How do you manage memory and performance issues when training large generative models and what coding strategies have helped

Question

I was facing issue related to memory and performance of my model. Can you give me code in python showing the management of memory of large generative model?

Answer 1

In order to manage the memory and performance of Generative AI Model  implement the following code:

 

In the code above we have used gradient checkpointing , inference mode , cache clearing and variable management. These techniques make it easier to handle large models on limited hardware.