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cuequivariance-torch

Official

Run equivariant PyTorch ops with cuEquivariance

Software Engineering#gpu#cuda#polynomials#torch#tensor-product#equivariant#segmented_polynomial
AuthorNVIDIA
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill helps researchers and engineers implement and run equivariant neural network components in PyTorch using cuEquivariance's SegmentedPolynomial backends and CUDA-accelerated primitives.

Core Features & Use Cases

  • High-performance primitives: SegmentedPolynomial, ChannelWiseTensorProduct, FullyConnectedTensorProduct, Linear, SphericalHarmonics, Rotation, Inversion, SymmetricContraction
  • Layers: BatchNorm, FullyConnectedTensorProductConv
  • Backend options: naive, uniform_1d, fused_tp, indexed_linear with CUDA acceleration when available
  • PyTorch integration: ready-to-use components for model building and training

Quick Start

Import cuequivariance_torch as cuet and instantiate a SegmentedPolynomial-based module to plug into your PyTorch model.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

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Please help me install this Skill:
Name: cuequivariance-torch
Download link: https://github.com/NVIDIA/cuEquivariance/archive/main.zip#cuequivariance-torch

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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