AWS Blog

In 2015, the Centers for Medicare and Medicaid Services (CMS) reported that healthcare spending made up 17.8% of the U.S. GDP – that’s almost $3.2 trillion or $9,990 per person. By 2025, the CMS estimates this number will increase to nearly 20%. As cloud technology evolves in the healthcare and life science industries, we are seeing how companies of all sizes are using AWS to provide powerful and innovative solutions to customers across the globe. This month we are excited to feature the following startups:

• ClearCare – helping home care agencies operate efficiently and grow their business.
• DNAnexus – providing a cloud-based global network for sharing and managing genomic data.

ClearCare (San Francisco, CA)

ClearCare envisions a future where home care is the only choice for aging in place. Home care agencies play a critical role in the economy and their communities by significantly lowering the overall cost of care, reducing the number of hospital admissions, and bending the cost curve of aging. Patients receiving home care typically have multiple chronic conditions and functional limitations, driving over $190 billion in healthcare spending in the U.S. each year. To offset these costs, health insurance payers are developing in-home care management programs for patients. ClearCare’s goal is to help home care agencies leverage technology to improve costs, outcomes, and quality of life for the aging population. The company’s powerful software platform is specifically designed for use by non-medical, in-home care agencies to manage their businesses.

Founder and CEO Geoff Nudd created ClearCare because of his own grandmother’s need for care. Keeping family members and caregivers up to date on a loved one’s well being can be difficult, so Geoff created what is now ClearCare’s Family Room, which enables caregivers and agency staff to check schedules and receive real-time updates about what’s happening in the home. Since then, agencies have provided feedback on others areas of their businesses that could be streamlined. ClearCare has now built over 20 modules to help home care agencies optimize operations with services including a telephony service, billing and payroll, and more. ClearCare now serves over 4,000 home care agencies, representing 500,000 caregivers and 400,000 seniors.

Using AWS, ClearCare is able to spin up reliable infrastructure for proofs of concept and iterate on those systems to quickly get value to market. The company runs many AWS services including Amazon Elasticsearch Service, Amazon RDS, and Amazon CloudFront. Amazon EMR and Amazon Athena have enabled ClearCare to build a Hadoop-based ETL and data warehousing system that processes terabytes of data each day. By utilizing these managed services, ClearCare has been able to go from concept to customer delivery in less than three months.

To learn more about ClearCare, check out their website.

DNAnexus (Mountain View, CA)

DNAnexus is accelerating the application of genomic data in precision medicine by providing a cloud-based platform for sharing and managing genomic and biomedical data and analysis tools. The company was founded in 2009 by Stanford graduate student Andreas Sundquist and two Stanford professors Arend Sidow and Serafim Batzoglou, to address the need for scaling secondary analysis of next-generation sequencing (NGS) data in the cloud. The founders quickly learned that users needed a flexible solution to build complex analysis workflows and tools that enable them to share and manage large volumes of data. DNAnexus is optimized to address the challenges of security, scalability, and collaboration for organizations that are pursuing genomic-based approaches to health, both in clinics and research labs. DNAnexus has a global customer base – spanning North America, Europe, Asia-Pacific, South America, and Africa – that runs a million jobs each month and is doubling their storage year-over-year. The company currently stores more than 10 petabytes of biomedical and genomic data. That is equivalent to approximately 100,000 genomes, or in simpler terms, over 50 billion Facebook photos!

DNAnexus is working with its customers to help expand their translational informatics research, which includes expanding into clinical trial genomic services. This will help companies developing different medicines to better stratify clinical trial populations and develop companion tests that enable the right patient to get the right medicine. In collaboration with Janssen Human Microbiome Institute, DNAnexus is also launching Mosaic – a community platform for microbiome research.

