13 - Distributed Training

This module covers parallel strategies and optimization techniques for large-scale model training.

Module Structure

13-distributed-training/
├── 01-data-parallel/           # Data Parallel
├── 02-model-parallel/          # Model Parallel
├── 03-mixed-precision/         # Mixed Precision
└── 04-large-scale-training/    # Large-Scale Training

Core Content

01 - Data Parallel

Technology	Description	Use Case
DDP	Distributed Data Parallel	Multi-GPU
FSDP	Fully Sharded Data Parallel	Large models
ZeRO	Zero Redundancy Optimizer	Memory optimization

02 - Model Parallel

Technology	Description
Tensor Parallel	Intra-layer partitioning
Pipeline Parallel	Inter-layer partitioning
Sequence Parallel	Sequence dimension partitioning

03 - Mixed Precision

Technology	Description
AMP	Automatic Mixed Precision
BF16	Brain Float 16
Gradient Scaling	Prevent underflow

04 - Large-Scale Training

Framework	Features
DeepSpeed	Microsoft, ZeRO optimization
Megatron-LM	NVIDIA, 3D parallelism
FSDP	PyTorch native

Learning Path

DDP → FSDP/ZeRO → Tensor Parallel → Pipeline Parallel → Mixed Precision → DeepSpeed