News For Dev-ops

Amazon Elastic Kubernetes Service (Amazon EKS) now supports Amazon Application Recovery Controller (ARC) zonal shift and autoshift for clusters using EKS Auto Mode . ARC helps you manage and coordinate recovery across AWS Regions and Availability Zones (AZs). With this launch, EKS Auto Mode automatically protects your compute during a zonal shift at no additional cost or configuration, helping you maintain Kubernetes application availability by shifting in-cluster network traffic away from an impaired AZ. Customers run highly available applications across multiple AZs in Amazon EKS to eliminate a single point of failure. Because EKS Auto Mode manages compute on your behalf, you get zonal shift support without setting flags, granting permissions, or managing Karpenter versions; simply enable ARC zonal shift on your cluster. When a zonal shift is activated, EKS Auto Mode stops provisioning new capacity in the impaired AZ and halts voluntary disruptions such as consolidation and drift ...

Today, AWS announced the availability of Voxtral-Mini-4B-Realtime-2602 in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. This model from Mistral AI is a multilingual, real-time speech-transcription model, enabling customers to build low-latency speech applications on AWS infrastructure. Voxtral-Mini-4B-Realtime excels at high-quality transcription of audio to text with a natively streaming architecture that enables real-time transcription. It supports multilingual transcription across 13 languages and offers configurable transcription delays, allowing users to balance latency and accuracy based on their needs. With SageMaker JumpStart, customers can deploy this model with just a few clicks to address their specific AI use cases. To get started with this model, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using ...

Today, AWS announced the availability of Qwen3-VL-Embedding-2B and Qwen3-Reranker-4B in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These models from Qwen are designed for information retrieval and cross-modal understanding, enabling customers to build comprehensive search pipelines on AWS infrastructure. The two models are typically used in tandem: the embedding model performs efficient initial recall, while the reranker refines results in a subsequent re-ranking stage. These models address different stages of the retrieval pipeline with specialized capabilities: Qwen3-VL-Embedding-2B accepts diverse inputs including text, images, screenshots, and videos, as well as inputs containing a mixture of these modalities, and generates semantically rich vectors that capture both visual and textual information in a shared space. It delivers performance across diverse multimodal tasks such as image-text retrieval, video-text matching...

In my earlier overview of how compilers on different architectures perform stack probes , Cole Tobin asked , "Why not have a page fault handler that detects the faulting address being the stack and page in the other pages?" Csaba Varga replied , "My guess: you don't want an invalid pointer dereference to allocate a huge chunk of stack, just because the pointer happens to be pointing where the stack might grow, eventually. You want an invalid pointer dereference to segfault most of the time." I agree with Csaba on this. If the entire stack were made of guard pages, then it means that a single page fault far below the stack limit could take arbitrary long and allocate arbitrarily large quantities of memory. The program might have said that it wants stacks to default to 1GB, and now a single page fault on the stack could result in a long pause as the system allocates 1GB of memory. If you study the problem in the debugger, what you see is that a single memory...

Amazon SageMaker HyperPod now supports AMI-based configuration for Slurm clusters that use continuous provisioning. Continuous provisioning adds nodes to the cluster as capacity becomes available, and this launch extends AMI-based configuration to clusters using this mode. With this support, clusters using continuous provisioning can be created without downloading, configuring, or uploading lifecycle configuration scripts to Amazon S3. AMI-based configuration provisions nodes with the software and configurations needed for a production-ready environment to run AI/ML training workloads, including required software such as Docker, Enroot, and Pyxis, and configurations such as Slurm accounting, SSH key generation, and log rotation. When using continuous provisioning, each node is configured from the AMI as it is added to the cluster, without the need to manage lifecycle configuration scripts, so nodes become available to schedule jobs sooner. To enable AMI-based configuration, omit the ...

AWS Config now supports 191 additional managed rules across key services including Amazon Bedrock, Amazon SageMaker, Amazon ECS, Amazon EKS, Amazon RDS, Amazon Redshift, Amazon S3, and Amazon CloudTrail. This expansion increases built-in governance coverage across AI workloads and core cloud infrastructure. Examples of the new managed rules include evaluating resource configurations for encryption, logging, public access, network security, data protection, and other operational best practices across AWS services.  With this launch, you can deploy these new managed rules individually or as part of a conformance pack in the AWS Regions where the corresponding AWS services are available.    AWS Certificate Manager  ACM_CERTIFICATE_RSA_CHECK   Amazon API Gateway  API_GWV2_ACCESS_LOGS_ENABLED   AWS AppSync  APPSYNC_AUTHORIZATION_CHECK   APPSYNC_LOGGING_ENABLED   Amazon Athena...