AWS provides DNAnexus and its customers the flexibility to grow and scale research programs. Building the technology infrastructure required to manage these projects in-house is expensive and time-consuming. DNAnexus removes that barrier for labs of any size by using AWS scalable cloud resources. The company deploys its customers’ genomic pipelines on Amazon EC2, using Amazon S3 for high-performance, high-durability storage, and Amazon Glacier for low-cost data archiving. DNAnexus is also an AWS Life Sciences Competency Partner.

Learn more about DNAnexus here.

—

-Tina

Our Health Customer Stories page lists just a few of the many customers that are building and running healthcare and life sciences applications that run on AWS. Customers like Verge Health, Care Cloud, and Orion Health trust AWS with Protected Health Information (PHI) and Personally Identifying Information (PII) as part of their efforts to comply with HIPAA and HITECH.

Sixteen More Services
In my last HIPAA Eligibility Update I shared the news that we added eight additional services to our list of HIPAA eligible services. Today I am happy to let you know that we have added another sixteen services to the list, bringing the total up to 46. Here are the newest additions, along with some short descriptions and links to some of my blog posts to jog your memory:

Amazon Aurora with PostgreSQL Compatibility – This brand-new addition to Amazon Aurora allows you to encrypt your relational databases using keys that you create and manage through AWS Key Management Service (KMS). When you enable encryption for an Amazon Aurora database, the underlying storage is encrypted, as are automated backups, read replicas, and snapshots. Read New – Encryption at Rest for Amazon Aurora to learn more.

Amazon CloudWatch Logs – You can use the logs to monitor and troubleshoot your systems and applications. You can monitor your existing system, application, and custom log files in near real-time, watching for specific phrases, values, or patterns. Log data can be stored durably and at low cost, for as long as needed. To learn more, read Store and Monitor OS & Application Log Files with Amazon CloudWatch and Improvements to CloudWatch Logs and Dashboards.

Amazon Connect – This self-service, cloud-based contact center makes it easy for you to deliver better customer service at a lower cost. You can use the visual designer to set up your contact flows, manage agents, and track performance, all without specialized skills. Read Amazon Connect – Customer Contact Center in the Cloud and New – Amazon Connect and Amazon Lex Integration to learn more.

Amazon ElastiCache for Redis – This service lets you deploy, operate, and scale an in-memory data store or cache that you can use to improve the performance of your applications. Each ElastiCache for Redis cluster publishes key performance metrics to Amazon CloudWatch. To learn more, read Caching in the Cloud with Amazon ElastiCache and Amazon ElastiCache – Now With a Dash of Redis.

Amazon Kinesis Streams – This service allows you to build applications that process or analyze streaming data such as website clickstreams, financial transactions, social media feeds, and location-tracking events. To learn more, read Amazon Kinesis – Real-Time Processing of Streaming Big Data and New: Server-Side Encryption for Amazon Kinesis Streams.

Amazon RDS for MariaDB – This service lets you set up scalable, managed MariaDB instances in minutes, and offers high performance, high availability, and a simplified security model that makes it easy for you to encrypt data at rest and in transit. Read Amazon RDS Update – MariaDB is Now Available to learn more.

Amazon RDS SQL Server – This service lets you set up scalable, managed Microsoft SQL Server instances in minutes, and also offers high performance, high availability, and a simplified security model. To learn more, read Amazon RDS for SQL Server and .NET support for AWS Elastic Beanstalk and Amazon RDS for Microsoft SQL Server – Transparent Data Encryption (TDE) to learn more.

Amazon Route 53 – This is a highly available Domain Name Server. It translates names like www.example.com into IP addresses. To learn more, read Moving Ahead with Amazon Route 53.

AWS Batch – This service lets you run large-scale batch computing jobs on AWS. You don’t need to install or maintain specialized batch software or build your own server clusters. Read AWS Batch – Run Batch Computing Jobs on AWS to learn more.

