Merge branch 'main' into support_evaluator_env

.github/workflows/unix.yml

@@ -1,13 +1,18 @@
 name: Unix
 
-on: [push, pull_request]
+on:
+  push:
+  pull_request:
+  # Run daily at midnight (UTC).
+  schedule:
+    - cron: '0 0 * * *'
 
 jobs:
   test:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.8]
+        python-version: [3.8, 3.9, '3.10', 3.11]
 
     steps:
     - uses: actions/checkout@v2

README.md

@@ -1,13 +1,81 @@
-<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/> -->
-
-# 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)
 
+<!-- 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>
+
+  <h3 align="center">
+    Optimization at scale, powered by 
+    <a href="https://libensemble.readthedocs.io/"><strong>libEnsemble</strong></a>
+  </h3>
+
+  <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>
+
 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.
+
+## 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).
+
+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).
+
+## 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}
+}
+```

doc/source/conf.py

@@ -80,7 +80,7 @@
         },
         {
             "name": "Slack",
-            "url": "https://optimas.slack.com/",
+            "url": "https://optimas-group.slack.com/",
             "icon": "fa-brands fa-slack",
         },
     ],

doc/source/index.rst

@@ -12,7 +12,7 @@ parallel optimization, from a typical laptop to exascale HPC systems. It is
 built on top of 
 `libEnsemble <https://libensemble.readthedocs.io/>`_.
 
-.. grid:: 3
+.. grid:: 1 1 3 3
     :gutter: 2
 
     .. grid-item-card:: User guide

doc/source/user_guide/basic_usage/analyze_output.rst

@@ -14,7 +14,7 @@ In every run, the following log files are generated:
 
 - ``libE-stats.txt``: log indicating the worker, start time, end time, etc. of
   each evaluation.
-- ``ensemble.log``: log of ``libEnsemble`` containg the main events of
+- ``ensemble.log``: log of ``libEnsemble`` containing the main events of
   the run. This includes the commands with each evaluation is launched.
 - ``libE_history_for_run_starting_<start_time>_after_sim_<last_simulation_number>.npy``:
   numpy file that contains the
@@ -57,7 +57,7 @@ generated. This is the case, for example, of the
 ``AxClient`` with the surrogate model used for Bayesian optimization.
 
 Generators that have this capability can also save the internal model
-model to file with a certain periodicity (set by the ``model_save_period``
+to file with a certain periodicity (set by the ``model_save_period``
 attribute). By default, these models will be saved in a
 ``exploration/model_history`` directory.
 

doc/source/user_guide/citation.rst

@@ -0,0 +1,38 @@
+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>`_:
+
+.. 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:
+
+.. 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}
+    }

doc/source/user_guide/dependencies.rst

@@ -0,0 +1,9 @@
+Dependencies
+============
+
+Optimas relies on the following packages:
+
+* `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.

doc/source/user_guide/index.rst

@@ -4,15 +4,25 @@ User guide
 ==========
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 1
    :caption: Installation
 
-   installation
+   dependencies
+   installation_local
+   installation_maxwell
+   installation_juwels
+   installation_perlmutter
 
 .. toctree::
    :maxdepth: 2
    :caption: Basic usage
 
    basic_usage/basic_setup
    basic_usage/running_with_simulations
-   basic_usage/analyze_output
+   basic_usage/analyze_output
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Citation
+
+   citation

doc/source/user_guide/installation_local.rst

@@ -3,30 +3,6 @@ Installation on a local computer
 
 The recommended approach is to install optimas in a ``conda`` environment.
 
-Install basic dependencies
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. code::
-
-    conda install numpy pandas
-
-Install PyTorch
-~~~~~~~~~~~~~~~
-
-If your computer does not feature a CUDA-capable GPU, install PyTorch for CPU:
-
-.. code::
-
-    conda install pytorch cpuonly -c pytorch
-
-
-If you have a CUDA-capable GPU and want to take make it available to optimas,
-install PyTorch with:
-
-.. code::
-
-    conda install pytorch pytorch-cuda=11.7 -c pytorch -c nvidia
-
 Install ``mpi4py``
 ~~~~~~~~~~~~~~~~~~
 If your system has already an MPI implementation installed, install ``mpi4py``