Serve an NLP model on GPU with Amazon SageMaker Multi-model endpoints (MME)

In this example, we will walk you through how to use NVIDIA Triton Inference Server on Amazon SageMaker MME with GPU feature to deploy two different HuggingFace T5 NLP model for Text Translation. In particular, this model will be using:

T5-small HuggingFace PyTorch Translation Model (Served using Triton's Python Backend)

Steps to run the notebook

1. Launch SageMaker notebook instance with g5.xlarge instance. This example can also be run on a SageMaker studio notebook instance but the steps that follow will focus on the notebook instance.

   • IMPORTANT: In Notebook instance settings, within Additional Configuration, for Volume Size in GB specify at least 100 GB.
   • For git repositories select the option Clone a public git repository to this notebook instance only and specify the Git repository URL

2. Once JupyterLab is ready, launch the t5_nlp_pytorch_python-gpu.ipynb notebook with conda_python3 conda kernel and run through this notebook to learn how to host multiple NLP models on g5.2xlarge GPU behind MME endpoint.