| commit | b11d3a2550b3ad9d96f42e7d15e2c418dd2b4c52 | [log] [tgz] |
|---|---|---|
| author | Eric Junyuan Xie <piiswrong@users.noreply.github.com> | Sun Jan 22 11:07:09 2017 -0800 |
| committer | GitHub <noreply@github.com> | Sun Jan 22 11:07:09 2017 -0800 |
| tree | 12dbc0fa2f89f6dc355e59836a8ae9f37459dd50 | |
| parent | 0964409534440812c90bc9aa01c06aa62f9e5bd0 [diff] |
[RELEASE] v0.9.3 official release (#4767)
MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix the flavours of symbolic programming and imperative programming to maximize efficiency and productivity. In its core, a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. The library is portable and lightweight, and it scales to multiple GPUs and multiple machines.
MXNet is also more than a deep learning project. It is also a collection of blue prints and guidelines for building deep learning system, and interesting insights of DL systems for hackers.
© Contributors, 2015-2017. Licensed under an Apache-2.0 license.
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. In Neural Information Processing Systems, Workshop on Machine Learning Systems, 2015
MXNet is initiated and designed in collaboration by the authors of cxxnet, minerva and purine2. The project reflects what we have learnt from the past projects. It combines important flavours of the existing projects for efficiency, flexibility and memory efficiency.