Slides: Distributed Training for ML

Explore distributed training techniques through this interactive presentation. Navigate through the slides using arrow keys or the navigation controls. Topics Covered Back to Basics: Understanding neural network fundamentals Why Distributed Training: Memory constraints and scaling challenges DDP (Data Distributed Parallel): Replicating models across GPUs Pipeline Parallelism: Splitting models across devices FSDP (Fully Sharded Data Parallel): Advanced sharding techniques Slides Use the arrow keys (← →) or click the navigation arrows to move between slides. Some slides include animations that you can step through using the animation controls at the bottom.