AWS CloudHSM – A cloud-based Hardware Security Module (HSM) for key storage and management at cloud scale. Designed for sensitive workloads, CloudHSM lets you manage your own keys using FIPS 140-2 Level 3 validated HSMs. To learn more, read AWS CloudHSM – Secure Key Storage and Cryptographic Operations and AWS CloudHSM Update – Cost Effective Hardware Key Management at Cloud Scale for Sensitive & Regulated Workloads.

AWS Key Management Service – This service makes it easy for you to create and control the encryption keys used to encrypt your data. It uses HSMs to protect your keys, and is integrated with AWS CloudTrail in order to provide you with a log of all key usage. Read New AWS Key Management Service (KMS) to learn more.

AWS Lambda – This service lets you run event-driven application or backend code without thinking about or managing servers. To learn more, read AWS Lambda – Run Code in the Cloud, AWS Lambda – A Look Back at 2016, and AWS Lambda – In Full Production with New Features for Mobile Devs.

Lambda@Edge – You can use this new feature of AWS Lambda to run Node.js functions across the global network of AWS locations without having to provision or manager servers, in order to deliver rich, personalized content to your users with low latency. Read Lambda@Edge – Intelligent Processing of HTTP Requests at the Edge to learn more.

AWS Snowball Edge – This is a data transfer device with 100 terabytes of on-board storage as well as compute capabilities. You can use it to move large amounts of data into or out of AWS, as a temporary storage tier, or to support workloads in remote or offline locations. To learn more, read AWS Snowball Edge – More Storage, Local Endpoints, Lambda Functions.

AWS Snowmobile – This is an exabyte-scale data transfer service. Pulled by a semi-trailer truck, each Snowmobile packs 100 petabytes of storage into a ruggedized 45-foot long shipping container. Read AWS Snowmobile – Move Exabytes of Data to the Cloud in Weeks to learn more (and to see some of my finest LEGO work).

AWS Storage Gateway – This hybrid storage service lets your on-premises applications use AWS cloud storage (Amazon Simple Storage Service (S3), Amazon Glacier, and Amazon Elastic File System) in a simple and seamless way, with storage for volumes, files, and virtual tapes. To learn more, read The AWS Storage Gateway – Integrate Your Existing On-Premises Applications with AWS Cloud Storage and File Interface to AWS Storage Gateway.

And there you go! Check out my earlier post for a list of resources that will help you to build applications that comply with HIPAA and HITECH.

— Jeff;

Leaves are crunching under my boots, Halloween is tomorrow, and pumpkin is having its annual moment in the sun – it’s fall everybody! And just in time to celebrate, we have whipped up a fresh batch of pumpkin spice Tech Talks. Grab your planner (Outlook calendar) and pencil these puppies in. This month we are covering re:Invent, serverless, and everything in between.

November 2017 – Schedule

Noted below are the upcoming scheduled live, online technical sessions being held during the month of November. Make sure to register ahead of time so you won’t miss out on these free talks conducted by AWS subject matter experts.

Webinars featured this month are:

Monday, November 6

Compute

9:00 – 9:40 AM PDT: Set it and Forget it: Auto Scaling Target Tracking Policies

Tuesday, November 7

Big Data

9:00 – 9:40 AM PDT: Real-time Application Monitoring with Amazon Kinesis and Amazon CloudWatch

Compute

10:30 – 11:10 AM PDT: Simplify Microsoft Windows Server Management with Amazon Lightsail

Mobile

12:00 – 12:40 PM PDT: Deep Dive on Amazon SES What’s New

Wednesday, November 8

Databases

10:30 – 11:10 AM PDT: Migrating Your Oracle Database to PostgreSQL

Compute

12:00 – 12:40 PM PDT: Run Your CI/CD Pipeline at Scale for a Fraction of the Cost

