croq-tune-iterative

GPU kernel tuning progress is saved through session checkpointing, which captures the tuning state before and after each autonomous run. This ensures process recoverability and allows you to resume lengthy tuning workflows seamlessly without losing data.

Human-in-the-loop control for GPU tuning allows user intervention and decision-making between autonomous tuning invocations. It enforces explicit checkpoint boundaries where users validate steps, ensuring safety and process continuity without disrupting the underlying tuning infrastructure.

You pause a GPU kernel tuning session by leveraging the wrapper's explicit checkpoint boundaries. The session state is automatically saved before each autonomous run, allowing you to pause, inspect configurations, and resume the workflow seamlessly while preserving progress.

Yes, the checkpointed GPU tuning wrapper requires integration with existing tuning infrastructure. It acts as a durable wrapper managing session state and user validation, rather than providing the core tuning engine itself, ensuring it fits into complex or lengthy scenarios.

Checkpointed workflows are best suited for complex or lengthy GPU kernel tuning scenarios. You should use them when process continuity and safety are critical, allowing explicit user validation steps and ensuring progress is preserved across lengthy tuning configurations.