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Amazon Polly Update – Time-Driven Prosody and Asynchronous Synthesis

AWS Blog - Tue, 07/17/2018 - 11:56

I hope that you are enjoying the Polly-powered audio that is available for the newest posts on this blog, including the DeepLens Challenge and the Storage Gateway Recap. As part of my blogging process, I now listen to the synthesized speech for my draft blog posts in order to get a better sense for how they flow.

Today we are launching two new features for Amazon Polly:

Time-Driven Prosody – You can now specify the desired duration for the synthesized speech that corresponds to part or all of the input text.

Asynchronous Synthesis – You can now process large blocks of text and store the synthesized speech in Amazon S3 with a single call.

Both of these features are available now and you can start using them today. Let’s take a closer look!

Time-Driven Prosody
Imagine that you are creating a multi-lingual version of a video or a self-running presentation. You write the script, record the video in one language, and then use Amazon Translate and Amazon Polly to create audio tracks in other languages. In order to keep each language in sync with the visual content, you need to exercise fine-grained control over the duration of each segment. That’s where this new feature comes in. You can now specify the maximum desired duration for any desired segments, counting on Polly to adjust the speech rate in order to limit the length of each segment.

The preceding paragraph generates 19 seconds of audio if I use Amazon Polly’s Joanna voice with no other options:

<speak> In order to keep each language in sync with the visual content, you need to exercise fine-grained control over the duration of each segment. That's where this new feature comes in. You can now specify the maximum desired duration for any desired segments, counting on Polly to adjust the speech rate in order to limit the length of each segment. </speak>

I can use a <prosody> tag to limit the length to 15 seconds:

<speak> <prosody amazon:max-duration="15s"> In order to keep each language in sync with the visual content, you need to exercise fine-grained control over the duration of each segment. That's where this new feature comes in. You can now specify the maximum desired duration for any desired segments, counting on Polly to adjust the speech rate in order to limit the length of each segment. </prosody> </speak>

I can control the duration at a more fine-grained level by using multiple <prosody> tags:

<prosody amazon:max-duration="10s"> In order to keep each language in sync with the visual content, you need to exercise fine-grained control over the duration of each segment. </prosody> <prosody amazon:max-duration="7s"> That's where this new feature comes in. You can now specify the maximum desired duration for any desired segments, counting on Polly to adjust the speech rate in order to limit the length of each segment. </prosody>

The Spanish equivalent (courtesy of Amazon Translate) of my English text is somewhat longer and the speed-up is apparent:

<speak> <prosody amazon:max-duration="15s"> Para mantener cada idioma sincronizado con el contenido visual, es necesario ejercer un control detallado sobre la duración de cada segmento. Ahí es donde entra esta nueva característica. Ahora puede especificar la duración máxima deseada para los segmentos deseados, contando con que Polly ajuste la velocidad de voz para limitar la longitud de cada segmento. </prosody> </speak>

The text inside of each time-limited <prosody> tag is limited to 1500 characters and nesting is not allowed (the inner tag will be ignored). In order to ensure that the audio remains comprehensible, Polly will speed up the audio by a maximum of 5x.

Asynchronous Synthesis
This feature makes it easier for you to use Polly to generate speech for long-form content such as articles or book chapters by allowing you to process up to 100,000 characters of text at a time using asynchronous requests. The synthesized speech is delivered to the S3 bucket of your choice, with failure notifications optionally routed to the Amazon Simple Notification Service (SNS) topic of your choice. The generated audio can be up to 6 hours long, and is typically ready within minutes. In addition to 100,000 characters of text, each request can include an additional 100,000 characters of Speech Synthesis Markup Language (SSML) markup.

Each asynchronous request creates a new speech synthesis task. You can initiate and manage tasks from the Polly Console, CLI (start-speech-synthesis-task), or API (StartSpeechSynthesisTask).

To test this feature I created a plain-text version of my thoroughly obsolete AWS book and inserted some SSML tags, turning it in to valid XML along the way. Then I open the Polly Console, click Text-to-Speech, paste the XML, and click Synthesize to S3:

I enter the name of my S3 bucket (which must be in region where I plan to create the task), and click Synthesize to proceed:

My task is created:

And I can see it in the list of tasks:

I receive an email when the synthesis is complete:

And the file is in my bucket as expected:

I did not spend a lot of time on the markup, but the results are impressive:

Interestingly enough, most of that chapter is still relevant. The rest of the book has been overtaken by history, and is best left there! Perhaps I’ll write another one sometime.

Anyway, as you can see (and hear) the asynchronous speech synthesis is powerful and easy to use. Give it a shot, build something cool, and tell me about it.

Jeff;

 

 

Categories: Cloud

Amazon EC2 Instance Update – Faster Processors and More Memory

AWS Blog - Tue, 07/17/2018 - 08:13

Last month I told you about the Nitro system and explained how it will allow us to broaden the selection of EC2 instances and to pick up the pace as we do so, with an ever-broadening selection of compute, storage, memory, and networking options. This will allow us to give you access to the latest technology very quickly, giving you the ability to choose the instance type that is the best match for your applications.

Today, I would like to tell you about three new instance types that are in the works and that will be available soon:

z1d – Compute-intensive instances running at up to 4.0 GHz, powered by sustained all-core Turbo Boost. They are ideal for Electronic Design Automation (EDA) and relational database workloads, and are also a great fit for several kinds of HPC workloads.

