Jump to Navigation

Cloud

Amazon EC2 Update – Additional Instance Types, Nitro System, and CPU Options

AWS Blog - Tue, 06/19/2018 - 08:47

I have a backlog of EC2 updates to share with you. We’ve been releasing new features and instance types at a rapid clip and it is time to catch up. Here’s a quick peek at where we are and where we are going…

Additional Instance Types
Here’s a quick recap of the most recent EC2 instance type announcements:

Compute-Intensive – The compute-intensive C5d instances provide a 25% to 50% performance improvement over the C4 instances. They are available in 5 regions and offer up to 72 vCPUs, 144 GiB of memory, and 1.8 TB of local NVMe storage.

General Purpose – The general purpose M5d instances are also available in 5 regions. They offer up to 96 vCPUs, 384 GiB of memory, and 3.6 TB of local NVMe storage.

Bare Metal – The i3.metal instances became generally available in 5 regions a couple of weeks ago. You can run performance analysis tools that are hardware-dependent, workloads that require direct access to bare-metal infrastructure, applications that need to run in non-virtualized environments for licensing or support reasons, and container environments such as Clear Containers, while you take advantage of AWS features such as Elastic Block Store (EBS), Elastic Load Balancing, and Virtual Private Clouds. Bare metal instances with 6 TB, 9 TB, 12 TB, and more memory are in the works, all designed specifically for SAP HANA and other in-memory workloads.

Innovation and the Nitro System
The Nitro system is a rich collection of building blocks that can be assembled in many different ways, giving us the flexibility to design and rapidly deliver EC2 instance types with an ever-broadening selection of compute, storage, memory, and networking options. We will deliver new instance types more quickly than ever in the months to come, with the goal of helping you to build, migrate, and run even more types of workloads.

Local NVMe Storage – The new C5d, M5d, and bare metal EC2 instances feature our Nitro local NVMe storage building block, which is also used in the Xen-virtualized I3 and F1 instances. This building block provides direct access to high-speed local storage over a PCI interface and transparently encrypts all data using dedicated hardware. It also provides hardware-level isolation between storage devices and EC2 instances so that bare metal instances can benefit from local NVMe storage.

Nitro Security Chip – A component that is part of our AWS server designs that continuously monitors and protects hardware resources and independently verifies firmware each time a system boots.

Nitro Hypervisor – A thin, quiescent hypervisor that manages memory and CPU allocation, and delivers performance that is indistinguishable from bare metal for most workloads (Brendan Gregg of Netflix benchmarked it at less than 1%).

Networking – Hardware support for the software defined network inside of each Virtual Private Cloud (VPC), Enhanced Networking, and Elastic Network Adapter.

Elastic Block Storage – Hardware EBS processing including CPU-intensive cryptographic operations.

Moving storage, networking, and security functions to hardware has important consequences for both bare metal and virtualized instance types:

Virtualized instances can make just about all of the host’s CPU power and memory available to the guest operating systems since the hypervisor plays a greatly diminished role.

Bare metal instances have full access to the hardware, but also have the same the flexibility and feature set as virtualized EC2 instances including CloudWatch metrics, EBS, and VPC.

To learn more about the hardware and software that make up the Nitro system, watch Amazon EC2 Bare Metal Instances or C5 Instances and the Evolution of Amazon EC2 Virtualization and take a look at The Nitro Project: Next-Generation EC2 Infrastructure.

CPU Options
This feature provides you with additional control over your EC2 instances and lets you optimize your instance for a particular workload. First, you can specify the desired number of vCPUs at launch time. This allows you to control the vCPU to memory ratio for Oracle and SQL Server workloads that need high memory, storage, and I/O but perform well with a low vCPU count. As a result, you can optimize your vCPU-based licensing costs when you Bring Your Own License (BYOL). Second, you can disable Intel® Hyper-Threading Technology (Intel® HT Technology) on instances that run compute-intensive workloads. These workloads sometimes exhibit diminished performance when Intel HT is enabled. Both of these options are available when you launch an instance using the AWS Command Line Interface (CLI) or one of the AWS SDKs. You simply specify the total number of cores and the number of threads per core using values chosen from the CPU Cores and Threads per CPU Core Per Instance Type table. Here’s how you would launch an instance with 6 CPU cores and Intel® HT Technology disabled:

$ aws ec2 run-instances --image-id ami-1a2b3c4d --instance-type r4.4xlarge --cpu-options "CoreCount=6,ThreadsPerCore=1"

To learn more, read about Optimizing CPU Options.

Help Wanted
The EC2 team is always hiring! Here are a few of their open positions:

Jeff;

Categories: Cloud

Amazon Polly Plugin for WordPress Update – Translate and Vocalize Your Content

AWS Blog - Tue, 06/19/2018 - 06:21

Earlier this year I showed you how to Give Your WordPress Blog a Voice with Amazon Polly and walked you through the steps involved in installing, configuring, and using the Amazon Polly for WordPress plugin. Today we are making this plugin even more powerful, adding the ability to translate your content into one or more languages and to produce audio versions of each translation. The translation is implemented using Amazon Translate, a neural machine translation service that is part of our portfolio of machine learning services.

The original version of the plugin works like this:

And the new version works like this:

This version of the plugin supports translation of English-language web content into Spanish, German, French, and Portuguese, with plans to support other languages in the future.

Updating and Configuring the Plugin
My earlier post covered the steps involved in launching an Amazon Lightsail instance and setting up the plugin, and I won’t repeat them here. The first step is to edit my existing IAM policy so that it allows calls to the TranslateText function:

Then I log in to the WordPress Admin dashboard, click Plugins, and see that a new version is available:

I click update now, and wait a few seconds for the update. Then I click Settings to enable translation:

I click Enable translation support and Save Changes, then come back and set up the details. I select all of the available target languages, leave the voices and labels as-is, and click Save Changes to move forward:

Creating Translations and Vocalizations
Now I can create a new post and exercise the plugin. I enter the title and text for the post as usual:

Before moving forward, I can click How much will this cost to convert? to check on costs.

The price seems reasonable to me. I publish the post, and then click Translate to generate audio in 4 other languages. This happens in a matter of seconds:

The published post now includes a player that lets me listen to the original audio or any of the 4 translations:

Here are the audio versions:

English: Spanish: German: French: Portuguese:

I have lots of customization options. For example, I can enable transcripts of the translated text:

The transcripts are shown in the post:

I can change the labels that are used for each language:

Here are the updated labels:

I can also specify the Polly voice for each target language:

Now Available
The updated plugin is available now and you can start using it today! As you can see, it uses the “magic” of machine translation and text-to-speech to make your web content accessible to a wider audience, in both written and spoken form.

Jeff;

 

Categories: Cloud

New – Redis 4.0 Compatibility in Amazon ElastiCache

AWS Blog - Thu, 06/14/2018 - 18:10

Amazon ElastiCache makes it easy for you to set up a fully managed in-memory data store and cache with Redis or Memcached. Today we’re pleased to launch compatibility with Redis 4.0 in ElastiCache. You can now launch Redis 4.0 compatible ElastiCache nodes or clusters, in all commercial AWS regions. ElastiCache Redis clusters can scale to terabytes of memory and millions of reads / writes per second to serve the most demanding needs of games, IoT devices, financial applications, and web applications.

Launching a Redis cluster in the AWS Management Console or AWS Command Line Interface (CLI) remains simple. I’m going to create a small cluster to play with the new Redis 4.0 features, to use the new version I just select a 4.0 release in “Engine version compatibility”. This will launch, at the time of this writing, a 4.0.10 compatible cluster.

New Features
  • Least Frequently Used (LFU) cache eviction policy – Redis 4.0 launched with a number of caching improvements including a new LFU cache eviction algorithm, customers may see better performance from LFU over Least Recently Used (LRU). Antirez’s blog has a deep dive on some of the changes.
  • Asynchronous FLUSHDB, FLUSHALL, and UNLINK – using the ASYNC option of the FLUSH commands allows users to make a non-blocking call to clear databases. Using UNLINK instead of DEL allows users to asynchronously delete individual keys. There’s also the SWAPDB command which can be useful to atomically switch between entire datasets.
  • Active memory defragmentation – Redis can now defragment memory while running which allows more efficient utilization of memory for customer data. This is off by default but you can modify the parameter group to turn it on. Customers should probably only turn it on if they’re running into fragmentation issues.
  • Online Cluster Resizing and Encryption in transit – with Redis 4.0 you can now use encryption in transit and online cluster resizing at the same time. With Online Cluster Resizing you can add and remove shards from a running cluster to dynamically scale-out or scale-in your Redis cluster and adapt to changes on demand. Previously this feature wasn’t able to be used with encryption in transit but now you can use both features simultaneously. This helps with workloads that require encryption for compliance purposes.
  • MEMORY commands – a whole new family of memory commands: DOCTOR, USAGE, STATS, PURGE, and MALLOC-STATS are available for gathering statistics or usage information on your redis nodes. Running MEMORY DOCTOR will tell you about any memory issues (and it will give you a nice sci-fi easter egg if no problems are detected). The MEMORY STATS command will return some useful statistics like “bytes-per-key” that aren’t available in the INFO commands.
Additional Resources

You can find more information in the documentation and in antirez’s blogs/release notes.

We hope customers can take advantage of these new features right away. As always, feel free to leave any comments below or reach out to us on twitter!

Randall

Categories: Cloud

AWS DeepLens Now Shipping – Order One Today!

AWS Blog - Wed, 06/13/2018 - 21:01

AWS DeepLens is a video camera that runs deep learning models directly on the device, out in the field. I wrote about the hardware and system software in depth last year; here’s a quick recap:

Hardware – 4 megapixel camera (1080P video), 2D microphone array, Intel Atom® Processor, dual-band Wi-Fi, USB and micro HDMI ports, 8 GB of memory for models and code.

Software – Ubuntu 16.04, AWS Greengrass Core, device-optimized versions of MXNet and Intel® clDNN library, support for other deep learning frameworks.

