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https://github.com/Libensemble/libensemble
A Python toolkit for coordinating asynchronous and dynamic ensembles of calculations.
https://github.com/Libensemble/libensemble
Last synced: 3 months ago
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A Python toolkit for coordinating asynchronous and dynamic ensembles of calculations.
- Host: GitHub
- URL: https://github.com/Libensemble/libensemble
- Owner: Libensemble
- License: bsd-3-clause
- Created: 2016-12-12T23:43:44.000Z (almost 8 years ago)
- Default Branch: main
- Last Pushed: 2024-05-22T23:31:47.000Z (6 months ago)
- Last Synced: 2024-05-23T00:26:35.717Z (6 months ago)
- Language: Python
- Homepage: https://libensemble.readthedocs.io/
- Size: 24.9 MB
- Stars: 61
- Watchers: 10
- Forks: 24
- Open Issues: 27
-
Metadata Files:
- Readme: README.rst
- Changelog: CHANGELOG.rst
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Support: SUPPORT.rst
Awesome Lists containing this project
README
|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 ``[email protected]`` 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