R5 – Memory-optimized instances running at up to 3.1 GHz powered by sustained all-core Turbo Boost, with up to 50% more vCPUs and 60% more memory than R4 instances.

R5d – Memory-optimized instances equipped with local NVMe storage (up to 3.6 TB for the largest R5d instance), and will be available in the same sizes and with the same specs as the R5 instances.

We are also planning to launch R5 Bare Metal, R5d Bare Metal, and z1d Bare Metal instances. As is the case with the existing i3.metal instances, you will be able to access low-level hardware features and to run applications that are not licensed or supported in virtualized environments.

z1d Instances
The z1d instances are designed for applications that can benefit from extremely high per-core performance. These include:

Electronic Design Automation – As chips become smaller and denser, the amount of compute power needed to design and verify the chips increases non-linearly. Semiconductor customers deploy jobs that span thousands of cores; having access to faster cores reduces turnaround time for each job and can also lead to a measurable reduction in software licensing costs.

HPC – In the financial services world, jobs that run analyses or compute risks also benefit from faster cores. Manufacturing organizations can run their Finite Element Analysis (FEA) and simulation jobs to completion more quickly.

Relational Database – CPU-bound workloads that run on a database that “features” high per-core license fees will enjoy both cost and performance benefits.

z1d instances use custom Intel® Xeon® Scalable Processors running at up to 4.0 GHz, powered by sustained all-core Turbo Boost. They will be available in 6 sizes, with up to 48 vCPUs, 384 GiB of memory, and 1.8 TB of local NVMe storage. On the network side, they feature ENA networking that will deliver up to 25 Gbps of bandwidth, and are EBS-Optimized by default for up to 14 Gbps of bandwidth. As usual, you can launch them in a Cluster Placement Group to increase throughput and reduce latency. Here are the sizes and specs:

Instance Name vCPUs Memory Local Storage EBS-Optimized Bandwidth Network Bandwidth z1d.large 2 16 GiB 1 x 75 GB NVMe SSD Up to 2.333 Gbps Up to 10 Gbps z1d.xlarge 4 32 GiB 1 x 150 GB NVMe SSD Up to 2.333 Gbps Up to 10 Gbps z1d.2xlarge 8 64 GiB 1 x 300 GB NVMe SSD 2.333 Gbps Up to 10 Gbps z1d.3xlarge 12 96 GiB 1 x 450 GB NVMe SSD 3.5 Gbps Up to 10 Gbps z1d.6xlarge 24 192 GiB 1 x 900 GB NVMe SSD 7.0 Gbps 10 Gbps z1d.12xlarge 48 384 GiB 2 x 900 GB NVMe SSD 14.0 Gbps 25 Gbps

The instances are HVM and VPC-only, and you will need to use an AMI with the appropriate ENA and NVMe drivers. Any AMI that runs on C5 or M5 instances will also run on z1d instances.

R5 Instances
Building on the earlier generations of memory-intensive instance types (M2, CR1, R3, and R4), the R5 instances are designed to support high-performance databases, distributed in-memory caches, in-memory analytics, and big data analytics. They use custom Intel® Xeon® Platinum 8000 Series (Skylake-SP) processors running at up to 3.1 GHz, again powered by sustained all-core Turbo Boost. The instances will be available in 6 sizes, with up to 96 vCPUs and 768 GiB of memory. Like the Z1d instances, they feature ENA networking and are EBS-Optimized by default, and can be launched in Placement Groups. Here are the sizes and specs:

Instance Name vCPUs Memory EBS-Optimized Bandwidth Network Bandwidth r5.large 2 16 GiB Up to 3.5 Gbps Up to 10 Gbps r5.xlarge 4 32 GiB Up to 3.5 Gbps Up to 10 Gbps r5.2xlarge 8 64 GiB Up to 3.5 Gbps Up to 10 Gbps r5.4xlarge 16 128 GiB 3.5 Gbps Up to 10 Gbps r5.12xlarge 48 384 GiB 7.0 Gbps 10 Gbps r5.24xlarge 96 768 GiB 14.0 Gbps 25 Gbps

Once again, the instances are HVM and VPC-only, and you will need to use an AMI with the appropriate ENA and NVMe drivers.

Learn More
The new EC2 instances announced today highlight our plan to continue innovating in order to better meet your needs! I’ll share additional information as soon as it is available.

Jeff;

 

 

Categories: Cloud

New – EC2 Compute Instances for AWS Snowball Edge

AWS Blog - Tue, 07/17/2018 - 07:43

I love factories and never miss an opportunity to take a tour. Over the years, I have been lucky enough to watch as raw materials and sub-assemblies are turned into cars, locomotives, memory chips, articulated buses, and more. I’m always impressed by the speed, precision, repeat-ability, and the desire to automate every possible step. On one recent tour, the IT manager told me that he wanted to be able to set up and centrally manage the global collection of on-premises industrialized PCs that monitor their machinery as easily and as efficiently as he does their EC2 instances and other cloud resources.

