Tag Archives: AWS Marketplace

NEW – Machine Learning algorithms and model packages now available in AWS Marketplace

Post Syndicated from Shaun Ray original https://aws.amazon.com/blogs/aws/new-machine-learning-algorithms-and-model-packages-now-available-in-aws-marketplace/

At AWS, our mission is to put machine learning in the hands of every developer. That’s why in 2017 we launched Amazon SageMaker. Since then it has become one of the fastest growing services in AWS history, used by thousands of customers globally. Customers using Amazon SageMaker can use optimized algorithms offered in Amazon SageMaker, to run fully-managed MXNet, TensorFlow, PyTorch, and Chainer algorithms, or bring their own algorithms and models. When it comes to building their own machine learning model, many customers spend significant time developing algorithms and models that are solutions to problems that have already been solved.

 

Introducing Machine Learning in AWS Marketplace

I am pleased to announce the new Machine Learning category of products offered by AWS Marketplace, which includes over 150+ algorithms and model packages, with more coming every day. AWS Marketplace offers a tailored selection for vertical industries like retail (35 products), media (19 products), manufacturing (17 products), HCLS (15 products), and more. Customers can find solutions to critical use cases like breast cancer prediction, lymphoma classifications, hospital readmissions, loan risk prediction, vehicle recognition, retail localizer, botnet attack detection, automotive telematics, motion detection, demand forecasting, and speech recognition.

Customers can search and browse a list of algorithms and model packages in AWS Marketplace. Once customers have subscribed to a machine learning solution, they can deploy it directly from the SageMaker console, a Jupyter Notebook, the SageMaker SDK, or the AWS CLI. Amazon SageMaker protects buyers data by employing security measures such as static scans, network isolation, and runtime monitoring.

The intellectual property of sellers on the AWS Marketplace is protected by encrypting the algorithms and model package artifacts in transit and at rest, using secure (SSL) connections for communications, and ensuring role based access for deployment of artifacts. AWS provides a secure way for the sellers to monetize their work with a frictionless self-service process to publish their algorithms and model packages.

 

Machine Learning category in Action

Having tried to build my own models in the past, I sure am excited about this feature. After browsing through the available algorithms and model packages from AWS Marketplace, I’ve decided to try the Deep Vision vehicle recognition model, published by Deep Vision AI. This model will allow us to identify the make, model and type of car from a set of uploaded images. You could use this model for insurance claims, online car sales, and vehicle identification in your business.

I continue to subscribe and accept the default options of recommended instance type and region. I read and accept the subscription contract, and I am ready to get started with our model.

My subscription is listed in the Amazon SageMaker console and is ready to use. Deploying the model with Amazon SageMaker is the same as any other model package, I complete the steps in this guide to create and deploy our endpoint.

With our endpoint deployed I can start asking the model questions. In this case I will be using a single image of a car; the model is trained to detect the model, maker, and year information from any angle. First, I will start off with a Volvo XC70 and see what results I get:

Results:

{'result': [{'mmy': {'make': 'Volvo', 'score': 0.97, 'model': 'Xc70', 'year': '2016-2016'}, 'bbox': {'top': 146, 'left': 50, 'right': 1596, 'bottom': 813}, 'View': 'Front Left View'}]}

My model has detected the make, model and year correctly for the supplied image. I was recently on holiday in the UK and stayed with a relative who had a McLaren 570s supercar. The thought that crossed my mind as the gulf-wing doors opened for the first time and I was about to be sitting in the car, was how much it would cost for the insurance excess if things went wrong! Quite apt for our use case today.

Results:

{'result': [{'mmy': {'make': 'Mclaren', 'score': 0.95, 'model': '570S', 'year': '2016-2017'}, 'bbox': {'top': 195, 'left': 126, 'right': 757, 'bottom': 494}, 'View': 'Front Right View'}]}

The score (0.95) measures how confident the model is that the result is right. The range of the score is 0.0 to 1.0. My score is extremely accurate for the McLaren car, with the make, model and year all correct. Impressive results for a relatively rare type of car on the road. I test a few more cars given to me by the launch team who are excitedly looking over my shoulder and now it’s time to wrap up.

Within ten minutes, I have been able to choose a model package, deploy an endpoint and accurately detect the make, model and year of vehicles, with no data scientists, expensive GPU’s for training or writing any code. You can be sure I will be subscribing to a whole lot more of these models from AWS Marketplace throughout re:Invent week and trying to solve other use cases in less than 15 minutes!

Access for the machine learning category in AWS Marketplace can be achieved through the Amazon SageMaker console, or directly through AWS Marketplace itself. Once an algorithm or model has been successfully subscribed to, it is accessible via the console, SDK, and AWS CLI. Algorithms and models from the AWS Marketplace can be deployed just like any other model or algorithm, by selecting the AWS Marketplace option as your package source. Once you have chosen an algorithm or model, you can deploy it to Amazon SageMaker by following this guide.

 

Availability & Pricing

Customers pay a subscription fee for the use of an algorithm or model package and the AWS resource fee. AWS Marketplace provides a consolidated monthly bill for all purchased subscriptions.

At launch, AWS Marketplace for Machine Learning includes algorithms and models from Deep Vision AI Inc, Knowledgent, RocketML, Sensifai, Cloudwick Technologies, Persistent Systems, Modjoul, H2Oai Inc, Figure Eight [Crowdflower], Intel Corporation, AWS Gluon Model Zoos, and more with new sellers being added regularly. If you are interested in selling machine learning algorithms and model packages, please reach out to [email protected]

 

 

NEW – AWS Marketplace makes it easier to govern software procurement with Private Marketplace

Post Syndicated from Shaun Ray original https://aws.amazon.com/blogs/aws/new-aws-marketplace-makes-it-easier-to-govern-software-procurement-with-private-marketplace/

Over six years ago, we launched AWS Marketplace with the ambitious goal of providing users of the cloud with the software applications and infrastructure they needed to run their business. Today, more than 200,000 AWS active customers are using software from AWS Marketplace from categories such as security, data and analytics, log analysis and machine learning. Those customers use over 650 million hours a month of Amazon EC2 for products in AWS Marketplace and have more than 950,000 active software subscriptions. AWS Marketplace offers 35 categories and more than 4,500 software listings from more than 1,400 Independent Software Vendors (ISVs) to help you on your cloud journey, no matter what stage of adoption you are up to.

Customers have told us that they love the flexibility and myriad of options that AWS Marketplace provides. Today, I am excited to announce we are offering even more flexibility for AWS Marketplace with the launch of Private Marketplace from AWS Marketplace.

Private Marketplace is a new feature that enables you to create a custom digital catalog of pre-approved products from AWS Marketplace. As an administrator, you can select products that meet your procurement policies and make them available for your users. You can also further customize Private Marketplace with company branding, such as logo, messaging, and color scheme. All controls for Private Marketplace apply across your entire AWS Organizations entity, and you can define fine-grained controls using Identity and Access Management for roles such as: administrator, subscription manager and end user.

Once you enable Private Marketplace, users within your AWS Organizations redirect to Private Marketplace when they sign into AWS Marketplace. Now, your users can quickly find, buy, and deploy products knowing they are pre-approved.

 

Private Marketplace in Action

To get started we need to be using a master account, if you have a single account, it will automatically be classified as a master account. If you are a member of an AWS Organizations managed account, the master account will need to enable Private Marketplace access. Once done, you can add subscription managers and administrators through AWS Identity and Access Management (IAM) policies.

 

1- My account meets the requirement of being a master, I can proceed to create a Private Marketplace. I click “Create Private Marketplace” and am redirected to the admin page where I can whitelist products from AWS Marketplace. To grant other users access to approve products for listing, I can use AWS Organizations policies to grant the AWSMarketplaceManageSubscriptions role.

2- I select some popular software and operating systems from the list and add them to Private Marketplace. Once selected we can now see our whitelisted products.

3- One thing that I appreciate, and I am sure that the administrators of their organization’s Private Marketplace will, is some customization to bring the style and branding inline with the company. In this case, we can choose the name, logo, color, and description of our Private Marketplace.

4- After a couple of minutes we have our freshly minted Private Marketplace ready to go, there is an explicit step that we need to complete to push our Private Marketplace live. This allows us to create and edit without enabling access to users.

 

5 -For the next part, we will switch to a member account and see what our Private Marketplace looks like.

