|libE_logo|
|PyPI| |Conda| |Spack|
|Tests| |Coverage| |Docs| |Style| |JOSS|
.. after_badges_rst_tag
=====================================================================
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
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:target: https://pypi.org/project/libensemble
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:target: https://anaconda.org/conda-forge/libensemble
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:target: https://github.com/Libensemble/libensemble/actions
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: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
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"description": "|libE_logo|\n\n|PyPI| |Conda| |Spack|\n\n|Tests| |Coverage| |Docs| |Style| |JOSS|\n\n.. after_badges_rst_tag\n\n=====================================================================\nlibEnsemble: A complete toolkit for dynamic ensembles of calculations\n=====================================================================\n\nAdaptive, portable, and scalable software for connecting \"deciders\" to experiments or simulations.\n\n\u2022 **Dynamic ensembles**: Generate parallel tasks on-the-fly based on previous computations.\n\u2022 **Extreme portability and scaling**: Run on or across laptops, clusters, and leadership-class machines.\n\u2022 **Heterogeneous computing**: Dynamically and portably assign CPUs, GPUs, or multiple nodes.\n\u2022 **Application monitoring**: Ensemble members can run, monitor, and cancel apps.\n\u2022 **Data-flow between tasks**: Running ensemble members can send and receive data.\n\u2022 **Low start-up cost**: No additional background services or processes required.\n\nlibEnsemble is effective at solving design, decision, and inference problems on parallel resources.\n\n`Quickstart`_\n\nInstallation\n============\n\nInstall libEnsemble and its dependencies from PyPI_ using pip::\n\n pip install libensemble\n\nOther install methods are described in the docs_.\n\nBasic Usage\n===========\n\nCreate an ``Ensemble``, then customize it with general settings, simulation and generator parameters,\nand an exit condition. Run the following four-worker example via ``python this_file.py``:\n\n.. code-block:: python\n\n import numpy as np\n\n from libensemble import Ensemble\n from libensemble.gen_funcs.sampling import uniform_random_sample\n from libensemble.sim_funcs.six_hump_camel import six_hump_camel\n from libensemble.specs import ExitCriteria, GenSpecs, LibeSpecs, SimSpecs\n\n if __name__ == \"__main__\":\n\n libE_specs = LibeSpecs(nworkers=4)\n\n sim_specs = SimSpecs(\n sim_f=six_hump_camel,\n inputs=[\"x\"],\n outputs=[(\"f\", float)],\n )\n\n gen_specs = GenSpecs(\n gen_f=uniform_random_sample,\n outputs=[(\"x\", float, 2)],\n user={\n \"gen_batch_size\": 50,\n \"lb\": np.array([-3, -2]),\n \"ub\": np.array([3, 2]),\n },\n )\n\n exit_criteria = ExitCriteria(sim_max=100)\n\n sampling = Ensemble(\n libE_specs=libE_specs,\n sim_specs=sim_specs,\n gen_specs=gen_specs,\n exit_criteria=exit_criteria,\n )\n\n sampling.add_random_streams()\n sampling.run()\n\n if sampling.is_manager:\n sampling.save_output(__file__)\n print(\"Some output data:\\n\", sampling.H[[\"x\", \"f\"]][:10])\n\n|Inline Example|\n\nTry some other examples live in Colab.\n\n+---------------------------------------------------------------+-------------------------------------+\n| Description | Try online |\n+===============================================================+=====================================+\n| Simple Ensemble that makes a Sine wave. | |Simple Ensemble| |\n+---------------------------------------------------------------+-------------------------------------+\n| Ensemble with an MPI application. | |Ensemble with an MPI application| |\n+---------------------------------------------------------------+-------------------------------------+\n| Optimization example that finds multiple minima. | |Optimization example| |\n+---------------------------------------------------------------+-------------------------------------+\n\nThere are many more examples in the `regression tests`_.