Tag Archives: launch

New – Create application-consistent snapshots using Amazon Data Lifecycle Manager and custom scripts

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-create-application-consistent-snapshots-using-amazon-data-lifecycle-manager-and-custom-scripts/

Amazon Data Lifecycle Manager now supports the use of pre-snapshot and post-snapshot scripts embedded in AWS Systems Manager documents. You can use these scripts to ensure that Amazon Elastic Block Store (Amazon EBS) snapshots created by Data Lifecycle Manager are application-consistent. Scripts can pause and resume I/O operations, flush buffered data to EBS volumes, and so forth. As part of this launch we are also publishing a set of detailed blog posts that show you how to use this feature with self-managed relational databases and Windows Volume Shadow Copy Service (VSS).

Data Lifecycle Manager (DLM) Recap
As a quick recap, Data Lifecycle Manager helps you to automate the creation, retention, and deletion of Amazon EBS volume snapshots. Once you have completed the prerequisite steps such as onboarding your EC2 instance to AWS Systems Manager, setting up an IAM role for DLM, and tagging your SSM documents, you simply create a lifecycle policy and indicate (via tags) the applicable Amazon Elastic Compute Cloud (Amazon EC2) instances, set a retention model, and let DLM do the rest. The policies specify when they are to be run, what is to be backed up, and how long the snapshots must be retained. For a full walk-through of DLM, read my 2018 blog post, New – Lifecycle Management for Amazon EBS Snapshots.

Application Consistent Snapshots
EBS snapshots are crash-consistent, meaning that they represent the state of the associated EBS volume at the time that the snapshot was created. This is sufficient for many types of applications, including those that do not use snapshots to capture the state of an active relational database. To make a snapshot that is application-consistent, it is necessary to take pending transactions into account (either waiting for them to finish or causing them to fail), momentarily pause further write operations, take the snapshot, and then resume normal operations.

And that’s where today’s launch comes in. DLM now has the ability to tell the instance to prepare for an application-consistent backup. The pre-snapshot script can manage pending transactions, flush in-memory data to persistent storage, freeze the filesystem, or even bring the application or database to a stop. Then the post-snapshot script can bring the application or database back to life, reload in-memory caches from persistent storage, thaw the filesystem, and so forth.

In addition to the base-level support for custom scripts, you can also use this feature to automate the creation of VSS Backup snapshots:

Pre and Post Scripts
The new scripts apply to DLM policies for instances. Let’s assume that I have created a policy that references SSM documents with pre-snapshot and post-snapshot scripts, and that it applies to a single instance. Here’s what happens when the policy is run per its schedule:

  1. The pre-snapshot script is started from the SSM document.
  2. Each command in the script is run and the script-level status (success or failure) is captured. If enabled in the policy, DLM will retry failed scripts.
  3. Multi-volume EBS snapshots are initiated for EBS volumes attached to the instance, with further control via the policy.
  4. The post-snapshot script is started from the SSM document,
  5. Each command in the script is run and and the script-level status (success or failure) is captured.

The policy contains options that give you control over the actions that are taken (retry, continue, or skip) when either of the scripts times out or fails. The status is logged, Amazon CloudWatch metrics are published, Amazon EventBridge events are emitted, and the status is also encoded in tags that are automatically assigned to each snapshot.

The pre-snapshot and post-snapshot scripts can perform any of the actions that are allowed in a command document: running shell scripts, running PowerShell scripts, and so forth. The actions must complete within the timeout specified in the policy, with an allowable range of 10 seconds to 120 seconds.

Getting Started
You will need to have a detailed understanding of your application or database in order to build a robust pair of scripts. In addition to handling the “happy path” when all goes well, your scripts need to plan for several failure scenarios. For example, a pre-snapshot script should fork a background task that will serve as a failsafe in case the post-snapshot script does not work as expected. Each script must return a shell-level status code, as detailed here.

Once I have written and tested my scripts and packaged them as SSM documents, I open the Data Lifecycle Manager page in the EC2 Console, select EBS snapshot policy, and click Next step:

I target all of my instances that are tagged with a Mode of Production, and use the default IAM role (if you use a different role, it must enable access to SSM), leave the rest of the values as-is, and proceed:

On the next page I scroll down to Pre and post scripts and expand the section. I click Enable pre and post scripts, choose Custom SSM document, and then select my SSM document from the menu. I also set the timeout and retry options, and choose to default to a crash-consistent backup if one of my scripts fails. I click Review policy, do one final check, and click Create policy on the following page:

My policy is created, and will take effect right away. After it has run at least once, I can inspect the CloudWatch metrics to check for starts, completions, and failures:

Additional Reading
Here are the first of the detailed blog posts that I promised you earlier:

We have more in the works for later this year and I will update the list above when they are published.

You can also read the documentation to learn more.

DLM Videos
While I’ve got your attention, I would like to share a couple of helpful videos with you:

This new feature is available now and you can start using it today!

— Jeff;

Amazon EC2 Instance Metadata Service IMDSv2 by default

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/amazon-ec2-instance-metadata-service-imdsv2-by-default/

Effective mid-2024, newly released Amazon EC2 instance types will use only version 2 of the EC2 Instance Metadata Service (IMDSv2). We are also taking a series of steps to make IMDSv2 the default choice for AWS Management Console Quick Starts and other launch pathways.

Background
This service is accessible from within an EC2 instance at a fixed IP address (169.254.169.254 via IPv4 or fd00:ec2::254 via IPv6 on Nitro instances). It gives you (or the code running on the instance) access to a wealth of static and dynamic data including the ID of the AMI that was used to launch the instance, block device mappings, temporary IAM credentials for roles that are attached to the instance, network interface information, user data, and much more, as detailed in Instance Metadata Categories.

The v1 service uses a request/response access method and the v2 service uses a session-oriented method, as detailed in this blog post. Both services are fully secure, but v2 provides additional layers of protection for four types of vulnerabilities that could be used to try to access IMDS.

Many applications and instances are already using and benefiting from IMDSv2, but the full range of benefits become available only when IMDSv1 is disabled at the AWS account level.

Migration Plan
Here are the significant steps that we have taken, and those that plan to take, on the road to making IMDSv2 the default choice for new AWS infrastructure (allow a tiny bit of wiggle room on the 2023 and 2024 dates):

November 2019 – We launched IMDSv2 and showed you how to use it to add defense in depth.

February 2020 – We began to verify that all newly published products from AWS Marketplace sellers and AWS Partners support IMDSv2.

March 2023 – We launched Amazon Linux 2023, which uses IMDSv2 by default for all launches.

September 2023 – We published a blog post to show you how to Get the full benefits of IMDSv2 and disable IMDSv1 across your AWS infrastructure.

November 2023 – Starting today, all console Quick Start launches will use IMDSv2-only (all Amazon and Partner Quick Start AMIs support this). Here’s how this is specified in the EC2 Console within Advanced details when launching an instance:

February 2024 – We plan to introduce a new API function that will allow you to control the use of IMDSv1 as the default at the account level. You can already control IMDSv1 usage in an IAM policy (taking away and limiting existing permission), or as an SCP that is applied globally across an account, an organizational unit (OU), or an entire organization. For example IAM policies read Work with instance metadata.

Mid-2024 – Newly released Amazon EC2 instance types will use IMDSv2 only by default. For transition support, you will still be able to enable/turn on IMDSv1 at launch or after launch on an instance live without the need for a restart or stop/start.

What to Do
Now is the time to get started on your migration from IMDSv1 to IMDSv2 using the Get the full benefits.. blog post as a guide. You should also become familiar with the Tools for helping with the transition to IMDSv2, along with the recommended path on the same page. In addition to recommending tools, this page shows you how to set up an IAM policy that disables the use of IMDSv1 and shows you how to use the MetadataNoToken CloudWatch metric to detect any remaining usage:

Another helpful resource can be found on AWS re:Post: How can I use Systems Manager automation to enforce that only IMDSv2 is used to access instance metadata from my Amazon EC2 instance?

We want this transition to be as smooth as possible for you and for your customers. If you need any additional help, please contact AWS Support.

— Jeff;

Announcing Amazon EC2 Capacity Blocks for ML to reserve GPU capacity for your machine learning workloads

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/announcing-amazon-ec2-capacity-blocks-for-ml-to-reserve-gpu-capacity-for-your-machine-learning-workloads/

Recent advancements in machine learning (ML) have unlocked opportunities for customers across organizations of all sizes and industries to reinvent new products and transform their businesses. However, the growth in demand for GPU capacity to train, fine-tune, experiment, and inference these ML models has outpaced industry-wide supply, making GPUs a scarce resource. Access to GPU capacity is an obstacle for customers whose capacity needs fluctuate depending on the research and development phase they’re in.

Today, we are announcing Amazon Elastic Compute Cloud (Amazon EC2) Capacity Blocks for ML, a new Amazon EC2 usage model that further democratizes ML by making it easy to access GPU instances to train and deploy ML and generative AI models. With EC2 Capacity Blocks, you can reserve hundreds of GPUs collocated in EC2 UltraClusters designed for high-performance ML workloads, using Elastic Fabric Adapter (EFA) networking in a peta-bit scale non-blocking network, to deliver the best network performance available in Amazon EC2.

This is an innovative new way to schedule GPU instances where you can reserve the number of instances you need for a future date for just the amount of time you require. EC2 Capacity Blocks are currently available for Amazon EC2 P5 instances powered by NVIDIA H100 Tensor Core GPUs in the AWS US East (Ohio) Region. With EC2 Capacity Blocks, you can reserve GPU instances in just a few clicks and plan your ML development with confidence. EC2 Capacity Blocks make it easy for anyone to predictably access EC2 P5 instances that offer the highest performance in EC2 for ML training.

EC2 Capacity Block reservations work similarly to hotel room reservations. With a hotel reservation, you specify the date and duration you want your room for and the size of beds you’d like─a queen bed or king bed, for example. Likewise, with EC2 Capacity Block reservations, you select the date and duration you require GPU instances and the size of the reservation (the number of instances). On your reservation start date, you’ll be able to access your reserved EC2 Capacity Block and launch your P5 instances. At the end of the EC2 Capacity Block duration, any instances still running will be terminated.

You can use EC2 Capacity Blocks when you need capacity assurance to train or fine-tune ML models, run experiments, or plan for future surges in demand for ML applications. Alternatively, you can continue using On-Demand Capacity Reservations for all other workload types that require compute capacity assurance, such as business-critical applications, regulatory requirements, or disaster recovery.

Getting started with Amazon EC2 Capacity Blocks for ML
To reserve your Capacity Blocks, choose Capacity Reservations on the Amazon EC2 console in the US East (Ohio) Region. You can see two capacity reservation options. Select Purchase Capacity Blocks for ML and then Get started to start looking for an EC2 Capacity Block.

Choose your total capacity and specify how long you need the EC2 Capacity Block. You can reserve an EC2 Capacity Block in the following sizes: 1, 2, 4, 8, 16, 32, or 64 p5.48xlarge instances. The total number of days that you can reserve EC2 Capacity Blocks is 1– 14 days in 1-day increments. EC2 Capacity Blocks can be purchased up to 8 weeks in advance.

EC2 Capacity Block prices are dynamic and depend on total available supply and demand at the time you purchase the EC2 Capacity Block. You can adjust the size, duration, or date range in your specifications to search for other EC2 Capacity Block options. When you select Find Capacity Blocks, AWS returns the lowest-priced offering available that meets your specifications in the date range you have specified. At this point, you will be shown the price for the EC2 Capacity Block.

After reviewing EC2 Capacity Blocks details, tags, and total price information, choose Purchase. The total price of an EC2 Capacity Block is charged up front, and the price does not change after purchase. The payment will be billed to your account within 12 hours after you purchase the EC2 Capacity Blocks.

All EC2 Capacity Blocks reservations start at 11:30 AM Coordinated Universal Time (UTC). EC2 Capacity Blocks can’t be modified or canceled after purchase.

You can also use AWS Command Line Interface (AWS CLI) and AWS SDKs to purchase EC2 Capacity Blocks. Use the describe-capacity-block-offerings API to provide your cluster requirements and discover an available EC2 Capacity Block for purchase.

$ aws ec2 describe-capacity-block-offerings \
          --instance-type p5.48xlarge \
          --instance-count 4 \
          --start-date-range 2023-10-30T00:00:00Z \
          --end-date-range 2023-11-01T00:00:00Z \
          –-capacity-duration 48

After you find an available EC2 Capacity Block with the CapacityBlockOfferingId and capacity information from the preceding command, you can use purchase-capacity-block-reservation API to purchase it.

$ aws ec2 purchase-capacity-block-reservation \
          --capacity-block-offering-id cbr-0123456789abcdefg \
          –-instance-platform Linux/UNIX

For more information about new EC2 Capacity Blocks APIs, see the Amazon EC2 API documentation.

Your EC2 Capacity Block has now been scheduled successfully. On the scheduled start date, your EC2 Capacity Block will become active. To use an active EC2 Capacity Block on your starting date, choose the capacity reservation ID for your EC2 Capacity Block. You can see a breakdown of the reserved instance capacity, which shows how the capacity is currently being utilized in the Capacity details section.

To launch instances into your active EC2 Capacity Block, choose Launch instances and follow the normal process of launching EC2 instances and running your ML workloads.

