The TensorFlow Special Interest Groups (TF SIGs) organize community contributions to key parts of the TensorFlow ecosystem. SIG leads and members work together to build and support important TensorFlow use cases.
SIGs are led by members of the open source community, including industry collaborators and Machine Learning Google Developer Experts (ML GDEs). TensorFlow's success is due in large part to their hard work and contributions.
We encourage you to join a SIG working on the area of TensorFlow's ecosystem you care most about. Not all SIGs will have the same level of energy, breadth of scope, or governance models — browse our SIG charters to learn more. Stay connected with SIG leads and members on the TensorFlow Forum, where you can subscribe to preferred tags and learn more about the regular SIG meetings.
SIG Addons builds and maintains a repository of community contributions that conform to well-established API patterns, but implement new functionality not available in core TensorFlow.
TensorFlow natively supports a large number of operators, layers, metrics, losses, optimizers, and more. However, in a fast-moving field like ML, there are many new developments that cannot be integrated into core TensorFlow (because their broad applicability is not yet clear, or it is mostly used by a smaller subset of the community). SIG Addons enables users to introduce new extensions to the TensorFlow ecosystem in a sustainable manner.
SIG Addons on GitHub Contributing Discuss on the Forum
SIG Build improves and extends the TensorFlow build process. SIG Build maintains a repository showcasing resources, guides, tools, and builds contributed by the community, for the community.
SIG Build on GitHub Contributing Discuss on the Forum
SIG IO maintains TensorFlow I/O, a collection of file systems and file formats that are not available in TensorFlow's built-in support.
SIG IO on GitHub Contributing Discuss on the Forum
SIG JVM maintains the TF Java bindings to let users use JVM for building, training and running machine learning models.
Java and other JVM languages, such as Scala or Kotlin, are frequently used in small-to-large enterprises all over the world, which makes TensorFlow a strategic choice for adopting machine learning at a large scale.
SIG JVM on GitHub Contributing Discuss on the Forum
SIG Models focuses on enabling contributions to the state-of-the-art model implementation in TensorFlow 2, and sharing best practices of using TensorFlow 2 for state-of-the-art research. Subgroups orient around different machine learning applications (Vision, NLP, etc.).
SIG Models host discussions and collaborations around the TensorFlow Model Garden and TensorFlow Hub. Learn how to contribute on GitHub below, or discuss Research & Models on the Forum.
TensorFlow Model Garden on GitHub Contributing
TensorFlow Hub on GitHub Contributing
SIG Micro discusses and shares updates on TensorFlow Lite for Microcontrollers, a port of TensorFlow Lite designed to run machine learning models on DSPs, microcontrollers and other devices with limited memory.
TensorFlow Lite Micro on GitHub Contributing Discuss on the Forum
SIG MLIR maintains MLIR dialects and utilities for TensorFlow, XLA and TF Lite, providing high performance compilers and optimization techniques that can be applied to TensorFlow graphs and code generation. Their overarching goal is to create common intermediate representation (IR) that reduces the cost to bring up new hardware, and improve usability for existing TensorFlow users.
SIG MLIR on GitHub Contributing Discuss on the Forum
SIG Networking maintains the TensorFlow Networking repository for platform-specific networking extensions to core TensorFlow and related utilities.
SIG Networking on GitHub Discuss on the Forum
SIG Recommenders maintains a collection of projects related to large-scale recommendation systems built upon TensorFlow contributed and maintained by the community. Those contributions are complementary to TensorFlow Core and TensorFlow Recommenders.
SIG Recommenders on GitHub Contributing Discuss on the Forum
SIG Rust maintains idiomatic Rust language bindings for TensorFlow.
SIG Rust on GitHub Contributing Discuss on the Forum
SIG TensorBoard facilitates discussion around TensorBoard—a suite of tools for inspecting, debugging and optimizing TensorFlow programs.
TensorBoard on GitHub Contributing Discuss on the Forum
SIG TF.js facilitates community-contributed components to TensorFlow.js and offers project support through the SIG.
TensorFlow.js on GitHub Contributing Discuss on the Forum
SIG TFX-Addons accelerates the sharing of customizations and additions to meet the needs of production ML, expand the vision, and help drive new directions for TensorFlow Extended (TFX) and the ML community.
SIG TFX-Addons on GitHub Contributing Discuss on the Forum
Didn't find what you were looking for? If you believe there is a strong need for a new TensorFlow SIG, please read the SIG playbook and follow instructions on how to propose it to our contributor community.