\n\nResources\n=========\n\n**Support:**\n\n- Ask questions or report issues on GitHub_.\n- Email ``libEnsemble@lists.mcs.anl.gov`` to request `libEnsemble Slack page`_.\n- Join the `libEnsemble mailing list`_ for updates about new releases.\n\n**Further Information:**\n\n- Documentation is provided by ReadtheDocs_.\n- Contributions_ to libEnsemble are welcome.\n- Browse production functions and workflows in the `Community Examples repository`_.\n\n**Cite libEnsemble:**\n\n.. code-block:: bibtex\n\n @article{Hudson2022,\n title = {{libEnsemble}: A Library to Coordinate the Concurrent\n Evaluation of Dynamic Ensembles of Calculations},\n author = {Stephen Hudson and Jeffrey Larson and John-Luke Navarro and Stefan M. Wild},\n journal = {{IEEE} Transactions on Parallel and Distributed Systems},\n volume = {33},\n number = {4},\n pages = {977--988},\n year = {2022},\n doi = {10.1109/tpds.2021.3082815}\n }\n\n.. |libE_logo| image:: https://raw.githubusercontent.com/Libensemble/libensemble/main/docs/images/libE_logo.png\n :align: middle\n :alt: libEnsemble\n.. |PyPI| image:: https://img.shields.io/pypi/v/libensemble.svg?color=blue\n :target: https://pypi.org/project/libensemble\n.. |Conda| image:: https://img.shields.io/conda/v/conda-forge/libensemble?color=blue\n :target: https://anaconda.org/conda-forge/libensemble\n.. |Spack| image:: https://img.shields.io/spack/v/py-libensemble?color=blue\n :target: https://packages.spack.io/package.html?name=py-libensemble\n.. |Tests| image:: https://github.com/Libensemble/libensemble/actions/workflows/extra.yml/badge.svg?branch=main\n :target: https://github.com/Libensemble/libensemble/actions\n.. |Coverage| image:: https://codecov.io/github/Libensemble/libensemble/graph/badge.svg\n :target: https://codecov.io/github/Libensemble/libensemble\n.. |Docs| image:: https://readthedocs.org/projects/libensemble/badge/?maxAge=2592000\n :target: https://libensemble.readthedocs.org/en/latest/\n :alt: Documentation Status\n.. |Style| image:: https://img.shields.io/badge/code%20style-black-000000.svg\n :target: https://github.com/psf/black\n :alt: Code style: black\n.. |JOSS| image:: https://joss.theoj.org/papers/10.21105/joss.06031/status.svg\n :target: https://doi.org/10.21105/joss.06031\n :alt: JOSS Status\n\n.. _Community Examples repository: https://github.com/Libensemble/libe-community-examples\n.. _conda-forge: https://conda-forge.org/\n.. _Contributions: https://github.com/Libensemble/libensemble/blob/main/CONTRIBUTING.rst\n.. _docs: https://libensemble.readthedocs.io/en/main/advanced_installation.html\n.. _GitHub: https://github.com/Libensemble/libensemble\n.. _libEnsemble mailing list: https://lists.mcs.anl.gov/mailman/listinfo/libensemble\n.. _libEnsemble Slack page: https://libensemble.slack.com\n.. _MPICH: http://www.mpich.org/\n.. _mpmath: http://mpmath.org/\n.. _PyPI: https://pypi.org\n.. _Quickstart: https://libensemble.readthedocs.io/en/main/introduction.html\n.. _ReadtheDocs: http://libensemble.readthedocs.org/\n.. _regression tests: https://github.com/Libensemble/libensemble/tree/main/libensemble/tests/regression_tests\n\n.. |Inline Example| image:: https://colab.research.google.com/assets/colab-badge.svg\n :target: http://colab.research.google.com/github/Libensemble/libensemble/blob/develop/examples/readme_notebook.ipynb\n\n.. |Simple Ensemble| image:: https://colab.research.google.com/assets/colab-badge.svg\n :target: http://colab.research.google.com/github/Libensemble/libensemble/blob/develop/examples/tutorials/simple_sine/sine_tutorial_notebook.ipynb\n\n.. |Ensemble with an MPI application| image:: https://colab.research.google.com/assets/colab-badge.svg\n :target: http://colab.research.google.com/github/Libensemble/libensemble/blob/develop/examples/tutorials/forces_with_executor/forces_tutorial_notebook.ipynb\n\n.. |Optimization example| image:: https://colab.research.google.com/assets/colab-badge.svg\n :target: http://colab.research.google.com/github/Libensemble/libensemble/blob/develop/examples/tutorials/aposmm/aposmm_tutorial_notebook.ipynb\n",
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