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개발 및 엔지니어링 · SKILL.md

training-and-evaluation

training-and-evaluation

Operate OpenPCDet train/test workflows, distributed launchers, checkpoint resume/eval_all, outputs, and evaluation troubleshooting.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: training-and-evaluation description: "Operate OpenPCDet train/test workflows, distributed launchers, checkpoint resume/eval_all, outputs, and evaluation troubleshooting." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

OpenPCDet Training and Evaluation

Use this sub-skill for training, evaluation, checkpoint resume, distributed jobs, output directories, result files, and CLI flags.

Fast route

  1. Verify runtime with ../../scripts/inspect_openpcdet_runtime.py --require-cuda-ops.
  2. Verify dataset products with ../data-preparation/SKILL.md.
  3. Summarize the target YAML with ../../scripts/summarize_openpcdet_config.py --cfg <config.yaml>.
  4. Build the train/test command with ../../scripts/plan_openpcdet_command.py.
  5. Read references/train-test-workflows.md for command semantics and failure modes.

Command helper examples

Print commands from the generated skill root:

python scripts/plan_openpcdet_command.py --repo <checkout> --mode train --cfg <config.yaml> --extra-tag <tag>
python scripts/plan_openpcdet_command.py --repo <checkout> --mode test --cfg <config.yaml> --ckpt <checkpoint.pth> --save-to-file

Add --execute only when the user has authorized the job and dataset/GPU budget.

Boundaries

  • Dataset info/database generation belongs to ../data-preparation/SKILL.md.
  • Config/model-family selection belongs to ../models-and-configs/SKILL.md.
  • Demo/custom point-cloud inference belongs to ../inference-and-custom-data/SKILL.md.
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