The response to this AWS re:Invent was immediate and gratifying! Educators, students, and developers signed up for hands-on sessions and started to build and train models right away. Their enthusiasm continued throughout the preview period and into this year’s AWS Summit season, where we did our best to provide all interested parties with access to devices, tools, and training.

Hackathons and Challenges
We made DeepLens devices available to participants in last month’s HackTillDawn. I was fortunate enough to be able to attend the event and to help to choose the three winners. It was amazing to watch the teams, most with no previous machine learning or computer vision experience, dive right in and build interesting, sophisticated applications designed to enhance the attendee experience at large-scale music festivals. The three winners went on to compete at EDC Vegas, where the Grand Prize winner (Find Your Totem) was chosen. Congrats to the team, and have fun at EDC Orlando!

We also ran the AWS DeepLens Challenge, asking participants to build machine learning projects that made use of DeepLens, with bonus points for the use of Amazon SageMaker and/or AWS Lambda. The submissions were as diverse as they were interesting, with applications designed for children, adults, and animals. Details on all of the submissions, including demo videos and source code, are available on the Community Projects page. The three winning applications were ReadToMe (first place), Dee (second place), and SafeHaven (third place).

From what I can tell, DeepLens has proven itself as an excellent learning vehicle. While speaking to the attendees at HackTillDawn, I learned that many of them were eager to get some hands-on experience that they could use to broaden their skillsets and to help them to progress in their careers.

Preview Updates
During the preview period, the DeepLens team has stayed heads-down, focusing on making the device even more capable. Significant additions include:

Gluon Support – Computer vision models can be built using Gluon (an imperative interface to MXNet), trained, imported to DeepLens, and deployed.

SageMaker Import – Models can be built and trained in Amazon SageMaker and then imported to DeepLens.

Model Optimizer – The optimizer runs on the device and optimizes downloaded MXNet models so that they run efficiently on the DeepLens GPU.

Now Shipping
I am happy to report that DeepLens is now shipping and available to order from Amazon.com. You can get one of your very own and start building your own deep learning applications within days. Devices can be shipped to addresses in the United States, with additional destinations in the works.

We are also rounding out the initial feature set with the addition of some important new capabilities:

Expanded Framework Support – DeepLens now supports the TensorFlow and Caffe frameworks.

Expanded MXNet Layer Support – DeepLens now supports the Deconvolution, L2Normalization, and LRN layers provided by MXNet.

Kinesis Video Streams – The video stream from the DeepLens camera can now be used in conjunction with Amazon Kinesis Video Streams. You can stream the raw camera feed to the cloud and then use Amazon Rekognition Video to extract objects, faces, and content from the video.

New Sample Project – DeepLens now includes a sample project for head pose detection (powered by TensorFlow). You can examine this sample to see how the model was constructed; here’s an excerpt from the notebook:

I am looking forward to seeing what you build with your very own DeepLens. Drop me a line and let me know!

Jeff;

Categories: Cloud

Amazon SageMaker Automatic Model Tuning: Using Machine Learning for Machine Learning

AWS Blog - Thu, 06/07/2018 - 17:35

Today I’m excited to announce the general availability of Amazon SageMaker Automatic Model Tuning. Automatic Model Tuning eliminates the undifferentiated heavy lifting required to search the hyperparameter space for more accurate models. This feature allows developers and data scientists to save significant time and effort in training and tuning their machine learning models. A Hyperparameter Tuning job launches multiple training jobs, with different hyperparameter combinations, based on the results of completed training jobs. SageMaker trains a “meta” machine learning model, based on Bayesian Optimization, to infer hyperparameter combinations for our training jobs. Let’s dive a little deeper.

Model Tuning in the Machine Learning Process

A developer’s typical machine learning process comprises 4 steps: exploratory data analysis (EDA), model design, model training, and model evaluation. SageMaker already makes each of those steps easy with access to powerful Jupyter notebook instances, built-in algorithms, and model training within the service. Focusing on the training portion of the process, we typically work with data and feed it into a model where we evaluate the model’s prediction against our expected result. We keep a portion of our overall input data, the evaluation data, away from the training data used to train the model. We can use the evaluation data to examine the behavior of our model on data it has never seen. In many cases after we’ve chosen an algorithm or built a custom model, we will need to search the space of possible hyperparameter configurations of that algorithm for the best results for our input data.

Hyperparameters control how our underlying algorithms operate and influence the performance of the model. They can be things like: the number of epochs to train for, the number of layers in the network, the learning rate, the optimization algorithms, and more. Typically, you start with random values, or common values for other problems, and iterate through adjustments as you begin to see what effect the changes have. In the past this was a painstakingly manual process. However, thanks to the work of some very talented researchers we can use SageMaker to eliminate almost all of the manual overhead. A user only needs to select the hyperparameters to tune, a range for each parameter to explore, and the total number of training jobs to budget. Let’s see how this works in practice.

Hyperparameter Tuning

To demonstrate this feature we’ll work with the standard MNIST dataset, the Apache MXNet framework, and the SageMaker Python SDK. Everything you see below is available in the SageMaker example notebooks.

First, I’ll create a traditional MXNet estimator using the SageMaker Python SDK on a Notebook Instance:

import boto3 import sagemaker from sagemaker.mxnet import MXNet role = sagemaker.get_execution_role() region = boto3.Session().region_name train_data_location = 's3://sagemaker-sample-data-{}/mxnet/mnist/train'.format(region) test_data_location = 's3://sagemaker-sample-data-{}/mxnet/mnist/test'.format(region) estimator = MXNet(entry_point='mnist.py', role=role, train_instance_count=1, train_instance_type='ml.m4.xlarge', sagemaker_session=sagemaker.Session(), base_job_name='HPO-mxnet', hyperparameters={'batch_size': 100})

This is probably quite similar to what you’ve seen in other SageMaker examples.

Now, we can import some tools for the Auto Model Tuning and create our hyperparameter ranges.

from sagemaker.tuner import HyperparameterTuner, IntegerParameter, CategoricalParameter, ContinuousParameter hyperparameter_ranges = {'optimizer': CategoricalParameter(['sgd', 'Adam']), 'learning_rate': ContinuousParameter(0.01, 0.2), 'num_epoch': IntegerParameter(10, 50)}

The tuning job will select parameters from these ranges and use those to determine the best place to focus training efforts. There are few types of parameters:

  • Categorical parameters use one value from a discrete set.
  • Continuous parameters can use any real number value between the minimum and maximum value.
  • Integer parameters can use any integer within the bounds specified.

Now that we have our ranges defined we want to define our success metric and a regular expression for finding that metric in the training job logs.

objective_metric_name = 'Validation-accuracy' metric_definitions = [{'Name': 'Validation-accuracy', 'Regex': 'Validation-accuracy=([0-9\\.]+)'}]

Now, with just these few things defined we can start our tuning job!

tuner = HyperparameterTuner(estimator, objective_metric_name, hyperparameter_ranges, metric_definitions, max_jobs=9, max_parallel_jobs=3) tuner.fit({'train': train_data_location, 'test': test_data_location})

Now, we can open up the SageMaker console, select the Hyperparameter tuning jobs sub-console and check out all our tuning jobs.

We can click on the job we just created to get some more detail and explore the results of the tuning.

By default the console will show us the best job and the parameters used but we can also check out each of the other jobs.

Hopping back over to our notebook instance, we have a handy analytics object from tuner.analytics() that we can use to visualize the results of the training with some bokeh plots. Some examples of this are provided in the SageMaker example notebooks.

This feature works for built-in algorithms, jobs created with the SageMaker Python SDK, or even bring-your-own training jobs in docker.

We can even create tuning jobs right in the console by clicking Create hyperparameter tuning job.

First we select a name for our job, an IAM role and which VPC it should run in, if any.

Next, we configure the training job. We can use built-in algorithms or a custom docker image. If we’re using a custom image this would be where we defined the regex to to find the objective metric in the logs. For now we’ll just select XGBoost and click next.

Now we’ll configure our tuning job parameters just like in the notebook example. I’ll select the area under the curve (AUC) as the objective metric to optimize. Since this is a builtin algorithm the regex for that metric was already filled in by the previous step. I’ll set the minimum and maximum number of rounds and click next.

In the next screen we can configure the input channels that our algorithm is expecting as well as the location to output the models. We’d typically have more than just the “train” channel and would have an “eval” channel as well.

Finally, we can configure the resource limits for this tuning job.

Now we’re off to the races tuning!

Additional Resources

To take advantage of automatic model tuning there are really only a few things users have to define: the hyperparameter ranges, the success metric and a regex to find it, the number of jobs to run in parallel, and the maximum number of jobs to run. For the built-in algorithms we don’t even need to define the regex. There’s a small trade-off between the number of parallel jobs used and the accuracy of the final model. Increasing max_parallel_jobs will cause the tuning job to finish much faster but a lower parallelism will generally provide a slightly better final result.

Amazon SageMaker Automatic Model Tuning is provided at no additional charge, you pay only for the underlying resources used by the training jobs that the tuning job launches. This feature is available now in all regions where SageMaker is available. This feature is available in the API and training jobs launched by automatic model tuning are visible in the console. You can find our more by reading the documentation.

I really think this feature will save developers a lot of time and effort and I’m excited to see what customers do with it. As always, we welcome your feedback in the comments or on Twitter!

Randall

Categories: Cloud

Amazon EKS – Now Generally Available

AWS Blog - Tue, 06/05/2018 - 10:58

We announced Amazon Elastic Container Service for Kubernetes and invited customers to take a look at a preview during re:Invent 2017. Today I am pleased to be able to let you know that Amazon EKS is available for use in production form. It has been certified as Kubnernetes conformant, and is ready to run your existing Kubernetes workloads.

Based on the most recent data from the Cloud Native Computing Foundation, we know that AWS is the leading environment for Kubernetes, with 57% of all companies who run Kubernetes choosing to do so on AWS. Customers tell us that Kubernetes is core to their IT strategy, and are already running hundreds of millions of containers on AWS every week. Amazon EKS simplifies the process of building, securing, operating, and maintaining Kubernetes clusters, and brings the benefits of container-based computing to organizations that want to focus on building applications instead of setting up a Kubernetes cluster from scratch.

