abench

Run reproducible ActivitySim runtime and memory experiments in Linux Docker, from macOS or Linux. One runner supports MTC, SANDAG ABM3, and other models through small YAML profiles. ActivitySim itself does not need to be installed on the host.

With uv, run without managing a Python environment:

uvx abench --help
uvx abench experiments.yaml

For a specific release use uvx abench@0.1.2 experiments.yaml; use uvx abench@latest to refresh to the latest release. Docker and model data must still be available locally. macOS and Linux hosts are supported.

Alternatively, install with pip:

python -m pip install abench
abench run --model-dir /path/to/sandag-abm3-example --profile sandag \
  --source activitysim=ActivitySim/activitysim@<full-40-character-SHA> \
  --source sharrow=ActivitySim/sharrow@<full-40-character-SHA> \
  --multiprocess --processes 4 --sharrow --households 28365 \
  --memory 32g --shm-size 8g --output-dir /path/to/experiments/sandag

Use --profile mtc for MTC; both profiles ship with the package. SANDAG defaults to its small benchmarking-data, not full-scale skims. MTC defaults to data_full. --data-dir overrides either. Model directories need not be Git repositories; Git revision/status are recorded where available and model files are always snapshotted. Existing example scripts and normal configs are untouched.

The host needs Python 3.10+, PyYAML (installed with abench), and a Linux Docker engine with cgroup v2 and memory.peak. Docker Desktop must have enough VM RAM for the chosen memory limit plus VM overhead. The default container is Debian Bookworm/Python 3.11. Current instrumentation requires ActivitySim's workflow.State API (1.4-era or newer); arbitrary historical revisions are not promised to work. Build/runtime failures retain diagnostics and a failure report.

Named experiment files

You can pass a model directory instead of a YAML file:

abench /path/to/model

Abench looks for .yaml and .yml files directly inside /path/to/model/.abench/ (no recursive search). In a terminal it lists them alphabetically and asks which experiment to run, then prompts for that experiment's inputs. Use ↑/↓ to move the highlighted selection and Enter to confirm. The first file is initially selected; a single file still gets a selection prompt. Only the selected file is loaded. run, validate, and prepare all support directory selection.

abench /path/to/model --help lists available files without prompting or running anything. Missing .abench directories and empty file lists produce clear errors. Without a terminal, or with --non-interactive, one file is selected automatically; multiple files require passing the desired YAML path directly.

Relative paths remain relative to the selected YAML file. For a file inside .abench, use model_dir: .. and, for example, data_dir: ../data_full and output_root: ../benchmark-runs/run-${timestamp} to reference the parent model. For declared downloads, keep unpack destinations inside .abench (for example, unpack: data_full with data_dir: data_full); asset destinations cannot use ... Moving an existing suite into .abench requires reviewing its paths.

Write common options once and override only what differs between runs:

schema_version: 1
inputs:
  households:
    type: integer
    default: 28365
    minimum: 0
  warmup_households:
    type: integer
    default: 5000
    minimum: 1
vars:
  model: /path/to/sandag-abm3-example
output_root: ./results/sandag-${timestamp}
defaults:
  model_dir: ${model}
  profile: sandag
  data_dir: ${model}/benchmarking-data
  config_overlay: ["${model}/configs_explicit_chunk"]
  multiprocess: true
  processes: 4
  sharrow: true
  households: ${households}
  warmup_households: ${warmup_households}
  memory: 80g
  shm_size: 8g
  sources:
    - sharrow=ActivitySim/sharrow@fc175b27d8e0c5d202721c67d96b050e6117b235
runs:
  main:
    sources:
      - activitysim=ActivitySim/activitysim@5c6fae24a91a57a2d6dfc2e1dbe062a61d94545a
  pr1110:
    sources:
      - activitysim=ActivitySim/activitysim@51e298a84276813946e1d623c9a5785e078e022f

Save it as sandag.yaml, then run:

abench sandag.yaml
# Or check all runs without building images or running models:
abench validate sandag.yaml

A ready-to-use SANDAG chunked suite is included in the repository. Its paths assume abench and the SANDAG repository are siblings. The pinned main revision is the one used in the earlier trials, not a moving branch reference.

