monitor-experiment

To monitor W&B training runs, you connect to an ongoing run by specifying its run ID, set the polling interval, and choose alert conditions to receive severity-graded alerts for loss spikes and gradient issues.

The best way to track long training experiments is using automated monitoring that evaluates metrics, step times, and validation outputs, generating structured progress reports to guide your decision to continue or halt.

Yes, you can automatically check model training health after 30 minutes; the Skill summarizes training progress, loss reduction, and validation outputs to guide decision-making in long training runs.

Yes, W&B monitoring works with local summaries; the Skill connects to W&B or reads local summaries to monitor training loss, gradient norms, step times, and validation outputs.

You can detect anomalies during model training by evaluating conditions such as loss spikes, gradient issues, and progress stalls, issuing severity-graded alerts to optimize your training workflows.