.ggmlr_normalize_gpu

Library-size normalisation followed by log1p, matching Seurat's
<code>NormalizeData(method = "LogNormalize")</code>:
<code>log1p(x / colSums(x) * scale_factor)</code>. The per-cell scaling and the
<code>log1p</code> run elementwise on the GPU (broadcast a per-cell factor across
genes); the column sums are a cheap reduction.

internal

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

.ggmlr_normalize_gpu function

<dl><dt>mat</dt>
<dd>Dense numeric matrix, features x cells (raw/counts).</dd>
<dt>scale_factor</dt>
<dd>Library size to scale each cell to (default 1e4).</dd>
<dt>backend</dt>
<dd><code>"vulkan"</code> or <code>"cpu"</code> (dispatch resolves "auto").</dd></dl>

Arguments

GPU-accelerated LogNormalize (op = "normalize") — .ggmlr_normalize_gpu

<dl>

<dt>mat</dt>
<dd>Dense numeric matrix, features x cells (raw/counts).</dd>


<dt>scale_factor</dt>
<dd>Library size to scale each cell to (default 1e4).</dd>


<dt>backend</dt>
<dd><code>"vulkan"</code> or <code>"cpu"</code> (dispatch resolves "auto").</dd>

</dl>

.ggmlr_normalize_gpu: GPU-accelerated LogNormalize (op = "normalize")

Description

Usage

Value

Arguments