torch Ecosystem Updates

We’ve just published a new round of CRAN releases across the torch ecosystem. Here’s a tour of what’s new in each package.

torch v0.17.0#

The most exciting experimental new feature is support for the cudatoolkit packages. With this, you no longer need a global CUDA toolkit installation in order to use torch on the GPU.

You can now do:

install.packages(
  "cuda12.8", 
  repos = c("https://mlverse.r-universe.dev", "https://cloud.r-project.org")
)
install.packages("torch")

The {cuda12.8} package bundles all the CUDA runtime libraries and torch can find it and use it by default. See more details in the installation docs.

We also highlight the update to LibTorch v2.8.0 led by Troy Hernandez (#1419).

Additionally, this release includes many small bug fixes and small additions to the API. See the full release notes in the changelog.

torchvision v0.9.0#

torchvision provides datasets, model architectures, and image transformations for computer vision. This is a big release with new models, datasets, and many improvements — largely driven by community contributors.

New models:#

model_maskrcnn_resnet50_fpn() and model_maskrcnn_resnet50_fpn_v2() for instance segmentation.
model_convnext_*_detection() for object detection (tiny/small/base).
model_convnext_*_fcn() and model_convnext_*_upernet() for semantic segmentation (tiny/small/base).

New datasets and features:#

vggface2_dataset() for loading the VGGFace2 dataset.
New coco_segmentation_dataset(), split from coco_detection_dataset(), reducing memory usage by ~50%.
Collection dataset catalog with search_collection(), get_collection_catalog(), and list_collection_datasets() for discovering and exploring datasets.
New visualization utilities draw_segmentation_masks() and vision_make_grid().

See the full release notes in the changelog.

A huge thank you to the community contributors who made this release possible: @cregouby, @ANAMASGARD, @Chandraveersingh1717, @DerrickUnleashed, and @srishtiii28.

Other releases#

Most of the other packages don’t have significant changes, and the releases add minimal improvements to docs, CI infrastructure and CRAN related updates.

luz v0.5.2 — Higher-level API for torch with a Keras-like interface for training neural networks.
hfhub v0.1.2 — Download and cache files from Hugging Face Hub repositories, making it easy to use pretrained models and datasets from R.
tok v0.2.2 — Fast tokenizers for R, powered by the Hugging Face Tokenizers library written in Rust. Supports BPE, WordPiece, and other tokenization algorithms.
torchdatasets v0.3.2 — Extra ready-to-use datasets for torch, complementing the built-in datasets in torchvision.
safetensors v0.2.1 — Read and write the Safetensors file format, a safe and fast format for storing and loading tensors.
tfevents v0.0.5 — Write event files compatible with TensorBoard from R for experiment tracking and visualization.
wav v0.2.0 — Read and write WAV files in R.