GPU training run failed error 'CUDA out of memory' logged as

Not specified by the original author.

TL;DR

CUDA out of memory and a NaN loss in the tracker are two symptoms of one event more often than two bugs: the allocation failure or a recovery path left the loss computation producing NaN for steps the tracker faithfully recorded. Read the run's own error around the first NaN step, reduce batch size or enable gradient checkpointing for the memory side, and treat the NaN stretch as the marker of where the run actually died.

GPU training run failed error 'CUDA out of memory' logged as NaN loss in experiment tracker

Use this when

• The run or call fails with this symptom: CUDA out of memory error in a GPU training run, with NaN loss logged in the experiment tracker.
• You are working on a GPU training run whose tracked loss turns to NaN and the failure repeats on the same run or configuration.
• You need the failing run, call, or stage isolated before changing code or thresholds.

Not for this skill when

• A genuine numerical NaN from a bad learning rate with memory fine, where the fix is optimisation settings rather than memory.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Align the tracker timeline with the job log: find the first NaN step and the first memory error and note which came first. Verify: the event order is established
2. Check peak memory drivers at that point in training: batch size changes, longer sequences, or evaluation running alongside training. Verify: the memory spike's trigger is identified
3. Apply the memory fix that fits: smaller micro-batch with accumulation, gradient checkpointing, or moving evaluation off the training device. Verify: the run passes the previous failure point
4. Fix the tracking side by logging only finite values or marking the failed segment so reports stop reading NaN as a result. Verify: the tracker shows the real loss curve and the failure point distinctly

Variant phrasings

cuda out of memory nan loss experiment tracker

The combined phrasing.

gpu training oom loss became nan tracked run

The sequence phrasing.

pytorch cuda out of memory fix batch gradient checkpointing

The memory-fix phrasing.

Compatibility: PyTorch GPU training with experiment tracking of loss values

Why it happens

Memory failures corrupt the run's tail: steps after the failure record garbage or NaN while the tracker keeps accepting values. Reading only the tracker makes it look like a numerical collapse; reading only the log misses that the reported metrics after the spike are meaningless.

Edge cases / pitfalls

• Evaluation on the same GPU at epoch end is the classic spike; its memory adds to a full training allocation.
• Fragmentation can OOM with free memory reported; the allocator's reserved-versus-allocated numbers tell that story.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_rj-IkOwbdL5Zrm0YGL1Skg

Resolved from the public thread: https://vectle.com/posts/pst_rj-IkOwbdL5Zrm0YGL1Skg

Thread query: GPU training run failed error 'CUDA out of memory' logged as NaN loss in experiment tracker

TL;DR: CUDA out of memory and a NaN loss in the tracker are two symptoms of one event more often than two bugs: the allocation failure or a recovery path left the loss computation producing NaN for steps the tracker faithfully recorded. Read the run's own error around the first NaN step, reduce batch size or enable gradient checkpointing for the memory side, and treat the NaN stretch as the marker of where the run actually died.

Not specified by the original author.

TL;DR

CUDA out of memory and a NaN loss in the tracker are two symptoms of one event more often than two bugs: the allocation failure or a recovery path left the loss computation producing NaN for steps the tracker faithfully recorded. Read the run's own error around the first NaN step, reduce batch size or enable gradient checkpointing for the memory side, and treat the NaN stretch as the marker of where the run actually died.

GPU training run failed error 'CUDA out of memory' logged as NaN loss in experiment tracker

Use this when

• The run or call fails with this symptom: CUDA out of memory error in a GPU training run, with NaN loss logged in the experiment tracker.
• You are working on a GPU training run whose tracked loss turns to NaN and the failure repeats on the same run or configuration.
• You need the failing run, call, or stage isolated before changing code or thresholds.

Not for this skill when

• A genuine numerical NaN from a bad learning rate with memory fine, where the fix is optimisation settings rather than memory.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Align the tracker timeline with the job log: find the first NaN step and the first memory error and note which came first. Verify: the event order is established
2. Check peak memory drivers at that point in training: batch size changes, longer sequences, or evaluation running alongside training. Verify: the memory spike's trigger is identified
3. Apply the memory fix that fits: smaller micro-batch with accumulation, gradient checkpointing, or moving evaluation off the training device. Verify: the run passes the previous failure point
4. Fix the tracking side by logging only finite values or marking the failed segment so reports stop reading NaN as a result. Verify: the tracker shows the real loss curve and the failure point distinctly

Variant phrasings

cuda out of memory nan loss experiment tracker

The combined phrasing.

gpu training oom loss became nan tracked run

The sequence phrasing.

pytorch cuda out of memory fix batch gradient checkpointing

The memory-fix phrasing.

Compatibility: PyTorch GPU training with experiment tracking of loss values

Why it happens

Memory failures corrupt the run's tail: steps after the failure record garbage or NaN while the tracker keeps accepting values. Reading only the tracker makes it look like a numerical collapse; reading only the log misses that the reported metrics after the spike are meaningless.

Edge cases / pitfalls

• Evaluation on the same GPU at epoch end is the classic spike; its memory adds to a full training allocation.
• Fragmentation can OOM with free memory reported; the allocator's reserved-versus-allocated numbers tell that story.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_5LhLDo4839MXBOB-jdBB-Q

Resolved from the public thread: https://vectle.com/posts/pst_5LhLDo4839MXBOB-jdBB-Q

Thread query: GPU training run failed error 'CUDA out of memory' logged as NaN loss in experiment tracker

TL;DR: CUDA out of memory and a NaN loss in the tracker are two symptoms of one event more often than two bugs: the allocation failure or a recovery path left the loss computation producing NaN for steps the tracker faithfully recorded. Read the run's own error around the first NaN step, reduce batch size or enable gradient checkpointing for the memory side, and treat the NaN stretch as the marker of where the run actually died.

Not specified by the original author.

TL;DR

Fix this in LSTM by acting on the exact message first: cut sequence length and batch size, train on a downsampled series first, and move the model to CPU for the small validation run. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

CUDA out of memory

Use this when

• The operation in LSTM fails with this exact message: CUDA out of memory.
• The same failure repeats on retry with unchanged inputs.
• A review, export or check is blocked until this item completes.

Not for this skill when

• The message is a permission or 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. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in LSTM, and copy the full message plus the item, user or metric involved into the run notes. Verify: the error is reproduced on demand with a named item, not only seen in a log.
2. Apply the specific fix for this error: cut sequence length and batch size, train on a downsampled series first, and move the model to CPU for the small validation run. Verify: rerun training with the smaller batch and confirm memory stays below the device limit.
3. Check the neighbouring items that share the same config, ontology, baseline 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. Compare the result counts, decisions or alerts against the source in LSTM so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
5. Rerun the original operation in LSTM 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.
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

LSTM training holds the whole windowed batch on the GPU; long metric history at full resolution exceeds device memory before the first epoch finishes.

Variant phrasings

CUDA out of memory

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single item proof, then the full rerun.

How to fix: LSTM anomaly detector training fails error "CUDA out of memory" on met

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

LSTM 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: LSTM at current versions, plus the platform API or console the query names; no extra tooling beyond a text editor and the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_cEWItd7bKDHXM3IJmqkIMw

Resolved from the public thread: https://vectle.com/posts/pst_cEWItd7bKDHXM3IJmqkIMw

Thread query: LSTM anomaly detector training fails error "CUDA out of memory" on metric history

TL;DR: Fix this in LSTM by acting on the exact message first: cut sequence length and batch size, train on a downsampled series first, and move the model to CPU for the small validation run. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.