Mostly AI error synthetic dataset generation stuck at encoding step never completes
Fixes the Mostly AI failure synthetic dataset generation stuck at encoding step - never completes by applying the specific fix in the steps, proving it on one small scope first, then rerunning and reconciling counts. Use when this error blocks generation, validation or export. Not for platform outages, which must be resolved first.
TL;DR
Fix this in Mostly AI by acting on the exact message first: cancel the stuck generation, inspect which column the encoder was processing when progress froze, usually a very high cardinality text or identifier column, reclassify that column as an identifier to exclude or cap its categories, and restart generation from the saved generator instead of recreating it. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.
synthetic dataset generation stuck at encoding step - never completesUse this when
- The operation in Mostly AI fails with this exact message or symptom: synthetic dataset generation stuck at encoding step - never completes.
- The same failure repeats on retry with unchanged inputs.
- Generation, validation or export is blocked until this item completes.
Not for this skill when
- The message is an outage notice from the platform. Resolve that first, then return here if this error remains.
- The operation completes and you only disagree with its output quality. That is a quality review, not this failure.
Steps
- Reproduce the failure with the smallest scope that still shows it in Mostly AI, and copy the full message plus the table, column or job involved into the run notes. Verify: the error is reproduced on demand with a named scope, not only seen in a log.
- Apply the specific fix for this error: cancel the stuck generation, inspect which column the encoder was processing when progress froze, usually a very high cardinality text or identifier column, reclassify that column as an identifier to exclude or cap its categories, and restart generation from the saved generator instead of recreating it. Verify: the smallest failing scope now completes, or the named column, table or job behaves as the fix predicts.
- Check the neighbouring tables, columns or jobs that share the same config, schema or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
- Rerun the original operation in Mostly AI on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
- Compare the result counts, distributions or status against the source in Mostly AI so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
- Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.
Why it happens
Encoding expands categorical columns before generation starts, and one unbounded cardinality column can make that step grow without bound so the job appears frozen at encoding with no error.
Variant phrasings
synthetic dataset generation stuck at encoding step - never completes
The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single scope proof, then the full rerun.
How to fix - Mostly AI error synthetic dataset generation stuck at encoding step never completes
The how-to phrasing for the same failure in Mostly AI. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.
Mostly AI troubleshooting for this failure
The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.
Compatibility: Mostly AI at current versions, plus the platform API or console the query names; no extra tooling beyond the source system access needed to verify counts.
Provenance
Resolved from the public thread: https://vectle.com/posts/pst_tiLjWLEvlRz1nHu0laVneQ