Installing Python packages with Spack

Obtaining Spack

To obtain the Spack distribution and start installing packages, see the Spack getting started guide

An example of how to get spack and maintain a forked repository is here.

Installing Python packages in Spack

All Python packages in Spack have the py- prefix. E.g. to install py-libensemble:

spack install py-libensemble

While most packages in Spack locate dependencies using RPATHS embedded in the binaries, Python packages must be loaded into the PYTHONPATH. The complication is that every Python package in Spack has it’s own Python site-packages sub-directory, instead of a combined site-packages.

When the spack install command is executed, modules are created for each package. To see your package directories do:

spack find --paths

Having installed your package, you can recursively load your modules with one command. But first you must have Spack’s shell commands enabled:

. $SPACK_ROOT/share/spack/setup-env.sh

Now load the package modules:

spack load py-libensemble

This will bring py-libensemble and all Python package dependencies into your PYTHONPATH. If you have more than one variant of the package, specify the abbreviated hash with a forward slash. E.g:

spack load py-libensemble /j4zmggq

Any Python packages will be added to the PYTHONPATH, when the modules are loaded.

If you do not have an environment module system installed, you may need to install lmod (also available in Spack):

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

Note An alternative to the recursive module load is to use Spack views. Also note that using a Spack environment will automatically create a view.

Using system installed Python

Many systems will have a Python module, which will supply a Python distribution, perhaps through a Conda environment.

I recommend specifying the module supplied Python within your ~/.spack/packages.yaml file. Packages included with this Python distribution (e.g. Numpy) can also be supplied. This file is used to specify dependencies that Spack must obtain from the given system (rather than building from scratch). E.g. to use the modules on Theta the packages.yaml may include:

packages:
  python:
    modules:
      python@3.6%intel@18.0.0.128 arch=cray-cnl6-mic_knl: intelpython36/2019.3.075
      buildable: False
  py-numpy:
    modules:
      py-numpy@1.16.1%intel@18.0.0.128 arch=cray-cnl6-mic_knl: intelpython36/2019.3.075
      buildable: False

Hint: Alternatively, you can access your Conda Python and built-in 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 3.7 and SciPy installed.

Note: The use of $CONDA_PREFIX in the packages.yaml no longer works, so you need to get the path from $CONDA_PREFIX first and substitute below in packages.yaml:

packages:
  python:
    buildable: false
    externals:
    - spec: python@3.7
      prefix: $CONDA_PREFIX
  py-numpy:
    buildable: false
    externals:
    - spec: py-numpy@3.7
      prefix: $CONDA_PREFIX/lib/python3.7/site-packages/numpy
  py-scipy:
    buildable: false
    externals:
    - spec: py-scipy@3.7
      prefix: $CONDA_PREFIX/lib/python3.7/site-packages/scipy

It is also recommended to use packages.yaml to specify the MPI libraries for your system. To see the concretized specification (and which packages are already installed):

spack spec --install-status py-libensemble

Spack packages come with variants. Note that some packages have an MPI variant; usually enabled by default. In some cases, the serial version may require turning off sub-variants. For example to build a simple serial version of PETSc with petsc4py:

spack install py-petsc4py~mpi ^petsc~mpi~hdf5~hypre~superlu-dist