemble/libE.py
+++ b/libensemble/libE.py
@@ -142,7 +142,7 @@
         H, persis_info, flag = libE(sim_specs, gen_specs, exit_criteria, persis_info,
                                     libE_specs=libE_specs)
 
-Alternatively, you may set the multiprocesing start method to ``'fork'`` via the following:
+Alternatively, you may set the multiprocessing start method to ``'fork'`` via the following:
 
     from multiprocessing import set_start_method
     set_start_method("fork")

From 8b4d3024a93efa28758651818508d1afb7617cf7 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 8 Jul 2022 10:13:06 -0500
Subject: [PATCH 030/196] removing some out-of-date or very old images

---
 docs/images/central_balsam.png              | Bin 269938 -> 0 bytes
 docs/images/centralized_new.png             | Bin 429889 -> 0 bytes
 docs/images/diagram_xml/centralized_new.xml |   2 --
 docs/images/diagram_xml/distributed_new.xml |   2 --
 docs/images/diagram_xml/funcx.xml           |   1 -
 docs/images/distributed_new.png             | Bin 388503 -> 0 bytes
 docs/images/funcx.png                       | Bin 886431 -> 0 bytes
 docs/images/libE_logo_smaller.png           | Bin 44157 -> 0 bytes
 docs/images/logo_manager_worker.png         | Bin 155087 -> 0 bytes
 docs/images/using_new.png                   | Bin 354301 -> 0 bytes
 docs/images/white.png                       | Bin 1221 -> 0 bytes
 docs/platforms/platforms_index.rst          |   6 ++++--
 docs/running_libE.rst                       |  10 ++++++++--
 docs/scipy2020.rst                          |  21 +++-----------------
 14 files changed, 15 insertions(+), 27 deletions(-)
 delete mode 100644 docs/images/central_balsam.png
 delete mode 100644 docs/images/centralized_new.png
 delete mode 100644 docs/images/diagram_xml/centralized_new.xml
 delete mode 100644 docs/images/diagram_xml/distributed_new.xml
 delete mode 100644 docs/images/diagram_xml/funcx.xml
 delete mode 100644 docs/images/distributed_new.png
 delete mode 100644 docs/images/funcx.png
 delete mode 100644 docs/images/libE_logo_smaller.png
 delete mode 100644 docs/images/logo_manager_worker.png
 delete mode 100644 docs/images/using_new.png
 delete mode 100644 docs/images/white.png

diff --git a/docs/images/central_balsam.png b/docs/images/central_balsam.png
deleted file mode 100644
index d185082970bf10849b6006638039b05dbece4475..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/docs/images/diagram_xml/centralized_new.xml b/docs/images/diagram_xml/centralized_new.xml
deleted file mode 100644
index e117931c0f..0000000000
--- a/docs/images/diagram_xml/centralized_new.xml
+++ /dev/null
@@ -1,2 +0,0 @@
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</diagram></mxfile>
\ No newline at end of file
diff --git a/docs/images/diagram_xml/distributed_new.xml b/docs/images/diagram_xml/distributed_new.xml
deleted file mode 100644
index 39509b0ebe..0000000000
--- a/docs/images/diagram_xml/distributed_new.xml
+++ /dev/null
@@ -1,2 +0,0 @@
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</diagram></mxfile>
\ No newline at end of file
diff --git a/docs/images/diagram_xml/funcx.xml b/docs/images/diagram_xml/funcx.xml
deleted file mode 100644
index 5017489f65..0000000000
--- a/docs/images/diagram_xml/funcx.xml
+++ /dev/null
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diff --git a/docs/platforms/platforms_index.rst b/docs/platforms/platforms_index.rst
index ecd8d436dc..f95c480f12 100644
--- a/docs/platforms/platforms_index.rst
+++ b/docs/platforms/platforms_index.rst
@@ -153,6 +153,8 @@ libE_specs option.
 When using the MPI Executor, it is possible to override the detected information using the
 `custom_info` argument. See the :doc:`MPI Executor<../executor/mpi_executor>` for more.
 
+ .. _funcx_ref:
+
 funcX - Remote User functions
 -----------------------------
 
@@ -161,9 +163,9 @@ internet access (laptops, login nodes, other servers, etc.), workers can be inst
 launch generator or simulator user function instances to separate resources from
 themselves via funcX_, a distributed, high-performance function-as-a-service platform:
 
-    .. image:: ../images/funcx.png
+    .. image:: ../images/funcxmodel.png
         :alt: running_with_funcx
-        :scale: 40
+        :scale: 50
         :align: center
 
 This is useful for running ensembles across machines and heterogeneous resources, but
diff --git a/docs/running_libE.rst b/docs/running_libE.rst
index 61cfd08ee8..a508ff7b28 100644
--- a/docs/running_libE.rst
+++ b/docs/running_libE.rst
@@ -110,13 +110,19 @@ Reverse-ssh interface
 ^^^^^^^^^^^^^^^^^^^^^
 
 Via specifying ``--comms ssh`` on the command line, libEnsemble workers can
-be launched to remote ssh-accessible systems without needing to specify ``'port'`` or ``'authkey'``.
+be launched to remote ssh-accessible systems without needing to specify ``'port'`` or ``'authkey'``. This allows users 
+to colocate workers, simulation or generator functions, and any applications they submit on the same machine. Such user
+functions can also be persistent, unlike when launching remote functions via :ref:`funcX<funcx_ref>`.
 
 The working directory and Python to run on the remote system need to be specified. Running a calling script may resemble::
 
     python myscript.py --comms ssh --workers machine1 machine2 --worker_pwd /home/workers --worker_python /home/.conda/.../python
 
-Note that currently, the interface assumes that all remote machines share a filesystem. We'll be adjusting this in the future.
+.. note::
+    Setting up public-key authentication on the worker host systems is recommended to avoid entering passwords.
+
+.. note::
+    This interface assumes that all remote machines share a filesystem. We'll be adjusting this in the future.
 
 Limitations of TCP mode
 ^^^^^^^^^^^^^^^^^^^^^^^
diff --git a/docs/scipy2020.rst b/docs/scipy2020.rst
index 8fbd0c98e7..ef9249ef24 100644
--- a/docs/scipy2020.rst
+++ b/docs/scipy2020.rst
@@ -8,11 +8,6 @@
  :width: 33 %
  :align: right
 
-.. image:: images/white.png
-  :align: center
-  :width: 33 %
-  :height: 1.2 in
-
 =========================================================================
 **libEnsemble**: A Python Library for Dynamic Ensemble-Based Computations
 =========================================================================
@@ -51,11 +46,6 @@ input and a ``sim_f`` function that performs and monitors simulations. The user
 parameterizes these functions and initiates libEnsemble in a *calling script*.
 Examples and templates of such scripts and functions are included in the library.
 
-.. image:: images/using_new.png
- :alt: Using libEnsemble
- :scale: 30 %
- :align: center
-
 For example, the ``gen_f`` may contain an optimization routine to generate new
 simulation parameters on-the-fly based on results from previous ``sim_f``
 simulations.
@@ -82,11 +72,6 @@ many-node simulations. The *manager* allocates workers to asynchronously execute
 ``gen_f`` generation functions and ``sim_f`` simulation functions based on
 produced output, directed by a provided ``alloc_f`` allocation function.
 
-.. image:: images/logo_manager_worker.png
- :alt: Managers and Workers
- :align: center
- :scale: 40 %
-
 Flexible Run Mechanisms
 -----------------------
 
@@ -97,14 +82,14 @@ run and launch tasks (user applications) on available nodes.
 
 * **Distributed**: Workers are distributed across allocated nodes and launch tasks in-place. Workers share nodes with their applications.
 
-.. image:: images/distributed_new.png
+.. image:: images/distributed_new_detailed.png
  :alt: Distributed
  :align: center
  :scale: 30 %
 
 * **Centralized**: Workers run on one or more dedicated nodes and launch tasks to the remaining allocated nodes.
 
-.. image:: images/centralized_new.png
+.. image:: images/centralized_new_detailed.png
  :alt: Centralized
  :align: center
  :scale: 30 %
@@ -132,7 +117,7 @@ Executor can interface with the **Balsam** library, which functions as a proxy
 job launcher that maintains and submits jobs from a database on front end launch
 nodes.
 
-.. image:: images/central_balsam.png
+.. image:: images/centralized_new_detailed_balsam.png
  :alt: Central Balsam
  :align: center
  :scale: 40 %

From 2b2faa6ca35c70344e29740afaf4f4b0a23e1504 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 8 Jul 2022 16:53:56 -0500
Subject: [PATCH 031/196] initial commit for moving some funcX and other logic
 from worker to runners.py

---
 libensemble/tests/.coveragerc |  1 +
 libensemble/utils/runners.py  | 68 +++++++++++++++++++++++++++++++++++
 libensemble/worker.py         | 64 ++-------------------------------
 3 files changed, 71 insertions(+), 62 deletions(-)
 create mode 100644 libensemble/utils/runners.py

diff --git a/libensemble/tests/.coveragerc b/libensemble/tests/.coveragerc
index 6156169def..3f24d55bbd 100644
--- a/libensemble/tests/.coveragerc
+++ b/libensemble/tests/.coveragerc
@@ -24,6 +24,7 @@ omit =
     */legacy_balsam_executor.py
     */forkable_pdb.py
     */parse_args.py
+    */runners.py
     */gen_funcs/old_aposmm.py
     */alloc_funcs/fast_alloc_to_aposmm.py
 exclude_lines =
diff --git a/libensemble/utils/runners.py b/libensemble/utils/runners.py
new file mode 100644
index 0000000000..6e65285761
--- /dev/null
+++ b/libensemble/utils/runners.py
@@ -0,0 +1,68 @@
+import logging
+import logging.handlers
+
+from libensemble.worker import Worker
+from libensemble.message_numbers import EVAL_SIM_TAG, EVAL_GEN_TAG
+
+logger = logging.getLogger(__name__)
+
+
+def _funcx_result(funcx_exctr, user_f, calc_in, persis_info, specs, libE_info):
+    libE_info["comm"] = None  # 'comm' object not pickle-able
+    Worker._set_executor(0, None)  # ditto for executor
+
+    future = funcx_exctr.submit(user_f, calc_in, persis_info, specs, libE_info, endpoint_id=specs["funcx_endpoint"])
+    remote_exc = future.exception()  # blocks until exception or None
+    if remote_exc is None:
+        return future.result()
+    else:
+        raise remote_exc
+
+
+def _get_funcx_exctr(sim_specs, gen_specs):
+    funcx_sim = len(sim_specs.get("funcx_endpoint", "")) > 0
+    funcx_gen = len(gen_specs.get("funcx_endpoint", "")) > 0
+
+    if any([funcx_sim, funcx_gen]):
+        try:
+            from funcx import FuncXClient
+            from funcx.sdk.executor import FuncXExecutor
+
+            return FuncXExecutor(FuncXClient()), funcx_sim, funcx_gen
+        except ModuleNotFoundError:
+            logger.warning("funcX use detected but funcX not importable. Is it installed?")
+            return None, False, False
+        except Exception:
+            return None, False, False
+    else:
+        return None, False, False
+
+
+def make_runners(sim_specs, gen_specs):
+    """Creates functions to run a sim or gen. These functions are either
+    called directly by the worker or submitted to a funcX endpoint."""
+
+    funcx_exctr, funcx_sim, funcx_gen = _get_funcx_exctr(sim_specs, gen_specs)
+    sim_f = sim_specs["sim_f"]
+
+    def run_sim(calc_in, persis_info, libE_info):
+        """Calls or submits the sim func."""
+        if funcx_sim and funcx_exctr:
+            return _funcx_result(funcx_exctr, sim_f, calc_in, persis_info, sim_specs, libE_info)
+        else:
+            return sim_f(calc_in, persis_info, sim_specs, libE_info)
+
+    if gen_specs:
+        gen_f = gen_specs["gen_f"]
+
+        def run_gen(calc_in, persis_info, libE_info):
+            """Calls or submits the gen func."""
+            if funcx_gen and funcx_exctr:
+                return _funcx_result(funcx_exctr, gen_f, calc_in, persis_info, gen_specs, libE_info)
+            else:
+                return gen_f(calc_in, persis_info, gen_specs, libE_info)
+
+    else:
+        run_gen = []
+
+    return {EVAL_SIM_TAG: run_sim, EVAL_GEN_TAG: run_gen}
diff --git a/libensemble/worker.py b/libensemble/worker.py
index ba1cdb1145..b3565c3b09 100644
--- a/libensemble/worker.py
+++ b/libensemble/worker.py
@@ -18,6 +18,7 @@
 from libensemble.output_directory import EnsembleDirectory
 
 from libensemble.utils.timer import Timer
+from libensemble.utils.runners import make_runners
 from libensemble.executors.executor import Executor
 from libensemble.resources.resources import Resources
 from libensemble.comms.logs import worker_logging_config
@@ -134,72 +135,11 @@ def __init__(self, comm, dtypes, workerID, sim_specs, gen_specs, libE_specs):
         self.stats_fmt = libE_specs.get("stats_fmt", {})
 
         self.calc_iter = {EVAL_SIM_TAG: 0, EVAL_GEN_TAG: 0}
-        self._run_calc = Worker._make_runners(sim_specs, gen_specs)
+        self._run_calc = make_runners(sim_specs, gen_specs)
         Worker._set_executor(self.workerID, self.comm)
         Worker._set_resources(self.workerID, self.comm)
         self.EnsembleDirectory = EnsembleDirectory(libE_specs=libE_specs)
 
-    @staticmethod
-    def _funcx_result(funcx_exctr, user_f, calc_in, persis_info, specs, libE_info):
-        libE_info["comm"] = None  # 'comm' object not pickle-able
-        Worker._set_executor(0, None)  # ditto for executor
-
-        future = funcx_exctr.submit(user_f, calc_in, persis_info, specs, libE_info, endpoint_id=specs["funcx_endpoint"])
-        remote_exc = future.exception()  # blocks until exception or None
-        if remote_exc is None:
-            return future.result()
-        else:
-            raise remote_exc
-
-    @staticmethod
-    def _get_funcx_exctr(sim_specs, gen_specs):
-        funcx_sim = len(sim_specs.get("funcx_endpoint", "")) > 0
-        funcx_gen = len(gen_specs.get("funcx_endpoint", "")) > 0
-
-        if any([funcx_sim, funcx_gen]):
-            try:
-                from funcx import FuncXClient
-                from funcx.sdk.executor import FuncXExecutor
-
-                return FuncXExecutor(FuncXClient()), funcx_sim, funcx_gen
-            except ModuleNotFoundError:
-                logger.warning("funcX use detected but funcX not importable. Is it installed?")
-                return None, False, False
-            except Exception:
-                return None, False, False
-        else:
-            return None, False, False
-
-    @staticmethod
-    def _make_runners(sim_specs, gen_specs):
-        """Creates functions to run a sim or gen. These functions are either
-        called directly by the worker or submitted to a funcX endpoint."""
-
-        funcx_exctr, funcx_sim, funcx_gen = Worker._get_funcx_exctr(sim_specs, gen_specs)
-        sim_f = sim_specs["sim_f"]
-
-        def run_sim(calc_in, persis_info, libE_info):
-            """Calls or submits the sim func."""
-            if funcx_sim and funcx_exctr:
-                return Worker._funcx_result(funcx_exctr, sim_f, calc_in, persis_info, sim_specs, libE_info)
-            else:
-                return sim_f(calc_in, persis_info, sim_specs, libE_info)
-
-        if gen_specs:
-            gen_f = gen_specs["gen_f"]
-
-            def run_gen(calc_in, persis_info, libE_info):
-                """Calls or submits the gen func."""
-                if funcx_gen and funcx_exctr:
-                    return Worker._funcx_result(funcx_exctr, gen_f, calc_in, persis_info, gen_specs, libE_info)
-                else:
-                    return gen_f(calc_in, persis_info, gen_specs, libE_info)
-
-        else:
-            run_gen = []
-
-        return {EVAL_SIM_TAG: run_sim, EVAL_GEN_TAG: run_gen}
-
     @staticmethod
     def _set_rset_team(rset_team):
         """Pass new rset_team to worker resources"""

From f7d1dd28f95737ae856e5e4405554c501fb21458 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 11 Jul 2022 10:43:53 -0500
Subject: [PATCH 032/196] move extract_H_ranges to new module

---
 libensemble/manager.py                        |  3 ++-
 libensemble/output_directory.py               | 19 ---------------
 .../unit_tests/test_sim_dir_properties.py     |  9 ++++----
 libensemble/tools/alloc_support.py            |  6 ++---
 libensemble/utils/misc.py                     | 23 +++++++++++++++++++
 libensemble/utils/runners.py                  |  2 +-
 libensemble/worker.py                         |  3 ++-
 7 files changed, 36 insertions(+), 29 deletions(-)
 create mode 100644 libensemble/utils/misc.py

diff --git a/libensemble/manager.py b/libensemble/manager.py
index 6cd5d1d853..c0f7e18ebf 100644
--- a/libensemble/manager.py
+++ b/libensemble/manager.py
@@ -12,6 +12,7 @@
 import numpy as np
 
 from libensemble.utils.timer import Timer
+from libensemble.utils.misc import extract_H_ranges
 
 from libensemble.message_numbers import (
     EVAL_SIM_TAG,
@@ -341,7 +342,7 @@ def _send_work_order(self, Work, w):
         work_name = calc_type_strings[Work["tag"]]
         logger.debug(
             "Manager sending {} work to worker {}. Rows {}".format(
-                work_name, w, EnsembleDirectory.extract_H_ranges(Work) or None
+                work_name, w, extract_H_ranges(Work) or None
             )
         )
         if len(work_rows):
diff --git a/libensemble/output_directory.py b/libensemble/output_directory.py
index da8a46bbc2..9ce404e379 100644
--- a/libensemble/output_directory.py
+++ b/libensemble/output_directory.py
@@ -1,8 +1,6 @@
 import os
 import re
 import shutil
-from itertools import groupby
-from operator import itemgetter
 
 from libensemble.utils.loc_stack import LocationStack
 from libensemble.tools.fields_keys import libE_spec_sim_dir_keys, libE_spec_gen_dir_keys, libE_spec_calc_dir_misc
@@ -84,23 +82,6 @@ def use_calc_dirs(self, type):
         else:
             return self.gen_use
 
-    @staticmethod
-    def extract_H_ranges(Work):
-        """Convert received H_rows into ranges for labeling"""
-        work_H_rows = Work["libE_info"]["H_rows"]
-        if len(work_H_rows) == 1:
-            return str(work_H_rows[0])
-        else:
-            # From https://stackoverflow.com/a/30336492
-            ranges = []
-            for diff, group in groupby(enumerate(work_H_rows.tolist()), lambda x: x[0] - x[1]):
-                group = list(map(itemgetter(1), group))
-                if len(group) > 1:
-                    ranges.append(str(group[0]) + "-" + str(group[-1]))
-                else:
-                    ranges.append(str(group[0]))
-            return "_".join(ranges)
-
     def _make_calc_dir(self, workerID, H_rows, calc_str, locs):
         """Create calc dirs and intermediate dirs, copy inputs, based on libE_specs"""
 
diff --git a/libensemble/tests/unit_tests/test_sim_dir_properties.py b/libensemble/tests/unit_tests/test_sim_dir_properties.py
index 71fc5759b7..e84e8f7ca0 100644
--- a/libensemble/tests/unit_tests/test_sim_dir_properties.py
+++ b/libensemble/tests/unit_tests/test_sim_dir_properties.py
@@ -3,20 +3,21 @@
 import numpy as np
 from libensemble.output_directory import EnsembleDirectory
 from libensemble.utils.loc_stack import LocationStack
+from libensemble.utils.misc import extract_H_ranges
 
 
 def test_range_single_element():
     """Single H_row labeling"""
 
     work = {"H_fields": ["x", "num_nodes", "procs_per_node"], "libE_info": {"H_rows": np.array([5]), "workerID": 1}}
-    assert EnsembleDirectory.extract_H_ranges(work) == "5", "Failed to correctly parse single H row"
+    assert extract_H_ranges(work) == "5", "Failed to correctly parse single H row"
 
 
 def test_range_two_separate_elements():
     """Multiple H_rows, non-sequential"""
 
     work = {"H_fields": ["x", "num_nodes", "procs_per_node"], "libE_info": {"H_rows": np.array([2, 8]), "workerID": 1}}
-    assert EnsembleDirectory.extract_H_ranges(work) == "2_8", "Failed to correctly parse nonsequential H rows"
+    assert extract_H_ranges(work) == "2_8", "Failed to correctly parse nonsequential H rows"
 
 
 def test_range_two_ranges():
@@ -26,7 +27,7 @@ def test_range_two_ranges():
         "H_fields": ["x", "num_nodes", "procs_per_node"],
         "libE_info": {"H_rows": np.array([0, 1, 2, 3, 7, 8]), "workerID": 1},
     }
-    assert EnsembleDirectory.extract_H_ranges(work) == "0-3_7-8", "Failed to correctly parse multiple H ranges"
+    assert extract_H_ranges(work) == "0-3_7-8", "Failed to correctly parse multiple H ranges"
 
 
 def test_range_mixes():
@@ -37,7 +38,7 @@ def test_range_mixes():
         "libE_info": {"H_rows": np.array([2, 3, 4, 6, 8, 9, 11, 14]), "workerID": 1},
     }
     assert (
-        EnsembleDirectory.extract_H_ranges(work) == "2-4_6_8-9_11_14"
+        extract_H_ranges(work) == "2-4_6_8-9_11_14"
     ), "Failed to correctly parse H row single elements and ranges."
 
 
diff --git a/libensemble/tools/alloc_support.py b/libensemble/tools/alloc_support.py
index e6d3edfb18..fee6304181 100644
--- a/libensemble/tools/alloc_support.py
+++ b/libensemble/tools/alloc_support.py
@@ -3,7 +3,7 @@
 from libensemble.message_numbers import EVAL_SIM_TAG, EVAL_GEN_TAG
 from libensemble.resources.resources import Resources
 from libensemble.resources.scheduler import ResourceScheduler, InsufficientFreeResources  # noqa: F401
-from libensemble.output_directory import EnsembleDirectory
+from libensemble.utils.misc import extract_H_ranges
 
 logger = logging.getLogger(__name__)
 # For debug messages - uncomment
@@ -180,7 +180,7 @@ def sim_work(self, wid, H, H_fields, H_rows, persis_info, **libE_info):
 
         logger.debug(
             "Alloc func packing SIM work for worker {}. Packing sim_ids: {}".format(
-                wid, EnsembleDirectory.extract_H_ranges(work) or None
+                wid, extract_H_ranges(work) or None
             )
         )
         return work
@@ -224,7 +224,7 @@ def gen_work(self, wid, H_fields, H_rows, persis_info, **libE_info):
 
         logger.debug(
             "Alloc func packing GEN work for worker {}. Packing sim_ids: {}".format(
-                wid, EnsembleDirectory.extract_H_ranges(work) or None
+                wid, extract_H_ranges(work) or None
             )
         )
         return work
diff --git a/libensemble/utils/misc.py b/libensemble/utils/misc.py
new file mode 100644
index 0000000000..583e7edfdf
--- /dev/null
+++ b/libensemble/utils/misc.py
@@ -0,0 +1,23 @@
+"""
+Misc internal functions
+"""
+
+from itertools import groupby
+from operator import itemgetter
+
+
+def extract_H_ranges(Work):
+    """Convert received H_rows into ranges for labeling"""
+    work_H_rows = Work["libE_info"]["H_rows"]
+    if len(work_H_rows) == 1:
+        return str(work_H_rows[0])
+    else:
+        # From https://stackoverflow.com/a/30336492
+        ranges = []
+        for diff, group in groupby(enumerate(work_H_rows.tolist()), lambda x: x[0] - x[1]):
+            group = list(map(itemgetter(1), group))
+            if len(group) > 1:
+                ranges.append(str(group[0]) + "-" + str(group[-1]))
+            else:
+                ranges.append(str(group[0]))
+        return "_".join(ranges)
diff --git a/libensemble/utils/runners.py b/libensemble/utils/runners.py
index 6e65285761..22275c2a32 100644
--- a/libensemble/utils/runners.py
+++ b/libensemble/utils/runners.py
@@ -1,13 +1,13 @@
 import logging
 import logging.handlers
 
-from libensemble.worker import Worker
 from libensemble.message_numbers import EVAL_SIM_TAG, EVAL_GEN_TAG
 
 logger = logging.getLogger(__name__)
 
 
 def _funcx_result(funcx_exctr, user_f, calc_in, persis_info, specs, libE_info):
+    from libensemble.worker import Worker
     libE_info["comm"] = None  # 'comm' object not pickle-able
     Worker._set_executor(0, None)  # ditto for executor
 
diff --git a/libensemble/worker.py b/libensemble/worker.py
index b3565c3b09..ad9751bdf4 100644
--- a/libensemble/worker.py
+++ b/libensemble/worker.py
@@ -17,6 +17,7 @@
 from libensemble.message_numbers import calc_type_strings, calc_status_strings
 from libensemble.output_directory import EnsembleDirectory
 
+from libensemble.utils.misc import extract_H_ranges
 from libensemble.utils.timer import Timer
 from libensemble.utils.runners import make_runners
 from libensemble.executors.executor import Executor
@@ -197,7 +198,7 @@ def _handle_calc(self, Work, calc_in):
         # from output_directory.py
         if calc_type == EVAL_SIM_TAG:
             enum_desc = "sim_id"
-            calc_id = EnsembleDirectory.extract_H_ranges(Work)
+            calc_id = extract_H_ranges(Work)
         else:
             enum_desc = "Gen no"
             # Use global gen count if available

From 27fba811c56437ec3844836e08d233bf4c8a6fdf Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 11 Jul 2022 11:09:43 -0500
Subject: [PATCH 033/196] fix function usage

---
 libensemble/output_directory.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/libensemble/output_directory.py b/libensemble/output_directory.py
index 9ce404e379..df52d688c2 100644
--- a/libensemble/output_directory.py
+++ b/libensemble/output_directory.py
@@ -3,6 +3,7 @@
 import shutil
 
 from libensemble.utils.loc_stack import LocationStack
+from libensemble.utils.misc import extract_H_ranges
 from libensemble.tools.fields_keys import libE_spec_sim_dir_keys, libE_spec_gen_dir_keys, libE_spec_calc_dir_misc
 from libensemble.message_numbers import EVAL_SIM_TAG, calc_type_strings
 
@@ -163,7 +164,7 @@ def prep_calc_dir(self, Work, calc_iter, workerID, calc_type):
             self.loc_stack = LocationStack()
 
         if calc_type == EVAL_SIM_TAG:
-            H_rows = self.extract_H_ranges(Work)
+            H_rows = extract_H_ranges(Work)
         else:
             H_rows = str(calc_iter[calc_type])
 

From 177c16ff2f3dd3d8b57e166e1c68200b3abeb6be Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 11 Jul 2022 13:33:52 -0500
Subject: [PATCH 034/196] odd symlink in repo base?

---
 persistent_sampling.py | 1 -
 1 file changed, 1 deletion(-)
 delete mode 120000 persistent_sampling.py

diff --git a/persistent_sampling.py b/persistent_sampling.py
deleted file mode 120000
index 539123e8be..0000000000
--- a/persistent_sampling.py
+++ /dev/null
@@ -1 +0,0 @@
-../../libensemble/gen_funcs/persistent_sampling.py
\ No newline at end of file

From 4e23b95c81805a1224b796cad5e8934d2175e82a Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 11 Jul 2022 13:57:57 -0500
Subject: [PATCH 035/196] some fixes, add if name == main line to simple forces

---
 .../forces/forces_simple/run_libe_forces.py   | 90 ++++++++++---------
 scripts/libesubmit                            | 12 +--
 2 files changed, 52 insertions(+), 50 deletions(-)

diff --git a/libensemble/tests/scaling_tests/forces/forces_simple/run_libe_forces.py b/libensemble/tests/scaling_tests/forces/forces_simple/run_libe_forces.py
index 3c4a296706..0d4bc23bad 100644
--- a/libensemble/tests/scaling_tests/forces/forces_simple/run_libe_forces.py
+++ b/libensemble/tests/scaling_tests/forces/forces_simple/run_libe_forces.py
@@ -9,47 +9,49 @@
 from libensemble.tools import parse_args, add_unique_random_streams
 from libensemble.executors import MPIExecutor
 
-# Parse number of workers, comms type, etc. from arguments
-nworkers, is_manager, libE_specs, _ = parse_args()
-
-# Initialize MPI Executor instance
-exctr = MPIExecutor()
-
-# Register simulation executable with executor
-sim_app = os.path.join(os.getcwd(), "../forces_app/forces.x")
-
-if not os.path.isfile(sim_app):
-    sys.exit("forces.x not found - please build first in ../forces_app dir")
-
-exctr.register_app(full_path=sim_app, app_name="forces")
-
-# State the sim_f, inputs, outputs
-sim_specs = {
-    "sim_f": run_forces,  # sim_f, imported above
-    "in": ["x"],  # Name of input for sim_f
-    "out": [("energy", float)],  # Name, type of output from sim_f
-}
-
-# State the gen_f, inputs, outputs, additional parameters
-gen_specs = {
-    "gen_f": uniform_random_sample,  # Generator function
-    "in": [],  # Generator input
-    "out": [("x", float, (1,))],  # Name, type and size of data from gen_f
-    "user": {
-        "lb": np.array([1000]),  # User parameters for the gen_f
-        "ub": np.array([3000]),
-        "gen_batch_size": 8,
-    },
-}
-
-# Create and work inside separate per-simulation directories
-libE_specs["sim_dirs_make"] = True
-
-# Instruct libEnsemble to exit after this many simulations
-exit_criteria = {"sim_max": 8}
-
-# Seed random streams for each worker, particularly for gen_f
-persis_info = add_unique_random_streams({}, nworkers + 1)
-
-# Launch libEnsemble
-H, persis_info, flag = libE(sim_specs, gen_specs, exit_criteria, persis_info=persis_info, libE_specs=libE_specs)
+if __name__ == "__main__":
+
+    # Parse number of workers, comms type, etc. from arguments
+    nworkers, is_manager, libE_specs, _ = parse_args()
+
+    # Initialize MPI Executor instance
+    exctr = MPIExecutor()
+
+    # Register simulation executable with executor
+    sim_app = os.path.join(os.getcwd(), "../forces_app/forces.x")
+
+    if not os.path.isfile(sim_app):
+        sys.exit("forces.x not found - please build first in ../forces_app dir")
+
+    exctr.register_app(full_path=sim_app, app_name="forces")
+
+    # State the sim_f, inputs, outputs
+    sim_specs = {
+        "sim_f": run_forces,  # sim_f, imported above
+        "in": ["x"],  # Name of input for sim_f
+        "out": [("energy", float)],  # Name, type of output from sim_f
+    }
+
+    # State the gen_f, inputs, outputs, additional parameters
+    gen_specs = {
+        "gen_f": uniform_random_sample,  # Generator function
+        "in": [],  # Generator input
+        "out": [("x", float, (1,))],  # Name, type and size of data from gen_f
+        "user": {
+            "lb": np.array([1000]),  # User parameters for the gen_f
+            "ub": np.array([3000]),
+            "gen_batch_size": 8,
+        },
+    }
+
+    # Create and work inside separate per-simulation directories
+    libE_specs["sim_dirs_make"] = True
+
+    # Instruct libEnsemble to exit after this many simulations
+    exit_criteria = {"sim_max": 8}
+
+    # Seed random streams for each worker, particularly for gen_f
+    persis_info = add_unique_random_streams({}, nworkers + 1)
+
+    # Launch libEnsemble
+    H, persis_info, flag = libE(sim_specs, gen_specs, exit_criteria, persis_info=persis_info, libE_specs=libE_specs)
diff --git a/scripts/libesubmit b/scripts/libesubmit
index 7f2cd09498..01c8fb5b70 100644
--- a/scripts/libesubmit
+++ b/scripts/libesubmit
@@ -93,7 +93,7 @@ if __name__ == "__main__":
     jobargs, unknown = parser.parse_known_args(sys.argv[1:])
 
     script = sys.argv[1]
-    if not jobargs.script.endswith(".json"):
+    if not script.endswith(".json"):
         parser.print_help()
         sys.exit("First argument doesn't appear to be a .json file.")
 
@@ -110,8 +110,8 @@ if __name__ == "__main__":
     if jobspec.executable == "python":
         jobspec.executable = str(jobargs.python_path)
     jobspec.attributes.duration = jobargs.wallclock
-    if jobspec.resources.node_count == 1:
-        jobspec.resources.node_count = jobargs.nnodes
+    if jobspec.resources["node_count"] == 1:
+        jobspec.resources["node_count"] = jobargs.nnodes
 
     # we enforced passing a python script in liberegister
     callscript = [i for i in jobspec.arguments if str(i).endswith(".py")][0]
@@ -134,10 +134,10 @@ if __name__ == "__main__":
 
         print(home + ":")
         for i in enumerate(check_dirs):
-            print(f"{i[0]}. {i[1]}")
+            print(f"   {i[0]+1}. /{i[1]}")
 
         inchoice = input("Specify a starting directory: ")
-        choice = home + "/" + check_dirs[int(inchoice)]
+        choice = home + "/" + check_dirs[int(inchoice)-1]
 
         def walkdir(folder):
             """Walk through every file in a directory"""
@@ -158,7 +158,7 @@ if __name__ == "__main__":
                 if callscript in filepath.split("/"):
                     candidate_script_paths.append(filepath)
                     tqdm.write(
-                        f"{len(candidate_script_paths)}. {'...' + filepath.split(choice)[1]}"
+                        f"   {len(candidate_script_paths)}. {filepath.split(choice)[1]}"
                     )
 
             exit = input("Specify a detected script: ")

From d83fe29d4278a3331e574dea539adb6a43ad79a5 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 12 Jul 2022 14:09:27 -0500
Subject: [PATCH 036/196] try windows once again on actions...?

---
 .github/workflows/ci.yml | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 1e209ee1c5..c7cb8d7517 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -42,6 +42,11 @@ jobs:
                       comms-type: l
                       mpi-version: 'mpich'
                       do-balsam: true
+                    - os: windows-latest
+                      python-version: "3.10"
+                      comms-type: l
+                      mpi-version: 'mpich'
+                      do-balsam: false
         env:
             HYDRA_LAUNCHER: 'fork'
             TERM: xterm-256color
@@ -192,10 +197,10 @@ jobs:
           run: |
            ./libensemble/tests/run-tests.sh -e -A "-W error" -z -${{ matrix.comms-type }}
 
-        #- name: Run simple tests, Ubuntu, Python 3.11
-        #  if: matrix.python-version == '3.11'
-        #  run: |
-        #   ./libensemble/tests/run-tests.sh -A "-W error" -z -${{ matrix.comms-type }}
+        - name: Run simple tests, Ubuntu, Python 3.11
+         if: matrix.os == 'windows-latest'
+         run: |
+          ./libensemble/tests/run-tests.sh -A "-W error" -z -${{ matrix.comms-type }}
 
         - name: Run extensive tests, macOS
           if: matrix.os == 'macos-latest'

From 026f10f367aa13f774f3d3c64d1c7e178280259c Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 12 Jul 2022 14:19:16 -0500
Subject: [PATCH 037/196] indentation

---
 .github/workflows/ci.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index c7cb8d7517..aae16f53d5 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -43,7 +43,7 @@ jobs:
                       mpi-version: 'mpich'
                       do-balsam: true
                     - os: windows-latest
-                      python-version: "3.10"
+                      python-version: '3.10'
                       comms-type: l
                       mpi-version: 'mpich'
                       do-balsam: false
@@ -198,9 +198,9 @@ jobs:
            ./libensemble/tests/run-tests.sh -e -A "-W error" -z -${{ matrix.comms-type }}
 
         - name: Run simple tests, Ubuntu, Python 3.11
-         if: matrix.os == 'windows-latest'
-         run: |
-          ./libensemble/tests/run-tests.sh -A "-W error" -z -${{ matrix.comms-type }}
+          if: matrix.os == 'windows-latest'
+          run: |
+            ./libensemble/tests/run-tests.sh -A "-W error" -z -${{ matrix.comms-type }}
 
         - name: Run extensive tests, macOS
           if: matrix.os == 'macos-latest'

From f6f9034df4f93c7d680c524e3a5897451d81c837 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 12 Jul 2022 14:26:56 -0500
Subject: [PATCH 038/196] try disabling mpich download on windows?

---
 .github/workflows/ci.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index aae16f53d5..e2b7521b22 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -102,7 +102,7 @@ jobs:
             conda install mpi4py
 
         - name: Install MPI, mpi4py from pip
-          if: matrix.python-version == '3.10'
+          if: matrix.python-version == '3.10' && matrix.os != 'windows-latest'
           run: |
             conda install ${{ matrix.mpi-version }}
             pip install mpi4py

From 1c57af1009405e3fb8369b7bd7fc1bf7a8095d86 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 12 Jul 2022 14:38:53 -0500
Subject: [PATCH 039/196] revoke generator dep install on windows?

---
 .github/workflows/ci.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index e2b7521b22..0c3aba2ee0 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -108,7 +108,7 @@ jobs:
             pip install mpi4py
 
         - name: Install generator dependencies
-          if: contains('3.7_3.8_3.9_3.10', matrix.python-version) && matrix.do-balsam == false
+          if: contains('3.7_3.8_3.9_3.10', matrix.python-version) && matrix.do-balsam == false && matrix.os != 'windows-latest'
           run: |
             python -m pip install --upgrade pip
             conda install nlopt

From 078a5f59e989e834201836e339bb2c6e7fe62c21 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 12 Jul 2022 16:32:32 -0500
Subject: [PATCH 040/196] try installing msmpi on windows, also make a reg test
 extra

---
 .github/workflows/ci.yml                                      | 4 ++--
 .../test_uniform_sampling_with_variable_resources.py          | 1 +
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 0c3aba2ee0..748082f1d2 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -45,7 +45,7 @@ jobs:
                     - os: windows-latest
                       python-version: '3.10'
                       comms-type: l
-                      mpi-version: 'mpich'
+                      mpi-version: 'msmpi'
                       do-balsam: false
         env:
             HYDRA_LAUNCHER: 'fork'
@@ -102,7 +102,7 @@ jobs:
             conda install mpi4py
 
         - name: Install MPI, mpi4py from pip
-          if: matrix.python-version == '3.10' && matrix.os != 'windows-latest'
+          if: matrix.python-version == '3.10'
           run: |
             conda install ${{ matrix.mpi-version }}
             pip install mpi4py
diff --git a/libensemble/tests/regression_tests/test_uniform_sampling_with_variable_resources.py b/libensemble/tests/regression_tests/test_uniform_sampling_with_variable_resources.py
index edc3ccbcbf..9c9804da04 100644
--- a/libensemble/tests/regression_tests/test_uniform_sampling_with_variable_resources.py
+++ b/libensemble/tests/regression_tests/test_uniform_sampling_with_variable_resources.py
@@ -14,6 +14,7 @@
 # Do not change these lines - they are parsed by run-tests.sh
 # TESTSUITE_COMMS: mpi local
 # TESTSUITE_NPROCS: 2 4
+# TESTSUITE_EXTRA: true
 
 import sys
 import numpy as np

From e758a5c49f287216b8dd8e839ef085054a117e14 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 13 Jul 2022 11:47:01 -0500
Subject: [PATCH 041/196] remove mpiexec --version, and ulimit just in case?

---
 .github/workflows/ci.yml | 2 --
 1 file changed, 2 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 748082f1d2..bef7e7833c 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -182,10 +182,8 @@ jobs:
         - name: Find MPI, Install libEnsemble, flake8, ulimit adjust
           run: |
             python install/find_mpi.py
-            mpiexec --version
             pip install -e .
             flake8 libensemble
-            ulimit -Sn 10000
 
         - name: Activate API unit test if using mpich
           if: matrix.mpi-version == 'mpich'

From e9edbb94a1a7a1fd8f5be9340dd3de011a777fea Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 13 Jul 2022 13:16:57 -0500
Subject: [PATCH 042/196] some attempts to catch and help resolve missing
 dependencies in psij scripts

---
 scripts/liberegister | 15 +++++++++++----
 scripts/libesubmit   | 23 +++++++++++++++++++----
 2 files changed, 30 insertions(+), 8 deletions(-)

diff --git a/scripts/liberegister b/scripts/liberegister
index 62bd90a49e..33047bfd95 100644
--- a/scripts/liberegister
+++ b/scripts/liberegister
@@ -5,13 +5,20 @@ import sys
 import shutil
 import argparse
 from pathlib import Path
-from psij import Job, JobSpec
-from psij.resource_spec import ResourceSpecV1
-from psij.job_attributes import JobAttributes
-from psij.serialize import Export
 from libensemble.version import __version__
 from libensemble.tools.parse_args import parser as callscript_parser
 
+try:
+    from psij import Job, JobSpec
+    from psij.resource_spec import ResourceSpecV1
+    from psij.job_attributes import JobAttributes
+    from psij.serialize import Export
+except ModuleNotFoundError:
+    print(f"*** libEnsemble {__version__} ***")
+    print("\nThe PSI/J Python interface is not installed. Please install it via the following:\n")
+    print("     git clone https://github.com/ExaWorks/psi-j-python.git")
+    print("     cd psi-j-python; pip install -e .\n")
+
 if __name__ == "__main__":
 
     parser = argparse.ArgumentParser(
diff --git a/scripts/libesubmit b/scripts/libesubmit
index 01c8fb5b70..00a7cf4deb 100644
--- a/scripts/libesubmit
+++ b/scripts/libesubmit
@@ -5,14 +5,29 @@ import sys
 import time
 import shutil
 import argparse
-from tqdm.auto import tqdm
 from pathlib import Path
-from psij import JobExecutor, Import, Export, JobSpec, Job
-from psij.resource_spec import ResourceSpecV1
-from psij.job_attributes import JobAttributes
+
 from libensemble.version import __version__
 from libensemble.resources import node_resources
 
+try:
+    from tqdm.auto import tqdm
+except ModuleNotFoundError:
+    print(f"*** libEnsemble {__version__} ***")
+    print("\ntqdm is not installed, but this only matters if libesubmit can't find your calling script.\n")
+    print("\ntqdm can be installed via:\n")
+    print("     pip install tqdm")
+
+try:
+    from psij import JobExecutor, Import, Export, JobSpec, Job
+    from psij.resource_spec import ResourceSpecV1
+    from psij.job_attributes import JobAttributes
+except ModuleNotFoundError:
+    print(f"*** libEnsemble {__version__} ***")
+    print("\nThe PSI/J Python interface is not installed. Please install it via the following:\n")
+    print("     git clone https://github.com/ExaWorks/psi-j-python.git")
+    print("     cd psi-j-python; pip install -e .\n")
+
 if __name__ == "__main__":
 
     parser = argparse.ArgumentParser(

From 4748b7909c2cf5ad1e6cb66375f38db78fa03ae2 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 13 Jul 2022 16:16:41 -0500
Subject: [PATCH 043/196] first attempt at including nsim_workers and
 nresource_sets in liberegister

---
 scripts/liberegister | 19 +++++++++++++++++--
 1 file changed, 17 insertions(+), 2 deletions(-)

diff --git a/scripts/liberegister b/scripts/liberegister
index 33047bfd95..01993d8e9f 100644
--- a/scripts/liberegister
+++ b/scripts/liberegister
@@ -125,9 +125,18 @@ if __name__ == "__main__":
             jobargs.calling_script,
             "--comms",
             jobargs.comms,
-            "--nworkers",
-            str(jobargs.nworkers),
         ]
+
+        nworkers = jobargs.nworkers
+        nsim_workers = jobargs.nsim_workers
+
+        ngen_workers = nworkers - nsim_workers
+        assert ngen_workers > 0, "nsim_workers cannot be greater than number of workers"
+
+        if nsim_workers is not None:
+            nworkers = nworkers or nsim_workers + 1
+            arguments.append["--nsim_workers", str(jobargs.nsim_workers)]
+
         resources = ResourceSpecV1(node_count=jobargs.nnodes)
     else:  # jobargs.comms == "mpi":
         arguments = [jobargs.calling_script]
@@ -135,6 +144,12 @@ if __name__ == "__main__":
             process_count=jobargs.nworkers + 1, processes_per_node=1
         )
 
+        if jobargs.nsim_workers:
+            arguments.append('--nsim_workers', str(jobargs.nsim_workers))
+
+    if jobargs.nresource_sets:
+        arguments.append('--nresource_sets', str(jobargs.nresource_sets))
+
     jobspec = JobSpec(
         name=jobargs.jobname,
         executable=str(executable),

From dff6559399bdf132bb4a8c111f6840c57a8545bd Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 14 Jul 2022 09:04:52 -0500
Subject: [PATCH 044/196] some slight clarification on nsim_workers

---
 libensemble/tools/parse_args.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/libensemble/tools/parse_args.py b/libensemble/tools/parse_args.py
index d365b33713..6b8b3b1126 100644
--- a/libensemble/tools/parse_args.py
+++ b/libensemble/tools/parse_args.py
@@ -18,7 +18,7 @@
 )
 parser.add_argument("--nworkers", type=int, nargs="?", help="Number of local forked processes")
 parser.add_argument(
-    "--nsim_workers", type=int, nargs="?", help="Number of workers for sims. 1+ zero-resource gen worker will be added"
+    "--nsim_workers", type=int, nargs="?", help="Number of workers for sims. 1+ zero-resource worker for a gen will be added"
 )
 parser.add_argument("--nresource_sets", type=int, nargs="?", help="Number of resource sets")
 parser.add_argument("--workers", type=str, nargs="+", help="List of worker nodes")
@@ -178,9 +178,9 @@ def parse_args():
 
         --comms,          Communications medium for manager and workers. Default is 'mpi'.
         --nworkers,       (For 'local' or 'tcp' comms) Set number of workers.
-        --nsim_workers,   (For 'local' or 'mpi' comms) A convenience option for common cases.
+        --nsim_workers,   (For 'local' or 'mpi' comms) A convenience option for cases with persistent generators.
                           If used with no other criteria, will generate one additional
-                          zero-resource worker for use as a generator. If the number of workers
+                          zero-resource worker for running a generator. If --nworkers
                           has also been specified, will generate enough zero-resource workers to
                           match the other criteria.
         --nresource_sets, Explicitly set the number of resource sets. This sets
@@ -192,7 +192,7 @@ def parse_args():
         Run with 'local' comms and 4 workers
         $ python calling_script --comms local --nworkers 4
 
-        Run with 'local' comms and 5 workers - one gen (no resources), and 4 sims.
+        Run with 'local' comms and 5 workers - one gen worker (no resources), and 4 sim workers.
         $ python calling_script --comms local --nsim_workers 4
 
         Run with 'local' comms with 4 workers and 8 resource sets. The extra resource sets will

From a311da2fa63464f40e9ca3ad7791c89800b40cc3 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 14 Jul 2022 09:11:33 -0500
Subject: [PATCH 045/196] in imports of persistent_aposmm unit test, only set
 fork if on macOS or linux

---
 libensemble/tests/unit_tests/test_persistent_aposmm.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/libensemble/tests/unit_tests/test_persistent_aposmm.py b/libensemble/tests/unit_tests/test_persistent_aposmm.py
index ea68193f0b..7ab999ad88 100644
--- a/libensemble/tests/unit_tests/test_persistent_aposmm.py
+++ b/libensemble/tests/unit_tests/test_persistent_aposmm.py
@@ -1,7 +1,9 @@
 import pytest
+import platform
 import multiprocessing
 
-multiprocessing.set_start_method("fork", force=True)
+if platform.system() in ['Linux', 'Darwin']:
+    multiprocessing.set_start_method("fork", force=True)
 
 import libensemble.gen_funcs
 

From 2dd65b7eaa0e1420ad3f7cb81c834fbd90ab5389 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 14 Jul 2022 10:30:55 -0500
Subject: [PATCH 046/196] fix logic so args are just passed though - evaluating
 how well they work isn't this script's job

---
 scripts/liberegister | 17 +++++------------
 1 file changed, 5 insertions(+), 12 deletions(-)

diff --git a/scripts/liberegister b/scripts/liberegister
index 01993d8e9f..c4a42671d6 100644
--- a/scripts/liberegister
+++ b/scripts/liberegister
@@ -127,15 +127,8 @@ if __name__ == "__main__":
             jobargs.comms,
         ]
 
-        nworkers = jobargs.nworkers
-        nsim_workers = jobargs.nsim_workers
-
-        ngen_workers = nworkers - nsim_workers
-        assert ngen_workers > 0, "nsim_workers cannot be greater than number of workers"
-
-        if nsim_workers is not None:
-            nworkers = nworkers or nsim_workers + 1
-            arguments.append["--nsim_workers", str(jobargs.nsim_workers)]
+        if jobargs.nworkers:
+            arguments.extend(["--nworkers", str(jobargs.nworkers)])
 
         resources = ResourceSpecV1(node_count=jobargs.nnodes)
     else:  # jobargs.comms == "mpi":
@@ -144,11 +137,11 @@ if __name__ == "__main__":
             process_count=jobargs.nworkers + 1, processes_per_node=1
         )
 
-        if jobargs.nsim_workers:
-            arguments.append('--nsim_workers', str(jobargs.nsim_workers))
+    if jobargs.nsim_workers:
+        arguments.extend(['--nsim_workers', str(jobargs.nsim_workers)])
 
     if jobargs.nresource_sets:
-        arguments.append('--nresource_sets', str(jobargs.nresource_sets))
+        arguments.extend(['--nresource_sets', str(jobargs.nresource_sets)])
 
     jobspec = JobSpec(
         name=jobargs.jobname,

From 187dafe9819fe0a8ffa0af1a06493030597437f5 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 14 Jul 2022 10:32:55 -0500
Subject: [PATCH 047/196] also dont import aposmm unless on linux or darwin

---
 libensemble/tests/unit_tests/test_persistent_aposmm.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libensemble/tests/unit_tests/test_persistent_aposmm.py b/libensemble/tests/unit_tests/test_persistent_aposmm.py
index 7ab999ad88..cc46e05a04 100644
--- a/libensemble/tests/unit_tests/test_persistent_aposmm.py
+++ b/libensemble/tests/unit_tests/test_persistent_aposmm.py
@@ -4,11 +4,11 @@
 
 if platform.system() in ['Linux', 'Darwin']:
     multiprocessing.set_start_method("fork", force=True)
+    from libensemble.gen_funcs.persistent_aposmm import aposmm, update_history_optimal
 
 import libensemble.gen_funcs
 
 libensemble.gen_funcs.rc.aposmm_optimizers = "nlopt"
-from libensemble.gen_funcs.persistent_aposmm import aposmm, update_history_optimal
 
 import numpy as np
 import libensemble.tests.unit_tests.setup as setup

From be048982d217e81859629bceed596e566b219543 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 14 Jul 2022 10:57:58 -0500
Subject: [PATCH 048/196] rearrange?

---
 libensemble/tests/unit_tests/test_persistent_aposmm.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/libensemble/tests/unit_tests/test_persistent_aposmm.py b/libensemble/tests/unit_tests/test_persistent_aposmm.py
index cc46e05a04..8f427d0634 100644
--- a/libensemble/tests/unit_tests/test_persistent_aposmm.py
+++ b/libensemble/tests/unit_tests/test_persistent_aposmm.py
@@ -2,14 +2,14 @@
 import platform
 import multiprocessing
 
-if platform.system() in ['Linux', 'Darwin']:
-    multiprocessing.set_start_method("fork", force=True)
-    from libensemble.gen_funcs.persistent_aposmm import aposmm, update_history_optimal
-
 import libensemble.gen_funcs
 
 libensemble.gen_funcs.rc.aposmm_optimizers = "nlopt"
 
+if platform.system() in ['Linux', 'Darwin']:
+    multiprocessing.set_start_method("fork", force=True)
+    from libensemble.gen_funcs.persistent_aposmm import aposmm, update_history_optimal
+
 import numpy as np
 import libensemble.tests.unit_tests.setup as setup
 from libensemble.sim_funcs.six_hump_camel import six_hump_camel_func, six_hump_camel_grad

From 0c263c4d960f525807d5c46545e74b402f19a1ae Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 14 Jul 2022 15:36:52 -0500
Subject: [PATCH 049/196] first approach to dreaded line explanation in FAQ

---
 docs/FAQ.rst | 18 ++++++++++++++++--
 1 file changed, 16 insertions(+), 2 deletions(-)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index da70c7f5bd..51a278db6b 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -221,8 +221,22 @@ This effectively puts libEnsemble in silent mode.
 
 See the :ref:`Logger Configuration<logger_config>` docs for more information.
 
-macOS-Specific Errors
----------------------
+macOS and Windows Errors
+------------------------
+
+**"RuntimeError: An attempt has been made to start a new process... this probably means that you are not using fork...
+" if __name__ == '__main__': freeze_support() ...**
+
+You need to place your main calling script code underneath an ``if __name__ == "__main__":`` block.
+
+Explanation: Python chooses one of three methods to start new processes when using multiprocessing (``--comms local`` with libEnsemble).
+These are ``'fork'``, ``'spawn'``, and ``'forkserver'``. ``'fork'`` is the default on Unix, and in our experience is quicker and more reliable,
+but ``'spawn'`` is the default on Windows and macOS (See the `Python multiprocessing docs`_).
+
+Prior to libEnsemble v0.9.2, if libEnsemble detected macOS, it would automatically switch the multiprocessing method to ``'fork'``. We
+decided to stop doing this to stop overriding defaults and avoid compatibility issues with some libraries.
+
+.. _`Python multiprocessing docs`: https://docs.python.org/3/library/multiprocessing.html
 
 **"Fatal error in MPI_Init_thread: Other MPI error, error stack: ... gethostbyname failed"**
 

From eb4c77258f1788dae920046992e6f5b41e14c707 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 15 Jul 2022 10:12:09 -0500
Subject: [PATCH 050/196] mention set_start_method alternative

---
 docs/FAQ.rst | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index 51a278db6b..3494fc1591 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -229,12 +229,20 @@ macOS and Windows Errors
 
 You need to place your main calling script code underneath an ``if __name__ == "__main__":`` block.
 
-Explanation: Python chooses one of three methods to start new processes when using multiprocessing (``--comms local`` with libEnsemble).
-These are ``'fork'``, ``'spawn'``, and ``'forkserver'``. ``'fork'`` is the default on Unix, and in our experience is quicker and more reliable,
-but ``'spawn'`` is the default on Windows and macOS (See the `Python multiprocessing docs`_).
+Explanation: Python chooses one of three methods to start new processes when using multiprocessing
+(``--comms local`` with libEnsemble). These are ``'fork'``, ``'spawn'``, and ``'forkserver'``. ``'fork'`` 
+is the default on Unix, and in our experience is quicker and more reliable, but ``'spawn'`` is the default 
+on Windows and macOS (See the `Python multiprocessing docs`_).
 
