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cuda.ml: R Interface for the RAPIDS cuML Suite of Libraries

R interface for RAPIDS cuML (<https://github.com/NVIDIA/cuml>), a suite of GPU-accelerated machine learning libraries powered by CUDA (<https://en.wikipedia.org/wiki/CUDA>).

Version: 0.4.0
Depends: R (≥ 4.1)
Imports: bundle, digest, ellipsis, filelock, hardhat, jsonlite, Rcpp (≥ 1.0.6), rlang (≥ 0.3.0)
Suggests: callr, glmnet, knitr, MASS, modeldata, palmerpenguins, parsnip, purrr, recipes, reticulate, rmarkdown, testthat (≥ 3.1.7), workflows, xgboost
OS_type: unix
Published: 2026-08-21
DOI: 10.32614/CRAN.package.cuda.ml
Author: Yitao Li ORCID iD [aut, cph], Tomasz Kalinowski [aut, cre, cph], Daniel Falbel [aut, cph], RStudio [cph, fnd]
Maintainer: Tomasz Kalinowski <tomasz at posit.co>
BugReports: https://github.com/mlverse/cuda.ml/issues
License: MIT + file LICENSE
Copyright: file inst/COPYRIGHTS
cuda.ml copyright details
URL: https://mlverse.github.io/cuda.ml/, https://github.com/mlverse/cuda.ml
NeedsCompilation: no
SystemRequirements: Native operations require Linux x86_64 with glibc 2.28 or newer. GPU-backed operations require a supported NVIDIA GPU and driver 580 or newer.
CRAN checks: cuda.ml results

Documentation:

Reference manual: cuda.ml.html , cuda.ml.pdf
Vignettes: Get started with cuda.ml (source, R code)
Install and manage cuda.ml (source, R code)
Save and restore models (source, R code)
nvForest inference and deployment (source, R code)
Use cuda.ml with tidymodels (source, R code)

Downloads:

Package source: cuda.ml_0.4.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): cuda.ml_0.4.0.tgz, r-oldrel (arm64): cuda.ml_0.4.0.tgz, r-release (x86_64): cuda.ml_0.4.0.tgz, r-oldrel (x86_64): cuda.ml_0.4.0.tgz
Old sources: cuda.ml archive

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.