Skip to main content

cuequivariance-ops-torch - GPU Accelerated Torch Extensions for Equivariant Primitives

Project description

cuequivariance-ops-torch

Introduction

cuequivariance_ops_torch provides CUDA kernels for the cuEquivariance project's PyTorch components. As such, it contains pytorch bindings to optimized kernels that cuEquivariance's operations map down to. In general, we advice that you access those kernels through cuEquivariance, but you may also find them useful on their own.

Installation

Please install using either pip install cuequivariance-ops-torch-cu11 or pip install cuequivariance-ops-torch-cu12 (depending on the CUDA toolkit you wish to use).

Documentation

For detailed usage information of the kernels, please refer to the doc-strings in their respective modules. For higher-level documentations, refer to cuEquivariance.

Usage

You can import the library from python:

import cuequivariance_ops_torch

Kernels are primarily exposed as torch.nn.Module, but also provide a lower-level interface as torch.library operators. Generally, the module is responsible for proper input transformation and initialization, and the operator execute the kernel. This allows you to export models using this operations using torch.export, and running inference on them using TensorRT.

Support and Feedback

Please contact the cuEquivariance developers for any issues you might encounter.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

cuequivariance_ops_torch_cu11-0.5.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (153.4 kB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64

cuequivariance_ops_torch_cu11-0.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (154.4 kB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

cuequivariance_ops_torch_cu11-0.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (154.7 kB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

File details

Details for the file cuequivariance_ops_torch_cu11-0.5.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for cuequivariance_ops_torch_cu11-0.5.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d24f1e7e25f3d1d7b4bf6d3ba25580869d4145f8a19844b9c8540d2655d73af2
MD5 185f188e78c645004572926325cbb4bd
BLAKE2b-256 5ac6e05a21ce773e00b20d26e29df45436b256f6cb1397e063a0d1ea02f6c46f

See more details on using hashes here.

File details

Details for the file cuequivariance_ops_torch_cu11-0.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for cuequivariance_ops_torch_cu11-0.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 5dd662e762d5d153f06bc2be759300a7d65b1e261ff753a88959f88c87909eed
MD5 0458e8e9faf408009b6355f83a58529b
BLAKE2b-256 527d86bf7a1dcce1ad28c80ea688ec0ef6e7bcd9a5f537317a055a26f67e18a4

See more details on using hashes here.

File details

Details for the file cuequivariance_ops_torch_cu11-0.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for cuequivariance_ops_torch_cu11-0.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 64d8d1b54cc68c57b96f2d3a7d10f8f8d99507b0c20b88823c22e419a8657dcd
MD5 4bc2305c911b49740149b11e47ff62ab
BLAKE2b-256 631cb93c90304ce0c6241fdea6c3a09109447cbbb7ac7d78a4f257b7086c10cc

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page