What methods would you use to reduce computational overhead when training a large generative model

Question

Can you tell me What methods would you use to reduce computational overhead when training a large generative model?

Answer 1

 Use gradient checkpointing to reduce memory usage and computational overhead during training. Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• Saves memory by discarding intermediate activations.

• Trades compute for memory: recomputes activations during backprop.

• Easy integration into PyTorch models.

• Useful for training large models on limited GPU resources.

Gradient checkpointing strategically reduces memory load by recomputing forward passes during backpropagation, hence enabling training of deeper generative models with limited hardware.