Create, List, Update, Delete Amazon EKS clusters. Deploy and manage software on EKS. Run distributed model training and inference examples.

This Guidance demonstrates how to deploy a machine learning inference architecture on Amazon Elastic Kubernetes Service (Amazon EKS). It addresses the basic implementation requirements as well as ways you can pack thousands of unique PyTorch deep learning (DL) models into a scalable architecture and evaluate performance

AWS DevOps for Docker - a sample project to help you build Docker containers and run them on AWS. In addition to running locally, this project can run your container on EC2, ECS, EKS, AWS Batch, and AWS Lambda.

A framework for building predictive modeling applications

A do-framework project to simplify deployment of Kubeflow on Amazon EKS

Openfold inference architecture for Amazon EKS

Create and manage Amazon SageMaker HyperPod clusters, run distributed model training

Deploy and scale distributed python applications on Amazon EKS using Ray

Run the AWS CLI and related utilities in a container. Manage VPC and EC2 resources.

Build Amazon Machine Images (AMIs) using tools running in a container

Create and manage High Performance Computing (HPC) clusters on AWS using Parallel Cluster