Stanford CS224N: NLP w/ DL | Spring 2024 | Lecture 12 - Efficient Training, Shikhar Murty

This episode explores the challenges and solutions in training large machine learning models on GPUs, particularly relevant for final projects in a deep learning course. The lecture begins by explaining the representation of numbers in computers, specifically focusing on floating-point data types like FP32 and FP16, and their implications for memory usage and precision in neural network training. Against this backdrop, the concept of mixed-precision training is introduced as a solution to out-of-memory errors, employing both FP16 and FP32 for optimal performance. More significantly, the lecture delves into multi-GPU training, introducing distributed data parallel (DDP) and its memory limitations. To address these limitations, the Zero Redundancy Optimizer (ZERO) techniques are explained, showcasing how sharding model parameters and optimizer states improves memory efficiency. Finally, the lecture covers parameter-efficient fine-tuning, particularly the Low-Rank Adaptation (LoRa) method, as a way to reduce computational costs and improve generalization when full fine-tuning is infeasible. This highlights the growing importance of efficient training methods in the face of increasing model sizes and environmental concerns. What this means for the future of deep learning is a shift towards more resource-conscious training practices, balancing accuracy with efficiency.