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name: running-and-training description: "Run, validate, and troubleshoot Scenic app/config/training workflows without accidentally launching expensive jobs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Scenic Running and Training Router

Use this sub-skill when a user wants to install-check Scenic, understand how the Scenic application runner passes --config, --workdir, dataset-service, RNG, and JAX backend flags into training, validate an experiment config before launch, build a safe training command, or troubleshoot training utility imports.

Read or run these bundled files

• Read references/running-and-configuration.md for app-run semantics, required flags, config shape, safe command construction, workdir/checkpoint behavior, dataset service notes, JAX backend flags, and no-training validation steps.
• Read references/training-api.md for lr_schedules, optimizers, train_utils, TrainState, checkpoint helpers, trainer flow, transfer/pretraining caveats, and source-script exclusion notes.
• Read references/troubleshooting.md when an install/import, config, dataset/model/trainer name, TensorFlow Addons/Keras, JAX backend, checkpoint, or expensive-training failure appears.
• Run scripts/scenic_config_probe.py when a user provides a Python config and asks whether it is structurally safe to launch. The helper imports the config, prints top-level keys, checks dataset/model/trainer/RNG/training-related fields, and never starts training.

Fast routing

• Config preflight or launch safety: run the bundled config probe first, then inspect missing or warning fields in running-and-configuration.md.
• Constructing a command: require a Python config file and a fresh or intentionally resumed workdir; include --config and --workdir; add --dataset_service_address only with a compatible config.
• Learning-rate, optimizer, TrainState, checkpoint, or transfer details: read training-api.md.
• tensorflow_addons, keras.src.engine, big_vision, or trainer import failures: read troubleshooting.md; avoid importing the trainer registry for simple config/LR/optimizer checks.

Boundaries

• Route model classes, model registry entries, Flax layer APIs, and metric/loss authoring to ../modeling-and-layers/SKILL.md.
• Route dataset registry details, TFDS/raw data layout, and preprocessing pipeline internals to ../data-pipelines/SKILL.md.
• Route project-specific mains, optional dependency catalogs, and project tools to ../baselines-and-projects/SKILL.md.

Do not tell future agents to open or run the original repository's tests, examples, notebooks, or source files for this sub-skill. Use the bundled references and helper script as the runtime knowledge base.