Today we are making that manager’s dream a reality, with the introduction of EC2 instances that run on AWS Snowball Edge devices! These ruggedized devices, with 100 TB of local storage, can be used to collect and process data in hostile environments with limited or non-existent Internet connections before shipping the processed data back to AWS for storage, aggregation, and detailed analysis. Here are the instance specs:

Instance Name vCPUs Memory sbe1.small 1 1 GiB sbe1.medium 1 2 GiB sbe1.large 2 4 GiB sbe1.xlarge 4 8 GiB sbe1.2xlarge 8 16 GiB sbe1.4xlarge 16 32 GiB

Each Snowball Edge device is powered by an Intel® Xeon® D processor running at 1.8 GHz, and supports any combination of instances that consume up to 24 vCPUs and 32 GiB of memory. You can build and test AMIs (Amazon Machine Images) in the cloud and then preload them onto the device as part of the ordering process (I’ll show you how in just a minute). You can use the EC2-compatible endpoint exposed by each device to programmatically start, stop, resume, and terminate instances. This allows you to use the existing CLI commands and to build tools and scripts to manage fleets of devices. It also allows you to take advantage of your existing EC2 skills and knowledge, and to put them to good use in a new environment.

There are three main setup steps:

  1. Creating a suitable AMI.
  2. Ordering a Snowball Edge Device.
  3. Connecting and Configuring the Device.

Let’s take an in-depth look at the first two steps. Time was tight and I didn’t have time to get hands-on experience with an actual device, so the third step will have to wait for another time.

Creating a Suitable AMI
I have the ability to choose up to 10 AMIs that will be preloaded onto the device. The AMIs must be owned by my AWS account, and must be based on one of the following Marketplace AMIs:

These AMIs have been tested for use on Snowball Edge devices and can be used as a starting point for customization. We will be adding additional options over time, so let us know what you need.

I decided to start with the newest Ubuntu AMI, and launch it on an M5 instance, taking care to specify the SSH keypair that I will eventually use to connect to the instance from my terminal client:

After my instance launches, I connect to it, customize it as desired for use on my device, and then return to the EC2 Console to create an AMI. I select the running instance, choose Create Image from the Actions menu, specify the details, and click Create Image:

The size of the root volume will determine how much of the device’s SSD storage is allocated to the instance when it launches. A total of one TB of space is available for all running instances, so keep your local file storage needs in mind as your analyze your use case and set up your AMIs. Also, Snowball Edge devices cannot make use of additional EBS volumes, so don’t bother including them in your AMI. My AMI is ready within minutes (To learn more about how to create AMIs, read Creating an Amazon EBS-Backed Linux AMI):

Now I am ready to order my first device!

Ordering a Snowball Edge Device
The ordering procedure lets me designate a shipping address and specify how I would like my Snowball device to be configured. I open the AWS Snowball Console and click Create job:

I specify the job type (they all support EC2 compute instances):

Then I select my shipping address, entering a new one if necessary (come and visit me):

Next, I define my job. I give it a name (SJ1), select the 100 TB device, and pick the S3 bucket that will receive data when the device is returned to AWS:

Now comes the fun part! I click Enable compute with EC2 and select the AMIs to be loaded on the Snowball Edge:

I click Add an AMI and find the one that I created earlier:

I can add up to ten AMIs to my job, but will stop at one for this post:

Next, I set up my IAM role and configure encryption:

Then I configure the optional SNS notifications. I can choose to receive notification for a wide variety of job status values:

My job is almost ready! I review the settings and click Create job to create it:

Connecting and Configuring the Device
After I create the job, I wait until my Snowball Edge device arrives. I connect it to my network, power it on, and then unlock it using my manifest and device code, as detailed in Unlock the Snowball Edge. Then I configure my EC2 CLI to use the EC2 endpoint on the device and launch an instance. Since I configured my AMI for SSH access, I can connect to it as if it were an EC2 instance in the cloud.

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

Long-Term Usage – You can keep the Snowball Edge devices on your premises and hard at work for as long as you would like. You’ll be billed for a one-time setup fee for each job; after 10 days you will pay an additional, per-day fee for each device. If you want to keep a device for an extended period of time, you can also pay upfront as part of a one or three year commitment.

Dev/Test – You should be able to do much of your development and testing on an EC2 instance running in the cloud; some of our early users are working in this way as part of a “Digital Twin” strategy.

S3 Access – Each Snowball Edge device includes an S3-compatible endpoint that you can access from your on-device code. You can also make use of existing S3 tools and applications.

Now Available
You can start ordering devices today and make use of this exciting new AWS feature right away.

Jeff;

 

 

Categories: Cloud

Amazon Kinesis Video Streams Adds Support For HLS Output Streams

AWS Blog - Fri, 07/13/2018 - 16:10

Today I’m excited to announce and demonstrate the new HTTP Live Streams (HLS) output feature for Amazon Kinesis Video Streams (KVS). If you’re not already familiar with KVS, Jeff covered the release for AWS re:Invent in 2017. In short, Amazon Kinesis Video Streams is a service for securely capturing, processing, and storing video for analytics and machine learning – from one device or millions. Customers are using Kinesis Video with machine learning algorithms to power everything from home automation and smart cities to industrial automation and security.

After iterating on customer feedback, we’ve launched a number of features in the past few months including a plugin for GStreamer, the popular open source multimedia framework, and docker containers which make it easy to start streaming video to Kinesis. We could talk about each of those features at length, but today is all about the new HLS output feature! Fair warning, there are a few pictures of my incredibly messy office in this post.

