A step-by-step tutorial on deploying BERT-based Hugging Face Transformer models on AWS Inferentia via Amazon SageMaker for accelerated, low-cost inference. Covers converting a model to AWS Neuron format using torch_neuron, writing a custom inference.py script, packaging and uploading the model to S3, deploying a real-time SageMaker endpoint, and benchmarking performance. Results show 5-6ms average latency per Neuron core for sequence length 128, with the ability to run 4 models in parallel across Neuron cores for higher throughput than GPUs at lower cost.
Table of contents
Tutorial1. Convert your Hugging Face Transformer to AWS Neuron2. Create a custom inference.py script for text-classification3. Create and upload the neuron model and inference script to Amazon S34. Deploy a Real-time Inference Endpoint on Amazon SageMaker5. Run and evaluate Inference performance of BERT on InferentiaConclusion4 Impressions