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Container wrapper

The container wrapper is a set of tools which wrap software installations inside a Apptainer/Singularity container to improve startup times, reduce I/O load, and lessen the number of files on large parallel file systems.

Additionally, the container wrapper will generate wrappers so that installed software can be used (almost) as if it were not containerized. Depending on tool selection and settings, either the whole host file system or a limited subset is visible during execution and installation. This means that it's possible to wrap installations using e.g. mpi4py while still relying on the host provided MPI installation.

This documentation covers a subset of the functionality and focuses on examples of wrapping conda and Python installations.

The container wrapper is experimental software

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

Basic conda installation

The tools provided by the container wrapper are accessible by loading the lumi-container-wrapper module that is available in the LUMI and CrayEnv software stacks.

module load LUMI/22.08 
module load lumi-container-wrapper

Then we can run the conda-containerize tool

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

where env.yml is a conda environment file.

This file can be written manually, e.g:

channels:
  - conda-forge
dependencies:
  - python=3.8.8
  - scipy
  - nglview

or generated from an existing environment

conda env export -n <target_env_name> > env.yaml 
Windows and MacOS will need to add the --from-history flag to the export command

or, alternatively,

conda list -n <target_env_name> --explicit > env.txt

Using the --explicit option only works if the existing environment is on a Linux machine with x86 CPU architecture. Otherwise the result will not be transferable to LUMI.

After the installation is done, you simply need to add the bin directory <install_dir>/bin to your PATH.

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

Then, you can call python and any other executables, conda has installed, in the same way as if you would have activated the environment.

If you also need to install some additional pip packages, you can do so by supplying 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. Enable this feature by adding the --mamba flag, e.g. conda-containerize new --mamba ...

Make sure that you have read and understood the license terms for miniconda as well as any used conda channels before using the command.

End-to-end example of a conda install

Using the previous env.yml

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
3.8.8
 $ 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 container wrapped conda installation

As the container wrapper installed software resides in a container, it cannot be directly modified. Small python packages can be added outside the container, in the usual way, using pip, but then the python packages are sitting on the parallel file system which is not recommended for larger installations.

To actually modify the installation inside the wrapping container, 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 pyyaml
pip install requests

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

Plain pip installations

Sometimes you don't need a full-blown conda environment or you may prefer to manage your python installations using pip. For this case we can use the container wrapper via

pip-containerize new --prefix <install_dir> req.txt

where req.txt is a standard pip requirements file. The above notes and options for modifying a conda installation apply to pip installations as well.

Note that the python version used by pip-containerize is the first python executable found on the PATH, so it's affected by loading modules.

Note

This python used to installed pip packages cannot itself be container-based as nesting of containers is not possible.

Additionally, you may use the --slim argument, which will use a pre-built minimal python container with a much newer version of python as a base. Without the --slim argument, the whole host system is available. However, by using the --slim argument, the system installations (i.e /usr, /lib64 ...) are no longer taken from the host, but are instead taken from the minimal python container.

Existing containers

The container wrapper also provides a tool to generate wrappers for existing Apptainer/Singularity containers, so that they can be used transparently without the need for prepending singularity exec ..., or modify scripts if switching between containerized versions of tools.

wrap-container -w </path/inside/container> <container> --prefix <install_dir> 

where <container> can be a filepath or any URL accepted by singularity (e.g docker//: oras//: or any other singularity accepted format), and -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.

Additional example

How it works

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