Fine-tuning Vision Transformer on Graphcore IPUs with Hugging Face Optimum Graphcore

AI Impact Summary

The article demonstrates end-to-end fine-tuning of Vision Transformer (ViT) models on Graphcore IPUs using the Hugging Face Optimum Graphcore library, leveraging pre-trained google/vit-base-patch16-224-in21k checkpoints and HF model hub deployment. It highlights IPU-specific optimizations (MIMD, IPU-Fabric, data and pipeline parallelism) to accelerate training for multi-label chest X-ray classification on the ChestX-ray14 dataset, suggesting substantial throughput and cost benefits over traditional CPU/GPU approaches. It also provides a concrete migration path (Optimum setup, notebooks, and Graphcore tutorials) enabling teams to reproduce and operationalize CV transformer workloads on IPUs for healthcare imaging tasks.