Custom R Environments with Mamba

Creating a Mamba environment for specific R versions

Tempest is now configured to run RStudio-Server on compute nodes.

RStudio-Server will accept any version of R you have installed. RCI recommends using Mamba (aka. Anaconda) environments as they are easily managed and R packages (extensions) are easily installed with all the proper dependencies.

 To install R versions beyond the default 4.2.1, log into Tempest through the Terminal:

ssh <NetID>@tempest-login.msu.montana.edu 

 To create a mamba environment with a new version of R:

module load Mamba
mamba init # ONLY run this if you haven't initialized the shell
mamba create -n <name> r-base='VERSION' #<name> is the name of the environment

To get a list of available R versions (only a subset of output shown below):

mamba search r-base  # r-base is the base R package

*Note: RCI recommends you use the Anaconda channel “conda-forge”, it is the most widely supported. This will be the default in the below commands.

Create a new mamba environment for your specific R version:

mamba create -n <name_of_environment> r-base='<name_of_R_version> 

# For example: 
mamba create -n r3.5.1 r-base='3.5.1'

Verify successful creation of the Mamba environment: 

mamba env list
# conda environments:
#
r3.5.1                   /home/<your_net_id>/.conda/envs/r3.5.1

The first column shows you the names of your mamba environment. The second shows you the location of your mamba environment

Running RStudio-Server with specific R version

• To access a web-based RStudio environment running on Tempest, follow the instructions below:

  • Navigate to the Tempest web access login page

  • Enter your NetID and Password to sign in to Tempest

• Open the RStudio initialization page
  
  1. Select the "Interactive Apps" Dropdown menu from the the top bar
  2. Select from the dropdown "RStudio on Tempest"

• Select the resources and account to run your RStudio session
  1. Select RStudio-Mamba on Tempest from the left sidebar
  2. Select the account and partition
     
     • Note: Your partition must match your account.
       • E.g. if using priority-<account_name> account, you must specificy the "priority" partition.
       • If using group-<account_name> account, you must specify the "unsafe" partition
  3. Select the compute resources you will need for your session
     • Note: Fewer resources will fit into the queue faster and your session will be more likely to start immediately. Use the bare minimum resources necessary.
       • E.g. For a simple rscript to process a dataframe < 1 GB in size, 8 CPUs and 2 GB of memory is plenty
  4. Select the number of hours you will need for your session
     • Note: you can save scripts and re-open them in different sessions, but session data will not be saved. Be sure to save all final results as output files.
  5. Launch your RStudio session

• Connect to your RStudio Server session
  • Once resources are available on the cluster for your session, it will automatically start. To enter the session click "Connect to Rstudio Server"

• Open a new Rscript and begin using RStudio
  • Note: Any packages installed will be specific for this R version and will be available in future sessions

# To install a package: 
install.packages("<package-name>")

# In a future session, you can use: 
library(<package-name>)