---

name: training-evaluation description: "Launch, troubleshoot, and inspect MMSegmentation training, validation, testing, metrics, logs, and distributed jobs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Training and Evaluation

Use this sub-skill when the task is to run or debug MMSegmentation training, validation, testing, metrics, log analysis, checkpoint resume/fine-tuning, distributed launch, Slurm jobs, AMP, TTA, offline result formatting, or test-time visualization.

Route Here When

• The user needs a safe MMSegmentation training/testing command, Runner.from_cfg guidance, distributed launch, or Slurm launch.
• A run failed around checkpoints, work_dirs, --resume, load_from, --amp, --cfg-options, launcher settings, ports, ranks, or unavailable CUDA.
• The user needs IoU, Cityscapes, or depth metric behavior explained, including output_dir, format_only, and saved prediction files.
• The user wants to summarize MMSegmentation JSON logs, compare loss/metric curves, or inspect training progress without starting a long run.
• Testing needs --out, TTA, visualized predictions, or hidden-test/offline submission formatting.

Do Not Handle Here

• Dataset registration, data roots, annotation format, pipelines, or config authoring details; route those to data-configuration.
• Implementing models, decode heads, losses, optimizers, hooks, or registries beyond runtime troubleshooting; route those to model-customization.
• Inference-only APIs such as init_model, inference_model, or inferencers; route those to inference.

Start With

1. Identify whether the user wants a dry-run command, a real execution, or a diagnosis of an existing log/error.
2. For training/testing commands, prefer the bundled safe wrappers first:
   • scripts/mmseg_train_wrapper.py --help
   • scripts/mmseg_test_wrapper.py --help
3. For log summaries, use scripts/analyze_mmseg_log.py --help; it has no MMSegmentation import requirement and works on small JSON-line fixtures.
4. If direct commands are clearer than wrappers, use the exact patterns in references/train-test-workflows.md and references/distributed-training.md.
5. For metric/output questions, check references/metrics-and-logs.md before changing evaluator config.
6. For failures, match the symptom in references/troubleshooting.md before changing code or re-running expensive jobs.

Key References

• references/train-test-workflows.md covers single-process training/testing, Runner.from_cfg, AMP, resume/fine-tune, TTA, visualization, output formatting, and work_dirs.
• references/metrics-and-logs.md covers IoUMetric, DepthMetric, Cityscapes-style evaluation, JSON log summaries, confusion-matrix inputs, and benchmark cautions.
• references/distributed-training.md covers torch.distributed.launch, multi-node environment variables, Slurm variables, port conflicts, and NPU launch shape.
• references/troubleshooting.md maps common training/evaluation failures to checks and safe fixes.

Safety Defaults

• The bundled training/testing wrappers are dry-run/preflight tools unless --execute is passed.
• Do not start full training, testing, distributed, Slurm, or benchmark jobs without explicit user approval and a bounded config/checkpoint scope.
• Prefer short help checks, config parsing, tiny log fixtures, or metric unit tests before native long-running jobs.
• Keep offline formatting and hidden-test workflows explicit: set evaluator format_only=True only when ground truth is unavailable or evaluation should be skipped.