---

name: scenic description: "Use Scenic, Google Research's JAX/Flax computer-vision research codebase, for configs, training, datasets, models, layers, baselines, and project-specific workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Scenic Repo Skill

Use this skill when a task is about Scenic (google-research/scenic): JAX/Flax vision research experiments, Scenic config files, app/training runners, dataset registry errors, BaseModel/layer APIs, or choosing/adapting a Scenic baseline or project.

Scenic is both a lightweight shared library (common_lib, dataset_lib, model_lib, train_lib) and a large project zoo under scenic.projects. It favors project-level forking/copying for research code and keeps reusable patterns in the shared libraries.

Start here

1. If setting up an environment or checking whether Scenic imports, read references/installation-and-environment.md and run scripts/inspect_scenic_package.py.
2. If the task names --config, --workdir, rng_seed, trainer_name, model_name, dataset_name, LR schedules, optimizers, checkpoints, or train launches, use sub-skills/running-and-training/SKILL.md.
3. If the task is about dataset registration, TFDS, FlexIO, COCO/TFRecord layouts, DatasetRegistry, @add_dataset, or unknown dataset errors, use sub-skills/data-pipelines/SKILL.md.
4. If the task is about BaseModel contracts, registered model names, Flax modules, attention/layers, matchers, losses, metrics, or tiny model checks, use sub-skills/modeling-and-layers/SKILL.md.
5. If the task asks which Scenic baseline/project to use, how a project-specific main.py/config/registry works, or which optional dependencies a project needs, use sub-skills/baselines-and-projects/SKILL.md.

Safe core checks

Run these from any environment where Scenic is installed:

python scripts/inspect_scenic_package.py
python scripts/run_scenic_smoke.py

The smoke helper is self-contained: it imports safe modules, checks a tiny JAX calculation, validates LR schedule construction, and lists dataset/model registry names. It does not run training, load datasets, download checkpoints, or execute original repository tests.

For optional trainer-registry import diagnostics:

python scripts/inspect_scenic_package.py --check-trainers
python scripts/run_scenic_smoke.py --check-trainers

A trainer import failure can be an optional transfer/BigTransfer/TensorFlow Addons dependency issue rather than a core package failure. Read references/troubleshooting.md and the running-and-training troubleshooting reference before changing package versions.

Route by user intent

User intent or signal	Read next
"How do I run this config?", --config, --workdir, dataset_service_address, JAX backend flags	running-and-training
Validate a config without launching a job	running-and-training and its scripts/scenic_config_probe.py
Unknown dataset, custom dataset registration, TFDS/FlexIO/COCO/TFRecord data layout	data-pipelines
Unrecognized model, model registry names, BaseModel, metrics/losses, matchers/layers	modeling-and-layers
Choose ViT/ResNet/DETR/CLIP/BERT/ViViT/MTV/OWL-ViT/UnLoc/Vid2Seq/DenseVOC/PixelLLM/etc.	baselines-and-projects
Optional dependencies such as DMVR, T5/T5X, CLIP/Torch, COCO/LVIS, pycocotools, TensorFlow Addons, BigVision	baselines-and-projects plus root troubleshooting
Shared image/video/debug/export helpers	references/common-utilities.md
Check whether this generated skill is stale for a checkout	references/repo-provenance.md

Operating rules

• Do not launch full training/evaluation until config, dataset availability, checkpoint paths, backend devices, and project dependencies are all known.
• Treat CPU smoke checks as validation for API/config guidance only. They do not prove GPU/TPU performance or multi-host correctness.
• Do not install every project requirements.txt. Pick the project/baseline first, then install the narrow optional dependency set it needs.
• Do not run data-conversion tools unless input data, output paths, credentials, side effects, and dependencies are explicitly approved.
• For project-specific models/trainers/datasets, prefer the project main.py and project registry pattern instead of assuming the root scenic.main registry knows every project object.
• Keep source-code modification and contribution-policy tasks separate from user-facing Scenic experiment use; if the user is editing Scenic itself, verify with focused tests and current project instructions.

Provenance and routing metadata

• Read references/repo-provenance.md before refreshing or trusting this skill for a different Scenic checkout.
• references/repo-routing-metadata.json is the structured router metadata used by the managed repo-skill importer when import is explicitly requested in a later run.