libensemble 1.6.0

A Python toolkit for coordinating asynchronous and dynamic ensembles of calculations.

libEnsemble: A complete toolkit for dynamic ensembles of calculations

libEnsemble empowers model-driven ensembles to solve design, decision, and inference problems on the world’s leading supercomputers such as Frontier, Aurora, and Perlmutter.

• 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.

Quickstart

New: libEnsemble nows supports the gest-api generator standard, and can run with Optimas and Xopt generators.

The to generate customized scripts for running ensembles with your MPI applications.

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:

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])

Try some other examples live in Colab.

Description	Try online
Simple Ensemble that makes a Sine wave.
Ensemble with an MPI application.
Optimization example that finds multiple minima.
Surrogate model generation with gpCAM.
Bayesian Optimization with Xopt.

There are many more examples in the regression tests and Community Examples repository.

Resources

Support:

• Ask questions or report issues on GitHub.

• Email [e-mail] 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:

@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}
}