-Prior to libEnsemble v0.9.2, if libEnsemble detected macOS, it would automatically switch the multiprocessing method to ``'fork'``. We
-decided to stop doing this to stop overriding defaults and avoid compatibility issues with some libraries.
+Prior to libEnsemble v0.9.2, if libEnsemble detected macOS, it would automatically switch the multiprocessing 
+method to ``'fork'``. We decided to stop doing this to avoid overriding defaults and compatibility issues with
+some libraries.
+
+If you'd prefer to use ``'fork'`` or not reformat your code, you can set the multiprocessing start method via 
+the following, placed near the top of your calling script::
+
+  import multiprocessing
+  multiprocessing.set_start_method('fork', force=True)
 
 .. _`Python multiprocessing docs`: https://docs.python.org/3/library/multiprocessing.html
 

From 9666c7525f8b88ba2d3ddf2deae2f4367bc2c569 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 15 Jul 2022 16:09:19 -0500
Subject: [PATCH 051/196] mention persistent gen again

---
 libensemble/tools/parse_args.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libensemble/tools/parse_args.py b/libensemble/tools/parse_args.py
index 6b8b3b1126..0a7cf0068b 100644
--- a/libensemble/tools/parse_args.py
+++ b/libensemble/tools/parse_args.py
@@ -18,7 +18,7 @@
 )
 parser.add_argument("--nworkers", type=int, nargs="?", help="Number of local forked processes")
 parser.add_argument(
-    "--nsim_workers", type=int, nargs="?", help="Number of workers for sims. 1+ zero-resource worker for a gen will be added"
+    "--nsim_workers", type=int, nargs="?", help="Number of workers for sims. 1+ zero-resource worker for a persistent generator will be added"
 )
 parser.add_argument("--nresource_sets", type=int, nargs="?", help="Number of resource sets")
 parser.add_argument("--workers", type=str, nargs="+", help="List of worker nodes")

From 4236914daf284b5ddcc8e524df1c00979d517fe8 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 15 Jul 2022 16:25:51 -0500
Subject: [PATCH 052/196] flakeasefoiahsoeifhaosiedfadfe8888888

---
 libensemble/tools/parse_args.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/libensemble/tools/parse_args.py b/libensemble/tools/parse_args.py
index 0a7cf0068b..d4cb5d7346 100644
--- a/libensemble/tools/parse_args.py
+++ b/libensemble/tools/parse_args.py
@@ -18,7 +18,10 @@
 )
 parser.add_argument("--nworkers", type=int, nargs="?", help="Number of local forked processes")
 parser.add_argument(
-    "--nsim_workers", type=int, nargs="?", help="Number of workers for sims. 1+ zero-resource worker for a persistent generator will be added"
+    "--nsim_workers",
+    type=int,
+    nargs="?",
+    help="Number of workers for sims. 1+ zero-resource worker for a persistent generator will be added",
 )
 parser.add_argument("--nresource_sets", type=int, nargs="?", help="Number of resource sets")
 parser.add_argument("--workers", type=str, nargs="+", help="List of worker nodes")

From fc16331ec480722a05579b8fbb9d62b39eb5d6ea Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Fri, 15 Jul 2022 16:59:53 -0500
Subject: [PATCH 053/196] Re-enamble PETSc tests

---
 ...tent_aposmm_pounders.py => test_persistent_aposmm_pounders.py} | 0
 ...nt_aposmm_tao_blmvm.py => test_persistent_aposmm_tao_blmvm.py} | 0
 ...rsistent_aposmm_tao_nm.py => test_persistent_aposmm_tao_nm.py} | 0
 3 files changed, 0 insertions(+), 0 deletions(-)
 rename libensemble/tests/regression_tests/{dont_run_test_persistent_aposmm_pounders.py => test_persistent_aposmm_pounders.py} (100%)
 rename libensemble/tests/regression_tests/{dont_run_test_persistent_aposmm_tao_blmvm.py => test_persistent_aposmm_tao_blmvm.py} (100%)
 rename libensemble/tests/regression_tests/{dont_run_test_persistent_aposmm_tao_nm.py => test_persistent_aposmm_tao_nm.py} (100%)

diff --git a/libensemble/tests/regression_tests/dont_run_test_persistent_aposmm_pounders.py b/libensemble/tests/regression_tests/test_persistent_aposmm_pounders.py
similarity index 100%
rename from libensemble/tests/regression_tests/dont_run_test_persistent_aposmm_pounders.py
rename to libensemble/tests/regression_tests/test_persistent_aposmm_pounders.py
diff --git a/libensemble/tests/regression_tests/dont_run_test_persistent_aposmm_tao_blmvm.py b/libensemble/tests/regression_tests/test_persistent_aposmm_tao_blmvm.py
similarity index 100%
rename from libensemble/tests/regression_tests/dont_run_test_persistent_aposmm_tao_blmvm.py
rename to libensemble/tests/regression_tests/test_persistent_aposmm_tao_blmvm.py
diff --git a/libensemble/tests/regression_tests/dont_run_test_persistent_aposmm_tao_nm.py b/libensemble/tests/regression_tests/test_persistent_aposmm_tao_nm.py
similarity index 100%
rename from libensemble/tests/regression_tests/dont_run_test_persistent_aposmm_tao_nm.py
rename to libensemble/tests/regression_tests/test_persistent_aposmm_tao_nm.py

From 25c5a8674e212ca5b27e15acd738ad2aef33cf49 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 18 Jul 2022 13:31:32 -0500
Subject: [PATCH 054/196] first approach to documenting psi/j scripts

---
 docs/platforms/example_scripts.rst |   4 +
 docs/running_libE.rst              | 141 +++++++++++++++++++++++++++++
 2 files changed, 145 insertions(+)

diff --git a/docs/platforms/example_scripts.rst b/docs/platforms/example_scripts.rst
index 3163aa52f0..f6e9989685 100644
--- a/docs/platforms/example_scripts.rst
+++ b/docs/platforms/example_scripts.rst
@@ -5,6 +5,10 @@ Below are example submission scripts used to configure and launch libEnsemble
 on a variety of high-powered systems. See :doc:`here<platforms_index>` for more
 information about the respective systems and configuration.
 
+Alternatively to interacting with the scheduler or configuring submission scripts,
+libEnsemble now features a portable set of :ref:`command-line utilities<liberegister>`
+for submitting workflows to almost any system or scheduler.
+
 Slurm - Basic
 -------------
 
diff --git a/docs/running_libE.rst b/docs/running_libE.rst
index a508ff7b28..eb9e6fca58 100644
--- a/docs/running_libE.rst
+++ b/docs/running_libE.rst
@@ -134,6 +134,147 @@ Further command line options
 
 See the **parse_args()** function in :doc:`Convenience Tools<utilities>` for further command line options.
 
+.. _liberegister:
+
+liberegister / libesubmit
+-------------------------
+
+libEnsemble now features a pair of command-line utilities for preparing and launching libEnsemble workflows onto almost
+any machine and any scheduler, using a `PSI/J`_ Python implementation. This is an alternative approach
+to maintaining system or scheduler-specific batch submission scripts.
+
+- `liberegister`
+
+Creates an initial, platform-independent PSI/J serialization of a libEnsemble submission. Run this utility on
+a calling script in a familiar manner::
+
+    liberegister my_calling_script.py --comms local --nworkers 4
+
+This produces an initial `my_calling_script.json` serialization conforming to PSI/J's specification:
+
+.. container:: toggle
+
+    .. container:: header
+
+       `my_calling_script.json`
+
+    .. code-block:: JSON
+
+        {
+            "version": 0.1,
+            "type": "JobSpec",
+            "data": {
+                "name": "libe-job",
+                "executable": "python",
+                "arguments": [
+                    "my_calling_script.py",
+                    "--comms",
+                    "local",
+                    "--nworkers",
+                    "4"
+                ],
+                "directory": null,
+                "inherit_environment": true,
+                "environment": {
+                    "PYTHONNOUSERSITE": "1"
+                },
+                "stdin_path": null,
+                "stdout_path": null,
+                "stderr_path": null,
+                "resources": {
+                    "node_count": 1,
+                    "process_count": null,
+                    "process_per_node": null,
+                    "cpu_cores_per_process": null,
+                    "gpu_cores_per_process": null,
+                    "exclusive_node_use": true
+                },
+                "attributes": {
+                    "duration": "30",
+                    "queue_name": null,
+                    "project_name": null,
+                    "reservation_id": null,
+                    "custom_attributes": {}
+                },
+                "launcher": null
+            }
+        }
+
+- `libesubmit`
+
+Further parameterizes a serialization, and submits a corresponding Job to the specified scheduler.
+Running ``qsub``, ``sbatch``, etc. on some batch submission script is not needed. For instance::
+
+    libesubmit my_calling_script.json -q debug -A project -s slurm --nnodes 8
+
+Results in::
+
+    *** libEnsemble 0.9.2+dev ***
+    Imported PSI/J serialization: my_calling_script.json. Preparing submission...
+    Calling script: my_calling_script.py
+    ...found! Proceding.
+    Submitting Job!: Job[id=ce4ead75-a3a4-42a3-94ff-c44b3b2c7e61, native_id=None, executor=None, status=JobStatus[NEW, time=1658167808.5125017]]
+
+    $ squeue --long --users=user
+    Mon Jul 18 13:10:15 2022
+             JOBID PARTITION     NAME     USER    STATE       TIME TIME_LIMI  NODES NODELIST(REASON)
+           2508936    debug  ce4ead75     user  PENDING       0:00     30:00      8 (Priority)
+
+This also produces a Job-specific representation, e.g:
+
+.. container:: toggle
+
+    .. container:: header
+
+       `8ba9de56.my_calling_script.json`
+
+    .. code-block:: JSON
+
+        {
+            "version": 0.1,
+            "type": "JobSpec",
+            "data": {
+                "name": "libe-job",
+                "executable": "/Users/jnavarro/miniconda3/envs/libe/bin/python3.8",
+                "arguments": [
+                    "my_calling_script.py",
+                    "--comms",
+                    "local",
+                    "--nworkers",
+                    "4"
+                ],
+                "directory": "/home/user/libensemble/scratch",
+                "inherit_environment": true,
+                "environment": {
+                    "PYTHONNOUSERSITE": "1"
+                },
+                "stdin_path": null,
+                "stdout_path": "8ba9de56.my_calling_script.out",
+                "stderr_path": "8ba9de56.my_calling_script.err",
+                "resources": {
+                    "node_count": 8,
+                    "process_count": null,
+                    "process_per_node": null,
+                    "cpu_cores_per_process": null,
+                    "gpu_cores_per_process": null,
+                    "exclusive_node_use": true
+                },
+                "attributes": {
+                    "duration": "30",
+                    "queue_name": "debug",
+                    "project_name": "project",
+                    "reservation_id": null,
+                    "custom_attributes": {}
+                },
+                "launcher": null
+            }
+        }
+
+If libesubmit is run on a ``.json`` serialization from liberegister and can't find the
+specified calling script, it'll help search for matching candidate scripts.
+
+.. _PSI/J: https://exaworks.org/psij
+
 Persistent Workers
 ------------------
 .. _persis_worker:

From 41ad54d53aee68cb98426a161ede2358a1fb1cff Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 19 Jul 2022 12:54:52 -0500
Subject: [PATCH 055/196] mention new optional dependencies in README

