{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"Provides CUDA kernels for equivariant neural network operations, loaded as a shared library when imported to enable GPU-accelerated equivariant primitives.","skillfed_tags":["cuda-kernels","equivariant-networks","gpu-acceleration"],"use_cases":["Accelerating equivariant neural networks on NVIDIA GPUs for tasks like 3D object recognition or molecular property prediction.","Building deep learning models that leverage rotational, translational, or permutation symmetries without manually implementing CUDA kernels.","Integrating GPU-optimized equivariant operations into larger PyTorch or TensorFlow workflows for scientific computing.","Developing graph neural networks or point cloud models that require equivariant convolutions or message passing.","Prototyping symmetry-aware AI models for physics simulations or chemistry applications on GPU hardware."],"what_it_does":"cuequivariance-ops-cu13 is a Python package that wraps CUDA kernels for equivariant neural network operations. When imported, it loads a precompiled shared library containing GPU kernels optimized for equivariant primitives\u2014mathematical operations that respect symmetries in data. The package itself contains no Python bindings; it acts as a bridge to the underlying CUDA implementation, designed to accelerate equivariant deep learning workloads on NVIDIA GPUs.\n\nThe package is part of the broader cuEquivariance ecosystem and is intended for developers building neural networks that exploit equivariance properties. It requires Python 3.10 or later and depends on nvidia-cublas for linear algebra operations, along with tqdm, nvidia-ml-py, and platformdirs for utility functions. Installation is platform-specific, with separate wheels for aarch64 and x86_64 architectures. Users should consult the cuEquivariance documentation for guidance on how to use the kernels within their applications.","worth_installing":"Yes, if you are building equivariant neural networks on NVIDIA GPUs and need GPU acceleration. The package is actively maintained, has no known vulnerabilities, and is part of an established NVIDIA ecosystem. However, accept the proprietary license terms carefully: the SDK is restricted to NVIDIA GPU systems, prohibits reverse engineering, and requires your application to have material functionality beyond the SDK itself. Not suitable if you need open-source licensing or non-NVIDIA GPU support."},"id":"cuequivariance-ops-cu13","links":{"html":"https://skillfed.io/packages/cuequivariance-ops-cu13","md":"https://skillfed.io/packages/cuequivariance-ops-cu13.md","pypi":"https://pypi.org/project/cuequivariance-ops-cu13/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-07","license_spdx":null,"license_treatment":"unclear","name":"cuequivariance-ops-cu13","python_support":"supports_current","summary":"cuequivariance-ops - GPU Accelerated Extensions for Equivariant Primitives"},"popularity":{"monthly_downloads":91302,"position":13526,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.11.1"}