{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"PyTorch bindings for NVIDIA's CUDA-accelerated library for building equivariant geometric neural networks using segmented polynomials and optimized tensor operations.","skillfed_tags":["geometric-deep-learning","cuda-acceleration","equivariance"],"use_cases":["Build rotation- and translation-invariant neural networks for molecular or crystal structure prediction","Accelerate training of geometric deep learning models on GPU with native CUDA kernels","Construct equivariant feature extractors for 3D point cloud or mesh-based computer vision tasks","Encode physical symmetries into neural network layers for physics simulation or inverse problems"],"what_it_does":"cuequivariance-torch is NVIDIA's PyTorch frontend for building geometric neural networks that respect symmetries like rotations and translations. It provides CUDA-accelerated operations on segmented polynomials and tensor products, designed to make equivariant models more data-efficient by encoding physical symmetries directly into the network architecture.\n\nThe package wraps optimized CUDA kernels behind a PyTorch-native API, letting you construct networks that automatically preserve equivariance properties. It depends on the core cuequivariance library and is intended for developers building physics-informed or geometry-aware deep learning models where respecting spatial symmetries improves both sample efficiency and model interpretability.","worth_installing":"Yes, if you are building equivariant geometric neural networks in PyTorch and have access to compatible CUDA hardware. The package is actively maintained, permissively licensed, and backed by NVIDIA. However, it is in Beta state, so evaluate stability for your production timeline. Verify that required CUDA kernel packages are available for your environment before committing."},"id":"cuequivariance-torch","links":{"html":"https://skillfed.io/packages/cuequivariance-torch","md":"https://skillfed.io/packages/cuequivariance-torch.md","pypi":"https://pypi.org/project/cuequivariance-torch/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-07","license_spdx":null,"license_treatment":"permissive","name":"cuequivariance-torch","python_support":"supports_current","summary":"CUDA accelerated equivariant operations"},"popularity":{"monthly_downloads":186266,"position":9991,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.11.1"}