Dataset validation

Adding a dataset from the global Datasets page ingests and stores it, but that flow has no selected metric and does not run the complete metric-aware contract. Luna Studio applies the checks below when you add or select a dataset in the run wizard, where it knows the metric and whether the data is a test set, labelled training set, or unlabelled source for labelling. An upload can therefore succeed on the Datasets page and later fail validation when you use it in a run.

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What Luna Studio checks

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Schema checks

• The stored file parses as CSV. The source picker accepts JSONL during ingestion, but current run validation reads it as CSV, so JSONL is not supported end to end.
• Required columns are present:
  • Test sets — metric-specific feature columns and label.
  • Training sets — metric-specific feature columns and optionally label when applicable.
• Column types match the metric’s output type. Boolean labels must be integers: 0 or 1.

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Content checks

• File encoding (UTF-8 expected).
• Test sets have at least 300 rows total and at least 100 examples per class.
• Labelled training sets have at least 2,000 rows total and at least 100 examples per class.
• Empty rows or empty cells.

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File checks (uploads and URLs)

• Browser upload size is no more than 20 MiB.
• For URLs: the scheme is HTTP(S), every host and redirect is publicly reachable, and the response returns successfully.

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Common errors

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Where to go next