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Apache MXNet | A flexible and efficient library for deep learning.
Apache MXNet | A flexible and efficient library for deep learning.
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APACHE MXNET: A FLEXIBLE AND EFFICIENT LIBRARY FOR DEEP LEARNING
A truly open source deep learning framework suitedfor flexible research
prototyping and
production.
Get Started
›
Key Features &Capabilities
All Features ›
Hybrid Front-End
A hybrid front-end seamlessly transitions between Gluon eager imperative mode and symbolic mode to provide both flexibility and speed.
Distributed Training
Scalable distributed training and performance optimization in research and production is enabled by the dual Parameter Server and Horovod support.
8 Language Bindings
Deep integration into Python and support for Scala, Julia, Clojure, Java, C++, R and Perl.
Tools & Libraries
A thriving ecosystem of tools and libraries extends MXNet and enable use-cases in computer vision, NLP, time series and more.
Ecosystem
All Projects ›
Explore a rich ecosystem of libraries, tools, and more to support development.
D2L.ai
An interactive deep learning book with code, math, and discussions. Used at Berkeley, University of Washington and more.
GluonCV
GluonCV is a computer vision toolkit with rich model zoo. From object detection to pose estimation.
GluonNLP
GluonNLP provides state-of-the-art deep learning models in NLP. For engineers and researchers to fast prototype research ideas and products.
GluonTS
Gluon Time Series (GluonTS) is the Gluon toolkit for probabilistic time series modeling, focusing on deep learning-based models.
Community
Join the Apache MXNet scientific community to contribute, learn, and get
answers to your questions.
GitHub
Report bugs, request features, discuss issues, and more.
Discuss Forum
Browse and join discussions on deep learning with MXNet and Gluon.
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Discuss advanced topics. Request access by mail dev@mxnet.apache.org
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apache/mxnet apachemxnet apachemxnet
A flexible and efficient library for deep learning.
"Copyright © 2017-2022, The Apache Software Foundation. Licensed under the Apache License, Version 2.0. Apache MXNet, MXNet, Apache, the Apache
feather, and the Apache MXNet project logo are either registered trademarks or trademarks of the
Apache Software Foundation."
GitHub - apache/mxnet: Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
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Apache MXNet 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.
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.
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.
Contents
Installation
Tutorials
Ecosystem
API Documentation
Examples
Stay Connected
Social Media
What's New
1.9.1 Release - MXNet 1.9.1 Release.
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
oneDNN for Faster CPU Performance
MXNet Memory Monger, Training Deeper Nets with Sublinear Memory Cost
Tutorial for NVidia GTC 2016
MXNet.js: Javascript Package for Deep Learning in Browser (without server)
Guide to Creating New Operators (Layers)
Go binding for inference
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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 .
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Get updates about new features and events.
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Apache MXNet on Twitter
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Apache MXNet on LinkedIn
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
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Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
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Apache MXNet - Wikipedia
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From Wikipedia, the free encyclopedia
This article contains content that is written like an advertisement. Please help improve it by removing promotional content and inappropriate external links, and by adding encyclopedic content written from a neutral point of view. (April 2020) (Learn how and when to remove this template message)
Apache MXNetDeveloper(s)Apache Software FoundationStable release1.9.1[1]
/ 10 May 2022; 21 months ago (10 May 2022)
Repositorygithub.com/apache/incubator-mxnet
Written inC++, Python, R, Java, Julia, JavaScript, Scala, Go, PerlOperating systemWindows, macOS, LinuxTypeLibrary for machine learning and deep learningLicenseApache License 2.0Websitemxnet.apache.org
Apache MXNet is an open-source deep learning software framework that trains and deploys deep neural networks. It is scalable, allows fast model training, and supports a flexible programming model and multiple programming languages (including C++, Python, Java, Julia, MATLAB, JavaScript, Go, R, Scala, Perl, and Wolfram Language). The MXNet library is portable and can scale to multiple GPUs[2] and machines. It was co-developed by Carlos Guestrin at the University of Washington (along with GraphLab).[3]
As of September 2023, it is no longer actively developed.[4]
Features[edit]
Apache MXNet is a scalable deep learning framework that supports deep learning models, such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs).
Scalability[edit]
MXNet can be distributed on dynamic cloud infrastructure using a distributed parameter server (based on research at Carnegie Mellon University, Baidu, and Google[5]). With multiple GPUs or CPUs, the framework approaches linear scale.
Flexibility[edit]
MXNet supports both imperative and symbolic programming. The framework allows developers to track, debug, save checkpoints, modify hyperparameters, and perform early stopping.
Multiple languages[edit]
MXNet supports Python, R, Scala, Clojure, Julia, Perl, MATLAB, and JavaScript for front-end development and C++ for back-end optimization.
Portability[edit]
Supports deployment of a trained model to low-end devices for inference, such as mobile devices (using Amalgamation[6]), Internet of things devices (using AWS Greengrass), serverless computing (using AWS Lambda), or containers. These low-end environments can have only weaker CPU or limited memory (RAM) and should be able to use the models that were trained on a higher-level environment (GPU-based cluster, for example)
Cloud Support[edit]
MXNet is supported by public cloud providers including Amazon Web Services (AWS)[7] and Microsoft Azure.[8] Amazon has chosen MXNet as its deep learning framework of choice at AWS.[9][10] Currently, MXNet is supported by Intel, Baidu, Microsoft, Wolfram Research, and research institutions such as Carnegie Mellon, MIT, the University of Washington, and the Hong Kong University of Science and Technology.[11]
See also[edit]
Comparison of deep learning software
Differentiable programming
References[edit]
^ "Release 1.9.1". 10 May 2022. Retrieved 30 June 2022.
^ "Building Deep Neural Networks in the Cloud with Azure GPU VMs, MXNet and Microsoft R Server". Microsoft. 15 September 2016. Archived from the original on August 15, 2023. Retrieved 13 May 2017.
^ "Carlos Guestrin". guestrin.su.domains. Archived from the original on September 22, 2023.
^ "Apache MXNet - Apache Attic".
^ "Scaling Distributed Machine Learning with the Parameter Server" (PDF). Archived (PDF) from the original on August 13, 2023. Retrieved 2014-10-08.
^ "Amalgamation". Archived from the original on 2018-08-08. Retrieved 2018-05-08.
^ "Apache MXNet on AWS - Deep Learning on the Cloud". Amazon Web Services, Inc. Retrieved 13 May 2017.
^ "Building Deep Neural Networks in the Cloud with Azure GPU VMs, MXNet and Microsoft R Server". Microsoft TechNet Blogs. 15 September 2016. Retrieved 6 September 2017.
