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The ShARC HPC cluster was decommissioned on the 30th of November 2023 at 17:00. It is no longer possible for users to access that cluster.


This page documents the “miniconda” installation on ShARC. This is the recommended way of using Python, and the best way to be able to configure custom sets of packages for your use.

“conda” a Python package manager, allows you to create “environments” which are sets of packages that you can modify. It does this by installing them in your home area. This page will guide you through loading conda and then creating and modifying environments so you can install and use whatever Python packages you need.

Using Conda Python

After connecting to ShARC (see Establishing a SSH connection), start an interactive session with the qrshx or qrsh command.

Conda Python can be loaded with:

module load apps/python/conda

The root conda environment (the default) provides Python 3 and no extra modules, it is automatically updated, and not recommended for general use, just as a base for your own environments. There is also a python2 environment, which is the same but with a Python 2 installation.


Due to Anaconda being installed in a module you must use the source command instead of conda when activating or deactivating environments!

Quickly Conda Environments

There are a small number of environments provided for everyone to use, these are the default root and python2 environments as well as various versions of Anaconda for Python 3 and Python 2.

The anaconda environments can be loaded through provided module files:

module load apps/python/anaconda2-4.2.0
module load apps/python/anaconda3-4.2.0

Where anaconda2 represents Python 2 installations and anaconda3 represents Python 3 installations. These commands will also load the apps/python/conda module and then activate the anaconda environment specified.


Anaconda 2.5.0 and higher are compiled with Intel MKL libraries which should result in higher numerical performance.

Using a Conda Environment

Once the conda module is loaded you have to load or create the desired conda environments. For the documentation on conda environments see the conda documentation.

You can load a conda environment with:

source activate python2

where python2 is the name of the environment, and unload one with:

source deactivate

which will return you to the root environment.

It is possible to list all the available environments with:

conda env list

Provided system-wide are a set of anaconda environments, these will be installed with the anaconda version number in the environment name, and never modified. They will therefore provide a static base for derivative environments or for using directly.

Creating a Conda Environment

Every user can create their own environments, and packages shared with the system-wide environments will not be reinstalled or copied to your file store, they will be symlinked, this reduces the space you need in your /home directory to install many different Python environments.

To create a clean environment with just Python 2 and numpy you can run:

conda create -n mynumpy python=2.7 numpy

This will download the latest release of Python 2.7 and numpy, and create an environment named mynumpy.

Any version of Python or list of packages can be provided:

conda create -n myscience python=3.5 numpy=1.15.2 scipy

If you wish to modify an existing environment, such as one of the anaconda installations, you can clone that environment:

conda create --clone anaconda3-4.2.0 -n myexperiment

This will create an environment called myexperiment which has all the anaconda 4.2.0 packages installed with Python 3.

Avoiding large Conda environments filling up your home directory

If you want to create one or more large Conda environments (e.g. containing bulky Deep Learning packages such as TensorFlow or PyTorch) then there’s a risk you’ll quickly use up your home directory’s 10GB storage quota.

Create a .condarc file in your home directory if it does not already exist. Add an envs_dirs: and pkgs_dirs: section as shown below:

- /data/username/anaconda/.pkg-cache/

- /data/username/anaconda/.envs

Make sure to replace username with your own username and then create these folders by running the following command:

mkdir -p /data/$USER/anaconda/.pkg-cache/  /data/$USER/anaconda/.envs

Installations of environments and package caching should now occur in your /data area.

Installing Packages Inside an Environment

Once you have created your own environment you can install additional packages or different versions of packages into it. There are two methods for doing this, conda and pip, if a package is available through conda it is strongly recommended that you use conda to install packages. You can search for packages using conda:

conda search pandas

then install the package using:

conda install pandas

if you are not in your environment you will get a permission denied error when trying to install packages, if this happens, create or activate an environment you own.

If a package is not available through conda you can search for and install it using pip, i.e.:

pip search colormath

pip install colormath

Using Python with MPI

There is an experimental set of packages for conda that have been compiled by the RSE and RCG teams, which allow you to use a MPI stack entirely managed by Conda. This allows you to easily create complex evironments and use MPI without worrying about other modules or system libraries.

To get access to these packages you need to run the following command to add the repo to your conda config:

conda config --add channels file:///usr/local/packages/apps/conda/conda-bld/

you should then be able to install the packages with the openmpi feature, which currently include openmpi, hdf5, mpi4py and h5py:

conda create -n my_mpi_env python=3.5 openmpi mpi4py

Currently, this channel provides Conda packages for:

  • mpi4py (and openmpi) for Python 3.4, 3.5, 3.6 and 2.7

  • h5py (and hdf5) with MPI support for Python 3.5 and 2.7

The build scripts for these packages can be found in this GitHub repository.

Further Conda Python Learning Resources

The resources and training courses below may be of interest:

Installation Notes

These are primarily for administrators of the system.

The conda package manager is installed in /usr/share/packages/apps/conda, it was installed using the miniconda installer.

It is important to regularly update the root environment to keep the conda package manager up to date. To do this login as a sa_ account (with write permissions to /usr/local/packages/apps/conda) and run:

$ conda update --all
$ conda update conda

Between updates, remove write permissions on certain dirs/files to prevent sysadmins from accidentally installing central conda envs instead of local ones / encountering errors when trying to create local envs:

chmod ugo-w /usr/local/packages/apps/conda /usr/local/packages/apps/conda/envs
chmod -R ugo-w /usr/local/packages/apps/conda/pkgs

Installing a New Version of Anaconda

Run the following as a sa_ user (with write permissions to /usr/local/packages/apps/conda:

$ conda create -n anaconda3-<VERSION> python=3 anaconda=<VERSION>
$ conda create -n anaconda2-<VERSION> python=2 anaconda=<VERSION>

Then copy the modulefile for the previous version of anaconda to the new version and update the name of the environment. Also you will need to append the new module to the conflict line in apps/python/.conda-environments.tcl.