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<p align="center"> | ||
<img width="450" src="https://user-images.githubusercontent.com/20479420/219680583-34ac9525-7715-4e2a-b4fe-74848e9f59b2.png" alt="optimas logo"/> | ||
</p> | ||
<!-- <hr/> --> | ||
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# Optimization at scale, powered by [libEnsemble](https://libensemble.readthedocs.io/) | ||
[![PyPI](https://img.shields.io/pypi/v/optimas)](https://pypi.org/project/optimas/) | ||
[![tests badge](https://github.com/optimas-org/optimas/actions/workflows/unix.yml/badge.svg)](https://github.com/optimas-org/optimas/actions) | ||
[![Documentation Status](https://readthedocs.org/projects/optimas/badge/?version=latest)](https://optimas.readthedocs.io/en/latest/?badge=latest) | ||
[![DOI](https://zenodo.org/badge/287560975.svg)](https://zenodo.org/badge/latestdoi/287560975) | ||
[![License](https://img.shields.io/pypi/l/optimas.svg)](license.txt) | ||
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<!-- PROJECT LOGO --> | ||
<br /> | ||
<div align="center"> | ||
<a href="https://github.com/othneildrew/Best-README-Template"> | ||
<img src="https://user-images.githubusercontent.com/20479420/219680583-34ac9525-7715-4e2a-b4fe-74848e9f59b2.png" alt="optimas logo" width="350"> | ||
</a> | ||
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<h3 align="center"> | ||
Optimization at scale, powered by | ||
<a href="https://libensemble.readthedocs.io/"><strong>libEnsemble</strong></a> | ||
</h3> | ||
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<p align="center"> | ||
<a href="https://optimas.readthedocs.io/"><strong>Explore the docs »</strong></a> | ||
<br /> | ||
<br /> | ||
<a href="https://optimas.readthedocs.io/en/latest/examples/index.html">View Examples</a> | ||
· | ||
<a href="https://optimas-group.slack.com/">Support</a> | ||
· | ||
<a href="https://optimas.readthedocs.io/en/latest/api/index.html">API Reference</a> | ||
</p> | ||
</div> | ||
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Optimas is a Python library for scalable optimization on massively-parallel supercomputers. See the [documentation](https://optimas.readthedocs.io/) for installation instructions, tutorials, and more information. | ||
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## Installation | ||
From PyPI | ||
```sh | ||
pip install optimas | ||
``` | ||
From GitHub | ||
```sh | ||
pip install git+https://github.com/optimas-org/optimas.git | ||
``` | ||
Make sure `mpi4py` is available in your environment prior to installing optimas (see [here](https://optimas.readthedocs.io/en/latest/user_guide/installation_local.html) for more details). | ||
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Optimas is regularly used and tested in large distributed HPC systems. | ||
We have prepared installation instructions for | ||
[JUWELS (JSC)](https://optimas.readthedocs.io/en/latest/user_guide/installation_juwels.html), | ||
[Maxwell (DESY)](https://optimas.readthedocs.io/en/latest/user_guide/installation_maxwell.html) and | ||
[Perlmutter (NERSC)](https://optimas.readthedocs.io/en/latest/user_guide/installation_perlmutter.html). | ||
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## Citing optimas | ||
If your usage of `optimas` leads to a scientific publication, please consider citing the original [paper](https://link.aps.org/doi/10.1103/PhysRevAccelBeams.26.084601): | ||
```bibtex | ||
@article{PhysRevAccelBeams.26.084601, | ||
title = {Bayesian optimization of laser-plasma accelerators assisted by reduced physical models}, | ||
author = {Ferran Pousa, A. and Jalas, S. and Kirchen, M. and Martinez de la Ossa, A. and Th\'evenet, M. and Hudson, S. and Larson, J. and Huebl, A. and Vay, J.-L. and Lehe, R.}, | ||
journal = {Phys. Rev. Accel. Beams}, | ||
volume = {26}, | ||
issue = {8}, | ||
pages = {084601}, | ||
numpages = {9}, | ||
year = {2023}, | ||
month = {Aug}, | ||
publisher = {American Physical Society}, | ||
doi = {10.1103/PhysRevAccelBeams.26.084601}, | ||
url = {https://link.aps.org/doi/10.1103/PhysRevAccelBeams.26.084601} | ||
} | ||
``` | ||
and libEnsemble: | ||
```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} | ||
} | ||
``` |
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Citing optimas | ||
============== | ||
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If your usage of ``optimas`` leads to a scientific publication, please consider | ||
citing the original `paper <https://link.aps.org/doi/10.1103/PhysRevAccelBeams.26.084601>`_: | ||
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.. code-block:: bibtex | ||
@article{PhysRevAccelBeams.26.084601, | ||
title = {Bayesian optimization of laser-plasma accelerators assisted by reduced physical models}, | ||
author = {Ferran Pousa, A. and Jalas, S. and Kirchen, M. and Martinez de la Ossa, A. and Th\'evenet, M. and Hudson, S. and Larson, J. and Huebl, A. and Vay, J.-L. and Lehe, R.}, | ||
journal = {Phys. Rev. Accel. Beams}, | ||
volume = {26}, | ||
issue = {8}, | ||
pages = {084601}, | ||
numpages = {9}, | ||
year = {2023}, | ||
month = {Aug}, | ||
publisher = {American Physical Society}, | ||
doi = {10.1103/PhysRevAccelBeams.26.084601}, | ||
url = {https://link.aps.org/doi/10.1103/PhysRevAccelBeams.26.084601} | ||
} | ||
and libEnsemble: | ||
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.. 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} | ||
} |
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Dependencies | ||
============ | ||
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Optimas relies on the following packages: | ||
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* `mpi4py <https://pypi.org/project/mpi4py/>`_ - Python bindings for MPI. Required for launching parallel simulations. | ||
* `libEnsemble <https://pypi.org/project/libensemble/>`_ - The backbone of optimas, orchestrates the concurrent evaluation of simulations, the resource detection and allocation, and the communication between simulations and manager. | ||
* `jinja2 <https://pypi.org/project/jinja2/>`_ - Needed to generate simulation scripts from templates. | ||
* `Ax <https://pypi.org/project/ax-platform/>`_ - Algorithms for Bayesian optimization. |
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