onnx_device_info

Returns information about backend placement: which backends are
available, how the scheduler splits the graph, and how many ops
are supported by GPU vs CPU-only.

Provides 'R' bindings to the 'GGML' tensor library for machine
learning, optimized for 'Vulkan' GPU acceleration with a transparent CPU
fallback. The package features a 'Keras'-like sequential API and a
'PyTorch'-style 'autograd' engine for building, training, and deploying
neural networks. Key capabilities include high-performance 5D tensor
operations, 'f16' precision, and efficient quantization. It supports
native 'ONNX' model import (50+ operators) and 'GGUF' weight loading
from the 'llama.cpp' and 'Hugging Face' ecosystems. Designed for
zero-overhead inference via dedicated weight buffering, it integrates
seamlessly as a 'parsnip' engine for 'tidymodels' and provides
first-class learners for the 'mlr3' framework.
See <https://github.com/ggml-org/ggml> for more information about the
underlying library.

Yuri Baramykov

ggmlR

'GGML' Tensor Operations for Machine Learning

onnx_device_info function

<dl><dt>model</dt>
<dd>An <code>onnx_model</code> object from <code>onnx_load()</code>.</dd></dl>

Arguments

ONNX model device/scheduler diagnostics — onnx_device_info

<dl>

<dt>model</dt>
<dd>An <code>onnx_model</code> object from <code>onnx_load()</code>.</dd>

</dl>

onnx_device_info: ONNX model device/scheduler diagnostics

Description

Usage

Value

Arguments