Tag Archives: launch

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

Announcing Amazon Managed Service for Apache Flink Renamed from Amazon Kinesis Data Analytics

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/announcing-amazon-managed-service-for-apache-flink-renamed-from-amazon-kinesis-data-analytics/

Today we are announcing the rename of Amazon Kinesis Data Analytics to Amazon Managed Service for Apache Flink, a fully managed and serverless service for you to build and run real-time streaming applications using Apache Flink.

We continue to deliver the same experience in your Flink applications without any impact on ongoing operations, developments, or business use cases. All your existing running applications in Kinesis Data Analytics will work as is without any changes.

Many customers use Apache Flink for data processing, including support for diverse use cases with a vibrant open-source community. While Apache Flink applications are robust and popular, they can be difficult to manage because they require scaling and coordination of parallel compute or container resources. With the explosion of data volumes, data types, and data sources, customers need an easier way to access, process, secure, and analyze their data to gain faster and deeper insights without compromising on performance and costs.

Using Amazon Managed Service for Apache Flink, you can set up and integrate data sources or destinations with minimal code, process data continuously with sub-second latencies from hundreds of data sources like Amazon Kinesis Data Streams and Amazon Managed Streaming for Apache Kafka (Amazon MSK), and respond to events in real-time. You can also analyze streaming data interactively with notebooks in just a few clicks with Amazon Managed Service for Apache Flink Studio with built-in visualizations powered by Apache Zeppelin.

With Amazon Managed Service for Apache Flink, you can deploy secure, compliant, and highly available applications. There are no servers and clusters to manage, no compute and storage infrastructure to set up, and you only pay for the resources your applications consume.

A History to Support Apache Flink
Since we launched Amazon Kinesis Data Analytics based on a proprietary SQL engine in 2016, we learned that SQL alone was not sufficient to provide the capabilities that customers needed for efficient stateful stream processing. So, we started investing in Apache Flink, a popular open-source framework and engine for processing real-time data streams.

In 2018, we provided support for Amazon Kinesis Data Analytics for Java as a programmable option for customers to build streaming applications using Apache Flink libraries and choose their own integrated development environment (IDE) to build their applications. In 2020, we repositioned Amazon Kinesis Data Analytics for Java to Amazon Kinesis Data Analytics for Apache Flink to emphasize our continued support for Apache Flink. In 2021, we launched Kinesis Data Analytics Studio (now, Amazon Managed Service for Apache Flink Studio) with a simple, familiar notebook interface for rapid development powered by Apache Zeppelin and using Apache Flink as the processing engine.

Since 2019, we have worked more closely with the Apache Flink community, increasing code contributions in the area of AWS connectors for Apache Flink such as those for Kinesis Data Streams and Kinesis Data Firehose, as well as sponsoring annual Flink Forward events. Recently, we contributed Async Sink to the Flink 1.15 release, which improved cloud interoperability and added more sink connectors and formats, among other updates.

Beyond connectors, we continue to work with the Flink community to contribute availability improvements and deployment options. To learn more, see Making it Easier to Build Connectors with Apache Flink: Introducing the Async Sink in the AWS Open Source Blog.

New Features in Amazon Managed Service for Apache Flink
As I mentioned, you can continue to run your existing Flink applications in Kinesis Data Analytics (now Amazon Managed Apache Flink) without making any changes. I want to let you know about a part of the service along with the console change and new feature,  a blueprint where you create an end-to-end data pipeline with just one click.

First, you can use the new console of Amazon Managed Service for Apache Flink directly under the Analytics section in AWS. To get started, you can easily create Streaming applications or Studio notebooks in the new console, with the same experience as before.

To create a streaming application in the new console, choose Create from scratch or Use a blueprint. With a new blueprint option, you can create and set up all the resources that you need to get started in a single step using AWS CloudFormation.

The blueprint is a curated collection of Apache Flink applications. The first of these has demo data being read from a Kinesis Data Stream and written to an Amazon Simple Storage Service (Amazon S3) bucket.

After creating the demo application, you can configure, run, and open the Apache Flink dashboard to monitor your Flink application’s health with the same experiences as before. You can change a code sample in the GitHub repository to perform different operations using the Flink libraries in your own local development environment.

Blueprints are designed to be extensible, and you can leverage them to create more complex applications to solve your business challenges based on Amazon Managed Service for Apache Flink. Learn more about how to use Apache Flink libraries in the AWS documentation.

You can also use a blueprint to create your Studio notebook using Apache Zeppelin as a new setup option. With this new blueprint option, you can also create and set up all the resources that you need to get started in a single step using AWS CloudFormation.

This blueprint includes Apache Flink applications with demo data being sent to an Amazon MSK topic and read in Managed Service for Apache Flink. With an Apache Zeppelin notebook, you can view, query, and analyze your streaming data. Deploying the blueprint and setting up the Studio notebook takes about ten minutes. Go get a cup of coffee while we set it up!

After creating the new Studio notebook, you can open an Apache Zeppelin notebook to run SQL queries in your note with the same experiences as before. You can view a code sample in the GitHub repository to learn more about how to use Apache Flink libraries.

You can run more SQL queries on this demo data such as user-defined functions, tumbling and hopping windows, Top-N queries, and delivering data to an S3 bucket for streaming.

You can also use Java, Python, or Scala to power up your SQL queries and deploy your note as a continuously running application, as shown in the blog posts, how to use the Studio notebook and query your Amazon MSK topics.

To learn more blueprint samples, see GitHub repositories such as reading from MSK Serverless and writing to Amazon S3, reading from MSK Serverless and writing to MSK Serverless, and reading from MSK Serverless and writing to Amazon S3.

Now Available
You can now use Amazon Managed Service for Apache Flink, renamed from Amazon Kinesis Data Analytics. All your existing running applications in Kinesis Data Analytics will work as is without any changes.

To learn more, visit the new product page and developer guide. You can send feedback to AWS re:Post for Amazon Managed Service for Apache Flink, or through your usual AWS Support contacts.

— Channy

New – Amazon EC2 Hpc7a Instances Powered by 4th Gen AMD EPYC Processors Optimized for High Performance Computing

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/new-amazon-ec2-hpc7a-instances-powered-by-4th-gen-amd-epyc-processors-optimized-for-high-performance-computing/

In January 2022, we launched Amazon EC2 Hpc6a instances for customers to efficiently run their compute-bound high performance computing (HPC) workloads on AWS with up to 65 percent better price performance over comparable x86-based compute-optimized instances.

As their jobs grow more complex, customers have asked for more cores with more compute performance and more memory and network performance to reduce the time to complete jobs. Additionally, as customers look to bring more of their HPC workloads to EC2, they have asked how we can make it easier to distribute processes to make the best use of memory and network bandwidth, to align with their workload requirements.

Today, we are announcing the general availability of Amazon EC2 Hpc7a instances, the next generation of instance types that are purpose-built for tightly coupled HPC workloads. Hpc7a instances powered by the 4th Gen AMD EPYC processors (Genoa) deliver up to 2.5 times better performance compared to Hpc6a instances. These instances offer 300 Gbps Elastic Fabric Adapter (EFA) bandwidth powered by the AWS Nitro System, for fast and low-latency internode communications.

Hpc7a instances feature Double Data Rate 5 (DDR5) memory, which provides 50 percent higher memory bandwidth compared to DDR4 memory to enable high-speed access to data in memory. These instances are ideal for compute-intensive, latency-sensitive workloads such as computational fluid dynamics (CFD) and numerical weather prediction (NWP).

If you are running on Hpc6a, you can use Hpc7a instances and take advantage of the 2 times higher core density, 2.1 times higher effective memory bandwidth, and 3 times higher network bandwidth to lower the time needed to complete jobs compared to Hpc6a instances.

Here’s a quick infographic that shows you how the Hpc7a instances and the 4th Gen AMD EPYC processor (Genoa) compare to the previous instances and processor:

Hpc7a instances feature sizes of up to 192 cores of the AMD EPYC processors CPUs with 768 GiB RAM. Here are the detailed specs:

Instance Name CPUs RAM (Gib)
 EFA Network Bandwidth (Gbps)
 Attached Storage
Hpc7a.12xlarge 24 768 Up to 300 EBS Only
Hpc7a.24xlarge 48 768 Up to 300 EBS Only
Hpc7a.48xlarge 96 768 Up to 300 EBS Only
Hpc7a.96xlarge 192 768 Up to 300 EBS Only

These instances provide higher compute, memory, and network performance to run the most compute-intensive workloads, such as CFD, weather forecasting, molecular dynamics, and computational chemistry on AWS.

Similar to EC2 Hpc7g instances released a month earlier, we are offering smaller instance sizes that makes it easier for customers to pick a smaller number of CPU cores to activate while keeping all other resources constant based on their workload requirements. For HPC workloads, common scenarios include providing more memory bandwidth per core for CFD workloads, allocating fewer cores in license-bound scenarios, and supporting more memory per core. To learn more, see Instance sizes in the Amazon EC2 Hpc7 family – a different experience in the AWS HPC Blog.

As with Hpc6a instances, you can use the Hpc7a instance to run your largest and most complex HPC simulations on EC2 and optimize for cost and performance. You can also use the new Hpc7a instances with AWS Batch and AWS ParallelCluster to simplify workload submission and cluster creation. You can also use Amazon FSx for Lustre for submillisecond latencies and up to hundreds of gigabytes per second of throughput for storage.

To achieve the best performance for HPC workloads, these instances have Simultaneous Multithreading (SMT) disabled, they’re available in a single Availability Zone, and they have limited external network and EBS bandwidth.

Now Available
Amazon EC2 Hpc7a instances are available today in three AWS Regions: US East (Ohio), EU (Ireland), and US GovCloud for purchase in On-Demand, Reserved Instances, and Savings Plans. For more information, see the Amazon EC2 pricing page.

To learn more, visit our Hpc7a instances page and get in touch with our HPC team, AWS re:Post for EC2, or through your usual AWS Support contacts.

