Advanced Installation

libEnsemble can be installed from pip, Conda, or Spack.

libEnsemble requires the following dependencies, which are typically automatically installed alongside libEnsemble:

Given libEnsemble’s compiled dependencies, the following installation methods each offer a trade-off between convenience and the ability to customize builds, including platform-specific optimizations.

We always recommend installing in a virtual environment from Conda or another source.

Further recommendations for selected HPC systems are given in the HPC platform guides.

To install the latest PyPI release:

pip install libensemble

To pip install libEnsemble from the latest develop branch:

python -m pip install --upgrade git+https://github.com/Libensemble/libensemble.git@develop

Installing with mpi4py

If you wish to use mpi4py with libEnsemble (choosing MPI out of the three communications options), then this should be installed to work with the existing MPI on your system. For example, the following line:

pip install mpi4py

will use the mpicc compiler wrapper on your PATH to identify the MPI library. To specify a different compiler wrapper, add the MPICC option. You also may wish to avoid existing binary builds; for example,:

MPICC=mpiicc pip install mpi4py --no-binary mpi4py

On Summit, the following line is recommended (with gcc compilers):

CC=mpicc MPICC=mpicc pip install mpi4py --no-binary mpi4py

Install libEnsemble with Conda from the conda-forge channel:

conda config --add channels conda-forge
conda install -c conda-forge libensemble

This package comes with some useful optional dependencies, including optimizers and will install quickly as ready binary packages.

Installing with mpi4py with Conda

If you wish to use mpi4py with libEnsemble (choosing MPI out of the three communications options), you can use the following.

Note

For clusters and HPC systems, always install mpi4py to use the system MPI library (see pip instructions above).

For a standalone build that comes with an MPI implementation, you can install libEnsemble using one of the following variants.

To install libEnsemble with MPICH:

conda install -c conda-forge libensemble=*=mpi_mpich*

To install libEnsemble with Open MPI:

conda install -c conda-forge libensemble=*=mpi_openmpi*

The asterisks will pick up the latest version and build.

Note

This syntax may not work without adjustments on macOS or any non-bash shell. In these cases, try:

conda install -c conda-forge libensemble='*'=mpi_mpich'*'

For a complete list of builds for libEnsemble on Conda:

conda search libensemble --channel conda-forge

Install libEnsemble using the Spack distribution:

spack install py-libensemble

The above command will install the latest release of libEnsemble with the required dependencies only. Other optional dependencies can be specified through variants. The following line installs libEnsemble version 0.7.2 with some common variants (e.g., using APOSMM):

spack install py-libensemble @0.7.2 +mpi +scipy +mpmath +petsc4py +nlopt

The list of variants can be found by running:

spack info py-libensemble

On some platforms you may wish to run libEnsemble without mpi4py, using a serial PETSc build. This is often preferable if running on the launch nodes of a three-tier system (e.g., Summit):

spack install py-libensemble +scipy +mpmath +petsc4py ^py-petsc4py~mpi ^petsc~mpi~hdf5~hypre~superlu-dist

The installation will create modules for libEnsemble and the dependent packages. These can be loaded by running:

spack load -r py-libensemble

Any Python packages will be added to the PYTHONPATH when the modules are loaded. If you do not have modules on your system you may need to install lmod (also available in Spack):

spack install lmod
. $(spack location -i lmod)/lmod/lmod/init/bash
spack load lmod

Alternatively, Spack could be used to build the serial petsc4py, and Conda could use this by loading the py-petsc4py module thus created.

Hint: When combining Spack and Conda, you can access your Conda Python and packages in your ~/.spack/packages.yaml while your Conda environment is activated, using CONDA_PREFIX For example, if you have an activated Conda environment with Python 3.9 and SciPy installed:

packages:
python:
    externals:
    - spec: "python"
    prefix: $CONDA_PREFIX
    buildable: False
py-numpy:
    externals:
    - spec: "py-numpy"
    prefix: $CONDA_PREFIX/lib/python3.9/site-packages/numpy
    buildable: False
py-scipy:
    externals:
    - spec: "py-scipy"
    prefix: $CONDA_PREFIX/lib/python3.9/site-packages/scipy
    buildable: True

For more information on Spack builds and any particular considerations for specific systems, see the spack_libe repository. In particular, this includes some example packages.yaml files (which go in ~/.spack/). These files are used to specify dependencies that Spack must obtain from the given system (rather than building from scratch). This may include Python and the packages distributed with it (e.g., numpy), and will often include the system MPI library.

Optional Dependencies for Additional Features

The following packages may be installed separately to enable additional features:

  • Balsam - Manage and submit applications to the Balsam service with our BalsamExecutor

  • pyyaml and tomli - Parameterize libEnsemble via yaml or toml

  • Globus Compute - Submit simulation or generator function instances to remote Globus Compute endpoints