Amazon S3 Express One Zone, a high-performance, single-Availability Zone (AZ) S3 storage class, now supports server-side encryption with AWS Key Management Service (KMS) keys (SSE-KMS). S3 Express One Zone already encrypts all objects stored in S3 directory buckets with Amazon S3 managed keys (SSE-S3) by default. Starting today, you can use AWS KMS customer managed keys to encrypt data at rest, with no impact on performance. This new encryption capability gives you an additional option to meet compliance and regulatory requirements when using S3 Express One Zone, which is designed to deliver consistent single-digit millisecond data access for your most frequently accessed data and latency-sensitive applications.

S3 directory buckets allow you to specify only one customer managed key per bucket for SSE-KMS encryption. Once the customer managed key is added, you cannot edit it to use a new key. On the other hand, with S3 general purpose buckets, you can use multiple KMS keys either by changing the default encryption configuration of the bucket or during S3 PUT requests. When using SSE-KMS with S3 Express One Zone, S3 Bucket Keys are always enabled. S3 Bucket Keys are free and reduce the number of requests to AWS KMS by up to 99%, optimizing both performance and costs.

Using SSE-KMS with Amazon S3 Express One Zone
To show you this new capability in action, I first create an S3 directory bucket in the Amazon S3 console following the steps to create a S3 directory bucket and use apne1-az4 as the Availability Zone. In Base name, I enter s3express-kms and a suffix that includes the Availability Zone ID wich is automatically added to create the final name. Then, I select the checkbox to acknowledge that Data is stored in a single Availability Zone.

In the Default encryption section, I choose Server-side encryption with AWS Key Management Service keys (SSE-KMS). Under AWS KMS Key I can Choose from your AWS KMS keys, Enter AWS KMS key ARN, or Create a KMS key. For this example, I previously created an AWS KMS key, which I selected from the list, and then choose Create bucket.

Now, any new object I upload to this S3 directory bucket will be automatically encrypted using my AWS KMS key.

SSE-KMS with Amazon S3 Express One Zone in action
To use SSE-KMS with S3 Express One Zone via the AWS Command Line Interface (AWS CLI), you need an AWS Identity and Access Management (IAM) user or role with the following policy . This policy allows the CreateSession API operation, which is necessary to successfully upload and download encrypted files to and from your S3 directory bucket.

{
	"Version": "2012-10-17",
	"Statement": [
		{
			"Effect": "Allow",
			"Action": [
				"s3express:CreateSession"
			],
			"Resource": [
				"arn:aws:s3express:*:<account>:bucket/s3express-kms--apne1-az4--x-s3"
			]
		},
		{
			"Effect": "Allow",
			"Action": [
				"kms:Decrypt",
				"kms:GenerateDataKey"
			],
			"Resource": [
				"arn:aws:kms:*:<account>:key/<keyId>"
			]
		}
	]
}

With the PutObject command, I upload a new file named confidential-doc.txt to my S3 directory bucket.

aws s3api put-object --bucket s3express-kms--apne1-az4--x-s3 
--key confidential-doc.txt 
--body confidential-doc.txt

As a success of the previous command I receive the following output:

{
    "ETag": ""664469eeb92c4218bbdcf92ca559d03b"",
    "ChecksumCRC32": "0duteA==",
    "ServerSideEncryption": "aws:kms",
    "SSEKMSKeyId": "arn:aws:kms:ap-northeast-1:<accountId>:key/<keyId>",
    "BucketKeyEnabled": true
}

Checking the object’s properties with HeadObject command, I see that it’s encrypted using SSE-KMS with the key that I created before:

aws s3api head-object --bucket s3express-kms--apne1-az4--x-s3 
--key confidential-doc.txt

I get the following output:

 
{
    "AcceptRanges": "bytes",
    "LastModified": "2024-08-21T15:29:22+00:00",
    "ContentLength": 5,
    "ETag": ""664469eeb92c4218bbdcf92ca559d03b"",
    "ContentType": "binary/octet-stream",
    "ServerSideEncryption": "aws:kms",
    "Metadata": {},
    "SSEKMSKeyId": "arn:aws:kms:ap-northeast-1:<accountId>:key/<keyId>",
    "BucketKeyEnabled": true,
    "StorageClass": "EXPRESS_ONEZONE"
}

I download the encrypted object with GetObject:

aws s3api get-object --bucket s3express-kms--apne1-az4--x-s3 
--key confidential-doc.txt output-confidential-doc.txt

As my session has the necessary permissions, the object is downloaded and decrypted automatically.

{
    "AcceptRanges": "bytes",
    "LastModified": "2024-08-21T15:29:22+00:00",
    "ContentLength": 5,
    "ETag": ""664469eeb92c4218bbdcf92ca559d03b"",
    "ContentType": "binary/octet-stream",
    "ServerSideEncryption": "aws:kms",
    "Metadata": {},
    "SSEKMSKeyId": "arn:aws:kms:ap-northeast-1:<accountId>:key/<keyId>",
    "BucketKeyEnabled": true,
    "StorageClass": "EXPRESS_ONEZONE"
}

For this second test, I use a different IAM user with a policy that is not granted the necessary KMS key permissions to download the object. This attempt fails with an AccessDenied error, demonstrating that the SSE-KMS encryption is functioning as intended.

An error occurred (AccessDenied) when calling the CreateSession operation: Access Denied

This demonstration shows how SSE-KMS works seamlessly with S3 Express One Zone, providing an additional layer of security while maintaining ease of use for authorized users.

Things to know
Getting started – You can enable SSE-KMS for S3 Express One Zone using the Amazon S3 console, AWS CLI, or AWS SDKs. Set the default encryption configuration of your S3 directory bucket to SSE-KMS and specify your AWS KMS key. Remember, you can only use one customer managed key per S3 directory bucket for its lifetime.

Regions – S3 Express One Zone support for SSE-KMS using customer managed keys is available in all AWS Regions where S3 Express One Zone is currently available.

Performance – Using SSE-KMS with S3 Express One Zone does not impact request latency. You’ll continue to experience the same single-digit millisecond data access.