6- We can see the five pieces of software I whitelisted and our customizations to our Private Marketplace. We can also see that these products are “Approved for Procurement” and can be subscribed to by our end users. Other products are still discoverable by our users, but cannot be subscribed to until an administrator whitelists the product.

 

Conclusion

Users in a Private Marketplace can launch products knowing that all products in their Private Marketplace comply with their company’s procurement policies. When users search for products in Private Marketplace, they can see which products are labeled as “Approved for Procurement” and quickly filter between their company’s catalog and the full catalog of software products in AWS Marketplace.

 

Pricing and Availability

Subscription costs remain the same as all products in AWS Marketplace once consumed. Private Marketplace from AWS Marketplace is available in all commercial regions today.

 

 

 

Leveraging AWS Marketplace Partner Storage Solutions for Microsoft

Post Syndicated from islawson original https://aws.amazon.com/blogs/architecture/leveraging-aws-marketplace-partner-storage-solutions-for-microsoft/

Designing a cloud storage solution to accommodate traditional enterprise software such as Microsoft SharePoint can be challenging. Microsoft SharePoint is complex and demands a lot of the underlying storage that’s used for its many databases and content repositories. To ensure that the selected storage platform can accommodate the availability, connectivity, and performance requirements recommended by Microsoft you need to use third-party storage solutions that build on and extend the functionality and performance of AWS storage services.

An appropriate storage solution for Microsoft SharePoint needs to provide data redundancy, high availability, fault tolerance, strong encryption, standard connectivity protocols, point-in-time data recovery, compression, ease of management, directory integration, and support.

AWS Marketplace is uniquely positioned as a procurement channel to find a third-party storage product that provides the additional technology layered on top of AWS storage services. The third-party storage products are provided and maintained by industry newcomers with born-in-the-cloud solutions as well as existing industry leaders. They include many mainstream storage products that are already familiar and commonly deployed in enterprises.

We recently released the “Leveraging AWS Marketplace Storage Solutions for Microsoft SharePoint” whitepaper to walk through the deployment and configuration of SoftNAS Cloud NAS, an AWS Marketplace third-party storage product that provides secure, highly available, redundant, and fault-tolerant storage to the Microsoft SharePoint collaboration suite.

About the Author

Israel Lawson is a senior solutions architect on the AWS Marketplace team.

Amazon Redshift – 2017 Recap

Post Syndicated from Larry Heathcote original https://aws.amazon.com/blogs/big-data/amazon-redshift-2017-recap/

We have been busy adding new features and capabilities to Amazon Redshift, and we wanted to give you a glimpse of what we’ve been doing over the past year. In this article, we recap a few of our enhancements and provide a set of resources that you can use to learn more and get the most out of your Amazon Redshift implementation.

In 2017, we made more than 30 announcements about Amazon Redshift. We listened to you, our customers, and delivered Redshift Spectrum, a feature of Amazon Redshift, that gives you the ability to extend analytics to your data lake—without moving data. We launched new DC2 nodes, doubling performance at the same price. We also announced many new features that provide greater scalability, better performance, more automation, and easier ways to manage your analytics workloads.

To see a full list of our launches, visit our what’s new page—and be sure to subscribe to our RSS feed.

Major launches in 2017

Amazon Redshift Spectrumextend analytics to your data lake, without moving data

We launched Amazon Redshift Spectrum to give you the freedom to store data in Amazon S3, in open file formats, and have it available for analytics without the need to load it into your Amazon Redshift cluster. It enables you to easily join datasets across Redshift clusters and S3 to provide unique insights that you would not be able to obtain by querying independent data silos.

With Redshift Spectrum, you can run SQL queries against data in an Amazon S3 data lake as easily as you analyze data stored in Amazon Redshift. And you can do it without loading data or resizing the Amazon Redshift cluster based on growing data volumes. Redshift Spectrum separates compute and storage to meet workload demands for data size, concurrency, and performance. Redshift Spectrum scales processing across thousands of nodes, so results are fast, even with massive datasets and complex queries. You can query open file formats that you already use—such as Apache Avro, CSV, Grok, ORC, Apache Parquet, RCFile, RegexSerDe, SequenceFile, TextFile, and TSV—directly in Amazon S3, without any data movement.

For complex queries, Redshift Spectrum provided a 67 percent performance gain,” said Rafi Ton, CEO, NUVIAD. “Using the Parquet data format, Redshift Spectrum delivered an 80 percent performance improvement. For us, this was substantial.

To learn more about Redshift Spectrum, watch our AWS Summit session Intro to Amazon Redshift Spectrum: Now Query Exabytes of Data in S3, and read our announcement blog post Amazon Redshift Spectrum – Exabyte-Scale In-Place Queries of S3 Data.

DC2 nodes—twice the performance of DC1 at the same price

We launched second-generation Dense Compute (DC2) nodes to provide low latency and high throughput for demanding data warehousing workloads. DC2 nodes feature powerful Intel E5-2686 v4 (Broadwell) CPUs, fast DDR4 memory, and NVMe-based solid state disks (SSDs). We’ve tuned Amazon Redshift to take advantage of the better CPU, network, and disk on DC2 nodes, providing up to twice the performance of DC1 at the same price. Our DC2.8xlarge instances now provide twice the memory per slice of data and an optimized storage layout with 30 percent better storage utilization.

Redshift allows us to quickly spin up clusters and provide our data scientists with a fast and easy method to access data and generate insights,” said Bradley Todd, technology architect at Liberty Mutual. “We saw a 9x reduction in month-end reporting time with Redshift DC2 nodes as compared to DC1.”

Read our customer testimonials to see the performance gains our customers are experiencing with DC2 nodes. To learn more, read our blog post Amazon Redshift Dense Compute (DC2) Nodes Deliver Twice the Performance as DC1 at the Same Price.

Performance enhancements— 3x-5x faster queries

On average, our customers are seeing 3x to 5x performance gains for most of their critical workloads.

We introduced short query acceleration to speed up execution of queries such as reports, dashboards, and interactive analysis. Short query acceleration uses machine learning to predict the execution time of a query, and to move short running queries to an express short query queue for faster processing.

We launched results caching to deliver sub-second response times for queries that are repeated, such as dashboards, visualizations, and those from BI tools. Results caching has an added benefit of freeing up resources to improve the performance of all other queries.

We also introduced late materialization to reduce the amount of data scanned for queries with predicate filters by batching and factoring in the filtering of predicates before fetching data blocks in the next column. For example, if only 10 percent of the table rows satisfy the predicate filters, Amazon Redshift can potentially save 90 percent of the I/O for the remaining columns to improve query performance.

We launched query monitoring rules and pre-defined rule templates. These features make it easier for you to set metrics-based performance boundaries for workload management (WLM) queries, and specify what action to take when a query goes beyond those boundaries. For example, for a queue that’s dedicated to short-running queries, you might create a rule that aborts queries that run for more than 60 seconds. To track poorly designed queries, you might have another rule that logs queries that contain nested loops.

Customer insights

Amazon Redshift and Redshift Spectrum serve customers across a variety of industries and sizes, from startups to large enterprises. Visit our customer page to see the success that customers are having with our recent enhancements. Learn how companies like Liberty Mutual Insurance saw a 9x reduction in month-end reporting time using DC2 nodes. On this page, you can find case studies, videos, and other content that show how our customers are using Amazon Redshift to drive innovation and business results.

In addition, check out these resources to learn about the success our customers are having building out a data warehouse and data lake integration solution with Amazon Redshift:

Partner solutions

You can enhance your Amazon Redshift data warehouse by working with industry-leading experts. Our AWS Partner Network (APN) Partners have certified their solutions to work with Amazon Redshift. They offer software, tools, integration, and consulting services to help you at every step. Visit our Amazon Redshift Partner page and choose an APN Partner. Or, use AWS Marketplace to find and immediately start using third-party software.

To see what our Partners are saying about Amazon Redshift Spectrum and our DC2 nodes mentioned earlier, read these blog posts:

Resources

Blog posts

Visit the AWS Big Data Blog for a list of all Amazon Redshift articles.

YouTube videos

GitHub

Our community of experts contribute on GitHub to provide tips and hints that can help you get the most out of your deployment. Visit GitHub frequently to get the latest technical guidance, code samples, administrative task automation utilities, the analyze & vacuum schema utility, and more.

Customer support

If you are evaluating or considering a proof of concept with Amazon Redshift, or you need assistance migrating your on-premises or other cloud-based data warehouse to Amazon Redshift, our team of product experts and solutions architects can help you with architecting, sizing, and optimizing your data warehouse. Contact us using this support request form, and let us know how we can assist you.

