Category Archives: Open Source Blog

News about Google’s open source projects and programs

Kubernetes 1.3 is here!

With all of the excitement being generated around the Kubernetes 1.3 release and the first anniversary of Kubernetes 1.0 (#k8sbday), now is a great time to point out some of the features that enterprise users should be taking note of.

If you’re not familiar with Kubernetes, let me get you up to speed.

Kubernetes is an open-source container automation framework that builds upon 15 years of experience of running production workloads at Google. Once you declare a desired state, Kubernetes works to drive your system toward that state. As a developer this means less time handling trivial tasks that a computer can automate and more time focusing on developing applications that provide value to users.

Additionally, Kubernetes aims to be a framework that you can operate at planetary scale, run anywhere, and never outgrow.

With the release of Kubernetes 1.3, Kubernetes is closer than ever to meeting those goals; the 1.3 release adds exciting features such as:
Aside from features, the coolest part about working with Kubernetes is hearing user stories. I’ll soon be publishing an interview with Joseph Jacks, co-founder of Kismatic, the enterprise Kubernetes company, on the Kubernetes blog.

Joseph is very active in the Kubernetes community and has extensive experience with Kubernetes in production. In the interview I ask him why he bet his business on Kubernetes, what could be better, and how he sees Kubernetes growing in the near future.

Kubernetes has many, many features to offer that I didn’t get to cover in this short write-up. If you know anyone that needs to ramp up on Kubernetes, the easiest way is the free course I created with Kelsey Hightower, Scalable Microservices with Kubernetes. The course covers the basic features of Kubernetes. If you want an overview of what’s new in Kubernetes 1.3, feel free to look at the “What’s new in Kubernetes 1.3” video or slides.

Finally for a more in-depth look at the 1.3 release, make sure to check out: 5 days of Kubernetes 1.3 blog series.

Want to learn more about container orchestration and cloud native platforms? Here’s some recommended reading to follow up with:
By Carter Morgan, Developer Programs Engineer

Announcing an Open Source ADC board for BeagleBone

Cross posted on the Google Research Blog
Working with electronics, we often find ourselves soldering up a half baked electronic circuit to detect some sort of signal. For example, last year we wanted to measure the strength of a carrier. We started with traditional analog circuits — amplifier, filter, envelope detector, threshold. You can see some of our prototypes in the image below; they get pretty messy.


While there's a certain satisfaction in taming a signal using the physical properties of capacitors, coils of wire and transistors, it's usually easier to digitize the signal with an Analog to Digital Converter (ADC) and manage it with Digital Signal Processing (DSP) instead of electronic parts. Tweaking software doesn't require a soldering iron, and lets us modify signals in ways that would require impossible analog circuits.


There are several standard solutions for digitizing a signal: connect a laptop to an oscilloscope or Data Acquisition System (DAQ) via USB or Ethernet, or use the onboard ADCs of a maker board like an Arduino. The former are sensitive and accurate, but also big and power hungry. The latter are cheap and tiny, but slower and have enough RAM for only milliseconds worth of high speed sample data.  


That led us to investigate single board computers like the BeagleBone and Raspberry Pi, which are small and cheap like an Arduino, but have specs like a smartphone.  And crucially, the BeagleBone's system-on-a-chip (SoC) combines a beefy ARMv7 CPU with two smaller Programmable Realtime Units (PRUs) that have access to all 512MB of system RAM.  This lets us dedicate the PRUs to the time-sensitive and repetitive task of reading each sample out of an external ADC, while the main CPU lets us use the data with the GNU/Linux tools we're used to.


The result is an open source BeagleBone cape we've named PRUDAQ.  It's built around the Analog Devices AD9201 ADC, which samples two inputs simultaneously at up to 20 megasamples per second, per channel.  Simultaneous sampling and high sample rates make it useful for software-defined radio (SDR) and scientific applications where a built-in ADC isn't quite up to the task.  


Our open source electrical design and sample code are available on GitHub, and GroupGets has boards ready to ship for $79.  We also were fortunate to have help from Google intern Kumar Abhishek. He added support for PRUDAQ to his Google Summer of Code project BeagleLogic that performs much better than our sample code.


We started PRUDAQ for our own needs, but quickly realized that others might also find it useful. We're excited to get your feedback through the email list.  Tell us what can be done with inexpensive fast ADCs paired with inexpensive fast CPUs!

