ggmlR: 'GGML' Tensor Operations for Machine Learning
Abstract
Provides 'R' bindings to the 'GGML' tensor library for machine learning, designed primarily for 'Vulkan' GPU acceleration with full CPU fallback. '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. 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. Serves as backend for 'LLM' (Large Language Model) inference via 'llamaR' and Stable Diffusion image generation via 'sdR'. See for more information about the underlying library.
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