cuequivariance-torch
CUDA accelerated equivariant operations
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later and CUDA-capable GPU; CUDA kernel packages (cuequivariance-ops-torch-cu12 or cu13) may be needed for full functionality
- Active maintenance with recent release.
- Low install friction via pure Python wheel.
License · maintenance · safety
permissive license (permissive) — Apache 2.0 permissive license allows commercial and derivative use with attribution and license propagation requirements.
last release 2026-08-07 (7 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 186,266 downloads/mo, #9,991 on PyPI
Alternatives
Verify before relying
pip install cuequivariance-torch
import cuequivariance_torch- Whether CUDA kernel packages (cuequivariance-ops-torch-cu12 or cu13) are required for actual use or optional
- Performance characteristics and typical speedup over non-equivariant baselines
- Maturity level beyond Beta status and stability guarantees for production workloads
What it is and 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.
The 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.
Use it for
- 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
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
cuequivariance-torch on PyPI
Before you install
Active maintenance with recent release. Low install friction via pure Python wheel. Single runtime dependency on cuequivariance core.
Requires Python 3.10 or later and CUDA-capable GPU; CUDA kernel packages (cuequivariance-ops-torch-cu12 or cu13) may be needed for full functionality
License in practice
Apache 2.0 permissive license allows commercial and derivative use with attribution and license propagation requirements.
Quickstart
pip install cuequivariance-torch
import cuequivariance_torchVerify before relying
- Whether CUDA kernel packages (cuequivariance-ops-torch-cu12 or cu13) are required for actual use or optional
- Performance characteristics and typical speedup over non-equivariant baselines
- Maturity level beyond Beta status and stability guarantees for production workloads
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagecuequivariance |
| Maintenance | Actively maintained 7 days since the last release |
| First released | |
| Downloads | 186,266 / month, #9,991 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 :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: cuequivariance_torch-0.11.1-py3-none-any.whl
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See also cuequivariance · e3nn-jax · mace-torch · cuequivariance-ops-torch-cu12 · cuequivariance-ops-cu12 · cuequivariance-ops-cu13 · e3nn · torch-geometric · torch · pytorch-msssim