Resuming a coaching course of from a saved state is a standard observe in machine studying. This entails loading beforehand saved parameters, optimizer states, and different related data into the mannequin and coaching atmosphere. This permits the continuation of coaching from the place it left off, moderately than ranging from scratch. For instance, think about coaching a posh mannequin requiring days and even weeks. If the method is interrupted on account of {hardware} failure or different unexpected circumstances, restarting coaching from the start could be extremely inefficient. The power to load a saved state permits for a seamless continuation from the final saved level.
This performance is crucial for sensible machine studying workflows. It presents resilience towards interruptions, facilitates experimentation with totally different hyperparameters after preliminary coaching, and allows 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 grew to become bigger and coaching instances elevated, the need for strong strategies to save lots of and restore coaching progress grew to become more and more obvious.
