Tag Archives: training

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

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/sagemaker-tokyo-summit-2018/

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.

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

Working with the Scout Association on digital skills for life

Post Syndicated from Philip Colligan original https://www.raspberrypi.org/blog/working-with-scout-association-digital-skills-for-life/

Today we’re launching a new partnership between the Scouts and the Raspberry Pi Foundation that will help tens of thousands of young people learn crucial digital skills for life. In this blog post, I want to explain what we’ve got planned, why it matters, and how you can get involved.

This is personal

First, let me tell you why this partnership matters to me. As a child growing up in North Wales in the 1980s, Scouting changed my life. My time with 2nd Rhyl provided me with countless opportunities to grow and develop new skills. It taught me about teamwork and community in ways that continue to shape my decisions today.

As my own kids (now seven and ten) have joined Scouting, I’ve seen the same opportunities opening up for them, and like so many parents, I’ve come back to the movement as a volunteer to support their local section. So this is deeply personal for me, and the same is true for many of my colleagues at the Raspberry Pi Foundation who in different ways have been part of the Scouting movement.

That shouldn’t come as a surprise. Scouting and Raspberry Pi share many of the same values. We are both community-led movements that aim to help young people develop the skills they need for life. We are both powered by an amazing army of volunteers who give their time to support that mission. We both care about inclusiveness, and pride ourselves on combining fun with learning by doing.

Raspberry Pi

Raspberry Pi started life in 2008 as a response to the problem that too many young people were growing up without the skills to create with technology. Our goal is that everyone should be able to harness the power of computing and digital technologies, for work, to solve problems that matter to them, and to express themselves creatively.

In 2012 we launched our first product, the world’s first $35 computer. Just six years on, we have sold over 20 million Raspberry Pi computers and helped kickstart a global movement for digital skills.

The Raspberry Pi Foundation now runs the world’s largest network of volunteer-led computing clubs (Code Clubs and CoderDojos), and creates free educational resources that are used by millions of young people all over the world to learn how to create with digital technologies. And lots of what we are able to achieve is because of partnerships with fantastic organisations that share our goals. For example, through our partnership with the European Space Agency, thousands of young people have written code that has run on two Raspberry Pi computers that Tim Peake took to the International Space Station as part of his Mission Principia.

Digital makers

Today we’re launching the new Digital Maker Staged Activity Badge to help tens of thousands of young people learn how to create with technology through Scouting. Over the past few months, we’ve been working with the Scouts all over the UK to develop and test the new badge requirements, along with guidance, project ideas, and resources that really make them work for Scouting. We know that we need to get two things right: relevance and accessibility.

Relevance is all about making sure that the activities and resources we provide are a really good fit for Scouting and Scouting’s mission to equip young people with skills for life. From the digital compass to nature cameras and the reinvented wide game, we’ve had a lot of fun thinking about ways we can bring to life the crucial role that digital technologies can play in the outdoors and adventure.

Compass Coding with Raspberry Pi

We are beyond excited to be launching a new partnership with the Raspberry Pi Foundation, which will help tens of thousands of young people learn digital skills for life.

We also know that there are great opportunities for Scouts to use digital technologies to solve social problems in their communities, reflecting the movement’s commitment to social action. Today we’re launching the first set of project ideas and resources, with many more to follow over the coming weeks and months.

Accessibility is about providing every Scout leader with the confidence, support, and kit to enable them to offer the Digital Maker Staged Activity Badge to their young people. A lot of work and care has gone into designing activities that require very little equipment: for example, activities at Stages 1 and 2 can be completed with a laptop without access to the internet. For the activities that do require kit, we will be working with Scout Stores and districts to make low-cost kit available to buy or loan.

We’re producing accessible instructions, worksheets, and videos to help leaders run sessions with confidence, and we’ll also be planning training for leaders. We will work with our network of Code Clubs and CoderDojos to connect them with local sections to organise joint activities, bringing both kit and expertise along with them.




Get involved

Today’s launch is just the start. We’ll be developing our partnership over the next few years, and we can’t wait for you to join us in getting more young people making things with technology.

Take a look at the brand-new Raspberry Pi resources designed especially for Scouts, to get young people making and creating right away.

The post Working with the Scout Association on digital skills for life appeared first on Raspberry Pi.

Connect, collaborate, and learn at AWS Global Summits in 2018

Post Syndicated from Tina Kelleher original https://aws.amazon.com/blogs/big-data/connect-collaborate-and-learn-at-aws-global-summits-in-2018/

Regardless of your career path, there’s no denying that attending industry events can provide helpful career development opportunities — not only for improving and expanding your skill sets, but for networking as well. According to this article from PayScale.com, experts estimate that somewhere between 70-85% of new positions are landed through networking.

Narrowing our focus to networking opportunities with cloud computing professionals who’re working on tackling some of today’s most innovative and exciting big data solutions, attending big data-focused sessions at an AWS Global Summit is a great place to start.

AWS Global Summits are free events that bring the cloud computing community together to connect, collaborate, and learn about AWS. As the name suggests, these summits are held in major cities around the world, and attract technologists from all industries and skill levels who’re interested in hearing from AWS leaders, experts, partners, and customers.

In addition to networking opportunities with top cloud technology providers, consultants and your peers in our Partner and Solutions Expo, you’ll also hone your AWS skills by attending and participating in a multitude of education and training opportunities.

Here’s a brief sampling of some of the upcoming sessions relevant to big data professionals:

May 31st : Big Data Architectural Patterns and Best Practices on AWS | AWS Summit – Mexico City

June 6th-7th: Various (click on the “Big Data & Analytics” header) | AWS Summit – Berlin

June 20-21st : [email protected] | Public Sector Summit – Washington DC

June 21st: Enabling Self Service for Data Scientists with AWS Service Catalog | AWS Summit – Sao Paulo

Be sure to check out the main page for AWS Global Summits, where you can see which cities have AWS Summits planned for 2018, register to attend an upcoming event, or provide your information to be notified when registration opens for a future event.

UK soldiers design Raspberry Pi bomb disposal robot

Post Syndicated from Helen Lynn original https://www.raspberrypi.org/blog/uk-soldiers-design-raspberry-pi-bomb-disposal-robot/

Three soldiers in the British Army have used a Raspberry Pi to build an autonomous robot, as part of their Foreman of Signals course.

Meet The Soldiers Revolutionising Bomb Disposal

Three soldiers from Blandford Camp have successfully designed and built an autonomous robot as part of their Foreman of Signals Course at the Dorset Garrison.

Autonomous robots

Forces Radio BFBS carried a story last week about Staff Sergeant Jolley, Sergeant Rana, and Sergeant Paddon, also known as the “Project ROVER” team. As part of their Foreman of Signals training, their task was to design an autonomous robot that can move between two specified points, take a temperature reading, and transmit the information to a remote computer. The team comments that, while semi-autonomous robots have been used as far back as 9/11 for tasks like finding people trapped under rubble, nothing like their robot and on a similar scale currently exists within the British Army.

The ROVER buggy

Their build is named ROVER, which stands for Remote Obstacle aVoiding Environment Robot. It’s a buggy that moves on caterpillar tracks, and it’s tethered; we wonder whether that might be because it doesn’t currently have an on-board power supply. A demo shows the robot moving forward, then changing its path when it encounters an obstacle. The team is using RealVNC‘s remote access software to allow ROVER to send data back to another computer.

Applications for ROVER

Dave Ball, Senior Lecturer in charge of the Foreman of Signals course, comments that the project is “a fantastic opportunity for [the team] to, even only halfway through the course, showcase some of the stuff they’ve learnt and produce something that’s really quite exciting.” The Project ROVER team explains that the possibilities for autonomous robots like this one are extensive: they include mine clearance, bomb disposal, and search-and-rescue campaigns. They point out that existing semi-autonomous hardware is not as easy to program as their build. In contrast, they say, “with the invention of the Raspberry Pi, this has allowed three very inexperienced individuals to program a robot very capable of doing these things.”

We make Raspberry Pi computers because we want building things with technology to be as accessible as possible. So it’s great to see a project like this, made by people who aren’t techy and don’t have a lot of computing experience, but who want to solve a problem and see that the Pi is an affordable and powerful tool that can help.

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Amazon Sumerian – Now Generally Available

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/amazon-sumerian-now-generally-available/

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;

 

Puerto Rico’s First Raspberry Pi Educator Workshop

Post Syndicated from Dana Augustin original https://www.raspberrypi.org/blog/puerto-rico-raspberry-pi-workshop/

Earlier this spring, an excited group of STEM educators came together to participate in the first ever Raspberry Pi and Arduino workshop in Puerto Rico.

Their three-day digital making adventure was led by MakerTechPR’s José Rullán and Raspberry Pi Certified Educator Alex Martínez. They ran the event as part of the Robot Makers challenge organized by Yees! and sponsored by Puerto Rico’s Department of Economic Development and Trade to promote entrepreneurial skills within Puerto Rico’s education system.

Over 30 educators attended the workshop, which covered the use of the Raspberry Pi 3 as a computer and digital making resource. The educators received a kit consisting of a Raspberry Pi 3 with an Explorer HAT Pro and an Arduino Uno. At the end of the workshop, the educators were able to keep the kit as a demonstration unit for their classrooms. They were enthusiastic to learn new concepts and immerse themselves in the world of physical computing.

In their first session, the educators were introduced to the Raspberry Pi as an affordable technology for robotic clubs. In their second session, they explored physical computing and the coding languages needed to control the Explorer HAT Pro. They started off coding with Scratch, with which some educators had experience, and ended with controlling the GPIO pins with Python. In the final session, they learned how to develop applications using the powerful combination of Arduino and Raspberry Pi for robotics projects. This gave them a better understanding of how they could engage their students in physical computing.

“The Raspberry Pi ecosystem is the perfect solution in the classroom because to us it is very resourceful and accessible.” – Alex Martínez

Computer science and robotics courses are important for many schools and teachers in Puerto Rico. The simple idea of programming a microcontroller from a $35 computer increases the chances of more students having access to more technology to create things.

Puerto Rico’s education system has faced enormous challenges after Hurricane Maria, including economic collapse and the government’s closure of many schools due to the exodus of families from the island. By attending training like this workshop, educators in Puerto Rico are becoming more experienced in fields like robotics in particular, which are key for 21st-century skills and learning. This, in turn, can lead to more educational opportunities, and hopefully the reopening of more schools on the island.

“We find it imperative that our children be taught STEM disciplines and skills. Our goal is to continue this work of spreading digital making and computer science using the Raspberry Pi around Puerto Rico. We want our children to have the best education possible.” – Alex Martínez

After attending Picademy in 2016, Alex has integrated the Raspberry Pi Foundation’s online resources into his classroom. He has also taught small workshops around the island and in the local Puerto Rican makerspace community. José is an electrical engineer, entrepreneur, educator and hobbyist who enjoys learning to use technology and sharing his knowledge through projects and challenges.

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Introducing the AWS Machine Learning Competency for Consulting Partners

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/introducing-the-aws-machine-learning-competency-for-consulting-partners/

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

AWS Online Tech Talks – May and Early June 2018

Post Syndicated from Devin Watson original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-may-and-early-june-2018/

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.

 

 

 

 

Analyze data in Amazon DynamoDB using Amazon SageMaker for real-time prediction

Post Syndicated from YongSeong Lee original https://aws.amazon.com/blogs/big-data/analyze-data-in-amazon-dynamodb-using-amazon-sagemaker-for-real-time-prediction/

Many companies across the globe use Amazon DynamoDB to store and query historical user-interaction data. DynamoDB is a fast NoSQL database used by applications that need consistent, single-digit millisecond latency.

Often, customers want to turn their valuable data in DynamoDB into insights by analyzing a copy of their table stored in Amazon S3. Doing this separates their analytical queries from their low-latency critical paths. This data can be the primary source for understanding customers’ past behavior, predicting future behavior, and generating downstream business value. Customers often turn to DynamoDB because of its great scalability and high availability. After a successful launch, many customers want to use the data in DynamoDB to predict future behaviors or provide personalized recommendations.

DynamoDB is a good fit for low-latency reads and writes, but it’s not practical to scan all data in a DynamoDB database to train a model. In this post, I demonstrate how you can use DynamoDB table data copied to Amazon S3 by AWS Data Pipeline to predict customer behavior. I also demonstrate how you can use this data to provide personalized recommendations for customers using Amazon SageMaker. You can also run ad hoc queries using Amazon Athena against the data. DynamoDB recently released on-demand backups to create full table backups with no performance impact. However, it’s not suitable for our purposes in this post, so I chose AWS Data Pipeline instead to create managed backups are accessible from other services.

To do this, I describe how to read the DynamoDB backup file format in Data Pipeline. I also describe how to convert the objects in S3 to a CSV format that Amazon SageMaker can read. In addition, I show how to schedule regular exports and transformations using Data Pipeline. The sample data used in this post is from Bank Marketing Data Set of UCI.

The solution that I describe provides the following benefits:

  • Separates analytical queries from production traffic on your DynamoDB table, preserving your DynamoDB read capacity units (RCUs) for important production requests
  • Automatically updates your model to get real-time predictions
  • Optimizes for performance (so it doesn’t compete with DynamoDB RCUs after the export) and for cost (using data you already have)
  • Makes it easier for developers of all skill levels to use Amazon SageMaker

All code and data set in this post are available in this .zip file.

Solution architecture

The following diagram shows the overall architecture of the solution.

The steps that data follows through the architecture are as follows:

  1. Data Pipeline regularly copies the full contents of a DynamoDB table as JSON into an S3
  2. Exported JSON files are converted to comma-separated value (CSV) format to use as a data source for Amazon SageMaker.
  3. Amazon SageMaker renews the model artifact and update the endpoint.
  4. The converted CSV is available for ad hoc queries with Amazon Athena.
  5. Data Pipeline controls this flow and repeats the cycle based on the schedule defined by customer requirements.

Building the auto-updating model

This section discusses details about how to read the DynamoDB exported data in Data Pipeline and build automated workflows for real-time prediction with a regularly updated model.

Download sample scripts and data

Before you begin, take the following steps:

  1. Download sample scripts in this .zip file.
  2. Unzip the src.zip file.
  3. Find the automation_script.sh file and edit it for your environment. For example, you need to replace 's3://<your bucket>/<datasource path>/' with your own S3 path to the data source for Amazon ML. In the script, the text enclosed by angle brackets—< and >—should be replaced with your own path.
  4. Upload the json-serde-1.3.6-SNAPSHOT-jar-with-dependencies.jar file to your S3 path so that the ADD jar command in Apache Hive can refer to it.

For this solution, the banking.csv  should be imported into a DynamoDB table.

Export a DynamoDB table

To export the DynamoDB table to S3, open the Data Pipeline console and choose the Export DynamoDB table to S3 template. In this template, Data Pipeline creates an Amazon EMR cluster and performs an export in the EMRActivity activity. Set proper intervals for backups according to your business requirements.

One core node(m3.xlarge) provides the default capacity for the EMR cluster and should be suitable for the solution in this post. Leave the option to resize the cluster before running enabled in the TableBackupActivity activity to let Data Pipeline scale the cluster to match the table size. The process of converting to CSV format and renewing models happens in this EMR cluster.

For a more in-depth look at how to export data from DynamoDB, see Export Data from DynamoDB in the Data Pipeline documentation.

Add the script to an existing pipeline

After you export your DynamoDB table, you add an additional EMR step to EMRActivity by following these steps:

  1. Open the Data Pipeline console and choose the ID for the pipeline that you want to add the script to.
  2. For Actions, choose Edit.
  3. In the editing console, choose the Activities category and add an EMR step using the custom script downloaded in the previous section, as shown below.

Paste the following command into the new step after the data ­­upload step:

s3://#{myDDBRegion}.elasticmapreduce/libs/script-runner/script-runner.jar,s3://<your bucket name>/automation_script.sh,#{output.directoryPath},#{myDDBRegion}

The element #{output.directoryPath} references the S3 path where the data pipeline exports DynamoDB data as JSON. The path should be passed to the script as an argument.

The bash script has two goals, converting data formats and renewing the Amazon SageMaker model. Subsequent sections discuss the contents of the automation script.

Automation script: Convert JSON data to CSV with Hive

We use Apache Hive to transform the data into a new format. The Hive QL script to create an external table and transform the data is included in the custom script that you added to the Data Pipeline definition.

When you run the Hive scripts, do so with the -e option. Also, define the Hive table with the 'org.openx.data.jsonserde.JsonSerDe' row format to parse and read JSON format. The SQL creates a Hive EXTERNAL table, and it reads the DynamoDB backup data on the S3 path passed to it by Data Pipeline.

Note: You should create the table with the “EXTERNAL” keyword to avoid the backup data being accidentally deleted from S3 if you drop the table.

The full automation script for converting follows. Add your own bucket name and data source path in the highlighted areas.

#!/bin/bash
hive -e "
ADD jar s3://<your bucket name>/json-serde-1.3.6-SNAPSHOT-jar-with-dependencies.jar ; 
DROP TABLE IF EXISTS blog_backup_data ;
CREATE EXTERNAL TABLE blog_backup_data (
 customer_id map<string,string>,
 age map<string,string>, job map<string,string>, 
 marital map<string,string>,education map<string,string>, 
 default map<string,string>, housing map<string,string>,
 loan map<string,string>, contact map<string,string>, 
 month map<string,string>, day_of_week map<string,string>, 
 duration map<string,string>, campaign map<string,string>,
 pdays map<string,string>, previous map<string,string>, 
 poutcome map<string,string>, emp_var_rate map<string,string>, 
 cons_price_idx map<string,string>, cons_conf_idx map<string,string>,
 euribor3m map<string,string>, nr_employed map<string,string>, 
 y map<string,string> ) 
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe' 
LOCATION '$1/';

INSERT OVERWRITE DIRECTORY 's3://<your bucket name>/<datasource path>/' 
SELECT concat( customer_id['s'],',', 
 age['n'],',', job['s'],',', 
 marital['s'],',', education['s'],',', default['s'],',', 
 housing['s'],',', loan['s'],',', contact['s'],',', 
 month['s'],',', day_of_week['s'],',', duration['n'],',', 
 campaign['n'],',',pdays['n'],',',previous['n'],',', 
 poutcome['s'],',', emp_var_rate['n'],',', cons_price_idx['n'],',',
 cons_conf_idx['n'],',', euribor3m['n'],',', nr_employed['n'],',', y['n'] ) 
FROM blog_backup_data
WHERE customer_id['s'] > 0 ; 

After creating an external table, you need to read data. You then use the INSERT OVERWRITE DIRECTORY ~ SELECT command to write CSV data to the S3 path that you designated as the data source for Amazon SageMaker.

Depending on your requirements, you can eliminate or process the columns in the SELECT clause in this step to optimize data analysis. For example, you might remove some columns that have unpredictable correlations with the target value because keeping the wrong columns might expose your model to “overfitting” during the training. In this post, customer_id  columns is removed. Overfitting can make your prediction weak. More information about overfitting can be found in the topic Model Fit: Underfitting vs. Overfitting in the Amazon ML documentation.

Automation script: Renew the Amazon SageMaker model

After the CSV data is replaced and ready to use, create a new model artifact for Amazon SageMaker with the updated dataset on S3.  For renewing model artifact, you must create a new training job.  Training jobs can be run using the AWS SDK ( for example, Amazon SageMaker boto3 ) or the Amazon SageMaker Python SDK that can be installed with “pip install sagemaker” command as well as the AWS CLI for Amazon SageMaker described in this post.

In addition, consider how to smoothly renew your existing model without service impact, because your model is called by applications in real time. To do this, you need to create a new endpoint configuration first and update a current endpoint with the endpoint configuration that is just created.

#!/bin/bash
## Define variable 
REGION=$2
DTTIME=`date +%Y-%m-%d-%H-%M-%S`
ROLE="<your AmazonSageMaker-ExecutionRole>" 


# Select containers image based on region.  
case "$REGION" in
"us-west-2" )
    IMAGE="174872318107.dkr.ecr.us-west-2.amazonaws.com/linear-learner:latest"
    ;;
"us-east-1" )
    IMAGE="382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:latest" 
    ;;
"us-east-2" )
    IMAGE="404615174143.dkr.ecr.us-east-2.amazonaws.com/linear-learner:latest" 
    ;;
"eu-west-1" )
    IMAGE="438346466558.dkr.ecr.eu-west-1.amazonaws.com/linear-learner:latest" 
    ;;
 *)
    echo "Invalid Region Name"
    exit 1 ;  
esac

# Start training job and creating model artifact 
TRAINING_JOB_NAME=TRAIN-${DTTIME} 
S3OUTPUT="s3://<your bucket name>/model/" 
INSTANCETYPE="ml.m4.xlarge"
INSTANCECOUNT=1
VOLUMESIZE=5 
aws sagemaker create-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION}  --algorithm-specification TrainingImage=${IMAGE},TrainingInputMode=File --role-arn ${ROLE}  --input-data-config '[{ "ChannelName": "train", "DataSource": { "S3DataSource": { "S3DataType": "S3Prefix", "S3Uri": "s3://<your bucket name>/<datasource path>/", "S3DataDistributionType": "FullyReplicated" } }, "ContentType": "text/csv", "CompressionType": "None" , "RecordWrapperType": "None"  }]'  --output-data-config S3OutputPath=${S3OUTPUT} --resource-config  InstanceType=${INSTANCETYPE},InstanceCount=${INSTANCECOUNT},VolumeSizeInGB=${VOLUMESIZE} --stopping-condition MaxRuntimeInSeconds=120 --hyper-parameters feature_dim=20,predictor_type=binary_classifier  

# Wait until job completed 
aws sagemaker wait training-job-completed-or-stopped --training-job-name ${TRAINING_JOB_NAME}  --region ${REGION}

# Get newly created model artifact and create model
MODELARTIFACT=`aws sagemaker describe-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION}  --query 'ModelArtifacts.S3ModelArtifacts' --output text `
MODELNAME=MODEL-${DTTIME}
aws sagemaker create-model --region ${REGION} --model-name ${MODELNAME}  --primary-container Image=${IMAGE},ModelDataUrl=${MODELARTIFACT}  --execution-role-arn ${ROLE}

# create a new endpoint configuration 
CONFIGNAME=CONFIG-${DTTIME}
aws sagemaker  create-endpoint-config --region ${REGION} --endpoint-config-name ${CONFIGNAME}  --production-variants  VariantName=Users,ModelName=${MODELNAME},InitialInstanceCount=1,InstanceType=ml.m4.xlarge

# create or update the endpoint
STATUS=`aws sagemaker describe-endpoint --endpoint-name  ServiceEndpoint --query 'EndpointStatus' --output text --region ${REGION} `
if [[ $STATUS -ne "InService" ]] ;
then
    aws sagemaker  create-endpoint --endpoint-name  ServiceEndpoint  --endpoint-config-name ${CONFIGNAME} --region ${REGION}    
else
    aws sagemaker  update-endpoint --endpoint-name  ServiceEndpoint  --endpoint-config-name ${CONFIGNAME} --region ${REGION}
fi

Grant permission

Before you execute the script, you must grant proper permission to Data Pipeline. Data Pipeline uses the DataPipelineDefaultResourceRole role by default. I added the following policy to DataPipelineDefaultResourceRole to allow Data Pipeline to create, delete, and update the Amazon SageMaker model and data source in the script.

{
 "Version": "2012-10-17",
 "Statement": [
 {
 "Effect": "Allow",
 "Action": [
 "sagemaker:CreateTrainingJob",
 "sagemaker:DescribeTrainingJob",
 "sagemaker:CreateModel",
 "sagemaker:CreateEndpointConfig",
 "sagemaker:DescribeEndpoint",
 "sagemaker:CreateEndpoint",
 "sagemaker:UpdateEndpoint",
 "iam:PassRole"
 ],
 "Resource": "*"
 }
 ]
}

Use real-time prediction

After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint. This approach is useful for interactive web, mobile, or desktop applications.

Following, I provide a simple Python code example that queries against Amazon SageMaker endpoint URL with its name (“ServiceEndpoint”) and then uses them for real-time prediction.

=== Python sample for real-time prediction ===

#!/usr/bin/env python
import boto3
import json 

client = boto3.client('sagemaker-runtime', region_name ='<your region>' )
new_customer_info = '34,10,2,4,1,2,1,1,6,3,190,1,3,4,3,-1.7,94.055,-39.8,0.715,4991.6'
response = client.invoke_endpoint(
    EndpointName='ServiceEndpoint',
    Body=new_customer_info, 
    ContentType='text/csv'
)
result = json.loads(response['Body'].read().decode())
print(result)
--- output(response) ---
{u'predictions': [{u'score': 0.7528127431869507, u'predicted_label': 1.0}]}

Solution summary

The solution takes the following steps:

  1. Data Pipeline exports DynamoDB table data into S3. The original JSON data should be kept to recover the table in the rare event that this is needed. Data Pipeline then converts JSON to CSV so that Amazon SageMaker can read the data.Note: You should select only meaningful attributes when you convert CSV. For example, if you judge that the “campaign” attribute is not correlated, you can eliminate this attribute from the CSV.
  2. Train the Amazon SageMaker model with the new data source.
  3. When a new customer comes to your site, you can judge how likely it is for this customer to subscribe to your new product based on “predictedScores” provided by Amazon SageMaker.
  4. If the new user subscribes your new product, your application must update the attribute “y” to the value 1 (for yes). This updated data is provided for the next model renewal as a new data source. It serves to improve the accuracy of your prediction. With each new entry, your application can become smarter and deliver better predictions.

Running ad hoc queries using Amazon Athena

Amazon Athena is a serverless query service that makes it easy to analyze large amounts of data stored in Amazon S3 using standard SQL. Athena is useful for examining data and collecting statistics or informative summaries about data. You can also use the powerful analytic functions of Presto, as described in the topic Aggregate Functions of Presto in the Presto documentation.

With the Data Pipeline scheduled activity, recent CSV data is always located in S3 so that you can run ad hoc queries against the data using Amazon Athena. I show this with example SQL statements following. For an in-depth description of this process, see the post Interactive SQL Queries for Data in Amazon S3 on the AWS News Blog. 

Creating an Amazon Athena table and running it

Simply, you can create an EXTERNAL table for the CSV data on S3 in Amazon Athena Management Console.

=== Table Creation ===
CREATE EXTERNAL TABLE datasource (
 age int, 
 job string, 
 marital string , 
 education string, 
 default string, 
 housing string, 
 loan string, 
 contact string, 
 month string, 
 day_of_week string, 
 duration int, 
 campaign int, 
 pdays int , 
 previous int , 
 poutcome string, 
 emp_var_rate double, 
 cons_price_idx double,
 cons_conf_idx double, 
 euribor3m double, 
 nr_employed double, 
 y int 
)
ROW FORMAT DELIMITED 
FIELDS TERMINATED BY ',' ESCAPED BY '\\' LINES TERMINATED BY '\n' 
LOCATION 's3://<your bucket name>/<datasource path>/';

The following query calculates the correlation coefficient between the target attribute and other attributes using Amazon Athena.

=== Sample Query ===

SELECT corr(age,y) AS correlation_age_and_target, 
 corr(duration,y) AS correlation_duration_and_target, 
 corr(campaign,y) AS correlation_campaign_and_target,
 corr(contact,y) AS correlation_contact_and_target
FROM ( SELECT age , duration , campaign , y , 
 CASE WHEN contact = 'telephone' THEN 1 ELSE 0 END AS contact 
 FROM datasource 
 ) datasource ;

Conclusion

In this post, I introduce an example of how to analyze data in DynamoDB by using table data in Amazon S3 to optimize DynamoDB table read capacity. You can then use the analyzed data as a new data source to train an Amazon SageMaker model for accurate real-time prediction. In addition, you can run ad hoc queries against the data on S3 using Amazon Athena. I also present how to automate these procedures by using Data Pipeline.

You can adapt this example to your specific use case at hand, and hopefully this post helps you accelerate your development. You can find more examples and use cases for Amazon SageMaker in the video AWS 2017: Introducing Amazon SageMaker on the AWS website.

 


Additional Reading

If you found this post useful, be sure to check out Serving Real-Time Machine Learning Predictions on Amazon EMR and Analyzing Data in S3 using Amazon Athena.

 


About the Author

Yong Seong Lee is a Cloud Support Engineer for AWS Big Data Services. He is interested in every technology related to data/databases and helping customers who have difficulties in using AWS services. His motto is “Enjoy life, be curious and have maximum experience.”

 

 

Own your own working Pokémon Pokédex!

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/deep-learning-pokedex/

Squeal with delight as your inner Pokémon trainer witnesses the wonder of Adrian Rosebrock’s deep learning Pokédex.

Creating a real-life Pokedex with a Raspberry Pi, Python, and Deep Learning

This video demos a real-like Pokedex, complete with visual recognition, that I created using a Raspberry Pi, Python, and Deep Learning. You can find the entire blog post, including code, using this link: https://www.pyimagesearch.com/2018/04/30/a-fun-hands-on-deep-learning-project-for-beginners-students-and-hobbyists/ Music credit to YouTube user “No Copyright” for providing royalty free music: https://www.youtube.com/watch?v=PXpjqURczn8

The history of Pokémon in 30 seconds

The Pokémon franchise was created by video game designer Satoshi Tajiri in 1995. In the fictional world of Pokémon, Pokémon Trainers explore the vast landscape, catching and training small creatures called Pokémon. To date, there are 802 different types of Pokémon. They range from the ever recognisable Pikachu, a bright yellow electric Pokémon, to the highly sought-after Shiny Charizard, a metallic, playing-card-shaped Pokémon that your mate Alex claims she has in mint condition, but refuses to show you.

Pokemon GIF

In the world of Pokémon, children as young as ten-year-old protagonist and all-round annoyance Ash Ketchum are allowed to leave home and wander the wilderness. There, they hunt vicious, deadly creatures in the hope of becoming a Pokémon Master.

Adrian’s deep learning Pokédex

Adrian is a bit of a deep learning pro, as demonstrated by his Santa/Not Santa detector, which we wrote about last year. For that project, he also provided a great explanation of what deep learning actually is. In a nutshell:

…a subfield of machine learning, which is, in turn, a subfield of artificial intelligence (AI).While AI embodies a large, diverse set of techniques and algorithms related to automatic reasoning (inference, planning, heuristics, etc), the machine learning subfields are specifically interested in pattern recognition and learning from data.

As with his earlier Raspberry Pi project, Adrian uses the Keras deep learning model and the TensorFlow backend, plus a few other packages such as Adrian’s own imutils functions and OpenCV.

Adrian trained a Convolutional Neural Network using Keras on a dataset of 1191 Pokémon images, obtaining 96.84% accuracy. As Adrian explains, this model is able to identify Pokémon via still image and video. It’s perfect for creating a Pokédex – an interactive Pokémon catalogue that should, according to the franchise, be able to identify and read out information on any known Pokémon when captured by camera. More information on model training can be found on Adrian’s blog.

Adrian Rosebeck deep learning pokemon pokedex

For the physical build, a Raspberry Pi 3 with camera module is paired with the Raspberry Pi 7″ touch display to create a portable Pokédex. And while Adrian comments that the same result can be achieved using your home computer and a webcam, that’s not how Adrian rolls as a Raspberry Pi fan.

Adrian Rosebeck deep learning pokemon pokedex

Plus, the smaller size of the Pi is perfect for one of you to incorporate this deep learning model into a 3D-printed Pokédex for ultimate Pokémon glory, pretty please, thank you.

Adrian Rosebeck deep learning pokemon pokedex

Adrian has gone into impressive detail about how the project works and how you can create your own on his blog, pyimagesearch. So if you’re interested in learning more about deep learning, and making your own Pokédex, be sure to visit.

The post Own your own working Pokémon Pokédex! appeared first on Raspberry Pi.

Continued: the answers to your questions for Eben Upton

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/eben-q-a-2/

Last week, we shared the first half of our Q&A with Raspberry Pi Trading CEO and Raspberry Pi creator Eben Upton. Today we follow up with all your other questions, including your expectations for a Raspberry Pi 4, Eben’s dream add-ons, and whether we really could go smaller than the Zero.

Live Q&A with Eben Upton, creator of the Raspberry Pi

Get your questions to us now using #AskRaspberryPi on Twitter

With internet security becoming more necessary, will there be automated versions of VPN on an SD card?

There are already third-party tools which turn your Raspberry Pi into a VPN endpoint. Would we do it ourselves? Like the power button, it’s one of those cases where there are a million things we could do and so it’s more efficient to let the community get on with it.

Just to give a counterexample, while we don’t generally invest in optimising for particular use cases, we did invest a bunch of money into optimising Kodi to run well on Raspberry Pi, because we found that very large numbers of people were using it. So, if we find that we get half a million people a year using a Raspberry Pi as a VPN endpoint, then we’ll probably invest money into optimising it and feature it on the website as we’ve done with Kodi. But I don’t think we’re there today.

Have you ever seen any Pis running and doing important jobs in the wild, and if so, how does it feel?

It’s amazing how often you see them driving displays, for example in radio and TV studios. Of course, it feels great. There’s something wonderful about the geographic spread as well. The Raspberry Pi desktop is quite distinctive, both in its previous incarnation with the grey background and logo, and the current one where we have Greg Annandale’s road picture.

The PIXEL desktop on Raspberry Pi

And so it’s funny when you see it in places. Somebody sent me a video of them teaching in a classroom in rural Pakistan and in the background was Greg’s picture.

Raspberry Pi 4!?!

There will be a Raspberry Pi 4, obviously. We get asked about it a lot. I’m sticking to the guidance that I gave people that they shouldn’t expect to see a Raspberry Pi 4 this year. To some extent, the opportunity to do the 3B+ was a surprise: we were surprised that we’ve been able to get 200MHz more clock speed, triple the wireless and wired throughput, and better thermals, and still stick to the $35 price point.

We’re up against the wall from a silicon perspective; we’re at the end of what you can do with the 40nm process. It’s not that you couldn’t clock the processor faster, or put a larger processor which can execute more instructions per clock in there, it’s simply about the energy consumption and the fact that you can’t dissipate the heat. So we’ve got to go to a smaller process node and that’s an order of magnitude more challenging from an engineering perspective. There’s more effort, more risk, more cost, and all of those things are challenging.

With 3B+ out of the way, we’re going to start looking at this now. For the first six months or so we’re going to be figuring out exactly what people want from a Raspberry Pi 4. We’re listening to people’s comments about what they’d like to see in a new Raspberry Pi, and I’m hoping by early autumn we should have an idea of what we want to put in it and a strategy for how we might achieve that.

Could you go smaller than the Zero?

The challenge with Zero as that we’re periphery-limited. If you run your hand around the unit, there is no edge of that board that doesn’t have something there. So the question is: “If you want to go smaller than Zero, what feature are you willing to throw out?”

It’s a single-sided board, so you could certainly halve the PCB area if you fold the circuitry and use both sides, though you’d have to lose something. You could give up some GPIO and go back to 26 pins like the first Raspberry Pi. You could give up the camera connector, you could go to micro HDMI from mini HDMI. You could remove the SD card and just do USB boot. I’m inventing a product live on air! But really, you could get down to two thirds and lose a bunch of GPIO – it’s hard to imagine you could get to half the size.

What’s the one feature that you wish you could outfit on the Raspberry Pi that isn’t cost effective at this time? Your dream feature.

Well, more memory. There are obviously technical reasons why we don’t have more memory on there, but there are also market reasons. People ask “why doesn’t the Raspberry Pi have more memory?”, and my response is typically “go and Google ‘DRAM price’”. We’re used to the price of memory going down. And currently, we’re going through a phase where this has turned around and memory is getting more expensive again.

Machine learning would be interesting. There are machine learning accelerators which would be interesting to put on a piece of hardware. But again, they are not going to be used by everyone, so according to our method of pricing what we might add to a board, machine learning gets treated like a $50 chip. But that would be lovely to do.

Which citizen science projects using the Pi have most caught your attention?

I like the wildlife camera projects. We live out in the countryside in a little village, and we’re conscious of being surrounded by nature but we don’t see a lot of it on a day-to-day basis. So I like the nature cam projects, though, to my everlasting shame, I haven’t set one up yet. There’s a range of them, from very professional products to people taking a Raspberry Pi and a camera and putting them in a plastic box. So those are good fun.

Raspberry Shake seismometer

The Raspberry Shake seismometer

And there’s Meteor Pi from the Cambridge Science Centre, that’s a lot of fun. And the seismometer Raspberry Shake – that sort of thing is really nice. We missed the recent South Wales earthquake; perhaps we should set one up at our Californian office.

How does it feel to go to bed every day knowing you’ve changed the world for the better in such a massive way?

What feels really good is that when we started this in 2006 nobody else was talking about it, but now we’re part of a very broad movement.

We were in a really bad way: we’d seen a collapse in the number of applicants applying to study Computer Science at Cambridge and elsewhere. In our view, this reflected a move away from seeing technology as ‘a thing you do’ to seeing it as a ‘thing that you have done to you’. It is problematic from the point of view of the economy, industry, and academia, but most importantly it damages the life prospects of individual children, particularly those from disadvantaged backgrounds. The great thing about STEM subjects is that you can’t fake being good at them. There are a lot of industries where your Dad can get you a job based on who he knows and then you can kind of muddle along. But if your dad gets you a job building bridges and you suck at it, after the first or second bridge falls down, then you probably aren’t going to be building bridges anymore. So access to STEM education can be a great driver of social mobility.

By the time we were launching the Raspberry Pi in 2012, there was this wonderful movement going on. Code Club, for example, and CoderDojo came along. Lots of different ways of trying to solve the same problem. What feels really, really good is that we’ve been able to do this as part of an enormous community. And some parts of that community became part of the Raspberry Pi Foundation – we merged with Code Club, we merged with CoderDojo, and we continue to work alongside a lot of these other organisations. So in the two seconds it takes me to fall asleep after my face hits the pillow, that’s what I think about.

We’re currently advertising a Programme Manager role in New Delhi, India. Did you ever think that Raspberry Pi would be advertising a role like this when you were bringing together the Foundation?

No, I didn’t.

But if you told me we were going to be hiring somewhere, India probably would have been top of my list because there’s a massive IT industry in India. When we think about our interaction with emerging markets, India, in a lot of ways, is the poster child for how we would like it to work. There have already been some wonderful deployments of Raspberry Pi, for example in Kerala, without our direct involvement. And we think we’ve got something that’s useful for the Indian market. We have a product, we have clubs, we have teacher training. And we have a body of experience in how to teach people, so we have a physical commercial product as well as a charitable offering that we think are a good fit.

It’s going to be massive.

What is your favourite BBC type-in listing?

There was a game called Codename: Druid. There is a famous game called Codename: Droid which was the sequel to Stryker’s Run, which was an awesome, awesome game. And there was a type-in game called Codename: Druid, which was at the bottom end of what you would consider a commercial game.

codename druid

And I remember typing that in. And what was really cool about it was that the next month, the guy who wrote it did another article that talks about the memory map and which operating system functions used which bits of memory. So if you weren’t going to do disc access, which bits of memory could you trample on and know the operating system would survive.

babbage versus bugs Raspberry Pi annual

See the full listing for Babbage versus Bugs in the Raspberry Pi 2018 Annual

I still like type-in listings. The Raspberry Pi 2018 Annual has a type-in listing that I wrote for a Babbage versus Bugs game. I will say that’s not the last type-in listing you will see from me in the next twelve months. And if you download the PDF, you could probably copy and paste it into your favourite text editor to save yourself some time.

The post Continued: the answers to your questions for Eben Upton appeared first on Raspberry Pi.

Announcing the new AWS Certified Security – Specialty exam

Post Syndicated from Janna Pellegrino original https://aws.amazon.com/blogs/architecture/announcing-the-new-aws-certified-security-specialty-exam/

Good news for cloud security experts: following our most popular beta exam ever, the AWS Certified Security – Specialty exam is here. This new exam allows experienced cloud security professionals to demonstrate and validate their knowledge of how to secure the AWS platform.

About the exam
The security exam covers incident response, logging and monitoring, infrastructure security, identity and access management, and data protection. The exam is open to anyone who currently holds a Cloud Practitioner or Associate-level certification. We recommend candidates have five years of IT security experience designing and implementing security solutions, and at least two years of hands-on experience securing AWS workloads.

The exam validates:

  • An understanding of specialized data classifications and AWS data protection mechanisms.
  • An understanding of data encryption methods and AWS mechanisms to implement them.
  • An understanding of secure Internet protocols and AWS mechanisms to implement them.
  • A working knowledge of AWS security services and features of services to provide a secure production environment.
  • Competency gained from two or more years of production deployment experience using AWS security services and features.
  • Ability to make trade-off decisions with regard to cost, security, and deployment complexity given a set of application requirements.
  • An understanding of security operations and risk.

Learn more and register >>

How to prepare
We have training and other resources to help you prepare for the exam:

AWS Training (aws.amazon.com/training)

Additional Resources

Learn more and register >>

Please contact us if you have questions about exam registration.

Good luck!

Announcing the new AWS Certified Security – Specialty exam

Post Syndicated from Ozlem Yilmaz original https://aws.amazon.com/blogs/security/announcing-the-new-aws-certified-security-specialty-exam/

Good news for cloud security experts: the AWS Certified Security — Specialty exam is here. This new exam allows experienced cloud security professionals to demonstrate and validate their knowledge of how to secure the AWS platform.

About the exam

The security exam covers incident response, logging and monitoring, infrastructure security, identity and access management, and data protection. The exam is open to anyone who currently holds a Cloud Practitioner or Associate-level certification. We recommend candidates have five years of IT security experience designing and implementing security solutions, and at least two years of hands-on experience securing AWS workloads.

The exam validates your understanding of:

  • Specialized data classifications and AWS data protection mechanisms
  • Data encryption methods and AWS mechanisms to implement them
  • Secure Internet protocols and AWS mechanisms to implement them
  • AWS security services and features of services to provide a secure production environment
  • Making tradeoff decisions with regard to cost, security, and deployment complexity given a set of application requirements
  • Security operations and risk

How to prepare

We have training and other resources to help you prepare for the exam.

AWS Training that includes:

Additional Resources

Learn more and register here, and please contact us if you have questions about exam registration.

Want more AWS Security news? Follow us on Twitter.

Start a CoderDojo with our free online training

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/start-a-coderdojo-with-our-free-online-training/

You can now sign up to our newest free online course Start a CoderDojo to learn more about CoderDojo and how you can easily set up one of these free coding clubs for young people in your area. With less than two weeks until the course begins, we wanted to tell you about the course’s content and why the course’s creator put it together for you.

Start a CoderDojo || free online learning || Raspberry Pi Foundation

Get support and advice on how to grow your confidence in coding and start a CoderDojo for young people in your area.

What is CoderDojo?

CoderDojo is a global network of free, volunteer-led, community-based programming clubs for young people aged 7 to 17. There are currently more than 1700 Dojos running regularly across 75 countries. All of these clubs were started by individuals who are passionate about giving young people the opportunity to learn to code. Some people assume you need technical skills to start a Dojo, but that’s not true. The most important thing is that you can bring people together for a shared goal.

What is covered on the course?

The course was developed by Philip, CoderDojo’s Educational Content Lead. It gives those who think empowering young people to be tech creators is important the resources and supports to achieve that goal by starting a Dojo. Divided over three weeks and running for about four hours in total, the course provides practical advice and resources on everything you need to know to plan and run a fun, social, and creative coding club for young people.

“In the first week, you’ll look at what coding is, at the worldwide CoderDojo community of coding clubs, and at the creative approach CoderDojos take to helping young people learn to code. In week two, you’ll move on to setting up your Dojo with a team, a venue, and any needed materials. You’ll also look at how to find young people to attend. Week three wraps up the course by giving you sample plans for a Dojo session and a Dojo’s year, and we’ll be talking about how to grow and develop your Dojo over time as your attendees become better coders.”
— Philip

Who is the course for?

Anyone interested in enabling young people to be tech creators should take this course. Parents, teachers, librarians, IT professionals, youth workers, and others have all started Dojos in their community. They say that “it’s an amazing experience that led [them] to expand [their] personal horizons”, and that they “find it really rewarding”.

The course is free and open to all — if you’re interested, then sign up now.

If you’re already mentoring at a Dojo, the course is a great opportunity to revise what you’ve learnt, and a chance to share your insights with newcomers in the discussion sections. Parents and guardians who wish to learn more about CoderDojo and are considering getting involved are also more than welcome to join.

The post Start a CoderDojo with our free online training appeared first on Raspberry Pi.

Artefacts in the classroom with Museum in a Box

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/museum-in-a-box/

Museum in a Box bridges the gap between museums and schools by creating a more hands-on approach to conservation education through 3D printing and digital making.

Artefacts in the classroom with Museum in a Box || Raspberry Pi Stories

Learn more: http://rpf.io/ Subscribe to our YouTube channel: http://rpf.io/ytsub Help us reach a wider audience by translating our video content: http://rpf.io/yttranslate Buy a Raspberry Pi from one of our Approved Resellers: http://rpf.io/ytproducts Find out more about the Raspberry Pi Foundation: Raspberry Pi http://rpf.io/ytrpi Code Club UK http://rpf.io/ytccuk Code Club International http://rpf.io/ytcci CoderDojo http://rpf.io/ytcd Check out our free online training courses: http://rpf.io/ytfl Find your local Raspberry Jam event: http://rpf.io/ytjam Work through our free online projects: http://rpf.io/ytprojects Do you have a question about your Raspberry Pi?

Fantastic collections and where to find them

Large, impressive statues are truly a sight to be seen. Take for example the 2.4m Hoa Hakananai’a at the British Museum. Its tall stature looms over you as you read its plaque to learn of the statue’s journey from Easter Island to the UK under the care of Captain Cook in 1774, and you can’t help but wonder at how it made it here in one piece.

Hoa Hakananai’a Captain Cook British Museum
Hoa Hakananai’a Captain Cook British Museum

But unless you live near a big city where museums are plentiful, you’re unlikely to see the likes of Hoa Hakananai’a in person. Instead, you have to content yourself with online photos or videos of world-famous artefacts.

And that only accounts for the objects that are on display: conservators estimate that only approximately 5 to 10% of museums’ overall collections are actually on show across the globe. The rest is boxed up in storage, inaccessible to the public due to risk of damage, or simply due to lack of space.

Museum in a Box

Museum in a Box aims to “put museum collections and expert knowledge into your hand, wherever you are in the world,” through modern maker practices such as 3D printing and digital making. With the help of the ‘Scan the World’ movement, an “ambitious initiative whose mission is to archive objects of cultural significance using 3D scanning technologies”, the Museum in a Box team has been able to print small, handheld replicas of some of the world’s most recognisable statues and sculptures.

Museum in a Box Raspberry Pi

Each 3D print gets NFC tags so it can initiate audio playback from a Raspberry Pi that sits snugly within the laser-cut housing of a ‘brain box’. Thus the print can talk directly to us through the magic of wireless technology, replacing the dense, dry text of a museum plaque with engaging speech.

Museum in a Box Raspberry Pi

The Museum in a Box team headed by CEO George Oates (featured in the video above) makes use of these 3D-printed figures alongside original artefacts, postcards, and more to bridge the gap between large, crowded, distant museums and local schools. Modeled after the museum handling collections that used to be sent to schools, Museum in a Box is a cheaper, more accessible alternative. Moreover, it not only allows for hands-on learning, but also encourages children to get directly involved by hacking its technology! With NFC technology readily available to the public, students can curate their own collections about their local area, record their own messages, and send their own box-sized museums on to schools in other towns or countries. In this way, Museum in a Box enables students to explore, and expand the reach of, their own histories.

Moving forward

With the technology perfected and interest in the project ever-growing, Museum in a Box has a busy year ahead. Supporting the new ‘Unstacked’ learning initiative, the team will soon be delivering ten boxes to the Smithsonian Libraries. The team has curated two collections specifically for this: an exploration into Asia-Pacific America experiences of migration to the USA throughout the 20th century, and a look into the history of science.

Smithsonian Library Museum in a Box Raspberry Pi

The team will also be making a box for the British Museum to support their Iraq Scheme initiative, and another box will be heading to the V&A to support their See Red programme. While primarily installed in the Lansbury Micro Museum, the box will also take to the road to visit the local Spotlight high school.

Museum in a Box at Raspberry Fields

Lastly, by far the most exciting thing the Museum in a Box team will be doing this year — in our opinion at least — is showcasing at Raspberry Fields! This is our brand-new festival of digital making that’s taking place on 30 June and 1 July 2018 here in Cambridge, UK. Find more information about it and get your ticket here.

The post Artefacts in the classroom with Museum in a Box appeared first on Raspberry Pi.

New – Machine Learning Inference at the Edge Using AWS Greengrass

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-machine-learning-inference-at-the-edge-using-aws-greengrass/

What happens when you combine the Internet of Things, Machine Learning, and Edge Computing? Before I tell you, let’s review each one and discuss what AWS has to offer.

Internet of Things (IoT) – Devices that connect the physical world and the digital one. The devices, often equipped with one or more types of sensors, can be found in factories, vehicles, mines, fields, homes, and so forth. Important AWS services include AWS IoT Core, AWS IoT Analytics, AWS IoT Device Management, and Amazon FreeRTOS, along with others that you can find on the AWS IoT page.

Machine Learning (ML) – Systems that can be trained using an at-scale dataset and statistical algorithms, and used to make inferences from fresh data. At Amazon we use machine learning to drive the recommendations that you see when you shop, to optimize the paths in our fulfillment centers, fly drones, and much more. We support leading open source machine learning frameworks such as TensorFlow and MXNet, and make ML accessible and easy to use through Amazon SageMaker. We also provide Amazon Rekognition for images and for video, Amazon Lex for chatbots, and a wide array of language services for text analysis, translation, speech recognition, and text to speech.

Edge Computing – The power to have compute resources and decision-making capabilities in disparate locations, often with intermittent or no connectivity to the cloud. AWS Greengrass builds on AWS IoT, giving you the ability to run Lambda functions and keep device state in sync even when not connected to the Internet.

ML Inference at the Edge
Today I would like to toss all three of these important new technologies into a blender! You can now perform Machine Learning inference at the edge using AWS Greengrass. This allows you to use the power of the AWS cloud (including fast, powerful instances equipped with GPUs) to build, train, and test your ML models before deploying them to small, low-powered, intermittently-connected IoT devices running in those factories, vehicles, mines, fields, and homes that I mentioned.

Here are a few of the many ways that you can put Greengrass ML Inference to use:

Precision Farming – With an ever-growing world population and unpredictable weather that can affect crop yields, the opportunity to use technology to increase yields is immense. Intelligent devices that are literally in the field can process images of soil, plants, pests, and crops, taking local corrective action and sending status reports to the cloud.

Physical Security – Smart devices (including the AWS DeepLens) can process images and scenes locally, looking for objects, watching for changes, and even detecting faces. When something of interest or concern arises, the device can pass the image or the video to the cloud and use Amazon Rekognition to take a closer look.

Industrial Maintenance – Smart, local monitoring can increase operational efficiency and reduce unplanned downtime. The monitors can run inference operations on power consumption, noise levels, and vibration to flag anomalies, predict failures, detect faulty equipment.

Greengrass ML Inference Overview
There are several different aspects to this new AWS feature. Let’s take a look at each one:

Machine Learning ModelsPrecompiled TensorFlow and MXNet libraries, optimized for production use on the NVIDIA Jetson TX2 and Intel Atom devices, and development use on 32-bit Raspberry Pi devices. The optimized libraries can take advantage of GPU and FPGA hardware accelerators at the edge in order to provide fast, local inferences.

Model Building and Training – The ability to use Amazon SageMaker and other cloud-based ML tools to build, train, and test your models before deploying them to your IoT devices. To learn more about SageMaker, read Amazon SageMaker – Accelerated Machine Learning.

Model Deployment – SageMaker models can (if you give them the proper IAM permissions) be referenced directly from your Greengrass groups. You can also make use of models stored in S3 buckets. You can add a new machine learning resource to a group with a couple of clicks:

These new features are available now and you can start using them today! To learn more read Perform Machine Learning Inference.

Jeff;

 

Amazon SageMaker Now Supports Additional Instance Types, Local Mode, Open Sourced Containers, MXNet and Tensorflow Updates

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/amazon-sagemaker-roundup-sf/

Amazon SageMaker continues to iterate quickly and release new features on behalf of customers. Starting today, SageMaker adds support for many new instance types, local testing with the SDK, and Apache MXNet 1.1.0 and Tensorflow 1.6.0. Let’s take a quick look at each of these updates.

New Instance Types

Amazon SageMaker customers now have additional options for right-sizing their workloads for notebooks, training, and hosting. Notebook instances now support almost all T2, M4, P2, and P3 instance types with the exception of t2.micro, t2.small, and m4.large instances. Model training now supports nearly all M4, M5, C4, C5, P2, and P3 instances with the exception of m4.large, c4.large, and c5.large instances. Finally, model hosting now supports nearly all T2, M4, M5, C4, C5, P2, and P3 instances with the exception of m4.large instances. Many customers can take advantage of the newest P3, C5, and M5 instances to get the best price/performance for their workloads. Customers also take advantage of the burstable compute model on T2 instances for endpoints or notebooks that are used less frequently.

Open Sourced Containers, Local Mode, and TensorFlow 1.6.0 and MXNet 1.1.0

Today Amazon SageMaker has open sourced the MXNet and Tensorflow deep learning containers that power the MXNet and Tensorflow estimators in the SageMaker SDK. The ability to write Python scripts that conform to simple interface is still one of my favorite SageMaker features and now those containers can be additionally customized to include any additional libraries. You can download these containers locally to iterate and experiment which can accelerate your debugging cycle. When you’re ready go from local testing to production training and hosting you just change one line of code.

These containers launch with support for Tensorflow 1.6.0 and MXNet 1.1.0 as well. Tensorflow has a number of new 1.6.0 features including support for CUDA 9.0, cuDNN 7, and AVX instructions which allows for significant speedups in many training applications. MXNet 1.1.0 adds a number of new features including a Text API mxnet.text with support for text processing, indexing, glossaries, and more. Two of the really cool pre-trained embeddings included are GloVe and fastText.
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Available Now
All of the features mentioned above are available today. As always please let us know on Twitter or in the comments below if you have any questions or if you’re building something interesting. Now, if you’ll excuse me I’m going to go experiment with some of those new MXNet APIs!

Randall

Police Assisted By MPAA Shut Down Pirate TV Box Sellers

Post Syndicated from Andy original https://torrentfreak.com/police-assisted-by-mpaa-shut-down-pirate-tv-box-sellers-180404/

Piracy configured set-top boxes are the next big thing, today. Millions have been sold around the world and anti-piracy groups are scrambling to rein them in.

Many strategies are being tested, from pressurizing developers of allegedly infringing addons to filing aggressive lawsuits against sites such as TVAddons, a Kodi addon repository now facing civil action in both the United States and Canada.

Also under fire are companies that sell set-top boxes that come ready configured for piracy. Both Tickbox TV and Dragon Media Inc are being sued by the Alliance for Creativity and Entertainment (ACE) in the US. At this stage, neither case looks promising for the defendants.

However, civil action isn’t the only way to deal with defendants in the United States, as a man and woman team from Tampa, Florida, have just discovered after being arrested by local police.

Mickael Cantrell and Nancy Major were allegedly the brains behind NBEETV, a company promising to supply set-top boxes that deliver “every movie, every tv show that’s ever been made, plus live sports with no blackouts” with “no monthly fees ever.”

As similar cases have shown, this kind of marketing spiel rarely ends well for defendants but the people behind NBEE TV (also known as FreeTVForLife Inc.) were either oblivious or simply didn’t care about the consequences.

A company press release dated April 2017 advertising the company’s NBPro 3+ box and tracked down by TF this week reveals the extent of the boasts.

“NBPRO 3+ is a TV box that offers instant access to watch every episode of any TV show without paying any monthly bill. One just must attach the loaded box to his TV and stream whatever they want, with no commercials,” the company wrote.

But while “Free TV for Life” was the slogan, that wasn’t the reality at the outset.

NBEETV’s Kodi-powered Android boxes were hellishly expensive with the NBPRO 1, NBPRO 3, NBPRO 5 costing $199.00, $279.00 and $359.00 respectively. This, however, was presented as a bargain alongside a claim that the “average [monthly] cable bill across the country is approximately $198.00” per month.

On top of the base product, NBEETV offered an 800 number for customer support and from their physical premises, they ran “training classes every Tuesday and Thursdays at 11:00” for people to better understand their products.

The location of that building isn’t mentioned in local media but a WHOIS on the company’s FreeTVForLife domain yields a confirmed address. It’s one that’s also been complained about in the past by an unhappy customer.

“Free TV for LIFE [redacted]..(next to K-Mart) Hudson, Fl.. 34667. We bought the Little black box costing $277.00. The pictures were not clear,” Rita S. wrote.

“The screen froze up on us all the time, even after hooking straight into the router. When we took the unit back they kept $80 of our money….were very rude, using the ************* word and we will not get the remainder of our money for 14-28 days according to the employee at the store. Buyers beware and I am telling everyone!!!”

While this customer was clearly unhappy, NBEETV claimed to be a “movement which is spreading across the country.” Unfortunately, that movement reached the eyes of the police, who didn’t think that the content being offered on the devices should have been presented for free.

“We saw [the boxes] had Black Panther, The Shape Of Water, Jumanji was on there as well,” said Detective Darren Hill.

“This is someone blatantly on the side of the road just selling them, with signage, a store front; advertising on the internet with a website.”

Detective Hill worked on the case with the MPAA but even from TorrentFreak’s limited investigations this week, the couple were incredibly easy to identify.

Aside from providing accurate and non-hidden address data in WHOIS records, Mickael Cantrell (also known as Michael Cantrell) put in his real name too. The listed email address is also easily traced back to a company called Nanny Bees Corporation which was operated by Cantrell and partner Nancy Major, who was also arrested in the NBEETV case.

Unfortunately for the couple, the blundering didn’t stop there. Their company YouTube channel, which is packed with tutorials, is also in Cantrell’s real name. Indeed, the photograph supplied to YouTube even matches the mugshot published by ABC Action News.

The publication reports that the Sheriff’s Office found the couple with around 50 ‘pirate’ boxes. The store operated by the couple has also been shutdown.

Finally, another curious aspect of NBEETV’s self-promotion comes via a blog post/press release dated August 2017 in which Cantrell suddenly ups the ante by becoming Michael W. Cantrell, Ph. D alongside some bold and unusual claims.

“Dr. Cantrell unleashes his latest innovation, a Smart TV Box that literally updates every ten minutes. Not only does the content (what you can view) but the whole platform updates automatically. If the Company changes an icon you receive the change in real time,” the release reads.

“Thanks to the Overlay Processor that Dr. Cantrell created, this processor named B-D.A.D (Binary Data Acceleration Dump) which enhances an Android unit’s operating power 5 times than the original bench test, has set a new industry standard around the world.”

Sounds epic….perhaps it powered the following video clip.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN reviews, discounts, offers and coupons.