Docs/pre 2 adjusts

.claude/skills/generate-scripts/references/aposmm.md

@@ -62,10 +62,6 @@ When using a SciPy method, must also supply `opt_return_codes` — e.g. [0] for
 | `lhs_divisions` | int | Latin hypercube partitions (0 or 1 = uniform) |
 | `rk_const` | float | Multiplier for r_k value |
 
-## Worker Configuration
-
-With `gen_on_manager=True`, the persistent generator runs on the manager process and all `nworkers` are available for simulations.
-
 ## Local Optimizer Methods
 
 ### SciPy (no extra install)

.pre-commit-config.yaml

@@ -37,4 +37,4 @@ repos:
   rev: v1.19.1
   hooks:
     - id: mypy
-      exclude: ^libensemble/utils/(launcher|loc_stack|runners|pydantic|output_directory)\.py$|libensemble/tests/(regression_tests|functionality_tests|unit_tests|scaling_tests)/.*
+      exclude: ^docs/conf\.py$|libensemble/utils/(launcher|loc_stack|runners|pydantic|output_directory)\.py$|libensemble/tests/(regression_tests|functionality_tests|unit_tests|scaling_tests)/.*

AGENTS.md

@@ -43,10 +43,10 @@ Information about Generators
 Its fields match ``sim_specs/gen_specs["out"]`` or ``vocs`` attributes, plus additional reserved fields for metadata.
 - Prior to libEnsemble v1.6.0, generators were plain functions. They often ran in "persistent" mode, meaning they executed in a
 long-running loop, sending and receiving points to and from the manager until the ensemble was complete.
-- A ``gest-api`` or "standardized" generator is a class that at a minimum implements ``suggest`` and ``ingest`` methods, and is parameterized by a ``vocs``.
-- See ``libensemble/generators.py`` for more information about the ``gest-api`` standard.
+- A ``gest-api`` or "standardized" generator is a class that inherits from ``gest_api.Generator``, implements ``suggest`` and ``ingest`` methods (which process lists of dictionaries, not NumPy arrays), and is parameterized by a ``vocs``.
+- See ``libensemble/gen_classes/external/sampling.py`` for simple examples of the pure ``gest-api`` interface. (Note: ``libensemble.generators.LibensembleGenerator`` exists to wrap legacy NumPy-based workflows, but pure ``gest_api.Generator`` is preferred).
 - Generators are often used for simple sampling, optimization, calibration, uncertainty quantification, and other simulation-based tasks.
-- **Automatic Variable Mapping**: Subclasses of ``LibensembleGenerator`` (like ``UniformSample``) automatically map all ``VOCS`` variables to a single multi-dimensional ``"x"`` field in the History array if no explicit ``variables_mapping`` is provided.
+- **Automatic Variable Mapping**: When using ``LibensembleGenerator`` subclasses, they automatically map all ``VOCS`` variables to a single multi-dimensional ``"x"`` field in the History array if no explicit ``variables_mapping`` is provided. Pure ``gest_api.Generator`` classes handle variables natively.
 - **Mandatory Input Fields**: Even for simple generators that don't ingest data, ``gen_specs["in"]`` or ``gen_specs["persis_in"]`` must be defined if using an allocation function like ``only_persistent_gens`` that attempts to send rows. If these are empty, the manager will raise an ``AssertionError`` stating that no fields were requested to be sent.
 - **Default Allocator**: ``only_persistent_gens`` is the default allocator for standardized ``gest-api`` generators. It treats these generators as persistent entities that communicate throughout the run.
 
@@ -97,3 +97,5 @@ When modernizing existing libEnsemble scripts (functionality tests, regression t
 - **Remove Explicit `AllocSpecs`**: In libEnsemble 2.0, `only_persistent_gens` is the default allocator. Scripts that previously used `give_sim_work_first` or other simple allocators can often remove `alloc_specs` entirely when switching to standardized generators.
 - **Generator Placement**: By default, generators run on the manager thread (Worker 0). This means all allocated workers are available for simulation tasks unless `gen_on_worker` is explicitly set to `True` in `libE_specs`.
 - **Mandatory Fields**: Ensure `gen_specs["in"]` or `gen_specs["persis_in"]` includes at least one field (e.g., `["sim_id"]`) if feedback is sent back to the generator, to satisfy the allocator's requirements.
+- **gest-api Simulators**: The gest-api pattern also applies to simulators. Set `SimSpecs.simulator` to a callable with signature `(input_dict: dict, **kwargs) -> dict` instead of providing a `sim_f`. libEnsemble automatically wraps it with `gest_api_sim` from `libensemble.sim_funcs.gest_api_wrapper` and handles all NumPy conversions. `SimSpecs.inputs` and `SimSpecs.outputs` can be derived automatically when `SimSpecs.vocs` is provided.
+- **`safe_mode` is opt-in**: `libE_specs["safe_mode"]` defaults to `False`, meaning protected History fields (`gen_worker`, `gen_started_time`, `gen_ended_time`, `sim_worker`, `sim_started`, `sim_started_time`, `sim_ended`, `sim_ended_time`, `gen_informed`, `gen_informed_time`, `kill_sent`) are freely overwritable by default. Set `safe_mode=True` to enable protection. Overwriting these fields without understanding their purpose may crash libEnsemble.

README.rst

@@ -41,39 +41,49 @@ Basic Usage
 ===========
 
 Create an ``Ensemble``, then customize it with general settings, simulation and generator parameters,
-and an exit condition. Run the following four-worker example via ``python this_file.py``:
+and an exit condition.
 