— Channy

New – Amazon EC2 M7a General Purpose Instances Powered by 4th Gen AMD EPYC Processors

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/new-amazon-ec2-m7a-general-purpose-instances-powered-by-4th-gen-amd-epyc-processors/

In November 2021, we launched Amazon EC2 M6a instances, powered by 3rd Gen AMD EPYC (Milan) processors, running at frequencies up to 3.6 GHz, which offer you up to 35 percent improvement in price performance compared to M5a instances. 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, general purpose Amazon EC2 M7a 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 M6a instances. This increased performance gives you the ability to process data faster, consolidate workloads, and lower the cost of ownership.

M7a instances support AVX-512, Vector Neural Network Instructions (VNNI) and brain floating point (bfloat16). These instances feature Double Data Rate 5 (DDR5) memory, which enable high-speed access to data in-memory, and deliver 2.25 times more memory bandwidth compared to M6a instances for lower latency.

M7a instances are SAP-certified and ideal for applications that benefit from high performance and high throughput, such as financial applications, application servers, simulation modeling, gaming, mid-size data stores, application development environments, and caching fleets.

M7a instances feature sizes of up to 192 vCPUs with 768 GiB RAM. Here are the detailed specs:

Name vCPUs Memory (GiB) Network Bandwidth (Gbps) EBS Bandwidth (Gbps)
m7a.medium 1 4 Up to 12.5 Up to 10
m7a.large 2 8 Up to 12.5 Up to 10
m7a.xlarge 4 16 Up to 12.5 Up to 10
m7a.2xlarge 8 32 Up to 12.5 Up to 10
m7a.4xlarge 16 64 Up to 12.5 Up to 10
m7a.8xlarge 32 128 12.5 10
m7a.12xlarge 48 192 18.75 15
m7a.16xlarge 64 256 25 20
m7a.24xlarge 96 384 37.5 30
m7a.32xlarge 128 512 50 40
m7a.48xlarge 192 768 50 40
m7a.metal-48xl 192 768 50 40

M7a instances have up to 50 Gbps enhanced networking and 40 Gbps EBS bandwidth, which is similar to M6a instances. But you have a new medium instance size, which enables you to right-size your workloads more accurately, offering 1 vCPUs, 4 GiB, and the largest size offering 192 vCPUs, 768 GiB.

The new instances are built on the AWS Nitro System, a collection of building blocks that offloads many of the traditional virtualization functions to dedicated hardware for high performance, high availability, and highly secure cloud instances.

Now Available
Amazon EC2 M7a instances are now available today 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 M7a instance 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

New — File Release for Amazon FSx for Lustre

Post Syndicated from Veliswa Boya original https://aws.amazon.com/blogs/aws/new-file-release-for-amazon-fsx-for-lustre/

Amazon FSx for Lustre provides fully managed shared storage with the scalability and high performance of the open-source Lustre file systems to support your Linux-based workloads. FSx for Lustre is for workloads where storage speed and throughput matter. This is because FSx for Lustre helps you avoid storage bottlenecks, increase utilization of compute resources, and decrease time to value for workloads that include artificial intelligence (AI) and machine learning (ML), high performance computing (HPC), financial modeling, and media processing. FSx for Lustre integrates natively with Amazon Simple Storage Service (Amazon S3), synchronizing changes in both directions with automatic import and export, so that you can access your Amazon S3 data lakes through a high-performance POSIX-compliant file system on demand.

Today, I’m excited to announce file release for FSx for Lustre. This feature helps you manage your data lifecycle by releasing file data that has been synchronized with Amazon S3. File release frees up storage space so that you can continue writing new data to the file system while retaining on-demand access to released files through the FSx for Lustre lazy loading from Amazon S3. You specify a directory to release from, and optionally a minimum amount of time since last access, so that only data from the specified directory, and the minimum amount of time since last access (if specified), is released. File release helps you with data lifecycle management by moving colder file data to S3 enabling you to take advantage of S3 tiering.

File release tasks are initiated using the AWS Management Console, or by making an API call using the AWS CLI, AWS SDK, or Amazon EventBridge Scheduler to schedule release tasks at regular intervals. You can choose to receive completion reports at the end of your release task if so desired.

Initiating a Release Task
As an example, let’s look at how to use the console to initiate a release task. To specify criteria for files to release (for example, directories or time since last access), we define release data repository tasks (DRTs). DRTs release all files that are synchronized with Amazon S3 and that meet the specified criteria. It’s worth noting that release DRTs are processed in sequence. This means that if you submit a release DRT while another DRT (for example, import or export) is in progress, the release DRT will be queued but not processed until after the import or export DRT has completed.

Note: For the data repository association to work, automatic backups for the file system must be disabled (use the Backups tab to do this). Secondly, ensure that the file system and the associated S3 bucket are in the same AWS Region.

I already have an FSx for Lustre file system my-fsx-test.

I create a data repository association, which is a link between a directory on the file system and an S3 bucket or prefix.

I specify the name of the S3 bucket or an S3 prefix to be associated with the file system.

After the data repository association has been created, I select Create release task.

The release task will release directories or files that you want to release based on your specific criteria (again, important to remember that these files or directories must be synchronized with an S3 bucket in order for the release to work). If you specified the minimum last access for release (in addition to the directory), files that have not been accessed more recently than that will be released.

In my example, I chose to Disable completion reports. However, if you choose to Enable completion reports, the release task will produce a report at the end of the release task.

Files that have been released can still be accessed using existing FSx for Lustre functionality to automatically retrieve data from Amazon S3 back to the file system on demand. This is because, although released, their metadata stays on the file system.

File release won’t automatically prevent your file system from becoming full. It remains important to ensure that you don’t write more data than the available storage capacity before you run the next release task.

Now Available
File release on FSx for Lustre is available today in all AWS Regions where FSx for Lustre is supported, on all new or existing S3-linked file systems running Lustre version 2.12 or later. With file release on FSx for Lustre, there is no additional cost. However, if you release files that you later access again from the file system, you will incur normal Amazon S3 request and data retrieval costs where applicable when those files are read back into the file system.

To learn more, visit the Amazon FSx for Lustre Page, and please send feedback to AWS re:Post for Amazon FSx for Lustre or through your usual AWS support contacts.

– Veliswa

Welcome to AWS Storage Day 2023

Post Syndicated from Veliswa Boya original https://aws.amazon.com/blogs/aws/welcome-to-aws-storage-day-2023/

Welcome to the fifth annual AWS Storage Day! This virtual event is happening today starting at 9:00 AM Pacific Time (12:00 PM Eastern Time) and is available for you to watch on the AWS On Air Twitch channel. The first AWS Storage Day was hosted in 2019, and this event has grown into an innovation day that we look forward to delivering to you every year. In last year’s Storage Day post, I wrote about the constant innovations in AWS Storage aimed at helping you put your data to work while keeping it secure and protected. This year, Storage Day is focused on storage for AI/ML, data protection and resiliency, and the benefits of moving to the cloud.

AWS Storage Day Key Themes
When it comes to storage for AI/ML, data volumes are increasing at an unprecedented rate, exploding from terabytes to petabytes and even to exabytes. With a modern data architecture on AWS, you can rapidly build scalable data lakes, use a broad and deep collection of purpose-built data services, scale your systems at a low cost without compromising performance, share data across organizational boundaries, and manage compliance, security, and governance, allowing you to make decisions with speed and agility at scale.
To train machine learning models and build Generative AI applications, you must have the right data strategy in place. So, I’m happy to see that, among the list of sessions to look forward to at the live event, the Optimize generative AI and ML with AWS Infrastructure session will discuss how you can transform your data into meaningful insights.

Whether you’re just getting started with the cloud, planning to migrate applications to AWS, or already building applications on AWS, we have resources to help you protect your data and meet your business continuity objectives. Our data protection and resiliency features and solutions can help you meet your business continuity goals and deliver disaster recovery during data loss events, across recovery point and time objectives (RPO and RTO). With the unprecedented data growth happening in the world today, determining where your data is stored, how it’s secured, and who has access to it is a higher priority than ever. Be sure to join the Protect data in AWS amid a rapidly evolving cyber landscape session to learn more.

When moving data to the cloud, you need to understand where you’re moving it for different use cases, the types of data you’re moving, and the network resources available, among other considerations. There are many reasons to move to the cloud, recently, Enterprise Strategy Group (ESG) validated that organizations reduced compute, networking, and storage costs by up to 66 percent by migrating on-premises workloads to AWS Cloud infrastructure. ESG confirmed that migrating on-premises workloads to AWS provides organizations with reduced costs, increased performance, improved operational efficiency, faster time to value, and improved business agility.
We have a number of sessions that discuss how to move to the cloud, based on your use case. I’m most looking forward to the Hybrid cloud storage and edge compute: AWS, where you need it session, which will discuss considerations for workloads that can’t fully move to the cloud.

Tune in to learn from experts about new announcements, leadership insights, and educational content related to the broad portfolio of AWS Storage services and features that address all these themes and more. Today, we have announcements related to Amazon Simple Storage Service (Amazon S3), Amazon FSx for Windows File Server, Amazon Elastic File System (Amazon EFS), Amazon FSx for OpenZFS, and more.

Let’s get into it.

15 Years of Amazon EBS
Not long ago, I was reading Jeff Barr’s post titled 15 Years of AWS Blogging! In this post, Jeff mentioned a few posts he wrote for the earliest AWS services and features. Amazon Elastic Block Store (Amazon EBS) is on this list as a service that simplifies the use of Amazon EC2.

Well, it’s been 15 years since the launch of Amazon EBS was announced, and today we celebrate 15 years of this service. If you were one of the original users who put Amazon EBS to good use and provided us with the very helpful feedback that helped us invent and simplify, iterate and improve, I’m sure you can’t believe how time flies. Today, Amazon EBS handles more than 100 trillion I/O operations daily, and over 390 million EBS volumes are created every day.

