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name: runtime-and-ops description: "Diagnose and prepare OpenPCDet runtime environments, CUDA extensions, spconv/cumm variants, and import readiness." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

OpenPCDet Runtime and Ops

Use this sub-skill when OpenPCDet installation, native extension build, CUDA/spconv compatibility, import readiness, or optional visualization dependencies are relevant.

Fast route

1. Read references/runtime-build-guide.md for the supported runtime stack and known build pitfalls.
2. Run the root helper ../../scripts/inspect_openpcdet_runtime.py against the target environment or checkout.
3. If compiled ops fail, use references/native-ops-troubleshooting.md before running train/test/demo.
4. If the task is dataset, command, inference, or model-specific after the environment is healthy, return to the root skill and route to the corresponding sub-skill.

Required runtime claims

• Full OpenPCDet train/eval/demo workflows require CUDA-capable PyTorch and compiled OpenPCDet CUDA extension modules.
• spconv/cumm must match the CUDA variant used by PyTorch; CPU-only sparse-conv checks are not a substitute.
• Dataset import can be affected by optional dataset dependencies; Argo2 uses kornia/av2 and was sensitive to kornia version in the construction environment.

Verification hooks

• Safe import probe: ../../scripts/inspect_openpcdet_runtime.py --require-cuda-ops.
• Config-only sanity probe: ../../scripts/summarize_openpcdet_config.py --cfg <config.yaml>.
• Native examples/training should run only after the runtime probe, config summary, and dataset-layout checks pass.