commit	672a9fa9000190467ce6b29ea1987df4a8fccaca	[log] [tgz]
author	Caspar <997476274@qq.com>	Tue Oct 05 14:22:09 2021 -0400
committer	GitHub <noreply@github.com>	Tue Oct 05 11:22:09 2021 -0700
tree	0162aa61e56f30fa8402cfddb3de3cd9bbe783d2
parent	3480ba2c6df02bb907d3a975d354efa8697c4e71 [diff]

Java2.0 proto (#20461)

* ADD: first commit

* ADD: load local libraries

* UPDATE: use header files of MXNet 2.0

* ADD: load binaries from environment variable, java properties or jar files.

* ADD: add symbol loading and closing
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* ADD: [WIP] Component MxNDArray

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* ADD: Component CachedOp

* REMOVE: module api which is no use

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* ADD: [WIP] add test cases for NdArray and CachedOp

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* ADD: implement of the forward function for MxSymbolblock

* ADD: implement of the forward function for MxSymbolblock

* ADD: Sample model downloader for MLP

* ADD: doc

* ADD: Front-end module for inference, class MxModel, Predictor and so on.

* FIX: Mxnet crash when process exits.

* FIX: remove and initialize 3rdparty directory

* FIX: revert version of submodules: dlpack, dmlc-core, googletest, ps-lite

* Revert "FIX: remove and initialize 3rdparty directory"

This reverts commit d097675e

* FIX: redownload files in 3rdparty

* FIX: reset --hard the version of a few submodules

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* FIX: reset --hard the version of a few submodules

* PERF: [WIP] optimize code structure and memory management and

* ADD: add copyright; remove Mx prefix for some classes

* ADD: add copyright

* FIX: group name, path to find header files

* UPDATE: README.md

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author cspchen <cspchen@amazon.com> 1629186478 +0800
committer cspchen <cspchen@amazon.com> 1629186485 +0800

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* MERGE: resolve conflicts

* MERGE: resolve conflicts

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parent 1ea18edce197b402cbfbaaaa54a94347501d92ab
author cspchen <cspchen@amazon.com> 1629186478 +0800
committer cspchen <cspchen@amazon.com> 1629186485 +0800

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* parent 1ea18edce197b402cbfbaaaa54a94347501d92ab
author cspchen <cspchen@amazon.com> 1629186478 +0800
committer cspchen <cspchen@amazon.com> 1629186485 +0800

FIX: loading on Linux platform

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* UPDATE: remove useless files

* FIX: licence to apache

* FIX: sanity check

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* FIX: sanity check

* FIX: remove unused files

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* DOC: add document

* FIX: doesn't work on osx

* FIX: clang static check

* FIX: sanity

* FIX: skip signal handler registration when building java package

* FIX: remove DataType String

* FIX: add license

Co-authored-by: cspchen <cspchen@amazon.com>

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tree: 0162aa61e56f30fa8402cfddb3de3cd9bbe783d2

README.md

Apache MXNet (incubating) for Deep Learning

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.

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.

Branch	Build Status
master
v1.x

Features

NumPy-like programming interface, and is integrated with the new, easy-to-use Gluon 2.0 interface. NumPy users can easily adopt MXNet and start in deep learning.
Automatic hybridization provides imperative programming with the performance of traditional symbolic programming.
Lightweight, memory-efficient, and portable to smart devices through native cross-compilation support on ARM, and through ecosystem projects such as TVM, TensorRT, OpenVINO.
Scales up to multi GPUs and distributed setting with auto parallelism through ps-lite, Horovod, and BytePS.
Extensible backend that supports full customization, allowing integration with custom accelerator libraries and in-house hardware without the need to maintain a fork.
Support for Python, Java, C++, R, Scala, Clojure, Go, Javascript, Perl, and Julia.
Cloud-friendly and directly compatible with AWS and Azure.

What's New

1.8.0 Release - MXNet 1.8.0 Release.
1.7.0 Release - MXNet 1.7.0 Release.
1.6.0 Release - MXNet 1.6.0 Release.
1.5.1 Release - MXNet 1.5.1 Patch Release.
1.5.0 Release - MXNet 1.5.0 Release.
1.4.1 Release - MXNet 1.4.1 Patch Release.
1.4.0 Release - MXNet 1.4.0 Release.
1.3.1 Release - MXNet 1.3.1 Patch Release.
1.3.0 Release - MXNet 1.3.0 Release.
1.2.0 Release - MXNet 1.2.0 Release.
1.1.0 Release - MXNet 1.1.0 Release.
1.0.0 Release - MXNet 1.0.0 Release.
0.12.1 Release - MXNet 0.12.1 Patch Release.
0.12.0 Release - MXNet 0.12.0 Release.
0.11.0 Release - MXNet 0.11.0 Release.
Apache Incubator - We are now an Apache Incubator project.
0.10.0 Release - MXNet 0.10.0 Release.
0.9.3 Release - First 0.9 official release.
0.9.1 Release (NNVM refactor) - NNVM branch is merged into master now. An official release will be made soon.
0.8.0 Release

Ecosystem News

Stay Connected

Channel	Purpose
Follow MXNet Development on Github	See what's going on in the MXNet project.
MXNet Confluence Wiki for Developers	MXNet developer wiki for information related to project development, maintained by contributors and developers. To request write access, send an email to send request to the dev list .
dev@mxnet.apache.org mailing list	The “dev list”. Discussions about the development of MXNet. To subscribe, send an email to dev-subscribe@mxnet.apache.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 .
Follow MXNet on Social Media	Get updates about new features and events.

Social Media

Keep connected with the latest MXNet news and updates.

History

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