Thursday, November 9

Databases

10:30 – 11:10 AM PDT: Migrating Your Oracle Database to PostgreSQL

Containers

9:00 – 9:40 AM PDT: Managing Container Images with Amazon ECR

Big Data

12:00 – 12:40 PM PDT: Amazon Elasticsearch Service Security Deep Dive

Monday, November 13

re:Invent

10:30 – 11:10 AM PDT: AWS re:Invent 2017: Know Before You Go

5:00 – 5:40 PM PDT: AWS re:Invent 2017: Know Before You Go

Tuesday, November 14

AI

9:00 – 9:40 AM PDT: Sentiment Analysis Using Apache MXNet and Gluon

10:30 – 11:10 AM PDT: Bringing Characters to Life with Amazon Polly Text-to-Speech

IoT

12:00 – 12:40 PM PDT: Essential Capabilities of an IoT Cloud Platform

Enterprise

2:00 – 2:40 PM PDT: Everything you wanted to know about licensing Windows workloads on AWS, but were afraid to ask

Wednesday, November 15

Security & Identity

9:00 – 9:40 AM PDT: How to Integrate AWS Directory Service with Office365

Storage

10:30 – 11:10 AM PDT: Disaster Recovery Options with AWS

Hands on Lab

12:30 – 2:00 PM PDT: Hands on Lab: Windows Workloads

Thursday, November 16

Serverless

9:00 – 9:40 AM PDT: Building Serverless Websites with Lambda@Edge

Hands on Lab

12:30 – 2:00 PM PDT: Hands on Lab: Deploy .NET Code to AWS from Visual Studio

– Sara

Driven by customer demand and made possible by on-going advances in the state-of-the-art, we’ve come a long way since the original m1.small instance that we launched in 2006, with instances that emphasize compute power, burstable performance, memory size, local storage, and accelerated computing.

The New P3
Today we are making the next generation of GPU-powered EC2 instances available in four AWS regions. Powered by up to eight NVIDIA Tesla V100 GPUs, the P3 instances are designed to handle compute-intensive machine learning, deep learning, computational fluid dynamics, computational finance, seismic analysis, molecular modeling, and genomics workloads.

P3 instances use customized Intel Xeon E5-2686v4 processors running at up to 2.7 GHz. They are available in three sizes (all VPC-only and EBS-only):

Each of the NVIDIA GPUs is packed with 5,120 CUDA cores and another 640 Tensor cores and can deliver up to 125 TFLOPS of mixed-precision floating point, 15.7 TFLOPS of single-precision floating point, and 7.8 TFLOPS of double-precision floating point. On the two larger sizes, the GPUs are connected together via NVIDIA NVLink 2.0 running at a total data rate of up to 300 GBps. This allows the GPUs to exchange intermediate results and other data at high speed, without having to move it through the CPU or the PCI-Express fabric.

What’s a Tensor Core?
I had not heard the term Tensor core before starting to write this post. According to this very helpful post on the NVIDIA Blog, Tensor cores are designed to speed up the training and inference of large, deep neural networks. Each core is able to quickly and efficiently multiply a pair of 4×4 half-precision (also known as FP16) matrices together, add the resulting 4×4 matrix to another half or single-precision (FP32) matrix, and store the resulting 4×4 matrix in either half or single-precision form. Here’s a diagram from NVIDIA’s blog post:

This operation is in the innermost loop of the training process for a deep neural network, and is an excellent example of how today’s NVIDIA GPU hardware is purpose-built to address a very specific market need. By the way, the mixed-precision qualifier on the Tensor core performance means that it is flexible enough to work with with a combination of 16-bit and 32-bit floating point values.

Performance in Perspective
I always like to put raw performance numbers into a real-world perspective so that they are easier to relate to and (hopefully) more meaningful. This turned out to be surprisingly difficult, given that the eight NVIDIA Tesla V100 GPUs on a single p3.16xlarge can do 125 trillion single-precision floating point multiplications per second.

