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Vision Transformer (ViT) Optimization on IPUs

1 post · Aug 2022

What this trail covers

Hugging Face Optimum provides integration for running and fine-tuning Vision Transformer (ViT) models on Graphcore Intelligence Processing Units (IPUs). This enables efficient training and inference of ViT models for computer vision tasks, leveraging IPU's parallel processing capabilities and pre-optimized model checkpoints from the Hugging Face Hub. The integration simplifies the development lifecycle for AI models on specialized hardware.

All Hugging Face trails
  1. Deep Dive: Vision Transformers On Hugging Face Optimum GraphcoreThis post details the process of fine-tuning pre-trained Vision Transformer (ViT) models on Graphcore IPUs using the Hugging Face Optimum Graphcore library. It provides a step-by-step guide and a notebook example for training a ViT model on the ChestX-ray14 dataset. The post highlights the benefits of using IPUs for ViT models due to their MIMD architecture and scale-out capabilities, and explains how Optimum simplifies the use of pre-trained checkpoints and integration with Graphcore hardware.