DistML.js: Installation-free Distributed Deep Learning Framework for Web Browsers

TL;DR

DistML.js is a browser-based distributed deep learning library that enables local training and inference, leveraging WebGL for fast computations and offering a PyTorch-like API for easy prototyping.

Contribution

It introduces a novel installation-free framework for distributed deep learning directly in web browsers with a user-friendly API and high-performance backend.

Findings

Supports model training and inference in browsers

Enables distributed learning with server communication

Utilizes WebGL for high-speed matrix computations

Abstract

We present "DistML.js", a library designed for training and inference of machine learning models within web browsers. Not only does DistML.js facilitate model training on local devices, but it also supports distributed learning through communication with servers. Its design and define-by-run API for deep learning model construction resemble PyTorch, thereby reducing the learning curve for prototyping. Matrix computations involved in model training and inference are executed on the backend utilizing WebGL, enabling high-speed calculations. We provide a comprehensive explanation of DistML.js's design, API, and implementation, alongside practical applications including data parallelism in learning. The source code is publicly available at https://github.com/mil-tokyo/distmljs.