Tag Archives: artificial intelligence

Marvellous retrofitted home assistants

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/retrofitted-home-assistants/

As more and more digital home assistants are appearing on the consumer market, it’s not uncommon to see the towering Amazon Echo or sleek Google Home when visiting friends or family. But we, the maker community, are rarely happy unless our tech stands out from the rest. So without further ado, here’s a roundup of some fantastic retrofitted home assistant projects you can recreate and give pride of place in your kitchen, on your bookshelf, or wherever else you’d like to talk to your virtual, disembodied PA.

Google AIY Robot Conversion

Turned an 80s Tomy Mr Money into a little Google AIY / Raspberry Pi based assistant.

Matt ‘Circuitbeard’ Brailsford’s Tomy Mr Money Google AIY Assistant is just one of many home-brew home assistants makers have built since the release of APIs for Amazon Alexa and Google Home. Here are some more…

Teddy Ruxpin

Oh Teddy, how exciting and mysterious you were when I unwrapped you back in the mideighties. With your awkwardly moving lips and twitching eyelids, you were the cream of the crop of robotic toys! How was I to know that during my thirties, you would become augmented with home assistant software and suddenly instil within me a fear unlike any I’d felt before? (Save for my lifelong horror of ET…)

Alexa Ruxpin – Raspberry Pi & Alexa Powered Teddy Bear

Please watch: “DIY Fidget LED Display – Part 1” https://www.youtube.com/watch?v=FAZIc82Duzk -~-~~-~~~-~~-~- There are tons of virtual assistants out on the market: Siri, Ok Google, Alexa, etc. I had this crazy idea…what if I made the virtual assistant real…kinda. I decided to take an old animatronic teddy bear and hack it so that it ran Amazon Alexa.

Several makers around the world have performed surgery on Teddy to install a Raspberry Pi within his stomach and integrate him with Amazon Alexa Voice or Google’s AIY Projects Voice kit. And because these makers are talented, they’ve also managed to hijack Teddy’s wiring to make his lips move in time with his responses to your commands. Freaky…

Speaking of freaky: check out Zack’s Furlexa — an Amazon Alexa Furby that will haunt your nightmares.

Give old tech new life

Devices that were the height of technology when you purchased them may now be languishing in your attic collecting dust. With new and improved versions of gadgets and gizmos being released almost constantly, it is likely that your household harbours a spare whosit or whatsit which you can dismantle and give a new Raspberry Pi heart and purpose.

Take, for example, Martin Mander’s Google Pi intercom. By gutting and thoroughly cleaning a vintage intercom, Martin fashioned a suitable housing the Google AIY Projects Voice kit to create a new home assistant for his house:

1986 Google Pi Intercom

This is a 1986 Radio Shack Intercom that I’ve converted into a Google Home style device using a Raspberry Pi and the Google AIY (Artificial Intelligence Yourself) kit that came free with the MagPi magazine (issue 57). It uses the Google Assistant to answer questions and perform actions, using IFTTT to integrate with smart home accessories and other web services.

Not only does this build look fantastic, it’s also a great conversation starter for any visitors who had a similar device during the eighties.

Also take a look at Martin’s 1970s Amazon Alexa phone for more nostalgic splendour.

Put it in a box

…and then I’ll put that box inside of another box, and then I’ll mail that box to myself, and when it arrives…

A GIF from the emperors new groove - Raspberry Pi Home Assistant

A GIF. A harmless, little GIF…and proof of the comms team’s obsession with The Emperor’s New Groove.

You don’t have to be fancy when it comes to housing your home assistant. And often, especially if you’re working with the smaller people in your household, the results of a simple homespun approach are just as delightful.

Here are Hannah and her dad Tom, explaining how they built a home assistant together and fit it inside an old cigar box:

Raspberry Pi 3 Amazon Echo – The Alexa Kids Build!

My 7 year old daughter and I decided to play around with the Raspberry Pi and build ourselves an Amazon Echo (Alexa). The video tells you about what we did and the links below will take you to all the sites we used to get this up and running.

Also see the Google AIY Projects Voice kit — the cardboard box-est of home assistant boxes.

Make your own home assistant

And now it’s your turn! I challenge you all (and also myself) to create a home assistant using the Raspberry Pi. Whether you decide to fit Amazon Alexa inside an old shoebox or Google Home inside your sister’s Barbie, I’d love to see what you create using the free home assistant software available online.

Check out these other home assistants for Raspberry Pi, and keep an eye on our blog to see what I manage to create as part of the challenge.

Ten virtual house points for everyone who shares their build with us online, either in the comments below or by tagging us on your social media account.

The post Marvellous retrofitted home assistants appeared first on Raspberry Pi.

AWS Contributes to Milestone 1.0 Release and Adds Model Serving Capability for Apache MXNet

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/aws-contributes-to-milestone-1-0-release-and-adds-model-serving-capability-for-apache-mxnet/

Post by Dr. Matt Wood

Today AWS announced contributions to the milestone 1.0 release of the Apache MXNet deep learning engine including the introduction of a new model-serving capability for MXNet. The new capabilities in MXNet provide the following benefits to users:

1) MXNet is easier to use: The model server for MXNet is a new capability introduced by AWS, and it packages, runs, and serves deep learning models in seconds with just a few lines of code, making them accessible over the internet via an API endpoint and thus easy to integrate into applications. The 1.0 release also includes an advanced indexing capability that enables users to perform matrix operations in a more intuitive manner.

  • Model Serving enables set up of an API endpoint for prediction: It saves developers time and effort by condensing the task of setting up an API endpoint for running and integrating prediction functionality into an application to just a few lines of code. It bridges the barrier between Python-based deep learning frameworks and production systems through a Docker container-based deployment model.
  • Advanced indexing for array operations in MXNet: It is now more intuitive for developers to leverage the powerful array operations in MXNet. They can use the advanced indexing capability by leveraging existing knowledge of NumPy/SciPy arrays. For example, it supports MXNet NDArray and Numpy ndarray as index, e.g. (a[mx.nd.array([1,2], dtype = ‘int32’]).

2) MXNet is faster: The 1.0 release includes implementation of cutting-edge features that optimize the performance of training and inference. Gradient compression enables users to train models up to five times faster by reducing communication bandwidth between compute nodes without loss in convergence rate or accuracy. For speech recognition acoustic modeling like the Alexa voice, this feature can reduce network bandwidth by up to three orders of magnitude during training. With the support of NVIDIA Collective Communication Library (NCCL), users can train a model 20% faster on multi-GPU systems.

