How does memory management work in deep learning?

How does memory management work in deep learning?

Memory management in deep learning involves efficiently allocating and deallocating memory for tensors and models during training.

What are some common techniques for optimizing memory usage?

Common techniques include gradient checkpointing, mixed precision training, and using memory-efficient data structures.

Can you explain gradient checkpointing?

Gradient checkpointing saves memory by storing only a subset of activations during the forward pass and recomputing others during backpropagation.

What impact does memory management have on training speed?

Effective memory management can significantly speed up training by reducing the overhead of memory allocation and preventing out-of-memory errors.