Using HPC applications learning path

If you want to run HPC applications, you can consider following the following training sessions.

graph TD Best_practices_for_scientific_computing[Best practices for scientific computing] --> Version_control_with_git[Version control with Git] Best_practices_for_scientific_computing[Best practices for scientific computing] --> Linux_intro[Linux introduction] Linux_intro --> HPC_intro[HPC introduction] HPC_intro --> Workflows_for_HPC[Workflows for HPC] HPC_intro --> Containers_on_HPC[Containers on HPC] HPC_intro --> MLOps_on_HPC[MLOps on HPC] click Best_practices_for_scientific_computing "https://gjbex.github.io/Best-practices-for-scientific-computing/" "Best practices for scientific computing" click Version_control_with_git "https://gjbex.github.io/Version-control-with-git" "Version control with Git" click Linux_intro "https://gjbex.github.io/Training-sessions/linux_intro" "Linux introduction" click HPC_intro "https://gjbex.github.io/Training-sessions/hpc_intro" "HPC introduction" click Workflows_for_HPC "https://gjbex.github.io/Workflows-for-HPC/" "Workflows for HPC" click Containers_on_HPC "https://gjbex.github.io/Containers-for-HPC/" "Containers on HPC" click MLOps_on_HPC "https://gjbex.github.io/MLOps-on-HPC/" "MLOps on HPC"

If you are new to running HPC applications in the context of scientific computing, you may want to start with "Best practices for scientific computing".

The next step is to familiarize yourself with the basics of working on the Linux command line and the HPC infrastructure.

Version control is an essential part of reproducible scientific research. For more information on this topic, see "Version control with git".

Depending on the HPC application you want to run, you may want to learn about "Workflows for HPC" to automate multi-step analyses, "Containers on HPC" to manage software environments, or "MLOps on HPC" when your work involves machine learning experiments.