Lilly Tech Systems
Learn Distributed Training
Scale AI model training across multiple GPUs and nodes. From data parallelism and model parallelism to DeepSpeed and FSDP — all for free.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Why distributed training? Scaling laws, communication overhead, and the distributed landscape.
2. Data Parallelism
Replicate the model, split data. AllReduce, gradient synchronization, and DDP in PyTorch.
3. Model Parallelism
Tensor parallelism, pipeline parallelism, and splitting large models across devices.
4. DeepSpeed
ZeRO optimizer stages, offloading, DeepSpeed configuration, and training billion-parameter models.
5. FSDP
Fully Sharded Data Parallel in PyTorch, sharding strategies, and memory-efficient training.
6. Best Practices
Choosing strategies, debugging distributed jobs, checkpointing, and production scaling.
What You'll Learn
By the end of this course, you will be able to:
Understand Distributed Concepts
Grasp data parallelism, model parallelism, gradient synchronization, and communication primitives.
Use PyTorch DDP
Set up DistributedDataParallel training across multiple GPUs and nodes with PyTorch.
Deploy DeepSpeed & FSDP
Train billion-parameter models using ZeRO stages and Fully Sharded Data Parallel.
Scale to Production
Handle checkpointing, fault tolerance, and cost optimization for large-scale training runs.