Training and Customization#

This directory contains comprehensive documentation for training and customizing models with Megatron Bridge. Learn how to configure training, optimize performance, and customize training workflows.

Quick Navigation#

I want to#

🚀 Get started with training → Start with Configuration Container Overview to understand the training setup

⚙️ Configure training parameters → See Training Loop Settings and Optimizer & Scheduler

📊 Monitor and profile training → Check Logging and Profiling guides

💾 Manage checkpoints → Read Checkpointing for saving and resuming training

⚡ Optimize performance → Explore Performance Guide and Performance Summary

🔧 Customize training → See PEFT, Distillation, and Entry Points

Core Training Documentation#

Configuration and Setup#

Document	Purpose	When to Read
Configuration Container Overview	Central configuration object for all training settings	First time setting up training
Entry Points	Training entry points and execution flow	Understanding how training starts
Training Loop Settings	Training loop parameters and configuration	Configuring batch sizes, iterations, validation

Optimization and Performance#

Document	Purpose	When to Read
Optimizer & Scheduler	Optimizer and learning rate scheduler configuration	Setting up optimization
Mixed Precision	Mixed precision training for memory efficiency	Reducing memory usage
Communication Overlap	Overlapping communication with computation	Optimizing distributed training
Attention Optimizations	Optimizing attention mechanisms	Improving training speed
Activation Recomputation	Gradient checkpointing strategies	Reducing memory footprint
CPU Offloading	Offloading to CPU for memory management	Working with limited GPU memory

Monitoring and Debugging#

Document	Purpose	When to Read
Logging	Logging configuration and TensorBoard/WandB integration	Monitoring training progress
Profiling	Performance profiling and analysis	Identifying bottlenecks
Resiliency	Handling failures and recovery	Building robust training pipelines

Advanced Features#

Document	Purpose	When to Read
PEFT	Parameter-Efficient Fine-Tuning (LoRA, etc.)	Fine-tuning with limited resources
Packed Sequences	Sequence packing for efficiency	Optimizing data loading
Distillation	Knowledge distillation techniques	Transferring knowledge between models
Checkpointing	Checkpoint saving, loading, and resuming	Managing training state

Training Workflow#

A typical training workflow involves:

Configure Training - Set up ConfigContainer with model, data, and training parameters
Prepare Data - Configure dataset loading and preprocessing
Set Optimization - Configure optimizer, scheduler, and mixed precision
Enable Monitoring - Set up logging and profiling
Configure Checkpointing - Set up checkpoint saving and resuming
Launch Training - Start training with configured entry points
Monitor Progress - Track metrics via logging and profiling
Resume if Needed - Use checkpointing to resume from saved state

Common Training Scenarios#

🆕 First-Time Training Setup#

Configuration Container Overview - Understand the configuration system
Entry Points - Learn how to start training
Training Loop Settings - Configure basic training parameters
Logging - Set up monitoring

⚡ Performance Optimization#

Performance Guide - Comprehensive optimization strategies
Mixed Precision - Enable mixed precision training
Communication Overlap - Optimize distributed training
Activation Recomputation - Reduce memory usage
Profiling - Identify bottlenecks

💾 Production Training#

Checkpointing - Reliable checkpoint management
Resiliency - Handle failures gracefully
Logging - Comprehensive monitoring
Profiling - Performance analysis

🔧 Customization#

PEFT - Parameter-efficient fine-tuning
Distillation - Knowledge distillation
Entry Points - Custom training workflows

Ready to start training? Begin with Configuration Container Overview or return to the main documentation.