Anaconda is a Python distribution. We ask our users to not install Anaconda on our clusters. We recommand that you consider other options like a virtual environment or a Singularity container, for the most complicated cases.
We are aware of the fact that Anaconda is widely used in several domains, such as data science, AI, bioinformatics etc. Anaconda is a useful solution for simplifying the management of Python and scientific libraries on a personal computer. However, on a cluster like those supported by HPC2N, the management of these libraries and dependencies should be done by our staff, in order to ensure compatibility and optimal performance. Here is a list of reasons:
A virtual environment offers you all the functionality which you need to use Python on our clusters. Here is how to convert to the use of virtual environments if you use Anaconda on your personal computer:
Your software should run - if it doesn't, don't hesitate to contact us.
In some situations, the complexity of the dependencies of a program requires the use of a solution where you can control the entire software environment. In these situations, we recommend the tool Singularity; note that a Docker image can be converted into a Singularity image. The only disadvantage of Singularity is its consumption of disk space. If your research group plans on using several images, it would be wise to collect all of them together in a single directory of the group's project space to avoid duplication.
A conda recipe forces the installation of R. This installation does not perform nearly as well as the version we provide as a module (which uses an optimized BLAS library for computantional speed). This conda installed R does not work well, and jobs launched with it may die and waste both computing resources as well as your time.
The content is copied with permission from Compute Canada and adjusted for HPC2N.