In the Advanced details section, choose Capacity Blocks as the purchase option and select the capacity reservation ID of the EC2 Capacity Block you’re trying to target.

As your EC2 Capacity Block end time approaches, Amazon EC2 will emit an event through Amazon EventBridge, letting you know your reservation is ending soon so you can checkpoint your workload. Any instances running in the EC2 Capacity Block go into a shutting-down state 30 minutes before your reservation ends. The amount you were charged for your EC2 Capacity Block does not include this time period. When your EC2 Capacity Block expires, any instances still running will be terminated.

Now available
Amazon EC2 Capacity Blocks are now available for p5.48xlarge instances in the AWS US East (Ohio) Region. You can view the price of an EC2 Capacity Block before you reserve it, and the total price of an EC2 Capacity Block is charged up-front at the time of purchase. For more information, see the EC2 Capacity Blocks pricing page.

To learn more, see the EC2 Capacity Blocks documentation and send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

— Channy

AWS Weekly Roundup – re:Post Selections, SNS and SQS FIFO improvements, multi-VPC ENI attachments, and more – October 30, 2023

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-repost-selections-sns-and-sqs-fifo-improvements-multi-vpc-eni-attachments-and-more-october-30-2023/

It’s less than a month to AWS re:Invent, but interesting news doesn’t slow down in the meantime. This week is my turn to help keep you up to date!

Last week’s launches
Here are some of the launches that caught my attention last week:

AWS re:Post – With re:Post, you have access to a community of experts that helps you become even more successful on AWS. With Selections, community members can organize knowledge in an aggregated view to create learning paths or curated content sets.

Amazon SNS – First-in-First-out (FIFO) topics now support the option to store and replay messages without needing to provision a separate archival resource. This improves the durability of your event-driven applications and can help you recover from downstream failure scenarios. Find out more in this AWS Comput Blog post – Archiving and replaying messages with Amazon SNS FIFO. Also, you can now use custom data identifiers to protect not only common sensitive data (such as names, addresses, and credit card numbers) but also domain-specific sensitive data, such as your company’s employee IDs. You can find additional info on this feature in this AWS Security blog post – Mask and redact sensitive data published to Amazon SNS using managed and custom data identifiers.

Amazon SQS – With the increased throughput quota for FIFO high throughput mode, you can process up to 18,000 transactions per second, per API action. Note the throughput quota depends on the AWS Region.

Amazon OpenSearch Service – OpenSearch Serverless now supports automated time-based data deletion with new index lifecycle policies. To determine the best strategy to deliver accurate and low latency vector search queries, OpenSearch can now intelligently evaluate optimal filtering strategies, like pre-filtering with approximate nearest neighbor (ANN) or filtering with exact k-nearest neighbor (k-NN). Also, OpenSearch Service now supports Internet Protocol Version 6 (IPv6).

Amazon EC2 – With multi-VPC ENI attachments, you can launch an instance with a primary elastic network interface (ENI) in one virtual private cloud (VPC) and attach a secondary ENI from another VPC. This helps maintain network-level segregation, but still allows specific workloads (like centralized appliances and databases) to communicate between them.

AWS CodePipeline – With parameterized pipelines, you can dynamically pass input parameters to a pipeline execution. You can now start a pipeline execution when a specific git tag is applied to a commit in the source repository.

Amazon MemoryDB – Now supports Graviton3-based R7g nodes that deliver up to 28 percent increased throughput compared to R6g. These nodes also deliver higher networking bandwidth.

Other AWS news
Here are a few posts from some of the other AWS and cloud blogs that I follow:

Networking & Content Delivery Blog – Some of the technical management and hardware decisions we make when building AWS network infrastructure: A Continuous Improvement Model for Interconnects within AWS Data Centers

Interconnect monitoring service infrastructure diagram

DevOps Blog – To help enterprise customers understand how many of developers use CodeWhisperer, how often they use it, and how often they accept suggestions: Introducing Amazon CodeWhisperer Dashboard and CloudWatch Metrics

Front-End Web & Mobile Blog – How to restrict access to your GraphQL APIs to consumers within a private network: Architecture Patterns for AWS AppSync Private APIs

Architecture Blog – Another post in this super interesting series: Let’s Architect! Designing systems for stream data processing

A serverless streaming data pipeline using Amazon Kinesis and AWS Glue

From Community.AWS: Load Testing WordPress Amazon Lightsail Instances and Future-proof Your .NET Apps With Foundation Model Choice and Amazon Bedrock.

Don’t miss the latest AWS open source newsletter by my colleague Ricardo.

Upcoming AWS events
Check your calendars and sign up for these AWS events

AWS Community Days – Join a community-led conference run by AWS user group leaders in your region: Jaipur (November 4), Vadodara (November 4), Brasil (November 4), Central Asia (Kazakhstan, Uzbekistan, Kyrgyzstan, and Mongolia on November 17-18), and Guatemala (November 18).

AWS re:Invent (November 27 – December 1) – Join us to hear the latest from AWS, learn from experts, and connect with the global cloud community. Browse the session catalog and attendee guides and check out the highlights for generative AI.

Here you can browse all upcoming AWS-led in-person and virtual events and developer-focused events.

And that’s all from me for this week. On to the next one!

— 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!

In the Works – AWS European Sovereign Cloud

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/in-the-works-aws-european-sovereign-cloud/

The AWS European Sovereign Cloud will allow government agencies, regulated industries, and the independent software vendors (ISVs) that support them to store sensitive data and run critical workloads on AWS infrastructure that is operated and supported by AWS employees located in and residents of the European Union (EU). The first Region will be located in Germany.

Background
Late last year we announced the AWS Digital Sovereignty Pledge and made a commitment to offer you (and all AWS customers) the most advanced set of sovereignty controls and features available in the cloud. Since that announcement we have taken several important steps forward in fulfillment of that pledge:

May 2023 – We announced that AWS Nitro System had been validated by an independent third-party to confirm that it contains no mechanism that allows anyone at AWS to access your data on AWS hosts. At the same time we announced that the AWS Key Management Service (KMS) External Key Store allows you to store keys outside of AWS and use them to encrypt data stored in AWS.

August 2023 – We announced AWS Dedicated Local Zones, infrastructure that is fully managed by AWS and built for exclusive use by a customer or community, and placed in a customer-specified location or data center.

AWS European Sovereign Cloud
The upcoming AWS European Sovereign Cloud will be separate from, and independent of, the eight existing AWS Regions already open in Frankfurt, Ireland, London, Milan, Paris, Stockholm, Spain, and Zurich. It will give you additional options for deployment, while providing AWS services, APIs, and tools that you are already familiar with. The design will help you meet your data residency, operational autonomy, and resiliency needs.

In order to maintain separation between this cloud and the existing AWS Global Cloud you will need to create a fresh AWS account. The metadata you create such as data labels, categories, permissions, and configurations will be stored within the EU. This does not apply to AWS account information such as spend and billing data, which will be aggregated and used to ensure that you get favorable pricing within any applicable volume usage tiers.

As I mentioned earlier, this cloud will be operated and supported by AWS employees located in and residents of the EU, with support available 24/7/365.

The AWS European Sovereign Cloud will be operationally independent of the other regions, with separate in-Region billing and usage metering systems.

Initial Region
The initial region will be located in Germany. It will launch with multiple Availability Zones, each in separate and distinct geographic locations, with enough distance between them to significantly reduce the risk of a single event impacting your business continuity. We will have additional details on the list of available services, instance types, and so forth as we get closer to the launch.

Over time, this and other regions in this cloud will also function as parent regions for AWS Outposts and Dedicated Local Zones. These options give you even more flexibility with regard to isolation and in-country data residency. If you would like to express your interest in Dedicated Local Zones in your country, please contact your AWS account manager.

Get Ready
You can start to build applications today in any of the existing regions and move them to the AWS European Sovereign Cloud when the region launches. You can also initiate conversations with your local regulatory authorities in order to better understand any issues that are specific to your particular location.

— Jeff;

Introducing Amazon MSK Replicator – Fully Managed Replication across MSK Clusters in Same or Different AWS Regions

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/introducing-amazon-msk-replicator-fully-managed-replication-across-msk-clusters-in-same-or-different-aws-regions/

Amazon Managed Streaming for Apache Kafka (Amazon MSK) provides a fully managed and highly available Apache Kafka service simplifying the way you process streaming data. When using Apache Kafka, a common architectural pattern is to replicate data from one cluster to another.

Cross-cluster replication is often used to implement business continuity and disaster recovery plans and increase application resilience across AWS Regions. Another use case, when building multi-Region applications, is to have copies of streaming data in multiple geographies stored closer to end consumers for lower latency access. You might also need to aggregate data from multiple clusters into one centralized cluster for analytics.

To address these needs, you would have to write custom code or install and manage open-source tools like MirrorMaker 2.0, available as part of Apache Kafka starting with version 2.4. However, these tools can be complex and time-consuming to set up for reliable replication, and require continuous monitoring and scaling.

Today, we’re introducing MSK Replicator, a new capability of Amazon MSK that makes it easier to reliably set up cross-Region and same-Region replication between MSK clusters, scaling automatically to handle your workload. You can use MSK Replicator with both provisioned and serverless MSK cluster types, including those using tiered storage.

With MSK Replicator, you can setup both active-passive and active-active cluster topologies to increase the resiliency of your Kafka application across Regions:

  • In an active-active setup, both MSK clusters are actively serving reads and writes.
  • In an active-passive setup, only one MSK cluster at a time is actively serving streaming data while the other cluster is on standby.

Let’s see how that works in practice.

Creating an MSK Replicator across AWS Regions
I have two MSK clusters deployed in different Regions. MSK Replicator requires that the clusters have IAM authentication enabled. I can continue to use other authentication methods such as mTLS or SASL for my other clients. The source cluster also needs to enable multi-VPC private connectivity.

MSK Replicator cross-Region architecture diagram.

From a network perspective, the security groups of the clusters allow traffic between the cluster and the security group used by the Replicator. For example, I can add self-referencing inbound and outbound rules that allow traffic from and to the same security group. For simplicity, I use the default VPC and its default security group for both clusters.

Before creating a replicator, I update the cluster policy of the source cluster to allow the MSK service (including replicators) to find and reach the cluster. In the Amazon MSK console, I select the source Region. I choose Clusters from the navigation pane and then the source cluster. First, I copy the source cluster ARN at the top. Then, in the Properties tab, I choose Edit cluster policy in the Security settings. There, I use the following JSON policy (replacing the source cluster ARN) and save the changes:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "Service": "kafka.amazonaws.com"
            },
            "Action": [
                "kafka:CreateVpcConnection",
                "kafka:GetBootstrapBrokers",
                "kafka:DescribeClusterV2"
            ],
            "Resource": "<SOURCE_CLUSTER_ARN>"
        }
    ]
}

I select the target Region in the console. I choose Replicators from the navigation pane and then Create replicator. Here, I enter a name and a description for the replicator.

Console screenshot.

In the Source cluster section, I select the Region of the source MSK cluster. Then, I choose Browse to select the source MSK cluster from the list. Note that Replicators can be created only for clusters that have a cluster policy set.

Console screenshot.

I leave Subnets and Security groups as their default values to use my default VPC and its default security group. This network configuration may be used to place elastic network interfaces (EINs) to facilitate communication with your cluster.

The Access control method for the source cluster is set to IAM role-based authentication. Optionally, I can turn on multiple authentication methods at the same time to continue to use clients that need other authentication methods like mTLS or SASL while the Replicator uses IAM. For cross-Region replication, the source cluster cannot have unauthenticated access enabled, because we use multi-VPC to access their source cluster.

Console screenshot.

In the Target cluster section, the Cluster region is set to the Region where I’m using the console. I choose Browse to select the target MSK cluster from the list.

Console screenshot.

Similar to what I did for the source cluster, I leave Subnets and Security groups as their default values. This network configuration is used to place the ENIs required to communicate with the target cluster. The Access control method for the target cluster is also set to IAM role-based authentication.

Console screenshot.

In the Replicator settings section, I use the default Topic replication configuration, so that all topics are replicated. Optionally, I can specify a comma-separated list of regular expressions that indicate the names of the topics to replicate or to exclude from replication. In the Additional settings, I can choose to copy topics configurations, access control lists (ACLs), and to detect and copy new topics.

Console screenshot.

Consumer group replication allows me to specify if consumer group offsets should be replicated so that, after a switchover, consuming applications can resume processing near where they left off in the primary cluster. I can specify a comma-separated list of regular expressions that indicate the names of the consumer groups to replicate or to exclude from replication. I can also choose to detect and copy new consumer groups. I use the default settings that replicate all consumer groups.

Console screenshot.

In Compression, I select None from the list of available compression types for the data that is being replicated.

Console screenshot.

The Amazon MSK console can automatically create a service execution role with the necessary permissions required for the Replicator to work. The role is used by the MSK service to connect to the source and target clusters, to read from the source cluster, and to write to the target cluster. However, I can choose to create and provide my own role as well. In Access permissions, I choose Create or update IAM role.

Console screenshot.

Finally, I add tags to the replicator. I can use tags to search and filter my resources or to track my costs. In the Replicator tags section, I enter Environment as the key and AWS News Blog as the value. Then, I choose Create.

Console screenshot.

After a few minutes, the replicator is running. Let’s put it into use!

