4. Installing additional software#

Before you start building/installing your own software/packages, you should first check if the software is already available in Hydra, either with its own module or as part of another module. If the software package you need is not available, we strongly recommend to request its installation to VUB-HPC Support. Installations carried out by VUB-HPC have several advantages:

  1. The HPC team will optimize the compilation for each CPU architecture present in Hydra, guaranteeing that your software/package runs efficiently on all nodes (and usually much faster than installations made by the users).

  2. Free software will be available to all users of Hydra and licensed software can be made available to specific groups of users.

  3. The package will be built in a reproducible way with EasyBuild: important for scientific reproducibility.

  4. Different versions of the software can be installed alongside each other.

If you still want to install additional software/packages yourself, you can find guidance for specific development environments in the sections below.

4.1. Compiling and testing your software on the HPC#

We strongly recommend to use a suitable buildenv module to compile software in Hydra. A buildenv module loads pre-defined collections of build tools and compilers, ensuring that you work in a controlled and reproducible environment.

Example loading a build environment for toolchain foss/2023a#
module load buildenv/default-foss-2023a

The buildenv module:

  • loads the compiler and any math and/or MPI libraries that may be included in the respective toolchain

  • defines compiler flags for optimal performance: CFLAGS, FFLAGS, CXXFLAGS, LIBBLAS, LIBLAPACK, LIBFFT, …

  • defines flags and paths to make sure the build system finds the right libraries: LIBRARY_PATH, LD_LIBRARY_PATH, LDFLAGS, …

If needed you can load additional development tools compatible with the buildenv module. For instance, the following modules can be loaded alongside buildenv/default-foss-2023a:

  • CMake/3.26.3-GCCcore-12.3.0

  • Autotools/20220317-GCCcore-12.3.0

  • pkgconf/1.9.5-GCCcore-12.3.0

  • Ninja/1.11.1-GCCcore-12.3.0

  • Meson/1.1.1-GCCcore-12.3.0

Users compiling their own software should be aware that software compiled on the login nodes may fail in older compute nodes if full hardware optimization is used. The CPU microarchitecture of the login nodes (Skylake) has some instruction sets not available in Hydra’s older compute nodes (e.g. Broadwell). Therefore, there are two options to compile your own software

Best performance

Compile on the login node (with -march=native). The resulting binaries can only run on Skylake nodes, but they offer the best performance on those nodes. Jobs can be restricted to run on Skylake nodes with the Slurm option --partition=skylake.

Best compatibility

Compile on any Broadwell node. Login to a Broadwell node with

srun --partition=broadwell --pty bash -l

and compile your code on it. The resulting binaries can run on any node on Hydra with decent performance. Alternatively, users knowing how to setup the compilation can compile on the login node with -march=broadwell -mtune=skylake.

Helpdesk The environment in the HPC might differ significantly from your development system. We can help you in case of problems or questions to transfer your development to the HPC.

See also

VSCdocSoftware development for more information.

4.2. Installing additional Python packages#

Helpdesk There is a large number of Python packages already available in Hydra, see the question How can I find specific Python or R packages? If the package you need is not available, we can install it for you.

  • If you would like to test some new Python package before requesting its installation, you can do so by using your personal site-packages directory in your home:

    1. Load the appropriate Python module, i.e. the specific Python version to use with the package:

      module load Python/3.11.3-GCCcore-12.3.0
    2. Install the Python package with pip in your user account. The following command will also download and install all required dependencies. The new package and all missing dependencies will be installed by default in ~/.local/lib/pythonX.Y/site-packages:

      pip install --user <new_python_package>
  • Developers, who require using/testing software in Python that is in active development, can also use pip to install their own packages in a personal site-packages directory:

    1. Load the appropriate Python module and install the Python package from the local directory containing the source code. In this case pip will also install any missing dependencies:

      module load Python/3.11.3-GCCcore-12.3.0
      pip install --user /path/to/your/source/code
    2. Optional Updating the installation can be done at any time with the command:

      pip install --user --no-deps --ignore-installed /path/to/your/source/code

4.3. Python virtual environments#

A virtual environment is an isolated Python environment in which you can safely install Python packages, independent from those installed in the system or in other virtual environments. For Python developers, using virtual environments is very convenient as it allows working on multiple software projects at the same time. On the other hand, as explained in section Additional Software, it is highly recommended to use the software modules as much as possible.

In this section, we show how you can combine modules with virtual environments in the HPC to get the best of two worlds.

  1. Select the cluster partition that you want to use with the new virtual environment and start an interactive shell in it


    Virtual environments are tied to the cluster partition used for their creation. The login nodes of Hydra can be used to create virtual environments that will run on the skylake partitions of the cluster.

    Example command to start an interactive shell in the broadwell partition#
    srun --partition=broadwell --pty bash -l
  2. Load a Python module as base of the virtual environment. Choose a Python version that is suitable for the additional Python packages that will be installed in the virtual environment:

    module load Python/3.11.3-GCCcore-12.3.0
  3. Optional Load modules with additional Python packages

    Pythons modules in the HPC already include a long list of Python packages, but many other modules are also available. A common module is SciPy-bundle, a bundle of data science packages such as numpy, pandas, and scipy:

    module load SciPy-bundle/2023.07-gfbf-2023a
  4. Create a virtual environment with virtualenv

    Example command to create a new virtual environment in the directory myenv#
    virtualenv --system-site-packages myenv
  5. Before we can use the virtual environment, we must activate it

    Once the virtual environment is active, its name will be displayed in front of the prompt#
    $ source myenv/bin/activate
    (myenv) $
  6. We recommend to always upgrade pip to the latest version:

    (myenv) $ python -m pip install --upgrade pip
  7. Now we can install additional Python packages, or different versions of available packages, in the the virtual environment.

    Example command to install a version of the requests package that is different from the version included in the Python module#
    (myenv) $ python -m pip install requests==2.27.1
  8. Once you finish your work in the virtual environment, use the command deactivate to exit it

    The command deactivate will bring you to the standard shell#
    (myenv) $ deactivate

Whenever you want to go back to any of your virtual environments make sure to:

  1. Load the same software modules that you used in the creation of the virtual environment:

    module load Python/3.11.3-GCCcore-12.3.0 SciPy-bundle/2023.07-gfbf-2023a
  2. Reactivate the virtual environment:

    $ source myenv/bin/activate
    (myenv) $

4.4. Installing additional R packages#

Developers can compile and install R packages in the local R library of their home directory. The R function install.packages() will specifically ask to use your personal library. Keep in mind that if your software requires code compilation beyond R, you might need a build environment as described in Compiling and testing your software on the HPC.

Handling a personal R library in Hydra can be tricky though, it can easily break the rest of R packages provided by software modules (i.e the R-bundle-CRAN module). This can be due to conflicts with the global R library, issues with the multiple CPU micro-architectures in the compute nodes or due to a version change of R after the installation of local R packages.

If you experience errors running R scripts that are related to a failed load of a package, it is helpful to check your script in a clean R environment without your personal R library:

  1. Remove all modules and load the desired version of R:

    module purge
    module load R/4.3.2-gfbf-2023a
    module load R-bundle-CRAN/2023.12-foss-2023a
  2. Disable the R library in your home directory:

    export R_LIBS_USER=''
  3. Enter into a clean R environment (not loading previous workspace):

    R --no-restore


You can check the paths where R will look for the requested packages (i.e. after a call to library()) with the function .libPaths(). The paths at the beginning of the list have precedence over the rest.

4.5. Installing additional Perl packages#

See VSCdocPerl package management