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Tykky is a set of tools which make software installations to HPC systems easier and more efficient using Apptainer containers.

Tykky use cases:

  • Conda installations, based on Conda environment.yml.
  • Pip installations, based on pip requirements.txt.
  • Container installations, based on existing Docker or Apptainer/Singularity images.

Tykky wraps installations inside an Apptainer/Singularity container to improve startup times, reduce IO load, and lessen the number of files on large parallel filesystems. Additionally, Tykky will generate wrappers so that installed software can be used (almost) as if it was not containerized. Depending on tool selection and settings, either the whole host filesystem or a limited subset is visible during execution and installation. This means that it's possible to wrap installation using e.g mpi4py relying on the host provided mpi installation.

This documentation covers a subset of the functionality and focuses on conda and Python, a large part of the advanced use-cases are not covered here yet.


As Tykky is still under development some of the more advanced features might change in exact usage and API.

Tykky module

To access Tykky tools:

1) Usually it is best to first unload all other modules:

module purge

2) Load Tykky module.

module load tykky

Conda based installation

First make sure that you have read and understood the license terms for miniconda and any used channels before using the command.

1) Create conda environment file env.yml:

An example of a suitable env.yml file would be:

  - conda-forge
  - python=3.8.8
  - scipy
  - nglview

2) Create new directory for installation . Likely /projappl/<your_project>/.. is a good place.

3) Create installation

conda-containerize new --prefix <install_dir> env.yml

4) Add the bin directory <install_dir>/bin to the path.

export PATH="<install_dir>/bin:$PATH"

5) You can call python and any other executables conda has installed in the same way as if you had activated the environment.

pip with conda

To install some additional pip packages, add the -r <req_file> argument e.g:

conda-containerize new -r req.txt --prefix <install_dir> env.yml


The tool also supports using mamba for installing packages. Mamba often finds suitable packages much faster than conda, so it is a good option when required package list is long. Enable this feature by adding the --mamba flag.

conda-containerize new --mamba --prefix <install_dir> env.yml

End-to-end example

Create new conda based installation using the previous env.yml file.

mkdir MyEnv
conda-containerize new --prefix MyEnv env.yml 
After the installation finishes we can add the installation directory to our PATH and use it like normal.

$ export PATH="$PWD/MyEnv/bin:$PATH"
$ python --version
$ python3
Python 3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27) 
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import scipy
>>> import nglview

Modifying a conda installation

Tykky installed software resides in a container, so it can not be directly modified. Small Python packages can be added normally using pip, but then the Python packages are sitting on the parallel filesystem so this is not recommended for any larger installations.

To actually modify the installation we can use the update keyword together with the --post-install <file> option which specifies a bash script with commands to run to update the installation. The commands are executed with the conda environment activated.

conda-containerize update <existing installation> --post-install <file> 

Where <file> could e.g contain:

conda  install -y numpy
conda  remove -y nglview
pip install requests

In this mode the whole host system is available including all software and modules.

Pip based installations

Sometimes you don't need a full blown conda environment or you might prefer pip to manage Python installations. For this case we can use:

pip-containerize new --prefix <install_dir> req.txt
Where req.txt is a standard pip requirements file. The notes and options for modifying a conda installation apply here as well.

Note that the Python version used by pip-containerize is the first Python executable found in the path, so it's affected by loaded modules.

Important: This python can not be itself container-based as nesting is not possible.

An additional flag --slim argument exists, which will instead use a pre-built minimal python container with a much newer version of python as a base. Without the --slim flag, the whole host system is available, and with the flag the system installations (i.e /usr, /lib64 ...) are no longer taken from the host, instead coming from within container.

Container based installations

Tykky also provides an option to:

  • Generate wrappers for tools in existing Apptainer/Singularity containers, so that they can be used transparently (no need to prepend singularity exec ..., or modify scripts if switching between containerized versions and "normal" installation).
  • Install tools available in Docker images, including generating wrappers.
wrap-container -w </path/inside/container> <container> --prefix <install_dir> 
  • <container> can be a local filepath or any URL accepted by singularity (e.g docker:// oras:// )
  • -w needs to be an absolute path (or comma separated list) inside the container. Wrappers will then be automatically created for the executables in the target directories / for the target path. If you do not know the path of executables in the container, open a shell inside the container and use which command. To open shell:
    • In case of existing local Apptainer/Singularity file: singularity shell xxx.sif.
    • In case of Docker or non-local Apptainer/Singularity file, create first the installation with some path and then start with created _debug_shell.

More complicated example

Example in tool repository.

How it works

See the README in the source code repository. The source code can be found in the GitHub repository.

Last update: October 19, 2022