AWS Batch Adds Job Queue Share Utilization Visibility -- AWSInsider New metric helps teams better manage capacity and optimize batch workload scheduling.

Amazon Web Services (AWS) Batch added job queue share utilization metrics, improving visibility into compute allocation under fair-share scheduling for better prioritization and capacity planning.

Read more: https://ow.ly/PgtW50YgXuH

#AWS #AWSBatch #Cloud

AWS Batch: Automating High-Performance Computing at Scale
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AWS Batch on EKS Adds Support for Unmanaged Compute Environments -- AWSInsider New option gives teams greater control over Kubernetes infrastructure while using AWS Batch for job scheduling.

Amazon Web Services (AWS) Batch on Amazon EKS now supports unmanaged compute environments, letting teams schedule batch jobs on existing, self-managed EKS clusters while keeping their own scaling, security, networking, and Kubernetes governance.

Read more: https://ow.ly/u8Lr50Y9T6x

#AWS #AWSBatch

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AWS Batch now supports default instance type options As of today, AWS Batch has introduced two new instance type options for allowed instance types in Compute Environment default-x86_64 (default) and default-arm64. These new options will automatically select the most cost-effective instance type across different generations, based on your job queue requirements, where AWS Batch previously supported only optimal instance type. This makes it easier to run your Batch workloads with newer generation EC2 instance families and can provide better performance at a lower cost. As new instance types become available in a region, they'll be automatically added to the corresponding default pool. To get started, you can select default-x86_64 or default-arm64 in the instanceType parameter for managed compute environments. There is no need to create a new compute environment—the existing 'optimal' option (which applies to M, C, and R EC2 instance familes) will continue to be supported and is not being deprecated, no action is needed. However, please be aware that only ENABLED and VALID Compute Environments (CEs) will be automatically updated with new instance types. If you have any DISABLED or INVALID CEs, they will receive updates once they are re-enabled and set to a VALID state. This capability is now available for AWS Batch in all commercial and the AWS GovCloud (US) Regions. To learn more, see our https://aws.amazon.com/blogs/hpc/introducing-default-instance-categories-for-aws-batch/, visit https://docs.aws.amazon.com/batch/latest/userguide/create-compute-environment-managed-ec2.html or the Batch troubleshooting https://docs.aws.amazon.com/batch/latest/userguide/troubleshooting.html.

AWS Batch now supports default instance type options

As of today, AWS Batch has introduced two new instance type options for allowed instance types in Compute Environment default-x86_64 (default) and default-arm64. These new options will automatically select the most cost-effec...

#AWS #AwsBatch

AWS Batch now supports default instance type options As of today, AWS Batch has introduced two new instance type options for allowed instance types in Compute Environment default-x86_64 (default) and default-arm64. These new options will automatically select the most cost-effective instance type across different generations, based on your job queue requirements, where AWS Batch previously supported only optimal instance type. This makes it easier to run your Batch workloads with newer generation EC2 instance families and can provide better performance at a lower cost. As new instance types become available in a region, they'll be automatically added to the corresponding default pool. To get started, you can select default-x86_64 or default-arm64 in the instanceType parameter for managed compute environments. There is no need to create a new compute environment—the existing 'optimal' option (which applies to M, C, and R EC2 instance familes) will continue to be supported and is not being deprecated, no action is needed. However, please be aware that only ENABLED and VALID Compute Environments (CEs) will be automatically updated with new instance types. If you have any DISABLED or INVALID CEs, they will receive updates once they are re-enabled and set to a VALID state. This capability is now available for AWS Batch in all commercial and the AWS GovCloud (US) Regions. To learn more, see our launch blog, visit Batch's documentation page or the Batch troubleshooting User Guide.

🆕 AWS Batch now supports default instance type options for default-x86_64 and default-arm64, automatically selecting cost-effective instances. No action needed for existing 'optimal' option. Available in all commercial and AWS GovCloud (US) regions.

#AWS #AwsBatch

AWS Batch now supports AWS Graviton-based Spot compute with AWS Fargate AWS Batch for ECS Fargate now supports AWS Graviton-based compute with AWS Fargate Spot. This capability helps you run fault-tolerant Arm-based applications with up to 70% discount compared to Fargate prices. AWS Graviton processors are custom-built by AWS to deliver the best price-performance for cloud workloads. AWS Batch for ECS Fargate enables customers to deploy and build workloads at scale in a serverless manner. Starting today, customers can further optimize for costs by running fault-tolerant Arm-based workloads on AWS Fargate Spot. To get started, create a new Fargate configured Compute Environment (CE), select ARM64 as the cpuArchitecture, and choose FARGATE_SPOT as the type. You can then connect it to existing job queues or create a new one for your workload. AWS Batch will leverage spare AWS Graviton-based compute capacity available in the AWS cloud for running your service or task. You can now get the simplicity of serverless compute with familiar cost optimization levers of Spot capacity with Graviton-based compute. This capability is now available for AWS Batch in all commercial and the AWS GovCloud (US) Regions. To learn more, see Batch’s updated https://docs.aws.amazon.com/batch/latest/APIReference/API_RuntimePlatform.html and AWS Batch for ECS Fargate https://docs.aws.amazon.com/batch/latest/userguide/fargate-compute-environments.html. 