  • Optimize network bandwidth with gradient compression: In distributed training, each machine must communicate frequently with others to update the weight-vectors and thereby collectively build a single model, leading to high network traffic. Gradient compression algorithm enables users to train models up to five times faster by compressing the model changes communicated by each instance.
  • Optimize the training performance by taking advantage of NCCL: NCCL implements multi-GPU and multi-node collective communication primitives that are performance optimized for NVIDIA GPUs. NCCL provides communication routines that are optimized to achieve high bandwidth over interconnection between multi-GPUs. MXNet supports NCCL to train models about 20% faster on multi-GPU systems.

3) MXNet provides easy interoperability: MXNet now includes a tool for converting neural network code written with the Caffe framework to MXNet code, making it easier for users to take advantage of MXNet’s scalability and performance.

  • Migrate Caffe models to MXNet: It is now possible to easily migrate Caffe code to MXNet, using the new source code translation tool for converting Caffe code to MXNet code.

MXNet has helped developers and researchers make progress with everything from language translation to autonomous vehicles and behavioral biometric security. We are excited to see the broad base of users that are building production artificial intelligence applications powered by neural network models developed and trained with MXNet. For example, the autonomous driving company TuSimple recently piloted a self-driving truck on a 200-mile journey from Yuma, Arizona to San Diego, California using MXNet. This release also includes a full-featured and performance optimized version of the Gluon programming interface. The ease-of-use associated with it combined with the extensive set of tutorials has led significant adoption among developers new to deep learning. The flexibility of the interface has driven interest within the research community, especially in the natural language processing domain.

Getting started with MXNet
Getting started with MXNet is simple. To learn more about the Gluon interface and deep learning, you can reference this comprehensive set of tutorials, which covers everything from an introduction to deep learning to how to implement cutting-edge neural network models. If you’re a contributor to a machine learning framework, check out the interface specs on GitHub.

To get started with the Model Server for Apache MXNet, install the library with the following command:

$ pip install mxnet-model-server

The Model Server library has a Model Zoo with 10 pre-trained deep learning models, including the SqueezeNet 1.1 object classification model. You can start serving the SqueezeNet model with just the following command:

$ mxnet-model-server \
  --models squeezenet=https://s3.amazonaws.com/model-server/models/squeezenet_v1.1/squeezenet_v1.1.model \
  --service dms/model_service/mxnet_vision_service.py

Learn more about the Model Server and view the source code, reference examples, and tutorials here: https://github.com/awslabs/mxnet-model-server/

-Dr. Matt Wood

Amazon EC2 Bare Metal Instances with Direct Access to Hardware

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-amazon-ec2-bare-metal-instances-with-direct-access-to-hardware/

When customers come to us with new and unique requirements for AWS, we listen closely, ask lots of questions, and do our best to understand and address their needs. When we do this, we make the resulting service or feature generally available; we do not build one-offs or “snowflakes” for individual customers. That model is messy and hard to scale and is not the way we work.

Instead, every AWS customer has access to whatever it is that we build, and everyone benefits. VMware Cloud on AWS is a good example of this strategy in action. They told us that they wanted to run their virtualization stack directly on the hardware, within the AWS Cloud, giving their customers access to the elasticity, security, and reliability (not to mention the broad array of services) that AWS offers.

We knew that other customers also had interesting use cases for bare metal hardware and didn’t want to take the performance hit of nested virtualization. They wanted access to the physical resources for applications that take advantage of low-level hardware features such as performance counters and Intel® VT that are not always available or fully supported in virtualized environments, and also for applications intended to run directly on the hardware or licensed and supported for use in non-virtualized environments.

Our multi-year effort to move networking, storage, and other EC2 features out of our virtualization platform and into dedicated hardware was already well underway and provided the perfect foundation for a possible solution. This work, as I described in Now Available – Compute-Intensive C5 Instances for Amazon EC2, includes a set of dedicated hardware accelerators.

Now that we have provided VMware with the bare metal access that they requested, we are doing the same for all AWS customers. I’m really looking forward to seeing what you can do with them!

New Bare Metal Instances
Today we are launching a public preview the i3.metal instance, the first in a series of EC2 instances that offer the best of both worlds, allowing the operating system to run directly on the underlying hardware while still providing access to all of the benefits of the cloud. The instance gives you direct access to the processor and other hardware, and has the following specifications:

  • Processing – Two Intel Xeon E5-2686 v4 processors running at 2.3 GHz, with a total of 36 hyperthreaded cores (72 logical processors).
  • Memory – 512 GiB.
  • Storage – 15.2 terabytes of local, SSD-based NVMe storage.
  • Network – 25 Gbps of ENA-based enhanced networking.

Bare Metal instances are full-fledged members of the EC2 family and can take advantage of Elastic Load Balancing, Auto Scaling, Amazon CloudWatch, Auto Recovery, and so forth. They can also access the full suite of AWS database, IoT, mobile, analytics, artificial intelligence, and security services.

Previewing Now
We are launching a public preview of the Bare Metal instances today; please sign up now if you want to try them out.

You can now bring your specialized applications or your own stack of virtualized components to AWS and run them on Bare Metal instances. If you are using or thinking about using containers, these instances make a great host for CoreOS.

An AMI that works on one of the new C5 instances should also work on an I3 Bare Metal Instance. It must have the ENA and NVMe drivers, and must be tagged for ENA.

Jeff;

 

Presenting Amazon Sumerian: An easy way to create VR, AR, and 3D experiences

Post Syndicated from Tara Walker original https://aws.amazon.com/blogs/aws/launch-presenting-amazon-sumerian/

If you have had an opportunity to read any of my blog posts or attended any session I’ve conducted at various conferences, you are probably aware that I am definitively a geek girl. I am absolutely enamored with all of the latest advancements that have been made in technology areas like cloud, artificial intelligence, internet of things and the maker space, as well as, with virtual reality and augmented reality. In my opinion, it is a wonderful time to be a geek. All the things that we dreamed about building while we sweated through our algorithms and discrete mathematics classes or the technology we marveled at when watching Star Wars and Star Trek are now coming to fruition.  So hopefully this means it will only be a matter of time before I can hyperdrive to other galaxies in space, but until then I can at least build the 3D virtual reality and augmented reality characters and images like those featured in some of my favorite shows.

Amazon Sumerian provides tools and resources that allows anyone to create and run augmented reality (AR), virtual reality (VR), and 3D applications with ease.  With Sumerian, you can build multi-platform experiences that run on hardware like the Oculus, HTC Vive, and iOS devices using WebVR compatible browsers and with support for ARCore on Android devices coming soon.

