cuequivariance-ops-cu13
cuequivariance-ops - GPU Accelerated Extensions for Equivariant Primitives
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires NVIDIA GPU hardware and CUDA-capable system; Python 3.10 or later; nvidia-cublas and other runtime dependencies must be installed.
- Medium install friction due to platform-specific wheel requirements (aarch64 and x86_64 manylinux builds).
- Active maintenance with a release 7 days ago.
License · maintenance · safety
(unclear) — Licensed under NVIDIA's proprietary SDK agreement with significant restrictions: use limited to systems with NVIDIA GPUs, no reverse engineering or sublicensing, and applications must have material additional functionality beyond the SDK. Distribution requires compliance with specific terms and NVIDIA notification obligations. Liability capped at US$10.00.
last release 2026-08-07 (7 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 91,302 downloads/mo, #13,526 on PyPI
Alternatives
Verify before relying
pip install cuequivariance-ops-cu13
import cuequivariance_ops_cu13
# Loads CUDA kernels; refer to cuEquivariance documentation for kernel usage- Specific CUDA compute capability requirements and supported GPU architectures not documented in the fact sheet.
- Whether the package works with non-NVIDIA CUDA toolchains or only official NVIDIA CUDA.
- Performance characteristics and typical use-case scale (e.g., model sizes, batch dimensions) not specified.
What it is and 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—mathematical 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.
The 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
cuequivariance-ops-cu13 on PyPI
Before you install
Medium install friction due to platform-specific wheel requirements (aarch64 and x86_64 manylinux builds). Active maintenance with a release 7 days ago. Requires Python 3.10 or later and depends on nvidia-cublas, tqdm, nvidia-ml-py, and platformdirs.
Requires NVIDIA GPU hardware and CUDA-capable system; Python 3.10 or later; nvidia-cublas and other runtime dependencies must be installed.
License in practice
Licensed under NVIDIA's proprietary SDK agreement with significant restrictions: use limited to systems with NVIDIA GPUs, no reverse engineering or sublicensing, and applications must have material additional functionality beyond the SDK. Distribution requires compliance with specific terms and NVIDIA notification obligations. Liability capped at US$10.00.
Quickstart
pip install cuequivariance-ops-cu13
import cuequivariance_ops_cu13
# Loads CUDA kernels; refer to cuEquivariance documentation for kernel usageVerify before relying
- Specific CUDA compute capability requirements and supported GPU architectures not documented in the fact sheet.
- Whether the package works with non-NVIDIA CUDA toolchains or only official NVIDIA CUDA.
- Performance characteristics and typical use-case scale (e.g., model sizes, batch dimensions) not specified.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 4 packagesnvidia-cublastqdmnvidia-ml-pyplatformdirs |
| Maintenance | Actively maintained 7 days since the last release |
| First released | |
| Downloads | 91,302 / month, #13,526 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersProgramming Language :: Python |
Evidence: cuequivariance_ops_cu13-0.11.1-py3-none-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; cuequivariance_ops_cu13-0.11.1-py3-none-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “cuda kernels equivariant operations”
- cuequivariance-ops-cu13Provides CUDA kernels for equivariant neural network operations,…
- cuequivariance-ops-torch-cu12Provides CUDA-accelerated PyTorch kernels and operators for…
- cuequivariance-ops-cu12Loads CUDA kernels for equivariant neural network operations on…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also cuequivariance-ops-cu12 · cuequivariance-ops-torch-cu12 · cuequivariance-torch · cuequivariance · nvidia-cudnn-cu13 · e3nn · nvidia-cudnn-cu11 · nvidia-cublas-cu11 · nvidia-cudnn-cu12 · e3nn-jax