Posted by Jason Holt, Software Engineer

Lessons from Professors’ Open Source Software Experience (POSSE) 2016


From Google Summer of Code to Google Code-in, the Open Source Programs Office does a lot to get students involved with open source. In order to learn more about supporting open source in academia, I attended the NSF funded Professors' Open Source Software Experience (POSSE) in Philadelphia. It was a great opportunity for us to better understand the challenges instructors face in weaving open source into their curriculum and hear solutions on how to bridge the gap.

Almost 30 university professors and community college lecturers attended the 3-day workshop. During the workshop, attendees worked in small groups getting hands on experience incorporating humanitarian free and open source software (HFOSS) into their teaching. Professors were able to talk, mingle and share best practices throughout the event.

The POSSE workshop is led by Heidi Ellis, Professor, Department of Computer Science and Information Technology at Western New England University, and Greg Hislop, Professor of Software Engineering and Senior Associate Dean for Academic Affairs at Drexel University. Heidi and Greg took over running POSSE five years after Red Hat began the program as an outreach effort to the higher education community. Red Hat continues as a collaborator in the effort. Around 40 university and community college professors participate in the program every year with over 100 individuals attending the workshop in the last four years.

Here are some of the challenges professors shared:
  • Very little guidance on how to bring FOSS into the classroom. No standard curriculum / syllabus available to reference. 
  • Time investment required to change the curriculum.
  • Will not be rewarded for teaching FOSS courses.
  • Will not get funds to travel for workshops/conferences unless it’s to present a paper at a conference.
  • Many administrations aren’t aware that adding open source is beneficial for students since more and more companies use open source and expect their new hires to be familiar with it.

The next POSSE will be Nov 17-19. Faculty who are interested in attending POSSE, please click here to apply.

We also discussed a number of open source programs that are currently working to engage students with open source software development:

Thanks to Heidi, Greg and the FOSS2Serve team for organizing POSSE 2016! We look forward to taking what we’ve learned and using it to better support FOSS education in academia.

By Feiran Helen Hu, Open Source Programs Office

GitHub on BigQuery: Analyze all the code



Google, in collaboration with GitHub, is releasing an incredible new open dataset on Google BigQuery. So far you've been able to monitor and analyze GitHub's pulse since 2011 (thanks GitHub Archive project!) and today we're adding the perfect complement to this. What could you do if you had access to analyze all the open source software in the world, with just one SQL command?

The Google BigQuery Public Datasets program now offers a full snapshot of the content of more than 2.8 million open source GitHub repositories in BigQuery. Thanks to our new collaboration with GitHub, you'll have access to analyze the source code of almost 2 billion files with a simple (or complex) SQL query. This will open the doors to all kinds of new insights and advances that we're just beginning to envision.

For example, let's say you're the author of a popular open source library. Now you'll be able to find every open source project on GitHub that's using it. Even more, you'll be able to guide the future of your project by analyzing how it's being used, and improve your APIs based on what your users are actually doing with it.

On the security side, we've seen how the most popular open source projects benefit from having multiple eyes and hands working on them. This visibility helps projects get hardened and buggy code cleaned up. What if you could search for errors with similar patterns in every other open source project? Would you notify their authors and send them pull requests? Well, now you can. Some concepts to keep in mind while working with BigQuery and the GitHub contents dataset:
To learn more, read GitHub's announcement and try some sample queries. Share your queries and findings in our reddit.com/r/bigquery and Hacker News posts. The ideas are endless, and I'll start collecting tips and links to other articles on this post on Medium.

Stay curious!

More statistics from Google Summer of Code 2016

Google Summer of Code
Google Summer of Code (GSoC) 2016 is officially at its halfway point. Mentors and students have just completed their midterm evaluations and it’s time for our second stats post. This time we take a closer look at our participating students.

First, we’d like to highlight the universities with the most student participants. Congratulations are due to the International Institute of Information Technology - Hyderabad for claiming the top spot for the third consecutive year!