This exciting new service, currently in preview, delivers features to allow you to design highly immersive and interactive 3D experiences from your browser. Some of these features are:

  • Editor: A web-based editor for constructing 3D scenes, importing assets, scripting interactions and special effects, with cross-platform publishing.
  • Object Library: a library of pre-built objects and templates.
  • Asset Import: Upload 3D assets to use in your scene. Sumerian supports importing FBX, OBJ, and coming soon Unity projects.
  • Scripting Library: provides a JavaScript scripting library via its 3D engine for advanced scripting capabilities.
  • Hosts: animated, lifelike 3D characters that can be customized for gender, voice, and language.
  • AWS Services Integration: baked in integration with Amazon Polly and Amazon Lex to add speech and natural language to into Sumerian hosts. Additionally, the scripting library can be used with AWS Lambda allowing use of the full range of AWS services.

Since Amazon Sumerian doesn’t require you to have 3D graphics or programming experience to build rich, interactive VR and AR scenes, let’s take a quick run to the Sumerian Dashboard and check it out.

From the Sumerian Dashboard, I can easily create a new scene with a push of a button.

A default view of the new scene opens and is displayed in the Sumerian Editor. With the Tara Blog Scene opened in the editor, I can easily import assets into my scene.

I’ll click the Import Asset button and pick an asset, View Room, to import into the scene. With the desired asset selected, I’ll click the Add button to import it.

Excellent, my asset was successfully imported into the Sumerian Editor and is shown in the Asset panel.  Now, I have the option to add the View Room object into my scene by selecting it in the Asset panel and then dragging it onto the editor’s canvas.

I’ll repeat the import asset process and this time I will add the Mannequin asset to the scene.

Additionally, with Sumerian, I can add scripting to Entity assets to make my scene even more exciting by adding a ScriptComponent to an entity and creating a script.  I can use the provided built-in scripts or create my own custom scripts. If I create a new custom script, I will get a blank script with some base JavaScript code that looks similar to the code below.

'use strict';
/* global sumerian */
//This is Me-- trying out the custom scripts - Tara

var setup = function (args, ctx) {
// Called when play mode starts.
};
var fixedUpdate = function (args, ctx) {
// Called on every physics update, after setup().
};
var update = function (args, ctx) {
// Called on every render frame, after setup().
};
var lateUpdate = function (args, ctx) {
// Called after all script "update" methods in the scene has been called.
};
var cleanup = function (args, ctx) {
// Called when play mode stops.
};
var parameters = [];

Very cool, I just created a 3D scene using Amazon Sumerian in a matter of minutes and I have only scratched the surface.

Summary

The Amazon Sumerian service enables you to create, build, and run virtual reality (VR), augmented reality (AR), and 3D applications with ease.  You don’t need any 3D graphics or specialized programming knowledge to get started building scenes and immersive experiences.  You can import FBX, OBJ, and Unity projects in Sumerian, as well as upload your own 3D assets for use in your scene. In addition, you can create digital characters to narrate your scene and with these digital assets, you have choices for the character’s appearance, speech and behavior.

You can learn more about Amazon Sumerian and sign up for the preview to get started with the new service on the product page.  I can’t wait to see what rich experiences you all will build.

Tara

 

How to Recover From Ransomware

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/complete-guide-ransomware/

Here’s the scenario. You’re working on your computer and you notice that it seems slower. Or perhaps you can’t access document or media files that were previously available.

You might be getting error messages from Windows telling you that a file is of an “Unknown file type” or “Windows can’t open this file.”

Windows error message

If you’re on a Mac, you might see the message “No associated application,” or “There is no application set to open the document.”

MacOS error message

Another possibility is that you’re completely locked out of your system. If you’re in an office, you might be looking around and seeing that other people are experiencing the same problem. Some are already locked out, and others are just now wondering what’s going on, just as you are.

Then you see a message confirming your fears.

wana decrypt0r ransomware message

You’ve been infected with ransomware.

You’ll have lots of company this year. The number of ransomware attacks on businesses tripled in the past year, jumping from one attack every two minutes in Q1 to one every 40 seconds by Q3.There were over four times more new ransomware variants in the first quarter of 2017 than in the first quarter of 2016, and damages from ransomware are expected to exceed $5 billion this year.

Growth in Ransomware Variants Since December 2015

Source: Proofpoint Q1 2017 Quarterly Threat Report

This past summer, our local PBS and NPR station in San Francisco, KQED, was debilitated for weeks by a ransomware attack that forced them to go back to working the way they used to prior to computers. Five months have passed since the attack and they’re still recovering and trying to figure out how to prevent it from happening again.

How Does Ransomware Work?

Ransomware typically spreads via spam or phishing emails, but also through websites or drive-by downloads, to infect an endpoint and penetrate the network. Once in place, the ransomware then locks all files it can access using strong encryption. Finally, the malware demands a ransom (typically payable in bitcoins) to decrypt the files and restore full operations to the affected IT systems.

Encrypting ransomware or “cryptoware” is by far the most common recent variety of ransomware. Other types that might be encountered are:

  • Non-encrypting ransomware or lock screens (restricts access to files and data, but does not encrypt them)
  • Ransomware that encrypts the Master Boot Record (MBR) of a drive or Microsoft’s NTFS, which prevents victims’ computers from being booted up in a live OS environment
  • Leakware or extortionware (exfiltrates data that the attackers threaten to release if ransom is not paid)
  • Mobile Device Ransomware (infects cell-phones through “drive-by downloads” or fake apps)

The typical steps in a ransomware attack are:

1
Infection
After it has been delivered to the system via email attachment, phishing email, infected application or other method, the ransomware installs itself on the endpoint and any network devices it can access.
2
Secure Key Exchange
The ransomware contacts the command and control server operated by the cybercriminals behind the attack to generate the cryptographic keys to be used on the local system.
3
Encryption
The ransomware starts encrypting any files it can find on local machines and the network.
4
Extortion
With the encryption work done, the ransomware displays instructions for extortion and ransom payment, threatening destruction of data if payment is not made.
5
Unlocking
Organizations can either pay the ransom and hope for the cybercriminals to actually decrypt the affected files (which in many cases does not happen), or they can attempt recovery by removing infected files and systems from the network and restoring data from clean backups.

Who Gets Attacked?

Ransomware attacks target firms of all sizes — 5% or more of businesses in the top 10 industry sectors have been attacked — and no no size business, from SMBs to enterprises, are immune. Attacks are on the rise in every sector and in every size of business.

Recent attacks, such as WannaCry earlier this year, mainly affected systems outside of the United States. Hundreds of thousands of computers were infected from Taiwan to the United Kingdom, where it crippled the National Health Service.