To compare current main against a PR, named suites also accept source mappings with branch or pr in place of commit:

schema_version: 1
inputs:
  activitysim_pr:
    type: integer
    required: true
    minimum: 1
    description: ActivitySim PR number to compare against current main
output_root: benchmark-runs/mtc-${timestamp}
defaults:
  profile: mtc
  households: 500000
  multiprocess: true
  processes: 4
  sharrow: true
  sources:
    - sharrow=ActivitySim/sharrow@fc175b27d8e0c5d202721c67d96b050e6117b235
runs:
  main:
    sources:
      - name: activitysim
        repository: ActivitySim/activitysim
        branch: main
  pr:
    label: ActivitySim PR ${activitysim_pr}
    sources:
      - name: activitysim
        repository: ActivitySim/activitysim
        pr: ${activitysim_pr}

Each mapping must provide exactly one of commit, branch, or pr. Branch/PR selectors work for any source package in a suite, including extras/subdirectories. Abench uses host Git and network access to resolve each selector once per suite, before running models, and passes only exact commits to the builds. pr selects refs/pull/<number>/head in the named repository, including PRs from forks; it does not select the synthetic merge commit. Use a positive integer PR number. branch names the branch without refs/heads/.

The resolved commits are printed and recorded in suite.json alongside the original YAML, and in each experiment's source provenance. A new invocation resolves the selectors again, including validate and prepare. To reproduce an earlier suite, replace selectors with commit: <recorded-full-SHA>. CLI overrides and model profiles still require exact commits.

Start the suite and answer its input prompts:

uvx abench ./activitysim-prototype-mtc/abench.yaml
# Preflight the same selection without running benchmarks:
uvx abench validate ./activitysim-prototype-mtc/abench.yaml

Declare user-facing settings in inputs, and keep internal reusable values and expressions in vars. Only inputs can be overridden. For example:

inputs:
  households:
    type: integer
    default: 500000
    minimum: 0
    description: Households to sample; 0 uses the full population
  mode:
    type: string
    default: chunked
    choices: [chunked, unchunked]
  sharrow:
    type: boolean
    default: true
vars:
  label: "${mode}, ${households} households"

In a terminal, run, validate, and prepare prompt for each input in YAML order. Inputs with choices use the same arrow-key menu, with the declared default initially highlighted (or the first option if required). Enter confirms the selection and preserves its declared type. Other inputs use text prompts. Press Enter to accept a displayed default. Required text inputs have no default and must be entered; empty or invalid answers prompt again with an explanation. Ctrl-C cancels before source resolution or downloads. Selected values are printed and saved in suite.json as input_values.

For scripting, --non-interactive uses defaults and supplied values without prompting. Non-terminal stdin behaves the same way; missing required inputs fail instead of hanging. Repeatable --set NAME=VALUE can supply values explicitly; these inputs are not prompted. Quote arguments containing spaces, for example --set 'label=My experiment' if label is declared as a string input. abench experiment.yaml --help lists the file's inputs, descriptions, defaults, and constraints without requiring input values, contacting GitHub, downloading data, or creating outputs.

