cuequivariance
CUDA accelerated equivariant operations
Decision gist · record as of 2026-08-14
Yes, if you are building geometric or physics-informed neural networks and need GPU acceleration for equivariant operations. The Apache 2.0 license is permissive, install friction is low, and maintenance is active. However, note that the package is in Beta, so expect potential API changes. Verify that the PyTorch or JAX bindings match your framework choice and that your CUDA version (12 or 13) is supported before committing.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- CUDA kernels require separate installation (cuequivariance-ops-torch-cu12/cu13 or cuequivariance-ops-jax-cu12/cu13) and a compatible NVIDIA GPU.
- Low install friction with a pure-wheel distribution.
- Active maintenance with a release 7 days ago.
License · maintenance · safety
permissive license (permissive) — Apache 2.0 permissive license allows commercial and derivative use with attribution and license preservation requirements for distributed modifications.
last release 2026-08-07 (7 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 228,157 downloads/mo, #9,156 on PyPI
Alternatives
Verify before relying
pip install cuequivariance
import cuequivariance
# For PyTorch: pip install cuequivariance-torch cuequivariance-ops-torch-cu12
# For JAX: pip install cuequivariance-jax cuequivariance-ops-jax-cu12- Specific performance benchmarks or speedup claims compared to non-accelerated implementations.
- Data efficiency gains from equivariance in real-world training scenarios.
- Scope and completeness of PyTorch and JAX API coverage.
- Beta state stability and likelihood of breaking API changes in future releases.
What it is and what it does
cuEquivariance is an NVIDIA library for constructing geometric neural networks that incorporate equivariance—the property of respecting symmetries like rotations and translations. It provides an API for describing segmented polynomials built from tensor products, optimized CUDA kernels for execution, and integration with PyTorch and JAX. The core idea is that AI models incorporating equivariance tend to be more data-efficient because they encode physical symmetries directly rather than learning them from data.
The package sits at the intersection of differential geometry and deep learning. It targets researchers and practitioners building physics-informed models, molecular simulations, or other domains where rotational and translational symmetry matter. Installation is straightforward for the core library, though GPU acceleration requires separate CUDA-specific packages. The library is in Beta, meaning its API and functionality may change.
Use it for
- Building molecular property prediction models that respect 3D rotation and translation symmetries.
- Developing physics simulations where equivariance reduces data requirements for training.
- Constructing point-cloud neural networks for 3D shape analysis with built-in symmetry handling.
- Integrating equivariant operations into existing PyTorch or JAX workflows for geometric deep learning.
- Accelerating segmented polynomial computations on GPU for large-scale scientific computing tasks.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building geometric or physics-informed neural networks and need GPU acceleration for equivariant operations.
The Apache 2.0 license is permissive, install friction is low, and maintenance is active. However, note that the package is in Beta, so expect potential API changes. Verify that the PyTorch or JAX bindings match your framework choice and that your CUDA version (12 or 13) is supported before committing.
Install
cuequivariance on PyPI
Before you install
Low install friction with a pure-wheel distribution. Active maintenance with a release 7 days ago. Requires Python 3.10 or later and depends on common scientific libraries (numpy, scipy, sympy, networkx, opt-einsum).
CUDA kernels require separate installation (cuequivariance-ops-torch-cu12/cu13 or cuequivariance-ops-jax-cu12/cu13) and a compatible NVIDIA GPU.
License in practice
Apache 2.0 permissive license allows commercial and derivative use with attribution and license preservation requirements for distributed modifications.
Quickstart
pip install cuequivariance
import cuequivariance
# For PyTorch: pip install cuequivariance-torch cuequivariance-ops-torch-cu12
# For JAX: pip install cuequivariance-jax cuequivariance-ops-jax-cu12Verify before relying
- Specific performance benchmarks or speedup claims compared to non-accelerated implementations.
- Data efficiency gains from equivariance in real-world training scenarios.
- Scope and completeness of PyTorch and JAX API coverage.
- Beta state stability and likelihood of breaking API changes in future releases.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesnetworkxnumpyopt-einsumscipysympy |
| Maintenance | Actively maintained 7 days since the last release |
| First released | |
| Downloads | 228,157 / month, #9,156 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-0.11.1-py3-none-any.whl
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See also cuequivariance-torch · cuequivariance-ops-cu12 · cuequivariance-ops-torch-cu12 · cuequivariance-ops-cu13 · e3nn-jax · e3nn · nvidia-cudnn-cu13 · nvidia-cudnn-cu12 · torch-geometric · nvidia-cudnn-cu11