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:

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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.