AWS Inside
Amazon EKS takes advantage of the fact that it is running in the AWS Cloud, making great use of many AWS services and features, while ensuring that everything you already know about Kubernetes remains applicable and helpful. Here’s an overview:

Multi-AZ – The Kubernetes control plane (the API server and the etcd database) are run in high-availability fashion across three AWS Availability Zones. Master nodes are monitored and replaced if they fail, and are also patched and updated automatically.

IAM Integration – Amazon EKS uses the Heptio Authenticator for authentication. You can make use of IAM roles and avoid the pain that comes with managing yet another set of credentials.

Load Balancer Support – You can route traffic to your worker nodes using the AWS Network Load Balancer, the AWS Application Load Balancer, or the original (classic) Elastic Load Balancer.

EBS – Kubernetes PersistentVolumes (used for cluster storage) are implemented as Amazon Elastic Block Store (EBS) volumes.

Route 53 – The External DNS project allows services in Kubernetes clusters to be accessed via Route 53 DNS records. This simplifies service discovery and supports load balancing.

Auto Scaling – Your clusters can make use of Auto Scaling, growing and shrinking in response to changes in load.

Container Interface – The Container Network Interface for Kubernetes uses Elastic Network Interfaces to provide static IP addresses for Kubernetes Pods.

For a more detailed look at these features, read about Amazon Elastic Container Service for Kubernetes.

Amazon EKS is built around a shared-responsibility model; the control plane nodes are managed by AWS and you run the worker nodes. This gives you high availability and simplifies the process of moving existing workloads to EKS. Here’s a very high-level overview:

 

Creating an Amazon EKS Cluster
To create a cluster, I provision the control plane, provision and connect the worker cluster, and launch my containers. In the example below I will create a new VPC for my worker cluster, but I can also use an existing one, as long as the desired subnets are tagged with the name of my Kubernetes cluster.

Following the directions in the Amazon EKS Getting Started Guide, I begin by creating an IAM role. Kubernetes assumes this role and uses it to create AWS resources such as Elastic Load Balancers. Once created, this role can be used for all of my clusters. I simply create a CloudFormation stack using the template referred to in the Getting Started Guide:

I acknowledge that the stack will create a role, and click Create to proceed:

The role is created in seconds, and the ARN is shown in the stack’s Output tab (I’ll need it later):

Next, I create a VPC (Virtual Private Cloud) using the sample template from the Getting Started Guide, with the following parameters:

The template creates a VPC that has two subnets, along with all of the necessary route tables, gateways, and security groups):

As is the case with the ARN, I will need the ID of the security group later.

Next, I download kubectl and set it up to use the Heptio Authenticator. The authenticator allows kubectl to make use of IAM authentication when it accesses my Kubernetes clusters. Instructions for downloading and setup are in the Getting Started Guide and I follow them as directed.

To wrap up the setup process, I ensure that I am running the latest version of the AWS Command Line Interface (CLI) (If I was running an older version, the eks command would not be available):

With my IAM role, my VPC, and my tooling all in place, I am ready to create my first Amazon EKS cluster!

I log in to the EKS Console using an IAM user that has administrative privileges (root credentials cannot be used due to the way that the Heptio Authenticator works) and click Create cluster:

I enter a name for my cluster (which must match the one that I entered when I created the VPC, because Kubernetes relies on tagging of subnets), along with the subnet IDs and the security group ID, both for the VPC, and click Create:

My control plane cluster starts out in CREATING status, and transitions to ACTIVE in 10 minutes or less:

Now I need to configure kubectl so that it can access my cluster. Before I can do this, I need to use the CLI to retrieve the certificate authority data:

$ aws eks describe-cluster --region us-west-2 --cluster-name jeff1 --query cluster.certificateAuthority.data

This command returns a long string of data that I’ll need in a minute.

I also retrieve the cluster endpoint from the console:

I make sure that I am in my home directory, create sub-directory .kube, and create file config-jeff1 in it. Then I open config-jeff1 in my editor, copy the templated config file from the Getting Started Guide and finalize the cluster endpoint, certificate, and cluster name. My file looks like this:

apiVersion: v1 clusters: - cluster: server: https://FDA1964D96C9EEF2B76684C103F31C67.sk1.us-west-2.eks.amazonaws.com certificate-authority-data: "...." name: kubernetes contexts: - context: cluster: kubernetes user: aws name: aws current-context: aws kind: Config preferences: {} users: - name: aws user: exec: apiVersion: client.authentication.k8s.io/v1alpha1 command: heptio-authenticator-aws args: - "token" - "-i"

Before I test kubectl, I need to ensure that my CLI is configured to use the same IAM user that I used when I logged in to the console to create the cluster:

And now I can run a quick test to verify that everything is working as expected:

At this point I have set up my master VPC and my Kubernetes control plane. I’m ready to create some worker nodes (EC2 instances). Once again, this is done using a CloudFormation template:

The stack is created in a couple of minutes and sets up IAM roles, security groups, and auto scaling:

Now I need to set up a configurator map so that the worker nodes know how to join the cluster. I download the map, add the ARN of the NodeInstanceRole from the stack, and apply the configuration:

Then I check and see that my nodes are ready:

Running the Guest Book Sample
My Kubnernetes cluster is all set and I can use the Guest Book application to test it out. I create the Kubernetes replication controllers and services:

$ kubectl apply -f https://raw.githubusercontent.com/kubernetes/kubernetes/v1.10.0/examples/guestbook-go/redis-master-controller.json replicationcontroller "redis-master" created $ kubectl apply -f https://raw.githubusercontent.com/kubernetes/kubernetes/v1.10.0/examples/guestbook-go/redis-master-service.json service "redis-master" created $ kubectl apply -f https://raw.githubusercontent.com/kubernetes/kubernetes/v1.10.0/examples/guestbook-go/redis-slave-controller.json replicationcontroller "redis-slave" created $ kubectl apply -f https://raw.githubusercontent.com/kubernetes/kubernetes/v1.10.0/examples/guestbook-go/redis-slave-service.json service "redis-slave" created $ kubectl apply -f https://raw.githubusercontent.com/kubernetes/kubernetes/v1.10.0/examples/guestbook-go/guestbook-controller.json replicationcontroller "guestbook" created $ kubectl apply -f https://raw.githubusercontent.com/kubernetes/kubernetes/v1.10.0/examples/guestbook-go/guestbook-service.json service "guestbook" created

I list the running services and capture the external IP address & port:

and visit the address in my web browser:

Things to Know
We make upstream contributions to the Kubernetes repo and to projects such as the CNI Plugin, the Heptio AWS Authenticator, and Virtual Kubelet. We are currently looking for Systems Development Engineers, DevOps Engineers, Product Managers, and Solution Architects with Kubernetes experience; check out the full list of open positions to learn more.

Amazon EKS is available today in the US East (N. Virginia) and US West (Oregon) Regions and will be expanding to others very soon. We have a detailed roadmap and plan to crank out plenty of additional features this year.

You pay $0.20 per hour for the EKS Control Plane, and usual EC2, EBS, and Load Balancing prices for resources that run in your account. See the EKS Pricing page for more information.

Jeff;

 

Categories: Cloud

SAP on AWS – Past, Present, and Future

AWS Blog - Tue, 06/05/2018 - 06:15

While many of my AWS colleagues are preparing for SAPPHIRE NOW, I thought this would be a good time to bring you up to date on what we have already done to make AWS a great home for SAP’s products and to share our plans to make it even better.

The Story So Far
Our enterprise customers want to bring gigantic, memory-intensive workloads to the AWS Cloud. with a special focus on large-scale production deployments of SAP HANA. Here’s what we have done so far to meet this important requirement:

May 2016 – We announced the x1.32xlarge instance type with 2 TB of memory, purpose-built for running SAP HANA in the cloud.

August 2016 – We announced SAP certification and support for scale-out clusters of up to 7 nodes and 14 TB of memory.

October 2016 – We announced the x1.16xlarge instance type with 1 TB of memory, perfect for testing and for smaller SAP HANA deployments, along with increased regional availability for both of the X1 instances.

May 2017 – We announced the x1e.32xlarge instance type with 4 TB of memory and SAP support for very large scale-out SAP HANA clusters of up to 17 nodes (34 TB of memory).

November 2017 – We announced SAP support for even larger on-demand SAP HANA clusters with up to 25 x1.32xlarge nodes (50 TB of memory).

Along the way, we have been working with customers like Brooks Brothers, Visy, Sumitomo Chemicals, and Kellogg’s to build business-critical HANA implementations on AWS. These customers (and many others) have improved their agility, realized cost savings, and increased performance as part of their move to the cloud.

Right Here, Right Now
As you may know, the C5 and M5 instances are powered by the latest Intel® Xeon® Scalable (Skylake) processors, and make use of our new lightweight, hardware-accelerated Nitro hypervisor. Both types of instances are fully certified by SAP, and deliver a measurable performance increase with respect to their predecessors. The Nitro Hypervisor provides consistent performance and increased compute and memory resources for virtualized EC2 instances by removing host system software components. It allows us to offer larger instance sizes (like c5.18xlarge) that make just about all of the server’s resources available to customers.

As an indication of our progress over the last couple of years, our first SAP certified NetWeaver installations on m2.4xlarge instances delivered 7400 SAPS (925 per vCPU). Today, the m5.24xlarge instances can deliver 135,230 SAPS (1409 per vCPU), our best performance to date. You can read the new SAP benchmarks for C5 and M5 instances, along with Measuring in SAPS, to learn more.

In the Works – Instances with More Memory
Our collaboration with SAP began in 2008 with the goal of providing our customers with options for running their mission-critical SAP systems in the cloud. We worked side-by-side with SAP to enable production deployments of HANA in 2014, and now offer a wide range of EC2 instances that are certified by SAP to run HANA.