If you’re new to Amazon EBS, join us for a fireside chat with Matt Garman, Senior Vice President, Sales, Marketing, and Global Services at AWS, and learn the strategy and customer challenges behind the launch of the service in 2008. You’ll also hear from long-term EBS customer, Stripe, about its growth with EBS since Stripe was launched 12 years ago.

Amazon EBS has continuously improved its scalability and performance to support more customer workloads as the direct storage attachment for Amazon EC2 instances. With the launch of Amazon EC2 M7i instances, powered by custom 4th Generation Intel Xeon Scalable processors, on August 2, you can attach up to 128 Amazon EBS volumes, an increase from 28 on a previous generation M6i instance. The higher number of volume attachments means you can increase storage density per instance and improve resource utilization, reducing total compute cost.

You can host up to 127 containers per instance for larger database applications and scale them more cost effectively before needing to provision more instances and only pay for resources you need. With a higher number of volume attachments, you can fully utilize the memory and vCPU available on these powerful M7i instances as your database storage footprint grows. EBS is also increasing the number of multi-volume snapshots you can create, for up to 128 EBS volumes attached to an instance, enabling you to create crash-consistent backups of all volumes attached to an instance.

Join the 15 years of innovations with Amazon EBS session for a discussion about how the original vision for Amazon EBS has evolved to meet your growing demands for cloud infrastructure.

Mountpoint for Amazon S3
Now generally available, Mountpoint for Amazon S3 is a new open source file client that delivers high throughput access, lowering compute costs for data lakes on Amazon S3. Mountpoint for Amazon S3 is a file client that translates local file system API calls to S3 object API calls. Using Mountpoint for Amazon S3, you can mount an Amazon S3 bucket as a local file system on your compute instance, to access your objects through a file interface with the elastic storage and throughput of Amazon S3. Mountpoint for Amazon S3 supports sequential and random read operations on existing files, and sequential write operations for creating new files.

The Deep dive and demo of Mountpoint for Amazon S3 session demonstrates how to use the file client to access objects in Amazon S3 using file APIs, making it easier to store data at scale and maximize the value of your data with analytics and machine learning workloads. Read this blog post to learn more about Mountpoint for Amazon S3 and how to get started, including a demo.

Put Cold Storage to Work Faster with Amazon S3 Glacier Flexible Retrieval
Amazon S3 Glacier Flexible Retrieval improves data restore time by up to 85 percent, at no additional cost. Faster data restores automatically apply to the Standard retrieval tier when using Amazon S3 Batch Operations. These restores begin to return objects within minutes, so you can process restored data faster. Processing restored data in parallel with ongoing restores helps you accelerate data workflows and quickly respond to business needs. Now, whether you’re transcoding media, restoring operational backups, training machine learning models, or analyzing historical data, you can speed up your data restores from archive.

Coupled with the S3 Glacier improvements to restore throughput by up to 10 times for millions of objects announced in 2022, S3 Glacier data restores of all sizes now benefit from both faster starts and shorter completion times.

Join the Maximize the value of cold data with Amazon S3 Glacier session to learn how Amazon S3 Glacier is helping organizations of all sizes and from all industries transform their data archiving to unlock business value, increase agility, and save on storage costs. Read this blog post to learn more about the Amazon S3 Glacier Flexible Retrieval performance improvements and follow step-by-step guidance on how to get started with faster standard retrievals from S3 Glacier Flexible Retrieval.

Supporting a Broad Spectrum of File Workloads
To serve a broad spectrum of use cases that rely on file systems, we offer a portfolio of file system services, each targeting a different set of needs. Amazon EFS is a serverless file system built to deliver an elastic experience for sharing data across compute resources. Amazon FSx makes it easier and cost-effective for you to launch, run, and scale feature-rich, high-performance file systems in the cloud, enabling you to move to the cloud with no changes to your code, processes, or how you manage your data.

Power ML research and big data analytics with Amazon EFS
Amazon EFS offers serverless and fully scalable file storage, designed for high scalability in both storage capacity and throughput performance. Just last week, we announced enhanced support for faster read and write IOPS, making it easier to power more demanding workloads. We’ve improved the performance capabilities of Amazon EFS by adding support for up to 55,000 read IOPS and up to 25,000 write IOPS per file system. These performance enhancements help you to run more demanding workflows, such as machine learning (ML) research with KubeFlow, financial simulations with IBM Symphony, and big data processing with Domino Data Lab, Hadoop, and Spark.

Join the Build and run analytics and SaaS applications at scale session to hear how recent Amazon EFS performance improvements can help power more workloads.

Multi-AZ file systems on Amazon FSx for OpenZFS
You can now use a multi-AZ deployment option when creating file systems on Amazon FSx for OpenZFS, making it easier to deploy file storage that spans multiple AWS Availability Zones to provide multi-AZ resilience for business-critical workloads. With this launch, you can take advantage of the power, agility, and simplicity of Amazon FSx for OpenZFS for a broader set of workloads, including business-critical workloads like database, line-of-business, and web-serving applications that require highly available shared storage that spans multiple AZs.

The new multi-AZ file systems are designed to deliver high levels of performance to serve a broad variety of workloads, including performance-intensive workloads such as financial services analytics, media and entertainment workflows, semiconductor chip design, and game development and streaming, up to 21 GB per second of throughput and over 1 million IOPS for frequently accessed cached data, and up to 10 GB per second and 350,000 IOPS for data accessed from persistent disk storage.

Join the Migrate NAS to AWS to reduce TCO and gain agility session to learn more about multi-AZs with Amazon FSx for OpenZFS.

New, Higher Throughput Capacity Levels on Amazon FSx for Windows File Server
Performance improvements for Amazon FSx for Windows File Server help you accelerate time-to-results for performance-intensive workloads such as SQL Server databases, media processing, cloud video editing, and virtual desktop infrastructure (VDI).

We’re adding four new, higher throughput capacity levels to increase the maximum I/O available up to 12 GB per second from the previous I/O of 2 GB per second. These throughput improvements come with correspondingly higher levels of disk IOPS, designed to deliver an increase up to 350,000 IOPS.

In addition, by using FSx for Windows File Server, you can provision IOPS higher than the default 3 IOPS per GiB for your SSD file system. This allows you to scale SSD IOPS independently from storage capacity, allowing you to optimize costs for performance-sensitive workloads.

Join the Migrate NAS to AWS to reduce TCO and gain agility session to learn more about the performance improvements for Amazon FSx for Windows File Server.

Logically Air-Gapped Vault for AWS Backup
AWS Backup is a fully managed, policy-based data protection solution that enables customers to centralize and automate backup restores across 19 AWS services (spanning compute, storage, and databases) and third-party applications such as VMware Cloud on AWS and on-premises, as well as SAP HANA on Amazon EC2.

Today, we’re announcing the preview of logically air-gapped vault as a new type of AWS Backup Vault that acts as an additional layer of protection to mitigate against malware events. With logically air-gapped vault, customers can recover their application data through a different trusted account.

Join the Deep dive on data recovery for ransomware events session to learn more about logically air-gapped vault for AWS Backup.

Copy Data to and from Other Clouds with AWS DataSync
AWS DataSync is an online data movement and discovery service that simplifies data migration and helps you quickly, easily, and securely transfer your file or object data to, from, and between AWS storage services. In addition to support of data migration to and from AWS storage services, DataSync supports copying to and from other clouds such as Google Cloud Storage, Azure Files, and Azure Blob Storage. Using DataSync, you can move your object data at scale between Amazon S3 compatible storage on other clouds and AWS storage services such as Amazon S3. We’re now expanding the support of DataSync for copying data to and from other clouds to include DigitalOcean Spaces, Wasabi Cloud Storage, Backblaze B2 Cloud Storage, Cloudflare R2 Storage, and Oracle Cloud Storage.

Join the Identify and accelerate data migrations at scale session to learn more about this expanded support for DataSync.

Join Us Online
Join us today for the AWS Storage Day virtual event on the AWS On Air channel on Twitch. The event will be live starting at 9:00 AM Pacific Time (12:00 PM Eastern Time) on August 9. All sessions will be available on demand approximately two days after Storage Day.

We look forward to seeing you on Twitch!

– Veliswa 

Mountpoint for Amazon S3 – Generally Available and Ready for Production Workloads

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/mountpoint-for-amazon-s3-generally-available-and-ready-for-production-workloads/

Mountpoint for Amazon S3 is an open source file client that makes it easy for your file-aware Linux applications to connect directly to Amazon Simple Storage Service (Amazon S3) buckets. Announced earlier this year as an alpha release, it is now generally available and ready for production use on your large-scale read-heavy applications: data lakes, machine learning training, image rendering, autonomous vehicle simulation, ETL, and more. It supports file-based workloads that perform sequential and random reads, sequential (append only) writes, and that don’t need full POSIX semantics.

Why Files?
Many AWS customers use the S3 APIs and the AWS SDKs to build applications that can list, access, and process the contents of an S3 bucket. However, many customers have existing applications, commands, tools, and workflows that know how to access files in UNIX style: reading directories, opening & reading existing files, and creating & writing new ones. These customers have asked us for an official, enterprise-ready client that supports performant access to S3 at scale. After speaking with these customers and asking lots of questions, we learned that performance and stability were their primary concerns, and that POSIX compliance was not a necessity.

When I first wrote about Amazon S3 back in 2006 I was very clear that it was intended to be used as an object store, not as a file system. While you would not want use the Mountpoint / S3 combo to store your Git repositories or the like, using it in conjunction with tools that can read and write files, while taking advantage of S3’s scale and durability, makes sense in many situations.

All About Mountpoint
Mountpoint is conceptually very simple. You create a mount point and mount an Amazon S3 bucket (or a path within a bucket) at the mount point, and then access the bucket using shell commands (ls, cat, dd, find, and so forth), library functions (open, close, read, write, creat, opendir, and so forth) or equivalent commands and functions as supported in the tools and languages that you already use.

