opensource.google.com

Menu

From MLPerf to MLCommons: moving machine learning forward

Thursday, December 3, 2020

Today, the community of machine learning researchers and engineers behind the MLPerf benchmark is launching an open engineering consortium called MLCommons. For us, this is the next step in a journey that started almost three years ago.


Early in 2018, we gathered a group of industry researchers and academics who had published work on benchmarking machine learning (ML), in a conference room to propose the creation of an industry standard benchmark to measure ML performance. Everyone had doubts: creating an industry standard is challenging under the best conditions and ML was (and is) a poorly understood stochastic process running on extremely diverse software and hardware. Yet, we all agreed to try.

Together, along with a growing community of researchers and academics, we created a new benchmark called MLPerf. The effort took off. MLPerf is now an industry standard with over 2,000 submitted results and multiple benchmarks suites that span systems from smartphones to supercomputers. Over that time, the fastest result submitted to MLPerf for training the classic ML network ResNet improved by over 13x.

We created MLPerf because we believed in three principles:
  • Machine learning has tremendous potential: Already, machine learning helps billions of people find and understand information through tools like Google’s search engine and translation service. Active research in machine learning could one day save millions of lives through improvements in healthcare and automotive safety.
  • Transforming machine learning from promising research into wide-spread industrial practice requires investment in common infrastructure -- especially metrics: Much like computing in the ‘80s, real innovation is mixed with hype and adopting new ideas is slow and cumbersome. We need good metrics to identify the best ideas, and good infrastructure to make adoption of new techniques fast and easy.
  • Developing common infrastructure is best done by an open, fast-moving collaboration: We need the vision of academics and the resources of industry. We need the agility of startups and the scale of leading tech companies. Working together, a diverse community can develop new ideas, launch experiments, and rapidly iterate to arrive at shared solutions.
Our belief in the principles behind MLPerf has only gotten stronger, and we are excited to be part of the next step for the MLPerf community with the launch of MLCommons.

MLCommons aims to accelerate machine learning to benefit everyone. MLCommons will build a a common set of tools for ML practitioners including:
  • Benchmarks to measure progress: MLCommons will leverage MLPerf to measure speed, but also expand benchmarking other aspects of ML such as accuracy and algorithmic efficiency. ML models continue to increase in size and consequently cost. Sustaining growth in capability will require learning how to do more (accuracy) with less (efficiency).
  • Public datasets to fuel research: MLCommons new People’s Speech project seeks to develop a public dataset that, in addition to being larger than any other public speech dataset by more than an order of magnitude, better reflects diverse languages and accents. Public datasets drive machine learning like nothing else; consider ImageNet’s impact on the field of computer vision. 
  • Best practices to accelerate development: MLCommons will make it easier to develop and deploy machine learning solutions by fostering consistent best practices. For instance, MLCommons’ MLCube project provides a common container interface for machine learning models to make them easier to share, experiment with (including benchmark), develop, and ultimately deploy.
Google believes in the potential of machine learning, the importance of common infrastructure, and the power of open, collaborative development. Our leadership in co-founding, and deep support in sustaining, MLPerf and MLCommons has echoed our involvement in other efforts like TensorFlow and NNAPI. Together with the MLCommons community, we can improve machine learning to benefit everyone.

Want to get involved? Learn more at mlcommons.org.


By Peter Mattson – ML Metrics, Naveen Kumar – ML Performance, and Cliff Young – Google Brain

Best practices for accessibility for virtual events

Tuesday, December 1, 2020





As everyone knows, most of our open source events have transformed from in-person to digital this year. However, due to the sudden change, not everything is accessible. We took this issue seriously and decided to work with one of our accessibility experts, Neighborhood Access, to share best practices for our community. We hope this will help you organize your digital events!

How Google’s 2020 summer interns became the newest contributors in open source

Thursday, November 19, 2020

Our internship program changed in structure this year to accommodate a virtual environment, and we enjoyed seeing the intern involvement in our open source teams. Now, as the Summer 2020 Interns have departed Google, we’ve seen widespread impact across these OSS projects. Some accomplishments from the intern community included:
  • Mohamed Ibrahim, a Software Engineering major at the University of Ontario Institute of Technology, interned on the Earth Engine team in Geo. He built a web app from scratch that allows Earth Engine developers, who are primarily climate and remote-sensing researchers, to build rich UIs for their Earth Engine Apps without needing to write any code. Mohamed also learned two coding languages unfamiliar to him, enabling him to write over 10,000 lines of TypeScript, 480 lines of Go, and merge over 30 PRs during one internship.
App creator demo
Web app demo
  • Vismita Uppalli, a Cloud intern and Computer Science major at the University of Virginia, wrote a tutorial showing how to use AI Platform Operators on Apache Airflow, which is now published in the official Airflow docs.
  • Colin Marsch interned with the Android team and published a blog post for Android developers, "Re-writing the AOSP DeskClock App in Kotlin," which has reached over 1,600 viewers! He is scheduled to graduate from the University of Waterloo with a major in Computer Science in Spring 2021.
  • Satyam Ralhan worked in the MyHeart team in Research to build a first-of-its-kind Android app that engages users in conversations to encourage healthy habits. He created a demo, which explores the different phases of the app and how it learns to personalize lifestyle suggestions for various kinds of users. He is in his fourth year at the Indian Institute of Technology, Kanpur, studying Computer Science and Engineering.
    MyHeart app demo
  • An Apigee intern, Nicole Gizzo, presented her work analyzing API vocabularies at the API Specifications Conference. She is majoring in Computer Science and Cognitive Science at Rensselaer Polytechnic Institute, and will graduate in May 2021.
  • The OSS Fuzzing Interns have found and reported over 600 bugs to critical open source projects like the Linux kernel and Nginx, over 100 of which were security vulnerabilities.
  • Madelyn Dubuk, a SWE Intern on the Cloud DPE team and a Computer Science major at USC, worked with three other interns to create a full stack web app to help better understand test flakiness, and enjoyed working directly with other interns.
Initial feedback from our interns indicates that their OSS contributions won’t stop when their internships end. Of the interns who worked on OSS projects, 69% plan to continue contributing to OSS, enjoying the ability to talk about their work and have a broader impact. Beyond the impact on OSS, we’ve seen tremendous professional growth for our interns. Lucia Cantu-Miller, an intern on the Chrome team and Computer Science major at ITESM Monterrey, reflected she was, “proud of seeing how I’ve grown during the internship. As the days passed I became more confident in my work and in asking questions, I have grown a lot as a person and as a professional student.” Lucia wasn’t the only intern to experience this as 98% of interns who worked on OSS feel that Google is a good place to start a career. The success of this summer’s Internship is due in large part to the many contributions of Google’s OSS community—from the intern hosts to the project champions and mentors—we can’t thank them enough for their support. 

By Emma Stamp, Google Engineering Education
.