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name: training-testing description: "Plan and validate MMDetection 3.3.0 training, resume, testing, evaluation, result dumping, and distributed or Slurm launch commands from configs without running them." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

training-testing

Use this sub-skill when the task involves launching or planning tools/train.py, tools/test.py, dist_train.sh, dist_test.sh, Slurm wrappers, resume behavior, work_dir outputs, command-line config overrides, --auto-scale-lr, CPU debugging, or result dumping.

Route first

• Config inheritance, auto_scale_lr fields, model zoo checkpoint choice, and config edits: use configuration-model-zoo first, then return here for launch commands.
• Dataset roots, COCO/VOC/Cityscapes annotation layout, evaluator metrics, and metric-specific output prefixes: use datasets-evaluation first.
• New hooks, runners, loops, optimizers, schedulers, or custom registries: use customization-extension first.
• Environment import failures such as ModuleNotFoundError: mmcv._ext: ensure full mmcv is installed instead of mmcv-lite; then return here.

Core workflow

1. Identify the config, checkpoint if testing, intended device topology, output work_dir, and any --cfg-options overrides.
2. For training, choose single-process, CPU debug, PyTorch distributed, or Slurm from training workflows.
3. For evaluation, visualization, TTA, and prediction dumps, use testing and evaluation.
4. Before giving a launch command, run the bundled dry-run planner to catch common flag mistakes:

python sub-skills/training-testing/scripts/build_train_test_command.py train CONFIG.py --work-dir work_dirs/EXP --auto-scale-lr
python sub-skills/training-testing/scripts/build_train_test_command.py test CONFIG.py CHECKPOINT.pth --gpus 8 --out results.pkl

5. If the user reports a failure, triage with training/testing troubleshooting.

Command patterns

• Single GPU train: python tools/train.py CONFIG.py --work-dir work_dirs/EXP [--resume [CKPT]] [--auto-scale-lr] [--cfg-options key=value ...].
• CPU debug train: CUDA_VISIBLE_DEVICES=-1 python tools/train.py CONFIG.py --work-dir work_dirs/debug ...; expect very slow execution.
• Distributed train: CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 bash tools/dist_train.sh CONFIG.py 4 ....
• Slurm train: GPUS=8 GPUS_PER_NODE=8 bash tools/slurm_train.sh PARTITION JOB CONFIG.py WORK_DIR ....
• Single GPU test: python tools/test.py CONFIG.py CHECKPOINT.pth [--work-dir WORK_DIR] [--out results.pkl] [--show-dir DIR] [--tta].
• Distributed test: PORT=29500 bash tools/dist_test.sh CONFIG.py CHECKPOINT.pth GPUS [--out results.pkl] ....

Always explain that shell wrappers are patterns tied to the user's checkout and cluster, while this skill's script only prints commands and validates arguments.