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Amazon FSx for Lustre launches new storage class with the lowest-cost and only fully elastic Lustre file storage

Post Syndicated from Veliswa Boya original https://aws.amazon.com/blogs/aws/amazon-fsx-for-lustre-adds-new-storage-class-with-the-lowest-cost-and-only-fully-elastic-lustre-file-storage/

Seismic imaging is a geophysical technique used to create detailed pictures of the Earth’s subsurface structure. It works by generating seismic waves that travel into the ground, reflect off various rock layers and structures, and return to the surface where they’re detected by sensitive instruments known as geophones or hydrophones. The huge volumes of acquired data often reach petabytes for a single survey and this presents significant storage, processing, and management challenges for researchers and energy companies.

Customers who run these seismic imaging workloads or other high performance computing (HPC) workloads, such as weather forecasting, advanced driver-assistance system (ADAS) training, or genomics analysis, already store the huge volumes of data on either hard disk drive (HDD)-based or a combination of HDD and solid state drive (SSD) file storage on premises. However, as these on premises datasets and workloads scale, customers find it increasingly challenging and expensive due to the need to make upfront capital investments to keep up with performance needs of their workloads and avoid running out of storage capacity.

Today, we’re announcing the general availability of the Amazon FSx for Lustre Intelligent-Tiering, a new storage class that delivers virtually unlimited scalability, the only fully elastic Lustre file storage, and the lowest cost Lustre file storage in the cloud. With a starting price of less than $0.005 per GB-month, FSx for Lustre Intelligent-Tiering offers the lowest cost high-performance file storage in the cloud, reducing storage costs for infrequently accessed data by up to 96 percent compared to other managed Lustre options. Elasticity means you no longer need to provision storage capacity upfront because your file system will grow and shrink as you add or delete data, and you pay only for the amount of data you store.

FSx for Lustre Intelligent-Tiering automatically optimizes costs by tiering cold data to the applicable lower-cost storage tier based on access patterns and includes an optional SSD read cache to improve performance for your most latency sensitive workloads. Intelligent-Tiering delivers high performance whether you’re starting with gigabytes of experimental data or working with large petabyte-scale datasets for your most demanding artificial intelligence/machine learning (AI/ML) and HPC workloads. With the flexibility to adjust your file system’s performance independent of storage, Intelligent-Tiering delivers up to 34 percent better price performance than on premises HDD file systems. The Intelligent-Tiering storage class is optimized for HDD-based or mixed HDD/SSD workloads that have a combination of hot and cold data. You can migrate and run such workloads to FSx for Lustre Intelligent-Tiering without application changes, eliminating storage capacity planning and management, while paying only for the resources that you use.

Prior to this launch, customers used the FSx for Lustre SSD storage class to accelerate ML and HPC workloads that need all-SSD performance and consistent low-latency access to all data. However, many workloads have a combination of hot and cold data and they don’t need all-SSD storage for colder portions of the data. FSx for Lustre is increasingly used in AI/ML workloads to increase graphics processing unit (GPU) utilization, and now it’s even more cost optimized to be one of the options for these workloads.

FSx for Lustre Intelligent-Tiering
Your data moves between three storage tiers (Frequent Access, Infrequent Access, and Archive) with no effort on your part, so you get automatic cost savings with no upfront costs or commitments. The tiering works as follows:

Frequent Access – Data that has been accessed within the last 30 days is stored in this tier.

Infrequent Access – Data that hasn’t been accessed for 30 – 90 days is stored in this tier, at a 44 percent cost reduction from Frequent Access.

Archive – Data that hasn’t been accessed for 90 or more days is stored in this tier, at a 65 percent cost reduction compared to Infrequent Access.

Regardless of the storage tier, your data is stored across multiple AWS Availability Zones for redundancy and availability, compared to typical on-premises implementations, which are usually confined within a single physical location. Additionally, your data can be retrieved instantly in milliseconds.

Creating a file system
I can create a file system using the AWS Management Console, AWS Command Line Interface (AWS CLI), API, or AWS CloudFormation. On the console, I choose Create file system to get started.


I select Amazon FSx for Lustre and choose Next.


Now, it’s time to enter the rest of the information to create the file system. I enter a name (veliswa_fsxINT_1) for my file system, and for deployment and storage class, I select Persistent, Intelligent-Tiering. I choose the desired Throughput capacity and the Metadata IOPS. The SSD read cache will be automatically configured by FSx for Lustre based on the specified throughput capacity. I leave the rest as the default, choose Next, and review my choices to create my file system.

With Amazon FSx for Lustre Intelligent-Tiering, you have the flexibility to provision the necessary performance for your workloads without having to provision any underlying storage capacity upfront.


I wanted to know which values were editable after creation, so I paid closer attention before finalizing the creation of the file system. I noted that Throughput capacity, Metadata IOPS, Security groups, SSD read cache, and a few others were editable later. After I start running the ML jobs, it might be necessary to increase the throughput capacity based on the volumes of data I’ll be processing, so this information is important to me.

The file system is now available. Considering that I’ll be running HPC workloads, I anticipate that I’ll be processing high volumes of data later, so I’ll increase the throughput capacity to 24 GB/s. After all, I only pay for the resources I use.



The SSD read cache is scaled automatically as your performance needs increase. You can adjust the cache size any time independently in user-provisioned mode or disable the read cache if you don’t need low-latency access.


Good to know

  • FSx for Lustre Intelligent-Tiering is designed to deliver up to multiple terabytes per second of total throughput.
  • FSx for Lustre with Elastic Fabric Adapter (EFA)/GPU Direct Storage (GDS) support provides up to 12x (up to 1200 Gbps) higher per-client throughput compared to the previous FSx for Lustre systems.
  • It can deliver up to tens of millions of IOPS for writes and cached reads. Data in the SSD read cache has submillisecond time-to-first-byte latencies, and all other data has time-to-first-byte latencies in the range of tens of milliseconds.

Now available
Here are a couple of things to keep in mind:

FSx Intelligent-Tiering storage class is available in the new FSx for Lustre file systems in the US East (N. Virginia, Ohio), US West (N. California, Oregon), Canada (Central), Europe (Frankfurt, Ireland, London, Stockholm), and Asia Pacific (Hong Kong, Mumbai, Seoul, Singapore, Sydney, Tokyo) AWS Regions.

You pay for data and metadata you store on your file system (GB/months). When you write data or when you read data that is not in the SSD read cache, you pay per operation. You pay for the total throughput capacity (in MBps/month), metadata IOPS (IOPS/month), and SSD read cache size for data and metadata (GB/month) you provision on your file system. To learn more, visit the Amazon FSx for Lustre Pricing page. To learn more about Amazon FSx for Lustre including this feature, visit the Amazon FSx for Lustre page.

Give Amazon FSx for Lustre Intelligent-Tiering a try in the Amazon FSx console today and send feedback to AWS re:Post for Amazon FSx for Lustre or through your usual AWS Support contacts.

– Veliswa.

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Amazon Aurora DSQL is now generally available

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/amazon-aurora-dsql-is-now-generally-available/

Today, we’re announcing the general availability of Amazon Aurora DSQL, the fastest serverless distributed SQL database with virtually unlimited scale, the highest availability, and zero infrastructure management for always available applications. You can remove the operational burden of patching, upgrades, and maintenance downtime and count on an easy-to-use developer experience to create a new database in a few quick steps.

When we introduced the preview of Aurora DSQL at AWS re:Invent 2024, our customers were excited by this innovative solution to simplify complex relational database challenges. In his keynote, Dr. Werner Vogels, CTO of Amazon.com, talked about managing complexity upfront in the design of Aurora DSQL. Unlike most traditional databases, Aurora DSQL is disaggregated into multiple independent components such as a query processor, adjudicator, journal, and crossbar.

These components have high cohesion, communicate through well-specified APIs, and scale independently based on your workloads. This architecture enables multi-Region strong consistency with low latency and globally synchronized time. To learn more about how Aurora DSQL works behind the scenes, watch Dr. Werner Vogels’ keynote and read about an Aurora DSQL story.

The architecture of Amazon Aurora DSQL
Your application can use the fastest distributed SQL reads and writes and scale to meet any workload demand without any database sharding or instance upgrades. With Aurora DSQL, its active-active distributed architecture is designed for 99.99 percent availability in a single Region and 99.999 percent availability across multiple Regions. This means your applications can continue to read and write with strong consistency, even in the rare case an application is unable to connect to a Region cluster endpoint.

In a single-Region configuration, Aurora DSQL commits all write transactions to a distributed transaction log and synchronously replicates all committed log data to user storage replicas in three Availability Zones. Cluster storage replicas are distributed across a storage fleet and automatically scale to ensure optimal read performance.

Multi-Region clusters provide the same resilience and connectivity as single-Region clusters while improving availability through two Regional endpoints, one for each peered cluster Region. Both endpoints of a peered cluster present a single logical database and support concurrent read and write operations with strong data consistency. A third Region acts as a log-only witness which means there is is no cluster resource or endpoint. This means you can balance applications and connections for geographic locations, performance, or resiliency purposes, making sure readers consistently see the same data.

