Google Developers Blog: March 2017

A new issue tracker for G Suite developers

Thursday, March 30, 2017

Originally Posted on the G Suite Developers BlogPosted by Ryan Roth, Developer Programs Engineer & Wesley Chun (@Wescpy), Developer Advocate, G SuiteGoogle Cloud Platformupgraded to Issue TrackerG Suitenew issue tracker

The new issue tracker for G Suite developers.

Google Apps Script: Report an issue - Request a feature

Google Drive SDK/API: Report an issue - Request a feature

Gmail API: Report an issue - Request a feature

Google Calendar API: Report an issue - Request a feature

Google Sheets API: Report an issue - Request a feature

Google Slides API: Report an issue - Request a feature

G Suite Activity API: Report an issue - Request a feature

G Suite Admin SDK: Report an issue - Request a feature

G Suite Marketplace: Report an issue - Request a feature

Google Classroom API: Report an issue - Request a feature

Google Contacts API: Report an issue - Request a feature

Google Picker API: Report an issue - Request a feature

Google Realtime API: Report an issue - Request a feature

Google SitesBas API: Report an issue - Request a feature

Google Tasks API: Report an issue - Request a feature

take a look at the documentation pagesFAQcheck out the Google Cloud Platform announcement

Game developers rejoice—new tools for developing on Actions on Google

Thursday, March 30, 2017

By Sunil Vemuri, Product Manager for Actions on Googlelaunchednew sound effect libraryairplanesslide whistlesbowlingcats purringthunderhandy guidemany old favoritestool and consulting companiesGoogle+ community

A New Home for Google Open Source

Tuesday, March 28, 2017

Originally on Google Open Source BlogPosted by Will Norris, Open Source Programs Office

Google Summer of CodeGoogle Code-inSoftware Freedom ConservancyApache Software Foundationmany othersopensource.google.com Helping you find interesting open sourceTensorFlowGoKubernetesLight My PianoNeuroglancerPeriph.iodirectory of our open source projects How we do open sourceTODO Grouphow we do open source at Googleprocess we follow for releasingsubmitting patchesmanage the open source code that we bring into the companyhowwhywhy we only use code under certain licenseswhy we require contributor license agreementslatest episode

Join us live on May 23rd as we announce the latest Ads, Analytics and DoubleClick innovations

Monday, March 27, 2017

Posted by Sridhar Ramaswamy Senior Vice President, Ads and Commerce
What: When:Duration: Where:Inside AdWords Blog

hereTwitterGoogle+FacebookLinkedIn

XLA - TensorFlow, compiled

Monday, March 6, 2017

By the XLA team within Google, in collaboration with the TensorFlow team
One of the design goals and core strengths of TensorFlow is its flexibility. TensorFlow was designed to be a flexible and extensible system for defining arbitrary data flow graphs and executing them efficiently in a distributed manner using heterogenous computing devices (such as CPUs and GPUs).
But flexibility is often at odds with performance. While TensorFlow aims to let you define any kind of data flow graph, it’s challenging to make all graphs execute efficiently because TensorFlow optimizes each op separately. When an op with an efficient implementation exists or when each op is a relatively heavyweight operation, all is well; otherwise, the user can still compose this op out of lower-level ops, but this composition is not guaranteed to run in the most efficient way.
This is why we’ve developed XLA (Accelerated Linear Algebra), a compiler for TensorFlow. XLA uses JIT compilation techniques to analyze the TensorFlow graph created by the user at runtime, specialize it for the actual runtime dimensions and types, fuse multiple ops together and emit efficient native machine code for them - for devices like CPUs, GPUs and custom accelerators (e.g. Google’s TPU).

