Registry / ai-ml / cuequivariance-ops-torch-cu13

cuequivariance-ops-torch-cu13

library0.10.0pypypiunverified

GPU-accelerated PyTorch extensions for equivariant neural network primitives, specifically compiled for CUDA 13. Current version 0.10.0, requires Python >=3.10. Part of the cuequivariance ecosystem for efficient group-equivariant operations.

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pip install cuequivariance-ops-torch-cu13

package ↗homepage ↗docs ↗

INSTALL

IMPORT

SIG · CUEQUIVARIANCE-OPS

cuequivariance-ops-torch-cu13

ai-mlpythonv0.10.0

Install

15.4s avg

Import

—

Disk

1044MB

Pass rate

4/ 10

Env Coverage4 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.10.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✕ build_error

✓ 15.8s

py 3.11

✕ build_error

✓ 16.1s

py 3.12

✕ build_error

✓ 14.2s

py 3.13

✕ build_error

✓ 15.7s

py 3.9

✕ build_error

1044MB installed

● package 1044MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

all relevant ops

✓ import cuequivariance_ops_torch as coe_ops

✗ from cuequivariance_ops_torch_cu13 import ...

The package name for import does not include the CUDA suffix; import using 'cuequivariance_ops_torch'.

Basic usage: import the package (without CUDA suffix) and call an operation on a CUDA tensor.

import torch import cuequivariance_ops_torch as coe_ops device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') x = torch.randn(10, 10, device=device) y = coe_ops.some_op(x) # Replace with actual operation print(y.shape)

Debug

Known issues

gotchaImport the package as 'cuequivariance_ops_torch', not with the CUDA suffix ('cuequivariance_ops_torch_cu13'). The suffix is only for pip installation to select the correct CUDA version.

fix

Use 'import cuequivariance_ops_torch as coe_ops' in code.

affects: all

breakingCUDA compatibility: The cu13 wheel only works with CUDA 13.x. Installing on a system with a different CUDA version will cause runtime errors (e.g., 'CUDA driver version is insufficient').

fix

Match the package suffix (e.g., cu12, cu13) to your installed CUDA toolkit version. Use 'pip install cuequivariance-ops-torch-cu12' for CUDA 12.

affects: 0.x

gotchaThe package may not be compatible with all CUDA compute capabilities; check the wheel's target architectures (e.g., sm_80, sm_90). Running on an unsupported GPU will raise a 'no kernel image' error.

fix

Ensure your GPU is supported (e.g., Ampere or newer). If not, you may need to build from source or use a CPU fallback.

affects: all

Upgrade

Version history

0.10.0latest on PyPI · released Apr 22, 2026

Audit

Dependencies

torchrequiredPyTorch with CUDA support is required at runtime.

cuequivariancerequiredCore library for equivariant operations.

Agent activity

36 hits · last 30 days

node

OpenAI (training)

Resources

packagehttps://pypi.org/project/cuequivariance-ops-torch-cu13/ ↗

homepagehttps://developer.nvidia.com/cuequivariance ↗

docshttps://docs.nvidia.com/cuda/cuequivariance/ ↗