HLS output is a convenient new feature that allows customers to create HLS endpoints for their Kinesis Video Streams, convenient for building custom UIs and tools that can playback live and on-demand video. The HLS-based playback capability is fully managed, so you don’t have to build any infrastructure to transmux the incoming media. You simply create a new streaming session, up to 5 (for now), with the new GetHLSStreamingSessionURL API and you’re off to the races. The great thing about HLS is that it’s already an industry standard and really easy to leverage in existing web-players like JW Player, hls.js, VideoJS, Google’s Shaka Player, or even rendering natively in mobile apps with Android’s Exoplayer and iOS’s AV Foundation. Let’s take a quick look at the API, feel free to skip to the walk-through below as well.

Kinesis Video HLS Output API

The documentation covers this in more detail than what we can go over in the Blog but I’ll cover the broad components.

  1. Get an endpoint with the GetDataEndpoint API
  2. Use that endpoint to get an HLS streaming URL with the GetHLSStreamingSessionURL API
  3. Render the content in the HLS URL with whatever tools you want!

This is pretty easy in a Jupyter notebook with a quick bit of Python and boto3.

import boto3 STREAM_NAME = "RandallDeepLens" kvs = boto3.client("kinesisvideo") # Grab the endpoint from GetDataEndpoint endpoint = kvs.get_data_endpoint( APIName="GET_HLS_STREAMING_SESSION_URL", StreamName=STREAM_NAME )['DataEndpoint'] # Grab the HLS Stream URL from the endpoint kvam = boto3.client("kinesis-video-archived-media", endpoint_url=endpoint) url = kvam.get_hls_streaming_session_url( StreamName=STREAM_NAME, PlaybackMode="LIVE" )['HLSStreamingSessionURL']

You can even visualize everything right away in Safari which can render HLS streams natively.

from IPython.display import HTML HTML(data='<video src="{0}" autoplay="autoplay" controls="controls" width="300" height="400"></video>'.format(url))

We can also stream directly from a AWS DeepLens with just a bit of code:

import DeepLens_Kinesis_Video as dkv import time aws_access_key = "super_fake" aws_secret_key = "even_more_fake" region = "us-east-1" stream_name ="RandallDeepLens" retention = 1 #in minutes. wait_time_sec = 60*300 #The number of seconds to stream the data # will create the stream if it does not already exist producer = dkv.createProducer(aws_access_key, aws_secret_key, "", region) my_stream = producer.createStream(stream_name, retention) my_stream.start() time.sleep(wait_time_sec) my_stream.stop()

How to use Kinesis Video Streams HLS Output Streams

We definitely need a Kinesis Video Stream, which we can create easily in the Kinesis Video Streams Console.

Now, we need to get some content into the stream. We have a few options here. Perhaps the easiest is the docker container. I decided to take the more adventurous route and compile the GStreamer plugin locally on my mac, following the scripts on github. Be warned, compiling this plugin takes a while and can cause your computer to transform into a space heater.

With our freshly compiled GStreamer binaries like gst-launch-1.0 and the kvssink plugin we can stream directly from my macbook’s webcam, or any other GStreamer source, into Kinesis Video Streams. I just use the kvssink output plugin and my data will wind up in the video stream. There are a few parameters to configure around this, so pay attention.

Here’s an example command that I ran to stream my macbook’s webcam to Kinesis Video Streams:

gst-launch-1.0 autovideosrc ! videoconvert \ ! video/x-raw,format=I420,width=640,height=480,framerate=30/1 \ ! vtenc_h264_hw allow-frame-reordering=FALSE realtime=TRUE max-keyframe-interval=45 bitrate=500 \ ! h264parse \ ! video/x-h264,stream-format=avc,alignment=au,width=640,height=480,framerate=30/1 \ ! kvssink stream-name="BlogStream" storage-size=1024 aws-region=us-west-2 log-config=kvslog

Now that we’re streaming some data into Kinesis, I can use the getting started sample static website to test my HLS stream with a few different video players. I just fill in my AWS credentials and ask it to start playing. The GetHLSStreamingSessionURL API supports a number of parameters so you can play both on-demand segments and live streams from various timestamps.

Additional Info

Data Consumed from Kinesis Video Streams using HLS is charged $0.0119 per GB in US East (N. Virginia) and US West (Oregon) and pricing for other regions is available on the service pricing page. This feature is available now, in all regions where Kinesis Video Streams is available.

The Kinesis Video team told me they’re working hard on getting more integration with the AWS Media services, like MediaLive, which will make it easier to serve Kinesis Video Stream content to larger audiences.

As always, let us know what you you think on twitter or in the comments. I’ve had a ton of fun playing around with this feature over the past few days and I’m excited to see customers build some new tools with it!

Randall

Categories: Cloud

New – Lifecycle Management for Amazon EBS Snapshots

AWS Blog - Thu, 07/12/2018 - 21:56

It is always interesting to zoom in on the history of a single AWS service or feature and watch how it has evolved over time in response to customer feedback. For example, Amazon Elastic Block Store (EBS) launched a decade ago and has been gaining more features and functionality every since. Here are a few of the most significant announcements:

Several of the items that I chose to highlight above make EBS snapshots more useful and more flexible. As you may already know, it is easy to create snapshots. Each snapshot is a point-in-time copy of the blocks that have changed since the previous snapshot, with automatic management to ensure that only the data unique to a snapshot is removed when it is deleted. This incremental model reduces your costs and minimizes the time needed to create a snapshot.

