Kategorie: Amazon AWS

Amazon EC2 High Memory U7i instances now available in additional regions

Amazon EC2 High Memory U7i-8TB instances (u7i-8tb.112xlarge) are now available in AWS Europe (Stockholm, Zurich) regions, U7in-16TB instances (u7in-16tb.224xlarge) are now available in the AWS US East (Ohio) region, and U7in-24TB instances (u7in-24tb.224xlarge) are now available in the AWS Europe (Stockholm) region. U7i instances are part of the AWS 7th generation and are powered by custom fourth-generation Intel Xeon Scalable processors (Sapphire Rapids). U7i-8TB instances offer 8 TiB of DDR5 memory, U7in-16TB instances offer 16 TiB of DDR5 memory, and U7in-24TB instances offer 24 TiB of DDR5 memory, enabling customers to scale transaction processing throughput in a fast-growing data environment.
U7i-8TB instances deliver 448 vCPUs and support up to 100 Gbps of Amazon EBS bandwidth, 100 Gbps of network bandwidth, and ENA Express. Both U7in-16TB and U7in-24TB instances deliver 896 vCPUs and support up to 100 Gbps of Amazon EBS bandwidth for faster data loading and backups, 200 Gbps of network bandwidth, and ENA Express. U7i instances are ideal for customers running mission-critical in-memory databases like SAP HANA, Oracle, and SQL Server.
To learn more about U7i instances, visit the High Memory instances page.
Quelle: aws.amazon.com

Amazon SageMaker HyperPod now supports automatic Slurm topology management

Amazon SageMaker HyperPod now automatically selects and continuously maintains the optimal network topology configuration for Slurm clusters based on the GPU instance types in the cluster. Network topology directly impacts distributed training performance — when jobs are placed on nodes that are topologically close, GPU-to-GPU communication is faster, NCCL collective operations are more efficient, and training throughput improves. HyperPod dynamically adapts the topology as the cluster evolves through scaling operations and node replacements, so job placement remains optimized throughout the cluster lifecycle without requiring manual updates to topology files or Slurm reconfiguration. HyperPod inspects the instance types across all instance groups at cluster creation, identifies the networking and interconnect characteristics of each instance type, and automatically selects the best-fit topology model. HyperPod supports tree topology for instance types with hierarchical interconnects such as ml.p5.48xlarge, ml.p5e.48xlarge, and ml.p5en.48xlarge, and block topology for instance types with uniform high-bandwidth connectivity such as ml.p6e-gb200.NVL72. For clusters with mixed instance types, HyperPod selects a compatible topology that works across all nodes. As the cluster changes through scale-up, scale-down, or node replacement events, HyperPod automatically updates the topology configuration without manual intervention, so the topology always reflects the actual state of the cluster.
To get started, create a SageMaker HyperPod Slurm cluster 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 about topology-aware scheduling, visit the Amazon SageMaker HyperPod documentation
Quelle: aws.amazon.com

AWS Parallel Computing Service now supports Slurm 25.11

AWS Parallel Computing Service (AWS PCS) now supports Slurm version 25.11, with support for a Prometheus-compatible OpenMetrics endpoint, and introduces new log types including scheduler audit logs. This release of Slurm 25.11 introduces expedited re-queue, which can automatically reschedule jobs affected by node issues at the highest priority to help your workloads recover faster. You can enable a new OpenMetrics endpoint for real-time visibility into jobs, nodes, and scheduling using your existing monitoring tools. AWS PCS can now also send Slurm database daemon (slurmdbd) and REST API daemon (slurmrestd) logs to Amazon CloudWatch Logs, Amazon S3, or Amazon Data Firehose, helping diagnose accounting issues and debug API integrations. Scheduler audit logs, previously included in operational logs, are now delivered as a dedicated log type, providing independent control over ingestion and storage costs. AWS PCS is a managed service that makes it easier for you to run and scale your high performance computing (HPC) workloads and build scientific and engineering models on AWS using Slurm. You can use AWS PCS to build complete, elastic environments that integrate compute, storage, networking, and visualization tools. AWS PCS simplifies cluster operations with managed updates and built-in observability features, helping to remove the burden of maintenance. You can work in a familiar environment, focusing on your research and innovation instead of worrying about infrastructure. These features are available in all AWS Regions where AWS PCS is available. Standard charges apply for log delivery destinations. To learn more about AWS PCS, refer to the service documentation.
Quelle: aws.amazon.com

