Merge pull request #1688 from Libensemble/release/v_1.6.0

jmlarson1 · web-flow · commit 42bade65efb1 · 2026-03-06T09:22:47.000-06:00
Updating develop after release 1.6
diff --git a/.wci.yml b/.wci.yml
@@ -16,8 +16,8 @@ description: |
 language: Python
 
 release:
-  version: 1.5.0
-  date: 2025-04-10
+  version: 1.6.0
+  date: 2026-03-04
 
 documentation:
   general: https://libensemble.readthedocs.io
diff --git a/CHANGELOG.rst b/CHANGELOG.rst
@@ -8,6 +8,45 @@ GitHub issues are referenced, and can be viewed with hyperlinks on the `github r
 
 .. _`github releases page`: https://github.com/Libensemble/libensemble/releases
 
+Release 1.6.0
+--------------
+
+:Date: March 04, 2026
+
+General Updates:
+
+* Support for ``gest-api`` generators (https://gest-api.readthedocs.io). #1307
+
+  * Support for any Xopt (v3.0+) and Optimas generators.
+  * libEnsemble's APOSMM, gpCAM, and random sampling generators are supplied in ``gest-api`` format.
+  * Support dictionary (Xopt-style) simulator functions.
+
+* Simulation container support - Executor precedent accepts ``%LIBENSEMBLE_SIM_DIR%`` placeholder. #1672
+
+Examples:
+
+* Adding test for ibcdfo with jax. #1591
+* Optimas/Xopt examples. #1620 / #1635 
+* Bayesian Optimization with Xopt tutorial / notebook.
+* Tasmanian generators moved to community examples.
+
+Dependencies:
+
+* ``gest-api`` is now a required dependency. #1666 
+* Remove Pydantic v1 support and Balsam. #1573
+* Python 3.14 supported. #1609 
+
+
+:Note:
+
+* Tests were run on Linux and MacOS with Python versions 3.10, 3.11, 3.12, 3.13, 3.14
+* Heterogeneous workflows tested on Aurora (ALCF) and Perlmutter (NERSC).
+
+:Known Issues:
+
+* See known issues section in the documentation.
+
+
 Release 1.5.0
 --------------
 
diff --git a/LICENSE b/LICENSE
@@ -1,6 +1,6 @@
 BSD 3-Clause License
 
-Copyright (c) 2018-2025, UChicago Argonne, LLC and the libEnsemble Development Team
+Copyright (c) 2018-2026, UChicago Argonne, LLC and the libEnsemble Development Team
 All Rights Reserved.
 
 Redistribution and use in source and binary forms, with or without
diff --git a/README.rst b/README.rst
@@ -22,8 +22,11 @@ and inference problems on the world's leading supercomputers such as Frontier, A
 
 `Quickstart`_
 
-**New:** Try out the |ScriptCreator| to generate customized scripts for running
-ensembles with your MPI applications.
+**New:** libEnsemble nows supports the `gest-api`_ generator standard, and can run with 
+Optimas and Xopt generators.
+
+The |ScriptCreator| to generate customized scripts for running ensembles with your
+MPI applications.
 
 Installation
 ============
@@ -44,52 +47,46 @@ and an exit condition. Run the following four-worker example via ``python this_f
 
     import numpy as np
 
-    from gest_api.vocs import VOCS
-
     from libensemble import Ensemble
-    from libensemble.gen_classes.sampling import UniformSample
+    from libensemble.gen_funcs.sampling import uniform_random_sample
     from libensemble.sim_funcs.six_hump_camel import six_hump_camel
     from libensemble.specs import ExitCriteria, GenSpecs, LibeSpecs, SimSpecs
 
     if __name__ == "__main__":
 
         libE_specs = LibeSpecs(nworkers=4)
 
-        variables_objectives = VOCS(
-            variables={
-                "x0": [-3, 3],
-                "x1": [-2, 2],
-            },
-            objectives={"f": "EXPLORE"},
-        )
-
-        generator = UniformSample(vocs=variables_objectives)
-
         sim_specs = SimSpecs(
             sim_f=six_hump_camel,
-            vocs=variables_objectives,
+            inputs=["x"],
+            outputs=[("f", float)],
         )
 
         gen_specs = GenSpecs(
-            generator=generator,
-            vocs=variables_objectives,
+            gen_f=uniform_random_sample,
+            outputs=[("x", float, 2)],
+            user={
+                "gen_batch_size": 50,
+                "lb": np.array([-3, -2]),
+                "ub": np.array([3, 2]),
+            },
         )
 
         exit_criteria = ExitCriteria(sim_max=100)
 
