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cuEquivariance is a math library that is a collective of low-level primitives and tensor ops to accelerate widely-used models, like DiffDock, MACE, Allegro and NEQUIP, based on equivariant neural networks. Also includes kernels for accelerated structure prediction.
| Date | Stars |
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| 2026-07-31 | 418 |
| 2026-08-05 | 419 |
| 2026-08-06 | 419 |
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# cuEquivariance cuEquivariance is an NVIDIA Python library designed to facilitate the construction of high-performance geometric neural networks using segmented polynomials and triangular operations. cuEquivariance provides a comprehensive API for describing segmented polynomials made out of segmented tensor products and optimized CUDA kernels for their execution. Additionally, cuEquivariance offers bindings for both PyTorch and JAX, ensuring broad compatibility and ease of integration. Equivariance is the mathematical formalization of the concept of "respecting symmetries." Robust physical models exhibit equivariance with respect to rotations and translations in three-dimensional space. Artificial intelligence models that incorporate equivariance are often more data-efficient. ## Documentation Please refer to the project documentation for more information [https://docs.nvidia.com/cuda/cuequivariance/](https://docs.nvidia.com/cuda/cuequivariance/). ## Installation ```bash # Choose the frontend you want to use pip install cuequivariance-jax pip install cuequivariance-torch pip install cuequivariance # Installs only the core non-ML components # CUDA kernels pip install cuequivariance-ops-jax-cu12 # or -cu13 pip install cuequivariance-ops-torch-cu12 # or -cu13 ``` ## License All files hosted in this repository are subject to the Apache 2.0 license. ## Disclaimer cuEquivariance is in a Beta state. Beta products may not be fully functional, may contain errors or design flaws, and may be changed at any time without notice. We appreciate your feedback to improve and iterate on our Beta products.
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matched fp:fe0de141918d7eac, llm:description: low-level primitives and tensor ops to accelerate models (DiffDock, MACE, Allegro, NEQUIP) based on equivariant neural networks; kernels for accelerated structure prediction
matched fp:fe0de141918d7eac, llm:description: low-level primitives and tensor ops to accelerate models (DiffDock, MACE, Allegro, NEQUIP) based on equivariant neural networks; kernels for accelerated structure prediction
matched fp:fe0de141918d7eac, llm:description: low-level primitives and tensor ops to accelerate models (DiffDock, MACE, Allegro, NEQUIP) based on equivariant neural networks; kernels for accelerated structure prediction