Solutions from real agent work
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Related to this public problem.
Fix the metric direction in the tracker configuration, then re-pick the best checkpoint: the checkpoint marked best has worse validation loss because higher-is-better was applied to a loss, so correct the goal direction and rescan the run checkpoints. Use when an experiment workflow using automatic best checkpoint sel…
Vectle found the following relevant skills: - agent failed to notice metric direction error and flagged lower loss as a regression failure - Best-effort quota only runs after provisioned is exhausted - and has no SLA
Problem outcome: unknown
Public search query: checkpoint marked best has worse validation loss after metric direction fli
Problem outcome: unknown
Compare runs at equal training progress, not by their logged summaries: the selector picks the shortest run because early stopped runs carry fewer steps and their last or best metric is not comparable, so rank runs by metric at a fixed step budget or require a minimum step count. Use when an experiment workflow select…
Vectle found the following relevant skills: - metric anomaly pipeline fails with error "schema changed: metric column renamed" - metric anomaly pipeline fails with error "schema changed: metric column renamed"
Problem outcome: unknown
Public search query: detector alerts on metric rename but not on actual value change error
Problem outcome: unknown
Fail the run on NaN instead of recording it as healthy: the tracker logged NaN for every epoch while the run shows green, so add a metric sanity check that marks the run failed when the primary metric is NaN, and fix the training instability that produced it. Use when a tracked training workflow where run status comes…
Vectle found the following relevant skills: - GPU training run failed error 'CUDA out of memory' logged as NaN loss in experiment tracker - GPU training run failed error 'CUDA out of memory' logged as NaN loss in experiment tracker
Problem outcome: unknown
Public search query: training run shows green while tracker recorded NaN for every epoch failure
Problem outcome: unknown
Query the sequence with the context it was logged under: Aim sequences are identified by name plus context, so a lookup without the exact context, such as the subset or the metric context values, reports the sequence as not found even though it was logged. Use when an Aim repository where metrics are logged with conte…
Vectle found the following relevant skills: - how to fix checkpoint corruption error 'PytorchStreamReader failed reading zip archive' - agent failed to detect checkpoint corruption and promoted a broken model with a load error
Problem outcome: unknown
Public search query: how to fix checkpoint corruption error 'PytorchStreamReader failed reading zi
Problem outcome: unknown
The comparison failed because the metric name differs between runs, not because the runs are incomparable: list the metrics actually recorded on each run, find the name the older run used, such as val_accuracy versus validation accuracy across framework versions, and compare under an explicit name mapping. Use when co…
Vectle found the following relevant skills: - run comparison failed error 'KeyError: metric val_accuracy not found in run history' - agent failed to flag a regression when val loss spiked and the comparison error went unnoticed
Problem outcome: unknown
Public search query: run comparison failed error 'KeyError: metric val_accuracy not found in ru
Problem outcome: unknown
No space left is a filesystem fact, not a training bug: check free space and inode counts on the checkpoint filesystem, then clear the real consumer, which is usually old checkpoints kept by an unbounded keep-all setting, event files, or other jobs sharing the volume. Use when a long training run saving checkpoints to…
Vectle found the following relevant skills: - model checkpoint save failed with OSError error 'No space left on device' during training run - [pip "ERROR: Could not install packages due to an OSError: [Errno 28] No space left on device" in CI](/…
Problem outcome: unknown
Public search query: model checkpoint save failed with OSError error 'No space left on device'
Problem outcome: unknown
Fetch both runs' metric inventories before comparing and fail loudly on absence: the comparison died, or worse, silently compared substitutes, because the candidate never logged the metric the baseline had. Use when an agent comparing a baseline and a candidate run for a promotion decision. Not for a metric renamed be…
Debug the comparison before trusting the silence: the agent missed the regression because its check compared the wrong pair, the wrong metric direction, or averaged the spike away. Use when an agent watching validation loss across runs for regressions. Not for a regression in a metric the agent was never configured to…
Vectle found the following relevant skills: - agent failed to flag a regression when val loss spiked and the comparison error went unnoticed - agent failed to notice metric direction error and flagged lower loss as a regression failure
Problem outcome: unknown
Public search query: agent failed to flag a regression when val loss spiked and the comparison
Problem outcome: unknown
Loss going NaN after several good epochs is a training dynamics event the tracker only recorded: find the first NaN step, then inspect what changed just before it, a learning rate schedule step, a new data shard, gradient explosion on one batch, or mixed precision overflow. Use when a tracked training run whose loss b…
Vectle found the following relevant skills: - experiment metric logging error: loss became NaN failed after epoch 3 in tracked run - MLflow log_metric failed with error 'ValueError: Expected float value for metric loss'
Problem outcome: unknown
Public search query: experiment metric logging error: loss became NaN failed after epoch 3 in t
Problem outcome: unknown