Google Summer of Code 2021 will bring some changes

Google Open Source is pleased to announce the 2021 cycle of the Google Summer of Code (GSoC) program, which will be our 17th consecutive year bringing students into open source communities. Over the past 16 years Google Summer of Code has brought over 16,000 student developers from 111 countries into 715 open source organizations big and small.

Some exciting changes are coming to the 2021 GSoC as we make adjustments to add more flexibility into the program for students and mentors alike.

• With the pandemic straining folks’ time we are changing the size of the projects and time commitment students are expected to spend on their projects. Starting in 2021, students will be focused on a 175-hour project over a 10-week coding period.
• As students are learning in many different educational formats in 2020, we are opening up the 2021 program to students 18 years and older who are:
1. Enrolled in post-secondary academic programs (including college, university, masters program, PhD program and/or undergraduate program, or licensed coding school, etc.) as of May 17, 2021; or,
2. Have graduated from a post-secondary academic program between December 1, 2020 and May 17, 2021.

We’re excited that GSoC will be able to continue to thrive as we welcome more students from around the world into open source in 2021! Applications for interested open source project organizations open on January 29th, and student applications open March 29, 2021.

Does your open source project want to learn more about how to apply to be a mentoring organization? This is a mentorship program so having mentors excited about teaching students how to be a part of your community and ready to guide students is key.

Visit the program site and read the mentor guide to learn more about what it means to be a mentor organization, how to prepare your community (hint: have plenty of enthusiastic mentors!), create appropriate project ideas, and tips for preparing your application. We welcome all types of organizations—large and small—and are very eager to involve first time projects. For 2021, we hope to welcome more organizations than ever before and are looking to accept at least 40 into their first GSoC.

Are you a student interested in learning how to prepare for the 2021 GSoC program? It’s never too early to start thinking about your proposal or about what type of open source organization you may want to work with. Read through the student guide for important tips on preparing your proposal and what to consider if you wish to apply for the program in late-March. You can also get inspired by checking out the 198 organizations that participated in Google Summer of Code 2020, as well as the projects that students worked on.

We encourage you to explore other resources and you can learn more on the program website.

Please spread the word to your friends as we hope these changes will help more excited folks apply to be students and mentoring organizations in GSoC 2021!

By Stephanie Taylor, Program Manager—Google Open Source

Peer Bonus Experiences: Building tiny models for the ML community with TensorFlow

Almost all the current state-of-the-art machine learning (ML) models take quite a lot of disk space. This makes them particularly inefficient in production situations. A bulky machine learning model can be exposed as a REST API and hosted on cloud services, but that same bulk may lead to hefty infrastructure costs. And some applications may need to operate in low-bandwidth environments, making cloud-hosted models less practical.

In a perfect world, your models would live alongside your application, saving data transfer costs and complying with any regulatory requirements restricting what data can be sent to the cloud. But storing multi-gigabyte models on today’s devices just isn’t practical. The field of on-device ML is dedicated to the development of tools and techniques to produce tiny—yet high performing!—ML models. Progress has been slow, but steady!

There has never been a better time to learn about on-device ML and successfully apply it in your own projects. With frameworks like TensorFlow Lite, you have an exceptional toolset to optimize your bulky models while retaining as much performance as possible. TensorFlow Lite also makes it very easy for mobile application developers to integrate ML models with tools like metadata and ML Model Binding, Android codegen, and others.

What is TensorFlow Lite?

“TensorFlow Lite is a production ready, cross-platform framework for deploying ML on mobile devices and embedded systems.” - TensorFlow Youtube

TensorFlow Lite provides first-class support for Native Android and iOS-based integrations (with many additional features, such as delegates). TensorFlow Lite also supports other tiny computing platforms, such as microcontrollers. TensorFlow Lite’s optimization APIs produce world-class, fast, and well-performing machine learning models.

Venturing into TensorFlow Lite

Last year, I started playing around with TensorFlow Lite while developing projects for Raspberry Pi for Computer Vision, using the official documentation and this course to fuel my initial learning. Following this interest, I decided to join a voluntary working group focused on creating sample applications, writing out tutorials, and creating tiny models. This working group consists of individuals from different backgrounds passionate about teaching on-device machine learning to others. The group is coordinated by Khanh LeViet (TensorFlow Lite team) and Hoi Lam (Android ML team). This is by far one of the most active working groups I have ever seen. And, back in our starting days, Khanh proposed a few different state-of-art machine learning models that were great fits for on-device machine learning:

• DeepLabV3 segmentation models that classify each image pixels to a particular category like so -

• Selfie2Anime model that can turn a selfie into an anime like so -

These ideas were enough for us to start spinning up Jupyter notebooks and VSCode. After months of work, we now have strong collaborations between machine learning GDEs and a bunch of different TensorFlow Lite models, sample applications, and tutorials for the community to learn from. Our collaborations have been fueled by the power of open source and all the tiny models that we have built together are available on TensorFlow Hub. There are numerous open source applications that we have built that demonstrate how to use these models.

The Cartoonizer model cartoonizes uploaded images

Margaret and I co-authored an end-to-end tutorial that was published from the official TensorFlow blog and published the TensorFlow Lite models on TensorFlow Hub. So far, the response we have received for this work has been truly mesmerizing. I’ve also shared my experiences with TensorFlow Lite in these blog posts and conference talks:

A Tale of Model Quantization in TF Lite
Plunging into Model Pruning in Deep Learning
A few good stuff in TF Lite
Doing more with TF Lite
Model Optimization 101

The power of collaboration

The working group is a tremendous opportunity for machine learning GDEs, Googlers, and passionate community individuals to collaborate and learn. We get to learn together, create together, and celebrate the joy of teaching others. I am immensely thankful, grateful, and humbled to be a part of this group. Lastly, I would like to wholeheartedly thank Khanh for being a pillar of support to us and for nominating me for the Google Open Source Peer Bonus Award.

By Sayak Paul, PyImageSearch—Guest Author

OpenTelemetry's First Release Candidates

OpenTelemetry has hit another milestone with the tracing specification reaching release candidate status.

With the specification now ready to go, expect to see tracing release candidates of the official APIs and SDKs over the next few weeks, along with updated exporters for Cloud Trace. In the coming months the same will follow for the metrics specification, followed by metrics release candidates of the APIs and SDKs and Cloud Monitoring exporters, followed by the project’s general availability. At this point we’ll switch our default application metrics and distributed tracing instrumentation from OpenCensus to OpenTelemetry.

This is exciting news for Google Cloud customers, as OpenTelemetry will enable even better observability experiences, both with Cloud Monitoring and Cloud Trace, or the third party monitoring and operations tools of your choice.

Originally posted on the on the OpenTelemetry blog.