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Announcing AWS Neuron SDK 2.25.0

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Today, AWS announces the general availability of Neuron SDK 2.25.0, delivering improvements for inference workloads and performance monitoring on AWS Inferentia and Trainium instances. This latest release adds context and data parallelism support as well as chunked attention for long sequence processing in inference, and updates the neuron-ls and neuron-monitor APIs with more information on node affinities and device utilization, respectively.

This release also introduces automatic aliasing (Beta) for fast tensor operations, and adds improvements for disaggregated serving (Beta). Finally, it provides upgraded AMIs and Deep Learning Containers for inference and training workloads on Neuron.

Neuron 2.25.0 is available in all AWS Regions where Inferentia and Trainium instances are offered.

To learn more and for a full list of new features and enhancements, see:

AWS IoT Core now supports customer-managed keys

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AWS IoT Core now supports customer-managed keys (CMK) through AWS Key Management Service (KMS), enabling you to encrypt data stored in IoT Core with your own encryption keys. This enhancement gives you greater control over your encryption key lifecycle, including creation, rotation, monitoring, and deletion. Moreover, when you opt for CMK, the service handles the transition by automatically re-encrypting existing data while helping to maintain uninterrupted IoT operations. Operating within the AWS shared responsibility model , this security enhancement helps organizations meet their security requirements without impacting their IoT applications.

This feature is now available in all AWS Regions where AWS IoT Core is supported. For details, visit the AWS IoT Core documentation .

Amazon SageMaker Unified Studio adds S3 file sharing options to projects

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Amazon SageMaker Unified Studio now offers a simplified file storage option in projects, providing data workers with an easier way to collaborate on their analytics and machine learning workflows without depending on Git. You can now choose between Git repositories (GitHub, GitLab or Bitbucket Cloud) or Amazon Simple Storage (S3) buckets for sharing code files between the various members of a project. While S3 is the default option, customers who want to use Git can still continue to have the same experience as they currently do.

With this launch, customers will see a consistent view of their files irrespective of the tool they are working in across SageMaker Unified Studio (such as JupyterLab, Code Editor or SQL query editor) making it easy to create, edit and share code. The S3 file storage option operates on a “last write wins” principle and supports basic file versioning when enabled by administrators. This option is particularly beneficial for data science teams who want to focus on their analytics and machine learning work without managing Git operations, while still maintaining a collaborative workspace for their project artifacts.

This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more about storage options in SageMaker Unified Studio projects, see Managing Project Files in the Amazon SageMaker Unified Studio User Guide .

Count Tokens API supported for Anthropic’s Claude models now in Amazon Bedrock

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The Count Tokens API is now available in Amazon Bedrock, enabling you to determine the token count for a given prompt or input being sent to a specific model ID prior to performing any inference.

By surfacing a prompt’s token count, the Count Tokens API allows you to more accurately project your costs, and provides you with greater transparency and control over your AI model usage. It allows you to proactively manage your token limits on Amazon Bedrock, helping to optimize your usage and avoid unexpected throttling. It also helps ensure your workloads fit within a model’s context length limit, allowing for more efficient prompt optimization.

At launch, the Count Tokens API will support Claude models, with the functionality available in all regions where these models are supported . For more information about this new feature, including supported models and use cases, visit the Count Tokens API documentation .

Amazon EKS enables namespace configuration for AWS and Community add-ons

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Amazon Elastic Kubernetes Service (Amazon EKS) now supports Kubernetes namespace configuration for AWS and Community add-ons, providing you greater control over how add-ons are organized within your Kubernetes cluster.

With namespace configuration, you can now specify a custom namespace during add-on installation, enabling better organization and isolation of add-on objects within your EKS cluster. This flexibility helps you align add-ons with your operational needs and existing namespace strategy. Once an add-on is installed in a specific namespace, you must remove and recreate the add-on to change its namespace.

This feature is available through the AWS Management Console, Amazon EKS APIs, AWS Command Line Interface (CLI), and infrastructure as code tools like AWS CloudFormation. Namespace configuration for AWS and Community add-ons is now available in all commercial AWS Regions. To learn more, visit the Amazon EKS documentation .

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