run-train

Deep learning training experiment tracking involves recording the exact training command, dataset assumptions, and checkpoint states into normalized outputs like SUMMARY.md and LOG.md, capturing completed_steps and best_metric for reproducibility auditing.

Resuming deep learning training from a checkpoint requires using a resume mode that records the checkpoint state, validates git state changes, and continues execution while appending new completed_steps and evaluation logs to the existing train_outputs.

Yes, short verification checks before a full training kickoff use short_run_verification mode to validate startup assumptions and capture initial metrics safely before committing computational resources to the complete training execution.

Timeouts during deep learning training limit execution by stopping the process and recording the stop_reason, ensuring partial progress like completed_steps and best_checkpoint is saved within the train_outputs before the session terminates.