Testing an MSK Replicator across AWS Regions
To connect to the source and target clusters, I already set up two Amazon Elastic Compute Cloud (Amazon EC2) instances in the two Regions. I followed the instructions in the MSK documentation to install the Apache Kafka client tools. Because I am using IAM authentication, the two instances have an IAM role attached that allows them to connect, send, and receive data from the clusters. To simplify networking, I used the default security group for the EC2 instances and the MSK clusters.

First, I create a new topic in the source cluster and send a few messages. I use Amazon EC2 Instance Connect to log into the EC2 instance in the source Region. I change the directory to the path where the Kafka client executables have been installed (the path depends on the version you use):

cd /home/ec2-user/kafka_2.12-2.8.1/bin

To connect to the source cluster, I need to know its bootstrap servers. Using the MSK console in the source Region, I choose Clusters from the navigation page and then the source cluster from the list. In the Cluster summary section, I choose View client information. There, I copy the list of Bootstrap servers. Because the EC2 instance is in the same VPC as the cluster, I copy the list in the Private endpoint (single-VPC) column.

Console screenshot.

Back to the EC2 instance, I put the list of bootstrap servers in the SOURCE_BOOTSTRAP_SERVERS environment variable.

export SOURCE_BOOTSTRAP_SERVERS=b-2.uscluster.esijym.c9.kafka.us-east-1.amazonaws.com:9098,b-3.uscluster.esijym.c9.kafka.us-east-1.amazonaws.com:9098,b-1.uscluster.esijym.c9.kafka.us-east-1.amazonaws.com:9098

Now, I create a topic on the source cluster.

./kafka-topics.sh --bootstrap-server $SOURCE_BOOTSTRAP_SERVERS --command-config client.properties --create --topic my-topic --partitions 6

Using the new topic, I send a few messages to the source cluster.

./kafka-console-producer.sh --broker-list $SOURCE_BOOTSTRAP_SERVERS --producer.config client.properties --topic my-topic
>Hello from the US
>These are my messages

Let’s see what happens in the target cluster. I connect to the EC2 instance in the target Region. Similar to what I did for the other instance, I get the list of bootstrap servers for the target cluster and put it into the TARGET_BOOTSTRAP_SERVERS environment variable.

On the target cluster, the source cluster alias is added as a prefix to the replicated topic names. To find the source cluster alias, I choose Replicators in the MSK console navigation pane. There, I choose the replicator I just created. In the Properties tab, I look up the Cluster alias in the Source cluster section.

Console screenshot.

I confirm the name of the replicated topic by looking at the list of topics in the target cluster (it’s the last one in the output list):

./kafka-topics.sh --list --bootstrap-server $TARGET_BOOTSTRAP_SERVERS --command-config client.properties
. . .
us-cluster-c78ec6d63588.my-topic

Now that I know the name of the replicated topic on the target cluster, I start a consumer to receive the messages originally sent to the source cluster:

./kafka-console-consumer.sh --bootstrap-server $TARGET_BOOTSTRAP_SERVERS --consumer.config client.properties --topic us-cluster-c78ec6d63588.my-topic --from-beginning
Hello from the US
These are my messages

Note that I can use a wildcard in the topic subscription (for example, .*my-topic) to automatically handle the prefix and have the same configuration in the source and target clusters.

As expected, all the messages I sent to the source cluster have been replicated and received by the consumer connected to the target cluster.

I can monitor the MSK Replicator latency, throughput, errors, and lag metrics using the Monitoring tab. Because this works through Amazon CloudWatch, I can easily create my own alarms and include these metrics in my dashboards.

To update the configuration to an active-active setup, I follow similar steps to create a replicator in the other Region and replicate streaming data between the clusters in the other direction. For details on how to manage failover and failback, see the MSK Replicator documentation.

Availability and Pricing
MSK Replicator is available today in: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Europe (Frankfurt), and Europe (Ireland).

With MSK Replicator, you pay per GB of data replicated and an hourly rate for each Replicator. You also pay Amazon MSK’s usual charges for your source and target MSK clusters and standard AWS charges for cross-Region data transfer. For more information, see MSK pricing.

Using MSK replicators, you can quickly implement cross-Region and same-Region replication to improve the resiliency of your architecture and store data close to your partners and end users. You can also use this new capability to get better insights by replicating streaming data to a single, centralized cluster where it is easier to run your analytics.

Simplify your data streaming architectures using Amazon MSK Replicator.

— Danilo

New Customization Capability in Amazon CodeWhisperer Generates Even Better Suggestions (Preview)

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/new-customization-capability-in-amazon-codewhisperer-generates-even-better-suggestions-preview/

An AI coding companion, such as Amazon CodeWhisperer, aims to improve developers’ productivity by helping them write code quickly and securely. However, in particular cases, developers need to have code recommendations based on their internal libraries and APIs they extensively use every day.

As most of the existing AI coding companion tools are trained only on open-source codes, they lack the capability to customize the code recommendations using private code repositories. This limitation presents a variety of challenges for developers. Developers have a difficulty learning how to use internal libraries correctly and avoid security problems. For large codebases, it requires hours of reading documentation to understand what code needs to be written to complete the task.

Now in Preview —  Amazon CodeWhisperer Customization Capability
Today, I’m excited to announce Amazon CodeWhisperer customization capability (in preview) that enables organizations to customize CodeWhisperer to generate specific code recommendations from private code repositories. With this feature, developers who are part of Amazon CodeWhisperer Professional tier can now receive real-time code recommendations that include their internal libraries, APIs, packages, classes, and methods.

Let’s say that you’re a developer working for a hypothetical food delivery company called AnyCompany. You’re given a task to process a list of unassigned food deliveries around the driver’s current location. Previously, with CodeWhisperer, it would not know the correct internal APIs to process unassigned food deliveries or getting driver’s current location as this isn’t publicly available information. 

Now, with customization capability, you can ask CodeWhisperer to provide recommendations that include specific code related to the company’s internal services. The following screenshot shows how CodeWhisperer generates codes based on the internal codebase just by writing a set of comments.

With the customization capability of utilizing your internal codebase, CodeWhisperer now understands the intent, determines which internal and public APIs are best suited to the task, and generates code recommendations.

How It Works
The explanation above described how you can use CodeWhisperer customization capability as a developer. Now, let me share how it works and how you can get started. 

To create a customization, you need to complete the following steps as a CodeWhisperer administrator. 

  1. Administer your end users as CodeWhisperer administrator.
  2. Connect to existing repositories. You can connect one or more code repositories in your GitHub, GitLab, or BitBucket account using AWS CodeStar Connections or manually upload all of your codes into an Amazon Simple Storage Service (Amazon S3) bucket.
  3. Create a customization. CodeWhisperer will customize its model based on your codebase.
  4. Activate the customization for your team members. Once the customization is created, you can review and manually activate the customization to make it available automatically in your team members’ IDEs.

This capability provides two main advantages: providing real-time customized code recommendations that are specific to organizations and ensuring the protection of valuable intellectual property. Organizations can now promote the use of code that meets their quality and security standards based on their codes in existing repositories.

Furthermore, CodeWhisperer helps to ensure the security of your codes by providing the option to encrypt your customization data using customer managed keys in AWS Key Management Service (AWS KMS). This customization data will be deleted once the customization job finishes. 

Let’s Get Started
Let me show you how you can use the Amazon CodeWhisperer customization capability.

To get started, I need to create a customization. I need to have administrator access to navigate to the Create customization page on the Amazon CodeWhisperer dashboard.

On the Create customization page, I can connect the desired private code repositories I want CodeWhisperer to train. Currently, CodeWhisperer customization capability supports connection to GitHub, GitLab, and Bitbucket via AWS CodeStar Connections. If I have codes that are not in any code repositories, I can also manually upload my codes into an S3 bucket and define the Amazon S3 URI.

The following screenshot shows that I have existing connections with my code repositories using AWS CodeStar Connections. I can also create a new connection by selecting Create new connection.

Then, I can select Create Customization so CodeWhisperer can start training the model based on the codes available in the connection. The duration to complete this process depends on the size of the code repositories.

When the customization is ready, CodeWhisperer will not activate it automatically. This gives me the flexibility to activate the customizations just when I need them. But before I demonstrate that, I’d like to explain the evaluation score.

In short, the evaluation score helps me to measure the customization’s accuracy in predicting and providing code recommendations based on the codes in my code repositories. It provides a score in one of three categories: 1) Very Good, with a score ranging from 7–10; 2) Fair, with a score ranging from 4–7; and 3) Poor, with a score ranging from 0–4. It’s recommended to activate the customization if the evaluation score is 6 or higher. If the evaluation score is less than desired, I need to make sure that I’m providing enough codes for customization and provide a new code dataset that extensively contains references to internal APIs.

Here, I can see the Evaluation score for my customization is 8, and I’m happy with this result. Then, I can select Activate to start using this customization.

Once I have activated the customizations, I can define the access to selected customizations by selecting Add users. Now, I can give access to the customizations for selected team members who have been added as users for Amazon CodeWhisperer Professional tier. To do that, I can follow the guide from the Administering end users page. 

Then, once my team members sign in via AWS Toolkit in their IDEs, they will see the available customizations and can start using them. 

With Amazon CodeWhisperer, I can create multiple customizations by providing different code repositories. This feature is useful if I want to build customizations for code recommendations for certain teams. 

As administrator, I can also monitor the performance of each of the customizations by navigating to the CodeWhisperer dashboard page. This page summarizes useful data such as user activity, how many lines of code were suggested by CodeWhisperer and accepted by my team members, and how many security scans have successfully been run from IDEs. 

Amazon CodeWhisperer customization capability also follows the supported IDEs as part of AWS Toolkit by Amazon CodeWhisperer, such as Visual Studio Code, IntelliJ JetBrains, Visual Studio, and AWS Cloud9. This feature also provides support for most popular programming languages, including Python, Java, JavaScript, TypeScript, and C#.

Join the Public Preview
By securely leveraging customer’s internal codebase, Amazon CodeWhisperer unlocks the full potential of generative AI-powered coding that is customized to your unique requirements.

Join the public preview now and learn more on how to get started on the Amazon CodeWhisperer Customization page.

Happy coding!
— Donnie

New – Seventh Generation Memory-optimized Amazon EC2 Instances (R7i)

Post Syndicated from Irshad Buchh original https://aws.amazon.com/blogs/aws/new-seventh-generation-memory-optimized-amazon-ec2-instances-r7i/

Earlier, we introduced a duo of Amazon Elastic Compute Cloud (Amazon EC2) instances to our lineup: the general-purpose Amazon EC2 M7i instances and the compute-optimized Amazon EC2 C7i instances.

Today, I’m happy to share that we’re expanding these seventh-generation x86-based offerings to include memory-optimized Amazon EC2 R7i instances. These instances are powered by custom 4th Generation Intel Xeon Scalable Processors (Sapphire Rapids) exclusive to AWS and will offer the highest compute performance among the comparable fourth-generation Intel processors in the cloud. The R7i instances are available in eleven sizes including two bare metal sizes (coming soon), and offer 15 percent improvement in price-performance compared to Amazon EC2 R6i instances.

Amazon EC2 R7i instances are SAP Certified and are an ideal fit for memory-intensive workloads such as high-performance databases (SQL and NoSQL databases), distributed web scale in-memory caches (Memcached and Redis), in-memory databases (SAP HANA), real-time big data analytics (Apache Hadoop and Spark clusters) and other enterprise applications. Amazon EC2 R7i offers larger instance sizes (48xlarge) with up to 192 vCPUs and 1,536 GiB of memory, including both virtual and bare metal instances, enabling you to consolidate your workloads and scale-up applications.

You can attach up to 128 EBS volumes to each R7i instance; by way of comparison, the R6i instances allow you to attach up to 28 volumes.

Here are the specs for the R7i instances:

Instance Name vCPUs
 Memory (GiB)
 Network Bandwidth
 EBS Bandwidth
r7i.large 2 16 GiB Up to 12.5 Gbps Up to 10 Gbps
r7i.xlarge 4 32 GiB Up to 12.5 Gbps Up to 10 Gbps
r7i.2xlarge 8 64 GiB Up to 12.5 Gbps Up to 10 Gbps
r7i.4xlarge 16 128 GiB Up to 12.5 Gbps Up to 10 Gbps
r7i.8xlarge 32 256 GiB 12.5 Gbps 10 Gbps
r7i.12xlarge 48 384 GiB 18.75 Gbps 15 Gbps
r7i.16xlarge 64 512 GiB 25 Gbps 20 Gbps
r7i.24xlarge 96 768 GiB 37.5 Gbps 30 Gbps
r7i.48xlarge 192 1,536 GiB 50 Gbps 40 Gbps

We’re also getting ready to launch two sizes of bare metal R7i instances soon:

Instance Name vCPUs
 Memory (GiB)
 Network Bandwidth
 EBS Bandwidth
r7i.metal-24xl 96 768 GiB Up to 37.5 Gbps Up to 30 Gbps
r7i.metal-48xl 192 1,536 GiB Up to 50.0 Gbps Up to 40 Gbps

Built-in Accelerators
The Sapphire Rapids processors include four built-in accelerators, each providing hardware acceleration for a specific workload:

  • Advanced Matrix Extensions (AMX) – The AMX extensions are designed to accelerate machine learning and other compute-intensive workloads that involve matrix operations. It improves the efficiency of these operations by providing specialized hardware instructions and registers tailored for matrix computations. Matrix operations, such as multiplication and convolution, are fundamental building blocks in various computational tasks, especially in machine learning algorithms.
  • Intel Data Streaming Accelerator (DSA) – DSA enhances data processing and analytics capabilities for a wide range of applications and enables developers to harness the full potential of their data-driven workloads. With DSA, you gain access to optimized hardware acceleration that delivers exceptional performance for data-intensive tasks.
  • Intel In-Memory Analytics Accelerator (IAA) – This accelerator runs database and analytic workloads faster, with the potential for greater power efficiency. In-memory compression, decompression, encryption at very high throughput, and a suite of analytics primitives support in-memory databases, open-source databases, and data stores like RocksDB and ClickHouse.
  • Intel QuickAssist Technology (QAT) – This accelerator offloads encryption, decryption, and compression, freeing up processor cores and reducing power consumption. It also supports merged compression and encryption in a single data flow. To learn more start at the Intel QuickAssist Technology (Intel QAT) Overview.