Pricing – You pay AWS KMS charges to generate and retrieve data keys used for encryption and decryption. Visit the AWS KMS pricing page for more details. In addition, when using SSE-KMS with S3 Express One Zone, S3 Bucket Keys are enabled by default for all data plane operations except for CopyObject and UploadPartCopy, and can’t be disabled. This reduces the number of requests to AWS KMS by up to 99%, optimizing both performance and costs.

AWS CloudTrail integration – You can audit SSE-KMS actions on S3 Express One Zone objects using AWS CloudTrail. Learn more about that in my previous blog post.

– Eli.

Hello, everyone!

It’s been an interesting week full of AWS news as usual, but also full of vibrant faces filling up the rooms in a variety of events happening this month.

Let’s start by covering some of the releases that have caught my attention this week.

My Top 3 AWS news of the week

Amazon RDS for MySQL zero-ETL integrations is now generally available and it comes with exciting new features. You are now able to configure zero-ETL integrations in your AWS CloudFormation templates, and you also now have the ability to set up multiple integrations from a source Amazon RDS for MySQL database with up to five Amazon Redshift warehouses. Lastly, you can now also apply data filters which determine which database and tables get automatically replicated. Read this blog post where I review aspects of this release and show you how to get started with data filtering if you want to know more. Incidentally, this release pairs well with another release this week: Amazon Redshift now allows you to alter the sort keys of tables replicated via zero-ETL integrations.

Oracle Database@AWS has been announced as part of a strategic partnership between Amazon Web Services (AWS) and Oracle. This offering allows customers to access Oracle Autonomous Database and Oracle Exadata Database Service directly within AWS simplifying cloud migration for enterprise workloads. Key features include zero-ETL integration between Oracle and AWS services for real-time data analysis, enhanced security, and optimized performance for hybrid cloud environments. This collaboration addresses the growing demand for multi-cloud flexibility and efficiency. It will be available in preview later in the year with broader availability in 2025 as it expands to new Regions.

Amazon OpenSearch Service now supports version 2.15, featuring improvements in search performance, query optimization, and AI-powered application capabilities. Key updates include radial search for vector space queries, optimizations for neural sparse and hybrid search, and the ability to enable vector and hybrid search on existing indexes. Additionally, it also introduces new features like a toxicity detection guardrail and an ML inference processor for enriching ingest pipelines. Read this guide to see how you can upgrade your Amazon OpenSearch Service domain.

So simple yet so good
These releases are simple in nature, but have a big impact.

AWS Resource Access Manager (RAM) now supports AWS PrivateLink – With this release, you can now securely share resources across AWS accounts with private connectivity, without exposing traffic to the public internet. This integration allows for more secure and streamlined access to shared services via VPC endpoints, improving network security and simplifying resource sharing across organizations.

AWS Network Firewall now supports AWS PrivateLink – another security quick-win, you can now securely access and manage Network Firewall resources without exposing traffic to the public internet.

AWS IAM Identity Center now enables users to customize their experience – You can set the language and visual mode preferences, including dark mode for improved readability and reduced eye strain. This update supports 12 different languages and enables users to adjust their settings for a more personalized experience when accessing AWS resources through the portal​.

Others
Amazon EventBridge Pipes now supports customer managed KMS keys – Amazon EventBridge Pipes now supports customer-managed keys for server-side encryption. This update allows customers to use their own AWS Key Management Service (KMS) keys to encrypt data when transferring between sources and targets, offering more control and security over sensitive event data. The feature enhances security for point-to-point integrations without the need for custom integration code. See instructions on how to configure this in the updated documentation. 

AWS Glue Data Catalog now supports enhanced storage optimization for Apache Iceberg tables – This includes automatic removal of unnecessary data files, orphan file management, and snapshot retention. These optimizations help reduce storage costs and improve query performance by continuously monitoring and compacting tables, making it easier to manage large-scale datasets stored in Amazon S3. See this Big Data blog post for a deep dive into this new feature.

Amazon MSK Replicator now supports the replication of Kafka topics across clusters while preserving identical topic names – This simplifies cross-cluster replication processes allowing users to replicate data across regions without needing to reconfigure client applications. This reduces setup complexity and enhances support for more seamless failovers in multi-cluster streaming architectures​. See this Amazon MSK Replicator developer guide to learn more about it.

Amazon SageMaker introduces sticky session routing for inference – This allows requests from the same client to be directed to the same model instance for the duration of a session improving consistency and reducing latency, particularly in real-time inference scenarios like chatbots or recommendation systems, where session-based interactions are crucial​. Read about how to configure it in this documentation guide.

Events
The AWS GenAI Lofts continue to pop up around the world! This week, developers in San Francisco had the opportunity to attend two very exciting events at the AWS Gen AI Loft in San Francisco including the “Generative AI on AWS” meetup last Tuesday, featuring discussions about extended reality, future AI tools, and more. Then things got playful on Thursday with the demonstration of an Amazon Bedrock-powered MineCraft bot and AI video game battles! If you’re around San Francisco before October 19th make sure to check out the schedule to see the list of events that you can join.

Make sure to check out the AWS GenAI Loft in Sao Paulo, Brazil, which opened recently, and the AWS GenAI Loft in London, which opens September 30th. You can already start registering for events before they fill up including one called “The future of development” that offers a whole day of targeted learning for developers to help them accelerate their skills.

Our AWS communities have also been very busy throwing incredible events! I was privileged to be a speaker at AWS Community Day Belfast where I got to finally meet all of the organizers of this amazing thriving community in Northern Ireland. If you haven’t been to a community day, I really recommend you check them out! You are sure to leave energized by the dedication and passion from communities leaders like Matt Coulter, Kristi Perreault, Matthew Wilson, Chloe McAteer, and their community members – not to mention the smiles all around. 🙂

Certifications
If you’ve been postponing taking an AWS certification exam, now is the perfect time! Register free for the AWS Certified: Associate Challenge before December 12, 2024 and get a 50% discount voucher to take any of the following exams: AWS Certified Solutions Architect – Associate, AWS Certified Developer – Associate, AWS Certified SysOps Administrator – Associate, or AWS Certified Data Engineer – Associate. My colleague Jenna Seybold has posted a collection of study material for each exam; check it out if you’re interested.