Because snapshots are so easy to create and use, our customers create a lot of them, and make great use of tags to categorize, organize, and manage them. Going back to my list, you can see that we have added multiple tagging features over the years.

Lifecycle Management – The Amazon Data Lifecycle Manager
We want to make it even easier for you to create, use, and benefit from EBS snapshots! Today we are launching Amazon Data Lifecycle Manager to automate the creation, retention, and deletion of Amazon EBS volume snapshots. Instead of creating snapshots manually and deleting them in the same way (or building a tool to do it for you), you simply create a policy, indicating (via tags) which volumes are to be snapshotted, set a retention model, fill in a few other details, and let Data Lifecycle Manager do the rest. Data Lifecycle Manager is powered by tags, so you should start by setting up a clear and comprehensive tagging model for your organization (refer to the links above to learn more).

It turns out that many of our customers have invested in tools to automate the creation of snapshots, but have skimped on the retention and deletion. Sooner or later they receive a surprisingly large AWS bill and find that their scripts are not working as expected. The Data Lifecycle Manager should help them to save money and to be able to rest assured that their snapshots are being managed as expected.

Creating and Using a Lifecycle Policy
Data Lifecycle Manager uses lifecycle policies to figure out when to run, which volumes to snapshot, and how long to keep the snapshots around. You can create the policies in the AWS Management Console, from the AWS Command Line Interface (CLI) or via the Data Lifecycle Manager APIs; I’ll use the Console today. Here are my EBS volumes, all suitably tagged with a department:

I access the Lifecycle Manager from the Elastic Block Store section of the menu:

Then I click Create Snapshot Lifecycle Policy to proceed:

Then I create my first policy:

I use tags to specify the volumes that the policy applies to. If I specify multiple tags, then the policy applies to volumes that have any of the tags:

I can create snapshots at 12 or 24 hour intervals, and I can specify the desired snapshot time. Snapshot creation will start no more than an hour after this time, with completion based on the size of the volume and the degree of change since the last snapshot.

I can use the built-in default IAM role or I can create one of my own. If I use my own role, I need to enable the EC2 snapshot operations and all of the DLM (Data Lifecycle Manager) operations; read the docs to learn more.

Newly created snapshots will be tagged with the aws:dlm:lifecycle-policy-id and  aws:dlm:lifecycle-schedule-name automatically; I can also specify up to 50 additional key/value pairs for each policy:

I can see all of my policies at a glance:

I took a short break and came back to find that the first set of snapshots had been created, as expected (I configured the console to show the two tags created on the snapshots):

Things to Know
Here are a couple of things to keep in mind when you start to use Data Lifecycle Manager to automate your snapshot management:

Data Consistency – Snapshots will contain the data from all completed I/O operations, also known as crash consistent.

Pricing – You can create and use Data Lifecyle Manager policies at no charge; you pay the usual storage charges for the EBS snapshots that it creates.

Availability – Data Lifecycle Manager is available in the US East (N. Virginia), US West (Oregon), and EU (Ireland) Regions.

Tags and Policies – If a volume has more than one tag and the tags match multiple policies, each policy will create a separate snapshot and both policies will govern the retention. No two policies can specify the same key/value pair for a tag.

Programmatic Access – You can create and manage policies programmatically! Take a look at the CreateLifecyclePolicy, GetLifecyclePolicies, and UpdateLifeCyclePolicy functions to get started. You can also write an AWS Lambda function in response to the createSnapshot event.

Error Handling – Data Lifecycle Manager generates a “DLM Policy State Change” event if a policy enters the error state.

In the Works – As you might have guessed from the name, we plan to add support for additional AWS data sources over time. We also plan to support policies that will let you do weekly and monthly snapshots, and also expect to give you additional scheduling flexibility.

Jeff;

Categories: Cloud

AWS Storage Gateway Recap – SMB Support, RefreshCache Event, and More

AWS Blog - Thu, 07/12/2018 - 07:24

To borrow my own words, the AWS Storage Gateway is a service that includes a multi-protocol storage appliance that fits in between your existing application and the AWS Cloud. Your applications see the gateway as a file system, a local disk volume, or a Virtual Tape Library, depending on how it was configured.

Today I would like to share a few recent updates to the File Gateway configuration of the Storage Gateway, and also show you how they come together to enable some new processing models. First, the most recent updates:

SMB Support – The File Gateway already supports access from clients that speak NFS (versions 3 and 4.1 are supported). Last month we added support for the Server Message Block (SMB) protocol. This allows Windows applications that communicate using v2 or v3 of SMB to store files as objects in S3 through the gateway, enabling hybrid cloud use cases such as backup, content distribution, and processing of machine learning and big data workloads. You can control access to the gateway using your existing on-premises Active Directory (AD) domain or a cloud-based domain hosted in AWS Directory Service, or you can use authenticated guest access. To learn more about this update, read AWS Storage Gateway Adds SMB Support to Store and Access Objects in Amazon S3 Buckets.

Cross-Account Permissions – Some of our customers run their gateways in one AWS account and configure them to upload to an S3 bucket owned by another account. This allows them to track departmental storage and retrieval costs using chargeback and showback models. In order to simplify this important use case, you can configure the gateway to provide the bucket owner with full permissions. This avoids a pain point which could arise if the bucket owner was unable to see the objects. To learn how to set this up, read Using a File Share for Cross-Account Access.

