Foundationpose Setup
NVIDIAOfficialCreated by zwdoescodeDistributed by NVIDIA
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- Apache-2.0
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured workflow for installing, configuring, and repairing the FoundationPose perception pipeline. It covers environment setup, dependency management for SAM3 and TAO, and the construction of FoundationStereo TensorRT engines. The skill ensures that the runtime environment meets strict hardware and software requirements, including specific glibc versions and CUDA toolkit configurations, while providing diagnostic steps for common library and engine-loading failures.
What it does
- Performs preflight checks for system requirements like glibc, CUDA, and GPU access.
- Installs and synchronizes the FoundationPose pipeline environment using uv.
- Manages sibling dependencies including SAM3 and the FoundationPose inference library.
- Builds optimized FoundationStereo TensorRT engines based on scene-specific shapes.
- Verifies pipeline integrity through component-specific test scripts.
- Troubleshoots common dependency conflicts and library loading errors.
When to use it
- When performing an initial installation of the FoundationPose perception pipeline.
- When encountering library loading errors such as missing libcudart.so or GLIBC version mismatches.
- When needing to build or rebuild a TAO depth engine for a specific dataset or scene.
- When resolving dependency conflicts between SAM3, TAO Deploy, and the pipeline environment.
Advantages
- Ensures environment consistency by validating system-level requirements before installation.
- Provides clear procedures for handling machine-specific engine builds.
- Maintains clean separation between the pipeline, inference library, and model weights.
- Offers targeted troubleshooting steps for common runtime and build-time failures.
Usage examples
- “Install the FoundationPose perception pipeline on this Ubuntu 24.04 GPU machine.”
- The agent will verify system requirements, set up the Python venv, clone necessary repositories, and run the installation sync.
- “The pipeline cannot load libcudart.so.13 after I moved the checkout.”
- The agent will inspect the current environment paths and library links to resolve the runtime loading error.
- “Build the TAO depth engine for my adapted T-LESS scenes.”
- The agent will guide the user through the engine construction process using the adapted scene data and appropriate precision settings.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/foundationpose-setup/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip foundationpose-setup.zip -d .agents/skills
Documentation by zwdoescode
FoundationPose perception pipeline setup
Purpose
Prepare the FoundationPose perception pipeline
for depth, segmentation, and pose inference. Environment installation and engine construction
belong here; dataset adaptation, inference, and pose evaluation belong to
foundationpose-pipeline when that skill is installed.
Requirements
Work from the product checkout, not the installed skill directory. Find the user's checkout by
checking for pyproject.toml (project foundationpose-perception-pipeline),
tools/build_tao_engine.py, and config/defaults.yaml. If absent and setup was requested, clone
the product URL above into the user's workspace and enter it. For advice-only requests, use the
supplied diagnostics without cloning or installing anything. Commands below use paths relative
to the product root; references/ links are relative to this skill.
Read the checkout's README.md Requirements and Install sections for the matching revision.
The supported stack requires Linux x86_64, glibc >= 2.38, GLIBCXX_3.4.31, NVIDIA driver >= 580,
a CUDA toolkit >= 12.8 with nvcc, Python 3.12, uv, Git, Docker with GPU access, wget, and unzip.
Start GPU sizing at 24 GB and measure the densest scene; 32 GB was tested. Budget depth-cache
disk as roughly width * height * 4 * 3 bytes per scene, plus predictions and models.
Keep sibling directories for sam3/, foundation-pose-inference-library/, and models/ beside
the product checkout. models/ contains the deployable ONNX and engine, not FoundationStereo source.
