Distributed Training for Large Models
Scale AI training across multiple GPUs and machines. Learn data parallelism, model parallelism, and pipeline parallelism strategies.
TL;DR
Distributed training splits work across GPUs/machines: data parallelism (different data, same model), model parallelism (split model across devices), or pipeline parallelism (different layers on different devices).
Parallelism strategies
Data parallelism: Each GPU has full model, processes different data batches. Most common, easiest to implement.
Model parallelism: Model split across GPUs (layers or tensor-wise). Needed for models too large for one GPU.
Pipeline parallelism: Split model into stages, process micro-batches through pipeline. Reduces bubble time.
Implementation frameworks
- PyTorch Distributed (DDP, FSDP)
- DeepSpeed (ZeRO optimization)
- Megatron-LM (Nvidia)
- Ray Train
Best practices
- Start with data parallelism
- Use gradient accumulation for effective larger batches
- Monitor GPU utilization
- Optimize data loading to prevent bottlenecks
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Key Terms Used in This Guide
Training
The process of feeding data to an AI system so it learns patterns and improves its predictions over time.
Model
The trained AI system that contains all the patterns it learned from data. Think of it as the 'brain' that makes predictions or decisions.
AI (Artificial Intelligence)
Making machines perform tasks that typically require human intelligenceālike understanding language, recognizing patterns, or making decisions.
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