Automatic recovery is the self-managed restart and continuation workflow triggered by training or infrastructure failure events - it removes manual pager-based intervention and shortens downtime after faults.

What Is Automatic recovery?

• Definition: System capability to detect failure, relaunch workload, and resume from valid state automatically.
• Trigger Sources: Heartbeat timeouts, process exit codes, scheduler eviction signals, and health-check failures.
• Recovery Inputs: Latest durable checkpoint, job specification, and resource availability rules.
• Control Logic: Includes retry limits, exponential backoff, and escalation policies.

Why Automatic recovery Matters

• Downtime Reduction: Automated recovery starts immediately, minimizing idle cluster time.
• Labor Efficiency: Fewer manual restarts reduce operator burden and off-hours interruptions.
• Consistency: Standardized recovery workflow avoids ad hoc human error during incidents.
• Higher Completion Rates: Long training jobs survive transient infrastructure faults more often.
• Operational Maturity: Self-healing behavior is core to dependable platform service levels.

How It Is Used in Practice

• Watchdog Integration: Use orchestration controllers to monitor liveness and enforce restart policy.
• Checkpoint Selection: Automate restore from last confirmed checkpoint with integrity validation.
• Escalation Design: Route repeated-failure cases to humans only after policy thresholds are exceeded.

Automatic recovery is a critical self-healing capability for production ML infrastructure - fast autonomous restart keeps training pipelines resilient and predictable.