Manage Amazon SageMaker HyperPod clusters using the HyperPod CLI and SDK

Training and deploying large AI models requires advanced distributed computing capabilities, but managing these distributed systems shouldn’t be complex for data scientists and machine learning (ML) practitioners. The command line interface (CLI) and software development kit (SDK) for Amazon SageMaker HyperPod with Amazon Elastic Kubernetes Service (Amazon EKS) orchestration simplify how you manage cluster infrastructure and use the service’s distributed training and inference capabilities.

The SageMaker HyperPod CLI provides data scientists with an intuitive command-line experience, abstracting away the underlying complexity of distributed systems. Built on top of the SageMaker HyperPod SDK, the CLI offers straightforward commands for managing HyperPod clusters and common workflows like launching training or fine-tuning jobs, deploying inference endpoints, and monitoring cluster performance. This makes it ideal for quick experimentation and iteration.

A layered architecture for simplicity

The HyperPod CLI and SDK follow a multi-layered, shared architecture. The CLI and the Python module serve ...