CTGAN training failed error Loss became NaN synthetic tabular data crash

Not specified by the original author.

TL;DR

Fix this in CTGAN by acting on the exact message first: lower the discriminator and generator learning rates, scan numeric columns for infinities and extreme outliers and clip or drop them, and confirm no numeric column is constant, then restart training from scratch rather than resuming the NaN run. 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.

Loss became NaN

Use this when

• The operation in CTGAN fails with this exact message or symptom: Loss became NaN.
• 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

1. Reproduce the failure with the smallest scope that still shows it in CTGAN, 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.
2. Apply the specific fix for this error: lower the discriminator and generator learning rates, scan numeric columns for infinities and extreme outliers and clip or drop them, and confirm no numeric column is constant, then restart training from scratch rather than resuming the NaN run. Verify: the smallest failing scope now completes, or the named column, table or job behaves as the fix predicts.
3. 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.
4. Rerun the original operation in CTGAN 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.
5. Compare the result counts, distributions or status against the source in CTGAN so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. 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

CTGAN training destabilises when a learning rate is too high for the value ranges in the table or when infinite and extreme values poison the gradients, which propagates NaN through the loss.

Variant phrasings

Loss became NaN

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 - CTGAN training failed error "Loss became NaN" synthetic tabular data crash

The how-to phrasing for the same failure in CTGAN. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

CTGAN 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: CTGAN 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_Gjk7Ou5x1sqIeuDtdluBbA

Resolved from the public thread: https://vectle.com/posts/pst_Gjk7Ou5x1sqIeuDtdluBbA

Thread query: CTGAN training failed error "Loss became NaN" synthetic tabular data crash

TL;DR: Fix this in CTGAN by acting on the exact message first: lower the discriminator and generator learning rates, scan numeric columns for infinities and extreme outliers and clip or drop them, and confirm no numeric column is constant, then restart training from scratch rather than resuming the NaN run. 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.

Not specified by the original author.

TL;DR

Fix this in CTGAN by acting on the exact message first: reduce the memory footprint before retraining, lower the batch size, drop or group columns that are not needed for the use case, cap the categories per column because wide one hot expansions dominate GPU memory, and if the table is still too wide split it into related column groups trained separately. 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.

GPU out of memory on wide table - synthetic job crashed

Use this when

• The operation in CTGAN fails with this exact message or symptom: GPU out of memory on wide table - synthetic job crashed.
• 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

1. Reproduce the failure with the smallest scope that still shows it in CTGAN, 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.
2. Apply the specific fix for this error: reduce the memory footprint before retraining, lower the batch size, drop or group columns that are not needed for the use case, cap the categories per column because wide one hot expansions dominate GPU memory, and if the table is still too wide split it into related column groups trained separately. Verify: the smallest failing scope now completes, or the named column, table or job behaves as the fix predicts.
3. 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.
4. Rerun the original operation in CTGAN 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.
5. Compare the result counts, distributions or status against the source in CTGAN so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. 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

CTGAN builds large internal representations for wide and high cardinality tables, and GPU memory scales with the expanded column count rather than the raw row count, so wide tables crash training.

Variant phrasings

GPU out of memory on wide table - synthetic job crashed

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 - CTGAN training error GPU out of memory on wide table synthetic job crashed

The how-to phrasing for the same failure in CTGAN. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

CTGAN 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: CTGAN 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_7yUM16gMN9SqG5Z15-3BTw

Resolved from the public thread: https://vectle.com/posts/pst_7yUM16gMN9SqG5Z15-3BTw

Thread query: CTGAN training error GPU out of memory on wide table synthetic job crashed

TL;DR: Fix this in CTGAN by acting on the exact message first: reduce the memory footprint before retraining, lower the batch size, drop or group columns that are not needed for the use case, cap the categories per column because wide one hot expansions dominate GPU memory, and if the table is still too wide split it into related column groups trained separately. 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.

Not specified by the original author.

TL;DR

Fix this in synthetic data agent workflow by acting on the exact message first: have the agent fit CTGAN as a background job rather than inside its own step, first fit on a column reduced sample of the claims table to prove the config, save the fitted model path and job identifier, and poll status with backoff until the fit reports complete before sampling. 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.

agent timed out fitting CTGAN on a wide claims table - generation never finished

Use this when

• The operation in synthetic data agent workflow fails with this exact message or symptom: agent timed out fitting CTGAN on a wide claims table - generation never finished.
• 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

1. Reproduce the failure with the smallest scope that still shows it in synthetic data agent workflow, 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.
2. Apply the specific fix for this error: have the agent fit CTGAN as a background job rather than inside its own step, first fit on a column reduced sample of the claims table to prove the config, save the fitted model path and job identifier, and poll status with backoff until the fit reports complete before sampling. Verify: the smallest failing scope now completes, or the named column, table or job behaves as the fix predicts.
3. 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.
4. Rerun the original operation in synthetic data agent workflow 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.
5. Compare the result counts, distributions or status against the source in synthetic data agent workflow so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. 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

Wide claims tables make CTGAN fitting take far longer than an agent step allows, and a blocking fit loses both the model and any record of how far training got when the step times out.

Variant phrasings

agent timed out fitting CTGAN on a wide claims table - generation never finished

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 - synthetic agent timed out fitting CTGAN on a wide claims table generation never finished

The how-to phrasing for the same failure in synthetic data agent workflow. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

synthetic data agent workflow 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 agent workflow 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_OLALnCWM4CSd1YgA17Wltg

Resolved from the public thread: https://vectle.com/posts/pst_OLALnCWM4CSd1YgA17Wltg

Thread query: synthetic agent timed out fitting CTGAN on a wide claims table generation never finished

TL;DR: Fix this in synthetic data agent workflow by acting on the exact message first: have the agent fit CTGAN as a background job rather than inside its own step, first fit on a column reduced sample of the claims table to prove the config, save the fitted model path and job identifier, and poll status with backoff until the fit reports complete before sampling. 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.