^ "MXNet - Deep Learning Framework of Choice at AWS - All Things Distributed". www.allthingsdistributed.com. 22 November 2016. Retrieved 13 May 2017.
^ "Amazon Has Chosen This Framework to Guide Deep Learning Strategy". Fortune. Retrieved 13 May 2017.
^ "MXNet, Amazon's deep learning framework, gets accepted into Apache Incubator". Retrieved 2017-03-08.
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mxnet 1.9.1
pip install mxnet
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Latest version
Released:
May 17, 2022
Apache MXNet is an ultra-scalable deep learning framework. This version uses openblas and MKLDNN.
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Project description
Apache MXNet (Incubating) Python Package
Apache MXNet is a deep learning framework designed for both efficiency and flexibility.
It allows you to mix the flavours of deep learning programs together to maximize the efficiency and your productivity.
For feature requests on the PyPI package, suggestions, and issue reports, create an issue by clicking here.
Prerequisites
This package supports Linux, Mac OSX, and Windows platforms. You may also want to check:
mxnet-cu112 with CUDA-11.2 support.
mxnet-cu110 with CUDA-11.0 support.
mxnet-cu102 with CUDA-10.2 support.
mxnet-cu101 with CUDA-10.1 support.
mxnet-cu100 with CUDA-10.0 support.
mxnet-native CPU variant without MKLDNN.
To use this package on Linux you need the libquadmath.so.0 shared library. On
Debian based systems, including Ubuntu, run sudo apt install libquadmath0 to
install the shared library. On RHEL based systems, including CentOS, run sudo yum install libquadmath to install the shared library. As libquadmath.so.0 is
a GPL library and MXNet part of the Apache Software Foundation, MXNet must not
redistribute libquadmath.so.0 as part of the Pypi package and users must
manually install it.
To install for other platforms (e.g. Windows, Raspberry Pi/ARM) or other versions, check Installing MXNet for instructions on building from source.
Installation
To install, use:
pip install mxnet
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Get Started | Apache MXNet
Get Started | Apache MXNet
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0.12.1
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This project has retired. For details please refer to its
Attic page.
Get Started
Apache MXNet Tutorials
›
Build and install Apache MXNet from source
To build and install Apache MXNet from the official Apache Software Foundation
signed source code please follow our Building From Source guide.
The signed source releases are available here
Platform and use-case specific instructions for using Apache MXNet
Please indicate your preferred configuration below to see specific instructions.
MXNet Version
v1.9.1
v1.9.1
v1.8.0
v1.7.0
v1.6.0
v1.5.1
v1.4.1
v1.3.1
v1.2.1
v1.1.0
v1.0.0
v0.12.1
v0.11.0
OS / Platform
Linux
MacOS
Windows
Cloud
Devices
Language
Python
Scala
Java
Clojure
R
Julia
Perl
Cpp
GPU / CPU
GPU
CPU
Device
Raspberry Pi
NVIDIA Jetson
Distribution
Pip
Docker
Build from Source
WARNING: the following PyPI package names are provided for your convenience but
they point to packages that are not provided nor endorsed by the Apache
Software Foundation. As such, they might contain software components with more
restrictive licenses than the Apache License and you’ll need to decide whether
they are appropriate for your usage. The packages linked here contain GPL GCC
Runtime Library components. Like all Apache Releases, the official Apache MXNet
releases consist of source code only and are found at the Download
page.
Run the following command:
pip install mxnet
pip install mxnet==1.8.0.post0
Start from 1.7.0 release, oneDNN(previously known as: MKL-DNN/DNNL) is enabled
in pip packages by default.
oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform
performance library of basic building blocks for deep learning applications.
The library is optimized for Intel Architecture Processors, Intel Processor
Graphics and Xe architecture-based Graphics. Support for other architectures
such as Arm* 64-bit Architecture (AArch64) and OpenPOWER* Power ISA (PPC64) is
experimental.
oneDNN is intended for deep learning applications and framework developers
interested in improving application performance on Intel CPUs and GPUs, more
details can be found here.
You can find performance numbers in the
MXNet tuning guide.
To install native MXNet without oneDNN, run the following command:
pip install mxnet-native==1.8.0.post0
pip install mxnet==1.7.0.post2
Start from 1.7.0 release, oneDNN(previously known as: MKL-DNN/DNNL) is enabled
in pip packages by default.
oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform
performance library of basic building blocks for deep learning applications.
The library is optimized for Intel Architecture Processors, Intel Processor
Graphics and Xe architecture-based Graphics. Support for other architectures
such as Arm* 64-bit Architecture (AArch64) and OpenPOWER* Power ISA (PPC64) is
experimental.
Please note that the Linux CPU pip wheels for AArch64 platforms are built with
oneDNN with Arm Performance Libraries (APL) integrated. Because APL's license
is not compatible with Apache license, you would need to
manually install APL
in your system.
oneDNN is intended for deep learning applications and framework developers
interested in improving application performance on Intel CPUs and GPUs, more
details can be found here.
You can find performance numbers in the
MXNet tuning guide.
To install native MXNet without oneDNN, run the following command:
pip install mxnet-native==1.7.0
pip install mxnet==1.6.0
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
performance numbers in the
MXNet tuning guide.
pip install mxnet-mkl==1.6.0
pip install mxnet==1.5.1
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
performance numbers in the
MXNet tuning guide.
pip install mxnet-mkl==1.5.1
pip install mxnet==1.4.1
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
performance numbers in the
MXNet tuning guide.
pip install mxnet-mkl==1.4.1
pip install mxnet==1.3.1
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
performance numbers in the
MXNet tuning guide.
pip install mxnet-mkl==1.3.1
pip install mxnet==1.2.1
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
performance numbers in the
MXNet tuning guide.
pip install mxnet-mkl==1.2.1
pip install mxnet==1.1.0
pip install mxnet==1.0.0
pip install mxnet==0.12.1
For MXNet 0.12.0:
pip install mxnet==0.12.0
pip install mxnet==0.11.0
You can then validate your MXNet installation.
NOTES:
mxnet-cu101 means the package is built with CUDA/cuDNN and the CUDA version is
10.1.
All MKL pip packages are experimental prior to version 1.3.0.
WARNING: the following links and names of binary distributions are provided for
your convenience but they point to packages that are not provided nor endorsed
by the Apache Software Foundation. As such, they might contain software
components with more restrictive licenses than the Apache License and you’ll
need to decide whether they are appropriate for your usage. Like all Apache
Releases, the official Apache MXNet releases consist of source code
only and are found at
the Download page.
Docker images with MXNet are available at DockerHub.
After you installed Docker on your machine, you can use them via:
$ docker pull mxnet/python
You can list docker images to see if mxnet/python docker image pull was successful.
