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name: install-and-inspect description: "Guides Open3D-ML installation, import smoke checks, backend compatibility, and common startup failures." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Install and Inspect

Use this sub-skill when you need to get Open3D-ML importable, confirm the installed backend surface, or diagnose an initialization problem before you start dataset, training, or visualization work.

What this sub-skill covers

• Public installation commands for Open3D, PyTorch, and related runtime pieces.
• Source-checkout integration through OPEN3D_ML_ROOT when using an external Open3D wheel with the Open3D-ML source tree.
• Import/version/backend smoke checks for open3d, open3d.ml, and open3d.ml.torch.
• Optional backend notes for TensorFlow, CUDA, GUI, TensorBoard, and OpenVINO.
• Common startup failures such as version mismatch, missing namespace wiring, NumPy/compiled-extension incompatibility, and backend-specific import gaps.

When to route here

• "How do I install Open3D-ML?"
• "Why does open3d.ml.torch fail to import?"
• "Is my Open3D wheel compatible with this torch version?"
• "How do I check whether CUDA/TensorFlow/OpenVINO is available?"
• "Why does a local checkout need OPEN3D_ML_ROOT?"

Use the bundled helper

Run scripts/check_open3d_ml.py when you want a fast, safe smoke check that prints a machine-readable summary of the installed distribution versions, backend importability, and optional config loading.

Reading order

1. Read references/install-and-backends.md for install choices and the verified backend matrix.
2. Read references/troubleshooting.md when the import smoke check fails or a backend is unavailable.
3. Use scripts/check_open3d_ml.py for a quick status report.

Boundary notes

Include:

• Package installation and editable/source integration.
• Import smoke checks and backend availability.
• Safe explanation of OPEN3D_ML_ROOT.

Exclude:

• Dataset layout validation; use datasets-and-preprocessing.
• Training command construction and config selection; use training-and-pipelines.
• Visualization fixtures or TensorBoard summaries; use visualization-and-extensions.

Minimal workflow

1. Install Open3D and a compatible PyTorch wheel.
2. Export OPEN3D_ML_ROOT only when using a source checkout with an external Open3D wheel.
3. Run scripts/check_open3d_ml.py --framework torch.
4. If the check fails, follow the troubleshooting reference before proceeding.

Good handoff signals

A future agent should be able to answer these from this sub-skill alone:

• Which packages must be present for a CPU PyTorch smoke check.
• How to tell whether the installed Open3D wheel exposes PyTorch ops.
• What to do when TensorFlow or OpenVINO is optional but unavailable.
• How to recover from torch/Open3D or NumPy/extension mismatch errors.