-        ensemble = Ensemble(
+        sampling = Ensemble(
             libE_specs=libE_specs,
             sim_specs=sim_specs,
             gen_specs=gen_specs,
             exit_criteria=exit_criteria,
         )
 
-        ensemble.add_random_streams()
-        ensemble.run()
+        sampling.add_random_streams()
+        sampling.run()
 
-        if ensemble.is_manager:
-            ensemble.save_output(__file__)
-            print("Some output data:\n", ensemble.H[["x", "f"]][:10])
+        if sampling.is_manager:
+            sampling.save_output(__file__)
+            print("Some output data:\n", sampling.H[["x", "f"]][:10])
 
 |Inline Example|
 
@@ -106,6 +103,8 @@ Try some other examples live in Colab.
 +---------------------------------------------------------------+-------------------------------------+
 | Surrogate model generation with gpCAM.                        | |Surrogate Modeling|                |
 +---------------------------------------------------------------+-------------------------------------+
+| Bayesian Optimization with Xopt.                              | |Bayesian Optimization with Xopt|   |
++---------------------------------------------------------------+-------------------------------------+
 
 There are many more examples in the `regression tests`_ and `Community Examples repository`_.
 
@@ -167,6 +166,7 @@ Resources
 .. _conda-forge: https://conda-forge.org/
 .. _Contributions: https://github.com/Libensemble/libensemble/blob/main/CONTRIBUTING.rst
 .. _docs: https://libensemble.readthedocs.io/en/main/advanced_installation.html
+.. _gest-api: https://gest-api.readthedocs.io
 .. _GitHub: https://github.com/Libensemble/libensemble
 .. _libEnsemble mailing list: https://lists.mcs.anl.gov/mailman/listinfo/libensemble
 .. _libEnsemble Slack page: https://libensemble.slack.com
@@ -192,6 +192,9 @@ Resources
 .. |Surrogate Modeling| image:: https://colab.research.google.com/assets/colab-badge.svg
   :target:  https://colab.research.google.com/github/Libensemble/libensemble/blob/develop/examples/tutorials/gpcam_surrogate_model/gpcam.ipynb
 
+.. |Bayesian Optimization with Xopt| image:: https://colab.research.google.com/assets/colab-badge.svg
+  :target:  https://colab.research.google.com/github/Libensemble/libensemble/blob/develop/examples/tutorials/xopt_bayesian_gen/xopt_EI_example.ipynb
+
 .. |ScriptCreator| image:: https://img.shields.io/badge/Script_Creator-purple?logo=magic
    :target: https://libensemble.github.io/script-creator/
    :alt: Script Creator
diff --git a/docs/examples/gest_api.rst b/docs/examples/gest_api.rst
@@ -2,7 +2,7 @@
 (New) Standardized Generators
 =============================
 
-libEnsemble now also supports all generators that implement the gest_api_ interface.
+libEnsemble also supports all generators that use the gest_api_ interface.
 
 .. code-block:: python
   :linenos:
@@ -78,26 +78,36 @@ Modeling and Approximation
 Verified Third Party Examples
 =============================
 
-Generators that implement the gest_api_ interface and are verified to work with libEnsemble.
+Generators that use the gest_api_ interface and are verified to work with libEnsemble.
 
 The standardized interface was developed in partnership with their authors.
 
 Xopt - https://github.com/xopt-org/Xopt
 ---------------------------------------
 
+Examples:
+
 `Expected Improvement`_
 
 `Nelder Mead`_
 
 Optimas - https://github.com/optimas-org/optimas
 ------------------------------------------------
 
+Examples:
+
 `Grid Sampling`_
 
+`Ax Multi-fidelity`_
+
 .. _gest_api: https://github.com/campa-consortium/gest-api
 .. _gpcam: https://gpcam.lbl.gov/
 .. _paper: https://link.springer.com/article/10.1007/s12532-017-0131-4
 
