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Amazon SageMaker HyperPod now supports partition-level topology for Slurm orchestrated clusters

Amazon SageMaker HyperPod now supports network topology configuration at the partition level for Slurm orchestrated clusters. A single cluster can now run tree topology in one partition and block topology in another, with each partition using the topology best suited to its instance types. This improves distributed training performance by keeping job placement aligned with the interconnect characteristics of each instance type, so GPU-to-GPU communication is faster, NCCL collective operations are more efficient, and training throughput improves. HyperPod determines the topology for each partition based on the instance types of its compute instance groups. Partitions with Amazon EC2 UltraServer instance types such as ml.p6e-gb200.36xlarge use block topology, and those with hierarchical-interconnect instance types such as ml.p5.48xlarge, ml.p5e.48xlarge, and ml.p5en.48xlarge use tree topology, while partitions with instance types that don’t provide network topology information remain fully schedulable. HyperPod maintains this configuration automatically as the cluster changes through scale-up, scale-down, and node replacement events, so each partition’s topology always reflects the current state of the cluster. To get started, create or update a SageMaker HyperPod Slurm cluster running Slurm 25.11 or later with supported GPU instance types. Topology-aware scheduling is enabled by default and requires no configuration. This feature is available in all AWS Regions where Amazon SageMaker HyperPod is supported. To learn more, see Using topology-aware scheduling in Amazon SageMaker HyperPod.
Quelle: aws.amazon.com

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Amazon GameLift Streams now supports IAM role credentials for stream sessions

Amazon GameLift Streams now supports assigning an IAM role to a stream session, enabling your application to securely access resources in your AWS account, such as Amazon S3 buckets and DynamoDB tables. With this launch, you can pass a RoleArn parameter when starting a stream session, and your application automatically receives short-lived, auto-refreshing AWS credentials through the standard AWS SDK credential resolution chain — no application code changes required.
Previously, customers who needed their streamed applications to access AWS services had to embed long-lived access keys in application bundles or pass them as environment variables, creating security and operational challenges. Now, Amazon GameLift Streams handles credential vending and automatic refresh using the same container credential provider mechanism trusted by Amazon ECS task roles and Amazon EKS Pod Identity. Role misconfigurations are validated at session start, surfacing clear errors immediately rather than during runtime.
You can also configure IAM roles directly in the Amazon GameLift Streams console, which provides a pre-filled trust policy template to simplify role setup.
IAM role support for stream sessions is available in all AWS Regions where Amazon GameLift Streams is available.
To learn more, see Session Credentials Setup in the Amazon GameLift Streams Developer Guide: https://docs.aws.amazon.com/gameliftstreams/latest/developerguide/session-credentials-setup.html 
Quelle: aws.amazon.com