Also, don’t forget that the brand new AWS Certified AI Practitioner exam is now available. It is in beta stage, but you can already take it. If you pass it before February 15, 2025, you get an Early Adopter badge to add to your collection.

Conclusion
I hope you enjoyed the news this week!

Keep learning!

Today, we are pleased to announce Amazon Elastic Kubernetes Service (EKS) support in Amazon SageMaker HyperPod — purpose-built infrastructure engineered with resilience at its core for foundation model (FM) development. This new capability enables customers to orchestrate HyperPod clusters using EKS, combining the power of Kubernetes with Amazon SageMaker HyperPod‘s resilient environment designed for training large models. Amazon SageMaker HyperPod helps efficiently scale across more than a thousand artificial intelligence (AI) accelerators, reducing training time by up to 40%.

Amazon SageMaker HyperPod now enables customers to manage their clusters using a Kubernetes-based interface. This integration allows seamless switching between Slurm and Amazon EKS for optimizing various workloads, including training, fine-tuning, experimentation, and inference. The CloudWatch Observability EKS add-on provides comprehensive monitoring capabilities, offering insights into CPU, network, disk, and other low-level node metrics on a unified dashboard. This enhanced observability extends to resource utilization across the entire cluster, node-level metrics, pod-level performance, and container-specific utilization data, facilitating efficient troubleshooting and optimization.

Launched at re:Invent 2023, Amazon SageMaker HyperPod has become a go-to solution for AI startups and enterprises looking to efficiently train and deploy large scale models. It is compatible with SageMaker’s distributed training libraries, which offer Model Parallel and Data Parallel software optimizations that help reduce training time by up to 20%. SageMaker HyperPod automatically detects and repairs or replaces faulty instances, enabling data scientists to train models uninterrupted for weeks or months. This allows data scientists to focus on model development, rather than managing infrastructure.

The integration of Amazon EKS with Amazon SageMaker HyperPod uses the advantages of Kubernetes, which has become popular for machine learning (ML) workloads due to its scalability and rich open-source tooling. Organizations often standardize on Kubernetes for building applications, including those required for generative AI use cases, as it allows reuse of capabilities across environments while meeting compliance and governance standards. Today’s announcement enables customers to scale and optimize resource utilization across more than a thousand AI accelerators. This flexibility enhances the developer experience, containerized app management, and dynamic scaling for FM training and inference workloads.

Amazon EKS support in Amazon SageMaker HyperPod strengthens resilience through deep health checks, automated node recovery, and job auto-resume capabilities, ensuring uninterrupted training for large scale and/or long-running jobs. Job management can be streamlined with the optional HyperPod CLI, designed for Kubernetes environments, though customers can also use their own CLI tools. Integration with Amazon CloudWatch Container Insights provides advanced observability, offering deeper insights into cluster performance, health, and utilization. Additionally, data scientists can use tools like Kubeflow for automated ML workflows. The integration also includes Amazon SageMaker managed MLflow, providing a robust solution for experiment tracking and model management.

At a high level, Amazon SageMaker HyperPod cluster is created by the cloud admin using the HyperPod cluster API and is fully managed by the HyperPod service, removing the undifferentiated heavy lifting involved in building and optimizing ML infrastructure. Amazon EKS is used to orchestrate these HyperPod nodes, similar to how Slurm orchestrates HyperPod nodes, providing customers with a familiar Kubernetes-based administrator experience.

Let’s explore how to get started with Amazon EKS support in Amazon SageMaker HyperPod
I start by preparing the scenario, checking the prerequisites, and creating an Amazon EKS cluster with a single AWS CloudFormation stack following the Amazon SageMaker HyperPod EKS workshop, configured with VPC and storage resources.

To create and manage Amazon SageMaker HyperPod clusters, I can use either the AWS Management Console or AWS Command Line Interface (AWS CLI). Using the AWS CLI, I specify my cluster configuration in a JSON file. I choose the Amazon EKS cluster created previously as the orchestrator of the SageMaker HyperPod Cluster. Then, I create the cluster worker nodes that I call “worker-group-1”, with a private Subnet, NodeRecovery set to Automatic to enable automatic node recovery and for OnStartDeepHealthChecks I add InstanceStress and InstanceConnectivity to enable deep health checks.

cat > eli-cluster-config.json << EOL
{
    "ClusterName": "example-hp-cluster",
    "Orchestrator": {
        "Eks": {
            "ClusterArn": "${EKS_CLUSTER_ARN}"
        }
    },
    "InstanceGroups": [
        {
            "InstanceGroupName": "worker-group-1",
            "InstanceType": "ml.p5.48xlarge",
            "InstanceCount": 32,
            "LifeCycleConfig": {
                "SourceS3Uri": "s3://${BUCKET_NAME}",
                "OnCreate": "on_create.sh"
            },
            "ExecutionRole": "${EXECUTION_ROLE}",
            "ThreadsPerCore": 1,
            "OnStartDeepHealthChecks": [
                "InstanceStress",
                "InstanceConnectivity"
            ],
        },
  ....
    ],
    "VpcConfig": {
        "SecurityGroupIds": [
            "$SECURITY_GROUP"
        ],
        "Subnets": [
            "$SUBNET_ID"
        ]
    },
    "ResilienceConfig": {
        "NodeRecovery": "Automatic"
    }
}
EOL

You can add InstanceStorageConfigs to provision and mount an additional Amazon EBS volumes on HyperPod nodes.

To create the cluster using the SageMaker HyperPod APIs, I run the following AWS CLI command:

aws sagemaker create-cluster  
--cli-input-json file://eli-cluster-config.json

The AWS command returns the ARN of the new HyperPod cluster.

{
"ClusterArn": "arn:aws:sagemaker:us-east-2:ACCOUNT-ID:cluster/wccy5z4n4m49"
}

I then verify the HyperPod cluster status in the SageMaker Console, awaiting until the status changes to InService.

aws sagemaker describe-cluster --cluster-name my-hyperpod-cluster

And I can monitor cluster performance and health insights using Amazon CloudWatch Container.

