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Using Python

Python is supported on ARCHER2 both for running intensive parallel jobs and also as an analysis tool. This section describes how to use Python in either of these scenarios.

The Python installations on ARCHER2 contain some of the most commonly used modules. If you wish to install additional Python modules, we recommend that you use the pip command after loading the cray-python module. This is described in more detail below.


Python 2 is not supported on ARCHER2 as it has been deprecated since the start of 2020.


When you log onto ARCHER2, no Python module is loaded by default. You will generally need to load the cray-python module to access the functionality described below.

HPE Cray Python distribution

The recommended way to use Python on ARCHER2 is to use the HPE Cray Python distribution.

The HPE Cray distribution provides Python 3 along with some of the most common packages used for scientific computation and data analysis. These include:

  • numpy and scipy - built using GCC against HPE Cray LibSci
  • mpi4py - built using GCC against HPE Cray MPICH
  • dask

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

module load cray-python


The HPE Cray Python distribution is built using GCC compilers. If you wish to compile your own Python, C/C++ or Fortran code to use with HPE Cray Python, you should ensure that you compile using PrgEnv-gnu to make sure they are compatible.

Adding your own packages

If the packages you require are not included in the HPE Cray Python distribution, further packages can be added using pip. However, as the /home file systems are not available on the compute nodes, you will need to modify the default install location that pip uses to point to a location on the /work file systems (by default, pip installs into $HOME/.local). To do this, you set the PYTHONUSERBASE environment variable to point to a location on /work, for example:

export PYTHONUSERBASE=/work/t01/t01/auser/.local

You will also need to ensure that:

  1. the location of commands installed by pip are available on the command line by modifying the PATH environment variable;
  2. any packages you install are available to Python by modifying the PYTHONPATH environment variable.

You can do this in the following way (once you have set PYTHONUSERBASE as described above):

export PYTHONPATH=$PYTHONUSERBASE/lib/python3.8/site-packages:$PYTHONPATH

We would recommend adding all three of these commands to your $HOME/.bashrc file to ensure they are set by default when you log in to ARCHER2.

Once, you have done this, you can use pip to add packages on top of the HPE Cray Python environment. This can be done using:

module load cray-python
pip install --user <package_name>

This uses the --user flag to ensure the packages are installed in the directory specified by PYTHONUSERBASE.

Setting up virtual environments

We recommend that you use the pipenv and/or virtualenv packages to manage your Python environments. A summary of how to get a virtual environment set up is contained in the below, but for further information, see:

Sometimes, you may need several different versions of the same package installed, for example, due to dependency issues. Virtual environments allow you to manage these conflicting requirements. The first step is to run the commands contained in the above section so that you can install the virtualenv package which will manage your environments. To install virtualenv run:

pip install --user virtualenv  # The --user flag indicates this should be installed in the user's package folder
Next you must create a folder for the virtual environment's files to live in and tell virtualenv to set this folder up for storing virtual environment 'stuff'. This is done by running the command
mkdir /work/t01/t01/auser/<<name of your virtual environment>>  # Create the folder
virtualenv -p /opt/cray/pe/python/ /work/t01/t01/asuser/<<name of your virtual environment>>  # -p flag means use this python interpreter
Finally, you're ready to activate your environment. This is done by running
source /work/t01/t01/auser/<<name of your virtual environment>>/bin/activate
Once your environment is activated you will be able to install packages as usual using pip install <<package name>>. These packages will only be available within this environment. When running code that requires these packages you must activate the environment, by adding the above source ... activate line of code to any submission scripts.

Running Python on the compute nodes

In this section we provide example Python job submission scripts for a variety of scenarios of using Python on the ARCHER2 compute nodes.

Example serial Python submission script

#!/bin/bash --login

#SBATCH --job-name=python_test
#SBATCH --ntasks=1
#SBATCH --time=00:10:00

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

# Load the Python module
module load cray-python

# If using a virtual environment
source <<path to virtual environment>>/bin/activate

# Run your Python progamme


If you have installed your own packages you will need to set PATH and PYTHONPATH as described above within your job submission script in order to accesss the commands and packages you have installed.

Example mpi4py job submission script

Programs 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 program using srun python instead of python my_prog,py. Failing to do so will cause a segmentation fault in your program 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 --ntasks-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

# Pass cpus-per-task setting to srun

# Run your Python programme
# Note that srun MUST be used to wrap the call to python, otherwise an error
# will occur
srun --hint=nomultithread --distribution=block:block python

Using JupyterLab on ARCHER2

It is possible to view and run Jupyter notebooks from both login nodes and compute nodes on ARCHER2.


You can test such notebooks on the login nodes, but please do not attempt to run any computationally intensive work. Jobs may get killed once they hit a CPU limit on login nodes.

Please follow these steps.

