RStudio and GPU Resources

Overview

Questions

• How do you launch RStudio from OnDemand?
• What is the RStudio interface layout?
• How do you request GPU resources for interactive apps?
• How do you verify GPU access in Jupyter or R?

Objectives

• Launch an RStudio session and navigate the interface
• Execute R code and install R packages
• Request GPU resources when launching interactive apps
• Verify GPU availability and use GPU-accelerated libraries

Launching RStudio

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1. Click Interactive Apps in the left sidebar
2. Select RStudio Server
3. Configure resources (similar to Jupyter – cores, memory, time)
4. Click Launch and wait for allocation
5. Click Connect to RStudio when the session is ready

RStudio Interface Layout

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Once launched, RStudio provides four panels:

• Script editor (top-left): Write and save R scripts
• Console (bottom-left): Execute R commands interactively
• Environment/History (top-right): View loaded variables and command history
• Files/Plots/Packages (bottom-right): Browse files, view plots, manage packages

Running R Code

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Write code in the script editor or directly in the console:

First create a small sample file to work with, then read it back, summarize, and plot (in real work you would read your own data from /bigdata/lab/<labname>/ instead):

R

library(tidyverse)

# Create a small sample dataset in your home directory
dir.create("~/rstudio-demo", showWarnings = FALSE)
write.csv(data.frame(variable1 = rnorm(8), variable2 = rnorm(8)),
          "~/rstudio-demo/data.csv", row.names = FALSE)

df <- read.csv("~/rstudio-demo/data.csv")
summary(df)

ggplot(df, aes(x = variable1, y = variable2)) +
  geom_point() +
  theme_minimal()

A working version of the example: write a small sample CSV first, then read, summarize, and plot it — all four RStudio panes in action.

Installing R Packages

In the console:

R

install.packages("ggplot2")
install.packages("dplyr")
library(ggplot2)

Or use the Packages tab in the bottom-right panel to install graphically.

Callout

Save Your Work

Interactive sessions have time limits. Save scripts and results frequently with Ctrl+S. Download important files or save to /bigdata for durability. Do not wait until your session expires – save early and often.

Requesting GPU Resources

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When launching Jupyter or RStudio, select GPU options in the resource form:

• GPU type: A100 (80 GB) or L40S (48 GB)
• GPU count: Usually 1 for interactive work
• Partition: Select gpu to access GPU nodes

Verifying GPU in Jupyter (Python)

PYTHON

import torch
print(torch.cuda.is_available())       # Should print True
print(torch.cuda.get_device_name(0))   # Shows GPU model

import tensorflow as tf
print(tf.config.list_physical_devices('GPU'))

Verifying GPU in R

R

library(tensorflow)
tf$config$list_physical_devices('GPU')

GPU-Accelerated Libraries

Once GPU is verified, use it for computation:

PYTHON

import torch
x = torch.randn(1000, 1000).cuda()
y = torch.mm(x, x)

Monitor GPU usage from a terminal with nvidia-smi.

Resource Guidelines

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• Small datasets (< 100 MB): 1–2 cores, 4 GB memory, no GPU
• Medium datasets (100 MB – 10 GB): 2–4 cores, 8–16 GB memory
• GPU work: Request only when needed – GPUs are a shared resource
• Close sessions when finished to free resources for others

Challenge

Challenge: Launch RStudio and Explore

1. Launch RStudio from OnDemand with 1 core, 4 GB memory, and 1 hour

2. In the console, load the tidyverse package: library(tidyverse)

3. Create a sample data frame:

   R

   df <- data.frame(
     x = rnorm(100),
     y = rnorm(100),
     group = sample(c("A", "B"), 100, replace = TRUE)
   )

4. Create a scatter plot:

   R

   ggplot(df, aes(x = x, y = y, color = group)) +
     geom_point() +
     theme_minimal() +
     labs(title = "Sample Scatter Plot")

5. Save the plot with ggsave("scatter.png")

Show me the solution

The expected challenge result: 100 random points colored by group, with the data frame in the Environment pane.

1. RStudio launches after resource allocation (1–5 minutes)
2. tidyverse loads with some startup messages (this is normal)
3. The data frame appears in the Environment panel showing 100 observations of 3 variables
4. A scatter plot appears in the Plots panel with points colored by group
5. The file “scatter.png” is saved in your working directory and visible in the Files panel

If tidyverse is not installed, run install.packages("tidyverse") first – this may take a few minutes.

Key Points

• Launch RStudio from Interactive Apps with configurable resources
• RStudio provides script editor, console, environment, and files/plots panels
• Install R packages from the console or the Packages tab
• Request GPU resources by selecting the gpu partition and GPU type (A100, L40S, RTX PRO 6000)
• Verify GPU access with torch.cuda.is_available() in Python or tf in R
• Save your work frequently – sessions have time limits