 .. code-block:: python
 
     import numpy as np
+    from gest_api.vocs import VOCS
 
     from libensemble import Ensemble
-    from libensemble.gen_funcs.sampling import uniform_random_sample
+    from libensemble.gen_classes.sampling import UniformSample
     from libensemble.sim_funcs.six_hump_camel import six_hump_camel
     from libensemble.specs import ExitCriteria, GenSpecs, LibeSpecs, SimSpecs
 
     if __name__ == "__main__":
+        # Define problem using VOCS
+        vocs = VOCS(
+            variables={"x": [-3, 3], "y": [-2, 2]},
+            objectives={"f": "MINIMIZE"},
+        )
 
+        # General settings
         libE_specs = LibeSpecs(nworkers=4)
 
+        # Simulation parameters
         sim_specs = SimSpecs(
             sim_f=six_hump_camel,
             inputs=["x"],
             outputs=[("f", float)],
         )
 
+        # Generator parameters (standardized generator)
         gen_specs = GenSpecs(
-            gen_f=uniform_random_sample,
+            generator=UniformSample(vocs),
+            inputs=["sim_id"],
+            persis_in=["x", "f"],
             outputs=[("x", float, 2)],
-            user={
-                "gen_batch_size": 50,
-                "lb": np.array([-3, -2]),
-                "ub": np.array([3, 2]),
-            },
+            vocs=vocs,
+            user={"gen_batch_size": 50},
         )
 
+        # Exit criteria
         exit_criteria = ExitCriteria(sim_max=100)
 
+        # Create and run ensemble
         sampling = Ensemble(
             libE_specs=libE_specs,
             sim_specs=sim_specs,

SUPPORT.rst

@@ -1,6 +1,10 @@
 Support
 -------
 
+Open issues on Github at:
+
+* https://github.com/Libensemble/libensemble/issues
+
 Join the libEnsemble mailing list at:
 
 * https://lists.mcs.anl.gov/mailman/listinfo/libensemble

docs/advanced_installation/advanced_installation.rst

@@ -0,0 +1,41 @@
+Advanced Installation
+=====================
+
+`pip <advanced_installation_pip.html>`__ \|\| `uv <advanced_installation_uv.html>`__ \|\| `pixi <advanced_installation_pixi.html>`__ \|\| `conda <advanced_installation_conda.html>`__ \|\| `Spack <advanced_installation_spack.html>`__
+
+libEnsemble can be installed from ``pip``, ``uv``, ``pixi``, ``Conda``, or ``Spack``.
+
+libEnsemble requires the following dependencies, which are typically
+automatically installed alongside libEnsemble:
+
+* Python_       ``>= 3.11``
+* NumPy_        ``>= 1.21``
+* psutil_       ``>= 5.9.4``
+* `pydantic`_   ``>= 2``
+* gest-api_     ``>= 0.1,<0.2``
+
+We recommend installing in a virtual environment from ``uv``, ``conda`` or another source.
+
+Further recommendations for selected HPC systems are given in the
+:ref:`HPC platform guides<platform-index>`.
+
+.. toctree::
+    :hidden:
+
+    advanced_installation_pip
+    advanced_installation_uv
+    advanced_installation_pixi
+    advanced_installation_conda
+    advanced_installation_spack
+
+Globus Compute
+--------------
+
+`Globus Compute`_ may be installed optionally to submit simulation function instances to remote Globus Compute endpoints.
+
+.. _Globus Compute: https://www.globus.org/compute
+.. _Python: http://www.python.org
+.. _NumPy: http://www.numpy.org
+.. _psutil: https://pypi.org/project/psutil/
+.. _pydantic: https://docs.pydantic.dev/1.10/
+.. _gest-api: https://github.com/campa-consortium/gest-api

docs/advanced_installation/advanced_installation_conda.rst

@@ -0,0 +1,49 @@
+conda
+=====
+
+`Advanced Installation <advanced_installation.html>`__ \|\| `pip <advanced_installation_pip.html>`__ \|\| `uv <advanced_installation_uv.html>`__ \|\| `pixi <advanced_installation_pixi.html>`__ \|\| **conda** \|\| `Spack <advanced_installation_spack.html>`__
+
+Install libEnsemble with Conda_ from the conda-forge channel::
+
+    conda config --add channels conda-forge
+    conda install -c conda-forge libensemble
+
+This package comes with some useful optional dependencies, including
+optimizers and will install quickly as ready binary packages.
+
+**Installing with mpi4py with Conda**
+
+If you wish to use ``mpi4py`` with libEnsemble (choosing MPI out of the three
+:doc:`communications options<../running_libE>`), you can use the
+following.
+
+.. note::
+    For clusters and HPC systems, always install ``mpi4py`` to use the
+    system MPI library (see pip instructions above).
+
+For a standalone build that comes with an MPI implementation, you can install
+libEnsemble using one of the following variants.
+
+To install libEnsemble with MPICH_::
+
+    conda install -c conda-forge libensemble=*=mpi_mpich*
+
+To install libEnsemble with `Open MPI`_::
+
+    conda install -c conda-forge libensemble=*=mpi_openmpi*
+
+The asterisks will pick up the latest version and build.
+
+.. note::
+    This syntax may not work without adjustments on macOS or any non-bash
+    shell. In these cases, try::
+
+        conda install -c conda-forge libensemble='*'=mpi_mpich'*'
+
+For a complete list of builds for libEnsemble on Conda::
+
+    conda search libensemble --channel conda-forge
+
+.. _Conda: https://docs.conda.io/en/latest/
+.. _MPICH: https://www.mpich.org/
+.. _Open MPI: https://www.open-mpi.org/