How do you optimize backpropagation when training large generative models on limited hardware

Question

Can you name the ways to optimize backpropagation when training generative models on limited hardware?

Answer 1

In order to optimize backpropagation when training generative models on limited hardware, refer to the following:

• Gradient Checkpointing: This technique helps reduce memory requirements during the backpropagation phase of training results. 
• Mixed Precision Training: This technique results in considerable training speedups, reduces memory footprint, and increases performance during model training and evaluation. 
• Model pruning: Technique used in deep learning to reduce the size of a model by eliminating unnecessary parameters. 
• Gradient Accumulation: This technique is used when training neural networks to support large batch sizes, given the limited available GPU memory.
• Distributed Training: This technique is used to divide the workload across multiple processors while training a huge deep-learning model.

These will help in successful optimization when training generative models on limited hardware.

Related Post: 

• How do you handle memory constraints when training large generative models
• How to implement gradient checkpointing