The US has not been so lucky in other attacks, though. The US ranks the highest in the number of ransomware attacks, followed by Germany and then France. Windows computers are the main targets, but ransomware strains exist for Macintosh and Linux, as well.

The unfortunate truth is that ransomware has become so wide-spread that for most companies it is a certainty that they will be exposed to some degree to a ransomware or malware attack. The best they can do is to be prepared and understand the best ways to minimize the impact of ransomware.

“Ransomware is more about manipulating vulnerabilities in human psychology than the adversary’s technological sophistication.” — James Scott, expert in Artificial Intelligence

Phishing emails, malicious email attachments, and visiting compromised websites have been common vehicles of infection (we wrote about protecting against phishing recently), but other methods have become more common in past months. Weaknesses in Microsoft’s Server Message Block (SMB) and Remote Desktop Protocol (RDP) have allowed cryptoworms to spread. Desktop applications — in one case an accounting package — and even Microsoft Office (Microsoft’s Dynamic Data Exchange — DDE) have been the agents of infection.

Recent ransomware strains such as Petya, CryptoLocker, and WannaCry have incorporated worms to spread themselves across networks, earning the nickname, “cryptoworms.”

How to Defeat Ransomware

1
Isolate the Infection
Prevent the infection from spreading by separating all infected computers from each other, shared storage, and the network.
2
Identify the Infection
From messages, evidence on the computer, and identification tools, determine which malware strain you are dealing with.
3
Report
Report to the authorities to support and coordinate measures to counter attacks.
4
Determine Your Options
You have a number of ways to deal with the infection. Determine which approach is best for you.
5
Restore and Refresh
Use safe backups and program and software sources to restore your computer or outfit a new platform.
6
Plan to Prevent Recurrence
Make an assessment of how the infection occurred and what you can do to put measures into place that will prevent it from happening again.

1 — Isolate the Infection

The rate and speed of ransomware detection is critical in combating fast moving attacks before they succeed in spreading across networks and encrypting vital data.

The first thing to do when a computer is suspected of being infected is to isolate it from other computers and storage devices. Disconnect it from the network (both wired and Wi-Fi) and from any external storage devices. Cryptoworms actively seek out connections and other computers, so you want to prevent that happening. You also don’t want the ransomware communicating across the network with its command and control center.

Be aware that there may be more than just one patient zero, meaning that the ransomware may have entered your organization or home through multiple computers, or may be dormant and not yet shown itself on some systems. Treat all connected and networked computers with suspicion and apply measures to ensure that all systems are not infected.

This Week in Tech (TWiT.tv) did a videocast showing what happens when WannaCry is released on an isolated system and encrypts files and trys to spread itself to other computers. It’s a great lesson on how these types of cryptoworms operate.

2 — Identify the Infection

Most often the ransomware will identify itself when it asks for ransom. There are numerous sites that help you identify the ransomware, including ID Ransomware. The No More Ransomware! Project provides the Crypto Sheriff to help identify ransomware.

Identifying the ransomware will help you understand what type of ransomware you have, how it propagates, what types of files it encrypts, and maybe what your options are for removal and disinfection. It also will enable you to report the attack to the authorities, which is recommended.

wanna decryptor 2.0 ransomware message

WannaCry Ransomware Extortion Dialog

3 — Report to the Authorities

You’ll be doing everyone a favor by reporting all ransomware attacks to the authorities. The FBI urges ransomware victims to report ransomware incidents regardless of the outcome. Victim reporting provides law enforcement with a greater understanding of the threat, provides justification for ransomware investigations, and contributes relevant information to ongoing ransomware cases. Knowing more about victims and their experiences with ransomware will help the FBI to determine who is behind the attacks and how they are identifying or targeting victims.

You can file a report with the FBI at the Internet Crime Complaint Center.

There are other ways to report ransomware, as well.

4 — Determine Your Options

Your options when infected with ransomware are:

  1. Pay the ransom
  2. Try to remove the malware
  3. Wipe the system(s) and reinstall from scratch

It’s generally considered a bad idea to pay the ransom. Paying the ransom encourages more ransomware, and in most cases the unlocking of the encrypted files is not successful.

In a recent survey, more than three-quarters of respondents said their organization is not at all likely to pay the ransom in order to recover their data (77%). Only a small minority said they were willing to pay some ransom (3% of companies have already set up a Bitcoin account in preparation).

Even if you decide to pay, it’s very possible you won’t get back your data.

5 — Restore or Start Fresh

You have the choice of trying to remove the malware from your systems or wiping your systems and reinstalling from safe backups and clean OS and application sources.

Get Rid of the Infection

There are internet sites and software packages that claim to be able to remove ransomware from systems. The No More Ransom! Project is one. Other options can be found, as well.

Whether you can successfully and completely remove an infection is up for debate. A working decryptor doesn’t exist for every known ransomware, and unfortunately it’s true that the newer the ransomware, the more sophisticated it’s likely to be and a perhaps a decryptor has not yet been created.

It’s Best to Wipe All Systems Completely

The surest way of being certain that malware or ransomware has been removed from a system is to do a complete wipe of all storage devices and reinstall everything from scratch. If you’ve been following a sound backup strategy, you should have copies of all your documents, media, and important files right up to the time of the infection.

Be sure to determine as well as you can from file dates and other information what was the date of infection. Consider that an infection might have been dormant in your system for a while before it activated and made significant changes to your system. Identifying and learning about the particular malware that attacked your systems will enable you to understand how that malware operates and what your best strategy should be for restoring your systems.

Backblaze Backup enables you to go back in time and specify the date prior to which you wish to restore files. That date should precede the date your system was infected.

Choose files to restore from earlier date in Backblaze Backup

If you’ve been following a good backup policy with both local and off-site backups, you should be able to use backup copies that you are sure were not connected to your network after the time of attack and hence protected from infection. Backup drives that were completely disconnected should be safe, as are files stored in the cloud, as with Backblaze Backup.

System Restores Are not the Best Strategy for Dealing with Ransomware and Malware

You might be tempted to use a System Restore point to get your system back up and running. System Restore is not a good solution for removing viruses or other malware. Since malicious software is typically buried within all kinds of places on a system, you can’t rely on System Restore being able to root out all parts of the malware. Instead, you should rely on a quality virus scanner that you keep up to date. Also, System Restore does not save old copies of your personal files as part of its snapshot. It also will not delete or replace any of your personal files when you perform a restoration, so don’t count on System Restore as working like a backup. You should always have a good backup procedure in place for all your personal files.

Local backups can be encrypted by ransomware. If your backup solution is local and connected to a computer that gets hit with ransomware, the chances are good your backups will be encrypted along with the rest of your data.