$ docker images # Use sudo if you skip Step 2
REPOSITORY TAG IMAGE ID CREATED SIZE
mxnet/python latest 00d026968b3c 3 weeks ago 1.41 GB
You can then validate the installation.
Please follow the build from source instructions linked above.
WARNING: the following PyPI package names are provided for your convenience but
they point to packages that are not provided nor endorsed by the Apache
Software Foundation. As such, they might contain software components with more
restrictive licenses than the Apache License and you’ll need to decide whether
they are appropriate for your usage. The packages linked here contain
proprietary parts of the NVidia CUDA SDK and GPL GCC Runtime Library components.
Like all Apache Releases, the official Apache MXNet releases
consist of source code only and are found at the Download
page.
Run the following command:
$ pip install mxnet-cu102
$ pip install mxnet-cu102==1.8.0.post0
$ pip install mxnet-cu102==1.7.0
$ pip install mxnet-cu102==1.6.0
$ pip install mxnet-cu101==1.5.1
$ pip install mxnet-cu101==1.4.1
$ pip install mxnet-cu92==1.3.1
$ pip install mxnet-cu92==1.2.1
$ pip install mxnet-cu91==1.1.0
$ pip install mxnet-cu90==1.0.0
$ pip install mxnet-cu90==0.12.1
$ pip install mxnet-cu80==0.11.0
You can then validate your MXNet installation.
NOTES:
mxnet-cu101 means the package is built with CUDA/cuDNN and the CUDA version is
10.1.
All MKL pip packages are experimental prior to version 1.3.0.
CUDA should be installed first. Starting from version 1.8.0, CUDNN and NCCL should be installed as well.
Important: Make sure your installed CUDA (CUDNN/NCCL if applicable) version matches the CUDA version in the pip package.
Check your CUDA version with the following command:
nvcc --version
You can either upgrade your CUDA install or install the MXNet package that supports your CUDA version.
WARNING: the following links and names of binary distributions are provided for
your convenience but they point to packages that are not provided nor endorsed
by the Apache Software Foundation. As such, they might contain software
components with more restrictive licenses than the Apache License and you’ll
need to decide whether they are appropriate for your usage. Like all Apache
Releases, the official Apache MXNet releases consist of source code
only and are found at
the Download page.
Docker images with MXNet are available at DockerHub.
Please follow the NVidia Docker installation
instructions to enable the usage
of GPUs from the docker containers.
After you installed Docker on your machine, you can use them via:
$ docker pull mxnet/python:gpu # Use sudo if you skip Step 2
You can list docker images to see if mxnet/python docker image pull was successful.
$ docker images # Use sudo if you skip Step 2
REPOSITORY TAG IMAGE ID CREATED SIZE
mxnet/python gpu 493b2683c269 3 weeks ago 4.77 GB
You can then validate the installation.
Please follow the build from source instructions linked above.
You will need to R v3.4.4+ and build MXNet from source. Please follow the
instructions linked above.
You can use the Maven packages defined in the following dependency to include MXNet in your Java
project. The Java API is provided as a subset of the Scala API and is intended for inference only.
Please refer to the MXNet-Java setup guide for a detailed set of
instructions to help you with the setup process.
You can use the Maven packages defined in the following dependency to include MXNet in your Clojure
project. To maximize leverage, the Clojure package has been built on the existing Scala package. Please
refer to the MXNet-Scala setup guide for a detailed set of instructions
to help you with the setup process that is required to use the Clojure dependency.
Previously available binaries distributed via Maven have been removed as they
redistributed Category-X binaries in violation of Apache Software Foundation
(ASF) policies.
At this point in time, no third-party binary Java packages are available. Please
follow the build from source instructions linked above.
Please follow the build from source instructions linked above.
Please follow the build from source instructions linked above.
To use the C++ package, build from source the USE_CPP_PACKAGE=1 option. Please
refer to the build from source instructions linked above.
WARNING: the following PyPI package names are provided for your convenience but
they point to packages that are not provided nor endorsed by the Apache
Software Foundation. As such, they might contain software components with more
restrictive licenses than the Apache License and you’ll need to decide whether
they are appropriate for your usage. The packages linked here contain GPL GCC
Runtime Library components. Like all Apache Releases, the official Apache MXNet
releases consist of source code only and are found at the Download
page.
Run the following command:
pip install mxnet
pip install mxnet==1.8.0.post0
Start from 1.7.0 release, oneDNN(previously known as: MKL-DNN/DNNL) is enabled
in pip packages by default.
oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform
performance library of basic building blocks for deep learning applications.
The library is optimized for Intel Architecture Processors, Intel Processor
Graphics and Xe architecture-based Graphics. Support for other architectures
such as Arm* 64-bit Architecture (AArch64) and OpenPOWER* Power ISA (PPC64) is
experimental.
oneDNN is intended for deep learning applications and framework developers
interested in improving application performance on Intel CPUs and GPUs, more
details can be found here.
You can find performance numbers in the
MXNet tuning guide.
To install native MXNet without oneDNN, run the following command:
pip install mxnet-native==1.8.0.post0
pip install mxnet==1.7.0.post2
Start from 1.7.0 release, oneDNN(previously known as: MKL-DNN/DNNL) is enabled
in pip packages by default.
oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform
performance library of basic building blocks for deep learning applications.
The library is optimized for Intel Architecture Processors, Intel Processor
Graphics and Xe architecture-based Graphics. Support for other architectures
such as Arm* 64-bit Architecture (AArch64) and OpenPOWER* Power ISA (PPC64) is
experimental.
Please note that the Linux CPU pip wheels for AArch64 platforms are built with
oneDNN with Arm Performance Libraries (APL) integrated. Because APL's license
is not compatible with Apache license, you would need to
manually install APL
in your system.
oneDNN is intended for deep learning applications and framework developers
interested in improving application performance on Intel CPUs and GPUs, more
details can be found here.
You can find performance numbers in the
MXNet tuning guide.
To install native MXNet without oneDNN, run the following command:
pip install mxnet-native==1.7.0
pip install mxnet==1.6.0
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
performance numbers in the
MXNet tuning guide.
pip install mxnet-mkl==1.6.0
pip install mxnet==1.5.1
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
performance numbers in the
MXNet tuning guide.
pip install mxnet-mkl==1.5.1
pip install mxnet==1.4.1
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
performance numbers in the
MXNet tuning guide.
pip install mxnet-mkl==1.4.1
pip install mxnet==1.3.1
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
performance numbers in the
MXNet tuning guide.
pip install mxnet-mkl==1.3.1
pip install mxnet==1.2.1
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
performance numbers in the
MXNet tuning guide.
pip install mxnet-mkl==1.2.1
pip install mxnet==1.1.0
pip install mxnet==1.0.0
pip install mxnet==0.12.1
For MXNet 0.12.0:
pip install mxnet==0.12.0
pip install mxnet==0.11.0
You can then validate your MXNet installation.
