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RFC: Creating SIG TF.js
# Creating SIG TF.js | Status | Proposed | :-------------- |:---------------------------------------------------- | | **RFC #** | | **Author(s)** | Ping Yu ([email protected]), Sandeep Gupta ([email protected]) | | **Sponsor** | Thea Lamkin ([email protected]) | | **Updated** | 2020-09-16 | ## Objective We propose to create a Tensorflow SIG to facilitate community-contributed components to tensorflow/tfjs. This document outlines the context goals, and engagement plan for the proposed Special Interest Group.
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rfcs/20200916-sig-tf-js.md

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# Creating SIG TF.js
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| Status | Proposed |
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:-------------- |:---------------------------------------------------- |
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| **RFC #** |
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| **Author(s)** | Ping Yu ([email protected]), Sandeep Gupta ([email protected]) |
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| **Sponsor** | Thea Lamkin ([email protected]) |
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| **Updated** | 2020-09-16 |
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## Objective
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We propose to create a Tensorflow SIG to facilitate community-contributed components
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to tensorflow/tfjs. This document outlines the context goals, and engagement plan for
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the proposed Special Interest Group.
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## Context
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JavaScript is a very versatile and widely-used language. With TensorFlow.js, web application
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developers and JS developers can now use Machine Learning with TensorFlow in their JS
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applications. TensorFlow.js is the leading framework for ML in JavaScript with huge user growth
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(2.1M npm downloads, more than 2x YoY growth in the number of avg daily downloads). With the
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growth of the library, we now support an increasing number of use cases across a variety of
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platforms and backends, with many more models, use-cases, and applications.
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The core TensorFlow.js engineering team has been working on building the infrastructure and
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tooling to enable ML to run in JavaScript powered applications, as well as adding support for
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more types of models. TensorFlow.js has more than 220 contributors on GitHub which include many
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passionate contributors (individual developers, GDEs, and enterprise users) who have been active
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participants in the TensorFlow.js ecosystem. We want to accelerate the community involvement in
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the project to help continue meet the needs and help drive new directions for the project.
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## Goals & Objectives
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We welcome community contributions on any area of TensorFlow.js, but the SIG will be focused on
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the following goals:
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- Driving TensorFlow.js features for Server-side execution with tfjs-node. This will include
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building tooling and infra for end to end, production-ready ML with TensorFlow.js.
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- Developing features for specific platforms and deployments (eg. React Native, IoT and embedded
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devices, etc);
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- Building workflows for Transfer learning features for customizing models
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- Adding support for newer models and application areas (eg. NLP, RL, etc), and data analysis
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algorithms
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- Conducting research and implementing prototypes for client-side, privacy preserving ML and
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Federated Learning.
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- Identifying performance issues, tradeoffs, and requirements.
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- Model benchmarking across browsers, hardware and devices.
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- Model encryption.
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- Model visualization.
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- These projects will move towards community ownership as work streams mature.
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## Membership
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We encourage any developers working at the intersection of machine learning and web/JS
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applications to join and participate in the activities of the SIG. Whether you are working on
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advancing the platform, prototyping or building specific applications, or authoring new libraries,
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we welcome your feedback on and contributions to Tensorflow.js and its tooling, and are eager to hear
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about any downstream results, implementations, and extensions.
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We have multiple channels for participation, and publicly archive discussions in our user group and
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announcements mailing lists:
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- [email protected] -- our general mailing list that all are welcome to join ([archive group](https://groups.google.com/a/tensorflow.org/g/tfjs))
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- [email protected] -- Announcement only mailing list for TF.js community ([archive group](https://groups.google.com/a/tensorflow.org/g/tfjs-announce))
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We will create a new mailing list for TensorFlow.js SIG members.
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### Other Resources
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- Repository: https://github.com/tensorflow/tfjs
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- Documentation: https://www.tensorflow.org/js
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## Organization and Governance
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SIG project leads will be added to a SIG TF.js maintainers team on GitHub to streamline their
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contributions to tensorflow/tfjs. As specific ideas and work streams develop, we will explore creating
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new, community-owned repos that the SIG will drive.
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## Contacts
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### Project Lead(s):
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- Ping Yu (Google)
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- Sandeep Gupta (Google)
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- Ton Ngo (IBM):
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- Server side support (GPU, training)
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- End-to-end pipeline on Kubernetes (Kubeflow)
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- Ricky Cao (Alibaba)
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- Building tooling and infra for end to end, production-ready ML with TensorFlow.js.
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- Pipeline with transfer learning
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- Model Encryption
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- Ningxin Hu (Intel)
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- Benchmarking and performance optimization
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- Gant Laborde (Infinite Red)
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- Platforms (React and React Native)
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- Adding support for newer models and application areas (eg. NLP, RL, etc),
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- Model visualization
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### SIG members:
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- Eric Li (Alibaba)
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- Zebai (Alibaba)
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- Paul Van Eck (IBM)
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- Ted Chang (IBM)
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- YiHong Wang (IBM)
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### Administrative questions:
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- Thea Lamkin (Google): thealamkin at google dot org
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- Joana Carrasqueira (Google): joanafilipa at google dot org
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- tf-community at tensorflow dot org
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### Meeting cadence: TBD
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