Added conda section

config/templates/hpc.template

@@ -31,6 +31,7 @@ nav:
       - Best Practices: best_practices.md
       - Graphical applications with VNC: VNC.md
       - Graphical applications with X2Go: x2go.md
+      - Conda: conda.md
 {%- if site == 'Gent' %}
       - GPU clusters: gpu.md
       - Cron scripts: crontab.md

mkdocs/docs/HPC/conda.md

@@ -0,0 +1,69 @@
+# Conda
+
+Conda is a software package manager for data science that allows unprivileged (non-administrative) users to search, 
+fetch, install, upgrade, use, and manage supported open-source software packages and 
+programming languages/libraries/environments in a directory they have write access to. 
+Conda allows users to create reproducible software environments
+
+We do not recommend using conda environments on HPC clusters due to several common issues, 
+which we will explain in this section. However, we do not forbid the use of conda entirely. In certain situations, 
+such as testing software, creating new workflows, or teaching a course, it is perfectly acceptable to use it.
+
+## Performance and Optimization
+
+Conda's packages are pre-compiled binaries that are designed to work on a wide range of systems. 
+This means they are not optimized for the specific architecture of HPC clusters, 
+leading to potential performance drawbacks compared to modules compiled specifically for the HPC environment.
+
+HPC modules on the other hand, are compiled for the specific architecture of the cluster,
+and are optimized for performance.
+
+## Compatibility and Dependency
+
+Using Conda in conjunction with centrally installed modules can lead to conflicts and unexpected errors, 
+making it difficult to debug and manage dependencies.
+
+If you do wish to use conda, we recommend only using packages from conda itself, even when they are available as modules.
+This will degrade performance, but will avoid conflicts between conda and module dependencies.
+
+## Package Availability
+
+Conda has a smaller repository of available packages compared to PyPI, the repository used by pip. 
+This can limit the availability of specific tools or libraries needed for certain workflows.
+
+## Environment and Installation Issues
+
+### Home Directory Usage
+
+by default, conda installs packages in the user's home directory, 
+which can quickly fill up disk quotas due to the large number of files and directories it creates. 
+This is particularly problematic in the HPC environment where home directory quotas are limited.
+
+If you do wish to use conda, we recommend setting the conda package directory to a location with more storage 
+space. This can be done by running: 
+
+```bash
+export CONDA_PKGS_DIRS=$VSC_DATA/conda_pkgs
+```
+
+before installing packages into a conda environment.
+
+
+### Modification of Configuration Files
+
+Conda modifies the .bashrc file in the user's home directory, 
+which can lead to conflicts and unintended side effects in the user's environment setup.
+
+You can avoid the change of the `.bashrc` file by activating the environment with `source activate full/path/to/myenv`
+instead of `conda activate myenv`.
+
+## Anaconda Licensing
+
+If your organization uses Conda packages from Anaconda channels, 
+be aware that Anaconda may now be actively enforcing its licensing terms. 
+Academic, non-profit, and research institutions that previously used Anaconda software for free may now be required to 
+purchase a commercial license if they have over 200 employees or contractors. 
+Anaconda has sent legal notices to institutions demanding compliance and threatening backdated billing. 
+This shift follows changes in Anaconda’s terms of service, 
+updated in March 2024, and may affect your access to Anaconda’s resources. 
+Ensure your organization reviews its usage and licensing agreements to avoid potential legal issues.