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.

DistML.js是一个用于在Web浏览器中训练和推断机器学习模型的库，它不仅可以在本地设备上进行模型训练，还支持与服务器进行分布式学习。该库的设计和基于运行的API用于构建深度学习模型，类似于PyTorch，从而降低了原型设计的学习曲线。通过利用WebGL在后端执行模型训练和推断所涉及的矩阵计算，实现了高速计算。该论文提供了对DistML.js设计、API和实现的全面解释，以及包括数据并行在内的实际应用。源代码公开可用于指定的URL。

DistML.js：无需安装的面向 Web 浏览器的分布式深度学习框架