distributed_training
PyTorch Lightning provides several strategies for training large models efficiently across multiple GPUs, nodes, and machines. Choose the right strategy based on model size and hardware configuration.
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Content
# Distributed Training - Comprehensive Guide
## Overview
PyTorch Lightning provides several strategies for training large models efficiently across multiple GPUs, nodes, and machines. Choose the right strategy based on model size and hardware configuration.
## Strategy Selection Guide
### When to Use Each Strategy
**Regular Training (Single Device)**
- Model size: Any size that fits in single GPU memory
- Use case: Prototyping, small models, debugging
**DDP (Distributed Data Parallel)**
- Model size: <500M parameters (e.g., ResNet50 ~80M parameters)
- When: Weights, activations, optimize
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