NOTES:
mxnet-cu101 means the package is built with CUDA/cuDNN and the CUDA version is
10.1.
All MKL pip packages are experimental prior to version 1.3.0.
WARNING: the following links and names of binary distributions are provided for
your convenience but they point to packages that are not provided nor endorsed
by the Apache Software Foundation. As such, they might contain software
components with more restrictive licenses than the Apache License and you’ll
need to decide whether they are appropriate for your usage. Like all Apache
Releases, the official Apache MXNet releases consist of source code
only and are found at
the Download page.
Docker images with MXNet are available at DockerHub.
After you installed Docker on your machine, you can use them via:
$ docker pull mxnet/python
You can list docker images to see if mxnet/python docker image pull was successful.
$ docker images # Use sudo if you skip Step 2
REPOSITORY TAG IMAGE ID CREATED SIZE
mxnet/python latest 00d026968b3c 3 weeks ago 1.41 GB
You can then validate the installation.
Please follow the build from source instructions linked above.
Please follow the build from source instructions linked above.
You will need to R v3.4.4+ and build MXNet from source. Please follow the
instructions linked above.
You can use the Maven packages defined in the following dependency to include MXNet in your Java
project. The Java API is provided as a subset of the Scala API and is intended for inference only.
Please refer to the MXNet-Java setup guide for a detailed set of
instructions to help you with the setup process.
You can use the Maven packages defined in the following dependency to include MXNet in your Clojure
project. To maximize leverage, the Clojure package has been built on the existing Scala package. Please
refer to the MXNet-Scala setup guide for a detailed set of instructions
to help you with the setup process that is required to use the Clojure dependency.
Previously available binaries distributed via Maven have been removed as they
redistributed Category-X binaries in violation of Apache Software Foundation
(ASF) policies.
At this point in time, no third-party binary Java packages are available. Please
follow the build from source instructions linked above.
Please follow the build from source instructions linked above.
Please follow the build from source instructions linked above.
To use the C++ package, build from source the USE_CPP_PACKAGE=1 option. Please
refer to the build from source instructions linked above.
WARNING: the following PyPI package names are provided for your convenience but
they point to packages that are not provided nor endorsed by the Apache
Software Foundation. As such, they might contain software components with more
restrictive licenses than the Apache License and you’ll need to decide whether
they are appropriate for your usage. The packages linked here contain GPL GCC
Runtime Library components. Like all Apache Releases, the official Apache MXNet
releases consist of source code only and are found at the Download
page.
Run the following command:
pip install mxnet
pip install mxnet==1.8.0.post0
Start from 1.7.0 release, oneDNN(previously known as: MKL-DNN/DNNL) is enabled
in pip packages by default.
oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform
performance library of basic building blocks for deep learning applications.
The library is optimized for Intel Architecture Processors, Intel Processor
Graphics and Xe architecture-based Graphics. Support for other architectures
such as Arm* 64-bit Architecture (AArch64) and OpenPOWER* Power ISA (PPC64) is
experimental.
oneDNN is intended for deep learning applications and framework developers
interested in improving application performance on Intel CPUs and GPUs, more
details can be found here.
You can find performance numbers in the
MXNet tuning guide.
To install native MXNet without oneDNN, run the following command:
pip install mxnet-native==1.8.0.post0
pip install mxnet==1.7.0.post2
Start from 1.7.0 release, oneDNN(previously known as: MKL-DNN/DNNL) is enabled
in pip packages by default.
oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform
performance library of basic building blocks for deep learning applications.
The library is optimized for Intel Architecture Processors, Intel Processor
Graphics and Xe architecture-based Graphics. Support for other architectures
such as Arm* 64-bit Architecture (AArch64) and OpenPOWER* Power ISA (PPC64) is
experimental.
Please note that the Linux CPU pip wheels for AArch64 platforms are built with
oneDNN with Arm Performance Libraries (APL) integrated. Because APL's license
is not compatible with Apache license, you would need to
manually install APL
in your system.
oneDNN is intended for deep learning applications and framework developers
interested in improving application performance on Intel CPUs and GPUs, more
details can be found here.
You can find performance numbers in the
MXNet tuning guide.
To install native MXNet without oneDNN, run the following command:
pip install mxnet-native==1.7.0
pip install mxnet==1.6.0
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
performance numbers in the
MXNet tuning guide.
pip install mxnet-mkl==1.6.0
pip install mxnet==1.5.1
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
performance numbers in the
MXNet tuning guide.
pip install mxnet-mkl==1.5.1
pip install mxnet==1.4.1
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
performance numbers in the
MXNet tuning guide.
pip install mxnet-mkl==1.4.1
pip install mxnet==1.3.1
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
performance numbers in the
MXNet tuning guide.
pip install mxnet-mkl==1.3.1
pip install mxnet==1.2.1
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find
performance numbers in the
MXNet tuning guide.
pip install mxnet-mkl==1.2.1
pip install mxnet==1.1.0
pip install mxnet==1.0.0
pip install mxnet==0.12.1
For MXNet 0.12.0:
pip install mxnet==0.12.0
pip install mxnet==0.11.0
You can then validate your MXNet installation.
NOTES:
mxnet-cu101 means the package is built with CUDA/cuDNN and the CUDA version is
10.1.
All MKL pip packages are experimental prior to version 1.3.0.
Please follow the build from source instructions linked above.
WARNING: the following PyPI package names are provided for your convenience but
they point to packages that are not provided nor endorsed by the Apache
Software Foundation. As such, they might contain software components with more
restrictive licenses than the Apache License and you’ll need to decide whether
they are appropriate for your usage. The packages linked here contain
proprietary parts of the NVidia CUDA SDK and GPL GCC Runtime Library components.
Like all Apache Releases, the official Apache MXNet releases
consist of source code only and are found at the Download
page.
Run the following command:
$ pip install mxnet-cu102
$ pip install mxnet-cu102==1.8.0.post0
$ pip install mxnet-cu102==1.7.0
$ pip install mxnet-cu102==1.6.0
$ pip install mxnet-cu101==1.5.1
$ pip install mxnet-cu101==1.4.1
$ pip install mxnet-cu92==1.3.1
$ pip install mxnet-cu92==1.2.1
$ pip install mxnet-cu91==1.1.0
$ pip install mxnet-cu90==1.0.0
$ pip install mxnet-cu90==0.12.1
$ pip install mxnet-cu80==0.11.0
You can then validate your MXNet installation.
