  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.
