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

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.

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

Resources

Support:

• Ask questions or report issues on GitHub.

• Email libEnsemble@lists.mcs.anl.gov 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}
}

See the tutorial for a step-by-step beginners guide.

See the user guide for more information.

Example Compatible Packages

libEnsemble and the Community Examples repository include example generator functions for the following libraries:

• APOSMM Asynchronously parallel optimization solver for finding multiple minima. Supported local optimization routines include:

  • DFO-LS Derivative-free solver for (bound constrained) nonlinear least-squares minimization

  • NLopt Library for nonlinear optimization, providing a common interface for various methods

  • scipy.optimize Open-source solvers for nonlinear problems, linear programming, constrained and nonlinear least-squares, root finding, and curve fitting.

  • PETSc/TAO Routines for the scalable (parallel) solution of scientific applications

• DEAP Distributed evolutionary algorithms

• Distributed optimization methods for minimizing sums of convex functions. Methods include:

  • Primal-dual sliding (https://arxiv.org/pdf/2101.00143).

  • Distributed gradient descent with gradient tracking (https://arxiv.org/abs/1908.11444).

  • Proximal sliding (https://arxiv.org/abs/1406.0919).

• ECNoise Estimating Computational Noise in Numerical Simulations

• Surmise Modular Bayesian calibration/inference framework

• Tasmanian Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN

• VTMOP Fortran package for large-scale multiobjective multidisciplinary design optimization

libEnsemble has also been used to coordinate many computationally expensive simulations. Select examples include:

• OPAL Object Oriented Parallel Accelerator Library. (See this IPAC manuscript.)

• WarpX Advanced electromagnetic particle-in-cell code.