---
 README.rst                  | 13 +++++++++++--
 docs/introduction_latex.rst |  3 +++
 2 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/README.rst b/README.rst
index 59da5a4f26..dec88fcb01 100644
--- a/README.rst
+++ b/README.rst
@@ -71,7 +71,7 @@ and cores, and can dynamically assign resources to workers.
 Dependencies
 ~~~~~~~~~~~~
 
-Required dependencies:
+**Required dependencies**:
 
 * Python_ 3.7 or above
 * NumPy_
@@ -83,7 +83,7 @@ When using  ``mpi4py`` for libEnsemble communications:
 * A functional MPI 1.x/2.x/3.x implementation, such as MPICH_, built with shared/dynamic libraries
 * mpi4py_ v2.0.0 or above
 
-Optional dependencies:
+**Optional dependencies**:
 
 * Balsam_
 
@@ -103,6 +103,12 @@ a function-as-a-service platform to which workers can submit remote generator or
 simulator function instances. This feature can help distribute an ensemble
 across systems and heterogeneous resources.
 
+* `psi-j-python`_
+
+As of v0.9.2+dev, libEnsemble features a set of command-line utilities for submitting
+libEnsemble jobs to almost any system or scheduler via a `PSI/J`_ Python interface. tqdm_
+is also required.
+
 The example simulation and generation functions and tests require the following:
 
 * SciPy_
@@ -330,6 +336,8 @@ See a complete list of `example user scripts`_.
 .. _petsc4py: https://bitbucket.org/petsc/petsc4py
 .. _PETSc/TAO: http://www.mcs.anl.gov/petsc
 .. _poster: https://figshare.com/articles/libEnsemble_A_Python_Library_for_Dynamic_Ensemble-Based_Computations/12559520
+.. _PSI/J: https://exaworks.org/psij
+.. _psi-j-python: https://github.com/ExaWorks/psi-j-python
 .. _psutil: https://pypi.org/project/psutil/
 .. _PyPI: https://pypi.org
 .. _pytest-cov: https://pypi.org/project/pytest-cov/
@@ -348,6 +356,7 @@ See a complete list of `example user scripts`_.
 .. _tarball: https://github.com/Libensemble/libensemble/releases/latest
 .. _Tasmanian: https://tasmanian.ornl.gov/
 .. _Theta: https://www.alcf.anl.gov/alcf-resources/theta
+.. _tqdm: https://tqdm.github.io/
 .. _user guide: https://libensemble.readthedocs.io/en/latest/programming_libE.html
 .. _VTMOP: https://github.com/Libensemble/libe-community-examples#vtmop
 .. _WarpX: https://warpx.readthedocs.io/en/latest/
diff --git a/docs/introduction_latex.rst b/docs/introduction_latex.rst
index c552e18146..424765ec47 100644
--- a/docs/introduction_latex.rst
+++ b/docs/introduction_latex.rst
@@ -54,6 +54,8 @@ We now present further information on running and testing libEnsemble.
 .. _petsc4py: https://bitbucket.org/petsc/petsc4py
 .. _PETSc/TAO: http://www.mcs.anl.gov/petsc
 .. _poster: https://figshare.com/articles/libEnsemble_A_Python_Library_for_Dynamic_Ensemble-Based_Computations/12559520
+.. _PSI/J: https://exaworks.org/psij
+.. _psi-j-python: https://github.com/ExaWorks/psi-j-python
 .. _psutil: https://pypi.org/project/psutil/
 .. _PyPI: https://pypi.org
 .. _pytest-cov: https://pypi.org/project/pytest-cov/
@@ -72,6 +74,7 @@ We now present further information on running and testing libEnsemble.
 .. _tarball: https://github.com/Libensemble/libensemble/releases/latest
 .. _Tasmanian: https://tasmanian.ornl.gov/
 .. _Theta: https://www.alcf.anl.gov/alcf-resources/theta
+.. _tqdm: https://tqdm.github.io/
 .. _user guide: https://libensemble.readthedocs.io/en/latest/programming_libE.html
 .. _VTMOP: https://informs-sim.org/wsc20papers/311.pdf
 .. _WarpX: https://warpx.readthedocs.io/en/latest/

From 3daa8c48a857342c5b41c231f5c167f0f2a56831 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Tue, 19 Jul 2022 14:18:48 -0500
Subject: [PATCH 056/196] Uncomment petsc in ci

---
 .github/workflows/ci.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 1e209ee1c5..9ae72fb9a9 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -111,8 +111,8 @@ jobs:
             conda install superlu_dist
             conda install hypre
             conda install mumps-mpi
-            # pip install petsc
-            # pip install petsc4py
+            conda install petsc
+            conda install petsc4py
             pip install DFO-LS
 
             pip install mpmath

From 83f4e65dbbba9daadd3dbb6092bf3c5315f8520c Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Tue, 19 Jul 2022 21:04:08 -0500
Subject: [PATCH 057/196] Re-enamble persistent_gp with git installed dragonfly

---
 .github/workflows/ci.yml                                      | 4 +++-
 ...nt_run_test_persistent_gp.py => run_test_persistent_gp.py} | 0
 2 files changed, 3 insertions(+), 1 deletion(-)
 rename libensemble/tests/regression_tests/{dont_run_test_persistent_gp.py => run_test_persistent_gp.py} (100%)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 9ae72fb9a9..adaff30a98 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -137,7 +137,9 @@ jobs:
             cp ./benchmarks/speed3d_c2c ../../libensemble/tests/regression_tests/
             # end heffte build and dependencies
 
-            pip install dragonfly-opt
+            # pip install dragonfly-opt
+            pip install git+https://github.com/dragonfly/dragonfly.git
+
             pip install torch
             pip install gpytorch
             pip install ax-platform
diff --git a/libensemble/tests/regression_tests/dont_run_test_persistent_gp.py b/libensemble/tests/regression_tests/run_test_persistent_gp.py
similarity index 100%
rename from libensemble/tests/regression_tests/dont_run_test_persistent_gp.py
rename to libensemble/tests/regression_tests/run_test_persistent_gp.py

From 969177c271dcea5a91d265b2c087ec9455a2984b Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Wed, 20 Jul 2022 11:33:05 -0500
Subject: [PATCH 058/196] Fix typo in forces build script

---
 .../tests/scaling_tests/forces/forces_app/build_forces.sh       | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libensemble/tests/scaling_tests/forces/forces_app/build_forces.sh b/libensemble/tests/scaling_tests/forces/forces_app/build_forces.sh
index 3615a076ec..cd9f5914f2 100755
--- a/libensemble/tests/scaling_tests/forces/forces_app/build_forces.sh
+++ b/libensemble/tests/scaling_tests/forces/forces_app/build_forces.sh
@@ -36,7 +36,7 @@ mpicc -O3 -o forces.x forces.c -lm
 # xlc_r -O3 -qsmp=omp -qoffload -o forces.x forces.c
 
 # Nvidia (nvc) compiler with mpicc and on Cray system with target (Perlmutter)
-# mpicc -O3 -fopenmp -mp=gpu -o forces_gpu.x forces_gpu.c
+# mpicc -O3 -fopenmp -mp=gpu -o forces.x forces.c
 # cc -O3 -fopenmp -mp=gpu -target-accel=nvidia80 -o forces.x forces.c
 
 # Spock/Crusher (AMD ROCm compiler)

From cd090e51b7b17223871abc75247e49cebf73d04a Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Fri, 22 Jul 2022 15:42:32 -0500
Subject: [PATCH 059/196] Replace manager_poll output string with the
 enumerated ints

---
 docs/executor/overview.rst                    |  2 +-
 docs/tutorials/calib_cancel_tutorial.rst      |  4 ++--
 libensemble/executors/executor.py             | 22 ++++++++++++-------
 libensemble/sim_funcs/executor_hworld.py      |  2 +-
 .../test_executor_manager_poll.py             |  9 ++++----
 5 files changed, 23 insertions(+), 16 deletions(-)

diff --git a/docs/executor/overview.rst b/docs/executor/overview.rst
index 069b1de5f3..ba0c15926e 100644
--- a/docs/executor/overview.rst
+++ b/docs/executor/overview.rst
@@ -76,7 +76,7 @@ In user simulation function::
 
         # Has manager sent a finish signal
         exctr.manager_poll()
-        if exctr.manager_signal == 'finish':
+        if exctr.manager_signal in [MAN_SIGNAL_KILL, MAN_SIGNAL_FINISH]:
             task.kill()
             my_cleanup()
 
diff --git a/docs/tutorials/calib_cancel_tutorial.rst b/docs/tutorials/calib_cancel_tutorial.rst
index 718d6e4398..030a8bf590 100644
--- a/docs/tutorials/calib_cancel_tutorial.rst
+++ b/docs/tutorials/calib_cancel_tutorial.rst
@@ -240,7 +240,7 @@ prepared for irregular sending /receiving of data.
 ..         # Poll task for finish and poll manager for kill signals
 ..         while(not task.finished):
 ..             exctr.manager_poll()
-..             if exctr.manager_signal == 'kill':
+..             if exctr.manager_signal == MAN_SIGNAL_KILL:
 ..                 task.kill()
 ..                 calc_status = MAN_SIGNAL_KILL
 ..                 break
@@ -256,7 +256,7 @@ prepared for irregular sending /receiving of data.
 .. While the launched task isn't finished, the simulator function periodically polls
 .. both the task's statuses and for signals from the manager via
 .. the :ref:`executor.manager_poll()<manager_poll_label>` function.
-.. Immediately after ``exctr.manager_signal`` is confirmed as ``'kill'``, the current
+.. Immediately after ``exctr.manager_signal`` is confirmed as ``MAN_SIGNAL_KILL``, the current
 .. task is killed and the function returns with the
 .. ``MAN_SIGNAL_KILL`` :doc:`calc_status<../data_structures/calc_status>`.
 .. This status will be logged in ``libE_stats.txt``.
diff --git a/libensemble/executors/executor.py b/libensemble/executors/executor.py
index 5fd19ae0d9..bb7c1d0ab8 100644
--- a/libensemble/executors/executor.py
+++ b/libensemble/executors/executor.py
@@ -54,6 +54,11 @@
 FAILED
 """.split()
 
+MAN_KILL_SIGNALS = """
+MAN_SIGNAL_FINISH
+MAN_SIGNAL_KILL
+""".split()
+
 
 class ExecutorException(Exception):
     "Raised for any exception in the Executor"
@@ -386,7 +391,7 @@ def __init__(self):
         This is typically created in the user calling script.
         """
 
-        self.manager_signal = "none"
+        self.manager_signal = None
         self.default_apps = {"sim": None, "gen": None}
         self.apps = {}
 
@@ -485,7 +490,7 @@ def manager_poll(self):
         The executor manager_signal attribute will be updated.
         """
 
-        self.manager_signal = "none"  # Reset
+        self.manager_signal = None  # Reset
 
         # Check for messages; disregard anything but a stop signal
         if not self.comm.mail_flag():
@@ -495,13 +500,14 @@ def manager_poll(self):
             return
 
         # Process the signal and push back on comm (for now)
-        logger.info("Worker received kill signal {} from manager".format(man_signal))
-        if man_signal == MAN_SIGNAL_FINISH:
-            self.manager_signal = "finish"
-        elif man_signal == MAN_SIGNAL_KILL:
-            self.manager_signal = "kill"
+        self.manager_signal = man_signal
+
+        if man_signal in MAN_KILL_SIGNALS:
+            # Only kill signals exist currently
+            logger.info("Worker received kill signal {} from manager".format(man_signal))
         else:
             logger.warning("Received unrecognized manager signal {} - ignoring".format(man_signal))
+
         self.comm.push_to_buffer(mtag, man_signal)
         return man_signal
 
@@ -542,7 +548,7 @@ def polling_loop(self, task, timeout=None, delay=0.1, poll_manager=False):
 
             if poll_manager:
                 man_signal = self.manager_poll()
-                if self.manager_signal != "none":
+                if self.manager_signal in MAN_KILL_SIGNALS:
                     task.kill()
                     calc_status = man_signal
                     break
diff --git a/libensemble/sim_funcs/executor_hworld.py b/libensemble/sim_funcs/executor_hworld.py
index afdf7703eb..0cdc91a6b3 100644
--- a/libensemble/sim_funcs/executor_hworld.py
+++ b/libensemble/sim_funcs/executor_hworld.py
@@ -25,7 +25,7 @@ def custom_polling_loop(exctr, task, timeout_sec=5.0, delay=0.3):
         time.sleep(delay)
 
         exctr.manager_poll()
-        if exctr.manager_signal == "finish":
+        if exctr.manager_signal == MAN_SIGNAL_FINISH:
             exctr.kill(task)
             calc_status = MAN_SIGNAL_FINISH  # Worker will pick this up and close down
             print("Task {} killed by manager on worker {}".format(task.id, exctr.workerID))
diff --git a/libensemble/tests/deprecated_tests/standalone_executor_tests/test_executor_manager_poll.py b/libensemble/tests/deprecated_tests/standalone_executor_tests/test_executor_manager_poll.py
index ccd50e4fe3..e74cfc8ed3 100644
--- a/libensemble/tests/deprecated_tests/standalone_executor_tests/test_executor_manager_poll.py
+++ b/libensemble/tests/deprecated_tests/standalone_executor_tests/test_executor_manager_poll.py
@@ -12,6 +12,7 @@
 
 import os
 from libensemble.executors.executor import Executor
+from libensemble.message_numbers import MAN_SIGNAL_KILL
 
 
 def build_simfunc():
@@ -68,14 +69,14 @@ def polling_loop(exctr, task, timeout_sec=20.0, delay=2.0):
 
         exctr.manager_poll(task)
 
-        if task.manager_signal == "kill":
+        if task.manager_signal == MAN_SIGNAL_KILL:
             print("Manager has sent kill signal - killing task")
             exctr.kill(task)
 
         # In future might support other manager signals eg:
-        elif task.manager_signal == "pause":
-            # checkpoint_task()
-            pass
+        # elif task.manager_signal == "pause":
+        #     checkpoint_task()
+        #     pass
 
         time.sleep(delay)
         print("Polling at time", time.time() - start)

From d6cbcefaac802d0dab7f4d820b1f48490cb1119d Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Fri, 22 Jul 2022 15:53:36 -0500
Subject: [PATCH 060/196] Add function manager_kill_received

---
 libensemble/executors/executor.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/libensemble/executors/executor.py b/libensemble/executors/executor.py
index bb7c1d0ab8..b2ecfaff70 100644
--- a/libensemble/executors/executor.py
+++ b/libensemble/executors/executor.py
@@ -511,6 +511,13 @@ def manager_poll(self):
         self.comm.push_to_buffer(mtag, man_signal)
         return man_signal
 
+    def manager_kill_received(self):
+        """Return True if received kill signal from the manager"""
+        man_signal = self.manager_poll()
+        if man_signal in MAN_KILL_SIGNALS:
+            return True
+        return False
+
     def polling_loop(self, task, timeout=None, delay=0.1, poll_manager=False):
         """Optional, blocking, generic task status polling loop. Operates until the task
         finishes, times out, or is optionally killed via a manager signal. On completion, returns a

From bfb0de915282fc293ba5ef94be324479325337a2 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Sat, 23 Jul 2022 00:01:51 -0500
Subject: [PATCH 061/196] Remove unused imports

---
 libensemble/executors/executor.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/libensemble/executors/executor.py b/libensemble/executors/executor.py
index b2ecfaff70..6673247df1 100644
--- a/libensemble/executors/executor.py
+++ b/libensemble/executors/executor.py
@@ -18,8 +18,6 @@
 
 from libensemble.message_numbers import (
     UNSET_TAG,
-    MAN_SIGNAL_FINISH,
-    MAN_SIGNAL_KILL,
     WORKER_DONE,
     TASK_FAILED,
     WORKER_KILL_ON_TIMEOUT,

From 42b2924a695261dd38217442255c440829ebdec6 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Mon, 25 Jul 2022 22:56:41 -0500
Subject: [PATCH 062/196] Add stay_active arg and cancel_points func

---
 libensemble/history.py                  |  2 +-
 libensemble/manager.py                  |  3 ++-
 libensemble/tools/persistent_support.py | 16 +++++++++++++---
 3 files changed, 16 insertions(+), 5 deletions(-)

diff --git a/libensemble/history.py b/libensemble/history.py
index deabef2982..232a8e3698 100644
--- a/libensemble/history.py
+++ b/libensemble/history.py
@@ -200,7 +200,7 @@ def update_history_x_in(self, gen_worker, D, safe_mode, gen_started_time):
             # Ensure there aren't any gaps in the generated sim_id values:
             assert np.all(
                 np.in1d(np.arange(self.index, np.max(D["sim_id"]) + 1), D["sim_id"])
-            ), "The generator function has produced sim_id that are not in order."
+            ), "The generator function has produced sim_ids that are not in order."
 
             num_new = len(np.setdiff1d(D["sim_id"], self.H["sim_id"]))
 
diff --git a/libensemble/manager.py b/libensemble/manager.py
index c0f7e18ebf..e2027bb219 100644
--- a/libensemble/manager.py
+++ b/libensemble/manager.py
@@ -416,7 +416,8 @@ def _update_state_on_worker_msg(self, persis_info, D_recv, w):
         calc_status = D_recv["calc_status"]
         Manager._check_received_calc(D_recv)
 
-        if w not in self.persis_pending and not self.W[w - 1]["active_recv"]:
+        stay_active = D_recv["libE_info"].get("stay_active", False)
+        if w not in self.persis_pending and not self.W[w - 1]["active_recv"] and not stay_active:
             self.W[w - 1]["active"] = 0
 
         if calc_status in [FINISHED_PERSISTENT_SIM_TAG, FINISHED_PERSISTENT_GEN_TAG]:
diff --git a/libensemble/tools/persistent_support.py b/libensemble/tools/persistent_support.py
index 0f302c077e..a5213c3af0 100644
--- a/libensemble/tools/persistent_support.py
+++ b/libensemble/tools/persistent_support.py
@@ -1,5 +1,6 @@
 from libensemble.message_numbers import STOP_TAG, PERSIS_STOP, UNSET_TAG, EVAL_GEN_TAG, EVAL_SIM_TAG, calc_type_strings
 import logging
+import numpy as np
 
 logger = logging.getLogger(__name__)
 
@@ -24,7 +25,7 @@ def __init__(self, libE_info, calc_type):
         ], "The calc_type: {} specifies neither a simulator nor generator.".format(self.calc_type)
         self.calc_str = calc_type_strings[self.calc_type]
 
-    def send(self, output, calc_status=UNSET_TAG):
+    def send(self, output, calc_status=UNSET_TAG, stay_active=False):
         """
         Send message from worker to manager.
 
@@ -41,6 +42,8 @@ def send(self, output, calc_status=UNSET_TAG):
         else:
             libE_info = self.libE_info
 
+        libE_info["stay_active"] = stay_active
+
         D = {
             "calc_out": output,
             "libE_info": libE_info,
@@ -90,7 +93,7 @@ def recv(self, blocking=True):
         logger.debug("Persistent {} received work rows from manager".format(self.calc_str))
         return tag, Work, calc_in
 
-    def send_recv(self, output, calc_status=UNSET_TAG):
+    def send_recv(self, output, calc_status=UNSET_TAG, stay_active=False):
         """
         Send message from worker to manager and receive response.
 
@@ -100,5 +103,12 @@ def send_recv(self, output, calc_status=UNSET_TAG):
         :returns: message tag, Work dictionary, calc_in array
 
         """
-        self.send(output, calc_status)
+        self.send(output, calc_status, stay_active)
         return self.recv()
+
+    def cancel_points(self, sim_ids):
+        H_o = np.zeros(len(sim_ids), dtype=[("sim_id", int), ("cancel_requested", bool)])
+        H_o["sim_id"] = sim_ids
+        H_o["cancel_requested"] = True
+        print(H_o)
+        self.send(H_o, stay_active=True)

From 5a1b1c87ef6330e69b316c6852bd0a64295c9ff8 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Mon, 25 Jul 2022 23:07:58 -0500
Subject: [PATCH 063/196] Add persistent sampling cancellations test

---
 libensemble/gen_funcs/persistent_sampling.py  | 33 +++++++++
 ...test_persistent_uniform_sampling_cancel.py | 74 +++++++++++++++++++
 2 files changed, 107 insertions(+)
 create mode 100644 libensemble/tests/regression_tests/test_persistent_uniform_sampling_cancel.py

diff --git a/libensemble/gen_funcs/persistent_sampling.py b/libensemble/gen_funcs/persistent_sampling.py
index 2bfabc1bcd..258421b685 100644
--- a/libensemble/gen_funcs/persistent_sampling.py
+++ b/libensemble/gen_funcs/persistent_sampling.py
@@ -7,7 +7,9 @@
     "persistent_uniform",
     "uniform_random_sample_with_variable_resources",
     "persistent_request_shutdown",
+    "uniform_nonblocking",
     "batched_history_matching",
+    "persistent_uniform_with_cancellations",
 ]
 
 
@@ -204,3 +206,34 @@ def batched_history_matching(H, persis_info, gen_specs, libE_info):
             Sigma = np.cov(H_o["x"][best_inds].T)
 
     return H_o, persis_info, FINISHED_PERSISTENT_GEN_TAG
+
+
+def persistent_uniform_with_cancellations(H, persis_info, gen_specs, libE_info):
+
+    ub = gen_specs["user"]["ub"]
+    lb = gen_specs["user"]["lb"]
+    n = len(lb)
+    b = gen_specs["user"]["initial_batch_size"]
+
+    # Start cancelling points from half initial batch onward
+    cancel_from = b // 2  # Should get at least this many points back
+
+    ps = PersistentSupport(libE_info, EVAL_GEN_TAG)
+
+    # Send batches until manager sends stop tag
+    tag = None
+    while tag not in [STOP_TAG, PERSIS_STOP]:
+
+        H_o = np.zeros(b, dtype=gen_specs["out"])
+        H_o["x"] = persis_info["rand_stream"].uniform(lb, ub, (b, n))
+        tag, Work, calc_in = ps.send_recv(H_o)
+
+        if hasattr(calc_in, "__len__"):
+            b = len(calc_in)
+
+            # Cancel as many points as got back
+            cancel_ids = list(range(cancel_from, cancel_from + b))
+            cancel_from += b
+            ps.cancel_points(cancel_ids)
+
+    return H_o, persis_info, FINISHED_PERSISTENT_GEN_TAG
diff --git a/libensemble/tests/regression_tests/test_persistent_uniform_sampling_cancel.py b/libensemble/tests/regression_tests/test_persistent_uniform_sampling_cancel.py
new file mode 100644
index 0000000000..347308cf55
--- /dev/null
+++ b/libensemble/tests/regression_tests/test_persistent_uniform_sampling_cancel.py
@@ -0,0 +1,74 @@
+"""
+Tests libEnsemble with a simple persistent uniform sampling generator
+function.
+
+Execute via one of the following commands (e.g. 3 workers):
+   mpiexec -np 4 python test_persistent_sampling.py
+   python test_persistent_uniform_sampling_cancel.py --nworkers 3 --comms local
+   python test_persistent_uniform_sampling_cancel.py --nworkers 3 --comms tcp
+
+When running with the above commands, the number of concurrent evaluations of
+the objective function will be 2, as one of the three workers will be the
+persistent generator.
+"""
+
+# Do not change these lines - they are parsed by run-tests.sh
+# TESTSUITE_COMMS: mpi local
+# TESTSUITE_NPROCS: 3 4
+
+import sys
+import numpy as np
+
+# Import libEnsemble items for this test
+from libensemble.libE import libE
+from libensemble.sim_funcs.rosenbrock import rosenbrock_eval as sim_f
+from libensemble.gen_funcs.persistent_sampling import persistent_uniform_with_cancellations as gen_f
+from libensemble.alloc_funcs.start_only_persistent import only_persistent_gens as alloc_f
+from libensemble.tools import parse_args, save_libE_output, add_unique_random_streams
+
+# Main block is necessary only when using local comms with spawn start method (default on macOS and Windows).
+if __name__ == "__main__":
+
+    nworkers, is_manager, libE_specs, _ = parse_args()
+
+    if nworkers < 2:
+        sys.exit("Cannot run with a persistent worker if only one worker -- aborting...")
+
+    n = 2
+
+    sim_specs = {
+        "sim_f": sim_f,
+        "in": ["x"],
+        "out": [("f", float), ("grad", float, n)],
+    }
+
+    gen_specs = {
+        "gen_f": gen_f,
+        "persis_in": ["x", "f", "grad", "sim_id"],
+        "out": [("x", float, (n,))],
+        "user": {
+            "initial_batch_size": 100,
+            "lb": np.array([-3, -2]),
+            "ub": np.array([3, 2]),
+        },
+    }
+
+    alloc_specs = {
+        "alloc_f": alloc_f,
+        "user": {"async_return": True},
+    }
+
+    exit_criteria = {"gen_max": 150, "wallclock_max": 300}
+
+    persis_info = add_unique_random_streams({}, nworkers + 1)
+
+    # Perform the run
+    H, persis_info, flag = libE(sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs)
+
+    if is_manager:
+
+        # For reproducible test, only tests if cancel requested on points - not whether got evaluated
+        assert np.all(H["cancel_requested"][:49] == False), "Values cancelled which should not be"  # noqa: E712
+        assert np.all(H["cancel_requested"][50:100]), "Values not cancelled which should be"
+
+        save_libE_output(H, persis_info, __file__, nworkers)

From 2ef41ae609512bc012a577bacf2785bd386aa252 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 26 Jul 2022 17:08:08 -0500
Subject: [PATCH 064/196] try using setup-mpi action

---
 .github/workflows/ci.yml | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index bef7e7833c..9c529c7afd 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -95,16 +95,19 @@ jobs:
             mkdir ../sdk; tar xf MacOSX10.14.sdk.tar.xz -C ../sdk
             conda install clang_osx-64=9.0.1
 
-        - name: Install MPI, mpi4py from conda
+        - name: Setup MPI (${{ matrix.mpi-version }})
+          uses: mpi4py/setup-mpi@v1
+          with:
+            mpi: ${{ matrix.mpi-version }}
+
+        - name: Install mpi4py from conda
           if: matrix.python-version != '3.10'
           run: |
-            conda install ${{ matrix.mpi-version }}
             conda install mpi4py
 
-        - name: Install MPI, mpi4py from pip
+        - name: Install mpi4py from pip
           if: matrix.python-version == '3.10'
           run: |
-            conda install ${{ matrix.mpi-version }}
             pip install mpi4py
 
         - name: Install generator dependencies

From 33f0972ec1131ee4304fb74b59def661d53c7b44 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 27 Jul 2022 11:29:30 -0500
Subject: [PATCH 065/196] do we have cl available?

---
 .github/workflows/ci.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 9c529c7afd..6a854b7eb3 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -201,6 +201,7 @@ jobs:
         - name: Run simple tests, Ubuntu, Python 3.11
           if: matrix.os == 'windows-latest'
           run: |
+            cl
             ./libensemble/tests/run-tests.sh -A "-W error" -z -${{ matrix.comms-type }}
 
         - name: Run extensive tests, macOS

From d76a9f068e8780b3e16dada61a5104d4cc1b8142 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 27 Jul 2022 12:08:49 -0500
Subject: [PATCH 066/196] do we already have either gcc or call?

---
 .github/workflows/ci.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 6a854b7eb3..89a66dd311 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -201,7 +201,8 @@ jobs:
         - name: Run simple tests, Ubuntu, Python 3.11
           if: matrix.os == 'windows-latest'
           run: |
-            cl
+            gcc
+            call
             ./libensemble/tests/run-tests.sh -A "-W error" -z -${{ matrix.comms-type }}
 
         - name: Run extensive tests, macOS

From 8a146fb5b3e22263daf60c47dea2a4ff292b7b8e Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 27 Jul 2022 12:21:32 -0500
Subject: [PATCH 067/196] can clang be found here?

---
 .github/workflows/ci.yml | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 89a66dd311..aa3572c05a 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -73,6 +73,13 @@ jobs:
             python --version
             pip install -I --upgrade certifi
 
+        - name: Windows - Add clang path to $PATH env
+          shell: bash
+          if: matrix.os == 'windows-latest'
+          run: |
+            echo "PATH=$PATH:C:\msys64\mingw64\bin" >> $GITHUB_ENV
+            clang
+
         - name: Install Ubuntu compilers
           if: matrix.os == 'ubuntu-latest'
           run: conda install gcc_linux-64

From a35420dfd6d7c104106660c10a428775c2c517e3 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 27 Jul 2022 13:35:53 -0500
Subject: [PATCH 068/196] need to see what packages conda installs

---
 .github/workflows/ci.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index aa3572c05a..3f6e760493 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -78,7 +78,6 @@ jobs:
           if: matrix.os == 'windows-latest'
           run: |
             echo "PATH=$PATH:C:\msys64\mingw64\bin" >> $GITHUB_ENV
-            clang
 
         - name: Install Ubuntu compilers
           if: matrix.os == 'ubuntu-latest'

From 76b80e7b35f63d6bdd75cc51384116e8d322cc32 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 27 Jul 2022 13:51:20 -0500
Subject: [PATCH 069/196] try suggested compilation/linking from microsoft
 docs?

---
 .github/workflows/ci.yml | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 3f6e760493..72ee2f897c 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -78,6 +78,7 @@ jobs:
           if: matrix.os == 'windows-latest'
           run: |
             echo "PATH=$PATH:C:\msys64\mingw64\bin" >> $GITHUB_ENV
+            echo "PATH=$PATH:C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\bin\amd64" >> $GITHUB_ENV
 
         - name: Install Ubuntu compilers
           if: matrix.os == 'ubuntu-latest'
@@ -204,11 +205,11 @@ jobs:
           run: |
            ./libensemble/tests/run-tests.sh -e -A "-W error" -z -${{ matrix.comms-type }}
 
-        - name: Run simple tests, Ubuntu, Python 3.11
+        - name: Run simple tests, Windows
           if: matrix.os == 'windows-latest'
           run: |
-            gcc
-            call
+            cl /I"C:\Program Files (x86)\Microsoft SDKs\MPI\Include" /I"C:\Program Files (x86)\Microsoft SDKs\MPI\Include\x64" /I. /I"C:\Program Files (x86)\Microsoft SDKs\Windows\v7.1A\Include" /I"C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\include" /c libensemble/tests/unit_tests/simdir/my_simtask.c
+            link /machine:x64 /out:my_simtask.exe /dynamicbase "msmpi.lib" /libpath:"C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64" /LIBPATH:"C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\lib\amd64" /LIBPATH:"C:\Program Files (x86)\Microsoft SDKs\Windows\v7.1A\Lib\x64" my_simtask.obj
             ./libensemble/tests/run-tests.sh -A "-W error" -z -${{ matrix.comms-type }}
 
         - name: Run extensive tests, macOS

From 7510e28146938065449c796b7f3b4adddf8c2c41 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 28 Jul 2022 15:14:10 -0500
Subject: [PATCH 070/196] introdue fix for save_every_k filenames on windows

---
 libensemble/manager.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/libensemble/manager.py b/libensemble/manager.py
index c0f7e18ebf..890cbb0f64 100644
--- a/libensemble/manager.py
+++ b/libensemble/manager.py
@@ -8,6 +8,7 @@
 import glob
 import logging
 import socket
+import platform
 import traceback
 import numpy as np
 
@@ -252,6 +253,8 @@ def _save_every_k(self, fname, count, k):
         """Saves history every kth step"""
         count = k * (count // k)
         filename = fname.format(self.date_start, count)
+        if platform.system() == "Windows":
+            filename = filename.replace(":", '-')  # ":" is invalid in windows filenames
         if not os.path.isfile(filename) and count > 0:
             for old_file in glob.glob(fname.format(self.date_start, "*")):
                 os.remove(old_file)

From 0b5ab198c57630e6ca60a57a5888d1ba5e583246 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 28 Jul 2022 17:01:17 -0500
Subject: [PATCH 071/196] first approach for FAQ windows-mpi entry

---
 docs/FAQ.rst | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index 3494fc1591..b90b0d88d9 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -224,6 +224,20 @@ See the :ref:`Logger Configuration<logger_config>` docs for more information.
 macOS and Windows Errors
 ------------------------
 
+**How can I run libEnsemble with MPI on Windows?**
+
+In our experience this may be quite difficult, if not impossible, since most MPI distributions have
+either dropped Windows support (MPICH and Open MPI) or are no longer being maintained (msmpi). Plus
+Windows tends to experience platform-specific issues that are difficult to reproduce and troubleshoot
+for us.
+
+If you want to try anyways, we recommend experimenting with the many Unix-like emulators, containers, virtual machines,
+and other such systems. The `Installing PETSc On Microsoft Windows`_ docs contains lots of valuable information.
+
+Otherwise, most local-comms libEnsemble examples appear to run just fine.
+
+.. _`Installing PETSc On Microsoft Windows`: https://petsc.org/release/install/windows/#recommended-installation-methods
+
 **"RuntimeError: An attempt has been made to start a new process... this probably means that you are not using fork...
 " if __name__ == '__main__': freeze_support() ...**
 

From 1bc14e1d43de49b265e96100a28d87de8adfeaa8 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 28 Jul 2022 17:20:45 -0500
Subject: [PATCH 072/196] small rewording, README references windows faq entry

---
 README.rst   | 5 ++++-
 docs/FAQ.rst | 2 ++
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/README.rst b/README.rst
index dec88fcb01..541d9a34bd 100644
--- a/README.rst
+++ b/README.rst
@@ -58,7 +58,7 @@ With a basic familiarity of Python and NumPy_, users can easily incorporate
 any other mathematics, machine-learning, or resource-management libraries into libEnsemble
 workflows.
 
-libEnsemble employs a manager/worker scheme that runs on MPI, multiprocessing,
+libEnsemble employs a manager/worker scheme that communicates via MPI, multiprocessing,
 or TCP. Workers control and monitor any level of work using the aforementioned
 generator and simulator functions, from small subnode tasks to huge many-node computations.
 
@@ -66,6 +66,9 @@ libEnsemble includes an Executor interface so application-launching functions ar
 portable, resilient, and flexible; it also automatically detects available nodes
 and cores, and can dynamically assign resources to workers.
 
+libEnsemble performs best on Unix-like systems like Linux and macOS. See the
+:ref:`FAQ<faqwindows>` for more information.
+
 .. before_dependencies_rst_tag
 
 Dependencies
diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index b90b0d88d9..866ecf400e 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -224,6 +224,8 @@ See the :ref:`Logger Configuration<logger_config>` docs for more information.
 macOS and Windows Errors
 ------------------------
 
+.. _faqwindows:
+
 **How can I run libEnsemble with MPI on Windows?**
 
 In our experience this may be quite difficult, if not impossible, since most MPI distributions have

From 7cc15eb2a4a833df2ca211d3b1efc667ba2a23b0 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 29 Jul 2022 11:29:16 -0500
Subject: [PATCH 073/196] word choice

---
 docs/FAQ.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index 866ecf400e..f639e82307 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -228,7 +228,7 @@ macOS and Windows Errors
 
 **How can I run libEnsemble with MPI on Windows?**
 
-In our experience this may be quite difficult, if not impossible, since most MPI distributions have
+In our experience this may be challenging, if not impossible, since most MPI distributions have
 either dropped Windows support (MPICH and Open MPI) or are no longer being maintained (msmpi). Plus
 Windows tends to experience platform-specific issues that are difficult to reproduce and troubleshoot
 for us.

From a6ed4993fee24563500ef5974f4e92ee8990ef45 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 29 Jul 2022 15:34:48 -0500
Subject: [PATCH 074/196] lets see what windows can run, mark some unit tests
 as extra

---
 .github/workflows/ci.yml                       | 18 +++++++++++-------
 libensemble/tests/unit_tests/test_launcher.py  |  2 +-
 libensemble/tests/unit_tests/test_libE_main.py |  2 +-
 .../unit_tests/test_sim_dir_properties.py      |  2 +-
 4 files changed, 14 insertions(+), 10 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 72ee2f897c..cc7a85b525 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -45,7 +45,7 @@ jobs:
                     - os: windows-latest
                       python-version: '3.10'
                       comms-type: l
-                      mpi-version: 'msmpi'
+                      mpi-version: 'none'
                       do-balsam: false
         env:
             HYDRA_LAUNCHER: 'fork'
@@ -108,12 +108,12 @@ jobs:
             mpi: ${{ matrix.mpi-version }}
 
         - name: Install mpi4py from conda
-          if: matrix.python-version != '3.10'
+          if: matrix.python-version != '3.10' && matrix.os != 'windows-latest'
           run: |
             conda install mpi4py
 
         - name: Install mpi4py from pip
-          if: matrix.python-version == '3.10'
+          if: matrix.python-version == '3.10' && matrix.os != 'windows-latest'
           run: |
             pip install mpi4py
 
@@ -133,6 +133,13 @@ jobs:
             pip install mpmath
             python -m pip install --upgrade git+https://github.com/mosesyhc/surmise.git@development/PCGPwM
 
+        - name: Install some generator dependencies on Windows
+          if: matrix.os == 'windows-latest'
+          run: |
+            python -m pip install --upgrade pip
+            conda install nlopt
+            conda install scipy
+
         - name: Install generator dependencies for Ubuntu tests
           if: matrix.os == 'ubuntu-latest' && matrix.do-balsam == false
           run: |
@@ -191,7 +198,6 @@ jobs:
 
         - name: Find MPI, Install libEnsemble, flake8, ulimit adjust
           run: |
-            python install/find_mpi.py
             pip install -e .
             flake8 libensemble
 
@@ -208,9 +214,7 @@ jobs:
         - name: Run simple tests, Windows
           if: matrix.os == 'windows-latest'
           run: |
-            cl /I"C:\Program Files (x86)\Microsoft SDKs\MPI\Include" /I"C:\Program Files (x86)\Microsoft SDKs\MPI\Include\x64" /I. /I"C:\Program Files (x86)\Microsoft SDKs\Windows\v7.1A\Include" /I"C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\include" /c libensemble/tests/unit_tests/simdir/my_simtask.c
-            link /machine:x64 /out:my_simtask.exe /dynamicbase "msmpi.lib" /libpath:"C:\Program Files (x86)\Microsoft SDKs\MPI\Lib\x64" /LIBPATH:"C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\lib\amd64" /LIBPATH:"C:\Program Files (x86)\Microsoft SDKs\Windows\v7.1A\Lib\x64" my_simtask.obj
-            ./libensemble/tests/run-tests.sh -A "-W error" -z -${{ matrix.comms-type }}
+           ./libensemble/tests/run-tests.sh -A "-W error" -z -${{ matrix.comms-type }}
 
         - name: Run extensive tests, macOS
           if: matrix.os == 'macos-latest'
diff --git a/libensemble/tests/unit_tests/test_launcher.py b/libensemble/tests/unit_tests/test_launcher.py
index d19dd5a309..9ef0a07f36 100644
--- a/libensemble/tests/unit_tests/test_launcher.py
+++ b/libensemble/tests/unit_tests/test_launcher.py
@@ -19,7 +19,7 @@ def test_form_command():
 
     assert args == aref, "Command templating test failed."
 
-
+@pytest.mark.extra
 def xtest_submit():
     "Test simple launch."
 
diff --git a/libensemble/tests/unit_tests/test_libE_main.py b/libensemble/tests/unit_tests/test_libE_main.py
index 0e183e4258..4680c49282 100644
--- a/libensemble/tests/unit_tests/test_libE_main.py
+++ b/libensemble/tests/unit_tests/test_libE_main.py
@@ -252,7 +252,7 @@ def test_checking_inputs_single():
     libE_specs["sim_dir_copy_files"] = ["./__init__.py"]
     check_inputs(libE_specs=libE_specs)
 
-
+@pytest.mark.extra
 def test_logging_disabling():
     remove_file_if_exists("ensemble.log")
     remove_file_if_exists("libE_stats.txt")
diff --git a/libensemble/tests/unit_tests/test_sim_dir_properties.py b/libensemble/tests/unit_tests/test_sim_dir_properties.py
index e84e8f7ca0..0c1464f5f5 100644
--- a/libensemble/tests/unit_tests/test_sim_dir_properties.py
+++ b/libensemble/tests/unit_tests/test_sim_dir_properties.py
@@ -88,7 +88,7 @@ def test_worker_dirs_but_no_sim_dirs():
     for dir in [inputdir, ensemble_dir]:
         shutil.rmtree(dir)
 
-
+@pytest.mark.extra
 def test_loc_stack_FileExists_exceptions():
     inputdir = "./calc"
     copyfile = "./calc/copy"

From 80788f207f96ace0533540f28c1c350b02641200 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 29 Jul 2022 15:43:55 -0500
Subject: [PATCH 075/196] disable mpi setup on windows

---
 .github/workflows/ci.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index cc7a85b525..01b5d88fd2 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -104,6 +104,7 @@ jobs:
 
         - name: Setup MPI (${{ matrix.mpi-version }})
           uses: mpi4py/setup-mpi@v1
+          if: matrix.os != 'windows-latest'
           with:
             mpi: ${{ matrix.mpi-version }}
 

From 1b0308ad423e70f4e550a30c330a9dd503b0c132 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 29 Jul 2022 15:54:31 -0500
Subject: [PATCH 076/196] flake8

---
 libensemble/tests/unit_tests/test_launcher.py           | 2 ++
 libensemble/tests/unit_tests/test_libE_main.py          | 1 +
 libensemble/tests/unit_tests/test_sim_dir_properties.py | 2 ++
 3 files changed, 5 insertions(+)

diff --git a/libensemble/tests/unit_tests/test_launcher.py b/libensemble/tests/unit_tests/test_launcher.py
index 9ef0a07f36..ee458f9f96 100644
--- a/libensemble/tests/unit_tests/test_launcher.py
+++ b/libensemble/tests/unit_tests/test_launcher.py
@@ -5,6 +5,7 @@
 """
 
 import sys
+import pytest
 import libensemble.utils.launcher as launcher
 
 
@@ -19,6 +20,7 @@ def test_form_command():
 
     assert args == aref, "Command templating test failed."
 
+
 @pytest.mark.extra
 def xtest_submit():
     "Test simple launch."
diff --git a/libensemble/tests/unit_tests/test_libE_main.py b/libensemble/tests/unit_tests/test_libE_main.py
index 4680c49282..b8cbd70c0e 100644
--- a/libensemble/tests/unit_tests/test_libE_main.py
+++ b/libensemble/tests/unit_tests/test_libE_main.py
@@ -252,6 +252,7 @@ def test_checking_inputs_single():
     libE_specs["sim_dir_copy_files"] = ["./__init__.py"]
     check_inputs(libE_specs=libE_specs)
 
+
 @pytest.mark.extra
 def test_logging_disabling():
     remove_file_if_exists("ensemble.log")
diff --git a/libensemble/tests/unit_tests/test_sim_dir_properties.py b/libensemble/tests/unit_tests/test_sim_dir_properties.py
index 0c1464f5f5..bdd5398575 100644
--- a/libensemble/tests/unit_tests/test_sim_dir_properties.py
+++ b/libensemble/tests/unit_tests/test_sim_dir_properties.py
@@ -1,4 +1,5 @@
 import os
+import pytest
 import shutil
 import numpy as np
 from libensemble.output_directory import EnsembleDirectory
@@ -88,6 +89,7 @@ def test_worker_dirs_but_no_sim_dirs():
     for dir in [inputdir, ensemble_dir]:
         shutil.rmtree(dir)
 
+
 @pytest.mark.extra
 def test_loc_stack_FileExists_exceptions():
     inputdir = "./calc"

From 660a55f698d2d61ff119e18af6c652990f245988 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 29 Jul 2022 16:14:42 -0500
Subject: [PATCH 077/196] try marking executor tests as extra?

---
 libensemble/tests/unit_tests/test_executor.py | 38 +++++++++++++++++--
 1 file changed, 35 insertions(+), 3 deletions(-)

diff --git a/libensemble/tests/unit_tests/test_executor.py b/libensemble/tests/unit_tests/test_executor.py
index 5b9e52fecc..4a01ad0036 100644
--- a/libensemble/tests/unit_tests/test_executor.py
+++ b/libensemble/tests/unit_tests/test_executor.py
@@ -7,10 +7,13 @@
 import time
 import pytest
 import socket
-import mpi4py
+import platform
 
-mpi4py.rc.initialize = False
-from mpi4py import MPI
+if platform.system() != "Windows":
+    import mpi4py
+
+    mpi4py.rc.initialize = False
+    from mpi4py import MPI
 
 from libensemble.resources.mpi_resources import MPIResourcesException
 from libensemble.executors.executor import Executor, ExecutorException, TimeoutExpired
@@ -202,6 +205,8 @@ def polling_loop_multitask(exctr, task_list, timeout_sec=4.0, delay=0.05):
 
 
 # Tests ========================================================================================
+
+@pytest.mark.extra
 def test_launch_and_poll():
     """Test of launching and polling task and exiting on task finish"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
@@ -216,6 +221,7 @@ def test_launch_and_poll():
     assert task.run_attempts == 1, "task.run_attempts should be 1. Returned " + str(task.run_attempts)
 
 
+@pytest.mark.extra
 def test_launch_and_wait():
     """Test of launching and waiting on task"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
@@ -232,6 +238,7 @@ def test_launch_and_wait():
     assert task.state == "FINISHED", "task.state should be FINISHED. Returned " + str(task.state)
 
 
+@pytest.mark.extra
 def test_launch_and_wait_timeout():
     """Test of launching and waiting on task timeout (and kill)"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
@@ -251,6 +258,7 @@ def test_launch_and_wait_timeout():
     assert task.state == "USER_KILLED", "task.state should be USER_KILLED. Returned " + str(task.state)
 
 
+@pytest.mark.extra
 def test_launch_wait_on_start():
     """Test of launching task with wait_on_start"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
@@ -267,6 +275,7 @@ def test_launch_wait_on_start():
     assert task.state == "FINISHED", "task.state should be FINISHED. Returned " + str(task.state)
 
 
+@pytest.mark.extra
 def test_kill_on_file():
     """Test of killing task based on something in output file"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
@@ -280,6 +289,7 @@ def test_kill_on_file():
     assert task.state == "USER_KILLED", "task.state should be USER_KILLED. Returned " + str(task.state)
 
 
+@pytest.mark.extra
 def test_kill_on_timeout():
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     setup_executor()
@@ -292,6 +302,7 @@ def test_kill_on_timeout():
     assert task.state == "USER_KILLED", "task.state should be USER_KILLED. Returned " + str(task.state)
 
 
+@pytest.mark.extra
 def test_kill_on_timeout_polling_loop_method():
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     setup_executor()
@@ -304,6 +315,7 @@ def test_kill_on_timeout_polling_loop_method():
     assert task.state == "USER_KILLED", "task.state should be USER_KILLED. Returned " + str(task.state)
 
 
+@pytest.mark.extra
 def test_launch_and_poll_multitasks():
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     setup_executor()
@@ -326,6 +338,7 @@ def test_launch_and_poll_multitasks():
         assert task.state == "FINISHED", "task.state should be FINISHED. Returned " + str(task.state)
 
 
+@pytest.mark.extra
 def test_get_task():
     """Return task from given task id"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
@@ -351,6 +364,7 @@ def test_get_task():
     assert A is None, "Task found when supplied taskid should not exist"
 
 
+@pytest.mark.extra
 @pytest.mark.timeout(30)
 def test_procs_and_machinefile_logic():
     """Test of supplying various input configurations."""
@@ -437,6 +451,7 @@ def test_procs_and_machinefile_logic():
     assert task.state == "FINISHED", "task.state should be FINISHED. Returned " + str(task.state)
 
 
+@pytest.mark.extra
 @pytest.mark.timeout(20)
 def test_doublekill():
     """Test attempt to kill already killed task
@@ -460,6 +475,7 @@ def test_doublekill():
     assert task.state == "USER_KILLED", "task.state should be USER_KILLED. Returned " + str(task.state)
 
 
+@pytest.mark.extra
 @pytest.mark.timeout(20)
 def test_finish_and_kill():
     """Test attempt to kill already finished task
@@ -486,6 +502,7 @@ def test_finish_and_kill():
     assert task.state == "FINISHED", "task.state should be FINISHED. Returned " + str(task.state)
 
 
+@pytest.mark.extra
 @pytest.mark.timeout(20)
 def test_launch_and_kill():
     """Test launching and immediately killing tasks with no poll"""
@@ -506,6 +523,7 @@ def test_launch_and_kill():
         assert task.state == "USER_KILLED", "task.state should be USER_KILLED. Returned " + str(task.state)
 
 
+@pytest.mark.extra
 def test_launch_as_gen():
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     setup_executor()
@@ -536,6 +554,7 @@ def test_launch_as_gen():
         assert 0
 
 
+@pytest.mark.extra
 def test_launch_no_app():
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     setup_executor_noapp()
@@ -556,6 +575,7 @@ def test_launch_no_app():
         assert 0
 
 
+@pytest.mark.extra
 def test_kill_task_with_no_submit():
     from libensemble.executors.executor import Task
 
@@ -586,6 +606,7 @@ def test_kill_task_with_no_submit():
         assert 0
 
 
+@pytest.mark.extra
 def test_poll_task_with_no_submit():
     from libensemble.executors.executor import Task
 
@@ -606,6 +627,7 @@ def test_poll_task_with_no_submit():
         assert 0
 
 
+@pytest.mark.extra
 def test_task_failure():
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     setup_executor()
@@ -618,6 +640,7 @@ def test_task_failure():
     assert task.state == "FAILED", "task.state should be FAILED. Returned " + str(task.state)
 
 
+@pytest.mark.extra
 def test_retries_launch_fail():
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     setup_executor_fakerunner()
@@ -631,6 +654,7 @@ def test_retries_launch_fail():
     assert task.run_attempts == 5, "task.run_attempts should be 5. Returned " + str(task.run_attempts)
 
 
+@pytest.mark.extra
 def test_retries_run_fail():
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     setup_executor()
@@ -644,6 +668,7 @@ def test_retries_run_fail():
     assert task.run_attempts == 5, "task.run_attempts should be 5. Returned " + str(task.run_attempts)
 
 
+@pytest.mark.extra
 def test_register_apps():
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     setup_executor()  # This registers an app my_simtask.x (default sim)
@@ -689,6 +714,7 @@ def test_register_apps():
         # assert e.args[1] == "Registered applications: ['my_simtask.x', 'fake_app1', 'fake_app2']"
 
 
+@pytest.mark.extra
 def test_serial_exes():
     setup_serial_executor()
     exctr = Executor.executor
@@ -699,6 +725,7 @@ def test_serial_exes():
     assert task.state == "FINISHED", "task.state should be FINISHED. Returned " + str(task.state)
 
 
+@pytest.mark.extra
 def test_serial_startup_times():
     setup_executor_startups()
     exctr = Executor.executor
@@ -724,6 +751,7 @@ def test_serial_startup_times():
     assert 0 < startup_time < 1, "Start up time for python program took " + str(startup_time)
 
 
+@pytest.mark.extra
 def test_futures_interface():
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     setup_executor()
@@ -737,6 +765,7 @@ def test_futures_interface():
     assert task.done(), "task.done() should return True after task finishes."
 
 
+@pytest.mark.extra
 def test_futures_interface_cancel():
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     setup_executor()
@@ -749,6 +778,7 @@ def test_futures_interface_cancel():
     assert task.cancelled() and task.done(), "Task should be both cancelled() and done() after cancellation."
 
 
+@pytest.mark.extra
 def test_dry_run():
     """Test of dry_run in poll"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
@@ -761,6 +791,7 @@ def test_dry_run():
     task.kill()
 
 
+@pytest.mark.extra
 def test_non_existent_app():
     """Tests exception on non-existent app"""
 
@@ -781,6 +812,7 @@ def test_non_existent_app():
         assert 0
 
 
+@pytest.mark.extra
 def test_non_existent_app_mpi():
     """Tests exception on non-existent app"""
 

From 07e61b1a7daf5ba7c94427fcf47911eaf381117b Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 29 Jul 2022 16:35:20 -0500
Subject: [PATCH 078/196] more extra tests

---
 libensemble/tests/unit_tests/test_manager_main.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/libensemble/tests/unit_tests/test_manager_main.py b/libensemble/tests/unit_tests/test_manager_main.py
index 0d9404f91e..97dc1ab9b0 100644
--- a/libensemble/tests/unit_tests/test_manager_main.py
+++ b/libensemble/tests/unit_tests/test_manager_main.py
@@ -1,14 +1,17 @@
 import time
+import platform
 import numpy as np
 import numpy.lib.recfunctions
-from mpi4py import MPI
 
 import libensemble.manager as man
 import libensemble.tests.unit_tests.setup as setup
 
-libE_specs = {"mpi_comm": MPI.COMM_WORLD}
+if platform.system() != "Windows":
+    from mpi4py import MPI
+    libE_specs = {"mpi_comm": MPI.COMM_WORLD}
 
 
+@pytest.mark.extra
 def test_term_test_1():
     # termination_test should be True when we want to stop
 
@@ -19,6 +22,7 @@ def test_term_test_1():
     assert not mgr.term_test()
 
 
+@pytest.mark.extra
 def test_term_test_2():
     # Test 2 - these could also be sep - with a setup or fixture....
     # Shouldn't terminate
@@ -39,6 +43,7 @@ def test_term_test_2():
     assert mgr.term_test()
 
 
+@pytest.mark.extra
 def test_term_test_3():
     # Test 3.
     # Terminate because enough time has passed

From e20ea6cf86aaf62288e27230ec92da6e3d1e333b Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 29 Jul 2022 17:09:47 -0500
Subject: [PATCH 079/196] import pytest

---
 libensemble/tests/unit_tests/test_manager_main.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/libensemble/tests/unit_tests/test_manager_main.py b/libensemble/tests/unit_tests/test_manager_main.py
index 97dc1ab9b0..38f7134ad5 100644
--- a/libensemble/tests/unit_tests/test_manager_main.py
+++ b/libensemble/tests/unit_tests/test_manager_main.py
@@ -1,4 +1,5 @@
 import time
+import pytest
 import platform
 import numpy as np
 import numpy.lib.recfunctions

From ed31504bbd1d95e91ab11952b6a4d219efd8f8a1 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 1 Aug 2022 10:23:14 -0500
Subject: [PATCH 080/196] we can probably install msmpi and mpi4py on windows,
 but can only use mpi4py, no other mpi stuff?

---
 .github/workflows/ci.yml                      | 7 +++----
 libensemble/tests/unit_tests/test_launcher.py | 3 +--
 libensemble/tests/unit_tests/test_mpi4py.py   | 3 +++
 3 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 01b5d88fd2..d0f8236a12 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -45,7 +45,7 @@ jobs:
                     - os: windows-latest
                       python-version: '3.10'
                       comms-type: l
-                      mpi-version: 'none'
+                      mpi-version: 'msmpi'
                       do-balsam: false
         env:
             HYDRA_LAUNCHER: 'fork'
@@ -104,17 +104,16 @@ jobs:
 
         - name: Setup MPI (${{ matrix.mpi-version }})
           uses: mpi4py/setup-mpi@v1
-          if: matrix.os != 'windows-latest'
           with:
             mpi: ${{ matrix.mpi-version }}
 
         - name: Install mpi4py from conda
-          if: matrix.python-version != '3.10' && matrix.os != 'windows-latest'
+          if: matrix.python-version != '3.10'
           run: |
             conda install mpi4py
 
         - name: Install mpi4py from pip
-          if: matrix.python-version == '3.10' && matrix.os != 'windows-latest'
+          if: matrix.python-version == '3.10'
           run: |
             pip install mpi4py
 
diff --git a/libensemble/tests/unit_tests/test_launcher.py b/libensemble/tests/unit_tests/test_launcher.py
index ee458f9f96..2be4a2528c 100644
--- a/libensemble/tests/unit_tests/test_launcher.py
+++ b/libensemble/tests/unit_tests/test_launcher.py
@@ -21,7 +21,6 @@ def test_form_command():
     assert args == aref, "Command templating test failed."
 
 
-@pytest.mark.extra
 def xtest_submit():
     "Test simple launch."
 
@@ -60,7 +59,7 @@ def xtest_submit():
     assert not launcher.process_is_stopped(process, 0.5), "Process stopped early."
     launcher.cancel(process, 0)
 
-
+@pytest.mark.extra
 def test_launch32():
     "If we are in Python > 3.2, still check that 3.2 wait func works"
     saved_wait = launcher.wait
diff --git a/libensemble/tests/unit_tests/test_mpi4py.py b/libensemble/tests/unit_tests/test_mpi4py.py
index f4a23980ee..7dd9b0fc27 100644
--- a/libensemble/tests/unit_tests/test_mpi4py.py
+++ b/libensemble/tests/unit_tests/test_mpi4py.py
@@ -1,3 +1,6 @@
+import pytest
+
+@pytest.mark.extra
 def test_mpi4py():
     from mpi4py import MPI
 

From e32a4a7e7a9ecd23828c8a29460c6c5af60657ec Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 1 Aug 2022 11:45:51 -0500
Subject: [PATCH 081/196] ugh flake8

---
 libensemble/tests/unit_tests/test_launcher.py | 1 +
 libensemble/tests/unit_tests/test_mpi4py.py   | 1 +
 2 files changed, 2 insertions(+)

diff --git a/libensemble/tests/unit_tests/test_launcher.py b/libensemble/tests/unit_tests/test_launcher.py
index 2be4a2528c..318c4e7c0d 100644
--- a/libensemble/tests/unit_tests/test_launcher.py
+++ b/libensemble/tests/unit_tests/test_launcher.py
@@ -59,6 +59,7 @@ def xtest_submit():
     assert not launcher.process_is_stopped(process, 0.5), "Process stopped early."
     launcher.cancel(process, 0)
 
+
 @pytest.mark.extra
 def test_launch32():
     "If we are in Python > 3.2, still check that 3.2 wait func works"
diff --git a/libensemble/tests/unit_tests/test_mpi4py.py b/libensemble/tests/unit_tests/test_mpi4py.py
index 7dd9b0fc27..606c38f79a 100644
--- a/libensemble/tests/unit_tests/test_mpi4py.py
+++ b/libensemble/tests/unit_tests/test_mpi4py.py
@@ -1,5 +1,6 @@
 import pytest
 
+
 @pytest.mark.extra
 def test_mpi4py():
     from mpi4py import MPI

From 108b13c393ad4ef6e7c8bd4155f918e962d39d7b Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 1 Aug 2022 12:01:36 -0500
Subject: [PATCH 082/196] do we have mpirun setup?

---
 .github/workflows/ci.yml                      | 1 +
 libensemble/tests/unit_tests/test_launcher.py | 1 +
 2 files changed, 2 insertions(+)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index d0f8236a12..909e1a690b 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -214,6 +214,7 @@ jobs:
         - name: Run simple tests, Windows
           if: matrix.os == 'windows-latest'
           run: |
+           mpirun -h
            ./libensemble/tests/run-tests.sh -A "-W error" -z -${{ matrix.comms-type }}
 
         - name: Run extensive tests, macOS
diff --git a/libensemble/tests/unit_tests/test_launcher.py b/libensemble/tests/unit_tests/test_launcher.py
index 318c4e7c0d..951eb287dc 100644
--- a/libensemble/tests/unit_tests/test_launcher.py
+++ b/libensemble/tests/unit_tests/test_launcher.py
@@ -69,6 +69,7 @@ def test_launch32():
     launcher.wait = saved_wait
 
 
+@pytest.mark.extra
 def test_launch33():
     "If we are in Python > 3.2, also check the new-style wait func"
     if launcher.wait == launcher.wait_py33:

From 47a9e6225a17dc07049ff8aa57653497ff63304a Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 1 Aug 2022 12:10:21 -0500
Subject: [PATCH 083/196] ...what about mpiexec? its listed on the microsoft
 docs

---
 .github/workflows/ci.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 909e1a690b..51a87c109a 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -214,7 +214,7 @@ jobs:
         - name: Run simple tests, Windows
           if: matrix.os == 'windows-latest'
           run: |
-           mpirun -h
+           mpiexec -h
            ./libensemble/tests/run-tests.sh -A "-W error" -z -${{ matrix.comms-type }}
 
         - name: Run extensive tests, macOS

From ac289ee8a6786e7389ad044ac88b8977946540cf Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 1 Aug 2022 12:36:51 -0500
Subject: [PATCH 084/196] its /help

---
 .github/workflows/ci.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 51a87c109a..21b6d2fac5 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -214,7 +214,7 @@ jobs:
         - name: Run simple tests, Windows
           if: matrix.os == 'windows-latest'
           run: |
-           mpiexec -h
+           mpiexec /help
            ./libensemble/tests/run-tests.sh -A "-W error" -z -${{ matrix.comms-type }}
 
         - name: Run extensive tests, macOS

From bcdd311213d5b8b6125a51decdbdc5cde2802b1b Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 1 Aug 2022 12:48:52 -0500
Subject: [PATCH 085/196] what can I do?

---
 .github/workflows/ci.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 21b6d2fac5..0f19be8ff8 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -214,7 +214,7 @@ jobs:
         - name: Run simple tests, Windows
           if: matrix.os == 'windows-latest'
           run: |
-           mpiexec /help
+           mpiexec
            ./libensemble/tests/run-tests.sh -A "-W error" -z -${{ matrix.comms-type }}
 
         - name: Run extensive tests, macOS

From f19af9cc487e2c0ef6a798d737be4669c36098fa Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 1 Aug 2022 14:04:00 -0500
Subject: [PATCH 086/196] first attempt at libEnsemble detecting msmpi

---
 .github/workflows/ci.yml               |  1 -
 libensemble/executors/mpi_runner.py    | 17 +++++++++++++++++
 libensemble/resources/mpi_resources.py | 10 +++++++++-
 3 files changed, 26 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 0f19be8ff8..d0f8236a12 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -214,7 +214,6 @@ jobs:
         - name: Run simple tests, Windows
           if: matrix.os == 'windows-latest'
           run: |
-           mpiexec
            ./libensemble/tests/run-tests.sh -A "-W error" -z -${{ matrix.comms-type }}
 
         - name: Run extensive tests, macOS
diff --git a/libensemble/executors/mpi_runner.py b/libensemble/executors/mpi_runner.py
index aa6f68c663..45fe8990b2 100644
--- a/libensemble/executors/mpi_runner.py
+++ b/libensemble/executors/mpi_runner.py
@@ -18,6 +18,7 @@ def get_runner(mpi_runner_type, runner_name=None):
             "aprun": APRUN_MPIRunner,
             "srun": SRUN_MPIRunner,
             "jsrun": JSRUN_MPIRunner,
+            "msmpi": MSMPI_MPIRunner,
             "custom": MPIRunner,
         }
         mpi_runner = mpi_runners[mpi_runner_type]
@@ -198,6 +199,22 @@ def __init__(self, run_command="aprun"):
             "{extra_args}",
         ]
 
+class MSMPI_MPIRunner(MPIRunner):
+    def __init__(self, run_command="mpiexec"):
+        self.run_command = run_command
+        self.subgroup_launch = False
+        self.mfile_support = False
+        self.arg_nprocs = ("-n")
+        self.arg_nnodes = ("--LIBE_NNODES_ARG_EMPTY",)
+        self.arg_ppn = ("-cores",)
+        self.mpi_command = [
+            self.run_command,
+            "-env {env}",
+            "-hosts {hostlist}",
+            "-n {num_procs}",
+            "-cores {procs_per_node}",
+            "{extra_args}",
+        ]
 
 class SRUN_MPIRunner(MPIRunner):
     def __init__(self, run_command="srun"):
diff --git a/libensemble/resources/mpi_resources.py b/libensemble/resources/mpi_resources.py
index 0abd7feb1b..bbe2764d48 100644
--- a/libensemble/resources/mpi_resources.py
+++ b/libensemble/resources/mpi_resources.py
@@ -36,7 +36,7 @@ def get_MPI_variant():
     Returns
     -------
     mpi_variant: string:
-        MPI variant 'aprun' or 'jsrun' or 'mpich' or 'openmpi' or 'srun'
+        MPI variant 'aprun' or 'jsrun' or 'msmpi' or 'mpich' or 'openmpi' or 'srun'
 
     """
 
@@ -52,6 +52,14 @@ def get_MPI_variant():
     except Exception:
         pass
 
+    try:
+        try_msmpi = subprocess.Popen(["mpiexec"], stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+        stdout, _ = try_msmpi.communicate()
+        if "Microsoft" in stdout.decode():
+            return "msmpi"
+    except Exception:
+        pass
+
     try:
         # Explore mpi4py.MPI.get_vendor() and mpi4py.MPI.Get_library_version() for mpi4py
         try_mpich = subprocess.Popen(["mpirun", "-npernode"], stdout=subprocess.PIPE, stderr=subprocess.STDOUT)

From 9ef9daa88d5f38707281bdb8e54b01a8342487f9 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 1 Aug 2022 14:08:30 -0500
Subject: [PATCH 087/196] fake8

---
 libensemble/executors/mpi_runner.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/libensemble/executors/mpi_runner.py b/libensemble/executors/mpi_runner.py
index 45fe8990b2..e08bc5e5be 100644
--- a/libensemble/executors/mpi_runner.py
+++ b/libensemble/executors/mpi_runner.py
@@ -199,6 +199,7 @@ def __init__(self, run_command="aprun"):
             "{extra_args}",
         ]
 
+
 class MSMPI_MPIRunner(MPIRunner):
     def __init__(self, run_command="mpiexec"):
         self.run_command = run_command
@@ -216,6 +217,7 @@ def __init__(self, run_command="mpiexec"):
             "{extra_args}",
         ]
 
+
 class SRUN_MPIRunner(MPIRunner):
     def __init__(self, run_command="srun"):
         self.run_command = run_command

From 5d82d8deb1aed94f489abb65857957100c30fc0c Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 1 Aug 2022 14:51:39 -0500
Subject: [PATCH 088/196] mark a terminate test as extra

---
 .../tests/unit_tests_nompi/conftest.py        | 21 +++++++++++++++++++
 .../tests/unit_tests_nompi/test_aaa_comms.py  |  2 ++
 2 files changed, 23 insertions(+)
 create mode 100644 libensemble/tests/unit_tests_nompi/conftest.py

diff --git a/libensemble/tests/unit_tests_nompi/conftest.py b/libensemble/tests/unit_tests_nompi/conftest.py
new file mode 100644
index 0000000000..3909b231f0
--- /dev/null
+++ b/libensemble/tests/unit_tests_nompi/conftest.py
@@ -0,0 +1,21 @@
+# https://stackoverflow.com/questions/47559524/pytest-how-to-skip-tests-unless-you-declare-an-option-flag/61193490#61193490
+
+import pytest
+
+
+def pytest_addoption(parser):
+    parser.addoption("--runextra", action="store_true", default=False, help="run extra tests")
+
+
+def pytest_configure(config):
+    config.addinivalue_line("markers", "extra: mark test as extra to run")
+
+
+def pytest_collection_modifyitems(config, items):
+    if config.getoption("--runextra"):
+        # --runextra given in cli: do not skip extra tests
+        return
+    skip_extra = pytest.mark.skip(reason="need --runextra option to run")
+    for item in items:
+        if "extra" in item.keywords:
+            item.add_marker(skip_extra)
diff --git a/libensemble/tests/unit_tests_nompi/test_aaa_comms.py b/libensemble/tests/unit_tests_nompi/test_aaa_comms.py
index 180bba29f6..6dde4ff492 100644
--- a/libensemble/tests/unit_tests_nompi/test_aaa_comms.py
+++ b/libensemble/tests/unit_tests_nompi/test_aaa_comms.py
@@ -9,6 +9,7 @@
 """
 
 import time
+import pytest
 import signal
 import libensemble.comms.comms as comms
 
@@ -55,6 +56,7 @@ def ignore_handler(signum, frame):
     print("Ignoring SIGTERM")
 
 
+@pytest.mark.extra
 def test_qcomm_proc_terminate3():
     "Test that a QCommProcess ignoring SIGTERM manages."
 

From 9725cb5de1e154120677c3e6d87bae3b69976ce1 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 1 Aug 2022 15:06:33 -0500
Subject: [PATCH 089/196] catch permission error on windows in test_logger

---
 libensemble/tests/unit_tests_logger/test_logger.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/libensemble/tests/unit_tests_logger/test_logger.py b/libensemble/tests/unit_tests_logger/test_logger.py
index eaaf811e6f..18555dd517 100644
--- a/libensemble/tests/unit_tests_logger/test_logger.py
+++ b/libensemble/tests/unit_tests_logger/test_logger.py
@@ -72,7 +72,10 @@ def test_set_filename():
     with open(alt_name, "r") as f:
         line = f.readline()
         assert "Cannot set filename after loggers initialized" in line
-    os.remove(alt_name)
+    try:
+        os.remove(alt_name)
+    except PermissionError:  # windows only
