Kategorie: Amazon AWS

SageMaker Notebook Instances now support G6e instance types

We are pleased to announce general availability of Amazon EC2 G6e instances on SageMaker notebook instances.
Amazon EC2 G6e instances are powered by up to 8 NVIDIA L40s Tensor Core GPUs with 48 GB of memory per GPU and third generation AMD EPYC processors. G6e instances deliver up to 2.5x better performance compared to EC2 G5 instances. Customers can use G6e instances to interactively test model deployment and for interactive model training use cases such as generative AI fine-tuning. You can use G6e instances to deploy large language models (LLMs) with up to 13B parameters and diffusion models for generating images, video, and audio.
Amazon EC2 G6e instances are available on SageMaker notebook instances in the AWS US East (N. Virginia and Ohio), US West (Oregon), Asia Pacific (Tokyo), Middle East (Dubai) and Europe (Frankfurt, Sweden, Spain) regions.
Visit developer guides for instructions on setting up and using JupyterLab and CodeEditor applications on SageMaker Studio and SageMaker notebook instances.
Quelle: aws.amazon.com

AWS introduces Lambda MicroVMs for isolated execution of user and AI-generated code

AWS introduces Lambda MicroVMs, a new serverless compute primitive that provides VM-level isolation, near-instant launch and resume speeds, and state preservation for executing user or AI-generated code. You can now give each user or job their own compute environment to securely run code without managing virtualization infrastructure or choosing between isolation, speed, and state retention.
Developers are increasingly building multi-tenant applications that execute code supplied by end users or AI for use cases such as interactive coding environments, data analytics platforms, coding assistants, and vulnerability scanning platforms. For these applications, developers need to allocate a separate, isolated execution environment per user or session to limit the impact of incorrect or malicious code on other concurrently running users or jobs. Previously, developers needed to choose between strong isolation, fast launch times, and state retention when building these applications. Starting today, Lambda MicroVMs provides you these capabilities without any trade-offs. You get VM-level isolation, near-instant launch speeds, and the ability to suspend and resume execution for up to 8 hours. Lambda MicroVMs is built on Firecracker virtualization, the technology powering more than 15 trillion monthly Lambda Function invocations. 
To get started, create a MicroVM image from your Dockerfile, then launch MicroVMs from that image. Give each user or job their own MicroVM with a dedicated HTTPS URL that supports popular connectivity protocols such as HTTP/2, gRPC, and WebSockets. 
Lambda MicroVMs is available today in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland). To learn more, visit the AWS Lambda MicroVMs developer guide and the launch blog post. Get started with MicroVMs through the AWS Lambda console, AWS CloudFormation, AWS Cloud Development Kit, or use the Agent Toolkit for AWS with your preferred Agentic development tools. You pay for baseline compute resources while your MicroVM is running, and only for the active duration of additional resources consumed when your workload exceeds the baseline. To learn more about pricing, see Lambda MicroVMs pricing.
Quelle: aws.amazon.com

AWS HealthOmics now supports Nextflow profiles

AWS HealthOmics now supports Nextflow profiles, enabling customers to activate predefined execution settings at run time. Nextflow profiles allow customers to define reusable settings and select them at the point of execution, making it easy to switch between execution settings without modifying workflow source code. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows.
With Nextflow profiles, you can cleanly separate platform-specific settings such as resource limits or execution options from core workflow logic. You can switch between development and production settings without creating separate workflow definitions. This reduces errors from manual edits, accelerates workflow portability, and saves time when scaling from development to production. If you use nf-core workflows, you can now activate the built-in and institutional profiles those pipelines already ship with.
You can now specify one or more Nextflow profiles in your workflow runs in all AWS HealthOmics Regions: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Singapore, Seoul). To learn more, visit the Nextflow Profiles section on HealthOmics Nextflow engine settings documentation.
Quelle: aws.amazon.com

AWS IAM Identity Center now supports separate quotas for AWS accounts and applications