Aurora DSQL is an ideal choice to support applications using microservices and event-driven architectures, and you can design highly scalable solutions for industries such as banking, ecommerce, travel, and retail. It’s also ideal for multi-tenant software as a service (SaaS) applications and data-driven services like payment processing, gaming platforms, and social media applications that require multi-Region scalability and resilience.

Getting started with Amazon Aurora DSQL
Aurora DSQL provides a easy-to-use experience, starting with a simple console experience. You can use familiar SQL clients to leverage existing skillsets, and integration with other AWS services to improve managing databases.

To create an Aurora DSQL cluster, go to the Aurora DSQL console and choose Create cluster. You can choose either Single-Region or Multi-Region configuration options to help you establish the right database infrastructure for your needs.

1. Create a single-Region cluster

To create a single-Region cluster, you only choose Create cluster. That’s all.

In a few minutes, you’ll see your Aurora DSQL cluster created. To connect your cluster, you can use your favorite SQL client such as PostgreSQL interactive terminal,  DBeaver, JetBrains DataGrip, or you can take various programmable approaches with a database endpoint and authentication token as a password. You can integrate with AWS Secrets Manager for automated token generation and rotation to secure and simplify managing credentials across your infrastructure.

To get the authentication token, choose Connect and Get Token in your cluster detail page. Copy the endpoint from Endpoint (Host) and the generated authentication token after Connect as admin is chosen in the Authentication token (Password) section.

Then, choose Open in CloudShell, and with a few clicks, you can seamlessly connect to your cluster.

After you connect the Aurora DSQL cluster, test your cluster by running sample SQL statements. You can also query SQL statements for your applications using your favorite programming languages: Python, Java, JavaScript, C++, Ruby, .NET, Rust, and Golang. You can build sample applications using a Django, Ruby on Rails, and AWS Lambda application to interact with Amazon Aurora DSQL.

2. Create a multi-Region cluster

To create a multi-Region cluster, you need to add the other cluster’s Amazon Resource Name (ARN) to peer the clusters.

To create the first cluster, choose Multi-Region in the console. You will also be required to choose the Witness Region, which receives data written to any peered Region but doesn’t have an endpoint. Choose Create cluster. If you already have a remote Region cluster, you can optionally enter its ARN.

Next, add an existing remote cluster or create your second cluster in another Region by choosing Create cluster.

Now, you can create the second cluster with your peer cluster ARN as the first cluster.

When the second cluster is created, you must peer the cluster in us-east-1 in order to complete the multi-Region creation.

Go to the first cluster page and choose Peer to confirm cluster peering for both clusters.

Now, your multi-Region cluster is created successfully. You can see details about the peers that are in other Regions in the Peers tab.

To get hands-on experience with Aurora DSQL, you can use this step-by-step workshop. It walks through the architecture, key considerations, and best practices as you build a sample retail rewards point application with active-active resiliency.

You can use the AWS SDKs, AWS Comand Line Interface (AWS CLI), and Aurora DSQL APIs to create and manage Aurora DSQL programmatically. To learn more, visit Setting up Aurora DSQL clusters in the Amazon Aurora DSQL User Guide.

What did we add after the preview?
We used your feedback and suggestions during the preview period to add new capabilities. We’ve highlighted a few of the new features and capabilities:

  • Console experience –We improved your cluster management experience to create and peer multi-Region clusters as well as easily connect using AWS CloudShell.
  • PostgreSQL features – We added support for views, unique secondary indexes for tables with existing data and launched Auto-Analyze which removes the need to manually maintain accurate table statistics. Learn about Aurora DSQL PostgreSQL-compatible features.
  • Integration with AWS services –We integrated various AWS services such as AWS Backup for a full snapshot backup and Aurora DSQL cluster restore, AWS PrivateLink for private network connectivity, AWS CloudFormation for managing Aurora DSQL resources, and AWS CloudTrail for logging Aurora DSQL operations.

Aurora DSQL now provides a Model Context Protocol (MCP) server to improve developer productivity by making it easy for your generative AI models and database to interact through natural language. For example, install Amazon Q Developer CLI and configure Aurora DSQL MCP server. Amazon Q Developer CLI now has access to an Aurora DSQL cluster. You can easily explore the schema of your database, understand the structure of the tables, and even execute complex SQL queries, all without having to write any additional integration code.

Now available
Amazon Aurora DSQL is available today in the AWS US East (N. Virginia), US East (Ohio), US West (Oregon) Regions for single- and multi-Region clusters (two peers and one witness Region), Asia Pacific (Osaka) and Asia Pacific (Tokyo) for single-Region clusters, and Europe (Ireland), Europe (London), and Europe (Paris) for single-Region clusters.

You’re billed on a monthly basis using a single normalized billing unit called Distributed Processing Unit (DPU) for all request-based activity such as read/write. Storage is based on the total size of your database and measured in GB-months. You are only charged for one logical copy of your data per single-Region cluster or multi-Region peered cluster. As a part of the AWS Free Tier, your first 100,000 DPUs and 1 GB-month of storage each month is free. To learn more, visit Amazon Aurora DSQL Pricing.

Give Aurora DSQL a try for free in the Aurora DSQL console. For more information, visit the Aurora DSQL User Guide and send feedback to AWS re:Post for Aurora DSQL or through your usual AWS support contacts.

— Channy

Retail Under Siege: What Recent Cyber Attacks Tell Us About Today’s Threat Landscape

Post Syndicated from Emma Burdett original https://blog.rapid7.com/2025/05/27/retail-under-siege-what-recent-cyber-attacks-tell-us-about-todays-threat-landscape/

Retail Under Siege: What Recent Cyber Attacks Tell Us About Today’s Threat Landscape

When several major UK organizations, including well-known retail brands, found themselves caught in a cyber attack earlier this year, it made headlines. But this incident wasn’t the first, and it won’t be the last. It reflects a growing trend where attackers exploit third-party vendors to breach multiple businesses through a single point of entry.

In one case, the compromise stemmed from a vulnerability in MOVEit Transfer, a widely used file transfer tool. Attackers exploited the flaw through Zellis, a payroll provider servicing organisations such as Boots, the Co-op, and parts of the NHS. From that single access point, they were able to exfiltrate sensitive employee data, including names, dates of birth, national insurance numbers, and in some cases, bank details. Some customer data was also affected, although not financial information.

This wasn’t just a breach. It was a blueprint—and a clear signal that even the most trusted brands are vulnerable when third-party risk is left unaddressed.

A back door into the business

The MOVEit vulnerability, first exposed in mid-2023, has become a favoured entry point for criminal groups looking to conduct high-volume, high-impact attacks. In this instance, attackers reportedly linked to the group Scattered Spider moved quickly, exploiting the flaw to access data at scale.

They didn’t need to phish credentials, crack passwords, or trick users. They found a vulnerable service buried in the supply chain and used automation and speed to do the rest.

This type of breach is becoming alarmingly common. Attackers increasingly target third-party software and services, i.e. vendors with connections to dozens or hundreds of organisations, because it maximises the potential return on effort. Instead of breaching one business at a time, they go upstream and compromise a shared dependency.

Scattered Spider in particular has shown a keen focus on the retail sector, where high transaction volumes, rich identity data, and complex supply chains create an attractive threat surface. As noted in Dark Reading, these groups are playing the long game—building persistent access, quietly exfiltrating data, and returning to monetise later.

This is third-party risk in action. And it’s only becoming more sophisticated.

Modern threat actors, old-school outcomes

Rapid7’s threat intelligence teams have tracked how ransomware groups and data extortion crews have professionalised their operations over the past two years. These groups are no longer operating in the shadows. They’re mimicking enterprise structures, with revenue sharing models, support desks, marketing channels, and on-demand tooling.

Groups like DragonForce, for instance, use a white-label ransomware-as-a-service model built on LockBit code, offering affiliates a fully managed platform for launching attacks. As Raj Samani, SVP and Chief Scientist at Rapid7, noted in recent research, these groups provide their affiliates with everything they need to run sophisticated campaigns: prebuilt infrastructure, encryption tools, data leak sites, and communication channels. Their tactics often involve dual extortion – stealing data and threatening to publish it unless a ransom is paid, adding public pressure to the private pain of a breach.

This business-like approach is exactly why ransomware remains one of the most dominant threats in 2025. Ransomware today is less about disruption and more about strategy. Our recent analysis explores how these attacks have evolved from smash-and-grab to long-game economics, with extortion tactics designed to exert maximum pressure over time.

But the financial hit is only one part of the damage. As Raj explores in this piece for the Cyber Threat Alliance, the broader impact of cybercrime often goes uncounted—from reputational fallout and operational disruption to the long-term toll it takes on people and trust. These are the consequences organisations must now plan for, not just respond to.

These tactics are playing out across the retail sector and beyond. Attackers are using known exploits, moving efficiently, and causing maximum disruption—not by inventing new techniques, but by taking advantage of weaknesses businesses continue to overlook.

The visibility gap

The obvious takeaway is that third-party risk is real, and growing. But there’s a deeper issue beneath the surface: many organisations lack the visibility they need to see where their risk truly lies.

As we’ve argued before, proactive visibility is foundational to strong cybersecurity. If you don’t have a live, accurate view of your external exposure across infrastructure, vendors, applications, and user behaviour, you’re already behind. And if you don’t understand how your systems interact with those of your partners, you can’t realistically assess the blast radius of a third-party breach.