Let’s go back to the dawn of the microprocessor era, and consider the Intel 8080A chip that powered the MITS Altair that I bought in the summer of 1977. With a 2 MHz clock, it was able to do about 832 multiplications per second (I used this data and corrected it for the faster clock speed). The p3.16xlarge is roughly 150 billion times faster. However, just 1.2 billion seconds have gone by since that summer. In other words, I can do 100x more calculations today in one second than my Altair could have done in the last 40 years!

What about the innovative 8087 math coprocessor that was an optional accessory for the IBM PC that was announced in the summer of 1981? With a 5 MHz clock and purpose-built hardware, it was able to do about 52,632 multiplications per second. 1.14 billion seconds have elapsed since then, p3.16xlarge is 2.37 billion times faster, so the poor little PC would be barely halfway through a calculation that would run for 1 second today.

Ok, how about a Cray-1? First delivered in 1976, this supercomputer was able to perform vector operations at 160 MFLOPS, making the p3.x16xlarge 781,000 times faster. It could have iterated on some interesting problem 1500 times over the years since it was introduced.

Comparisons between the P3 and today’s scale-out supercomputers are harder to make, given that you can think of the P3 as a step-and-repeat component of a supercomputer that you can launch on as as-needed basis.

Run One Today
In order to take full advantage of the NVIDIA Tesla V100 GPUs and the Tensor cores, you will need to use CUDA 9 and cuDNN7. These drivers and libraries have already been added to the newest versions of the Windows AMIs and will be included in an updated Amazon Linux AMI that is scheduled for release on November 7th. New packages are already available in our repos if you want to to install them on your existing Amazon Linux AMI.

The newest AWS Deep Learning AMIs come preinstalled with the latest releases of Apache MxNet, Caffe2, and Tensorflow (each with support for the NVIDIA Tesla V100 GPUs), and will be updated to support P3 instances with other machine learning frameworks such as Microsoft Cognitive Toolkit and PyTorch as soon as these frameworks release support for the NVIDIA Tesla V100 GPUs. You can also use the NVIDIA Volta Deep Learning AMI for NGC.

P3 instances are available in the US East (Northern Virginia), US West (Oregon), EU (Ireland), and Asia Pacific (Tokyo) Regions in On-Demand, Spot, Reserved Instance, and Dedicated Host form.

— Jeff;

Late last year I told you about our plans to add PostgreSQL compatibility to Amazon Aurora. We launched the private beta shortly after that announcement, and followed it up earlier this year with an open preview. We’ve received lots of great feedback during the beta and the preview and have done our best to make sure that the product meets your needs and exceeds your expectations!

Now Generally Available
I am happy to report that Amazon Aurora with PostgreSQL Compatibility is now generally available and that you can use it today in four AWS Regions, with more to follow. It is compatible with PostgreSQL 9.6.3 and scales automatically to support up to 64 TB of storage, with 6-way replication behind the scenes to improve performance and availability.

Just like Amazon Aurora with MySQL compatibility, this edition is fully managed and is very easy to set up and to use. On the performance side, you can expect up to 3x the throughput that you’d get if you ran PostgreSQL on your own (you can read Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases to learn more about how we did this).

You can launch a PostgreSQL-compatible Amazon Aurora instance from the RDS Console by selecting Amazon Aurora as the engine and PostgreSQL-compatible as the edition, and clicking on Next:

Then choose your instance class, single or Multi-AZ deployment (good for dev/test and production, respectively), set the instance name, and the administrator credentials, and click on Next:

You can choose between six instance classes (2 to 64 vCPUs and 15.25 to 488 GiB of memory):

The db.r4 instance class is new addition to Aurora and to RDS, and gives you an additional size at the top-end. The db.r4.16xlarge will give you additional write performance, and may allow you to use a single Aurora database instead of two or more sharded databases.