AWS IAM Identity Center now supports separate quotas for the number of AWS accounts and applications that can be configured in an IAM Identity Center instance. By default, you can configure up to 7,000 AWS accounts and up to 7,000 applications independently, so that using more of one does not consume capacity from the other. Quotas can be further increased by submitting a quota increase request through AWS Service Quotas console.
Customers with existing higher limits are automatically granted the same limit for both accounts and applications, with no action required. Organizations managing thousands of AWS accounts can now onboard applications without consuming account quota capacity.
This update is available in all AWS Regions where IAM Identity Center is available.
To learn more, see Quotas for IAM Identity Center. Visit the IAM Identity Center product page to get started.
Quelle: aws.amazon.com

AWS Batch now supports customer-ordered instance allocation strategies

AWS Batch now offers the Best Fit Progressive Ordered (BFPO) and Spot Capacity Optimized Prioritized (SCOP) allocation strategies, giving you more control over instance type prioritization in your compute environments. BFPO and SCOP enable you to manually define instance type ordering based on your workload-specific performance characteristics. To use these features in AWS Batch, specify BEST_FIT_PROGRESSIVE_ORDERED allocation strategy for your on-demand compute environments or SPOT_CAPACITY_OPTIMIZED_PRIORITIZED for your Amazon EC2 Spot compute environments and provide an ordered list of instance types or families. These features are available via the AWS Batch API (CreateComputeEnvironment or UpdateComputeEnvironment) or the AWS Batch Management Console. BFPO and SCOP allocation strategies are supported today in all AWS Regions where AWS Batch is available. For more information, see the AWS Batch User Guide.
Quelle: aws.amazon.com

AWS Network Firewall updates default drop action for improved connection reliability

AWS Network Firewall now uses “Application drop established (server-directed only)” as the default stateful action for all newly created firewall policies, replacing the previous default of “Application drop established (bidirectional)” (formerly named “Application layer drop established”). No action is required to benefit from this change when creating new policies. AWS Network Firewall is a managed service that lets you deploy network protections across your Amazon VPCs. Previously, the “Application drop established (bidirectional)” default could silently drop legitimate server-to-client TCP packets, such as window updates, keep-alives, and resets — causing intermittent connection failures that were difficult to diagnose. With the safer default now in place, new policies avoid this issue. If your existing environment requires “Application drop established (bidirectional)” to support post-quantum cryptography (PQC) fragmented TLS handshakes, refer to our documentation for guidance on on switching to “Application drop established (server-directed only)” or adding the “to_server” flag to your TCP drop rules so legitimate flow control packets are not blocked. This update is available in all AWS Regions where AWS Network Firewall is offered. To get started, see Managing evaluation order for Suricata compatible rules in the AWS Network Firewall service documentation.
Quelle: aws.amazon.com

Amazon CloudWatch Synthetics now supports multilocation canaries

Today, Amazon CloudWatch Synthetics announces support for multilocation canaries, allowing developers and site reliability engineers to run the same canary across multiple AWS Regions simultaneously from a single point of management. Previously, monitoring application availability from multiple geographic locations required creating and managing separate canaries in each Region, adding operational overhead and increasing the risk of configuration drift. With multilocation canaries, you create and manage a canary in one primary Region, and CloudWatch Synthetics automatically replicates it to the additional Regions you choose, consolidating all run data, metrics, and artifacts in the primary Region.
Multilocation canaries help you ensure consistent user experience worldwide, identify region-specific performance bottlenecks, and validate that third-party dependencies like CDNs and payment processors work across all locations. Replica canaries run independently, giving you resilient monitoring coverage across geographic locations. You can also configure alarms that activate only when issues are detected from multiple locations, increasing alert confidence and helping your team focus on real customer-impacting problems. Amazon CloudWatch Synthetics multilocation canaries are available in all AWS commercial Regions that support CloudWatch Synthetics. You can upgrade existing single-region canaries to multilocation by adding replica Regions without recreating them. For more information about regional availability, see the AWS Region table.
To learn more about CloudWatch Synthetics, see Using synthetic monitoring in the Amazon CloudWatch User Guide. To get started, visit the Amazon CloudWatch product page.
Quelle: aws.amazon.com

