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name: training-and-pipelines description: "Guides Open3D-ML config-driven training, inference, evaluation, registry use, and model selection." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Training and Pipelines

Use this sub-skill when you want to train, test, or run inference with an Open3D-ML model, or when you need to construct a config-driven pipeline from a model, dataset, and pipeline definition.

What this sub-skill covers

• Repo config files and the Config loader/merge logic.
• Registry-driven creation of datasets, models, and pipelines.
• Semantic segmentation and object detection workflows.
• PyTorch model and pipeline usage, with TensorFlow described as an optional backend when a compatible build exists.
• Safe command construction for a pipeline launcher without running training.
• Model zoo and checkpoint selection guidance.

When to route here

• "How do I train RandLANet on SemanticKITTI?"
• "How do I run inference with PointPillars?"
• "Why does a config override not take effect?"
• "How do I build the right model/dataset/pipeline combination?"
• "How do I compare semantic segmentation and object detection workflows?"

Use the bundled helper

Run scripts/build_run_pipeline_command.py when you want a safe, non-training summary of how a config-driven pipeline would be launched.

Reading order

1. Read references/config-reference.md for Config loading and merge rules.
2. Read references/workflows.md for direct API and config-driven usage.
3. Read references/model-overview.md for model families and dataset pairings.
4. Read references/troubleshooting.md when a registry or version mismatch occurs.

Boundary notes

Include:

• Model/dataset/pipeline selection and command construction.
• Config file anatomy and override semantics.
• Direct API or launcher-shaped workflows for training, testing, and inference.

Exclude:

• Dataset layout validation; use datasets-and-preprocessing.
• Visualization and TensorBoard details; use visualization-and-extensions.
• Generic install/backend troubleshooting; use install-and-inspect.

Minimal workflow

1. Pick a config file or a model/dataset/pipeline trio.
2. Resolve the class names via the registry.
3. Check the command shape with scripts/build_run_pipeline_command.py.
4. Execute the direct API or launcher in a suitable environment.

Good handoff signals

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

• How Config.load_from_file and CLI-style overrides interact.
• Which models are available for segmentation and detection.
• How to detect a registry miss versus a backend mismatch.
• Which workflows are CPU-safe versus backend-dependent.