mlverse/torch
R Interface to Torch
What it solves
It provides a native R interface to the PyTorch library, allowing R users to build and train machine learning models without needing a separate Python installation.
How it works
The package enables the creation of torch tensors from R objects and provides access to PyTorch's core functionality, including an autograd system for calculating derivatives automatically.
Who it’s for
Data scientists and researchers using the R programming language who want to implement deep learning and machine learning workflows.
Highlights
- Native R implementation of PyTorch functionality
- Support for tensor operations and conversion between R arrays and torch tensors
- Built-in autograd feature for automatic differentiation
- CUDA support for GPU acceleration
Related
- Project
pytorch/pytorchPyTorch is an open‑source Python library that offers GPU‑accelerated tensor operations and a dynamic, tape‑based autograd system for building and training deep neural networks. It integrates tightly with Python, supports extensions in C/C++ and other languages, and provides fast, low‑overhead execution on CPUs, NVIDIA GPUs, AMD ROCm, and Intel GPUs.
- Project
LaurentMazare/tch-rsRust bindings for the PyTorch C++ API (libtorch), enabling the use of PyTorch's tensor operations and deep learning tools in Rust.
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meta-pytorch/torchcodecA PyTorch-native library for decoding and encoding videos, audio, and images directly into tensors for ML training and inference.
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facebookresearch/pytorch3dA PyTorch-based library for 3D Computer Vision research providing differentiable rendering and efficient tools for manipulating 3D meshes and point clouds.
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Ascend/pytorchTorchNPU is a PyTorch adapter plugin that enables developers to run deep learning models on Huawei Ascend AI processors by mapping PyTorch operations to Ascend hardware.