Package {gpuinfo}

Title: Lightweight Hardware and GPU Compute Detection
Version: 0.1.0
Description: Detects central processing unit and graphics processing unit hardware and reports the apparent availability of 'CUDA', 'Metal', 'ROCm', and 'OpenCL' compute backends. Detection uses operating-system information, documented platform interfaces, and optional command-line utilities, without requiring a GPU framework, 'Python', or a vendor software development kit. Backend interpretation follows the official 'CUDA' https://docs.nvidia.com/cuda/cuda-driver-api/, 'Metal' https://developer.apple.com/documentation/metal, 'ROCm' https://rocm.docs.amd.com/, and 'OpenCL' https://registry.khronos.org/OpenCL/ documentation. Missing hardware, drivers, libraries, and utilities are handled safely.
License: MIT + file LICENSE
URL: https://github.com/tkcaccia/gpuinfo
BugReports: https://github.com/tkcaccia/gpuinfo/issues
Encoding: UTF-8
NeedsCompilation: yes
Depends: R (≥ 3.6.0)
Suggests: jsonlite, knitr, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder: knitr
Config/testthat/edition: 3
Config/roxygen2/version: 8.1.0
Packaged: 2026-09-15 14:45:12 UTC; stefano
Author: Stefano Cacciatore ORCID iD [aut, cre]
Maintainer: Stefano Cacciatore <tkcaccia@gmail.com>
Repository: CRAN
Date/Publication: 2026-09-26 16:40:08 UTC

gpuinfo: Lightweight hardware and GPU-backend detection

Description

gpuinfo provides framework-independent, read-only detection of CPU and GPU hardware and the apparent availability of CUDA, Metal, ROCm, and OpenCL. It does not require Python, a machine-learning framework, or a vendor SDK.

Public API

The package intentionally exposes only seven functions. Use hardware_info() for the complete structured report, gpu_sitrep() for a human-readable diagnostic report, and has_gpu(), has_cuda(), has_metal(), has_rocm(), or has_opencl() for strict scalar checks.

Interpretation

A positive backend predicate means that gpuinfo found the relevant visible hardware and enough driver/runtime support to regard that backend as usable. It does not prove that another R package was compiled with that backend. For example, has_cuda() can be TRUE while an installed deep-learning package is CPU-only.

Detection is limited to devices visible to the current process. Containers, schedulers, remote sessions, and variables such as CUDA_VISIBLE_DEVICES can intentionally hide physical devices. Missing tools, libraries, drivers, or permissions produce FALSE, empty tables, or NA fields rather than an error.

Detection behavior

Probes may load vendor libraries dynamically and may run read-only system utilities such as nvidia-smi, system_profiler, lspci, rocminfo, or clinfo when available. The package does not modify drivers, environment variables, device settings, or files, and it does not make network requests.

Author(s)

Maintainer: Stefano Cacciatore tkcaccia@gmail.com (ORCID)

Authors:

See Also

hardware_info(), gpu_sitrep()

Create a GPU diagnostic report

Description

Creates a structured diagnostic report suitable for an issue, bug report, or hardware-validation record. When printed, the report includes the R and operating-system versions, CPU, visible GPUs, CUDA components, other backend results, and the selected fallback backend.

Usage

gpu_sitrep(format = c("text", "json"))

Arguments

format

Format used by the object's print() method. "text" displays a human-readable report. "json" displays the complete JSON representation and requires the suggested jsonlite package.

Details

The function performs the same read-only detection as hardware_info(). It does not install software, initialize a framework, change device visibility, or transmit the report. Review the output before sharing it because system and environment details may identify the computer or execution environment.

Value

A visible gpuinfo_sitrep object containing the structured output of hardware_info(). The object can be inspected with normal list operations; assigning it to a variable produces no console output.

See Also

hardware_info()

Examples

report <- gpu_sitrep()
report$cpu$vendor
print(report)

if (requireNamespace("jsonlite", quietly = TRUE)) {
    print(gpu_sitrep("json"))
}

Collect hardware information

Description

Creates a single structured snapshot of the CPU, visible GPUs, compute backends, execution environment, and validation evidence. This is the main entry point when more detail than the ⁠has_*()⁠ predicates is required.

Usage

hardware_info(as = c("list", "json"))

Arguments

as

Output representation. "list" returns native R objects. "json" returns a pretty-printed JSON character scalar and requires the suggested jsonlite package. Missing values become JSON null values.

Details

Detection is read-only and framework-independent. It describes the hardware and system software visible to the current R process; it does not determine whether torch, TensorFlow, or another package was compiled with GPU support. Containers, schedulers, permissions, and device-visibility environment variables can affect the result.

Value

A nested list containing R, system, CPU, GPU, backend, environment, and package-validation details when as = "list"; otherwise a JSON character scalar containing the same information.

