Congratulations!
Efficient AI Model Training with PyTorch
Dennis Lee
Data Engineer
Course journey
- Train models across multiple devices
- Ready to tackle large models with billions of parameters
- Challenges: hardware constraints, lengthy training times, memory limitations
Data preparation
Distribute data and model across devices
Distributed training
Trainer and Accelerator interfaces
Trainer and Accelerator interfaces
Trainer and Accelerator interfaces
Efficient training
Drivers of efficiency
Drivers of efficiency
- Memory efficiency
- Gradient accumulation: train on larger batches
- Gradient checkpointing: decrease model footprint
Drivers of efficiency
- Memory efficiency
- Gradient accumulation: train on larger batches
- Gradient checkpointing: decrease model footprint
- Communication efficiency: local SGD
Drivers of efficiency
- Memory efficiency
- Gradient accumulation: train on larger batches
- Gradient checkpointing: decrease model footprint
- Communication efficiency: local SGD
- Computational efficiency: mixed precision training
Optimizers
Optimizer tradeoffs
Optimizer tradeoffs
Optimizer tradeoffs
Equipped to excel in distributed training
