|author||bgawrych <firstname.lastname@example.org>||Thu Aug 04 17:08:23 2022 +0200|
|committer||GitHub <email@example.com>||Thu Aug 04 17:08:23 2022 +0200|
Add size threshold for few oneDNN operators (#21106) * Add size threshold for few oneDNN operators * add const * Add PR number * Apply suggestions from code review Co-authored-by: bartekkuncer <firstname.lastname@example.org> * Refactor binary op * Fix true_divide Co-authored-by: Bartlomiej Gawrych <email@example.com> Co-authored-by: bartekkuncer <firstname.lastname@example.org>
Apache MXNet 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, scalable to many GPUs and machines.
Apache MXNet is more than a deep learning project. It is a community on a mission of democratizing AI. 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.
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|email@example.com mailing list||The “dev list”. Discussions about the development of MXNet. To subscribe, send an email to firstname.lastname@example.org .|
|discuss.mxnet.io||Asking & answering MXNet usage questions.|
|Apache Slack #mxnet Channel||Connect with MXNet and other Apache developers. To join the MXNet slack channel send request to the dev list .|
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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.
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