With a good backup solution that is isolated from your local computers, such as Backblaze Backup, you can easily obtain the files you need to get your system working again. You have the flexility to determine which files to restore, from which date you want to restore, and how to obtain the files you need to restore your system.

Choose how to obtain your backup files

You’ll need to reinstall your OS and software applications from the source media or the internet. If you’ve been managing your account and software credentials in a sound manner, you should be able to reactivate accounts for applications that require it.

If you use a password manager, such as 1Password or LastPass, to store your account numbers, usernames, passwords, and other essential information, you can access that information through their web interface or mobile applications. You just need to be sure that you still know your master username and password to obtain access to these programs.

6 — How to Prevent a Ransomware Attack

“Ransomware is at an unprecedented level and requires international investigation.” — European police agency EuroPol

A ransomware attack can be devastating for a home or a business. Valuable and irreplaceable files can be lost and tens or even hundreds of hours of effort can be required to get rid of the infection and get systems working again.

Security experts suggest several precautionary measures for preventing a ransomware attack.

  1. Use anti-virus and anti-malware software or other security policies to block known payloads from launching.
  2. Make frequent, comprehensive backups of all important files and isolate them from local and open networks. Cybersecurity professionals view data backup and recovery (74% in a recent survey) by far as the most effective solution to respond to a successful ransomware attack.
  3. Keep offline backups of data stored in locations inaccessible from any potentially infected computer, such as external storage drives or the cloud, which prevents them from being accessed by the ransomware.
  4. Install the latest security updates issued by software vendors of your OS and applications. Remember to Patch Early and Patch Often to close known vulnerabilities in operating systems, browsers, and web plugins.
  5. Consider deploying security software to protect endpoints, email servers, and network systems from infection.
  6. Exercise cyber hygiene, such as using caution when opening email attachments and links.
  7. Segment your networks to keep critical computers isolated and to prevent the spread of malware in case of attack. Turn off unneeded network shares.
  8. Turn off admin rights for users who don’t require them. Give users the lowest system permissions they need to do their work.
  9. Restrict write permissions on file servers as much as possible.
  10. Educate yourself, your employees, and your family in best practices to keep malware out of your systems. Update everyone on the latest email phishing scams and human engineering aimed at turning victims into abettors.

It’s clear that the best way to respond to a ransomware attack is to avoid having one in the first place. Other than that, making sure your valuable data is backed up and unreachable by ransomware infection will ensure that your downtime and data loss will be minimal or avoided completely.

Have you endured a ransomware attack or have a strategy to avoid becoming a victim? Please let us know in the comments.

The post How to Recover From Ransomware appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Amazon and Microsoft Ramp Up their AI Efforts

Post Syndicated from Chris De Santis original https://www.anchor.com.au/blog/2017/11/amazon-microsoft-ai-efforts/

In recent years, Amazon Web Services and Microsoft have shifted to more of a focus on artificial intelligence (AI), and as a result, have vastly increased their investment in the implementation and advancement of AI over their fleets of online products and services.

Recently, at Sydney’s Microsoft Summit (16-17 November 2017), Microsoft announced that they have stepped up their investment in the AI arena by releasing and showcasing new tools to enhance human and organisational capabilities. These tools include Visual Studio Tools for AI, Azure IoT Edge, Microsoft Translator, and Seeing AI.

The reasoning that Microsoft gave to justify their focus is that the popularity of AI has been steadily increasing, which can be attributed to three main factors: cloud computing, powerful algorithms, and multitudes of data.

Amazon, on the other hand, are planning to introduce a number of new AI products into the market, in order to catch up to the likes of Microsoft and Google. Through a project codenamed “Ironman”, AWS are working internally as well as partnering with startups to take a few-years-old AWS data warehousing service and gearing it to organise data for workloads employing machine learning techniques.

For more information more information on recent AI endeavours in the cloud computing market, check out our previous article below:

AI in the Cloud Market: AWS & Microsoft Lend a Big Hand

Amazon and Microsoft Ramp Up their AI Efforts

The post Amazon and Microsoft Ramp Up their AI Efforts appeared first on AWS Managed Services by Anchor.

Community Profile: Matthew Timmons-Brown

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/community-profile-matthew-timmons-brown/

This column is from The MagPi issue 57. You can download a PDF of the full issue for free, or subscribe to receive the print edition in your mailbox or the digital edition on your tablet. All proceeds from the print and digital editions help the Raspberry Pi Foundation achieve its charitable goals.

“I first set up my YouTube channel because I noticed a massive lack of video tutorials for the Raspberry Pi,” explains Matthew Timmons-Brown, known to many as The Raspberry Pi Guy. At 18 years old, the Cambridge-based student has more than 60 000 subscribers to his channel, making his account the most successful Raspberry Pi–specific YouTube account to date.

Matthew Timmons-Brown

Matt gives a talk at the Raspberry Pi 5th Birthday weekend event

The Raspberry Pi Guy

If you’ve attended a Raspberry Pi event, there’s a good chance you’ve already met Matt. And if not, you’ll have no doubt come across one or more of his tutorials and builds online. On more than one occasion, his work has featured on the Raspberry Pi blog, with his yearly Raspberry Pi roundup videos being a staple of the birthday celebrations.

Matthew Timmons-Brown

With his website, Matt aimed to collect together “the many strands of The Raspberry Pi Guy” into one, neat, cohesive resource — and it works. From newcomers to the credit card-sized computer to hardened Pi veterans, The Raspberry Pi Guy offers aid and inspiration for many. Looking for a review of the Raspberry Pi Zero W? He’s filmed one. Looking for a step-by-step guide to building a Pi-powered Amazon Alexa? No problem, there’s one of those too.

Make your Raspberry Pi artificially intelligent! – Amazon Alexa Personal Assistant Tutorial

Artificial Intelligence. A hefty topic that has dominated the field since computers were first conceived. What if I told you that you could put an artificial intelligence service on your own $30 computer?! That’s right! In this tutorial I will show you how to create your own artificially intelligent personal assistant, using Amazon’s Alexa voice recognition and information service!

Raspberry Pi electric skateboard

Last summer, Matt introduced the world to his Raspberry Pi-controlled electric skateboard, soon finding himself plastered over local press as well as the BBC and tech sites like Adafruit and geek.com. And there’s no question as to why the build was so popular. With YouTubers such as Casey Neistat increasing the demand for electric skateboards on a near-daily basis, the call for a cheaper, home-brew version has quickly grown.