Fusing composable ops for increased performance

Consider the tf.nn.softmax op, for example. It computes the softmax activations of its parameter as follows:

Softmax can be implemented as a composition of primitive TensorFlow ops (exponent, reduction, elementwise division, etc.): softmax = exp(logits) / reduce_sum(exp(logits), dim)
This could potentially be slow, due to the extra data movement and materialization of temporary results that aren’t needed outside the op. Moreover, on co-processors like GPUs such a decomposed implementation could result in multiple “kernel launches” that make it even slower.
XLA is the secret compiler sauce that helps TensorFlow optimize compositions of primitive ops automatically. Tensorflow, augmented with XLA, retains flexibility without sacrificing runtime performance, by analyzing the graph at runtime, fusing ops together and producing efficient machine code for the fused subgraphs.
For example, a decomposed implementation of softmax as shown above would be optimized by XLA to be as fast as the hand-optimized compound op.
More generally, XLA can take whole subgraphs of TensorFlow operations and fuse them into efficient loops that require a minimal number of kernel launches. For example:

Many of the operations in this graph can be fused into a single element-wise loop. Consider a single element of the bias vector being added to a single element from the matmul result, for example. The result of this addition is a single element that can be compared with 0 (for ReLU). The result of the comparison can be exponentiated and divided by the sum of exponents of all inputs, resulting in the output of softmax. We don’t really need to create the intermediate arrays for matmul, add, and ReLU in memory.

s[j] = softmax[j](ReLU(bias[j] + matmul_result[j]))

A fused implementation can compute the end result within a single element-wise loop, without allocating needless memory. In more advanced scenarios, these operations can even be fused into the matrix multiplication.

XLA helps TensorFlow retain its flexibility while eliminating performance concerns.

On internal benchmarks, XLA shows up to 50% speedups over TensorFlow without XLA on Nvidia GPUs. The biggest speedups come, as expected, in models with long sequences of elementwise operations that can be fused to efficient loops. However, XLA should still be considered experimental, and some benchmarks may experience slowdowns.

In this talk from TensorFlow Developer Summit, Chris Leary and Todd Wang describe how TensorFlow can make use of XLA, JIT, AOT, and other compilation techniques to minimize execution time and maximize computing resources.

Extreme specialization for executable size reduction

In addition to improved performance, TensorFlow models can benefit from XLA for restricted-memory environments (such as mobile devices) due to the executable size reduction it provides. tfcompile is a tool that leverages XLA for ahead-of-time compilation (AOT) - a whole graph is compiled to XLA, which then emits tight machine code that implements the ops in the graph. Coupled with a minimal runtime this scheme provides considerable size reductions.

For example, given a 3-deep, 60-wide stacked LSTM model on android-arm, the original TF model size is 2.6 MB (1 MB runtime + 1.6 MB graph); when compiled with XLA, the size goes down to 600 KB.

Click here for a higher resolution image.

This size reduction is achieved by the full specialization of the model implied by its static compilation. When the model runs, the full power and flexibility of the TensorFlow runtime is not required - only the ops implementing the actual graph the user is interested in are compiled to native code. That said, the performance of the code emitted by the CPU backend of XLA is still far from optimal; this part of the project requires more work.

Support for alternative backends and devices

To execute TensorFlow graphs on a new kind of computing device today, one has to re-implement all the TensorFlow ops (kernels) for the new device. Depending on the device, this can be a very significant amount of work.

By design, XLA makes supporting new devices much easier by adding custom backends. Since TensorFlow can target XLA, one can add a new device backend to XLA and thus enable it to run TensorFlow graphs. XLA provides a significantly smaller implementation surface for new devices, since XLA operations are just the primitives (recall that XLA handles the decomposition of complex ops on its own). We’ve documented the process for adding a custom backend to XLA on this page. Google uses this mechanism to target TPUs from XLA.

Conclusion and looking forward

XLA is still in early stages of development. It is showing very promising results for some use cases, and it is clear that TensorFlow can benefit even more from this technology in the future. We decided to release XLA to TensorFlow Github early to solicit contributions from the community and to provide a convenient surface for optimizing TensorFlow for various computing devices, as well as retargeting the TensorFlow runtime and models to run on new kinds of hardware.

