pypddl-datasets 0.0.4

Download PDDL planning benchmark suites: classical, numeric, profiling, and learning splits.

planning-benchmarks

PDDL planning benchmark suites — classical, numeric, profiling, and generated learning splits — published as the pypddl-datasets Python package. The package itself is small: benchmark data is downloaded on first use from the matching GitHub release and cached locally.

Usage

pip install pypddl-datasets

import pypddl_datasets as pb

pb.list_suites()          # ['autoscale-agile-strips', ..., 'ipc-optimal-strips', ...]

task = pb.fetch_task("classical/tests/gripper/test-1.pddl")
task.domain_path          # .../gripper/domain.pddl   (correct also where instances
task.task_path            # .../gripper/test-1.pddl    carry their own domain files)
task.domain, task.problem # "classical-tests-gripper", "test-1.pddl" — lab-safe display names

domain = pb.fetch_domain("classical/downward-benchmarks/gripper")
domain.path               # the domain directory
domain.tasks              # list[Task], downloaded once and cached

suite = pb.fetch_suite("ipc-optimal-strips")   # Suite(path, domains)
for domain in suite.domains:
    for task in domain.tasks:
        run_planner(task.domain_path, task.task_path)

Most suites have a -test companion (e.g. "ipc-optimal-strips-test") whose entries are one representative task per domain — a cheap smoke run before committing to a full suite. pb.export_suite(suite, dest) materializes a suite as a plain directory tree for non-Python tools.

Domains can be filtered by their declared PDDL requirements — supported is a capability ceiling (keep what your planner handles), requires a feature floor (keep what exercises a feature). The data declares exactly the atomic requirements each file uses (strict-validated; aggregates like :adl never appear), and all queries are metadata-only (no download):

from pypddl_datasets import Requirement as R

SUPPORTED = {R.STRIPS, R.TYPING, R.ACTION_COSTS, R.NEGATIVE_PRECONDITIONS}

pb.task_requirements("classical/tests/gripper/test-1.pddl")  # frozenset({R.STRIPS})
pb.domain_requirements("classical/tests/gripper")     # union over the domain's tasks
pb.find_tasks(requires={R.CONDITIONAL_EFFECTS})       # task names, per-task precision
pb.find_domains(suite="ipc-satisficing-strips", supported=SUPPORTED)
pb.find_suites(supported=SUPPORTED)                   # suites runnable in full
pb.fetch_suite("ipc-satisficing-strips", supported=SUPPORTED)   # filtered fetch

pb.list_domains() lists every individually fetchable domain. The cache location defaults to the platform cache dir and can be overridden with the PYPDDL_DATASETS_CACHE environment variable. On machines without internet access, set PYPDDL_DATASETS_DATA to a local checkout's data/ directory and domains resolve there without downloading.

Repository layout

• src/pypddl_datasets/ — the package: fetch API, suite definitions, and the instance generators (including the train/valid/test split configurations).
• data/ — all benchmark data, organized as <formalism>/<collection>/<domain> (classical/, numeric/). Not shipped in the package; released as a single archive on data-v* GitHub releases, downloaded and unpacked once per machine on first use.
• data/classical/generated/<domain>-{train,valid,test}/ — fixed learning splits produced by the generators. These committed instances are the reproducibility contract; regenerate with python -m pypddl_datasets.generators.classical.<domain>.generate_instances.
• scripts/package_data.py — turns data/ into the byte-reproducible data.tar.gz release archive.
• scripts/extract_requirements.py — regenerates the committed requirements.{tasks,domains,suites}.json metadata files (run after changing data; a test guards freshness).
• validate.py — parses every domain/problem with pypddl; must pass for a data release to go out.

Releasing

Data and package releases are decoupled; data releases are permanent (published package versions pin them by tag).

1. Data changed? Tag and push data-v<N>. The workflow validates all PDDL (aborting the release on failure), packages data.tar.gz, and uploads it to the data-v<N> GitHub release. Copy the sha256 it prints into DATA_SHA256 (and bump DATA_VERSION) in src/pypddl_datasets/__init__.py.
2. Package release: bump version in pyproject.toml, tag and push v<version>. The workflow verifies the tag matches the version and that the pinned data release exists with the pinned hash, then builds and publishes to PyPI via trusted publishing. No assets are uploaded — code releases cost nothing in storage.

Contributing data

Clone with Git LFS (large instances are LFS-tracked):

git lfs install
git clone [e-mail]:planning-and-learning/planning-benchmarks.git
cd planning-benchmarks && git lfs pull

Track PDDL files larger than 10 MiB before staging them:

find data -type f -name '*.pddl' -size +10M -print0 | xargs -0 git lfs track --filename
git add .gitattributes data

Domain directories must contain their .pddl files directly (that is how scripts/package_data.py discovers them), and domain names flattened with / → - must stay unique (Task.domain; test-guarded). Run pytest tests to check suite definitions stay consistent.