ggmlR

Description

Provides 'R' bindings to the 'GGML' tensor library for machine learning, designed primarily for 'Vulkan' GPU acceleration with full CPU fallback. Requires 'Vulkan' 1.2+ with legacy pipeline barriers (avoids 'Synchronization2' due to 'RADV' performance issues); supports 'Push Descriptors' ('VK_KHR_push_descriptor') to eliminate descriptor pool overhead when available. 'Vulkan' support is auto-detected at build time on Linux (when 'libvulkan-dev' and 'glslc' are installed) and on Windows (when 'Vulkan' 'SDK' is installed and 'VULKAN_SDK' environment variable is set); all operations fall back to CPU transparently when no GPU is available. Supports tensors up to 5D natively (GGML_MAX_DIMS=5). Implements tensor operations, neural network layers, 'quantization', and a 'Keras'-like sequential model API for building and training networks. Includes 'AdamW' (Adam with Weight decay) and 'SGD' (Stochastic Gradient Descent) optimizers with 'MSE' (Mean Squared Error) and cross-entropy losses. Also provides a dynamic 'autograd' engine ('PyTorch'-style) with data-parallel training via 'dp_train()', broadcast arithmetic, 'f16' (half-precision) support on 'Vulkan' GPU, and a multi-head attention layer for building Transformer architectures. Supports 'ONNX' model import via built-in zero-dependency 'protobuf' parser: load 'pretrained' 'ONNX' models from 'PyTorch', 'TensorFlow', or other frameworks and run inference on 'Vulkan' GPU or CPU. Covers 50+ 'ONNX' ops including convolutions, attention primitives, normalization, quantized ops, shape operations, 'ScatterElements' (with 'Vulkan' 'atomicAdd' for GNN scatter-add), and fused custom ops (RelPosBias2D for 'BoTNet') — sufficient to run real-world models such as 'RoBERTa', 'BERT', 'GPT-NeoX', 'SqueezeNet', 'Inception v3', 'BAT-ResNeXt', 'BoTNet', and 'MNIST' out of the box. Reads 'GGUF' files natively: load 'pretrained' weights from any 'gguf'-compatible source ('llama.cpp', 'Hugging Face') with automatic weight conversion and metadata access. Uses a dedicated weight buffer architecture for zero-overhead repeated inference — weights are loaded to GPU once and never re-transferred. Serves as backend for 'LLM' (Large Language Model) inference via 'llamaR' and Stable Diffusion image generation via 'sd2R'. See <https://github.com/ggml-org/ggml> for more information about the underlying library.

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installed size is 16.2Mb
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GNU make is a SystemRequirements.

installed size is 18.1Mb
  sub-directories of 1Mb or more:
    help   1.2Mb
    lib    7.7Mb
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GNU make is a SystemRequirements.

installed size is  5.9Mb
  sub-directories of 1Mb or more:
    help   1.2Mb
    lib    1.4Mb
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installed size is 16.0Mb
  sub-directories of 1Mb or more:
    help   1.1Mb
    lib    6.9Mb
    libs   6.8Mb

installed size is 18.5Mb
  sub-directories of 1Mb or more:
    help   2.1Mb
    lib    7.6Mb
    libs   7.6Mb

installed size is  5.7Mb
  sub-directories of 1Mb or more:
    help   1.1Mb
    lib    1.4Mb
    libs   2.1Mb

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