Dataset versions

Publish immutable dataset snapshots and inspect exact version membership.

Version history in the product

Dataset history with published versions and membership deltas

Dataset history is the visual counterpart to dataset.versions() and version-specific item reads: publisher, timestamp, item count, delta, activity, and exact version actions remain auditable.

Outcome: Publish immutable dataset snapshots and inspect exact version membership.

version = dataset.publish_version(
    "Added edge cases",
    description="Reviewed false-negative cohort",
)

for item in dataset.versions():
    print(item.version_number, item.title)

for member in version.items():
    print(member.id, member.name)

attached = client.attach_dataset(project, dataset, version=version.version_number)

dataset.list_items(version=N) and DatasetVersion.items() read a published snapshot. client.attach_dataset(..., version="latest") attaches the current published version; pass an integer when reproducibility matters.

Operating contract

Concern	Required behavior
Execution surface	Pinned unitlab==3.0.0 application environment on Python 3.10+.
Identity	Least-privilege API key supplied through approved configuration.
Target resolution	Stable resource IDs and an explicitly bounded target set.
Success evidence	Typed return fields, server-side state, and downstream acceptance of the result.

Failure and recovery boundary

Condition	Response
Authentication or authorization fails	Stop, correct the service identity or access model, rotate exposed credentials, and rerun a read-only check.
Validation or entitlement rejects the operation	Correct the input or entitlement; do not retry an unchanged request.
A request times out	Inspect remote state before repeating a mutation because the server may have accepted it.
Asynchronous processing exceeds its deadline	Preserve the Batch Queue or release ID, continue bounded monitoring, and inspect item-level failures.
Only part of a batch succeeds	Keep successful identifiers, isolate failed rows, and retry only the corrected subset.

Pin and test the production SDK version. Use stable IDs, keep secrets out of logs and command history, and record the correlation ID, target IDs, counts, final state, and redacted error details for material state changes.

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