You can also set many advanced options on the next page, starting with network options such as the VPC and public accessibility:

You can set the cluster name and other database options. Encryption is easy to use and enabled by default; you can use the built-in default master key or choose one of your own:

You can also set failover behavior, the retention period for snapshot backups, and choose to enable collection of detailed (OS-level) metrics via Enhanced Monitoring:

After you have set it up to your liking, click on Launch DB Instance to proceed!

The new instances (primary and secondary since I specified Multi-AZ) are up and running within minutes:

Each PostgreSQL-compatible instance publishes 44 metrics to CloudWatch automatically:

With enhanced monitoring enabled, each instance collects additional per-instance and per-process metrics. It can be enabled when the instance is launched, or afterward, via Modify Instance. Here are some of the metrics collected when enhanced monitoring is enabled:

Clicking on Manage Graphs lets you choose which metrics are shown:

Per-process metrics are also available:

You can scale your read capacity by creating up to 15 Aurora replicas:

The cluster provides a single reader endpoint that you can access in order to load-balance requests across the replicas:

Performance Insights
As I noted earlier, Performance Insights is turned on automatically. This Amazon Aurora feature is wired directly into the database engine and allows you to look deep inside of each query, seeing the database resources that it uses and how they contribute to the overall response time. Here’s the initial view:

I can slice the view by SQL query in order to see how many concurrent copies of each query are running:

There are more views and options than I can fit in this post; to learn more take a look at Using Performance Insights.

Migrating to Amazon Aurora with PostgreSQL Compatibility
AWS Database Migration Service and the Schema Conversion Tool are ready to help you to move data stored in commercial and open-source databases to Amazon Aurora. The Schema Conversion Tool will perform a quick assessment of your database schemas and your code in order to help you to choose between MySQL and PostgreSQL. Our new, limited-time, Free DMS program allows you to use DMS and SCT to migrate to Aurora at no cost, with access to several types of DMS Instances for up to 6 months.

If you are already using PostgreSQL, you will be happy to hear that we support a long list of extensions including PostGIS and dblink.

Available Now
You can use Amazon Aurora with PostgreSQL Compatibility today in the US East (Northern Virginia), EU (Ireland), US West (Oregon), and US East (Ohio) Regions, with others to follow as soon as possible.

— Jeff;

You have long had the ability to tag your AWS resources and to see cost breakouts on a per-tag basis. Cost allocation was launched in 2012 (see AWS Cost Allocation for Customer Bills) and we have steadily added support for additional services, most recently DynamoDB (Introducing Cost Allocation Tags for Amazon DynamoDB), Lambda (AWS Lambda Supports Tagging and Cost Allocations), and EBS (New – Cost Allocation for AWS Snapshots).

Today, we are launching tag-based cost allocation for Amazon Simple Queue Service (SQS). You can now assign tags to your queues and use them to manage your costs at any desired level: application, application stage (for a loosely coupled application that communicates via queues), project, department, or developer. After you have tagged your queues, you can use the AWS Tag Editor to search queues that have tags of interest.

Here’s how I would add three tags (app, stage, and department) to one of my queues:

This feature is available now in all AWS Regions and you can start using in today! To learn more about tagging, read Tagging Your Amazon SQS Queues. To learn more about cost allocation via tags, read Using Cost Allocation Tags. To learn more about how to use message queues to build loosely coupled microservices for modern applications, read our blog post (Building Loosely Coupled, Scalable, C# Applications with Amazon SQS and Amazon SNS) and watch the recording of our recent webinar, Decouple and Scale Applications Using Amazon SQS and Amazon SNS.

If you are coming to AWS re:Invent, plan to attend session ARC 330: How the BBC Built a Massive Media Pipeline Using Microservices. In the talk you will find out how they used SNS and SQS to improve the elasticity and reliability of the BBC iPlayer architecture.

— Jeff;

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.