Things to know
Here are some key things you should know about Amazon EKS support in Amazon SageMaker HyperPod:

Resilient Environment – This integration provides a more resilient training environment with deep health checks, automated node recovery, and job auto-resume. SageMaker HyperPod automatically detects, diagnoses, and recovers from faults, allowing you to continually train foundation models for weeks or months without disruption. This can reduce training time by up to 40%.

Enhanced GPU Observability – Amazon CloudWatch Container Insights provides detailed metrics and logs for your containerized applications and microservices. This enables comprehensive monitoring of cluster performance and health.

Scientist-Friendly Tool – This launch includes a custom HyperPod CLI for job management, Kubeflow Training Operators for distributed training, Kueue for scheduling, and integration with SageMaker Managed MLflow for experiment tracking. It also works with SageMaker’s distributed training libraries, which provide Model Parallel and Data Parallel optimizations to significantly reduce training time. These libraries, combined with auto-resumption of jobs, enable efficient and uninterrupted training of large models.

Flexible Resource Utilization – This integration enhances developer experience and scalability for FM workloads. Data scientists can efficiently share compute capacity across training and inference tasks. You can use your existing Amazon EKS clusters or create and attach new ones to HyperPod compute, bring your own tools for job submission, queuing and monitoring.

To get started with Amazon SageMaker HyperPod on Amazon EKS, you can explore resources such as the SageMaker HyperPod EKS Workshop, the aws-do-hyperpod project, and the awsome-distributed-training project. This release is generally available in the AWS Regions where Amazon SageMaker HyperPod is available except Europe(London). For pricing information, visit the Amazon SageMaker Pricing page.

This blog post was a collaborative effort. I would like to thank Manoj Ravi, Adhesh Garg, Tomonori Shimomura, Alex Iankoulski, Anoop Saha, and the entire team for their significant contributions in compiling and refining the information presented here. Their collective expertise was crucial in creating this comprehensive article.

– Eli.

Last week, the latest AWS Heroes arrived! AWS Heroes are amazing technical experts who generously share their insights, best practices, and innovative solutions to help others.

The AWS GenAI Lofts are in full swing with San Francisco and São Paulo open now, and London, Paris, and Seoul coming in the next couple of months. Here’s an insider view from a workshop in San Francisco last week.

Last week’s launches
Here are the launches that got my attention.

Storage Browser for Amazon S3 (alpha release) – An open source Amplify UI React component that you can add to your web applications to provide your end users with a simple interface for data stored in S3. The component uses the new ListCallerAccessGrants API to list all S3 buckets, prefixes, and objects they can access, as defined by their S3 Access Grants.

AWS Network Load Balancer – Now supports a configurable TCP idle timeout. For more information, see this Networking & Content Devliery Blog post.

AWS Gateway Load Balancer – Also supports a configurable TCP idle timeout. More info is available in this blog post.

Amazon ECS – Now supports AWS Graviton-based Spot compute with AWS Fargate. This allows to run fault-tolerant Arm-based applications with up to 70% lower costs compared to on-demand.

Zone Groups for Availability Zones in AWS Regions – We are working on extending the Zone Group construct to Availability Zones (AZs) with a consistent naming format across all AWS Regions.

Amazon Managed Service for Apache Flink – Now supports Apache Flink 1.20. You can upgrade to benefit from bug fixes, performance improvements, and new functionality added by the Flink community.

AWS Glue – Now provides job queuing. If quotas or limits are insufficient to start a Glue job, AWS Glue will now automatically queue the job and wait for limits to free up.

Amazon DynamoDB – Now supports Attribute-Based Access Control (ABAC) for tables and indexes (limited preview). ABAC is an authorization strategy that defines access permissions based on tags attached to users, roles, and AWS resources. Read more in this Database Blog post.

Amazon Bedrock – Stability AI’s top text-to-image models (Stable Image Ultra, Stable Diffusion 3 Large, and Stable Image Core) are now available to generate high-quality visuals with speed and precision.

Amazon Bedrock Agents – Now supports Anthropic Claude 3.5 Sonnet, including Anthropic recommended tool use for function calling which can improve developer and end user experience.

Amazon Sagemaker Studio – You can now use Amazon EMR Serverless directly from your Studio Notebooks to interactively query, explore and visualize data, and run Apache Spark jobs.

Amazon SageMaker – Introducing sagemaker-core, a new Python SDK that provides an object-oriented interface for interacting with SageMaker resources such as TrainingJob, Model, and Endpoint resource classes.

AWS AppSync – Improves monitoring by including DEBUG and INFO logging levels for its GraphQL APIs. You now have more granular control over log verbosity to make it easier to troubleshoot your APIs while optimizing readability and costs.

Amazon WorkSpaces Pools – You can now bring your Windows 10 or 11 licenses and provide a consistent desktop experience when switching between on-premise and virtual desktops.

Amazon SES – A new enhanced onboarding experience to help discover and activate key SES features, including recommendations for optimal setup and the option to enable the Virtual Deliverability Manager to enhance email deliverability.

Amazon Redshift – Now the Amazon Redshift Data API support session reuse to retain the context of a session from one query execution to another, reducing connection setup latency on repeated queries to the same data warehouse.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS news
Here are some additional projects, blog posts, and news items that you might find interesting:

Amazon Q Developer Code Challenge – At the 2024 AWS Summit in Sydney, we put two teams (one using Amazon Q Developer, one not) in a battle of coding prowess, starting with basic math and string manipulation, up to including complex algorithms and intricate ciphers. Here are the results.

AWS named as a Leader in the first Gartner Magic Quadrant for AI Code Assistants – It’s great to see how new technologies make the whole software development lifecycle easier and increase developer productivity.

Build powerful RAG pipelines with LlamaIndex and Amazon Bedrock – A deep dive tutorial that covers simple and advanced use cases.

Evaluating prompts at scale with Prompt Management and Prompt Flows for Amazon Bedrock – To implement an automated prompt evaluation system to streamline prompt development and improve the overall quality of AI-generated content.

