Distributed Training Patterns

What problem does it solve?

This Skill addresses the challenge of training large machine learning models that exceed the memory or computational capacity of a single GPU, enabling efficient scaling across multiple GPUs and nodes.

Core Features & Use Cases

• Distributed Data Parallel (DDP): Standard PyTorch DDP setup for multi-GPU training.
• Fully Sharded Data Parallel (FSDP): Advanced memory optimization for massive models, sharding parameters, gradients, and optimizer states.
• DeepSpeed ZeRO-3: Configuration for extreme-scale training with advanced memory optimization and communication strategies.
• Mixed Precision Training: Utilizes AMP and GradScaler for FP16 training to reduce memory usage and speed up computation.
• Gradient Checkpointing: Trades compute for memory by recomputing activations during the backward pass.
• Efficient Data Loading: Strategies for optimized data loading across distributed ranks.
• Use Case: Train a multi-billion parameter LLM by distributing its layers and optimizer states across dozens of GPUs using FSDP or DeepSpeed ZeRO-3, while leveraging mixed precision and gradient checkpointing to fit within hardware constraints.

Quick Start

Use the distributed training skill to set up a PyTorch DDP training loop for a custom model and dataset.