NOTES:
mxnet-cu101 means the package is built with CUDA/cuDNN and the CUDA version is
10.1.
All MKL pip packages are experimental prior to version 1.3.0.
CUDA should be installed first. Starting from version 1.8.0, CUDNN and NCCL should be installed as well.
Important: Make sure your installed CUDA (CUDNN/NCCL if applicable) version matches the CUDA version in the pip package.
Check your CUDA version with the following command:
nvcc --version
You can either upgrade your CUDA install or install the MXNet package that supports your CUDA version.
Please follow the build from source instructions linked above.
You will need to R v3.4.4+ and build MXNet from source. Please follow the
instructions linked above.
You can use the Maven packages defined in the following dependency to include MXNet in your Java
project. The Java API is provided as a subset of the Scala API and is intended for inference only.
Please refer to the MXNet-Java setup guide for a detailed set of
instructions to help you with the setup process.
You can use the Maven packages defined in the following dependency to include MXNet in your Clojure
project. To maximize leverage, the Clojure package has been built on the existing Scala package. Please
refer to the MXNet-Scala setup guide for a detailed set of instructions
to help you with the setup process that is required to use the Clojure dependency.
Previously available binaries distributed via Maven have been removed as they
redistributed Category-X binaries in violation of Apache Software Foundation
(ASF) policies.
At this point in time, no third-party binary Java packages are available. Please
follow the build from source instructions linked above.
Please follow the build from source instructions linked above.
Please follow the build from source instructions linked above.
To use the C++ package, build from source the USE_CPP_PACKAGE=1 option. Please
refer to the build from source instructions linked above.
MXNet is available on several cloud providers with GPU support. You can also
find GPU/CPU-hybrid support for use cases like scalable inference, or even
fractional GPU support with AWS Elastic Inference.
WARNING: the following cloud provider packages are provided for your convenience
but they point to packages that are not provided nor endorsed by the Apache
Software Foundation. As such, they might contain software components with more
restrictive licenses than the Apache License and you’ll need to decide whether
they are appropriate for your usage. Like all Apache Releases, the official
Apache MXNet releases consist of source code only and are found at
the Download page.
Alibaba
NVIDIA
VM
Amazon Web Services
Amazon SageMaker - Managed training and deployment of
MXNet models
AWS Deep Learning AMI - Preinstalled
Conda environments
for Python 2 or 3 with MXNet, CUDA, cuDNN, MKL-DNN, and AWS Elastic Inference
Dynamic Training on
AWS -
experimental manual EC2 setup or semi-automated CloudFormation setup
NVIDIA VM
Google Cloud Platform
NVIDIA
VM
Microsoft Azure
NVIDIA
VM
Oracle Cloud
NVIDIA VM
All NVIDIA VMs use the NVIDIA MXNet Docker
container.
Follow the container usage
instructions found in
NVIDIA’s container repository.
MXNet should work on any cloud provider’s CPU-only instances. Follow the Python
pip install instructions, Docker instructions, or try the following preinstalled
option.
WARNING: the following cloud provider packages are provided for your convenience
but they point to packages that are not provided nor endorsed by the Apache
Software Foundation. As such, they might contain software components with more
restrictive licenses than the Apache License and you’ll need to decide whether
they are appropriate for your usage. Like all Apache Releases, the official
Apache MXNet releases consist of source code only and are found at
the Download page.
Amazon Web Services
AWS Deep Learning AMI - Preinstalled
Conda environments
for Python 2 or 3 with MXNet and MKL-DNN.
MXNet supports the Debian based Raspbian ARM based operating system so you can run MXNet on
Raspberry Pi 3B
devices.
These instructions will walk through how to build MXNet for the Raspberry Pi and install the
Python bindings
for the library.
You can do a dockerized cross compilation build on your local machine or a native build
on-device.
The complete MXNet library and its requirements can take almost 200MB of RAM, and loading
large models with
the library can take over 1GB of RAM. Because of this, we recommend running MXNet on the
Raspberry Pi 3 or
an equivalent device that has more than 1 GB of RAM and a Secure Digital (SD) card that has
at least 4 GB of
free memory.
Quick installation
You can use this pre-built Python
wheel
on a
Raspberry Pi 3B with Stretch. You will likely need to install several dependencies to get
MXNet to work.
Refer to the following Build section for details.
Docker installation
Step 1 Install Docker on your machine by following the docker installation
instructions.
Note - You can install Community Edition (CE)
Step 2 [Optional] Post installation steps to manage Docker as a non-root user.
Follow the four steps in this docker
documentation
to allow managing docker containers without sudo.
Build
This cross compilation build is experimental.
Please use a Native build with gcc 4 as explained below, higher compiler versions
currently cause test
failures on ARM.
The following command will build a container with dependencies and tools,
and then compile MXNet for ARMv7.
You will want to run this on a fast cloud instance or locally on a fast PC to save time.
The resulting artifact will be located in build/mxnet-x.x.x-py2.py3-none-any.whl.
Copy this file to your Raspberry Pi.
The previously mentioned pre-built wheel was created using this method.
ci/build.py -p armv7
Install using a pip wheel
Your Pi will need several dependencies.
Install MXNet dependencies with the following:
sudo apt-get update
sudo apt-get install -y \
apt-transport-https \
build-essential \
ca-certificates \
cmake \
curl \
git \
libatlas-base-dev \
libcurl4-openssl-dev \
libjemalloc-dev \
liblapack-dev \
libopenblas-dev \
libopencv-dev \
libzmq3-dev \
ninja-build \
python-dev \
python-pip \
software-properties-common \
sudo \
unzip \
virtualenv \
wget
Install virtualenv with:
sudo pip install virtualenv
Create a Python 2.7 environment for MXNet with:
virtualenv -p `which python` mxnet_py27
You may use Python 3, however the wine bottle detection
example
for the
Pi with camera requires Python 2.7.
Activate the environment, then install the wheel we created previously, or install this
prebuilt
wheel.
source mxnet_py27/bin/activate
pip install mxnet-x.x.x-py2.py3-none-any.whl
Test MXNet with the Python interpreter:
$ python
>>> import mxnet
If there are no errors then you’re ready to start using MXNet on your Pi!
Native Build
Installing MXNet from source is a two-step process:
Build the shared library from the MXNet C++ source code.
Install the supported language-specific packages for MXNet.
Step 1 Build the Shared Library
On Raspbian versions Wheezy and later, you need the following dependencies:
Git (to pull code from GitHub)
libblas (for linear algebraic operations)
libopencv (for computer vision operations. This is optional if you want to save RAM and
Disk Space)
A C++ compiler that supports C++ 11. The C++ compiler compiles and builds MXNet source
code. Supported
compilers include the following:
G++ (4.8 or later). Make sure to use gcc 4 and not 5 or 6
as there are
known bugs with these compilers.
