how to fix synthetic data seed not reproducible error different output same seed fails
Fixes the synthetic data generator failure seed not reproducible - different output same seed 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 synthetic data generator by acting on the exact message first: seed every random source the stack uses, the generator library, python random, numpy and torch where present, run generation in one process with a fixed row order, and record library versions, because an unseeded source or a version change breaks reproducibility even with the same headline seed. 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.
seed not reproducible - different output same seedUse this when
- The operation in synthetic data generator fails with this exact message or symptom: seed not reproducible - different output same seed.
- 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 synthetic data generator, 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: seed every random source the stack uses, the generator library, python random, numpy and torch where present, run generation in one process with a fixed row order, and record library versions, because an unseeded source or a version change breaks reproducibility even with the same headline seed. 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 synthetic data generator 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 synthetic data generator 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
Reproducibility needs every random generator in the stack seeded and a deterministic execution order, and one unseeded library or parallel shuffle silently changes the output.
Variant phrasings
seed not reproducible - different output same seed
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 - how to fix synthetic data seed not reproducible error different output same seed fails
The how-to phrasing for the same failure in synthetic data generator. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.
synthetic data generator 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: synthetic data generator 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_lz6OD9NVGrQbXTK1jmLvCA