Summary of Distml.js: Installation-free Distributed Deep Learning Framework For Web Browsers, by Masatoshi Hidaka et al.
DistML.js: Installation-free Distributed Deep Learning Framework for Web Browsers
by Masatoshi Hidaka, Tomohiro Hashimoto, Yuto Nishizawa, Tatsuya Harada
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces DistML.js, a library for training and inference of machine learning models within web browsers. It enables local model training, distributed learning through server communication, and high-speed calculations using WebGL. The design and API are inspired by PyTorch, making it easy to prototype deep learning models. The authors provide an overview of the library’s design, implementation, and practical applications, including data parallelism in learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DistML.js is a new tool that lets you train machine learning models right on your computer or phone. It also helps with distributed learning, where many devices work together to learn from large amounts of data. The way it works is inspired by another popular tool called PyTorch, making it easy for developers to use. This paper explains how DistML.js was made and shows some examples of what you can do with it. |
Keywords
» Artificial intelligence » Deep learning » Inference » Machine learning