This is where a Continuous Threat Exposure Management (CTEM) approach is essential. CTEM isn’t about reacting to every vulnerability alert. It’s about identifying which exposures are most likely to be exploited and putting the processes in place to resolve them before attackers take advantage.

That means:

  • Mapping your external attack surface, including shadow assets and forgotten systems
  • Actively monitoring your vendors and data flows, not just annually but continuously
  • Understanding exploitability, not just vulnerability, to focus on risk, not noise
  • Running simulations, tabletops, and breach-and-attack testing to stress-test your response before the real thing hits

The goal isn’t perfection. It’s preparedness.

From theory to action

The real takeaway for security leaders isn’t “this could happen to us.” It’s the recognition that some version of this is already happening—whether they know it or not.

Attackers are scanning your environment. They’re probing your vendors. They’re replaying leaked credentials and looking for unpatched services. What they find, and how quickly you detect and respond defines the outcome.

This is why we encourage organisations to move from reactive defence to proactive control. You don’t need to boil the ocean. But you do need a plan that accounts for real-world attacker behaviour, not just compliance checklists.

At Rapid7, we advocate for a layered, risk-informed approach. That includes:

  • Exposure management that gives you live insight into where your business is vulnerable
  • Attack surface management that helps you find and fix weaknesses before they’re exploited
  • Managed detection and response (MDR) services that augment your team’s ability to act quickly and effectively

But more than any product or service, the most important element is mindset. Security is no longer something you install or outsource. It’s something you practice every day, across every level of the business.

Shared responsibility in a connected world

Breaches like this one also raise important questions for consumers.

As Rapid7 CTO EMEA Thom Langford recently pointed out, individuals can take practical steps to reduce their risk. That includes using a password manager to store strong, unique passwords, enabling multi-factor authentication (MFA), and avoiding the storage of card details in retail accounts. For frequent online shoppers, virtual or disposable cards offer an extra layer of protection.

Still, the burden cannot rest on individuals alone. Organisations must design systems that make secure choices the default. That means encrypting data at rest and in transit, enforcing MFA by default, and never storing sensitive credentials in plaintext.

In a hyper-connected digital economy, trust is everything. And trust is built through transparency, responsiveness, and consistent investment in security—even when there’s no breach in the headlines.

A final word

These attacks aren’t happening because a single business made a mistake. They’re happening because attackers are evolving and because the systems we all rely on are more interconnected than ever.

Security leaders can’t control every vendor or patch every flaw in someone else’s software. But they can control how they prepare, how they prioritise, and how they respond.

The organisations that come out stronger are the ones that treat security as a continuous discipline – one rooted in visibility, resilience, and readiness.

Because in 2025, the question isn’t whether you’ll be targeted.

It’s whether you’ll be ready.

Introducing Claude 4 in Amazon Bedrock, the most powerful models for coding from Anthropic

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/claude-opus-4-anthropics-most-powerful-model-for-coding-is-now-in-amazon-bedrock/

Anthropic launched the next generation of Claude models today—Opus 4 and Sonnet 4—designed for coding, advanced reasoning, and the support of the next generation of capable, autonomous AI agents. Both models are now generally available in Amazon Bedrock, giving developers immediate access to both the model’s advanced reasoning and agentic capabilities.

Amazon Bedrock expands your AI choices with Anthropic’s most advanced models, giving you the freedom to build transformative applications with enterprise-grade security and responsible AI controls. Both models extend what’s possible with AI systems by improving task planning, tool use, and agent steerability.

With Opus 4’s advanced intelligence, you can build agents that handle long-running, high-context tasks like refactoring large codebases, synthesizing research, or coordinating cross-functional enterprise operations. Sonnet 4 is optimized for efficiency at scale, making it a strong fit as a subagent or for high-volume tasks like code reviews, bug fixes, and production-grade content generation.

When building with generative AI, many developers work on long-horizon tasks. These workflows require deep, sustained reasoning, often involving multistep processes, planning across large contexts, and synthesizing diverse inputs over extended timeframes. Good examples of these workflows are developer AI agents that help you to refactor or transform large projects. Existing models may respond quickly and fluently, but maintaining coherence and context over time—especially in areas like coding, research, or enterprise workflows—can still be challenging.

Claude Opus 4
Claude Opus 4 is the most advanced model to date from Anthropic, designed for building sophisticated AI agents that can reason, plan, and execute complex tasks with minimal oversight. Anthropic benchmarks show it is the best coding model available on the market today. It excels in software development scenarios where extended context, deep reasoning, and adaptive execution are critical. Developers can use Opus 4 to write and refactor code across entire projects, manage full-stack architectures, or design agentic systems that break down high-level goals into executable steps. It demonstrates strong performance on coding and agent-focused benchmarks like SWE-bench and TAU-bench, making it a natural choice for building agents that handle multistep development workflows. For example, Opus 4 can analyze technical documentation, plan a software implementation, write the required code, and iteratively refine it—while tracking requirements and architectural context throughout the process.

Claude Sonnet 4
Claude Sonnet 4 complements Opus 4 by balancing performance, responsiveness, and cost, making it well-suited for high-volume production workloads. It’s optimized for everyday development tasks with enhanced performance, such as powering code reviews, implementing bug fixes, and new feature development with immediate feedback loops. It can also power production-ready AI assistants for near real-time applications. Sonnet 4 is a drop-in replacement from Claude Sonnet 3.7. In multi-agent systems, Sonnet 4 performs well as a task-specific subagent—handling responsibilities like targeted code reviews, search and retrieval, or isolated feature development within a broader pipeline. You can also use Sonnet 4 to manage continuous integration and delivery (CI/CD) pipelines, perform bug triage, or integrate APIs, all while maintaining high throughput and developer-aligned output.

Opus 4 and Sonnet 4 are hybrid reasoning models offering two modes: near-instant responses and extended thinking for deeper reasoning. You can choose near-instant responses for interactive applications, or enable extended thinking when a request benefits from deeper analysis and planning. Thinking is especially useful for long-context reasoning tasks in areas like software engineering, math, or scientific research. By configuring the model’s thinking budget—for example, by setting a maximum token count—you can tune the tradeoff between latency and answer depth to fit your workload.

How to get started
To see Opus 4 or Sonnet 4 in action, enable the new model in your AWS account. Then, you can start coding using the Bedrock Converse API with model IDanthropic.claude-opus-4-20250514-v1:0 for Opus 4 and anthropic.claude-sonnet-4-20250514-v1:0 for Sonnet 4. We recommend using the Converse API, because it provides a consistent API that works with all Amazon Bedrock models that support messages. This means you can write code one time and use it with different models.

For example, let’s imagine I write an agent to review code before merging changes in a code repository. I write the following code that uses the Bedrock Converse API to send a system and user prompts. Then, the agent consumes the streamed result.

private let modelId = "us.anthropic.claude-sonnet-4-20250514-v1:0"

// Define the system prompt that instructs Claude how to respond
let systemPrompt = """
You are a senior iOS developer with deep expertise in Swift, especially Swift 6 concurrency. Your job is to perform a code review focused on identifying concurrency-related edge cases, potential race conditions, and misuse of Swift concurrency primitives such as Task, TaskGroup, Sendable, @MainActor, and @preconcurrency.

You should review the code carefully and flag any patterns or logic that may cause unexpected behavior in concurrent environments, such as accessing shared mutable state without proper isolation, incorrect actor usage, or non-Sendable types crossing concurrency boundaries.

Explain your reasoning in precise technical terms, and provide recommendations to improve safety, predictability, and correctness. When appropriate, suggest concrete code changes or refactorings using idiomatic Swift 6
"""
let system: BedrockRuntimeClientTypes.SystemContentBlock = .text(systemPrompt)

// Create the user message with text prompt and image
let userPrompt = """
Can you review the following Swift code for concurrency issues? Let me know what could go wrong and how to fix it.
"""
let prompt: BedrockRuntimeClientTypes.ContentBlock = .text(userPrompt)

// Create the user message with both text and image content
let userMessage = BedrockRuntimeClientTypes.Message(
    content: [prompt],
    role: .user
)

// Initialize the messages array with the user message
var messages: [BedrockRuntimeClientTypes.Message] = []
messages.append(userMessage)

// Configure the inference parameters
let inferenceConfig: BedrockRuntimeClientTypes.InferenceConfiguration = .init(maxTokens: 4096, temperature: 0.0)

// Create the input for the Converse API with streaming
let input = ConverseStreamInput(inferenceConfig: inferenceConfig, messages: messages, modelId: modelId, system: [system])

// Make the streaming request
do {
    // Process the stream
    let response = try await bedrockClient.converseStream(input: input)

    // Iterate through the stream events
    for try await event in stream {
        switch event {
        case .messagestart:
            print("AI-assistant started to stream"")

        case let .contentblockdelta(deltaEvent):
            // Handle text content as it arrives
            if case let .text(text) = deltaEvent.delta {
                self.streamedResponse + = text
                print(text, termination: "")
            }

        case .messagestop:
            print("\n\nStream ended")
            // Create a complete assistant message from the streamed response
            let assistantMessage = BedrockRuntimeClientTypes.Message(
                content: [.text(self.streamedResponse)],
                role: .assistant
            )
            messages.append(assistantMessage)

        default:
            break
        }
    }

To help you get started, my colleague Dennis maintains a broad range of code examples for multiple use cases and a variety of programming languages.

