Resuming a coaching course of from a saved state is a typical observe in machine studying. This includes loading beforehand saved parameters, optimizer states, and different related info into the mannequin and coaching surroundings. This permits the continuation of coaching from the place it left off, slightly than ranging from scratch. For instance, think about coaching a fancy mannequin requiring days and even weeks. If the method is interrupted attributable to {hardware} failure or different unexpected circumstances, restarting coaching from the start can be extremely inefficient. The power to load a saved state permits for a seamless continuation from the final saved level.
This performance is important for sensible machine studying workflows. It affords resilience towards interruptions, facilitates experimentation with completely different hyperparameters after preliminary coaching, and permits environment friendly utilization of computational sources. Traditionally, checkpointing and resuming coaching have advanced alongside developments in computing energy and the rising complexity of machine studying fashions. As fashions turned bigger and coaching occasions elevated, the need for sturdy strategies to avoid wasting and restore coaching progress turned more and more obvious.
