Permanently Installing R Packages for HPC Interactive Jobs

A step-by-step guide to persistent package installations in RStudio on HPC

Key Highlights

Set Up a Persistent Library: Create a user library in your home or shared directory and configure R to use it.
Environment and Configuration: Leverage files such as .Renviron or .Rprofile to ensure your settings persist across sessions.
Operational Workflow: Use module management and proper node usage patterns to install and maintain packages effectively.

Understanding the HPC Environment

On a High-Performance Computing (HPC) system, interactive sessions such as those provided by RStudio are often temporary in nature. When you start an interactive job, files and settings that reside in a temporary environment may be deleted upon session termination. This is particularly relevant when installing R packages, as the default library paths may point to directories that are cleared once a job ends. It is therefore essential to adopt a strategy that installs packages in a location that persists between sessions.

The Role of Persistent Directories

HPC systems typically grant users access to a persistent file system, such as directories within your home directory or a shared location. By installing R packages in these persistent directories, you ensure that they remain available for all future interactive sessions. User-specific libraries can be defined by setting up environment variables or modifying configuration files so that R always looks for packages in these directories.

Setting Up a Custom R Library

The first step towards permanently installing R packages is to create a custom library directory in a location that is not affected by the temporary nature of job sessions. The following detailed steps will help you configure your environment to point to this persistent directory:

Step 1: Create a Custom Library Directory

Begin by creating a directory within your home folder or a shared persistent storage area where you have write permissions. This directory will serve as a repository for all your installed R packages. For example, you can run the following command in your HPC login node or within an interactive job:

# Create a directory for R packages
mkdir -p ~/R/library

This command creates the directory if it does not already exist.

Step 2: Configure R to Use the Custom Library

To ensure that R recognizes and uses this custom library, you need to configure the R environment to include your newly created directory. This can be accomplished in several ways:

Using .Renviron

One recommended approach is to set the R_LIBS or R_LIBS_USER environment variable in your .Renviron file, which is automatically sourced when R starts. You can add the following line to your ~/.Renviron file:

# Append the custom library path to R_LIBS
echo 'R_LIBS=~/R/library' >> ~/.Renviron

This ensures that the custom library will always be included in R’s search path.

Using .Rprofile

Alternatively, you can set the library path dynamically during the R startup process by editing your ~/.Rprofile file. Include the following command in your .Rprofile:

# Append the custom directory to the library paths during R startup
.libPaths(c("~/R/library", .libPaths()))

This approach ensures that every time R starts, it will prioritize your persistent library.

Step 3: Setting Environment Variables Directly in R

In some situations, you may want to specify the library location directly within an interactive session. In your R console (or within RStudio), execute the following command:

# Set R_LIBS_USER to point to your custom library directory for the current session
Sys.setenv(R_LIBS_USER = "~/R/library")

This method is useful if you do not have the option to modify startup configuration files, though it must be run in each session unless automated.

Installing and Verifying R Packages

Once your environment is configured to use the custom persistent library, you can install R packages as normal. Here are detailed instructions for carrying out the installation:

Step 4: Load Appropriate Software Modules

On HPC systems, modules are often used to manage software environments. Prior to installation, ensure that the R module (or other required modules) is loaded, particularly when using RStudio. Execute:

# Load the R module - may vary by system
module load R

This step ensures that the right version of R and its dependencies are active.

Step 5: Install Your R Packages

With your persistent library set up and environment modules loaded, start an R session in RStudio and install the required packages. For example, to install the "ggplot2" package, type:

# Install ggplot2 into the persistent library
install.packages("ggplot2")

The installation command will download the package and its dependencies, placing them in the designated persistent directory.

Step 6: Verifying the Installed Packages

After installation, it is essential to verify that the package is correctly installed and available in your persistent library. You can do this by loading the library in an R session:

# Load the package to verify installation
library(ggplot2)

If the package loads without error, it confirms that your installation was successful and that R is using the appropriate library path.

Persisting the Configuration for Future Jobs

One of the most crucial steps is to persist your configuration across interactive sessions. By modifying the .Renviron or .Rprofile files, R will always load with your custom persistent library. This means that once you install a package, you do not need to reinstall it in subsequent interactive jobs.

Workflow Table: Persistent Package Installation Steps

Step	Description	Command or File
1	Create a persistent directory for R packages.	`mkdir -p ~/R/library`
2	Configure R to use the persistent library via .Renviron.	`echo 'R_LIBS=~/R/library' >> ~/.Renviron`
3	Alternatively, add library paths in .Rprofile.	`.libPaths(c("~/R/library", .libPaths()))`
4	Load the appropriate R module in HPC.	`module load R`
5	Install packages in an R session.	`install.packages("packageName")`
6	Verify package availability by loading the library.	`library(packageName)`

Additional Considerations and Best Practices

While the steps above should help you install and persist R packages across HPC interactive jobs, there are a few more aspects to consider to ensure a smooth experience:

Using Conda Environments

Some R packages have complex dependencies that might be easier to manage within a Conda environment. By creating an isolated Conda environment that includes R and your desired packages, you can simplify dependency management. However, the overall strategy remains similar: point the R package installation to a persistent directory. For example, create and activate a Conda environment, then install R and configure the library paths within that environment.

Managing Node Usage and System Quotas

HPC systems often have strict policies regarding resource usage, especially on login nodes versus compute nodes. Always use login nodes to perform administrative tasks like package installations, and be mindful of home directory quotas. Installing numerous packages may require careful management of disk space and quotas. If necessary, consider using shared storage areas designated for user libraries.

Version Management and Compatibility

Different projects or sessions might require different versions of R or its packages. To manage compatibility issues:

Maintain separate libraries for different R versions.
Document which packages and versions are used for specific projects.
Regularly update your configuration files to reflect changes in library paths or available package updates.

Automation and Scripting

For users who frequently set up interactive sessions, automating environment setup using scripts can be highly beneficial. You might consider writing startup scripts that load necessary modules, set environment variables, and configure paths so that every interactive session is automatically ready for use with your persistent library.

Troubleshooting and Support

Even with a well-planned setup, you might occasionally encounter issues. Some common troubleshooting steps include:

Verifying Environment Variables

Check that variables like R_LIBS or R_LIBS_USER are correctly set by inspecting your R session’s environment:

# Check current library paths
.libPaths()

This command will display all locations R is searching for packages.

Reviewing Configuration Files

Ensure that any modifications to .Renviron or .Rprofile are syntactically correct. Incorrect formatting can prevent R from correctly reading these files, leading to the default installation locations being used.

Consulting HPC Documentation and Support Teams

If issues persist, it is advisable to consult the documentation provided by your HPC center or contact their support team. Many HPC centers have specific guidelines, FAQs, or best practices for installing software and managing persistent data. These resources can provide insights tailored to your system's configuration.