If you are an Amazon Redshift customer, we offer a no-cost health check program. Our team of database engineers and solutions architects give you recommendations for optimizing Amazon Redshift and Amazon Redshift Spectrum for your specific workloads. To learn more, email us at [email protected].

If you have any questions, email us at [email protected].

 


Additional Reading

If you found this post useful, be sure to check out Amazon Redshift Spectrum – Exabyte-Scale In-Place Queries of S3 Data, Using Amazon Redshift for Fast Analytical Reports and How to Migrate Your Oracle Data Warehouse to Amazon Redshift Using AWS SCT and AWS DMS.


About the Author

Larry Heathcote is a Principle Product Marketing Manager at Amazon Web Services for data warehousing and analytics. Larry is passionate about seeing the results of data-driven insights on business outcomes. He enjoys family time, home projects, grilling out and the taste of classic barbeque.

 

 

 

The Floodgates Are Open – Increased Network Bandwidth for EC2 Instances

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/the-floodgates-are-open-increased-network-bandwidth-for-ec2-instances/

I hope that you have configured your AMIs and your current-generation EC2 instances to use the Elastic Network Adapter (ENA) that I told you about back in mid-2016. The ENA gives you high throughput and low latency, while minimizing the load on the host processor. It is designed to work well in the presence of multiple vCPUs, with intelligent packet routing backed up by multiple transmit and receive queues.

Today we are opening up the floodgates and giving you access to more bandwidth in all AWS Regions. Here are the specifics (in each case, the actual bandwidth is dependent on the instance type and size):

EC2 to S3 – Traffic to and from Amazon Simple Storage Service (S3) can now take advantage of up to 25 Gbps of bandwidth. Previously, traffic of this type had access to 5 Gbps of bandwidth. This will be of benefit to applications that access large amounts of data in S3 or that make use of S3 for backup and restore.

EC2 to EC2 – Traffic to and from EC2 instances in the same or different Availability Zones within a region can now take advantage of up to 5 Gbps of bandwidth for single-flow traffic, or 25 Gbps of bandwidth for multi-flow traffic (a flow represents a single, point-to-point network connection) by using private IPv4 or IPv6 addresses, as described here.

EC2 to EC2 (Cluster Placement Group) – Traffic to and from EC2 instances within a cluster placement group can continue to take advantage of up to 10 Gbps of lower-latency bandwidth for single-flow traffic, or 25 Gbps of lower-latency bandwidth for multi-flow traffic.

To take advantage of this additional bandwidth, make sure that you are using the latest, ENA-enabled AMIs on current-generation EC2 instances. ENA-enabled AMIs are available for Amazon Linux, Ubuntu 14.04 & 16.04, RHEL 7.4, SLES 12, and Windows Server (2008 R2, 2012, 2012 R2, and 2016). The FreeBSD AMI in AWS Marketplace is also ENA-enabled, as is VMware Cloud on AWS.

Jeff;

Building Blocks of Amazon ECS

Post Syndicated from Tiffany Jernigan original https://aws.amazon.com/blogs/compute/building-blocks-of-amazon-ecs/

So, what’s Amazon Elastic Container Service (ECS)? ECS is a managed service for running containers on AWS, designed to make it easy to run applications in the cloud without worrying about configuring the environment for your code to run in. Using ECS, you can easily deploy containers to host a simple website or run complex distributed microservices using thousands of containers.

Getting started with ECS isn’t too difficult. To fully understand how it works and how you can use it, it helps to understand the basic building blocks of ECS and how they fit together!

Let’s begin with an analogy

Imagine you’re in a virtual reality game with blocks and portals, in which your task is to build kingdoms.

In your spaceship, you pull up a holographic map of your upcoming destination: Nozama, a golden-orange planet. Looking at its various regions, you see that the nearest one is za-southwest-1 (SW Nozama). You set your destination, and use your jump drive to jump to the outer atmosphere of za-southwest-1.

As you approach SW Nozama, you see three portals, 1a, 1b, and 1c. Each portal lets you transport directly to an isolated zone (Availability Zone), where you can start construction on your new kingdom (cluster), Royaume.

With your supply of blocks, you take the portal to 1b, and erect the surrounding walls of your first territory (instance)*.

Before you get ahead of yourself, there are some rules to keep in mind. For your territory to be a part of Royaume, the land ordinance requires construction of a building (container), specifically a castle, from which your territory’s lord (agent)* rules.

You can then create architectural plans (task definitions) to build your developments (tasks), consisting of up to 10 buildings per plan. A development can be built now within this or any territory, or multiple territories.

If you do decide to create more territories, you can either stay here in 1b or take a portal to another location in SW Nozama and start building there.

Amazon EC2 building blocks

We currently provide two launch types: EC2 and Fargate. With Fargate, the Amazon EC2 instances are abstracted away and managed for you. Instead of worrying about ECS container instances, you can just worry about tasks. In this post, the infrastructure components used by ECS that are handled by Fargate are marked with a *.

Instance*

EC2 instances are good ol’ virtual machines (VMs). And yes, don’t worry, you can connect to them (via SSH). Because customers have varying needs in memory, storage, and computing power, many different instance types are offered. Just want to run a small application or try a free trial? Try t2.micro. Want to run memory-optimized workloads? R3 and X1 instances are a couple options. There are many more instance types as well, which cater to various use cases.

AMI*

Sorry if you wanted to immediately march forward, but before you create your instance, you need to choose an AMI. An AMI stands for Amazon Machine Image. What does that mean? Basically, an AMI provides the information required to launch an instance: root volume, launch permissions, and volume-attachment specifications. You can find and choose a Linux or Windows AMI provided by AWS, the user community, the AWS Marketplace (for example, the Amazon ECS-Optimized AMI), or you can create your own.

Region

AWS is divided into regions that are geographic areas around the world (for now it’s just Earth, but maybe someday…). These regions have semi-evocative names such as us-east-1 (N. Virginia), us-west-2 (Oregon), eu-central-1 (Frankfurt), ap-northeast-1 (Tokyo), etc.

Each region is designed to be completely isolated from the others, and consists of multiple, distinct data centers. This creates a “blast radius” for failure so that even if an entire region goes down, the others aren’t affected. Like many AWS services, to start using ECS, you first need to decide the region in which to operate. Typically, this is the region nearest to you or your users.

Availability Zone

AWS regions are subdivided into Availability Zones. A region has at minimum two zones, and up to a handful. Zones are physically isolated from each other, spanning one or more different data centers, but are connected through low-latency, fiber-optic networking, and share some common facilities. EC2 is designed so that the most common failures only affect a single zone to prevent region-wide outages. This means you can achieve high availability in a region by spanning your services across multiple zones and distributing across hosts.

Amazon ECS building blocks

Container

Well, without containers, ECS wouldn’t exist!

Are containers virtual machines?
Nope! Virtual machines virtualize the hardware (benefits), while containers virtualize the operating system (even more benefits!). If you look inside a container, you would see that it is made by processes running on the host, and tied together by kernel constructs like namespaces, cgroups, etc. But you don’t need to bother about that level of detail, at least not in this post!

Why containers?
Containers give you the ability to build, ship, and run your code anywhere!

Before the cloud, you needed to self-host and therefore had to buy machines in addition to setting up and configuring the operating system (OS), and running your code. In the cloud, with virtualization, you can just skip to setting up the OS and running your code. Containers make the process even easier—you can just run your code.

Additionally, all of the dependencies travel in a package with the code, which is called an image. This allows containers to be deployed on any host machine. From the outside, it looks like a host is just holding a bunch of containers. They all look the same, in the sense that they are generic enough to be deployed on any host.

With ECS, you can easily run your containerized code and applications across a managed cluster of EC2 instances.

Are containers a fairly new technology?
The concept of containerization is not new. Its origins date back to 1979 with the creation of chroot. However, it wasn’t until the early 2000s that containers became a major technology. The most significant milestone to date was the release of Docker in 2013, which led to the popularization and widespread adoption of containers.

What does ECS use?
While other container technologies exist (LXC, rkt, etc.), because of its massive adoption and use by our customers, ECS was designed first to work natively with Docker containers.

Container instance*

Yep, you are back to instances. An instance is just slightly more complex in the ECS realm though. Here, it is an ECS container instance that is an EC2 instance running the agent, has a specifically defined IAM policy and role, and has been registered into your cluster.