Available today in Amazon Bedrock
This release gives developers immediate access in Amazon Bedrock, a fully managed, serverless service, to the next generation of Claude models developed by Anthropic. Whether you’re already building with Claude in Amazon Bedrock or just getting started, this seamless access makes it faster to experiment, prototype, and scale with cutting-edge foundation models—without managing infrastructure or complex integrations.

Claude Opus 4 is available in the following AWS Regions in North America: US East (Ohio, N. Virginia) and US West (Oregon). Claude Sonnet 4 is available not only in AWS Regions in North America but also in APAC, and Europe: US East (Ohio, N. Virginia), US West (Oregon), Asia Pacific (Hyderabad, Mumbai, Osaka, Seoul, Singapore, Sydney, Tokyo), and Europe (Spain). You can access the two models through cross-Region inference. Cross-Region inference helps to automatically select the optimal AWS Region within your geography to process your inference request.

Opus 4 tackles your most challenging development tasks, while Sonnet 4 excels at routine work with its optimal balance of speed and capability.

Learn more about the pricing and how to use these new models in Amazon Bedrock today!

— seb

AMD Takes Aim for Workstation AI Market With Radeon AI Pro R9700

Post Syndicated from Ryan Smith original https://www.servethehome.com/amd-takes-aim-for-workstation-ai-market-with-radeon-ai-pro-r9700/

In what has quickly become a busy week for workstation and server class video cards at this year’s Computex trade show, AMD has become the latest video card vendor to join the fray with the announcement of their flagship Radeon AI PRO R9700 video card. Based on AMD’s newest RDNA 4 GPU architecture, AMD is […]

The post AMD Takes Aim for Workstation AI Market With Radeon AI Pro R9700 appeared first on ServeTheHome.

Centralize visibility of Kubernetes clusters across AWS Regions and accounts with EKS Dashboard

Post Syndicated from Micah Walter original https://aws.amazon.com/blogs/aws/centralize-visibility-of-kubernetes-clusters-across-aws-regions-and-accounts-with-eks-dashboard/

Today, we are announcing EKS Dashboard, a centralized display that enables cloud architects and cluster administrators to maintain organization-wide visibility across their Kubernetes clusters. With EKS Dashboard, customers can now monitor clusters deployed across different AWS Regions and accounts through a unified view, making it easier to track cluster inventory, assess compliance, and plan operational activities like version upgrades.

As organizations scale their Kubernetes deployments, they often run multiple clusters across different environments to enhance availability, ensure business continuity, or maintain data sovereignty. However, this distributed approach can make it challenging to maintain visibility and control, especially in decentralized setups spanning multiple Regions and accounts. Today, many customers resort to third-party tools for centralized cluster visibility, which adds complexity through identity and access setup, licensing costs, and maintenance overhead.

EKS Dashboard simplifies this experience by providing native dashboard capabilities within the AWS Console. The Dashboard provides insights into 3 different resources including clusters, managed node groups, and EKS add-ons, offering aggregated insights into cluster distribution by Region, account, version, support status, forecasted extended support EKS control plane costs, and cluster health metrics. Customers can drill down into specific data points with automatic filtering, enabling them to quickly identify and focus on clusters requiring attention.

Setting up EKS Dashboard

Customers can access the Dashboard in EKS console through AWS Organizations’ management and delegated administrator accounts. The setup process is straightforward and includes simply enabling trusted access as a one-time setup in the Amazon EKS console’s organizations settings page. Trusted access is available from the Dashboard settings page. Enabling trusted access will allow the management account to view the Dashboard. For more information on setup and configuration, see the official AWS Documentation.

Screenshot of EKS Dashboard settings

A quick tour of EKS Dashboard

The dashboard provides both graphical, tabular, and map views of your Kubernetes clusters, with advanced filtering, and search capabilities. You can also export data for further analysis or custom reporting.

Screenshot of EKS Dashboard interface

EKS Dashboard overview with key info about your clusters.

Screenshot of EKS Dashboard interface

There is a wide variety of available widgets to help visualize your clusters.

Screenshot of EKS Dashboard interface

You can visualize your managed node groups by instance type distribution, launch templates, AMI versions, and more

Screenshot of EKS Dashboard interface

There is even a map view where you can see all of your clusters across the globe.

Beyond EKS clusters

EKS Dashboard isn’t limited to just Amazon EKS clusters; it can also provide visibility into connected Kubernetes clusters running on-premises or on other cloud providers. While connected clusters may have limited data fidelity compared to native Amazon EKS clusters, this capability enables truly unified visibility for organizations running hybrid or multi-cloud environments.

Available now

EKS Dashboard is available today in the US East (N. Virginia) Region and is able to aggregate data from all commercial AWS Regions. There is no additional charge for using the EKS Dashboard. To learn more, visit the Amazon EKS documentation.

This new capability demonstrates our continued commitment to simplifying Kubernetes operations for our customers, enabling them to focus on building and scaling their applications rather than managing infrastructure. We’re excited to see how customers use EKS Dashboard to enhance their Kubernetes operations.

— Micah;

Configure System Integrity Protection (SIP) on Amazon EC2 Mac instances

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/configure-system-integrity-protection-sip-on-amazon-ec2-mac-instances/

I’m pleased to announce developers can now programmatically disable Apple System Integrity Protection (SIP) on their Amazon EC2 Mac instances. System Integrity Protection (SIP), also known as rootless, is a security feature introduced by Apple in OS X El Capitan (2015, version 10.11). It’s designed to protect the system from potentially harmful software by restricting the power of the root user account. SIP is enabled by default on macOS.

SIP safeguards the system by preventing modification of protected files and folders, restricting access to system-owned files and directories, and blocking unauthorized software from selecting a startup disk. The primary goal of SIP is to address the security risk linked to unrestricted root access, which could potentially allow malware to gain full control of a device with just one password or vulnerability. By implementing this protection, Apple aims to ensure a higher level of security for macOS users, especially considering that many users operate on administrative accounts with weak or no passwords.

While SIP provides excellent protection against malware for everyday use, developers might occasionally need to temporarily disable it for development and testing purposes. For instance, when creating a new device driver or system extension, disabling SIP is necessary to install and test the code. Additionally, SIP might block access to certain system settings required for your software to function properly. Temporarily disabling SIP grants you the necessary permissions to fine-tune programs for macOS. However, it’s crucial to remember that this is akin to briefly disabling the vault door for authorized maintenance, not leaving it permanently open.

Disabling SIP on a Mac requires physical access to the machine. You have to restart the machine in recovery mode, then disable SIP with the csrtutil command line tool, then restart the machine again.

Until today, you had to operate with the standard SIP settings on EC2 Mac instances. The physical access requirement and the need to boot in recovery mode made integrating SIP with the Amazon EC2 control plane and EC2 API challenging. But that’s no longer the case! You can now disable and re-enable SIP at will on your Amazon EC2 Mac instances. Let me show you how.

Let’s see how it works
Imagine I have an Amazon EC2 Mac instance started. It’s a mac2-m2.metal instance, running on an Apple silicon M2 processor. Disabling or enabling SIP is as straightforward as calling a new EC2 API: CreateMacSystemIntegrityProtectionModificationTask. This API is asynchronous; it starts the process of changing the SIP status on your instance. You can monitor progress using another new EC2 API: DescribeMacModificationTasks. All I need to know is the instance ID of the machine I want to work with.

Prerequisites
On Apple silicon based EC2 Mac instances and more recent type of machines, before calling the new EC2 API, I must set the ec2-user user password and enable secure token for that user on macOS. This requires connecting to the machine and typing two commands in the terminal.

# on the target EC2 Mac instance
# Set a password for the ec2-user user
~ % sudo /usr/bin/dscl . -passwd /Users/ec2-user
New Password: (MyNewPassw0rd)

# Enable secure token, with the same password, for the ec2-user
# old password is the one you just set with dscl
~ % sysadminctl -newPassword MyNewPassw0rd -oldPassword MyNewPassw0rd
2025-03-05 13:16:57.261 sysadminctl[3993:3033024] Attempting to change password for ec2-user…
2025-03-05 13:16:58.690 sysadminctl[3993:3033024] SecKeychainCopyLogin returned -25294
2025-03-05 13:16:58.690 sysadminctl[3993:3033024] Failed to update keychain password (-25294)
2025-03-05 13:16:58.690 sysadminctl[3993:3033024] - Done

# The error about the KeyChain is expected. I never connected with the GUI on this machine, so the Login keychain does not exist
# you can ignore this error.  The command below shows the list of keychains active in this session
~ % security list
    "/Library/Keychains/System.keychain"

# Verify that the secure token is ENABLED
~ % sysadminctl -secureTokenStatus ec2-user
2025-03-05 13:18:12.456 sysadminctl[4017:3033614] Secure token is ENABLED for user ec2-user

Change the SIP status
I don’t need to connect to the machine to toggle the SIP status. I only need to know its instance ID. I open a terminal on my laptop and use the AWS Command Line Interface (AWS CLI) to retrieve the Amazon EC2 Mac instance ID.

 aws ec2 describe-instances \
         --query "Reservations[].Instances[?InstanceType == 'mac2-m2.metal' ].InstanceId" \
         --output text

i-012a5de8da47bdff7

Now, still from the terminal on my laptop, I disable SIP with the create-mac-system-integrity-protection-modification-task command:

echo '{"rootVolumeUsername":"ec2-user","rootVolumePassword":"MyNewPassw0rd"}' > tmpCredentials
aws ec2 create-mac-system-integrity-protection-modification-task \
--instance-id "i-012a5de8da47bdff7" \
--mac-credentials fileb://./tmpCredentials \
--mac-system-integrity-protection-status "disabled" && rm tmpCredentials

{
    "macModificationTask": {
        "instanceId": "i-012a5de8da47bdff7",
        "macModificationTaskId": "macmodification-06a4bb89b394ac6d6",
        "macSystemIntegrityProtectionConfig": {},
        "startTime": "2025-03-14T14:15:06Z",
        "taskState": "pending",
        "taskType": "sip-modification"
    }
}

After the task is started, I can check its status with the aws ec2 describe-mac-modification-tasks command.