Our goal is to make it as easy as possible to run HANA and to provide you with instance sizes that are a great fit for many different applications and installations. At the last SAPPHIRE NOW conference, we announced our plans to launch EC2 instances with 8 TB to 16 TB of memory. Today I would like to tell you a bit more about the specs and sizes for these instances.

We are planning to launch high-memory EC2 Bare Metal instances with 6 TB, 9 TB, and 12 TB of memory, designed from the ground up to run mission-critical deployments of SAP HANA. Like the existing Bare Metal instances, these instances allow the operating system to run directly on the underlying hardware while still providing access to all of the benefits of the cloud as full-fledged members of the EC2 family.

The instances run on an 8-socket platform built with Intel Xeon Scalable (Skylake) processors. They can be launched in a VPC, offer ENA-based Enhanced Networking and EBS-optimization by default, and are available on EC2 Dedicated Hosts. You will be able to launch them in all of the usual ways, and to use IAM to control authentication, authorization, and auditing. Instances will be able to make use of multiple EBS volumes, each storing up to 16 TB of data, for elastic capacity.

I did not have the opportunity to go hands-on with the new instances, but my colleagues shared a few screen shots with me! Here’s some of the output from dmesg on an instance with 6 TB of memory:

And here’s what lscpu displays:

We plan to make these instances available in private preview this summer, and to move them to general availability this fall. While 12 TB instances are certainly a big step forward, we don’t plan to stop there, and are working on even bigger ones — instances with more than 16 TB of memory are in the works as well!

If you would like to join the private preview for these new instances, please contact us.

Amazon AppStream 2.0 with SAP GUI
In other AWS / SAP news, you can now use Amazon AppStream 2.0 to visualize the SAP GUI in any browser that is HTML5-compatible.

This is a clean, simple, and efficient alternative to installing the SAP GUI on every desktop. Response time improves, as does user productivity, because less data moves between client and server. Replacing hundreds or thousands of installed copies of SAP GUI with a centrally managed image also reduces the overall management effort.

To learn more about this cool new way to make the SAP GUI available to your users, read Deploying SAP GUI on Amazon AppStream 2.0.

Say Hello at SAPPHIRE NOW
The AWS team will be in booth 642 at SAPPHIRE this week with a full set of sessions from our team, our customers, and our partners in our in-booth theater. Many of our customers will also be telling their stories during sessions throughout the event. A listing of available sessions and activities can be found here.

Jeff;

Categories: Cloud

EC2 Instance Update – M5 Instances with Local NVMe Storage (M5d)

AWS Blog - Mon, 06/04/2018 - 13:18

Earlier this month we launched the C5 Instances with Local NVMe Storage and I told you that we would be doing the same for additional instance types in the near future!

Today we are introducing M5 instances equipped with local NVMe storage. Available for immediate use in 5 regions, these instances are a great fit for workloads that require a balance of compute and memory resources. Here are the specs:

Instance Name vCPUs RAM Local Storage EBS-Optimized Bandwidth Network Bandwidth m5d.large 2 8 GiB 1 x 75 GB NVMe SSD Up to 2.120 Gbps Up to 10 Gbps m5d.xlarge 4 16 GiB 1 x 150 GB NVMe SSD Up to 2.120 Gbps Up to 10 Gbps m5d.2xlarge 8 32 GiB 1 x 300 GB NVMe SSD Up to 2.120 Gbps Up to 10 Gbps m5d.4xlarge 16 64 GiB 1 x 600 GB NVMe SSD 2.210 Gbps Up to 10 Gbps m5d.12xlarge 48 192 GiB 2 x 900 GB NVMe SSD 5.0 Gbps 10 Gbps m5d.24xlarge 96 384 GiB 4 x 900 GB NVMe SSD 10.0 Gbps 25 Gbps

The M5d instances are powered by Custom Intel® Xeon® Platinum 8175M series processors running at 2.5 GHz, including support for AVX-512.

You can use any AMI that includes drivers for the Elastic Network Adapter (ENA) and NVMe; this includes the latest Amazon Linux, Microsoft Windows (Server 2008 R2, Server 2012, Server 2012 R2 and Server 2016), Ubuntu, RHEL, SUSE, and CentOS AMIs.

Here are a couple of things to keep in mind about the local NVMe storage on the M5d instances:

Naming – You don’t have to specify a block device mapping in your AMI or during the instance launch; the local storage will show up as one or more devices (/dev/nvme*1 on Linux) after the guest operating system has booted.

Encryption – Each local NVMe device is hardware encrypted using the XTS-AES-256 block cipher and a unique key. Each key is destroyed when the instance is stopped or terminated.

Lifetime – Local NVMe devices have the same lifetime as the instance they are attached to, and do not stick around after the instance has been stopped or terminated.

Available Now
M5d instances are available in On-Demand, Reserved Instance, and Spot form in the US East (N. Virginia), US West (Oregon), EU (Ireland), US East (Ohio), and Canada (Central) Regions. Prices vary by Region, and are just a bit higher than for the equivalent M5 instances.

Jeff;

 

Categories: Cloud

AWS Online Tech Talks – June 2018

AWS Blog - Mon, 06/04/2018 - 11:01

AWS Online Tech Talks – June 2018

Join us this month to learn about AWS services and solutions. New this month, we have a fireside chat with the GM of Amazon WorkSpaces and our 2nd episode of the “How to re:Invent” series. We’ll also cover best practices, deep dives, use cases and more! Join us and register today!

Note – All sessions are free and in Pacific Time.

Tech talks featured this month:

 

Analytics & Big Data

June 18, 2018 | 11:00 AM – 11:45 AM PTGet Started with Real-Time Streaming Data in Under 5 Minutes – Learn how to use Amazon Kinesis to capture, store, and analyze streaming data in real-time including IoT device data, VPC flow logs, and clickstream data.
June 20, 2018 | 11:00 AM – 11:45 AM PT – Insights For Everyone – Deploying Data across your Organization – Learn how to deploy data at scale using AWS Analytics and QuickSight’s new reader role and usage based pricing.

 

AWS re:Invent
June 13, 2018 | 05:00 PM – 05:30 PM PTEpisode 2: AWS re:Invent Breakout Content Secret Sauce – Hear from one of our own AWS content experts as we dive deep into the re:Invent content strategy and how we maintain a high bar.
Compute

June 25, 2018 | 01:00 PM – 01:45 PM PTAccelerating Containerized Workloads with Amazon EC2 Spot Instances – Learn how to efficiently deploy containerized workloads and easily manage clusters at any scale at a fraction of the cost with Spot Instances.

June 26, 2018 | 01:00 PM – 01:45 PM PTEnsuring Your Windows Server Workloads Are Well-Architected – Get the benefits, best practices and tools on running your Microsoft Workloads on AWS leveraging a well-architected approach.

 

Containers
June 25, 2018 | 09:00 AM – 09:45 AM PTRunning Kubernetes on AWS – Learn about the basics of running Kubernetes on AWS including how setup masters, networking, security, and add auto-scaling to your cluster.

 

Databases

June 18, 2018 | 01:00 PM – 01:45 PM PTOracle to Amazon Aurora Migration, Step by Step – Learn how to migrate your Oracle database to Amazon Aurora.
DevOps

June 20, 2018 | 09:00 AM – 09:45 AM PTSet Up a CI/CD Pipeline for Deploying Containers Using the AWS Developer Tools – Learn how to set up a CI/CD pipeline for deploying containers using the AWS Developer Tools.

 

Enterprise & Hybrid
June 18, 2018 | 09:00 AM – 09:45 AM PTDe-risking Enterprise Migration with AWS Managed Services – Learn how enterprise customers are de-risking cloud adoption with AWS Managed Services.

June 19, 2018 | 11:00 AM – 11:45 AM PTLaunch AWS Faster using Automated Landing Zones – Learn how the AWS Landing Zone can automate the set up of best practice baselines when setting up new

 

AWS Environments

June 21, 2018 | 11:00 AM – 11:45 AM PTLeading Your Team Through a Cloud Transformation – Learn how you can help lead your organization through a cloud transformation.

June 21, 2018 | 01:00 PM – 01:45 PM PTEnabling New Retail Customer Experiences with Big Data – Learn how AWS can help retailers realize actual value from their big data and deliver on differentiated retail customer experiences.

June 28, 2018 | 01:00 PM – 01:45 PM PTFireside Chat: End User Collaboration on AWS – Learn how End User Compute services can help you deliver access to desktops and applications anywhere, anytime, using any device.
IoT

June 27, 2018 | 11:00 AM – 11:45 AM PTAWS IoT in the Connected Home – Learn how to use AWS IoT to build innovative Connected Home products.

 

Machine Learning

June 19, 2018 | 09:00 AM – 09:45 AM PTIntegrating Amazon SageMaker into your Enterprise – Learn how to integrate Amazon SageMaker and other AWS Services within an Enterprise environment.

June 21, 2018 | 09:00 AM – 09:45 AM PTBuilding Text Analytics Applications on AWS using Amazon Comprehend – Learn how you can unlock the value of your unstructured data with NLP-based text analytics.

 

Management Tools

June 20, 2018 | 01:00 PM – 01:45 PM PTOptimizing Application Performance and Costs with Auto Scaling – Learn how selecting the right scaling option can help optimize application performance and costs.

 

Mobile
June 25, 2018 | 11:00 AM – 11:45 AM PTDrive User Engagement with Amazon Pinpoint – Learn how Amazon Pinpoint simplifies and streamlines effective user engagement.

 

Security, Identity & Compliance

June 26, 2018 | 09:00 AM – 09:45 AM PTUnderstanding AWS Secrets Manager – Learn how AWS Secrets Manager helps you rotate and manage access to secrets centrally.
June 28, 2018 | 09:00 AM – 09:45 AM PTUsing Amazon Inspector to Discover Potential Security Issues – See how Amazon Inspector can be used to discover security issues of your instances.

 

Serverless

June 19, 2018 | 01:00 PM – 01:45 PM PTProductionize Serverless Application Building and Deployments with AWS SAM – Learn expert tips and techniques for building and deploying serverless applications at scale with AWS SAM.

