Using Python

Python for general users

Cray Python 3 distribution

Python 3 on ARCHER2 is provided by Cray.

The central installation provides many of the most common packages used for scientific computation and data analysis. These include:

  • numpy and scipy - built against Cray LibSci
  • mpi4py - built against Cray MPT
  • dask

The Python 3 module can be loaded (either on the front-end or in a submission script) using:

module load cray-python

Adding your own packages

If the packages you require are not included in the central Python distribution, further packages can easily be built on top using pip. This can be done using:

pip install --user <package_name>

This uses the “--user” flag to ensure the packages are installed in your user directory. This means that you can have your own Python environment that is independent of the central installation.


This section on the conda tool is adapted from the NERSC Python documentation at

Creating conda environments

The conda tool allows you to build your own custom Python installation through “environments”. First, create a conda environment using conda create. Specify a name for your environment using the “-n” flag, and the version of the Python interpreter you want installed. For example, the following will create a Python 3.6 environment called “myenv”:

conda create -n myenv python=3.6

You will then be asked to confirm the package managements steps that will be taken, along with the installation location.

Activating conda environments

There are two options for activating a conda environments: source activate and conda activate.

source activate is the only option for versions of conda prior to 4.6.

You can activate an environment with the name “myenv” with the command:

source activate myenv

and then deactivate it with:

source deactivate

conda activate is available in conda 4.6 and later. It can be more complex to use than source activate, but has some advantages. Before running conda activate for the first time, first run the command conda init. This sets the current Python environment as the default. It does this by adding lines to your .bashrc file that will be run at login. (These lines are enclosed by the lines # >>> conda initialize >>> and # <<< conda initialize <<<, and can be removed or edited manually if needed.)

Having set your default environment, your custom environments can be activated using:

conda activate myenv

and deactivated using:

conda deactivate

Installing Packages

conda can also be used to find and install packages into your environments. For example, the following can be used to find and then install a package (in this case, scipy):

conda search scipy
conda install scipy

Example Python submission script

#!/bin/bash --login

#SBATCH --name=python_test
#SBATCH --nodes=1
#SBATCH --tasks-per-node=1
#SBATCH --cpus-per-task=1
#SBATCH --time=00:10:00

# Replace [budget code] below with your project code (e.g. t01)
#SBATCH --account=[budget code]
#SBATCH --partition=standard
#SBATCH --qos=standard

# Load the Python module
module load cray-python

# Run your Python progamme


Programmes that have been parallelised with mpi4py can be run on multiple processors on ARCHER2. A sample submission script is given below. The primary difference from the Python submission script in the previous section is that we must run the programme using srun python instead of python my_prog,py. Failing to do so will cause a segmentation fault in your programme when it reaches the line from mpi4py import MPI.

#!/bin/bash --login
# Slurm job options (job-name, compute nodes, job time)
#SBATCH --job-name=mpi4py_test
#SBATCH --nodes=1
#SBATCH --tasks-per-node=2
#SBATCH --cpus-per-task=1
#SBATCH --time=0:10:0

# Replace [budget code] below with your budget code (e.g. t01)
#SBATCH --account=[budget code]
#SBATCH --partition=standard
#SBATCH --qos=standard

# Load the Python module
module load cray-python

# Run your Python programme
# Note that srun MUST be used to wrap the call to python, otherwise an error
# will occur
srun python