Requester Pays – Bucket owners are responsible for storage costs. Owners pay for data transfer costs by default, but also have the option to have the requester pay. To support this use case, the File Gateway now supports S3’s Requester Pays Buckets. Data collectors and aggregators can use this feature to share data with research organizations such as universities and labs without incurring the costs of access themselves. File Gateway provides file based access to the S3 objects, caches recently accessed data locally, helping requesters reduce latency and costs. To learn more, read about Creating an NFS File Share and Creating an SMB File Share.

File Upload Notification – The gateway caches files locally, and uploads them to a designated S3 bucket in the background. Late last year we gave you the ability to request notification (in the form of a CloudWatch Event) when new files have been uploaded. You can use this to initiate cloud-based processing or to implement advanced logging. To learn more, read Getting File Upload Notification and study the NotifyWhenUploaded function.

Cache Refresh Event – You have long had the ability to use the RefreshCache function to make sure that the gateway is aware of objects that have been added, removed, or replaced in the bucket. The new Storage Gateway Cache Refresh Event lets you know that the cache is now in sync with S3, and can be used as a signal to initiate local processing. To learn more, read Getting Refresh Cache Notification.

Hybrid Processing Using File Gateway
You can use the File Upload Notification and Cache Refresh to automate some of your routine hybrid process tasks!

Let’s say that you run a geographically distributed office or retail business, with locations all over the world. Raw data (metrics, cash register receipts, or time sheets) is collected at each location, and then uploaded to S3 using a File Gateway hosted at each location. As the data arrives, you use the File Upload Notifications to process each S3 object, perhaps using an AWS Lambda function that invokes Amazon Athena to run a stock set of queries against each one. The data arrives over the course of a couple of hours, and results accumulate in another bucket. At the end of the reporting period, the intermediate results are processed, custom reports are generated for each branch location, and then stored in another bucket (this bucket, as it turns out, is also associated with a gateway, and each gateway will have cached copies of the prior versions of the reports). After you generate your reports, you can refresh each of the gateway caches, wait for the corresponding notifications, and then send an email to the branch managers to tell them that their new report is available.

Here’s a video (and presentation) with more information about this processing model:

Now Available
All of the features listed above are available now and you can start using them today in all regions where Storage Gateway is available.

Jeff;

Categories: Cloud

AWS re:Invent 2018 is Coming – Are You Ready?

AWS Blog - Tue, 07/10/2018 - 14:45

As I write this, there are just 138 days until re:Invent 2018. My colleagues on the events team are going all-out to make sure that you, our customer, will have the best possible experience in Las Vegas. After meeting with them, I decided to write this post so that you can have a better understanding of what we have in store, know what to expect, and have time to plan and to prepare.

Dealing with Scale
We started out by talking about some of the challenges that come with scale. Approximately 43,000 people (AWS customers, partners, members of the press, industry analysts, and AWS employees) attended in 2017 and we are expecting an even larger crowd this year. We are applying many of the scaling principles and best practices that apply to cloud architectures to the physical, logistical, and communication challenges that are part-and-parcel of an event that is this large and complex.

We want to make it easier for you to move from place to place, while also reducing the need for you to do so! Here’s what we are doing:

Campus Shuttle – In 2017, hundreds of buses traveled on routes that took them to a series of re:Invent venues. This added a lot of latency to the system and we were not happy about that. In 2018, we are expanding the fleet and replacing the multi-stop routes with a larger set of point-to-point connections, along with additional pick-up and drop-off points at each venue. You will be one hop away from wherever you need to go.

Ride Sharing – We are partnering with Lyft and Uber (both powered by AWS) to give you another transportation option (download the apps now to be prepared). We are partnering with the Las Vegas Monorail and the taxi companies, and are also working on a teleportation service, but do not expect it to be ready in time.

Session Access – We are setting up a robust overflow system that spans multiple re:Invent venues, and are also making sure that the most popular sessions are repeated in more than one venue.

Improved Mobile App – The re:Invent mobile app will be more lively and location-aware. It will help you to find sessions with open seats, tell you what is happening around you, and keep you informed of shuttle and other transportation options.

Something for Everyone
We want to make sure that re:Invent is a warm and welcoming place for every attendee, with business and social events that we hope are progressive and inclusive. Here’s just some of what we have in store:

You can also take advantage of our mother’s rooms, gender-neutral restrooms, and reflection rooms. Check out the community page to learn more!

Getting Ready
Now it is your turn! Here are some suggestions to help you to prepare for re:Invent:

  • Register – Registration is now open! Every year I get email from people I have not talked to in years, begging me for last-minute access after re:Invent sells out. While it is always good to hear from them, I cannot always help, even if we were in first grade together.
  • Watch – We’re producing a series of How to re:Invent webinars to help you get the most from re:Invent. Watch What’s New and Breakout Content Secret Sauce ASAP, and stay tuned for more.
  • Plan – The session catalog is now live! View the session catalog to see the initial list of technical sessions. Decide on the topics of interest to you and to your colleagues, and choose your breakout sessions, taking care to pay attention to the locations. There will be over 2,000 sessions so choose with care and make this a team effort.
  • Pay Attention – We are putting a lot of effort into preparatory content – this blog post, the webinars, and more. Watch, listen, and learn!
  • Train – Get to work on your cardio! You can easily walk 10 or more miles per day, so bring good shoes and arrive in peak condition.

