Posted by Joana Carrasqueira, TensorFlow Program Manager and Thea Lamkin, Open Source Program Manager, in collaboration with TensorFlow SIG Leads.
TensorFlow SIGs (Special Interest Groups) organize community contributions to key parts of the TensorFlow ecosystem, and enable community members to contribute and maintain new features in important areas.
SIG leads and members work together to build and support important TensorFlow use cases, and are a vital part of our open source community. It all started with the SIG Build, and we now have 13 Active SIGs, with more on the way.
In this article, you’ll learn about the SIGs that exist today, and how you can get involved. Many SIGs are led by members of the open source community, from industry collaborators to Machine Learning Google Developer Experts (ML GDEs). TensorFlow’s success is due in large part to the hard work and contributions of our vibrant community. We welcome contributors to join the SIGs working on the parts of TensorFlow’s ecosystem they are most excited to collaborate on. Here is an overview of the SIGs and their areas of focus, contributed by their leads:
In a fast-moving field like Machine Learning, there are many new developments that cannot be integrated into core TensorFlow. SIG Addons was created to tackle this problem by maintaining a repository of bleeding edge contributions that conform to well-established API patterns, but implement new functionality not available in core TensorFlow and adopted some of the parts of tf.contrib.
Started as a forum for development topics like new architecture support and packaging improvements, SIG Build grew to a discussion center dedicated to building, testing, packaging, and distributing TensorFlow that bridges internal and external TensorFlow development. The goal of this group is to ensure TensorFlow is a good citizen in the wider OSS ecosystem (Python, C++, Linux, Windows, MacOS).
SIG IO is a repository of dataset, streaming, and file systems extension support for TensorFlow. Recent accomplishments include the release of v.0.13.0 (with TF 2.2), added Video Studio Code tutorial, and added AVIF imagine file format support.
SIG JVM provides comprehensive support for building, training and serving TensorFlow models on top of Java Virtual Machine (JVM). This group focuses on using Java but also includes other popular JVM languages, like Kotlin and Scala. Some of the recent accomplishments include adding n-dimensional data access in native memory and the creation of a high-level API similar to Keras for building models.
This group focuses on care and feeding of the tf.Keras API (new features, docs, guides), Keras Tuner, AutoKeras, and Keras applications.
SIG Micro is a discussion and collaboration group around running TensorFlow models on Microcontrontrollers, DSPs, and other highly resource constrained embedded devices.
The goal of this group is to foster an open discussion on high performance compilers and how optimization techniques can be applied to TensorFlow graphs. Ultimately this project aims to create a common intermediate representation that reduces the cost of new hardware and improves usability for existing TensorFlow users.
SIG Networking aims to add support for different network fabrics and protocols. The group evaluates proposals and designs in this area and maintains code in the tensorflow/networking repository. Join us, if you are interested in improving TensorFlow on different types of networks or underlying drivers and libraries!
SIG Reccomenders (New!)
SIG Recommenders was created to drive discussion and collaborations around using TensorFlow for large scale recommendation systems (Recommenders). We hope to encourage sharing of best practices in the industry, get consensus and product feedback to help evolve TensorFlow support for recommenders, and facilitate the contributions of RFCs and PRs in this domain.
SIG Rust was created for users and contributors on the TensorFlow Rust binding project. It provides stable support for running models created in other languages, and can both train and evaluate.
To contribute to TensorFlow Rust, join the conversation at our monthly meeting.
The purpose of SIG Swift is to host design reviews, discuss upcoming API changes, share project roadmap, and encourage collaboration in the Swift for TensorFlow (S4TF) open-source community.
SIG TensorBoard was created for discussion and collaboration around TensorBoard, the visualization tool for TensorFlow. The goal of this group is to engage the TensorBoard user and developer community and get feedback; encourage development of new TensorBoard plugins; promote collaboration ML via TensorBoard.dev; and encourage community improvements to TensorBoard.
SIG TF.js (New!)
Thank you to our SIG Leads for their work and leadership:
|Picture: 1st TensorFlow Contributor Summit, Santa Clara, 2019.|
Sean Morgan, Tzu-Wei Sung | SIG Addons
Jason Zaman, Austin Anderson | SIG Build
Yong Tang, Anthony Dmitriev, Derek Murray | SIG IO
Karl Lessard, Adam Pocock, Rajagopal Ananthanarayanan | SIG JVM
Francois Chollet | SIG Keras
Neil Tan, Pete Warden | SIG Micro
Tatiana Shpeisman, Pankaj Kanwar | SIG MLIR
Bairen Yi, Jeroen Bedorf | SIG Networking
Bo Liu, Haidong Rong, Yong Li, Wei Wei | SIG Recommenders
Adam Crume | SIG Rust
Ewa Matejska | SIG Swift
Mani Varadarajan, Gal Oshri | SIG TensorBoard
Sandeep Gupta, Ping Yu | SIG TF.js