MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
Abstrak
MXNet is a multi-language machine learning (ML) library to ease the development of ML algorithms, especially for deep neural networks. Embedded in the host language, it blends declarative symbolic expression with imperative tensor computation. It offers auto differentiation to derive gradients. MXNet is computation and memory efficient and runs on various heterogeneous systems, ranging from mobile devices to distributed GPU clusters. This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion. Our preliminary experiments reveal promising results on large scale deep neural network applications using multiple GPU machines.
Topik & Kata Kunci
Penulis (10)
Tianqi Chen
Mu Li
Yutian Li
Min Lin
Naiyan Wang
Minjie Wang
Tianjun Xiao
Bing Xu
Chiyuan Zhang
Zheng Zhang
Akses Cepat
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Cek di sumber asli →- Tahun Terbit
- 2015
- Bahasa
- en
- Total Sitasi
- 2317×
- Sumber Database
- Semantic Scholar
- Akses
- Open Access ✓