Resuming a coaching course of from a saved state is a typical follow in machine studying. This includes loading beforehand saved parameters, optimizer states, and different related data into the mannequin and coaching surroundings. This allows 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 resulting from {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 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.
