Transformer models are powerful neural networks that have become the standard for delivering advanced performance for tasks such as natural language processing (NLP), computer vision, and online recommendations. (Fun fact: People use transformers every time they do an internet search on Google* or Microsoft Bing*.)
But there is a challenge: Training these deep learning models at scale requires a large amount of computing power. This can make the process time-consuming, complex, and costly.
This session shares a solution: An end-to-end training and inference optimization for transformers.
Join your hosts from Intel and Hugging Face* (notable for its transformers library) to learn:
- How to do multi-node, distributed CPU fine-tuning for transformers with hyperparameter optimization using the Hugging Face transformers and Accelerate library, and Intel® Extension for PyTorch*.
- How to easily do inference optimization (including model quantization and distillation using Optimum for Intel) with the interface between the transformers library and Intel tools and libraries.
Watch a showcase of transformer performance on the latest Intel® Xeon® Scalable processors.
Skill level: Intermediate