supervising-training-runs

Detect NaN loss and stalls during long neural network training runs by using a separate watchdog process that monitors structured JSONL telemetry and exits on anomalies like non-finite loss or stale metrics. This prevents wasted GPU time on failed jobs.

The best way to monitor multi-hour fine-tuning jobs is to log per-step and per-eval metrics to structured JSONL files. You can check progress with quick tail reads instead of parsing noisy log dumps, while a watchdog alerts you to throughput collapse.

Yes, you can set up early anomaly alerts for GPU jobs by running a watchdog supervisor that monitors JSONL telemetry and triggers an exit when evaluation metrics stay near chance level, saving your GPU window before the run completes.

Safely resume a failed fine-tuning job after a dead process by using fail-loud checkpointed monitoring with completion sentinels. This approach ensures long jobs are easier to resume and safer to recover from unexpected crashes.

Yes, you need to run a preflight smoke test to bench the real workload and audit the data before committing to a full GPU window. This validation step catches label bugs and configuration issues early, preventing wasted compute hours.