What is gradient checkpointing and how does it work?

What is gradient checkpointing and how does it work?

Gradient checkpointing is a technique used in deep learning to save memory during training. It works by storing only a subset of intermediate activations and recomputing the others during the backward pass, which reduces memory usage at the cost of additional computation.

Why is it important to use gradient checkpointing?

It's important because it allows training of larger models or using larger batch sizes without running out of memory, making it feasible to work with more complex architectures.

Are there any downsides to using gradient checkpointing?

Yes, the main downside is the increased computation time due to the need to recompute activations, which can slow down the training process.

Can you give an example of when to use it?

You might use gradient checkpointing when training very deep neural networks or when working with limited GPU memory, such as in natural language processing tasks with large transformer models.