Amazon MQ for RabbitMQ now supports private networking connectivity

Amazon MQ for RabbitMQ now supports private networking, enabling your brokers to connect to private resources in your VPC without exposing those resources publicly.. This helps you meet your security and compliance requirements when your brokers need to reach private identity providers (such as LDAP and OAuth 2.0), other Amazon MQ for RabbitMQ brokers, or self-hosted RabbitMQ brokers. Previously, this connectivity for RabbitMQ Federation, Shovel, or authentication required Network Load Balancer and NAT Gateway workarounds. Amazon MQ establishes this connectivity using Amazon VPC Lattice, AWS Resource Access Manager (AWS RAM), and AWS PrivateLink, and manages the underlying infrastructure on your behalf. To get started, create a VPC Lattice resource gateway, package your resource configurations into an AWS RAM resource share, and associate it with your broker. Private networking is available only for Amazon MQ for RabbitMQ brokers, in all AWS Regions where Amazon VPC Lattice is available. To learn more, see Private networking in the Amazon MQ Developer Guide and the Amazon MQ pricing page.
Quelle: aws.amazon.com

Amazon EC2 G7 instances are now generally available

Today, AWS announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) G7 instances, accelerated by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs. G7 instances deliver up to 4.6x AI inference performance and up to 2.1x graphics performance compared to G6.
You can use G7 instances for AI inference workloads such as language translation, video and image analysis, speech recognition, and recommender systems. Additionally, G7 instances also accelerate graphics workloads such as creating and rendering real-time, cinematic-quality graphics, and game streaming, as well as data analytics workloads such as large-scale data processing pipelines. G7 instances feature up to 8 NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs with 32 GB of memory per GPU, custom Intel Xeon 6 processors, and up to 700 Gbps of Elastic Fabric Adapter (EFA) networking bandwidth.
You can start using Amazon EC2 G7 instances today in two AWS Regions: US East (Ohio) and US West (Oregon). You can purchase G7 instances as On-Demand Instances, as part of Savings Plans, or Spot Instances. 
To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit this blog post and the G7 instance page.
Quelle: aws.amazon.com

Amazon ECS announces faster service auto scaling

Amazon ECS service auto scaling now detects and responds to load changes faster with support for high resolution (20-second) metrics and metric publishing optimizations. In AWS benchmarking tests, time to trigger scale-out improved from 363 seconds to 86 seconds (76% faster, 4.2x), and total time to scale and provision new tasks improved from 386 seconds to 109 seconds (72% faster, 3.5x). Faster service auto scaling also enables you to reduce baseline capacity and lower compute costs while maintaining service reliability and performance as workload demand fluctuates. Amazon ECS service auto scaling automatically adjusts task counts to meet workload demand with comprehensive scaling policies, including predictive scaling for recurring traffic patterns, scheduled scaling for planned events, and target tracking to scale dynamically on real-time metrics. With today’s launch, target tracking policies for CPU and memory utilization now support 20-second metric resolution, in addition to the default 60-second resolution, for faster scaling signal detection. To get started, use the AWS Console, CLI, CloudFormation, or AWS SDKs to configure 20-second resolution for CPU or memory utilization metrics when creating or updating your ECS service, then configure a target tracking policy selecting the corresponding high-resolution predefined metric. This feature is available in all AWS commercial and AWS GovCloud (US) Regions, across all ECS compute options: AWS Fargate, Amazon ECS Managed Instances, and Amazon EC2. High-resolution metrics are subject to standard CloudWatch charges; for a pricing example, see Amazon CloudWatch pricing. To learn more, see our documentation and the launch blog post.
Quelle: aws.amazon.com