MXNet is a deep learning framework and a multi-language library. It is designed to develop machine learning algorithms for deep neural networks. It is a dataset of blueprints and guidelines for creating deep learning systems.
It allows users to mix symbolic and imperative programming to maximize efficiency and productivity. It contains dynamic dependency scheduler that synchronizes declarative symbolic expression and imperative tensor computation. It is lightweight and embeds in multiple languages. It is scalable to different systems ranging from mobile devices to distributed GPU clusters.
MXNet is a project by Apache Software Foundation (ASF), sponsored by the Apache Incubator.
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Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang and Zheng Zhang
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
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