Introduction: This workshop will discuss how to use Jupyter Notebooks and Labs on ARCC HPC clusters and introduce a series of best practices.
Course Goals:
Demonstrate the Jupyter service across a variety of available languages and kernels
Demonstrate how to convert an existing Conda environment into a kernel that can be used within a Jupyter session
Notes:
The workshop modules work best in a sequential manner as a story introducing concepts and providing examples, but sections can be used separately to focus on a particular concept.
You will need to modify usernames, project names, and folder locations, to apply to yourself.
Where are R Packages Installed on the Cluster? Understand where R installs packages and where libraries are located, as well as inspecting general R system configuration.
R Conda Environments and Installed Packages: Understand R environments build with Conda.
R Packages and System Modules: Installing some R packages requires understanding what libraries are available on the System.
Creating a Shared Library of R Packages: Demonstrate how to use an R library to create a shared set of R packages.
Using R and RStudio within OnDemand: Detail the process of using R and RStudio via the OnDemand service.
Using an R Conda Environment with RStudio: Detail how to use an R Conda Environment within RStudio.
Create an R Kernel for a Jupyter Notebook: Detail how to update an R Conda environment so it can be used as a kernel within ARCC’s Jupyter service.
Parallel R: Introduction: Introduction some high-level aspects of using R in parallel relating to the cluster.
Using R/RStudio on the Cluster: Summary: Summarize the concepts covered across the workshop.