DistML.js: Installation-free Distributed Deep Learning Framework for Web Browsers
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909235439206400 |
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| author | Hidaka, Masatoshi Hashimoto, Tomohiro Nishizawa, Yuto Harada, Tatsuya |
| author_facet | Hidaka, Masatoshi Hashimoto, Tomohiro Nishizawa, Yuto Harada, Tatsuya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01023 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DistML.js: Installation-free Distributed Deep Learning Framework for Web Browsers Hidaka, Masatoshi Hashimoto, Tomohiro Nishizawa, Yuto Harada, Tatsuya Machine Learning 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. |
| title | DistML.js: Installation-free Distributed Deep Learning Framework for Web Browsers |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.01023 |