Under the covers, the Linux Virtual Filesystem (VFS) translates these operations into calls to Mountpoint, which in turns translates them into calls to S3: LIST, GET, PUT, and so forth. Mountpoint strives to make good use of network bandwidth, increasing throughput and allowing you to reduce your compute costs by getting more work done in less time.

Mountpoint can be used from an Amazon Elastic Compute Cloud (Amazon EC2) instance, or within an Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (EKS) container. It can also be installed on your existing on-premises systems, with access to S3 either directly or over an AWS Direct Connect connection via AWS PrivateLink for Amazon S3.

Installing and Using Mountpoint for Amazon S3
Mountpoint is available in RPM format and can easily be installed on an EC2 instance running Amazon Linux. I simply fetch the RPM and install it using yum:

$ wget https://s3.amazonaws.com/mountpoint-s3-release/latest/x86_64/mount-s3.rpm
$ sudo yum install ./mount-s3.rpm

For the last couple of years I have been regularly fetching images from several of the Washington State Ferry webcams and storing them in my wsdot-ferry bucket:

I collect these images in order to track the comings and goings of the ferries, with a goal of analyzing them at some point to find the best times to ride. My goal today is to create a movie that combines an entire day’s worth of images into a nice time lapse. I start by creating a mount point and mounting the bucket:

$ mkdir wsdot-ferry
$  mount-s3 wsdot-ferry wsdot-ferry

I can traverse the mount point and inspect the bucket:

$ cd wsdot-ferry
$ ls -l | head -10
total 0
drwxr-xr-x 2 jeff jeff 0 Aug  7 23:07 2020_12_30
drwxr-xr-x 2 jeff jeff 0 Aug  7 23:07 2020_12_31
drwxr-xr-x 2 jeff jeff 0 Aug  7 23:07 2021_01_01
drwxr-xr-x 2 jeff jeff 0 Aug  7 23:07 2021_01_02
drwxr-xr-x 2 jeff jeff 0 Aug  7 23:07 2021_01_03
drwxr-xr-x 2 jeff jeff 0 Aug  7 23:07 2021_01_04
drwxr-xr-x 2 jeff jeff 0 Aug  7 23:07 2021_01_05
drwxr-xr-x 2 jeff jeff 0 Aug  7 23:07 2021_01_06
drwxr-xr-x 2 jeff jeff 0 Aug  7 23:07 2021_01_07
$
$  cd 2020_12_30
$ ls -l
total 0
drwxr-xr-x 2 jeff jeff 0 Aug  7 23:07 fauntleroy_holding
drwxr-xr-x 2 jeff jeff 0 Aug  7 23:07 fauntleroy_way
drwxr-xr-x 2 jeff jeff 0 Aug  7 23:07 lincoln
drwxr-xr-x 2 jeff jeff 0 Aug  7 23:07 trenton
drwxr-xr-x 2 jeff jeff 0 Aug  7 23:07 vashon_112_north
drwxr-xr-x 2 jeff jeff 0 Aug  7 23:07 vashon_112_south
drwxr-xr-x 2 jeff jeff 0 Aug  7 23:07 vashon_bunker_north
drwxr-xr-x 2 jeff jeff 0 Aug  7 23:07 vashon_bunker_south
drwxr-xr-x 2 jeff jeff 0 Aug  7 23:07 vashon_holding
$
$ cd fauntleroy_holding
$  ls -l | head -10
total 2680
-rw-r--r-- 1 jeff jeff  19337 Feb 10  2021 17-12-01.jpg
-rw-r--r-- 1 jeff jeff  19380 Feb 10  2021 17-15-01.jpg
-rw-r--r-- 1 jeff jeff  19080 Feb 10  2021 17-18-01.jpg
-rw-r--r-- 1 jeff jeff  17700 Feb 10  2021 17-21-01.jpg
-rw-r--r-- 1 jeff jeff  17016 Feb 10  2021 17-24-01.jpg
-rw-r--r-- 1 jeff jeff  16638 Feb 10  2021 17-27-01.jpg
-rw-r--r-- 1 jeff jeff  16713 Feb 10  2021 17-30-01.jpg
-rw-r--r-- 1 jeff jeff  16647 Feb 10  2021 17-33-02.jpg
-rw-r--r-- 1 jeff jeff  16750 Feb 10  2021 17-36-01.jpg
$

I can create my animation with a single command:

$ ffmpeg -framerate 10 -pattern_type glob -i "*.jpg" ferry.gif

And here’s what I get:

As you can see, I used Mountpoint to access the existing image files and to write the newly created animation back to S3. While this is a fairly simple demo, it does show how you can use your existing tools and skills to process objects in an S3 bucket. Given that I have collected several million images over the years, being able to process them without explicitly syncing them to my local file system is a big win.

Mountpoint for Amazon S3 Facts
Here are a couple of things to keep in mind when using Mountpoint:

Pricing – There are no new charges for the use of Mountpoint; you pay only for the underlying S3 operations. You can also use Mountpoint to access requester-pays buckets.

Performance – Mountpoint is able to take advantage of the elastic throughput offered by S3, including data transfer at up to 100 Gb/second between each EC2 instance and S3.

Credentials – Mountpoint accesses your S3 buckets using the AWS credentials that are in effect when you mount the bucket. See the CONFIGURATION doc for more information on credentials, bucket configuration, use of requester pays, some tips for the use of S3 Object Lambda, and more.

Operations & Semantics – Mountpoint supports basic file operations, and can read files up to 5 TB in size. It can list and read existing files, and it can create new ones. It cannot modify existing files or delete directories, and it does not support symbolic links or file locking (if you need POSIX semantics, take a look at Amazon FSx for Lustre). For more information about the supported operations and their interpretation, read the SEMANTICS document.

Storage Classes – You can use Mountpoint to access S3 objects in all storage classes except S3 Glacier Flexible Retrieval, S3 Glacier Deep Archive, S3 Intelligent-Tiering Archive Access Tier, and S3 Intelligent-Tiering Deep Archive Access Tier.

Open Source – Mountpoint is open source and has a public roadmap. Your contributions are welcome; be sure to read our Contributing Guidelines and our Code of Conduct first.

Hop On
As you can see, Mountpoint is really cool and I am guessing that you are going to find some awesome ways to put it to use in your applications. Check it out and let me know what you think!

— Jeff;

New – Improve Amazon S3 Glacier Flexible Restore Time By Up To 85% Using Standard Retrieval Tier and S3 Batch Operations

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/new-improve-amazon-s3-glacier-flexible-restore-time-by-up-to-85-using-standard-retrieval-tier-and-s3-batch-operations/

Last year, Amazon S3 Glacier celebrated its tenth anniversary. Amazon S3 Glacier is the leader in cloud cold storage, and I wrote about its innovations over the last decade.

The Amazon S3 Glacier storage classes provide you with long-term, secure, and durable storage options to optimally archive your data at the lowest cost. The Amazon S3 Glacier storage classes (Amazon S3 Glacier Instant Retrieval, Amazon S3 Glacier Flexible Retrieval, and Amazon S3 Glacier Deep Archive) are purpose-built for colder data, providing you with retrieval flexibility from milliseconds to days, in addition to the ability to store archive data for as low as $1 per terabyte per month.

Many customers tell us that they are keeping their data for longer periods of time because they recognize its future value potential, and that they are already monetizing subsets of their archival data, or plan to use large sets of their archive data in the future. Modern data archiving is not only about optimizing storage costs for cold data; it’s also about setting up mechanisms so that when you need to put that data to work for your business, you can access it as quickly as your business requirements demand.

In 2022, AWS customers restored over 32 billion objects from Amazon S3 Glacier. Customers need to retrieve archived objects quickly when transcoding media, restoring operational backups, training machine learning (ML) models, or analyzing historical data. While customers using S3 Glacier Instant Retrieval can access their data in just milliseconds, S3 Glacier Flexible Retrieval is lower cost and provides three retrieval options: expedited retrievals in 1–5 minutes, standard retrievals in 3–5 hours, and free bulk retrievals in 5–12 hours. S3 Glacier Deep Archive is our lowest cost storage class and provides data retrieval within 12 hours using the standard retrieval option or 48 hours using the bulk retrieval option.

In November 2022, Amazon S3 Glacier improved restore throughput by up to 10 times at no additional cost when retrieving large volumes of archived data in S3 Glacier Flexible Retrieval and S3 Glacier Deep Archive. With Amazon S3 Batch Operations, you can automatically initiate requests at a faster rate, allowing you to restore billions of objects containing petabytes of data.

To continue the decade-long trend of cold storage innovation, we are announcing today the general availability of faster Standard retrievals from S3 Glacier Flexible Retrieval by up to 85 percent, at no additional cost. Faster data restores automatically apply to the Standard retrieval tier when using S3 Batch Operations.

Using S3 Batch Operations, you can restore archived data at scale by providing a manifest of objects to be retrieved and specifying a retrieval tier. With S3 Batch Operations, restores in the Standard retrieval tier now typically begin to return objects to you within minutes, down from 3–5 hours, so you can easily speed up your data restores from archive.

Additionally, S3 Batch Operations improves overall restore throughput by applying new performance optimizations to your jobs. As a result, you can restore your data faster and process restored objects sooner. Processing restored data in parallel with ongoing restores helps you accelerate data workflows and quickly respond to business needs.

Getting Started with Faster Standard Retrievals from S3 Glacier Flexible Retrieval
To restore archived data with this performance improvement, you can use S3 Batch Operations to perform both large- and small-scale batch operations on S3 objects. S3 Batch Operations can perform a single operation on lists of S3 objects that you specify. You can use S3 Batch Operations through the AWS Management Console, AWS Command Line Interface (AWS CLI), SDKs, or REST API.

