devel:languages:R:autoCRAN

Large parts of CRAN (cran.r-project.org) mirrored to OBS in a fully automatic way.

This repo contains a large part of CRAN automatically converted to rpm packages. *ALL* packages in the repo are created and kept uptodate(!) in a fully automatic way using the R package CRAN2OBS (gitlab.com/dsteuer/CRAN2OBS). At the moment CRAN2OBS is still subject to many changes, but it already works well enough to bring about 15k packages from CRAN to Suse. If you find packages not working, please contact me. Do not push packages here by hand after manually altering anything in a spec file, please. If you find an important package still missing, send a note, please. May be it is easy to add fitting rules to the scripts. Attention: there are Prefer: lines in the project config. Should be rechecked from time to time. https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/ devel:languages:R:autoCRANsupp

Supplements for the autoCRAN project

autoCRANsupp contains *only* - libraries needed to build a worthy number of R packages that are not in factory/tumbleweed, i.e. udunits2-1 - a link to d:l:R:released/R-base to provide newer versions for older SuSE releases. A lot of packages need the latest R. This project will be as small as possible. In a best case scenario only R-base will remain here to be included for building autoCRAN. https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRANsupp/openSUSE_Tumbleweed/ openSUSE:Factory

The next openSUSE distribution

openSUSE Tumbleweed: The Bleeding Edge, Perfected. Tumbleweed is the ultimate rolling release distribution, providing the latest software as it’s released, built upon a foundation of world-class stability and testing. * Always Current: Get the newest kernel, IDEs, desktops, and applications automatically. * Powerfully Stable: Experience the velocity of a rolling release without sacrificing the reliability you depend on. * Engineered for Professionals: The top choice for Developers, Power Users, and openSUSE Contributors who need the best tools for the job. If you demand the latest stable software, your choice is Tumbleweed. Staging dashboard is located at: https://build.opensuse.org/staging_workflows/openSUSE:Factory List of known devel projects: https://build.opensuse.org/package/view_file/openSUSE:Factory:Staging/dashboard/devel_projects Have a look at http://en.opensuse.org/Portal:Factory for more details. https://download.opensuse.org/tumbleweed/repo/oss/ openSUSE:Tumbleweed

Tumbleweed

Tumbleweed is the openSUSE Rolling Release This OBS Project represents the content of the currently published snapshot. The newer repository for next publish can be found in openSUSE:Factory standard repository. https://download.opensuse.org/repositories/openSUSE:/Tumbleweed/standard/ openSUSE:Tumbleweed

Tumbleweed

The next openSUSE distribution

'GGML' Tensor Operations for Machine Learning

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.