Amazon Redshift data ingestion options – An overview of the available ingestion methods and how they work for different use cases.

Upcoming AWS events
Check your calendars and sign up for upcoming AWS events:

AWS Summits – Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. AWS Summits for this year are coming to an end. There are two more left that you can still register: Toronto (September 11), and Ottawa (October 9).

AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs driven by expert AWS users and industry leaders from around the world. Upcoming AWS Community Days are in the SF Bay Area (September 13), where our own Antje Barth is a keynote speaker, Argentina (September 14), Armenia (September 14), and DACH (in Munich on September 17).

AWS GenAI Lofts – Collaborative spaces and immersive experiences that showcase AWS’s cloud and AI expertise, while providing startups and developers with hands-on access to AI products and services, exclusive sessions with industry leaders, and valuable networking opportunities with investors and peers. Find a GenAI Loft location near you and don’t forget to register.

Browse all upcoming AWS-led in-person and virtual events here.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Danilo

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

With the arrival of September, AWS re:Invent 2024 is now 3 months away and I am very excited for the new upcoming services and announcements at the conference. I remember attending re:Invent 2019, just before the COVID-19 pandemic. It was the biggest in-person re:Invent with 60,000+ attendees and it was my second one. It was amazing to be in that atmosphere! Registration is now open for AWS re:Invent 2024. Come join us in Las Vegas for five exciting days of keynotes, breakout sessions, chalk talks, interactive learning opportunities, and career-changing connections!

Now let’s look at the last week’s new announcements.

Last week’s launches
Here are the launches that got my attention.

Announcing AWS Parallel Computing Service – AWS Parallel Computing Service (AWS PCS) is a new managed service that lets you run and scale high performance computing (HPC) workloads on AWS. You can build scientific and engineering models and run simulations using a fully managed Slurm scheduler with built-in technical support and a rich set of customization options. Tailor your HPC environment to your specific needs and integrate it with your preferred software stack. Build complete HPC clusters that integrates compute, storage, networking, and visualization resources, and seamlessly scale from zero to thousands of instances. To learn more, visit AWS Parallel Computing Service and read Channy’s blog post.

Amazon EC2 status checks now support reachability health of attached EBS volumes – You can now use Amazon EC2 status checks to directly monitor if the Amazon EBS volumes attached to your instances are reachable and able to complete I/O operations. With this new status check, you can quickly detect attachment issues or volume impairments that may impact the performance of your applications running on Amazon EC2 instances. You can further integrate these status checks within Auto Scaling groups to monitor the health of EC2 instances and replace impacted instances to ensure high availability and reliability of your applications. Attached EBS status checks can be used along with the instance status and system status checks to monitor the health of your instances. To learn more, refer to the Status checks for Amazon EC2 instances documentation.

Amazon QuickSight now supports sharing views of embedded dashboards – You can now share views of embedded dashboards in Amazon QuickSight. This feature allows you to enable more collaborative capabilities in your application with embedded QuickSight dashboards. Additionally, you can enable personalization capabilities such as bookmarks for anonymous users. You can share a unique link that displays only your changes while staying within the application, and use dashboard or console embedding to generate a shareable link to your application page with QuickSight’s reference encapsulated using the QuickSight Embedding SDK. QuickSight Readers can then send this shareable link to their peers. When their peer accesses the shared link, they are taken to the page on the application that contains the embedded QuickSight dashboard. For more information, refer to Embedded view documentation.

Amazon Q Business launches IAM federation for user identity authentication – Amazon Q Business is a fully managed service that deploys a generative AI business expert for your enterprise data. You can use the Amazon Q Business IAM federation feature to connect your applications directly to your identity provider to source user identity and user attributes for these applications. Previously, you had to sync your user identity information from your identity provider into AWS IAM Identity Center, and then connect your Amazon Q Business applications to IAM Identity Center for user authentication. At launch, Amazon Q Business IAM federation will support the OpenID Connect (OIDC) and SAML2.0 protocols for identity provider connectivity. To learn more, visit Amazon Q Business documentation.

Amazon Bedrock now supports cross-Region inference – Amazon Bedrock announces support for cross-Region inference, an optional feature that enables you to seamlessly manage traffic bursts by utilizing compute across different AWS Regions. If you are using on-demand mode, you’ll be able to get higher throughput limits (up to 2x your allocated in-Region quotas) and enhanced resilience during periods of peak demand by using cross-Region inference. By opting in, you no longer have to spend time and effort predicting demand fluctuations. Instead, cross-Region inference dynamically routes traffic across multiple Regions, ensuring optimal availability for each request and smoother performance during high-usage periods. You can control where your inference data flows by selecting from a pre-defined set of Regions, helping you comply with applicable data residency requirements and sovereignty laws. Find the list at Supported Regions and models for cross-Region inference. To get started, refer to the Amazon Bedrock documentation or this Machine Learning blog.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

We launched existing services and instance types in additional Regions:

• Amazon EC2 C6gd and R6gd instances are now available in AWS Europe (Spain) Region. C6gd instances are ideal for compute-intensive workloads such as high performance computing (HPC), batch processing, and CPU-based machine learning inference. R6gd instances are built for running memory-intensive workloads such as open-source databases, in-memory caches, and real time big data analytics.
• Amazon OpenSearch Serverless now available in the AWS GovCloud (US-West) Region. OpenSearch Serverless is a serverless deployment option for Amazon OpenSearch Service that makes it simple to run search and analytics workloads without the complexities of infrastructure management.
• AWS Global Accelerator launches new edge location in Cairo, Egypt. AWS Global Accelerator is a networking service that helps you improve the availability and performance of the applications that you offer to your global users.
• Amazon Redshift Serverless is now available in the AWS Asia Pacific (Jakarta) Region. Amazon Redshift Serverless is a serverless option of Amazon Redshift that makes it more efficient to run and scale analytics in seconds without the need to set up and manage data warehouse infrastructure.
• Amazon OpenSearch Service now supports Graviton3 (C7g, M7g, R7g, R7gd) instances. Amazon OpenSearch Service now supports AWS Graviton3 instances, which deliver up to 25% better performance over Graviton2-based instances.