Clang (3.9 - 6)
Install these dependencies using the following commands in any directory:
sudo apt-get update
sudo apt-get -y install git cmake ninja-build build-essential g++-4.9 c++-4.9 liblapack*
libblas* libopencv*
libopenblas* python3-dev python-dev virtualenv
Clone the MXNet source code repository using the following git command in your home
directory:
git clone https://github.com/apache/mxnet.git --recursive
cd mxnet
Build:
mkdir -p build && cd build
cmake \
-DUSE_SSE=OFF \
-DUSE_CUDA=OFF \
-DUSE_OPENCV=ON \
-DUSE_OPENMP=ON \
-DUSE_MKL_IF_AVAILABLE=OFF \
-DUSE_SIGNAL_HANDLER=ON \
-DCMAKE_BUILD_TYPE=Release \
-GNinja ..
ninja -j$(nproc)
Some compilation units require memory close to 1GB, so it’s recommended that you enable swap
as
explained below and be cautious about increasing the number of jobs when building (-j)
Executing these commands start the build process, which can take up to a couple hours, and
creates a file
called libmxnet.so in the build directory.
If you are getting build errors in which the compiler is being killed, it is likely that the
compiler is running out of memory (especially if you are on Raspberry Pi 1, 2 or Zero, which
have
less than 1GB of RAM), this can often be rectified by increasing the swapfile size on the Pi
by
editing the file /etc/dphys-swapfile and changing the line CONF_SWAPSIZE=100 to
CONF_SWAPSIZE=1024,
then running:
sudo /etc/init.d/dphys-swapfile stop
sudo /etc/init.d/dphys-swapfile start
free -m # to verify the swapfile size has been increased
Step 2 Build cython modules (optional)
$ pip install Cython
$ make cython # You can set the python executable with `PYTHON` flag, e.g., make cython
PYTHON=python3
MXNet tries to use the cython modules unless the environment variable
MXNET_ENABLE_CYTHON is set to 0.
If loading the cython modules fails, the default behavior is falling back to ctypes without
any warning. To
raise an exception at the failure, set the environment variable MXNET_ENFORCE_CYTHON to
1. See
here for more details.
Step 3 Install MXNet Python Bindings
To install Python bindings run the following commands in the MXNet directory:
cd python
pip install --upgrade pip
pip install -e .
Note that the -e flag is optional. It is equivalent to --editable and means that if you
edit the source
files, these changes will be reflected in the package installed.
Alternatively you can create a whl package installable with pip with the following command:
ci/docker/runtime_functions.sh build_wheel python/ $(realpath build)
You are now ready to run MXNet on your Raspberry Pi device. You can get started by following
the tutorial on
Real-time Object Detection with MXNet On The Raspberry
Pi.
Note - Because the complete MXNet library takes up a significant amount of the Raspberry
Pi’s limited RAM,
when loading training data or large models into memory, you might have to turn off the GUI
and terminate
running processes to free RAM.
NVIDIA Jetson Devices
To install MXNet on a Jetson TX or Nano, please refer to the Jetson installation
guide.
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apache/mxnet apachemxnet apachemxnet
A flexible and efficient library for deep learning.
"Copyright © 2017-2022, The Apache Software Foundation. Licensed under the Apache License, Version 2.0. Apache MXNet, MXNet, Apache, the Apache
feather, and the Apache MXNet project logo are either registered trademarks or trademarks of the
Apache Software Foundation."
Apache MXNet – What Is It and Why Does It Matter?
Apache MXNet – What Is It and Why Does It Matter?
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Apache MXNet
Apache MXNet is a flexible and scalable deep learning framework that supports many deep learning models, programming languages, and features a development interface that’s highly regarded for its ease of use.
What is Apache MXNet?
MXNet is an open-source deep learning framework that allows you to define, train, and deploy deep neural networks on a wide array of devices, from cloud infrastructure to mobile devices. It’s highly scalable, allowing for fast model training, and supports a flexible programming model and multiple languages.
MXNet lets you mix symbolic and imperative programming flavors to maximize both efficiency and productivity. It’s built on 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 MXNet library is portable and lightweight. It’s accelerated with the NVIDIA Pascal™ GPUs and scales across multiple GPUs and multiple nodes, allowing you to train models faster.
Why Apache MXNet?
Apache MXNet offers the following key features and benefits:
Hybrid frontend: The imperative symbolic hybrid Gluon API provides an easy way to prototype, train, and deploy models without sacrificing training speed. Developers need just a few lines of Gluon code to build linear regression, CNN, and recurrent LSTM models for such uses as object detection, speech recognition, and recommendation engines.
Scalability: Designed from the ground up for cloud infrastructure, MXNet uses a distributed parameter server that can achieve an almost linear scale using multiple GPUs or CPUs. Deep learning workloads can be distributed across multiple GPUs with near-linear scalability and auto-scaling. Tests run by Amazon Web Services found that MXNet performed 109 times faster across a cluster of 128 GPUs than with a single GPU. It’s because of the ability to scale to multiple GPUs (across multiple hosts) along with development speed and portability that AWS adopted MXNet as its deep learning framework of choice over alternatives such as TensorFlow, Theano, and Torch.
Ecosystem: MXNet has toolkits and libraries for computer vision, natural language processing, time series, and more.
Languages: MXNet-supported languages include Python, C++, R, Scala, Julia, Matlab, and JavaScript. MXNet also compiles to C++, producing a lightweight neural network model representation that can run on everything from low-powered devices like Raspberry Pi to cloud servers.
How does MXNet work?
Created by a consortium of academic institutions and incubated at the Apache Software Foundation, MXNet (or “mix-net”) was designed to blend the advantages of different programming approaches to deep learning model development—imperative, which specifies exactly “how” computation is performed, and declarative or symbolic, which focuses on “what” should be performed.
Image reference: https://www.cs.cmu.edu/~muli/file/mxnet-learning-sys.pdf
Imperative Programming Mode
MXNet’s NDArray, with imperative programming, is MXNet’s primary tool for storing and transforming data. NDArray is used to represent and manipulate the inputs and outputs of a model as multi-dimensional arrays. NDArray is similar to NumPy’s ndarrays, but they can run on GPUs to accelerate computing.
Imperative programming has the advantage that it’s familiar to developers with procedural programming backgrounds, it’s more natural for parameter updates and interactive debugging.