And as you probably guessed, in these instances, you are running containers. 

AMI*

These container instances can use any AMI as long as it has the following specifications: a modern Linux distribution with the agent and the Docker Daemon with any Docker runtime dependencies running on it.

Want it more simplified? Well, AWS created the Amazon ECS-Optimized AMI for just that. Not only does that AMI come preconfigured with all of the previously mentioned specifications, it’s tested and includes the recommended ecs-init upstart process to run and monitor the agent.

Cluster

An ECS cluster is a grouping of (container) instances* (or tasks in Fargate) that lie within a single region, but can span multiple Availability Zones – it’s even a good idea for redundancy. When launching an instance (or tasks in Fargate), unless specified, it registers with the cluster named “default”. If “default” doesn’t exist, it is created. You can also scale and delete your clusters.

Agent*

The Amazon ECS container agent is a Go program that runs in its own container within each EC2 instance that you use with ECS. (It’s also available open source on GitHub!) The agent is the intermediary component that takes care of the communication between the scheduler and your instances. Want to register your instance into a cluster? (Why wouldn’t you? A cluster is both a logical boundary and provider of pool of resources!) Then you need to run the agent on it.

Task

When you want to start a container, it has to be part of a task. Therefore, you have to create a task first. Succinctly, tasks are a logical grouping of 1 to N containers that run together on the same instance, with N defined by you, up to 10. Let’s say you want to run a custom blog engine. You could put together a web server, an application server, and an in-memory cache, each in their own container. Together, they form a basic frontend unit.

Task definition

Ah, but you cannot create a task directly. You have to create a task definition that tells ECS that “task definition X is composed of this container (and maybe that other container and that other container too!).” It’s kind of like an architectural plan for a city. Some other details it can include are how the containers interact, container CPU and memory constraints, and task permissions using IAM roles.

Then you can tell ECS, “start one task using task definition X.” It might sound like unnecessary planning at first. As soon as you start to deal with multiple tasks, scaling, upgrades, and other “real life” scenarios, you’ll be glad that you have task definitions to keep track of things!

Scheduler*

So, the scheduler schedules… sorry, this should be more helpful, huh? The scheduler is part of the “hosted orchestration layer” provided by ECS. Wait a minute, what do I mean by “hosted orchestration”? Simply put, hosted means that it’s operated by ECS on your behalf, without you having to care about it. Your applications are deployed in containers running on your instances, but the managing of tasks is taken care of by ECS. One less thing to worry about!

Also, the scheduler is the component that decides what (which containers) gets to run where (on which instances), according to a number of constraints. Say that you have a custom blog engine to scale for high availability. You could create a service, which by default, spreads tasks across all zones in the chosen region. And if you want each task to be on a different instance, you can use the distinctInstance task placement constraint. ECS makes sure that not only this happens, but if a task fails, it starts again.

Service

To ensure that you always have your task running without managing it yourself, you can create a service based on the task that you defined and ECS ensures that it stays running. A service is a special construct that says, “at any given time, I want to make sure that N tasks using task definition X1 are running.” If N=1, it just means “make sure that this task is running, and restart it if needed!” And with N>1, you’re basically scaling your application until you hit N, while also ensuring each task is running.

So, what now?

Hopefully you, at the very least, learned a tiny something. All comments are very welcome!

Want to discuss ECS with others? Join the amazon-ecs slack group, which members of the community created and manage.

Also, if you’re interested in learning more about the core concepts of ECS and its relation to EC2, here are some resources:

Pages
Amazon ECS landing page
AWS Fargate landing page
Amazon ECS Getting Started
Nathan Peck’s AWSome ECS

Docs
Amazon EC2
Amazon ECS

Blogs
AWS Compute Blog
AWS Blog

GitHub code
Amazon ECS container agent
Amazon ECS CLI

AWS videos
Learn Amazon ECS
AWS videos
AWS webinars

 

— tiffany

 @tiffanyfayj

 

Now Open AWS EU (Paris) Region

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/now-open-aws-eu-paris-region/

Today we are launching our 18th AWS Region, our fourth in Europe. Located in the Paris area, AWS customers can use this Region to better serve customers in and around France.

The Details
The new EU (Paris) Region provides a broad suite of AWS services including Amazon API Gateway, Amazon Aurora, Amazon CloudFront, Amazon CloudWatch, CloudWatch Events, Amazon CloudWatch Logs, Amazon DynamoDB, Amazon Elastic Compute Cloud (EC2), EC2 Container Registry, Amazon ECS, Amazon Elastic Block Store (EBS), Amazon EMR, Amazon ElastiCache, Amazon Elasticsearch Service, Amazon Glacier, Amazon Kinesis Streams, Polly, Amazon Redshift, Amazon Relational Database Service (RDS), Amazon Route 53, Amazon Simple Notification Service (SNS), Amazon Simple Queue Service (SQS), Amazon Simple Storage Service (S3), Amazon Simple Workflow Service (SWF), Amazon Virtual Private Cloud, Auto Scaling, AWS Certificate Manager (ACM), AWS CloudFormation, AWS CloudTrail, AWS CodeDeploy, AWS Config, AWS Database Migration Service, AWS Direct Connect, AWS Elastic Beanstalk, AWS Identity and Access Management (IAM), AWS Key Management Service (KMS), AWS Lambda, AWS Marketplace, AWS OpsWorks Stacks, AWS Personal Health Dashboard, AWS Server Migration Service, AWS Service Catalog, AWS Shield Standard, AWS Snowball, AWS Snowball Edge, AWS Snowmobile, AWS Storage Gateway, AWS Support (including AWS Trusted Advisor), Elastic Load Balancing, and VM Import.

The Paris Region supports all sizes of C5, M5, R4, T2, D2, I3, and X1 instances.

There are also four edge locations for Amazon Route 53 and Amazon CloudFront: three in Paris and one in Marseille, all with AWS WAF and AWS Shield. Check out the AWS Global Infrastructure page to learn more about current and future AWS Regions.

The Paris Region will benefit from three AWS Direct Connect locations. Telehouse Voltaire is available today. AWS Direct Connect will also become available at Equinix Paris in early 2018, followed by Interxion Paris.

All AWS infrastructure regions around the world are designed, built, and regularly audited to meet the most rigorous compliance standards and to provide high levels of security for all AWS customers. These include ISO 27001, ISO 27017, ISO 27018, SOC 1 (Formerly SAS 70), SOC 2 and SOC 3 Security & Availability, PCI DSS Level 1, and many more. This means customers benefit from all the best practices of AWS policies, architecture, and operational processes built to satisfy the needs of even the most security sensitive customers.

AWS is certified under the EU-US Privacy Shield, and the AWS Data Processing Addendum (DPA) is GDPR-ready and available now to all AWS customers to help them prepare for May 25, 2018 when the GDPR becomes enforceable. The current AWS DPA, as well as the AWS GDPR DPA, allows customers to transfer personal data to countries outside the European Economic Area (EEA) in compliance with European Union (EU) data protection laws. AWS also adheres to the Cloud Infrastructure Service Providers in Europe (CISPE) Code of Conduct. The CISPE Code of Conduct helps customers ensure that AWS is using appropriate data protection standards to protect their data, consistent with the GDPR. In addition, AWS offers a wide range of services and features to help customers meet the requirements of the GDPR, including services for access controls, monitoring, logging, and encryption.

From Our Customers
Many AWS customers are preparing to use this new Region. Here’s a small sample:

Societe Generale, one of the largest banks in France and the world, has accelerated their digital transformation while working with AWS. They developed SG Research, an application that makes reports from Societe Generale’s analysts available to corporate customers in order to improve the decision-making process for investments. The new AWS Region will reduce latency between applications running in the cloud and in their French data centers.

SNCF is the national railway company of France. Their mobile app, powered by AWS, delivers real-time traffic information to 14 million riders. Extreme weather, traffic events, holidays, and engineering works can cause usage to peak at hundreds of thousands of users per second. They are planning to use machine learning and big data to add predictive features to the app.

Radio France, the French public radio broadcaster, offers seven national networks, and uses AWS to accelerate its innovation and stay competitive.

Les Restos du Coeur, a French charity that provides assistance to the needy, delivering food packages and participating in their social and economic integration back into French society. Les Restos du Coeur is using AWS for its CRM system to track the assistance given to each of their beneficiaries and the impact this is having on their lives.

