Quick Setup • devil

Quick Start

This guide provides streamlined installation instructions for the devil package. Choose the installation method that best suits your computational needs.

Standard Installation

For most users, the standard CPU-based installation is recommended:

# Install from GitHub
devtools::install_github("caravagnalab/devil")

If you don’t have devtools installed:

install.packages("devtools")
devtools::install_github("caravagnalab/devil")

GPU-Accelerated Installation

For users with NVIDIA GPUs who want to leverage GPU acceleration for faster computations.

Prerequisites

Before installing the GPU-accelerated version, ensure you have:

CUDA Toolkit (version 12.0 or higher) - Download here
cuTENSOR Library - Download here
Environment Variables properly configured:
- CUDA_HOME: Path to your CUDA installation directory
- CUTENSOR_HOME: Path to your cuTENSOR installation directory

Verifying Your Environment

Check if your environment variables are set correctly:

# Check CUDA_HOME
Sys.getenv("CUDA_HOME")

# Check CUTENSOR_HOME
Sys.getenv("CUTENSOR_HOME")

Both commands should return valid directory paths. If they return empty strings, you’ll need to set these variables before proceeding.

Installation Command

Once prerequisites are met:

devtools::install_github("caravagnalab/devil", configure.args = "--with-cuda")

The package will automatically detect your CUDA installation and compile with GPU support.

Troubleshooting

If installation fails:

Verify CUDA toolkit installation: nvcc --version in your terminal
Confirm environment variables are set system-wide, not just in your R session
Ensure your GPU drivers are up to date
Check that your CUDA version is compatible with your GPU architecture

For additional help, please open an issue on the GitHub repository.

Next Steps

After successful installation, load the package:

library(devil)

Continue to the main vignettes to learn how to use devil for your analysis.