+        pass
 
     logs = LogConfig.config
     logs.logger_set = True

From 51f4c0baac2450958842b5ce8cdb92cecfc2adbe Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 1 Aug 2022 15:35:33 -0500
Subject: [PATCH 090/196] move test_stats_output to functionality tests,
 experimenting with skipping tests on windows

---
 install/testing_requirements.txt                     |  1 +
 .../test_executor_hworld_pass_fail.py                |  3 ++-
 .../test_executor_hworld_timeout.py                  |  3 ++-
 .../functionality_tests/test_sim_dirs_per_worker.py  |  1 +
 .../test_stats_output.py                             |  0
 .../test_persistent_aposmm_external_localopt.py      |  2 +-
 libensemble/tests/run-tests.sh                       | 12 +++++++++++-
 7 files changed, 18 insertions(+), 4 deletions(-)
 rename libensemble/tests/{regression_tests => functionality_tests}/test_stats_output.py (100%)

diff --git a/install/testing_requirements.txt b/install/testing_requirements.txt
index c33b06649e..e3d464f3d5 100644
--- a/install/testing_requirements.txt
+++ b/install/testing_requirements.txt
@@ -5,3 +5,4 @@ pytest-cov==2.12.1
 pytest-timeout==1.4.2
 mock==4.0.3
 coveralls==3.2.0
+python-dateutil
\ No newline at end of file
diff --git a/libensemble/tests/functionality_tests/test_executor_hworld_pass_fail.py b/libensemble/tests/functionality_tests/test_executor_hworld_pass_fail.py
index ccfa4d1e45..90cb7dfd9b 100644
--- a/libensemble/tests/functionality_tests/test_executor_hworld_pass_fail.py
+++ b/libensemble/tests/functionality_tests/test_executor_hworld_pass_fail.py
@@ -26,9 +26,10 @@
 
 # Do not change these lines - they are parsed by run-tests.sh
 # TESTSUITE_COMMS: mpi local tcp
-# TESTSUITE_OS_SKIP: OSX
+# TESTSUITE_OS_SKIP: OSX WIN
 # TESTSUITE_NPROCS: 2 3 4
 # TESTSUITE_OMPI_SKIP: true
+# TESTSUITE_EXTRA: true
 
 # Main block is necessary only when using local comms with spawn start method (default on macOS and Windows).
 if __name__ == "__main__":
diff --git a/libensemble/tests/functionality_tests/test_executor_hworld_timeout.py b/libensemble/tests/functionality_tests/test_executor_hworld_timeout.py
index e953d23739..b39348114c 100644
--- a/libensemble/tests/functionality_tests/test_executor_hworld_timeout.py
+++ b/libensemble/tests/functionality_tests/test_executor_hworld_timeout.py
@@ -26,7 +26,8 @@
 # TESTSUITE_COMMS: mpi local tcp
 # TESTSUITE_NPROCS: 2 3 4
 # TESTSUITE_OMPI_SKIP: true
-# TESTSUITE_OS_SKIP: OSX
+# TESTSUITE_OS_SKIP: OSX WIN
+# TESTSUITE_EXTRA: true
 
 # Main block is necessary only when using local comms with spawn start method (default on macOS and Windows).
 if __name__ == "__main__":
diff --git a/libensemble/tests/functionality_tests/test_sim_dirs_per_worker.py b/libensemble/tests/functionality_tests/test_sim_dirs_per_worker.py
index eeb507b2be..a8ec5c8596 100644
--- a/libensemble/tests/functionality_tests/test_sim_dirs_per_worker.py
+++ b/libensemble/tests/functionality_tests/test_sim_dirs_per_worker.py
@@ -13,6 +13,7 @@
 # Do not change these lines - they are parsed by run-tests.sh
 # TESTSUITE_COMMS: mpi local tcp
 # TESTSUITE_NPROCS: 2 4
+# TESTSUITE_EXTRA: true
 
 import numpy as np
 import os
diff --git a/libensemble/tests/regression_tests/test_stats_output.py b/libensemble/tests/functionality_tests/test_stats_output.py
similarity index 100%
rename from libensemble/tests/regression_tests/test_stats_output.py
rename to libensemble/tests/functionality_tests/test_stats_output.py
diff --git a/libensemble/tests/regression_tests/test_persistent_aposmm_external_localopt.py b/libensemble/tests/regression_tests/test_persistent_aposmm_external_localopt.py
index 7d24043446..96b37b58b5 100644
--- a/libensemble/tests/regression_tests/test_persistent_aposmm_external_localopt.py
+++ b/libensemble/tests/regression_tests/test_persistent_aposmm_external_localopt.py
@@ -19,7 +19,7 @@
 # Do not change these lines - they are parsed by run-tests.sh
 # TESTSUITE_COMMS: local mpi tcp
 # TESTSUITE_NPROCS: 4
-# TESTSUITE_OS_SKIP: OSX
+# TESTSUITE_OS_SKIP: OSX WIN
 # TESTSUITE_EXTRA: true
 
 import sys
diff --git a/libensemble/tests/run-tests.sh b/libensemble/tests/run-tests.sh
index 0b506f4858..84bcd5a361 100755
--- a/libensemble/tests/run-tests.sh
+++ b/libensemble/tests/run-tests.sh
@@ -490,6 +490,7 @@ if [ "$root_found" = true ]; then
           NPROCS_LIST=$(sed -n '/# TESTSUITE_NPROCS/s/# TESTSUITE_NPROCS: //p' $TEST_SCRIPT)
           OS_SKIP_LIST=$(sed -n '/# TESTSUITE_OS_SKIP/s/# TESTSUITE_OS_SKIP: //p' $TEST_SCRIPT)
           OMPI_SKIP=$(sed -n '/# TESTSUITE_OMPI_SKIP/s/# TESTSUITE_OMPI_SKIP: //p' $TEST_SCRIPT)
+          echo $OS_SKIP_LIST
           if [ "$REG_RUN_LARGEST_TEST_ONLY" = true ]; then  NPROCS_LIST=${NPROCS_LIST##*' '}; fi
           for NPROCS in ${NPROCS_LIST}
           do
@@ -500,11 +501,20 @@ if [ "$root_found" = true ]; then
             if [ "$RUN_LOCAL" = true ] && [ "$LAUNCHER" = local ]; then RUN_TEST=true; fi
             if [ "$RUN_TCP" = true ]   && [ "$LAUNCHER" = tcp ];   then RUN_TEST=true; fi
 
-            if [[ "$OSTYPE" = *"darwin"* ]] && [[ "$OS_SKIP_LIST" = "OSX" ]]; then
+            if [[ "$OSTYPE" = *"darwin"* ]] && [[ "$OS_SKIP_LIST" = *"OSX"* ]]; then
               echo "Skipping test number for OSX: " $test_num
               continue
             fi
 
+            if [[ "$OS_SKIP_LIST" = *"WIN"* ]]; then
+              echo "Skipping test number for Windows: " $test_num
+            fi
+
+            if [[ "$OSTYPE" = *"windows"* ]] && [[ "$OS_SKIP_LIST" = *"WIN"* ]]; then
+              echo "Skipping test number for Windows: " $test_num
+              continue
+            fi
+
             if [[ "$OMPI_SKIP" = "true" ]] && [[ "$MPIEXEC_FLAGS" = "--oversubscribe" ]] && [[ "$RUN_MPI" = true ]]; then
               echo "Skipping test number for Open MPI: " $test_num
               continue

From 183c59ca6c0f46cc80f60353da4491dcfb4daea7 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 28 Jul 2022 15:14:10 -0500
Subject: [PATCH 091/196] introdue fix for save_every_k filenames on windows

---
 libensemble/manager.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/libensemble/manager.py b/libensemble/manager.py
index c0f7e18ebf..890cbb0f64 100644
--- a/libensemble/manager.py
+++ b/libensemble/manager.py
@@ -8,6 +8,7 @@
 import glob
 import logging
 import socket
+import platform
 import traceback
 import numpy as np
 
@@ -252,6 +253,8 @@ def _save_every_k(self, fname, count, k):
         """Saves history every kth step"""
         count = k * (count // k)
         filename = fname.format(self.date_start, count)
+        if platform.system() == "Windows":
+            filename = filename.replace(":", '-')  # ":" is invalid in windows filenames
         if not os.path.isfile(filename) and count > 0:
             for old_file in glob.glob(fname.format(self.date_start, "*")):
                 os.remove(old_file)

From c52fa60114fd5ccd88b7f0932467d4cf344fdc23 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 1 Aug 2022 16:11:01 -0500
Subject: [PATCH 092/196] remove unneeded run-tests section

---
 libensemble/tests/run-tests.sh | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/libensemble/tests/run-tests.sh b/libensemble/tests/run-tests.sh
index 84bcd5a361..1ac7af83a5 100755
--- a/libensemble/tests/run-tests.sh
+++ b/libensemble/tests/run-tests.sh
@@ -506,10 +506,6 @@ if [ "$root_found" = true ]; then
               continue
             fi
 
-            if [[ "$OS_SKIP_LIST" = *"WIN"* ]]; then
-              echo "Skipping test number for Windows: " $test_num
-            fi
-
             if [[ "$OSTYPE" = *"windows"* ]] && [[ "$OS_SKIP_LIST" = *"WIN"* ]]; then
               echo "Skipping test number for Windows: " $test_num
               continue

From 577f9ab70364462371235f2ef06d593e752a264f Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 1 Aug 2022 16:21:23 -0500
Subject: [PATCH 093/196] move testing module

---
 .../{regression_tests => functionality_tests}/check_libE_stats.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename libensemble/tests/{regression_tests => functionality_tests}/check_libE_stats.py (100%)

diff --git a/libensemble/tests/regression_tests/check_libE_stats.py b/libensemble/tests/functionality_tests/check_libE_stats.py
similarity index 100%
rename from libensemble/tests/regression_tests/check_libE_stats.py
rename to libensemble/tests/functionality_tests/check_libE_stats.py

From 8dc0c7f30eefcdbb640aaaa6d54fc1462cb916b7 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 1 Aug 2022 17:38:23 -0500
Subject: [PATCH 094/196] specify nprocs to -hosts option

---
 libensemble/executors/mpi_runner.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libensemble/executors/mpi_runner.py b/libensemble/executors/mpi_runner.py
index e08bc5e5be..1028e6e238 100644
--- a/libensemble/executors/mpi_runner.py
+++ b/libensemble/executors/mpi_runner.py
@@ -211,7 +211,7 @@ def __init__(self, run_command="mpiexec"):
         self.mpi_command = [
             self.run_command,
             "-env {env}",
-            "-hosts {hostlist}",
+            "-hosts {num_procs} {hostlist}",  # msmpi wants nprocs before hostnames
             "-n {num_procs}",
             "-cores {procs_per_node}",
             "{extra_args}",

From 6688bdb08befefc6c8ea12d86fa5bd66fe57d69c Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 1 Aug 2022 17:45:49 -0500
Subject: [PATCH 095/196] apparently if were using -hosts we cant use -n

---
 libensemble/executors/mpi_runner.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/libensemble/executors/mpi_runner.py b/libensemble/executors/mpi_runner.py
index 1028e6e238..4f117d2aca 100644
--- a/libensemble/executors/mpi_runner.py
+++ b/libensemble/executors/mpi_runner.py
@@ -212,7 +212,6 @@ def __init__(self, run_command="mpiexec"):
             self.run_command,
             "-env {env}",
             "-hosts {num_procs} {hostlist}",  # msmpi wants nprocs before hostnames
-            "-n {num_procs}",
             "-cores {procs_per_node}",
             "{extra_args}",
         ]

From 84d869a9e59a7bb76f4dcade39146f25aad27faf Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 2 Aug 2022 14:08:23 -0500
Subject: [PATCH 096/196] have msmpi runner just use num_procs, since -hosts
 may have issues when specifying local machine?:

---
 libensemble/executors/mpi_runner.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/libensemble/executors/mpi_runner.py b/libensemble/executors/mpi_runner.py
index 4f117d2aca..30b6b2bf28 100644
--- a/libensemble/executors/mpi_runner.py
+++ b/libensemble/executors/mpi_runner.py
@@ -205,13 +205,13 @@ def __init__(self, run_command="mpiexec"):
         self.run_command = run_command
         self.subgroup_launch = False
         self.mfile_support = False
-        self.arg_nprocs = ("-n")
+        self.arg_nprocs = ("-n", "-np")
         self.arg_nnodes = ("--LIBE_NNODES_ARG_EMPTY",)
         self.arg_ppn = ("-cores",)
         self.mpi_command = [
             self.run_command,
             "-env {env}",
-            "-hosts {num_procs} {hostlist}",  # msmpi wants nprocs before hostnames
+            "-n {num_procs}",
             "-cores {procs_per_node}",
             "{extra_args}",
         ]

From 0433da4d3508711b4b70767f5814a374d56d6ca4 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 2 Aug 2022 14:23:43 -0500
Subject: [PATCH 097/196] disable a test on windows

---
 .../regression_tests/test_persistent_uniform_gen_decides_stop.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/libensemble/tests/regression_tests/test_persistent_uniform_gen_decides_stop.py b/libensemble/tests/regression_tests/test_persistent_uniform_gen_decides_stop.py
index 164aaa62e2..96b9af52fa 100644
--- a/libensemble/tests/regression_tests/test_persistent_uniform_gen_decides_stop.py
+++ b/libensemble/tests/regression_tests/test_persistent_uniform_gen_decides_stop.py
@@ -14,6 +14,7 @@
 # Do not change these lines - they are parsed by run-tests.sh
 # TESTSUITE_COMMS: mpi local tcp
 # TESTSUITE_NPROCS: 5
+# TESTSUITE_OS_SKIP: WIN
 
 import sys
 import numpy as np

From 3c0640a6eaf5406c3c6a285cf643fba26a5cd305 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 2 Aug 2022 14:29:05 -0500
Subject: [PATCH 098/196] ensure again that jobs are installing correct MPI

---
 .github/workflows/ci.yml | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 2cb0460519..cd0e0453fa 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -104,17 +104,24 @@ jobs:
 
         - name: Setup MPI (${{ matrix.mpi-version }})
           uses: mpi4py/setup-mpi@v1
+          if: matrix.os == 'windows-latest'
           with:
             mpi: ${{ matrix.mpi-version }}
 
-        - name: Install mpi4py from conda
-          if: matrix.python-version != '3.10'
+        - name: Install mpi4py on Windows
+          if: matrix.os == 'windows-latest'
+          run: pip install mpi4py
+
+        - name: Install mpi4py and MPI from conda
+          if: matrix.python-version != '3.10' && matrix.os != 'windows-latest'
           run: |
+            conda install ${{ matrix.mpi-version }}
             conda install mpi4py
 
-        - name: Install mpi4py from pip
-          if: matrix.python-version == '3.10'
+        - name: Install mpi4py from pip, MPI from conda
+          if: matrix.python-version == '3.10' && matrix.os != 'windows-latest'
           run: |
+            conda install ${{ matrix.mpi-version }}
             pip install mpi4py
 
         - name: Install generator dependencies

From 7fe7fa03366dfb24d317ae732068846d7eef94ba Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 2 Aug 2022 14:46:50 -0500
Subject: [PATCH 099/196] make nlopt test non-extra

---
 .../tests/regression_tests/test_persistent_aposmm_nlopt.py       | 1 -
 1 file changed, 1 deletion(-)

diff --git a/libensemble/tests/regression_tests/test_persistent_aposmm_nlopt.py b/libensemble/tests/regression_tests/test_persistent_aposmm_nlopt.py
index 6b65de580b..1c21a1f88f 100644
--- a/libensemble/tests/regression_tests/test_persistent_aposmm_nlopt.py
+++ b/libensemble/tests/regression_tests/test_persistent_aposmm_nlopt.py
@@ -14,7 +14,6 @@
 # Do not change these lines - they are parsed by run-tests.sh
 # TESTSUITE_COMMS: local mpi tcp
 # TESTSUITE_NPROCS: 3
-# TESTSUITE_EXTRA: true
 
 import sys
 import multiprocessing

From 06a92aa1e46b9a36d1db51789a143856166e61df Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 2 Aug 2022 16:42:27 -0500
Subject: [PATCH 100/196] install mpmath on windows, remove start method force
 for single aposmm regression test

---
 .github/workflows/ci.yml                                       | 1 +
 .../tests/regression_tests/test_persistent_aposmm_nlopt.py     | 3 ---
 2 files changed, 1 insertion(+), 3 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index cd0e0453fa..67e240e9a2 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -146,6 +146,7 @@ jobs:
             python -m pip install --upgrade pip
             conda install nlopt
             conda install scipy
+            pip install mpmath
 
         - name: Install generator dependencies for Ubuntu tests
           if: matrix.os == 'ubuntu-latest' && matrix.do-balsam == false
diff --git a/libensemble/tests/regression_tests/test_persistent_aposmm_nlopt.py b/libensemble/tests/regression_tests/test_persistent_aposmm_nlopt.py
index 1c21a1f88f..905eab9e88 100644
--- a/libensemble/tests/regression_tests/test_persistent_aposmm_nlopt.py
+++ b/libensemble/tests/regression_tests/test_persistent_aposmm_nlopt.py
@@ -37,9 +37,6 @@
 # Main block is necessary only when using local comms with spawn start method (default on macOS and Windows).
 if __name__ == "__main__":
 
-    # Temporary solution while we investigate/resolve slowdowns with "spawn" start method.
-    multiprocessing.set_start_method("fork", force=True)
-
     nworkers, is_manager, libE_specs, _ = parse_args()
 
     if is_manager:

From 714c0af5239f4d4ca280d0f15a4a2e219495217c Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 2 Aug 2022 17:06:49 -0500
Subject: [PATCH 101/196] organize entries into macos/windows, add
 clarification on installing msmpi and mpi4py

---
 docs/FAQ.rst | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index f639e82307..86155d6758 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -226,20 +226,25 @@ macOS and Windows Errors
 
 .. _faqwindows:
 
-**How can I run libEnsemble with MPI on Windows?**
+**Windows - How can I run libEnsemble with MPI comms?**
 
-In our experience this may be challenging, if not impossible, since most MPI distributions have
-either dropped Windows support (MPICH and Open MPI) or are no longer being maintained (msmpi). Plus
+In our experience this may be challenging, since most MPI distributions have
+either dropped Windows support (MPICH and Open MPI) or are no longer being maintained (`msmpi``). Plus
 Windows tends to experience platform-specific issues that are difficult to reproduce and troubleshoot
 for us.
 
 If you want to try anyways, we recommend experimenting with the many Unix-like emulators, containers, virtual machines,
-and other such systems. The `Installing PETSc On Microsoft Windows`_ docs contains lots of valuable information.
+and other such systems. The `Installing PETSc On Microsoft Windows`_ docs contain lots of valuable information.
 
-Otherwise, most local-comms libEnsemble examples appear to run just fine.
+Otherwise, install `msmpi` and `mpi4py` from conda and experiment, or use local comms.
 
 .. _`Installing PETSc On Microsoft Windows`: https://petsc.org/release/install/windows/#recommended-installation-methods
 
+**Windows - 'A required privilege is not held by the client'**
+
+Assuming you were trying to use the `sim_dir_symlink_files` or `gen_dir_symlink_files` options, this indicates that to
+allow libEnsemble to create symlinks, you need to run your current `cmd` shell as administrator.
+
 **"RuntimeError: An attempt has been made to start a new process... this probably means that you are not using fork...
 " if __name__ == '__main__': freeze_support() ...**
 
@@ -262,13 +267,13 @@ the following, placed near the top of your calling script::
 
 .. _`Python multiprocessing docs`: https://docs.python.org/3/library/multiprocessing.html
 
-**"Fatal error in MPI_Init_thread: Other MPI error, error stack: ... gethostbyname failed"**
+**"macOS - Fatal error in MPI_Init_thread: Other MPI error, error stack: ... gethostbyname failed"**
 
 Resolve this by appending ``127.0.0.1   [your hostname]`` to /etc/hosts.
 Unfortunately, ``127.0.0.1   localhost`` isn't satisfactory for preventing this
 error.
 
-**How do I stop the Firewall Security popups when running with the Executor?**
+**macOS - How do I stop the Firewall Security popups when running with the Executor?**
 
 There are several ways to address this nuisance, but all involve trial and error.
 An easy (but insecure) solution is temporarily disabling the firewall through

From 0f54dc5cc93329f306fffc66d1d5252f139d32bf Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 2 Aug 2022 17:08:45 -0500
Subject: [PATCH 102/196] flake8

---
 .../tests/regression_tests/test_persistent_aposmm_nlopt.py       | 1 -
 1 file changed, 1 deletion(-)

diff --git a/libensemble/tests/regression_tests/test_persistent_aposmm_nlopt.py b/libensemble/tests/regression_tests/test_persistent_aposmm_nlopt.py
index 905eab9e88..cb58850a0e 100644
--- a/libensemble/tests/regression_tests/test_persistent_aposmm_nlopt.py
+++ b/libensemble/tests/regression_tests/test_persistent_aposmm_nlopt.py
@@ -16,7 +16,6 @@
 # TESTSUITE_NPROCS: 3
 
 import sys
-import multiprocessing
 import numpy as np
 
 # Import libEnsemble items for this test

From 9a647c852213fcce290721f32826c7fa5f326a52 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 3 Aug 2022 09:04:34 -0500
Subject: [PATCH 103/196] how does OSTYPE present on windows action?

---
 .github/workflows/ci.yml       | 1 +
 libensemble/tests/run-tests.sh | 1 -
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 67e240e9a2..7e6e072454 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -224,6 +224,7 @@ jobs:
         - name: Run simple tests, Windows
           if: matrix.os == 'windows-latest'
           run: |
+           echo $OSTYPE
            ./libensemble/tests/run-tests.sh -A "-W error" -z -${{ matrix.comms-type }}
 
         - name: Run extensive tests, macOS
diff --git a/libensemble/tests/run-tests.sh b/libensemble/tests/run-tests.sh
index 1ac7af83a5..9f92086f49 100755
--- a/libensemble/tests/run-tests.sh
+++ b/libensemble/tests/run-tests.sh
@@ -490,7 +490,6 @@ if [ "$root_found" = true ]; then
           NPROCS_LIST=$(sed -n '/# TESTSUITE_NPROCS/s/# TESTSUITE_NPROCS: //p' $TEST_SCRIPT)
           OS_SKIP_LIST=$(sed -n '/# TESTSUITE_OS_SKIP/s/# TESTSUITE_OS_SKIP: //p' $TEST_SCRIPT)
           OMPI_SKIP=$(sed -n '/# TESTSUITE_OMPI_SKIP/s/# TESTSUITE_OMPI_SKIP: //p' $TEST_SCRIPT)
-          echo $OS_SKIP_LIST
           if [ "$REG_RUN_LARGEST_TEST_ONLY" = true ]; then  NPROCS_LIST=${NPROCS_LIST##*' '}; fi
           for NPROCS in ${NPROCS_LIST}
           do

From 936f1eee3ceab39a716f4a6971093868bcf3992e Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 3 Aug 2022 09:16:57 -0500
Subject: [PATCH 104/196] OSTYPE value on windows is msys

---
 .github/workflows/ci.yml       | 1 -
 libensemble/tests/run-tests.sh | 2 +-
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 7e6e072454..67e240e9a2 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -224,7 +224,6 @@ jobs:
         - name: Run simple tests, Windows
           if: matrix.os == 'windows-latest'
           run: |
-           echo $OSTYPE
            ./libensemble/tests/run-tests.sh -A "-W error" -z -${{ matrix.comms-type }}
 
         - name: Run extensive tests, macOS
diff --git a/libensemble/tests/run-tests.sh b/libensemble/tests/run-tests.sh
index 9f92086f49..b0b3a53f97 100755
--- a/libensemble/tests/run-tests.sh
+++ b/libensemble/tests/run-tests.sh
@@ -505,7 +505,7 @@ if [ "$root_found" = true ]; then
               continue
             fi
 
-            if [[ "$OSTYPE" = *"windows"* ]] && [[ "$OS_SKIP_LIST" = *"WIN"* ]]; then
+            if [[ "$OSTYPE" = *"msys"* ]] && [[ "$OS_SKIP_LIST" = *"WIN"* ]]; then
               echo "Skipping test number for Windows: " $test_num
               continue
             fi

From f0c1fc656e5b8771ab71432b22a7e73281121b1b Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 12 Aug 2022 17:24:54 -0500
Subject: [PATCH 105/196] initial commit, for funcx unit test

---
 .../tests/unit_tests/test_make_runners.py     | 80 +++++++++++++++++++
 1 file changed, 80 insertions(+)
 create mode 100644 libensemble/tests/unit_tests/test_make_runners.py

diff --git a/libensemble/tests/unit_tests/test_make_runners.py b/libensemble/tests/unit_tests/test_make_runners.py
new file mode 100644
index 0000000000..c1916b935f
--- /dev/null
+++ b/libensemble/tests/unit_tests/test_make_runners.py
@@ -0,0 +1,80 @@
+import os
+import numpy as np
+import pytest
+import mock
+
+import libensemble.tests.unit_tests.setup as setup
+from libensemble.tools.fields_keys import libE_fields
+from libensemble.resources.resources import Resources
+from libensemble.utils import runners
+
+
+def test_normal_runners():
+    sim_specs, gen_specs, exit_criteria = setup.make_criteria_and_specs_0()
+
+
+
+
+def test_manager_exception():
+    """Checking dump of history and pickle file on abort"""
+    sim_specs, gen_specs, exit_criteria = setup.make_criteria_and_specs_0()
+    remove_file_if_exists(hfile_abort)
+    remove_file_if_exists(pfile_abort)
+
+    with mock.patch("libensemble.manager.manager_main") as managerMock:
+        managerMock.side_effect = Exception
+        # Collision between libE.py and libE() (after mods to __init__.py) means
+        #   libensemble.libE.comms_abort tries to refer to the function, not file
+        with mock.patch("libensemble.comms_abort") as abortMock:
+            abortMock.side_effect = Exception
+            # Need fake MPI to get past the Manager only check and dump history
+            with pytest.raises(Exception):
+                libE_specs = {"mpi_comm": fake_mpi, "disable_resource_manager": True}
+                libE(sim_specs, gen_specs, exit_criteria, libE_specs=libE_specs)
+                pytest.fail("Expected exception")
+            assert os.path.isfile(hfile_abort), "History file not dumped"
+            assert os.path.isfile(pfile_abort), "Pickle file not dumped"
+            os.remove(hfile_abort)
+            os.remove(pfile_abort)
+
+            # Test that History and Pickle files NOT created when disabled
+            with pytest.raises(Exception):
+                libE_specs = {"mpi_comm": fake_mpi, "save_H_and_persis_on_abort": False}
+                libE(sim_specs, gen_specs, exit_criteria, libE_specs=libE_specs)
+                pytest.fail("Expected exception")
+            assert not os.path.isfile(hfile_abort), "History file dumped"
+            assert not os.path.isfile(pfile_abort), "Pickle file dumped"
+
+
+# Note - this could be combined now with above tests as fake_MPI prevents need for use of mock module
+# Only way that is better is that this will simply hit first code exception - (when fake_MPI tries to isend)
+# While first test triggers on call to manager
+def test_exception_raising_manager_with_abort():
+    """Running until fake_MPI tries to send msg to test (mocked) comm.Abort is called
+
+    Manager should raise MPISendException when fakeMPI tries to send message, which
+    will be caught by libE and raise MPIAbortException from fakeMPI.Abort"""
+    with pytest.raises(MPIAbortException):
+        sim_specs, gen_specs, exit_criteria = setup.make_criteria_and_specs_0()
+        libE_specs = {"mpi_comm": fake_mpi, "disable_resource_manager": True}
+        libE(sim_specs, gen_specs, exit_criteria, libE_specs=libE_specs)
+        pytest.fail("Expected MPIAbortException exception")
+
+
+def test_exception_raising_manager_no_abort():
+    """Running until fake_MPI tries to send msg to test (mocked) comm.Abort is called
+
+    Manager should raise MPISendException when fakeMPI tries to send message, which
+    will be caught by libE and raise MPIAbortException from fakeMPI.Abort"""
+    libE_specs = {"abort_on_exception": False, "mpi_comm": fake_mpi, "disable_resource_manager": True}
+    with pytest.raises(LoggedException):
+        sim_specs, gen_specs, exit_criteria = setup.make_criteria_and_specs_0()
+        libE(sim_specs, gen_specs, exit_criteria, libE_specs=libE_specs)
+        pytest.fail("Expected MPISendException exception")
+
+
+
+if __name__ == "__main__":
+    test_manager_exception()
+    test_exception_raising_manager_with_abort()
+    test_exception_raising_manager_no_abort()
\ No newline at end of file

From 532fec4a5af389d052dcb44620268eebcc659879 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 15 Aug 2022 13:47:19 -0500
Subject: [PATCH 106/196] first approach for refactoring runners.py

---
 .../tests/unit_tests/test_make_runners.py     |  67 ++++++-----
 libensemble/utils/runners.py                  | 113 +++++++++---------
 libensemble/worker.py                         |   4 +-
 3 files changed, 96 insertions(+), 88 deletions(-)

diff --git a/libensemble/tests/unit_tests/test_make_runners.py b/libensemble/tests/unit_tests/test_make_runners.py
index c1916b935f..757eddf6dc 100644
--- a/libensemble/tests/unit_tests/test_make_runners.py
+++ b/libensemble/tests/unit_tests/test_make_runners.py
@@ -6,14 +6,46 @@
 import libensemble.tests.unit_tests.setup as setup
 from libensemble.tools.fields_keys import libE_fields
 from libensemble.resources.resources import Resources
-from libensemble.utils import runners
+from libensemble.message_numbers import EVAL_SIM_TAG, EVAL_GEN_TAG
+from libensemble.utils.runners import Runners
 
 
-def test_normal_runners():
+def get_ufunc_args():
     sim_specs, gen_specs, exit_criteria = setup.make_criteria_and_specs_0()
 
+    L = exit_criteria["sim_max"]
+    H = np.zeros(L, dtype=list(set(libE_fields + sim_specs["out"] + gen_specs["out"])))
+
+    H["sim_id"][-L:] = -1
+    H["sim_started_time"][-L:] = np.inf
+
+    sim_ids = np.zeros(1, dtype=int)
+    Work = {"tag": EVAL_SIM_TAG, "persis_info": {}, "libE_info": {"H_rows": sim_ids}, "H_fields": sim_specs["in"]}
+    calc_in = H[Work["H_fields"]][Work["libE_info"]["H_rows"]]
+    return calc_in, sim_specs, gen_specs
+
+
+def test_normal_runners():
+    calc_in, sim_specs, gen_specs = get_ufunc_args()
+
+    runners = Runners(sim_specs, gen_specs)
+    assert not runners.has_funcx_sim and not runners.has_funcx_gen, \
+        "funcX use should not be detected without setting endpoint fields"
+
+    ro = runners.make_runners()
+    assert all([i in ro for i in [EVAL_SIM_TAG, EVAL_GEN_TAG]]), \
+        "Both user function tags should be included in runners dictionary"
 
 
+def test_normal_no_gen():
+    calc_in, sim_specs, gen_specs = get_ufunc_args()
+
+    runners = Runners(sim_specs, {})
+    ro = runners.make_runners()
+
+    assert not ro[2], \
+        "generator function shouldn't be provided if not using gen_specs"
+
 
 def test_manager_exception():
     """Checking dump of history and pickle file on abort"""
@@ -46,35 +78,8 @@ def test_manager_exception():
             assert not os.path.isfile(pfile_abort), "Pickle file dumped"
 
 
-# Note - this could be combined now with above tests as fake_MPI prevents need for use of mock module
-# Only way that is better is that this will simply hit first code exception - (when fake_MPI tries to isend)
-# While first test triggers on call to manager
-def test_exception_raising_manager_with_abort():
-    """Running until fake_MPI tries to send msg to test (mocked) comm.Abort is called
-
-    Manager should raise MPISendException when fakeMPI tries to send message, which
-    will be caught by libE and raise MPIAbortException from fakeMPI.Abort"""
-    with pytest.raises(MPIAbortException):
-        sim_specs, gen_specs, exit_criteria = setup.make_criteria_and_specs_0()
-        libE_specs = {"mpi_comm": fake_mpi, "disable_resource_manager": True}
-        libE(sim_specs, gen_specs, exit_criteria, libE_specs=libE_specs)
-        pytest.fail("Expected MPIAbortException exception")
-
-
-def test_exception_raising_manager_no_abort():
-    """Running until fake_MPI tries to send msg to test (mocked) comm.Abort is called
-
-    Manager should raise MPISendException when fakeMPI tries to send message, which
-    will be caught by libE and raise MPIAbortException from fakeMPI.Abort"""
-    libE_specs = {"abort_on_exception": False, "mpi_comm": fake_mpi, "disable_resource_manager": True}
-    with pytest.raises(LoggedException):
-        sim_specs, gen_specs, exit_criteria = setup.make_criteria_and_specs_0()
-        libE(sim_specs, gen_specs, exit_criteria, libE_specs=libE_specs)
-        pytest.fail("Expected MPISendException exception")
-
 
 
 if __name__ == "__main__":
-    test_manager_exception()
-    test_exception_raising_manager_with_abort()
-    test_exception_raising_manager_no_abort()
\ No newline at end of file
+    test_normal_runners()
+    test_no_gen()
diff --git a/libensemble/utils/runners.py b/libensemble/utils/runners.py
index 22275c2a32..a24e6a547f 100644
--- a/libensemble/utils/runners.py
+++ b/libensemble/utils/runners.py
@@ -5,64 +5,67 @@
 
 logger = logging.getLogger(__name__)
 
+class Runners:
+    """Determines and returns methods for workers to run user functions.
+
+        Currently supported: direct-call and funcX
+    """
+
+    def __init__(self, sim_specs, gen_specs):
+        self.sim_specs = sim_specs
+        self.gen_specs = gen_specs
+        self.sim_f = sim_specs["sim_f"]
+        self.gen_f = gen_specs.get("gen_f")
+        self.has_funcx_sim = len(sim_specs.get("funcx_endpoint", "")) > 0
+        self.has_funcx_gen = len(gen_specs.get("funcx_endpoint", "")) > 0
+
+        if any([self.has_funcx_sim, self.has_funcx_gen]):
+            try:
+                from funcx import FuncXClient
+                from funcx.sdk.executor import FuncXExecutor
+                self.funcx_exctr = FuncXExecutor(FuncXClient())
+
+            except ModuleNotFoundError:
+                logger.warning("funcX use detected but funcX not importable. Is it installed?")
+
+
+    def make_runners(self):
+        """Creates functions to run a sim or gen. These functions are either
+        called directly by the worker or submitted to a funcX endpoint."""
+
+        def run_sim(calc_in, persis_info, libE_info):
+            """Calls or submits the sim func."""
+            if self.has_funcx_sim and self.funcx_exctr:
+                return self._funcx_result(calc_in, persis_info, self.sim_specs, libE_info, self.sim_f)
+            else:
+                return self._normal_result(calc_in, persis_info, self.sim_specs, libE_info, self.sim_f)
+
+        if self.gen_specs:
+
+            def run_gen(calc_in, persis_info, libE_info):
+                """Calls or submits the gen func."""
+                if self.has_funcx_gen and self.funcx_exctr:
+                    return self._funcx_result(calc_in, persis_info, self.gen_specs, libE_info, self.gen_f)
+                else:
+                    return self._normal_result(calc_in, persis_info, self.gen_specs, libE_info, self.gen_f)
 
-def _funcx_result(funcx_exctr, user_f, calc_in, persis_info, specs, libE_info):
-    from libensemble.worker import Worker
-    libE_info["comm"] = None  # 'comm' object not pickle-able
-    Worker._set_executor(0, None)  # ditto for executor
-
-    future = funcx_exctr.submit(user_f, calc_in, persis_info, specs, libE_info, endpoint_id=specs["funcx_endpoint"])
-    remote_exc = future.exception()  # blocks until exception or None
-    if remote_exc is None:
-        return future.result()
-    else:
-        raise remote_exc
-
-
-def _get_funcx_exctr(sim_specs, gen_specs):
-    funcx_sim = len(sim_specs.get("funcx_endpoint", "")) > 0
-    funcx_gen = len(gen_specs.get("funcx_endpoint", "")) > 0
-
-    if any([funcx_sim, funcx_gen]):
-        try:
-            from funcx import FuncXClient
-            from funcx.sdk.executor import FuncXExecutor
-
-            return FuncXExecutor(FuncXClient()), funcx_sim, funcx_gen
-        except ModuleNotFoundError:
-            logger.warning("funcX use detected but funcX not importable. Is it installed?")
-            return None, False, False
-        except Exception:
-            return None, False, False
-    else:
-        return None, False, False
-
-
-def make_runners(sim_specs, gen_specs):
-    """Creates functions to run a sim or gen. These functions are either
-    called directly by the worker or submitted to a funcX endpoint."""
-
-    funcx_exctr, funcx_sim, funcx_gen = _get_funcx_exctr(sim_specs, gen_specs)
-    sim_f = sim_specs["sim_f"]
-
-    def run_sim(calc_in, persis_info, libE_info):
-        """Calls or submits the sim func."""
-        if funcx_sim and funcx_exctr:
-            return _funcx_result(funcx_exctr, sim_f, calc_in, persis_info, sim_specs, libE_info)
         else:
-            return sim_f(calc_in, persis_info, sim_specs, libE_info)
+            run_gen = []
 
-    if gen_specs:
-        gen_f = gen_specs["gen_f"]
+        return {EVAL_SIM_TAG: run_sim, EVAL_GEN_TAG: run_gen}
 
-        def run_gen(calc_in, persis_info, libE_info):
-            """Calls or submits the gen func."""
-            if funcx_gen and funcx_exctr:
-                return _funcx_result(funcx_exctr, gen_f, calc_in, persis_info, gen_specs, libE_info)
-            else:
-                return gen_f(calc_in, persis_info, gen_specs, libE_info)
 
-    else:
-        run_gen = []
+    def _normal_result(self, calc_in, persis_info, specs, libE_info, user_f):
+        return user_f(calc_in, persis_info, specs, libE_info)
+
+    def _funcx_result(self, calc_in, persis_info, specs, libE_info, user_f):
+        from libensemble.worker import Worker
+        libE_info["comm"] = None  # 'comm' object not pickle-able
+        Worker._set_executor(0, None)  # ditto for executor
 
-    return {EVAL_SIM_TAG: run_sim, EVAL_GEN_TAG: run_gen}
+        future = self.funcx_exctr.submit(user_f, calc_in, persis_info, specs, libE_info, endpoint_id=specs["funcx_endpoint"])
+        remote_exc = future.exception()  # blocks until exception or None
+        if remote_exc is None:
+            return future.result()
+        else:
+            raise remote_exc
\ No newline at end of file
diff --git a/libensemble/worker.py b/libensemble/worker.py
index ad9751bdf4..d0890c8e53 100644
--- a/libensemble/worker.py
+++ b/libensemble/worker.py
@@ -19,7 +19,7 @@
 
 from libensemble.utils.misc import extract_H_ranges
 from libensemble.utils.timer import Timer
-from libensemble.utils.runners import make_runners
+from libensemble.utils.runners import Runners
 from libensemble.executors.executor import Executor
 from libensemble.resources.resources import Resources
 from libensemble.comms.logs import worker_logging_config
@@ -136,7 +136,7 @@ def __init__(self, comm, dtypes, workerID, sim_specs, gen_specs, libE_specs):
         self.stats_fmt = libE_specs.get("stats_fmt", {})
 
         self.calc_iter = {EVAL_SIM_TAG: 0, EVAL_GEN_TAG: 0}
-        self._run_calc = make_runners(sim_specs, gen_specs)
+        self._run_calc = Runners(sim_specs, gen_specs).make_runners()
         Worker._set_executor(self.workerID, self.comm)
         Worker._set_resources(self.workerID, self.comm)
         self.EnsembleDirectory = EnsembleDirectory(libE_specs=libE_specs)

From a1535c4594b4818fe508ba96f786fc1a8b4ce39e Mon Sep 17 00:00:00 2001
From: Jeffrey Larson <jmlarson@anl.gov>
Date: Mon, 15 Aug 2022 15:21:42 -0500
Subject: [PATCH 107/196] Black

---
 libensemble/manager.py                                  | 6 ++----
 libensemble/tests/unit_tests/test_executor.py           | 1 +
 libensemble/tests/unit_tests/test_manager_main.py       | 1 +
 libensemble/tests/unit_tests/test_persistent_aposmm.py  | 2 +-
 libensemble/tests/unit_tests/test_sim_dir_properties.py | 4 +---
 libensemble/tools/alloc_support.py                      | 8 ++------
 libensemble/utils/runners.py                            | 1 +
 setup.py                                                | 8 ++++----
 8 files changed, 13 insertions(+), 18 deletions(-)

diff --git a/libensemble/manager.py b/libensemble/manager.py
index 890cbb0f64..16bba267b6 100644
--- a/libensemble/manager.py
+++ b/libensemble/manager.py
@@ -254,7 +254,7 @@ def _save_every_k(self, fname, count, k):
         count = k * (count // k)
         filename = fname.format(self.date_start, count)
         if platform.system() == "Windows":
-            filename = filename.replace(":", '-')  # ":" is invalid in windows filenames
+            filename = filename.replace(":", "-")  # ":" is invalid in windows filenames
         if not os.path.isfile(filename) and count > 0:
             for old_file in glob.glob(fname.format(self.date_start, "*")):
                 os.remove(old_file)
@@ -344,9 +344,7 @@ def _send_work_order(self, Work, w):
         work_rows = Work["libE_info"]["H_rows"]
         work_name = calc_type_strings[Work["tag"]]
         logger.debug(
-            "Manager sending {} work to worker {}. Rows {}".format(
-                work_name, w, extract_H_ranges(Work) or None
-            )
+            "Manager sending {} work to worker {}. Rows {}".format(work_name, w, extract_H_ranges(Work) or None)
         )
         if len(work_rows):
             if "repack_fields" in globals():
diff --git a/libensemble/tests/unit_tests/test_executor.py b/libensemble/tests/unit_tests/test_executor.py
index 4a01ad0036..dc2e613eac 100644
--- a/libensemble/tests/unit_tests/test_executor.py
+++ b/libensemble/tests/unit_tests/test_executor.py
@@ -206,6 +206,7 @@ def polling_loop_multitask(exctr, task_list, timeout_sec=4.0, delay=0.05):
 
 # Tests ========================================================================================
 
+
 @pytest.mark.extra
 def test_launch_and_poll():
     """Test of launching and polling task and exiting on task finish"""
diff --git a/libensemble/tests/unit_tests/test_manager_main.py b/libensemble/tests/unit_tests/test_manager_main.py
index 38f7134ad5..a75d320fad 100644
--- a/libensemble/tests/unit_tests/test_manager_main.py
+++ b/libensemble/tests/unit_tests/test_manager_main.py
@@ -9,6 +9,7 @@
 
 if platform.system() != "Windows":
     from mpi4py import MPI
+
     libE_specs = {"mpi_comm": MPI.COMM_WORLD}
 
 
diff --git a/libensemble/tests/unit_tests/test_persistent_aposmm.py b/libensemble/tests/unit_tests/test_persistent_aposmm.py
index 8f427d0634..7b5fa7749d 100644
--- a/libensemble/tests/unit_tests/test_persistent_aposmm.py
+++ b/libensemble/tests/unit_tests/test_persistent_aposmm.py
@@ -6,7 +6,7 @@
 
 libensemble.gen_funcs.rc.aposmm_optimizers = "nlopt"
 
-if platform.system() in ['Linux', 'Darwin']:
+if platform.system() in ["Linux", "Darwin"]:
     multiprocessing.set_start_method("fork", force=True)
     from libensemble.gen_funcs.persistent_aposmm import aposmm, update_history_optimal
 
diff --git a/libensemble/tests/unit_tests/test_sim_dir_properties.py b/libensemble/tests/unit_tests/test_sim_dir_properties.py
index bdd5398575..6e377bbdc5 100644
--- a/libensemble/tests/unit_tests/test_sim_dir_properties.py
+++ b/libensemble/tests/unit_tests/test_sim_dir_properties.py
@@ -38,9 +38,7 @@ def test_range_mixes():
         "H_fields": ["x", "num_nodes", "procs_per_node"],
         "libE_info": {"H_rows": np.array([2, 3, 4, 6, 8, 9, 11, 14]), "workerID": 1},
     }
-    assert (
-        extract_H_ranges(work) == "2-4_6_8-9_11_14"
-    ), "Failed to correctly parse H row single elements and ranges."
+    assert extract_H_ranges(work) == "2-4_6_8-9_11_14", "Failed to correctly parse H row single elements and ranges."
 
 
 def test_copy_back():
diff --git a/libensemble/tools/alloc_support.py b/libensemble/tools/alloc_support.py
index fee6304181..79ae6ed31e 100644
--- a/libensemble/tools/alloc_support.py
+++ b/libensemble/tools/alloc_support.py
@@ -179,9 +179,7 @@ def sim_work(self, wid, H, H_fields, H_rows, persis_info, **libE_info):
         }
 
         logger.debug(
-            "Alloc func packing SIM work for worker {}. Packing sim_ids: {}".format(
-                wid, extract_H_ranges(work) or None
-            )
+            "Alloc func packing SIM work for worker {}. Packing sim_ids: {}".format(wid, extract_H_ranges(work) or None)
         )
         return work
 
@@ -223,9 +221,7 @@ def gen_work(self, wid, H_fields, H_rows, persis_info, **libE_info):
         }
 
         logger.debug(
-            "Alloc func packing GEN work for worker {}. Packing sim_ids: {}".format(
-                wid, extract_H_ranges(work) or None
-            )
+            "Alloc func packing GEN work for worker {}. Packing sim_ids: {}".format(wid, extract_H_ranges(work) or None)
         )
         return work
 
diff --git a/libensemble/utils/runners.py b/libensemble/utils/runners.py
index 22275c2a32..b75037bd29 100644
--- a/libensemble/utils/runners.py
+++ b/libensemble/utils/runners.py
@@ -8,6 +8,7 @@
 
 def _funcx_result(funcx_exctr, user_f, calc_in, persis_info, specs, libE_info):
     from libensemble.worker import Worker
+
     libE_info["comm"] = None  # 'comm' object not pickle-able
     Worker._set_executor(0, None)  # ditto for executor
 
diff --git a/setup.py b/setup.py
index 942fe716a9..94ac971693 100644
--- a/setup.py
+++ b/setup.py
@@ -99,10 +99,10 @@ def run_tests(self):
             "sphinx_rtd_theme",
         ],
     },
-
-    scripts=['scripts/liberegister',
-             'scripts/libesubmit'],
-
+    scripts=[
+        "scripts/liberegister",
+        "scripts/libesubmit",
+    ],
     classifiers=[
         "Development Status :: 4 - Beta",
         "Intended Audience :: Developers",

From d6428cdcf71d7af68dbd0462416d4769bf203260 Mon Sep 17 00:00:00 2001
From: Jeffrey Larson <jmlarson@anl.gov>
Date: Mon, 15 Aug 2022 15:22:14 -0500
Subject: [PATCH 108/196] Whitespace

---
 docs/FAQ.rst          | 8 ++++----
 docs/running_libE.rst | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index 3494fc1591..864345ac44 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -230,15 +230,15 @@ macOS and Windows Errors
 You need to place your main calling script code underneath an ``if __name__ == "__main__":`` block.
 
 Explanation: Python chooses one of three methods to start new processes when using multiprocessing
-(``--comms local`` with libEnsemble). These are ``'fork'``, ``'spawn'``, and ``'forkserver'``. ``'fork'`` 
-is the default on Unix, and in our experience is quicker and more reliable, but ``'spawn'`` is the default 
+(``--comms local`` with libEnsemble). These are ``'fork'``, ``'spawn'``, and ``'forkserver'``. ``'fork'``
+is the default on Unix, and in our experience is quicker and more reliable, but ``'spawn'`` is the default
 on Windows and macOS (See the `Python multiprocessing docs`_).
 
-Prior to libEnsemble v0.9.2, if libEnsemble detected macOS, it would automatically switch the multiprocessing 
+Prior to libEnsemble v0.9.2, if libEnsemble detected macOS, it would automatically switch the multiprocessing
 method to ``'fork'``. We decided to stop doing this to avoid overriding defaults and compatibility issues with
 some libraries.
 
-If you'd prefer to use ``'fork'`` or not reformat your code, you can set the multiprocessing start method via 
+If you'd prefer to use ``'fork'`` or not reformat your code, you can set the multiprocessing start method via
 the following, placed near the top of your calling script::
 
   import multiprocessing
diff --git a/docs/running_libE.rst b/docs/running_libE.rst
index eb9e6fca58..0ea1d36a6e 100644
--- a/docs/running_libE.rst
+++ b/docs/running_libE.rst
@@ -110,7 +110,7 @@ Reverse-ssh interface
 ^^^^^^^^^^^^^^^^^^^^^
 
 Via specifying ``--comms ssh`` on the command line, libEnsemble workers can
-be launched to remote ssh-accessible systems without needing to specify ``'port'`` or ``'authkey'``. This allows users 
+be launched to remote ssh-accessible systems without needing to specify ``'port'`` or ``'authkey'``. This allows users
 to colocate workers, simulation or generator functions, and any applications they submit on the same machine. Such user
 functions can also be persistent, unlike when launching remote functions via :ref:`funcX<funcx_ref>`.
 

From 7783a18f06ddd8c8b74c829bc5e4488af469682d Mon Sep 17 00:00:00 2001
From: Jeffrey Larson <jmlarson@anl.gov>
Date: Mon, 15 Aug 2022 15:27:13 -0500
Subject: [PATCH 109/196] Slight wording and black

---
 docs/FAQ.rst                                  | 21 +++++++++----------
 docs/running_libE.rst                         |  2 +-
 libensemble/manager.py                        |  6 ++----
 .../unit_tests/test_sim_dir_properties.py     |  4 +---
 libensemble/tools/alloc_support.py            |  8 ++-----
 libensemble/utils/runners.py                  |  1 +
 setup.py                                      |  8 +++----
 7 files changed, 21 insertions(+), 29 deletions(-)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index 86155d6758..a1d056ddcc 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -228,15 +228,14 @@ macOS and Windows Errors
 
 **Windows - How can I run libEnsemble with MPI comms?**
 
-In our experience this may be challenging, since most MPI distributions have
-either dropped Windows support (MPICH and Open MPI) or are no longer being maintained (`msmpi``). Plus
-Windows tends to experience platform-specific issues that are difficult to reproduce and troubleshoot
-for us.
+In our experience this may be challenging, because most MPI distributions have
+either dropped Windows support (MPICH and Open MPI) or are no longer being maintained (`msmpi``).
+Windows also tends to produce more platform-specific issues that are difficult to reproduce and troubleshoot.
 
-If you want to try anyways, we recommend experimenting with the many Unix-like emulators, containers, virtual machines,
-and other such systems. The `Installing PETSc On Microsoft Windows`_ docs contain lots of valuable information.
+If users wish to try, we recommend experimenting with the many Unix-like emulators, containers, virtual machines,
+and other such systems. The `Installing PETSc On Microsoft Windows`_ documentation contains valuable information.
 
-Otherwise, install `msmpi` and `mpi4py` from conda and experiment, or use local comms.
+Otherwise, install `msmpi` and `mpi4py` from conda and experiment, or use ``local`` comms.
 
 .. _`Installing PETSc On Microsoft Windows`: https://petsc.org/release/install/windows/#recommended-installation-methods
 
@@ -251,15 +250,15 @@ allow libEnsemble to create symlinks, you need to run your current `cmd` shell a
 You need to place your main calling script code underneath an ``if __name__ == "__main__":`` block.
 
 Explanation: Python chooses one of three methods to start new processes when using multiprocessing
-(``--comms local`` with libEnsemble). These are ``'fork'``, ``'spawn'``, and ``'forkserver'``. ``'fork'`` 
-is the default on Unix, and in our experience is quicker and more reliable, but ``'spawn'`` is the default 
+(``--comms local`` with libEnsemble). These are ``'fork'``, ``'spawn'``, and ``'forkserver'``. ``'fork'``
+is the default on Unix, and in our experience is quicker and more reliable, but ``'spawn'`` is the default
 on Windows and macOS (See the `Python multiprocessing docs`_).
 
-Prior to libEnsemble v0.9.2, if libEnsemble detected macOS, it would automatically switch the multiprocessing 
+Prior to libEnsemble v0.9.2, if libEnsemble detected macOS, it would automatically switch the multiprocessing
 method to ``'fork'``. We decided to stop doing this to avoid overriding defaults and compatibility issues with
 some libraries.
 
-If you'd prefer to use ``'fork'`` or not reformat your code, you can set the multiprocessing start method via 
+If you'd prefer to use ``'fork'`` or not reformat your code, you can set the multiprocessing start method via
 the following, placed near the top of your calling script::
 
   import multiprocessing
diff --git a/docs/running_libE.rst b/docs/running_libE.rst
index eb9e6fca58..0ea1d36a6e 100644
--- a/docs/running_libE.rst
+++ b/docs/running_libE.rst
@@ -110,7 +110,7 @@ Reverse-ssh interface
 ^^^^^^^^^^^^^^^^^^^^^
 
 Via specifying ``--comms ssh`` on the command line, libEnsemble workers can
-be launched to remote ssh-accessible systems without needing to specify ``'port'`` or ``'authkey'``. This allows users 
+be launched to remote ssh-accessible systems without needing to specify ``'port'`` or ``'authkey'``. This allows users
 to colocate workers, simulation or generator functions, and any applications they submit on the same machine. Such user
 functions can also be persistent, unlike when launching remote functions via :ref:`funcX<funcx_ref>`.
 
diff --git a/libensemble/manager.py b/libensemble/manager.py
index 890cbb0f64..16bba267b6 100644
--- a/libensemble/manager.py
+++ b/libensemble/manager.py
@@ -254,7 +254,7 @@ def _save_every_k(self, fname, count, k):
         count = k * (count // k)
         filename = fname.format(self.date_start, count)
         if platform.system() == "Windows":
-            filename = filename.replace(":", '-')  # ":" is invalid in windows filenames
+            filename = filename.replace(":", "-")  # ":" is invalid in windows filenames
         if not os.path.isfile(filename) and count > 0:
             for old_file in glob.glob(fname.format(self.date_start, "*")):
                 os.remove(old_file)
@@ -344,9 +344,7 @@ def _send_work_order(self, Work, w):
         work_rows = Work["libE_info"]["H_rows"]
         work_name = calc_type_strings[Work["tag"]]
         logger.debug(
-            "Manager sending {} work to worker {}. Rows {}".format(
-                work_name, w, extract_H_ranges(Work) or None
-            )
+            "Manager sending {} work to worker {}. Rows {}".format(work_name, w, extract_H_ranges(Work) or None)
         )
         if len(work_rows):
             if "repack_fields" in globals():
diff --git a/libensemble/tests/unit_tests/test_sim_dir_properties.py b/libensemble/tests/unit_tests/test_sim_dir_properties.py
index e84e8f7ca0..32e3d70bda 100644
--- a/libensemble/tests/unit_tests/test_sim_dir_properties.py
+++ b/libensemble/tests/unit_tests/test_sim_dir_properties.py
@@ -37,9 +37,7 @@ def test_range_mixes():
         "H_fields": ["x", "num_nodes", "procs_per_node"],
         "libE_info": {"H_rows": np.array([2, 3, 4, 6, 8, 9, 11, 14]), "workerID": 1},
     }
-    assert (
-        extract_H_ranges(work) == "2-4_6_8-9_11_14"
-    ), "Failed to correctly parse H row single elements and ranges."
+    assert extract_H_ranges(work) == "2-4_6_8-9_11_14", "Failed to correctly parse H row single elements and ranges."
 
 
 def test_copy_back():
diff --git a/libensemble/tools/alloc_support.py b/libensemble/tools/alloc_support.py
index fee6304181..79ae6ed31e 100644
--- a/libensemble/tools/alloc_support.py
+++ b/libensemble/tools/alloc_support.py
@@ -179,9 +179,7 @@ def sim_work(self, wid, H, H_fields, H_rows, persis_info, **libE_info):
         }
 
         logger.debug(
-            "Alloc func packing SIM work for worker {}. Packing sim_ids: {}".format(
-                wid, extract_H_ranges(work) or None
-            )
+            "Alloc func packing SIM work for worker {}. Packing sim_ids: {}".format(wid, extract_H_ranges(work) or None)
         )
         return work
 
@@ -223,9 +221,7 @@ def gen_work(self, wid, H_fields, H_rows, persis_info, **libE_info):
         }
 
         logger.debug(
-            "Alloc func packing GEN work for worker {}. Packing sim_ids: {}".format(
-                wid, extract_H_ranges(work) or None
-            )
+            "Alloc func packing GEN work for worker {}. Packing sim_ids: {}".format(wid, extract_H_ranges(work) or None)
         )
         return work
 
diff --git a/libensemble/utils/runners.py b/libensemble/utils/runners.py
index 22275c2a32..b75037bd29 100644
--- a/libensemble/utils/runners.py
+++ b/libensemble/utils/runners.py
@@ -8,6 +8,7 @@
 
 def _funcx_result(funcx_exctr, user_f, calc_in, persis_info, specs, libE_info):
     from libensemble.worker import Worker
+
     libE_info["comm"] = None  # 'comm' object not pickle-able
     Worker._set_executor(0, None)  # ditto for executor
 
diff --git a/setup.py b/setup.py
index 942fe716a9..94ac971693 100644
--- a/setup.py
+++ b/setup.py
@@ -99,10 +99,10 @@ def run_tests(self):
             "sphinx_rtd_theme",
         ],
     },
-
-    scripts=['scripts/liberegister',
-             'scripts/libesubmit'],
-
+    scripts=[
+        "scripts/liberegister",
+        "scripts/libesubmit",
+    ],
     classifiers=[
         "Development Status :: 4 - Beta",
         "Intended Audience :: Developers",

From 286b7e6a1f290051da0aa3e669f566781d5d98c1 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 15 Aug 2022 15:37:27 -0500
Subject: [PATCH 110/196] finishing first draft of testing runners/funcx code,
 plus black

---
 .../tests/unit_tests/test_make_runners.py     | 103 +++++++++++-------
 libensemble/utils/runners.py                  |  43 ++++++--
 2 files changed, 97 insertions(+), 49 deletions(-)

diff --git a/libensemble/tests/unit_tests/test_make_runners.py b/libensemble/tests/unit_tests/test_make_runners.py
index 757eddf6dc..014ef77484 100644
--- a/libensemble/tests/unit_tests/test_make_runners.py
+++ b/libensemble/tests/unit_tests/test_make_runners.py
@@ -20,7 +20,12 @@ def get_ufunc_args():
     H["sim_started_time"][-L:] = np.inf
 
     sim_ids = np.zeros(1, dtype=int)
-    Work = {"tag": EVAL_SIM_TAG, "persis_info": {}, "libE_info": {"H_rows": sim_ids}, "H_fields": sim_specs["in"]}
+    Work = {
+        "tag": EVAL_SIM_TAG,
+        "persis_info": {},
+        "libE_info": {"H_rows": sim_ids},
+        "H_fields": sim_specs["in"],
+    }
     calc_in = H[Work["H_fields"]][Work["libE_info"]["H_rows"]]
     return calc_in, sim_specs, gen_specs
 
@@ -29,12 +34,14 @@ def test_normal_runners():
     calc_in, sim_specs, gen_specs = get_ufunc_args()
 
     runners = Runners(sim_specs, gen_specs)
-    assert not runners.has_funcx_sim and not runners.has_funcx_gen, \
-        "funcX use should not be detected without setting endpoint fields"
+    assert (
+        not runners.has_funcx_sim and not runners.has_funcx_gen
+    ), "funcX use should not be detected without setting endpoint fields"
 
     ro = runners.make_runners()
-    assert all([i in ro for i in [EVAL_SIM_TAG, EVAL_GEN_TAG]]), \
-        "Both user function tags should be included in runners dictionary"
+    assert all(
+        [i in ro for i in [EVAL_SIM_TAG, EVAL_GEN_TAG]]
+    ), "Both user function tags should be included in runners dictionary"
 
 
 def test_normal_no_gen():
@@ -43,43 +50,65 @@ def test_normal_no_gen():
     runners = Runners(sim_specs, {})
     ro = runners.make_runners()
 
-    assert not ro[2], \
-        "generator function shouldn't be provided if not using gen_specs"
+    assert not ro[2], "generator function shouldn't be provided if not using gen_specs"
 
 
-def test_manager_exception():
-    """Checking dump of history and pickle file on abort"""
-    sim_specs, gen_specs, exit_criteria = setup.make_criteria_and_specs_0()
-    remove_file_if_exists(hfile_abort)
-    remove_file_if_exists(pfile_abort)
-
-    with mock.patch("libensemble.manager.manager_main") as managerMock:
-        managerMock.side_effect = Exception
-        # Collision between libE.py and libE() (after mods to __init__.py) means
-        #   libensemble.libE.comms_abort tries to refer to the function, not file
-        with mock.patch("libensemble.comms_abort") as abortMock:
-            abortMock.side_effect = Exception
-            # Need fake MPI to get past the Manager only check and dump history
-            with pytest.raises(Exception):
-                libE_specs = {"mpi_comm": fake_mpi, "disable_resource_manager": True}
-                libE(sim_specs, gen_specs, exit_criteria, libE_specs=libE_specs)
-                pytest.fail("Expected exception")
-            assert os.path.isfile(hfile_abort), "History file not dumped"
-            assert os.path.isfile(pfile_abort), "Pickle file not dumped"
-            os.remove(hfile_abort)
-            os.remove(pfile_abort)
-
-            # Test that History and Pickle files NOT created when disabled
-            with pytest.raises(Exception):
-                libE_specs = {"mpi_comm": fake_mpi, "save_H_and_persis_on_abort": False}
-                libE(sim_specs, gen_specs, exit_criteria, libE_specs=libE_specs)
-                pytest.fail("Expected exception")
-            assert not os.path.isfile(hfile_abort), "History file dumped"
-            assert not os.path.isfile(pfile_abort), "Pickle file dumped"
+def test_funcx_runner_init():
+    calc_in, sim_specs, gen_specs = get_ufunc_args()
+
+    sim_specs["funcx_endpoint"] = "1234"
+    runners = Runners(sim_specs, gen_specs)
+    assert (
+        runners.funcx_exctr is not None
+    ), "FuncXExecutor should have been instantiated when funcx_endpoint found in specs"
+
+
+def test_funcx_runner_pass():
+    calc_in, sim_specs, gen_specs = get_ufunc_args()
+
+    sim_specs["funcx_endpoint"] = "1234"
+    runners = Runners(sim_specs, gen_specs)
+
+    #  Creating Mock funcXExecutor and funcX future object - no exception
+    funcx_mock = mock.Mock()
+    funcx_future = mock.Mock()
+    funcx_mock.submit.return_value = funcx_future
+    funcx_future.exception.return_value = None
+    funcx_future.result.return_value = (True, True)
+
+    runners.funcx_exctr = funcx_mock
+    ro = runners.make_runners()
+
+    libE_info = {"H_rows": np.array([2, 3, 4]), "workerID": 1, "comm": "fakecomm"}
+    out, persis_info = ro[1](calc_in, {}, libE_info)
+
+    assert all([out, persis_info]), "funcX runner correctly returned results"
+
+
+def test_funcx_runner_fail():
+    calc_in, sim_specs, gen_specs = get_ufunc_args()
+
+    gen_specs["funcx_endpoint"] = "4321"
+    runners = Runners(sim_specs, gen_specs)
+
+    #  Creating Mock funcXExecutor and funcX future object - no exception
+    funcx_mock = mock.Mock()
+    funcx_future = mock.Mock()
+    funcx_mock.submit.return_value = funcx_future
+    funcx_future.exception.return_value = Exception
+
+    runners.funcx_exctr = funcx_mock
+    ro = runners.make_runners()
 
+    libE_info = {"H_rows": np.array([2, 3, 4]), "workerID": 1, "comm": "fakecomm"}
 
+    with pytest.raises(Exception):
+        out, persis_info = ro[2](calc_in, {}, libE_info)
+        pytest.fail("Expected exception")
 
 
 if __name__ == "__main__":
     test_normal_runners()
-    test_no_gen()
+    test_normal_no_gen()
+    test_funcx_runner_pass()
+    test_funcx_runner_fail()
diff --git a/libensemble/utils/runners.py b/libensemble/utils/runners.py
index a24e6a547f..82b09dce1c 100644
--- a/libensemble/utils/runners.py
+++ b/libensemble/utils/runners.py
@@ -5,10 +5,11 @@
 
 logger = logging.getLogger(__name__)
 
+
 class Runners:
     """Determines and returns methods for workers to run user functions.
 
-        Currently supported: direct-call and funcX
+    Currently supported: direct-call and funcX
     """
 
     def __init__(self, sim_specs, gen_specs):
@@ -18,54 +19,72 @@ def __init__(self, sim_specs, gen_specs):
         self.gen_f = gen_specs.get("gen_f")
         self.has_funcx_sim = len(sim_specs.get("funcx_endpoint", "")) > 0
         self.has_funcx_gen = len(gen_specs.get("funcx_endpoint", "")) > 0
+        self.funcx_exctr = None
 
         if any([self.has_funcx_sim, self.has_funcx_gen]):
             try:
                 from funcx import FuncXClient
                 from funcx.sdk.executor import FuncXExecutor
+
                 self.funcx_exctr = FuncXExecutor(FuncXClient())
 
             except ModuleNotFoundError:
-                logger.warning("funcX use detected but funcX not importable. Is it installed?")
-
+                logger.warning(
+                    "funcX use detected but funcX not importable. Is it installed?"
+                )
 
     def make_runners(self):
         """Creates functions to run a sim or gen. These functions are either
         called directly by the worker or submitted to a funcX endpoint."""
 
         def run_sim(calc_in, persis_info, libE_info):
-            """Calls or submits the sim func."""
+            """Determines how to run sim."""
             if self.has_funcx_sim and self.funcx_exctr:
-                return self._funcx_result(calc_in, persis_info, self.sim_specs, libE_info, self.sim_f)
+                result = self._funcx_result
             else:
-                return self._normal_result(calc_in, persis_info, self.sim_specs, libE_info, self.sim_f)
+                result = self._normal_result
+
+            return result(calc_in, persis_info, self.sim_specs, libE_info, self.sim_f)
 
         if self.gen_specs:
 
             def run_gen(calc_in, persis_info, libE_info):
-                """Calls or submits the gen func."""
+                """Determines how to run gen."""
                 if self.has_funcx_gen and self.funcx_exctr:
-                    return self._funcx_result(calc_in, persis_info, self.gen_specs, libE_info, self.gen_f)
+                    result = self._funcx_result
                 else:
-                    return self._normal_result(calc_in, persis_info, self.gen_specs, libE_info, self.gen_f)
+                    result = self._normal_result
+
+                return result(
+                    calc_in, persis_info, self.gen_specs, libE_info, self.gen_f
+                )
 
         else:
             run_gen = []
 
         return {EVAL_SIM_TAG: run_sim, EVAL_GEN_TAG: run_gen}
 
-
     def _normal_result(self, calc_in, persis_info, specs, libE_info, user_f):
+        """User function called in-place"""
         return user_f(calc_in, persis_info, specs, libE_info)
 
     def _funcx_result(self, calc_in, persis_info, specs, libE_info, user_f):
+        """User function submitted to funcX"""
         from libensemble.worker import Worker
+
         libE_info["comm"] = None  # 'comm' object not pickle-able
         Worker._set_executor(0, None)  # ditto for executor
 
-        future = self.funcx_exctr.submit(user_f, calc_in, persis_info, specs, libE_info, endpoint_id=specs["funcx_endpoint"])
+        future = self.funcx_exctr.submit(
+            user_f,
+            calc_in,
+            persis_info,
+            specs,
+            libE_info,
+            endpoint_id=specs["funcx_endpoint"],
+        )
         remote_exc = future.exception()  # blocks until exception or None
         if remote_exc is None:
             return future.result()
         else:
-            raise remote_exc
\ No newline at end of file
+            raise remote_exc

From 737c93bd12aadd8411682e10e11474b3f8a3c7bb Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 15 Aug 2022 15:38:30 -0500
Subject: [PATCH 111/196] install funcx on ci

---
 .github/workflows/ci.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index adaff30a98..687e0d9217 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -149,6 +149,7 @@ jobs:
             pip install -r install/testing_requirements.txt
             pip install psutil
             pip install pyyaml
+            pip install funcx
 
         - name: Install Tasmanian on Ubuntu
           if: matrix.os == 'ubuntu-latest' && matrix.do-balsam == false

From 274b1d10b2d728e60cd7c086f4f34f2c8595d2ec Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 15 Aug 2022 15:40:48 -0500
Subject: [PATCH 112/196] yes exception

---
 libensemble/tests/unit_tests/test_make_runners.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libensemble/tests/unit_tests/test_make_runners.py b/libensemble/tests/unit_tests/test_make_runners.py
index 014ef77484..5a37c7bf0e 100644
--- a/libensemble/tests/unit_tests/test_make_runners.py
+++ b/libensemble/tests/unit_tests/test_make_runners.py
@@ -91,7 +91,7 @@ def test_funcx_runner_fail():
     gen_specs["funcx_endpoint"] = "4321"
     runners = Runners(sim_specs, gen_specs)
 
-    #  Creating Mock funcXExecutor and funcX future object - no exception
+    #  Creating Mock funcXExecutor and funcX future object - yes exception
     funcx_mock = mock.Mock()
     funcx_future = mock.Mock()