Advanced Matrix Extensions are available on all sizes of R7i instances. The Intel QAT, Intel IAA, and Intel DSA accelerators will be available on the r7i.metal-24xl and r7i.metal-48xl instances.

Now Available
The new instances are available in the US East (Ohio, N. Virginia), US West (Oregon), Europe (Spain), Europe (Stockholm), and Europe (Ireland) AWS Regions.

Purchasing Options
R7i instances are available in On-Demand, Reserved, Savings Plan, and Spot Instance form. R7i instances are also available in Dedicated Host and Dedicated Instance form.

— Irshad

AWS Weekly Roundup – EBS Status Check, Textract Custom Queries, Amazon Linux 2, and more – October 16, 2023

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-ebs-status-check-textract-custom-queries-amazon-linux-2-and-more-october-16-2023/

With just 41 days until AWS re:Invent 2023 opens, I’m doing my best to stay heads-down and focused on working with the entire AWS News Blog team to create plenty of awesome new posts for your reading pleasure! I’ll take a short break this morning to share some of the most exciting launches and other news from last week. Here we go!

Last Week’s Launches
Here are some of the launches that captured my attention:

Amazon EBS – The new Attached EBS Status Check CloudWatch metric lets you monitor the status of all of the Amazon Elastic Block Store (Amazon EBS) volumes attached to a particular Amazon Elastic Compute Cloud (Amazon EC2) instance, verifying that the volumes are reachable and able to complete I/O operations.

AWS Systems Manager – You can now enable AWS Systems Manager by default for all EC2 instances within an Organization. This lets you confirm that core Systems Manager capabilities are present on all new and existing instances.

Amazon EC2 – You can now set unused or obsolete AMIs to a disabled state. This makes the AMI private if it was previously shared, hides it from DescribeImages by default, and prevents new instances from being launched from it.

Amazon Textract – You can now use Custom Queries to adapt Textract’s Queries feature to improve extraction accuracy for business-specific documents. You upload sample documents, label the data, and generate an adapter, which you then use in calls to the AnalyzeDocument function.

Amazon OpenSearch Service – You can now create Search Pipelines for easier processing of queries and results. Each search pipeline can contain multiple processing steps: query rewriters, natural language processors, result rerankers, and filters; several standard processors are also included.

Amazon Linux 2 – The latest quarterly release (AL2023.2) of Amazon Linux 2 includes a core set of Ansible features as well as a curated set of community collections. It also includes Amazon Corretto 21, and many other new features and capabilities.

Amazon Rekognition – You can now train custom adapters to reduce the number of false positives and false negative flagged by Amazon Rekognition, giving you the power to tailor the deep learning model to improve performance for your specific use case.

Amazon RDS – Amazon Relational Database Service (RDS) now supports PostgreSQL, MySQL, and MariaDB databases on M6in, M6idn, R6in, and R6idn database instances.

X in Y – We launched existing services and instance types in additional regions:

Other AWS News
Here are some other blog posts and news items that you might like:

On the Community.AWS Blog, Seth Eliot listed Twelve Resilience Sessions at AWS re:Invent You Won’t Want to Miss, Brooke Jamieson explained How to Learn Generative AI from Scratch, and Daniel Wirjo shared some Patterns for Building Generative AI Applications on Amazon Bedrock.

On the AWS Insights blog, fellow news blogger Irshad Buchh explained why Two billion downloads of Terraform AWS Provider shows value of IaC for infrastructure management.

The AWS IoT Blog explained How to build a scalable, multi-tenant IoT SaaS platform on AWS using a multi-account strategy.

The Amazon SES Blog showed you how to Automate marketing campaigns with real-time customer data using Amazon Pinpoint.

The AWS Big Data Blog showed you how to Orchestrate Amazon EMR Serverless jobs with AWS Step functions.

The AWS Compute Blog talked about Filtering events in Amazon EventBridge with wildcard pattern matching.

The AWS Storage Blog talked about Retaining Amazon EC2 AMI snapshots for compliance using Amazon EBS Snapshots Archive.

The AWS Architecture Blog talked about how Internet Travel Service ITS adopts microservices architecture for improved air travel search engine.

Some other great sources of AWS news include:

Upcoming AWS Events
Check your calendars and sign up for these AWS events:

AWS Community Days – Join a community-led conference run by AWS user group leaders in your region: Italy (October 18), UAE (October 21), Jaipur (November 4), Vadodara (November 4), and Brasil (November 4).

AWS InnovateAWS Innovate: Every Application Edition – Join our free online conference to explore cutting-edge ways to enhance security and reliability, optimize performance on a budget, speed up application development, and revolutionize your applications with generative AI. Register for AWS Innovate Online Americas and EMEA on October 19 and AWS Innovate Online Asia Pacific & Japan on October 26.

AWS re:Invent 2023AWS re:Invent (November 27 – December 1) – Join us to hear the latest from AWS, learn from experts, and connect with the global cloud community. Browse the session catalog and attendee guides and check out the re:Invent highlights for generative AI.

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

And that’s a wrap. Check back next Monday for another Weekly Roundup!

— Jeff;

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

New – Amazon EC2 C7a Instances Powered By 4th Gen AMD EPYC Processors for Compute Optimized Workloads

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/new-amazon-ec2-c7a-instances-powered-by-4th-gen-amd-epyc-processors-for-compute-optimized-workloads/

We launched the compute optimized Amazon EC2 C6a instances in February 2022 powered by 3rd Gen AMD EPYC (Milan) processors, running at frequencies up to 3.6 GHz.

Today, we’re announcing the general availability of new, compute optimized Amazon EC2 C7a instances, powered by the 4th Gen AMD EPYC (Genoa) processors with a maximum frequency of 3.7 GHz, which offer up to 50 percent higher performance compared to C6a instances. You can use this increased performance to process data faster, consolidate workloads, and lower the cost of ownership.

C7a instances offer up to 50 percent higher performance compared to C6a instances. These instances are ideal for running compute-intensive workloads such as high-performance web servers, batch processing, ad serving, machine learning, multiplayer gaming, video encoding, high performance computing (HPC) such as scientific modeling, and machine learning.

C7a instances support AVX-512, Vector Neural Network Instructions (VNNI), and brain floating point (bfloat16). These instances feature Double Data Rate 5 (DDR5) memory, which enables high-speed access to data in-memory, and deliver 2.25 times more memory bandwidth compared to the previous generation instances for lower latency.

C7a instances feature sizes of up to 192 vCPUs with 384 GiB RAM, which you have a new medium instance size, which enables you to right-size your workloads more accurately, offering 1 vCPU, 2 GiB. Here are the detailed specs:

Name vCPUs Memory (GiB) Network Bandwidth (Gbps) EBS Bandwidth (Gbps)
c7a.medium 1 2 Up to 12.5 Up to 10
c7a.large 2 4 Up to 12.5 Up to 10
c7a.xlarge 4 8 Up to 12.5 Up to 10
c7a.2xlarge 8 16 Up to 12.5 Up to 10
c7a.4xlarge 16 32 Up to 12.5 Up to 10
c7a.8xlarge 32 64 12.5 10
c7a.12xlarge 48 96 18.75 15
c7a.16xlarge 64 128 25 20
c7a.24xlarge 96 192 37.5 30
c7a.32xlarge 128 256 50 40
c7a.48xlarge 192 384 50 40
c7a.metal-48xl 192 384 50 40

C7a instances have up to 50 Gbps enhanced networking and 40 Gbps EBS bandwidth, and you can attach up to 128 EBS volumes to an instance, compared to up to 28 EBS volume attachments with the previous generation instances.

C7a instances support always-on memory encryption with AMD secure memory encryption (SME) and new AVX-512 instructions for accelerating encryption and decryption algorithms, convolutional neural network (CNN) based algorithms, financial analytics, and video encoding workloads. C7a instances also support AES-256 compared to AES-128 in C6a instances for enhanced security.

These instances are built on the AWS Nitro System and support Elastic Fabric Adapter (EFA) for workloads that benefit from lower network latency and highly scalable inter-node communication, such as high-performance computing and video processing.

Now Available
Amazon EC2 C7a instances are now available in the following AWS Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), and EU (Ireland). As usual with Amazon EC2, you only pay for what you use. For more information, see the Amazon EC2 pricing page.

To learn more, visit the EC2 C7a instances page and AWS/AMD partner page. You can send feedback to [email protected], AWS re:Post for EC2, or through your usual AWS Support contacts.

— Channy

Amazon DataZone Now Generally Available – Collaborate on Data Projects across Organizational Boundaries

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/amazon-datazone-now-generally-available-collaborate-on-data-projects-across-organizational-boundaries/

Today, we’re announcing the general availability of Amazon DataZone, a new data management service to catalog, discover, analyze, share, and govern data between data producers and consumers in your organization.

At AWS re:Invent 2022, we preannounced Amazon DataZone, and in March 2023, we previewed it publicly.

During the keynote of the last re:Invent, Swami Sivasubramanian, vice president of Databases, Analytics, and Machine Learning at AWS said “I have had the benefit of being an early customer of DataZone to run the AWS weekly business review meeting where we assemble data from our sales pipeline and revenue projections to inform our business strategy.”

During the keynote, a demo led by Shikha Verma, head of product for Amazon DataZone, demonstrated how organizations can use the product to create more effective advertising campaigns and get the most out of their data.

“Every enterprise is made up of multiple teams that own and use data across a variety of data stores. Data people have to pull this data together but do not have an easy way to access or even have visibility to this data. DataZone provides a unified environment where everyone in an organization—from data producers to consumers, can go to access and share data in a governed manner.”

With Amazon DataZone, data producers populate the business data catalog with structured data assets from AWS Glue Data Catalog and Amazon Redshift tables. Data consumers search and subscribe to data assets in the data catalog and share with other business use case collaborators. Consumers can analyze their subscribed data assets with tools—such as Amazon Redshift or Amazon Athena query editors—that are directly accessed from the Amazon DataZone portal. The integrated publishing-and-subscription workflow provides access-auditing capabilities across projects.

Introducing Amazon DataZone
For those of you who aren’t yet familiar with Amazon DataZone, let me introduce you to its key concept and capabilities.

Amazon DataZone Domain represents the distinct boundary of a line of business (LOB) or a business area within an organization that can manage it’s own data, including it’s own data assets and its own definition of data or business terminology, and may have it’s own governing standards. The domain includes all core components such as the data portal, business data catalog, projects and environments, and built-in workflows.

  1. Data portal (outside the AWS Management Console) – This is a web application where different users can go to catalog, discover, govern, share, and analyze data in a self-service fashion. The data portal authenticates users with AWS Identity and Access Manager (IAM) credentials or existing credentials from your identity provider through the AWS IAM Identity Center.
  2. Business data catalog – In your catalog, you can define the taxonomy or the business glossary. You can use this component to catalog data across your organization with business context and thus enable everyone in your organization to find and understand data quickly.
  3. Data projects & environments – You can use projects to simplify access to the AWS analytics by creating business use case–based groupings of people, data assets, and analytics tools. Amazon DataZone projects provide a space where project members can collaborate, exchange data, and share data assets. Within projects, you can create environments that provide the necessary infrastructure to project members such as analytics tools and storage so that project members can easily produce new data or consume data they have access to.
  4. Governance and access control – You can use built-in workflows that allow users across the organization to request access to data in the catalog and owners of the data to review and approve those subscription requests. Once a subscription request is approved, Amazon DataZone can automatically grant access by managing permission at underlying data stores such as AWS Lake Formation and Amazon Redshift.

To learn more, see Amazon DataZone Terminology and Concepts.

Getting Started with Amazon DataZone
To get started, consider a scenario where a product marketing team wants to run campaigns to drive product adoption. To do this, they need to analyze product sales data owned by a sales team. In this walkthrough, the sales team, which acts as the data producer, publishes sales data in Amazon DataZone. Then the marketing team, which acts as the data consumer, subscribes to sales data and analyzes it in order to build a campaign strategy.

To understand how the DataZone works, let’s walk through a condensed version of the Getting started guide for Amazon DataZone.

1. Create a Domain
When you first start using DataZone, you start by creating a domain and all core components such as business data catalog, projects, and environments in the data portal, then exist within that domain. Go to the Amazon DataZone console and choose Create domain.

Enter Domain name and a descrption and leave all other values as default.

