---

name: training-evaluation-configs description: "Route MMOCR config-driven training, testing, evaluation, model-zoo selection, distributed/Slurm launch, AMP, resume, and TTA preflights." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

training-evaluation-configs

Use this router when the task is to inspect, edit, preflight, or launch MMOCR config-driven training/testing/evaluation. It covers Python config inheritance, --cfg-options, model-zoo family selection, checkpoint matching, train.py/test.py, distributed scripts, Slurm scripts, AMP, resume/load behavior, prediction dumping, and test-time augmentation.

Route first

• Config syntax, _base_ inheritance, default scope, dataloaders, evaluators, work_dir, resume, load_from, or one-off override checks: read config reference and run the safe config smoke helper.
• Single-device training/testing, distributed launch, Slurm launch, offline evaluation, visualization, --amp, --auto-scale-lr, --resume, --save-preds, or --tta: read training and evaluation workflows.
• Model-family choice, model-index aliases, config/checkpoint matching, evaluator family, or AMP support by algorithm: read model zoo reference.
• Broken _base_ paths, shell quoting, missing data paths, missing checkpoints, checkpoint/config mismatch, CPU/CUDA fallback, unsupported AMP, port conflicts, Slurm variables, or work_dir/checkpoint/resume confusion: read troubleshooting.

Boundaries

Route elsewhere when the task is not an end-to-end training/evaluation/config problem:

• Dataset download, conversion, annotation format, dataset-zoo authoring, LMDB preparation, or generated dataset bases: ../data-preparation/.
• OCR/KIE inferencer APIs, prediction-only inference, model-name inference smoke tests, or high-level OCR chains: ../ocr-inference/.
• Model internals, custom components, registries, data samples, metrics as classes, transforms, visualizers, or project extension code: ../model-api-components/.

Safe operating pattern

1. Load the target config with the smoke helper before any real train/test command.
2. Confirm default_scope, model.type, dataloader dataset types/batch sizes, evaluator types, work_dir, resume, load_from, TTA fields, and distributed backend.
3. Match the config family to the checkpoint and evaluator before enabling expensive launch options.
4. Choose the smallest launcher that fits the hardware: single-device first, then distributed, then Slurm.
5. Enable --amp, --auto-scale-lr, --resume, --save-preds, or --tta only after the workflow and model-zoo references say the route is valid.

Source-script policy

• The bundled smoke helper only calls mmengine.Config.fromfile and cfg.merge_from_dict; it never builds a runner, dataset, model, evaluator, or training loop.
• Official train.py, test.py, distributed, and Slurm scripts are real execution routes. Document and use them only when the user intends to start training or evaluation.
• Offline evaluation and visualization tools are reference-only in this sub-skill because they build evaluators, datasets, visualizers, or simulated runners.