Country School 2016 Accepted Students 2015 Accepted Students 12 Year Total
India International Institute of Information Technology - Hyderabad 50 62 252
Sri Lanka University of Moratuwa 29 44 320
Romania University POLITEHNICA of Bucharest 24 14 155
India Birla Institute of Technology and Science Pilani, Goa Campus 22 15 110
India Birla Institute of Technology and Science, Pilani Campus 22 18 116
India Indian Institute of Technology, Bombay 18 13 75
India Indian Institute of Technology, Kharagpur 15 8 92
India Indian Institute of Technology, Roorkee 15 8 57
India Indraprastha Institute of Information Technology 15 7 27
India Amrita Institute Of Technology & Science, Amritapuri 13 5 33
India Indian Institute of Technology, Guwahati 13 5 38
Cameroon University of Buea 12 10 26
India Delhi Technological University 12 9 60
India Indian Institute of Technology BHU Varanasi 12 12 37
Germany TU Munich 11 7 45


Next, we are proud to announce that 2016 marks the largest number of female GSoC participants to date — 12% of accepted students are female, up 2.2% from 2015. This is good progress, but we are certain we can do better in the future to diversify our program. The Google Open Source team will continue our outreach to many organizations, for example, Grace Hopper and Black Girls Code, to increase this number even more 2017. If you have any suggestions of organizations we should work with, please let us know in the comments.

Finally, each year we like to look at the majors of students. As expected, the most common area of study for our participants is Computer Science (approximately 78%), but this year we have a wide variety of studies including Linguistics, Law, Music Technology and Psychology.  The majority of our students this year are undergraduates (67%), followed by Masters (23%) and then PhD students (9%).



Although reviewing GSoC statistics each year is great fun, we want to stress that being “first place” is not the point of the program. Our goal is to get more and more students involved in creating free and open source software. We hope Google Summer of Code encourages contributions to projects that have the potential to make a difference worldwide. Congratulations to the students from all over the globe and keep up the good work!

By Mary Radomile, Open Source Programs Office

Coding has begun for Google Summer of Code 2016

2016 Google Summer of Code


Today marks the start of coding for the 12th annual Google Summer of Code. With the community bonding period complete, about 1,200 students now begin 12 weeks of writing code for 178 different open source organizations.

We are excited to see the contributions this year’s students will make to the open source community. 

For more information on important dates for the program please visit our timeline. Stay tuned as we will highlight some of the new mentoring organizations over the next few months.

Have a great summer and happy coding!

By Josh Simmons, Open Source Programs Office

Google Summer of Code 2016 statistics: Part one

Google Summer of Code
We share statistics from Google Summer of Code (GSoC) every year — now that 2016 is chugging along we’ve got some exciting numbers to share! 1,202 students from all over the globe are currently in the community bonding period, a time where participants learn more about the organization they will be contributing to before coding officially begins on May 23. This includes becoming familiar with the community practices and processes, setting up a development environment, or contributing small (or large) patches and bug fixes.

We’ll start our statistics reporting this year with the total number of students participating from each country:

Country Accepted Students Country Accepted Students Country Accepted Students
Albania
1
Greece
10
Romania
31
Algeria
1
Guatemala
1
Russian Federation
52
Argentina
3
Hong Kong
2
Serbia
2
Armenia
3
Hungary
7
Singapore
7
Australia
6
India
454
Slovak Republic
3
Austria
19
Ireland
3
Slovenia
4
Belarus
5
Israel
2
South Africa
2
Belgium
5
Italy
23
South Korea
6
Bosnia-Herzegovina
1
Japan
12
Spain
33
Brazil
21
Kazakhstan
2
Sri Lanka
54
Bulgaria
2
Kenya
3
Sweden
5
Cambodia
1
Latvia
3
Switzerland
2
Cameroon
1
Lithuania
1
Taiwan
7
Canada
23
Luxembourg
1
Thailand
1
China
34
Macedonia
1
Turkey
12
Croatia
2
Mexico
2
Ukraine
13
Czech Republic
6
Netherlands
9
United Kingdom
18
Denmark
2
New Zealand
2
United States
118
Egypt
10
Pakistan
4
Uruguay
1
Estonia
1
Paraguay
1
Venezuela
1
Finland
3
Philippines
2
Vietnam
4
France
19
Poland
28
 
 
Germany
66
Portugal
7
 
 


We’d like to welcome a new country to the GSoC family. 2016 brings us one student from Albania!

In our upcoming statistics posts, we will delve deeper into the numbers by looking at  universities with the most accepted students, gender numbers, mentor countries and more. If you have additional statistics that you would like us to share, please leave a comment below and we will consider including them in an upcoming post.