DIY 30KM/H ELECTRIC SKATEBOARD – RASPBERRY PI/WIIMOTE POWERED

Over the summer, I made my own electric skateboard using a £4 Raspberry Pi Zero. Controlled with a Nintendo Wiimote, capable of going 30km/h, and with a range of over 10km, this project has been pretty darn fun. In this video, you see me racing around Cambridge and I explain the ins and outs of this project.

Using a Raspberry Pi Zero, a Nintendo Wii Remote, and a little help from members of the Cambridge Makespace community, Matt built a board capable of reaching 30km/h, with a battery range of 10km per charge. Alongside Neistat, Matt attributes the project inspiration to Australian student Tim Maier, whose build we previously covered in The MagPi.

Matthew Timmons-Brown and Eben Upton standing in a car park looking at a smartphone

LiDAR

Despite the success and the fun of the electric skateboard (including convincing Raspberry Pi Trading CEO Eben Upton to have a go for local television news coverage), the project Matt is most proud of is his wireless LiDAR system for theoretical use on the Mars rovers.

Matthew Timmons-Brown's LiDAR project for scanning terrains with lasers

Using a tablet app to define the angles, Matt’s A Level coursework LiDAR build scans the surrounding area, returning the results to the touchscreen, where they can be manipulated by the user. With his passion for the cosmos and the International Space Station, it’s no wonder that this is Matt’s proudest build.

Built for his A Level Computer Science coursework, the build demonstrates Matt’s passion for space and physics. Used as a means of surveying terrain, LiDAR uses laser light to measure distance, allowing users to create 3D-scanned, high-resolution maps of a specific area. It is a perfect technology for exploring unknown worlds.

Matthew Timmons-Brown and two other young people at a reception in the Houses of Parliament

Matt was invited to St James’s Palace and the Houses of Parliament as part of the Raspberry Pi community celebrations in 2016

Joining the community

In a recent interview at Hills Road Sixth Form College, where he is studying mathematics, further mathematics, physics, and computer science, Matt revealed where his love of electronics and computer science started. “I originally became interested in computer science in 2012, when I read a tiny magazine article about a computer that I would be able to buy with pocket money. This was a pretty exciting thing for a 12-year-old! Your own computer… for less than £30?!” He went on to explain how it became his mission to learn all he could on the subject and how, months later, his YouTube channel came to life, cementing him firmly into the Raspberry Pi community

The post Community Profile: Matthew Timmons-Brown appeared first on Raspberry Pi.

AWS Online Tech Talks – November 2017

Post Syndicated from Sara Rodas original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-november-2017/

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

November 2017 – Schedule

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

Webinars featured this month are:

Monday, November 6

Compute

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

Tuesday, November 7

Big Data

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

Compute

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

Mobile

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

Wednesday, November 8

Databases

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

Compute

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

Thursday, November 9

Databases

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

Containers

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

Big Data

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

Monday, November 13

re:Invent

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

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

Tuesday, November 14

AI

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

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

IoT

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

Enterprise

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

Wednesday, November 15

Security & Identity

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

Storage

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

Hands on Lab

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

Thursday, November 16

Serverless

9:00 – 9:40 AM PDT: Building Serverless Websites with [email protected]

Hands on Lab

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

– Sara

Linux Foundation debuts Community Data License Agreement

Post Syndicated from jake original https://lwn.net/Articles/737212/rss

The Linux Foundation has announced a pair of licenses for data that are modeled on the two broad categories of free-software licenses: permissive and copyleft. The Community Data License Agreement (CDLA) comes in two flavors: Sharing that “encourages contributions of data back to the data community” and Permissive that allows the data to be used without any further requirements.

Inspired by the collaborative software development models of open source software, the CDLA licenses are designed to enable individuals and organizations of all types to share data as easily as they currently share open source software code. Soundly drafted licensing models can help people form communities to assemble, curate and maintain vast amounts of data, measured in petabytes and exabytes, to bring new value to communities of all types, to build new business opportunities and to power new applications that promise to enhance safety and services.
The growth of big data analytics, machine learning and artificial intelligence (AI) technologies has allowed people to extract unprecedented levels of insight from data. Now the challenge is to assemble the critical mass of data for those tools to analyze. The CDLA licenses are designed to help governments, academic institutions, businesses and other organizations open up and share data, with the goal of creating communities that curate and share data openly.

AI in the Cloud Market: AWS & Microsoft Lend a Big Hand

Post Syndicated from Chris De Santis original https://www.anchor.com.au/blog/2017/10/aws-microsoft-launch-ai-platform/

Artificial intelligence (or AI) doesn’t necessarily play a big role in the current cloud hosting market, but Amazon Web Services (AWS) and Microsoft are looking to change that.

AI is starting to grow at an alarming rate and may be a significant role-player in the near future. According to Bernie Trudel, chairman of the Asia Cloud Computing Association (ACCA), AI “will become the killer application that will drive cloud computing forward”. He continues to mention that, although AI only accounts for 1% of the today’s global cloud computing market, its overall IT market share is growing at 52%, and its expected to rapidly grow to 10% of cloud revenue by 2025.

Trudel made notable that, although the big players in the cloud game are currently offering AI capabilities, the cloud-based AI market is still in its early stages. These big players include AWS, Microsoft, Google, and IBM. He also continues to state that AWS is certainly the leader in the cloud market, but they’re playing catch-up in terms of an AI perspective.

AWS 💘 Microsoft?

Here’s the funny bit–that a day or two after Trudel said all of this at Cloud Expo Asia, AWS announce (on their blog) their combined effort with Microsoft to create a new open-source deep-learning interface that “allows developers to more easily and quickly build machine learning models”. In other words, Gluon is an AI application for developers to create their own AI models, to the benefit of their own cloud applications and technical endeavours.

If you’d like to learn more about Gluon and the details of the project, head over to the AWS blog here.

AWS + Microsoft

 

The post AI in the Cloud Market: AWS & Microsoft Lend a Big Hand appeared first on AWS Managed Services by Anchor.