Introducing Python Fire, a library for automatically generating command line interfaces

Friday, March 3, 2017

By David Bieber, Software Engineer on Google Brain

Originally posted on the Google Open Source Blog

Today we are pleased to announce the open-sourcing of Python Fire. Python Fire generates command line interfaces (CLIs) from any Python code. Simply call the Fire function in any Python program to automatically turn that program into a CLI. The library is available from pypi via `pip install fire`, and the source is available on GitHub.

Python Fire will automatically turn your code into a CLI without you needing to do any additional work. You don't have to define arguments, set up help information, or write a main function that defines how your code is run. Instead, you simply call the `Fire` function from your main module, and Python Fire takes care of the rest. It uses inspection to turn whatever Python object you give it -- whether it's a class, an object, a dictionary, a function, or even a whole module -- into a command line interface, complete with tab completion and documentation, and the CLI will stay up-to-date even as the code changes.

To illustrate this, let's look at a simple example.

#!/usr/bin/env python

import fire

class Example(object):

def hello(self, name='world'):

"""Says hello to the specified name."""

return 'Hello {name}!'.format(name=name)

def main():

fire.Fire(Example)

if __name__ == '__main__':

main()

When the Fire function is run, our command will be executed. Just by calling Fire, we can now use the Example class as if it were a command line utility.

$ ./example.py hello

Hello world!

$ ./example.py hello David

Hello David!

$ ./example.py hello --name=Google

Hello Google!

Of course, you can continue to use this module like an ordinary Python library, enabling you to use the exact same code both from Bash and Python. If you're writing a Python library, then you no longer need to update your main method or client when experimenting with it; instead you can simply run the piece of your library that you're experimenting with from the command line. Even as the library changes, the command line tool stays up to date.

At Google, engineers use Python Fire to generate command line tools from Python libraries. We have an image manipulation tool built by using Fire with the Python Imaging Library, PIL. In Google Brain, we use an experiment management tool built with Fire, allowing us to manage experiments equally well from Python or from Bash.

Every Fire CLI comes with an interactive mode. Run the CLI with the `--interactive` flag to launch an IPython REPL with the result of your command, as well as other useful variables already defined and ready to use. Be sure to check out Python Fire's documentation for more on this and the other useful features Fire provides.

Between Python Fire's simplicity, generality, and power, we hope you find it a useful library for your own projects.

Apply now to Launchpad Accelerator---now including Africa and Europe!

Wednesday, March 1, 2017

Posted By: Roy Glasberg, Global Lead, Launchpad Program & Acceleratorhere

Asia: India, Indonesia, Thailand, Vietnam, Malaysia and the Philippines

Latin America: Argentina, Brazil, Chile, Colombia and Mexico

Africa: Kenya, Nigeria and South Africa

Europe: Czech Republic, Hungary and Poland

What are the benefits?
What do we look for when selecting startups?

Be a technological startup.

Be targeting their local markets.

Have proven product-market fit (beyond ideation stage).

Be based in the countries listed above.

The problem you are trying to solve. How does it create value for users? How are you addressing a real challenge for your home city, country or region?

Does your management team have a leadership mindset and the drive to become an influencer?

Will you share what you learn in Silicon Valley for the benefit of other startups in your local ecosystem?

“Level up” your gaming business with new innovations for apps

Wednesday, March 1, 2017

Inside AdMob BlogPosted by Sissie Hsiao, Product Director, Mobile Advertising, Google. Last played Fire Emblem Heroes for AndroidMobile games mean more than just fun. They mean business. Big business. According to App Annie, game developers should capture almost half of the $189B global market for in-app purchases and advertising by 2020¹.Later today, at the Games Developer Conference (GDC) in San Francisco, I look forward to sharing a series of new innovations across ad formats, monetization tools and measurement insights for apps.