AWS Batch now supports AWS Graviton-based Spot compute with AWS Fargate

AWS Batch for ECS Fargate now supports AWS Graviton-based compute with AWS Fargate Spot. This capability helps you run fault-tolerant Arm-based applications with up to 70% discount compared to F...

#AWS #AwsBatch #AwsFargate

AWS Batch now supports AWS Graviton-based Spot compute with AWS Fargate AWS Batch for ECS Fargate now supports AWS Graviton-based compute with AWS Fargate Spot. This capability helps you run fault-tolerant Arm-based applications with up to 70% discount compared to Fargate prices. AWS Graviton processors are custom-built by AWS to deliver the best price-performance for cloud workloads. AWS Batch for ECS Fargate enables customers to deploy and build workloads at scale in a serverless manner. Starting today, customers can further optimize for costs by running fault-tolerant Arm-based workloads on AWS Fargate Spot. To get started, create a new Fargate configured Compute Environment (CE), select ARM64 as the cpuArchitecture, and choose FARGATE_SPOT as the type. You can then connect it to existing job queues or create a new one for your workload. AWS Batch will leverage spare AWS Graviton-based compute capacity available in the AWS cloud for running your service or task. You can now get the simplicity of serverless compute with familiar cost optimization levers of Spot capacity with Graviton-based compute. This capability is now available for AWS Batch in all commercial and the AWS GovCloud (US) Regions. To learn more, see Batch’s updated RuntimePlatform API and AWS Batch for ECS Fargate documentation.

🆕 AWS Batch now supports Graviton-based Spot compute with Fargate, offering up to 70% savings for Arm-based applications. Use ARM64 compute environments with FARGATE_SPOT for cost-optimized, serverless workloads in all commercial and GovCloud regions.

#AWS #AwsBatch #AwsFargate

Use PascalCase when trying to pass parameters and environment variables to a Job Queue, not camelCase.

A tip for anyone attempting to pass environment variables from Amazon Event Bridge events to containerized tasks in the AWS Batch side of things:

Use PascalCase, not camelCase. The capitalization is important.

#AmazonAWS #EventBridge #JobQueue #AWSBatch #Containers

AWS Batch now supports Amazon Elastic Container Service Exec and AWS FireLens log router AWS Batch now supports Amazon Elastic Container Service (ECS) Exec and AWS FireLens log router for AWS Batch on Amazon ECS and AWS Fargate. With ECS Exec you can track the progress of your application and troubleshoot issue by by running interactive commands against the containers in your AWS Batch job. AWS FireLens allows you to stream logs of your AWS Batch jobs to your chosen destinations including Amazon CloudWatch, Amazon S3, Amazon OpenSearch Service, Amazon Redshift, partner services such as Splunk and more. You can configure ECS Exec and AWS FireLens while registering a new AWS Batch job definition or making a revision to an existing job definition. For more information, see Register Job Definition page in the https://docs.aws.amazon.com/batch/latest/APIReference/API_RegisterJobDefinition.html and Amazon ECS Developer Guide for https://docs.aws.amazon.com/AmazonECS/latest/developerguide/ecs-exec.html and https://docs.aws.amazon.com/AmazonECS/latest/developerguide/firelens-taskdef.html. AWS Batch supports developers, scientists, and engineers in running efficient batch processing for ML model training, simulations, and analysis at any scale. ECS Exec and AWS FireLens are supported in any https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/ where AWS Batch is available.

AWS Batch now supports Amazon Elastic Container Service Exec and AWS FireLens log router

AWS Batch now supports Amazon Elastic Container Service (ECS) Exec and AWS FireLens log router for AWS Batch on Amazon ECS and AWS Fargate. With ECS Exec you can track the progress of your ...

#AWS #AwsBatch

AWS Batch now supports Amazon Elastic Container Service Exec and AWS FireLens log router AWS Batch now supports Amazon Elastic Container Service (ECS) Exec and AWS FireLens log router for AWS Batch on Amazon ECS and AWS Fargate. With ECS Exec you can track the progress of your application and troubleshoot issue by by running interactive commands against the containers in your AWS Batch job. AWS FireLens allows you to stream logs of your AWS Batch jobs to your chosen destinations including Amazon CloudWatch, Amazon S3, Amazon OpenSearch Service, Amazon Redshift, partner services such as Splunk and more. You can configure ECS Exec and AWS FireLens while registering a new AWS Batch job definition or making a revision to an existing job definition. For more information, see Register Job Definition page in the AWS Batch API reference and Amazon ECS Developer Guide for ECS Exec and AWS FireLense. AWS Batch supports developers, scientists, and engineers in running efficient batch processing for ML model training, simulations, and analysis at any scale. ECS Exec and AWS FireLens are supported in any AWS Region where AWS Batch is available.

🆕 AWS Batch now supports ECS Exec and AWS FireLens for better container troubleshooting and log streaming to CloudWatch, S3, OpenSearch, Redshift, and more. Configure during job definition registration. Available in all AWS Regions where AWS Batch operates.