-.. _Expected Improvement: https://github.com/xopt-org/Xopt/blob/main/xopt/generators/bayesian/expected_improvement.py
-.. _Nelder Mead: https://github.com/xopt-org/Xopt/blob/main/xopt/generators/sequential/neldermead.py
-.. _Grid Sampling: https://github.com/optimas-org/optimas/blob/main/optimas/generators/grid_sampling.py
+.. _Expected Improvement: https://github.com/Libensemble/libensemble/blob/develop/libensemble/tests/regression_tests/test_xopt_EI.py
+
+.. _Nelder Mead: https://github.com/Libensemble/libensemble/blob/develop/libensemble/tests/regression_tests/test_xopt_nelder_mead.py
+
+.. _Grid Sampling: https://github.com/Libensemble/libensemble/blob/develop/libensemble/tests/regression_tests/test_optimas_grid_sample.py
+
+.. _Ax Multi-fidelity: https://github.com/Libensemble/libensemble/blob/develop/libensemble/tests/regression_tests/test_optimas_ax_mf.py
diff --git a/docs/function_guides/ask_tell_generator.rst b/docs/function_guides/ask_tell_generator.rst
diff --git a/docs/function_guides/function_guide_index.rst b/docs/function_guides/function_guide_index.rst
@@ -13,7 +13,6 @@ These guides describe common development patterns and optional components:
    :caption: Writing User Functions
 
    generator
-   ask_tell_generator
    simulator
    allocator
    sim_gen_alloc_api
diff --git a/docs/tutorials/xopt_bayesian_gen.rst b/docs/tutorials/xopt_bayesian_gen.rst
@@ -168,4 +168,4 @@ Reset generator and change to libEnsemble-style simulator:
         assert np.array_equal(H["c1"], H["x1"])
 
 .. |Open in Colab| image:: https://colab.research.google.com/assets/colab-badge.svg
-  :target:  http://colab.research.google.com/github/Libensemble/libensemble/blob/examples/xopt_generators/examples/tutorials/xopt_bayesian_gen/xopt_EI_example.ipynb
+  :target:  https://colab.research.google.com/github/Libensemble/libensemble/blob/develop/examples/tutorials/xopt_bayesian_gen/xopt_EI_example.ipynb
diff --git a/examples/tutorials/xopt_bayesian_gen/xopt_EI_example.ipynb b/examples/tutorials/xopt_bayesian_gen/xopt_EI_example.ipynb
@@ -14,6 +14,26 @@
     "2. Using a libEnsemble-style simulator function\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Check installed packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "if 'google.colab' in sys.modules:\n",
+    "    !pip install gest-api\n",
+    "    !pip install xopt\n",
+    "    !pip install libensemble"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/libensemble/gen_funcs/aposmm_localopt_support.py b/libensemble/gen_funcs/aposmm_localopt_support.py
@@ -45,7 +45,7 @@ class APOSMMException(Exception):
     if "dfols" in optimizers:
         import dfols  # noqa: F401
     if "ibcdfo_pounders" in optimizers:
-        from ibcdfo import run_pounders
+        from ibcdfo import run_pounders  # noqa: F401
     if "ibcdfo_manifold_sampling" in optimizers:
         from ibcdfo import run_MSP  # noqa: F401
     if "scipy" in optimizers:
@@ -434,7 +434,7 @@ def run_local_ibcdfo_manifold_sampling(user_specs, comm_queue, x0, f0, child_can
     not be sent back to the manager).
     """
     from ibcdfo import run_MSP  # noqa: F811
-    
+
     n = len(x0)
     # Define bound constraints (lower <= x <= upper)
     lb = np.zeros(n)
@@ -490,7 +490,7 @@ def run_local_ibcdfo_pounders(user_specs, comm_queue, x0, f0, child_can_read, pa
     not be sent back to the manager).
     """
     from ibcdfo import run_pounders  # noqa: F811
-    
+
     n = len(x0)
     # Define bound constraints (lower <= x <= upper)
     lb = np.zeros(n)
diff --git a/libensemble/tests/regression_tests/test_xopt_nelder_mead.py b/libensemble/tests/regression_tests/test_xopt_nelder_mead.py
@@ -14,6 +14,7 @@
 # TESTSUITE_COMMS: mpi local
 # TESTSUITE_NPROCS: 2
 # TESTSUITE_EXTRA: true
+# TESTSUITE_EXCLUDE: true
 
 import numpy as np
 from gest_api.vocs import VOCS
diff --git a/libensemble/version.py b/libensemble/version.py
@@ -1 +1 @@
-__version__ = "1.5.0+dev"
+__version__ = "1.6.0"