Partners and Sponsors
Participating sponsors are a core part of the learning, networking, and after hours activities at re:Invent.

For APN Partners, re:Invent is the single largest opportunity to interact with AWS customers, delivering both business development and product differentiation. If you are interested in becoming a re:Invent sponsor, read the re:Invent Sponsorship Prospectus.

For re:Invent attendees, I urge you to take time to meet with Sponsoring APN Partners in both the Venetian and Aria Expo halls. Sponsors offer diverse skills, Competencies, services and expertise to help attendees solve a variety of different business challenges. Check out the list of re:Invent Sponsors to learn more.

See You There
Once you are on site, be sure to take advantage of all that re:Invent has to offer.

If you are not sure where to go or what to do next, we’ll have some specially trained content experts to guide you.

I am counting down the days, gearing up to crank out a ton of blog posts for re:Invent, and looking forward to saying hello to friends new and old.

Jeff;

PS – We will be adding new sessions to the session catalog over the summer, so be sure to check back every week!

 

Categories: Cloud

DeepLens Challenge #1 Starts Today – Use Machine Learning to Drive Inclusion

AWS Blog - Tue, 07/10/2018 - 10:12

Are you ready to develop and show off your machine learning skills in a way that has a positive impact on the world? If so, get your hands on an AWS DeepLens video camera and join the AWS DeepLens Challenge!

About the Challenge
Working together with our friends at Intel, we are launching the first in a series of eight themed challenges today, all centered around improving the world in some way. Each challenge will run for two weeks and is designed to help you to get some hands-on experience with machine learning.

We will announce a fresh challenge every two weeks on the AWS Machine Learning Blog. Each challenge will have a real-world theme, a technical focus, a sample project, and a subject matter expert. You have 12 days to invent and implement a DeepLens project that resonates with the theme, and to submit a short, compelling video (four minutes or less) to represent and summarize your work.

We’re looking for cool submissions that resonate with the theme and that make great use of DeepLens. We will watch all of the videos and then share the most intriguing ones.

Challenge #1 – Inclusivity Challenge
The first challenge was inspired by the Special Olympics, which took place in Seattle last week. We invite you to use your DeepLens to create a project that drives inclusion, overcomes barriers, and strengthens the bonds between people of all abilities. You could gauge the physical accessibility of buildings, provide audio guidance using Polly for people with impaired sight, or create educational projects for children with learning disabilities. Any project that supports this theme is welcome.

For each project that meets the entry criteria we will make a donation of $249 (the retail price of an AWS DeepLens) to the Northwest Center, a non-profit organization based in Seattle. This organization works to advance equal opportunities for children and adults of all abilities and we are happy to be able to help them to further their mission. Your work will directly benefit this very worthwhile goal!

As an example of what we are looking for, ASLens is a project created by Chris Coombs of Melbourne, Australia. It recognizes and understands American Sign Language (ASL) and plays the audio for each letter. Chris used Amazon SageMaker and Polly to implement ASLens (you can watch the video, learn more and read the code).

To learn more, visit the DeepLens Challenge page. Entries for the first challenge are due by midnight (PT) on July 22nd and I can’t wait to see what you come up with!

Jeff;

PS – The DeepLens Resources page is your gateway to tutorial videos, documentation, blog posts, and other helpful information.

Categories: Cloud

AWS Heroes – New Categories Launch

AWS Blog - Thu, 07/05/2018 - 11:55

As you may know, in 2014 we launched the AWS Community Heroes program to recognize a vibrant group of AWS experts. These standout individuals use their extensive knowledge to teach customers and fellow-techies about AWS products and services across a range of mediums. As AWS grows, new groups of Heroes emerge.

Today, we’re excited to recognize prominent community leaders by expanding the AWS Heroes program. Unlike Community Heroes (who tend to focus on advocating a wide-range of AWS services within their community), these new Heroes are specialists who focus their efforts and advocacy on a specific technology. Our first new heroes are the AWS Serverless Heroes and AWS Container Heroes. Please join us in welcoming them as the passion and enthusiasm for AWS knowledge-sharing continues to grow in technical communities.

AWS Serverless Heroes

Serverless Heroes are early adopters and spirited pioneers of the AWS serverless ecosystem. They evangelize AWS serverless technologies online and in-person as well as open source contributions to GitHub and the AWS Serverless Application Repository, these Serverless Heroes help evolve the way developers, companies, and the community at large build modern applications. Our initial cohort of Serverless Heroes includes:

Yan Cui

Aleksandar Simovic

Forrest Brazeal

Marcia Villalba

Erica Windisch

Peter Sbarski

Slobodan Stojanović

Rob Gruhl

Michael Hart

Ben Kehoe

Austen Collins

Announcing AWS Container Heroes

Container Heroes are prominent trendsetters who are deeply connected to the ever-evolving container community. They possess extensive knowledge of multiple Amazon container services, are always keen to learn the latest trends, and are passionate about sharing their insights with anyone running containers on AWS. Please meet the first AWS Container Heroes:

 

Casey Lee

Tung Nguyen

Philipp Garbe

Yusuke Kuoka

Mike Fielder

The trends within the AWS community are ever-changing.  We look forward to recognizing a wide variety of Heroes in the future. Stay tuned for additional updates to the Hero program in coming months, and be sure to visit the Heroes website to learn more.

