Ray Summit 2022
Hugging Face Transformers is a popular open-source project that features state-of-the-art machine learning for PyTorch, TensorFlow, and JAX. It allows for easy access to state-of-the-art NLP, computer vision, audio, and multimodal models.
The computational and memory requirements for fine-tuning and training those models can be significant. To deal with this issue, the Ray team has developed a Hugging Face integration for Ray AI Runtime (AIR), allowing Transformers model training to be easily parallelized across multiple CPUs or GPUs in a Ray cluster, saving time and money, all the while allowing to take advantage of the rich Ray ML ecosystem thanks to a common API.
In this session, we explore the integration between Hugging Face and Ray AIR, allowing users to scale their model training and data loading seamlessly. We will dive deep into the implementation and API and explore how we can use Ray AIR to create an end-to-end Hugging Face workflow, from data ingest through fine tuning and HPO to inference and serving.
Antoni Baum is a software engineer at Anyscale, working on Ray Tune, XGBoost-Ray, and other ML libraries. In his spare time, he contributes to various open source projects, trying to make machine learning more accessible and approachable.
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