Distributed training is a critical technique in modern machine learning, enabling faster model development by leveraging multiple computing resources. Here are its core concepts:
Key Principles 💡
- Data Parallelism: Splitting the dataset across devices, with each device computing gradients independently.
- Model Parallelism: Partitioning the model itself across devices, ideal for large models.
- Hybrid Parallelism: Combines data and model parallelism for optimal performance.
Technical Challenges ⚠️
- High communication overhead between devices
- Load balancing across heterogeneous hardware
- Fault tolerance in distributed systems
- Debugging complex coordination issues
For deeper insights, check our guide on Distributed Training Benefits. 📚