• Every input requires a type: string, integer, number, or boolean. Give it either a typed YAML default or required: true, but not both.
• Optional choices restricts allowed values; minimum/maximum are inclusive bounds for numeric inputs. Numbers must be finite. Boolean overrides accept true or false (case insensitive), not yes, no, 1, or 0.
• Values are converted according to their declared type, never parsed as YAML. Strings retain literal text, including =, spaces, and ${...}. Defaults and input declarations are literal too; use vars for derived expressions.
• Defaults are applied first, followed by command-line overrides, then ${...} expansion. Inputs and vars share one namespace; duplicate names and the reserved name timestamp are errors. Input names use letters, digits, and underscores and cannot start with a digit.
• Unknown inputs, duplicate overrides, and invalid explicit values fail before source resolution or downloads. Missing required inputs prompt in a terminal and fail in non-interactive mode. There is no overriding vars.
• The YAML file is unchanged. suite.json records typed cli_overrides, effective input_values, the expanded configuration, and exact source resolutions. experiments.yaml preserves the original file, so replay its recorded overrides too when reproducing a run.

branch: main is freshly resolved on every launch; there is no saved branch SHA to update manually. For the MTC example, enter the PR number when prompted, then press Enter twice to accept 500,000 households and 4 processes.

These features are available in abench 0.1.1 and later.

• defaults accepts CLI options using underscores (shm_size, config_overlay, etc.). Use multiprocess: false for serial execution and sharrow: false to disable Sharrow. sources accepts the same strings/mappings as model profiles.
• runs is an ordered mapping of names to overrides. Each run inherits defaults; ordinary values and lists are replaced. Sources merge by normalized package name, so changing ActivitySim does not discard the shared Sharrow pin.
• ${name} substitutes a scalar from inputs or vars; vars can reference other vars and inputs. A whole-value reference preserves its type, including numbers/booleans. Undefined references and cycles are errors. No shell or environment expansion is performed. ${timestamp} is a built-in UTC launch identifier shared by all runs, with microseconds to avoid reusing output directories.
• All explicit paths in the suite are relative to the YAML file, independent of the terminal's current directory. This includes overlays and custom profile paths. Built-in mtc/sandag profile names retain their meaning. When omitted, model_dir defaults to the YAML file's directory; the model profile still supplies its usual default data/config paths.
• The suite owns output locations: output_root/<run-name>/. Set output_root once instead of output_dir in each run. Existing roots are rejected.
• All runs are preflighted before the first starts, then run sequentially in file order. Failure stops the suite and retains partial results. The combined report is output_root/comparison.html; individual runs retain their own reports. experiments.yaml and suite.json record the original file and expanded plan.
• File invocations accept --set and --non-interactive; other model options belong in defaults or the relevant run.

This experiment file describes which tests to run. A model profile such as benchmark.yaml describes how to configure a model, and remains reusable across suites.

Terminal progress identifies the experiment number, image build, warmup, and measured attempts/retries. During builds and model phases, an interactive terminal shows live elapsed time, current and peak cgroup memory (when available), and a panel with the last 12 lines of the run log. Ctrl-C cancels the command. Full console output stays in the printed log paths; elapsed time and memory status are saved every 15 seconds in a sibling console.progress.log (or build.progress.log for builds). Redirected output and non-interactive terminals only print start and finish summaries. These are status updates, not an estimated completion percentage.

Publishing benchmark results to a PR

A suite can post one results comment after its final comparison report. Add an explicit target (the repository is always on github.com):

inputs:
  pr:
    type: integer
    required: true
    minimum: 1
publish:
  github:
    repository: ActivitySim/activitysim
    pr: ${pr}
    baseline: main

baseline names a run in runs and defaults to the first run. The posting target is independent of source selection: use pr: ${pr} in an ActivitySim source mapping to benchmark that PR's pinned head as well. See examples/sandag-pr.yaml for a complete example with a pinned baseline; adjust its model/data paths and baseline commit for your model.

abench examples/sandag-pr.yaml --set pr=1110
# Run benchmarks, but only generate the local comment and charts:
abench examples/sandag-pr.yaml --set pr=1110 --publish-dry-run
# Inspect or publish retained results without rerunning benchmarks:
abench publish /path/to/suite-output --dry-run
abench publish /path/to/suite-output
# Read local status, or verify the comment still exists on GitHub:
abench publish /path/to/suite-output --status
abench publish /path/to/suite-output --verify
# The permanent launcher works from any working directory:
/path/to/suite-output/publication/publish.sh
/path/to/suite-output/publication/publish.sh --dry-run

