1. Congratulations!
Congratulations!
2. Course journey
You've completed your journey in distributed training. Along the way, you've learned how to train models across multiple devices. You are ready to tackle large models with trillions of parameters and the challenges that they present, including hardware constraints, lengthy training times, and memory limitations.
3. Data preparation
To deal with these challenges, you started with data preparation.
4. Distribute data and model across devices
Accelerator enabled you to distribute data and models across devices for parallel processing. Using the Accelerate library, you prepared images, audio, and text to feed into Transformer models.
5. Distributed training
After data preparation, you learned about two approaches for distributed training,
6. Trainer and Accelerator interfaces
using Trainer's interface with no training loops
7. Trainer and Accelerator interfaces
or Accelerator's interface for customizing training loops.
8. Trainer and Accelerator interfaces
You applied distributed training to various applications, like answering agricultural questions, simplifying language translations, and evaluating e-commerce reviews, illustrating the versatility of the techniques.
9. Efficient training
Then you investigated ways to increase training efficiency.
10. Drivers of efficiency
You examined key drivers of efficiency: memory, communication, and computation, and you learned about techniques to address these factors.
11. Drivers of efficiency
Gradient accumulation allowed you to effectively train on larger batches, and gradient checkpointing decreased a model's memory footprint.
12. Drivers of efficiency
Local SGD reduced inter-device communication.
13. Drivers of efficiency
Mixed precision training enabled faster computations.
14. Optimizers
Next you focused on optimizers as a way to increase training efficiency.
15. Optimizer tradeoffs
You saw that AdamW was a common choice and a benchmark for other optimizers.
16. Optimizer tradeoffs
Adafactor saved memory by using fewer parameters,
17. Optimizer tradeoffs
while 8-bit Adam stored parameters with low precision to reduce a model's memory footprint.
18. Equipped to excel in distributed training
You are now equipped with the knowledge and tools to build distributed AI-powered services.
19. Kudos!
Kudos on your unparalleled success!