{
    "macModificationTasks": [
        {
            "instanceId": "i-012a5de8da47bdff7",
            "macModificationTaskId": "macmodification-06a4bb89b394ac6d6",
            "macSystemIntegrityProtectionConfig": {
                "debuggingRestrictions": "",
                "dTraceRestrictions": "",
                "filesystemProtections": "",
                "kextSigning": "",
                "nvramProtections": "",
                "status": "disabled"
            },
            "startTime": "2025-03-14T14:15:06Z",
            "tags": [],
            "taskState": "in-progress",
            "taskType": "sip-modification"
        },
...

The instance initiates the process and a series of reboots, during which it becomes unreachable. This process can take 60–90 minutes to complete. After that, when I see the status in the console becoming available again, I connect to the machine through SSH or EC2 Instance Connect, as usual.

➜  ~ ssh [email protected]
Warning: Permanently added '54.99.9.99' (ED25519) to the list of known hosts.
Last login: Mon Feb 26 08:52:42 2024 from 1.1.1.1

    ┌───┬──┐   __|  __|_  )
    │ ╷╭╯╷ │   _|  (     /
    │  └╮  │  ___|\___|___|
    │ ╰─┼╯ │  Amazon EC2
    └───┴──┘  macOS Sonoma 14.3.1

➜  ~ uname -a
Darwin Mac-mini.local 23.3.0 Darwin Kernel Version 23.3.0: Wed Dec 20 21:30:27 PST 2023; root:xnu-10002.81.5~7/RELEASE_ARM64_T8103 arm64

➜ ~ csrutil --status 
System Integrity Protection status: disabled.

When to disable SIP
Disabling SIP should be approached with caution because it opens up the system to potential security risks. However, as I mentioned in the introduction of this post, you might need to disable SIP when developing device drivers or kernel extensions for macOS. Some older applications might also not function correctly when SIP is enabled.

Disabling SIP is also required to turn off Spotlight indexing. Spotlight can help you quickly find apps, documents, emails and other items on your Mac. It’s very convenient on desktop machines, but not so much on a server. When there is no need to index your documents as they change, turning off Spotlight will release some CPU cycles and disk I/O.

Things to know
There are a couple of additional things to know about disabling SIP on Amazon EC2 Mac:

  • Disabling SIP is available through the API and AWS SDKs, the AWS CLI, and the AWS Management Console.
  • On Apple silicon, the setting is volume based. So if you replace the root volume, you need to disable SIP again. On Intel, the setting is Mac host based, so if you replace the root volume, SIP will still be disabled.
  • After disabling SIP, it will be enabled again if you stop and start the instance. Rebooting an instance doesn’t change its SIP status.
  • SIP status isn’t transferable between EBS volumes. This means SIP will be disabled again after you restore an instance from an EBS snapshot or if you create an AMI from an instance where SIP is enabled.

These new APIs are available in all Regions where Amazon EC2 Mac is available, at no additional cost. Try them today.

— seb

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AMD Reveals Ryzen Threadripper 9000 Family: Big Zen 5 Chips for HEDT and Workstations

Post Syndicated from Ryan Smith original https://www.servethehome.com/amd-reveals-ryzen-threadripper-9000-family-big-zen-5-chips-for-hedt-and-workstations/

Headlining this evening’s announcements for AMD is a fresh batch of CPUs for the HEDT and workstation markets. Taking to the stage at Computex 2025, AMD’s Jack Huynh announced that the company’s Threadripper family of CPUs is receiving its long-awaited Zen 5 facelift, upgrading the CPUs to AMD’s latest architecture. The chonky chips, with up […]

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AMD Computex 2025 Live Coverage

Post Syndicated from Ryan Smith original https://www.servethehome.com/amd-computex-2025-live-coverage/

The final major keynote for this year’s Computex trade show comes from AMD. And, with competitor Intel not presenting at this year’s show, AMD is the sole x86 vendor holding a keynote this year. AMD’s theme for this year’s keynote is a grab-bag of gaming, PC, and workstations. Notably, this keynote is not being helmed […]

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Introducing the AWS Product Lifecycle page and AWS service availability updates

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/introducing-the-aws-product-lifecycle-page-and-aws-service-availability-updates/

Today, we’re introducing the AWS Product Lifecycle page, a centralized resource that provides comprehensive information about service availability changes across AWS.

The new AWS Product Lifecycle page consolidates all service availability information in one convenient location. This dedicated resource offers detailed visibility into three key categories of changes: 1) services closing access to new customers, 2) services that have announced end of support, and 3) services that have reached their end of support date. For each service listed, you can access specific end-of-support dates, recommended migration paths, and links to relevant documentation, enabling more efficient planning for service transitions.

The AWS Product Lifecycle page helps you stay informed about changes that may affect your workloads and enables more efficient planning for service transitions. The centralized nature of this resource reduces the time and effort needed to track service lifecycle information, allowing you to focus more on your core business objectives and less on administrative overhead.

Today on the new Product Lifecycle page, you will see updates about the following changes to services and capabilities:

AWS service availability updates in 2025
After careful consideration, we’re announcing availability changes for a select group of AWS services and features. We understand that the decision to end support for a service or feature significantly impacts your operations. We approach such decisions only after thorough evaluation, and when end of support is necessary, we provide detailed guidance on available alternatives and comprehensive support for migration.

Services closing access to new customers
We’re closing access to new customers after June 20, 2025, for the following services or capabilities listed. Existing customers will be able to continue to use the service.

Services that have announced end of support
The following services will no longer be supported. To find out more about service specific end-of-support dates, as well as detailed migration information, please visit individual service documentation pages.

Services that have reached their end of support
The following services have reached their end of support date and can no longer be accessed:

  • AWS Private 5G
  • AWS DataSync Discovery

The AWS Product Lifecycle page is available and all the changes described in this post are listed on the new page now. We recommend that you bookmark this page and check out What’s New with AWS? for upcoming AWS service availability updates. For more information about using this new resource, contact us or your usual AWS Support contacts for specific guidance on transitioning affected workloads.

— seb

Join AWS Cloud Infrastructure Day to learn cutting-edge innovations building global cloud infrastructure

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/join-aws-cloud-infrastructure-day-to-learn-cutting-edge-innovations-building-global-cloud-infrastructure/

I want to introduce the AWS Cloud Infrastructure Day to provide a comprehensive showcase of latest innovations in AWS cloud infrastructure. This event will highlight cutting-edge advances across compute, artificial intelligence and machine learning (AI/ML), storage solutions, networking capabilities, serverless, and accelerated technologies, and global infrastructure.

Join us for AWS Cloud Infrastructure Day, a free-to-attend one-day virtual event on May 22, 2025, starting at 11:00 AM PDT (2:00 PM EDT). We will stream the event simultaneously across multiple platforms, including LinkedIn Live, Twitter, YouTube, and Twitch.

Here are some of the highlights you can expect from this event:

Willem Visser, VP of EC2 Technology will open with the introduction of the AWS journey since 2006, when Amazon Elastic Compute Cloud (Amazon EC2) was launched with the goal of customer-obsessed innovation. He will speak about the progress made over nearly two decades in cloud infrastructure to support both startups and enterprise workloads based on scale, capacity, and flexibility.

You can learn how AWS developed beyond computing instances to create a complete cloud infrastructure, including the parallel evolution of services like storage and networking capabilities.

Todd Kennedy, Principal Engineer, GoDaddy, will share GoDaddy’s Graviton adoption journey and the benefits it reaped from Graviton. Todd will walk through an example to demonstrate moving Rust workloads to Graviton. Learn how GoDaddy achieved 40 percent compute cost savings and over 20 percent performance gains.

This event covers a variety of topics related to AWS Cloud infrastructure. Here are interesting topics that caught my interest:

  • Generative AI at the edge – You can learn how to select, fine-tune, and deploy small language models (SLMs) for on-premises and edge use cases due to data residency requirements using AWS hybrid and edge services.
  • Serverless for agentic AI auditability – You can learn how AWS Step Functions and AWS Lambda transform opaque agentic AI system operations into transparent, auditable workflows.
  • Accelerated computing – You can get a close look at AWS innovation across silicon, server, and data centers and learn how customers are using AI chips. Learn how you can get started and reduce your generative AI costs.
  • Networking capability – You can learn how AWS infrastructure—from physical fiber to software-defined networking—enables unparalleled performance and reliability at global scale. The session covers modern application networking patterns while emphasizing secure connectivity solutions for hybrid environments.

