5.7 Evaluating and Using Models on New Data
- Part 2: Training & Saving a Model Checkpoint
- Part 3: Loading a Model from a Checkpoint & Evaluating the Model
What we covered in this video lecture
In this lecture, we explored the concept of checkpoints. During the training of a model, the model performance evolves as it is exposed to more data. For various reasons, it is recommended to save the state of the model at various intervals throughout the training process. Once training is completed, we can then load the checkpoint that corresponds to the highest performance.
Additionally, checkpoints allow for the training process to be resumed from its previous state in the event of an interruption. The Lightning checkpoints are fully compatible with plain PyTorch and can be easily used in either framework.
Additional resources if you want to learn more
If you want to learn additional detail about checkpoints in Lightning, check out the official documentation here.
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