#AWS #AwsBatch

AWS Batch now supports resource aware scheduling AWS Batch now supports job scheduling that takes into account consumable resources (CRs) such as third-party license tokens, database access bandwidth, budgetary limits, and more. With resource aware scheduling you can set up sets of tokens representing these resources, which will then be consumed by the running AWS Batch jobs. This will help you reduce job failures and wasted compute time caused by missing or rate-limited resources, which in turn will improve utilization of infrastructure and reduce costs. You can create, manage, and monitor consumption of your CRs using AWS Batch Management Console or the new AWS Batch consumable resource APIs such as CreateConsumableResource, DescribeConsumableResource, UpdateConsumableResource, DeleteConsumableResource, and ListJobsByConsumableResource. Once you set up your consumable resources, you can associate up to 5 CRs with your AWS Batch jobs while creating or updating AWS Batch job definitions. For more information, see Consumable Resources page in the https://docs.aws.amazon.com/batch/latest/APIReference/API_Operations.html, https://docs.aws.amazon.com/batch/latest/APIReference/API_Operations.html, and our https://aws.amazon.com/blogs/hpc/how-to-use-rate-limited-resources-in-aws-batch-jobs-with-resource-aware-scheduling/. AWS Batch supports developers, scientists, and engineers in running efficient batch processing for ML model training, simulations, and analysis at any scale. Resource aware scheduling is available for all types of AWS Batch compute environments in any https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/ where AWS Batch is available.  

AWS Batch now supports resource aware scheduling

AWS Batch now supports job scheduling that takes into account consumable resources (CRs) such as third-party license tokens, database access bandwidth, budgetary limits, and more. With resource aware scheduling you can set up set...

#AWS #AwsBatch

AWS Batch now supports resource aware scheduling AWS Batch now supports job scheduling that takes into account consumable resources (CRs) such as third-party license tokens, database access bandwidth, budgetary limits, and more. With resource aware scheduling you can set up sets of tokens representing these resources, which will then be consumed by the running AWS Batch jobs. This will help you reduce job failures and wasted compute time caused by missing or rate-limited resources, which in turn will improve utilization of infrastructure and reduce costs. You can create, manage, and monitor consumption of your CRs using AWS Batch Management Console or the new AWS Batch consumable resource APIs such as CreateConsumableResource, DescribeConsumableResource, UpdateConsumableResource, DeleteConsumableResource, and ListJobsByConsumableResource. Once you set up your consumable resources, you can associate up to 5 CRs with your AWS Batch jobs while creating or updating AWS Batch job definitions. For more information, see Consumable Resources page in the AWS Batch User Guide, AWS Batch API Reference, and our AWS HPC Blog post. AWS Batch supports developers, scientists, and engineers in running efficient batch processing for ML model training, simulations, and analysis at any scale. Resource aware scheduling is available for all types of AWS Batch compute environments in any AWS Region where AWS Batch is available.

🆕 AWS Batch now supports resource-aware scheduling to manage licenses, bandwidth, and budget, reducing failures and costs. Use APIs to create and monitor up to 5 consumable resources per job. Available globally for efficient batch processing.

#AWS #AwsBatch

AWS Batch launches new features for access control and management of AWS Batch on EKS workloads AWS Batch on EKS now supports configurable Kubernetes namespaces, Persistent Volume Claims (PVCs), subPath to Kubernetes pod container volumes, and Kubernetes pod annotations. Using different Kubernetes namespaces for your AWS Batch jobs, you can improve workload isolation by defining job permission boundaries both within EKS cluster and for access to other AWS services. With Kubernetes PVCs and subPath you can give your AWS Batch jobs access only to the right data or particular subPath within a data volume. Finally, EKS pod annotations make it easier to integrate with external tools and other AWS services such as AWS Secrets Manager by allowing you to attach necessary metadata directly to your AWS Batch job. You can configure Kubernetes namespaces, PVCs, subPath, and annotations while registering a new AWS Batch job definition or making a revision to an existing job definition. You can also override the namespace and annotations from your job definition when you submit the job. For more information, see https://docs.aws.amazon.com/batch/latest/APIReference/API_RegisterJobDefinition.html and https://docs.aws.amazon.com/batch/latest/APIReference/API_SubmitJob.html pages in the AWS Batch API reference and our AWS HPC https://aws.amazon.com/blogs/hpc/adding-configurable-namespaces-persistent-volume-claims-and-other-features-for-aws-batch-on-amazon-eks/. AWS Batch supports developers, scientists, and engineers in running efficient batch processing for ML model training, simulations, and analysis at any scale. Configurable Kubernetes namespaces, PVCs, subPath, and annotations are available in any https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/ where AWS Batch is available.  

AWS Batch launches new features for access control and management of AWS Batch on EKS workloads

AWS Batch on EKS now supports configurable Kubernetes namespaces, Persistent Volume Claims (PVCs), subPath to Kubernetes pod container volumes, and Kubernetes pod annotations. Using ...

#AWS #AwsBatch