Metadata-Version: 2.1
Name: libensemble
Version: 1.4.3
Summary: Library to coordinate the concurrent evaluation of dynamic ensembles of calculations
Home-page: https://github.com/Libensemble/libensemble
Author: Jeffrey Larson, Stephen Hudson, Stefan M. Wild, David Bindel and John-Luke Navarro
Author-email: libensemble@lists.mcs.anl.gov
License: BSD 3-clause
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries :: Python Modules
License-File: LICENSE
Requires-Dist: numpy>=1.21
Requires-Dist: psutil>=5.9.4
Requires-Dist: pydantic>=1.10
Requires-Dist: tomli>=1.2.1
Requires-Dist: pyyaml>=6.0
Provides-Extra: docs
Requires-Dist: autodoc_pydantic; extra == "docs"
Requires-Dist: sphinx<9; extra == "docs"
Requires-Dist: sphinx_design; extra == "docs"
Requires-Dist: sphinx_rtd_theme; extra == "docs"
Requires-Dist: sphinxcontrib-bibtex; extra == "docs"
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Requires-Dist: sphinx-copybutton; extra == "docs"

|libE_logo|

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|Tests| |Coverage| |Docs| |Style| |JOSS|

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=====================================================================
libEnsemble: A complete toolkit for dynamic ensembles of calculations
=====================================================================

Adaptive, portable, and scalable software for connecting "deciders" to experiments or simulations.

• **Dynamic ensembles**: Generate parallel tasks on-the-fly based on previous computations.
• **Extreme portability and scaling**: Run on or across laptops, clusters, and leadership-class machines.
• **Heterogeneous computing**: Dynamically and portably assign CPUs, GPUs, or multiple nodes.
• **Application monitoring**: Ensemble members can run, monitor, and cancel apps.
• **Data-flow between tasks**: Running ensemble members can send and receive data.
• **Low start-up cost**: No additional background services or processes required.

libEnsemble is effective at solving design, decision, and inference problems on parallel resources.

`Quickstart`_

Installation
============

Install libEnsemble and its dependencies from PyPI_ using pip::

    pip install libensemble

Other install methods are described in the docs_.

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``:

.. code-block:: python

    import numpy as np

    from libensemble import Ensemble
    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)

        sim_specs = SimSpecs(
            sim_f=six_hump_camel,
            inputs=["x"],
            outputs=[("f", float)],
        )

        gen_specs = GenSpecs(
            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)

        sampling = Ensemble(
            libE_specs=libE_specs,
            sim_specs=sim_specs,
            gen_specs=gen_specs,
            exit_criteria=exit_criteria,
        )

        sampling.add_random_streams()
        sampling.run()

        if sampling.is_manager:
            sampling.save_output(__file__)
            print("Some output data:\n", sampling.H[["x", "f"]][:10])

|Inline Example|

Try some other examples live in Colab.

+---------------------------------------------------------------+-------------------------------------+
| Description                                                   | Try online                          |
+===============================================================+=====================================+
| Simple Ensemble that makes a Sine wave.                       | |Simple Ensemble|                   |
+---------------------------------------------------------------+-------------------------------------+
| Ensemble with an MPI application.                             | |Ensemble with an MPI application|  |
+---------------------------------------------------------------+-------------------------------------+
| Optimization example that finds multiple minima.              | |Optimization example|              |
+---------------------------------------------------------------+-------------------------------------+

There are many more examples in the `regression tests`_.

Resources
=========

**Support:**

- Ask questions or report issues on GitHub_.
- Email ``libEnsemble@lists.mcs.anl.gov`` to request `libEnsemble Slack page`_.
- Join the `libEnsemble mailing list`_ for updates about new releases.

**Further Information:**

- Documentation is provided by ReadtheDocs_.
- Contributions_ to libEnsemble are welcome.
- Browse production functions and workflows in the `Community Examples repository`_.

**Cite libEnsemble:**

.. code-block:: bibtex

  @article{Hudson2022,
    title   = {{libEnsemble}: A Library to Coordinate the Concurrent
               Evaluation of Dynamic Ensembles of Calculations},
    author  = {Stephen Hudson and Jeffrey Larson and John-Luke Navarro and Stefan M. Wild},
    journal = {{IEEE} Transactions on Parallel and Distributed Systems},
    volume  = {33},
    number  = {4},
    pages   = {977--988},
    year    = {2022},
    doi     = {10.1109/tpds.2021.3082815}
  }

.. |libE_logo| image:: https://raw.githubusercontent.com/Libensemble/libensemble/main/docs/images/libE_logo.png
   :align: middle
   :alt: libEnsemble
.. |PyPI| image:: https://img.shields.io/pypi/v/libensemble.svg?color=blue
   :target: https://pypi.org/project/libensemble
.. |Conda| image:: https://img.shields.io/conda/v/conda-forge/libensemble?color=blue
   :target: https://anaconda.org/conda-forge/libensemble
.. |Spack| image:: https://img.shields.io/spack/v/py-libensemble?color=blue
   :target: https://packages.spack.io/package.html?name=py-libensemble
.. |Tests| image:: https://github.com/Libensemble/libensemble/actions/workflows/extra.yml/badge.svg?branch=main
   :target: https://github.com/Libensemble/libensemble/actions
.. |Coverage| image:: https://codecov.io/github/Libensemble/libensemble/graph/badge.svg
   :target: https://codecov.io/github/Libensemble/libensemble
.. |Docs| image:: https://readthedocs.org/projects/libensemble/badge/?maxAge=2592000
   :target: https://libensemble.readthedocs.org/en/latest/
   :alt: Documentation Status
.. |Style| image:: https://img.shields.io/badge/code%20style-black-000000.svg
   :target: https://github.com/psf/black
   :alt: Code style: black
.. |JOSS| image:: https://joss.theoj.org/papers/10.21105/joss.06031/status.svg
   :target: https://doi.org/10.21105/joss.06031
   :alt: JOSS Status

.. _Community Examples repository: https://github.com/Libensemble/libe-community-examples
.. _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
.. _GitHub: https://github.com/Libensemble/libensemble
.. _libEnsemble mailing list: https://lists.mcs.anl.gov/mailman/listinfo/libensemble
.. _libEnsemble Slack page: https://libensemble.slack.com
.. _MPICH: http://www.mpich.org/
.. _mpmath: http://mpmath.org/
.. _PyPI: https://pypi.org
.. _Quickstart: https://libensemble.readthedocs.io/en/main/introduction.html
.. _ReadtheDocs: http://libensemble.readthedocs.org/
.. _regression tests: https://github.com/Libensemble/libensemble/tree/main/libensemble/tests/regression_tests

.. |Inline Example| image:: https://colab.research.google.com/assets/colab-badge.svg
  :target:  http://colab.research.google.com/github/Libensemble/libensemble/blob/develop/examples/readme_notebook.ipynb

.. |Simple Ensemble| image:: https://colab.research.google.com/assets/colab-badge.svg
  :target:  http://colab.research.google.com/github/Libensemble/libensemble/blob/develop/examples/tutorials/simple_sine/sine_tutorial_notebook.ipynb

.. |Ensemble with an MPI application| image:: https://colab.research.google.com/assets/colab-badge.svg
  :target:  http://colab.research.google.com/github/Libensemble/libensemble/blob/develop/examples/tutorials/forces_with_executor/forces_tutorial_notebook.ipynb

.. |Optimization example| image:: https://colab.research.google.com/assets/colab-badge.svg
  :target:  http://colab.research.google.com/github/Libensemble/libensemble/blob/develop/examples/tutorials/aposmm/aposmm_tutorial_notebook.ipynb
