Skip to main content

Usd Optimize

Usd Optimize is a scene optimization library for OpenUSD: a broad set of operations for processing and optimizing USD stages — geometry, materials, hierarchy, and analysis. It ships as a standalone C++ library with Python bindings, so you can embed scene optimization in your own applications and pipelines without installing Omniverse Kit.

This usd-optimize wheel provides the precompiled Python package. It also registers a set of performance validators for usd-validation-nvidia, which nvidia_usd_validate discovers automatically once this package is installed.

Requirements

  • Python 3.12 — the wheel is built for CPython 3.12 (cp312).
  • Windows: the Microsoft Visual C++ 2015–2022 Redistributable (x64) must be installed. The USD runtime DLLs import MSVCP140.dll / VCRUNTIME140.dll / VCRUNTIME140_1.dll, which are not bundled. It is widely present but not part of a base Windows install; install it with winget install --id Microsoft.VCRedist.2015+.x64 if import pxr fails with DLL load failed while importing _tf. (A python.org interpreter ships the VCRUNTIME140 DLLs but not MSVCP140, so the redistributable is still required.)

Documentation

License

Licensed under the Apache License 2.0.

Metadata

Release files for usd-optimize 1.4.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distributions (wheels)

Table of built distributions (wheels) for usd-optimize 1.4.0
File Interpreter ABI Platform Actions
usd_optimize-1.4.0-cp312-cp312-win_amd64.whl CPython 3.12 CPython 3.12 Windows x86-64 Details for usd_optimize-1.4.0-cp312-cp312-win_amd64.whl
usd_optimize-1.4.0-cp312-cp312-manylinux_2_35_x86_64.whl CPython 3.12 CPython 3.12 Linux glibc 2.35+ x86-64 Details for usd_optimize-1.4.0-cp312-cp312-manylinux_2_35_x86_64.whl
usd_optimize-1.4.0-cp312-cp312-manylinux_2_35_aarch64.whl CPython 3.12 CPython 3.12 Linux glibc 2.35+ ARM64 Details for usd_optimize-1.4.0-cp312-cp312-manylinux_2_35_aarch64.whl

Total release size: 199.1 MB

Release files / usd_optimize-1.4.0-cp312-cp312-win_amd64.whl

Download URL usd_optimize-1.4.0-cp312-cp312-win_amd64.whl
Size 75.9 MB
Tags CPython 3.12 Windows x86-64
SHA-256 checksum
How to use checksums
8709fea88abadc1155a69fc758dcd960d1de23359bc33221a1d6cfe66e59acac
BLAKE2b-256 checksum
How to use checksums
126cf0c0b14873ee29a7f704faf24e0dc92fb3e5abb86bba696c18f65c1fc0c9
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.14.8

Release files / usd_optimize-1.4.0-cp312-cp312-manylinux_2_35_x86_64.whl

Download URL usd_optimize-1.4.0-cp312-cp312-manylinux_2_35_x86_64.whl
Size 61.8 MB
Tags CPython 3.12 Linux glibc 2.35+ x86-64
SHA-256 checksum
How to use checksums
f0e79985796f081f4c974a9a03635a2e55ece3912e2e75889c0b77d8437c2994
BLAKE2b-256 checksum
How to use checksums
d48c6a4c1bc3f849dbefb32a2f276edabbc6fb83a03e5bb8934ca73945b793c9
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.14.8

Release files / usd_optimize-1.4.0-cp312-cp312-manylinux_2_35_aarch64.whl

Download URL usd_optimize-1.4.0-cp312-cp312-manylinux_2_35_aarch64.whl
Size 61.4 MB
Tags CPython 3.12 Linux glibc 2.35+ ARM64
SHA-256 checksum
How to use checksums
6c900eb83c35f5847119c05256f954787c5ee1290877377df5bc6f601a65cef0
BLAKE2b-256 checksum
How to use checksums
dbfcdfb02a33148ed7a27901b96a8f5278e2bbdd9d2b74bf14f8e1813d11719d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.14.8

Security for usd-optimize 1.4.0

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page