Instructions
Preflight before installing. Check glibc, GLIBCXX, driver,
nvcc, tools, and Docker GPU access. An Ubuntu 22.04 host with glibc 2.35 cannot load the shipped FoundationPose library; report the unsupported runtime and stop setup there. Do not replace system libc or try to solve this withLD_LIBRARY_PATH. See installation.Install into the product's Python 3.12 venv. Follow installation for uv, SAM3, the FoundationPose build, and TAO Deploy. On a fresh venv use
uv sync --extra foundationpose; on an existing venv useuv sync --inexact --extra foundationposeto preserve out-of-band packages.Verify checkpoint access. SAM3 is gated at Hugging Face; an existing authorized token or usable cached checkpoint is sufficient. Request user action only if access is missing. FoundationPose and the documented FoundationStereo export are public Hugging Face downloads; credential hunting is not the first response to a network failure.
Prepare the depth engine. Read engine construction. Use the user's ONNX location or the sibling
models/directory. Adapt the dataset before measuring the engine shape; usetools/bop_adapt/adapt.py --config <profile> --src <source>as described in the checkout's README Dataset adaptation section. Only registered adapters are supported. Build with--shape-from-sceneon an adapted scene and FP32 unless the user requests a precision experiment. Setoverrides.depth.engineinconfig/<profile>.yaml.Set runtime paths and verify. From the product root:
source .venv/bin/activate export FOUNDATIONPOSE_ROOT="$(realpath ../foundation-pose-inference-library)" PIPELINE_SITE="$(realpath .venv/lib/python3.12/site-packages)" export LD_LIBRARY_PATH="${PIPELINE_SITE}/tensorrt_libs:${PIPELINE_SITE}/nvidia/cu13/lib:${LD_LIBRARY_PATH:-}" python tools/verify_sam3.py python tools/verify_foundationpose.py python tools/verify_foundationstereo.py --config <profile> --engine <engine-path> python test/check_engine_depth_smoke.py --config <profile> --engine <engine-path>The first three verify components; the last also needs an adapted dataset. Expect
backend=tao,normalization=imagenet, the intended fixed shape, and nocropping N rowswarning. An unloaded or unavailable engine is an incomplete verification, not a pass.
Troubleshooting
| Symptom | Action |
|---|---|
GLIBC_2.38 not found | Use a supported OS/runtime; a venv or library search path cannot upgrade host libc. |
libcudart.so.13 missing | Check the product venv runtime wheels and absolute library paths before retrying pose. |
| SAM3 breaks after sync | Use --inexact; confirm numpy 1.26.x and reinstall the sibling SAM3 package if pruned. |
pycuda build cannot find cuda.h | Check the CUDA toolkit, nvcc on PATH, or CUDA_ROOT. |
| TAO import or dependency conflict | Use TAO Deploy 7.1.0 with --no-deps; sync declared dependencies with --inexact. |
| Engine sidecar mismatch or cropping | Rebuild for this GPU, TensorRT version, precision, and adapted scene shape. |
Examples
"Install the FoundationPose perception pipeline on this Ubuntu 24.04 GPU machine."
"The pipeline cannot load libcudart.so.13 after I moved the checkout."
"Build the TAO depth engine for my adapted T-LESS scenes."
Limitations and completion
Engines are machine-specific and must not be committed. With no dataset, download the ONNX and report shape-dependent engine construction and scene validation as pending; do not invent a rig shape. The pipeline's Apache license does not cover separately downloaded model weights; retain their upstream terms and SAM3's access requirements.
Report which preflight, install, checkpoint, engine, and verification steps actually passed, the checkout and engine paths, versions used, and remaining blockers. Do not equate installation or a smoke check with measured pose accuracy.
Files
- ATTRIBUTION.mdAdded by AutoGineer922 B
- BENCHMARK.md7.4 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md5.9 KiB
- agents/openai.yaml262 B
- evals/config.yml149 B
- evals/evals.json4.7 KiB
- references/engine.md3.4 KiB
- references/installation.md5.5 KiB
- skill-card.md4.2 KiB
- skill.oms.sig5.4 KiB
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Attribution and licence
- Created by
- zwdoescode
- Publisher
- NVIDIA (Official)
- Distributed by
- NVIDIA
- Source
- NVIDIA/skills · skills/foundationpose-setup · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download foundationpose-setup.zip