By Mary Radomile, Open Source Programs

Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source

Originally posted on the Google Research Blog

By Slav Petrov, Senior Staff Research Scientist

At Google, we spend a lot of time thinking about how computer systems can read and understand human language in order to process it in intelligent ways. Today, we are excited to share the fruits of our research with the broader community by releasing SyntaxNet, an open-source neural network framework implemented in TensorFlow that provides a foundation for Natural Language Understanding (NLU) systems. Our release includes all the code needed to train new SyntaxNet models on your own data, as well as Parsey McParseface, an English parser that we have trained for you and that you can use to analyze English text.

Parsey McParseface is built on powerful machine learning algorithms that learn to analyze the linguistic structure of language, and that can explain the functional role of each word in a given sentence. Because Parsey McParseface is the most accurate such model in the world, we hope that it will be useful to developers and researchers interested in automatic extraction of information, translation, and other core applications of NLU.

How does SyntaxNet work?

SyntaxNet is a framework for what’s known in academic circles as a syntactic parser, which is a key first component in many NLU systems. Given a sentence as input, it tags each word with a part-of-speech (POS) tag that describes the word's syntactic function, and it determines the syntactic relationships between words in the sentence, represented in the dependency parse tree. These syntactic relationships are directly related to the underlying meaning of the sentence in question. To take a very simple example, consider the following dependency tree for Alice saw Bob:


This structure encodes that Alice and Bob are nouns and saw is a verb. The main verb saw is the root of the sentence and Alice is the subject (nsubj) of saw, while Bob is its direct object (dobj). As expected, Parsey McParseface analyzes this sentence correctly, but also understands the following more complex example:


This structure again encodes the fact that Alice and Bob are the subject and object respectively of saw, in addition that Alice is modified by a relative clause with the verb reading, that saw is modified by the temporal modifier yesterday, and so on. The grammatical relationships encoded in dependency structures allow us to easily recover the answers to various questions, for example whom did Alice see?, who saw Bob?, what had Alice been reading about? or when did Alice see Bob?.

Why is Parsing So Hard For Computers to Get Right?

One of the main problems that makes parsing so challenging is that human languages show remarkable levels of ambiguity. It is not uncommon for moderate length sentences - say 20 or 30 words in length - to have hundreds, thousands, or even tens of thousands of possible syntactic structures. A natural language parser must somehow search through all of these alternatives, and find the most plausible structure given the context. As a very simple example, the sentence Alice drove down the street in her car has at least two possible dependency parses:


The first corresponds to the (correct) interpretation where Alice is driving in her car; the second corresponds to the (absurd, but possible) interpretation where the street is located in her car. The ambiguity arises because the preposition in can either modify drove or street; this example is an instance of what is called prepositional phrase attachment ambiguity.

Humans do a remarkable job of dealing with ambiguity, almost to the point where the problem is unnoticeable; the challenge is for computers to do the same. Multiple ambiguities such as these in longer sentences conspire to give a combinatorial explosion in the number of possible structures for a sentence. Usually the vast majority of these structures are wildly implausible, but are nevertheless possible and must be somehow discarded by a parser.

SyntaxNet applies neural networks to the ambiguity problem. An input sentence is processed from left to right, with dependencies between words being incrementally added as each word in the sentence is considered. At each point in processing many decisions may be possible—due to ambiguity—and a neural network gives scores for competing decisions based on their plausibility. For this reason, it is very important to use beam search in the model. Instead of simply taking the first-best decision at each point, multiple partial hypotheses are kept at each step, with hypotheses only being discarded when there are several other higher-ranked hypotheses under consideration. An example of a left-to-right sequence of decisions that produces a simple parse is shown below for the sentence I booked a ticket to Google.
Furthermore, as described in our paper, it is critical to tightly integrate learning and search in order to achieve the highest prediction accuracy. Parsey McParseface and other SyntaxNet models are some of the most complex networks that we have trained with the TensorFlow framework at Google. Given some data from the Google supported Universal Treebanks project, you can train a parsing model on your own machine.

So How Accurate is Parsey McParseface?

On a standard benchmark consisting of randomly drawn English newswire sentences (the 20 year old Penn Treebank), Parsey McParseface recovers individual dependencies between words with over 94% accuracy, beating our own previous state-of-the-art results, which were already better than any previous approach. While there are no explicit studies in the literature about human performance, we know from our in-house annotation projects that linguists trained for this task agree in 96-97% of the cases. This suggests that we are approaching human performance—but only on well-formed text. Sentences drawn from the web are a lot harder to analyze, as we learned from the Google WebTreebank (released in 2011). Parsey McParseface achieves just over 90% of parse accuracy on this dataset.