 

Storage

June 26, 2018 | 11:00 AM – 11:45 AM PTDeep Dive: Hybrid Cloud Storage with AWS Storage Gateway – Learn how you can reduce your on-premises infrastructure by using the AWS Storage Gateway to connecting your applications to the scalable and reliable AWS storage services.
June 27, 2018 | 01:00 PM – 01:45 PM PTChanging the Game: Extending Compute Capabilities to the Edge – Discover how to change the game for IIoT and edge analytics applications with AWS Snowball Edge plus enhanced Compute instances.
June 28, 2018 | 11:00 AM – 11:45 AM PTBig Data and Analytics Workloads on Amazon EFS – Get best practices and deployment advice for running big data and analytics workloads on Amazon EFS.

Categories: Cloud

Some quick thoughts on the public discussion regarding facial recognition and Amazon Rekognition this past week

AWS Blog - Fri, 06/01/2018 - 13:47

We have seen a lot of discussion this past week about the role of Amazon Rekognition in facial recognition, surveillance, and civil liberties, and we wanted to share some thoughts.

Amazon Rekognition is a service we announced in 2016. It makes use of new technologies – such as deep learning – and puts them in the hands of developers in an easy-to-use, low-cost way. Since then, we have seen customers use the image and video analysis capabilities of Amazon Rekognition in ways that materially benefit both society (e.g. preventing human trafficking, inhibiting child exploitation, reuniting missing children with their families, and building educational apps for children), and organizations (enhancing security through multi-factor authentication, finding images more easily, or preventing package theft). Amazon Web Services (AWS) is not the only provider of services like these, and we remain excited about how image and video analysis can be a driver for good in the world, including in the public sector and law enforcement.

There have always been and will always be risks with new technology capabilities. Each organization choosing to employ technology must act responsibly or risk legal penalties and public condemnation. AWS takes its responsibilities seriously. But we believe it is the wrong approach to impose a ban on promising new technologies because they might be used by bad actors for nefarious purposes in the future. The world would be a very different place if we had restricted people from buying computers because it was possible to use that computer to do harm. The same can be said of thousands of technologies upon which we all rely each day. Through responsible use, the benefits have far outweighed the risks.

Customers are off to a great start with Amazon Rekognition; the evidence of the positive impact this new technology can provide is strong (and growing by the week), and we’re excited to continue to support our customers in its responsible use.

-Dr. Matt Wood, general manager of artificial intelligence at AWS

Categories: Cloud

Amazon SageMaker Updates – Tokyo Region, CloudFormation, Chainer, and GreenGrass ML

AWS Blog - Thu, 05/31/2018 - 18:35

Today, at the AWS Summit in Tokyo we announced a number of updates and new features for Amazon SageMaker. Starting today, SageMaker is available in Asia Pacific (Tokyo)! SageMaker also now supports CloudFormation. A new machine learning framework, Chainer, is now available in the SageMaker Python SDK, in addition to MXNet and Tensorflow. Finally, support for running Chainer models on several devices was added to AWS Greengrass Machine Learning.

Amazon SageMaker Chainer Estimator


Chainer is a popular, flexible, and intuitive deep learning framework. Chainer networks work on a “Define-by-Run” scheme, where the network topology is defined dynamically via forward computation. This is in contrast to many other frameworks which work on a “Define-and-Run” scheme where the topology of the network is defined separately from the data. A lot of developers enjoy the Chainer scheme since it allows them to write their networks with native python constructs and tools.

Luckily, using Chainer with SageMaker is just as easy as using a TensorFlow or MXNet estimator. In fact, it might even be a bit easier since it’s likely you can take your existing scripts and use them to train on SageMaker with very few modifications. With TensorFlow or MXNet users have to implement a train function with a particular signature. With Chainer your scripts can be a little bit more portable as you can simply read from a few environment variables like SM_MODEL_DIR, SM_NUM_GPUS, and others. We can wrap our existing script in a if __name__ == '__main__': guard and invoke it locally or on sagemaker.

import argparse import os if __name__ =='__main__': parser = argparse.ArgumentParser() # hyperparameters sent by the client are passed as command-line arguments to the script. parser.add_argument('--epochs', type=int, default=10) parser.add_argument('--batch-size', type=int, default=64) parser.add_argument('--learning-rate', type=float, default=0.05) # Data, model, and output directories parser.add_argument('--output-data-dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR']) parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR']) parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN']) parser.add_argument('--test', type=str, default=os.environ['SM_CHANNEL_TEST']) args, _ = parser.parse_known_args() # ... load from args.train and args.test, train a model, write model to args.model_dir.

Then, we can run that script locally or use the SageMaker Python SDK to launch it on some GPU instances in SageMaker. The hyperparameters will get passed in to the script as CLI commands and the environment variables above will be autopopulated. When we call fit the input channels we pass will be populated in the SM_CHANNEL_* environment variables.

from sagemaker.chainer.estimator import Chainer # Create my estimator chainer_estimator = Chainer( entry_point='example.py', train_instance_count=1, train_instance_type='ml.p3.2xlarge', hyperparameters={'epochs': 10, 'batch-size': 64} ) # Train my estimator chainer_estimator.fit({'train': train_input, 'test': test_input}) # Deploy my estimator to a SageMaker Endpoint and get a Predictor predictor = chainer_estimator.deploy( instance_type="ml.m4.xlarge", initial_instance_count=1 )

Now, instead of bringing your own docker container for training and hosting with Chainer, you can just maintain your script. You can see the full sagemaker-chainer-containers on github. One of my favorite features of the new container is built-in chainermn for easy multi-node distribution of your chainer training jobs.

There’s a lot more documentation and information available in both the README and the example notebooks.

AWS GreenGrass ML with Chainer

AWS GreenGrass ML now includes a pre-built Chainer package for all devices powered by Intel Atom, NVIDIA Jetson, TX2, and Raspberry Pi. So, now GreenGrass ML provides pre-built packages for TensorFlow, Apache MXNet, and Chainer! You can train your models on SageMaker then easily deploy it to any GreenGrass-enabled device using GreenGrass ML. You can find out more details in the post on the IoT Blog.

JAWS UG

I want to give a quick shout out to all of our wonderful and inspirational friends in the JAWS UG who attended the AWS Summit in Tokyo today. I’ve very much enjoyed seeing your pictures of the summit. Thanks for making Japan an amazing place for AWS developers! I can’t wait to visit again and meet with all of you.

Randall

Categories: Cloud

New – Pay-per-Session Pricing for Amazon QuickSight, Another Region, and Lots More

AWS Blog - Thu, 05/31/2018 - 10:31

Amazon QuickSight is a fully managed cloud business intelligence system that gives you Fast & Easy to Use Business Analytics for Big Data. QuickSight makes business analytics available to organizations of all shapes and sizes, with the ability to access data that is stored in your Amazon Redshift data warehouse, your Amazon Relational Database Service (RDS) relational databases, flat files in S3, and (via connectors) data stored in on-premises MySQL, PostgreSQL, and SQL Server databases. QuickSight scales to accommodate tens, hundreds, or thousands of users per organization.

Today we are launching a new, session-based pricing option for QuickSight, along with additional region support and other important new features. Let’s take a look at each one:

Pay-per-Session Pricing
Our customers are making great use of QuickSight and take full advantage of the power it gives them to connect to data sources, create reports, and and explore visualizations.

However, not everyone in an organization needs or wants such powerful authoring capabilities. Having access to curated data in dashboards and being able to interact with the data by drilling down, filtering, or slicing-and-dicing is more than adequate for their needs. Subscribing them to a monthly or annual plan can be seen as an unwarranted expense, so a lot of such casual users end up not having access to interactive data or BI.

In order to allow customers to provide all of their users with interactive dashboards and reports, the Enterprise Edition of Amazon QuickSight now allows Reader access to dashboards on a Pay-per-Session basis. QuickSight users are now classified as Admins, Authors, or Readers, with distinct capabilities and prices:

Authors have access to the full power of QuickSight; they can establish database connections, upload new data, create ad hoc visualizations, and publish dashboards, all for $9 per month (Standard Edition) or $18 per month (Enterprise Edition).

Readers can view dashboards, slice and dice data using drill downs, filters and on-screen controls, and download data in CSV format, all within the secure QuickSight environment. Readers pay $0.30 for 30 minutes of access, with a monthly maximum of $5 per reader.

Admins have all authoring capabilities, and can manage users and purchase SPICE capacity in the account. The QuickSight admin now has the ability to set the desired option (Author or Reader) when they invite members of their organization to use QuickSight. They can extend Reader invites to their entire user base without incurring any up-front or monthly costs, paying only for the actual usage.

To learn more, visit the QuickSight Pricing page.

A New Region
QuickSight is now available in the Asia Pacific (Tokyo) Region:

The UI is in English, with a localized version in the works.

Hourly Data Refresh
Enterprise Edition SPICE data sets can now be set to refresh as frequently as every hour. In the past, each data set could be refreshed up to 5 times a day. To learn more, read Refreshing Imported Data.

Access to Data in Private VPCs
This feature was launched in preview form late last year, and is now available in production form to users of the Enterprise Edition. As I noted at the time, you can use it to implement secure, private communication with data sources that do not have public connectivity, including on-premises data in Teradata or SQL Server, accessed over an AWS Direct Connect link. To learn more, read Working with AWS VPC.

Parameters with On-Screen Controls
QuickSight dashboards can now include parameters that are set using on-screen dropdown, text box, numeric slider or date picker controls. The default value for each parameter can be set based on the user name (QuickSight calls this a dynamic default). You could, for example, set an appropriate default based on each user’s office location, department, or sales territory. Here’s an example:

To learn more, read about Parameters in QuickSight.

URL Actions for Linked Dashboards
You can now connect your QuickSight dashboards to external applications by defining URL actions on visuals. The actions can include parameters, and become available in the Details menu for the visual. URL actions are defined like this:

You can use this feature to link QuickSight dashboards to third party applications (e.g. Salesforce) or to your own internal applications. Read Custom URL Actions to learn how to use this feature.

