Distributed Model Training & Parallel Processing
Why Distributed Training?
Large datasets and deep learning models require significant compute resources. Distributed training spreads workloads across multiple machines or GPUs.
Key Concepts
- Data parallelism
- Model parallelism
- Parameter synchronization
Distributed systems reduce training time and improve scalability.
