ggml_ops_registry

Returns the registry of operations the single-cell adapter can dispatch. Use
this to check capabilities (and required parameters) before building a
<code>ggml_task</code> — capability is declared, never discovered at runtime.

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

ggml_ops_registry function

<dl><dt>op</dt>
<dd>Optional operation name. If supplied, returns that single entry (or
<code>NULL</code> if unknown); otherwise a named list of all entries.</dd></dl>

Arguments

Supported single-cell operations — ggml_ops_registry

<dl>

<dt>op</dt>
<dd>Optional operation name. If supplied, returns that single entry (or
<code>NULL</code> if unknown); otherwise a named list of all entries.</dd>

</dl>

ggml_ops_registry: Supported single-cell operations

Description

Usage

Value

Arguments

Examples