Resuming a coaching course of from a saved state is a standard apply in machine studying. This entails loading beforehand saved parameters, optimizer states, and different related info into the mannequin and coaching atmosphere. This allows the continuation of coaching from the place it left off, fairly 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 could 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 provides resilience in opposition to interruptions, facilitates experimentation with totally different hyperparameters after preliminary coaching, and permits environment friendly utilization of computational sources. Traditionally, checkpointing and resuming coaching have developed alongside developments in computing energy and the rising complexity of machine studying fashions. As fashions grew to become bigger and coaching occasions elevated, the need for sturdy strategies to save lots of and restore coaching progress grew to become more and more obvious.