To create a batch job, choose Batch Operations on the left navigation pane of the Amazon S3 console and choose Create job. You can select one of the manifest formats, a list of S3 objects that contains object keys that you want to retrieve. If your manifest format is a CSV file, each row in the file must include the bucket name, object key, and, optionally, the object version.

In the next step, choose the operation that you want to perform on all objects listed in the manifest. The Restore operation initiates restore requests for archived objects on a list of S3 objects that you specify. Using a restore operation results in a restore request for every object that is specified in the manifest.

When you restore with the Standard retrieval tier from the S3 Glacier Flexible Retrieval storage class, you automatically get faster retrievals.

You can also create a restore job with S3InitiateRestoreObject job using the AWS CLI:

$aws s3control create-job \
     --region us-east-1 \
     --account-id 123456789012 \
     --operation '{"S3InitiateRestoreObject": { "ExpirationInDays": 1, "GlacierJobTier":"STANDARD"} }' \
     --report '{"Bucket":"arn:aws:s3:::reports-bucket ","Prefix":"batch-op-restore-job", "Format":" S3BatchOperations_CSV_20180820","Enabled":true,"ReportScope":"FailedTasksOnly"}' \
     --manifest '{"Spec":{"Format":"S3BatchOperations_CSV_20180820", "Fields":["Bucket","Key"]},"Location":{"ObjectArn":"arn:aws:s3:::inventory-bucket/inventory_for_restore.csv", "ETag":"<ETag>"}}' \
     --role-arn arn:aws:iam::123456789012:role/s3batch-role

You can then check the status of the job submission of the requests by running the following CLI command:

$ aws s3control describe-job \
     --region us-east-1 \
     --account-id 123456789012 \
     --job-id <JobID> \
     --query 'Job'.'ProgressSummary'

You can view and update the job status, add notifications and logging, track job failures, and generate completion reports. S3 Batch Operations job activity is recorded as events in AWS CloudTrail. For tracking job events, you can create a custom rule in Amazon EventBridge and send these events to the target notification resource of your choice, such as Amazon Simple Notification Service (Amazon SNS).

When you create an S3 Batch Operations job, you can also request a completion report for all tasks or just for failed tasks. The completion report contains additional information for each task, including the object key name and version, status, error codes, and descriptions of any errors.

For more information, see Tracking job status and completion reports in the Amazon S3 User Guide.

Here is the result of a sample retrieval job with 250 objects, each sized 100 MB. As you can see from the Previous restore performance line (blue line at the right), these restores would typically finish in 3–5 hours using Standard retrievals. Now, when you use Standard retrievals with S3 Batch Operations, your job typically starts within minutes, as shown in the Improved restore performance line (orange line at the left), improving data restore time by up to 85 percent.

To learn more, see Restoring archived objects at scale from the Amazon S3 Glacier storage classes on the AWS Storage Blog and Restoring an archived object in the Amazon S3 User Guide.

Now Available
Faster standard retrievals for Amazon S3 Glacier Flexible Retrieval are now available in all AWS Regions, including the AWS GovCloud (US) Regions and China Regions. This performance improvement is available to you at no additional cost. You are charged for S3 Batch Operations and data retrievals. For more information, see the S3 pricing page.

Lastly, we published a new ebook titled “Maximize the value of cold storage with Amazon S3 Glacier“. Read this ebook to learn how Amazon S3 Glacier is helping organizations of all sizes and from all industries transform their data archiving to unlock business value, increase agility, and save on storage costs.

To learn more, visit the S3 Glacier storage classes page and getting started guide, and send feedback to AWS re:Post for S3 Glacier or through your usual AWS Support contacts.

I’m really excited for you to start using this new feature, and I look forward to hearing about even more ways you are reinventing your business with archive data.

— Channy

New Seventh-Generation General Purpose Amazon EC2 Instances (M7i-Flex and M7i)

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-seventh-generation-general-purpose-amazon-ec2-instances-m7i-flex-and-m7i/

Today we are launching Amazon Elastic Compute Cloud (Amazon EC2) M7i-Flex and M7i instances powered by custom 4th generation Intel Xeon Scalable processors available only on AWS, that offer the best performance among comparable Intel processors in the cloud – up to 15% faster than Intel processors utilized by other cloud providers. M7i-Flex instances are available in the five most common sizes, and are designed to give you up to 19% better price/performance than M6i instances for many workloads. The M7i instances are available in nine sizes (with two size of bare metal instances in the works), and offer 15% better price/performance than the previous generation of Intel-powered instances.

M7i-Flex Instances
The M7i-Flex instances are a lower-cost variant of the M7i instances, with 5% better price/performance and 5% lower prices. They are great for applications that don’t fully utilize all compute resources. The M7i-Flex instances deliver a baseline of 40% CPU performance, and can scale up to full CPU performance 95% of the time. M7i-Flex instances are ideal for running general purpose workloads such as web and application servers, virtual desktops, batch processing, micro-services, databases and enterprise applications. If you are currently using earlier generations of general-purposes instances, you can adopt M7i-Flex instances without having to make changes to your application or your workload.

Here are the specs for the M7i-Flex instances:

Instance Name vCPUs
 Memory
 Network Bandwidth
 EBS Bandwidth
m7i-flex.large 2 8 GiB up to 12.5 Gbps up to 10 Gbps
m7i-flex.xlarge 4 16 GiB up to 12.5 Gbps up to 10 Gbps
m7i-flex.2xlarge 8 32 GiB up to 12.5 Gbps up to 10 Gbps
m7i-flex.4xlarge 16 64 GiB up to 12.5 Gbps up to 10 Gbps
m7i-flex.8xlarge 32 128 GiB up to 12.5 Gbps up to 10 Gbps

M7i Instances
For workloads such as large application servers and databases, gaming servers, CPU based machine learning, and video streaming that need the largest instance sizes or high CPU continuously, you can get price/performance benefits by using M7i instances.

Here are the specs for the M7i instances:

Instance Name vCPUs
 Memory
 Network Bandwidth
 EBS Bandwidth
m7i.large 2 8 GiB up to 12.5 Gbps up to 10 Gbps
m7i.xlarge 4 16 GiB up to 12.5 Gbps up to 10 Gbps
m7i.2xlarge 8 32 GiB up to 12.5 Gbps up to 10 Gbps
m7i.4xlarge 16 64 GiB up to 12.5 Gbps up to 10 Gbps
m7i.8xlarge 32 128 GiB 12.5 Gbps 10 Gbps
m7i.12xlarge 48 192 GiB 18.75 Gbps 15 Gbps
m7i.16xlarge 64 256 GiB 25.0 Gbps 20 Gbps
m7i.24xlarge 96 384 GiB 37.5 Gbps 30 Gbps
m7i.48xlarge 192 768 GiB 50 Gbps 40 Gbps

You can attach up to 128 EBS volumes to each M7i instance; by way of comparison, the M6i instances allow you to attach up to 28 volumes.

We are also getting ready to launch two sizes of bare metal M7i instances:

Instance Name vCPUs
 Memory
 Network Bandwidth
 EBS Bandwidth
m7i.metal-24xl 96 384 GiB 37.5 Gbps 30 Gbps
m7i.metal-48xl 192 768 GiB 50.0 Gbps 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) – This set of extensions to the x86 instruction set improve deep learning and inferencing, and support workloads such as natural language processing, recommendation systems, and image recognition. The extensions provide high-speed multiplication operations on 2-dimensional matrices of INT8 or BF16 values. To learn more, read Chapter 3 of the Intel AMX Instruction Set Reference.
  • Intel Data Streaming Accelerator (DSA) – This accelerator drives high performance for storage, networking, and data-intensive workloads by offloading common data movement tasks between CPU, memory, caches, network devices, and storage devices, improving streaming data movement and transformation operations. Read Introducing the Intel Data Streaming Accelerator (Intel DSA) to learn more.
  • 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, and encryption at very high throughput, and a suite of analytics primitives support in-memory databases, open source database, and data stores like RocksDB and ClickHouse. To learn more, read the Intel In-Memory Analytics Accelerator (Intel IAA) Architecture Specification.
  • 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.

Some of these accelerators require the use of specific kernel versions, drivers, and/or compilers.

The Advanced Matrix Extensions are available on all sizes of M7i and M7i-Flex instances. The Intel QAT, Intel IAA, and Intel DSA accelerators will be available on the m7i.metal-24xl and m7i.metal-48xl instances.

Details
Here are a couple of things to keep in mind about the M7i-Flex and M7i instances:

Regions – The new instances are available in the US East (Ohio, N. Virginia), US West (Oregon), and Europe (Ireland) AWS Regions, and we plan to expand to additional regions throughout the rest of 2023.

Purchasing Options – M7i-Flex amd M7i instances are available in On-Demand, Reserved Instance, Savings Plan, and Spot form. M7i instances are also available in Dedicated Host and Dedicated Instance form.

— Jeff;

Prime Day 2023 Powered by AWS – All the Numbers

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/prime-day-2023-powered-by-aws-all-the-numbers/

As part of my annual tradition to tell you about how AWS makes Prime Day possible, I am happy to be able to share some chart-topping metrics (check out my 2016, 2017, 2019, 2020, 2021, and 2022 posts for a look back).

This year I bought all kinds of stuff for my hobbies including a small drill press, filament for my 3D printer, and irrigation tools. I also bought some very nice Alphablock books for my grandkids. According to our official release, the first day of Prime Day was the single largest sales day ever on Amazon and for independent sellers, with more than 375 million items purchased.

Prime Day by the Numbers
As always, Prime Day was powered by AWS. Here are some of the most interesting and/or mind-blowing metrics:

Amazon Elastic Block Store (Amazon EBS) – The Amazon Prime Day event resulted in an incremental 163 petabytes of EBS storage capacity allocated – generating a peak of 15.35 trillion requests and 764 petabytes of data transfer per day. Compared to the previous year, Amazon increased the peak usage on EBS by only 7% Year-over-Year yet delivered +35% more traffic per day due to efficiency efforts including workload optimization using Amazon Elastic Compute Cloud (Amazon EC2) AWS Graviton-based instances. Here’s a visual comparison:

AWS CloudTrail – AWS CloudTrail processed over 830 billion events in support of Prime Day 2023.

