Resuming a training process from a saved state is a common practice in machine learning. This involves loading previously stored parameters, optimizer states, and other relevant information into the model and training environment. This enables the continuation of training from where it left off, rather than starting from scratch. For example, imagine training a complex model requiring days or even weeks. If the process is interrupted due to hardware failure or other unforeseen circumstances, restarting training from the beginning would be highly inefficient. The ability to load a saved state allows for a seamless continuation from the last saved point.
This functionality is essential for practical machine learning workflows. It offers resilience against interruptions, facilitates experimentation with different hyperparameters after initial training, and enables efficient utilization of computational resources. Historically, checkpointing and resuming training have evolved alongside advancements in computing power and the growing complexity of machine learning models. As models became larger and training times increased, the necessity for robust methods to save and restore training progress became increasingly apparent.