AlloResto by JustEat (a leader in the French FoodTech industry), is using AWS to to scale during traffic peaks and to accelerate their innovation process.

AWS Consulting and Technology Partners
We are already working with a wide variety of consulting, technology, managed service, and Direct Connect partners in France. Here’s a partial list:

AWS Premier Consulting PartnersAccenture, Capgemini, Claranet, CloudReach, DXC, and Edifixio.

AWS Consulting PartnersABC Systemes, Atos International SAS, CoreExpert, Cycloid, Devoteam, LINKBYNET, Oxalide, Ozones, Scaleo Information Systems, and Sopra Steria.

AWS Technology PartnersAxway, Commerce Guys, MicroStrategy, Sage, Software AG, Splunk, Tibco, and Zerolight.

AWS in France
We have been investing in Europe, with a focus on France, for the last 11 years. We have also been developing documentation and training programs to help our customers to improve their skills and to accelerate their journey to the AWS Cloud.

As part of our commitment to AWS customers in France, we plan to train more than 25,000 people in the coming years, helping them develop highly sought after cloud skills. They will have access to AWS training resources in France via AWS Academy, AWSome days, AWS Educate, and webinars, all delivered in French by AWS Technical Trainers and AWS Certified Trainers.

Use it Today
The EU (Paris) Region is open for business now and you can start using it today!

Jeff;

 

AWS PrivateLink Update – VPC Endpoints for Your Own Applications & Services

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-privatelink-update-vpc-endpoints-for-your-own-applications-services/

Earlier this month, my colleague Colm MacCárthaigh told you about AWS PrivateLink and showed you how to use it to access AWS services such as Amazon Kinesis Streams, AWS Service Catalog, EC2 Systems Manager, the EC2 APIs, and the ELB APIs by way of VPC Endpoints. The endpoint (represented by one or more Elastic Network Interfaces or ENIs) resides within your VPC and has IP addresses drawn from the VPC’s subnets, without the need for an Internet or NAT Gateway. This model is clear and easy to understand, not to mention secure and scalable!

Endpoints for Private Connectivity
Today we are building upon the initial launch and extending the PrivateLink model, allowing you to set up and use VPC Endpoints to access your own services and those made available by others. Even before we launched PrivateLink for AWS services, we had a lot of requests for this feature, so I expect it to be pretty popular. For example, one customer told us that they plan to create hundreds of VPCs, each hosting and providing a single microservice (read Microservices on AWS to learn more).

Companies can now create services and offer them for sale to other AWS customers, for access via a private connection. They create a service that accepts TCP traffic, host it behind a Network Load Balancer, and then make the service available, either directly or in AWS Marketplace. They will be notified of new subscription requests and can choose to accept or reject each one. I expect that this feature will be used to create a strong, vibrant ecosystem of service providers in 2018.

The service provider and the service consumer run in separate VPCs and AWS accounts and communicate solely through the endpoint, with all traffic flowing across Amazon’s private network. Service consumers don’t have to worry about overlapping IP addresses, arrange for VPC peering, or use a VPC Gateway. You can also use AWS Direct Connect to connect your existing data center to one of your VPCs in order to allow your cloud-based applications to access services running on-premises, or vice versa.

Providing and Consuming Services
This new feature puts a lot of power at your fingertips. You can set it all up using the VPC APIs, the VPC CLI, or the AWS Management Console. I’ll use the console, and will show you how to provide and then consume a service. I am going to do both within a single AWS account, but that’s just for demo purposes.

Let’s talk about providing a service. It must run behind a Network Load Balancer and must be accessible over TCP. It can be hosted on EC2 instances, ECS containers, or on-premises (configured as an IP target), and should be able to scale in order to meet the expected level of demand. For low latency and fault tolerance, we recommend using an NLB with targets in every AZ of its region. Here’s mine:

I open up the VPC Console and navigate to Endpoint Services, then click on Create Endpoint Service:

I choose my NLB (just one in this case, but I can choose two or more and they will be mapped to consumers on a round-robin basis). By clicking on Acceptance required, I get to control access to my endpoint on a request-by-request basis:

I click on Create service and my service is ready immediately:

If I was going to make this service available in AWS Marketplace, I would go ahead and create a listing now. Since I am going to be the producer and the consumer in this blog post, I’ll skip that step. I will, however, copy the Service name for use in the next step.

I return to the VPC Dashboard and navigate to Endpoints, then click on Create endpoint. Then I select Find service by name, paste the service name, and click on Verify to move ahead. Then I select the desired AZs, and a subnet in each one, pick my security groups, and click on Create endpoint:

Because I checked Acceptance required when I created the endpoint service, the connection is pending acceptance:

Back on the endpoint service side (typically in a separate AWS account), I can see and accept the pending request:

The endpoint becomes available and ready to use within a minute or so. If I was creating a service and selling access on a paid basis, I would accept the request as part of a larger, and perhaps automated, onboarding workflow for a new customer.

On the consumer side, my new endpoint is accessible via DNS name:

Services provided by AWS and services in AWS Marketplace are accessible through split-horizon DNS. Accessing the service through this name will resolve to the “best” endpoint, taking Region and Availability Zone into consideration.

In the Marketplace
As I noted earlier, this new PrivateLink feature creates an opportunity for new and existing sellers in AWS Marketplace. The following SaaS offerings are already available as endpoints and I expect many more to follow (read Sell on AWS Marketplace to get started):

CA TechnologiesCA App Experience Analytics Essentials.

Aqua SecurityAqua Container Image Security Scanner.

DynatraceCloud-Native Monitoring powered by AI.

Cisco StealthwatchPublic Cloud Monitoring – Metered, Public Cloud Monitoring – Contracts.

SigOptML Optimization & Tuning.

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

Jeff;

 

Bringing Datacenter-Scale Hardware-Software Co-design to the Cloud with FireSim and Amazon EC2 F1 Instances

Post Syndicated from Mia Champion original https://aws.amazon.com/blogs/compute/bringing-datacenter-scale-hardware-software-co-design-to-the-cloud-with-firesim-and-amazon-ec2-f1-instances/

The recent addition of Xilinx FPGAs to AWS Cloud compute offerings is one way that AWS is enabling global growth in the areas of advanced analytics, deep learning and AI. The customized F1 servers use pooled accelerators, enabling interconnectivity of up to 8 FPGAs, each one including 64 GiB DDR4 ECC protected memory, with a dedicated PCIe x16 connection. That makes this a powerful engine with the capacity to process advanced analytical applications at scale, at a significantly faster rate. For example, AWS commercial partner Edico Genome is able to achieve an approximately 30X speedup in analyzing whole genome sequencing datasets using their DRAGEN platform powered with F1 instances.

While the availability of FPGA F1 compute on-demand provides clear accessibility and cost advantages, many mainstream users are still finding that the “threshold to entry” in developing or running FPGA-accelerated simulations is too high. Researchers at the UC Berkeley RISE Lab have developed “FireSim”, powered by Amazon FPGA F1 instances as an open-source resource, FireSim lowers that entry bar and makes it easier for everyone to leverage the power of an FPGA-accelerated compute environment. Whether you are part of a small start-up development team or working at a large datacenter scale, hardware-software co-design enables faster time-to-deployment, lower costs, and more predictable performance. We are excited to feature FireSim in this post from Sagar Karandikar and his colleagues at UC-Berkeley.

―Mia Champion, Sr. Data Scientist, AWS

Mapping an 8-node FireSim cluster simulation to Amazon EC2 F1

As traditional hardware scaling nears its end, the data centers of tomorrow are trending towards heterogeneity, employing custom hardware accelerators and increasingly high-performance interconnects. Prototyping new hardware at scale has traditionally been either extremely expensive, or very slow. In this post, I introduce FireSim, a new hardware simulation platform under development in the computer architecture research group at UC Berkeley that enables fast, scalable hardware simulation using Amazon EC2 F1 instances.

FireSim benefits both hardware and software developers working on new rack-scale systems: software developers can use the simulated nodes with new hardware features as they would use a real machine, while hardware developers have full control over the hardware being simulated and can run real software stacks while hardware is still under development. In conjunction with this post, we’re releasing the first public demo of FireSim, which lets you deploy your own 8-node simulated cluster on an F1 Instance and run benchmarks against it. This demo simulates a pre-built “vanilla” cluster, but demonstrates FireSim’s high performance and usability.

Why FireSim + F1?

