Python is an integrated technical computing environment that combines sophisticated computation, advanced graphics and visualization, and a high-level programming language.

Learning Python

The Research Computing groups offers a free 10-part video series called “Python for Scientists & Engineers”. Click here to start learning Python.

Python on Rivanna

The default Python is required for system purposes and is generally too old for applications. We offer Python through the Anaconda distribution from Continuum Analytics. Anaconda bundles a large number of popular modules and packages, as well as the Spyder IDE, an iPython console, and Jupyter notebooks. To see all available versions, run

module spider anaconda
ModuleVersion Module Load Command
anaconda2019.10-py2.7 module load anaconda/2019.10-py2.7
anaconda2020.11-py3.8 module load anaconda/2020.11-py3.8

The module version suffices, e.g. py2.7 and py3.8, indicate the version of the Python interpreter.

Python and MPI

Built to complement the rich, open source Python community, the Anaconda platform provides an enterprise-ready data analytics platform that empowers companies to adopt a modern open data science analytics architecture. On Rivanna, we provide mpi4py libraries via dedicated modules that are built using the GCC compiler and OpenMPI libraries.

ModuleVersion Module Load Command
mpi4py3.0.0-py2.7 module load gcc/7.1.0 openmpi/3.1.4 mpi4py/3.0.0-py2.7
mpi4py3.0.3 module load gcc/9.2.0 openmpi/3.1.6 mpi4py/3.0.3

As long as an MPI toolchain (e.g. gcc + openmpi) is loaded, you can install mpi4py using any Python/Ancaonda module via pip install --user mpi4py.

View list of all installed packages

After loading an anaconda module, a list of all installed packages can be viewed by running this command:

conda list

A large number of packages are included in Anaconda. If you need a package not available in the bundle, you can install it yourself with pip or conda.

Package installation with pip

module load anaconda
pip install --user yourpackage

The --user option will install it into your home directory. It is bound to a particular Python version (namely, X.Y in anaconda/****.**-pyX.Y) and will have to be reinstalled if Anaconda is upgraded. To import pip-installed packages in a Python script, please remember to load the same anaconda module that was used to install the packages.

Package installation with conda

Certain Python packages are available pre-bundled via public Conda channels. Conda packages are installed in environments, i.e. specific directories. This is useful to isolate incompatible packages so that they do not conflict with each other. Only one Conda environment can be active at any given time. The Anaconda distribution provides a root environment that contains all of the preinstalled Anaconda packages. In addition, users can create their own Conda environments in their home directory.

Use one of the following two commands to install a particular package provided via a specific Conda channel in your own custom environment.

Creating a new environment For example, if you want to install the epic package into a new environment named custom_env that does not exist yet, run this command:

module load anaconda
conda create -n custom_env -c bioconda epic

Updating an existing environment To install the same package into an existing environment, run:

conda install -n custom_env -c bioconda epic
  • -n custom_env: Specifies the name of the environment where the package(s) will be installed.
  • -c bioconda: Specifies that the packages to be installed are provided by the bioconda channel.
  • epic: The name of the Conda package to be installed. Multiple packages separated by whitespaces can be listed.

The user environments are installed under ~/.conda/envs.

Using Conda environments

After loading an anaconda module on Rivanna, the root environment with default packages is activated. To list all environments, run this command:

conda env list

To use packages in your own environment named custom_env, run this command:

source activate custom_env

To switch back to the default root environment, run this command:

source deactivate custom_env

Example SLURM script

#SBATCH -n 1
#SBATCH -t 01:00:00
#SBATCH -o myprog.out
#SBATCH -p standard
#SBATCH -A mygroup

module purge
module load anaconda # or anaconda/2019.10-py2.7 for Python 2
# optional: uncomment next line to use your custom Conda environment; replace 'custom_env' with actual env name
# source activate custom_env

python myscript.py