Encore – Bookable 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;

Starting today, you can connect to your Amazon Elasticsearch Service domains from within an Amazon VPC without the need for NAT instances or Internet gateways. VPC support for Amazon ES is easy to configure, reliable, and offers an extra layer of security. With VPC support, traffic between other services and Amazon ES stays entirely within the AWS network, isolated from the public Internet. You can manage network access using existing VPC security groups, and you can use AWS Identity and Access Management (IAM) policies for additional protection. VPC support for Amazon ES domains is available at no additional charge.

Getting Started

Creating an Amazon Elasticsearch Service domain in your VPC is easy. Follow all the steps you would normally follow to create your cluster and then select “VPC access”.

That’s it. There are no additional steps. You can now access your domain from within your VPC!

Things To Know

To support VPCs, Amazon ES places an endpoint into at least one subnet of your VPC. Amazon ES places an Elastic Network Interface (ENI) into the VPC for each data node in the cluster. Each ENI uses a private IP address from the IPv4 range of your subnet and receives a public DNS hostname. If you enable zone awareness, Amazon ES creates endpoints in two subnets in different availability zones, which provides greater data durability.

You need to set aside three times the number of IP addresses as the number of nodes in your cluster. You can divide that number by two if Zone Awareness is enabled. Ideally, you would create separate subnets just for Amazon ES.

A few notes:

• Currently, you cannot move existing domains to a VPC or vice-versa. To take advantage of VPC support, you must create a new domain and migrate your data.
• Currently, Amazon ES does not support Amazon Kinesis Firehose integration for domains inside a VPC.

To learn more, see the Amazon ES documentation.

– Randall

I first told you about Amazon Lightsail last year in my blog post, Amazon Lightsail – the Power of AWS, the Simplicity of a VPS. Since last year’s launch, thousands of customers have used Lightsail to get started with AWS, launching Linux-based Virtual Private Servers.

Today we are adding support for Windows-based Virtual Private Servers. You can launch a VPS that runs Windows Server 2012 R2, Windows Server 2016, or Windows Server 2016 with SQL Server 2016 Express and be up and running in minutes. You can use your VPS to build, test, and deploy .NET or Windows applications without having to set up or run any infrastructure. Backups, DNS management, and operational metrics are all accessible with a click or two.

Servers are available in five sizes, with 512 MB to 8 GB of RAM, 1 or 2 vCPUs, and up to 80 GB of SSD storage. Prices (including software licenses) start at $10 per month:

You can try out a 512 MB server for one month (up to 750 hours) at no charge.

Launching a Windows VPS
To launch a Windows VPS, log in to Lightsail , click on Create instance, and select the Microsoft Windows platform. Then click on Apps + OS if you want to run SQL Server 2016 Express, or OS Only if Windows is all you need:

If you want to use a Powershell script to customize your instance after it launches for the first time, click on Add launch script and enter the script:

Choose your instance plan, enter a name for your instance(s), and select the quantity to be launched, then click on Create:

Your instance will be up and running within a minute or so:

Click on the instance, and then click on Connect using RDP:

This will connect using a built-in, browser-based RDP client (you can also use the IP address and the credentials with another client):

Available Today
This feature is available today in the US East (Northern Virginia), US East (Ohio), US West (Oregon), EU (London), EU (Ireland), EU (Frankfurt), Asia Pacific (Singapore), Asia Pacific (Mumbai), Asia Pacific (Sydney), and Asia Pacific (Tokyo) Regions.

— Jeff;

Post by Dr. Matt Wood

Today, AWS and Microsoft announced Gluon, a new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance.

Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components. Developers who are new to machine learning will find this interface more familiar to traditional code, since machine learning models can be defined and manipulated just like any other data structure. More seasoned data scientists and researchers will value the ability to build prototypes quickly and utilize dynamic neural network graphs for entirely new model architectures, all without sacrificing training speed.

Gluon is available in Apache MXNet today, a forthcoming Microsoft Cognitive Toolkit release, and in more frameworks over time.