Other AWS events
AWS GenAI Lofts are collaborative spaces and immersive experiences that showcase AWS’s cloud and AI expertise, while providing startups and developers with hands-on access to AI products and services, exclusive sessions with industry leaders, and valuable networking opportunities with investors and peers. Find a GenAI Loft location near you and don’t forget to register.

credit: Antje Barth

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

AWS Summits are free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. AWS Summits for this year are coming to an end. There are 3 more left that you can still register: Jakarta (September 5), Toronto (September 11), and Ottawa (October 9).

AWS Community Days feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world. While AWS Summits 2024 are almost over, AWS Community Days are in full swing. Upcoming AWS Community Days are in Belfast (September 6), SF Bay Area (September 13), where our own Antje Barth is a keynote speaker, Argentina (September 14), and Armenia (September 14).

Browse all upcoming AWS led in-person and virtual events here.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Esra

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Today we are announcing AWS Parallel Computing Service (AWS PCS), a new managed service that helps customers set up and manage high performance computing (HPC) clusters so they seamlessly run their simulations at virtually any scale on AWS. Using the Slurm scheduler, they can work in a familiar HPC environment, accelerating their time to results instead of worrying about infrastructure.

In November 2018, we introduced AWS ParallelCluster, an AWS supported open-source cluster management tool that helps you to deploy and manage HPC clusters in the AWS Cloud. With AWS ParallelCluster, customers can also quickly build and deploy proof of concept and production HPC compute environments. They can use AWS ParallelCluster Command-Line interface, API, Python library, and the user interface installed from open source packages. They are responsible for updates, which can include tearing down and redeploying clusters. Many customers, though, have asked us for a fully managed AWS service to eliminate operational jobs in building and operating HPC environments.

AWS PCS simplifies HPC environments managed by AWS and is accessible through the AWS Management Console, AWS SDK, and AWS Command-Line Interface (AWS CLI). Your system administrators can create managed Slurm clusters that use their compute and storage configurations, identity, and job allocation preferences. AWS PCS uses Slurm, a highly scalable, fault-tolerant job scheduler used across a wide range of HPC customers, for scheduling and orchestrating simulations. End users such as scientists, researchers, and engineers can log in to AWS PCS clusters to run and manage HPC jobs, use interactive software on virtual desktops, and access data. You can bring their workloads to AWS PCS quickly, without significant effort to port code.

You can use fully managed NICE DCV remote desktops for remote visualization, and access job telemetry or application logs to enable specialists to manage your HPC workflows in one place.

AWS PCS is designed for a wide range of traditional and emerging, compute or data-intensive, engineering and scientific workloads across areas such as computational fluid dynamics, weather modeling, finite element analysis, electronic design automation, and reservoir simulations using familiar ways of preparing, executing, and analyzing simulations and computations.

Getting started with AWS Parallel Computing Service
To try out AWS PCS, you can use our tutorial for creating a simple cluster in the AWS documentation. First, you create a virtual private cloud (VPC) with an AWS CloudFormation template and shared storage in Amazon Elastic File System (Amazon EFS) within your account for the AWS Region where you will try AWS PCS. To learn more, visit Create a VPC and Create shared storage in the AWS documentation.

1. Create a cluster
In the AWS PCS console, choose Create cluster, a persistent resource for managing resources and running workloads.

Next, enter your cluster name and choose the controller size of your Slurm scheduler. You can choose Small (up to 32 nodes and 256 jobs), Medium (up to 512 nodes and 8,192 jobs), or Large (up to 2,048 nodes and 16,384 jobs) for the limits of cluster workloads. In the Networking section, choose your created VPC, subnet to launch the cluster, and security group applied to your cluster.

Optionally, you can set the Slurm configuration such as an idle time before compute nodes will scale down, a Prolog and Epilog scripts directory on launched compute nodes, and a resource selection algorithm parameter used by Slurm.

Choose Create cluster. It takes some time for the cluster to be provisioned.

2. Create compute node groups
After creating your cluster, you can create compute node groups, a virtual collection of Amazon Elastic Compute Cloud (Amazon EC2) instances that AWS PCS uses to provide interactive access to a cluster or run jobs in a cluster. When you define a compute node group, you specify common traits such as EC2 instance types, minimum and maximum instance count, target VPC subnets, Amazon Machine Image (AMI), purchase option, and custom launch configuration. Compute node groups require an instance profile to pass an AWS Identity and Access Management (IAM) role to an EC2 instance and an EC2 launch template that AWS PCS uses to configure EC2 instances it launches. To learn more, visit Create a launch template And Create an instance profile in the AWS documentation.

To create a compute node group in the console, go to your cluster and choose the Compute node groups tab and the Create compute node group button.

You can create two compute node groups: a login node group to be accessed by end users and a job node group to run HPC jobs.

To create a compute node group running HPC jobs, enter a compute node name and select a previously-created EC2 launch template, IAM instance profile, and subnets to launch compute nodes in your cluster VPC.

Next, choose your preferred EC2 instance types to use when launching compute nodes and the minimum and maximum instance count for scaling. I chose the hpc6a.48xlarge instance type and scale limit up to eight instances. For a login node, you can choose a smaller instance, such as one c6i.xlarge instance. You can also choose either the On-demand or Spot EC2 purchase option if the instance type supports. Optionally, you can choose a specific AMI.

Choose Create. It takes some time for the compute node group to be provisioned. To learn more, visit Create a compute node group to run jobs and Create a compute node group for login nodes in the AWS documentation.

3. Create and run your HPC jobs
After creating your compute node groups, you submit a job to a queue to run it. The job remains in the queue until AWS PCS schedules it to run on a compute node group, based on available provisioned capacity. Each queue is associated with one or more compute node groups, which provide the necessary EC2 instances to do the processing.

To create a queue in the console, go to your cluster and choose the Queues tab and the Create queue button.

Enter your queue name and choose your compute node groups assigned to your queue.

Choose Create and wait while the queue is being created.

When the login compute node group is active, you can use AWS Systems Manager to connect to the EC2 instance it created. Go to the Amazon EC2 console and choose your EC2 instance of the login compute node group. To learn more, visit Create a queue to submit and manage jobs and Connect to your cluster in the AWS documentation.

