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Customize DeepSeek-R1 671b model using Amazon SageMaker HyperPod recipes – Part 2

AWS Machine Learning Blog

You can execute each step in the training pipeline by initiating the process through the SageMaker control plane using APIs, AWS Command Line Interface (AWS CLI), or the SageMaker ModelTrainer SDK. Alternatively, you can also use AWS Systems Manager and run a command such as the following to start the session.

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Accelerate pre-training of Mistral’s Mathstral model with highly resilient clusters on Amazon SageMaker HyperPod

AWS Machine Learning Blog

The compute clusters used in these scenarios are composed of more than thousands of AI accelerators such as GPUs or AWS Trainium and AWS Inferentia , custom machine learning (ML) chips designed by Amazon Web Services (AWS) to accelerate deep learning workloads in the cloud. at a minimum).

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Moderate your Amazon IVS live stream using Amazon Rekognition

AWS Machine Learning Blog

You can deploy this solution to your AWS account using the AWS Cloud Development Kit (AWS CDK) package available in our GitHub repo. Using the AWS Management Console , you can create a recording configuration and link it to an Amazon IVS channel. In this section, we briefly introduce the system architecture.

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Mitigating risk: AWS backbone network traffic prediction using GraphStorm

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The AWS global backbone network is the critical foundation enabling reliable and secure service delivery across AWS Regions. Specifically, we need to predict how changes to one part of the AWS global backbone network might affect traffic patterns and performance across the entire system.

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Build multi-agent systems with LangGraph and Amazon Bedrock

AWS Machine Learning Blog

AWS has introduced a multi-agent collaboration capability for Amazon Bedrock Agents , enabling developers to build, deploy, and manage multiple AI agents working together on complex tasks. Nodes Python functions that encode the logic of your agents. For this post, we use the us-west-2 AWS Region. A valid AWS account.

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Ray jobs on Amazon SageMaker HyperPod: scalable and resilient distributed AI

AWS Machine Learning Blog

They require efficient systems for distributing workloads across multiple GPU accelerated servers, and optimizing developer velocity as well as performance. Ray is an open source framework that makes it straightforward to create, deploy, and optimize distributed Python jobs. in the aws-do-ray GitHub repo. The fsdp-ray.py