How can I solve slow convergence when training large generative models

Question

With the help of Python programming, can you tell me How I can solve slow convergence when training large generative models?

Answer 1

To solve slow convergence when training large generative models, you can use techniques like learning rate schedules, gradient clipping, and mixed precision training to speed up training.

Here is the code reference you can refer to:

In the above code, we are using the following: 

• Learning Rate Schedule: Reducing the learning rate periodically (lr_schedule) helps the model converge faster and avoid overshooting.
• Gradient Clipping: Prevents gradients from exploding by clipping them to a range, ensuring stable updates.
• Mixed Precision Training: Reduced precision (Float16) is used to speed up training, especially on GPUs.

Hence, by referring to the above, you can solve slow convergence when training large generative models.