This event is perfect for technical decision-makers and developers and offers deep technical insights and hands-on demonstrations of the latest AWS Cloud infrastructure solutions.

To learn more details, review the event schedule and register for AWS Cloud Infrastructure Day.

— Channy

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Amazon Inspector enhances container security by mapping Amazon ECR images to running containers

Post Syndicated from Elizabeth Fuentes original https://aws.amazon.com/blogs/aws/amazon-inspector-enhances-container-security-by-mapping-amazon-ecr-images-to-running-containers/

When running container workloads, you need to understand how software vulnerabilities create security risks for your resources. Until now, you could identify vulnerabilities in your Amazon Elastic Container Registry (Amazon ECR) images, but couldn’t determine if these images were active in containers or track their usage. With no visibility if these images were being used on running clusters, you had limited ability to prioritize fixes based on actual deployment and usage patterns.

Starting today, Amazon Inspector offers two new features that enhance vulnerability management, giving you a more comprehensive view of your container images. First, Amazon Inspector now maps Amazon ECR images to running containers, enabling security teams to prioritize vulnerabilities based on containers currently running in your environment. With these new capabilities, you can analyze vulnerabilities in your Amazon ECR images and prioritize findings based on whether they are currently running and when they last ran in your container environment. Additionally, you can see the cluster Amazon Resource Name (ARN), number EKS pods or ECS tasks where an image is deployed, helping you prioritize fixes based on usage and severity.

Second, we’re extending vulnerability scanning support to minimal base images including scratch, distroless, and Chainguard images, and extending support for additional ecosystems including Go toolchain, Oracle JDK & JRE, Amazon Corretto, Apache Tomcat, Apache httpd, WordPress (core, themes, plugins), and Puppeteer, helping teams maintain robust security even in highly optimized container environments.

Through continual monitoring and tracking of images running on containers, Amazon Inspector helps teams identify which container images are actively running in their environment and where they’re deployed, detecting Amazon ECR images running on containers in Amazon Elastic Container Service (Amazon ECS) and Amazon Elastic Kubernetes Service (Amazon EKS), and any associated vulnerabilities. This solution supports teams managing Amazon ECR images across single AWS accounts, cross-account scenarios, and AWS Organizations with delegated administrator capabilities, enabling centralized vulnerability management based on container images running patterns.

Let’s see it in action
Amazon ECR image scanning helps identify vulnerabilities in your container images through enhanced scanning, which integrates with Amazon Inspector to provide automated, continual scanning of your repositories. To use this new feature you have to enable enhanced scanning through the Amazon ECR console, you can do it by following the steps in the Configuring enhanced scanning for images in Amazon ECR documentation page. I already have Amazon ECR enhanced scanning, so I don’t have to do any action.

In the Amazon Inspector console, I navigate to General settings and select ECR scanning settings from the navigation panel. Here, I can configure the new Image re-scan mode settings by choosing between Last in-use date and Last pull date. I leave it as it is by default with Last in-use date and set the Image last in use date to 14 days. These settings make it so that Inspector monitors my images based on when they were running in the last 14 days in my Amazon ECS or Amazon EKS environments. After applying these settings, Amazon Inspector starts tracking information about images running on containers and incorporating it into vulnerability findings, helping me focus on images actively running in containers in my environment.

After it’s configured, I can view information about images running on containers in the Details menu, where I can see last in-use and pull dates, along with EKS pods or ECS tasks count.

When selecting the number of Deployed ECS Tasks/EKS Pods, I can see the cluster ARN, last use dates, and Type for each image.

For cross-account visibility demonstration, I have a repository with EKS pods deployed in two accounts. In the Resources coverage menu, I navigate to Container repositories, select my repository name and choose the Image tag. As before, I can see the number of deployed EKS pods/ECS tasks.

When I select the number of deployed EKS pods/ECS tasks, I can see that it is running in a different account.

In the Findings menu, I can review any vulnerabilities, and by selecting one, I can find the Last in use date and Deployed ECS Tasks/EKS Pods involved in the vulnerability under Resource affected data, helping me prioritize remediation based on actual usage.

In the All Findings menu, you can now search for vulnerabilities within account management, using filters such as Account ID, Image in use count and Image last in use at.

Key features and considerations
Monitoring based on container image lifecycle – Amazon Inspector now determines image activity based on: image push date ranging duration 14, 30, 60, 90, or 180 days or lifetime, image pull date from 14, 30, 60, 90, or 180 days, stopped duration from never to 14, 30, 60, 90, or 180 days and status of image running on the container. This flexibility lets organizations tailor their monitoring strategy based on actual container image usage rather than only repository events. For Amazon EKS and Amazon ECS workloads, last in use, push and pull duration are set to 14 days, which is now the default for new customers.

Image runtime-aware finding details – To help prioritize remediation efforts, each finding in Amazon Inspector now includes the lastInUseAt date and InUseCount, indicating when an image was last running on the containers and the number of deployed EKS pods/ ECS tasks currently using it. Amazon Inspector monitors both Amazon ECR last pull date data and images running on Amazon ECS tasks or Amazon EKS pods container data for all accounts, updating this information at least once daily. Amazon Inspector integrates these details into all findings reports and seamlessly works with Amazon EventBridge. You can filter findings based on the lastInUseAt field using rolling window or fixed range options, and you can filter images based on their last running date within the last 14, 30, 60, or 90 days.

Comprehensive security coverage – Amazon Inspector now provides unified vulnerability assessments for both traditional Linux distributions and minimal base images including scratch, distroless, and Chainguard images through a single service. This extended coverage eliminates the need for multiple scanning solutions while maintaining robust security practices across your entire container ecosystem, from traditional distributions to highly optimized container environments. The service streamlines security operations by providing comprehensive vulnerability management through a centralized platform, enabling efficient assessment of all container types.

Enhanced cross-account visibility – Security management across single accounts, cross-account setups, and AWS Organizations is now supported through delegated administrator capabilities. Amazon Inspector shares images running on container information within the same organization, which is particularly valuable for accounts maintaining golden image repositories. Amazon Inspector provides all ARNs for Amazon EKS and Amazon ECS clusters where images are running, if the resource belongs to the account with an API, providing comprehensive visibility across multiple AWS accounts. The system updates deployed EKS pods or ECS tasks information at least one time daily and automatically maintains accuracy as accounts join or leave the organization.

Availability and pricing – The new container mapping capabilities are available now in all AWS Regions where Amazon Inspector is offered at no additional cost. To get started, visit the AWS Inspector documentation. For pricing details and Regional availability, refer to the AWS Inspector pricing page.

PS: Writing a blog post at AWS is always a team effort, even when you see only one name under the post title. In this case, I want to thank Nirali Desai, for her generous help with technical guidance, and expertise, which made this overview possible and comprehensive.

— Eli

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Sanmina Buys ZT Systems Manufacturing from AMD

Post Syndicated from Patrick Kennedy original https://www.servethehome.com/sanmina-buys-zt-systems-manufacturing-from-amd/

Last year, AMD Bought ZT Systems in a Surprise Move. The motivation quickly became apparent as AMD needed to move into rack-scale and data center scale infrastructure, not just individual systems and 8 GPU baseboards. ZT Systems both designed and manufactured these systems for hyper-scalers like Microsoft Azure. Along with that $4.9B deal announcement, AMD also […]

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Qualcomm Teases New Data Center CPUs and AI Inference Accelerators in the Works

Post Syndicated from Ryan Smith original https://www.servethehome.com/qualcomm-teases-new-data-center-cpus-and-ai-inference-accelerators-in-the-works/

Closing out their hour-long, AI-focused Computex keynote, Qualcomm CEO Cristiano Amon ended his presentation with a brief teaser of what’s in store for the server side of Qualcomm’s hardware business. The company has repeatedly dabbled in the server market with products such as their Cloud AI 100 inference accelerators. But thus far Qualcomm has not […]

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NVIDIA Announces NVLink Fusion: Bringing NVLink to Third-Party CPUs and Accelerators

Post Syndicated from Ryan Smith original https://www.servethehome.com/nvidia-announces-nvlink-fusion-bringing-nvlink-to-third-party-cpus-and-accelerators/

As part of today’s headline Computex keynote, NVIDIA announced a new member in their suite of NVLink interconnect technologies: NVLink Fusion. And this latest entry may be the most interesting yet, as NVIDIA is taking a big step towards opening up the NVLink ecosystem to third-party CPUs and accelerators, releasing IP and hardware designed to […]

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Qualcomm Computex 2025 Live Coverage

Post Syndicated from Ryan Smith original https://www.servethehome.com/qualcomm-computex-2025-live-coverage/

The second major Computex keynote for the day comes from Qualcomm, who is quickly becoming a regular presenter at the show. This year’s keynote is being presented by company CEO Cristiano Amon, and the theme is “The heart of your PC.” Qualcomm is making it clear from the start that this year’s keynote is going […]

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NVIDIA Computex 2025 Keynote Live Coverage

Post Syndicated from Ryan Smith original https://www.servethehome.com/nvidia-computex-2025-keynote-live-coverage/

Kicking off today is the annual Computex tradeshow in Taiwan. Home to countless system and device manufacturers, Computex is a cornucopia of consumer electronics, and these days is also the biggest PC-centric show of the year. And even though it takes place in May, barely half-way through the year, the show routinely sets the stage […]

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New Amazon EC2 P6-B200 instances powered by NVIDIA Blackwell GPUs to accelerate AI innovations

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/new-amazon-ec2-p6-b200-instances-powered-by-nvidia-blackwell-gpus-to-accelerate-ai-innovations/

Today, we’re announcing the general availability of Amazon Elastic Compute Cloud (Amazon EC2) P6-B200 instances powered by NVIDIA B200 to address customer needs for high performance and scalability in artificial intelligence (AI), machine learning (ML), and high performance computing (HPC) applications.

