Project uploads

Upload local multimodal files into one project Batch Queue and handle partial failure explicitly.

Where this operation appears in the product

Project upload dialog with Local, CLI, SDK, and Storage tabs

Project › Datasets › Upload exposes the same project-scoped ingestion boundary used by the SDK and CLI examples on this page.

Outcome: Upload local multimodal files into one project Batch Queue and handle partial failure explicitly.

batch = project.upload("./data", fps=2.0, batch_size=100)
print(batch.uploaded, batch.failed)

# File-transfer failure is available immediately.
batch.raise_on_failure()

# Server processing is asynchronous.
status = batch.wait(timeout=1800, show_progress=True)
print(status.total, status.completed, status.failed)

fps defaults to 1.0 and is used for video ingestion. batch_size controls files per upload batch. Local validation detects recognized extensions; server processing can still fail after transfer. Treat upload transfer and Batch Queue processing as two separate checkpoints.

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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