HPC/Applications/python

There are many Python installations on Carbon, each normally prepared for access by a module load modulename shell command. Modules are grouped and named according to the type and scope of the Python installation provided. Each module gives you access to a Python interpreter and typically one or more Python-native packages for import, and to supporting software.

As with other modules, each modulename will look like a Unix file name that has one or more directory components. You can (and normally should) use abbreviated module names, with recommended name forms shown below. For abbreviated names, the module shell command will select the most suitable module from the modules that would complete your abbreviated name. This will usually be the module with the highest version number or an administrator-designated default.

What follows are the types of Python installations and their module naming convention. For the current full list of these modules, run the shell command module avail python. Please contact [email protected] for help to choose an approach.

Python standard distributions

Contains

A Python interpreter and solely its standard packages (which vary by version), as distributed by the Python Software Foundation at python.org.

Module nomenclature

python/pyMajor.pyMinor/compilerName-compMajor.compMinor/pyMajor.pyMinor.pyPatch-moduleBuild

Examples of full module names

python/2.7/gcc-4.1/2.7.3-1

python/2.7/gcc-4.4/2.7.11-1

python/3.5/gcc-4.4/3.5.1-1

Recommended module abbreviations

python/2.7

python/3.5

When to use

Use one of these modules as base for installing one or a few Python-native packages yourself, as long as the dependencies are not too odious or performance is not critical. For more complex requirements, choose a vendor-distributed or Carbon-customized Python suite as shown below.

Caution: Packages that were installed under one Python version (or module) usually are not accessible through another version, especially packages that contain binaries like shared libraries and executables.

Python bundles within the OS

Contains

The Python interpreter that comes with the operating system. This particular installation is required for many system internals and is the one expected by system-provided add-on packages (installed via rpm or yum). Fortunately, system functionality is not affected (except for possible resource exhaustion) when you choose a different Python installation for user application tasks.

Module nomenclature

python-osname/pyMajor.pyMinor/compilerName-compMajor.compMinor/pyMajor.pyMinor.pyPatch

Examples of full module names

python-centos/2.4/gcc-4.1/2.4.3

python-centos/2.6/gcc-4.4/2.6.6

python-centos/2.7/gcc-4.8/2.7.5

Recommended module abbreviations

(none)

When to use

You normally do not need to load these modules. They are included here for visibility in the module lineup, and as a prerequisite for some older modules which provided Python-native packages independently (outside a package manager), and which are usually tied to the version of the Python interpreter under which they were installed.

Python suites with vendor-distributed package set

Contains

Python software suite with the package selection as distributed by the vendor.

Module nomenclature

python-distributor/pyMajor.pyMinor/distMajor/distributor_defined_version[…]-moduleBuild

Examples of full module names

python-intel/2.7/2018/2.7.14-2018.1.023-1

python-intel/3.5/2017/3.5.3-2017.3.052-1

python-intel/3.6/2018/3.6.3-2018.1.023-1

python-anaconda/2.7/4/2.7.11-4.0.0-2

Recommended module abbreviations

python-intel/2.7

python-intel/3.6

python-anaconda/2.7 (deprecated)

python-anaconda/3.5 (deprecated)

When to use

Use one of these modules for general scientific computing projects, or as ready-made base for installing your own packages when one of the #Python standard distributions proved insufficient.

Python-based software suites with a broad sope became popular in recent years. They typically contain:

• A Python interpreter.
• The Conda/pip package management systems (see more context).
• A wide-ranging set of Python-native packages, extensions, and add-on executables, often including:
  • NumPy/SciPy – Ecosystem for scientific computing in Python
  • Idle – Python Integrated DeveLopment Environment
  • iPython and Jupyter – Interactive computation system
  • Cython – Compiler for a superset of Python and C/C++
• All libraries and supporting binaries required by the above, e.g.:
  • BLAS/LAPACK – Linear algebra libraries
  • Tcl – Interpreter for the Tool Command Language
  • MPI – Runtime environment for parallel applications using Message Passing Interface, including the mpirun or mpiexec launch commands.

The last item can be problematic because libraries and supporting binaries included in a Python suite can interfere with other software modules on Carbon. For access by users, HPC software typically leverages conventional Unix environment variables like PATH, LD_LIBRARY_PATH, and PYTHONPATH. These environment variables are interpreted front-to-back, which implies priorities and can easily lead to resources from one module overshadowing those from another when they use the same name.

In particular, a Python suite with an included MPI runtime makes parallel computing easily accessible under Python, but this means a "mere python module" can clash with other MPI implementations used on Carbon. Loading a module for a Python suite that contains MPI and a separate MPI module at the same time can be done but then requires more detail for the MPI launcher in job scripts. Without that, non-obvious failures will result for either bundled parallel applications within the Python suite or (worse) unrelated compiled applications on Carbon.

Caution: Avoid loading modules at the same time for both a Python suite with an included MPI and a separate MPI implementation, especially in your dot-files (.bashrc, .modules-el*).

Various workarounds for MPI clashes exist:

• Altering the module load order.
• Launching the non-python MPI variant with qualified paths like $FOO_HOME/bin/mpirun.
• If MPI is not needed on the Python side, use a custom virtual environment that has MPI excluded (described in the next section).

Python suites with customized package set

Contains

Python virtual environment with a package selection customized for Carbon.

Module nomenclature

python-env-distributor/pyMajor.pyMinor/distMajor/pyVersion[…]-moduleBuild

Python with a default selection of packages deemed generally useful on Carbon.

python-env-distributor-purpose/pyMajor.pyMinor/distMajor/pyVersion[…]-moduleBuild

Same, with alternative package selections for specific uses, created on request.

Examples of full module names

python-env-intel/2.7/2018/2.7-01

python-env-intel/3.5/2017/3.5-01

python-env-intel/3.6/2018/3.6-01

python-env-anaconda/2.7/4/2.7.11-09 (deprecated)

Recommended module abbreviations

python-env-intel/2.7

python-env-intel/3.6

python-env-anaconda/2.7 (deprecated)

python-env-anaconda/3.5 (deprecated)

When to use

Use one of these modules projects in domains related to nanoscience on Carbon, like atomistic or photonic modeling.

Packages from nanoscience-related domains are typically not included in vendor-distributed suites. On Carbon, a set of virtual environments is routinely provided for each vendor distribution, with a growing list of such packages added on and ready to use.

To request a custom environment or that packages be added to an existing environment, please contact [email protected].