Amazon DynamoDB – DynamoDB powers multiple high-traffic Amazon properties and systems including Alexa, the Amazon.com sites, and all Amazon fulfillment centers. Over the course of Prime Day, these sources made trillions of calls to the DynamoDB API. DynamoDB maintained high availability while delivering single-digit millisecond responses and peaking at 126 million requests per second.

Amazon Aurora – On Prime Day, 5,835 database instances running the PostgreSQL-compatible and MySQL-compatible editions of Amazon Aurora processed 318 billion transactions, stored 2,140 terabytes of data, and transferred 836 terabytes of data.

Amazon Simple Email Service (SES) – Amazon SES sent 56% more emails for Amazon.com during Prime Day 2023 vs. 2022, delivering 99.8% of those emails to customers.

Amazon CloudFront – Amazon CloudFront handled a peak load of over 500 million HTTP requests per minute, for a total of over 1 trillion HTTP requests during Prime Day.

Amazon SQS – During Prime Day, Amazon SQS set a new traffic record by processing 86 million messages per second at peak. This is 22% increase from Prime Day of 2022, where SQS supported 70.5M messages/sec.

Amazon Elastic Compute Cloud (EC2) – During Prime Day 2023, Amazon used tens of millions of normalized AWS Graviton-based Amazon EC2 instances, 2.7x more than in 2022, to power over 2,600 services. By using more Graviton-based instances, Amazon was able to get the compute capacity needed while using up to 60% less energy.

Amazon Pinpoint – Amazon Pinpoint sent tens of millions of SMS messages to customers during Prime Day 2023 with a delivery success rate of 98.3%.

Prepare to Scale
Every year I reiterate the same message: rigorous preparation is key to the success of Prime Day and our other large-scale events. If you are preparing for a similar chart-topping event of your own, I strongly recommend that you take advantage of AWS Infrastructure Event Management (IEM). As part of an IEM engagement, my colleagues will provide you with architectural and operational guidance that will help you to execute your event with confidence!

— Jeff;

Now Open – AWS Israel (Tel Aviv ) Region

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/now-open-aws-israel-tel-aviv-region/

In June 2021, Jeff Barr announced the upcoming AWS Israel (Tel Aviv) Region. Today we’re announcing the general availability of the AWS Israel (Tel Aviv) Region, with three Availability Zones and the il-central-1 API name.

The new Tel Aviv Region gives customers an additional option for running their applications and serving users from data centers located in Israel. Customers can securely store data in Israel while serving users in the vicinity with even lower latency.

AWS Services in the AWS Israel (Tel Aviv) Region
In the new Tel Aviv Region, you can use C5, C5d, C6g, C6gn, C6i, C6id, D3, G5, I3, I3en, I4i, M5, M5d, M6g, M6gd, M6i, M6id, P4de (public preview only), R5, R5d, R6g, R6i, R6id, T3, T3a, T4g instances, and a long list of AWS services including: Amazon API Gateway, AWS AppConfig, AWS Application Auto Scaling, Amazon Aurora, Aurora PostgreSQL, AWS Budgets, AWS Certificate Manager, AWS CloudFormation, Amazon Cloudfront, AWS Cloud Map, AWS CloudTrail, Amazon CloudWatch, Amazon CloudWatch Events, Amazon CloudWatch Logs, AWS CodeBuild, AWS CodeDeploy, AWS Config, AWS Cost Explorer, AWS Database Migration Service, AWS Direct Connect, AWS Directory Service, Amazon DynamoDB, Amazon Elastic Block Store (Amazon EBS), Amazon Elastic Compute Cloud (Amazon EC2), Amazon EC2 Auto Scaling, EC2 Image Builder, Amazon Elastic Container Registry (Amazon ECR), Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Kubernetes Service, Amazon ElastiCache, AWS Elastic Beanstalk, Elastic Load Balancing, Elastic Load Balancing – Network (NLB), Amazon EMR, Amazon EventBridge, AWS Fargate, Glacier, AWS Health Dashboard, AWS Identity and Access Management (IAM), Amazon Kinesis Data Streams, Amazon Kinesis Data Firehose, AWS Key Management Service (AWS KMS), AWS Lambda, AWS Marketplace, AWS Mobile SDK for iOS and Android, Amazon OpenSearch Service, AWS Organizations, Amazon Redshift, AWS Resource Access Manager, Amazon Relational Database Service (Amazon RDS), Resource Groups, Amazon Route 53, Amazon Virtual Private Cloud (Amazon VPC), AWS Secrets Manager, AWS Shield Standard, AWS Shield Advanced, Amazon Simple Notification Service (Amazon SNS), Amazon Simple Queue Service (Amazon SQS), Amazon Simple Storage Service (Amazon S3), Amazon Simple Workflow Service (Amazon SWF), AWS Step Functions, AWS Support API, AWS Systems Manager, AWS Trusted Advisor, VM Import/Export, AWS VPN, AWS WAF, and AWS X-Ray.

AWS in Israel
According to the Israel Ministry of Economic Industry, Israel is in the front line of the cloud computing era and “is known to be the ‘start-up nation’ of the number of global start-ups being produced. Over the past decade, Israel has produced over 2,000 start-ups, the majority of these start-ups are driven by software as a service (SaaS). Israeli cloud technology remains a strong promise in the market as new start-ups are continuously penetrating the market.”

AWS began supporting startups in Israel in 2013 through its AWS Activate program. In Israel, AWS works with accelerator organizations such as 8200 EISP, F2 Venture Capital – thejunction, and TechStars as well as venture capital firms like Entrée Capital, Bessemer Venture Partners, Pitango, Vertex Ventures Israel, and Viola Group to support the rapid growth of their portfolio companies.

Back in 2014, we opened an AWS office and a research and development (R&D) center in Israel. Since then, Amazon has expanded its R&D presence in the country, which now includes Prime Air and Alexa Shopping.

In 2015, AWS acquired Annapurna Labs, an Israeli microelectronics company, which has developed advanced compute, networking, security, and storage technologies for AWS—such as AWS-designed Graviton processors, AWS Inferentia, AWS Trainium chips, and the AWS Nitro System.

In 2018, we expanded to new offices in Tel Aviv, including AWS Experience Tel Aviv on Floor28 to support the growth of Israeli startups, enterprises, and government customers through technology-focused events and educational activities. Now, AWS Experience Tel Aviv on Floor28 is an education hub where anyone interested in AWS can attend industry events, workshops, and meetups, and receive free, in-person technical and business guidance from AWS experts.

In 2019, we launched the first AWS infrastructure in Israel, opening an Amazon CloudFront edge location. In 2020, we brought AWS Outposts and AWS Direct Connect to Israel, providing Israeli organizations with the ability to run AWS technology in their own data centers and establish dedicated connections back to the AWS Cloud.

In April 2021, the government of Israel announced that it had selected AWS as its primary cloud provider as part of the Nimbus contract. The Nimbus framework will enable government departments—including the ministries, education, healthcare, and municipalities—to accelerate their digital transformation by using AWS technologies.

AWS continues to invest in upskilling local developers, students, and the next generation of IT leaders in Israel through programs such as AWS Educate, AWS Academy, AWS re/Start, and other Training and Certification programs.

AWS Educate and Academy programs are providing free resources to accelerate cloud-related learning and preparing today’s students in Israel for the jobs of the future. Israel colleges already participating in the AWS Academy program include the Bar Ilan University, Ben-Gurion University of the Negev, Holon Institute of Technology, Jerusalem College of Technology, and University of Haifa. We also launched AWS re/Start to focus on helping unemployed or underemployed individuals to launch a new cloud career. You can now apply to AWS re/Start programs through Appleseeds, Sigma Labs Jerusalem, and Analiza Cyber Intelligence in Israel.

AWS Customers in Israel
We have many amazing customers in Israel who are doing incredible things with AWS, for example:

AI21 Labs – AI21 Labs offers access to its state-of-the-art proprietary language models through AI21 Studio for businesses to build their own generative artificial intelligence applications, as well as its consumer product, Wordtune, the first AI-based writing assistant to understand context and meaning. AI21 Labs scaled to hundreds of GPUs efficiently and cost effectively to build the Jurassic-2 family of language models. These models were trained with distributed and parallelized infrastructure based on Amazon EC2 P4d instances 400 Gbps high-performance networking supported by Elastic Fabric Adaptor (EFA).

Bank Leumi – Leumi is one of the leading banks in Israel and has over 200 branches across the country and dedicated teams using AWS to build an advanced banking services marketplace. In just 5 months, Leumi migrated 16 on-premises applications from its former Kubernetes solution to Amazon EKS Anywhere with no service interruptions. The bank’s new environment facilitates a consistent, scalable approach to deployments, saving time and money and increasing innovation velocity.

CyberArk – CyberArk is an AWS partner in the identity security industry. Centered on privileged access management, CyberArk provides the most comprehensive security SaaS offering on AWS for any identity—human or machine—across business applications, distributed workforces, hybrid cloud workloads, and throughout the DevOps lifecycle. CyberArk Identity Security Intelligence has integrated with AWS CloudTrail Lake to increase visibility and responsiveness associated with targeted threats. CyberArk Audit also delivers security event information to Amazon Security Lake.

Ichilov Hospital – The I-Medata Innovation Center of Ichilov Hospital uses AWS Control Tower to facilitate the fast, consistent, and secure creation of AWS accounts while protecting sensitive medical data. The center also relies on Amazon SageMaker to enable its scientists to build, train, and deploy advanced machine learning models for early detection of deterioration in COVID-19 patients. They had full protection of sensitive medical data on AWS while continuing to enable the productivity of researchers.

You can find more customer stories from Israel.

