| commit | cc861f0a3fecea8fb94c9687396e27966e5ddcde | [log] [tgz] |
|---|---|---|
| author | Zhaoqi Zhu <zhaoqizh@usc.edu> | Fri Jul 19 22:41:09 2019 -0700 |
| committer | Sandeep Krishnamurthy <sandeep.krishna98@gmail.com> | Fri Jul 19 22:41:09 2019 -0700 |
| tree | 07bc3aceab80d51f5cdeed55861187a2882ffc2a | |
| parent | d14fa692b075bc70c58d8e5ad2bd8d442f7449b9 [diff] |
Tensor Inspector Tutorial (#15517) * add tensor inspector tutorial * link docs * link docs * add license * Revert "add license" This reverts commit 32881e5acff6a0dc833e52f9c82c4967df40f006. * Revert "link docs" This reverts commit f93ae219262513e8ce52c0d68abd8eb3f40b2ed5. * Revert "link docs" This reverts commit 160b8912e14746f2de1b660c6b24a24197ffcb46. * Revert "add tensor inspector tutorial" This reverts commit 3b53981a4fe932e8ae80e4ea1ab5cd0260a12574. * add tensor inspector doc * fix api name * add new test and limitations section * fix * update urls and other fixes * fix urls * fix
| Master | Docs | License |
|---|---|---|
 |  |  |
Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains 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. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines.
MXNet is more than a deep learning project. It is a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.
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 emerged from a collaboration by the authors of cxxnet, minerva, and purine2. The project reflects what we have learned from the past projects. MXNet combines aspects of each of these projects to achieve flexibility, speed, and memory efficiency.