FPGA-accelerated hardware simulation is by no means a new concept. However, previous attempts to use FPGAs for simulation have been fraught with usability, scalability, and cost issues. FireSim takes advantage of EC2 F1 and open-source hardware to address the traditional problems with FPGA-accelerated simulation:
Problem #1: FPGA-based simulations have traditionally been expensive, difficult to deploy, and difficult to reproduce.
FireSim uses public-cloud infrastructure like F1, which means no upfront cost to purchase and deploy FPGAs. Developers and researchers can distribute pre-built AMIs and AFIs, as in this public demo (more details later in this post), to make experiments easy to reproduce. FireSim also automates most of the work involved in deploying an FPGA simulation, essentially enabling one-click conversion from new RTL to deploying on an FPGA cluster.

Problem #2: FPGA-based simulations have traditionally been difficult (and expensive) to scale.
Because FireSim uses F1, users can scale out experiments by spinning up additional EC2 instances, rather than spending hundreds of thousands of dollars on large FPGA clusters.

Problem #3: Finding open hardware to simulate has traditionally been difficult. Finding open hardware that can run real software stacks is even harder.
FireSim simulates RocketChip, an open, silicon-proven, RISC-V-based processor platform, and adds peripherals like a NIC and disk device to build up a realistic system. Processors that implement RISC-V automatically support real operating systems (such as Linux) and even support applications like Apache and Memcached. We provide a custom Buildroot-based FireSim Linux distribution that runs on our simulated nodes and includes many popular developer tools.

Problem #4: Writing hardware in traditional HDLs is time-consuming.
Both FireSim and RocketChip use the Chisel HDL, which brings modern programming paradigms to hardware description languages. Chisel greatly simplifies the process of building large, highly parameterized hardware components.

How to use FireSim for hardware/software co-design

FireSim drastically improves the process of co-designing hardware and software by acting as a push-button interface for collaboration between hardware developers and systems software developers. The following diagram describes the workflows that hardware and software developers use when working with FireSim.

Figure 2. The FireSim custom hardware development workflow.

The hardware developer’s view:

  1. Write custom RTL for your accelerator, peripheral, or processor modification in a productive language like Chisel.
  2. Run a software simulation of your hardware design in standard gate-level simulation tools for early-stage debugging.
  3. Run FireSim build scripts, which automatically build your simulation, run it through the Vivado toolchain/AWS shell scripts, and publish an AFI.
  4. Deploy your simulation on EC2 F1 using the generated simulation driver and AFI
  5. Run real software builds released by software developers to benchmark your hardware

The software developer’s view:

  1. Deploy the AMI/AFI generated by the hardware developer on an F1 instance to simulate a cluster of nodes (or scale out to many F1 nodes for larger simulated core-counts).
  2. Connect using SSH into the simulated nodes in the cluster and boot the Linux distribution included with FireSim. This distribution is easy to customize, and already supports many standard software packages.
  3. Directly prototype your software using the same exact interfaces that the software will see when deployed on the real future system you’re prototyping, with the same performance characteristics as observed from software, even at scale.

FireSim demo v1.0

Figure 3. Cluster topology simulated by FireSim demo v1.0.

This first public demo of FireSim focuses on the aforementioned “software-developer’s view” of the custom hardware development cycle. The demo simulates a cluster of 1 to 8 RocketChip-based nodes, interconnected by a functional network simulation. The simulated nodes work just like “real” machines:  they boot Linux, you can connect to them using SSH, and you can run real applications on top. The nodes can see each other (and the EC2 F1 instance on which they’re deployed) on the network and communicate with one another. While the demo currently simulates a pre-built “vanilla” cluster, the entire hardware configuration of these simulated nodes can be modified after FireSim is open-sourced.

In this post, I walk through bringing up a single-node FireSim simulation for experienced EC2 F1 users. For more detailed instructions for new users and instructions for running a larger 8-node simulation, see FireSim Demo v1.0 on Amazon EC2 F1. Both demos walk you through setting up an instance from a demo AMI/AFI and booting Linux on the simulated nodes. The full demo instructions also walk you through an example workload, running Memcached on the simulated nodes, with YCSB as a load generator to demonstrate network functionality.

Deploying the demo on F1

In this release, we provide pre-built binaries for driving simulation from the host and a pre-built AFI that contains the FPGA infrastructure necessary to simulate a RocketChip-based node.

Starting your F1 instances

First, launch an instance using the free FireSim Demo v1.0 product available on the AWS Marketplace on an f1.2xlarge instance. After your instance has booted, log in using the user name centos. On the first login, you should see the message “FireSim network config completed.” This sets up the necessary tap interfaces and bridge on the EC2 instance to enable communicating with the simulated nodes.

AMI contents

The AMI contains a variety of tools to help you run simulations and build software for RISC-V systems, including the riscv64 toolchain, a Buildroot-based Linux distribution that runs on the simulated nodes, and the simulation driver program. For more details, see the AMI Contents section on the FireSim website.

Single-node demo

First, you need to flash the FPGA with the FireSim AFI. To do so, run:

[[email protected]_ADDR ~]$ sudo fpga-load-local-image -S 0 -I agfi-00a74c2d615134b21

To start a simulation, run the following at the command line:

[[email protected]_ADDR ~]$ boot-firesim-singlenode

This automatically calls the simulation driver, telling it to load the Linux kernel image and root filesystem for the Linux distro. This produces output similar to the following:

Simulations Started. You can use the UART console of each simulated node by attaching to the following screens:

There is a screen on:

2492.fsim0      (Detached)

1 Socket in /var/run/screen/S-centos.

You could connect to the simulated UART console by connecting to this screen, but instead opt to use SSH to access the node instead.

First, ping the node to make sure it has come online. This is currently required because nodes may get stuck at Linux boot if the NIC does not receive any network traffic. For more information, see Troubleshooting/Errata. The node is always assigned the IP address 192.168.1.10:

[[email protected]_ADDR ~]$ ping 192.168.1.10

This should eventually produce the following output:

PING 192.168.1.10 (192.168.1.10) 56(84) bytes of data.

From 192.168.1.1 icmp_seq=1 Destination Host Unreachable

64 bytes from 192.168.1.10: icmp_seq=1 ttl=64 time=2017 ms

64 bytes from 192.168.1.10: icmp_seq=2 ttl=64 time=1018 ms

64 bytes from 192.168.1.10: icmp_seq=3 ttl=64 time=19.0 ms

At this point, you know that the simulated node is online. You can connect to it using SSH with the user name root and password firesim. It is also convenient to make sure that your TERM variable is set correctly. In this case, the simulation expects TERM=linux, so provide that:

[[email protected]_ADDR ~]$ TERM=linux ssh [email protected]

The authenticity of host ‘192.168.1.10 (192.168.1.10)’ can’t be established.

ECDSA key fingerprint is 63:e9:66:d0:5c:06:2c:1d:5c:95:33:c8:36:92:30:49.

Are you sure you want to continue connecting (yes/no)? yes

Warning: Permanently added ‘192.168.1.10’ (ECDSA) to the list of known hosts.

[email protected]’s password:

#

At this point, you’re connected to the simulated node. Run uname -a as an example. You should see the following output, indicating that you’re connected to a RISC-V system:

# uname -a

Linux buildroot 4.12.0-rc2 #1 Fri Aug 4 03:44:55 UTC 2017 riscv64 GNU/Linux

Now you can run programs on the simulated node, as you would with a real machine. For an example workload (running YCSB against Memcached on the simulated node) or to run a larger 8-node simulation, see the full FireSim Demo v1.0 on Amazon EC2 F1 demo instructions.

Finally, when you are finished, you can shut down the simulated node by running the following command from within the simulated node:

# poweroff

You can confirm that the simulation has ended by running screen -ls, which should now report that there are no detached screens.

Future plans

At Berkeley, we’re planning to keep improving the FireSim platform to enable our own research in future data center architectures, like FireBox. The FireSim platform will eventually support more sophisticated processors, custom accelerators (such as Hwacha), network models, and peripherals, in addition to scaling to larger numbers of FPGAs. In the future, we’ll open source the entire platform, including Midas, the tool used to transform RTL into FPGA simulators, allowing users to modify any part of the hardware/software stack. Follow @firesimproject on Twitter to stay tuned to future FireSim updates.