Amazon EC2 P6-B200 instances accelerate a broad range of GPU-enabled workloads but are especially well-suited for large-scale distributed AI training and inferencing for foundation models (FMs) with reinforcement learning (RL) and distillation, multimodal training and inference, and HPC applications such as climate modeling, drug discovery, seismic analysis, and insurance risk modeling.

When combined with Elastic Fabric Adapter (EFAv4) networking, hyperscale clustering by EC2 UltraClusters, and advanced virtualization and security capabilities by AWS Nitro System, you can train and serve FMs with increased speed, scale, and security. These instances also deliver up to two times the performance for AI training (time to train) and inference (tokens/sec) compared to EC2 P5en instances.

You can accelerate time-to-market for training FMs and deliver faster inference throughput, which lowers inference cost and helps increase adoption of generative AI applications as well as increased processing performance for HPC applications.

EC2 P6-B200 instances specifications
New EC2 P6-B200 instances provide eight NVIDIA B200 GPUs with 1440 GB of high bandwidth GPU memory, 5th Generation Intel Xeon Scalable processors (Emerald Rapids), 2 TiB of system memory, and 30 TB of local NVMe storage.

Here are the specs for EC2 P6-B200 instances:

Instance size GPUs (NVIDIA B200) GPU
memory (GB)		 vCPUs GPU Peer to peer (GB/s) Instance storage (TB) Network bandwidth (Gbps) EBS bandwidth (Gbps)
P6-b200.48xlarge 8 1440 HBM3e 192 1800 8 x 3.84 NVMe SSD 8 x 400 100

These instances feature up to 125 percent improvement in GPU TFLOPs, 27 percent increase in GPU memory size, and 60 percent increase in GPU memory bandwidth compared to P5en instances.

P6-B200 instances in action
You can use P6-B200 instances in the US West (Oregon) AWS Region through EC2 Capacity Blocks for ML. To reserve your EC2 Capacity Blocks, choose Capacity Reservations on the Amazon EC2 console.

Select Purchase Capacity Blocks for ML and then choose your total capacity and specify how long you need the EC2 Capacity Block for p6-b200.48xlarge instances. The total number of days that you can reserve EC2 Capacity Blocks is 1-14 days, 21 days, 28 days, or multiples of 7 up to 182 days. You can choose your earliest start date for up to 8 weeks in advance.

Now, your EC2 Capacity Block will be scheduled successfully. The total price of an EC2 Capacity Block is charged up front, and the price doesn’t change after purchase. The payment will be billed to your account within 12 hours after you purchase the EC2 Capacity Blocks. To learn more, visit Capacity Blocks for ML in the Amazon EC2 User Guide.

When launching P6-B200 instances, you can use AWS Deep Learning AMIs (DLAMI) to support EC2 P6-B200 instances. DLAMI provides ML practitioners and researchers with the infrastructure and tools to quickly build scalable, secure, distributed ML applications in preconfigured environments.

To run instances, you can use AWS Management Console, AWS Command Line Interface (AWS CLI) or AWS SDKs.

You can integrate EC2 P6-B200 instances seamlessly with various AWS managed services such as Amazon Elastic Kubernetes Services (Amazon EKS), Amazon Simple Storage Service (Amazon S3), and Amazon FSx for Lustre. Support for Amazon SageMaker HyperPod is also coming soon.

Now available
Amazon EC2 P6-B200 instances are available today in the US West (Oregon) Region and can be purchased as EC2 Capacity blocks for ML.

Give Amazon EC2 P6-B200 instances a try in the Amazon EC2 console. To learn more, refer to the Amazon EC2 P6 instance page and send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

— Channy

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Accelerate CI/CD pipelines with the new AWS CodeBuild Docker Server capability

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/accelerate-ci-cd-pipelines-with-the-new-aws-codebuild-docker-server-capability/

Starting today, you can use AWS CodeBuild Docker Server capability to provision a dedicated and persistent Docker server directly within your CodeBuild project. With Docker Server capability, you can accelerate your Docker image builds by centralizing image building to a remote host, which reduces wait times and increases overall efficiency.

From my benchmark, with this Docker Server capability, I reduced the total building time by 98 percent, from 24 minutes and 54 seconds to 16 seconds. Here’s a quick look at this feature from my AWS CodeBuild projects.

AWS CodeBuild is a fully managed continuous integration service that compiles source code, runs tests, and produces software packages ready for deployment. Building Docker images is one of the most common use cases for CodeBuild customers, and the service has progressively improved this experience over time by releasing features such as Docker layer caching and reserved capacity features to improve Docker build performance.

With the new Docker Server capability, you can reduce build time for your applications by providing a persistent Docker server with consistent caching. When enabled in a CodeBuild project, a dedicated Docker server is provisioned with persistent storage that maintains your Docker layer cache. This server can handle multiple concurrent Docker build operations, with all builds benefiting from the same centralized cache.

Using AWS CodeBuild Docker Server
Let me walk you through a demonstration that showcases the benefits with the new Docker Server capability.

For this demonstration, I’m building a complex, multi-layered Docker image based on the official AWS CodeBuild curated Docker images repository, specifically the Dockerfile for building a standard Ubuntu image. This image contains numerous dependencies and tools required for modern continuous integration and continuous delivery (CI/CD) pipelines, making it a good example of the type of large Docker builds that development teams regularly perform.


# Copyright 2020-2024 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Amazon Software License (the "License"). You may not use this file except in compliance with the License.
# A copy of the License is located at
#
#    http://aws.amazon.com/asl/
#
# or in the "license" file accompanying this file.
# This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, express or implied.
# See the License for the specific language governing permissions and limitations under the License.
FROM public.ecr.aws/ubuntu/ubuntu:20.04 AS core

ARG DEBIAN_FRONTEND="noninteractive"

# Install git, SSH, Git, Firefox, GeckoDriver, Chrome, ChromeDriver,  stunnel, AWS Tools, configure SSM, AWS CLI v2, env tools for runtimes: Dotnet, NodeJS, Ruby, Python, PHP, Java, Go, .NET, Powershell Core,  Docker, Composer, and other utilities
COMMAND REDACTED FOR BREVITY
# Activate runtime versions specific to image version.
RUN n $NODE_14_VERSION
RUN pyenv  global $PYTHON_39_VERSION
RUN phpenv global $PHP_80_VERSION
RUN rbenv  global $RUBY_27_VERSION
RUN goenv global  $GOLANG_15_VERSION

# Configure SSH
COPY ssh_config /root/.ssh/config
COPY runtimes.yml /codebuild/image/config/runtimes.yml
COPY dockerd-entrypoint.sh /usr/local/bin/dockerd-entrypoint.sh
COPY legal/bill_of_material.txt /usr/share/doc/bill_of_material.txt
COPY amazon-ssm-agent.json /etc/amazon/ssm/amazon-ssm-agent.json

ENTRYPOINT ["/usr/local/bin/dockerd-entrypoint.sh"]

This Dockerfile creates a comprehensive build environment with multiple programming languages, build tools, and dependencies – exactly the type of image that would benefit from persistent caching.

In the build specification (buildspec), I use the docker buildx build . command:

version: 0.2
phases:
  build:
    commands:
      - cd ubuntu/standard/5.0
      - docker buildx build -t codebuild-ubuntu:latest .

To enable the Docker Server capability, I navigate to the AWS CodeBuild console and select Create project. I can also enable this capability when editing existing CodeBuild projects.

I fill in all details and configuration. In the Environment section, I select Additional configuration.

Then, I scroll down and find Docker server configuration and select Enable docker server for this project. When I select this option, I can choose a compute type configuration for the Docker server. When I’m finished with the configurations, I create this project.

Now, let’s see the Docker Server capability in action.

The initial build takes approximately 24 minutes and 54 seconds to complete because it needs to download and compile all dependencies from scratch. This is expected for the first build of such a complex image.

For subsequent builds with no code changes, the build takes only 16 seconds and that shows 98% reduction in build time.

Looking at the logs, I can see that with Docker Server, most layers are pulled from the persistent cache:

The persistent caching provided by the Docker Server maintains all layers between builds, which is particularly valuable for large, complex Docker images with many layers. This demonstrates how Docker Server can dramatically improve throughput for teams running numerous Docker builds in their CI/CD pipelines.

