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name: training-and-evaluation description: "Route Det3D training, inference, evaluation, checkpoint, resume, and distributed-launch tasks." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Training and Evaluation

Use this route for train.py, test.py, distributed testing, checkpoint resume/finetuning, result export, and metric evaluation. Read the CLI and workflow references before constructing a command. Use scripts/plan_launch.py to validate and print a launch plan without starting a job.

Safe sequence

1. Confirm the config, prepared dataset info files, checkpoint (for test), work directory, GPU count, and launcher.
2. Preflight imports and CUDA/compiled-op readiness via runtime-ops.
3. Plan a single-GPU dry run or distributed environment explicitly; do not infer distributed mode only from a shell's CUDA_VISIBLE_DEVICES.
4. Start with a bounded smoke/evaluation case if a real fixture exists.
5. Preserve config, Det3D version, class metadata, logs, and checkpoint paths in the work directory for reproducibility.

Training and normal evaluation are GPU/data/checkpoint workflows. CPU import or config parsing is not a substitute. Full native runs are intentionally not performed by this skill without user-provided data and an approved budget.

Use datasets-and-preprocessing for data generation, configuration-and-models for architecture edits, and visualization-and-analysis for result artifacts.