Categories: Cloud

PHP 7.3.0 alpha 3 Released

PHP News - Thu, 07/05/2018 - 02:41
Categories: PHP

AWS Online Tech Talks – July 2018

AWS Blog - Mon, 07/02/2018 - 15:05

Join us this month to learn about AWS services and solutions featuring topics on Amazon EMR, Amazon SageMaker, AWS Lambda, Amazon S3, Amazon WorkSpaces, Amazon EC2 Fleet and more! We also have our third episode of the “How to re:Invent” where we’ll dive deep with the AWS Training and Certification team on Bootcamps, Hands-on Labs, and how to get AWS Certified at re:Invent. Register now! We look forward to seeing you. Please note – all sessions are free and in Pacific Time.

 

Tech talks featured this month:

 

Analytics & Big Data

July 23, 2018 | 11:00 AM – 12:00 PM PT – Large Scale Machine Learning with Spark on EMR – Learn how to do large scale machine learning on Amazon EMR.

July 25, 2018 | 01:00 PM – 02:00 PM PT – Introduction to Amazon QuickSight: Business Analytics for Everyone – Get an introduction to Amazon Quicksight, Amazon’s BI service.

July 26, 2018 | 11:00 AM – 12:00 PM PT – Multi-Tenant Analytics on Amazon EMR – Discover how to make an Amazon EMR cluster multi-tenant to have different processing activities on the same data lake.

 

Compute

July 31, 2018 | 11:00 AM – 12:00 PM PT – Accelerate Machine Learning Workloads Using Amazon EC2 P3 Instances – Learn how to use Amazon EC2 P3 instances, the most powerful, cost-effective and versatile GPU compute instances available in the cloud.

August 1, 2018 | 09:00 AM – 10:00 AM PT – Technical Deep Dive on Amazon EC2 Fleet – Learn how to launch workloads across instance types, purchase models, and AZs with EC2 Fleet to achieve the desired scale, performance and cost.

 

Containers

July 25, 2018 | 11:00 AM – 11:45 AM PT – How Harry’s Shaved Off Their Operational Overhead by Moving to AWS Fargate – Learn how Harry’s migrated their messaging workload to Fargate and reduced message processing time by more than 75%.

 

Databases

July 23, 2018 | 01:00 PM – 01:45 PM PT – Purpose-Built Databases: Choose the Right Tool for Each Job – Learn about purpose-built databases and when to use which database for your application.

July 24, 2018 | 11:00 AM – 11:45 AM PT – Migrating IBM Db2 Databases to AWS – Learn how to migrate your IBM Db2 database to the cloud database of your choice.

 

DevOps

July 25, 2018 | 09:00 AM – 09:45 AM PT – Optimize Your Jenkins Build Farm – Learn how to optimize your Jenkins build farm using the plug-in for AWS CodeBuild.

 

Enterprise & Hybrid

July 31, 2018 | 09:00 AM – 09:45 AM PT – Enable Developer Productivity with Amazon WorkSpaces – Learn how your development teams can be more productive with Amazon WorkSpaces.

August 1, 2018 | 11:00 AM – 11:45 AM PT – Enterprise DevOps: Applying ITIL to Rapid Innovation – Innovation doesn’t have to equate to more risk for your organization. Learn how Enterprise DevOps delivers agility while maintaining governance, security and compliance.

 

IoT

July 30, 2018 | 01:00 PM – 01:45 PM PT – Using AWS IoT & Alexa Skills Kit to Voice-Control Connected Home Devices – Hands-on workshop that covers how to build a simple backend service using AWS IoT to support an Alexa Smart Home skill.

 

Machine Learning

July 23, 2018 | 09:00 AM – 09:45 AM PT – Leveraging ML Services to Enhance Content Discovery and Recommendations – See how customers are using computer vision and language AI services to enhance content discovery & recommendations.

July 24, 2018 | 09:00 AM – 09:45 AM PT – Hyperparameter Tuning with Amazon SageMaker’s Automatic Model Tuning – Learn how to use Automatic Model Tuning with Amazon SageMaker to get the best machine learning model for your datasets, to tune hyperparameters.

July 26, 2018 | 09:00 AM – 10:00 AM PT – Build Intelligent Applications with Machine Learning on AWS – Learn how to accelerate development of AI applications using machine learning on AWS.

 

re:Invent

July 18, 2018 | 08:00 AM – 08:30 AM PT – Episode 3: Training & Certification Round-Up – Join us as we dive deep with the AWS Training and Certification team on Bootcamps, Hands-on Labs, and how to get AWS Certified at re:Invent.

 

Security, Identity, & Compliance

July 30, 2018 | 11:00 AM – 11:45 AM PT – Get Started with Well-Architected Security Best Practices – Discover and walk through essential best practices for securing your workloads using a number of AWS services.

 

Serverless

July 24, 2018 | 01:00 PM – 02:00 PM PT – Getting Started with Serverless Computing Using AWS Lambda – Get an introduction to serverless and how to start building applications with no server management.

 

Storage

July 30, 2018 | 09:00 AM – 09:45 AM PT – Best Practices for Security in Amazon S3 – Learn about Amazon S3 security fundamentals and lots of new features that help make security simple.

Categories: Cloud

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by Dr. Radut