AWS Machine Learning Blog

SEPTEMBER 18, 2024

It is important to consider the massive amount of compute often required to train these models. When using compute clusters of massive size, a single failure can often throw a training job off course and may require multiple hours of discovery and remediation from customers.

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Clustering AWS ML ML 124

AWS Machine Learning Blog

MAY 14, 2025

The following diagram illustrates the solution architecture for training using SageMaker HyperPod. With HyperPod, users can begin the process by connecting to the login/head node of the Slurm cluster. Alternatively, you can also use AWS Systems Manager and run a command such as the following to start the session.

Clustering 81

Clustering AWS ML ML 81

AWS Machine Learning Blog

APRIL 10, 2025

As cluster sizes grow, the likelihood of failure increases due to the number of hardware components involved. Each hardware failure can result in wasted GPU hours and requires valuable engineering time to identify and resolve the issue, making the system prone to downtime that can disrupt progress and delay completion.

ML 120

ML ML Clustering AWS 120