     funcx_mock.submit.return_value = funcx_future

From e6a0b06d85c8670a21161f1e737c1ee21b7868d5 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 15 Aug 2022 15:42:07 -0500
Subject: [PATCH 113/196] enabling coverage of runners.py

---
 libensemble/tests/.coveragerc | 1 -
 1 file changed, 1 deletion(-)

diff --git a/libensemble/tests/.coveragerc b/libensemble/tests/.coveragerc
index 3f24d55bbd..6156169def 100644
--- a/libensemble/tests/.coveragerc
+++ b/libensemble/tests/.coveragerc
@@ -24,7 +24,6 @@ omit =
     */legacy_balsam_executor.py
     */forkable_pdb.py
     */parse_args.py
-    */runners.py
     */gen_funcs/old_aposmm.py
     */alloc_funcs/fast_alloc_to_aposmm.py
 exclude_lines =

From cfe530066defda85913c315de30809d4e34b374c Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 15 Aug 2022 16:03:18 -0500
Subject: [PATCH 114/196] oh yeah, black doesnt fix unused imports

---
 libensemble/tests/unit_tests/test_make_runners.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/libensemble/tests/unit_tests/test_make_runners.py b/libensemble/tests/unit_tests/test_make_runners.py
index 5a37c7bf0e..f3b95794ec 100644
--- a/libensemble/tests/unit_tests/test_make_runners.py
+++ b/libensemble/tests/unit_tests/test_make_runners.py
@@ -1,11 +1,9 @@
-import os
 import numpy as np
 import pytest
 import mock
 
 import libensemble.tests.unit_tests.setup as setup
 from libensemble.tools.fields_keys import libE_fields
-from libensemble.resources.resources import Resources
 from libensemble.message_numbers import EVAL_SIM_TAG, EVAL_GEN_TAG
 from libensemble.utils.runners import Runners
 

From bf60cdc84688bbc395157bce6295bd64bf5aa6dd Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 15 Aug 2022 16:27:38 -0500
Subject: [PATCH 115/196] try pinning setuptools?

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 942fe716a9..a2bcb101fe 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@ def run_tests(self):
         "libensemble.tests.regression_tests",
     ],
     package_data={"libensemble.sim_funcs.branin": ["known_minima_and_func_values"]},
-    install_requires=["numpy", "psutil", "setuptools"],
+    install_requires=["numpy", "psutil", "setuptools==64.0.1"],
     # If run tests through setup.py - downloads these but does not install
     tests_require=[
         "pytest>=3.1",

From 52f2545f247277c48be73ef82bab203103857a1e Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 15 Aug 2022 17:07:38 -0500
Subject: [PATCH 116/196] try installing specific setuptools right before
 dragonfly

---
 .github/workflows/ci.yml | 1 +
 setup.py                 | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 687e0d9217..b0fdc1d655 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -138,6 +138,7 @@ jobs:
             # end heffte build and dependencies
 
             # pip install dragonfly-opt
+            pip uninstall setuptools; pip install setuptools==64.0.1
             pip install git+https://github.com/dragonfly/dragonfly.git
 
             pip install torch
diff --git a/setup.py b/setup.py
index a2bcb101fe..942fe716a9 100644
--- a/setup.py
+++ b/setup.py
@@ -71,7 +71,7 @@ def run_tests(self):
         "libensemble.tests.regression_tests",
     ],
     package_data={"libensemble.sim_funcs.branin": ["known_minima_and_func_values"]},
-    install_requires=["numpy", "psutil", "setuptools==64.0.1"],
+    install_requires=["numpy", "psutil", "setuptools"],
     # If run tests through setup.py - downloads these but does not install
     tests_require=[
         "pytest>=3.1",

From 2855ea2e38a19ed903c1800ecc3a569070ab90b9 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 16 Aug 2022 09:36:58 -0500
Subject: [PATCH 117/196] lets see whats changed since yesterday

---
 .github/workflows/ci.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index b0fdc1d655..687e0d9217 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -138,7 +138,6 @@ jobs:
             # end heffte build and dependencies
 
             # pip install dragonfly-opt
-            pip uninstall setuptools; pip install setuptools==64.0.1
             pip install git+https://github.com/dragonfly/dragonfly.git
 
             pip install torch

From 71ec57b021d47c05ca5339e194b14ca732c670b3 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 16 Aug 2022 10:14:58 -0500
Subject: [PATCH 118/196] initial commit

---
 libensemble/tests/.coveragerc                     | 1 -
 libensemble/tests/unit_tests/test_make_runners.py | 5 +++++
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/libensemble/tests/.coveragerc b/libensemble/tests/.coveragerc
index 6156169def..3e1a312766 100644
--- a/libensemble/tests/.coveragerc
+++ b/libensemble/tests/.coveragerc
@@ -20,7 +20,6 @@ omit =
     */regression_tests/*
     */sim_funcs/helloworld.py
     */sim_funcs/executor_hworld.py
-    */balsam_executor.py
     */legacy_balsam_executor.py
     */forkable_pdb.py
     */parse_args.py
diff --git a/libensemble/tests/unit_tests/test_make_runners.py b/libensemble/tests/unit_tests/test_make_runners.py
index f3b95794ec..f57619cbda 100644
--- a/libensemble/tests/unit_tests/test_make_runners.py
+++ b/libensemble/tests/unit_tests/test_make_runners.py
@@ -28,6 +28,7 @@ def get_ufunc_args():
     return calc_in, sim_specs, gen_specs
 
 
+@pytest.mark.extra
 def test_normal_runners():
     calc_in, sim_specs, gen_specs = get_ufunc_args()
 
@@ -42,6 +43,7 @@ def test_normal_runners():
     ), "Both user function tags should be included in runners dictionary"
 
 
+@pytest.mark.extra
 def test_normal_no_gen():
     calc_in, sim_specs, gen_specs = get_ufunc_args()
 
@@ -51,6 +53,7 @@ def test_normal_no_gen():
     assert not ro[2], "generator function shouldn't be provided if not using gen_specs"
 
 
+@pytest.mark.extra
 def test_funcx_runner_init():
     calc_in, sim_specs, gen_specs = get_ufunc_args()
 
@@ -61,6 +64,7 @@ def test_funcx_runner_init():
     ), "FuncXExecutor should have been instantiated when funcx_endpoint found in specs"
 
 
+@pytest.mark.extra
 def test_funcx_runner_pass():
     calc_in, sim_specs, gen_specs = get_ufunc_args()
 
@@ -83,6 +87,7 @@ def test_funcx_runner_pass():
     assert all([out, persis_info]), "funcX runner correctly returned results"
 
 
+@pytest.mark.extra
 def test_funcx_runner_fail():
     calc_in, sim_specs, gen_specs = get_ufunc_args()
 

From d5232c034e5afddccba1fde956acc6710e60c526 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Tue, 16 Aug 2022 14:04:03 -0500
Subject: [PATCH 119/196] testing structure, plus quick fix

---
 .../balsam_executors/balsam_executor.py       |   6 +-
 .../tests/unit_tests/test_executor_balsam.py  | 110 ++++++++++++++++++
 2 files changed, 113 insertions(+), 3 deletions(-)
 create mode 100644 libensemble/tests/unit_tests/test_executor_balsam.py

diff --git a/libensemble/executors/balsam_executors/balsam_executor.py b/libensemble/executors/balsam_executors/balsam_executor.py
index 969b10ba35..d4a58a3ce3 100644
--- a/libensemble/executors/balsam_executors/balsam_executor.py
+++ b/libensemble/executors/balsam_executors/balsam_executor.py
@@ -12,7 +12,7 @@
 
 In order to initiate a Balsam executor, the calling script should contain ::
 
-    from libensemble.executors import BalsamExecutor
+    from libensemble.executors.balsam_executors import BalsamExecutor
     exctr = BalsamExecutor()
 
 Key differences to consider between this executor and libEnsemble's others is
@@ -21,7 +21,7 @@
 
 This process may resemble::
 
-    from libensemble.executors import BalsamExecutor
+    from libensemble.executors.balsam_executors import BalsamExecutor
     from balsam.api import ApplicationDefinition
 
     class HelloApp(ApplicationDefinition):
@@ -284,7 +284,7 @@ def add_app(self, name, site, exepath, desc):
         """Sync application with Balsam service"""
         pass
 
-    def register_app(self, BalsamApp, app_name, calc_type=None, desc=None, precedent=None):
+    def register_app(self, BalsamApp, app_name=None, calc_type=None, desc=None, precedent=None):
         """Registers a Balsam ``ApplicationDefinition`` to libEnsemble. This class
         instance *must* have a ``site`` and ``command_template`` specified. See
         the Balsam docs for information on other optional fields.
diff --git a/libensemble/tests/unit_tests/test_executor_balsam.py b/libensemble/tests/unit_tests/test_executor_balsam.py
new file mode 100644
index 0000000000..ec2c03d0ed
--- /dev/null
+++ b/libensemble/tests/unit_tests/test_executor_balsam.py
@@ -0,0 +1,110 @@
+# !/usr/bin/env python
+# Integration Test of executor module for libensemble
+# Test does not require running full libensemble
+import os
+import re
+import sys
+import time
+import mock
+import pytest
+import socket
+
+from balsam.api import ApplicationDefinition
+
+from libensemble.resources.mpi_resources import MPIResourcesException
+from libensemble.executors.executor import Executor, ExecutorException, TimeoutExpired
+from libensemble.executors.executor import NOT_STARTED_STATES, Application
+
+NCORES = 1
+py_startup = "simdir/py_startup.py"
+
+class TestLibeApp(ApplicationDefinition):
+    site = "libe-unit-test"
+    command_template = "python simdir/py_startup.py"
+
+
+def setup_module(module):
+    try:
+        print("setup_module module:%s" % module.__name__)
+    except AttributeError:
+        print("setup_module (direct run) module:%s" % module)
+    if Executor.executor is not None:
+        del Executor.executor
+        Executor.executor = None
+
+
+def teardown_module(module):
+    try:
+        print("teardown_module module:%s" % module.__name__)
+    except AttributeError:
+        print("teardown_module (direct run) module:%s" % module)
+    if Executor.executor is not None:
+        del Executor.executor
+        Executor.executor = None
+
+
+# This would typically be in the user calling script
+def setup_executor():
+    """Set up a Balsam Executor with sim app"""
+    from libensemble.executors.balsam_executors import BalsamExecutor
+    exctr = BalsamExecutor()
+
+
+
+# Tests ========================================================================================
+
+def test_register_app():
+    """Test of registering an ApplicationDefinition"""
+    print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
+    setup_executor()
+    exctr = Executor.executor
+    exctr.register_app(TestLibeApp, calc_type="sim", precedent="fake/dir")
+    assert isinstance(exctr.apps["python"], Application), \
+        "Application object not created based on registered Balsam AppDef"
+
+def test_submit_app_defaults():
+    """Test of submitting an ApplicationDefinition"""
+    print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
+    exctr = Executor.executor
+
+
+def test_submit_app_workdir():
+    pass
+
+def test_submit_app_dry():
+    pass
+
+def test_submit_app_wait():
+    pass
+
+def test_submit_alloc():
+    pass
+
+def test_revoke_alloc():
+    pass
+
+def test_task_timing():
+    pass
+
+def test_task_poll():
+    pass
+
+def test_task_wait():
+    pass
+
+def test_task_kill():
+    pass
+
+if __name__ == "__main__":
+    setup_module(__file__)
+    test_register_app()
+    test_submit_app_defaults()
+    test_submit_app_workdir()
+    test_submit_app_dry()
+    test_submit_app_wait()
+    test_submit_alloc()
+    test_revoke_alloc()
+    test_task_timing()
+    test_task_poll()
+    test_task_wait()
+    test_task_kill()
\ No newline at end of file

From 8675415e5f3064240483886524c2efdb59f13933 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 17 Aug 2022 09:48:50 -0500
Subject: [PATCH 120/196] try building heffte faster

---
 .github/workflows/ci.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 687e0d9217..fa2cecee0b 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -132,7 +132,7 @@ jobs:
             cd heffte/build
             pwd
             cmake -D CMAKE_BUILD_TYPE=Release -D BUILD_SHARED_LIBS=ON -D CMAKE_INSTALL_PREFIX=./ -D Heffte_ENABLE_AVX=ON -D Heffte_ENABLE_FFTW=ON ../
-            make
+            make -j 4
             make install
             cp ./benchmarks/speed3d_c2c ../../libensemble/tests/regression_tests/
             # end heffte build and dependencies

From 3e46174ce89e3a19644b4931bf4fa6a6fdc495c2 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 17 Aug 2022 11:16:37 -0500
Subject: [PATCH 121/196] patch FuncXClient with mock to avoid login prompt

---
 .../tests/unit_tests/test_make_runners.py     | 67 +++++++++++--------
 1 file changed, 39 insertions(+), 28 deletions(-)

diff --git a/libensemble/tests/unit_tests/test_make_runners.py b/libensemble/tests/unit_tests/test_make_runners.py
index f3b95794ec..929dde6da3 100644
--- a/libensemble/tests/unit_tests/test_make_runners.py
+++ b/libensemble/tests/unit_tests/test_make_runners.py
@@ -55,58 +55,69 @@ def test_funcx_runner_init():
     calc_in, sim_specs, gen_specs = get_ufunc_args()
 
     sim_specs["funcx_endpoint"] = "1234"
-    runners = Runners(sim_specs, gen_specs)
-    assert (
-        runners.funcx_exctr is not None
-    ), "FuncXExecutor should have been instantiated when funcx_endpoint found in specs"
+
+    with mock.patch("funcx.FuncXClient"):
+
+        runners = Runners(sim_specs, gen_specs)
+
+        assert (
+            runners.funcx_exctr is not None
+        ), "FuncXExecutor should have been instantiated when funcx_endpoint found in specs"
 
 
 def test_funcx_runner_pass():
     calc_in, sim_specs, gen_specs = get_ufunc_args()
 
     sim_specs["funcx_endpoint"] = "1234"
-    runners = Runners(sim_specs, gen_specs)
 
-    #  Creating Mock funcXExecutor and funcX future object - no exception
-    funcx_mock = mock.Mock()
-    funcx_future = mock.Mock()
-    funcx_mock.submit.return_value = funcx_future
-    funcx_future.exception.return_value = None
-    funcx_future.result.return_value = (True, True)
+    with mock.patch("funcx.FuncXClient"):
 
-    runners.funcx_exctr = funcx_mock
-    ro = runners.make_runners()
+        runners = Runners(sim_specs, gen_specs)
 
-    libE_info = {"H_rows": np.array([2, 3, 4]), "workerID": 1, "comm": "fakecomm"}
-    out, persis_info = ro[1](calc_in, {}, libE_info)
+        #  Creating Mock funcXExecutor and funcX future object - no exception
+        funcx_mock = mock.Mock()
+        funcx_future = mock.Mock()
+        funcx_mock.submit.return_value = funcx_future
+        funcx_future.exception.return_value = None
+        funcx_future.result.return_value = (True, True)
 
-    assert all([out, persis_info]), "funcX runner correctly returned results"
+        runners.funcx_exctr = funcx_mock
+        ro = runners.make_runners()
+
+        libE_info = {"H_rows": np.array([2, 3, 4]), "workerID": 1, "comm": "fakecomm"}
+        out, persis_info = ro[1](calc_in, {}, libE_info)
+
+        assert all([out, persis_info]), "funcX runner correctly returned results"
 
 
 def test_funcx_runner_fail():
     calc_in, sim_specs, gen_specs = get_ufunc_args()
 
     gen_specs["funcx_endpoint"] = "4321"
-    runners = Runners(sim_specs, gen_specs)
 
-    #  Creating Mock funcXExecutor and funcX future object - yes exception
-    funcx_mock = mock.Mock()
-    funcx_future = mock.Mock()
-    funcx_mock.submit.return_value = funcx_future
-    funcx_future.exception.return_value = Exception
+    with mock.patch("funcx.FuncXClient"):
 
-    runners.funcx_exctr = funcx_mock
-    ro = runners.make_runners()
+        runners = Runners(sim_specs, gen_specs)
+
+        #  Creating Mock funcXExecutor and funcX future object - yes exception
+        funcx_mock = mock.Mock()
+        funcx_future = mock.Mock()
+        funcx_mock.submit.return_value = funcx_future
+        funcx_future.exception.return_value = Exception
+
+        runners.funcx_exctr = funcx_mock
+        ro = runners.make_runners()
 
-    libE_info = {"H_rows": np.array([2, 3, 4]), "workerID": 1, "comm": "fakecomm"}
+        libE_info = {"H_rows": np.array([2, 3, 4]), "workerID": 1, "comm": "fakecomm"}
 
-    with pytest.raises(Exception):
-        out, persis_info = ro[2](calc_in, {}, libE_info)
-        pytest.fail("Expected exception")
+        with pytest.raises(Exception):
+            out, persis_info = ro[2](calc_in, {}, libE_info)
+            pytest.fail("Expected exception")
 
 
 if __name__ == "__main__":
     test_normal_runners()
     test_normal_no_gen()
+    test_funcx_runner_init()
     test_funcx_runner_pass()
     test_funcx_runner_fail()

From 6433f3bad6024440cb9cea0d85393f9cc8e1f6d5 Mon Sep 17 00:00:00 2001
From: Jeffrey Larson <jmlarson@anl.gov>
Date: Wed, 17 Aug 2022 13:32:47 -0500
Subject: [PATCH 122/196] Trying dropping explicit torch,gpytorch from build

---
 .github/workflows/ci.yml | 2 --
 1 file changed, 2 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index adaff30a98..41421f8d60 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -140,8 +140,6 @@ jobs:
             # pip install dragonfly-opt
             pip install git+https://github.com/dragonfly/dragonfly.git
 
-            pip install torch
-            pip install gpytorch
             pip install ax-platform
 
         - name: Install other testing dependencies

From 728c8659d93dc98a3f40bb183e4b26ae3f3341e1 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 17 Aug 2022 13:34:47 -0500
Subject: [PATCH 123/196] spawn adherence refactor, should run on macos now

---
 .../test_persistent_gp_multitask_ax.py        | 41 +++++++++----------
 1 file changed, 20 insertions(+), 21 deletions(-)

diff --git a/libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py b/libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py
index ce8176d258..e70d490a79 100644
--- a/libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py
+++ b/libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py
@@ -17,7 +17,6 @@
 # TESTSUITE_COMMS: local mpi
 # TESTSUITE_NPROCS: 5
 # TESTSUITE_EXTRA: true
-# TESTSUITE_OS_SKIP: OSX
 
 import numpy as np
 from libensemble.libE import libE
@@ -30,6 +29,26 @@
 
 import warnings
 
+def run_simulation(H, persis_info, sim_specs, libE_info):
+    # Extract input parameters
+    values = list(H["x"][0])
+    x0 = values[0]
+    x1 = values[1]
+    # Extract fidelity parameter
+    task = H["task"][0]
+    if task == "expensive_model":
+        z = 8
+    elif task == "cheap_model":
+        z = 1
+
+    libE_output = np.zeros(1, dtype=sim_specs["out"])
+    calc_status = WORKER_DONE
+
+    # Function that depends on the resolution parameter
+    libE_output["f"] = -(x0 + 10 * np.cos(x0 + 0.1 * z)) * (x1 + 5 * np.cos(x1 - 0.2 * z))
+
+    return libE_output, persis_info, calc_status
+
 # Main block is necessary only when using local comms with spawn start method (default on macOS and Windows).
 if __name__ == "__main__":
 
@@ -48,26 +67,6 @@
         "n_opt_lofi": 4,
     }
 
-    def run_simulation(H, persis_info, sim_specs, libE_info):
-        # Extract input parameters
-        values = list(H["x"][0])
-        x0 = values[0]
-        x1 = values[1]
-        # Extract fidelity parameter
-        task = H["task"][0]
-        if task == "expensive_model":
-            z = 8
-        elif task == "cheap_model":
-            z = 1
-
-        libE_output = np.zeros(1, dtype=sim_specs["out"])
-        calc_status = WORKER_DONE
-
-        # Function that depends on the resolution parameter
-        libE_output["f"] = -(x0 + 10 * np.cos(x0 + 0.1 * z)) * (x1 + 5 * np.cos(x1 - 0.2 * z))
-
-        return libE_output, persis_info, calc_status
-
     sim_specs = {
         "sim_f": run_simulation,
         "in": ["x", "task"],

From f241d13acdcce00faebc4ae9327fafab06e0c8a6 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 17 Aug 2022 13:35:24 -0500
Subject: [PATCH 124/196] what happens if gpytorch and torch aren't specified?

---
 .github/workflows/ci.yml | 2 --
 1 file changed, 2 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 67e240e9a2..449eeb4541 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -170,8 +170,6 @@ jobs:
             # pip install dragonfly-opt
             pip install git+https://github.com/dragonfly/dragonfly.git
 
-            pip install torch
-            pip install gpytorch
             pip install ax-platform
 
         - name: Install other testing dependencies

From b83dc8cb589578ea14d4c268f28f1733b6e2434c Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 17 Aug 2022 13:41:22 -0500
Subject: [PATCH 125/196] seriously

---
 .../tests/regression_tests/test_persistent_gp_multitask_ax.py   | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py b/libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py
index e70d490a79..4c59006605 100644
--- a/libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py
+++ b/libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py
@@ -29,6 +29,7 @@
 
 import warnings
 
+
 def run_simulation(H, persis_info, sim_specs, libE_info):
     # Extract input parameters
     values = list(H["x"][0])
@@ -49,6 +50,7 @@ def run_simulation(H, persis_info, sim_specs, libE_info):
 
     return libE_output, persis_info, calc_status
 
+
 # Main block is necessary only when using local comms with spawn start method (default on macOS and Windows).
 if __name__ == "__main__":
 

From e302b8497489d780ca1b5ede852f9f3c99e09dad Mon Sep 17 00:00:00 2001
From: Jeffrey Larson <jmlarson@anl.gov>
Date: Wed, 17 Aug 2022 14:04:55 -0500
Subject: [PATCH 126/196] Typos

---
 docs/running_libE.rst | 2 +-
 scripts/libesubmit    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/running_libE.rst b/docs/running_libE.rst
index eb9e6fca58..30f3c3b44e 100644
--- a/docs/running_libE.rst
+++ b/docs/running_libE.rst
@@ -212,7 +212,7 @@ Results in::
     *** libEnsemble 0.9.2+dev ***
     Imported PSI/J serialization: my_calling_script.json. Preparing submission...
     Calling script: my_calling_script.py
-    ...found! Proceding.
+    ...found! Proceeding.
     Submitting Job!: Job[id=ce4ead75-a3a4-42a3-94ff-c44b3b2c7e61, native_id=None, executor=None, status=JobStatus[NEW, time=1658167808.5125017]]
 
     $ squeue --long --users=user
diff --git a/scripts/libesubmit b/scripts/libesubmit
index 00a7cf4deb..9adca85369 100644
--- a/scripts/libesubmit
+++ b/scripts/libesubmit
@@ -188,7 +188,7 @@ if __name__ == "__main__":
         jobspec.arguments[jobspec.arguments.index(callscript)] = new_callscript
 
     else:
-        print("...found! Proceding.")
+        print("...found! Proceeding.")
 
     # Little bit strange I have to re-initialize this class to re-serialize
     if not jobspec.resources[

From 83c3f54da754bb6c31b00ea146f964432a2a2c85 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 17 Aug 2022 14:08:45 -0500
Subject: [PATCH 127/196] install ax on macos

---
 .github/workflows/ci.yml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 449eeb4541..1d0b3d3c4a 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -138,6 +138,7 @@ jobs:
             pip install DFO-LS
 
             pip install mpmath
+            pip install ax-platform
             python -m pip install --upgrade git+https://github.com/mosesyhc/surmise.git@development/PCGPwM
 
         - name: Install some generator dependencies on Windows
@@ -170,8 +171,6 @@ jobs:
             # pip install dragonfly-opt
             pip install git+https://github.com/dragonfly/dragonfly.git
 
-            pip install ax-platform
-
         - name: Install other testing dependencies
           run: |
             pip install -r install/testing_requirements.txt

From 7971b97d4d86df4f54f9d34cf0556cac512558fe Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 17 Aug 2022 16:21:08 -0500
Subject: [PATCH 128/196] additional completed unit tests

---
 .github/workflows/ci.yml                      |  1 +
 .../balsam_executors/balsam_executor.py       |  4 --
 .../tests/unit_tests/test_executor_balsam.py  | 66 ++++++++++++++-----
 3 files changed, 52 insertions(+), 19 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 687e0d9217..e05d96dc6e 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -150,6 +150,7 @@ jobs:
             pip install psutil
             pip install pyyaml
             pip install funcx
+            pip install balsam
 
         - name: Install Tasmanian on Ubuntu
           if: matrix.os == 'ubuntu-latest' && matrix.do-balsam == false
diff --git a/libensemble/executors/balsam_executors/balsam_executor.py b/libensemble/executors/balsam_executors/balsam_executor.py
index d4a58a3ce3..50c94e0d07 100644
--- a/libensemble/executors/balsam_executors/balsam_executor.py
+++ b/libensemble/executors/balsam_executors/balsam_executor.py
@@ -523,10 +523,6 @@ def submit(
 
         if machinefile is not None:
             logger.warning("machinefile arg ignored - not supported in Balsam")
-            jassert(
-                num_procs or num_nodes or procs_per_node,
-                "No procs/nodes provided - aborting",
-            )
 
         task = BalsamTask(app, app_args, workdir, None, None, self.workerID)
 
diff --git a/libensemble/tests/unit_tests/test_executor_balsam.py b/libensemble/tests/unit_tests/test_executor_balsam.py
index ec2c03d0ed..b183069ff6 100644
--- a/libensemble/tests/unit_tests/test_executor_balsam.py
+++ b/libensemble/tests/unit_tests/test_executor_balsam.py
@@ -7,9 +7,7 @@
 import time
 import mock
 import pytest
-import socket
-
-from balsam.api import ApplicationDefinition
+from dataclasses import dataclass
 
 from libensemble.resources.mpi_resources import MPIResourcesException
 from libensemble.executors.executor import Executor, ExecutorException, TimeoutExpired
@@ -18,10 +16,15 @@
 NCORES = 1
 py_startup = "simdir/py_startup.py"
 
-class TestLibeApp(ApplicationDefinition):
+# fake Balsam app
+@dataclass
+class TestLibeApp:
     site = "libe-unit-test"
     command_template = "python simdir/py_startup.py"
 
+    def sync():
+        pass
+
 
 def setup_module(module):
     try:
@@ -54,7 +57,7 @@ def setup_executor():
 # Tests ========================================================================================
 
 def test_register_app():
-    """Test of registering an ApplicationDefinition"""
+    """Test of registering an App"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     setup_executor()
     exctr = Executor.executor
@@ -63,25 +66,59 @@ def test_register_app():
         "Application object not created based on registered Balsam AppDef"
 
 def test_submit_app_defaults():
-    """Test of submitting an ApplicationDefinition"""
+    """Test of submitting an App"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     exctr = Executor.executor
+    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job"):
+        task = exctr.submit(calc_type="sim")
+
+    assert task in exctr.list_of_tasks, \
+        "new task not added to executor's list of tasks"
+
+    assert task == exctr.get_task(task.id), \
+        "task retrieved via task ID doesn't match new task"
 
 
 def test_submit_app_workdir():
-    pass
+    """Test of submitting an App with a workdir"""
+    print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
+    exctr = Executor.executor
+    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job"):
+        task = exctr.submit(calc_type="sim", workdir="output", machinefile="nope")
+
+    assert task.workdir == os.path.join(exctr.workflow_name, "output"), \
+        "workdir not properly defined for new task"
 
 def test_submit_app_dry():
-    pass
+    """Test of dry-run submitting an App"""
+    print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
+    exctr = Executor.executor
+    task = exctr.submit(calc_type="sim", dry_run=True)
+
+    assert all([task.dry_run, task.done()]), \
+        "new task from dry_run wasn't marked as such, or set as done"
 
 def test_submit_app_wait():
-    pass
+    print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
+    exctr = Executor.executor
+    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job") as job:
+        job.return_value.state = "RUNNING"
+        task = exctr.submit(calc_type="sim", wait_on_start=True)
 
-def test_submit_alloc():
-    pass
+    assert task.running(), \
+        "new task is not marked as running after wait_on_start"
 
-def test_revoke_alloc():
-    pass
+def test_submit_revoke_alloc():
+    print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
+    exctr = Executor.executor
+    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.BatchJob") as batchjob:
+        alloc = exctr.submit_allocation(site_id="libe-unit-test", num_nodes=1, wall_time_min=30)
+
+        assert alloc in exctr.allocations, \
+            "batchjob object not appended to executor's list of allocations"
+
+        alloc.scheduler_id = 1
+        exctr.revoke_allocation(alloc)
 
 def test_task_timing():
     pass
@@ -102,8 +139,7 @@ def test_task_kill():
     test_submit_app_workdir()
     test_submit_app_dry()
     test_submit_app_wait()
-    test_submit_alloc()
-    test_revoke_alloc()
+    test_submit_revoke_alloc()
     test_task_timing()
     test_task_poll()
     test_task_wait()

From d0daf31ebe015ffbfb52192c98308e0a9e60d31d Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 17 Aug 2022 17:01:03 -0500
Subject: [PATCH 129/196] additional test

---
 .../tests/unit_tests/test_executor_balsam.py  | 29 ++++++++++++-------
 1 file changed, 18 insertions(+), 11 deletions(-)

diff --git a/libensemble/tests/unit_tests/test_executor_balsam.py b/libensemble/tests/unit_tests/test_executor_balsam.py
index b183069ff6..947f65a866 100644
--- a/libensemble/tests/unit_tests/test_executor_balsam.py
+++ b/libensemble/tests/unit_tests/test_executor_balsam.py
@@ -99,6 +99,7 @@ def test_submit_app_dry():
         "new task from dry_run wasn't marked as such, or set as done"
 
 def test_submit_app_wait():
+    """Test of exctr.submit blocking until app is running"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     exctr = Executor.executor
     with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job") as job:
@@ -109,7 +110,7 @@ def test_submit_app_wait():
         "new task is not marked as running after wait_on_start"
 
 def test_submit_revoke_alloc():
-    print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
+    """Test creating and revoking BatchJob objects through the executor"""
     exctr = Executor.executor
     with mock.patch("libensemble.executors.balsam_executors.balsam_executor.BatchJob") as batchjob:
         alloc = exctr.submit_allocation(site_id="libe-unit-test", num_nodes=1, wall_time_min=30)
@@ -120,17 +121,25 @@ def test_submit_revoke_alloc():
         alloc.scheduler_id = 1
         exctr.revoke_allocation(alloc)
 
-def test_task_timing():
-    pass
-
-def test_task_poll():
-    pass
-
 def test_task_wait():
-    pass
+    """Test of killing (cancelling) a balsam app"""
+    print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
+    exctr = Executor.executor
+    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job"):
+        task = exctr.submit(calc_type="sim")
+
+    task.wait()
 
 def test_task_kill():
-    pass
+    """Test of killing (cancelling) a balsam app"""
+    print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
+    exctr = Executor.executor
+    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job"):
+        task = exctr.submit(calc_type="sim")
+
+    task.kill()
+    assert task.finished and task.state == "USER_KILLED", \
+        "task not set as killed after kill method"
 
 if __name__ == "__main__":
     setup_module(__file__)
@@ -140,7 +149,5 @@ def test_task_kill():
     test_submit_app_dry()
     test_submit_app_wait()
     test_submit_revoke_alloc()
-    test_task_timing()
-    test_task_poll()
     test_task_wait()
     test_task_kill()
\ No newline at end of file

From 19aa13ddabae79e4978b9e0783b3219fd3a5240a Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 18 Aug 2022 10:11:19 -0500
Subject: [PATCH 130/196] presumably finish up unit test

---
 .../tests/unit_tests/test_executor_balsam.py      | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/libensemble/tests/unit_tests/test_executor_balsam.py b/libensemble/tests/unit_tests/test_executor_balsam.py
index 947f65a866..2247f9bb80 100644
--- a/libensemble/tests/unit_tests/test_executor_balsam.py
+++ b/libensemble/tests/unit_tests/test_executor_balsam.py
@@ -7,7 +7,6 @@
 import time
 import mock
 import pytest
-from dataclasses import dataclass
 
 from libensemble.resources.mpi_resources import MPIResourcesException
 from libensemble.executors.executor import Executor, ExecutorException, TimeoutExpired
@@ -17,7 +16,6 @@
 py_startup = "simdir/py_startup.py"
 
 # fake Balsam app
-@dataclass
 class TestLibeApp:
     site = "libe-unit-test"
     command_template = "python simdir/py_startup.py"
@@ -125,10 +123,19 @@ def test_task_wait():
     """Test of killing (cancelling) a balsam app"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     exctr = Executor.executor
-    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job"):
+    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job") as job:
         task = exctr.submit(calc_type="sim")
+        job.return_value.state = "JOB_FINISHED"
+
+    task.wait(timeout=3)
+    assert task.state == "FINISHED", \
+        "task was not finished after wait method"
+
+    assert not task.running(), \
+        "task shouldn't be running after wait method returns"
 
-    task.wait()
+    assert task.done(), \
+        "task should be 'done' after wait method"
 
 def test_task_kill():
     """Test of killing (cancelling) a balsam app"""

From b6c7fe80ff44a451ca06c0181bc181647ed589ed Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 18 Aug 2022 10:17:18 -0500
Subject: [PATCH 131/196] cleanup

---
 .../tests/unit_tests/test_executor_balsam.py  | 88 +++++++++++--------
 1 file changed, 53 insertions(+), 35 deletions(-)

diff --git a/libensemble/tests/unit_tests/test_executor_balsam.py b/libensemble/tests/unit_tests/test_executor_balsam.py
index 2247f9bb80..6d27baf868 100644
--- a/libensemble/tests/unit_tests/test_executor_balsam.py
+++ b/libensemble/tests/unit_tests/test_executor_balsam.py
@@ -2,18 +2,12 @@
 # Integration Test of executor module for libensemble
 # Test does not require running full libensemble
 import os
-import re
 import sys
-import time
 import mock
 import pytest
 
-from libensemble.resources.mpi_resources import MPIResourcesException
-from libensemble.executors.executor import Executor, ExecutorException, TimeoutExpired
-from libensemble.executors.executor import NOT_STARTED_STATES, Application
+from libensemble.executors.executor import Executor, Application
 
-NCORES = 1
-py_startup = "simdir/py_startup.py"
 
 # fake Balsam app
 class TestLibeApp:
@@ -48,21 +42,26 @@ def teardown_module(module):
 def setup_executor():
     """Set up a Balsam Executor with sim app"""
     from libensemble.executors.balsam_executors import BalsamExecutor
-    exctr = BalsamExecutor()
 
+    exctr = BalsamExecutor()  # noqa F841
 
 
 # Tests ========================================================================================
 
+
+@pytest.mark.extra
 def test_register_app():
     """Test of registering an App"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     setup_executor()
     exctr = Executor.executor
     exctr.register_app(TestLibeApp, calc_type="sim", precedent="fake/dir")
-    assert isinstance(exctr.apps["python"], Application), \
-        "Application object not created based on registered Balsam AppDef"
+    assert isinstance(
+        exctr.apps["python"], Application
+    ), "Application object not created based on registered Balsam AppDef"
+
 
+@pytest.mark.extra
 def test_submit_app_defaults():
     """Test of submitting an App"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
@@ -70,13 +69,14 @@ def test_submit_app_defaults():
     with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job"):
         task = exctr.submit(calc_type="sim")
 
-    assert task in exctr.list_of_tasks, \
-        "new task not added to executor's list of tasks"
+    assert task in exctr.list_of_tasks, "new task not added to executor's list of tasks"
 
-    assert task == exctr.get_task(task.id), \
-        "task retrieved via task ID doesn't match new task"
+    assert task == exctr.get_task(
+        task.id
+    ), "task retrieved via task ID doesn't match new task"
 
 
+@pytest.mark.extra
 def test_submit_app_workdir():
     """Test of submitting an App with a workdir"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
@@ -84,59 +84,74 @@ def test_submit_app_workdir():
     with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job"):
         task = exctr.submit(calc_type="sim", workdir="output", machinefile="nope")
 
-    assert task.workdir == os.path.join(exctr.workflow_name, "output"), \
-        "workdir not properly defined for new task"
+    assert task.workdir == os.path.join(
+        exctr.workflow_name, "output"
+    ), "workdir not properly defined for new task"
 
+
+@pytest.mark.extra
 def test_submit_app_dry():
     """Test of dry-run submitting an App"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     exctr = Executor.executor
     task = exctr.submit(calc_type="sim", dry_run=True)
 
-    assert all([task.dry_run, task.done()]), \
-        "new task from dry_run wasn't marked as such, or set as done"
+    assert all(
+        [task.dry_run, task.done()]
+    ), "new task from dry_run wasn't marked as such, or set as done"
+
 
+@pytest.mark.extra
 def test_submit_app_wait():
     """Test of exctr.submit blocking until app is running"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     exctr = Executor.executor
-    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job") as job:
+    with mock.patch(
+        "libensemble.executors.balsam_executors.balsam_executor.Job"
+    ) as job:
         job.return_value.state = "RUNNING"
         task = exctr.submit(calc_type="sim", wait_on_start=True)
 
-    assert task.running(), \
-        "new task is not marked as running after wait_on_start"
+    assert task.running(), "new task is not marked as running after wait_on_start"
 
+
+@pytest.mark.extra
 def test_submit_revoke_alloc():
     """Test creating and revoking BatchJob objects through the executor"""
     exctr = Executor.executor
-    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.BatchJob") as batchjob:
-        alloc = exctr.submit_allocation(site_id="libe-unit-test", num_nodes=1, wall_time_min=30)
+    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.BatchJob"):
+        alloc = exctr.submit_allocation(
+            site_id="libe-unit-test", num_nodes=1, wall_time_min=30
+        )
 
-        assert alloc in exctr.allocations, \
-            "batchjob object not appended to executor's list of allocations"
+        assert (
+            alloc in exctr.allocations
+        ), "batchjob object not appended to executor's list of allocations"
 
         alloc.scheduler_id = 1
         exctr.revoke_allocation(alloc)
 
+
+@pytest.mark.extra
 def test_task_wait():
     """Test of killing (cancelling) a balsam app"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     exctr = Executor.executor
-    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job") as job:
+    with mock.patch(
+        "libensemble.executors.balsam_executors.balsam_executor.Job"
+    ) as job:
         task = exctr.submit(calc_type="sim")
         job.return_value.state = "JOB_FINISHED"
 
     task.wait(timeout=3)
-    assert task.state == "FINISHED", \
-        "task was not finished after wait method"
+    assert task.state == "FINISHED", "task was not finished after wait method"
 
-    assert not task.running(), \
-        "task shouldn't be running after wait method returns"
+    assert not task.running(), "task shouldn't be running after wait method returns"
 
-    assert task.done(), \
-        "task should be 'done' after wait method"
+    assert task.done(), "task should be 'done' after wait method"
 
+
+@pytest.mark.extra
 def test_task_kill():
     """Test of killing (cancelling) a balsam app"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
@@ -145,8 +160,10 @@ def test_task_kill():
         task = exctr.submit(calc_type="sim")
 
     task.kill()
-    assert task.finished and task.state == "USER_KILLED", \
-        "task not set as killed after kill method"
+    assert (
+        task.finished and task.state == "USER_KILLED"
+    ), "task not set as killed after kill method"
+
 
 if __name__ == "__main__":
     setup_module(__file__)
@@ -157,4 +174,5 @@ def test_task_kill():
     test_submit_app_wait()
     test_submit_revoke_alloc()
     test_task_wait()
-    test_task_kill()
\ No newline at end of file
+    test_task_kill()
+    teardown_module(__file__)

From a20a4f61f94d007653261c47c72de331f595e6c2 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 18 Aug 2022 10:21:23 -0500
Subject: [PATCH 132/196] skip a flakey test on windows

---
 .../regression_tests/test_persistent_sim_uniform_sampling.py     | 1 +
 1 file changed, 1 insertion(+)

diff --git a/libensemble/tests/regression_tests/test_persistent_sim_uniform_sampling.py b/libensemble/tests/regression_tests/test_persistent_sim_uniform_sampling.py
index 8ecaa06247..5cb692a42e 100644
--- a/libensemble/tests/regression_tests/test_persistent_sim_uniform_sampling.py
+++ b/libensemble/tests/regression_tests/test_persistent_sim_uniform_sampling.py
@@ -15,6 +15,7 @@
 # Do not change these lines - they are parsed by run-tests.sh
 # TESTSUITE_COMMS: mpi local tcp
 # TESTSUITE_NPROCS: 3 4
+# TESTSUITE_OS_SKIP: WIN
 
 import sys
 import numpy as np

From 3e1d22ec1e3bcc5568200e2af91424826f6835a2 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 18 Aug 2022 10:31:52 -0500
Subject: [PATCH 133/196] make sure legacy balsam job doesnt install new balsam

---
 .github/workflows/ci.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index e05d96dc6e..fd48695211 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -145,6 +145,7 @@ jobs:
             pip install ax-platform
 
         - name: Install other testing dependencies
+          if: matrix.do-balsam == false
           run: |
             pip install -r install/testing_requirements.txt
             pip install psutil
@@ -162,6 +163,7 @@ jobs:
           env:
               BALSAM_DB_PATH: $HOME/test-balsam
           run: |
+            pip install -r install/testing_requirements.txt
             wget https://github.com/argonne-lcf/balsam/archive/refs/tags/0.5.0.tar.gz
             mkdir ../balsam; tar xf 0.5.0.tar.gz -C ../balsam;
             python install/configure_balsam_install.py

From 61100651645e89cd618e2efce14286158e5ad991 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 18 Aug 2022 15:35:47 -0500
Subject: [PATCH 134/196] first attempt at simple executor functionality test,
 plus the adjustments to executor_hworld to make every test work

---
 libensemble/sim_funcs/executor_hworld.py      | 181 +++++++++---------
 .../test_executor_simple.py                   |  87 +++++++++
 2 files changed, 181 insertions(+), 87 deletions(-)
 create mode 100644 libensemble/tests/functionality_tests/test_executor_simple.py

diff --git a/libensemble/sim_funcs/executor_hworld.py b/libensemble/sim_funcs/executor_hworld.py
index afdf7703eb..1cca0fbd16 100644
--- a/libensemble/sim_funcs/executor_hworld.py
+++ b/libensemble/sim_funcs/executor_hworld.py
@@ -78,100 +78,107 @@ def executor_hworld(H, persis_info, sim_specs, libE_info):
     wait = False
     args_for_sim = "sleep 1"
 
-    if ELAPSED_TIMEOUT:
-        args_for_sim = "sleep 60"  # Manager kill - if signal received else completes
-        timeout = 65.0
+    if "six_hump_camel" not in exctr.default_app("sim").full_path:
 
-    else:
-        global sim_ended_count
-        sim_ended_count += 1
-        timeout = 6.0
-        launch_shc = False
-        print(sim_ended_count)
-
-        if sim_ended_count == 1:
-            args_for_sim = "sleep 1"  # Should finish
-        elif sim_ended_count == 2:
-            args_for_sim = "sleep 1 Error"  # Worker kill on error
-        elif sim_ended_count == 3:
-            wait = True
-            args_for_sim = "sleep 1"  # Should finish
-            launch_shc = True
-        elif sim_ended_count == 4:
-            args_for_sim = "sleep 8"  # Worker kill on timeout
-            timeout = 1.0
-        elif sim_ended_count == 5:
-            args_for_sim = "sleep 2 Fail"  # Manager kill - if signal received else completes
-
-    if USE_BALSAM:
-        task = exctr.submit(
-            calc_type="sim",
-            num_procs=cores,
-            app_args=args_for_sim,
-            hyperthreads=True,
-            machinefile="notused",
-            stdout="notused",
-            wait_on_start=True,
-        )
-    else:
-        task = exctr.submit(calc_type="sim", num_procs=cores, app_args=args_for_sim, hyperthreads=True)
-
-    if wait:
-        task.wait()
-        if not task.finished:
-            calc_status = UNSET_TAG
-        if task.state == "FINISHED":
-            calc_status = WORKER_DONE
-        elif task.state == "FAILED":
-            calc_status = TASK_FAILED
+        if ELAPSED_TIMEOUT:
+            args_for_sim = "sleep 60"  # Manager kill - if signal received else completes
+            timeout = 65.0
 
-    else:
-        if not ELAPSED_TIMEOUT:
-            if sim_ended_count >= 2 and not USE_BALSAM:
-                calc_status = exctr.polling_loop(task, timeout=timeout, delay=0.3, poll_manager=True)
-                if sim_ended_count == 2 and task.stdout_exists() and "Error" in task.read_stdout():
-                    calc_status = WORKER_KILL_ON_ERR
+        else:
+            global sim_ended_count
+            sim_ended_count += 1
+            timeout = 6.0
+            launch_shc = False
+            print(sim_ended_count)
+
+            if sim_ended_count == 1:
+                args_for_sim = "sleep 1"  # Should finish
+            elif sim_ended_count == 2:
+                args_for_sim = "sleep 1 Error"  # Worker kill on error
+            elif sim_ended_count == 3:
+                wait = True
+                args_for_sim = "sleep 1"  # Should finish
+                launch_shc = True
+            elif sim_ended_count == 4:
+                args_for_sim = "sleep 8"  # Worker kill on timeout
+                timeout = 1.0
+            elif sim_ended_count == 5:
+                args_for_sim = "sleep 2 Fail"  # Manager kill - if signal received else completes
+
+        if USE_BALSAM:
+            task = exctr.submit(
+                calc_type="sim",
+                num_procs=cores,
+                app_args=args_for_sim,
+                hyperthreads=True,
+                machinefile="notused",
+                stdout="notused",
+                wait_on_start=True,
+            )
+        else:
+            task = exctr.submit(calc_type="sim", num_procs=cores, app_args=args_for_sim, hyperthreads=True)
 
-            else:
-                task, calc_status = custom_polling_loop(exctr, task, timeout)
+        if wait:
+            task.wait()
+            if not task.finished:
+                calc_status = UNSET_TAG
+            if task.state == "FINISHED":
+                calc_status = WORKER_DONE
+            elif task.state == "FAILED":
+                calc_status = TASK_FAILED
 
         else:
-            calc_status = exctr.polling_loop(task, timeout=timeout, delay=0.3, poll_manager=True)
-
-    if USE_BALSAM:
-        task.read_file_in_workdir("ensemble.log")
-        try:
-            task.read_stderr()
-        except ValueError:
-            pass
-
-        task = exctr.submit(
-            app_name="sim_hump_camel_dry_run",
-            num_procs=cores,
-            app_args=args_for_sim,
-            hyperthreads=True,
-            machinefile="notused",
-            stdout="notused",
-            wait_on_start=True,
-            dry_run=True,
-            stage_inout=os.getcwd(),
-        )
+            if not ELAPSED_TIMEOUT:
+                if sim_ended_count >= 2 and not USE_BALSAM:
+                    calc_status = exctr.polling_loop(task, timeout=timeout, delay=0.3, poll_manager=True)
+                    if sim_ended_count == 2 and task.stdout_exists() and "Error" in task.read_stdout():
+                        calc_status = WORKER_KILL_ON_ERR
 
-        task.poll()
-        task.wait()
-
-    # This is temp - return something - so doing six_hump_camel_func again...
-    batch = len(H["x"])
-    H_o = np.zeros(batch, dtype=sim_specs["out"])
-    for i, x in enumerate(H["x"]):
-        H_o["f"][i] = six_hump_camel_func(x)
-        if launch_shc:
-            # Test launching a named app.
-            app_args = " ".join(str(val) for val in list(x[:]))
-            task = exctr.submit(app_name="six_hump_camel", num_procs=1, app_args=app_args)
+                else:
+                    task, calc_status = custom_polling_loop(exctr, task, timeout)
+
+            else:
+                calc_status = exctr.polling_loop(task, timeout=timeout, delay=0.3, poll_manager=True)
+
+        if USE_BALSAM:
+            task.read_file_in_workdir("ensemble.log")
+            try:
+                task.read_stderr()
+            except ValueError:
+                pass
+
+            task = exctr.submit(
+                app_name="sim_hump_camel_dry_run",
+                num_procs=cores,
+                app_args=args_for_sim,
+                hyperthreads=True,
+                machinefile="notused",
+                stdout="notused",
+                wait_on_start=True,
+                dry_run=True,
+                stage_inout=os.getcwd(),
+            )
+
+            task.poll()
             task.wait()
-            output = np.float64(task.read_stdout())
-            assert np.isclose(H_o["f"][i], output)
+
+    else:
+        launch_shc = True
+        calc_status = UNSET_TAG
+
+        # This is temp - return something - so doing six_hump_camel_func again...
+        batch = len(H["x"])
+        H_o = np.zeros(batch, dtype=sim_specs["out"])
+        for i, x in enumerate(H["x"]):
+            H_o["f"][i] = six_hump_camel_func(x)
+            if launch_shc:
+                # Test launching a named app.
+                app_args = " ".join(str(val) for val in list(x[:]))
+                task = exctr.submit(app_name="six_hump_camel", num_procs=1, app_args=app_args)
+                task.wait()
+                output = np.float64(task.read_stdout())
+                assert np.isclose(H_o["f"][i], output)
+                calc_status = WORKER_DONE
 
     # This is just for testing at calling script level - status of each task
     H_o["cstat"] = calc_status
diff --git a/libensemble/tests/functionality_tests/test_executor_simple.py b/libensemble/tests/functionality_tests/test_executor_simple.py
new file mode 100644
index 0000000000..f201ccd4ea
--- /dev/null
+++ b/libensemble/tests/functionality_tests/test_executor_simple.py
@@ -0,0 +1,87 @@
+"""
+Runs libEnsemble testing the executor functionality.
+
+Execute via one of the following commands (e.g. 3 workers):
+   mpiexec -np 4 python test_executor_hworld.py
+   python test_executor_hworld.py --nworkers 3 --comms local
+   python test_executor_hworld.py --nworkers 3 --comms tcp
+
+The number of concurrent evaluations of the objective function will be 4-1=3.
+"""
+
+import os
+import numpy as np
+import multiprocessing
+
+# Import libEnsemble items for this test
+from libensemble.message_numbers import WORKER_DONE, WORKER_KILL_ON_ERR, WORKER_KILL_ON_TIMEOUT, TASK_FAILED
+from libensemble.libE import libE
+from libensemble.sim_funcs.executor_hworld import executor_hworld as sim_f
+import libensemble.sim_funcs.six_hump_camel as six_hump_camel
+from libensemble.gen_funcs.sampling import uniform_random_sample as gen_f
+from libensemble.tools import parse_args, add_unique_random_streams
+from libensemble.tests.regression_tests.common import build_simfunc
+from libensemble.executors.mpi_executor import MPIExecutor
+
+
+# Do not change these lines - they are parsed by run-tests.sh
+# TESTSUITE_COMMS: mpi local
+# TESTSUITE_NPROCS: 4
+# TESTSUITE_OMPI_SKIP: true
+
+# Main block is necessary only when using local comms with spawn start method (default on macOS and Windows).
+if __name__ == "__main__":
+
+    nworkers, is_manager, libE_specs, _ = parse_args()
+
+    cores_per_task = 1
+    cores_all_tasks = nworkers * cores_per_task
+
+    sim_app2 = six_hump_camel.__file__
+
+    exctr = MPIExecutor()
+    exctr.register_app(full_path=sim_app2, app_name="six_hump_camel", calc_type="sim")  # Named app
+
+    sim_specs = {
+        "sim_f": sim_f,
+        "in": ["x"],
+        "out": [("f", float), ("cstat", int)],
+        "user": {"cores": cores_per_task},
+    }
+
+    gen_specs = {
+        "gen_f": gen_f,
+        "in": ["sim_id"],
+        "out": [("x", float, (2,))],
+        "user": {
+            "lb": np.array([-3, -2]),
+            "ub": np.array([3, 2]),
+            "gen_batch_size": nworkers,
+        },
+    }
+
+    persis_info = add_unique_random_streams({}, nworkers + 1)
+
+    # num sim_ended_count conditions in executor_hworld
+    exit_criteria = {"sim_max": nworkers * 5}
+
+    # Perform the run
+    H, persis_info, flag = libE(sim_specs, gen_specs, exit_criteria, persis_info, libE_specs=libE_specs)
+
+    if is_manager:
+        print("\nChecking expected task status against Workers ...\n")
+
+        calc_status_list_in = np.asarray(
+            [WORKER_DONE] * 5
+        )
+        calc_status_list = np.repeat(calc_status_list_in, nworkers)
+
+        # For debug
+        print("Expecting: {}".format(calc_status_list))
+        print("Received:  {}\n".format(H["cstat"]))
+
+        assert np.array_equal(H["cstat"], calc_status_list), "Error - unexpected calc status. Received: " + str(
+            H["cstat"]
+        )
+
+        print("\n\n\nRun completed.")

From a1510b41628098e5ca818ebd60ea8028032ded39 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 18 Aug 2022 16:34:42 -0500
Subject: [PATCH 135/196] try adding an mpi windows job

---
 .github/workflows/ci.yml | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index b5c82deb1f..a12fa82163 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -47,6 +47,12 @@ jobs:
                       comms-type: l
                       mpi-version: 'msmpi'
                       do-balsam: false
+                    - os: windows-latest
+                      python-version: '3.10'
+                      comms-type: m
+                      mpi-version: 'msmpi'
+                      do-balsam: false
+
         env:
             HYDRA_LAUNCHER: 'fork'
             TERM: xterm-256color

From 1a593e7e2d735abc98d223e02d0c9d61a0d3407b Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 18 Aug 2022 17:15:58 -0500
Subject: [PATCH 136/196] reformatting

---
 .../test_executor_simple.py                   | 23 +++++++++----------
 1 file changed, 11 insertions(+), 12 deletions(-)

diff --git a/libensemble/tests/functionality_tests/test_executor_simple.py b/libensemble/tests/functionality_tests/test_executor_simple.py
index f201ccd4ea..4db571bdf5 100644
--- a/libensemble/tests/functionality_tests/test_executor_simple.py
+++ b/libensemble/tests/functionality_tests/test_executor_simple.py
@@ -9,18 +9,15 @@
 The number of concurrent evaluations of the objective function will be 4-1=3.
 """
 
-import os
 import numpy as np
-import multiprocessing
 
 # Import libEnsemble items for this test
-from libensemble.message_numbers import WORKER_DONE, WORKER_KILL_ON_ERR, WORKER_KILL_ON_TIMEOUT, TASK_FAILED
+from libensemble.message_numbers import WORKER_DONE
 from libensemble.libE import libE
 from libensemble.sim_funcs.executor_hworld import executor_hworld as sim_f
 import libensemble.sim_funcs.six_hump_camel as six_hump_camel
 from libensemble.gen_funcs.sampling import uniform_random_sample as gen_f
 from libensemble.tools import parse_args, add_unique_random_streams
-from libensemble.tests.regression_tests.common import build_simfunc
 from libensemble.executors.mpi_executor import MPIExecutor
 
 
@@ -40,7 +37,9 @@
     sim_app2 = six_hump_camel.__file__
 
     exctr = MPIExecutor()
-    exctr.register_app(full_path=sim_app2, app_name="six_hump_camel", calc_type="sim")  # Named app
+    exctr.register_app(
+        full_path=sim_app2, app_name="six_hump_camel", calc_type="sim"
+    )  # Named app
 
     sim_specs = {
         "sim_f": sim_f,
@@ -66,22 +65,22 @@
     exit_criteria = {"sim_max": nworkers * 5}
 
     # Perform the run
-    H, persis_info, flag = libE(sim_specs, gen_specs, exit_criteria, persis_info, libE_specs=libE_specs)
+    H, persis_info, flag = libE(
+        sim_specs, gen_specs, exit_criteria, persis_info, libE_specs=libE_specs
+    )
 
     if is_manager:
         print("\nChecking expected task status against Workers ...\n")
 
-        calc_status_list_in = np.asarray(
-            [WORKER_DONE] * 5
-        )
+        calc_status_list_in = np.asarray([WORKER_DONE] * 5)
         calc_status_list = np.repeat(calc_status_list_in, nworkers)
 
         # For debug
         print("Expecting: {}".format(calc_status_list))
         print("Received:  {}\n".format(H["cstat"]))
 
-        assert np.array_equal(H["cstat"], calc_status_list), "Error - unexpected calc status. Received: " + str(
-            H["cstat"]
-        )
+        assert np.array_equal(
+            H["cstat"], calc_status_list
+        ), "Error - unexpected calc status. Received: " + str(H["cstat"])
 
         print("\n\n\nRun completed.")

From 32d8a0e01bdb35e641597bd1b4ab71e228c67a82 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Thu, 18 Aug 2022 17:25:23 -0500
Subject: [PATCH 137/196] rewording faq for windows based on feeback

---
 docs/FAQ.rst | 21 +++++++++++++++------
 1 file changed, 15 insertions(+), 6 deletions(-)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index a1d056ddcc..0c0538a366 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -226,23 +226,32 @@ macOS and Windows Errors
 
 .. _faqwindows:
 
+**Can I run libEnsemble on Windows**
+
+Although we run many libEnsemble workflows successfully on Windows using both MPI and local comms, we do not 
+rigorously support Windows, and recommend unix-like systems as a preference. Windows tends to produce more 
+platform-specific issues that are difficult to reproduce and troubleshoot.
+
+Feel free to check our `Github Actions`_ page to see what tests we run regularly on Windows.
+
+.. _`Github Actions`: https://github.com/Libensemble/libensemble/actions
+
 **Windows - How can I run libEnsemble with MPI comms?**
 
-In our experience this may be challenging, because most MPI distributions have
-either dropped Windows support (MPICH and Open MPI) or are no longer being maintained (`msmpi``).
-Windows also tends to produce more platform-specific issues that are difficult to reproduce and troubleshoot.
+We have run workflows with MPI comms. However, as most MPI distributions have either dropped Windows support
+ (MPICH and Open MPI) or are no longer being maintained (``msmpi``), we cannot guarantee success.
 
 If users wish to try, we recommend experimenting with the many Unix-like emulators, containers, virtual machines,
 and other such systems. The `Installing PETSc On Microsoft Windows`_ documentation contains valuable information.
 
-Otherwise, install `msmpi` and `mpi4py` from conda and experiment, or use ``local`` comms.
+Otherwise, install ``msmpi`` and ``mpi4py`` from conda and experiment, or use ``local`` comms.
 
 .. _`Installing PETSc On Microsoft Windows`: https://petsc.org/release/install/windows/#recommended-installation-methods
 
 **Windows - 'A required privilege is not held by the client'**
 
-Assuming you were trying to use the `sim_dir_symlink_files` or `gen_dir_symlink_files` options, this indicates that to
-allow libEnsemble to create symlinks, you need to run your current `cmd` shell as administrator.
+Assuming you were trying to use the ``sim_dir_symlink_files`` or ``gen_dir_symlink_files`` options, this indicates that to
+allow libEnsemble to create symlinks, you need to run your current ``cmd`` shell as administrator.
 
 **"RuntimeError: An attempt has been made to start a new process... this probably means that you are not using fork...
 " if __name__ == '__main__': freeze_support() ...**

From 32e1b9330faca79b8df30d6edeb8871df05e2f63 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 19 Aug 2022 09:08:32 -0500
Subject: [PATCH 138/196] fix small error

---
 libensemble/sim_funcs/executor_hworld.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/libensemble/sim_funcs/executor_hworld.py b/libensemble/sim_funcs/executor_hworld.py
index 1cca0fbd16..4f06617166 100644
--- a/libensemble/sim_funcs/executor_hworld.py
+++ b/libensemble/sim_funcs/executor_hworld.py
@@ -77,6 +77,7 @@ def executor_hworld(H, persis_info, sim_specs, libE_info):
 
     wait = False
     args_for_sim = "sleep 1"
+    calc_status = UNSET_TAG
 
     if "six_hump_camel" not in exctr.default_app("sim").full_path:
 

From 8d722de91d2b2fcb512785e48f78505ec9f5765b Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 19 Aug 2022 09:47:05 -0500
Subject: [PATCH 139/196] probably shouldve caught these issues earlier

---
 libensemble/sim_funcs/executor_hworld.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/libensemble/sim_funcs/executor_hworld.py b/libensemble/sim_funcs/executor_hworld.py
index 4f06617166..2ea0c92863 100644
--- a/libensemble/sim_funcs/executor_hworld.py
+++ b/libensemble/sim_funcs/executor_hworld.py
@@ -79,6 +79,9 @@ def executor_hworld(H, persis_info, sim_specs, libE_info):
     args_for_sim = "sleep 1"
     calc_status = UNSET_TAG
 
+    batch = len(H["x"])
+    H_o = np.zeros(batch, dtype=sim_specs["out"])
+
     if "six_hump_camel" not in exctr.default_app("sim").full_path:
 
         if ELAPSED_TIMEOUT:
@@ -168,8 +171,6 @@ def executor_hworld(H, persis_info, sim_specs, libE_info):
         calc_status = UNSET_TAG
 
         # This is temp - return something - so doing six_hump_camel_func again...
-        batch = len(H["x"])
-        H_o = np.zeros(batch, dtype=sim_specs["out"])
         for i, x in enumerate(H["x"]):
             H_o["f"][i] = six_hump_camel_func(x)
             if launch_shc:

From 4ea9b06693a79b347603883f1185db9b546bc302 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 19 Aug 2022 10:09:42 -0500
Subject: [PATCH 140/196] skip comm dup test on windows

---
 .../test_1d_uniform_sampling_with_comm_dup.py                    | 1 +
 1 file changed, 1 insertion(+)

diff --git a/libensemble/tests/functionality_tests/test_1d_uniform_sampling_with_comm_dup.py b/libensemble/tests/functionality_tests/test_1d_uniform_sampling_with_comm_dup.py
index 987b9eea99..ab81c9ce89 100644
--- a/libensemble/tests/functionality_tests/test_1d_uniform_sampling_with_comm_dup.py
+++ b/libensemble/tests/functionality_tests/test_1d_uniform_sampling_with_comm_dup.py
@@ -13,6 +13,7 @@
 # Do not change these lines - they are parsed by run-tests.sh
 # TESTSUITE_COMMS: mpi
 # TESTSUITE_NPROCS: 2 4
+# TESTSUITE_OS_SKIP: WIN
 
 import sys
 import numpy as np

From 271450fe6839fd870ab19c8ecc79eeb8a33f4ce9 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 19 Aug 2022 14:39:09 -0500
Subject: [PATCH 141/196] mock EventLog since objects interface unavailable
 with no login

---
 .../tests/unit_tests/test_executor_balsam.py  | 26 ++++++++++++++++---
 1 file changed, 22 insertions(+), 4 deletions(-)

diff --git a/libensemble/tests/unit_tests/test_executor_balsam.py b/libensemble/tests/unit_tests/test_executor_balsam.py
index 6d27baf868..cce9eb80cc 100644
--- a/libensemble/tests/unit_tests/test_executor_balsam.py
+++ b/libensemble/tests/unit_tests/test_executor_balsam.py
@@ -5,6 +5,8 @@
 import sys
 import mock
 import pytest
+import datetime
+from dataclasses import dataclass
 
 from libensemble.executors.executor import Executor, Application
 
@@ -18,6 +20,12 @@ def sync():
         pass
 
 
+# fake EventLog object
+@dataclass
+class TestLogEvent:
+    timestamp: datetime.datetime = None
+
+
 def setup_module(module):
     try:
         print("setup_module module:%s" % module.__name__)
@@ -109,8 +117,16 @@ def test_submit_app_wait():
     with mock.patch(
         "libensemble.executors.balsam_executors.balsam_executor.Job"
     ) as job:
-        job.return_value.state = "RUNNING"
-        task = exctr.submit(calc_type="sim", wait_on_start=True)
+        with mock.patch(
+            "libensemble.executors.balsam_executors.balsam_executor.EventLog"
+        ) as log:
+            job.return_value.state = "RUNNING"
+            log.objects.filter.return_value = [
+                TestLogEvent(
+                    timestamp=datetime.datetime(2022, 4, 21, 20, 29, 33, 455144)
+                )
+            ]
+            task = exctr.submit(calc_type="sim", wait_on_start=True)
 
     assert task.running(), "new task is not marked as running after wait_on_start"
 
@@ -143,7 +159,8 @@ def test_task_wait():
         task = exctr.submit(calc_type="sim")
         job.return_value.state = "JOB_FINISHED"
 
-    task.wait(timeout=3)
+    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.EventLog"):
+        task.wait(timeout=3)
     assert task.state == "FINISHED", "task was not finished after wait method"
 
     assert not task.running(), "task shouldn't be running after wait method returns"
@@ -159,7 +176,8 @@ def test_task_kill():
     with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job"):
         task = exctr.submit(calc_type="sim")
 
-    task.kill()
+    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.EventLog"):
+        task.kill()
     assert (
         task.finished and task.state == "USER_KILLED"
     ), "task not set as killed after kill method"

From 7a97698f4213df936aaf4a4a76c26f53acf4e574 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 19 Aug 2022 15:12:55 -0500
Subject: [PATCH 142/196] pytest on ci is trying to 'collect' the function?

---
 libensemble/tests/unit_tests/test_executor_balsam.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/libensemble/tests/unit_tests/test_executor_balsam.py b/libensemble/tests/unit_tests/test_executor_balsam.py
index cce9eb80cc..ec853d562e 100644
--- a/libensemble/tests/unit_tests/test_executor_balsam.py
+++ b/libensemble/tests/unit_tests/test_executor_balsam.py
@@ -22,7 +22,7 @@ def sync():
 
 # fake EventLog object
 @dataclass
-class TestLogEvent:
+class LogEventTest:
     timestamp: datetime.datetime = None
 
 
@@ -122,7 +122,7 @@ def test_submit_app_wait():
         ) as log:
             job.return_value.state = "RUNNING"
             log.objects.filter.return_value = [
-                TestLogEvent(
+                LogEventTest(
                     timestamp=datetime.datetime(2022, 4, 21, 20, 29, 33, 455144)
                 )
             ]

From ac6ae928e0be4ec4a5ee381e4f0900a48d99937f Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Fri, 19 Aug 2022 16:33:29 -0500
Subject: [PATCH 143/196] lets just skip the ax test on macos for now

---
 .../tests/regression_tests/test_persistent_gp_multitask_ax.py    | 1 +
 1 file changed, 1 insertion(+)

diff --git a/libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py b/libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py
index 4c59006605..0fc03a6cee 100644
--- a/libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py
+++ b/libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py
@@ -17,6 +17,7 @@
 # TESTSUITE_COMMS: local mpi
 # TESTSUITE_NPROCS: 5
 # TESTSUITE_EXTRA: true
+# TESTSUITE_OS_SKIP: OSX
 
 import numpy as np
 from libensemble.libE import libE

From 771a29e7360c98066618ce30dee43a880d9aaddf Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 22 Aug 2022 13:07:04 -0500
Subject: [PATCH 144/196] not assuming balsam can return unknown state, add
 timeout to revoke_allocation, add task.wait tests

---
 .../balsam_executors/balsam_executor.py       |  28 +++--
 .../tests/unit_tests/test_executor_balsam.py  | 104 ++++++++++++++----
 2 files changed, 97 insertions(+), 35 deletions(-)

diff --git a/libensemble/executors/balsam_executors/balsam_executor.py b/libensemble/executors/balsam_executors/balsam_executor.py
index 50c94e0d07..739d7af620 100644
--- a/libensemble/executors/balsam_executors/balsam_executor.py
+++ b/libensemble/executors/balsam_executors/balsam_executor.py
@@ -162,13 +162,11 @@ def _set_complete(self, dry_run=False):
             ]:
                 self.success = True
                 self.state = "FINISHED"
-            elif balsam_state in STATES:  # In my states
-                self.state = balsam_state
             else:
-                logger.warning("Task finished, but in unrecognized " "Balsam state {}".format(balsam_state))
-                self.state = "UNKNOWN"
+                self.state = balsam_state
+
 
-            logger.info("Task {} ended with state {}".format(self.name, self.state))
+        logger.info("Task {} ended with state {}".format(self.name, self.state))
 
     def poll(self):
         """Polls and updates the status attributes of the supplied task. Requests
@@ -202,12 +200,8 @@ def poll(self):
 
         elif balsam_state in ["RUN_ERROR", "RUN_TIMEOUT", "FAILED"]:
             self.state = "FAILED"
+            self._set_complete()
 
-        else:
-            raise ExecutorException(
-                "Task state returned from Balsam is not in known list of "
-                "Balsam states. Task state is {}".format(balsam_state)
-            )
 
     def wait(self, timeout=None):
         """Waits on completion of the task or raises ``TimeoutExpired``.
@@ -239,6 +233,7 @@ def wait(self, timeout=None):
             "POSTPROCESSED",
             "STAGED_OUT",
             "JOB_FINISHED",
+            "RUN_ERROR", "RUN_TIMEOUT", "FAILED"
         ]:
             time.sleep(0.2)
             self.process.refresh_from_db()
@@ -280,7 +275,7 @@ def serial_setup(self):
         """Balsam serial setup includes emptying database and adding applications"""
         pass
 
-    def add_app(self, name, site, exepath, desc):
+    def add_app(self, *args):
         """Sync application with Balsam service"""
         pass
 
@@ -398,7 +393,7 @@ def submit_allocation(
 
         return allocation
 
-    def revoke_allocation(self, allocation):
+    def revoke_allocation(self, allocation, timeout=60):
         """
         Terminates a Balsam ``BatchJob`` machine allocation remotely. Balsam apps should
         no longer be submitted to this allocation. Best to run after libEnsemble
@@ -409,16 +404,25 @@ def revoke_allocation(self, allocation):
 
         allocation: ``BatchJob`` object
             a ``BatchJob`` with a corresponding machine allocation that should be cancelled.
+
+        timeout: int, optional
+            Timeout and warn user after this many seconds of attempting to revoke an allocation.
         """
         allocation.refresh_from_db()
 
+        start = time.time()
+
         while not allocation.scheduler_id:
             time.sleep(1)
             allocation.refresh_from_db()
+            if time.time() - start > timeout:
+                logger.warning("Unable to terminate Balsam BatchJob. You may need to login to the machine and manually remove it.")
+                return False
 
         batchjob = BatchJob.objects.get(scheduler_id=allocation.scheduler_id)
         batchjob.state = "pending_deletion"
         batchjob.save()
+        return True
 
     def set_resources(self, resources):
         self.resources = resources
diff --git a/libensemble/tests/unit_tests/test_executor_balsam.py b/libensemble/tests/unit_tests/test_executor_balsam.py
index ec853d562e..a5ecdd7782 100644
--- a/libensemble/tests/unit_tests/test_executor_balsam.py
+++ b/libensemble/tests/unit_tests/test_executor_balsam.py
@@ -8,7 +8,7 @@
 import datetime
 from dataclasses import dataclass
 
-from libensemble.executors.executor import Executor, Application
+from libensemble.executors.executor import Executor, Application, ExecutorException, TimeoutExpired
 
 
 # fake Balsam app
@@ -63,11 +63,21 @@ def test_register_app():
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     setup_executor()
     exctr = Executor.executor
+
+    exctr.serial_setup()  # does nothing, compatibility with legacy-balsam-exctr
+    exctr.add_app("hello", "world")  # does nothing, compatibility with legacy-balsam-exctr
+    exctr.set_resources("hello")  # does nothing, compatibility with other executors
+
     exctr.register_app(TestLibeApp, calc_type="sim", precedent="fake/dir")
     assert isinstance(
         exctr.apps["python"], Application
     ), "Application object not created based on registered Balsam AppDef"
 
+    exctr.register_app(TestLibeApp, app_name="test")
+    assert isinstance(
+        exctr.apps["test"], Application
+    ), "Application object not created based on registered Balsam AppDef"
+
 
 @pytest.mark.extra
 def test_submit_app_defaults():
@@ -76,6 +86,7 @@ def test_submit_app_defaults():
     exctr = Executor.executor
     with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job"):
         task = exctr.submit(calc_type="sim")
+        task = exctr.submit(app_name="test")
 
     assert task in exctr.list_of_tasks, "new task not added to executor's list of tasks"
 
@@ -83,6 +94,10 @@ def test_submit_app_defaults():
         task.id
     ), "task retrieved via task ID doesn't match new task"
 
+    with pytest.raises(ExecutorException):
+        task = exctr.submit()
+        pytest.fail("Expected exception")
+
 
 @pytest.mark.extra
 def test_submit_app_workdir():
@@ -103,6 +118,7 @@ def test_submit_app_dry():
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     exctr = Executor.executor
     task = exctr.submit(calc_type="sim", dry_run=True)
+    task.poll()
 
     assert all(
         [task.dry_run, task.done()]
@@ -114,26 +130,23 @@ def test_submit_app_wait():
     """Test of exctr.submit blocking until app is running"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     exctr = Executor.executor
-    with mock.patch(
-        "libensemble.executors.balsam_executors.balsam_executor.Job"
-    ) as job:
-        with mock.patch(
-            "libensemble.executors.balsam_executors.balsam_executor.EventLog"
-        ) as log:
+    with mock.patch( "libensemble.executors.balsam_executors.balsam_executor.Job") as job:
+        with mock.patch("libensemble.executors.balsam_executors.balsam_executor.EventLog") as log:
             job.return_value.state = "RUNNING"
-            log.objects.filter.return_value = [
-                LogEventTest(
-                    timestamp=datetime.datetime(2022, 4, 21, 20, 29, 33, 455144)
-                )
-            ]
+            log.objects.filter.return_value = [LogEventTest(timestamp=datetime.datetime(2022, 4, 21, 20, 29, 33, 455144))]
+            task = exctr.submit(calc_type="sim", wait_on_start=True)
+            assert task.running(), "new task is not marked as running after wait_on_start"
+
+            log.objects.filter.return_value = [LogEventTest(timestamp=None)]
             task = exctr.submit(calc_type="sim", wait_on_start=True)
+            assert task.runtime == 0, "runtime should be 0 without Balsam timestamp evaluated"
 
-    assert task.running(), "new task is not marked as running after wait_on_start"
 
 
 @pytest.mark.extra
 def test_submit_revoke_alloc():
     """Test creating and revoking BatchJob objects through the executor"""
+    print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     exctr = Executor.executor
     with mock.patch("libensemble.executors.balsam_executors.balsam_executor.BatchJob"):
         alloc = exctr.submit_allocation(
@@ -144,30 +157,74 @@ def test_submit_revoke_alloc():
             alloc in exctr.allocations
         ), "batchjob object not appended to executor's list of allocations"
 
+        alloc.scheduler_id = None
+        assert not exctr.revoke_allocation(alloc, timeout=3), \
+            "unable to revoke allocation if Balsam never returns scheduler ID"
+
         alloc.scheduler_id = 1
-        exctr.revoke_allocation(alloc)
+        assert exctr.revoke_allocation(alloc, timeout=3), \
+            "should've been able to revoke allocation if scheduler ID available"
 
 
 @pytest.mark.extra
-def test_task_wait():
+def test_task_poll():
     """Test of killing (cancelling) a balsam app"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     exctr = Executor.executor
-    with mock.patch(
-        "libensemble.executors.balsam_executors.balsam_executor.Job"
-    ) as job:
-        task = exctr.submit(calc_type="sim")
-        job.return_value.state = "JOB_FINISHED"
+    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job") as job:
+        with mock.patch("libensemble.executors.balsam_executors.balsam_executor.EventLog"):
+            task = exctr.submit(calc_type="sim")
 
-    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.EventLog"):
-        task.wait(timeout=3)
-    assert task.state == "FINISHED", "task was not finished after wait method"
+            job.return_value.state = "PREPROCESSED"
+            task.poll()
+            assert task.state == "WAITING", "task should've been considered waiting based on balsam state"
+
+            job.return_value.state = "FAILED"
+            task.poll()
+            assert task.state == "FAILED", "task should've been considered failed based on balsam state"
+
+            task = exctr.submit(calc_type="sim")
+
+            job.return_value.state = "JOB_FINISHED"
+            task.poll()
+            assert task.state == "FINISHED", "task was not finished after wait method"
 
     assert not task.running(), "task shouldn't be running after wait method returns"
 
     assert task.done(), "task should be 'done' after wait method"
 
 
+@pytest.mark.extra
+def test_task_wait():
+    """Test of killing (cancelling) a balsam app"""
+    print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
+    exctr = Executor.executor
+    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job") as job:
+        with mock.patch("libensemble.executors.balsam_executors.balsam_executor.EventLog"):  # need to patch since wait polls
+            task = exctr.submit(calc_type="sim")
+
+            job.return_value.state = "RUNNING"
+            with pytest.raises(TimeoutExpired):
+                task.wait(timeout=3)
+                pytest.fail("Expected exception")
+
+            job.return_value.state = "JOB_FINISHED"
+            task.wait(timeout=3)
+            task.wait(timeout=3)  # should return immediately since self._check_poll() should return False
+            assert task.state == "FINISHED", "task was not finished after wait method"
+            assert not task.running(), "task shouldn't be running after wait method returns"
+            assert task.done(), "task should be 'done' after wait method"
+
+            task = exctr.submit(calc_type="sim", dry_run=True)
+            task.wait()  # should also return immediately since dry_run
+
+            task = exctr.submit(calc_type="sim")
+            job.return_value.state = "FAILED"
+            task.wait(timeout=3)
+            assert task.state == "FAILED", "Matching Balsam state should've been assigned to task"
+
+
+
 @pytest.mark.extra
 def test_task_kill():
     """Test of killing (cancelling) a balsam app"""
@@ -191,6 +248,7 @@ def test_task_kill():
     test_submit_app_dry()
     test_submit_app_wait()
     test_submit_revoke_alloc()
+    test_task_poll()
     test_task_wait()
     test_task_kill()
     teardown_module(__file__)

From da9fd70e3132076522be91f7a34cb618080307d8 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Mon, 22 Aug 2022 13:09:44 -0500
Subject: [PATCH 145/196] reformatting

---
 .../balsam_executors/balsam_executor.py       | 30 ++++---
 .../tests/unit_tests/test_executor_balsam.py  | 81 ++++++++++++++-----
 2 files changed, 79 insertions(+), 32 deletions(-)

diff --git a/libensemble/executors/balsam_executors/balsam_executor.py b/libensemble/executors/balsam_executors/balsam_executor.py
index 739d7af620..b4a4d79aab 100644
--- a/libensemble/executors/balsam_executors/balsam_executor.py
+++ b/libensemble/executors/balsam_executors/balsam_executor.py
@@ -79,7 +79,6 @@ class HelloApp(ApplicationDefinition):
     ExecutorException,
     TimeoutExpired,
     jassert,
-    STATES,
 )
 from libensemble.executors import Executor
 
@@ -121,7 +120,9 @@ def __init__(
     def _get_time_since_balsam_submit(self):
         """Return time since balsam task entered ``RUNNING`` state"""
 
-        event_query = EventLog.objects.filter(job_id=self.process.id, to_state="RUNNING")
+        event_query = EventLog.objects.filter(
+            job_id=self.process.id, to_state="RUNNING"
+        )
         if not len(event_query):
             return 0
         balsam_launch_datetime = event_query[0].timestamp
@@ -165,7 +166,6 @@ def _set_complete(self, dry_run=False):
             else:
                 self.state = balsam_state
 
-
         logger.info("Task {} ended with state {}".format(self.name, self.state))
 
     def poll(self):
@@ -202,7 +202,6 @@ def poll(self):
             self.state = "FAILED"
             self._set_complete()
 
-
     def wait(self, timeout=None):
         """Waits on completion of the task or raises ``TimeoutExpired``.
 
@@ -233,7 +232,9 @@ def wait(self, timeout=None):
             "POSTPROCESSED",
             "STAGED_OUT",
             "JOB_FINISHED",
-            "RUN_ERROR", "RUN_TIMEOUT", "FAILED"
+            "RUN_ERROR",
+            "RUN_TIMEOUT",
+            "FAILED",
         ]:
             time.sleep(0.2)
             self.process.refresh_from_db()
@@ -279,7 +280,9 @@ def add_app(self, *args):
         """Sync application with Balsam service"""
         pass
 
-    def register_app(self, BalsamApp, app_name=None, calc_type=None, desc=None, precedent=None):
+    def register_app(
+        self, BalsamApp, app_name=None, calc_type=None, desc=None, precedent=None
+    ):
         """Registers a Balsam ``ApplicationDefinition`` to libEnsemble. This class
         instance *must* have a ``site`` and ``command_template`` specified. See
         the Balsam docs for information on other optional fields.
@@ -303,7 +306,9 @@ def register_app(self, BalsamApp, app_name=None, calc_type=None, desc=None, prec
         """
 
         if precedent is not None:
-            logger.warning("precedent is ignored in Balsam executor - add to command template")
+            logger.warning(
+                "precedent is ignored in Balsam executor - add to command template"
+            )
 
         if not app_name:
             app_name = BalsamApp.command_template.split(" ")[0]
@@ -416,7 +421,9 @@ def revoke_allocation(self, allocation, timeout=60):
             time.sleep(1)
             allocation.refresh_from_db()
             if time.time() - start > timeout:
-                logger.warning("Unable to terminate Balsam BatchJob. You may need to login to the machine and manually remove it.")
+                logger.warning(
+                    "Unable to terminate Balsam BatchJob. You may need to login to the machine and manually remove it."
+                )
                 return False
 
         batchjob = BatchJob.objects.get(scheduler_id=allocation.scheduler_id)
@@ -561,10 +568,13 @@ def submit(
 
             if not task.timer.timing and not task.finished:
                 task.timer.start()
-                task.submit_time = task.timer.tstart  # Time not date - may not need if using timer.
+                task.submit_time = (
+                    task.timer.tstart
+                )  # Time not date - may not need if using timer.
 
             logger.info(
-                "Submitted Balsam App to site {}: " "nodes {} ppn {}".format(App.site, num_nodes, procs_per_node)
+                "Submitted Balsam App to site {}: "
+                "nodes {} ppn {}".format(App.site, num_nodes, procs_per_node)
             )
 
         self.list_of_tasks.append(task)
diff --git a/libensemble/tests/unit_tests/test_executor_balsam.py b/libensemble/tests/unit_tests/test_executor_balsam.py
index a5ecdd7782..b08b48c772 100644
--- a/libensemble/tests/unit_tests/test_executor_balsam.py
+++ b/libensemble/tests/unit_tests/test_executor_balsam.py
@@ -8,7 +8,12 @@
 import datetime
 from dataclasses import dataclass
 
-from libensemble.executors.executor import Executor, Application, ExecutorException, TimeoutExpired
+from libensemble.executors.executor import (
+    Executor,
+    Application,
+    ExecutorException,
+    TimeoutExpired,
+)
 
 
 # fake Balsam app
@@ -65,7 +70,9 @@ def test_register_app():
     exctr = Executor.executor
 
     exctr.serial_setup()  # does nothing, compatibility with legacy-balsam-exctr
-    exctr.add_app("hello", "world")  # does nothing, compatibility with legacy-balsam-exctr
+    exctr.add_app(
+        "hello", "world"
+    )  # does nothing, compatibility with legacy-balsam-exctr
     exctr.set_resources("hello")  # does nothing, compatibility with other executors
 
     exctr.register_app(TestLibeApp, calc_type="sim", precedent="fake/dir")
@@ -130,17 +137,28 @@ def test_submit_app_wait():
     """Test of exctr.submit blocking until app is running"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     exctr = Executor.executor
-    with mock.patch( "libensemble.executors.balsam_executors.balsam_executor.Job") as job:
-        with mock.patch("libensemble.executors.balsam_executors.balsam_executor.EventLog") as log:
+    with mock.patch(
+        "libensemble.executors.balsam_executors.balsam_executor.Job"
+    ) as job:
+        with mock.patch(
+            "libensemble.executors.balsam_executors.balsam_executor.EventLog"
+        ) as log:
             job.return_value.state = "RUNNING"
-            log.objects.filter.return_value = [LogEventTest(timestamp=datetime.datetime(2022, 4, 21, 20, 29, 33, 455144))]
+            log.objects.filter.return_value = [
+                LogEventTest(
+                    timestamp=datetime.datetime(2022, 4, 21, 20, 29, 33, 455144)
+                )
+            ]
             task = exctr.submit(calc_type="sim", wait_on_start=True)
-            assert task.running(), "new task is not marked as running after wait_on_start"
+            assert (
+                task.running()
+            ), "new task is not marked as running after wait_on_start"
 
             log.objects.filter.return_value = [LogEventTest(timestamp=None)]
             task = exctr.submit(calc_type="sim", wait_on_start=True)
-            assert task.runtime == 0, "runtime should be 0 without Balsam timestamp evaluated"
-
+            assert (
+                task.runtime == 0
+            ), "runtime should be 0 without Balsam timestamp evaluated"
 
 
 @pytest.mark.extra
@@ -158,12 +176,14 @@ def test_submit_revoke_alloc():
         ), "batchjob object not appended to executor's list of allocations"
 
         alloc.scheduler_id = None
-        assert not exctr.revoke_allocation(alloc, timeout=3), \
-            "unable to revoke allocation if Balsam never returns scheduler ID"
+        assert not exctr.revoke_allocation(
+            alloc, timeout=3
+        ), "unable to revoke allocation if Balsam never returns scheduler ID"
 
         alloc.scheduler_id = 1
-        assert exctr.revoke_allocation(alloc, timeout=3), \
-            "should've been able to revoke allocation if scheduler ID available"
+        assert exctr.revoke_allocation(
+            alloc, timeout=3
+        ), "should've been able to revoke allocation if scheduler ID available"
 
 
 @pytest.mark.extra
@@ -171,17 +191,25 @@ def test_task_poll():
     """Test of killing (cancelling) a balsam app"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     exctr = Executor.executor
-    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job") as job:
-        with mock.patch("libensemble.executors.balsam_executors.balsam_executor.EventLog"):
+    with mock.patch(
+        "libensemble.executors.balsam_executors.balsam_executor.Job"
+    ) as job:
+        with mock.patch(
+            "libensemble.executors.balsam_executors.balsam_executor.EventLog"
+        ):
             task = exctr.submit(calc_type="sim")
 
             job.return_value.state = "PREPROCESSED"
             task.poll()
-            assert task.state == "WAITING", "task should've been considered waiting based on balsam state"
+            assert (
+                task.state == "WAITING"
+            ), "task should've been considered waiting based on balsam state"
 
             job.return_value.state = "FAILED"
             task.poll()
-            assert task.state == "FAILED", "task should've been considered failed based on balsam state"
+            assert (
+                task.state == "FAILED"
+            ), "task should've been considered failed based on balsam state"
 
             task = exctr.submit(calc_type="sim")
 
@@ -199,8 +227,12 @@ def test_task_wait():
     """Test of killing (cancelling) a balsam app"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     exctr = Executor.executor
-    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job") as job:
-        with mock.patch("libensemble.executors.balsam_executors.balsam_executor.EventLog"):  # need to patch since wait polls
+    with mock.patch(
+        "libensemble.executors.balsam_executors.balsam_executor.Job"
+    ) as job:
+        with mock.patch(
+            "libensemble.executors.balsam_executors.balsam_executor.EventLog"
+        ):  # need to patch since wait polls
             task = exctr.submit(calc_type="sim")
 
             job.return_value.state = "RUNNING"
@@ -210,9 +242,13 @@ def test_task_wait():
 
             job.return_value.state = "JOB_FINISHED"
             task.wait(timeout=3)
-            task.wait(timeout=3)  # should return immediately since self._check_poll() should return False
+            task.wait(
+                timeout=3
+            )  # should return immediately since self._check_poll() should return False
             assert task.state == "FINISHED", "task was not finished after wait method"
-            assert not task.running(), "task shouldn't be running after wait method returns"
+            assert (
+                not task.running()
+            ), "task shouldn't be running after wait method returns"
             assert task.done(), "task should be 'done' after wait method"
 
             task = exctr.submit(calc_type="sim", dry_run=True)
@@ -221,8 +257,9 @@ def test_task_wait():
             task = exctr.submit(calc_type="sim")
             job.return_value.state = "FAILED"
             task.wait(timeout=3)
-            assert task.state == "FAILED", "Matching Balsam state should've been assigned to task"
-
+            assert (
+                task.state == "FAILED"
+            ), "Matching Balsam state should've been assigned to task"
 
 
 @pytest.mark.extra

From 2ea3f7550117ac67ffa613906a289a3d02233c9f Mon Sep 17 00:00:00 2001
From: Jeffrey Larson <jmlarson@anl.gov>
Date: Thu, 25 Aug 2022 11:40:18 -0500
Subject: [PATCH 146/196] Running black

---
 .../balsam_executors/balsam_executor.py       | 19 ++---
 .../test_executor_simple.py                   | 14 ++--
 .../tests/unit_tests/test_executor_balsam.py  | 80 +++++--------------
 libensemble/utils/runners.py                  |  8 +-
 4 files changed, 32 insertions(+), 89 deletions(-)

diff --git a/libensemble/executors/balsam_executors/balsam_executor.py b/libensemble/executors/balsam_executors/balsam_executor.py
index b4a4d79aab..05a8bf5e09 100644
--- a/libensemble/executors/balsam_executors/balsam_executor.py
+++ b/libensemble/executors/balsam_executors/balsam_executor.py
@@ -120,9 +120,7 @@ def __init__(
     def _get_time_since_balsam_submit(self):
         """Return time since balsam task entered ``RUNNING`` state"""
 
-        event_query = EventLog.objects.filter(
-            job_id=self.process.id, to_state="RUNNING"
-        )
+        event_query = EventLog.objects.filter(job_id=self.process.id, to_state="RUNNING")
         if not len(event_query):
             return 0
         balsam_launch_datetime = event_query[0].timestamp
@@ -280,9 +278,7 @@ def add_app(self, *args):
         """Sync application with Balsam service"""
         pass
 
-    def register_app(
-        self, BalsamApp, app_name=None, calc_type=None, desc=None, precedent=None
-    ):
+    def register_app(self, BalsamApp, app_name=None, calc_type=None, desc=None, precedent=None):
         """Registers a Balsam ``ApplicationDefinition`` to libEnsemble. This class
         instance *must* have a ``site`` and ``command_template`` specified. See
         the Balsam docs for information on other optional fields.
@@ -306,9 +302,7 @@ def register_app(
         """
 
         if precedent is not None:
-            logger.warning(
-                "precedent is ignored in Balsam executor - add to command template"
-            )
+            logger.warning("precedent is ignored in Balsam executor - add to command template")
 
         if not app_name:
             app_name = BalsamApp.command_template.split(" ")[0]
@@ -568,13 +562,10 @@ def submit(
 
             if not task.timer.timing and not task.finished:
                 task.timer.start()
-                task.submit_time = (
-                    task.timer.tstart
-                )  # Time not date - may not need if using timer.
+                task.submit_time = task.timer.tstart  # Time not date - may not need if using timer.
 
             logger.info(
-                "Submitted Balsam App to site {}: "
-                "nodes {} ppn {}".format(App.site, num_nodes, procs_per_node)
+                "Submitted Balsam App to site {}: " "nodes {} ppn {}".format(App.site, num_nodes, procs_per_node)
             )
 
         self.list_of_tasks.append(task)
diff --git a/libensemble/tests/functionality_tests/test_executor_simple.py b/libensemble/tests/functionality_tests/test_executor_simple.py
index 4db571bdf5..c09e5037bc 100644
--- a/libensemble/tests/functionality_tests/test_executor_simple.py
+++ b/libensemble/tests/functionality_tests/test_executor_simple.py
@@ -37,9 +37,7 @@
     sim_app2 = six_hump_camel.__file__
 
     exctr = MPIExecutor()
-    exctr.register_app(
-        full_path=sim_app2, app_name="six_hump_camel", calc_type="sim"
-    )  # Named app
+    exctr.register_app(full_path=sim_app2, app_name="six_hump_camel", calc_type="sim")  # Named app
 
     sim_specs = {
         "sim_f": sim_f,
@@ -65,9 +63,7 @@
     exit_criteria = {"sim_max": nworkers * 5}
 
     # Perform the run
-    H, persis_info, flag = libE(
-        sim_specs, gen_specs, exit_criteria, persis_info, libE_specs=libE_specs
-    )
+    H, persis_info, flag = libE(sim_specs, gen_specs, exit_criteria, persis_info, libE_specs=libE_specs)
 
     if is_manager:
         print("\nChecking expected task status against Workers ...\n")
@@ -79,8 +75,8 @@
         print("Expecting: {}".format(calc_status_list))
         print("Received:  {}\n".format(H["cstat"]))
 
-        assert np.array_equal(
-            H["cstat"], calc_status_list
-        ), "Error - unexpected calc status. Received: " + str(H["cstat"])
+        assert np.array_equal(H["cstat"], calc_status_list), "Error - unexpected calc status. Received: " + str(
+            H["cstat"]
+        )
 
         print("\n\n\nRun completed.")
diff --git a/libensemble/tests/unit_tests/test_executor_balsam.py b/libensemble/tests/unit_tests/test_executor_balsam.py
index b08b48c772..8c64f57d23 100644
--- a/libensemble/tests/unit_tests/test_executor_balsam.py
+++ b/libensemble/tests/unit_tests/test_executor_balsam.py
@@ -70,9 +70,7 @@ def test_register_app():
     exctr = Executor.executor
 
     exctr.serial_setup()  # does nothing, compatibility with legacy-balsam-exctr
-    exctr.add_app(
-        "hello", "world"
-    )  # does nothing, compatibility with legacy-balsam-exctr
+    exctr.add_app("hello", "world")  # does nothing, compatibility with legacy-balsam-exctr
     exctr.set_resources("hello")  # does nothing, compatibility with other executors
 
     exctr.register_app(TestLibeApp, calc_type="sim", precedent="fake/dir")
@@ -97,9 +95,7 @@ def test_submit_app_defaults():
 
     assert task in exctr.list_of_tasks, "new task not added to executor's list of tasks"
 
-    assert task == exctr.get_task(
-        task.id
-    ), "task retrieved via task ID doesn't match new task"
+    assert task == exctr.get_task(task.id), "task retrieved via task ID doesn't match new task"
 
     with pytest.raises(ExecutorException):
         task = exctr.submit()
@@ -114,9 +110,7 @@ def test_submit_app_workdir():
     with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job"):
         task = exctr.submit(calc_type="sim", workdir="output", machinefile="nope")
 
-    assert task.workdir == os.path.join(
-        exctr.workflow_name, "output"
-    ), "workdir not properly defined for new task"
+    assert task.workdir == os.path.join(exctr.workflow_name, "output"), "workdir not properly defined for new task"
 
 
 @pytest.mark.extra
@@ -127,9 +121,7 @@ def test_submit_app_dry():
     task = exctr.submit(calc_type="sim", dry_run=True)
     task.poll()
 
-    assert all(
-        [task.dry_run, task.done()]
-    ), "new task from dry_run wasn't marked as such, or set as done"
+    assert all([task.dry_run, task.done()]), "new task from dry_run wasn't marked as such, or set as done"
 
 
 @pytest.mark.extra
@@ -137,28 +129,18 @@ def test_submit_app_wait():
     """Test of exctr.submit blocking until app is running"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     exctr = Executor.executor
-    with mock.patch(
-        "libensemble.executors.balsam_executors.balsam_executor.Job"
-    ) as job:
-        with mock.patch(
-            "libensemble.executors.balsam_executors.balsam_executor.EventLog"
-        ) as log:
+    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job") as job:
+        with mock.patch("libensemble.executors.balsam_executors.balsam_executor.EventLog") as log:
             job.return_value.state = "RUNNING"
             log.objects.filter.return_value = [
-                LogEventTest(
-                    timestamp=datetime.datetime(2022, 4, 21, 20, 29, 33, 455144)
-                )
+                LogEventTest(timestamp=datetime.datetime(2022, 4, 21, 20, 29, 33, 455144))
             ]
             task = exctr.submit(calc_type="sim", wait_on_start=True)
-            assert (
-                task.running()
-            ), "new task is not marked as running after wait_on_start"
+            assert task.running(), "new task is not marked as running after wait_on_start"
 
             log.objects.filter.return_value = [LogEventTest(timestamp=None)]
             task = exctr.submit(calc_type="sim", wait_on_start=True)
-            assert (
-                task.runtime == 0
-            ), "runtime should be 0 without Balsam timestamp evaluated"
+            assert task.runtime == 0, "runtime should be 0 without Balsam timestamp evaluated"
 
 
 @pytest.mark.extra
@@ -167,13 +149,9 @@ def test_submit_revoke_alloc():
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     exctr = Executor.executor
     with mock.patch("libensemble.executors.balsam_executors.balsam_executor.BatchJob"):
-        alloc = exctr.submit_allocation(
-            site_id="libe-unit-test", num_nodes=1, wall_time_min=30
-        )
+        alloc = exctr.submit_allocation(site_id="libe-unit-test", num_nodes=1, wall_time_min=30)
 
-        assert (
-            alloc in exctr.allocations
-        ), "batchjob object not appended to executor's list of allocations"
+        assert alloc in exctr.allocations, "batchjob object not appended to executor's list of allocations"
 
         alloc.scheduler_id = None
         assert not exctr.revoke_allocation(
@@ -191,25 +169,17 @@ def test_task_poll():
     """Test of killing (cancelling) a balsam app"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     exctr = Executor.executor
-    with mock.patch(
-        "libensemble.executors.balsam_executors.balsam_executor.Job"
-    ) as job:
-        with mock.patch(
-            "libensemble.executors.balsam_executors.balsam_executor.EventLog"
-        ):
+    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job") as job:
+        with mock.patch("libensemble.executors.balsam_executors.balsam_executor.EventLog"):
             task = exctr.submit(calc_type="sim")
 
             job.return_value.state = "PREPROCESSED"
             task.poll()
-            assert (
-                task.state == "WAITING"
-            ), "task should've been considered waiting based on balsam state"
+            assert task.state == "WAITING", "task should've been considered waiting based on balsam state"
 
             job.return_value.state = "FAILED"
             task.poll()
-            assert (
-                task.state == "FAILED"
-            ), "task should've been considered failed based on balsam state"
+            assert task.state == "FAILED", "task should've been considered failed based on balsam state"
 
             task = exctr.submit(calc_type="sim")
 
@@ -227,9 +197,7 @@ def test_task_wait():
     """Test of killing (cancelling) a balsam app"""
     print("\nTest: {}\n".format(sys._getframe().f_code.co_name))
     exctr = Executor.executor
-    with mock.patch(
-        "libensemble.executors.balsam_executors.balsam_executor.Job"
-    ) as job:
+    with mock.patch("libensemble.executors.balsam_executors.balsam_executor.Job") as job:
         with mock.patch(
             "libensemble.executors.balsam_executors.balsam_executor.EventLog"
         ):  # need to patch since wait polls
@@ -242,13 +210,9 @@ def test_task_wait():
 
             job.return_value.state = "JOB_FINISHED"
             task.wait(timeout=3)
-            task.wait(
-                timeout=3
-            )  # should return immediately since self._check_poll() should return False
+            task.wait(timeout=3)  # should return immediately since self._check_poll() should return False
             assert task.state == "FINISHED", "task was not finished after wait method"
-            assert (
-                not task.running()
-            ), "task shouldn't be running after wait method returns"
+            assert not task.running(), "task shouldn't be running after wait method returns"
             assert task.done(), "task should be 'done' after wait method"
 
             task = exctr.submit(calc_type="sim", dry_run=True)
@@ -257,9 +221,7 @@ def test_task_wait():
             task = exctr.submit(calc_type="sim")
             job.return_value.state = "FAILED"
             task.wait(timeout=3)
-            assert (
-                task.state == "FAILED"
-            ), "Matching Balsam state should've been assigned to task"
+            assert task.state == "FAILED", "Matching Balsam state should've been assigned to task"
 
 
 @pytest.mark.extra
@@ -272,9 +234,7 @@ def test_task_kill():
 
     with mock.patch("libensemble.executors.balsam_executors.balsam_executor.EventLog"):
         task.kill()
-    assert (
-        task.finished and task.state == "USER_KILLED"
-    ), "task not set as killed after kill method"
+    assert task.finished and task.state == "USER_KILLED", "task not set as killed after kill method"
 
 
 if __name__ == "__main__":
diff --git a/libensemble/utils/runners.py b/libensemble/utils/runners.py
index 82b09dce1c..806b68258b 100644
--- a/libensemble/utils/runners.py
+++ b/libensemble/utils/runners.py
@@ -29,9 +29,7 @@ def __init__(self, sim_specs, gen_specs):
                 self.funcx_exctr = FuncXExecutor(FuncXClient())
 
             except ModuleNotFoundError:
-                logger.warning(
-                    "funcX use detected but funcX not importable. Is it installed?"
-                )
+                logger.warning("funcX use detected but funcX not importable. Is it installed?")
 
     def make_runners(self):
         """Creates functions to run a sim or gen. These functions are either
@@ -55,9 +53,7 @@ def run_gen(calc_in, persis_info, libE_info):
                 else:
                     result = self._normal_result
 
-                return result(
-                    calc_in, persis_info, self.gen_specs, libE_info, self.gen_f
-                )
+                return result(calc_in, persis_info, self.gen_specs, libE_info, self.gen_f)
 
         else:
             run_gen = []

From 7b08f483511f71bb27a3e09edef66185729db3e4 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Thu, 25 Aug 2022 22:23:21 -0500
Subject: [PATCH 147/196] Use cancel_points function in Surmise

---
 libensemble/gen_funcs/persistent_surmise_calib.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/libensemble/gen_funcs/persistent_surmise_calib.py b/libensemble/gen_funcs/persistent_surmise_calib.py
index 31842fb3fb..66da5d48d0 100644
--- a/libensemble/gen_funcs/persistent_surmise_calib.py
+++ b/libensemble/gen_funcs/persistent_surmise_calib.py
@@ -97,11 +97,7 @@ def cancel_columns(obs_offset, c, n_x, pending, ps):
                 sim_ids_to_cancel.append(sim_id_cancel)
                 pending[i, c] = 0
 
-    # Send only these fields to existing H rows and libEnsemble will slot in the change.
-    H_o = np.zeros(len(sim_ids_to_cancel), dtype=[("sim_id", int), ("cancel_requested", bool)])
-    H_o["sim_id"] = sim_ids_to_cancel
-    H_o["cancel_requested"] = True
-    ps.send(H_o)
+    ps.cancel_points(sim_ids_to_cancel)
 
 
 def assign_priority(n_x, n_thetas):

From 725a1a27bd7ccb20887825de1415e9fff1f7a8fc Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Thu, 25 Aug 2022 22:31:16 -0500
Subject: [PATCH 148/196] Change cancel_points to request_cancel_sim_ids

---
 libensemble/gen_funcs/persistent_sampling.py      | 2 +-
 libensemble/gen_funcs/persistent_surmise_calib.py | 2 +-
 libensemble/tools/persistent_support.py           | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/libensemble/gen_funcs/persistent_sampling.py b/libensemble/gen_funcs/persistent_sampling.py
index 258421b685..0cdacfe0a2 100644
--- a/libensemble/gen_funcs/persistent_sampling.py
+++ b/libensemble/gen_funcs/persistent_sampling.py
@@ -234,6 +234,6 @@ def persistent_uniform_with_cancellations(H, persis_info, gen_specs, libE_info):
             # Cancel as many points as got back
             cancel_ids = list(range(cancel_from, cancel_from + b))
             cancel_from += b
-            ps.cancel_points(cancel_ids)
+            ps.request_cancel_sim_ids(cancel_ids)
 
     return H_o, persis_info, FINISHED_PERSISTENT_GEN_TAG
diff --git a/libensemble/gen_funcs/persistent_surmise_calib.py b/libensemble/gen_funcs/persistent_surmise_calib.py
index 66da5d48d0..4e8945f7f5 100644
--- a/libensemble/gen_funcs/persistent_surmise_calib.py
+++ b/libensemble/gen_funcs/persistent_surmise_calib.py
@@ -97,7 +97,7 @@ def cancel_columns(obs_offset, c, n_x, pending, ps):
                 sim_ids_to_cancel.append(sim_id_cancel)
                 pending[i, c] = 0
 
-    ps.cancel_points(sim_ids_to_cancel)
+    ps.request_cancel_sim_ids(sim_ids_to_cancel)
 
 
 def assign_priority(n_x, n_thetas):
diff --git a/libensemble/tools/persistent_support.py b/libensemble/tools/persistent_support.py
index a5213c3af0..5ec78408e0 100644
--- a/libensemble/tools/persistent_support.py
+++ b/libensemble/tools/persistent_support.py
@@ -106,7 +106,7 @@ def send_recv(self, output, calc_status=UNSET_TAG, stay_active=False):
         self.send(output, calc_status, stay_active)
         return self.recv()
 
-    def cancel_points(self, sim_ids):
+    def request_cancel_sim_ids(self, sim_ids):
         H_o = np.zeros(len(sim_ids), dtype=[("sim_id", int), ("cancel_requested", bool)])
         H_o["sim_id"] = sim_ids
         H_o["cancel_requested"] = True

From 0c532af43c43df85b20e081a5b614ceb30179927 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Fri, 26 Aug 2022 11:49:19 -0500
Subject: [PATCH 149/196] Change stay_active to keep_state

---
 libensemble/manager.py                  |  4 ++--
 libensemble/tools/persistent_support.py | 10 +++++-----
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/libensemble/manager.py b/libensemble/manager.py
index 37b1c9f3ab..8c3f1aab71 100644
--- a/libensemble/manager.py
+++ b/libensemble/manager.py
@@ -417,8 +417,8 @@ def _update_state_on_worker_msg(self, persis_info, D_recv, w):
         calc_status = D_recv["calc_status"]
         Manager._check_received_calc(D_recv)
 
-        stay_active = D_recv["libE_info"].get("stay_active", False)
-        if w not in self.persis_pending and not self.W[w - 1]["active_recv"] and not stay_active:
+        keep_state = D_recv["libE_info"].get("keep_state", False)
+        if w not in self.persis_pending and not self.W[w - 1]["active_recv"] and not keep_state:
             self.W[w - 1]["active"] = 0
 
         if calc_status in [FINISHED_PERSISTENT_SIM_TAG, FINISHED_PERSISTENT_GEN_TAG]:
diff --git a/libensemble/tools/persistent_support.py b/libensemble/tools/persistent_support.py
index 5ec78408e0..c0b3a5f4d7 100644
--- a/libensemble/tools/persistent_support.py
+++ b/libensemble/tools/persistent_support.py
@@ -25,7 +25,7 @@ def __init__(self, libE_info, calc_type):
         ], "The calc_type: {} specifies neither a simulator nor generator.".format(self.calc_type)
         self.calc_str = calc_type_strings[self.calc_type]
 
-    def send(self, output, calc_status=UNSET_TAG, stay_active=False):
+    def send(self, output, calc_status=UNSET_TAG, keep_state=False):
         """
         Send message from worker to manager.
 
@@ -42,7 +42,7 @@ def send(self, output, calc_status=UNSET_TAG, stay_active=False):
         else:
             libE_info = self.libE_info
 
-        libE_info["stay_active"] = stay_active
+        libE_info["keep_state"] = keep_state
 
         D = {
             "calc_out": output,
@@ -93,7 +93,7 @@ def recv(self, blocking=True):
         logger.debug("Persistent {} received work rows from manager".format(self.calc_str))
         return tag, Work, calc_in
 
-    def send_recv(self, output, calc_status=UNSET_TAG, stay_active=False):
+    def send_recv(self, output, calc_status=UNSET_TAG, keep_state=False):
         """
         Send message from worker to manager and receive response.
 
@@ -103,7 +103,7 @@ def send_recv(self, output, calc_status=UNSET_TAG, stay_active=False):
         :returns: message tag, Work dictionary, calc_in array
 
         """
-        self.send(output, calc_status, stay_active)
+        self.send(output, calc_status, keep_state)
         return self.recv()
 
     def request_cancel_sim_ids(self, sim_ids):
@@ -111,4 +111,4 @@ def request_cancel_sim_ids(self, sim_ids):
         H_o["sim_id"] = sim_ids
         H_o["cancel_requested"] = True
         print(H_o)
-        self.send(H_o, stay_active=True)
+        self.send(H_o, keep_state=True)

From 17cc00f8b4d78e4eecfc717852f62c267c266217 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Fri, 26 Aug 2022 12:44:53 -0500
Subject: [PATCH 150/196] Add docstring to request_cancel_sim_ids

---
 libensemble/tools/persistent_support.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/libensemble/tools/persistent_support.py b/libensemble/tools/persistent_support.py
index c0b3a5f4d7..7e45ed8125 100644
--- a/libensemble/tools/persistent_support.py
+++ b/libensemble/tools/persistent_support.py
@@ -107,6 +107,12 @@ def send_recv(self, output, calc_status=UNSET_TAG, keep_state=False):
         return self.recv()
 
     def request_cancel_sim_ids(self, sim_ids):
+        """Request cancellation of sim_ids
+
+        :param sim_ids: A list of sim_ids to cancel
+
+        A message is sent to the manager to mark requested sim_ids as cancel_requested
+        """
         H_o = np.zeros(len(sim_ids), dtype=[("sim_id", int), ("cancel_requested", bool)])
         H_o["sim_id"] = sim_ids
         H_o["cancel_requested"] = True

From 6b9f8df3f6969d45267b0724a059c6e3ccac1c2a Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Fri, 26 Aug 2022 12:56:04 -0500
Subject: [PATCH 151/196] Change surmise install from fork to main repo

---
 .github/workflows/ci.yml  | 2 +-
 docs/examples/surmise.rst | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 9161f5375c..cd058c75bc 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -145,7 +145,7 @@ jobs:
 
             pip install mpmath
             pip install ax-platform
-            python -m pip install --upgrade git+https://github.com/mosesyhc/surmise.git@development/PCGPwM
+            pip install surmise.git
 
         - name: Install some generator dependencies on Windows
           if: matrix.os == 'windows-latest'
diff --git a/docs/examples/surmise.rst b/docs/examples/surmise.rst
index f036883ee3..d1ec539224 100644
--- a/docs/examples/surmise.rst
+++ b/docs/examples/surmise.rst
@@ -3,9 +3,9 @@ persistent_surmise
 
 Required: Surmise_
 
-Note that currently the github fork https://github.com/mosesyhc/surmise should be used::
+Surmise can be pip installed::
 
-    python -m pip install --upgrade git+https://github.com/mosesyhc/surmise.git
+    pip install surmise
 
 The :doc:`Borehole Calibration tutorial<../tutorials/calib_cancel_tutorial>` uses this generator as an example of the capability to cancel pending simulations.
 

From 06c016ffcf31ecf72aaf40e8d4832a02274ba0b1 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Fri, 26 Aug 2022 13:19:57 -0500
Subject: [PATCH 152/196] Update surmise tutorial

---
 docs/tutorials/calib_cancel_tutorial.rst | 41 +++++++++---------------
 1 file changed, 15 insertions(+), 26 deletions(-)

diff --git a/docs/tutorials/calib_cancel_tutorial.rst b/docs/tutorials/calib_cancel_tutorial.rst
index 718d6e4398..fb86db2fdb 100644
--- a/docs/tutorials/calib_cancel_tutorial.rst
+++ b/docs/tutorials/calib_cancel_tutorial.rst
@@ -25,12 +25,8 @@ Overview of the Calibration Problem
 
 The generator function featured in this tutorial can be found in
 ``gen_funcs/persistent_surmise_calib.py`` and uses the `surmise`_ library for its
-calibration surrogate model interface.
-
-.. note::
-    Note that this repository is a fork of
-    the main surmise repository, but it retains support for the "PCGPwM" emulation
-    method used in the generator. surmise is under active development.
+calibration surrogate model interface. The surmise library uses the  "PCGPwM"
+emulation method in this example.
 
 Say there is a computer model :math:`f(\theta, x)` to be calibrated.  To calibrate
 is to find some parameter :math:`\theta_0` such that :math:`f(\theta_0, x)` closely
@@ -105,27 +101,25 @@ cancelled ("obviated"). If so, the generator then calls ``cancel_columns()``::
         ...
         c_obviate = info['obviatesugg']  # suggested
         if len(c_obviate) > 0:
-            cancel_columns(obs_offset, c_obviate, n_x, pending, comm)
+            cancel_columns(obs_offset, c_obviate, n_x, pending, ps)
 
 ``obs_offset`` is an offset that excludes the observations when mapping points in surmise
 data structures to ``sim_id``'s, ``c_obviate`` is a selection
 of columns to cancel, ``n_x`` is the number of ``x`` values, and ``pending`` is used
-to check that points marked for cancellation have not already returned. ``comm`` is a
-communicator object from :doc:`libE_info<../data_structures/work_dict>` used to send
-and receive messages from the Manager.
+to check that points marked for cancellation have not already returned. ``ps`` is the
+instantiation of the *PersistentSupport* class that is set up for persistent generators, and
+provides and interface for communication with the manager.
 
 Within ``cancel_columns()``, each column in ``c_obviate`` is iterated over, and if a
 point is ``pending`` and thus has not yet been evaluated by a simulation,
 its ``sim_id`` is appended to a list to be sent to the Manager for cancellation.
-A new, separate local :doc:`History array<../history_output_logging>` is defined with the
-selected ``'sim_id'`` s and the ``'cancel_requested'`` field set to ``True``. This array is
-then sent to the Manager using the ``send_mgr_worker_msg`` persistent generator
-helper function. Each of these helper functions is described :ref:`here<p_gen_routines>`.
-The entire ``cancel_columns()`` routine is listed below:
+Cancellation is requested using the helper function ``request_cancel_sim_ids`` provided
+by the *PersistentSupport* class.  Each of these helper functions is described
+:ref:`here<p_gen_routines>`. The entire ``cancel_columns()`` routine is listed below:
 
 .. code-block:: python
 
-    def cancel_columns(obs_offset, c, n_x, pending, comm):
+    def cancel_columns(obs_offset, c, n_x, pending, ps):
         """Cancel columns"""
         sim_ids_to_cancel = []
         columns = np.unique(c)
@@ -137,11 +131,7 @@ The entire ``cancel_columns()`` routine is listed below:
                     sim_ids_to_cancel.append(sim_id_cancel)
                     pending[i, c] = 0
 
-        # Send only these fields to existing H rows and libEnsemble will slot in the change.
-        H_o = np.zeros(len(sim_ids_to_cancel), dtype=[('sim_id', int), ('cancel_requested', bool)])
-        H_o['sim_id'] = sim_ids_to_cancel
-        H_o['cancel_requested'] = True
-        send_mgr_worker_msg(comm, H_o)
+        ps.request_cancel_sim_ids(sim_ids_to_cancel)
 
 In future calls to the allocation function by the manager, points that would have
 been distributed for simulation work but are now marked with "cancel_requested" will not
@@ -174,11 +164,10 @@ This is calculated from other parameters in the calling script.
 
 By default, workers (including persistent workers), are only
 allocated work when they're in an :doc:`idle or non-active state<../data_structures/worker_array>`.
-However, since this generator must asynchronously update its model and
-cancel pending evaluations, the worker running this generator remains
-in an *active receive* state, until it becomes non-persistent. This means
-both the manager and persistent worker (generator in this case) must be
-prepared for irregular sending /receiving of data.
+However, since this generator must asynchronously update its model, the worker
+running this generator remains in an *active receive* state, until it becomes
+non-persistent. This means both the manager and persistent worker (generator in
+this case) must be prepared for irregular sending /receiving of data.
 
 .. Manager - Cancellation, History Updates, and Allocation
 .. -------------------------------------------------------

From c8aff6879dab4161487eb5b35a61317e00313faf Mon Sep 17 00:00:00 2001
From: Jeffrey Larson <jmlarson@anl.gov>
Date: Fri, 26 Aug 2022 13:45:58 -0500
Subject: [PATCH 153/196] Whitespace

---
 docs/FAQ.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/FAQ.rst b/docs/FAQ.rst
index 0c0538a366..e29896c241 100644
--- a/docs/FAQ.rst
+++ b/docs/FAQ.rst
@@ -228,8 +228,8 @@ macOS and Windows Errors
 
 **Can I run libEnsemble on Windows**
 
-Although we run many libEnsemble workflows successfully on Windows using both MPI and local comms, we do not 
-rigorously support Windows, and recommend unix-like systems as a preference. Windows tends to produce more 
+Although we run many libEnsemble workflows successfully on Windows using both MPI and local comms, we do not
+rigorously support Windows, and recommend unix-like systems as a preference. Windows tends to produce more
 platform-specific issues that are difficult to reproduce and troubleshoot.
 
 Feel free to check our `Github Actions`_ page to see what tests we run regularly on Windows.

From 45ded3fe4ea137deb38c2d71059518ecf0e9244e Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Fri, 26 Aug 2022 23:47:59 -0500
Subject: [PATCH 154/196] Update work dictionary docs

---
 .github/workflows/ci.yml            |  2 +-
 docs/data_structures/libE_specs.rst |  2 +-
 docs/data_structures/work_dict.rst  | 22 ++++++++++++++--------
 3 files changed, 16 insertions(+), 10 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index cd058c75bc..833613bd0f 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -145,7 +145,7 @@ jobs:
 
             pip install mpmath
             pip install ax-platform
-            pip install surmise.git
+            pip install surmise
 
         - name: Install some generator dependencies on Windows
           if: matrix.os == 'windows-latest'
diff --git a/docs/data_structures/libE_specs.rst b/docs/data_structures/libE_specs.rst
index f31cabb0a1..46fcd15aa2 100644
--- a/docs/data_structures/libE_specs.rst
+++ b/docs/data_structures/libE_specs.rst
@@ -33,7 +33,7 @@ libEnsemble is primarily customized by setting options within a ``libE_specs`` d
             processes are then terminated. multiprocessing default: 1
         'kill_canceled_sims' [bool]:
             Will libE try to kill sims that user functions mark 'cancel_requested' as True.
-            If False, the manager avoid this moderate overhead.
+            If False, the manager avoids this moderate overhead.
             Default: True
 
         Directory management options:
diff --git a/docs/data_structures/work_dict.rst b/docs/data_structures/work_dict.rst
index 35af11cdff..d056a634d3 100644
--- a/docs/data_structures/work_dict.rst
+++ b/docs/data_structures/work_dict.rst
@@ -3,27 +3,33 @@
 work dictionary
 ===============
 
-The work dictionary contains integer keys ``i`` and dictionary values to be
-given to worker ``i``. ``Work[i]`` has the following form::
+The work dictionary constains metadata that is used by the manager to send a packet
+of work to a worker. The dictionary uses integer keys ``i`` and values that determine
+the data given to worker ``i``. ``Work[i]`` has the following form::
 
     Work[i]: [dict]:
 
         Required keys:
-        'persis_info' [dict]: Any persistent info to be sent to worker 'i'
         'H_fields' [list]: The field names of the history 'H' to be sent to worker 'i'
+        'persis_info' [dict]: Any persistent info to be sent to worker 'i'
         'tag' [int]: 'EVAL_SIM_TAG'/'EVAL_GEN_TAG' if worker 'i' is to call sim/gen_func
-        'libE_info' [dict]: Info sent to/from worker to help manager update the 'H'
+        'libE_info' [dict]: Info sent to/from worker to help manager update the 'H' array
 
-        Optional keys are:
+        libE_info contains the following:
         'H_rows' [list of ints]: History rows to send to worker 'i'
-        'blocking' [list of ints]: Workers to be blocked by this calculation
-        'persistent' [bool]: True if worker 'i' will enter persistent mode
+        'rset_team' [list of ints]: The resource sets to be assigned (if dynamic scheduling is used)
+        'persistent' [bool]: True if worker 'i' will enter persistent mode (Default: False)
+
+The work dictionary is typically set using the ``gen_work`` or ``sim_work``
+:doc:`helper functions<../function_guides/allocator>` in the allocation function.
+``H_fields``, for example, is usually packed from either ``sim_specs["in"]``, ``gen_specs["in"]``
+or the equivalent "persis_in" variants.
 
 .. seealso::
   For allocation functions giving work dictionaries using persistent workers,
   see `start_only_persistent.py`_ or `start_persistent_local_opt_gens.py`_.
   For a use case where the allocation and generator functions combine to do
-  simulation evaluations with different resources (blocking some workers), see
+  simulation evaluations with different resources, see
   `test_uniform_sampling_with_variable_resources.py`_.
 
 .. _start_only_persistent.py: https://github.com/Libensemble/libensemble/blob/develop/libensemble/alloc_funcs/start_only_persistent.py

From 26f2738c9e3f34c5aeead1543ae6e113016e5679 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Sat, 27 Aug 2022 18:28:41 -0500
Subject: [PATCH 155/196] Document keep_state option in docstrings

---
 libensemble/tools/persistent_support.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/libensemble/tools/persistent_support.py b/libensemble/tools/persistent_support.py
index 7e45ed8125..9709728fd7 100644
--- a/libensemble/tools/persistent_support.py
+++ b/libensemble/tools/persistent_support.py
@@ -31,6 +31,9 @@ def send(self, output, calc_status=UNSET_TAG, keep_state=False):
 
         :param output: Output array to be sent to manager
         :param calc_status: Optional, Provides a task status
+        :param keep_state: Optional, If True the manager will not modify the workers state
+            (usually the manager changes the worker's state to inactive, indiciating ready
+            to receive more work, unless using active receive mode).
 
         :returns: None
 
@@ -56,6 +59,7 @@ def send(self, output, calc_status=UNSET_TAG, keep_state=False):
     def recv(self, blocking=True):
         """
         Receive message to worker from manager.
+        :param blocking: Optional, If True (default), will block until a message is received.
 
         :returns: message tag, Work dictionary, calc_in array
 
@@ -99,6 +103,9 @@ def send_recv(self, output, calc_status=UNSET_TAG, keep_state=False):
 
         :param output: Output array to be sent to manager
         :param calc_status: Optional, Provides a task status
+        :param keep_state: Optional, If True the manager will not modify the workers state
+            (usually the manager changes the worker's state to inactive, indiciating ready
+            to receive more work, unless using active receive mode).
 
         :returns: message tag, Work dictionary, calc_in array
 

From 3d8dbf57384c298b02d1a9d5181f1fc6a4da2328 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Sat, 27 Aug 2022 19:39:28 -0500
Subject: [PATCH 156/196] Update docs for keep_state and cancel function

---
 docs/function_guides/generator.rst | 55 +++++++++++++++++++-----------
 1 file changed, 35 insertions(+), 20 deletions(-)

diff --git a/docs/function_guides/generator.rst b/docs/function_guides/generator.rst
index 57d301b82a..b8e666a7c5 100644
--- a/docs/function_guides/generator.rst
+++ b/docs/function_guides/generator.rst
@@ -52,13 +52,17 @@ any other libraries to serve their needs.
 Persistent Generators
 ---------------------
 
-While normal generators return after completing their calculation, persistent
-generators receive Work units, perform computations, and communicate results
-directly to the manager in a loop, not returning until explicitly instructed by
-the manager. The calling worker becomes a dedicated :ref:`persistent worker<persis_worker>`.
+While non-persistent generators return after completing their calculation, persistent
+generators receive work units, perform computations, and communicate results
+directly to the manager in a loop. A persistent generator returns either when
+explicitly instructed by the manager, or by exiting its main loop based on some
+condition. The allocation function can determine what to do once a persistent
+generator finishes, such as ending the ensemble.
+
+The calling worker becomes a dedicated :ref:`persistent worker<persis_worker>`.
 A ``gen_f`` is initiated as persistent by the ``alloc_f``.
 
-Most users prefer persistent generators since they do not need to be
+Many users prefer persistent generators since they do not need to be
 re-initialized every time their past work is completed and evaluated by a
 simulation, and can evaluate returned simulation results over the course of
 an entire libEnsemble routine as a single function instance. The :doc:`APOSMM<../examples/aposmm>`
@@ -133,27 +137,17 @@ function (see :ref:`start_only_persistent<start_only_persistent_label>`).
 
 The user is responsible for ensuring there are no communication deadlocks
 in this mode. Note that in manager/worker message exchanges, only the worker-side
-receive is blocking.
+receive is blocking by default (a non-blocking option is available).
 
 Cancelling Simulations
 ----------------------
 
 Previously submitted simulations can be cancelled by sending a message to the manager.
-To do this as a separate communication, a persistent generator should be
-in *active receive* mode to prevent a deadlock.
 
-To send out cancellations of previously submitted simulations, the generator
-can initiate a history array with just the ``sim_id`` and ``cancel_requested`` fields.
-Then fill in the ``sim_id``'s to cancel and set the ``cancel_requested`` field to ``True``.
-In the following example, ``sim_ids_to_cancel`` is a list of integers.
+To do this a PersistentSupport helper function is provided.
 
-.. code-block:: python
-
-    # Send only these fields to existing H rows and libEnsemble will slot in the change.
-    H_o = np.zeros(len(sim_ids_to_cancel), dtype=[('sim_id', int), ('cancel_requested', bool)])
-    H_o['sim_id'] = sim_ids_to_cancel
-    H_o['cancel_requested'] = True
-    my_support.send(H_o)
+.. currentmodule:: libensemble.tools.persistent_support.PersistentSupport
+.. autofunction:: request_cancel_sim_ids
 
 If a generated point is cancelled by the generator before it has been given to a
 worker for evaluation, then it will never be given. If it has already returned from the
@@ -165,6 +159,27 @@ by a user function, otherwise it will be ignored.
 The :doc:`Borehole Calibration tutorial<../tutorials/calib_cancel_tutorial>` gives an example
 of the capability to cancel pending simulations.
 
+Modification of existing points
+-------------------------------
+
+To change existing fields of the history array, the generator can initiate an output array
+with just the ``sim_id`` and fields to be modified (in place of ``gen_specs["out"]``,
+and then send to the manager as with regular communications. Any such message received by
+the manager will modify the given fields for the given *sim_ids*. If the
+changes are separate from the generation of new points and should not mark the generator as
+ready to receive new points, then send with the ``keep_state`` argument to *True*.
+
+For example, the cancellation function ``request_cancel_sim_ids`` could be replicated by
+the following (where ``sim_ids_to_cancel`` is a list of integers):
+
+.. code-block:: python
+
+    # Send only these fields to existing H rows and libEnsemble will slot in the change.
+    H_o = np.zeros(len(sim_ids_to_cancel), dtype=[('sim_id', int), ('cancel_requested', bool)])
+    H_o['sim_id'] = sim_ids_to_cancel
+    H_o['cancel_requested'] = True
+    ps.send(H_o, keep_state=True)
+
 Generator initiated shutdown
 ----------------------------
 
@@ -176,5 +191,5 @@ the ensemble as soon a persistent generator returns. The usual return values sho
 Examples
 --------
 
-Examples of normal and persistent generator functions
+Examples of non-persistent and persistent generator functions
 can be found :doc:`here<../examples/gen_funcs>`.

From 3cc94707870f619d6f0644a57977577f0a543153 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Sun, 28 Aug 2022 22:05:58 -0500
Subject: [PATCH 157/196] Remove keep_state from send_recv

An unlikely use-case.
---
 libensemble/tools/persistent_support.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/libensemble/tools/persistent_support.py b/libensemble/tools/persistent_support.py
index 9709728fd7..6fc102ff35 100644
--- a/libensemble/tools/persistent_support.py
+++ b/libensemble/tools/persistent_support.py
@@ -59,6 +59,7 @@ def send(self, output, calc_status=UNSET_TAG, keep_state=False):
     def recv(self, blocking=True):
         """
         Receive message to worker from manager.
+
         :param blocking: Optional, If True (default), will block until a message is received.
 
         :returns: message tag, Work dictionary, calc_in array
@@ -97,20 +98,17 @@ def recv(self, blocking=True):
         logger.debug("Persistent {} received work rows from manager".format(self.calc_str))
         return tag, Work, calc_in
 
-    def send_recv(self, output, calc_status=UNSET_TAG, keep_state=False):
+    def send_recv(self, output, calc_status=UNSET_TAG):
         """
         Send message from worker to manager and receive response.
 
         :param output: Output array to be sent to manager
         :param calc_status: Optional, Provides a task status
-        :param keep_state: Optional, If True the manager will not modify the workers state
-            (usually the manager changes the worker's state to inactive, indiciating ready
-            to receive more work, unless using active receive mode).
 
         :returns: message tag, Work dictionary, calc_in array
 
         """
-        self.send(output, calc_status, keep_state)
+        self.send(output, calc_status)
         return self.recv()
 
     def request_cancel_sim_ids(self, sim_ids):

From 09daf6badafa9ae170c99e9f4e45a42cbe321c70 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Sun, 28 Aug 2022 22:30:58 -0500
Subject: [PATCH 158/196] Update persistent generator docs

---