For example, in the Service access section, if you choose Create and use a new role by default, Amazon DataZone will automatically create a new role with necessary permissions that authorize DataZone to make API calls on behalf of users within the domain. Check the Quick setup option where DataZone can take care of all the setup steps.

Finally, choose Create domain. Amazon DataZone creates the necessary IAM roles and enables this domain to use resources within your account such as AWS Glue Data Catalog, Amazon Redshift, and Amazon Athena. Domain creation can take several minutes to complete. Wait for the domain to have a status of Available.

2. Create a Project and Environment in the Data Portal
After the domain is successfully created, select it, and on the domain’s summary page, note the data portal URL for the root domain. You can use this URL to access your Amazon DataZone data portal. Choose Open data portal.

To create a new data project as the sales team to publish sales data, choose Create Project.

In the dialogbox, enter “Sales producer project” as the Name, then enter a Description for this project and choose Create.

Once you have the project, you need to create a environment to work with data and analytics tools such as Amazon Athena or Amazon Redshift in this project. Choose Create environment in the overview page or after clicking the Environment tab.

Enter “publish-environment” as the Name, then enter a Description for this environment and choose Environment profile. An environment profile is a pre-defined template that includes technical details required to create an environment such as which AWS account, Region, VPC details, and resources and tools are added to the project.

You can select a couple of default environment profiles. Choosing DataLakeProfile enables you to publish data from your Amazon S3 and AWS Glue based data lakes. It also simplifies querying the AWS Glue tables that you have access to using Amazon Athena.

Next, ignore all the optional parameters and choose Create environment. It takes about a minute for the environment to create certain resources in your AWS account such as IAM roles, an Amazon S3 suffix, AWS Glue databases, and an Athena workgroup, which makes it easier for members of a project to produce and consume data in the data lake.

3. Publish Data in the Data Portal
You have the environment to publish your data in your AWS Glue table. To create this table in Amazon Athena, choose Query data with the Athena link on the right-hand side of the Environments page.

This opens the Athena query editor in a new tab. Select publishenvironment_pub_db from the database dropdown and then paste the following query into the query editor. This will create a table called catalog_sales in the environment’s AWS Glue database.

CREATE TABLE catalog_sales AS 
SELECT 146776932 AS order_number, 23 AS quantity, 23.4 AS wholesale_cost, 45.0 as list_price, 43.0 as sales_price, 2.0 as discount, 12 as ship_mode_sk,13 as warehouse_sk, 23 as item_sk, 34 as catalog_page_sk, 232 as ship_customer_sk, 4556 as bill_customer_sk
UNION ALL SELECT 46776931, 24, 24.4, 46, 44, 1, 14, 15, 24, 35, 222, 4551
UNION ALL SELECT 46777394, 42, 43.4, 60, 50, 10, 30, 20, 27, 43, 241, 4565
UNION ALL SELECT 46777831, 33, 40.4, 51, 46, 15, 16, 26, 33, 40, 234, 4563
UNION ALL SELECT 46779160, 29, 26.4, 50, 61, 8, 31, 15, 36, 40, 242, 4562
UNION ALL SELECT 46778595, 43, 28.4, 49, 47, 7, 28, 22, 27, 43, 224, 4555
UNION ALL SELECT 46779482, 34, 33.4, 64, 44, 10, 17, 27, 43, 52, 222, 4556
UNION ALL SELECT 46779650, 39, 37.4, 51, 62, 13, 31, 25, 31, 52, 224, 4551
UNION ALL SELECT 46780524, 33, 40.4, 60, 53, 18, 32, 31, 31, 39, 232, 4563
UNION ALL SELECT 46780634, 39, 35.4, 46, 44, 16, 33, 19, 31, 52, 242, 4557
UNION ALL SELECT 46781887, 24, 30.4, 54, 62, 13, 18, 29, 24, 52, 223, 4561

You can see the two databases in the dropdown menu. The publishenvironment_pub_db is to provide you with a space to produce new data and choose to publish it to the DataZone catalog. The other one, publishenvironment_sub_db is for project members when they subscribe to or access to data in the catalog within that project.

Make sure that the catalog_sales table is successfully created. Now you have a data asset that can be published into the Amazon DataZone catalog.

As the data producer, you can now go back to the data portal and publish this table into the DataZone catalog. Choose the Data tab in the top menu and Data sources in the left navigation pane.

You can see a default data source automatically created in your environment. When you open this data source, you will see your environments’ publishing database where we just created the catalog_sales table.

This data source will bring in all the tables it finds in the publishing database into the DataZone. By default, automated metadata generation is enabled, which means that any asset that the data source bring into the DataZone will automatically generate the business names of the table and columns for that asset. Choose Run in this data source.

Once the data source has finished running, you can see the catalog sales table in the Data Source Runs.

You can open this asset and see that the publishing job could automatically extract the technical metadata including the schema of the table and several other technical details such as AWS account, Region, and physical location of the data.

If they look correct, you can simply accept these recommendations either by clicking the brain icon in each recommended item or the Accept all button for all recommendations. When you are ready to publish, choose Publish asset and reconfirm in the dialog box.

4. Subscribe Data as a Data Consumer
Now let’s switch the role to a marketing team and see how you can subscribe to or request access this table. Repeat to create a new project called “Marketing consumer project” and a new environment called “subscriber-environment” as the data consumer using the same steps as before.

In the new created project, when you type “catalog sales” in the search bar, you can see the published table in the search results. Choose the Catalog Sales Data.

In the catalog, choose Subscribe.

In the Subscribe to Catalog Sales Data window, select your marketing consumer project, provide a reason for the subscription request, and then choose Subscribe.

When you get a subscription request as a data producer, it will notify you through a task in the sales producer project. Since you are acting as both subscriber and publisher here, you will see a notification.

When you click on this notification, it will open the subscription request including which project has requested access, who the requestor is, and why they need access. Choose Approve and provide a reason for approval.

Now that subscription has been approved, you can see catalog sales data in your marketing consumer project. To confirm this, choose the Data tab in the top menu and Data sources in the left navigation pane.


To analyze your subscribe data, choose the Environments tab in the top menu and Subscribe-environment you created in the marketing consumer project. It shows a new Query Data link in the right pane.

We can see that the catalog sales table is showing up under subscription database.
To make sure that we have access to this table, we can preview it and we can see that the query executes successfully.

This opens the Athena query editor in a new tab. Select subscribeenvironment_sub_db from the database dropdown, and then enter your query into the query editor.

You can now run any queries on the sales data table that you have subscribed to as a consumer (marketing team) and that was published into the business data catalog by a producer (sales team).

For more detailed demos such as publishing AWS Glue tables and Amazon Redshift tables and view, see the YouTube playlist.

What’s New at GA?
During the preview, we had lots of interest and great feedback from customers. I want to quickly review the features and introduce some improvements:

Enterprise-Ready Business Catalog – To add business context and make data discoverable by everyone in the organization, you can customize the catalog with automated metadata generation which uses machine learning to automatically generate business names of data assets and columns within those assets. We also improved metadata curation functionality. At GA, you can attach multiple business glossary terms to assets and glossary terms to individual columns in the asset.

Self-Service for Data Users – To provide data autonomy for users to publish and consume data, you can customize and bring any type of asset to the catalog using APIs. Data publishers can automate metadata discovery through ingestion jobs or manually publish files from Amazon Simple Storage Service (Amazon S3). Data consumers can use faceted search to quickly find and understand the data. Users can be notified of updates in the system or actions to be taken. These events are emitted to the customer’s event bus using Amazon EventBridge to customize actions.

Simplified Access to analysis – At GA, projects will serve as business use case-based logical containers. You can create a project and collaborate on specific business use case-based groupings of people, data, and analytics tools. Within the project, you can create an environment that provides the necessary infrastructure to project members such as analytics tools and storage so that project members can easily produce new data or consume data they have access to. This allows users to add multiple capabilities and analytics tools to the same project depending on their needs.

Governed Data Sharing – Data producers own and manage access to data with a subscription approval workflow that allows consumers to request access and data owners to approve. You can now set up subscription terms to be attached to assets when published and automate subscription grant fulfillment for AWS managed data lakes and Amazon Redshift with customizations using EventBridge events for other sources.

Now Available
Amazon DataZone is now generally available in eleven AWS Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (Stockholm), and South America (São Paulo).

You can use the free trial of Amazon DataZone, which includes 50 users at no additional cost for the first 3 calendar months of usage. The free trial starts when you first create an Amazon DataZone domain in an AWS account. If you exceed the number of monthly users during your trial, you will be charged at the standard pricing.

To learn more, visit the product page and user guide. You can send feedback to AWS re:Post for Amazon DataZone or through your usual AWS Support contacts.

— Channy

AWS Weekly Roundup – Amazon Bedrock Is Now Generally Available, Attend AWS Innovate Online, and More – Oct 2, 2023

Post Syndicated from Veliswa Boya original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-bedrock-is-now-generally-available-attend-aws-innovate-online-and-more-oct-2-2023/

Last week I attended the AWS Summit Johannesburg. This was the first summit to be hosted in my own country and my own city since 2019 so it was very special to have the opportunity to attend. It was great to get to meet with so many of our customers and hear how they are building on AWS.

Now on to the AWS updates. I’ve compiled a few announcements and upcoming events you need to know about. Let’s get started!

Last Week’s Launches
Amazon Bedrock Is Now Generally Available – Amazon Bedrock was announced in preview in April of this year as part of a set of new tools for building with generative AI on AWS. Last week’s announcement of this service being generally available was received with a lot of excitement and customers have already been sharing what they are building with Amazon Bedrock. I quite enjoyed this lighthearted post from AWS Serverless Hero Jones Zachariah Noel about the “Bengaluru with traffic-filled roads” image he produced using Stability AI’s Stable Diffusion XL image generation model on Amazon Bedrock.

Amazon MSK Introduces Managed Data Delivery from Apache Kafka to Your Data Lake – Amazon MSK was released in 2019 to help our customers reduce the work needed to set up, scale, and manage Apache Kafka in production. Now you can continuously load data from an Apache Kafka cluster to Amazon Simple Storage Service (Amazon S3).

Other AWS News
A few more news items and blog posts you might have missed:

The Community.AWS Blog is where builders share and learn with the community of cloud enthusiasts. Contributors to this blog include AWS employees, AWS Heroes, AWS Community Builders, and other members of the AWS Community. Last week, AWS Hero Johannes Koch published this awesome post on how to build a simple website using Flutter that interacts with a serverless backend powered by AppSync-merged APIs.

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

Upcoming AWS Events
We have the following upcoming events:

AWS Cloud Days (October 10, 24) – Connect and collaborate with other like-minded folks while learning about AWS at the AWS Cloud Day in Athens and Prague.

AWS Innovate Online (October 19) – Register for AWS Innovate Online to learn how you can build, run, and scale next-generation applications on the most extensive cloud platform. There will be 80+ sessions delivered in five languages and you’ll receive a certificate of attendance to showcase all you’ve learned.

We’re focused on improving our content to provide a better customer experience, and we need your feedback to do so. Take this quick survey to share insights on your experience with the AWS Blog. Note that this survey is hosted by an external company, so the link doesn’t lead to our website. AWS handles your information as described in the AWS Privacy Notice.

– Veliswa

Amazon Bedrock Is Now Generally Available – Build and Scale Generative AI Applications with Foundation Models

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/amazon-bedrock-is-now-generally-available-build-and-scale-generative-ai-applications-with-foundation-models/

This April, we announced Amazon Bedrock as part of a set of new tools for building with generative AI on AWS. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies, including AI21 Labs, Anthropic, Cohere, Stability AI, and Amazon, along with a broad set of capabilities to build generative AI applications, simplifying the development while maintaining privacy and security.

Today, I’m happy to announce that Amazon Bedrock is now generally available! I’m also excited to share that Meta’s Llama 2 13B and 70B parameter models will soon be available on Amazon Bedrock.

Amazon Bedrock

Amazon Bedrock’s comprehensive capabilities help you experiment with a variety of top FMs, customize them privately with your data using techniques such as fine-tuning and retrieval-augmented generation (RAG), and create managed agents that perform complex business tasks—all without writing any code. Check out my previous posts to learn more about agents for Amazon Bedrock and how to connect FMs to your company’s data sources.

Note that some capabilities, such as agents for Amazon Bedrock, including knowledge bases, continue to be available in preview. I’ll share more details on what capabilities continue to be available in preview towards the end of this blog post.

Since Amazon Bedrock is serverless, you don’t have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.

Amazon Bedrock is integrated with Amazon CloudWatch and AWS CloudTrail to support your monitoring and governance needs. You can use CloudWatch to track usage metrics and build customized dashboards for audit purposes. With CloudTrail, you can monitor API activity and troubleshoot issues as you integrate other systems into your generative AI applications. Amazon Bedrock also allows you to build applications that are in compliance with the GDPR and you can use Amazon Bedrock to run sensitive workloads regulated under the U.S. Health Insurance Portability and Accountability Act (HIPAA).

Get Started with Amazon Bedrock
You can access available FMs in Amazon Bedrock through the AWS Management Console, AWS SDKs, and open-source frameworks such as LangChain.

In the Amazon Bedrock console, you can browse FMs and explore and load example use cases and prompts for each model. First, you need to enable access to the models. In the console, select Model access in the left navigation pane and enable the models you would like to access. Once model access is enabled, you can try out different models and inference configuration settings to find a model that fits your use case.

