CLI: datasets and versions

Create datasets, add sources, publish versions, and inspect draft or versioned membership.

Product state behind the commands

Dataset history for published CLI-visible versions

CLI dataset commands create and inspect the same version history shown in the product. Use explicit version numbers in automation.

Outcome: Create datasets, add sources, publish versions, and inspect draft or versioned membership.

unitlab dataset list --json
unitlab dataset create "Training set" --folder-id FOLDER_ID --json
unitlab dataset add-sources DATASET_ID --asset-id ASSET_ID --json
unitlab dataset unpublished-changes DATASET_ID --json
unitlab dataset publish DATASET_ID "Added edge cases" --json
unitlab dataset versions DATASET_ID --json
unitlab dataset items DATASET_ID --version 3 --json

Publish only after reviewing draft membership. Use explicit version numbers when attaching data or recording a reproducible pipeline.

Operating contract

Concern	Required behavior
Execution surface	Pinned unitlab==3.0.0 command in the intended Python 3.10+ environment.
Machine contract	Use --json; human-readable output is not an automation interface.
Target resolution	Resolve stable IDs with a read command before a state-changing command.
Success evidence	Exit status, JSON result, returned IDs, and a read-after-write state check.

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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Explore related Unitlab capabilities: training-data curation workflows