 docs/function_guides/generator.rst | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/docs/function_guides/generator.rst b/docs/function_guides/generator.rst
index b8e666a7c5..57aad917c9 100644
--- a/docs/function_guides/generator.rst
+++ b/docs/function_guides/generator.rst
@@ -133,7 +133,9 @@ By default, a persistent worker (generator in this case) models the manager/work
 communications of a regular worker (i.e., the generator is expected to alternately
 receive and send data in a *ping pong* fashion). To have an irregular communication
 pattern, a worker can be initiated in *active receive* mode by the allocation
-function (see :ref:`start_only_persistent<start_only_persistent_label>`).
+function (see :ref:`start_only_persistent<start_only_persistent_label>`). In this mode,
+the persistent worker will always be considered ready to receive more data
+(e.g.,~ evaluation results). It can also send to the manager at any time.
 
 The user is responsible for ensuring there are no communication deadlocks
 in this mode. Note that in manager/worker message exchanges, only the worker-side
@@ -162,12 +164,13 @@ of the capability to cancel pending simulations.
 Modification of existing points
 -------------------------------
 
-To change existing fields of the history array, the generator can initiate an output array
-with just the ``sim_id`` and fields to be modified (in place of ``gen_specs["out"]``,
-and then send to the manager as with regular communications. Any such message received by
-the manager will modify the given fields for the given *sim_ids*. If the
-changes are separate from the generation of new points and should not mark the generator as
-ready to receive new points, then send with the ``keep_state`` argument to *True*.
+To change existing fields of the history array, the generator can initialize an output
+array where the *dtype* contains the ``sim_id`` and the fields to be modified (in
+place of ``gen_specs["out"]``), and then send to the manager as with regular
+communications. Any such message received by the manager will modify the given fields
+for the given *sim_ids*. If the changes are separate from the generation of new points
+and should not mark the generator as ready to receive new points, then send with the
+``keep_state`` argument set to *True*.
 
 For example, the cancellation function ``request_cancel_sim_ids`` could be replicated by
 the following (where ``sim_ids_to_cancel`` is a list of integers):

From f837d2caf7b12672a3f99a398a1ef73769a40fd6 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Mon, 29 Aug 2022 19:36:34 -0500
Subject: [PATCH 159/196] Revert "Change surmise install from fork to main
 repo"

This reverts commit 6b9f8df3f6969d45267b0724a059c6e3ccac1c2a.
---
 .github/workflows/ci.yml  | 2 +-
 docs/examples/surmise.rst | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 833613bd0f..9161f5375c 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -145,7 +145,7 @@ jobs:
 
             pip install mpmath
             pip install ax-platform
-            pip install surmise
+            python -m pip install --upgrade git+https://github.com/mosesyhc/surmise.git@development/PCGPwM
 
         - name: Install some generator dependencies on Windows
           if: matrix.os == 'windows-latest'
diff --git a/docs/examples/surmise.rst b/docs/examples/surmise.rst
index d1ec539224..f036883ee3 100644
--- a/docs/examples/surmise.rst
+++ b/docs/examples/surmise.rst
@@ -3,9 +3,9 @@ persistent_surmise
 
 Required: Surmise_
 
-Surmise can be pip installed::
+Note that currently the github fork https://github.com/mosesyhc/surmise should be used::
 
-    pip install surmise
+    python -m pip install --upgrade git+https://github.com/mosesyhc/surmise.git
 
 The :doc:`Borehole Calibration tutorial<../tutorials/calib_cancel_tutorial>` uses this generator as an example of the capability to cancel pending simulations.
 

From 98aefde87213c49c6940e9012590fbdcd1d0dc16 Mon Sep 17 00:00:00 2001
From: Jeffrey Larson <jmlarson@anl.gov>
Date: Mon, 29 Aug 2022 21:30:37 -0500
Subject: [PATCH 160/196] Small edits

---
 docs/tutorials/calib_cancel_tutorial.rst | 4 ++--
 libensemble/tools/persistent_support.py  | 7 ++++---
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/docs/tutorials/calib_cancel_tutorial.rst b/docs/tutorials/calib_cancel_tutorial.rst
index fb86db2fdb..34e60c88de 100644
--- a/docs/tutorials/calib_cancel_tutorial.rst
+++ b/docs/tutorials/calib_cancel_tutorial.rst
@@ -108,7 +108,7 @@ data structures to ``sim_id``'s, ``c_obviate`` is a selection
 of columns to cancel, ``n_x`` is the number of ``x`` values, and ``pending`` is used
 to check that points marked for cancellation have not already returned. ``ps`` is the
 instantiation of the *PersistentSupport* class that is set up for persistent generators, and
-provides and interface for communication with the manager.
+provides an interface for communication with the manager.
 
 Within ``cancel_columns()``, each column in ``c_obviate`` is iterated over, and if a
 point is ``pending`` and thus has not yet been evaluated by a simulation,
@@ -167,7 +167,7 @@ allocated work when they're in an :doc:`idle or non-active state<../data_structu
 However, since this generator must asynchronously update its model, the worker
 running this generator remains in an *active receive* state, until it becomes
 non-persistent. This means both the manager and persistent worker (generator in
-this case) must be prepared for irregular sending /receiving of data.
+this case) must be prepared for irregular sending/receiving of data.
 
 .. Manager - Cancellation, History Updates, and Allocation
 .. -------------------------------------------------------
diff --git a/libensemble/tools/persistent_support.py b/libensemble/tools/persistent_support.py
index 6fc102ff35..5ed5751ade 100644
--- a/libensemble/tools/persistent_support.py
+++ b/libensemble/tools/persistent_support.py
@@ -31,9 +31,10 @@ def send(self, output, calc_status=UNSET_TAG, keep_state=False):
 
         :param output: Output array to be sent to manager
         :param calc_status: Optional, Provides a task status
-        :param keep_state: Optional, If True the manager will not modify the workers state
-            (usually the manager changes the worker's state to inactive, indiciating ready
-            to receive more work, unless using active receive mode).
+        :param keep_state: Optional, If True the manager will not modify its
+            record of the workers state (usually the manager changes the
+            worker's state to inactive, indiciating the worker ready to receive
+            more work, unless using active receive mode).
 
         :returns: None
 

From 4344ee5f4da3f19064b1d22f68b535daf4e491f0 Mon Sep 17 00:00:00 2001
From: Jeffrey Larson <jmlarson@anl.gov>
Date: Tue, 30 Aug 2022 08:26:36 -0500
Subject: [PATCH 161/196] No space after one-line docstrings

---
 examples/tutorials/aposmm/tutorial_six_hump_camel.py   |  1 -
 libensemble/comms/logs.py                              |  2 --
 .../executors/balsam_executors/balsam_executor.py      |  4 ----
 .../balsam_executors/legacy_balsam_executor.py         |  3 ---
 libensemble/gen_funcs/old_aposmm.py                    |  1 -
 libensemble/gen_funcs/persistent_aposmm.py             |  1 -
 libensemble/gen_funcs/surmise_calib_support.py         |  1 -
 libensemble/libE.py                                    | 10 ----------
 libensemble/manager.py                                 |  4 ----
 libensemble/output_directory.py                        |  3 ---
 libensemble/resources/node_resources.py                |  1 -
 libensemble/resources/resources.py                     |  1 -
 libensemble/resources/scheduler.py                     |  1 -
 libensemble/resources/worker_resources.py              |  2 --
 libensemble/sim_funcs/branin/branin_obj.py             |  1 -
 libensemble/sim_funcs/noisy_vector_mapping.py          |  1 -
 .../balsam_tests/setup_balsam_tests.py                 |  1 -
 .../regression_tests/test_uniform_sampling_cancel.py   |  1 -
 libensemble/tests/unit_tests/test_executor.py          |  2 --
 libensemble/tests/unit_tests/test_scipy.py             |  2 --
 .../tests/unit_tests/test_sim_dir_properties.py        |  4 ----
 libensemble/tools/parse_args.py                        |  1 -
 libensemble/worker.py                                  |  4 ----
 23 files changed, 52 deletions(-)

diff --git a/examples/tutorials/aposmm/tutorial_six_hump_camel.py b/examples/tutorials/aposmm/tutorial_six_hump_camel.py
index 3d8a2bb348..fb9ea363c2 100644
--- a/examples/tutorials/aposmm/tutorial_six_hump_camel.py
+++ b/examples/tutorials/aposmm/tutorial_six_hump_camel.py
@@ -3,7 +3,6 @@
 
 def six_hump_camel(H, persis_info, sim_specs, _):
     """Six-Hump Camel sim_f."""
-
     batch = len(H["x"])  # Num evaluations each sim_f call.
     H_o = np.zeros(batch, dtype=sim_specs["out"])  # Define output array H
 
diff --git a/libensemble/comms/logs.py b/libensemble/comms/logs.py
index 997f312641..628f5bb59a 100644
--- a/libensemble/comms/logs.py
+++ b/libensemble/comms/logs.py
@@ -120,7 +120,6 @@ def init_worker_logger(logr, lev):
 
 def worker_logging_config(comm, worker_id=None):
     """Add a comm handler with worker ID filter to the indicated logger."""
-
     logconfig = LogConfig.config
     logger = logging.getLogger(logconfig.name)
     slogger = logging.getLogger(logconfig.stats_name)
@@ -142,7 +141,6 @@ def worker_logging_config(comm, worker_id=None):
 
 def manager_logging_config():
     """Add file-based logging at manager."""
-
     stat_timer = Timer()
     stat_timer.start()
 
diff --git a/libensemble/executors/balsam_executors/balsam_executor.py b/libensemble/executors/balsam_executors/balsam_executor.py
index 05a8bf5e09..d0c5a1cc1e 100644
--- a/libensemble/executors/balsam_executors/balsam_executor.py
+++ b/libensemble/executors/balsam_executors/balsam_executor.py
@@ -119,7 +119,6 @@ def __init__(
 
     def _get_time_since_balsam_submit(self):
         """Return time since balsam task entered ``RUNNING`` state"""
-
         event_query = EventLog.objects.filter(job_id=self.process.id, to_state="RUNNING")
         if not len(event_query):
             return 0
@@ -132,7 +131,6 @@ def _get_time_since_balsam_submit(self):
 
     def calc_task_timing(self):
         """Calculate timing information for this task"""
-
         # Get runtime from Balsam
         self.runtime = self._get_time_since_balsam_submit()
 
@@ -245,7 +243,6 @@ def wait(self, timeout=None):
 
     def kill(self):
         """Cancels the supplied task. Killing is unsupported at this time."""
-
         self.process.delete()
 
         logger.info("Killing task {}".format(self.name))
@@ -264,7 +261,6 @@ class BalsamExecutor(Executor):
 
     def __init__(self):
         """Instantiate a new ``BalsamExecutor`` instance."""
-
         super().__init__()
 
         self.workflow_name = "libe_workflow"
diff --git a/libensemble/executors/balsam_executors/legacy_balsam_executor.py b/libensemble/executors/balsam_executors/legacy_balsam_executor.py
index cf1a7b3aee..4369e6ff40 100644
--- a/libensemble/executors/balsam_executors/legacy_balsam_executor.py
+++ b/libensemble/executors/balsam_executors/legacy_balsam_executor.py
@@ -65,7 +65,6 @@ def read_stderr(self):
 
     def _get_time_since_balsam_submit(self):
         """Return time since balsam task entered RUNNING state"""
-
         # If wait_on_start then can could calculate runtime same a base executor
         # but otherwise that will return time from task submission. Get from Balsam.
 
@@ -79,7 +78,6 @@ def _get_time_since_balsam_submit(self):
 
     def calc_task_timing(self):
         """Calculate timing information for this task"""
-
         # Get runtime from Balsam
         self.runtime = self._get_time_since_balsam_submit()
 
@@ -177,7 +175,6 @@ def wait(self, timeout=None):
 
     def kill(self, wait_time=None):
         """Kills or cancels the supplied task"""
-
         dag.kill(self.process)
 
         # Could have Wait here and check with Balsam its killed -
diff --git a/libensemble/gen_funcs/old_aposmm.py b/libensemble/gen_funcs/old_aposmm.py
index 6c93af2d7f..6510528e42 100644
--- a/libensemble/gen_funcs/old_aposmm.py
+++ b/libensemble/gen_funcs/old_aposmm.py
@@ -884,7 +884,6 @@ def look_in_history(x, Run_H, vector_return=False):
 
 def calc_rk(n, n_s, rk_const, lhs_divisions=0):
     """ Calculate the critical distance r_k """
-
     if lhs_divisions == 0:
         r_k = rk_const*(log(n_s)/n_s)**(1/n)
     else:
diff --git a/libensemble/gen_funcs/persistent_aposmm.py b/libensemble/gen_funcs/persistent_aposmm.py
index b82f61ce10..b0a9ae6421 100644
--- a/libensemble/gen_funcs/persistent_aposmm.py
+++ b/libensemble/gen_funcs/persistent_aposmm.py
@@ -567,7 +567,6 @@ def decide_where_to_start_localopt(H, n, n_s, rk_const, ld=0, mu=0, nu=0):
 
 def calc_rk(n, n_s, rk_const, lhs_divisions=0):
     """ Calculate the critical distance r_k """
-
     if lhs_divisions == 0:
         r_k = rk_const*(log(n_s)/n_s)**(1/n)
     else:
diff --git a/libensemble/gen_funcs/surmise_calib_support.py b/libensemble/gen_funcs/surmise_calib_support.py
index b75c8c3c62..c4d475cad3 100644
--- a/libensemble/gen_funcs/surmise_calib_support.py
+++ b/libensemble/gen_funcs/surmise_calib_support.py
@@ -1,5 +1,4 @@
 """Contains supplemental methods for gen function in persistent_surmise_calib.py."""
-
 import numpy as np
 import scipy.stats as sps
 
diff --git a/libensemble/libE.py b/libensemble/libE.py
index fa8de62929..fa36b2e06b 100644
--- a/libensemble/libE.py
+++ b/libensemble/libE.py
@@ -297,7 +297,6 @@ def manager(
     on_cleanup=None,
 ):
     """Generic manager routine run."""
-
     logger.info("Logger initializing: [workerID] precedes each line. [0] = Manager")
     logger.info("libE version v{}".format(__version__))
 
@@ -364,7 +363,6 @@ def comms_abort(mpi_comm):
 
 def libE_mpi_defaults(libE_specs):
     """Fill in default values for MPI-based communicators."""
-
     from mpi4py import MPI
 
     if "mpi_comm" not in libE_specs:
@@ -375,7 +373,6 @@ def libE_mpi_defaults(libE_specs):
 
 def libE_mpi(sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs, H0):
     """MPI version of the libE main routine"""
-
     libE_specs, mpi_comm_null = libE_mpi_defaults(libE_specs)
 
     if libE_specs["mpi_comm"] == mpi_comm_null:
@@ -415,7 +412,6 @@ def libE_mpi(sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE
 
 def libE_mpi_manager(mpi_comm, sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs, H0):
     """Manager routine runs on rank 0."""
-
     from libensemble.comms.mpi import MainMPIComm
 
     hist = History(alloc_specs, sim_specs, gen_specs, exit_criteria, H0)
@@ -455,7 +451,6 @@ def on_abort():
 
 def libE_mpi_worker(libE_comm, sim_specs, gen_specs, libE_specs):
     """Worker routines run on ranks > 0."""
-
     from libensemble.comms.mpi import MainMPIComm
 
     comm = MainMPIComm(libE_comm)
@@ -468,7 +463,6 @@ def libE_mpi_worker(libE_comm, sim_specs, gen_specs, libE_specs):
 
 def start_proc_team(nworkers, sim_specs, gen_specs, libE_specs, log_comm=True):
     """Launch a process worker team."""
-
     resources = Resources.resources
     executor = Executor.executor
 
@@ -493,7 +487,6 @@ def kill_proc_team(wcomms, timeout):
 
 def libE_local(sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs, H0):
     """Main routine for thread/process launch of libE."""
-
     nworkers = libE_specs["nworkers"]
 
     check_inputs(libE_specs, alloc_specs, sim_specs, gen_specs, exit_criteria, H0)
@@ -559,7 +552,6 @@ def libE_tcp_default_ID():
 
 def libE_tcp(sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs, H0):
     """Main routine for TCP multiprocessing launch of libE."""
-
     check_inputs(libE_specs, alloc_specs, sim_specs, gen_specs, exit_criteria, H0)
 
     is_worker = True if "workerID" in libE_specs else False
@@ -612,7 +604,6 @@ def libE_tcp_start_team(manager, nworkers, workers, ip, port, authkey, launchf):
 
 def libE_tcp_mgr(sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs, H0):
     """Main routine for TCP multiprocessing launch of libE at manager."""
-
     hist = History(alloc_specs, sim_specs, gen_specs, exit_criteria, H0)
 
     # Set up a worker launcher
@@ -660,7 +651,6 @@ def cleanup():
 
 def libE_tcp_worker(sim_specs, gen_specs, libE_specs):
     """Main routine for TCP worker launched by libE."""
-
     ip = libE_specs["ip"]
     port = libE_specs["port"]
     authkey = libE_specs["authkey"]
diff --git a/libensemble/manager.py b/libensemble/manager.py
index 16bba267b6..a7e7ff9100 100644
--- a/libensemble/manager.py
+++ b/libensemble/manager.py
@@ -324,13 +324,11 @@ def _set_resources(self, Work, w):
 
     def _freeup_resources(self, w):
         """Free up resources assigned to the worker"""
-
         if self.resources:
             self.resources.resource_manager.free_rsets(w)
 
     def _send_work_order(self, Work, w):
         """Sends an allocation function order to a worker"""
-
         logger.debug("Manager sending work unit to worker {}".format(w))
 
         if self.resources:
@@ -359,7 +357,6 @@ def _send_work_order(self, Work, w):
 
     def _update_state_on_alloc(self, Work, w):
         """Updates a workers' active/idle status following an allocation order"""
-
         self.W[w - 1]["active"] = Work["tag"]
         if "libE_info" in Work:
             if "persistent" in Work["libE_info"]:
@@ -549,7 +546,6 @@ def _sim_max_given(self):
 
     def _get_alloc_libE_info(self):
         """Selected statistics useful for alloc_f"""
-
         return {
             "any_idle_workers": any(self.W["active"] == 0),
             "exit_criteria": self.exit_criteria,
diff --git a/libensemble/output_directory.py b/libensemble/output_directory.py
index df52d688c2..f905e037e0 100644
--- a/libensemble/output_directory.py
+++ b/libensemble/output_directory.py
@@ -77,7 +77,6 @@ def make_copyback_check(self):
 
     def use_calc_dirs(self, type):
         """Determines calc_dirs enabling for each calc type"""
-
         if type == EVAL_SIM_TAG:
             return self.sim_use
         else:
@@ -85,7 +84,6 @@ def use_calc_dirs(self, type):
 
     def _make_calc_dir(self, workerID, H_rows, calc_str, locs):
         """Create calc dirs and intermediate dirs, copy inputs, based on libE_specs"""
-
         if calc_str == "sim":
             input_dir = self.sim_input_dir
             do_calc_dirs = self.sim_dirs_make
@@ -159,7 +157,6 @@ def _make_calc_dir(self, workerID, H_rows, calc_str, locs):
 
     def prep_calc_dir(self, Work, calc_iter, workerID, calc_type):
         """Determines choice for calc_dir structure, then performs calculation."""
-
         if not self.loc_stack:
             self.loc_stack = LocationStack()
 
diff --git a/libensemble/resources/node_resources.py b/libensemble/resources/node_resources.py
index b76a649b66..cd6771c901 100644
--- a/libensemble/resources/node_resources.py
+++ b/libensemble/resources/node_resources.py
@@ -48,7 +48,6 @@ def _get_remote_cpu_resources(launcher):
 
 def _get_cpu_resources_from_env(env_resources=None):
     """Returns logical and physical cores per node by querying environment or None"""
-
     if not env_resources:
         return None
 
diff --git a/libensemble/resources/resources.py b/libensemble/resources/resources.py
index e08e735a65..ecc76f7c7e 100644
--- a/libensemble/resources/resources.py
+++ b/libensemble/resources/resources.py
@@ -52,7 +52,6 @@ class Resources:
     @classmethod
     def init_resources(cls, libE_specs):
         """Initiate resource management"""
-
         # If disable_resource_manager is True, then Resources.resources will remain None.
         disable_resource_manager = libE_specs.get("disable_resource_manager", False)
         if not disable_resource_manager:
diff --git a/libensemble/resources/scheduler.py b/libensemble/resources/scheduler.py
index e27aff6159..89d02f6876 100644
--- a/libensemble/resources/scheduler.py
+++ b/libensemble/resources/scheduler.py
@@ -165,7 +165,6 @@ def assign_resources(self, rsets_req):
 
     def find_rsets_any_slots(self, rsets_by_group, max_grpsize, rsets_req, ngroups, rsets_per_group):
         """Find optimal non-matching slots across groups"""
-
         tmp_rsets_by_group = copy.deepcopy(rsets_by_group)
         max_upper_bound = max_grpsize + 1
 
diff --git a/libensemble/resources/worker_resources.py b/libensemble/resources/worker_resources.py
index bfc9f96403..8633f8ef6c 100644
--- a/libensemble/resources/worker_resources.py
+++ b/libensemble/resources/worker_resources.py
@@ -65,7 +65,6 @@ def __init__(self, num_workers, resources):
 
     def assign_rsets(self, rset_team, worker_id):
         """Mark the resource sets given by rset_team as assigned to worker_id"""
-
         if rset_team:
             rteam = self.rsets["assigned"][rset_team]
             for i, wid in enumerate(rteam):
@@ -307,7 +306,6 @@ def set_slot_count(self):
     @staticmethod
     def get_local_nodelist(workerID, rset_team, split_list, rsets_per_node):
         """Returns the list of nodes available to the given worker and the slot dictionary"""
-
         if workerID is None:
             raise WorkerResourcesException("Worker has no workerID - aborting")
 
diff --git a/libensemble/sim_funcs/branin/branin_obj.py b/libensemble/sim_funcs/branin/branin_obj.py
index 87c4e19811..a6189353f2 100644
--- a/libensemble/sim_funcs/branin/branin_obj.py
+++ b/libensemble/sim_funcs/branin/branin_obj.py
@@ -9,7 +9,6 @@
 
 def call_branin(H, persis_info, sim_specs, _):
     """Evaluates the Branin function"""
-
     batch = len(H["x"])
 
     H_o = np.zeros(batch, dtype=sim_specs["out"])
diff --git a/libensemble/sim_funcs/noisy_vector_mapping.py b/libensemble/sim_funcs/noisy_vector_mapping.py
index f9a88553d7..26938d1e8a 100644
--- a/libensemble/sim_funcs/noisy_vector_mapping.py
+++ b/libensemble/sim_funcs/noisy_vector_mapping.py
@@ -26,7 +26,6 @@ def func_wrapper(H, persis_info, sim_specs, libE_info):
 
 def noisy_function(x):
     """ """
-
     x1 = x[0]
     x2 = x[1]
     term1 = (4 - 2.1 * x1**2 + (x1**4) / 3) * x1**2
diff --git a/libensemble/tests/deprecated_tests/balsam_tests/setup_balsam_tests.py b/libensemble/tests/deprecated_tests/balsam_tests/setup_balsam_tests.py
index 062fbf031d..1ff285e0dd 100755
--- a/libensemble/tests/deprecated_tests/balsam_tests/setup_balsam_tests.py
+++ b/libensemble/tests/deprecated_tests/balsam_tests/setup_balsam_tests.py
@@ -1,7 +1,6 @@
 #!/usr/bin/env python
 
 """ Script to set up apps and jobs for balsam tests """
-
 # Note: To see use of command line interface see bash_scripts/setup_balsam_tests.sh script.
 #       Currently that script does not create deps between jobs so may run simultaneously
 #       This script tests setup within python (could in theory be integrated with job!)
diff --git a/libensemble/tests/regression_tests/test_uniform_sampling_cancel.py b/libensemble/tests/regression_tests/test_uniform_sampling_cancel.py
index 2dd4f66877..83d25bfb24 100644
--- a/libensemble/tests/regression_tests/test_uniform_sampling_cancel.py
+++ b/libensemble/tests/regression_tests/test_uniform_sampling_cancel.py
@@ -34,7 +34,6 @@
 
 def create_H0(persis_info, gen_specs, sim_max):
     """Create an H0 for give_pregenerated_sim_work"""
-
     # Manually creating H0
     ub = gen_specs["user"]["ub"]
     lb = gen_specs["user"]["lb"]
diff --git a/libensemble/tests/unit_tests/test_executor.py b/libensemble/tests/unit_tests/test_executor.py
index dc2e613eac..9f41be02cf 100644
--- a/libensemble/tests/unit_tests/test_executor.py
+++ b/libensemble/tests/unit_tests/test_executor.py
@@ -795,7 +795,6 @@ def test_dry_run():
 @pytest.mark.extra
 def test_non_existent_app():
     """Tests exception on non-existent app"""
-
     from libensemble.executors.executor import Executor
 
     exctr = Executor()
@@ -816,7 +815,6 @@ def test_non_existent_app():
 @pytest.mark.extra
 def test_non_existent_app_mpi():
     """Tests exception on non-existent app"""
-
     from libensemble.executors.mpi_executor import MPIExecutor
 
     exctr = MPIExecutor()
diff --git a/libensemble/tests/unit_tests/test_scipy.py b/libensemble/tests/unit_tests/test_scipy.py
index cb8347bd72..e50a525229 100644
--- a/libensemble/tests/unit_tests/test_scipy.py
+++ b/libensemble/tests/unit_tests/test_scipy.py
@@ -10,7 +10,6 @@ def test_cdist_issue():
         pytest.skip("scipy or its dependencies not importable. Skipping.")
 
     """There is an issue (at least in scipy 1.1.0) with cdist segfaulting."""
-
     H = np.zeros(
         20,
         dtype=[
@@ -40,7 +39,6 @@ def test_cdist_issue():
 @pytest.mark.extra
 def test_save():
     """Seeing if I can save parts of the H array."""
-
     from libensemble.tests.regression_tests.support import uniform_or_localopt_gen_out as gen_out
 
     n = 2
diff --git a/libensemble/tests/unit_tests/test_sim_dir_properties.py b/libensemble/tests/unit_tests/test_sim_dir_properties.py
index 6e377bbdc5..29e79370d0 100644
--- a/libensemble/tests/unit_tests/test_sim_dir_properties.py
+++ b/libensemble/tests/unit_tests/test_sim_dir_properties.py
@@ -9,21 +9,18 @@
 
 def test_range_single_element():
     """Single H_row labeling"""
-
     work = {"H_fields": ["x", "num_nodes", "procs_per_node"], "libE_info": {"H_rows": np.array([5]), "workerID": 1}}
     assert extract_H_ranges(work) == "5", "Failed to correctly parse single H row"
 
 
 def test_range_two_separate_elements():
     """Multiple H_rows, non-sequential"""
-
     work = {"H_fields": ["x", "num_nodes", "procs_per_node"], "libE_info": {"H_rows": np.array([2, 8]), "workerID": 1}}
     assert extract_H_ranges(work) == "2_8", "Failed to correctly parse nonsequential H rows"
 
 
 def test_range_two_ranges():
     """Multiple sequences of H_rows"""
-
     work = {
         "H_fields": ["x", "num_nodes", "procs_per_node"],
         "libE_info": {"H_rows": np.array([0, 1, 2, 3, 7, 8]), "workerID": 1},
@@ -33,7 +30,6 @@ def test_range_two_ranges():
 
 def test_range_mixes():
     """Mix of single rows and sequences of H_rows"""
-
     work = {
         "H_fields": ["x", "num_nodes", "procs_per_node"],
         "libE_info": {"H_rows": np.array([2, 3, 4, 6, 8, 9, 11, 14]), "workerID": 1},
diff --git a/libensemble/tools/parse_args.py b/libensemble/tools/parse_args.py
index d4cb5d7346..c5e32ff3c2 100644
--- a/libensemble/tools/parse_args.py
+++ b/libensemble/tools/parse_args.py
@@ -66,7 +66,6 @@ def _mpi_parse_args(args):
 
 def _local_parse_args(args):
     """Parses arguments for forked processes using multiprocessing."""
-
     libE_specs = {"comms": "local"}
     nworkers = args.nworkers
 
diff --git a/libensemble/worker.py b/libensemble/worker.py
index d0890c8e53..ae8e8196e7 100644
--- a/libensemble/worker.py
+++ b/libensemble/worker.py
@@ -259,7 +259,6 @@ def _handle_calc(self, Work, calc_in):
 
     def _get_calc_msg(self, enum_desc, calc_id, calc_type, timer, status):
         """Construct line for libE_stats.txt file"""
-
         calc_msg = "{} {}: {} {}".format(enum_desc, calc_id, calc_type, timer)
 
         if self.stats_fmt.get("task_timing", False) or self.stats_fmt.get("task_datetime", False):
@@ -277,7 +276,6 @@ def _get_calc_msg(self, enum_desc, calc_id, calc_type, timer, status):
 
     def _recv_H_rows(self, Work):
         """Unpacks Work request and receives any history rows"""
-
         libE_info = Work["libE_info"]
         calc_type = Work["tag"]
         if len(libE_info["H_rows"]) > 0:
@@ -292,7 +290,6 @@ def _recv_H_rows(self, Work):
 
     def _handle(self, Work):
         """Handles a work request from the manager"""
-
         # Check work request and receive second message (if needed)
         libE_info, calc_type, calc_in = self._recv_H_rows(Work)
 
@@ -326,7 +323,6 @@ def _handle(self, Work):
 
     def run(self):
         """Runs the main worker loop."""
-
         try:
             logger.info("Worker {} initiated on node {}".format(self.workerID, socket.gethostname()))
 

From b8a781d8ca8be4f565cc852b0038c7e26e5e8e70 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Tue, 30 Aug 2022 11:40:01 -0500
Subject: [PATCH 162/196] Update generator guide  wording

---
 docs/function_guides/generator.rst | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/docs/function_guides/generator.rst b/docs/function_guides/generator.rst
index 57aad917c9..4f775e7c6f 100644
--- a/docs/function_guides/generator.rst
+++ b/docs/function_guides/generator.rst
@@ -168,9 +168,10 @@ To change existing fields of the history array, the generator can initialize an
 array where the *dtype* contains the ``sim_id`` and the fields to be modified (in
 place of ``gen_specs["out"]``), and then send to the manager as with regular
 communications. Any such message received by the manager will modify the given fields
-for the given *sim_ids*. If the changes are separate from the generation of new points
-and should not mark the generator as ready to receive new points, then send with the
-``keep_state`` argument set to *True*.
+for the given *sim_ids*. If the changes do not correspond with newly generated points,
+then the generator needs to communicate to the manager that it is not ready
+to receive completed evaluations. Send to the manager with the ``keep_state`` argument
+set to *True*.
 
 For example, the cancellation function ``request_cancel_sim_ids`` could be replicated by
 the following (where ``sim_ids_to_cancel`` is a list of integers):

From 24fa2cc6b1f7cfa7081f3dad23b1b301ac0a228a Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Tue, 30 Aug 2022 13:22:44 -0500
Subject: [PATCH 163/196] Fix wait docstring on timeout

---
 .../executors/balsam_executors/balsam_executor.py        | 4 +---
 .../executors/balsam_executors/legacy_balsam_executor.py | 3 +--
 libensemble/executors/executor.py                        | 9 +++------
 3 files changed, 5 insertions(+), 11 deletions(-)

diff --git a/libensemble/executors/balsam_executors/balsam_executor.py b/libensemble/executors/balsam_executors/balsam_executor.py
index 969b10ba35..f62e4a4f9e 100644
--- a/libensemble/executors/balsam_executors/balsam_executor.py
+++ b/libensemble/executors/balsam_executors/balsam_executor.py
@@ -220,9 +220,7 @@ def wait(self, timeout=None):
         timeout: int or float,  optional
             Time in seconds after which a TimeoutExpired exception is raised.
             If not set, then simply waits until completion.
-            Note that the task is not automatically killed if libEnsemble
-            timeouts from reaching exit_criteria["wallclock_max"].
-
+            Note that the task is not automatically killed on timeout.
         """
 
         if self.dry_run:
diff --git a/libensemble/executors/balsam_executors/legacy_balsam_executor.py b/libensemble/executors/balsam_executors/legacy_balsam_executor.py
index cf1a7b3aee..8f2eb0c96c 100644
--- a/libensemble/executors/balsam_executors/legacy_balsam_executor.py
+++ b/libensemble/executors/balsam_executors/legacy_balsam_executor.py
@@ -152,8 +152,7 @@ def wait(self, timeout=None):
         timeout: int or float,  optional
             Time in seconds after which a TimeoutExpired exception is raised.
             If not set, then simply waits until completion.
-            Note that the task is not automatically killed if libEnsemble
-            timeouts from reaching exit_criteria["wallclock_max"].
+            Note that the task is not automatically killed on timeout.
         """
 
         if self.dry_run:
diff --git a/libensemble/executors/executor.py b/libensemble/executors/executor.py
index 6673247df1..69c06f330f 100644
--- a/libensemble/executors/executor.py
+++ b/libensemble/executors/executor.py
@@ -247,8 +247,7 @@ def wait(self, timeout=None):
         timeout: int or float,  optional
             Time in seconds after which a TimeoutExpired exception is raised.
             If not set, then simply waits until completion.
-            Note that the task is not automatically killed if libEnsemble
-            timeouts from reaching exit_criteria["wallclock_max"].
+            Note that the task is not automatically killed on timeout.
         """
 
         if self.dry_run:
@@ -273,8 +272,7 @@ def result(self, timeout=None):
         timeout: int or float,  optional
             Time in seconds after which a TimeoutExpired exception is raised.
             If not set, then simply waits until completion.
-            Note that the task is not automatically killed if libEnsemble
-            timeouts from reaching exit_criteria["wallclock_max"].
+            Note that the task is not automatically killed on timeout.
         """
 
         self.wait(timeout=timeout)
@@ -289,8 +287,7 @@ def exception(self, timeout=None):
         timeout: int or float,  optional
             Time in seconds after which a TimeoutExpired exception is raised.
             If not set, then simply waits until completion.
-            Note that the task is not automatically killed if libEnsemble
-            timeouts from reaching exit_criteria["wallclock_max"].
+            Note that the task is not automatically killed on timeout.
         """
 
         self.wait(timeout=timeout)

From 05217fbfeb09ba846ccad1c53ad20ba8c7280095 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Tue, 30 Aug 2022 14:07:36 -0500
Subject: [PATCH 164/196] Update for executor autodocs

---
 docs/executor/executor.rst            | 5 ++++-
 docs/executor/mpi_executor.rst        | 1 +
 libensemble/executors/executor.py     | 1 +
 libensemble/executors/mpi_executor.py | 8 +++++++-
 4 files changed, 13 insertions(+), 2 deletions(-)

diff --git a/docs/executor/executor.rst b/docs/executor/executor.rst
index 6d12a387a0..9d9dd6520d 100644
--- a/docs/executor/executor.rst
+++ b/docs/executor/executor.rst
@@ -24,7 +24,10 @@ To run MPI applications and use detected resources, use an alternative Executor
 class, as shown above.
 
 .. autoclass:: Executor
-  :members: __init__, register_app, submit
+  :members:
+  :exclude-members: serial_setup, sim_default_app, gen_default_app, get_app, default_app, set_resources, get_task, set_workerID, set_worker_info, new_tasks_timing
+
+  .. automethod:: __init__
 
 .. _task_tag:
 
diff --git a/docs/executor/mpi_executor.rst b/docs/executor/mpi_executor.rst
index 60fc1cc782..98b4a30eff 100644
--- a/docs/executor/mpi_executor.rst
+++ b/docs/executor/mpi_executor.rst
@@ -7,6 +7,7 @@ MPI Executor - MPI apps
 .. autoclass:: MPIExecutor
   :show-inheritance:
   :inherited-members:
+  :exclude-members: serial_setup, sim_default_app, gen_default_app, get_app, default_app, set_resources, get_task, set_workerID, set_worker_info, new_tasks_timing
 
   .. automethod:: __init__
 
diff --git a/libensemble/executors/executor.py b/libensemble/executors/executor.py
index 69c06f330f..981d6294c9 100644
--- a/libensemble/executors/executor.py
+++ b/libensemble/executors/executor.py
@@ -350,6 +350,7 @@ class Executor:
     **Object Attributes:**
 
     :ivar list list_of_tasks: A list of tasks created in this executor
+    :ivar int manager_signal: The most recent manager signal received since manager_poll() was called.
     """
 
     executor = None
diff --git a/libensemble/executors/mpi_executor.py b/libensemble/executors/mpi_executor.py
index 7b0697b583..49a2fe3e1a 100644
--- a/libensemble/executors/mpi_executor.py
+++ b/libensemble/executors/mpi_executor.py
@@ -23,7 +23,13 @@
 
 
 class MPIExecutor(Executor):
-    """The MPI executor can create, poll and kill runnable MPI tasks"""
+    """The MPI executor can create, poll and kill runnable MPI tasks
+
+    **Object Attributes:**
+
+    :ivar list list_of_tasks: A list of tasks created in this executor
+    :ivar int manager_signal: The most recent manager signal received since manager_poll() was called.
+    """
 
     def __init__(self, custom_info={}):
         """Instantiate a new MPIExecutor instance.

From b3f42e98d975936e969c245cb4dba9b54badf342 Mon Sep 17 00:00:00 2001
From: Jeffrey Larson <jmlarson@anl.gov>
Date: Tue, 30 Aug 2022 15:23:48 -0500
Subject: [PATCH 165/196] Moving current_time

---
 libensemble/tests/run-tests.sh | 36 +++++++++++++++++-----------------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/libensemble/tests/run-tests.sh b/libensemble/tests/run-tests.sh
index b0b3a53f97..12fb2b72e9 100755
--- a/libensemble/tests/run-tests.sh
+++ b/libensemble/tests/run-tests.sh
@@ -92,24 +92,6 @@ print_summary_line() {
   done
 }
 
-#Get current time in seconds
-#In:  Nothing
-#Out: Returns time in seconds (seconds since 1970-01-01 00:00:00 UTC) as a string
-#     Or if bc not available uses SECONDS (whole seconds that script has been running)
-current_time() {
-  local time
-  #Is bc present
-  USE_BC=f
-  bc --version >> /dev/null && USE_BC=t
-  if [ $USE_BC = 't' ]; then
-    #time=$(date +%s.%N)
-    time=$(python -c 'import time; print(time.time())')
-  else
-    time=$SECONDS
-  fi;
-  echo "$time"
-}
-
 #Return a time difference
 #In:  Start and End times as strings
 #Out: Time difference as a string
@@ -339,6 +321,24 @@ fi;
 PYTHON_RUN="python$PYTHON_VER $PYTHON_FLAGS"
 echo -e "Python run: $PYTHON_RUN"
 
+#Get current time in seconds
+#In:  Nothing
+#Out: Returns time in seconds (seconds since 1970-01-01 00:00:00 UTC) as a string
+#     Or if bc not available uses SECONDS (whole seconds that script has been running)
+current_time() {
+  local time
+  #Is bc present
+  USE_BC=f
+  bc --version >> /dev/null && USE_BC=t
+  if [ $USE_BC = 't' ]; then
+    #time=$(date +%s.%N)
+    time=$($PYTHON_RUN -c 'import time; print(time.time())')
+  else
+    time=$SECONDS
+  fi;
+  echo "$time"
+}
+
 textreset=$(tput sgr0)
 fail_color=$(tput bold; tput setaf 1) #red
 pass_color=$(tput bold; tput setaf 2) #green

From 39efbe7ef9b64e5f75d291ec5011524f9aec5f72 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Tue, 30 Aug 2022 16:46:07 -0500
Subject: [PATCH 166/196] Put filterwarnings above gen import in Ax test

---
 .flake8                                               |  1 +
 .../test_persistent_gp_multitask_ax.py                | 11 ++++++-----
 2 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/.flake8 b/.flake8
index 3837b2b01b..882936703b 100644
--- a/.flake8
+++ b/.flake8
@@ -43,6 +43,7 @@ per-file-ignores =
         libensemble/gen_funcs/persistent_aposmm.py:E402, E501
         libensemble/tests/regression_tests/test_persistent_aposmm*:E402
         libensemble/tests/regression_tests/dont_run_test_persistent_aposmm*:E402
+        libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py:E402
         libensemble/tests/regression_tests/test_uniform_sampling_then_persistent_localopt_runs.py:E402
         libensemble/tests/functionality_tests/test_active_persistent_worker_abort.py:E402
         libensemble/tests/deprecated_tests/test_old_aposmm*:E402
diff --git a/libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py b/libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py
index 0fc03a6cee..1d0e2fabd4 100644
--- a/libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py
+++ b/libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py
@@ -26,10 +26,15 @@
 from libensemble.tools import save_libE_output, add_unique_random_streams
 from libensemble.tools import parse_args
 from libensemble.message_numbers import WORKER_DONE
-from libensemble.gen_funcs.persistent_ax_multitask import persistent_gp_mt_ax_gen_f
 
 import warnings
 
+# Ax uses a deprecated warn command.
+warnings.filterwarnings("ignore", category=UserWarning)
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+from libensemble.gen_funcs.persistent_ax_multitask import persistent_gp_mt_ax_gen_f
+
 
 def run_simulation(H, persis_info, sim_specs, libE_info):
     # Extract input parameters
@@ -55,10 +60,6 @@ def run_simulation(H, persis_info, sim_specs, libE_info):
 # Main block is necessary only when using local comms with spawn start method (default on macOS and Windows).
 if __name__ == "__main__":
 
-    # Ax uses a deprecated warn command.
-    warnings.filterwarnings("ignore", category=UserWarning)
-    warnings.filterwarnings("ignore", category=DeprecationWarning)
-
     nworkers, is_manager, libE_specs, _ = parse_args()
 
     mt_params = {

From 9dbe7504eca4e8aa016a63b0d29dfcc89d511bf9 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Tue, 30 Aug 2022 18:54:49 -0500
Subject: [PATCH 167/196] Temp. disable Ax test due to botorch error

---
 ...multitask_ax.py => dontrun_test_persistent_gp_multitask_ax.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename libensemble/tests/regression_tests/{test_persistent_gp_multitask_ax.py => dontrun_test_persistent_gp_multitask_ax.py} (100%)

diff --git a/libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py b/libensemble/tests/regression_tests/dontrun_test_persistent_gp_multitask_ax.py
similarity index 100%
rename from libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py
rename to libensemble/tests/regression_tests/dontrun_test_persistent_gp_multitask_ax.py

From c8d49b12fe284fbc2c078d9ccd67ed9a410de9f5 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Tue, 30 Aug 2022 19:10:49 -0500
Subject: [PATCH 168/196] Update flake8 exceptions

---
 .flake8 | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.flake8 b/.flake8
index 882936703b..6efeae09f5 100644
--- a/.flake8
+++ b/.flake8
@@ -44,6 +44,7 @@ per-file-ignores =
         libensemble/tests/regression_tests/test_persistent_aposmm*:E402
         libensemble/tests/regression_tests/dont_run_test_persistent_aposmm*:E402
         libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py:E402
+        libensemble/tests/regression_tests/dontrun_test_persistent_gp_multitask_ax.py:E402
         libensemble/tests/regression_tests/test_uniform_sampling_then_persistent_localopt_runs.py:E402
         libensemble/tests/functionality_tests/test_active_persistent_worker_abort.py:E402
         libensemble/tests/deprecated_tests/test_old_aposmm*:E402

From ba841cbe1b992f4104dc9a1542062ad557beb9e0 Mon Sep 17 00:00:00 2001
From: Jeffrey Larson <jmlarson@anl.gov>
Date: Wed, 31 Aug 2022 07:38:46 -0500
Subject: [PATCH 169/196] Typos

---
 docs/data_structures/work_dict.rst      | 2 +-
 libensemble/tools/persistent_support.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/data_structures/work_dict.rst b/docs/data_structures/work_dict.rst
index d056a634d3..64a479c772 100644
--- a/docs/data_structures/work_dict.rst
+++ b/docs/data_structures/work_dict.rst
@@ -3,7 +3,7 @@
 work dictionary
 ===============
 
-The work dictionary constains metadata that is used by the manager to send a packet
+The work dictionary contains metadata that is used by the manager to send a packet
 of work to a worker. The dictionary uses integer keys ``i`` and values that determine
 the data given to worker ``i``. ``Work[i]`` has the following form::
 
diff --git a/libensemble/tools/persistent_support.py b/libensemble/tools/persistent_support.py
index 5ed5751ade..fbe5ed5ee5 100644
--- a/libensemble/tools/persistent_support.py
+++ b/libensemble/tools/persistent_support.py
@@ -33,7 +33,7 @@ def send(self, output, calc_status=UNSET_TAG, keep_state=False):
         :param calc_status: Optional, Provides a task status
         :param keep_state: Optional, If True the manager will not modify its
             record of the workers state (usually the manager changes the
-            worker's state to inactive, indiciating the worker ready to receive
+            worker's state to inactive, indicating the worker is ready to receive
             more work, unless using active receive mode).
 
         :returns: None

From 0ea7a52ee67d6e45896e03617887a84e667e3a5c Mon Sep 17 00:00:00 2001
From: Jeffrey Larson <jmlarson@anl.gov>
Date: Wed, 31 Aug 2022 12:50:55 -0500
Subject: [PATCH 170/196] More active wording

---
 README.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.rst b/README.rst
index 541d9a34bd..5a62ae2471 100644
--- a/README.rst
+++ b/README.rst
@@ -31,8 +31,8 @@ Introduction to libEnsemble
 
 libEnsemble is a Python_ toolkit for coordinating workflows of asynchronous and dynamic ensembles of calculations.
 
-libEnsemble can help users take advantage of massively parallel resources to solve design,
-decision, and inference problems and expand the class of problems that can benefit from
+libEnsemble helps users take advantage of massively parallel resources to solve design,
+decision, and inference problems and expands the class of problems that can benefit from
 increased parallelism.
 
 libEnsemble aims for:

From 9dc32fb1f434455e618fb6b3dcc2d3d0efd90fcb Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Tue, 30 Aug 2022 18:54:49 -0500
Subject: [PATCH 171/196] Temp. disable Ax test due to botorch error

(cherry picked from commit 9dbe7504eca4e8aa016a63b0d29dfcc89d511bf9)
---
 ...multitask_ax.py => dontrun_test_persistent_gp_multitask_ax.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename libensemble/tests/regression_tests/{test_persistent_gp_multitask_ax.py => dontrun_test_persistent_gp_multitask_ax.py} (100%)

diff --git a/libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py b/libensemble/tests/regression_tests/dontrun_test_persistent_gp_multitask_ax.py
similarity index 100%
rename from libensemble/tests/regression_tests/test_persistent_gp_multitask_ax.py
rename to libensemble/tests/regression_tests/dontrun_test_persistent_gp_multitask_ax.py

From 1ae0e4d66a0038e005d942c0b6ec5e738cab3332 Mon Sep 17 00:00:00 2001
From: Jeffrey Larson <jmlarson@anl.gov>
Date: Wed, 31 Aug 2022 13:47:09 -0500
Subject: [PATCH 172/196] Black

---
 .../regression_tests/dontrun_test_persistent_gp_multitask_ax.py  | 1 +
 1 file changed, 1 insertion(+)

diff --git a/libensemble/tests/regression_tests/dontrun_test_persistent_gp_multitask_ax.py b/libensemble/tests/regression_tests/dontrun_test_persistent_gp_multitask_ax.py
index 9a50dc341b..1d0e2fabd4 100644
--- a/libensemble/tests/regression_tests/dontrun_test_persistent_gp_multitask_ax.py
+++ b/libensemble/tests/regression_tests/dontrun_test_persistent_gp_multitask_ax.py
@@ -35,6 +35,7 @@
 
 from libensemble.gen_funcs.persistent_ax_multitask import persistent_gp_mt_ax_gen_f
 
+
 def run_simulation(H, persis_info, sim_specs, libE_info):
     # Extract input parameters
     values = list(H["x"][0])

From f7578bd011971a59750549504346fe2cf51c1d80 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Thu, 1 Sep 2022 19:04:52 -0500
Subject: [PATCH 173/196] Convert string to integer comparisons

---
 libensemble/executors/executor.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/libensemble/executors/executor.py b/libensemble/executors/executor.py
index 981d6294c9..12146675a0 100644
--- a/libensemble/executors/executor.py
+++ b/libensemble/executors/executor.py
@@ -18,6 +18,8 @@
 
 from libensemble.message_numbers import (
     UNSET_TAG,
+    MAN_SIGNAL_FINISH,
+    MAN_SIGNAL_KILL,
     WORKER_DONE,
     TASK_FAILED,
     WORKER_KILL_ON_TIMEOUT,
@@ -52,10 +54,7 @@
 FAILED
 """.split()
 
-MAN_KILL_SIGNALS = """
-MAN_SIGNAL_FINISH
-MAN_SIGNAL_KILL
-""".split()
+MAN_KILL_SIGNALS = [MAN_SIGNAL_FINISH, MAN_SIGNAL_KILL]
 
 
 class ExecutorException(Exception):

From 67d936b97de7454b43beb7bbf5370f2a48d41266 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Thu, 1 Sep 2022 19:28:18 -0500
Subject: [PATCH 174/196] Move MAN_KILL_SIGNALS to message_numbers

---
 libensemble/executors/executor.py | 5 +----
 libensemble/message_numbers.py    | 2 ++
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/libensemble/executors/executor.py b/libensemble/executors/executor.py
index 12146675a0..d23b3ead27 100644
--- a/libensemble/executors/executor.py
+++ b/libensemble/executors/executor.py
@@ -18,8 +18,7 @@
 
 from libensemble.message_numbers import (
     UNSET_TAG,
-    MAN_SIGNAL_FINISH,
-    MAN_SIGNAL_KILL,
+    MAN_KILL_SIGNALS,
     WORKER_DONE,
     TASK_FAILED,
     WORKER_KILL_ON_TIMEOUT,
@@ -54,8 +53,6 @@
 FAILED
 """.split()
 
-MAN_KILL_SIGNALS = [MAN_SIGNAL_FINISH, MAN_SIGNAL_KILL]
-
 
 class ExecutorException(Exception):
     "Raised for any exception in the Executor"
diff --git a/libensemble/message_numbers.py b/libensemble/message_numbers.py
index 7392fd2086..adfcbc2448 100644
--- a/libensemble/message_numbers.py
+++ b/libensemble/message_numbers.py
@@ -41,6 +41,8 @@
 # last_calc_status_rst_tag
 CALC_EXCEPTION = 35  # Reserved: Automatically used if user_f raised an exception
 
+MAN_KILL_SIGNALS = [MAN_SIGNAL_FINISH, MAN_SIGNAL_KILL]
+
 calc_status_strings = {
     UNSET_TAG: "Not set",
     FINISHED_PERSISTENT_SIM_TAG: "Persis sim finished",

From d9e475f0508b1b03d597acab55acacc2dce328a8 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Thu, 1 Sep 2022 19:30:10 -0500
Subject: [PATCH 175/196] Update test to show both kills sent and kills done

---
 libensemble/sim_funcs/borehole_kills.py       | 19 +++++++++++--------
 .../test_persistent_surmise_killsims.py       |  6 ++++--
 2 files changed, 15 insertions(+), 10 deletions(-)

diff --git a/libensemble/sim_funcs/borehole_kills.py b/libensemble/sim_funcs/borehole_kills.py
index 65a4bbdbd3..1a6fc22c83 100644
--- a/libensemble/sim_funcs/borehole_kills.py
+++ b/libensemble/sim_funcs/borehole_kills.py
@@ -1,7 +1,7 @@
 import numpy as np
 from libensemble.executors.executor import Executor
 from libensemble.sim_funcs.surmise_test_function import borehole_true
-from libensemble.message_numbers import TASK_FAILED, MAN_SIGNAL_KILL, UNSET_TAG
+from libensemble.message_numbers import UNSET_TAG, TASK_FAILED, MAN_KILL_SIGNALS
 
 
 def subproc_borehole(H, delay):
@@ -23,7 +23,7 @@ def subproc_borehole(H, delay):
     task = exctr.submit(app_name="borehole", app_args=args, stdout="out.txt", stderr="err.txt")
     calc_status = exctr.polling_loop(task, delay=0.01, poll_manager=True)
 
-    if calc_status in [MAN_SIGNAL_KILL, TASK_FAILED]:
+    if calc_status in MAN_KILL_SIGNALS + [TASK_FAILED]:
         f = np.inf
     else:
         f = float(task.read_stdout())
@@ -46,12 +46,15 @@ def borehole(H, persis_info, sim_specs, libE_info):
 
     f, calc_status = subproc_borehole(H, delay)
 
-    # Failure model (excluding observations)
-    if sim_id > sim_specs["user"]["num_obs"]:
-        if (f / borehole_true(H["x"])) > 1.25:
-            f = np.inf
-            calc_status = TASK_FAILED
-            print("Failure of sim_id {}".format(sim_id), flush=True)
+    if calc_status in MAN_KILL_SIGNALS and "sim_killed" in H_o.dtype.names:
+        H_o["sim_killed"] = True  # For calling script to print only.
+    else:
+        # Failure model (excluding observations)
+        if sim_id > sim_specs["user"]["num_obs"]:
+            if (f / borehole_true(H["x"])) > 1.25:
+                f = np.inf
+                calc_status = TASK_FAILED
+                print("Failure of sim_id {}".format(sim_id), flush=True)
 
     H_o["f"] = f
     return H_o, persis_info, calc_status
diff --git a/libensemble/tests/regression_tests/test_persistent_surmise_killsims.py b/libensemble/tests/regression_tests/test_persistent_surmise_killsims.py
index 62874e2a7c..3c6e11b98e 100644
--- a/libensemble/tests/regression_tests/test_persistent_surmise_killsims.py
+++ b/libensemble/tests/regression_tests/test_persistent_surmise_killsims.py
@@ -80,7 +80,8 @@
     sim_specs = {
         "sim_f": sim_f,
         "in": ["x", "thetas"],
-        "out": [("f", float)],
+        "out": [("f", float),
+                ('sim_killed', bool)],  # 'sim_killed' is just used to display at the end.
         "user": {
             "num_obs": n_x,
             "init_sample_size": init_sample_size,
@@ -130,7 +131,8 @@
 
     if is_manager:
         print("Cancelled sims", H["sim_id"][H["cancel_requested"]])
-        print("Killed sims", H["sim_id"][H["kill_sent"]])
+        print("Kills sent by manager to running simulations", H["sim_id"][H["kill_sent"]])
+        print("Killed sims", H["sim_id"][H["sim_killed"]])
         sims_done = np.count_nonzero(H["sim_ended"])
         save_libE_output(H, persis_info, __file__, nworkers)
         assert sims_done == max_evals, "Num of completed simulations should be {}. Is {}".format(max_evals, sims_done)

From 8726b01f9ffeb4f265cd547d56494ae793feacd5 Mon Sep 17 00:00:00 2001
From: jlnav <jnavarro@anl.gov>
Date: Wed, 14 Sep 2022 13:21:24 -0500
Subject: [PATCH 176/196] only warn about creating sim ids sequentially if
 provided gen func not from libensemble

---
 libensemble/libE.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/libensemble/libE.py b/libensemble/libE.py
index fa36b2e06b..3df4497834 100644
--- a/libensemble/libE.py
+++ b/libensemble/libE.py
@@ -301,7 +301,8 @@ def manager(
     logger.info("libE version v{}".format(__version__))
 
     if "out" in gen_specs and ("sim_id", int) in gen_specs["out"]:
-        logger.manager_warning(_USER_SIM_ID_WARNING)
+        if "libensemble.gen_funcs" not in gen_specs["gen_f"].__module__:
+            logger.manager_warning(_USER_SIM_ID_WARNING)
 
     save_H = libE_specs.get("save_H_and_persis_on_abort", True)
 

From 5fa732e6f43bc861f2fd92c5e897921f3dade908 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Fri, 16 Sep 2022 15:03:01 -0500
Subject: [PATCH 177/196] Add manager_kill_received to executor timeout test

---
 libensemble/sim_funcs/executor_hworld.py      | 25 ++++++---------
 .../test_executor_hworld_timeout.py           | 31 ++++++++++---------
 2 files changed, 27 insertions(+), 29 deletions(-)

diff --git a/libensemble/sim_funcs/executor_hworld.py b/libensemble/sim_funcs/executor_hworld.py
index 8692e0378e..f86e15b7ba 100644
--- a/libensemble/sim_funcs/executor_hworld.py
+++ b/libensemble/sim_funcs/executor_hworld.py
@@ -24,8 +24,7 @@ def custom_polling_loop(exctr, task, timeout_sec=5.0, delay=0.3):
     while task.runtime < timeout_sec:
         time.sleep(delay)
 
-        exctr.manager_poll()
-        if exctr.manager_signal == MAN_SIGNAL_FINISH:
+        if exctr.manager_kill_received():
             exctr.kill(task)
             calc_status = MAN_SIGNAL_FINISH  # Worker will pick this up and close down
             print("Task {} killed by manager on worker {}".format(task.id, exctr.workerID))
@@ -84,16 +83,17 @@ def executor_hworld(H, persis_info, sim_specs, libE_info):
 
     if "six_hump_camel" not in exctr.default_app("sim").full_path:
 
+        global sim_ended_count
+        sim_ended_count += 1
+        print("sim_ended_count", sim_ended_count, flush=True)
+
         if ELAPSED_TIMEOUT:
             args_for_sim = "sleep 60"  # Manager kill - if signal received else completes
             timeout = 65.0
 
         else:
-            global sim_ended_count
-            sim_ended_count += 1
             timeout = 6.0
             launch_shc = False
-            print(sim_ended_count)
 
             if sim_ended_count == 1:
                 args_for_sim = "sleep 1"  # Should finish
@@ -132,17 +132,12 @@ def executor_hworld(H, persis_info, sim_specs, libE_info):
                 calc_status = TASK_FAILED
 
         else:
-            if not ELAPSED_TIMEOUT:
-                if sim_ended_count >= 2 and not USE_BALSAM:
-                    calc_status = exctr.polling_loop(task, timeout=timeout, delay=0.3, poll_manager=True)
-                    if sim_ended_count == 2 and task.stdout_exists() and "Error" in task.read_stdout():
-                        calc_status = WORKER_KILL_ON_ERR
-
-                else:
-                    task, calc_status = custom_polling_loop(exctr, task, timeout)
-
-            else:
+            if sim_ended_count >= 2 and not USE_BALSAM:
                 calc_status = exctr.polling_loop(task, timeout=timeout, delay=0.3, poll_manager=True)
+                if sim_ended_count == 2 and task.stdout_exists() and "Error" in task.read_stdout():
+                    calc_status = WORKER_KILL_ON_ERR
+            else:
+                task, calc_status = custom_polling_loop(exctr, task, timeout)
 
         if USE_BALSAM:
             task.read_file_in_workdir("ensemble.log")
diff --git a/libensemble/tests/functionality_tests/test_executor_hworld_timeout.py b/libensemble/tests/functionality_tests/test_executor_hworld_timeout.py
index b39348114c..ab287bf02f 100644
--- a/libensemble/tests/functionality_tests/test_executor_hworld_timeout.py
+++ b/libensemble/tests/functionality_tests/test_executor_hworld_timeout.py
@@ -86,23 +86,26 @@
 
     persis_info = add_unique_random_streams({}, nworkers + 1)
 
-    exit_criteria = {"wallclock_max": 30}
+    exit_criteria = {"wallclock_max": 10}
 
-    # Perform the run
-    H, persis_info, flag = libE(sim_specs, gen_specs, exit_criteria, persis_info, libE_specs=libE_specs)
+    iterations = 2
+    for i in range(iterations):
 
-    if is_manager:
-        print("\nChecking expected task status against Workers ...\n")
+        # Perform the run
+        H, persis_info, flag = libE(sim_specs, gen_specs, exit_criteria, persis_info, libE_specs=libE_specs)
+
+        if is_manager:
+            print("\nChecking expected task status against Workers ...\n")
 
-        calc_status_list_in = np.asarray([0])
-        calc_status_list = np.repeat(calc_status_list_in, nworkers)
+            calc_status_list_in = np.asarray([0])
+            calc_status_list = np.repeat(calc_status_list_in, nworkers)
 
-        # For debug
-        print("Expecting: {}".format(calc_status_list))
-        print("Received:  {}\n".format(H["cstat"]))
+            # For debug
+            print("Expecting: {}".format(calc_status_list))
+            print("Received:  {}\n".format(H["cstat"]))
 
-        assert np.array_equal(H["cstat"], calc_status_list), "Error - unexpected calc status. Received: " + str(
-            H["cstat"]
-        )
+            assert np.array_equal(H["cstat"], calc_status_list), "Error - unexpected calc status. Received: " + str(
+                H["cstat"]
+            )
 
-        print("\n\n\nRun completed.")
+            print("\n\n\nRun completed.")

From f23323ba964abdc6057db5e0f16d3f9401126d6a Mon Sep 17 00:00:00 2001
From: Jeffrey Larson <jmlarson@anl.gov>
Date: Mon, 19 Sep 2022 14:25:48 -0500
Subject: [PATCH 178/196] Black/edit

---
 .../regression_tests/test_persistent_surmise_killsims.py    | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/libensemble/tests/regression_tests/test_persistent_surmise_killsims.py b/libensemble/tests/regression_tests/test_persistent_surmise_killsims.py
index 3c6e11b98e..510b78ab7a 100644
--- a/libensemble/tests/regression_tests/test_persistent_surmise_killsims.py
+++ b/libensemble/tests/regression_tests/test_persistent_surmise_killsims.py
@@ -80,8 +80,10 @@
     sim_specs = {
         "sim_f": sim_f,
         "in": ["x", "thetas"],
-        "out": [("f", float),
-                ('sim_killed', bool)],  # 'sim_killed' is just used to display at the end.
+        "out": [
+            ("f", float),
+            ("sim_killed", bool),  # "sim_killed" is used only for display at the end of this test
+        ],
         "user": {
             "num_obs": n_x,
             "init_sample_size": init_sample_size,

From a4f5fb9652aef791c56cae26a67281dfb898a75f Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Mon, 19 Sep 2022 15:00:39 -0500
Subject: [PATCH 179/196] One iteration for TCP

---
 .../functionality_tests/test_executor_hworld_timeout.py    | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/libensemble/tests/functionality_tests/test_executor_hworld_timeout.py b/libensemble/tests/functionality_tests/test_executor_hworld_timeout.py
index ab287bf02f..fa8bb45d08 100644
--- a/libensemble/tests/functionality_tests/test_executor_hworld_timeout.py
+++ b/libensemble/tests/functionality_tests/test_executor_hworld_timeout.py
@@ -88,7 +88,12 @@
 
     exit_criteria = {"wallclock_max": 10}
 
-    iterations = 2
+    # TCP does not support multiple libE calls
+    if libE_specs["comms"] == "tcp":
+        iterations = 1
+    else:
+        iterations = 2
+
     for i in range(iterations):
 
         # Perform the run

From 825b57bcb0eadab4777d474ee0c66983697e24f5 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Mon, 19 Sep 2022 22:42:29 -0500
Subject: [PATCH 180/196] Change CUDA test from zrw to num_resource_sets

---
 .../test_persistent_sampling_CUDA_variable_resources.py    | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/libensemble/tests/regression_tests/test_persistent_sampling_CUDA_variable_resources.py b/libensemble/tests/regression_tests/test_persistent_sampling_CUDA_variable_resources.py
index 96ec1279fe..0240460607 100644
--- a/libensemble/tests/regression_tests/test_persistent_sampling_CUDA_variable_resources.py
+++ b/libensemble/tests/regression_tests/test_persistent_sampling_CUDA_variable_resources.py
@@ -30,7 +30,12 @@
 
     nworkers, is_manager, libE_specs, _ = parse_args()
 
-    libE_specs["zero_resource_workers"] = [1]
+    # The persistent gen does not need resources
+
+    libE_specs["num_resource_sets"] = nworkers - 1  # Any worker can be the gen
+
+    # libE_specs["zero_resource_workers"] = [1]  # If first worker must be gen, use this instead
+
     libE_specs["sim_dirs_make"] = True
     libE_specs["ensemble_dir_path"] = "./ensemble_CUDA_variable_w" + str(nworkers)
 

From 93c96701c67c4aaa88c4018acc0e1e4556dafcb9 Mon Sep 17 00:00:00 2001
From: Jeffrey Larson <jmlarson@anl.gov>
Date: Tue, 20 Sep 2022 15:42:59 -0500
Subject: [PATCH 181/196] Adding another pointer to contributing doc

---
 README.rst                  | 4 +++-
 docs/introduction_latex.rst | 1 +
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/README.rst b/README.rst
index 5a62ae2471..923248103b 100644
--- a/README.rst
+++ b/README.rst
@@ -238,7 +238,8 @@ Resources
 **Further Information:**
 
 - Documentation is provided by ReadtheDocs_.
-- An overview of libEnsemble's structure and capabilities is given in this manuscript_ and poster_
+- We welcome others to Contribute_ to libEnsemble.
+- An overview of libEnsemble's structure and capabilities is given in this manuscript_ and poster_.
 - Examples of production user functions and complete workflows can be viewed, downloaded, and contributed to in the libEnsemble `Community Examples repository`_.
 
 **Citation:**
@@ -314,6 +315,7 @@ See a complete list of `example user scripts`_.
 .. _Community Examples repository: https://github.com/Libensemble/libe-community-examples
 .. _Conda: https://docs.conda.io/en/latest/
 .. _conda-forge: https://conda-forge.org/
+.. _Contribute: https://github.com/Libensemble/libensemble/blob/main/CONTRIBUTING.rst
 .. _Coveralls: https://coveralls.io/github/Libensemble/libensemble?branch=main
 .. _DEAP: https://deap.readthedocs.io/en/master/overview.html
 .. _DFO-LS: https://github.com/numericalalgorithmsgroup/dfols
diff --git a/docs/introduction_latex.rst b/docs/introduction_latex.rst
index 424765ec47..30b62fde76 100644
--- a/docs/introduction_latex.rst
+++ b/docs/introduction_latex.rst
@@ -30,6 +30,7 @@ We now present further information on running and testing libEnsemble.
 .. _Community Examples repository: https://github.com/Libensemble/libe-community-examples
 .. _Conda: https://docs.conda.io/en/latest/
 .. _conda-forge: https://conda-forge.org/
+.. _Contribute: https://github.com/Libensemble/libensemble/blob/main/CONTRIBUTING.rst
 .. _Coveralls: https://coveralls.io/github/Libensemble/libensemble?branch=main
 .. _DEAP: https://deap.readthedocs.io/en/master/overview.html
 .. _DFO-LS: https://github.com/numericalalgorithmsgroup/dfols

From ff2841c8d818fe7c7ab19203fc97a70b10b30b53 Mon Sep 17 00:00:00 2001
From: Jeffrey Larson <jmlarson@anl.gov>
Date: Tue, 20 Sep 2022 15:54:03 -0500
Subject: [PATCH 182/196] Responding to requested changes

---
 README.rst                  | 4 ++--
 docs/introduction_latex.rst | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.rst b/README.rst
index 923248103b..dd16366995 100644
--- a/README.rst
+++ b/README.rst
@@ -238,7 +238,7 @@ Resources
 **Further Information:**
 
 - Documentation is provided by ReadtheDocs_.
-- We welcome others to Contribute_ to libEnsemble.
+- Contributions_ to libEnsemble are welcome.
 - An overview of libEnsemble's structure and capabilities is given in this manuscript_ and poster_.
 - Examples of production user functions and complete workflows can be viewed, downloaded, and contributed to in the libEnsemble `Community Examples repository`_.
 
@@ -315,7 +315,7 @@ See a complete list of `example user scripts`_.
 .. _Community Examples repository: https://github.com/Libensemble/libe-community-examples
 .. _Conda: https://docs.conda.io/en/latest/
 .. _conda-forge: https://conda-forge.org/
-.. _Contribute: https://github.com/Libensemble/libensemble/blob/main/CONTRIBUTING.rst
+.. _Contributions: https://github.com/Libensemble/libensemble/blob/main/CONTRIBUTING.rst
 .. _Coveralls: https://coveralls.io/github/Libensemble/libensemble?branch=main
 .. _DEAP: https://deap.readthedocs.io/en/master/overview.html
 .. _DFO-LS: https://github.com/numericalalgorithmsgroup/dfols
diff --git a/docs/introduction_latex.rst b/docs/introduction_latex.rst
index 30b62fde76..95d081f28c 100644
--- a/docs/introduction_latex.rst
+++ b/docs/introduction_latex.rst
@@ -30,7 +30,7 @@ We now present further information on running and testing libEnsemble.
 .. _Community Examples repository: https://github.com/Libensemble/libe-community-examples
 .. _Conda: https://docs.conda.io/en/latest/
 .. _conda-forge: https://conda-forge.org/
-.. _Contribute: https://github.com/Libensemble/libensemble/blob/main/CONTRIBUTING.rst
+.. _Contributions: https://github.com/Libensemble/libensemble/blob/main/CONTRIBUTING.rst
 .. _Coveralls: https://coveralls.io/github/Libensemble/libensemble?branch=main
 .. _DEAP: https://deap.readthedocs.io/en/master/overview.html
 .. _DFO-LS: https://github.com/numericalalgorithmsgroup/dfols

From d1dfe7f8fd727d6dc24f94ea522f0e74cca7b28e Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Tue, 20 Sep 2022 21:07:27 -0500
Subject: [PATCH 183/196] Change zrw doc to prioritize num_resource_sets

---
 .../zero_resource_workers.rst                 | 63 ++++++++++++++-----
 1 file changed, 48 insertions(+), 15 deletions(-)

diff --git a/docs/resource_manager/zero_resource_workers.rst b/docs/resource_manager/zero_resource_workers.rst
index 546e4ea83b..948f25478d 100644
--- a/docs/resource_manager/zero_resource_workers.rst
+++ b/docs/resource_manager/zero_resource_workers.rst
@@ -4,34 +4,67 @@ Zero-resource workers
 ~~~~~~~~~~~~~~~~~~~~~
 
