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Deprecating Conda

CSC has deprecated the direct usage of Conda installations on our supercomputers' (Puhti and Mahti) shared file systems. This is due to the performance issues of Conda-based environments on parallel file systems, causing long start-up delays and system-wide slowdowns when using Python scripts. We strongly recommend users moving away from their own installations which use Conda directly on a shared drive (such as /projappl, /scratch on user's home directory). We are also gradually deprecating CSC-installed modules that are based directly on Conda. Conda can still be used indirectly, for example wrapped as a Singularity container.

Conda environments typically contain tens or even hundreds of thousands of files, and starting a Conda application requires reading a large number of them. Unfortunately all parallel file systems, which are optimized for large number of clients, have a poor single-client performance. You notice this as a longer initial start up time for Conda applications, and extra stress on the Lustre metadata server.

As an alternative to direct Conda usage we recommend:

  1. Use CSC's pre-installed environments available through the module system

    Check if any of CSC's pre-installed environments would be suitable for your project. If the existing environment is missing a few critical packages, you can often install the missing packages on your own.

    Our Python and R pages also contain further details on how to install your own packages to our modules. You can also contact CSC's servicedesk with requests for missing packages.

  2. Wrap your Conda or pip environment using CSC's tool

    CSC is currently developing a tool for wrapping existing Conda or pip installations into a smaller set of files using Singularity and squashfs technologies. The tool is still being developed, but is already being used internally for CSC's own installations.

  3. Use your own custom containers

    This is a great alternative for developing software locally on a workstation, and then deploying it on other workstation, cluster, or on cloud platforms. CSC's supercomputers support Singularity containers, which are are just single big files for Lustre, thus avoiding much of the problems. Many software projects offer Docker-containers which can often easily be converted to Singularity format. Inside of the container you can naturally use for example Conda to manage the packages without causing any file system issues.

    Read our documentation on how to create your own Singularity container

Last edited Thu Jan 27 2022