Acknowledgements

FireSim is the joint work of many students and faculty at Berkeley: Sagar Karandikar, Donggyu Kim, Howard Mao, David Biancolin, Jack Koenig, Jonathan Bachrach, and Krste Asanović. This work is partially funded by AWS through the RISE Lab, by the Intel Science and Technology Center for Agile HW Design, and by ASPIRE Lab sponsors and affiliates Intel, Google, HPE, Huawei, NVIDIA, and SK hynix.

Getting Ready for AWS re:Invent 2017

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/getting-ready-for-aws-reinvent-2017/

With just 40 days remaining before AWS re:Invent begins, my colleagues and I want to share some tips that will help you to make the most of your time in Las Vegas. As always, our focus is on training and education, mixed in with some after-hours fun and recreation for balance.

Locations, Locations, Locations
The re:Invent Campus will span the length of the Las Vegas strip, with events taking place at the MGM Grand, Aria, Mirage, Venetian, Palazzo, the Sands Expo Hall, the Linq Lot, and the Encore. Each venue will host tracks devoted to specific topics:

MGM Grand – Business Apps, Enterprise, Security, Compliance, Identity, Windows.

Aria – Analytics & Big Data, Alexa, Container, IoT, AI & Machine Learning, and Serverless.

Mirage – Bootcamps, Certifications & Certification Exams.

Venetian / Palazzo / Sands Expo Hall – Architecture, AWS Marketplace & Service Catalog, Compute, Content Delivery, Database, DevOps, Mobile, Networking, and Storage.

Linq Lot – Alexa Hackathons, Gameday, Jam Sessions, re:Play Party, Speaker Meet & Greets.

EncoreBookable meeting space.

If your interests span more than one topic, plan to take advantage of the re:Invent shuttles that will be making the rounds between the venues.

Lots of Content
The re:Invent Session Catalog is now live and you should start to choose the sessions of interest to you now.

With more than 1100 sessions on the agenda, planning is essential! Some of the most popular “deep dive” sessions will be run more than once and others will be streamed to overflow rooms at other venues. We’ve analyzed a lot of data, run some simulations, and are doing our best to provide you with multiple opportunities to build an action-packed schedule.

We’re just about ready to let you reserve seats for your sessions (follow me and/or @awscloud on Twitter for a heads-up). Based on feedback from earlier years, we have fine-tuned our seat reservation model. This year, 75% of the seats for each session will be reserved and the other 25% are for walk-up attendees. We’ll start to admit walk-in attendees 10 minutes before the start of the session.

Las Vegas never sleeps and neither should you! This year we have a host of late-night sessions, workshops, chalk talks, and hands-on labs to keep you busy after dark.

To learn more about our plans for sessions and content, watch the Get Ready for re:Invent 2017 Content Overview video.

Have Fun
After you’ve had enough training and learning for the day, plan to attend the Pub Crawl, the re:Play party, the Tatonka Challenge (two locations this year), our Hands-On LEGO Activities, and the Harley Ride. Stay fit with our 4K Run, Spinning Challenge, Fitness Bootcamps, and Broomball (a longstanding Amazon tradition).

See You in Vegas
As always, I am looking forward to meeting as many AWS users and blog readers as possible. Never hesitate to stop me and to say hello!

Jeff;

 

 

Now Available – Microsoft SQL Server 2017 for Amazon EC2

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/now-available-microsoft-sql-server-2017-for-amazon-ec2/

Microsoft SQL Server 2017 (launched just a few days ago) includes lots of powerful new features including support for graph databases, automatic database tuning, and the ability to create clusterless Always On Availability Groups. It can also be run on Linux and in Docker containers.

Run on EC2
I’m happy to announce that you can now launch EC2 instances that run Windows Server 2016 and four editions (Web, Express, Standard, and Enterprise) of SQL Server 2017. The AMIs (Amazon Machine Images) are available today in all AWS Regions and run on a wide variety of EC2 instance types, including the new x1e.32xlarge with 128 vCPUs and almost 4 TB of memory.

You can launch these instances from the AWS Management Console or through AWS Marketplace. Here’s what they look like in the console:

And in AWS Marketplace:

Licensing Options Galore
You have lots of licensing options for SQL Server:

Pay As You Go – This option works well if you would prefer to avoid buying licenses, are already running an older version of SQL Server, and want to upgrade. You don’t have to deal with true-ups, software compliance audits, or Software Assurance and you don’t need to make a long-term purchase. If you are running the Standard Edition of SQL Server, you also benefit from our recent price reduction, with savings of up to 52%.

License Mobility – This option lets your use your active Software Assurance agreement to bring your existing licenses to EC2, and allows you to run SQL Server on Windows or Linux instances.

Bring Your Own Licenses – This option lets you take advantage of your existing license investment while minimizing upgrade costs. You can run SQL Server on EC2 Dedicated Instances or EC2 Dedicated Hosts, with the potential to reduce operating costs by licensing SQL Server on a per-core basis. This option allows you to run SQL Server 2017 on EC2 Linux instances (SUSE, RHEL, and Ubuntu are supported) and also supports Docker-based environments running on EC2 Windows and Linux instances. To learn more about these options, read the Installation Guidance for SQL Server on Linux and Run SQL Server 2017 Container Image with Docker.

Learn More
To learn more about SQL Server 2017 and to explore your licensing options in depth, take a look at the SQL Server on AWS page.

If you need advice and guidance as you plan your migration effort, check out the AWS Partners who have qualified for the Microsoft Workloads competency and focus on database solutions.

Amazon RDS support for SQL Server 2017 is planned for November. This will give you a fully managed option.

Plan to join the AWS team at the PASS Summit (November 1-3 in Seattle) and at AWS re:Invent (November 27th to December 1st in Las Vegas).

Jeff;

PS – Special thanks to my colleague Tom Staab (Partner Solutions Architect) for his help with this post!

New – Per-Second Billing for EC2 Instances and EBS Volumes

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-per-second-billing-for-ec2-instances-and-ebs-volumes/

Back in the old days, you needed to buy or lease a server if you needed access to compute power. When we launched EC2 back in 2006, the ability to use an instance for an hour, and to pay only for that hour, was big news. The pay-as-you-go model inspired our customers to think about new ways to develop, test, and run applications of all types.

Today, services like AWS Lambda prove that we can do a lot of useful work in a short time. Many of our customers are dreaming up applications for EC2 that can make good use of a large number of instances for shorter amounts of time, sometimes just a few minutes.

Per-Second Billing for EC2 and EBS
Effective October 2nd, usage of Linux instances that are launched in On-Demand, Reserved, and Spot form will be billed in one-second increments. Similarly, provisioned storage for EBS volumes will be billed in one-second increments.

Per-second billing also applies to Amazon EMR and AWS Batch:

Amazon EMR – Our customers add capacity to their EMR clusters in order to get their results more quickly. With per-second billing for the EC2 instances in the clusters, adding nodes is more cost-effective than ever.

AWS Batch – Many of the batch jobs that our customers run complete in less than an hour. AWS Batch already launches and terminates Spot Instances; with per-second billing batch processing will become even more economical.

Some of our more sophisticated customers have built systems to get the most value from EC2 by strategically choosing the most advantageous target instances when managing their gaming, ad tech, or 3D rendering fleets. Per-second billing obviates the need for this extra layer of instance management, and brings the costs savings to all customers and all workloads.

While this will result in a price reduction for many workloads (and you know we love price reductions), I don’t think that’s the most important aspect of this change. I believe that this change will inspire you to innovate and to think about your compute-bound problems in new ways. How can you use it to improve your support for continuous integration? Can it change the way that you provision transient environments for your dev and test workloads? What about your analytics, batch processing, and 3D rendering?

One of the many advantages of cloud computing is the elastic nature of provisioning or deprovisioning resources as you need them. By billing usage down to the second we will enable customers to level up their elasticity, save money, and customers will be positioned to take advantage of continuing advances in computing.

Things to Know
This change is effective in all AWS Regions and will be effective October 2, for all Linux instances that are newly launched or already running. Per-second billing is not currently applicable to instances running Microsoft Windows or Linux distributions that have a separate hourly charge. There is a 1 minute minimum charge per-instance.

List prices and Spot Market prices are still listed on a per-hour basis, but bills are calculated down to the second, as is Reserved Instance usage (you can launch, use, and terminate multiple instances within an hour and get the Reserved Instance Benefit for all of the instances). Also, bills will show times in decimal form, like this:

The Dedicated Per Region Fee, EBS Snapshots, and products in AWS Marketplace are still billed on an hourly basis.