Available Now
The new Tel Aviv Region is ready to support your business. You can find a detailed list of the services available in this Region on the AWS Regional Services List.

With this launch, AWS now spans 102 Availability Zones in 32 geographic Regions around the world. We have also announced plans for 12 more Availability Zones and four more Regions in Canada, Malaysia, New Zealand, and Thailand.

To learn more, see the Global Infrastructure page, give it a try, and send feedback through your usual AWS support contacts in Israel.

— Channy

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New – AWS Public IPv4 Address Charge + Public IP Insights

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-aws-public-ipv4-address-charge-public-ip-insights/

We are introducing a new charge for public IPv4 addresses. Effective February 1, 2024 there will be a charge of $0.005 per IP per hour for all public IPv4 addresses, whether attached to a service or not (there is already a charge for public IPv4 addresses you allocate in your account but don’t attach to an EC2 instance).

Public IPv4 Charge
As you may know, IPv4 addresses are an increasingly scarce resource and the cost to acquire a single public IPv4 address has risen more than 300% over the past 5 years. This change reflects our own costs and is also intended to encourage you to be a bit more frugal with your use of public IPv4 addresses and to think about accelerating your adoption of IPv6 as a modernization and conservation measure.

This change applies to all AWS services including Amazon Elastic Compute Cloud (Amazon EC2), Amazon Relational Database Service (RDS) database instances, Amazon Elastic Kubernetes Service (EKS) nodes, and other AWS services that can have a public IPv4 address allocated and attached, in all AWS regions (commercial, AWS China, and GovCloud). Here’s a summary in tabular form:

Public IP Address Type Current Price/Hour (USD) New Price/Hour (USD)
(Effective February 1, 2024)
In-use Public IPv4 address (including Amazon provided public IPv4 and Elastic IP) assigned to resources in your VPC, Amazon Global Accelerator, and AWS Site-to-site VPN tunnel No charge $0.005
Additional (secondary) Elastic IP Address on a running EC2 instance $0.005 $0.005
Idle Elastic IP Address in account $0.005 $0.005

The AWS Free Tier for EC2 will include 750 hours of public IPv4 address usage per month for the first 12 months, effective February 1, 2024. You will not be charged for IP addresses that you own and bring to AWS using Amazon BYOIP.

Starting today, your AWS Cost and Usage Reports automatically include public IPv4 address usage. When this price change goes in to effect next year you will also be able to use AWS Cost Explorer to see and better understand your usage.

As I noted earlier in this post, I would like to encourage you to consider accelerating your adoption of IPv6. A new blog post shows you how to use Elastic Load Balancers and NAT Gateways for ingress and egress traffic, while avoiding the use of a public IPv4 address for each instance that you launch. Here are some resources to show you how you can use IPv6 with widely used services such as EC2, Amazon Virtual Private Cloud (Amazon VPC), Amazon Elastic Kubernetes Service (EKS), Elastic Load Balancing, and Amazon Relational Database Service (RDS):

Earlier this year we enhanced EC2 Instance Connect and gave it the ability to connect to your instances using private IPv4 addresses. As a result, you no longer need to use public IPv4 addresses for administrative purposes (generally using SSH or RDP).

Public IP Insights
In order to make it easier for you to monitor, analyze, and audit your use of public IPv4 addresses, today we are launching Public IP Insights, a new feature of Amazon VPC IP Address Manager that is available to you at no cost. In addition to helping you to make efficient use of public IPv4 addresses, Public IP Insights will give you a better understanding of your security profile. You can see the breakdown of public IP types and EIP usage, with multiple filtering options:

You can also see, sort, filter, and learn more about each of the public IPv4 addresses that you are using:

Using IPv4 Addresses Efficiently
By using the new IP Insights tool and following the guidance that I shared above, you should be ready to update your application to minimize the effect of the new charge. You may also want to consider using AWS Direct Connect to set up a dedicated network connection to AWS.

Finally, be sure to read our new blog post, Identify and Optimize Public IPv4 Address Usage on AWS, for more information on how to make the best use of public IPv4 addresses.

— Jeff;

New Amazon EC2 Instances (C7gd, M7gd, and R7gd) Powered by AWS Graviton3 Processor with Local NVMe-based SSD Storage

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/new-amazon-ec2-instances-c7gd-m7gd-and-r7gd-powered-by-aws-graviton3-processor-with-local-nvme-based-ssd-storage/

We launched Amazon EC2 C7g instances in May 2022 and M7g and R7g instances in February 2023. Powered by the latest AWS Graviton3 processors, the new instances deliver up to 25 percent higher performance, up to two times higher floating-point performance, and up to 2 times faster cryptographic workload performance compared to AWS Graviton2 processors.

Graviton3 processors deliver up to 3 times better performance compared to AWS Graviton2 processors for machine learning (ML) workloads, including support for bfloat16. They also support DDR5 memory that provides 50 percent more memory bandwidth compared to DDR4. Graviton3 also uses up to 60 percent less energy for the same performance as comparable EC2 instances, which helps you reduce your carbon footprint.

The C7g instances are well suited for compute-intensive workloads, such as high performance computing (HPC), batch processing, ad serving, video encoding, gaming, scientific modeling, distributed analytics, and CPU-based machine learning inference. The M7g instances are for general purpose workloads such as application servers, microservices, gaming servers, mid-sized data stores, and caching fleets. The R7g instances are a great fit for memory-intensive workloads such as open-source databases, in-memory caches, and real-time big data analytics.

Today, we’re adding a d variant to all three instance families. The new Amazon EC2 C7gd, M7gd, and R7gd instance types have NVM Express (NVMe) locally attached up to 2 x 1.9 TB SSD drives that are physically connected to the host server and provide block-level storage that is coupled to the lifetime of the instance. These instances have up to 45 percent better real-time NVMe storage performance than comparable Graviton2-based instances.

These are a great fit for applications that need access to high-speed, low-latency local storage, including those that need temporary storage of data for scratch space, temporary files, and caches. The data on an instance store volume persists only during the life of the associated EC2 instance.

Here are the specs for these instances:

Instance Size vCPU Memory
(GiB)		 Local NVMe Storage (GB) Network Bandwidth
(Gbps)		 EBS Bandwidth
(Gbps)
C7gd/M7gd/R7gd C7gd/M7gd/R7gd C7gd/M7gd/R7gd
medium 1 2/ 4 / 8 1 x 59 Up to 12.5 Up to 10
large 2 4 / 8 / 16 1 x 118 Up to 12.5 Up to 10
xlarge 4 8 / 16 / 32 1 x 237 Up to 12.5 Up to 10
2xlarge 8 16 / 32 / 64 1 x 474 Up to 15 Up to 10
4xlarge 16 32 / 64 / 128 1 x 950 Up to 15 Up to 10
8xlarge 32 64 / 128 / 256 1 x 1900 15 10
12xlarge 48 96 / 192/ 384 2 x 1425 22.5 15
16xlarge 64 128 / 256 / 512 2 x 1900 30 20

These instances are built on the AWS Nitro System, a combination of AWS-designed dedicated hardware and a lightweight hypervisor that allows the delivery of isolated multitenancy, private networking, and fast local storage. They provide up to 20 Gbps Amazon Elastic Block Store (Amazon EBS) bandwidth and up to 30 Gbps network bandwidth. The 16xlarge instances also support Elastic Fabric Adapter (EFA) for applications that need a high level of inter-node communication.

Now Available
Amazon EC2 C7gd, M7gd, and R7gd instances are now available in the following AWS Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), and Europe (Ireland). As usual with Amazon EC2, you only pay for what you use. For more information, see the Amazon EC2 pricing page.

If you’re optimizing applications for Arm architecture, be sure to have a look at our Getting Started collection of resources or learn more about AWS Graviton3-based EC2 instances.

To learn more, visit our Amazon EC2 C7g instances, M7g instances or R7g instances page, and please send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

— Channy

New: AWS Local Zone in Phoenix, Arizona – More Instance Types, More EBS Storage Classes, and More Services

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-aws-local-zone-in-phoenix-arizona-more-instance-types-more-ebs-storage-classes-and-more-services/

I am happy to announce that a new AWS Local Zone in Phoenix, Arizona is now open and ready for you to use, with more instance types, storage classes, and services than ever before.

We launched the first AWS Local Zone in 2019 (AWS Now Available from a Local Zone in Los Angeles) with the goal of making a select set of EC2 instance types, EBS volume types, and other AWS services available with single-digit millisecond when accessed from Los Angeles and other locations in Southern California. Since then, we have launched a second Local Zone in Los Angeles, along with 15 more in other parts of the United States and another 17 around the world, 34 in all. We are also planning to build 19 more Local Zones outside of the US (see the Local Zones Locations page for a complete list).

Local Zones In Action
Our customers make use of Local Zones in many different ways. Popular use cases include real-time gaming, hybrid migrations, content creation for media & entertainment, live video streaming, engineering simulations, and AR/VR at the edge. Here are a couple of great examples that will give you a taste of what is possible:

Arizona State University (ASU) – Known for its innovation and research, ASU is among the largest universities in the U.S. with 173,000 students and 20,000 faculty and staff. Local Zones help them to accelerate the delivery of online services and storage, giving them a level of performance that is helping them to transform the educational experience for students and staff.

DISH Wireless -Two years ago they began to build a cloud-native, fully virtualized 5G network on AWS, making use of Local Zones to support latency-sensitive real-time 5G applications and workloads at the network edge (read Telco Meets AWS Cloud to learn more). The new Local Zone in Phoenix will allow them to further enhance the strength and reliability of their network by extending their 5G core to the edge.

We work closely with these and many other customers to make sure that the Local Zone(s) that they use are a great fit for their use cases. In addition to the already-strong set of instance types, storage classes, and services that are part-and-parcel of every Local Zone, we add others on an as-needed basis.