Publication requires GitHub CLI 2.99 or newer with gh pr comment --attach, an authenticated account, and repository write access for image uploads. Use gh auth login --hostname github.com (OAuth), or a classic personal access token through GH_TOKEN. Other token types and GitHub Enterprise Server image uploads are not supported by this first release. gh remains optional for ordinary runs and publication dry runs. Authentication and PR access are checked before asset preparation or benchmark execution when publication is enabled; validation and prepare-only commands do not publish. Tokens are never written to experiment metadata or passed into benchmark containers.

The comment contains elapsed time and peak memory, percentage changes against the baseline for valid runs with matching comparison settings, run settings, source commits, memory traces, and component-runtime charts. Invalid runs have no change claims and are excluded from the runtime chart; unstarted runs are identified. PR head/base commits are recorded at startup, and a head change during execution is noted when posting. These are observations, not statistical significance tests.

The publication/ directory retains comment.md, memory.svg, runtimes.svg, and state.json with the comment ID/URL and publication status. STATUS.md shows whether publication is pending, failed, uncertain, or published, with attempt time, errors, a retry command, and the comment link. It reflects local knowledge; --verify checks GitHub without uploading or editing anything. Verification failures preserve the previous publication record. A missing previously published comment is flagged and is not automatically recreated.

The executable publish.sh is permanent: it is safe to call repeatedly and passes through --dry-run, --status, or --verify. It prefers abench on PATH, then uses uvx --refresh --from <recorded-checkout> when a development checkout was available when the bundle was prepared. It reports an actionable error if neither is available and never stores credentials. Output paths with spaces are supported. Existing suites can create these helpers with abench publish OUTPUT --status without reading measurement data or contacting GitHub.

The final console message explicitly says whether results were published, gives the comment URL on success, or gives the launcher command when action is needed. A connection failure after posting begins is marked as an uncertain outcome; retrying reconciles the comment marker before attempting another post.

The exact upload body is retained as upload.md. A retry searches for the suite's unique comment marker before posting; a recorded successful publication is a no-op. Independent suite executions get separate comments. Simultaneous publication of the same output directory is blocked. Partial uploads can leave unused GitHub attachments, but the local reports are retained and a retry can complete the comment.

A posting error fails the command and prints the retry command. If the benchmark also failed, its failure remains primary and the publication error is printed separately. The full HTML/JSON comparison remains local; this release uploads only the summary and SVG figures, not raw logs, model data, or the interactive HTML. Images must fit GitHub's 10 MB attachment limit, and summaries are limited to 60,000 characters. Publication transport tests simulate GitHub; a live attachment smoke test should use a designated test PR, never a production PR by default.

Run controls

On macOS, benchmark commands automatically run /usr/bin/caffeinate -i to prevent idle system sleep for the entire invocation, including preparation, builds, warmup, measured attempts, reporting, and PR publication. The display can still sleep. The assertion is released on completion or cancellation; it also expires if the abench process exits unexpectedly. Other platforms are unchanged. Use --allow-sleep to opt out, for example abench experiments.yaml --allow-sleep. Report-only, publish-only, validation, preparation-only, and help commands do not start caffeinate. This prevents idle sleep; it does not override explicit sleep or closing a laptop lid.

• --single-process (default), or --multiprocess --processes N. The count applies to every sliced stage; coordinators are additional processes.
• --sharrow (default) or --no-sharrow. Sharrow enabled requires its source pin.
• --households N (default 1,000); zero uses the original full input population. abench never replicates households. Positive samples must match realized output.
• --config-overlay configs_explicit_chunk adds config directories in listed priority order. Relative overlay paths are relative to the model directory.
• --memory 16g, --shm-size 8g, --interval 0.5, and optional --platform.
• --output-dir must be new. --label names an experiment, and --compare accepts earlier experiment directories. Compatible compiled flows are reused automatically; the serial warmup still runs.

abench validate accepts the same experiment arguments without --output-dir. It checks the profile, required inputs, CSV population size, source pin syntax, and Docker capabilities without building an image or running the model. It does not prove Git commit availability, package compatibility, or skim consistency; those are checked by the build and model run.

