Managing a PyTorch Training Process with Checkpoints and Early Stopping
A large deep learning model can take a long time to train. You lose a lot of work if the training process interrupted in the middle. But sometimes, you actually want to interrupt the training process in the middle because you know going any further would not give you a better model. In this post, you will discover how to control the training loop in PyTorch such that you can resume an interrupted process, or early stop the training loop.
After completing this post, you will know:
- The importance of checkpointing neural network models when training
- How to checkpoint a model during training and retore it later
- How to terminate training loop early with checkpointing
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