Training and deploying a graphics processing unit (GPU)-supported machine learning (ML) model requires an initial setup and initialization of certain environment variables to fully unlock the benefits of NVIDIA GPUs. However, it can be time-consuming to set up the environment and make it compatible with Amazon SageMaker architecture on the Amazon Web Services (AWS) Cloud.
This pattern helps you train and build a custom GPU-supported ML model using Amazon SageMaker. It provides steps to train and deploy a custom CatBoost model built on an open-source Amazon reviews dataset. You can then benchmark its performance on a p3.16xlarge Amazon Elastic Compute Cloud (Amazon EC2) instance.
This pattern is useful if your organization wants to deploy existing GPU-supported ML models on SageMaker. Your data scientists can follow the steps in this pattern to create NVIDIA GPU-supported containers and deploy ML models on those containers