Dashboard Sharing
You can now share QuickSight dashboards across every user in an account.

Larger SPICE Tables
The per-data set limit for SPICE tables has been raised from 10 GB to 25 GB.

Upgrade to Enterprise Edition
The QuickSight administrator can now upgrade an account from Standard Edition to Enterprise Edition with a click. This enables provisioning of Readers with pay-per-session pricing, private VPC access, row-level security for dashboards and data sets, and hourly refresh of data sets. Enterprise Edition pricing applies after the upgrade.

Available Now
Everything I listed above is available now and you can start using it today!

You can try QuickSight for 60 days at no charge, and you can also attend our June 20th Webinar.

Jeff;

 

Categories: Cloud

Amazon Neptune Generally Available

AWS Blog - Wed, 05/30/2018 - 13:41

Amazon Neptune is now Generally Available in US East (N. Virginia), US East (Ohio), US West (Oregon), and EU (Ireland). Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. At the core of Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with millisecond latencies. Neptune supports two popular graph models, Property Graph and RDF, through Apache TinkerPop Gremlin and SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets. Neptune can be used to power everything from recommendation engines and knowledge graphs to drug discovery and network security. Neptune is fully-managed with automatic minor version upgrades, backups, encryption, and fail-over. I wrote about Neptune in detail for AWS re:Invent last year and customers have been using the preview and providing great feedback that the team has used to prepare the service for GA.

Now that Amazon Neptune is generally available there are a few changes from the preview:

Launching an Amazon Neptune Cluster

Launching a Neptune cluster is as easy as navigating to the AWS Management Console and clicking create cluster. Of course you can also launch with CloudFormation, the CLI, or the SDKs.

You can monitor your cluster health and the health of individual instances through Amazon CloudWatch and the console.

Additional Resources

We’ve created two repos with some additional tools and examples here. You can expect continuous development on these repos as we add additional tools and examples.

  • Amazon Neptune Tools Repo
    This repo has a useful tool for converting GraphML files into Neptune compatible CSVs for bulk loading from S3.
  • Amazon Neptune Samples Repo
    This repo has a really cool example of building a collaborative filtering recommendation engine for video game preferences.
Purpose Built Databases

There’s an industry trend where we’re moving more and more onto purpose-built databases. Developers and businesses want to access their data in the format that makes the most sense for their applications. As cloud resources make transforming large datasets easier with tools like AWS Glue, we have a lot more options than we used to for accessing our data. With tools like Amazon Redshift, Amazon Athena, Amazon Aurora, Amazon DynamoDB, and more we get to choose the best database for the job or even enable entirely new use-cases. Amazon Neptune is perfect for workloads where the data is highly connected across data rich edges.

I’m really excited about graph databases and I see a huge number of applications. Looking for ideas of cool things to build? I’d love to build a web crawler in AWS Lambda that uses Neptune as the backing store. You could further enrich it by running Amazon Comprehend or Amazon Rekognition on the text and images found and creating a search engine on top of Neptune.

As always, feel free to reach out in the comments or on twitter to provide any feedback!

Randall

Categories: Cloud

Simplify Login with Application Load Balancer Built-in Authentication

AWS Blog - Wed, 05/30/2018 - 13:28

Today I’m excited to announce built-in authentication support in Application Load Balancers (ALB). ALB can now securely authenticate users as they access applications, letting developers eliminate the code they have to write to support authentication and offload the responsibility of authentication from the backend. The team built a great live example where you can try out the authentication functionality.

Identity-based security is a crucial component of modern applications and as customers continue to move mission critical applications into the cloud, developers are asked to write the same authentication code again and again. Enterprises want to use their on-premises identities with their cloud applications. Web developers want to use federated identities from social networks to allow their users to sign-in. ALB’s new authentication action provides authentication through social Identity Providers (IdP) like Google, Facebook, and Amazon through Amazon Cognito. It also natively integrates with any OpenID Connect protocol compliant IdP, providing secure authentication and a single sign-on experience across your applications.

How Does ALB Authentication Work?

Authentication is a complicated topic and our readers may have differing levels of expertise with it. I want to cover a few key concepts to make sure we’re all on the same page. If you’re already an authentication expert and you just want to see how ALB authentication works feel free to skip to the next section!

  • Authentication verifies identity.
  • Authorization verifies permissions, the things an identity is allowed to do.
  • OpenID Connect (OIDC) is a simple identity, or authentication, layer built on top on top of the OAuth 2.0 protocol. The OIDC specification document is pretty well written and worth a casual read.
  • Identity Providers (IdPs) manage identity information and provide authentication services. ALB supports any OIDC compliant IdP and you can use a service like Amazon Cognito or Auth0 to aggregate different identities from various IdPs like Active Directory, LDAP, Google, Facebook, Amazon, or others deployed in AWS or on premises.

When we get away from the terminology for a bit, all of this boils down to figuring out who a user is and what they’re allowed to do. Doing this securely and efficiently is hard. Traditionally, enterprises have used a protocol called SAML with their IdPs, to provide a single sign-on (SSO) experience for their internal users. SAML is XML heavy and modern applications have started using OIDC with JSON mechanism to share claims. Developers can use SAML in ALB with Amazon Cognito’s SAML support. Web app or mobile developers typically use federated identities via social IdPs like Facebook, Amazon, or Google which, conveniently, are also supported by Amazon Cognito.

ALB Authentication works by defining an authentication action in a listener rule. The ALB’s authentication action will check if a session cookie exists on incoming requests, then check that it’s valid. If the session cookie is set and valid then the ALB will route the request to the target group with X-AMZN-OIDC-* headers set. The headers contain identity information in JSON Web Token (JWT) format, that a backend can use to identify a user. If the session cookie is not set or invalid then ALB will follow the OIDC protocol and issue an HTTP 302 redirect to the identity provider. The protocol is a lot to unpack and is covered more thoroughly in the documentation for those curious.

ALB Authentication Walkthrough

I have a simple Python flask app in an Amazon ECS cluster running in some AWS Fargate containers. The containers are in a target group routed to by an ALB. I want to make sure users of my application are logged in before accessing the authenticated portions of my application. First, I’ll navigate to the ALB in the console and edit the rules.

I want to make sure all access to /account* endpoints is authenticated so I’ll add new rule with a condition to match those endpoints.

Now, I’ll add a new rule and create an Authenticate action in that rule.

I’ll have ALB create a new Amazon Cognito user pool for me by providing some configuration details.


After creating the Amazon Cognito pool, I can make some additional configuration in the advanced settings.

I can change the default cookie name, adjust the timeout, adjust the scope, and choose the action for unauthenticated requests.

I can pick Deny to serve a 401 for all unauthenticated requests or I can pick Allow which will pass through to the application if unauthenticated. This is useful for Single Page Apps (SPAs). For now, I’ll choose Authenticate, which will prompt the IdP, in this case Amazon Cognito, to authenticate the user and reload the existing page.

 

Now I’ll add a forwarding action for my target group and save the rule.

Over on the Facebook side I just need to add my Amazon Cognito User Pool Domain to the whitelisted OAuth redirect URLs.

I would follow similar steps for other authentication providers.

Now, when I navigate to an authenticated page my Fargate containers receive the originating request with the X-Amzn-Oidc-* headers set by ALB. Using the information in those headers (claims-data, identity, access-token) my application can implement authorization.

All of this was possible without having to write a single line of code to deal with each of the IdPs. However, it’s still important for the implementing applications to verify the signature on the JWT header to ensure the request hasn’t been tampered with.

Additional Resources

Of course everything we’ve seen today is also available in the the API and AWS Command Line Interface (CLI). You can find additional information on the feature in the documentation. This feature is provided at no additional charge.

Be sure to check out the live example as well.

With authentication built-in to ALB, developers can focus on building their applications instead of rebuilding authentication for every application, all the while maintaining the scale, availability, and reliability of ALB. I think this feature is a pretty big deal and I can’t wait to see what customers build with it. Let us know what you think of this feature in the comments or on twitter!

Randall

Categories: Cloud

EC2 Instance Update – C5 Instances with Local NVMe Storage (C5d)

AWS Blog - Thu, 05/17/2018 - 18:05

As you can see from my EC2 Instance History post, we add new instance types on a regular and frequent basis. Driven by increasingly powerful processors and designed to address an ever-widening set of use cases, the size and diversity of this list reflects the equally diverse group of EC2 customers!

Near the bottom of that list you will find the new compute-intensive C5 instances. With a 25% to 50% improvement in price-performance over the C4 instances, the C5 instances are designed for applications like batch and log processing, distributed and or real-time analytics, high-performance computing (HPC), ad serving, highly scalable multiplayer gaming, and video encoding. Some of these applications can benefit from access to high-speed, ultra-low latency local storage. For example, video encoding, image manipulation, and other forms of media processing often necessitates large amounts of I/O to temporary storage. While the input and output files are valuable assets and are typically stored as Amazon Simple Storage Service (S3) objects, the intermediate files are expendable. Similarly, batch and log processing runs in a race-to-idle model, flushing volatile data to disk as fast as possible in order to make full use of compute resources.

New C5d Instances with Local Storage
In order to meet this need, we are introducing C5 instances equipped with local NVMe storage. Available for immediate use in 5 regions, these instances are a great fit for the applications that I described above, as well as others that you will undoubtedly dream up! Here are the specs:

Instance Name vCPUs RAM Local Storage EBS Bandwidth Network Bandwidth c5d.large 2 4 GiB 1 x 50 GB NVMe SSD Up to 2.25 Gbps Up to 10 Gbps c5d.xlarge 4 8 GiB 1 x 100 GB NVMe SSD Up to 2.25 Gbps Up to 10 Gbps c5d.2xlarge 8 16 GiB 1 x 225 GB NVMe SSD Up to 2.25 Gbps Up to 10 Gbps c5d.4xlarge 16 32 GiB 1 x 450 GB NVMe SSD 2.25 Gbps Up to 10 Gbps c5d.9xlarge 36 72 GiB 1 x 900 GB NVMe SSD 4.5 Gbps 10 Gbps c5d.18xlarge 72 144 GiB 2 x 900 GB NVMe SSD 9 Gbps 25 Gbps

Other than the addition of local storage, the C5 and C5d share the same specs. Both are powered by 3.0 GHz Intel Xeon Platinum 8000-series processors, optimized for EC2 and with full control over C-states on the two largest sizes, giving you the ability to run two cores at up to 3.5 GHz using Intel Turbo Boost Technology.