Symbolic Programming Mode
Neural networks transform input data by applying layers of nested functions to input parameters. Each layer consists of a linear function followed by a nonlinear transformation. The goal of deep learning is to optimize these parameters (consisting of weights and biases) by computing their partial derivatives (gradients) with respect to a loss metric. In forward propagation, the neural network takes the input parameters and outputs a confidence score to the nodes in the next layer until the output layer is reached where the error of the score is calculated. With backpropagation inside of a process called gradient descent, the errors are sent back through the network again and the weights are adjusted, improving the model.
Graphs are data structures consisting of connected nodes (called vertices) and edges. Every modern framework for deep learning is based on the concept of graphs, where neural networks are represented as a graph structure of computations.
MXNet symbolic programming allows functions to be defined abstractly through computation graphs. With symbolic programming, complex functions are first expressed in terms of placeholder values. Then these functions can be executed by binding them to real values. Symbolic programming also provides predefined neural network layers allowing to express large models concisely with less repetitive work and better performance.
Image reference: https://www.cs.cmu.edu/~muli/file/mxnet-learning-sys.pdf
Symbolic programming has the following advantages:
The clear boundaries of a computation graph provide more optimization opportunities by the backend MXNet executor
It’s easier to specify the computation graph for neural network configurations
Hybrid Programming Mode with the Gluon API
One of the key advantages of MXNet is its included hybrid programming interface, Gluon, which bridges the gap between the imperative and symbolic interfaces while keeping the capabilities and advantages of both. Gluon is an easy-to-learn language that produces fast portable models. With the Gluon API, you can prototype your model imperatively using NDArray. Then you can switch to symbolic mode with the hybridize command for faster model training and inference. In symbolic mode, the model runs faster as an optimized graph with the backend MXNet executor and can be easily exported for inference in different language bindings like java or C++.
Why MXNet Is Better on GPUs
Architecturally, the CPU is composed of just a few cores with lots of cache memory that can handle a few software threads at a time. In contrast, a GPU is composed of hundreds of cores that can handle thousands of threads simultaneously.
Because neural nets are created from large numbers of identical neurons, they’re highly parallel by nature. This parallelism maps naturally to GPUs, providing a significant computation speed-up over CPU-only training. GPUs have become the platform of choice for training large, complex neural network-based systems for this reason. The parallel nature of inference operations also lend themselves well for execution on GPUs.
With improved algorithms, bigger datasets, and GPU-accelerated computation, deep learning neural networks have become an indispensable tool for image recognition, speech recognition, language translation, and more in numerous industries. MXNet was developed with the goal to offer powerful tools to help developers exploit the full capabilities of GPUs and cloud computing.
Simply stated, the more GPUs you put to work on an MXNet training algorithm, the faster the job completes. The framework is a standout in scalable performance with nearly linear speed improvements as more GPUs are brought to bear. MXNet was also designed to scale automatically according to available GPUs, a plus for performance tuning.
Use Cases
Smartphone Apps
MXNet is well-suited for image recognition, and its ability to support models that run on low-power, limited-memory platforms make it a good choice for mobile phone deployment. Models built with MXNet have been shown to provide highly reliable image recognition results running natively on laptop computers. Combining local and cloud processors could enable powerful distributed applications in areas like augmented reality, object, and scene identification.
Voice and image recognition applications also have intriguing possibilities for people with disabilities. For example, mobile apps could help vision-impaired people to better perceive their surroundings and people with hearing impairments to translate voice conversations into text.
Autonomous Vehicles
Self-driving cars and trucks must process an enormous amount of data to make decisions in near-real-time. The complex networks that are developing to support fleets of autonomous vehicles use distributed processing to an unprecedented degree to coordinate everything from the braking decisions in a single car to traffic management across an entire city.
TuSimple—which is building an autonomous freight network of mapped routes that allow for autonomous cargo shipments across the southwestern U.S.—chose MXNet as its foundational platform for artificial intelligence model development. The company is bringing self-driving technology to an industry plagued with a chronic driver shortage as well as high overhead due to accidents, shift schedules, and fuel inefficiencies.
TuSimple chose MXNet because of its cross-platform portability, training efficiency, and scalability. One factor was a benchmark that compared MXNet against TensorFlow and found that in an environment with eight GPUs, MXNet was faster, more memory-efficient, and more accurate.
Why MXNet Matters to…
Data scientists
Machine learning is a growing part of the data science landscape. For those who are unfamiliar with the fine points of deep learning model development, MXNet is a good place to start. Its broad language support, Gluon API, and flexibility are well-suited to organizations developing their own deep learning skill sets. Amazon’s endorsement ensures that MXNet will be around for the long term and that the third-party ecosystem will continue to grow. Many experts recommend MXNet as a good starting point for future excursions into more complex deep learning frameworks.
Machine learning researchers
MXNet is often used by researchers for its ability to prototype quickly, which makes it easier to transform their research ideas into models and assess results. It also supports imperative programming which gives much more control to the researchers for computation. This particular framework has also shown significant performance on certain types of the model when compared to other frameworks due to the great utilization of CPUs and GPUs.
Software developers
Flexibility is a valued commodity in software engineering and MXNet is about as flexible a deep learning framework as can be found. In addition to its broad language support, it works with various data formats, including Amazon S3 cloud storage, and can scale up or down to fit most platforms. In 2019, MXNet added support for Horovod, a distributed learning framework developed by Uber. This offers software engineers even more flexibility in specifying deployment environments, which may include everything from laptops to cloud servers.
MXNet with NVIDIA GPUs
MXNet recommends NVIDIA GPUs to train and deploy neural networks because it offers significantly more computation power compared to CPUs, providing huge performance boosts in raining and inference. Developers can easily get started with MXNet using NGC (NVIDIA GPU Cloud). Here, users can pull containers having pre-trained models available on a variety of tasks such as computer vision, natural language processing, etc. with all the dependencies and framework in one container. With NVIDA TensorRT™, inference performance can be improved significantly on MXNet when using NVIDIA GPUs.
NVIDIA Deep Learning for Developers
GPU-accelerated deep learning frameworks offer the flexibility to design and train custom deep neural networks and provide interfaces to commonly used programming languages such as Python and C/C++. Widely used deep learning frameworks such as MXNet, PyTorch, TensorFlow, and others rely on NVIDIA GPU-accelerated libraries to deliver high-performance, multi-GPU accelerated training.
NVIDIA GPU Accelerated, End-to-End Data Science
The NVIDIA RAPIDS™ suite of open-source software libraries, built on CUDA-X AI, provides the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA CUDA primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.
With the RAPIDS GPU DataFrame, data can be loaded onto GPUs using a Pandas-like interface, and then used for various connected machine learning and graph analytics algorithms without ever leaving the GPU. This level of interoperability is made possible through libraries like Apache Arrow. This allows acceleration for end-to-end pipelines—from data prep to machine learning to deep learning.
