Brain.js: GPU Accelerated Neural Networks in JavaScript

This page summarizes the projects mentioned and recommended in the original post on news.ycombinator.com

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  • Dannjs

    Easy to use Deep Neural Network Library for JavaScript.

  • tract

    Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference

    There's also tract, from sonos[0]. 100% rust.

    I'm currently trying to use it to do speech recognition with a variant of the Conformer architecture (exported to ONNX).

    The final goal is to do it in WASM client-side.

    [0] https://github.com/sonos/tract

  • Appwrite

    Appwrite - The Open Source Firebase alternative introduces iOS support . Appwrite is an open source backend server that helps you build native iOS applications much faster with realtime APIs for authentication, databases, files storage, cloud functions and much more!

  • UNN.js

    Deep Learning in JS. Alternative to TensorFlow and ConvNetJS, that is 4x faster.

  • Brain.js

    🤖 GPU accelerated Neural networks in JavaScript for Browsers and Node.js

  • gpu.js

    GPU Accelerated JavaScript

    Thanks for pointing this out. I've submitted a PR to resolve this: https://github.com/gpujs/gpu.js/issues/757

    That being said, if you're not building from source (you're running an LTS version of node on a supported platform), you don't need to worry about python or many of the build deps.

  • wonnx

    A GPU-accelerated ONNX inference run-time written 100% in Rust, ready for the web

    Thanks! it looks like the wonnx CLI itself falls back to tract to do inference on CPU if a GPU is not available[0], and it also sounds like setting up llvmpipe/lavapipe on WASM is much harder (if not impossible?) than just shipping tract, so the approach I'll take is probably a wonnx+tract approach.

    [0] https://github.com/webonnx/wonnx/issues/116#issuecomment-114...

  • ml5-library

    Friendly machine learning for the web! 🤖

    See also: https://ml5js.org/

    "The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies."

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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