You can use any AMI that includes drivers for the Elastic Network Adapter (ENA) and NVMe; this includes the latest Amazon Linux, Microsoft Windows (Server 2008 R2, Server 2012, Server 2012 R2 and Server 2016), Ubuntu, RHEL, SUSE, and CentOS AMIs.

Here are a couple of things to keep in mind about the local NVMe storage:

Naming – You don’t have to specify a block device mapping in your AMI or during the instance launch; the local storage will show up as one or more devices (/dev/nvme*1 on Linux) after the guest operating system has booted.

Encryption – Each local NVMe device is hardware encrypted using the XTS-AES-256 block cipher and a unique key. Each key is destroyed when the instance is stopped or terminated.

Lifetime – Local NVMe devices have the same lifetime as the instance they are attached to, and do not stick around after the instance has been stopped or terminated.

Available Now
C5d instances are available in On-Demand, Reserved Instance, and Spot form in the US East (N. Virginia), US West (Oregon), EU (Ireland), US East (Ohio), and Canada (Central) Regions. Prices vary by Region, and are just a bit higher than for the equivalent C5 instances.

Jeff;

PS – We will be adding local NVMe storage to other EC2 instance types in the months to come, so stay tuned!

Categories: Cloud

AWS IoT 1-Click – Use Simple Devices to Trigger Lambda Functions

AWS Blog - Wed, 05/16/2018 - 10:25

We announced a preview of AWS IoT 1-Click at AWS re:Invent 2017 and have been refining it ever since, focusing on simplicity and a clean out-of-box experience. Designed to make IoT available and accessible to a broad audience, AWS IoT 1-Click is now generally available, along with new IoT buttons from AWS and AT&T.

I sat down with the dev team a month or two ago to learn about the service so that I could start thinking about my blog post. During the meeting they gave me a pair of IoT buttons and I started to think about some creative ways to put them to use. Here are a few that I came up with:

Help Request – Earlier this month I spent a very pleasant weekend at the HackTillDawn hackathon in Los Angeles. As the participants were hacking away, they occasionally had questions about AWS, machine learning, Amazon SageMaker, and AWS DeepLens. While we had plenty of AWS Solution Architects on hand (decked out in fashionable & distinctive AWS shirts for easy identification), I imagined an IoT button for each team. Pressing the button would alert the SA crew via SMS and direct them to the proper table.

Camera ControlTim Bray and I were in the AWS video studio, prepping for the first episode of Tim’s series on AWS Messaging. Minutes before we opened the Twitch stream I realized that we did not have a clean, unobtrusive way to ask the camera operator to switch to a closeup view. Again, I imagined that a couple of IoT buttons would allow us to make the request.

Remote Dog Treat Dispenser – My dog barks every time a stranger opens the gate in front of our house. While it is great to have confirmation that my Ring doorbell is working, I would like to be able to press a button and dispense a treat so that Luna stops barking!

Homes, offices, factories, schools, vehicles, and health care facilities can all benefit from IoT buttons and other simple IoT devices, all managed using AWS IoT 1-Click.

All About AWS IoT 1-Click
As I said earlier, we have been focusing on simplicity and a clean out-of-box experience. Here’s what that means:

Architects can dream up applications for inexpensive, low-powered devices.

Developers don’t need to write any device-level code. They can make use of pre-built actions, which send email or SMS messages, or write their own custom actions using AWS Lambda functions.

Installers don’t have to install certificates or configure cloud endpoints on newly acquired devices, and don’t have to worry about firmware updates.

Administrators can monitor the overall status and health of each device, and can arrange to receive alerts when a device nears the end of its useful life and needs to be replaced, using a single interface that spans device types and manufacturers.

I’ll show you how easy this is in just a moment. But first, let’s talk about the current set of devices that are supported by AWS IoT 1-Click.

Who’s Got the Button?
We’re launching with support for two types of buttons (both pictured above). Both types of buttons are pre-configured with X.509 certificates, communicate to the cloud over secure connections, and are ready to use.

The AWS IoT Enterprise Button communicates via Wi-Fi. It has a 2000-click lifetime, encrypts outbound data using TLS, and can be configured using BLE and our mobile app. It retails for $19.99 (shipping and handling not included) and can be used in the United States, Europe, and Japan.

The AT&T LTE-M Button communicates via the LTE-M cellular network. It has a 1500-click lifetime, and also encrypts outbound data using TLS. The device and the bundled data plan is available an an introductory price of $29.99 (shipping and handling not included), and can be used in the United States.

We are very interested in working with device manufacturers in order to make even more shapes, sizes, and types of devices (badge readers, asset trackers, motion detectors, and industrial sensors, to name a few) available to our customers. Our team will be happy to tell you about our provisioning tools and our facility for pushing OTA (over the air) updates to large fleets of devices; you can contact them at iot1click@amazon.com.

AWS IoT 1-Click Concepts
I’m eager to show you how to use AWS IoT 1-Click and the buttons, but need to introduce a few concepts first.

Device – A button or other item that can send messages. Each device is uniquely identified by a serial number.

Placement Template – Describes a like-minded collection of devices to be deployed. Specifies the action to be performed and lists the names of custom attributes for each device.

Placement – A device that has been deployed. Referring to placements instead of devices gives you the freedom to replace and upgrade devices with minimal disruption. Each placement can include values for custom attributes such as a location (“Building 8, 3rd Floor, Room 1337”) or a purpose (“Coffee Request Button”).

Action – The AWS Lambda function to invoke when the button is pressed. You can write a function from scratch, or you can make use of a pair of predefined functions that send an email or an SMS message. The actions have access to the attributes; you can, for example, send an SMS message with the text “Urgent need for coffee in Building 8, 3rd Floor, Room 1337.”

Getting Started with AWS IoT 1-Click
Let’s set up an IoT button using the AWS IoT 1-Click Console:

If I didn’t have any buttons I could click Buy devices to get some. But, I do have some, so I click Claim devices to move ahead. I enter the device ID or claim code for my AT&T button and click Claim (I can enter multiple claim codes or device IDs if I want):

The AWS buttons can be claimed using the console or the mobile app; the first step is to use the mobile app to configure the button to use my Wi-Fi:

Then I scan the barcode on the box and click the button to complete the process of claiming the device. Both of my buttons are now visible in the console:

I am now ready to put them to use. I click on Projects, and then Create a project:

I name and describe my project, and click Next to proceed:

Now I define a device template, along with names and default values for the placement attributes. Here’s how I set up a device template (projects can contain several, but I just need one):

The action has two mandatory parameters (phone number and SMS message) built in; I add three more (Building, Room, and Floor) and click Create project:

I’m almost ready to ask for some coffee! The next step is to associate my buttons with this project by creating a placement for each one. I click Create placements to proceed. I name each placement, select the device to associate with it, and then enter values for the attributes that I established for the project. I can also add additional attributes that are peculiar to this placement:

I can inspect my project and see that everything looks good:

I click on the buttons and the SMS messages appear:

I can monitor device activity in the AWS IoT 1-Click Console:

And also in the Lambda Console:

The Lambda function itself is also accessible, and can be used as-is or customized:

As you can see, this is the code that lets me use {{*}}include all of the placement attributes in the message and {{Building}} (for example) to include a specific placement attribute.

Now Available
I’ve barely scratched the surface of this cool new service and I encourage you to give it a try (or a click) yourself. Buy a button or two, build something cool, and let me know all about it!

Pricing is based on the number of enabled devices in your account, measured monthly and pro-rated for partial months. Devices can be enabled or disabled at any time. See the AWS IoT 1-Click Pricing page for more info.

To learn more, visit the AWS IoT 1-Click home page or read the AWS IoT 1-Click documentation.

Jeff;

 

Categories: Cloud

Amazon Sumerian – Now Generally Available

AWS Blog - Tue, 05/15/2018 - 10:55

We announced Amazon Sumerian at AWS re:Invent 2017. As you can see from Tara‘s blog post (Presenting Amazon Sumerian: An Easy Way to Create VR, AR, and 3D Experiences), Sumerian does not require any specialized programming or 3D graphics expertise. You can build VR, AR, and 3D experiences for a wide variety of popular hardware platforms including mobile devices, head-mounted displays, digital signs, and web browsers.

I’m happy to announce that Sumerian is now generally available. You can create realistic virtual environments and scenes without having to acquire or master specialized tools for 3D modeling, animation, lighting, audio editing, or programming. Once built, you can deploy your finished creation across multiple platforms without having to write custom code or deal with specialized deployment systems and processes.

Sumerian gives you a web-based editor that you can use to quickly and easily create realistic, professional-quality scenes. There’s a visual scripting tool that lets you build logic to control how objects and characters (Sumerian Hosts) respond to user actions. Sumerian also lets you create rich, natural interactions powered by AWS services such as Amazon Lex, Polly, AWS Lambda, AWS IoT, and Amazon DynamoDB.

Sumerian was designed to work on multiple platforms. The VR and AR apps that you create in Sumerian will run in browsers that supports WebGL or WebVR and on popular devices such as the Oculus Rift, HTC Vive, and those powered by iOS or Android.

