checkpoint

Data that captures the state of a model's parameters either during training or after training is completed. For example, during training, you can: 1. Stop training, perhaps intentionally or perhaps as the result of certain errors. 2. Capture the checkpoint. 3. Later, reload the checkpoint, possibly on different hardware. 4. Restart training.

Practitioners refer to checkpoint when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.