While the accuracy is not perfect, it’s certainly high enough to be useful in many applications. The major source of errors at this point are examples such as the prepositional phrase attachment ambiguity described above, which require real world knowledge (e.g. that a street is not likely to be located in a car) and deep contextual reasoning. Machine learning (and in particular, neural networks) have made significant progress in resolving these ambiguities. But our work is still cut out for us: we would like to develop methods that can learn world knowledge and enable equal understanding of natural language across all languages and contexts.

To get started, see the SyntaxNet code and download the Parsey McParseface parser model. Happy parsing from the main developers, Chris Alberti, David Weiss, Daniel Andor, Michael Collins & Slav Petrov.

Googlers on the road: OSCON 2016 in Austin

Developers and open source enthusiasts converge on Austin, Texas in just under two weeks for O’Reilly Media’s annual open source conference, OSCON, and the Community Leadership Summit (CLS) that precedes it. CLS runs May 14-15 at the Austin Convention Center followed by OSCON from May 16-19.

OSCON 2014 program chairs including Googler Sarah Novotny.
Photo licensed by O'Reilly Media under CC-BY-NC 2.0.

This year we have 10 Googlers hosting sessions covering topics including web development, machine learning, devops, astronomy and open source. A list of all of the talks hosted by Googlers alongside related events can be found below.

If you’re a student, educator, mentor, past or present participant in Google Summer of Code or Google Code-in, or just interested in learning more about the two programs, make sure to join us Monday evening for our Birds of a Feather session.

Have questions about Kubernetes, Google Summer of Code, open source at Google or just want to meet some Googlers? Stop by booth #307 in the Expo Hall.


Thursday, May 12th - GDG Austin
7:00pm   Google Developers Group Austin Meetup


Sunday, May 15th - Community Leadership Summit

Monday, May 16th
7:00pm   Google Summer of Code and Google Code-in Birds of a Feather


Tuesday, May 17th

Wednesday, May 18th

Thursday, May 19th
11:00am  Kubernetes hackathon at OSCON Contribute hosted by Brian Dorsey, Nikhil Jindal, Janet Kuo, Jeff Mendoza, John Mulhausen, Sarah Novotny, Terrence Ryan and Chao Xu
5:10pm    PANOPTES: Open source planet discovery by Jennifer Tong and Wilfred Gee

Haven’t registered for OSCON yet? You can knock 25% off the cost of registration by using discount code Google25, or attend parts of the event including our Birds of a Feather session for free by using discount code OSCON16XPO.

See you at OSCON!

By Josh Simmons, Open Source Programs Office

XRay: a function call tracing system

At Google we spend a lot of time debugging and tuning the performance of our production systems. Some standard practices when doing this involves using profilers, debuggers, and analysis of logs and execution traces. Doing this at scale, in production, is difficult. One of the ways for getting high fidelity data from production systems is to build applications with instrumentation, and then reconstruct the instrumentation data into a form humans can consume (summary statistics, reports, etc.). Instrumentation comes at a cost though, sometimes too high to make it feasible to deploy in production.

Getting this balance right is hard. This is why we've developed XRay, a function call tracing system that has very little overhead when not enabled, but can be dynamically turned on and only impose moderate costs. XRay works as a combination of compiler-inserted instrumentation points which functionally do nothing (called "nop sleds") and a library that can be enabled and disabled at runtime which replaces the nop sleds with the appropriate instrumentation instructions.

We've been using XRay to debug internal systems, from core infrastructure services like Bigtable to ad serving systems. XRay's detailed function tracing has enabled several teams in Google to debug issues that would be really hard to solve without XRay.

We think XRay is an important piece of technology, not only at Google, but for developers around the world. It's because of this that we're working on making XRay opensource. To kick-start that process, we're releasing a white paper describing the technical details of XRay. In the following weeks, we will be engaging the LLVM community, where we are committed to making XRay available for wide use and distribution.

We hope that by open-sourcing XRay we can contribute to the advancement of debugging real-world applications. We're looking forward to working with the LLVM community and other projects to make the data XRay generates useful for debugging a wide variety of applications.

By Dean Michael Berris, Google Engineering