Amazon Athena simplifies federated queries with managed connectors

Amazon Athena now offers managed connectors for 12 data sources, including Amazon DynamoDB, PostgreSQL, MySQL, and Snowflake. Managed connectors are AWS Glue Data Catalog federated connectors that Athena creates and manages on your behalf, so you can query data outside Amazon S3 without deploying or maintaining connector resources in your AWS account. With Athena, you can interactively query relational, non-relational, object, and custom data sources without moving or duplicating data. To get started with managed connectors, you create a connection for your data source in Athena. Athena automatically sets up and manages connector resources on your behalf, registering the data source as a federated catalog in AWS Glue Data Catalog. You can then query the data source alongside your Amazon S3 data and optionally set up fine-grained access controls through AWS Lake Formation. Federated queries with managed connectors are available in all AWS Regions where Athena is available, except the AWS GovCloud (US) Regions and the China Regions. To learn more, visit Use Amazon Athena Federated Query in the Athena User Guide.
Quelle: aws.amazon.com

Amazon Quick now supports multiple owners for admin-managed SharePoint and Google Drive knowledge bases

Amazon Quick now enables you to add co-owners to knowledge bases and data source connections for admin-managed Microsoft SharePoint Online and Google Drive integrations. This makes it easier to collaborate across teams and reuse existing connections without re-entering credentials. Knowledge base owners can share their knowledge bases with two roles: Owner (full management access including editing, syncing, sharing, and deleting) and Viewer (query-only access). Co-owner sharing with the Owner role is available exclusively for admin-managed SharePoint and Google Drive knowledge bases. All other knowledge base types support Viewer sharing only. To share, navigate to the actions menu next to any knowledge base or use the Permissions tab. Administrators can also share data source connections, allowing other users to create knowledge bases from the same connection. Data source sharing supports Owner (create knowledge bases and edit connection details) and Viewer (create knowledge bases only) roles. To share a data source, go to Manage account > Manage assets > Data sources and select the connection to share. This feature is available in all AWS Regions where Amazon Quick is available. For more information, see Knowledge Base Sharing in the Amazon Quick User Guide. Amazon Quick is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (London), and Europe (Ireland). For more information, visit the Amazon Quick page.
Quelle: aws.amazon.com

AWS Elastic Beanstalk AI-powered environment analysis now supports Windows

AWS Elastic Beanstalk AI-powered environment analysis is now available on Windows Server platforms. Previously available on Amazon Linux 2 and AL2023, this feature now extends to Windows-based environments, enabling you to quickly identify root causes and get recommended solutions for environment health issues. Elastic Beanstalk collects recent events, instance health, and logs from your Windows environment and sends them to Amazon Bedrock for analysis. With this expansion, developers and operations teams running .NET applications and other Windows workloads on Elastic Beanstalk can now diagnose and resolve environment issues faster without manually reviewing logs and events. You can request an AI analysis from the Elastic Beanstalk console using the AI Analysis button or using the AWS CLI with the RequestEnvironmentInfo and RetrieveEnvironmentInfo API operations. The analysis provides step-by-step troubleshooting recommendations tailored to your Windows environment’s current state.
AI-powered environment analysis is available in all AWS Regions where both AWS Elastic Beanstalk and Amazon Bedrock are available. For more information about the AI-powered environment analysis and for a full list of supported platform versions, see the Elastic Beanstalk developer guide. To learn more about Elastic Beanstalk, visit the Elastic Beanstalk product page.
Quelle: aws.amazon.com

Second-generation AWS Outposts racks now supported in the AWS Asia Pacific (Seoul, Sydney) and Europe (Paris) Regions