Introducing Gluon: a new library for machine learning from AWS and Microsoft

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/introducing-gluon-a-new-library-for-machine-learning-from-aws-and-microsoft/

Post by Dr. Matt Wood

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

Gluon Logo

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

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

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

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

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

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

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

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

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

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

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

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

-Dr. Matt Wood

Things Go Better With Step Functions

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/things-go-better-with-step-functions/

I often give presentations on Amazon’s culture of innovation, and start out with a slide that features a revealing quote from Amazon founder Jeff Bezos:

I love to sit down with our customers and to learn how we have empowered their creativity and to pursue their dreams. Earlier this year I chatted with Patrick from The Coca-Cola Company in order to learn how they used AWS Step Functions and other AWS services to support the Coke.com Vending Pass program. This program includes drink rewards earned by purchasing products at vending machines equipped to support mobile payments using the Coca-Cola Vending Pass. Participants swipe their NFC-enabled phones to complete an Apple Pay or Android Pay purchase, identifying themselves to the vending machine and earning credit towards future free vending purchases in the process

After the swipe, a combination of SNS topics and AWS Lambda functions initiated a pair of calls to some existing backend code to count the vending points and update the participant’s record. Unfortunately, the backend code was slow to react and had some timing dependencies, leading to missing updates that had the potential to confuse Vending Pass participants. The initial solution to this issue was very simple: modify the Lambda code to include a 90 second delay between the two calls. This solved the problem, but ate up process time for no good reason (billing for the use of Lambda functions is based on the duration of the request, in 100 ms intervals).

In order to make their solution more cost-effective, the team turned to AWS Step Functions, building a very simple state machine. As I wrote in an earlier blog post, Step Functions coordinate the components of distributed applications and microservices at scale, using visual workflows that are easy to build.

Coke built a very simple state machine to simplify their business logic and reduce their costs. Yours can be equally simple, or they can make use of other Step Function features such as sequential and parallel execution and the ability to make decisions and choose alternate states. The Coke state machine looks like this:

The FirstState and the SecondState states (Task states) call the appropriate Lambda functions while Step Functions implements the 90 second delay (a Wait state). This modification simplified their logic and reduced their costs. Here’s how it all fits together:

 

What’s Next
This initial success led them to take a closer look at serverless computing and to consider using it for other projects. Patrick told me that they have already seen a boost in productivity and developer happiness. Developers no longer need to wait for servers to be provisioned, and can now (as Jeff says) unleash their creativity and pursue their dreams. They expect to use Step Functions to improve the scalability, functionality, and reliability of their applications, going far beyond the initial use for the Coca-Cola Vending Pass. For example, Coke has built a serverless solution for publishing nutrition information to their food service partners using Lambda, Step Functions, and API Gateway.

Patrick and his team are now experimenting with machine learning and artificial intelligence. They built a prototype application to analyze a stream of photos from Instagram and extract trends in tastes and flavors. The application (built as a quick, one-day prototype) made use of Lambda, Amazon DynamoDB, Amazon API Gateway, and Amazon Rekognition and was, in Patrick’s words, a “big win and an enabler.”

In order to build serverless applications even more quickly, the development team has created an internal CI/CD reference architecture that builds on the Serverless Application Framework. The architecture includes a guided tour of Serverless and some boilerplate code to access internal services and assets. Patrick told me that this model allows them to easily scale promising projects from “a guy with a computer” to an entire development team.

Patrick will be on stage at AWS re:Invent next to my colleague Tim Bray. To meet them in person, be sure to attend SRV306 – State Machines in the Wild! How Customers Use AWS Step Functions.

Jeff;

Natural Language Processing at Clemson University – 1.1 Million vCPUs & EC2 Spot Instances

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/natural-language-processing-at-clemson-university-1-1-million-vcpus-ec2-spot-instances/

My colleague Sanjay Padhi shared the guest post below in order to recognize an important milestone in the use of EC2 Spot Instances.

Jeff;


A group of researchers from Clemson University achieved a remarkable milestone while studying topic modeling, an important component of machine learning associated with natural language processing, breaking the record for creating the largest high-performance cluster by using more than 1,100,000 vCPUs on Amazon EC2 Spot Instances running in a single AWS region. The researchers conducted nearly half a million topic modeling experiments to study how human language is processed by computers. Topic modeling helps in discovering the underlying themes that are present across a collection of documents. Topic models are important because they are used to forecast business trends and help in making policy or funding decisions. These topic models can be run with many different parameters and the goal of the experiments is to explore how these parameters affect the model outputs.

The Experiment
Professor Amy Apon, Co-Director of the Complex Systems, Analytics and Visualization Institute at Clemson University with Professor Alexander Herzog and graduate students Brandon Posey and Christopher Gropp in collaboration with members of the AWS team as well as AWS Partner Omnibond performed the experiments.  They used software infrastructure based on CloudyCluster that provisions high performance computing clusters on dynamically allocated AWS resources using Amazon EC2 Spot Fleet. Spot Fleet is a collection of biddable spot instances in EC2 responsible for maintaining a target capacity specified during the request. The SLURM scheduler was used as an overlay virtual workload manager for the data analytics workflows. The team developed additional provisioning and workflow automation software as shown below for the design and orchestration of the experiments. This setup allowed them to evaluate various topic models on different data sets with massively parallel parameter sweeps on dynamically allocated AWS resources. This framework can easily be used beyond the current study for other scientific applications that use parallel computing.

Ramping to 1.1 Million vCPUs
The figure below shows elastic, automatic expansion of resources as a function of time, in the US East (Northern Virginia) Region. At just after 21:40 (GMT-1) on Aug. 26, 2017, the number of vCPUs utilized was 1,119,196. Clemson researchers also took advantage of the new per-second billing for the EC2 instances that they launched. The vCPU count usage is comparable to the core count on the largest supercomputers in the world.

Here’s the breakdown of the EC2 instance types that they used:

Campus resources at Clemson funded by the National Science Foundation were used to determine an effective configuration for the AWS experiments as compared to campus resources, and the AWS cloud resources complement the campus resources for large-scale experiments.

Meet the Team
Here’s the team that ran the experiment (Professor Alexander Herzog, graduate students Christopher Gropp and Brandon Posey, and Professor Amy Apon):

Professor Apon said about the experiment:

I am absolutely thrilled with the outcome of this experiment. The graduate students on the project are amazing. They used resources from AWS and Omnibond and developed a new software infrastructure to perform research at a scale and time-to-completion not possible with only campus resources. Per-second billing was a key enabler of these experiments.

Boyd Wilson (CEO, Omnibond, member of the AWS Partner Network) told me:

Participating in this project was exciting, seeing how the Clemson team developed a provisioning and workflow automation tool that tied into CloudyCluster to build a huge Spot Fleet supercomputer in a single region in AWS was outstanding.

About the Experiment
The experiments test parameter combinations on a range of topics and other parameters used in the topic model. The topic model outputs are stored in Amazon S3 and are currently being analyzed. The models have been applied to 17 years of computer science journal abstracts (533,560 documents and 32,551,540 words) and full text papers from the NIPS (Neural Information Processing Systems) Conference (2,484 documents and 3,280,697 words). This study allows the research team to systematically measure and analyze the impact of parameters and model selection on model convergence, topic composition and quality.