  1. Install JupyterLab in your work directory.

    module load cray-python
    export PYTHONUSERBASE=/work/t01/t01/auser/.local
    # source <<path to virtual environment>>/bin/activate  # If using a virtualenvironment uncomment this line and remove the --user flag from the next
    pip install --user jupyterlab

  2. If you want to test JupyterLab on the login node please go straight to step 3. To run your Jupyter notebook on a compute node, you first need to run an interactive session.

    srun --nodes=1 --exclusive --time=00:20:00 --account=<your_budget> \
         --partition=standard --qos=short --reservation=shortqos \
         --pty /bin/bash
    Your prompt will change to something like below.
    In this case, the node id is nid001015. Now execute the following on the compute node.
    cd /work/t01/t01/auser # Update the path to your work directory
    export PYTHONUSERBASE=$(pwd)/.local
    export HOME=$(pwd)
    module load cray-python
    # source <<path to virtual environment>>/bin/activate  # If using a virtualenvironment uncomment this line

  3. Run the JupyterLab server.

    export JUPYTER_RUNTIME_DIR=$(pwd)
    jupyter lab --ip= --no-browser
    Once it's started, you will see a URL printed in the terminal window of the form<port_number>/lab?token=<string>; we'll need this URL for step 6.

  4. Please skip this step if you are connecting from a machine running Windows. Open a new terminal window on your laptop and run the following command.

    ssh <username> -L<port_number>:<node_id>:<port_number>
    where <username> is your username, and <node_id> is the id of the node you're currently on (for a login node, this will be uan01, or similar; on a compute node, it will be a mix of numbers and letters). In our example, <node_id> is nid001015. Note, please use the same port number as that shown in the URL of step 3. This number may vary, likely values are 8888 or 8889.

  5. Please skip this step if you are connecting from Linux or macOS. If you are connecting from Windows, you should use MobaXterm to configure an SSH tunnel as follows.

    • Click on the Tunnelling button above the MobaXterm terminal. Create a new tunnel by clicking on New SSH tunnel in the window that opens.
    • In the new window that opens, make sure the Local port forwarding radio button is selected.
    • In the forwarded port text box on the left under My computer with MobaXterm, enter the port number indicated in the JupyterLab server output (e.g., 8888 or 8890).
    • In the three text boxes on the bottom right under SSH server enter, your ARCHER2 username and then 22.
    • At the top right, under Remote server, enter the id of the login or compute node running the JupyterLab server and the associated port number.
    • Click on the Save button.
    • In the tunnelling window, you will now see a new row for the settings you just entered. If you like, you can give a name to the tunnel in the leftmost column to identify it.
    • Click on the small key icon close to the right for the new connection to tell MobaXterm which SSH private key to use when connecting to ARCHER2. You should tell it to use the same .ppk private key that you normally use when connecting to ARCHER2.
    • The tunnel should now be configured. Click on the small start button (like a play '>' icon) for the new tunnel to open it. You'll be asked to enter your ARCHER2 account password -- please do so.
  6. Now, if you open a browser window locally, you should be able to navigate to the URL from step 3, and this should display the JupyterLab server. If JupyterLab is running on a compute node, the notebook will be available for the length of the interactive session you have requested.


Please do not use the other http address given by the JupyterLab output, the one formatted http://<node_id>:<port_number>/lab?token=<string>. Your local browser will not recognise the <node_id> part of the address.

Using Dask Job-Queue on ARCHER2

The Dask-jobqueue project makes it easy to deploy Dask on ARCHER2. You can find more information in the Dask Job-Queue documentation.

Please follow these steps:

  1. Install Dask-Jobqueue
module load cray-python
export PYTHONUSERBASE=/work/t01/t01/auser/.local

pip install --user dask-jobqueue --upgrade
  1. Using Dask

Dask-jobqueue creates a Dask Scheduler in the Python process where the cluster object is instantiated. A script for running dask jobs on ARCHER2 might look something like this:

from dask_jobqueue import SLURMCluster
cluster = SLURMCluster(cores=128, 
                       python='srun python',
                       shebang="#!/bin/bash --login",
                       env_extra=['module load cray-python',
                                  'export PYTHONUSERBASE=/work/t01/t01/auser/.local/',
                                  'export PATH=$PYTHONUSERBASE/bin:$PATH',
                                  'export PYTHONPATH=$PYTHONUSERBASE/lib/python3.8/site-packages:$PYTHONPATH'])

cluster.scale(jobs=2)    # Deploy two single-node jobs

from dask.distributed import Client
client = Client(cluster)  # Connect this local process to remote workers

# wait for jobs to arrive, depending on the queue, this may take some time
import dask.array as da
x = …              # Dask commands now use these distributed resources

This script can be run on the login nodes and it submits the Dask jobs to the job queue. Users should ensure that the computationally intensive work is done with the Dask commands which run on the compute nodes.

The cluster object parameters specify the characteristics for running on a single compute node. The header_skip option is required as we are running on exclusive nodes where you should not specify the memory requirements, however Dask requires you to supply this option.

Jobs are be deployed with the cluster.scale command, where the jobs option sets the number of single node jobs requested. Job scripts are generated (from the cluster object) and these are submitted to the queue to begin running once the resources are available. You can check the status of the jobs by running squeue -u $USER in a separate terminal.

If you wish to see the generated job script you can use: