Using TensorFlow and JupyterHub in Classrooms

We’ve published a new solution and a companion GitHub repository that guides you through setting up a Google Container Engine cluster to run JupyterHub to automatically provision secure Jupyter containers for each user in a classroom or team. Don’t let the title of this article mislead you, not only does it use TensorFlow and JupyterHub, it’s actually an open source and cloud smorgasbord based on the Jupyter and Kubernetes platforms.



Jupyter is a powerful open source technology that gives you a platform to write and execute code to analyze, visualize and share the discoveries you find in your big data set. You can download a number of different Docker images preconfigured with many different notebook extensions and software packages to help you on any kind of data-science quest.

If you’re exploring on your own, and really want to get started quickly, you can get this all running on your local computer, but what if you want to take your expertise and lead a classroom of people along the same path? You have to either configure everything for them or walk them through configuring their own machines with all the required software.

This is where JupyterHub comes in, as a management layer in front of Jupyter instances, allowing you to configure users, using custom authentication, and giving you a Python interface to spawn new Jupyter instances for each user. Even with JupyterHub, you still need a way to provision physical and virtual hardware for the students.

Enter Kubernetes, an open source system for automating deploying, scaling and managing containerized applications. Google Container Engine is a fully managed service based on Kubernetes, allowing you to create clusters easily on Google Cloud Platform.

This solution comes with a JupyterHub Spawner class that allows it to create Kubernetes Pods, which are Docker images running Jupyter, for each user. It also comes with all the automation scripts required to create a Container Engine cluster and let you easily customize your setup.

When your students log into JupyterHub using Google OAuth2, they can choose from a list of several pre-built Jupyter images, including a newly updated “datalab-jupyter” image, which comes with the Google Datalab open source notebook extension enabling integration with BigQuery, Google Cloud ML, StackDriver, and it also has TensorFlow and the Apache Beam Python SDK for Google Cloud DataFlow installed.  Users can also choose to run any of the pre-configured Jupyter docker-stack images, or you can build your own Docker images to run any special libraries or Jupyter configurations you want.

We hope that this solution allows you to get your classroom or team environment running quickly so you can focus on learning rather than configuring machines.

By Brad Svee, Cloud Solutions Architect

Dart in 2017 and beyond

We’re here at the Dart Developer Summit in Munich, Germany. Over 250 developers from more than 50 companies from all over the world just finished watching the keynote.

This is a summary of the topics we covered:

Dart is the fastest growing programming language at Google, with a 3.5x increase in lines of code since last year. We like to think that this is because of our focus on developer productivity: teams report 25% to 100% increase in speed of development. Google has bet its biggest business on Dart — the web apps built on Dart bring over $70B per year.

Google AdSense recently launched a ground-up redesign of their web app, built with Dart. Earlier this year, we announced that the next generation of AdWords is built with Dart. There are more exciting Dart products at Google that we’re looking forward to reveal. Outside Google, companies such as Wrike, Workiva, Soundtrap, Blossom, DG Logic, Sonar Design have all been using and enjoying Dart for years.

Our five year investment in this language is reaping fruit. But we’re not finished.

We learned that people who use Dart love its terse and readable syntax. So we’re keeping that.

We have also learned that Dart developers really enjoy the language’s powerful static analysis. So we’re making it better. With strong mode, Dart’s type system becomes sound (meaning that it rejects all incorrect programs). We’re also introducing support for generic methods.

We have validated that the programming language itself is just a part of the puzzle. Dart comes with ‘batteries included.’ Developers really like Dart’s core libraries — we will keep them tight, efficient and comprehensive. We will also continue to invest in tooling such as pub (our integrated packaging system), dartfmt (our automatic formatter) and, of course, the analyzer.

On the web, we have arrived at a framework that is an excellent fit for Dart: AngularDart. All the Google web apps mentioned above use it. It has been in production at Google since February. AngularDart is designed for Dart, and it’s getting better every week. In the past 4 months, AngularDart’s output has gotten 40% smaller, and our AngularDart web apps got 15% faster.

Today, we’re launching AngularDart 2.0 final. Tune in to the next session.

With that, we’re also releasing — as a developer preview — the AngularDart components that Google uses for its major web apps. These Material Design widgets are being developed by hundreds of Google engineers and are thoroughly tested. They are written purely in Dart.

We’re also making Dart easier to use with existing JavaScript libraries. For example, you will be able to use our tool to convert TypeScript .d.ts declarations into Dart libraries.

We’re making the development cycle much faster. Thanks to Dart Dev Compiler, compilation to JavaScript will take less than a second across all modern browsers.

We believe all this makes Dart an even better choice for web development than before. Dart has been here for a long time and it’s not going anywhere. It’s cohesive and dependable, which is what a lot of web developers want.

We’re also very excited about Flutter — a project to help developers build high-performance, high-fidelity, mobile apps for iOS and Android from a single codebase in Dart. More on that tomorrow.

We hope you’ll enjoy these coming two days. Tune in on the live stream or follow #dartsummit on Twitter.

By Filip Hracek, Developer Relations Program Manager

Google Summer of Code 2016 wrap-up: GNU Radio

This post is the third installment in our series of wrap-up posts reflecting on Google Summer of Code 2016. Check out the first and second posts in the series.

Originally posted on GNU Radio Blog

The summer has come to an end -- along with the Summer of Code for GNU Radio. It was a great season in terms of student participation, and as the students are preparing their last commits, this seems a good time to summarize their efforts.

All students presented their work (either in person, or via poster) at this year’s GNU Radio Conference in Boulder, Colorado.

gr-inspector

With gr-inspector, GNU Radio now has its own out-of-tree module, which serves as a repository for signal analysis algorithms, but also as a collection of fantastic examples. This module was created and worked on by Sebastian Müller, who was funded by Google Summer of Code (GSoC), and Christopher Richardson, who participated as a Summer of Code in Space (SOCIS) student funded by the European Space Agency. Sebastian also created a video demonstrating some of the features:


Both Sebastian and Chris have written up their efforts on their own blogs.

PyBOMBS GUI

Ravi Sharan was our other GSoC student, primarily working on a GUI for PyBOMBS, our installation helper tool. Ravi also worked on a bunch of other things, and has summarized his efforts as well.

The PyBOMBS GUI is written in Qt, and is a nice extension to our out-of-tree module ecosystem:

While some developers prefer the comfort of their command line environments, we hope that the PyBOMBS GUI will ease the entry for more new developers. The GUI ties in nicely with CGRAN, and with the correct setup, users can directly launch installation of out-of-tree modules from their browser.

Want to participate? Have ideas?

We will definitely apply for GSoC and SOCIS again next year! If you want to participate as a student, it helps a lot to get involved with the community early on. We also recommend you sign up for the mailing list, and get involved with GNU Radio by using it, reporting and fixing issues, or even publishing your own out-of-tree module. For more ideas, take a look at our summer of code wiki pages.

If you simply have ideas for future projects, those are welcome too! Suggest those on the mailing list, or simply edit the wiki page.

By Martin Braun, Organization Administrator for GNU Radio