Distributed Training
term_id: distributed_training
Category: training_techniques
Definition
Distributed Training accelerates model convergence by parallelizing computation over multiple GPUs or nodes. Techniques include data parallelism, where each worker processes a subset of data, and model parallelism, where different layers are split across devices. This approach is essential for training large-scale deep learning models that exceed the memory capacity of a single device, enabling faster experimentation and deployment.
Summary
A method of training machine learning models by splitting data or computations across multiple devices or servers.
Key Concepts
- Data Parallelism
- Model Parallelism
- GPU Clusters
- Gradient Synchronization
Use Cases
- Training large language models
- Accelerating computer vision dataset processing
- Reducing training time for complex neural networks