zhreshold commented on a change in pull request #17566: [WIP] Discussion on 
merging BMXNet 2 contributions
URL: https://github.com/apache/incubator-mxnet/pull/17566#discussion_r379595336
 
 

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 File path: README.md
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-
-<div align="center">
-  <a href="https://mxnet.incubator.apache.org/";><img 
src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet_logo_2.png";></a><br>
-</div>
-
-Apache MXNet (incubating) for Deep Learning
-=====
-| Master         | Docs          | License  |
-| :-------------:|:-------------:|:--------:|
-| [![Build 
Status](http://jenkins.mxnet-ci.amazon-ml.com/job/incubator-mxnet/job/master/badge/icon)](http://jenkins.mxnet-ci.amazon-ml.com/job/incubator-mxnet/job/master/)
  | [![Documentation 
Status](http://jenkins.mxnet-ci.amazon-ml.com/job/restricted-website-build/badge/icon)](https://mxnet.incubator.apache.org/)
 | [![GitHub license](http://dmlc.github.io/img/apache2.svg)](./LICENSE) |
-
-![banner](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/banner.png)
-
-Apache MXNet (incubating) is a deep learning framework designed for both 
*efficiency* and *flexibility*.
-It allows you to ***mix*** [symbolic and imperative 
programming](https://mxnet.incubator.apache.org/architecture/index.html#deep-learning-system-design-concepts)
-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](https://mxnet.incubator.apache.org/architecture/index.html#deep-learning-system-design-concepts)
 for building
-deep learning systems, and interesting insights of DL systems for hackers.
-
-Ask Questions
--------------
-* Please use [discuss.mxnet.io](https://discuss.mxnet.io/) for asking 
questions.
-* Please use [mxnet/issues](https://github.com/apache/incubator-mxnet/issues) 
for reporting bugs.
-* [Frequent Asked Questions](https://mxnet.incubator.apache.org/faq/faq.html)
-
-How to Contribute
------------------
-* [Contribute to 
MXNet](https://mxnet.incubator.apache.org/community/contribute.html)
-
-What's New
-----------
-* [Version 1.4.1 
Release](https://github.com/apache/incubator-mxnet/releases/tag/1.4.1) - MXNet 
1.4.1 Patch Release.
-* [Version 1.4.0 
Release](https://github.com/apache/incubator-mxnet/releases/tag/1.4.0) - MXNet 
1.4.0 Release.
-* [Version 1.3.1 
Release](https://github.com/apache/incubator-mxnet/releases/tag/1.3.1) - MXNet 
1.3.1 Patch Release.
-* [Version 1.3.0 
Release](https://github.com/apache/incubator-mxnet/releases/tag/1.3.0) - MXNet 
1.3.0 Release.
-* [Version 1.2.0 
Release](https://github.com/apache/incubator-mxnet/releases/tag/1.2.0) - MXNet 
1.2.0 Release.
-* [Version 1.1.0 
Release](https://github.com/apache/incubator-mxnet/releases/tag/1.1.0) - MXNet 
1.1.0 Release.
-* [Version 1.0.0 
Release](https://github.com/apache/incubator-mxnet/releases/tag/1.0.0) - MXNet 
1.0.0 Release.
-* [Version 0.12.1 
Release](https://github.com/apache/incubator-mxnet/releases/tag/0.12.1) - MXNet 
0.12.1 Patch Release.
-* [Version 0.12.0 
Release](https://github.com/apache/incubator-mxnet/releases/tag/0.12.0) - MXNet 
0.12.0 Release.
-* [Version 0.11.0 
Release](https://github.com/apache/incubator-mxnet/releases/tag/0.11.0) - MXNet 
0.11.0 Release.
-* [Apache Incubator](http://incubator.apache.org/projects/mxnet.html) - We are 
now an Apache Incubator project.
-* [Version 0.10.0 Release](https://github.com/dmlc/mxnet/releases/tag/v0.10.0) 
- MXNet 0.10.0 Release.
-* [Version 0.9.3 Release](./docs/architecture/release_note_0_9.md) - First 0.9 
official release.
-* [Version 0.9.1 Release (NNVM 
refactor)](./docs/architecture/release_note_0_9.md) - NNVM branch is merged 
into master now. An official release will be made soon.
-* [Version 0.8.0 Release](https://github.com/dmlc/mxnet/releases/tag/v0.8.0)
-* [Updated Image Classification with new Pre-trained 