Any dependency from GitHub source

Repeat --source for any Python distribution, including add-on extensions:

--source 'my-addon[fast]=ExampleOrg/model-addon@<SHA>#subdirectory=python/addon'

The left side is the distribution name (which can differ from its import module). Each source uses an exact full SHA and an organization/repository name. Extras and a repository subdirectory are optional. Profiles can declare the same entries as strings or mappings:

sources:
  - name: my-addon
    repository: ExampleOrg/model-addon
    commit: '0123456789abcdef0123456789abcdef01234567'
    extras: [fast]
    subdirectory: python/addon

CLI sources override profile sources by normalized distribution name. Duplicate CLI entries are errors. --activitysim-commit and --sharrow-commit remain aliases for the official repositories; conflicting alias/source declarations are errors.

Inside Docker, abench verifies each checkout's Git object, builds a wheel, checks its distribution name, then installs all source wheels together with other requirements. It runs pip check and verifies installed wheel identities. Exact source commits, resolved versions, and the full dependency environment are saved. All source dependencies must agree: conflicting requirements fail the build.

Use profile requirements for additional registry requirements and constraints for resolver bounds or exact transitive pins. Profiles may select python_image (a compatible Debian-based image, optionally pinned by digest). The default image includes a compiler and HDF5 headers. Packages requiring other system libraries can use a prebuilt compatible base image. Private GitHub authentication and custom OS provisioning are outside the initial interface.

Source pins do not freeze unpinned transitive/build dependencies or base images. Retain images and dependency manifests for strict reproduction. Build isolation may fetch build requirements; constraints currently govern the final environment, not those isolated build environments.

Add another model

Create benchmark.yaml in its model directory, then use --model-dir:

schema_version: 1
name: My regional model
configs: [configs]
mp_configs: [configs_mp]
snapshot: [configs, configs_mp, extensions]
extensions: [extensions]
data_dir: data
required_inputs:
  - [households.csv, households.parquet]
  - persons.csv
  - land_use.csv
  - skims.omx
settings:
  use_shadow_pricing: false
  rng_base_seed: 0
input_tables:
  households: {}
  persons: {}
  land_use:
    totals: [TOTPOP, TOTHH, TOTEMP]
    zone_columns: [TAZ]
output_tables:
  households: {}
  persons: {}
  tours: {}
  trips:
    categories: [trip_mode, primary_purpose]

Paths in a profile are relative to the model directory (except data_dir, which may be absolute). configs and mp_configs are ordered, highest priority first. snapshot must cover config files, local extension modules, and adapter modules; entries must not overlap or contain directory symlinks. Data is mounted read-only and should not change during a run. Profiles are trusted model code/configuration.

Settings precedence, lowest to highest: normal model configs → profile settings → user overlays → required CLI controls (sample, SP/MP, worker counts, Sharrow, and fail-fast). Generated inheriting profile configs preserve this ordering when ActivitySim reconstructs worker settings. Component overlays remain independent.

Optional profile fields:

Field	Purpose
models_from, exclude_models	Take a YAML models list and explicitly omit diagnostic steps. Otherwise use normal settings.
mp_settings	Read multiprocess_steps from a separate YAML file.
extensions	Import modules through ActivitySim's registration mechanism, including spawned workers. Installing a package alone does not register its components.
adapter: module:function	Optional function(state, spec, phase) initialization hook, called once in the model parent before execution. Use state.import_extensions for worker setup; parent-only mutations are not automatically worker initialization.
input_tables, output_tables	Logical table names mapped to summary options: file (stem), totals, categories, and zone_columns for land use. CSV and Parquet are supported. Use logical households for sample validation.
output_prefix	Default final_; applied to output file stems.
household_table	Default households.csv, used for early CSV sample validation. Parquet samples are checked after execution.
zone_label	Display label for land-use rows, such as zones or MAZs.

