Resuming a coaching course of from a saved state is a typical observe in machine studying. This entails loading beforehand saved parameters, optimizer states, and different related info into the mannequin and coaching setting. This permits the continuation of coaching from the place it left off, quite 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 flexibility 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 gives resilience towards interruptions, facilitates experimentation with totally different hyperparameters after preliminary coaching, and allows environment friendly utilization of computational assets. Traditionally, checkpointing and resuming coaching have developed alongside developments in computing energy and the rising complexity of machine studying fashions. As fashions turned bigger and coaching occasions elevated, the need for strong strategies to save lots of and restore coaching progress turned more and more obvious.
