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

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Can you name the ways to optimize backpropagation when training generative models on limited hardware?
Nov 8 in Generative AI by Ashutosh
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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.

answered Nov 8 by ashu

selected Nov 8 by Ashutosh

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