Returned components

The report contains the following named components:

r

R version and platform.

system

Operating-system name, release, version, and architecture.

cpu

CPU model, vendor, architecture, logical-core count, and OS.

gpu

One row per detected visible GPU, including device id, vendor, model, reported memory, backend, and NVIDIA compute capability when known. An empty data frame means that no visible GPU was detected.

accelerators

A vendor-neutral accelerator table with memory and backend-status information.

capabilities

Per-device precision, memory, and backend capability information. Unknown capabilities are represented by NA.

cuda

NVIDIA GPU, driver, runtime, Toolkit, nvcc, versions, and apparent CUDA usability. The CUDA version advertised by nvidia-smi is a driver-supported maximum, not necessarily an installed Toolkit.

metal

macOS platform, Apple silicon, device, framework, support, and apparent Metal usability.

rocm

AMD GPU, kernel driver, runtime, utilities, version, and apparent ROCm usability.

opencl

Loader, platforms, devices, vendors, memory, FP64 support, and apparent OpenCL availability.

available_backends

Detected usable backends, always including the CPU fallback.

best_backend

The first usable backend in the package priority order CUDA, Metal, ROCm, OpenCL, then CPU.

environment

Container, CI, WSL, scheduler, and device-visibility information. Values are reported without changing the environment.

validation

Real-hardware validation evidence shipped with this package version. This describes maintainer testing, not the current host.

See Also

gpu_sitrep(), has_gpu(), has_cuda(), has_metal(), has_rocm(), has_opencl()

Examples

info <- hardware_info()
names(info)
info$cpu
info$gpu
info$cuda$usable

if (requireNamespace("jsonlite", quietly = TRUE)) {
    json <- hardware_info("json")
}

Test whether CUDA appears usable

Description

Returns TRUE when an NVIDIA GPU is visible through an apparently working NVIDIA driver. Detection uses a dynamically loaded CUDA driver library and, when available, read-only nvidia-smi queries.

Usage

has_cuda()

Details

The CUDA Toolkit and nvcc compiler are not required: applications can use a GPU through a driver and a bundled runtime without a local Toolkit. A positive result does not imply that torch, TensorFlow, or another R package was built with CUDA support. Inspect hardware_info()$cuda to distinguish GPU, driver, runtime, Toolkit, compiler, and version information.

Value

One non-missing logical value: TRUE when CUDA appears usable, otherwise FALSE.

See Also

hardware_info(), has_gpu()

Examples

has_cuda()
hardware_info()$cuda

Test whether a GPU is detected

Description

Returns TRUE when at least one graphics processor is visible to the current R process. Detection combines native vendor-library queries with portable operating-system and command-line fallbacks when available.

Usage

has_gpu()

Details

This tests hardware visibility, not whether any particular compute backend or R framework can use the device. A GPU with a missing driver can therefore be detected while all backend predicates remain FALSE. Conversely, a container or scheduler can hide a physical GPU from the process.

Value

One non-missing logical value: TRUE when a visible GPU is detected, otherwise FALSE.

See Also

hardware_info(), has_cuda(), has_metal(), has_rocm(), has_opencl()

Examples

has_gpu()

Test whether Metal appears usable

Description

Returns TRUE on macOS when a Metal-capable graphics device is visible. Detection uses the Metal framework when it can be loaded and supplements it with system_profiler and Apple-silicon platform information.

Usage

has_metal()

Details

This reports operating-system and hardware capability, not framework-specific support such as torch MPS. A positive result therefore does not guarantee that another R package can execute work through Metal. Detailed fields are available in hardware_info()$metal.

Value

One non-missing logical value: TRUE when Metal appears usable, otherwise FALSE. It is FALSE on non-macOS platforms.

See Also

hardware_info(), has_gpu()

Examples

has_metal()
hardware_info()$metal

Test whether OpenCL is available

Description

Returns TRUE when an OpenCL loader is present, at least one platform can be enumerated, and at least one compute device is visible. Detection uses the OpenCL library directly and can supplement it with the read-only clinfo utility.

Usage

has_opencl()

Details

OpenCL devices can be GPUs, CPUs, or other accelerators, so this predicate is not synonymous with has_gpu(). It also does not guarantee that a particular R package has OpenCL support. Inspect hardware_info()$opencl for platform, device, vendor, memory, and FP64 details.

Value

One non-missing logical value: TRUE when OpenCL appears available, otherwise FALSE.

See Also

hardware_info(), has_gpu()

Examples

has_opencl()
hardware_info()$opencl

Test whether ROCm appears usable

Description

Returns TRUE when an AMD GPU is visible and a usable ROCm/HIP runtime path is apparent. Detection can use the HIP runtime library, ⁠/dev/kfd⁠, rocminfo, rocm-smi, hipcc, and common ROCm installation locations.

Usage

has_rocm()

Details

Merely finding ROCm files is insufficient: the predicate requires compatible visible hardware and runtime evidence. A positive result does not guarantee that another R package was compiled with ROCm support. Inspect hardware_info()$rocm for the individual detection fields and version.

Value

One non-missing logical value: TRUE when ROCm appears usable, otherwise FALSE.

See Also

hardware_info(), has_gpu()

Examples

has_rocm()
hardware_info()$rocm