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Optimization of PIC simulation with libEnsemble

The scripts in this repository allow to optimize PIC simulation.

Installing

On a local computer

Then install other dependencies:

pip install libensemble
pip install -r requirements.txt

Then

git clone https://github.com/RemiLehe/libE_opt.git
cd libE_opt
pip install .

On Summit

In the instructions below, before the git clone command, cd into your $MEMBERWORK folder, and create a dedicated directory.

For FBPIC simulations

Install according to: https://fbpic.github.io/install/install_summit.html

Then install other dependencies:

source activate $SCRATCH/fbpic_env
pip install libensemble
git clone https://github.com/RemiLehe/libE_opt.git
cd libE_opt
pip install .
source deactivate

For WarpX simulations

conda create -n libE_opt
source activate libE_opt
conda install -c conda-forge mamba
mamba install -c conda-forge openpmd-viewer openpmd-api pandas botorch ax-platform
pip install libensemble
git clone https://github.com/RemiLehe/libE_opt.git
cd libE_opt
pip install .

On Lawrencium

Install according to: https://fbpic.github.io/install/install_lawrencium.html

Then install other dependencies:

pip install libensemble
pip install -r requirements.txt

cd into your $SCRATCH folder, and create a dedicated directory. Then run:

git clone https://github.com/RemiLehe/libE_opt.git
cd libE_opt
pip install .

Usage

cd into the folder optimization_folder, and run the script create_new_optimization.py. (In order to see the usage of this script, type python create_new_optimization.py -h.) Then follow the printed instructions.

Note that the script will create a new folder, with a number of files, which you can modify before submitting/launching the optimization job:

  • template_simulation_script.py: an simulation script, templated with jinja2 syntax.
  • varying_parameters.py: list of varying parameters, along with their bounds. These variables should match the templated variables in template_simulation_script.py.
  • mf_parameters.py (optional): defines fidelity parameters for multi-fidelity optimization.
  • analysis_script.py: analyzes the result of the simulation and extract the objective function f.

You can also resume a previous libE optimization (of the exact same problem), by copying the .npy file that was produced by libE into the new folder, and renaming it as past_history.npy.

You can easily postprocess the optimization in a Jupyter notebook by using:

from libe_opt.post_processing import PostProcOptimization
pp = PostProcOptimization('path/to/your/optimization')