Prebuilt Package

Installing a Prebuilt Usd Optimize Package

This skill covers consuming a published binary drop (e.g. usd_optimize_usd_<usd_ver>_py_<py_ver>@<version>.<platform>.release). It is not for building from source — for that, use the build skill or repo.bat build / repo.sh build.

OS-specific install walkthroughs (interpreter install, environment-variable syntax, virtual-env activation):

• Windows: docs/install-prebuilt-windows.md
• Linux: docs/install-prebuilt-linux.md

The notes below are platform-neutral and capture the load-bearing details.

What this skill covers

Search this doc for keywords like usdpy, PYTHONPATH, LD_LIBRARY_PATH, entry-point, register_all, analysisMode, nvidia_usd_validate, auditwheel to jump.

• What's in a prebuilt drop — directory layout (include/, lib/, python/, usdpy/).
• Prerequisites — interpreter / OS requirements.
• Smoke-check the install — minimal verification commands.
• What does NOT work in the drop — repo.sh, source rebuilds, dev driver scripts.
• Public API surface — what's importable.
• Purpose / Limitations / Troubleshooting — scope summary, scope boundaries, common failure modes (PYTHONPATH, libusd alignment, validator plugin auto-loading).

Companion skills: build (build from source instead), validators (run-time validator infrastructure), run-validators / run-operations (dev driver scripts that need a source build, NOT a drop).

What's in a prebuilt drop

No Python interpreter is bundled — the consumer supplies one.

Prerequisites

1. Python version must match the py_<ver> token in the package name. The bundled pxr extension modules link against a specific CPython ABI; loading them under any other Python version fails at import time. On Windows the failure surfaces as ImportError: Module use of python<XY>.dll conflicts with this version of Python.; on Linux it surfaces as undefined-symbol or version-mismatch errors from the dynamic linker. The matching interpreter is the consumer's responsibility — see the OS-specific guide above.
2. PYTHONPATH must include both python and usdpy. Missing usdpy produces ModuleNotFoundError: No module named 'pxr'.
3. The platform's library-search path must include both lib and extraLibs, set before the interpreter starts. That's PATH on Windows and LD_LIBRARY_PATH on Linux. The path is consulted at module-load time only — exporting it after the Python process is running has no effect; restart the interpreter.

Linux — libpython on disk vs ldconfig -p. Bundled pxr needs libpython3.X.so.1.0 from your interpreter; ldconfig -p only reflects the linker cache and can be stale. Verify with the snippet in docs/install-prebuilt-linux.md (Python prerequisites): set PYLIBDIR from sysconfig.get_config_var("LIBDIR"), ls there, and compare to ldconfig -p | grep libpython…. Interpretation: (1) no .so under PYLIBDIR → install shared libpython or rebuild Python with --enable-shared; (2) .so under /usr/lib or /lib (multiarch counts) but ldconfig -p empty → run sudo ldconfig once — avoid reinstall churn or redundant LD_LIBRARY_PATH when libs already sit in the default linker layout; (3) .so only under pyenv/conda-style prefixes → add that PYLIBDIR to LD_LIBRARY_PATH (conda activation usually does this).

Smoke-check the install

A short script that proves the bindings load and an op runs against an in-memory stage. Save as smoke_check.py and run with the matching Python:

from usd_optimize.core import ExecutionContext, UsdOptimizeCore
from pxr import Usd, UsdGeom

core = UsdOptimizeCore.getInstance()
assert len(core.getOperations()) > 0

stage = Usd.Stage.CreateInMemory()
UsdGeom.Xform.Define(stage, "/World")
UsdGeom.Cube.Define(stage, "/World/c1")
UsdGeom.Cube.Define(stage, "/World/c2")
ctx = ExecutionContext()
ctx.set_stage(stage)
results = core.executeConfig(ctx, [
    {"operation": "deletePrims", "primPaths": ["/World/c1"]},
])
assert all(ok for ok, _err, _out in results) and sum(1 for _ in stage.TraverseAll()) == 2
print("OK")

If this prints OK the drop is healthy. Any positive op-registry count confirms the plugins loaded; the exact count varies by build.

What does NOT work in the drop

• repo.sh build / repo.bat test — repo.sh/repo.bat is not shipped. The README's "Quickstart" applies to the source repo, not to the binary drop.
• python/tests/test.python/run_discover.py — the bundled test_validators_*.py modules require PyPI usd-validation-nvidia (usd_validation_nvidia). Without it, imports fail before unittest runs. If any module fails to import, run_discover.py exits with code 1 without executing tests. Even with imports fixed, most modules still expect fixtures under tests/data. The self-contained cases in test_core_python_bindings.py (test_executionContext, test_executionContext_reportPath_roundtrip, test_executionContext_reportPath_survives_executeOperation, test_usdOptimizeCore, test_operation) match the smoke check above.

Public API surface

The supported entry point for standalone consumers is the UsdOptimizeCore singleton in usd_optimize.core (canonical examples: docs/overview.rst):

• executeConfig(context, config) — runs a list of {"operation": …, …} descriptor dicts against the stage bound to the ExecutionContext. Returns one (success, error, output) tuple per operation. Takes a Python list — for JSON input, pass json.loads(text) / json.load(f), not the path or raw text.
• executeOperation(name, context, args) — runs a single operation; returns one (success, error, output) tuple.
• mapConfig(config_json) — applies the operation/argument renames in lib/operation_mapping.json (JSON string in, JSON string out) so older configs keep working.

Operation keys accepted by executeConfig are the strings from UsdOptimizeCore.getInstance().getOperations() at runtime (count varies by build). Bundled tests under python/tests/test.python/ illustrate descriptor JSON for many operations. lib/operation_mapping.json is only a small backward-compatibility alias table for mapConfig(), not the full operation list.

Purpose

Stand up a working Usd Optimize install from a prebuilt binary drop — no source clone, no repo.sh/repo.bat, no compiler. Cover the layout of the drop, the strict interpreter / library-path requirements, the canonical smoke check, and the boundaries of the supported public API so consumers can integrate the package into their own pipeline without reaching for the dev tooling.

Limitations

This is a runtime / consumer skill, not a development skill. The following are intentionally out of scope:

• Source rebuilds. A drop has no repo.sh / repo.bat and no compiler toolchain. Use the build skill against a checkout instead.
• Dev driver scripts. tools/validators/run.sh and similar require a source tree with _build/<platform>/<config>/; they will not work against a drop. (A drop ships its own bin/usdOptimize CLI for running operations.)
• Most bundled tests. run_discover.py needs every test_*.py to import cleanly first; missing usd-validation-nvidia breaks the test_validators_*.py set immediately. Any import failure prevents the unittest phase. Use the smoke check above as the supported install verification.
• Mixing your own USD with the drop's usdpy. pxr and the C++ core must resolve to the same libusd build — point PYTHONPATH at the drop's usdpy and don't shadow it with another USD install.
• Non-published platforms. Drops are produced for specific platform/USD/Python combinations (encoded in the package name). Other combinations need a source build.

Troubleshooting