Models](./example/image-classification)
-* [Notebooks How to Use MXNet](https://github.com/d2l-ai/d2l-en)
-* [MKLDNN for Faster CPU Performance](./docs/tutorials/mkldnn/MKLDNN_README.md)
-* [MXNet Memory Monger, Training Deeper Nets with Sublinear Memory 
Cost](https://github.com/dmlc/mxnet-memonger)
-* [Tutorial for NVidia GTC 2016](https://github.com/dmlc/mxnet-gtc-tutorial)
-* [Embedding Torch layers and functions in 
MXNet](https://mxnet.incubator.apache.org/faq/torch.html)
-* [MXNet.js: Javascript Package for Deep Learning in Browser (without server)
-](https://github.com/dmlc/mxnet.js/)
-* [Design Note: Design Efficient Deep Learning Data Loading 
Module](https://mxnet.incubator.apache.org/architecture/note_data_loading.html)
-* [MXNet on Mobile 
Device](https://mxnet.incubator.apache.org/faq/smart_device.html)
-* [Distributed 
Training](https://mxnet.incubator.apache.org/faq/multi_devices.html)
-* [Guide to Creating New Operators 
(Layers)](https://mxnet.incubator.apache.org/faq/new_op.html)
-* [Go binding for inference](https://github.com/songtianyi/go-mxnet-predictor)
-* [Amalgamation and Go Binding for 
Predictors](https://github.com/jdeng/gomxnet/) - Outdated
-* [Large Scale Image 
Classification](https://github.com/apache/incubator-mxnet/tree/master/example/image-classification)
-
-Contents
---------
-* [Documentation](https://mxnet.incubator.apache.org/) and  
[Tutorials](https://mxnet.incubator.apache.org/tutorials/)
-* [Design Notes](https://mxnet.incubator.apache.org/architecture/index.html)
-* [Code 
Examples](https://github.com/apache/incubator-mxnet/tree/master/example)
-* [Installation](https://mxnet.incubator.apache.org/install/index.html)
-* [Pretrained 
Models](http://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html)
-
-Features
---------
-* Design notes providing useful insights that can re-used by other DL projects
-* Flexible configuration for arbitrary computation graph
-* Mix and match imperative and symbolic programming to maximize flexibility 
and efficiency
-* Lightweight, memory efficient and portable to smart devices
-* Scales up to multi GPUs and distributed setting with auto parallelism
-* Support for 
[Python](https://github.com/apache/incubator-mxnet/tree/master/python), 
[Scala](https://github.com/apache/incubator-mxnet/tree/master/scala-package), 
[C++](https://github.com/apache/incubator-mxnet/tree/master/cpp-package), 
[Java](https://github.com/apache/incubator-mxnet/tree/master/scala-package), 
[Clojure](https://github.com/apache/incubator-mxnet/tree/master/contrib/clojure-package),
 [R](https://github.com/apache/incubator-mxnet/tree/master/R-package), 
[Go](https://github.com/jdeng/gomxnet/), 
[Javascript](https://github.com/dmlc/mxnet.js/), 
[Perl](https://github.com/apache/incubator-mxnet/tree/master/perl-package), 
[Matlab](https://github.com/apache/incubator-mxnet/tree/master/matlab), and 
[Julia](https://github.com/apache/incubator-mxnet/tree/master/julia)
-* Cloud-friendly and directly compatible with S3, HDFS, and Azure
-
-License
--------
-Licensed under an 
[Apache-2.0](https://github.com/apache/incubator-mxnet/blob/master/LICENSE) 
license.
-
-Reference Paper
----------------
-
-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](https://github.com/dmlc/web-data/raw/master/mxnet/paper/mxnet-learningsys.pdf).
-In Neural Information Processing Systems, Workshop on Machine Learning 
Systems, 2015
-
-History
--------
-MXNet emerged from a collaboration by the authors of 
[cxxnet](https://github.com/dmlc/cxxnet), 
[minerva](https://github.com/dmlc/minerva), and 
[purine2](https://github.com/purine/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.
+# BMXNet 2 // Hasso Plattner Institute
 
 Review comment:
   Leave the README as is, and move this README to examples/bmxnet2

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