For specialized data formats, an adapter can arrange compatible CSV/Parquet summary outputs. The initial generic reader does not interpret arbitrary binary model outputs.

Measurement and reports

Sharrow runs first execute the model in a separate single-process warmup, using min(target households, 5000) households by default. For --households 0, the target is the full available population, so warmup uses at most 5000 of those households. Set --warmup-households N (or warmup_households: N in experiment YAML) to change this positive cap. Warmup always uses one process; measured runs retain their requested sample and worker count. Model config directories, seed, chunk overlays, and flow cache path are retained from the target experiment.

Compiled flows are automatically reused across runs and suites, including changes in ActivitySim revisions, sample sizes, process counts, and model configs. The persistent host cache defaults to ~/.cache/abench/flows. Change it with --flow-cache-dir PATH (flow_cache_dir in YAML), or disable automatic reads and writes with --no-reuse-flows (reuse_flows: false). --cache-from remains an explicit seed option with its existing stricter dependency checks.

Containers set NUMBA_CACHE_DIR to /results/cache/flows/.numba, a writable directory included in those snapshots. This covers installed-library helpers (such as sharrow.maths) as well as generated flows without writing into root-owned site-packages or relying on the container user's home directory. Warmup may recompile entries from older snapshots that used Numba's default paths.

Compatibility uses the installed Sharrow, Numba, llvmlite, and NumPy versions, plus their source repository/commit identities when applicable, Python version, and container architecture/CPU features. ActivitySim and model settings are excluded from this key: Sharrow identifies generated flows by their contents, and Numba checks cached signatures. Changed flows can compile during warmup. An existing cache does not guarantee that warmup will need no compilation.

Each experiment receives a private copy with source timestamps preserved. Warmup always runs, and successful warmups atomically update the persistent cache before measurement starts. Compatible simultaneous experiments wait for each other to avoid losing compiled signatures when attempts update Numba cache indexes. Only cache/flows is shared, never model data, outputs, or shared-memory artifacts. Cache identity and reuse counts are recorded in flow-cache-identity.json and experiment.json. The persistent cache can be deleted between runs to reclaim disk space; older experiments created before this feature are not automatically imported.

A smaller serial warmup may not exercise every flow/type signature needed by the measured run. Each measured attempt therefore records flow compilation and allows it to finish. If compilation occurred, the completed attempt becomes cache preparation, and none of its runtime or memory results qualify as benchmark results. Its diagnostics and outputs are retained under attempts/attempt-001, attempts/attempt-002, etc. Newly compiled flows are published to the shared cache.

The model then restarts in a fresh container with fresh outputs and model caches, using the same settings and expanded flow cache. Only an attempt with zero flow compilations is accepted. All attempts keep permanent directories under attempts/; measured/ links to the accepted attempt. By default abench allows two additional attempts (three total). Set --cache-retries N or cache_retries: N in YAML; zero allows no retries. If compilation persists, the experiment fails with the final attempt's diagnostics retained. Ordinary model errors, OOMs, and output validation failures stop immediately and are never retried as cache preparation.

The report and experiment.json include attempt history. Each attempt retains cache-miss-details-*.jsonl, its settings, component timings, and memory samples. Ordinary non-flow compilation and disk-cache loading remain included in accepted measurements. Flow tracking uses private Numba internals and is covered by real compilation, cache-hit, and Docker retry tests.

Memory is the whole-container cgroup v2 charge, counting shared pages once. Blue is memory.current (including file cache, shared memory, and kernel costs). Green dashed is anon + shmem, a subset excluding ordinary file cache and kernel costs. Never add the lines. Swap is recorded separately and disabled by equal memory/memory+swap limits. /dev/shm capacity is within that limit.

