Principle:LLMBook zh LLMBook zh github io Training Loop Execution
| Knowledge Sources | |
|---|---|
| Domains | Deep_Learning, Training |
| Last Updated | 2026-02-08 00:00 GMT |
Overview
The managed training loop pattern that handles forward pass, backward pass, gradient accumulation, optimization, logging, and checkpointing for model training.
Description
Training Loop Execution abstracts away the boilerplate of PyTorch model training into a managed loop. Instead of writing manual training code (forward, backward, optimizer step, logging), a managed trainer handles all of these concerns, including distributed training support, mixed-precision (bf16/fp16), gradient accumulation, learning rate scheduling, and periodic checkpointing.
Usage
Use this principle when training any model with HuggingFace ecosystem tools. The managed approach reduces bugs and ensures consistent training behavior across different hardware configurations.
Theoretical Basis
A standard training loop performs:
- Forward pass: Compute model outputs and loss.
- Backward pass: Compute gradients via backpropagation.
- Gradient accumulation: Optionally accumulate gradients over multiple steps.
- Optimizer step: Update model parameters.
- Learning rate scheduling: Adjust learning rate per step.
- Logging: Record training metrics.
- Checkpointing: Periodically save model state.