Vision Transformers on Graphcore IPUs via Hugging Face Optimum Graphcore for ChestX-ray14

AI Impact Summary

The post demonstrates end-to-end fine-tuning of Vision Transformer (ViT) models on Graphcore IPUs via the Hugging Face Optimum Graphcore library, including pre-trained weights and a ChestX-ray14 multi-label classification example. It highlights IPU-specific optimizations—data and pipeline parallelism enabled by the IPU-Fabric and the MIMD architecture—to accelerate CV training and increase throughput. By leveraging google/vit-base-patch16-224-in21k checkpoints and the Graphcore-ViT model card, it provides a practical, production-oriented path to experiment and deploy ViT on Graphcore hardware without training from scratch.