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SIG TF.js

Objective

To facilitate community-contributed components to tensorflow/tfjs.

Context

JavaScript is a very versatile and widely-used language. With TensorFlow.js, web application developers and JS developers can now use Machine Learning with TensorFlow in their JS applications. TensorFlow.js is the leading framework for ML in JavaScript with huge user growth (2.1M npm downloads, more than 2x YoY growth in the number of avg daily downloads). With the growth of the library, we now support an increasing number of use cases across a variety of platforms and backends, with many more models, use-cases, and applications.

The core TensorFlow.js engineering team has been working on building the infrastructure and tooling to enable ML to run in JavaScript powered applications, as well as adding support for more types of models. TensorFlow.js has more than 220 contributors on GitHub which include many passionate contributors (individual developers, GDEs, and enterprise users) who have been active participants in the TensorFlow.js ecosystem. We want to accelerate the community involvement in the project to help continue meet the needs and help drive new directions for the project.

Goals & Objectives

We welcome community contributions on any area of TensorFlow.js, but the SIG will be focused on the following goals:

  • Driving TensorFlow.js features for Server-side execution with tfjs-node. This will include building tooling and infra for end to end, production-ready ML with TensorFlow.js.
  • Developing features for specific platforms and deployments (eg. React Native, IoT and embedded devices, etc);
  • Building workflows for Transfer learning features for customizing models
  • Adding support for newer models and application areas (eg. NLP, RL, etc), and data analysis algorithms
  • Conducting research and implementing prototypes for client-side, privacy preserving ML and Federated Learning.
  • Identifying performance issues, tradeoffs, and requirements.
  • Model benchmarking across browsers, hardware and devices.
  • Model encryption.
  • Model visualization.
  • These projects will move towards community ownership as work streams mature.

Membership

We encourage any developers working at the intersection of machine learning and web/JS applications to join and participate in the activities of the SIG. Whether you are working on advancing the platform, prototyping or building specific applications, or authoring new libraries, we welcome your feedback on and contributions to Tensorflow.js and its tooling, and are eager to hear about any downstream results, implementations, and extensions. To participate, please request to join our SIG mailing list.

We have multiple channels for participation, and publicly archive discussions in our user group and announcements mailing lists:

Monthly Meetings

We meet on the first Tuesday of every month. If you are interested in joining the meeting, add the calendar event to your calendar or email tf-community-team at google dot com.

Other Resources

Organization and Governance

SIG project leads will be added to a SIG TF.js maintainers team on GitHub to streamline their contributions to tensorflow/tfjs. As specific ideas and work streams develop, we will explore creating new, community-owned repos that the SIG will drive.

Contacts

Project Lead(s):

  • Ping Yu (Google)
  • Sandeep Gupta (Google)
  • Jason Mayes (Google)
  • Ton Ngo (IBM):
    • Server side support (GPU, training)
    • End-to-end pipeline on Kubernetes (Kubeflow)
  • Ricky Cao (Alibaba)
    • Building tooling and infra for end to end, production-ready ML with TensorFlow.js.
    • Pipeline with transfer learning
    • Model Encryption
  • Ningxin Hu (Intel)
    • Benchmarking and performance optimization
  • Gant Laborde (Infinite Red)
    • Platforms (React and React Native)
    • Adding support for newer models and application areas (eg. NLP, RL, etc),
    • Model visualization

SIG members:

  • Adam Bouhenguel (Tesserai)
  • Clément Walter (IBM)
  • Eric Li (Alibaba)
  • Mrinal Mathur (ARM)
  • Patrick Haralabidis (Carlisle Homes)
  • Paul Van Eck (IBM)
  • Rising Odegua (Datopian)
  • Saswat Samal (Student)
  • Shivay Lamba (Metvy)
  • Ted Chang (IBM)
  • Va Barbosa (IBM)
  • YiHong Wang (IBM)
  • Zebai (Alibaba)
  • Belem Zhang (Intel)