For example, here’s a contract entity extraction use case example using Cohere’s Command model:

Amazon Bedrock

The example shows a prompt with a sample response, the inference configuration parameter settings for the example, and the API request that runs the example. If you select Open in Playground, you can explore the model and use case further in an interactive console experience.

Amazon Bedrock offers chat, text, and image model playgrounds. In the chat playground, you can experiment with various FMs using a conversational chat interface. The following example uses Anthropic’s Claude model:

Amazon Bedrock

As you evaluate different models, you should try various prompt engineering techniques and inference configuration parameters. Prompt engineering is a new and exciting skill focused on how to better understand and apply FMs to your tasks and use cases. Effective prompt engineering is about crafting the perfect query to get the most out of FMs and obtain proper and precise responses. In general, prompts should be simple, straightforward, and avoid ambiguity. You can also provide examples in the prompt or encourage the model to reason through more complex tasks.

Inference configuration parameters influence the response generated by the model. Parameters such as Temperature, Top P, and Top K give you control over the randomness and diversity, and Maximum Length or Max Tokens control the length of model responses. Note that each model exposes a different but often overlapping set of inference parameters. These parameters are either named the same between models or similar enough to reason through when you try out different models.

We discuss effective prompt engineering techniques and inference configuration parameters in more detail in week 1 of the Generative AI with Large Language Models on-demand course, developed by AWS in collaboration with DeepLearning.AI. You can also check the Amazon Bedrock documentation and the model provider’s respective documentation for additional tips.

Next, let’s see how you can interact with Amazon Bedrock via APIs.

Using the Amazon Bedrock API
Working with Amazon Bedrock is as simple as selecting an FM for your use case and then making a few API calls. In the following code examples, I’ll use the AWS SDK for Python (Boto3) to interact with Amazon Bedrock.

List Available Foundation Models
First, let’s set up the boto3 client and then use list_foundation_models() to see the most up-to-date list of available FMs:

import boto3
import json

bedrock = boto3.client(
    service_name='bedrock', 
    region='us-east-1'
)

bedrock.list_foundation_models()

Run Inference Using Amazon Bedrock’s InvokeModel API
Next, let’s perform an inference request using Amazon Bedrock’s InvokeModel API and boto3 runtime client. The runtime client manages the data plane APIs, including the InvokeModel API.

Amazon Bedrock

The InvokeModel API expects the following parameters:

{
    "modelId": <MODEL_ID>,
    "contentType": "application/json",
    "accept": "application/json",
    "body": <BODY>
}

The modelId parameter identifies the FM you want to use. The request body is a JSON string containing the prompt for your task, together with any inference configuration parameters. Note that the prompt format will vary based on the selected model provider and FM. The contentType and accept parameters define the MIME type of the data in the request body and response and default to application/json. For more information on the latest models, InvokeModel API parameters, and prompt formats, see the Amazon Bedrock documentation.

Example: Text Generation Using AI21 Lab’s Jurassic-2 Model
Here is a text generation example using AI21 Lab’s Jurassic-2 Ultra model. I’ll ask the model to tell me a knock-knock joke—my version of a Hello World.

bedrock_runtime = boto3.client(
    service_name='bedrock-runtime', 
    region='us-east-1'
)

modelId = 'ai21.j2-ultra-v1' 
accept = 'application/json'
contentType = 'application/json'

body = json.dumps(
    {"prompt": "Knock, knock!", 
     "maxTokens": 200,
     "temperature": 0.7,
     "topP": 1,
    }
)

response = bedrock_runtime.invoke_model(
    body=body, 
	modelId=modelId, 
	accept=accept, 
	contentType=contentType
)

response_body = json.loads(response.get('body').read())

Here’s the response:

outputText = response_body.get('completions')[0].get('data').get('text')
print(outputText)

Who's there? 
Boo! 
Boo who? 
Don't cry, it's just a joke!

You can also use the InvokeModel API to interact with embedding models.

Example: Create Text Embeddings Using Amazon’s Titan Embeddings Model
Text embedding models translate text inputs, such as words, phrases, or possibly large units of text, into numerical representations, known as embedding vectors. Embedding vectors capture the semantic meaning of the text in a high-dimension vector space and are useful for applications such as personalization or search. In the following example, I’m using the Amazon Titan Embeddings model to create an embedding vector.

prompt = "Knock-knock jokes are hilarious."

body = json.dumps({
    "inputText": prompt,
})

model_id = 'amazon.titan-embed-g1-text-02'
accept = 'application/json' 
content_type = 'application/json'

response = bedrock_runtime.invoke_model(
    body=body, 
    modelId=model_id, 
    accept=accept, 
    contentType=content_type
)

response_body = json.loads(response['body'].read())
embedding = response_body.get('embedding')

The embedding vector (shortened) will look similar to this:

[0.82421875, -0.6953125, -0.115722656, 0.87890625, 0.05883789, -0.020385742, 0.32421875, -0.00078201294, -0.40234375, 0.44140625, ...]

Note that Amazon Titan Embeddings is available today. The Amazon Titan Text family of models for text generation continues to be available in limited preview.

Run Inference Using Amazon Bedrock’s InvokeModelWithResponseStream API
The InvokeModel API request is synchronous and waits for the entire output to be generated by the model. For models that support streaming responses, Bedrock also offers an InvokeModelWithResponseStream API that lets you invoke the specified model to run inference using the provided input but streams the response as the model generates the output.

Amazon Bedrock

Streaming responses are particularly useful for responsive chat interfaces to keep the user engaged in an interactive application. Here is a Python code example using Amazon Bedrock’s InvokeModelWithResponseStream API:

response = bedrock_runtime.invoke_model_with_response_stream(
    modelId=modelId, 
    body=body)

stream = response.get('body')
if stream:
    for event in stream:
        chunk=event.get('chunk')
        if chunk:
            print(json.loads(chunk.get('bytes').decode))

Data Privacy and Network Security
With Amazon Bedrock, you are in control of your data, and all your inputs and customizations remain private to your AWS account. Your data, such as prompts, completions, and fine-tuned models, is not used for service improvement. Also, the data is never shared with third-party model providers.

Your data remains in the Region where the API call is processed. All data is encrypted in transit with a minimum of TLS 1.2 encryption. Data at rest is encrypted with AES-256 using AWS KMS managed data encryption keys. You can also use your own keys (customer managed keys) to encrypt the data.

You can configure your AWS account and virtual private cloud (VPC) to use Amazon VPC endpoints (built on AWS PrivateLink) to securely connect to Amazon Bedrock over the AWS network. This allows for secure and private connectivity between your applications running in a VPC and Amazon Bedrock.

Governance and Monitoring
Amazon Bedrock integrates with IAM to help you manage permissions for Amazon Bedrock. Such permissions include access to specific models, playground, or features within Amazon Bedrock. All AWS-managed service API activity, including Amazon Bedrock activity, is logged to CloudTrail within your account.

Amazon Bedrock emits data points to CloudWatch using the AWS/Bedrock namespace to track common metrics such as InputTokenCount, OutputTokenCount, InvocationLatency, and (number of) Invocations. You can filter results and get statistics for a specific model by specifying the model ID dimension when you search for metrics. This near real-time insight helps you track usage and cost (input and output token count) and troubleshoot performance issues (invocation latency and number of invocations) as you start building generative AI applications with Amazon Bedrock.

Billing and Pricing Models
Here are a couple of things around billing and pricing models to keep in mind when using Amazon Bedrock:

Billing – Text generation models are billed per processed input tokens and per generated output tokens. Text embedding models are billed per processed input tokens. Image generation models are billed per generated image.

Pricing Models – Amazon Bedrock offers two pricing models, on-demand and provisioned throughput. On-demand pricing allows you to use FMs on a pay-as-you-go basis without having to make any time-based term commitments. Provisioned throughput is primarily designed for large, consistent inference workloads that need guaranteed throughput in exchange for a term commitment. Here, you specify the number of model units of a particular FM to meet your application’s performance requirements as defined by the maximum number of input and output tokens processed per minute. For detailed pricing information, see Amazon Bedrock Pricing.

Now Available
Amazon Bedrock is available today in AWS Regions US East (N. Virginia) and US West (Oregon). To learn more, visit Amazon Bedrock, check the Amazon Bedrock documentation, explore the generative AI space at community.aws, and get hands-on with the Amazon Bedrock workshop. You can send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS contacts.

(Available in Preview) The Amazon Titan Text family of text generation models, Stability AI’s Stable Diffusion XL image generation model, and agents for Amazon Bedrock, including knowledge bases, continue to be available in preview. Reach out through your usual AWS contacts if you’d like access.

(Coming Soon) The Llama 2 13B and 70B parameter models by Meta will soon be available via Amazon Bedrock’s fully managed API for inference and fine-tuning.

Start building generative AI applications with Amazon Bedrock, today!

— Antje

AWS Weekly Roundup: Amazon EC2 M2 Pro Mac, Amazon Coretto 21, Amazon CloudWatch Synthetics, and more (Sept. 25, 2023)

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-ec2-m2-pro-mac-amazon-coretto-21-amazon-cloudwatch-synthetics-and-more-sept-25-2023/

This week, I’m in Jakarta to support AWS User Group Indonesia and AWS Cloud Day Indonesia. Yesterday, I attended a community event – a collaboration between AWS User Group Indonesia and Hacktiv8 with “Innovating Yourself as Early-Stage Developers” as the main theme. We had a blast and I had a wonderful time connecting with speakers and developers.

Next up, AWS Cloud Day Indonesia. I’ll be at the Developer Lounge, come and say hi!

Last Week’s Launches
Here are some of the launches that caught my attention last week:

Add Your Swift Packages to AWS CodeArtifact – In this article, Seb describes how Swift developers who write code for Apple platforms (iOS, iPadOS, macOS, tvOS, watchOS, visionOS or Swift) applications running on the server side can use AWS CodeArtifact to securely store and retrieve their package dependencies. What I really like is how developers can still use standard developer tools, such as Xcode, xcodebuild, and the Swift Package Manager (the swift package command) to interact with AWS CodeArtifact and facilitate integration into the development workflow.

Amazon EC2 M2 Pro Mac Instances Built on Apple Silicon M2 Pro Mac Mini Computers – Channy wrote how developers can use Amazon EC2 M2 Pro Mac to run memory intensive builds and test workloads, modernize their CI/CD and accelerate their product time to market. With 2x RAM, 1.5x CPU cores, and more than 2x GPU cores compared to EC2 M1 Mac instances, Apple developers can now run more tests in parallel using multiple Xcode simulators.

Synthetics Python runtime version 2.0 for Amazon CloudWatch Synthetics – With Amazon CloudWatch Synthetics, you can continually verify your customer experience and discover issues before your customers do by creating canaries. Canaries are configurable scripts that run on a schedule, to monitor your endpoints and APIs. In this announcement, you can use Synthetics Python runtime version syn-python-selenium-2.0 to create canaries.

Amazon QuickSight adds new layout and sparkline to KPI visual – Effortlessly design visually appealing KPIs on Amazon Quicksight with these new updates. Quicksight introduces a range of enhancements with user-friendly experience, including templated KPI layouts, support for sparklines, improvements in conditional formatting, and a revamped format pane.

Amazon Location Services announces a price reduction of up to 75 percent for tracking and geofencing – Amazon Location Service just announced a four-tiered pricing model for tracking and geofencing to help you scale and cost-effectively run your operations and business. If you use geofencing, you might see your bill decrease by 20 percent to 70 percent, and tracking by up to 75 percent.

Amazon Corretto 21 is now generally available – Happy news for Java developers. Amazon Coretto 21 with long term support (LTS) is generally available for Linux, Windows and macOS.

AWS App Runner launches improvements for Auto-Scaling configuration management – Now you can use new APIs and parameters for AWS App Runner service to manage your App Runner services and define your auto-scaling configuration (ASC). For example, setting default ASC, update existing ASC and list all App Runner services that are using an ASC resource.

Amazon SNS message data protection with redaction or masking – With Amazon SNS, now you can discover and protect certain types of personally identifiable information (PII) and protected health information (PHI). You can define your data protection policies and SNS will scan messages in real-time for sensitive data.

Upcoming AWS and Community Events
Check your calendars and sign up for these AWS events:

And let’s learn from our fellow builders and join AWS Community Days:

  • AWS Community Day Zimbabwe (Sept. 30),
  • AWS Community Day Chile (Sept. 30),
  • AWS Community Day Bulgaria Bulgaria (Oct. 7).

Visit the landing page to check out all the upcoming AWS Community Days.

Happy building!
— Donnie

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

New – Amazon EC2 M2 Pro Mac Instances Built on Apple Silicon M2 Pro Mac Mini Computers

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/new-amazon-ec2-m2-pro-mac-instances-built-on-apple-silicon-m2-pro-mac-mini-computers/

Today, we are announcing the general availability of Amazon EC2 M2 Pro Mac instances. These instances deliver up to 35 percent faster performance over the existing M1 Mac instances when building and testing applications for Apple platforms.