To run a job using Slurm, you prepare a submission script that specifies the job requirements and submit it to a queue with the sbatch command. Typically, this is done from a shared directory so the login and compute nodes have a common space for accessing files.

You can also run a message passing interface (MPI) job in AWS PCS using Slurm. To learn more, visit Run a single node job with Slurm or Run a multi-node MPI job with Slurm in the AWS documentation.

You can connect a fully-managed NICE DCV remote desktop for visualization. To get started, use the CloudFormation template from HPC Recipes for AWS GitHub repository.

In this example, I used the OpenFOAM motorBike simulation to calculate the steady flow around a motorcycle and rider. This simulation was run with 288 cores of three hpc6a instances. The output can be visualized in the ParaView session after logging in to the web interface of DCV instance.

Finally, after you are done HPC jobs with the cluster and node groups that you created, you should delete the resources that you created to avoid unnecessary charges. To learn more, visit Delete your AWS resources in the AWS documentation.

Things to know
Here are a couple of things that you should know about this feature:

• Slurm versions – AWS PCS initially supports Slurm 23.11 and offers mechanisms designed to enable customers to upgrade their Slurm major versions once new versions are added. Additionally, AWS PCS is designed to automatically update the Slurm controller with patch versions. To learn more, visit Slurm versions in the AWS documentation.
• Capacity Reservations – You can reserve EC2 capacity in a specific Availability Zone and for a specific duration using On-Demand Capacity Reservations to make sure that you have the necessary compute capacity available when you need it. To learn more, visit Capacity Reservations in the AWS documentation.
• Network file systems – You can attach network storage volumes where data and files can be written and accessed, including Amazon FSx for NetApp ONTAP, Amazon FSx for OpenZFS, and Amazon File Cache as well as Amazon EFS and Amazon FSx for Lustre. You can also use self-managed volumes, such as NFS servers. To learn more, visit Network file systems in the AWS documentation.

Now available
AWS Parallel Computing Service is now available in the US East (N. Virginia), AWS US East (Ohio), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), Europe (Stockholm) Regions.

AWS PCS launches all resources in your AWS account. You will be billed appropriately for those resources. For more information, see the AWS PCS Pricing page.

Give it a try and send feedback to AWS re:Post or through your usual AWS Support contacts.

— Channy

P.S. Special thanks to Matthew Vaughn, a principal developer advocate at AWS for his contribution in creating a HPC testing environment.

The AWS User Group Japan (JAWS-UG) hosted JAWS PANKRATION 2024 themed ‘No Border’. This is a 24-hour online event where AWS Heroes, AWS Community Builders, AWS User Group leaders, and others from around the world discuss topics ranging from cultural discussions to technical talks. One of the speakers at this event, Kevin Tuei, an AWS Community Builder based in Kenya, highlighted the importance of building in public and sharing your knowledge with others, a very fitting talk for this kind of event.

Last week’s launches
Here are some launches that got my attention during the previous week.

Amazon S3 now supports conditional writes – We’ve added support for conditional writes in Amazon S3 which check for existence of an object before creating it. With this feature, you can now simplify how distributed applications with multiple clients concurrently update data in parallel across shared datasets. Each client can conditionally write objects, making sure that it does not overwrite any objects already written by another client.

AWS Lambda introduces recursive loop detection APIs – With the recursive loop detection APIs you can now set recursive loop detection configuration on individual AWS Lambda functions. This allows you to turn off recursive loop detection on functions that intentionally use recursive patterns, avoiding disruption of these workloads. Using these APIs, you can avoid disruption to any intentionally recursive workflows as Lambda expands support of recursive loop detection to other AWS services. Configure recursive loop detection for Lambda functions through the Lambda Console, the AWS command line interface (CLI), or Infrastructure as Code tools like AWS CloudFormation, AWS Serverless Application Model (AWS SAM), or AWS Cloud Development Kit (CDK). This new configuration option is supported in AWS SAM CLI version 1.123.0 and CDK v2.153.0.

General availability of Amazon Bedrock batch inference API – You can now use Amazon Bedrock to process prompts in batch to get responses for model evaluation, experimentation, and offline processing. Using the batch API makes it more efficient to run inference with foundation models (FMs). It also allows you to aggregate responses and analyze them in batches. To get started, visit Run batch inference.

Other AWS news
Launched in July 2024, AWS GenAI Lofts is a global tour designed to foster innovation and community in the evolving landscape of generative artificial intelligence (AI) technology. The lofts bring collaborative pop-up spaces to key AI hotspots around the world, offering developers, startups, and AI enthusiasts a platform to learn, build, and connect. The events are ongoing. Find a location near you and be sure to attend soon.

Upcoming AWS events
AWS Summits – These are free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Whether you’re in the Americas, Asia Pacific & Japan, or EMEA region, learn more about future AWS Summit events happening in your area. On a personal note, I look forward to being one of the keynote speakers at the AWS Summit Johannesburg happening this Thursday. Registrations are still open and I look forward to seeing you there if you’ll be attending.

AWS Community Days – Join an AWS Community Day event just like the one I mentioned at the beginning of this post to participate in technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from your area. If you’re in New York, there’s an event happening in your area this week.

You can browse all upcoming in-person and virtual events here.

That’s all for this week. Check back next Monday for another Weekly Roundup!

– Veliswa

In March of last year, Jeff Barr announced the plan for an AWS Region in Malaysia. Today, I’m pleased to share the general availability of the AWS Asia Pacific (Malaysia) Region with three Availability Zones and API name ap-southeast-5.

The AWS Asia Pacific (Malaysia) Region is the first infrastructure Region in Malaysia and the thirteenth Region in Asia Pacific, joining the existing Asia Pacific Regions in Hong Kong, Hyderabad, Jakarta, Melbourne, Mumbai, Osaka, Seoul, Singapore, Sydney, and Tokyo and the Mainland China Beijing and Ningxia Regions.

The Petronas Twin Towers in the heart of Kuala Lumpur’s central business district.