During the preview period, we have been working with a broad spectrum of customers to put Sumerian to the test and to create proof of concept (PoC) projects designed to highlight an equally broad spectrum of use cases, including employee education, training simulations, field service productivity, virtual concierge, design and creative, and brand engagement. Fidelity Labs (the internal R&D unit of Fidelity Investments), was the first to use a Sumerian host to create an engaging VR experience. Cora (the host) lives within a virtual chart room. She can display stock quotes, pull up company charts, and answer questions about a company’s performance. This PoC uses Amazon Polly to implement text to speech and Amazon Lex for conversational chatbot functionality. Read their blog post and watch the video inside to see Cora in action:

Now that Sumerian is generally available, you have the power to create engaging AR, VR, and 3D experiences of your own. To learn more, visit the Amazon Sumerian home page and then spend some quality time with our extensive collection of Sumerian Tutorials.

Jeff;

 

Categories: Cloud

Amazon Aurora Backtrack – Turn Back Time

AWS Blog - Thu, 05/10/2018 - 02:54

We’ve all been there! You need to make a quick, seemingly simple fix to an important production database. You compose the query, give it a once-over, and let it run. Seconds later you realize that you forgot the WHERE clause, dropped the wrong table, or made another serious mistake, and interrupt the query, but the damage has been done. You take a deep breath, whistle through your teeth, wish that reality came with an Undo option. Now what?

New Amazon Aurora Backtrack
Today I would like to tell you about the new backtrack feature for Amazon Aurora. This is as close as we can come, given present-day technology, to an Undo option for reality.

This feature can be enabled at launch time for all newly-launched Aurora database clusters. To enable it, you simply specify how far back in time you might want to rewind, and use the database as usual (this is on the Configure advanced settings page):

Aurora uses a distributed, log-structured storage system (read Design Considerations for High Throughput Cloud-Native Relational Databases to learn a lot more); each change to your database generates a new log record, identified by a Log Sequence Number (LSN). Enabling the backtrack feature provisions a FIFO buffer in the cluster for storage of LSNs. This allows for quick access and recovery times measured in seconds.

After that regrettable moment when all seems lost, you simply pause your application, open up the Aurora Console, select the cluster, and click Backtrack DB cluster:

Then you select Backtrack and choose the point in time just before your epic fail, and click Backtrack DB cluster:

Then you wait for the rewind to take place, unpause your application and proceed as if nothing had happened. When you initiate a backtrack, Aurora will pause the database, close any open connections, drop uncommitted writes, and wait for the backtrack to complete. Then it will resume normal operation and being to accept requests. The instance state will be backtracking while the rewind is underway:

The console will let you know when the backtrack is complete:

If it turns out that you went back a bit too far, you can backtrack to a later time. Other Aurora features such as cloning, backups, and restores continue to work on an instance that has been configured for backtrack.

I’m sure you can think of some creative and non-obvious use cases for this cool new feature. For example, you could use it to restore a test database after running a test that makes changes to the database. You can initiate the restoration from the API or the CLI, making it easy to integrate into your existing test framework.

Things to Know
This option applies to newly created MySQL-compatible Aurora database clusters and to MySQL-compatible clusters that have been restored from a backup. You must opt-in when you create or restore a cluster; you cannot enable it for a running cluster.

This feature is available now in all AWS Regions where Amazon Aurora runs, and you can start using it today.

Jeff;

Categories: Cloud

Introducing the AWS Machine Learning Competency for Consulting Partners

AWS Blog - Thu, 05/10/2018 - 02:37

Today I’m excited to announce a new Machine Learning Competency for Consulting Partners in the Amazon Partner Network (APN). This AWS Competency program allows APN Consulting Partners to demonstrate a deep expertise in machine learning on AWS by providing solutions that enable machine learning and data science workflows for their customers. This new AWS Competency is in addition to the Machine Learning comptency for our APN Technology Partners, that we launched at the re:Invent 2017 partner summit.

These APN Consulting Partners help organizations solve their machine learning and data challenges through:

  • Providing data services that help data scientists and machine learning practitioners prepare their enterprise data for training.
  • Platform solutions that provide data scientists and machine learning practitioners with tools to take their data, train models, and make predictions on new data.
  • SaaS and API solutions to enable predictive capabilities within customer applications.
Why work with an AWS Machine Learning Competency Partner?

The AWS Competency Program helps customers find the most qualified partners with deep expertise. AWS Machine Learning Competency Partners undergo a strict validation of their capabilities to demonstrate technical proficiency and proven customer success with AWS machine learning tools.

If you’re an AWS customer interested in machine learning workloads on AWS, check out our AWS Machine Learning launch partners below:

 

Interested in becoming an AWS Machine Learning Competency Partner?

APN Partners with experience in Machine Learning can learn more about becoming an AWS Machine Learning Competency Partner here. To learn more about the benefits of joining the AWS Partner Network, see our APN Partner website.

Thanks to the AWS Partner Team for their help with this post!
Randall

Categories: Cloud

AWS Online Tech Talks – May and Early June 2018

AWS Blog - Wed, 05/09/2018 - 18:13

AWS Online Tech Talks – May and Early June 2018  

Join us this month to learn about some of the exciting new services and solution best practices at AWS. We also have our first re:Invent 2018 webinar series, “How to re:Invent”. Sign up now to learn more, we look forward to seeing you.

Note – All sessions are free and in Pacific Time.

Tech talks featured this month:

Analytics & Big Data

May 21, 2018 | 11:00 AM – 11:45 AM PT Integrating Amazon Elasticsearch with your DevOps Tooling – Learn how you can easily integrate Amazon Elasticsearch Service into your DevOps tooling and gain valuable insight from your log data.

May 23, 2018 | 11:00 AM – 11:45 AM PTData Warehousing and Data Lake Analytics, Together – Learn how to query data across your data warehouse and data lake without moving data.

May 24, 2018 | 11:00 AM – 11:45 AM PTData Transformation Patterns in AWS – Discover how to perform common data transformations on the AWS Data Lake.

Compute

May 29, 2018 | 01:00 PM – 01:45 PM PT – Creating and Managing a WordPress Website with Amazon Lightsail – Learn about Amazon Lightsail and how you can create, run and manage your WordPress websites with Amazon’s simple compute platform.

May 30, 2018 | 01:00 PM – 01:45 PM PTAccelerating Life Sciences with HPC on AWS – Learn how you can accelerate your Life Sciences research workloads by harnessing the power of high performance computing on AWS.

Containers

May 24, 2018 | 01:00 PM – 01:45 PM PT – Building Microservices with the 12 Factor App Pattern on AWS – Learn best practices for building containerized microservices on AWS, and how traditional software design patterns evolve in the context of containers.

Databases

May 21, 2018 | 01:00 PM – 01:45 PM PTHow to Migrate from Cassandra to Amazon DynamoDB – Get the benefits, best practices and guides on how to migrate your Cassandra databases to Amazon DynamoDB.

May 23, 2018 | 01:00 PM – 01:45 PM PT5 Hacks for Optimizing MySQL in the Cloud – Learn how to optimize your MySQL databases for high availability, performance, and disaster resilience using RDS.

DevOps

May 23, 2018 | 09:00 AM – 09:45 AM PT.NET Serverless Development on AWS – Learn how to build a modern serverless application in .NET Core 2.0.

Enterprise & Hybrid

May 22, 2018 | 11:00 AM – 11:45 AM PTHybrid Cloud Customer Use Cases on AWS – Learn how customers are leveraging AWS hybrid cloud capabilities to easily extend their datacenter capacity, deliver new services and applications, and ensure business continuity and disaster recovery.

IoT

May 31, 2018 | 11:00 AM – 11:45 AM PTUsing AWS IoT for Industrial Applications – Discover how you can quickly onboard your fleet of connected devices, keep them secure, and build predictive analytics with AWS IoT.

Machine Learning

May 22, 2018 | 09:00 AM – 09:45 AM PTUsing Apache Spark with Amazon SageMaker – Discover how to use Apache Spark with Amazon SageMaker for training jobs and application integration.

May 24, 2018 | 09:00 AM – 09:45 AM PTIntroducing AWS DeepLens – Learn how AWS DeepLens provides a new way for developers to learn machine learning by pairing the physical device with a broad set of tutorials, examples, source code, and integration with familiar AWS services.

Management Tools

May 21, 2018 | 09:00 AM – 09:45 AM PTGaining Better Observability of Your VMs with Amazon CloudWatch – Learn how CloudWatch Agent makes it easy for customers like Rackspace to monitor their VMs.

Mobile

May 29, 2018 | 11:00 AM – 11:45 AM PT – Deep Dive on Amazon Pinpoint Segmentation and Endpoint Management – See how segmentation and endpoint management with Amazon Pinpoint can help you target the right audience.

Networking

May 31, 2018 | 09:00 AM – 09:45 AM PTMaking Private Connectivity the New Norm via AWS PrivateLink – See how PrivateLink enables service owners to offer private endpoints to customers outside their company.

Security, Identity, & Compliance

May 30, 2018 | 09:00 AM – 09:45 AM PT – Introducing AWS Certificate Manager Private Certificate Authority (CA) – Learn how AWS Certificate Manager (ACM) Private Certificate Authority (CA), a managed private CA service, helps you easily and securely manage the lifecycle of your private certificates.

June 1, 2018 | 09:00 AM – 09:45 AM PTIntroducing AWS Firewall Manager – Centrally configure and manage AWS WAF rules across your accounts and applications.

Serverless

May 22, 2018 | 01:00 PM – 01:45 PM PTBuilding API-Driven Microservices with Amazon API Gateway – Learn how to build a secure, scalable API for your application in our tech talk about API-driven microservices.

Storage

May 30, 2018 | 11:00 AM – 11:45 AM PTAccelerate Productivity by Computing at the Edge – Learn how AWS Snowball Edge support for compute instances helps accelerate data transfers, execute custom applications, and reduce overall storage costs.

June 1, 2018 | 11:00 AM – 11:45 AM PTLearn to Build a Cloud-Scale Website Powered by Amazon EFS – Technical deep dive where you’ll learn tips and tricks for integrating WordPress, Drupal and Magento with Amazon EFS.

 

 

 

 

Categories: Cloud

Pages

Subscribe to LAMP, Database and Cloud Technical Information aggregator - Cloud


Main menu 2

by Dr. Radut