New EC2 M2 Pro Mac instances are powered by Apple M2 Pro Mac Mini computers featuring 12 core CPU, 19 core GPU, 32 GiB of memory, and 16 core Apple Neural Engine and uniquely enabled by the AWS Nitro System through high-speed Thunderbolt connections, offering these Mac mini computers as fully integrated and managed compute instances with up to 10 Gbps of Amazon VPC network bandwidth and up to 8 Gbps of Amazon EBS storage bandwidth. EC2 M2 Pro Mac instances support macOS Ventura (version 13.2 or later) as AMIs.

A Story of EC2 Mac Instances
When Jeff Barr first introduced Amazon EC2 Mac Instances in 2020, customers were surprised to be able to run macOS on Amazon EC2 to build, test, package, and sign applications developed with Xcode applications for the Apple platform, including macOS, iOS, iPadOS, tvOS, and watchOS.

In his keynote in AWS re:Invent 2020, Peter DeSantis revealed the secret to build EC2 Mac instances powered by the AWS Nitro System, which makes it possible to offer Apple Mac mini computers as fully integrated and managed compute instances with Amazon VPC networking and Amazon EBS storage, just like any other EC2 instances.

“We did not need to make any changes to the Mac hardware. We simply connected a Nitro controller via the Mac’s Thunderbolt connection. When you launch a Mac instance, your Mac-compatible Amazon Machine Image (AMI) runs directly on the Mac Mini, with no hypervisor. The Nitro controller sets up the instance and provides secure access to the network and any storage attached. And that Mac Mini can now natively use any AWS service.”

In July 2022, we introduced Amazon EC2 M1 Mac Instances built around the Apple-designed M1 System on Chip (SoC). Developers building for iPhone, iPad, Apple Watch, and Apple TV applications can choose either x86-based EC2 Mac instances or Arm-based EC2 M1 instances. If you want to re-architect your apps to natively support Macs with Apple Silicon using EC2 M1 instances, you can build and test your apps to deliver up to 60 percent better price performance over the EC2 Mac instances for iPhone and Mac app build workloads with all the benefits of AWS.

Many customers take advantage of EC2 Mac instances to deliver a complete end-to-end build pipeline on macOS on AWS. With EC2 Mac instances, they can scale their iOS build fleet; easily use custom macOS environments with AMIs; and debug any build or test failures with fully reproducible macOS environments.

Customers have reported up to 4x reduction in build times, up to 3x increase in parallel builds, up to 80 percent reduction in machine-related build failures, and up to 50 percent reduction in fleet size. They can continue to prioritize their time on innovating products and features while reducing the tedious effort required to manage on-premises macOS infrastructure.

To accelerate this innovation, EC2 Mac instances recently began to support replacing root volumes on a running EC2 Mac instance, enabling you to restore the root volume of an EC2 Mac instance to its initial launch state or to a specific snapshot, without requiring you to stop or terminate the instance.

You can also use in-place operating system updates from within the guest environment on EC2 M1 Mac instances to a specific or latest macOS version, including the beta version, by registering your instances with the Apple Developer Program. Developers can now integrate the latest macOS features into their applications and test existing applications for compatibility before public macOS releases.

Getting Started with EC2 M2 Pro Instances
As with other EC2 Mac instances, EC2 M2 Pro Mac instances also support Dedicated Host tenancy with a minimum host allocation duration of 24 hours to align with macOS licensing.

To get started, you should allocate a Mac-dedicated host, a physical server fully dedicated for your own use in your AWS account. After the host is allocated, you can launch, stop, and start your own macOS environment as one instance on that host for one dedicated host.

After the host is allocated, you can start an EC2 Mac instance on it. The procedure is no different from starting any EC2 instance type. Choose your macOS AMI version and select the mac2-m2pro.metal instance type in the Application and OS Images section.

In the Advanced details section, select Dedicated host in Tenancy and a dedicated host you just created in Tenancy host ID.

When you use EC2 Mac instances for the first time, you can use SSH to connect to the newly launched instance as usual or enable Apple Remote Desktop and start a VNC session to the EC2 instance. To learn more, see Sebastien’s series of articles to launch and connect your Mac instance.

When you no longer need the Mac dedicated host, you can terminate your running Mac instance and release the underlying host. Note again that after being allocated, a Mac dedicated host can only be released after 24 hours to align with Apple’s macOS licensing.

Now Available
Amazon EC2 M2 Pro Mac instances are available in the US West (Oregon) and US East (Ohio) AWS Regions, with additional regions coming soon.

To learn more or get started, see Amazon EC2 Mac Instances or visit the EC2 Mac documentation.  You can send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

— Channy

New – NVMe Reservations for Amazon Elastic Block Store io2 Volumes

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-nvme-reservations-for-amazon-elastic-block-store-io2-volumes/

Amazon Elastic Block Store (Amazon EBS) io2 and io2 Block Express volumes now support storage fencing using NVMe reservations. As I learned while writing this post, storage fencing is used to regulate access to storage for a compute or database cluster, ensuring that just one host in the cluster has permission to write to the volume at any given time. For example, you can set up SQL Server Failover Cluster Instances (FCI) and get higher application availability within a single Availability Zone without the need for database replication.

As a quick refresher, io2 Block Express volumes are designed to meet the needs of the most demanding I/O-intensive applications running on Nitro-based Amazon Elastic Compute Cloud (Amazon EC2) instances. Volumes can be as big as 64 TiB, and deliver SAN-like performance with up to 256,000 IOPS/volume and 4,000 MB/second of throughput, all with 99.999% durability and sub-millisecond latency. The volumes support other advanced EBS features including encryption and Multi-Attach, and can be reprovisioned online without downtime. To learn more, you can read Amazon EBS io2 Block Express Volumes with Amazon EC2 R5b Instances Are Now Generally Available.

Using Reservations
To make use of reservations, you simply create an io2 volume with Multi-Attach enabled, and then attach it to one or more Nitro-based EC2 instances (see Provisioned IOPS Volumes for a full list of supported instance types):

If you have existing io2 Block Express volumes, you can enable reservations by detaching the volumes from all of the EC2 instances, and then reattaching them. Reservations will be enabled as soon as you make the first attachment. If you are running Windows Server using AMIs data-stamped 2023.08 or earlier you will need to install the aws_multi_attach driver as described in AWS NVMe Drivers for Windows Instances.

Things to Know
Here are a couple of things to keep in mind regarding NVMe reservations:

Operating System Support – You can use NVMe reservations with Windows Server (2012 R2 and above, 2016, 2019, and 2022), SUSE SLES 12 SP3 and above, RHEL 8.3 and above, and Amazon Linux 2 & later (read NVMe reservations to learn more).

Cluster and Volume Managers – Windows Server Failover Clustering is supported; we are currently working to qualify other cluster and volume managers.

Charges – There are no additional charges for this feature. Each reservation counts as an I/O operation.

— Jeff;

AWS Weekly Roundup: C7i Instances, Knowledge Base for Amazon Bedrock, and More (Sept. 18, 2023)

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-c7i-instances-knowledge-base-for-amazon-bedrock-and-more-sept-18-2023/

While daylight is getting shorter in the Northern hemisphere, we’ve got two new EC2 instance types optimized for compute and memory and many new capabilities for other services. Last week there was also the EMEA AWS Heroes Summit in Munich, an amazing day full of insights and passion. Here’s a nice picture of the participants!

AWS Heroes Summit EMEA 2023 in Munich

Last Week’s Launches
Here are some of the launches that caught my attention last week:

C7i Instances – Powered by custom 4th Generation Intel Xeon Scalable processors (code-named Sapphire Rapids) and available only on AWS, these compute-optimized instances offer up to 15 percent better performance over comparable x86-based Intel processors used by other cloud providers. A great choice for all compute-intensive workloads, such as batch processing, distributed analytics, high performance computing (HPC), ad serving, highly scalable multiplayer gaming, and video encoding, C7i instances deliver up to 15 percent better price performance versus C6i instances.

vCPUs
 Memory (GiB)
 Network Bandwidth
 EBS Bandwidth
c7i.large 2 4 Up to 12.5 Gbps Up to 10 Gbps
c7i.xlarge 4 8 Up to 12.5 Gbps Up to 10 Gbps
c7i.2xlarge 8 16 Up to 12.5 Gbps Up to 10 Gbps
c7i.4xlarge 16 32 Up to 12.5 Gbps Up to 10 Gbps
c7i.8xlarge 32 64 12.5 Gbps 10 Gbps
c7i.12xlarge 48 96 18.75 Gbps 15 Gbps
c7i.16xlarge 64 128 25 Gbps 20 Gbps
c7i.24xlarge 96 192 37.5 Gbps 30 Gbps
c7i.48xlarge 192 384 50 Gbps 40 Gbps
c7i.metal-24xl* 96 192 37.5 Gbps 30 Gbps
c7i.metal-48xl* 192 384 50 Gbps 40 Gbps

*Bare metal instances are coming soon.

To facilitate efficient offload and acceleration of data operations and optimize performance for workloads, C7i instances support built-in Intel accelerators such as Data Streaming Accelerator (DSA), In-Memory Analytics Accelerator (IAA), QuickAssist Technology (QAT), and the new Intel Advanced Matrix Extensions (AMX) that accelerate matrix multiplication operations for applications such as CPU-based ML.

EC2 R7a Instances – Powered by 4th Gen AMD EPYC processors (code-named Genoa) with a maximum frequency of 3.7 GHz, these memory optimized instances deliver up to 50 percent higher performance compared to R6a instances and are ideal for high performance, memory-intensive workloads such as SQL and NoSQL databases, distributed web scale in-memory caches, in-memory databases, real-time big data analytics, and Electronic Design Automation (EDA) applications. Read more in Channy’s blog post.

Knowledge Base for Amazon Bedrock (Preview) – To deliver more relevant and contextual responses, Bedrock can now manage both the ingestion workflow and runtime orchestration to connect your organization’s private data sources to foundation models (FMs) and enable retrieval augmented generation (RAG) for your generative AI applications. To store data, you can choose from a range of vector databases including the vector engine for Amazon OpenSearch Serverless, Pinecone, and Redis Enterprise Cloud. Read more in Antje’s blog post.

High Query Rates with Amazon OpenSearch Serverless Extends Auto-Scaling – You can now rely on OpenSearch Serverless to help manage unpredictable surges in your search and query traffic and efficiently handle tens of thousands of query transactions per minute.

Amazon EMR on EKS – You can now improve resource utilization and simplify infrastructure management by using EMR to run Apache Flink (Public Preview) on the same Amazon EKS cluster as your other applications. Also, to provide a secure, stable, high-performance environment with the latest enhancements such as kernel, toolchain, glibc, and openssl, you can now use Amazon Linux 2023 as the operating system together with Java 17 as Java runtime to run your workloads with Amazon EMR on EKS.

Amazon Connect – Amazon Connect Cases now supports uploading attachments to a case, enabling agents to have the information they need at their fingertips in order to resolve cases, and displaying the author name for comments that are written on cases, to more easily track who contributed to the resolution of the case and collaborate more effectively. To receive near real-time stream of contact (voice calls, chat, and task) events (for example, call is queued) in a contact center, you can now subscribe to the new Contact Data Updated event.

Custom Notifications for AWS Chatbot – This lets you include additional information, such as number of orders or current throttling limits, when monitoring the health and performance of your AWS applications in Microsoft Teams and Slack channels.

AWS IAM Identity Center Session Duration Increased Up to 90 Days – You now have more flexibility based on your security context and desired end-user experience. Previously, the maximum duration was 7 days. The default session duration continues to be 8 hours and existing customer-configured session limits will remain unchanged.

Full Support of GraphQL APIs in Amplify Studio – You can now generate forms connected to your API, manage records in your API with Data Manager, and create data-bound Figma to React components for GraphQL APIs created with Amplify Studio or Amplify CLI. Previously, these data-powered features were only available when using Amplify DataStore.

Nested Filtering for AWS AppSync WebSockets-Based Subscriptions – You now have additional control over how data should be published out to connected clients by using filtering rules that allow you to target specific sub-items within the published data. Read more in this blog post.

API Gateway Console Refresh – There are usability improvements to REST and WebSocket API workflows (now visually aligned with the console experience of HTTP APIs) and dark mode support. Accessibility enhancements also help to better integrate with assistive technology.

Override Retention Capability for AWS Supply Chain – Manual forecast adjustments made by a demand planner are now automatically saved and reapplied from one planning cycle to the next.

Other AWS News

Serverless Development on AWS – Book CoverServerless Development on AWS – AWS Hero Sheen Brisals and his colleague Luke Hedger revealed that they are sharing their expertise with a book that helps build enterprise-scale serverless solutions on AWS. The book outlines the adoption requirements in terms of people, mindset, and workloads, and details architectural patterns, security, and data best practices for building serverless applications.

More posts from AWS blogs – Here are a few posts from some of the other AWS and cloud blogs that I follow:

Upcoming AWS Events
Check your calendars and sign up for these AWS events:

AWS On Tour, Sept. 18-Oct. 6 – The AWS Developer Relations team is boarding a bus and traveling across European cities (London, Paris, Brussels, Amsterdam, Frankfurt, Zurich, Milan, Lyon, and Barcelona) to share their experiences and help you improve productivity.

AWS Global Summits, Sept. 26 – The last in-person AWS Summit of the year will be held in Johannesburg on Sept. 26.

CDK Day, Sept. 29 – Learn more at the website about this community-led fully virtual event with tracks in English and Spanish about CDK and related projects.

