Introduction: This workshop will discuss how to use Jupyter Notebooks and Jupyter Labs on ARCC HPC clusters and introduce a series of best practices.
Course Goals:
Introduce what Jupyter is and why it’s useful
Identify the difference between Jupyter Lab and Jupyter Notebooks and when one tool is better than the other
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.
Intro to Jupyter
Notebooks vs Labs
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.