How do you implement model checkpointing in PyTorch while training Generative AI models

Question

Can you explain, using code, how you implement model checkpointing in PyTorch while training Generative AI models?

Answer 1

To implement model checkpointing in PyTorch for Generative AI models, you can save and load model weights, optimizer states, and other relevant data during training. Here is the code given below:

In the above code, we are using state_dict: Stores model and optimizer parameters, Epoch Tracking, which saves and resumes training from the last saved epoch and uses a file naming convention (checkpoint_epoch.pth) for better management.

Hence, this ensures you can pause and resume training without losing progress.