Second-generation AWS Outposts racks are now supported in the AWS Asia Pacific (Seoul, Sydney) and Europe (Paris) Regions. Outposts racks extend AWS infrastructure, AWS services, APIs, and tools to virtually any on-premises data center or colocation space for a truly consistent hybrid experience. Organizations from startups to enterprises and the public sector in and outside of South Korea, Australia, and France can now order their Outposts racks connected to this new supported region, optimizing for their latency and data residency needs. Outposts allows customers to run workloads that need low latency access to on-premises systems locally while connecting back to their home Region for application management. Customers can also use Outposts and AWS services to manage and process data that needs to remain on-premises to meet data residency requirements. This regional expansion provides additional flexibility in the AWS Regions that customers’ Outposts can connect to. To learn more about second-generation Outposts racks, read this blog post and user guide. For the most updated list of countries and territories and the AWS Regions where second-generation Outposts racks are supported, check out the Outposts rack FAQs page.
Quelle: aws.amazon.com

Attributed Revenue Dashboard Now Available in AWS Partner Central

Today, AWS announces the launch of the Attributed Revenue dashboard in AWS Partner Central in the AWS Console, giving Partners self-service visibility into the revenue impact of their solutions as measured by Partner Revenue Measurement. The dashboard displays aggregated monthly attributed revenue by Partner product, AWS service, and billing period. It provides consolidated insights from all three Partner Revenue Measurement capabilities—Resource Tagging, User Agent string, and AWS Marketplace Metering—in a single view.
Partners who implement Partner Revenue Measurement can now access the Attributed Revenue Dashboard through Partner Analytics to view monthly consumption patterns, monitor revenue trends over time, and verify that their implementation is actively measuring AWS service consumption driven by their solutions. Partners with multiple AWS Marketplace seller accounts can connect subsidiary accounts to see aggregated revenue across all connected accounts.
The Attributed Revenue Dashboard is available in all commercial regions for Partners that have migrated to AWS Partner Central in the AWS Console. To learn more about Partner Revenue Measurement, review the onboarding guide.
Quelle: aws.amazon.com

Amazon SageMaker supports notebooks and data agent for IdC domains

Amazon SageMaker Unified Studio now supports serverless notebooks with a built-in data agent for AWS IAM Identity Center (IdC) domains. Previously, the notebook experience and data agent were available only in IAM domains. With this launch, customers who use IdC for authentication and access management can access the high-performance, serverless notebook environment for analytics and machine learning (ML) workloads.
The serverless notebook gives data engineers, analysts, and data scientists one place to perform SQL queries, execute Python code, process large-scale data jobs, run ML workloads, and create visualizations. A built-in AI data agent accelerates development by generating code and SQL statements from natural language prompts and guides users through their tasks. Customers can flexibly combine SQL, Python, and natural language within a single interactive workspace, removing the need to switch between different tools based on the workload. For example, you can start with SQL queries to explore your data, use Python for advanced analytics or to build ML models, or use natural language prompts to generate code automatically. The notebook is backed by Amazon Athena for Apache Spark, scaling from interactive SQL queries to petabyte-scale data processing.
You can use the SageMaker notebook and data agent features in all AWS Regions where Amazon SageMaker Unified Studio is supported. To learn more, see the SageMaker notebooks user guide and the SageMaker data agent user guide. 
Quelle: aws.amazon.com

Amazon EC2 C8i instances are now available in Europe (Ireland) and Asia Pacific (New Zealand) regions

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C8i instances are available in the Europe (Ireland) and Asia Pacific (New Zealand) regions. These instances are powered by custom Intel Xeon 6 processors, available only on AWS, delivering the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. C8i instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances. They deliver up to 20% higher performance than C7i instances, with even higher gains for specific workloads. The C8i instances deliver up to 60% faster for NGINX web applications, up to 40% faster for AI deep learning recommendation models, and 35% faster for Memcached stores compared to C7i. C8i instances are a great choice for all memory-intensive workloads, especially for workloads that need the largest instance sizes or continuous high CPU usage. C8i instances offer 13 sizes including 2 bare metal sizes and the new 96xlarge size for the largest applications. To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information about the new C8i instances visit the AWS News blog.
Quelle: aws.amazon.com