Looking Forward
This study constitutes an interaction between computer science, artificial intelligence, and high performance computing. Papers describing the full study are being submitted for peer-reviewed publication. I hope that you enjoyed this brief insight into the ways in which AWS is helping to break the boundaries in the frontiers of natural language processing!

Sanjay Padhi, Ph.D, AWS Research and Technical Computing

 

An intro to machine learning (opensource.com)

Post Syndicated from corbet original https://lwn.net/Articles/734319/rss

Ulrich Drepper, once again an engineer at Red Hat, writes
about machine learning
on opensource.com.
Machine learning and artificial intelligence (ML/AI) mean different
things to different people, but the newest approaches have one thing in
common: They are based on the idea that a program’s output should be
created mostly automatically from a high-dimensional and possibly huge
dataset, with minimal or no intervention or guidance from a human. Open
source tools are used in a variety of machine learning and artificial
intelligence projects. In this article, I’ll provide an overview of the
state of machine learning today.

An intro to machine learning (Opensource.com)

Post Syndicated from corbet original https://lwn.net/Articles/734319/rss

Ulrich Drepper, once again an engineer at Red Hat, writes
about machine learning
on opensource.com.
Machine learning and artificial intelligence (ML/AI) mean different
things to different people, but the newest approaches have one thing in
common: They are based on the idea that a program’s output should be
created mostly automatically from a high-dimensional and possibly huge
dataset, with minimal or no intervention or guidance from a human. Open
source tools are used in a variety of machine learning and artificial
intelligence projects. In this article, I’ll provide an overview of the
state of machine learning today.

Simplify Your Jenkins Builds with AWS CodeBuild

Post Syndicated from Paul Roberts original https://aws.amazon.com/blogs/devops/simplify-your-jenkins-builds-with-aws-codebuild/

Jeff Bezos famously said, “There’s a lot of undifferentiated heavy lifting that stands between your idea and that success.” He went on to say, “…70% of your time, energy, and dollars go into the undifferentiated heavy lifting and only 30% of your energy, time, and dollars gets to go into the core kernel of your idea.”

If you subscribe to this maxim, you should not be spending valuable time focusing on operational issues related to maintaining the Jenkins build infrastructure. Companies such as Riot Games have over 1.25 million builds per year and have written several lengthy blog posts about their experiences designing a complex, custom Docker-powered Jenkins build farm. Dealing with Jenkins slaves at scale is a job in itself and Riot has engineers focused on managing the build infrastructure.

Typical Jenkins Build Farm

 

As with all technology, the Jenkins build farm architectures have evolved. Today, instead of manually building your own container infrastructure, there are Jenkins Docker plugins available to help reduce the operational burden of maintaining these environments. There is also a community-contributed Amazon EC2 Container Service (Amazon ECS) plugin that helps remove some of the overhead, but you still need to configure and manage the overall Amazon ECS environment.

There are various ways to create and manage your Jenkins build farm, but there has to be a way that significantly reduces your operational overhead.

Introducing AWS CodeBuild

AWS CodeBuild is a fully managed build service that removes the undifferentiated heavy lifting of provisioning, managing, and scaling your own build servers. With CodeBuild, there is no software to install, patch, or update. CodeBuild scales up automatically to meet the needs of your development teams. In addition, CodeBuild is an on-demand service where you pay as you go. You are charged based only on the number of minutes it takes to complete your build.

One AWS customer, Recruiterbox, helps companies hire simply and predictably through their software platform. Two years ago, they began feeling the operational pain of maintaining their own Jenkins build farms. They briefly considered moving to Amazon ECS, but chose an even easier path forward instead. Recuiterbox transitioned to using Jenkins with CodeBuild and are very happy with the results. You can read more about their journey here.

Solution Overview: Jenkins and CodeBuild

To remove the heavy lifting from managing your Jenkins build farm, AWS has developed a Jenkins AWS CodeBuild plugin. After the plugin has been enabled, a developer can configure a Jenkins project to pick up new commits from their chosen source code repository and automatically run the associated builds. After the build is successful, it will create an artifact that is stored inside an S3 bucket that you have configured. If an error is detected somewhere, CodeBuild will capture the output and send it to Amazon CloudWatch logs. In addition to storing the logs on CloudWatch, Jenkins also captures the error so you do not have to go hunting for log files for your build.

 

AWS CodeBuild with Jenkins Plugin

 

The following example uses AWS CodeCommit (Git) as the source control management (SCM) and Amazon S3 for build artifact storage. Logs are stored in CloudWatch. A development pipeline that uses Jenkins with CodeBuild plugin architecture looks something like this:

 

AWS CodeBuild Diagram

Initial Solution Setup

To keep this blog post succinct, I assume that you are using the following components on AWS already and have applied the appropriate IAM policies:

·         AWS CodeCommit repo.

·         Amazon S3 bucket for CodeBuild artifacts.

·         SNS notification for text messaging of the Jenkins admin password.

·         IAM user’s key and secret.

·         A role that has a policy with these permissions. Be sure to edit the ARNs with your region, account, and resource name. Use this role in the AWS CloudFormation template referred to later in this post.

 

Jenkins Installation with CodeBuild Plugin Enabled

To make the integration with Jenkins as frictionless as possible, I have created an AWS CloudFormation template here: https://s3.amazonaws.com/proberts-public/jenkins.yaml. Download the template, sign in the AWS CloudFormation console, and then use the template to create a stack.

 

CloudFormation Inputs

Jenkins Project Configuration

After the stack is complete, log in to the Jenkins EC2 instance using the user name “admin” and the password sent to your mobile device. Now that you have logged in to Jenkins, you need to create your first project. Start with a Freestyle project and configure the parameters based on your CodeBuild and CodeCommit settings.

 

AWS CodeBuild Plugin Configuration in Jenkins

 

Additional Jenkins AWS CodeBuild Plugin Configuration

 

After you have configured the Jenkins project appropriately you should be able to check your build status on the Jenkins polling log under your project settings:

 

Jenkins Polling Log

 

Now that Jenkins is polling CodeCommit, you can check the CodeBuild dashboard under your Jenkins project to confirm your build was successful:

Jenkins AWS CodeBuild Dashboard

Wrapping Up

In a matter of minutes, you have been able to provision Jenkins with the AWS CodeBuild plugin. This will greatly simplify your build infrastructure management. Now kick back and relax while CodeBuild does all the heavy lifting!


About the Author

Paul Roberts is a Strategic Solutions Architect for Amazon Web Services. When he is not working on Serverless, DevOps, or Artificial Intelligence, he is often found in Lake Tahoe exploring the various mountain ranges with his family.