Additional things to know
Here are a couple of things to note:

  • Architecture support – The feature is available for both x86 (Linux) and ARM builds.
  • Pricing – To learn more about pricing for Docker Server capability, refer to the AWS CodeBuild pricing page.
  • Availability – This feature is available in all AWS Regions where AWS CodeBuild is offered. For more information about the AWS Regions where CodeBuild is available, see the AWS Regions page.

You can learn more about the Docker Server feature in the AWS CodeBuild documentation.

Happy building! —

Donnie Prakoso

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Accelerate the modernization of Mainframe and VMware workloads with AWS Transform

Post Syndicated from Matheus Guimaraes original https://aws.amazon.com/blogs/aws/accelerate-the-modernization-of-mainframe-and-vmware-workloads-with-aws-transform/

Generative AI has brought many new possibilities to organizations. It has equipped them with new abilities to retire technical debt, modernize legacy systems, and build agile infrastructure to help unlock the value that is trapped in their internal data. However, many enterprises still rely heavily on legacy IT infrastructure, particularly mainframes and VMware-based systems. These platforms have been the backbone of critical operations for decades, but they hinder organizations’ ability to innovate, scale effectively, and reduce technical debt in an era where cloud-first strategies dominate. The need to modernize these workloads is clear, but the journey has traditionally been complex and risky.

The complexity spans multiple dimensions. Financially, organizations face mounting licensing costs and expensive migration projects. Technically, they must untangle legacy dependencies while meeting compliance requirements. Organizationally, they must manage the transition of teams who’ve built careers around legacy systems and navigate undocumented institutional knowledge.

AWS Transform directly addresses these challenges with purpose-built agentic AI that accelerates and de-risks your legacy modernization. It automates the assessment, planning, and transformation of both mainframe and VMware workloads into cloud based architectures, streamlining the entire process. Through intelligent insights, automated code transformation, and human-in-the-loop workflows, organizations can now tackle even the most challenging modernization projects with greater confidence and efficiency.

Mainframe workload migration
AWS Transform for mainframe is the first agentic AI service for modernizing mainframe workloads at scale. The specialized mainframe agent accelerates mainframe modernization by automating complex, resource-intensive tasks across every phase of modernization — from initial assessment to final deployment. It streamlines the migration of legacy applications built on IBM z/OS Db2, including COBOL, CICS, DB2, and VSAM, to modern cloud environments–cutting modernization timelines from years to months.

Let’s look at a few examples of how AWS Transform can help you through different aspects of the migration process.

Code analysis – AWS Transform provides comprehensive insights into your codebase, automatically examining mainframe codebases, creating detailed dependency graphs, measuring code complexity, and identifying component relationships

Documentation – AWS Transform for mainframe creates comprehensive technical and functional documentation of mainframe applications, preserving critical knowledge about features, program logic, and data flows. You can interact with the generated documentation through an AI-powered chat interface to discover and retrieve information quickly.

Business rule extraction – AWS Transform extracts and presents complex logic in plain language so you can gain visibility into business processes embedded within legacy applications. This enables both business and technical stakeholders to gain a greater understanding of application functionality.

Code decomposition – AWS Transform offers sophisticated code decomposition tools, including interactive dependency graphs and domain separation capabilities, enabling users to visualize and modify relationships between components while identifying key business functions. The solution also streamlines migration planning through an interactive wave sequence planner that considers user preferences to generate optimized migration strategies.

Modernization Wave Planning – With its specialized agent, AWS Transform for mainframe creates prioritized modernization wave sequences based on code and data dependencies, code volume, and business priorities. It enables modernization teams to make data-driven, customized migration plans that align to their specific organizational needs.

Code refactoring – AWS Transform can refactor millions of lines of mainframe code in minutes, converting COBOL, VSAM, and DB2 systems into modern Java Spring Boot applications while maintaining functional equivalence and transforming CICS transactions into web services and JCL batch processes into Groovy scripts. The solution provides high-quality output through configurable settings and bundled runtime capabilities, producing Java code that emphasizes readability, maintainability, and technical excellence.

Deployments – AWS Transform provides customizable deployment templates that streamline the deployment process through user-defined inputs. For added efficiency, the solution bundles the selected runtime version with the migrated application, enabling seamless deployment as a complete package.

By integrating intelligent documentation analysis, business rules extraction, and human-in-the-loop collaboration capabilities, AWS Transform helps organizations accelerate their mainframe transformation while reducing risk and maintaining business continuity.

VMware modernization
With rapid changes in VMware licensing and support model, organizations are increasingly exploring alternatives despite the difficulties associated with migrating and modernizing VMware workloads. This is aggravated by the fact that the accumulation of technical debt typically creates complex, poorly documented environments managed by multiple teams, leading to vendor lock-in and collaboration challenges that hinder migration efforts further.

AWS Transform is the first agentic AI service for VMware modernization of its kind that helps you to overcome those difficulties. It can offset risk and accelerate the modernization of VMware workloads by automating application discovery, dependency mapping, migration planning, network conversion, and EC2 instance optimization, reducing manual effort and accelerating cloud adoption.

The process is organized into four phases: inventory discovery, wave planning, network conversion, and server migration. It uses agentic AI capabilities to analyze and map complex VMware environments, converting network configurations into AWS built-in constructs and helps you to orchestrate dependency-aware migration waves for seamless cutovers. In addition, it also provides a collaborative web interface that keeps AWS teams, partners, and customers aligned throughout the modernization journey.

Let’s take a quick tour to see how this works.

Setting up
Before you can start using the service, you must first enable it by navigating to the AWS Transform console. AWS Transform requires AWS IAM Identity Center (IdC) to manage users and setup appropriate permissions. If you don’t yet have IdC set up it will ask you to configure it first and return to the AWS Transform console later to continue the process.

With IdC available, you can then proceed to choosing the encryption settings. AWS Transform gives you the option to use a default AWS managed key or you can use your own custom keys through AWS Key Management Service (AWS KMS).

After completing this step, AWS Transform will be enabled. You can manage admin access to the console by navigating to Users and using the search box to find them. You must create users or groups in IdC first if they don’t already exist. The service console will help admins provision users who will get access to the web app. Each provisioned user receives an email with a link to set password and get their personalized URL for the webapp.

You interact with AWS Transform through a dedicated web experience. To get the url, navigate to Settings where you can check your configurations and copy the links to the AWS Transform web experience where you and your teams can start using the service.

Discovery
AWS Transform can discover your VMware environment either automatically through AWS Application Discovery Service collectors or you can provide your own data by importing existing RVTools export files.

To get started, choose the Create or select connectors task and provide the account IDs for one or more AWS accounts that will be used for discovery. This will generate links that you can follow to authorize each account for usage within AWS Transform. You can then move on to the Perform discovery task, where you can choose to install AWS Application Discovery Service collectors or upload your own files such as exports from RVTools.

Provisioning
The steps for the provisioning phase are similar to the ones described earlier for discovery. You connect target AWS accounts by entering their account IDs and validating the authorization requests which will then enable the next steps such as the Generate VPC configuration step. Here, you can import your RVTools files or NSX exports from Import/Export from NSX, if applicable, and enable AWS Transform to understand your networking requirements.

You should then continue working through the job plan until you reach the point where it’s ready to deploy your Amazon Virtual Private Cloud (Amazon VPC). All the infrastructure as code (IaC) code is stored in Amazon Simple Storage Service (Amazon S3) buckets in the target AWS account.

Review the proposed changes and, if you’re happy, start the deployment process of the AWS resources to the target accounts.

Deployment
AWS Transform requires you to set up AWS Application Migration Service (MGN) in the target AWS accounts to automate the migration process. Choose the Initiate VM migration task and use the link to navigate to the service console, then follow the instructions to configure it.

After setting up service permissions, you’ll proceed to the implementation phase of the waves created by AWS Transform and start the migration process. For each wave, you’ll first be asked to make various choices such as setting the sizing preference and tenancy for the Amazon Elastic Compute Cloud (Amazon EC2) instances. Confirm your selections and continue following the instructions given by AWS Transform until you reach the Deploy replication agents stage, where you can start the migration for that wave.

After you start the waves migration process, you can switch to the dashboard at any time to check on progress.

With its agentic AI capabilities, AWS Transform offers a powerful solution for accelerating and de-risking mainframe and VMware modernization workloads. By automating complex assessment and transformation processes, AWS Transform reduces the time associated with legacy system migration while minimizing the potential for errors and business disruption enabling more agile, efficient, and future-ready IT environments within your organization.

Things to know
Availability –  AWS Transform for mainframe is available in US East (N. Virginia) and Europe (Frankfurt) Regions. AWS Transform for VMware offers different availability options for data collection and migrations. Please refer to the AWS Transform for VMware FAQ for more details.

Pricing –  Currently, we offer our core features—including assessment and transformation—at no cost to AWS customers.

Here are a few links for further reading.

Dive deeper into mainframe modernization and learn more about about AWS Transform for mainframe.

Explore more about VMware modernization and how to get started with your VMware migration journey.

Check out this interactive demo of AWS Transform for mainframe and this interactive demo of AWS Transform for VMware.

— Matheus Guimaraes | @codingmatheus

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