AWS re:Invent, Nov. 27-Dec. 1 – Browsing the session catalog is a nice way to start planning your re:Invent. Join us to hear the latest from AWS, learn from experts, and connect with the global cloud community.

AWS Community Days – Join a community-led conference run by AWS user group leaders in your region: Netherlands (Sept. 20), Spain (Sept. 23), Zimbabwe (Sept. 30), Peru (Sept. 30), Chile (Sept. 30), and Bulgaria (Oct. 7). Visit the landing page to check out all the upcoming AWS Community Days.

You can browse all upcoming AWS-led in-person and virtual events, and developer-focused events such as AWS DevDay.

— 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!

New – Amazon EC2 R7a Instances Powered By 4th Gen AMD EPYC Processors for Memory Optimized Workloads

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/new-amazon-ec2-r7a-instances-powered-by-4th-gen-amd-epyc-processors-for-memory-optimized-workloads/

We launched the memory optimized Amazon EC2 R6a instances in July 2022 powered by 3rd Gen AMD EPYC (Milan) processors, running at frequencies up to 3.6 GHz. Many customers who run workloads that are dependent on x86 instructions, such as SAP, are looking for ways to optimize their cloud utilization. They’re taking advantage of the compute choice that EC2 offers.

Today, we’re announcing the general availability of new memory optimized Amazon EC2 R7a instances powered by 4th Gen AMD EPYC (Genoa) processors with a maximum frequency of 3.7 GHz, which offer up to 50 percent higher performance compared to the previous generation instances. You can use this increased performance to process data faster, consolidate workloads, and lower the cost of ownership.

R7a instances also support AVX-512, Vector Neural Network Instructions (VNNI), and brain floating point (bfloat16). These instances feature Double Data Rate 5 (DDR5) memory, which enables high-speed access to data in-memory, and deliver 2.25 times more memory bandwidth compared to R6a instances for lower latency. Moreover, these instances support always-on memory encryption using AMD secure memory encryption (SME).

These instances are SAP-certified and ideal for high performance, memory-intensive workloads, such as SQL and NoSQL databases, distributed web scale in-memory caches, in-memory databases, real-time big data analytics, and Electronic Design Automation (EDA) applications.

R7a instances feature sizes of up to 192 vCPUs with 1536 GiB RAM. Here are the detailed specs:

Name vCPUs Memory (GiB) Network Bandwidth (Gbps) EBS Bandwidth (Gbps)
r7a.medium 1 8 Up to 12.5 Up to 10
r7a.large 2 16 Up to 12.5 Up to 10
r7a.xlarge 4 32 Up to 12.5 Up to 10
r7a.2xlarge 8 64 Up to 12.5 Up to 10
r7a.4xlarge 16 128 Up to 12.5 Up to 10
r7a.8xlarge 32 256 12.5 10
r7a.12xlarge 48 384 18.75 15
r7a.16xlarge 64 512 25 20
r7a.24xlarge 96 768 37.5 30
r7a.32xlarge 128 1024 50 40
r7a.48xlarge 192 1536 50 40

R7a instances have up to 50 Gbps enhanced networking and 40 Gbps EBS bandwidth, which is similar to R6a instances. You have a new medium instance size, which you can use to right-size your workloads more accurately, offering 1 vCPUs, 8 GiB. Additionally, with R7a instances you can attach up to 128 EBS volumes to an instance compared to up to 28 EBS volume attachments with R6a instances. R7a instances support AES-256 compared to AES-128 in R6a instances for enhanced security.

R7a instances are built on the AWS Nitro System and support Elastic Fabric Adapter (EFA) for workloads that benefit from lower network latency and highly scalable inter-node communication, such as high-performance computing and video processing.

Now Available
Amazon EC2 R7a instances are now available in AWS Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), and EU (Ireland). As usual with Amazon EC2, you only pay for what you use. For more information, see the Amazon EC2 pricing page.

To learn more, visit the EC2 R7a instances page, and AWS/AMD partner page. You can send feedback to [email protected], AWS re:Post for EC2, or through your usual AWS Support contacts.

— Channy

AWS Weekly Roundup: R7iz Instances, Amazon Connect, CloudWatch Logs, and Lots More (Sept. 11, 2023)

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-r7iz-instances-amazon-connect-cloudwatch-logs-and-lots-more-sept-11-2023/

Looks like it is my turn once again to write the AWS Weekly Roundup. I wrote and published the first one on April 16, 2012 — just 4,165 short day ago!

Last Week’s Launches
Here are some of the launches that caught my eye last week:

R7iz Instances – Optimized for high CPU performance and designed for your memory-intensive workloads, these instances are powered by the fastest 4th Generation Intel Xeon Scalable-based (Sapphire Rapids) instances in the cloud. They are available in eight sizes, with 2 to 128 vCPUs and 16 to 1024 GiB of memory, along with generous allocations of network and EBS bandwidth:

vCPUs
 Memory (GiB)
 Network Bandwidth
 EBS Bandwidth
r7iz.large 2 16 Up to 12.5 Gbps Up to 10 Gbps
r7iz.xlarge 4 32 Up to 12.5 Gbps Up to 10 Gbps
r7iz.2xlarge 8 64 Up to 12.5 Gbps Up to 10 Gbps
r7iz.4xlarge 16 128 Up to 12.5 Gbps Up to 10 Gbps
r7iz.8xlarge 32 256 12.5 Gbps 10 Gbps
r7iz.12xlarge 48 384 25 Gbps 19 Gbps
r7iz.16xlarge 64 512 25 Gbps 20 Gbps
r7iz.32xlarge 128 1024 50 Gbps 40 Gbps

As Veliswa shared in her post, the R7iz instances also include four built-in accelerators, and are available in two AWS regions.

Amazon Connect APIs for View Resources – A new set of View APIs allows you to programmatically create and manage the view resources (UI templates) used in the step-by-step guides that are displayed in the agent’s UI.

Daily Disbursements to Marketplace Sellers – Sellers can now set disbursement preferences and opt-in to receiving outstanding balances on a daily basis for increased flexibility, including the ability to match payments to existing accounting processes.

Enhanced Error Handling for AWS Step Functions – You can now construct detailed error messages in Step Functions Fail states, and you can set a maximum limit on retry intervals.

Amazon CloudWatch Logs RegEx Filtering – You can now use regular expressions in your Amazon CloudWatch Logs filter patterns. You can, for example, define a single filter that matches multiple IP subnets or HTTP status codes instead of having to use multiple filters, as was previously the case. Each filter pattern can have up to two regular expression patterns.

Amazon SageMaker – There’s a new (and quick) Studio setup experience, support for Multi Model Endpoints for PyTorch, and the ability to use SageMaker’s geospatial capabilities on GPU-based instances when using Notebooks.

X in Y – We launched existing services and instance types in new regions:

Other AWS News
Here are some other AWS updates and news:

AWS Fundamentals – The second edition of this awesome book, AWS for the Real World, Not for Certifications, is now available. In addition to more than 400 pages that cover 16 vital AWS services, each chapter includes a detailed and attractive infographic. Here’s a small-scale sample:

More posts from AWS blogs  – Here are a few posts from some of the other AWS and cloud blogs that I follow:

Upcoming AWS Events
Check your calendars and sign up for these AWS events:

AWS End User Computing Innovation Day, Sept. 13 – The one-day virtual event is designed to help IT teams tasked with providing the tools employees need to do their jobs, especially in today’s challenging times. Learn more.

AWS Global Summits, Sept. 26 – The last in-person AWS Summit will be held in Johannesburg on Sept. 26th. You can also watch on-demand videos of the latest Summit events such as Berlin, Bogotá, Paris, Seoul, Sydney, Tel Aviv, and Washington DC in the AWS YouTube channels.

CDK Day, Sept. 29 – A community-led fully virtual event with tracks in English and Spanish about CDK and related projects. Learn more at the website.

AWS re:Invent, Nov. 27-Dec. 1 – AWS re:Invent 2023Ready to start planning your re:Invent? Browse the session catalog now. Join us to hear the latest from AWS, learn from experts, and connect with the global cloud community.

AWS Community Days, multiple dates AWS Community Day– Join a community-led conference run by AWS user group leaders in your region: Munich (Sept. 14), Argentina (Sept. 16), Spain (Sept. 23), Peru (Sept. 30), and Chile (Sept. 30). Visit the landing page to check out all the upcoming AWS Community Days.

You can browse all upcoming AWS-led in-person and virtual events, and developer-focused events such as AWS DevDay.

— Jeff;

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

The newest AWS Heroes are here – September 2023

Post Syndicated from Taylor Jacobsen original https://aws.amazon.com/blogs/aws/the-newest-aws-heroes-are-here-september-2023/

Each quarter, the AWS Heroes program recognizes technical enthusiasts who lift up the greater AWS community through various approaches. While these inspirational individuals are driven to knowledge share, they sometimes discover novel and fun ways of using technology, such as leveraging LEDs to create a magical display of holiday lights. Many are also contributing heavily in their local communities by leading user groups, bootcamps, and workshops, speaking at conferences to share solutions, and beyond.

Without further ado, we’re eager to introduce the latest cohort of Heroes to the world—let’s give them a grand welcome!

Alex Lau – Hong Kong

Community Hero Alex Lau is a Lead Instructor of Tecky Academy with a focus on full stack, mobile apps, and AWS technologies. Enthusiastic about teaching and sharing, Alex has been an active leader in the Hong Kong developer community since 2015. He has organized annual hackathons and founded a coding bootcamp, growing the community to over 1,000 members. Earlier this year, he took the stage at the AWS Summit Hong Kong to introduce the cutting edge of AWS technologies, and also led a session during the Hong Kong AWS GenAI Solution Day.

Brian H. Hough– Boston, USA

DevTools Hero Brian H. Hough is the founder of the Tech Stack Playbook®, a software engineering firm serving enterprise and startup clients, and a media brand with over 10k+ followers. His talks, presentations, and work have been featured by AWS, freeCodeCamp, MongoDB, and NASA. Brian has also served as a mentor for AWS’ All Builders Welcome Grant Program and other tech communities, as he enjoys lifting up the voices of builders and empowering everyone to build the future they want to see in the world. In addition, he has spoken about full-stack development, microservices, MLOps, and Infrastructure as Code at conferences including, AWS re:Invent, AWS Summit New York, Geekle’s Worldwide Software Architecture Summit, DataSaturday, and more.

Dheeraj Choudhary – Maharashtra, India

Community Hero Dheeraj Choudhary is a lead engineer focused on the AWS cloud and the DevOps domain with over 10+ years of IT experience. He specializes in DevOps and build and release engineering, and software configuration management. As an AWS User Group Pune leader, he is passionate about co-organizing physical meetups and AWS Community Days. Additionally, Dheeraj is an active international speaker at AWS community events, and conducts guest lectures and workshops on AWS cloud computing at colleges and universities in Pune.

Evandro Pires – Blumenau, Brazil

Serverless Hero Evandro Pires is a CTO who started programming when he was 12 years old. His background is in technology and entrepreneurship, and he has led important projects in internet and mobile banking, and AI and low code for SaaS solutions. Since 2020, Evandro founded and hosts a podcast dedicated to serverless called, “Sem Servidor.” Evandro is also the organizer of the first ServerlessDays in LATAM.

Kazuki Miura – Hokkaido, Japan

Community Hero Kazuki Miura is a senior engineer at Hokkaido Television Broadcasting Co., Ltd. (HTB). He is involved in the development and operation of the company’s video on demand service and e-commerce service. Kazuki continues to share his knowledge gained through the development of web services widely with the Japanese AWS User Group (JAWS-UG).

Linda Mohamed – Vienna, Austria

Community Hero Linda Mohamed has been navigating the tech landscape for over a decade. She is currently at EBCONT where her primary focus and specialization is in cloud technologies, IT process optimization, and agile methodologies. Linda also holds the title of Chairperson for the AWS Community DACH Support Association, and is an active member of a funding advisory board. When she is not guiding companies on their cloud journey, she is diving into AI/ML services and technologies, and sharing her insights at AWS community events and other tech platforms.

Monica Colangelo– Milan, Italy

DevTools Hero Monica Colangelo is a principal cloud architect with 15-years in the IT industry. Her experience spans across operations, infrastructure, and notably, DevOps. Automation and operational excellence have always been central to her work, guiding her approach and solutions. Monica is also a regular speaker at tech conferences, sharing her expertise and insights. Furthermore, she is an advocate for diversity and emphasizes the need for a stronger representation of women in the tech sector.

Nick Triantafillou – Wollongong, Australia

Community Hero Nick Triantafillou is a cloud engineer, educator, User Group founder, and Christmas Light enthusiast. He was one of the original course instructors at the cloud education startup A Cloud Guru, having taught over 1 million students the fundamentals of AWS, and produced the world’s first AWS Certified DevOps Engineer course. He is also the founder of his local Wollongong AWS User Group, co-founder of the Sydney Serverless Meetup, and has assisted in the planning and operation of both the ServerlessConf and ServerlessDays ANZ conferences. He currently runs “NickExplainsAWS,” where he is attempting to make a video about every single AWS service on TikTok and YouTube. In addition, every December Nick brings traffic to a standstill by installing over 75,000 LEDs on his house for his serverless, AWS powered light show spectacular.

Learn More

If you’d like to learn more about the new Heroes or connect with a Hero near you, please visit the AWS Heroes website or browse the AWS Heroes Content Library.

— Taylor