liblaf-cherries 4.0.10

What Cherries does

Cherries records ordinary Python experiments in a local, content-addressed store. A successful cherries.main(main) run stages inputs and declared outputs in a fresh work directory, captures source and runtime evidence, seals a small record and manifest, and stores retained bytes once by SHA-256. It does not require a background service or create Git commits. Comet is opt-in with CHERRIES_COMET=1; its SDK is not loaded otherwise.

Use a separate experiment Git repository for authored studies. Keep Apple and Melon there as submodules; keep generated run payloads in the configured Cherries store. Git LFS is for curated fixtures and selected reviewable assets, not every output, checkpoint, or log.

Install and configure

The local recorder currently supports Linux with Python 3.12 or newer.

uv add liblaf-cherries

Place settings at the experiment repository root. cherries.local.toml is useful for a machine-specific data volume and can stay Git ignored.

# cherries.toml
[collection]
id = "<collection-uuid>"

[capture]
roots = ["libs/apple", "libs/melon", "tools"]

[execution]
failure_payload = "discard"

[archive.main]
path = "/archive/cherries/phace-exp"

# cherries.local.toml
[collection]
storage = "/data/cherries/phace-exp"

CHERRIES_STORAGE overrides collection.storage. Initialize a collection explicitly before sharing it with other machines:

cherries --project-dir . --storage /data/cherries/phace-exp init \
  --collection-id "<collection-uuid>"

Choose a local data volume through collection.storage or --storage. Configure archive.main.path before using archive or sync with --remote main.

Run a sealed experiment

Keep module scope passive: do not read experiment data, create outputs, initialize a solver, or call Cherries asset, metric, parameter, or step helpers until main. Config defaults are raw source strings or paths.

from liblaf import cherries


class Config(cherries.BaseConfig):
    mesh: str = "sha256:<full-digest>"
    steps: int = 200


def main(cfg: Config) -> None:
    mesh = cherries.input(cfg.mesh, name="mesh.vtu")
    output = cherries.output("solution.txt")
    cherries.temp("solver-cache")
    output.write_text(f"{mesh.name}: {cfg.steps}\n")
    cherries.log_metric("steps", cfg.steps)


if __name__ == "__main__":
    cherries.main(main)

Run the script normally; BaseConfig accepts kebab-case flags.

uv run python exp/2026/10/05/mouthopen/src/10-run.py --steps 200

input() accepts a local path, a full sha256: asset ID, a sha256-tree: bundle ID, or run:<record-id>/<logical-path>. It copies verified bytes into work/<id>/inputs and registers lineage before use. output() returns a path under work/<id>/outputs; every declared output must exist at completion. log_output() imports an already-created external file. temp() uses disposable work/<id>/scratch and is not retained by default.

Cherries captures the entry script, Git HEAD, binary working-tree diff, selected untracked source, runtime facts, resolved parameters, inputs, logs, metrics, and outputs. Capture begins after normal module imports, so it records source at the main boundary and does not claim replay verification. An execution exception keeps a diagnostic event and may discard only the unsealed work payload. A missing output or recording failure leaves the work stage for recovery and raises an error.

Inspect, archive, and restore

All commands are foreground operations. Add --json before the subcommand for machine-readable output.

cherries --storage /data/cherries/phace-exp browse --quality unreviewed
cherries --storage /data/cherries/phace-exp browse --failed
cherries --storage /data/cherries/phace-exp show <record-id>
cherries --storage /data/cherries/phace-exp read <record-id> RUN.md
cherries --storage /data/cherries/phace-exp path <record-id> outputs/solution.txt
cherries --storage /data/cherries/phace-exp path --release <lease-id>
cherries --storage /data/cherries/phace-exp archive <record-id> --remote /archive/cherries --evict
cherries --storage /data/cherries/phace-exp restore <record-id> --remote /archive/cherries

path materializes the selected record file only and creates a durable read lease; release it when finished. restore verifies and materializes a complete record view. archive verifies object bytes, uploads the complete closure, and writes the remote commit marker last. --evict only releases an eligible local view after remote verification; it never removes canonical bytes merely because one logical record was archived.

Archive multiple records in one command (archive <id1> <id2> ...). A batch publishes collection metadata once, verifies every selected run independently, then checkpoints new location events and concurrent metadata additions.

A local-directory remote has an atomic filesystem boundary. A generic rclone remote requires --coordinated, which is an explicit assertion that an external publisher serializes the collection; Cherries does not invent a marker-file lock or stale-owner takeover. Metadata-only synchronization is available through cherries sync --remote ... [--coordinated].

sync publishes SHA-bound collection control, sealed receipts, events, and a payload-free checkpoint marker last. Checkpoint import can merge receipt metadata and lineage; it does not establish that payload objects are remotely available. Failed attempts appear only in local browse --failed; they are not remotely published or imported.

For Python follow-up work, use a closeable reader hold:

with cherries.open_run("<record-id>") as saved:
    path = saved.path("outputs/solution.txt")

Pass workspace=Path("analysis/compare") to attach the source to an existing analysis workspace. Close the accessor or use a context manager in either mode: its temporary reader hold protects active reads even if the workspace is closed. The workspace's own source hold remains until cherries analysis close.

cherries rerun <record-id> --prepare-only --workspace replay/<record-id> reconstructs a workspace from the saved project/submodule HEADs, binary patches, selected untracked source, and recorded input mapping. It requires the captured local Git bases and a source-stable experiment receipt. Omit --prepare-only to execute with uv run (using --locked when uv.lock is saved). It creates a new attempt and leaves replay_verified false until scientific checks establish replay. Close a retained prepared workspace with cherries rerun --close <path>.

Review and follow-up analysis

Successful records start unreviewed. Review, labels, and retention flags are append-only metadata rather than changes to the sealed receipt.

cherries review <record-id> --quality good --note "Useful comparison"
cherries label add <record-id> mouthopen promising
cherries mark <record-id> --important
cherries mark <record-id> --keep-local
cherries analysis new analysis/compare --source <record-id>
cherries analysis save analysis/compare --output out/figure.png --used-in weekly/2026-10-05
cherries analysis close analysis/compare

An analysis workspace holds its source records while it is open. Saving it creates a lightweight dependent record. RUN.md and analysis.json retain their names; workspace src/... becomes source/..., and explicitly selected out/... files become outputs/... in the saved record. Read saved scripts and figures using those record-relative paths, including for existing saved analyses. A record with dependents cannot be discarded. Local single-machine maintenance is explicit and receipt-bound:

cherries maintenance pause
cherries discard <record-id> --plan
cherries discard --apply <plan-path>
cherries prune --plan
cherries prune --apply <plan-path>
cherries maintenance resume <pause-token>

Pause freezes reference creation. It fails closed with active or pending work, legacy provenance, or foreign participants. Plans bind to the pause receipt and local inventory; an inventory change requires a new plan. This does not implement remote or distributed deletion.

Development

uv run pytest
mise run lint
mise run docs:build

See docs/README.md for the detailed runtime and CLI contract, and docs/design/run-records.md for the remaining distributed-maintenance and replay design.