{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"Provides CUDA-accelerated PyTorch kernels and operators for equivariant neural network primitives, including specialized Pairformer inference operations optimized for low-precision tensor computations.","skillfed_tags":["cuda-kernels","equivariant-networks","inference-optimization"],"use_cases":["Accelerate Pairformer inference in equivariant models using optimized CUDA kernels","Export equivariant neural network models via torch.export for deployment with TensorRT on NVIDIA GPUs","Integrate low-precision (BF16/FP16) equivariant operations into production inference pipelines","Access raw CUDA kernels for custom equivariant layer implementations beyond the standard API"],"what_it_does":"cuequivariance-ops-torch-cu12 is a PyTorch extension that wraps optimized CUDA kernels for equivariant neural network operations. It is designed primarily as a lower-level component accessed through cuEquivariance, though the kernels can be used directly. The package exposes operations as both torch.nn.Module objects (for training and model export) and torch.library custom operators (for torch.compile tracing and TensorRT inference).\n\nThe package includes specialized primitives for Pairformer inference, such as attention pair bias masking and combined triangle multiplication. These operations are tuned for specific configurations (BF16 precision, D=256, H=16, sequence lengths 384 or 512) and use native low-precision tensor-core products with FP32 accumulation. It requires CUDA 12, torch, and scipy, and is distributed as platform-specific wheels for x86_64 and aarch64 Linux only.","worth_installing":"Yes, with conditions. Install if you are building equivariant neural networks on NVIDIA GPUs and need production-grade inference performance, or if you are already using cuEquivariance and want direct kernel access. Do not install if you lack CUDA 12 hardware, need cross-platform support, or require an open-source-compatible license. The proprietary restrictions and hardware lock-in are significant trade-offs for the performance gains."},"id":"cuequivariance-ops-torch-cu12","links":{"html":"https://skillfed.io/packages/cuequivariance-ops-torch-cu12","md":"https://skillfed.io/packages/cuequivariance-ops-torch-cu12.md","pypi":"https://pypi.org/project/cuequivariance-ops-torch-cu12/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-07","license_spdx":null,"license_treatment":"unclear","name":"cuequivariance-ops-torch-cu12","python_support":"supports_current","summary":"cuequivariance-ops-torch - GPU Accelerated Torch Extensions for Equivariant Primitives"},"popularity":{"monthly_downloads":122622,"position":11944,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.11.1"}