 Users with persistent ``gen_f`` functions may notice that the persistent workers
-are still automatically assigned system resources. This can be wasteful if those
-workers only run ``gen_f`` routines in-place and don't use the Executor to submit
-applications to allocated nodes:
+are still automatically assigned resources. This can be wasteful if those workers
+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
+
+If three nodes are available in the node allocation, the result may look like the
+following.
 
     .. image:: ../images/persis_wasted_node.png
         :alt: persis_wasted_node
         :scale: 40
         :align: center
 
-This can be resolved by using the libE_specs option ``zero_resource_workers``:
 
-.. code-block:: python
+To avoid the the wasted node above, add an extra worker::
 
-    libE_specs['zero_resource_workers'] = [1]
+    python run_ensemble_persistent_gen.py --comms local --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.
 
-in the calling script. Set the parameter ``zero_resource_workers`` to a list of
-worker IDs that should not have system resources assigned.
+.. code-block:: python
+
+    nworkers, is_manager, libE_specs, _ = parse_args()
+    libE_specs['num_resource_sets'] = nworkers - 1
 
-Worker 1 will not be allocated resources. Note that additional worker
-processes can be added to take advantage of the free resources (if using the
-same resource set) for simulation instances:
+When the ``num_resource_sets`` option is used, libEnsemble will use the dynamic resource scheduler,
+and any worker may assign work to any node. This works well for most users.
 
     .. image:: ../images/persis_add_worker.png
         :alt: persis_add_worker
         :scale: 40
         :align: center
 
-An alternative, when resource sets are being used, it to set the ``num_resource_sets``
-libE_specs option explicitly to the required value. The difference with declaring
-``zero_resource_workers`` is that a fixed worker will have zero resources (this must
-be supported by the allocation function, see :ref:`start_only_persistent<start_only_persistent_label>`)
+In general, the number of resource sets should be set to enable the maximum concurrency desired by
+the ensemble, taking in to account generators and simulators. The users can set generator resources
+by setting ``persis_info['gen_resources']`` to an integer value, representing the number of resource
+sets to give to the generator. The default is zero.
+
+The available nodes are always divided by the number of resource sets, and there may be multiple nodes
+or a partition of a node in each resource set. If the split is uneven, resource sets are not split between
+nodes. E.g.~ If there are two nodes and five resource sets, one node will have three resource sets, and
+the other will have two.
+
+Placing zero-resource functions on a fixed worker
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+If the generator must must always be on worker one, then instead of using ``num_resource_sets``, use the ``zero_resource_workers`` *libE_specs* option:
+
+
+.. code-block:: python
+
+    libE_specs['zero_resource_workers'] = [1]
+
+
+in the calling script and worker one will not be allocated resources. In general, set the parameter\ ``zero_resource_workers`` to a list of worker IDs that should not have resources assigned.
+
+This approach can be useful if running in :doc:`distributed mode<../platforms/platforms_index>`.
+
+The use of the ``zero_resource_workers`` *libE_specs* option must be supported by the allocation
+function, see :ref:`start_only_persistent<start_only_persistent_label>`)
+

From 21a0f1e97d290f8b5778ff4e5229cdb431d5d7e3 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Tue, 20 Sep 2022 22:55:12 -0500
Subject: [PATCH 184/196] Update parse_args so nsim_workers sets resource sets,
 not zrw

---
 libensemble/tools/parse_args.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/libensemble/tools/parse_args.py b/libensemble/tools/parse_args.py
index c5e32ff3c2..438a1665df 100644
--- a/libensemble/tools/parse_args.py
+++ b/libensemble/tools/parse_args.py
@@ -21,7 +21,7 @@
     "--nsim_workers",
     type=int,
     nargs="?",
-    help="Number of workers for sims. 1+ zero-resource worker for a persistent generator will be added",
+    help="Number of workers for sims. 1+ unresourced workers for a persistent generator will be added",
 )
 parser.add_argument("--nresource_sets", type=int, nargs="?", help="Number of resource sets")
 parser.add_argument("--workers", type=str, nargs="+", help="List of worker nodes")
@@ -59,7 +59,8 @@ def _mpi_parse_args(args):
     # Convenience option which sets other libE_specs options.
     nsim_workers = args.nsim_workers
     if nsim_workers is not None:
-        libE_specs["zero_resource_workers"] = _get_zrw(nworkers, nsim_workers)
+        # libE_specs["zero_resource_workers"] = _get_zrw(nworkers, nsim_workers)
+        libE_specs["num_resource_sets"] = libE_specs.get("num_resource_sets", nsim_workers)
 
     return nworkers, is_manager, libE_specs, args.tester_args
 
@@ -76,7 +77,8 @@ def _local_parse_args(args):
     nsim_workers = args.nsim_workers
     if nsim_workers is not None:
         nworkers = nworkers or nsim_workers + 1
-        libE_specs["zero_resource_workers"] = _get_zrw(nworkers, nsim_workers)
+        # libE_specs["zero_resource_workers"] = _get_zrw(nworkers, nsim_workers)
+        libE_specs["num_resource_sets"] = libE_specs.get("num_resource_sets", nsim_workers)
 
     nworkers = nworkers or 4
     libE_specs["nworkers"] = nworkers
@@ -182,9 +184,10 @@ def parse_args():
         --nworkers,       (For 'local' or 'tcp' comms) Set number of workers.
         --nsim_workers,   (For 'local' or 'mpi' comms) A convenience option for cases with persistent generators.
                           If used with no other criteria, will generate one additional
-                          zero-resource worker for running a generator. If --nworkers
-                          has also been specified, will generate enough zero-resource workers to
-                          match the other criteria.
+                          worker for running a generator and set the number of resource sets to
+                          the given value. If --nworkers has also been specified, will generate
+                          enough additional workers to match the other criteria. If --nresource_sets
+                          is also specified, will not override resource sets.
         --nresource_sets, Explicitly set the number of resource sets. This sets
                           libE_specs['num_resource_sets']. By default, resources will be
                           divided by workers (excluding zero_resource_workers).

From ab3960e0887cbb244a04d782c5829f9baab7e936 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Wed, 21 Sep 2022 10:14:27 -0500
Subject: [PATCH 185/196] Update --nsim_workers docs

---
 docs/resource_manager/zero_resource_workers.rst | 10 +++++++++-
 libensemble/tools/parse_args.py                 | 13 +++++++------
 2 files changed, 16 insertions(+), 7 deletions(-)

diff --git a/docs/resource_manager/zero_resource_workers.rst b/docs/resource_manager/zero_resource_workers.rst
index 948f25478d..4006a05a75 100644
--- a/docs/resource_manager/zero_resource_workers.rst
+++ b/docs/resource_manager/zero_resource_workers.rst
@@ -40,6 +40,13 @@ and any worker may assign work to any node. This works well for most users.
         :scale: 40
         :align: center
 
+**Optional**: An alternative way to express the above would be to use the command line::
+
+    python run_ensemble_persistent_gen.py --comms local --nsim_workers 3
+
+This would automatically set the ``num_resource_sets`` option and add a single worker for the
+persistent generator - a common use-case.
+
 In general, the number of resource sets should be set to enable the maximum concurrency desired by
 the ensemble, taking in to account generators and simulators. The users can set generator resources
 by setting ``persis_info['gen_resources']`` to an integer value, representing the number of resource
@@ -61,7 +68,8 @@ If the generator must must always be on worker one, then instead of using ``num_
     libE_specs['zero_resource_workers'] = [1]
 
 
-in the calling script and worker one will not be allocated resources. In general, set the parameter\ ``zero_resource_workers`` to a list of worker IDs that should not have resources assigned.
+in the calling script and worker one will not be allocated resources. In general, set the parameter
+``zero_resource_workers`` to a list of worker IDs that should not have resources assigned.
 
 This approach can be useful if running in :doc:`distributed mode<../platforms/platforms_index>`.
 
diff --git a/libensemble/tools/parse_args.py b/libensemble/tools/parse_args.py
index 438a1665df..469ad443e1 100644
--- a/libensemble/tools/parse_args.py
+++ b/libensemble/tools/parse_args.py
@@ -182,15 +182,16 @@ def parse_args():
 
         --comms,          Communications medium for manager and workers. Default is 'mpi'.
         --nworkers,       (For 'local' or 'tcp' comms) Set number of workers.
-        --nsim_workers,   (For 'local' or 'mpi' comms) A convenience option for cases with persistent generators.
-                          If used with no other criteria, will generate one additional
-                          worker for running a generator and set the number of resource sets to
-                          the given value. If --nworkers has also been specified, will generate
-                          enough additional workers to match the other criteria. If --nresource_sets
-                          is also specified, will not override resource sets.
         --nresource_sets, Explicitly set the number of resource sets. This sets
                           libE_specs['num_resource_sets']. By default, resources will be
                           divided by workers (excluding zero_resource_workers).
+        --nsim_workers,   (For 'local' or 'mpi' comms) A convenience option for cases with
+                          persistent generators - sets the number of simulation workers.
+                          If used with no other criteria, one additional worker for running a
+                          generator will be added, and the number of resource sets will be assigned
+                          the given value. If '--nworkers' has also been specified, will generate
+                          enough additional workers to match the other criteria. If '--nresource_sets'
+                          is also specified, will not override resource sets.
 
         Example command lines:
 

From dab1ff21b4b265c7623f82392ac0f3b7d85a712d Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Wed, 21 Sep 2022 10:18:47 -0500
Subject: [PATCH 186/196] Reformatting zrw docs

---
 .../zero_resource_workers.rst                 | 49 +++++++++++--------
 1 file changed, 28 insertions(+), 21 deletions(-)

diff --git a/docs/resource_manager/zero_resource_workers.rst b/docs/resource_manager/zero_resource_workers.rst
index 4006a05a75..f2e2044876 100644
--- a/docs/resource_manager/zero_resource_workers.rst
+++ b/docs/resource_manager/zero_resource_workers.rst
@@ -24,43 +24,48 @@ To avoid the the wasted node above, add an extra worker::
 
     python run_ensemble_persistent_gen.py --comms local --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.
+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.
 
 .. code-block:: python
 
     nworkers, is_manager, libE_specs, _ = parse_args()
     libE_specs['num_resource_sets'] = nworkers - 1
 
-When the ``num_resource_sets`` option is used, libEnsemble will use the dynamic resource scheduler,
-and any worker may assign work to any node. This works well for most users.
+When the ``num_resource_sets`` option is used, libEnsemble will use the dynamic
+resource scheduler, and any worker may assign work to any node. This works well
+for most users.
 
     .. image:: ../images/persis_add_worker.png
         :alt: persis_add_worker
         :scale: 40
         :align: center
 
-**Optional**: An alternative way to express the above would be to use the command line::
+**Optional**: An alternative way to express the above would be to use the command
+line::
 
     python run_ensemble_persistent_gen.py --comms local --nsim_workers 3
 
-This would automatically set the ``num_resource_sets`` option and add a single worker for the
-persistent generator - a common use-case.
+This would automatically set the ``num_resource_sets`` option and add a single
+worker for the persistent generator - a common use-case.
 
-In general, the number of resource sets should be set to enable the maximum concurrency desired by
-the ensemble, taking in to account generators and simulators. The users can set generator resources
-by setting ``persis_info['gen_resources']`` to an integer value, representing the number of resource
-sets to give to the generator. The default is zero.
+In general, the number of resource sets should be set to enable the maximum
+concurrency desired by the ensemble, taking in to account generators and simulators.
+The users can set generator resources by setting ``persis_info['gen_resources']``
+to an integer value, representing the number of resource sets to give to the
+generator. The default is zero.
 
-The available nodes are always divided by the number of resource sets, and there may be multiple nodes
-or a partition of a node in each resource set. If the split is uneven, resource sets are not split between
-nodes. E.g.~ If there are two nodes and five resource sets, one node will have three resource sets, and
-the other will have two.
+The available nodes are always divided by the number of resource sets, and there
+may be multiple nodes or a partition of a node in each resource set. If the split
+is uneven, resource sets are not split between nodes. E.g.~ If there are two nodes
+and five resource sets, one node will have three resource sets, and the other will
+have two.
 
 Placing zero-resource functions on a fixed worker
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-If the generator must must always be on worker one, then instead of using ``num_resource_sets``, use the ``zero_resource_workers`` *libE_specs* option:
+If the generator must must always be on worker one, then instead of using
+``num_resource_sets``, use the ``zero_resource_workers`` *libE_specs* option:
 
 
 .. code-block:: python
@@ -68,11 +73,13 @@ If the generator must must always be on worker one, then instead of using ``num_
     libE_specs['zero_resource_workers'] = [1]
 
 
-in the calling script and worker one will not be allocated resources. In general, set the parameter
-``zero_resource_workers`` to a list of worker IDs that should not have resources assigned.
+in the calling script and worker one will not be allocated resources. In general,
+set the parameter ``zero_resource_workers`` to a list of worker IDs that should not
+have resources assigned.
 
-This approach can be useful if running in :doc:`distributed mode<../platforms/platforms_index>`.
+This approach can be useful if running in
+:doc:`distributed mode<../platforms/platforms_index>`.
 
-The use of the ``zero_resource_workers`` *libE_specs* option must be supported by the allocation
-function, see :ref:`start_only_persistent<start_only_persistent_label>`)
+The use of the ``zero_resource_workers`` *libE_specs* option must be supported by
+the allocation function, see :ref:`start_only_persistent<start_only_persistent_label>`)
 

From fadaf1b44539b23f54fb2d9400f974ff55bb3434 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Wed, 21 Sep 2022 11:52:06 -0500
Subject: [PATCH 187/196] Update dynamic resources docs

---
 docs/resource_manager/overview.rst | 48 ++++++++++++++----------------
 1 file changed, 23 insertions(+), 25 deletions(-)

diff --git a/docs/resource_manager/overview.rst b/docs/resource_manager/overview.rst
index 7a2dff656e..317a4b3f29 100644
--- a/docs/resource_manager/overview.rst
+++ b/docs/resource_manager/overview.rst
@@ -8,32 +8,28 @@ libEnsemble comes with built-in resource management. This entails the
 :ref:`detection of available resources<resource_detection>` (e.g., nodelists and
 core counts), and the allocation of resources to workers.
 
-By default, the provisioned resources are divided by the number of workers (excluding
-any workers given in the ``zero_resource_workers`` libE_specs option). libEnsemble's
-:doc:`MPI Executor<../executor/mpi_executor>` is aware of these supplied resources,
-and if not given any of ``num_nodes``, ``num_procs``, or ``procs_per_node`` in the submit
-function, it will try to use all nodes and CPU cores available to the worker.
+By default, the provisioned resources are divided by the number of workers.
+libEnsemble's :doc:`MPI Executor<../executor/mpi_executor>` is aware of
+these supplied resources, and if not given any of ``num_nodes``, ``num_procs``,
+or ``procs_per_node`` in the submit function, it will try to use all nodes and
+CPU cores available to the worker.
 
 Detected resources can be overridden using the libE_specs option :ref:`resource_info<resource_info>`.
 
-Resource management can be disabled by setting
-``libE_specs['disable_resource_manager'] = True``. This will prevent libEnsemble
-from doing any resource detection or management.
-
 Variable resource assignment
 ----------------------------
 
 In slightly more detail, the resource manager divides resources into **resource sets**.
 One resource set is the smallest unit of resources that can be assigned (and
 dynamically reassigned) to workers. By default, the provisioned resources are
-divided by the number of workers (excluding any workers given in the ``zero_resource_workers``
-``libE_specs`` option). However, it can also be set directly by the ``num_resource_sets``
-``libE_specs`` option. If the latter is set, the dynamic resource assignment algorithm
-will always be used.
+divided by the number of workers (excluding any workers given in the
+``zero_resource_workers`` ``libE_specs`` option). However, it can also be set
+directly by the ``num_resource_sets`` ``libE_specs`` option. If the latter is set,
+the dynamic resource assignment algorithm will always be used.
 
 If there are more resource sets than nodes, then the resource sets on each node
-will be given a slot number, enumerated from zero. For example, if there are three slots
-on a node, they will have slot numbers 0, 1, and 2.
+will be given a slot number, enumerated from zero. For example, if there are three
+slots on a node, they will have slot numbers 0, 1, and 2.
 
 The resource manager will not split a resource set over nodes, rather the resource
 sets on each node will be the integer division of resource sets over nodes, with
@@ -41,7 +37,8 @@ the remainder dealt out from the first node. Even breakdowns are generally
 preferable, however.
 
 For example, say a given system has four GPUs per node, and the user has run
-libEnsemble on two nodes, with eight workers. The default division of resources would be:
+libEnsemble on two nodes, with eight workers. The default division of resources
+would be:
 
 .. image:: ../images/variable_resources1.png
 
@@ -66,7 +63,7 @@ In the calling script, use a ``gen_specs['out']`` field called ``resource_sets``
                          ('x', float, n)]
                 }
 
-For an example calling script, see The libEnsemble regression test
+For an example calling script, see the regression test
 `test_persistent_sampling_CUDA_variable_resources.py`_
 
 In the generator, the ``resource_sets`` field must be set to a value for each point
@@ -122,10 +119,9 @@ For example, in *six_hump_camel_CUDA_variable_resources*, the environment variab
     :emphasize-lines: 3
 
     resources = Resources.resources.worker_resources
-    if resources.even_slots:  # Need same slots on each node
-        resources.set_env_to_slots("CUDA_VISIBLE_DEVICES")  # Use convenience function.
-        num_nodes = resources.local_node_count
-        cores_per_node = resources.slot_count  # One CPU per GPU
+    resources.set_env_to_slots("CUDA_VISIBLE_DEVICES")  # Use convenience function.
+    num_nodes = resources.local_node_count
+    cores_per_node = resources.slot_count  # One CPU per GPU
 
 In the figure above, this would result in worker one setting::
 
@@ -196,17 +192,19 @@ Persistent generator
 You have *one* persistent generator and want *eight* workers for running concurrent
 simulations. In this case you can run with *nine* workers.
 
-Either use one zero resource worker, if the generator should always be the same worker:
+Either explicitly set eight resource sets (recommended):
 
 .. code-block:: python
 
-    libE_specs['zero_resource_workers'] = [1]
+    libE_specs['num_resource_sets'] = 8
 
-Or explicitly set eight resource sets:
+Or if the generator should always be the same worker, use one zero resource worker:
 
 .. code-block:: python
 
-    libE_specs['num_resource_sets'] = 8
+    libE_specs['zero_resource_workers'] = [1]
+
+For the second option, an allocation function supporting zero resource workers must be used.
 
 Using the two-node example above, the initial worker mapping in this example will be:
 

From 2392f05b7489e629dccd42a66dcb1f5edd3d7140 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Wed, 21 Sep 2022 11:54:16 -0500
Subject: [PATCH 188/196] Update resources contents page - more descriptions

---
 docs/resource_manager/resources_index.rst       | 8 ++++++--
 docs/resource_manager/zero_resource_workers.rst | 1 -
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/docs/resource_manager/resources_index.rst b/docs/resource_manager/resources_index.rst
index a1dae387d1..c1eabef0da 100644
--- a/docs/resource_manager/resources_index.rst
+++ b/docs/resource_manager/resources_index.rst
@@ -5,13 +5,17 @@ Resource Manager
 
 libEnsemble comes with built-in resource management. This entails the detection of available resources (e.g. nodelists and core counts), and the allocation of resources to workers.
 
+Resource management can be disabled by setting
+``libE_specs['disable_resource_manager'] = True``. This will prevent libEnsemble
+from doing any resource detection or management.
+
 .. toctree::
    :maxdepth: 2
    :titlesonly:
    :caption: Resource Manager:
 
+   Zero-resource workers (e.g.,~ Persistent gen does not need resources) <zero_resource_workers>
    overview
    resource_detection
-   zero_resource_workers
    scheduler_module
-   worker_resources
+   Worker Resources Module (query resources for current worker) <worker_resources>
diff --git a/docs/resource_manager/zero_resource_workers.rst b/docs/resource_manager/zero_resource_workers.rst
index f2e2044876..c676fa9a88 100644
--- a/docs/resource_manager/zero_resource_workers.rst
+++ b/docs/resource_manager/zero_resource_workers.rst
@@ -82,4 +82,3 @@ This approach can be useful if running in
 
 The use of the ``zero_resource_workers`` *libE_specs* option must be supported by
 the allocation function, see :ref:`start_only_persistent<start_only_persistent_label>`)
-

From 2824b7ef74d075a0aeeb0363b8d36e1f26de8798 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Wed, 21 Sep 2022 12:26:23 -0500
Subject: [PATCH 189/196] Update zrw to num_resource_sets elsewhere in docs

---
 docs/data_structures/libE_specs.rst    |  4 +++-
 docs/platforms/perlmutter.rst          |  2 +-
 docs/platforms/platforms_index.rst     |  2 +-
 docs/tutorials/forces_gpu_tutorial.rst | 14 +++++---------
 4 files changed, 10 insertions(+), 12 deletions(-)

diff --git a/docs/data_structures/libE_specs.rst b/docs/data_structures/libE_specs.rst
index 46fcd15aa2..85af8594f6 100644
--- a/docs/data_structures/libE_specs.rst
+++ b/docs/data_structures/libE_specs.rst
@@ -147,7 +147,9 @@ libEnsemble is primarily customized by setting options within a ``libE_specs`` d
             Distributed mode means workers share nodes with applications.
             Default: False
         'zero_resource_workers' [list of ints]:
-            List of workers that require no resources.
+            List of workers that require no resources. For when a fixed mapping of workers
+            to resources is required. Otherwise, use "num_resource_sets".
+            For use with supported allocation functions.
         'resource_info' [dict]:
             Provide resource information that will override automatically detected resources.
             The allowable fields are given below in 'Overriding Auto-detection'
diff --git a/docs/platforms/perlmutter.rst b/docs/platforms/perlmutter.rst
index c2f97bdbd3..96998949f7 100644
--- a/docs/platforms/perlmutter.rst
+++ b/docs/platforms/perlmutter.rst
@@ -114,7 +114,7 @@ four resource sets (the example generator does not need dedicated resources):
 
 .. code-block:: python
 
-    libE_specs['zero_resource_workers'] = [1]
+    libE_specs['num_resource_sets'] = 4
 
 The MPIExecutor is also initiated in the calling script, ensuring that ``srun`` is picked up::
 
diff --git a/docs/platforms/platforms_index.rst b/docs/platforms/platforms_index.rst
index f95c480f12..3b719c26b0 100644
--- a/docs/platforms/platforms_index.rst
+++ b/docs/platforms/platforms_index.rst
@@ -139,7 +139,7 @@ Zero-resource workers
 
 Users with persistent ``gen_f`` functions may notice that the persistent workers
 are still automatically assigned system resources. This can be resolved by
-using :ref:`zero resource workers<zero_resource_workers>`.
+:ref:`fixing the number of resource sets<zero_resource_workers>`.
 
 Overriding Auto-detection
 -------------------------
diff --git a/docs/tutorials/forces_gpu_tutorial.rst b/docs/tutorials/forces_gpu_tutorial.rst
index 0ce1054cda..7917862f3f 100644
--- a/docs/tutorials/forces_gpu_tutorial.rst
+++ b/docs/tutorials/forces_gpu_tutorial.rst
@@ -132,17 +132,13 @@ eight workers. For example::
 
     python run_libe_forces.py --comms local --nworkers 8
 
-If you are running one persistent generator which does not require
-resources, then assign nine workers, and set the following in your
-calling script::
-
-    libE_specs['zero_resource_workers'] = [1]
-
-Or - if you do not care which worker runs the generator, you could fix the
-*resource_sets*::
+Note that if you are running one persistent generator which does not require
+resources, then assign nine workers, and fix the number of *resource_sets*::
 
     libE_specs['num_resource_sets'] = 8
 
+See :ref:`zero resource workers<zero_resource_workers>` for more ways to express this.
+
 Changing number of GPUs per worker
 ----------------------------------
 
@@ -163,7 +159,7 @@ as a ``gen_specs['out']`` in your calling script. Simply assign the
 generated.
 
 In this case the above code would still work, assigning one CPU processor and
-one GPU to each rank. If you want to have one rank with multiple GPUs, then
+one GPU to each MPI rank. If you want to have one rank with multiple GPUs, then
 change source lines 29/30 accordingly.
 
 Further guidance on varying resource to workers can be found under the

From e0477c8e293779809f7ff081b3305a20a6ad0cbb Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Wed, 21 Sep 2022 17:18:43 -0500
Subject: [PATCH 190/196] Update more test to use num_resource_sets or both

---
 .../test_mpi_runners_zrw_subnode_uneven.py    | 33 +++++++++++++++----
 ...st_runlines_adaptive_workers_persistent.py |  5 +--
 2 files changed, 29 insertions(+), 9 deletions(-)

diff --git a/libensemble/tests/functionality_tests/test_mpi_runners_zrw_subnode_uneven.py b/libensemble/tests/functionality_tests/test_mpi_runners_zrw_subnode_uneven.py
index 933d7c0544..c633036c1c 100644
--- a/libensemble/tests/functionality_tests/test_mpi_runners_zrw_subnode_uneven.py
+++ b/libensemble/tests/functionality_tests/test_mpi_runners_zrw_subnode_uneven.py
@@ -7,6 +7,13 @@
    mpiexec -np 7 python test_mpi_runners_zrw_subnode_uneven.py
    python test_mpi_runners_zrw_subnode_uneven.py --nworkers 6 --comms local
    python test_mpi_runners_zrw_subnode_uneven.py --nworkers 6 --comms tcp
+
+The resource sets are split unevenly between the two nodes (e.g. 3 and 2).
+
+Two tests are run. In the first, num_resource_sets is used, and thus the dynamic scheduler.
+This will fill node two slots first as there are fewer resource sets on node two, and the
+scheduler will preference a smaller space for assigning the task. On the second test,
+zero_resource_workers are used, and the static scheduler will fill node one first.
 """
 
 import sys
@@ -36,7 +43,6 @@
     sim_app = "/path/to/fakeapp.x"
     comms = libE_specs["comms"]
 
-    libE_specs["zero_resource_workers"] = [1]
     libE_specs["dedicated_mode"] = True
     libE_specs["enforce_worker_core_bounds"] = True
 
@@ -45,8 +51,7 @@
     logger.set_filename(log_file)
 
     # For varying size test - relate node count to nworkers
-    in_place = libE_specs["zero_resource_workers"]
-    n_gens = len(in_place)
+    n_gens = 1
     nsim_workers = nworkers - n_gens
 
     if nsim_workers % 2 == 0:
@@ -96,7 +101,6 @@
     }
 
     alloc_specs = {"alloc_f": alloc_f, "out": []}
-    persis_info = add_unique_random_streams({}, nworkers + 1)
     exit_criteria = {"sim_max": (nsim_workers) * rounds}
 
     test_list_base = [
@@ -141,7 +145,22 @@
         "persis_gens": n_gens,
     }
 
-    # Perform the run
-    H, persis_info, flag = libE(sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs)
+    iterations = 2
+    for prob_id in range(iterations):
 
-    # All asserts are in sim func
+        if prob_id == 0:
+            # Uses dynamic scheduler - will find node 2 slots first (as fewer)
+            libE_specs["num_resource_sets"] = nworkers - 1  # Any worker can be the gen
+            sim_specs["user"]["offset_for_scheduler"] = True  # Changes expected values
+            persis_info = add_unique_random_streams({}, nworkers + 1)
+
+        else:
+            # Uses static scheduler - will find node 1 slots first
+            del libE_specs["num_resource_sets"]
+            libE_specs["zero_resource_workers"] = [1]  # Gen must be worker 1
+            sim_specs["user"]["offset_for_scheduler"] = False
+            persis_info = add_unique_random_streams({}, nworkers + 1)
+
+        # Perform the run
+        H, persis_info, flag = libE(sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs)
+        # All asserts are in sim func
diff --git a/libensemble/tests/functionality_tests/test_runlines_adaptive_workers_persistent.py b/libensemble/tests/functionality_tests/test_runlines_adaptive_workers_persistent.py
index 38f27fc1f1..9b1c49a170 100644
--- a/libensemble/tests/functionality_tests/test_runlines_adaptive_workers_persistent.py
+++ b/libensemble/tests/functionality_tests/test_runlines_adaptive_workers_persistent.py
@@ -36,8 +36,9 @@
 
     nworkers, is_manager, libE_specs, _ = parse_args()
 
-    libE_specs["zero_resource_workers"] = [1]
-    num_gens = len(libE_specs["zero_resource_workers"])
+    num_gens = 1
+    libE_specs["num_resource_sets"] = nworkers - num_gens  # Any worker can be the gen
+
     total_nodes = (nworkers - num_gens) // 4  # 4 resourced workers per node.
 
     if total_nodes == 1:

From 9e93c031d2a5a9d742fb6a019ea6c59ff792290b Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Wed, 21 Sep 2022 22:58:04 -0500
Subject: [PATCH 191/196] Testing just running nrs

---
 .../test_mpi_runners_zrw_subnode_uneven.py         | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/libensemble/tests/functionality_tests/test_mpi_runners_zrw_subnode_uneven.py b/libensemble/tests/functionality_tests/test_mpi_runners_zrw_subnode_uneven.py
index c633036c1c..1e071bfe32 100644
--- a/libensemble/tests/functionality_tests/test_mpi_runners_zrw_subnode_uneven.py
+++ b/libensemble/tests/functionality_tests/test_mpi_runners_zrw_subnode_uneven.py
@@ -145,7 +145,7 @@
         "persis_gens": n_gens,
     }
 
-    iterations = 2
+    iterations = 1
     for prob_id in range(iterations):
 
         if prob_id == 0:
@@ -154,12 +154,12 @@
             sim_specs["user"]["offset_for_scheduler"] = True  # Changes expected values
             persis_info = add_unique_random_streams({}, nworkers + 1)
 
-        else:
-            # Uses static scheduler - will find node 1 slots first
-            del libE_specs["num_resource_sets"]
-            libE_specs["zero_resource_workers"] = [1]  # Gen must be worker 1
-            sim_specs["user"]["offset_for_scheduler"] = False
-            persis_info = add_unique_random_streams({}, nworkers + 1)
+        # else:
+            # # Uses static scheduler - will find node 1 slots first
+            # del libE_specs["num_resource_sets"]
+            # libE_specs["zero_resource_workers"] = [1]  # Gen must be worker 1
+            # sim_specs["user"]["offset_for_scheduler"] = False
+            # persis_info = add_unique_random_streams({}, nworkers + 1)
 
         # Perform the run
         H, persis_info, flag = libE(sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs)

From f7fd581f58aea40bc01640daa722fefb510000d4 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Thu, 22 Sep 2022 10:31:31 -0500
Subject: [PATCH 192/196] Test only - with resource change

---
 libensemble/resources/resources.py             | 18 ++++++++++--------
 .../test_mpi_runners_zrw_subnode_uneven.py     | 12 ++++++------
 2 files changed, 16 insertions(+), 14 deletions(-)

diff --git a/libensemble/resources/resources.py b/libensemble/resources/resources.py
index ecc76f7c7e..74661e08b8 100644
--- a/libensemble/resources/resources.py
+++ b/libensemble/resources/resources.py
@@ -13,7 +13,7 @@
 
 logger = logging.getLogger(__name__)
 # To change logging level for just this module
-# logger.setLevel(logging.DEBUG)
+# #logger.setLevel(logging.DEBUG)
 
 
 class ResourcesException(Exception):
@@ -56,8 +56,9 @@ def init_resources(cls, libE_specs):
         disable_resource_manager = libE_specs.get("disable_resource_manager", False)
         if not disable_resource_manager:
             top_level_dir = os.getcwd()
-            if Resources.resources is None:
-                Resources.resources = Resources(libE_specs=libE_specs, top_level_dir=top_level_dir)
+            # if Resources.resources is None:
+            # del Resources.resources
+            Resources.resources = Resources(libE_specs=libE_specs, top_level_dir=top_level_dir)
 
     def __init__(self, libE_specs, top_level_dir=None):
         """Initiate a new resources object"""
@@ -163,8 +164,8 @@ def __init__(self, libE_specs, top_level_dir=None):
         self.num_resource_sets = libE_specs.get("num_resource_sets", None)
         self.enforce_worker_core_bounds = libE_specs.get("enforce_worker_core_bounds", False)
 
-        if self.dedicated_mode:
-            logger.debug("Running in dedicated mode")
+        # if self.dedicated_mode:
+            # logger.debug("Running in dedicated mode")
 
         resource_info = libE_specs.get("resource_info", {})
         cores_on_node = resource_info.get("cores_on_node", None)
@@ -227,6 +228,7 @@ def add_comm_info(self, libE_nodes):
                 self.global_nodelist = GlobalResources.remove_nodes(self.global_nodelist, self.libE_nodes)
                 if not self.global_nodelist:
                     logger.warning("Warning. Node-list for tasks is empty. Remove dedicated_mode or add nodes")
+                    pass
 
     @staticmethod
     def is_nodelist_shortnames(nodelist):
@@ -258,18 +260,18 @@ def get_global_nodelist(node_file=Resources.DEFAULT_NODEFILE, rundir=None, env_r
         node_filepath = os.path.join(top_level_dir, node_file)
         global_nodelist = []
         if os.path.isfile(node_filepath):
-            logger.debug("node_file found - getting nodelist from node_file")
+            # logger.debug("node_file found - getting nodelist from node_file")
             with open(node_filepath, "r") as f:
                 for line in f:
                     global_nodelist.append(line.rstrip())
         else:
-            logger.debug("No node_file found - searching for nodelist in environment")
+            # logger.debug("No node_file found - searching for nodelist in environment")
             if env_resources:
                 global_nodelist = env_resources.get_nodelist()
 
             if not global_nodelist:
                 # Assume a standalone machine
-                logger.info("Can not find nodelist from environment. Assuming standalone")
+                # logger.info("Can not find nodelist from environment. Assuming standalone")
                 # global_nodelist.append(env_resources.shortnames([socket.gethostname()])[0])
                 global_nodelist.append(socket.gethostname())
 
diff --git a/libensemble/tests/functionality_tests/test_mpi_runners_zrw_subnode_uneven.py b/libensemble/tests/functionality_tests/test_mpi_runners_zrw_subnode_uneven.py
index 1e071bfe32..3b685ee325 100644
--- a/libensemble/tests/functionality_tests/test_mpi_runners_zrw_subnode_uneven.py
+++ b/libensemble/tests/functionality_tests/test_mpi_runners_zrw_subnode_uneven.py
@@ -154,12 +154,12 @@
             sim_specs["user"]["offset_for_scheduler"] = True  # Changes expected values
             persis_info = add_unique_random_streams({}, nworkers + 1)
 
-        # else:
-            # # Uses static scheduler - will find node 1 slots first
-            # del libE_specs["num_resource_sets"]
-            # libE_specs["zero_resource_workers"] = [1]  # Gen must be worker 1
-            # sim_specs["user"]["offset_for_scheduler"] = False
-            # persis_info = add_unique_random_streams({}, nworkers + 1)
+        else:
+            # Uses static scheduler - will find node 1 slots first
+            del libE_specs["num_resource_sets"]
+            libE_specs["zero_resource_workers"] = [1]  # Gen must be worker 1
+            sim_specs["user"]["offset_for_scheduler"] = False
+            persis_info = add_unique_random_streams({}, nworkers + 1)
 
         # Perform the run
         H, persis_info, flag = libE(sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs)

From efe906c158b0f898ba50eeaf638d1d226d708058 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Thu, 22 Sep 2022 10:51:15 -0500
Subject: [PATCH 193/196] Fix indent

---
 libensemble/resources/resources.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/libensemble/resources/resources.py b/libensemble/resources/resources.py
index 74661e08b8..baaf99baad 100644
--- a/libensemble/resources/resources.py
+++ b/libensemble/resources/resources.py
@@ -164,9 +164,6 @@ def __init__(self, libE_specs, top_level_dir=None):
         self.num_resource_sets = libE_specs.get("num_resource_sets", None)
         self.enforce_worker_core_bounds = libE_specs.get("enforce_worker_core_bounds", False)
 
-        # if self.dedicated_mode:
-            # logger.debug("Running in dedicated mode")
-
         resource_info = libE_specs.get("resource_info", {})
         cores_on_node = resource_info.get("cores_on_node", None)
         node_file = resource_info.get("node_file", None)

From 2234fdb656d4166427012db9cca032ef11f83fe0 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Thu, 22 Sep 2022 14:15:33 -0500
Subject: [PATCH 194/196] Actually run both iterations

---
 .../test_mpi_runners_zrw_subnode_uneven.py                    | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/libensemble/tests/functionality_tests/test_mpi_runners_zrw_subnode_uneven.py b/libensemble/tests/functionality_tests/test_mpi_runners_zrw_subnode_uneven.py
index 3b685ee325..8bbe372884 100644
--- a/libensemble/tests/functionality_tests/test_mpi_runners_zrw_subnode_uneven.py
+++ b/libensemble/tests/functionality_tests/test_mpi_runners_zrw_subnode_uneven.py
@@ -145,7 +145,7 @@
         "persis_gens": n_gens,
     }
 
-    iterations = 1
+    iterations = 2
     for prob_id in range(iterations):
 
         if prob_id == 0:
@@ -163,4 +163,4 @@
 
         # Perform the run
         H, persis_info, flag = libE(sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs, libE_specs)
-        # All asserts are in sim func
+        # Run-line asserts are in sim func

From c489b362c01d3ea58813b28e49b7cc81ef2af5f0 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Thu, 22 Sep 2022 16:30:56 -0500
Subject: [PATCH 195/196] Add link to slots/rsets diagram

---
 docs/resource_manager/overview.rst     |  2 ++
 docs/tutorials/forces_gpu_tutorial.rst | 40 +++++++++++++++++++-------
 2 files changed, 31 insertions(+), 11 deletions(-)

diff --git a/docs/resource_manager/overview.rst b/docs/resource_manager/overview.rst
index 317a4b3f29..93f8b02009 100644
--- a/docs/resource_manager/overview.rst
+++ b/docs/resource_manager/overview.rst
@@ -40,6 +40,8 @@ For example, say a given system has four GPUs per node, and the user has run
 libEnsemble on two nodes, with eight workers. The default division of resources
 would be:
 
+.. _rsets-diagram:
+
 .. image:: ../images/variable_resources1.png
 
 Variable Size simulations
diff --git a/docs/tutorials/forces_gpu_tutorial.rst b/docs/tutorials/forces_gpu_tutorial.rst
index 7917862f3f..bae79dcc6d 100644
--- a/docs/tutorials/forces_gpu_tutorial.rst
+++ b/docs/tutorials/forces_gpu_tutorial.rst
@@ -22,9 +22,12 @@ GPU build lines in build_forces.sh_ or similar for your platform.
 The libEnsemble scripts in this example are available under forces_gpu_ in
 the libEnsemble repository.
 
-Note that at time of writing the calling script ``run_libe_forces.py`` is identical
-to that in ``forces_simple``. The ``forces_simf`` file has slight modifications to
-assign GPUs.
+Note that at time of writing the calling script **run_libe_forces.py** is functionally
+the same as that in *forces_simple*, but contains some commented out lines that can
+be used for a variable resources example. The *forces_simf.py* file has slight modifications
+to assign GPUs.
+
+Videos demonstrate running this example on Perlmutter_ and Spock_.
 
 Simulation function
 -------------------
@@ -106,6 +109,11 @@ and the line::
 will set the environment variable ``CUDA_VISIBLE_DEVICES`` to match the assigned
 slots (partitions on the node).
 
+.. note::
+    **slots** refers to the ``resource sets`` enumerated on a node (starting with
+    zero). If a resource set has more than one node, then each node is considered to
+    have slot zero. [:ref:`diagram<rsets-diagram>`]
+
 Note that if you are on a system that automatically assigns free GPUs on the node,
 then setting ``CUDA_VISIBLE_DEVICES`` is not necessary unless you want to ensure
 workers are strictly bound to GPUs. For example, on many **SLURM** systems, you
@@ -133,7 +141,8 @@ eight workers. For example::
     python run_libe_forces.py --comms local --nworkers 8
 
 Note that if you are running one persistent generator which does not require
-resources, then assign nine workers, and fix the number of *resource_sets*::
+resources, then assign nine workers, and fix the number of *resource_sets* in
+you calling script::
 
     libE_specs['num_resource_sets'] = 8
 
@@ -153,14 +162,20 @@ Varying resources
 -----------------
 
 The same code can be used when varying worker resources. In this case, you may
-choose to set one worker per GPU (as we did originally). Then add ``resource_sets``
-as a ``gen_specs['out']`` in your calling script. Simply assign the
-``resource_sets`` field of :doc:`H<../data_structures/history_array>` for each point
-generated.
+add an integer field called ``resource_sets`` as a ``gen_specs['out']`` in your
+calling script.
+
+In the generator function, assign the ``resource_sets`` field of
+:doc:`H<../data_structures/history_array>` for each point generated. For example
+if a larger simulation requires two MPI tasks (and two GPUs), set ``resource_sets``
+field to *2* for that sim_id in the generator function.
+
+The calling script run_libe_forces.py_ contains alternative commented out lines for
+a variable resource example. Search for "Uncomment for var resources"
 
-In this case the above code would still work, assigning one CPU processor and
-one GPU to each MPI rank. If you want to have one rank with multiple GPUs, then
-change source lines 29/30 accordingly.
+In this case, the simulator function will still work, assigning one CPU processor
+and one GPU to each MPI rank. If you want to have one rank with multiple GPUs,
+then change source lines 29/30 accordingly.
 
 Further guidance on varying resource to workers can be found under the
 :doc:`resource manager<../resource_manager/resources_index>`.
@@ -225,3 +240,6 @@ resource conflicts on each node.
 .. _forces_gpu: https://github.com/Libensemble/libensemble/blob/develop/libensemble/tests/scaling_tests/forces/forces_gpu
 .. _forces.c: https://github.com/Libensemble/libensemble/blob/develop/libensemble/tests/scaling_tests/forces/forces_app/forces.c
 .. _build_forces.sh: https://github.com/Libensemble/libensemble/blob/develop/libensemble/tests/scaling_tests/forces/forces_app/build_forces.sh
+.. _Perlmutter: https://www.youtube.com/watch?v=Av8ctYph7-Y
+.. _Spock: https://www.youtube.com/watch?v=XHXcslDORjU
+.. _run_libe_forces.py: https://github.com/Libensemble/libensemble/blob/develop/libensemble/tests/scaling_tests/forces/forces_gpu/run_libe_forces.py

From a57a4897f52b898bb9ce41d3e95b8b571f24be10 Mon Sep 17 00:00:00 2001
From: shudson <shudson@anl.gov>
Date: Thu, 22 Sep 2022 16:39:25 -0500
Subject: [PATCH 196/196] Remove excess comments

---
 libensemble/resources/resources.py | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/libensemble/resources/resources.py b/libensemble/resources/resources.py
index baaf99baad..3f35cf509e 100644
--- a/libensemble/resources/resources.py
+++ b/libensemble/resources/resources.py
@@ -13,7 +13,7 @@
 
 logger = logging.getLogger(__name__)
 # To change logging level for just this module
-# #logger.setLevel(logging.DEBUG)
+# logger.setLevel(logging.DEBUG)
 
 
 class ResourcesException(Exception):
@@ -56,8 +56,6 @@ def init_resources(cls, libE_specs):
         disable_resource_manager = libE_specs.get("disable_resource_manager", False)
         if not disable_resource_manager:
             top_level_dir = os.getcwd()
-            # if Resources.resources is None:
-            # del Resources.resources
             Resources.resources = Resources(libE_specs=libE_specs, top_level_dir=top_level_dir)
 
     def __init__(self, libE_specs, top_level_dir=None):
@@ -257,18 +255,15 @@ def get_global_nodelist(node_file=Resources.DEFAULT_NODEFILE, rundir=None, env_r
         node_filepath = os.path.join(top_level_dir, node_file)
         global_nodelist = []
         if os.path.isfile(node_filepath):
-            # logger.debug("node_file found - getting nodelist from node_file")
             with open(node_filepath, "r") as f:
                 for line in f:
                     global_nodelist.append(line.rstrip())
         else:
-            # logger.debug("No node_file found - searching for nodelist in environment")
             if env_resources:
                 global_nodelist = env_resources.get_nodelist()
 
             if not global_nodelist:
                 # Assume a standalone machine
-                # logger.info("Can not find nodelist from environment. Assuming standalone")
                 # global_nodelist.append(env_resources.shortnames([socket.gethostname()])[0])
                 global_nodelist.append(socket.gethostname())