Jeff;

 

Now Available – EC2 Instances with 4 TB of Memory

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/now-available-ec2-instances-with-4-tb-of-memory/

Earlier this year I told you about our plan to launch EC2 instances with up to 16 TB of memory. Today I am happy to announce that the new x1e.32xlarge instances with 4 TB of DDR4 memory are available in four AWS Regions. As I wrote in my earlier post, these instances are designed to run SAP HANA and other memory intensive, in-memory applications. Many of our customers are already running production SAP applications on the existing x1.32xlarge instances. With today’s launch, these customers can now store and process far larger data sets, making them a great fit for larger production deployments.

Like the x1.32xlarge, the x1e.32xlarge is powered by quad socket Intel Xeon E7 8880 v3 Haswell processors running at 2.3GHz (128 vCPUs), with large L3 caches, plenty of memory bandwidth, and support for C-state and P-state management.

On the network side, the instances offer up to 25 Gbps of network bandwidth when launched within an EC2 placement group, powered by the Elastic Network Adapter (ENA), with support for up to 8 Elastic Network Interfaces (ENIs) per instance. The instances are EBS-optimized by default, with an additional 14 Gbps of dedicated bandwidth to your EBS volumes, and support for up to 80,000 IOPS per instance. Each instance also includes a pair of 1,920 GB SSD volumes.

A Few Notes
Here are a couple of things to keep in mind regarding the x1e.32xlarge:

SAP Certification – The x1e.32xlarge instances are our largest cloud-native instances certified and supported by SAP for production HANA deployments of SAP Business Suite on HANA (SoH), SAP Business Warehouse on HANA (BWoH), and the next-generation SAP S/4HANA ERP and SAP BW/4HANA data warehouse solution. If you are already running SAP HANA workloads on smaller X1 instances, scaling up will be quick and easy. The SAP HANA on the AWS Cloud Quick Start Reference Deployment has been updated and will help you to set up a deployment that follows SAP and AWS standards for high performance and reliability. The SAP HANA Hardware Directory and the SAP HANA Sizing Guidelines are also relevant.

Reserved Instances – The regional size flexibility for Reserved Instances does not apply across x1 and x1e.

Now Available
The x1e.32xlarge instances can be launched in On-Demand and Reserved Instance form via the AWS Management Console, AWS Command Line Interface (CLI), AWS SDKs, and AWS Marketplace in the US East (Northern Virginia), US West (Oregon), EU (Ireland), and Asia Pacific (Tokyo) Regions.

I would also like to make you aware of a couple of other upgrades to the X1 instances:

EBS – As part of today’s launch, existing X1 instances also support up to 14 Gbps of dedicated bandwidth to EBS, along with 80,000 IOPS per instance.

Network – Earlier this week, we announced that existing x1.32xlarge instances also support up to 25 Gbps of network bandwidth within placement groups.

Jeff;

Journey into Deep Learning with AWS

Post Syndicated from Tara Walker original https://aws.amazon.com/blogs/aws/journey-into-deep-learning-with-aws/

If you are anything like me, Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning are completely fascinating and exciting topics. As AI, ML, and Deep Learning become more widely used, for me it means that the science fiction written by Dr. Issac Asimov, the robotics and medical advancements in Star Wars, and the technologies that enabled Captain Kirk and his Star Trek crew “to boldly go where no man has gone before” can become achievable realities.

 

Most people interested in the aforementioned topics are familiar with the AI and ML solutions enabled by Deep Learning, such as Convolutional Neural Networks for Image and Video Classification, Speech Recognition, Natural Language interfaces, and Recommendation Engines. However, it is not always an easy task setting up the infrastructure, environment, and tools to enable data scientists, machine learning practitioners, research scientists, and deep learning hobbyists/advocates to dive into these technologies. Most developers desire to go quickly from getting started with deep learning to training models and developing solutions using deep learning technologies.

For these reasons, I would like to share some resources that will help to quickly build deep learning solutions whether you are an experienced data scientist or a curious developer wanting to get started.

Deep Learning Resources

The Apache MXNet is Amazon’s deep learning framework of choice. With the power of Apache MXNet framework and NVIDIA GPU computing, you can launch your scalable deep learning projects and solutions easily on the AWS Cloud. As you get started on your MxNet deep learning quest, there are a variety of self-service tutorials and datasets available to you:

  • Launch an AWS Deep Learning AMI: This guide walks you through the steps to launch the AWS Deep Learning AMI with Ubuntu
  • MXNet – Create a computer vision application: This hands-on tutorial uses a pre-built notebook to walk you through using neural networks to build a computer vision application to identify handwritten digits
  • AWS Machine Learning Datasets: AWS hosts datasets for Machine Learning on the AWS Marketplace that you can access for free. These large datasets are available for anyone to analyze the data without requiring the data to be downloaded or stored.
  • Predict and Extract – Learn to use pre-trained models for predictions: This hands-on tutorial will walk you through how to use pre-trained model for predicting and feature extraction using the full Imagenet dataset.

 

AWS Deep Learning AMIs

AWS offers Amazon Machine Images (AMIs) for use on Amazon EC2 for quick deployment of an infrastructure needed to start your deep learning journey. The AWS Deep Learning AMIs are pre-configured with popular deep learning frameworks built using Amazon EC2 instances on Amazon Linux, and Ubuntu that can be launched for AI targeted solutions and models. The deep learning frameworks supported and pre-configured on the deep learning AMI are:

  • Apache MXNet
  • TensorFlow
  • Microsoft Cognitive Toolkit (CNTK)
  • Caffe
  • Caffe2
  • Theano
  • Torch
  • Keras

Additionally, the AWS Deep Learning AMIs install preconfigured libraries for Jupyter notebooks with Python 2.7/3.4, AWS SDK for Python, and other data science related python packages and dependencies. The AMIs also come with NVIDIA CUDA and NVIDIA CUDA Deep Neural Network (cuDNN) libraries preinstalled with all the supported deep learning frameworks and the Intel Math Kernel Library is installed for Apache MXNet framework. You can launch any of the Deep Learning AMIs by visiting the AWS Marketplace using the Try the Deep Learning AMIs link.

Summary

It is a great time to dive into Deep Learning. You can accelerate your work in deep learning by using the AWS Deep Learning AMIs running on the AWS cloud to get your deep learning environment running quickly or get started learning more about Deep Learning on AWS with MXNet using the AWS self-service resources.  Of course, you can learn even more information about Deep Learning, Machine Learning, and Artificial Intelligence on AWS by reviewing the AWS Deep Learning page, the Amazon AI product page, and the AWS AI Blog.

May the Deep Learning Force be with you all.

Tara

AWS Marketplace Update – SaaS Contracts in Action

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-marketplace-update-saas-contracts-in-action/

AWS Marketplace lets AWS customers find and use products and services offered by members of the AWS Partner Network (APN). Some marketplace offerings are billed on an hourly basis, many with a cost-saving annual option designed to line up with the procurement cycles of our enterprise customers. Other offerings are available in SaaS (Software as a Service) form and are billed based on consumption units specified by the seller. The SaaS model (described in New – SaaS subscriptions on AWS Marketplace) give sellers the flexibility to bill for actual usage: number of active hosts, number of requests, GB of log files processed, and so forth.

Recently we extended the SaaS model with the addition of SaaS contracts, which my colleague Brad Lyman introduced in his post, Announcing SaaS Contracts, a Feature to Simplify SaaS Procurement on AWS Marketplace. The contracts give our customers the opportunity save money by setting up monthly subscriptions that can be expanded to cover a one, two, or three year contract term, with automatic, configurable renewals. Sellers can provide services that require up-front payment or that offer discounts in exchange for a usage commitment.

Since Brad has already covered the seller side of this powerful and flexible new model, I would like to show you what it is like to purchase a SaaS contract. Let’s say that I want to use Splunk Cloud. I simply search for it as usual:

I click on Splunk Cloud and see that it is available in SaaS Contract form:

I can also see and review the pricing options, noting that pricing varies by location, index volume, and subscription duration:

I click on Continue. Since I do not have a contract with Splunk for this software, I’ll be redirected to the vendor’s site to create one as part of the process. I choose my location, index volume, and contract duration, and opt for automatic renewal, and then click on Create Contract:

This sets up my subscription, and I need only set up my account with Splunk:

I click on Set Up Your Account and I am ready to move forward by setting up my custom URL on the Splunk site:

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

Jeff;