For example, Local Zones in Los Angeles, Miami, and other locations have additional instance types; several Local Zones have additional Amazon Elastic Block Store (Amazon EBS) storage classes, and others have extra services such as Application Load Balancer, Amazon FSx, Amazon EMR, Amazon ElastiCache, Amazon Relational Database Service (RDS), Amazon GameLift, and AWS Application Migration Service (AWS MGN). You can see this first-hand on the Local Zones Features page.

And Now, Phoenix
As I mentioned earlier, this Local Zone has more instance types, storage classes, and services than earlier Local Zones. Here’s what’s inside:

Instance Types – Compared to all other Local Zones with the T3, C5(d), R5(d), and G4dn instance types, the Phoenix Local Zone includes C6i, M6i, R6i, and Cg6n instances.

EBS Volume Types  – In addition to the gp2 volumes that are available in all Local Zones, the Phoenix Local Zone includes gp3 (General Purpose SSD) , io1 (Provisioned IOPS SSD) , st1 (Throughput Optimized HDD), and sc1 (Cold HDD) storage.

Services – In addition to Amazon Elastic Compute Cloud (Amazon EC2), Amazon Elastic Block Store (Amazon EBS), AWS Shield, Amazon Virtual Private Cloud (Amazon VPC), Amazon Elastic Container Service (Amazon ECS). Amazon Elastic Kubernetes Service (EKS), Application Load Balancer, and AWS Direct Connect, the Phoenix LZ includes NAT Gateway.

Pricing Models – In addition to On-Demand and Savings Plans, the Phoenix Local Zone includes Spot.

Going forward, we plan to launch more Local Zones that are similarly equipped.

Opting-In to the Phoenix Local Zone
The original Phoenix Local Zone was launched in 2022 and remains available to customers who have already enabled it. The Zone that we are announcing today can be enabled by new and existing customers.

To get started with this or any other Local Zone, I must first enable it. To do this, I open the EC2 Console, select the parent region (US West (Oregon)) from the menu, and then click EC2 Dashboard in the left-side navigation:

Then I click on Zones in the Account attributes box:

Next, I scroll down to the new Phoenix Local Zone (us-west-2-phx-2), and click Manage:

I click Enabled, and then Update zone group:

I confirm that I want to enable the Zone Group, and click Ok:

And I am all set. I can create EBS volumes, launch EC2 instances, and make use of the other services in this Local Zone.

— Jeff;

New – Amazon EC2 P5 Instances Powered by NVIDIA H100 Tensor Core GPUs for Accelerating Generative AI and HPC Applications

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/new-amazon-ec2-p5-instances-powered-by-nvidia-h100-tensor-core-gpus-for-accelerating-generative-ai-and-hpc-applications/

In March 2023, AWS and NVIDIA announced a multipart collaboration focused on building the most scalable, on-demand artificial intelligence (AI) infrastructure optimized for training increasingly complex large language models (LLMs) and developing generative AI applications.

We preannounced Amazon Elastic Compute Cloud (Amazon EC2) P5 instances powered by NVIDIA H100 Tensor Core GPUs and AWS’s latest networking and scalability that will deliver up to 20 exaflops of compute performance for building and training the largest machine learning (ML) models. This announcement is the product of more than a decade of collaboration between AWS and NVIDIA, delivering the visual computing, AI, and high performance computing (HPC) clusters across the Cluster GPU (cg1) instances (2010), G2 (2013), P2 (2016), P3 (2017), G3 (2017), P3dn (2018), G4 (2019), P4 (2020), G5 (2021), and P4de instances (2022).

Most notably, ML model sizes are now reaching trillions of parameters. But this complexity has increased customers’ time to train, where the latest LLMs are now trained over the course of multiple months. HPC customers also exhibit similar trends. With the fidelity of HPC customer data collection increasing and data sets reaching exabyte scale, customers are looking for ways to enable faster time to solution across increasingly complex applications.

Introducing EC2 P5 Instances
Today, we are announcing the general availability of Amazon EC2 P5 instances, the next-generation GPU instances to address those customer needs for high performance and scalability in AI/ML and HPC workloads. P5 instances are powered by the latest NVIDIA H100 Tensor Core GPUs and will provide a reduction of up to 6 times in training time (from days to hours) compared to previous generation GPU-based instances. This performance increase will enable customers to see up to 40 percent lower training costs.

P5 instances provide 8 x NVIDIA H100 Tensor Core GPUs with 640 GB of high bandwidth GPU memory, 3rd Gen AMD EPYC processors, 2 TB of system memory, and 30 TB of local NVMe storage. P5 instances also provide 3200 Gbps of aggregate network bandwidth with support for GPUDirect RDMA, enabling lower latency and efficient scale-out performance by bypassing the CPU on internode communication.

Here are the specs for these instances:

Instance
Size		 vCPUs Memory
(GiB)		 GPUs
(H100)		 Network Bandwidth
(Gbps)		 EBS Bandwidth
(Gbps)		 Local Storage
(TB)
P5.48xlarge 192 2048 8 3200 80 8 x 3.84

Here’s a quick infographic that shows you how the P5 instances and NVIDIA H100 Tensor Core GPUs compare to previous instances and processors:

P5 instances are ideal for training and running inference for increasingly complex LLMs and computer vision models behind the most demanding and compute-intensive generative AI applications, including question answering, code generation, video and image generation, speech recognition, and more. P5 will provide up to 6 times lower time to train compared with previous generation GPU-based instances across those applications. Customers who can use lower precision FP8 data types in their workloads, common in many language models that use a transformer model backbone, will see further benefit at up to 6 times performance increase through support for the NVIDIA transformer engine.

HPC customers using P5 instances can deploy demanding applications at greater scale in pharmaceutical discovery, seismic analysis, weather forecasting, and financial modeling. Customers using dynamic programming (DP) algorithms for applications like genome sequencing or accelerated data analytics will also see further benefit from P5 through support for a new DPX instruction set.

This enables customers to explore problem spaces that previously seemed unreachable, iterate on their solutions at a faster clip, and get to market more quickly.

You can see the detail of instance specifications along with comparisons of instance types between p4d.24xlarge and new p5.48xlarge below:

Feature p4d.24xlarge p5.48xlarge Comparision
Number & Type of Accelerators 8 x NVIDIA A100 8 x NVIDIA H100 –
FP8 TFLOPS per Server – 16,000 640% vs.A100 FP16
FP16 TFLOPS per Server 2,496 8,000
GPU Memory 40 GB 80 GB 200%
GPU Memory Bandwidth 12.8 TB/s 26.8 TB/s 200%
CPU Family Intel Cascade Lake AMD Milan –
vCPUs 96  192 200%
Total System Memory 1152 GB 2048 GB 200%
Networking Throughput 400 Gbps 3200 Gbps 800%
EBS Throughput 19 Gbps 80 Gbps 400%
Local Instance Storage 8 TBs NVMe 30 TBs NVMe 375%
GPU to GPU Interconnect 600 GB/s 900 GB/s 150%

Second-generation Amazon EC2 UltraClusters and Elastic Fabric Adaptor
P5 instances provide market-leading scale-out capability for multi-node distributed training and tightly coupled HPC workloads. They offer up to 3,200 Gbps of networking using the second-generation Elastic Fabric Adaptor (EFA) technology, 8 times compared with P4d instances.

To address customer needs for large-scale and low latency, P5 instances are deployed in the second-generation EC2 UltraClusters, which now provide customers with lower latency across up to 20,000+ NVIDIA H100 Tensor Core GPUs. Providing the largest scale of ML infrastructure in the cloud, P5 instances in EC2 UltraClusters deliver up to 20 exaflops of aggregate compute capability.

EC2 UltraClusters use Amazon FSx for Lustre, fully managed shared storage built on the most popular high-performance parallel file system. With FSx for Lustre, you can quickly process massive datasets on demand and at scale and deliver sub-millisecond latencies. The low-latency and high-throughput characteristics of FSx for Lustre are optimized for deep learning, generative AI, and HPC workloads on EC2 UltraClusters.

FSx for Lustre keeps the GPUs and ML accelerators in EC2 UltraClusters fed with data, accelerating the most demanding workloads. These workloads include LLM training, generative AI inferencing, and HPC workloads, such as genomics and financial risk modeling.

Getting Started with EC2 P5 Instances
To get started, you can use P5 instances in the US East (N. Virginia) and US West (Oregon) Region.

When launching P5 instances, you will choose AWS Deep Learning AMIs (DLAMIs) to support P5 instances. DLAMI provides ML practitioners and researchers with the infrastructure and tools to quickly build scalable, secure distributed ML applications in preconfigured environments.

You will be able to run containerized applications on P5 instances with AWS Deep Learning Containers using libraries for Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service  (Amazon EKS).  For a more managed experience, you can also use P5 instances via Amazon SageMaker, which helps developers and data scientists easily scale to tens, hundreds, or thousands of GPUs to train a model quickly at any scale without worrying about setting up clusters and data pipelines. HPC customers can leverage AWS Batch and ParallelCluster with P5 to help orchestrate jobs and clusters efficiently.

Existing P4 customers will need to update their AMIs to use P5 instances. Specifically, you will need to update your AMIs to include the latest NVIDIA driver with support for NVIDIA H100 Tensor Core GPUs. They will also need to install the latest CUDA version (CUDA 12), CuDNN version, framework versions (e.g., PyTorch, Tensorflow), and EFA driver with updated topology files. To make this process easy for you, we will provide new DLAMIs and Deep Learning Containers that come prepackaged with all the needed software and frameworks to use P5 instances out of the box.

Now Available
Amazon EC2 P5 instances are available today in AWS Regions: US East (N. Virginia) and US West (Oregon). For more information, see the Amazon EC2 pricing page. To learn more, visit our P5 instance page and explore AWS re:Post for EC2 or through your usual AWS Support contacts.

You can choose a broad range of AWS services that have generative AI built in, all running on the most cost-effective cloud infrastructure for generative AI. To learn more, visit Generative AI on AWS to innovate faster and reinvent your applications.

— Channy