Working with Modules and Installing Software


Teaching: 15 min
Exercises: 30 min
  • How do I get access to additional software?

  • Understand how to load a module

  • Understand how to install pip packages for Python

Using & installing software

On most cluster systems, all the software installed is not immediately available for use; instead, it is loaded into your environment incrementally using a module system.

The module command controls this. You can get a list of available software with the module avail command. This should return a long list of available software.

One common piece of software that isn’t installed on the Supercomputing Wales hubs (without a module) is Python version 3. If we attempt to run python3 from the command line it will respond with an error:

[s.jane.doe@sl1 ~]$ python3
-bash: python3: command not found

Note that the login nodes and the compute nodes have identical configurations in terms of what software is available, so you can discover if your program will run from the login nodes

From the output of the module avail command there should have been an entry anaconda/2019.03 in the /app/local/modules/langauges section near the bottom. Anaconda is a distribution including a variety of Python packages, and tools for managing them. Let’s go ahead and load it.

[s.jane.doe@sl1 ~]$ module load anaconda/2019.03
[s.jane.doe@sl1 ~]$ source activate
[s.jane.doe@sl1 ~]$ python3
Python 3.7.0 (default, Jul 13 2018, 10:08:05)
[GCC Intel(R) C++ gcc 4.8.5 mode] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> exit()
[s.jane.doe@sl1 ~]$

Legacy HPC Wales and Raven Modules

All of the old modules which were available on HPC Wales and the old Cardiff Raven system are available by running either module load hpcw or module load raven. (Raven modules are only available on Hawk.) Please note that much of this software is outdated and may by suboptimal as its not compiled to take advantage of the newer CPU architectures on Supercomputing Wales.

Installing Python modules

Python is a popular language for researchers to use and has modules (aka libraries) that do all kinds of useful things, but sometimes the module you need won’t be installed. Many modules can be installed using the pip (or pip3 with Python 3) command. Since we don’t have administrative rights to the supercomputer, we can’t install these into the shared base Anaconda environment; instead, we need to create a local one.

$ conda create -n scw_test python=3.7

This tells the conda command to create a new environment, to give it the name scw_test, and to install Python 3.7 into it. Conda will take a little time to work out what it needs to install, and once you confirm by typing y, then place it in a new directory in ~/.conda/envs.

Once your environment is created, you can activate it so you can use it to work in with the command

$ conda activate scw_test

The prefix at the start of your prompt will now change from base to scw_test, to indicate the environment that you have active. Similarly, which python now returns ~/.conda/envs/scw_test/bin/python, indicating that this is now where Python will run from if you run python.

So far we have created a relatively bare environemnt, but we can install packages into the new environment just as we can on our own machines. Let’s now install Matplotlib into this environment:

$ conda install matplotlib

Conda automatically works out which extra packages need to be present for Matplotlib to work, and then prompts to install them.

You could alternatively have specified matplotlib directly to the conda create command, and it would have been installed when the environment was created.

If you wanted to create a full Anaconda Python installation to base your environment on, you can do this with conda create -n [name] anaconda; we don’t do this here because it creates a very large number of files and takes a substantial time to download and install. In general, it’s better to only install packages that you need, since they are less likely to conflict with each other.

Using pip works very similarly to Python; provided the Anaconda module is loaded and your Conda environment is activated, then pip will automatically install into your Conda environment and not try to install at the system level.

For more detail on using Python with Supercomputing Wales, see the training on High Performance Python.

How to install other software yourself

For Perl modules and R packages, we encourage you to set up directories in your home and/or lab folders for installing your own copies locally. Instructions on how to install these are available from the How To pages on the Supercomputing Wales portal.

Requesting additional software

If software you need is not installed, we encourage you to do local installs in your home or project shared directory for bleeding-edge releases, software you are testing, or software used only by your project. For programs that are commonly used by your domain, field, or department, please submit a software install request. Note that due to demand and the complex nature of software installs, it may take a while for us to complete these requests.

Commercial software will require the appropriate licenses.


Running a Python script

  1. Create a new file using nano and call it Give it the following contents:
    import matplotlib as mpl
    mpl.use('Agg') #set the backend to Agg to make a png file instead of displaying on screen
    import matplotlib.pyplot as plt
  2. Create a new job submission script called containing the following:
    #!/bin/bash --login
    # job name
    #SBATCH --job-name=plotgraph
    # job stdout file
    #SBATCH --output=plotgraph.out.%J
    # job stderr file
    #SBATCH --error=plotgraph.err.%J
    # maximum job time in D-HH:MM
    #SBATCH --time=0-00:01
    # number of tasks you are requesting
    #SBATCH --ntasks=1
    # memory per process in MB
    #SBATCH --mem=2
    # number of nodes needed
    #SBATCH --nodes=1
    # specify our current project
    # change this for your own work
    #SBATCH --account=scwXXXX
    # specify the reservation we have for the training workshop
    # remove this for your own work
    # replace XX with the code provided by your instructor
    #SBATCH --reservation=scwXXXX_YY
    module load anaconda/2019.03
    source activate scw_test
  3. Run the job with sbatch Did the job complete successfully? Are there any errors in the error file?
  4. Fix the cause of the errors by adjusting (or removing) the appropriate parameter.
  5. Copy back the resulting file, temp.png using SCP or SFTP and view it on your computer.
  6. Add the following lines to the Python script (between the plt.plot line and the plt.savefig line) to make it embed the job ID into the title of the image:
    import os
    jobid = str(os.environ.get('SLURM_JOBID'))
    plt.title(f'Job id {jobid}')

    Run the job again and look at the output image. Try using some of the other Slurm environment variables from this list, too.


  1. You will get errors about the amount of memory being used. These might show up as a segmentation fault rather than a memory error. For some reason this script can be very slow to run sometimes, so increasing the timeout can help too—only do this if you are getting timeout errors. Around 50MB of memory and 5 minutes should be enough.
  2. Your Python code should now read:
import matplotlib as mpl
mpl.use('Agg') #set the backend to Agg to make a png file instead of displaying on screen
import os
import matplotlib.pyplot as plt
plt.title(f'Job id {jobid}')

Key Points

  • A lot of software is only available by loading extra modules. This helps prevent problems where two packages are incompatible.

  • Python 3 is one such package.

  • If you want to install pip packages use the --user option to store the packages in your home directory.