New playable and video ad formats to get more people into your game

Integrations to help you create better monetization experiences

Measurement tools that provide insights about how players are interacting with your game

Let more users try your game with a playable ad formatThere’s no better way for a new user to experience your game than to actually play it. So today, we introduced playables, an interactive ad format in Universal App Campaigns that allows users to play a lightweight version of your game, right when they see it in any of the 1M+ apps in the Google Display Network. studio.justad.mobi-Management-studio-test_ad.php-browser&saved&id=703423(Nexus 5X)_nexus5x-portrait.png

studio.justad.mobi-Management-studio-test_ad.php-browser&saved&id=703423(Nexus 5X)_nexus5x-portrait.png

Jam City’s playable ad for Cookie JamPlayables help you get more qualified installs from users who tried your game in the ad and made the choice to download it for more play time. By attracting already-engaged users into your app, playables help you drive the long-term outcomes you care about — rounds played, levels beat, trophies won, purchases made and more."Jam City wants to put our games in the hands of more potential players as quickly as possible. Playables get new users into the game right from the ad, which we've found drives more engagement and long-term customer value." Josh Yguado, President & COO Jam City, maker of Panda Pop and Cookie Jam.Playables will be available for developers through Universal App Campaigns in the coming months, and will be compatible with HTML5 creatives built through Google Web Designer or third-party agencies.Improve the video experience with ads designed for mobile viewingMost mobile video ad views on the Google Display Network are watched on devices held vertically². This can create a poor experience when users encounter video ad creatives built for horizontal viewing.Developers using Universal App Campaigns will soon be able to use an auto-flip feature that automatically orients your video ads to match the way users are holding their phones. If you upload a horizontal video creative in AdWords, we will automatically create a second, vertical version for you. Cookie Jam horizontal video and vertical-optimized video created through auto-flip technologyThe auto-flip feature uses Google's machine learning technology to identify the most important objects in every frame of your horizontal video creative. It then produces an optimized, vertical version of your video ad that highlights those important components of your original asset. Early tests show that click-through rates are about 20% higher on these dynamically-generated vertical videos than on horizontal video ads watched vertically³.Unlock new business with rewarded video formats, and free, unlimited reportingDevelopers have embraced AdMob's platform to mediate rewarded video ads as a way to let users watch ads in exchange for an in-app reward. Today, we are delighted to announce that we are bringing Google’s video app install advertising demand from AdWords to AdMob, significantly increasing rewarded demand available to developers. Advertisers that use Universal App Campaigns can seamlessly reach this engaged, game-playing audience using your existing video creatives.We are also investing in better measurement tools for developers by bringing the power of Firebase Analytics to more game developers with a generally available C++ SDK and an SDK for Unity, a leading gaming engine.

C++ and Unity developers can now access Firebase Analytics for real-time player insightsWith Firebase Analytics, C++ and Unity developers can now capture billions of daily events — like level completes and play time — to get more nuanced player insights and gain a deeper understanding of metrics like daily active users, average revenue per user and player lifetime value.This is an exciting time to be a game developer. It’s been a privilege to meet so many of you at GDC 2017 and learn about the amazing games that you’re all building. We hope the innovations we announced today help you grow long-term gaming businesses and we look forward to continuing on this journey with you.Until next year, GDC!1 - App Monetization Report, November 2016, App Annie2 - More than 80% of video ad views in mobile apps on the Google Display Network are from devices held vertically video, Google Internal Data3 - Google Internal Data

Sep	OCT	Nov
	09
2017	2018	2019

A new issue tracker for G Suite developers

Game developers rejoice—new tools for developing on Actions on Google

A New Home for Google Open Source

Join us live on May 23rd as we announce the latest Ads, Analytics and DoubleClick innovations

XLA - TensorFlow, compiled

Introducing Python Fire, a library for automatically generating command line interfaces

Apply now to Launchpad Accelerator---now including Africa and Europe!

“Level up” your gaming business with new innovations for apps

Labels

Archive

Company-wide

Products

Developers