The new AWS Region in Malaysia will play a pivotal role in supporting the Malaysian government’s strategic Madani Economy Framework. This initiative aims to improve the living standards of all Malaysians by 2030 while supporting innovation in Malaysia and across ASEAN. The construction and operation of the new AWS Region is estimated to add approximately $12.1 billion (MYR 57.3 billion) to Malaysia’s gross domestic product (GDP) and will support an average of more than 3,500 full-time equivalent jobs at external businesses annually through 2038.

The AWS Region in Malaysia will help to meet the high demand for cloud services while supporting innovation in Malaysia and across Southeast Asia.

AWS in Malaysia
In 2016, Amazon Web Services (AWS) established a presence with its first AWS office in Malaysia. Since then, AWS has provided continuous investments in infrastructure and technology to help drive digital transformations in Malaysia in support of hundreds of thousands of active customers each month.

Amazon CloudFront – In 2017, AWS announced the launch of the first edge location in Malaysia, which helps improve performance and availability for end users. Today, there are four Amazon CloudFront locations in Malaysia.

AWS Direct Connect – To continue helping our customers in Malaysia improve application performance, secure data, and reduce networking costs, in 2017, AWS announced the opening of additional Direct Connect locations in Malaysia. Today, there are two AWS Direct Connect locations in Malaysia.

AWS Outposts – As a fully managed service that extends AWS infrastructure and AWS services, AWS Outposts is ideal for applications that need to run on-premises to meet low latency requirements. Since 2020, customers in Malaysia have been able to order AWS Outposts to be installed at their datacenters and on-premises locations.

AWS customers in Malaysia
Cloud adoption in Malaysia has been steadily gaining momentum in recent years. Here are some examples of AWS customers in Malaysia and how they are using AWS for various workloads:

PayNet – PayNet is Malaysia’s national payments network and shared central infrastructure for the financial market in Malaysia. PayNet uses AWS to run critical national payment workloads, including the MyDebit online cashless payments system and e-payment reporting.

Pos Malaysia Berhad (Pos Malaysia) – Pos Malaysia is the national post and parcel service provider, holding the sole mandate to deliver services under the universal postal service obligation for Malaysia. They migrated critical applications to AWS, which increased their business agility and ability to deliver enhanced customer experiences. Also, they scaled their compute capacity to handle deliveries to more than 11 million addresses and a network of more than 3,500 retail touchpoints using Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic Block Store (Amazon EBS), ensuring disruption-free services.

Deriv – Deriv, one of the world’s largest online brokers, is using Amazon Q Business to increase productivity, efficiency, and innovation in its operations across customer support, marketing, and recruiting departments. With Amazon Q Business, Deriv has been able to boost productivity and reduce onboarding time by 45 percent.

Asia Pacific University – As one of the leading tech universities in Malaysia, Asia Pacific University (APU) uses AWS serverless technology such as Lambda to reduce operational costs. The automated scalability of AWS services has led to high availability and faster deployment that ensure APU’s applications and services are accessible to the students and staff at all times, enhancing the overall user experience. 

Aerodyne – Aerodyne Group is a DT3 (Drone Tech, Data Tech, and Digital Transformation) solutions provider of drone-based enterprise solutions. They’re running their DRONOS software as a service (SaaS) platform on AWS to help drone operators worldwide grow their businesses.

Building cloud skills together
AWS and various organizations in Malaysia have been working closely to build necessary cloud skills for builders in Malaysia. Here are some of the initiatives:

Program AKAR powered by AWS re/Start – Program AKAR is the first financial services-aligned cloud skills program initiated by AWS and PayNet. This new program aims to bridge the growing skills gap in Malaysia’s digital economy by equipping university students with transferrable skills for careers in the sector. As part of this initial collaboration, PayNet, AWS re/Start, and WEPS have committed to starting the program with 100 students in 2024, with the first 50 from Asia Pacific University serving as a pilot. 

AWS Academy — AWS Academy aims to bridge the gap between industry and academia by preparing students for industry-recognized certifications and careers in the cloud with a free and ready-to-teach cloud computing curriculum. AWS Academy currently runs courses in 48 Malaysian universities, covering various domains. Since 2018, 23,000 students have been trained through this program.

AWS Skills Guild at PETRONAS – PETRONAS, a global energy and solutions provider with a presence in over 50 countries, has been an AWS customer since 2014. AWS is also collaborating with PETRONAS to train their employees using the AWS Skills Guild program.

AWS’s contribution to sustainability in Malaysia
With The Climate Pledge, Amazon is committed to reaching net-zero carbon across its business by 2040 and is on a path to powering its operations with 100 percent renewable energy by 2025.

In September 2023, AWS announced its collaboration with Petronas and Gentari, a global clean energy company, to accelerate sustainability and decarbonization efforts in the global energy transition. Shortly after, in December 2023, AWS customer PKT Logistics Group became the first Malaysian company to join over 300 global companies in The Climate Pledge to accelerate the world’s path to net-zero carbon.

In July 2024, AWS and Zero Waste Management collaborated on the first-ever AWS InCommunities Malaysia initiative, Green Wira Programme, to train educators to build sustainability initiatives in schools to advance Malaysia’s sustainable future.

Amazon is committed to investing and innovating across its businesses to help create a more sustainable future.

Things to know
AWS Community in Malaysia – Malaysia is also home to one AWS Hero, nine AWS Community Builders and about 9,000 community members of three AWS User Groups in various cities in Malaysia. If you’re interested in joining AWS User Groups Malaysia, visit their Meetup and Facebook pages.

AWS Global footprint – With this launch, AWS now spans 108 Availability Zones within 34 geographic Regions around the world. We have also announced plans for 18 more Availability Zones and six more AWS Regions in Mexico, New Zealand, the Kingdom of Saudi Arabia, Taiwan, Thailand, and the AWS European Sovereign Cloud.

Available now – The new Asia Pacific (Malaysia) Region is ready to support your business, and you can find a detailed list of the services available in this Region on the AWS Services by Region page.

To learn more, please visit the AWS Global Infrastructure page, and start building on ap-southeast-5!

Happy building!
— Donnie