Neural Networks vs Developers
Machine learning with neural networks (including ‘deep learning’) has three main components: data for training; a neural network model, and an algorithm which trains the neural network. You can think of the neural network in a similar way to a directed graph; it has a series of inputs (which represent the data), which connect to a series of outputs (the prediction), through a series of connected layers and weights. During training, the algorithm adjusts the weights in the network based on the error in the network output. This is the process by which the network learns; it is a memory and compute intensive process which can take days.

Deep learning frameworks such as Caffe2, Cognitive Toolkit, TensorFlow, and Apache MXNet are, in part, an answer to the question ‘how can we speed this process up? Just like query optimizers in databases, the more a training engine knows about the network and the algorithm, the more optimizations it can make to the training process (for example, it can infer what needs to be re-computed on the graph based on what else has changed, and skip the unaffected weights to speed things up). These frameworks also provide parallelization to distribute the computation process, and reduce the overall training time.

However, in order to achieve these optimizations, most frameworks require the developer to do some extra work: specifically, by providing a formal definition of the network graph, up-front, and then ‘freezing’ the graph, and just adjusting the weights.

The network definition, which can be large and complex with millions of connections, usually has to be constructed by hand. Not only are deep learning networks unwieldy, but they can be difficult to debug and it’s hard to re-use the code between projects.

The result of this complexity can be difficult for beginners and is a time-consuming task for more experienced researchers. At AWS, we’ve been experimenting with some ideas in MXNet around new, flexible, more approachable ways to define and train neural networks. Microsoft is also a contributor to the open source MXNet project, and were interested in some of these same ideas. Based on this, we got talking, and found we had a similar vision: to use these techniques to reduce the complexity of machine learning, making it accessible to more developers.

Enter Gluon: dynamic graphs, rapid iteration, scalable training
Gluon introduces four key innovations.

1. Friendly API: Gluon networks can be defined using a simple, clear, concise code – this is easier for developers to learn, and much easier to understand than some of the more arcane and formal ways of defining networks and their associated weighted scoring functions.
2. Dynamic networks: the network definition in Gluon is dynamic: it can bend and flex just like any other data structure. This is in contrast to the more common, formal, symbolic definition of a network which the deep learning framework has to effectively carve into stone in order to be able to effectively optimize computation during training. Dynamic networks are easier to manage, and with Gluon, developers can easily ‘hybridize’ between these fast symbolic representations and the more friendly, dynamic ‘imperative’ definitions of the network and algorithms.
3. The algorithm can define the network: the model and the training algorithm are brought much closer together. Instead of separate definitions, the algorithm can adjust the network dynamically during definition and training. Not only does this mean that developers can use standard programming loops, and conditionals to create these networks, but researchers can now define even more sophisticated algorithms and models which were not possible before. They are all easier to create, change, and debug.
4. High performance operators for training: which makes it possible to have a friendly, concise API and dynamic graphs, without sacrificing training speed. This is a huge step forward in machine learning. Some frameworks bring a friendly API or dynamic graphs to deep learning, but these previous methods all incur a cost in terms of training speed. As with other areas of software, abstraction can slow down computation since it needs to be negotiated and interpreted at run time. Gluon can efficiently blend together a concise API with the formal definition under the hood, without the developer having to know about the specific details or to accommodate the compiler optimizations manually.

The team here at AWS, and our collaborators at Microsoft, couldn’t be more excited to bring these improvements to developers through Gluon. We’re already seeing quite a bit of excitement from developers and researchers alike.

Getting started with Gluon
Gluon is available today in Apache MXNet, with support coming for the Microsoft Cognitive Toolkit in a future release. We’re also publishing the front-end interface and the low-level API specifications so it can be included in other frameworks in the fullness of time.

You can get started with Gluon today. Fire up the AWS Deep Learning AMI with a single click and jump into one of 50 fully worked, notebook examples. If you’re a contributor to a machine learning framework, check out the interface specs on GitHub.

-Dr. Matt Wood