Component runtimes show worker mean, population SD, count, and maximum. Worker SD is imbalance, not confidence across repeated trials. The component dropdown highlights each worker's actual window; overlaps darken and gaps remain clear. Overall elapsed includes startup, coordination, and checkpoint writes between components. Kernel peak includes startup; warmup and post-run summaries are excluded. Shared VM page-cache ownership can influence container charges: this is not a cold-input I/O benchmark.

abench report --compare /path/to/run-a /path/to/run-b \
  --output-dir /path/to/comparison.html

Reports are offline HTML/SVG/JavaScript plus normalized JSON. They retain fastest successful component highlighting and use common memory axes. Existing MTC and SANDAG schema-1 experiments remain readable, including approximate legacy timing windows where only completion logs exist. New experiments use schema 2 and include source manifests, resolved profile/settings, model and harness snapshots, file hashes, Docker details, and source installation provenance. Input file size/mtime records are provenance hints, not content hashes of large skims.

Development and CI

python -m pip install -e '.[test]' -r tests/requirements.txt
python -m pytest -m 'not docker'
pre-commit run --all-files
python -m build
ABENCH_DOCKER_TESTS=1 python -m pytest tests/test_docker.py -v

Install Node.js to exercise the offline chart selector test. GitHub Actions runs unit tests on Python 3.10–3.12, lint/format checks, wheel packaging checks, and Linux Docker integration. The Docker tests build pinned ActivitySim/Sharrow sources and run a four-household extension workflow in serial and multiprocess modes, checking warmup, generated-code cache reuse, per-worker timings, merged outputs, memory, and reports. They do not require either example repository or large datasets.

Adapted from the MTC and SANDAG benchmark harnesses developed in this workspace. The original measurement approach was informed by WSP's Lighthouse production benchmark. See LICENSE for the retained BSD license.

Releases

See RELEASING.md for Trusted Publishing setup and release instructions.

Downloading model data

Experiment suites may declare data_assets using ActivitySim's external-example asset names, URLs, SHA-256 checksums, and unpack destinations:

data_assets:
  name: prototype_mtc_extended
  assets:
    data_full.tar.zst:
      url: https://github.com/ActivitySim/activitysim-prototype-mtc/releases/download/v1.3.4/data_full.tar.zst
      sha256: b402506a61055e2d38621416dd9a5c7e3cf7517c0a9ae5869f6d760c03284ef3
      unpack: data_full

abench experiment.yaml prepares these assets before validating model inputs or starting Docker. abench prepare experiment.yaml only prepares data. abench validate experiment.yaml remains read-only: it checks declarations and requires inputs to be present (use prepare first for a fresh clone). Asset destinations are relative to the experiment YAML, regardless of model_dir; point each run's data_dir to the appropriate destination. Variable substitutions also work here.

The default download cache is exactly platformdirs.user_cache_dir("ActivitySim")/External-Examples/<name>/—the same layout used by activitysim.examples.external.download_external_example. name is optional; cache_dir can override the root before appending name. Keep names and checksums identical to ActivitySim's declarations to share files. For assets cached by direct download_asset(link=True) calls, set cache_dir to that call's platformdirs.user_data_dir("ActivitySim") and omit name. No host ActivitySim installation is needed.

Checksums are required and verified before reuse. For a .gz URL whose asset name omits .gz, the checksum covers the decompressed file, as in ActivitySim. Archives (.tar.zst, .tar.gz, .zip) use the archive checksum and preserve their internal paths when unpacking. Verified extracted contents are cached under .abench-extracted/<sha256>/ beside the archive. Whole unpacked directories are linked to the suite, and abench resolves data_dir before Docker mounts it. Individual files are copied so external file symlinks cannot break inside Docker. Existing destinations must match; modified inputs are never silently replaced. Archive links, special files, and paths escaping the destination are rejected. The original instructions and resolved cache locations are saved in suite.json.

Data preparation is available in abench 0.1.1 and later.