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name: nuplan-devkit description: "Use for Motional nuPlan autonomous-driving planning workflows: dataset and map access, scenario filtering, planner implementation, open- or closed-loop simulation, metrics, nuBoard, training/preprocessing, nuplan_cli, and submission packaging." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

nuPlan devkit

Use this skill for the nuPlan-devkit 1.2.2 Python package and its autonomous vehicle planning benchmark workflows. It is a router, not a replacement for the focused sub-skills. Keep the dataset, map version, experiment output, and backend assumptions explicit before suggesting a command.

Fast routing

• Actor states, frames, geometry, trajectories, or tensor transforms: read core-geometry.
• Dataset roots, SQLite/ORM queries, sensor blobs, maps, or scenario filters: read data-and-maps.
• Planner code, simulation, metric calculation/aggregation, result files, or nuBoard: read simulation-and-evaluation.
• Feature/target builders, caching, model construction, or training: read training-and-preprocessing.
• nuplan_cli, gRPC submission packaging, Docker/EvalAI protocol, or protected submission files: read submission-and-cli.

For a task that crosses routes, load the upstream route first: data-and-maps for a scenario/data question, then simulation-and-evaluation for execution; training-and-preprocessing shares the scenario/data contract; submission-and-cli shares the planner trajectory contract.

Installation and first checks

The package targets Python 3.9+ and exposes the nuplan_cli console script. Use a private environment and install the package with the dependency variant appropriate to the requested route. The older documented compatibility point uses NumPy 1.23.4, Hydra 1.1.0rc1, SQLAlchemy 1.4.27, and, for model workflows, PyTorch 1.9.0 with the repository's matching CUDA wheel on Linux. Do not add training, CUDA, browser, Docker, or S3 dependencies just to answer a database or geometry question.

After installation, run the bundled read-only check from this skill directory:

python scripts/check_environment.py
python scripts/check_environment.py --cuda
nuplan_cli --help
nuplan_cli db --help

--cuda is optional and only checks a visible CUDA runtime; it does not prove that a planner or training job fits a particular GPU. The check never downloads nuPlan data, starts a service, or writes experiment files.

Required runtime contract

Set these variables before data-backed workflows:

• NUPLAN_DATA_ROOT: parent containing nuplan-v1.1/ DB splits and sensor blobs; normally read-only.
• NUPLAN_MAPS_ROOT: directory containing map-version metadata and GeoPackages.
• NUPLAN_EXP_ROOT: writable experiment/cache/output root.
• NUPLAN_MAP_VERSION: usually nuplan-maps-v1.0 when a map version is needed.

Validate the local layout before invoking a builder, planner, or training job. Do not let a missing default DB or blob path trigger an implicit download. Use the data route's validator and stop at a help-only CLI check when data is not present.

Verification boundaries

The generated guidance is verified against package source, docs, installed API signatures, CLI help, static helpers, and selected CPU/mock-native cases. Full nuPlan dataset downloads, S3 access, Docker Compose, EvalAI uploads, notebook execution, benchmark-scale training, and portable CUDA performance are explicitly environment-dependent. Read the relevant troubleshooting reference before treating a failure as a planner or model defect.

Read references/installation-and-environment.md for dependency variants, environment variables, and verification boundaries. Read references/troubleshooting.md for cross-cutting import, data, Hydra, backend, and output failures. Read references/repo-provenance.md before deciding whether this skill is current for another checkout.

Handoff discipline

Report the exact package version, dataset split/map version, config overrides, worker mode, output root, and which backend/data/remote actions were actually run. Do not claim a full benchmark result from a config parse or a one-batch smoke test. Keep credentials, private paths, generated reports, and source checkout assumptions out of reusable instructions.