RAPIDS supports device memory sharing between many popular data science libraries. This keeps data on the GPU and avoids costly copying back and forth to host memory.
Next Steps
Learn more:
GPU-Accelerated Data Science with RAPIDS | NVIDIA
xNet Deep Learning Framework and GPU Acceleration | NVIDIA Data Center
NVIDIA deep learning home page.
The NVIDIA Deep Learning Institute
NVIDIA developers site
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Installing MXNet — mxnet documentation
Installing MXNet — mxnet documentation
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Installing MXNet¶
Indicate your preferred configuration. Then, follow the customized commands to install MXNet.
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$ pip install mxnet
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find performance numbers in the MXNet tuning guide.
$ pip install mxnet-mkl
$ pip install mxnet==1.4.1
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find performance numbers in the MXNet tuning guide.
$ pip install mxnet-mkl==1.4.1
$ pip install mxnet==1.3.1
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find performance numbers in the MXNet tuning guide.
$ pip install mxnet-mkl==1.3.1
$ pip install mxnet==1.2.1
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find performance numbers in the MXNet tuning guide.
$ pip install mxnet-mkl==1.2.1
$ pip install mxnet==1.1.0
$ pip install mxnet==1.0.0
$ pip install mxnet==0.12.1
For MXNet 0.12.0:
$ pip install mxnet==0.12.0
$ pip install mxnet==0.11.0
$ pip install mxnet --pre
MKL-DNN enabled pip packages are optimized for Intel hardware. You can find performance numbers in the MXNet tuning guide.
$ pip install mxnet-mkl --pre
Check the chart below for other options, refer to PyPI for other MXNet pip packages, or validate your MXNet installation.
NOTES:
mxnet-cu101mkl means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 10.1.
All MKL pip packages are experimental prior to version 1.3.0.
Docker images with MXNet are available at Docker Hub.
Step 1 Install Docker on your machine by following the docker installation instructions.
Note - You can install Community Edition (CE) to get started with MXNet.
Step 2 [Optional] Post installation steps to manage Docker as a non-root user.
Follow the four steps in this docker documentation to allow managing docker containers without sudo.
If you skip this step, you need to use sudo each time you invoke Docker.
Step 3 Pull the MXNet docker image.
$ docker pull mxnet/python # Use sudo if you skip Step 2
You can list docker images to see if mxnet/python docker image pull was successful.
$ docker images # Use sudo if you skip Step 2
REPOSITORY TAG IMAGE ID CREATED SIZE
mxnet/python latest 00d026968b3c 3 weeks ago 1.41 GB
Using the latest MXNet with Intel MKL-DNN is recommended for the fastest inference speeds with MXNet.
$ docker pull mxnet/python:1.3.0_cpu_mkl # Use sudo if you skip Step 2
$ docker images # Use sudo if you skip Step 2
REPOSITORY TAG IMAGE ID CREATED SIZE
mxnet/python 1.3.0_cpu_mkl deaf9bf61d29 4 days ago 678 MB
Step 4 Validate the installation.
To build from source, refer to the MXNet Ubuntu installation guide.
$ pip install mxnet-cu101
$ pip install mxnet-cu101==1.4.1
$ pip install mxnet-cu92==1.3.1
$ pip install mxnet-cu92==1.2.1
$ pip install mxnet-cu91==1.1.0
$ pip install mxnet-cu90==1.0.0
$ pip install mxnet-cu90==0.12.1
$ pip install mxnet-cu80==0.11.0
$ pip install mxnet-cu101 --pre
MXNet offers MKL pip packages that will be much faster when running on Intel hardware.
Check the chart below for other options, refer to PyPI for other MXNet pip packages, or validate your MXNet installation.
NOTES:
mxnet-cu101mkl means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 10.1.
All MKL pip packages are experimental prior to version 1.3.0.
CUDA should be installed first. Instructions can be found in the CUDA dependencies section of the MXNet Ubuntu installation guide.
Important: Make sure your installed CUDA version matches the CUDA version in the pip package. Check your CUDA version with the following command:
nvcc --version
You can either upgrade your CUDA install or install the MXNet package that supports your CUDA version.
Docker images with MXNet are available at Docker Hub.
Step 1 Install Docker on your machine by following the docker installation instructions.
Note - You can install Community Edition (CE) to get started with MXNet.
Step 2 [Optional] Post installation steps to manage Docker as a non-root user.
Follow the four steps in this docker documentation to allow managing docker containers without sudo.
If you skip this step, you need to use sudo each time you invoke Docker.
Step 3 Install nvidia-docker-plugin following the installation instructions. nvidia-docker-plugin is required to enable the usage of GPUs from the docker containers.
Step 4 Pull the MXNet docker image.
$ docker pull mxnet/python:gpu # Use sudo if you skip Step 2
You can list docker images to see if mxnet/python docker image pull was successful.
$ docker images # Use sudo if you skip Step 2
REPOSITORY TAG IMAGE ID CREATED SIZE
mxnet/python gpu 493b2683c269 3 weeks ago 4.77 GB
Using the latest MXNet with Intel MKL-DNN is recommended for the fastest inference speeds with MXNet.
$ docker pull mxnet/python:1.3.0_cpu_mkl # Use sudo if you skip Step 2
$ docker images # Use sudo if you skip Step 2
REPOSITORY TAG IMAGE ID CREATED SIZE
mxnet/python 1.3.0_gpu_cu92_mkl adcb3ab19f50 4 days ago 4.23 GB
Step 5 Validate the installation.
Refer to the MXNet Ubuntu installation guide.
The default version of R that is installed with apt-get is insufficient. You will need to first install R v3.4.4+ and build MXNet from source.
After you have setup R v3.4.4+ and MXNet, you can build and install the MXNet R bindings with the following, assuming that incubator-mxnet is the source directory you used to build MXNet as follows:
$ cd incubator-mxnet
$ make rpkg
The default version of R that is installed with apt-get is insufficient. You will need to first install R v3.4.4+ and build MXNet from source.
After you have setup R v3.4.4+ and MXNet, you can build and install the MXNet R bindings with the following, assuming that incubator-mxnet is the source directory you used to build MXNet as follows:
$ cd incubator-mxnet
$ make rpkg
You can use the Maven packages defined in the following dependency to include MXNet in your Scala project. Please refer to the MXNet-Scala setup guide for a detailed set of instructions to help you with the setup process.
You can use the Maven packages defined in the following dependency to include MXNet in your Scala project. Please refer to the MXNet-Scala setup guide for a detailed set of instructions to help you with the setup process.
You can use the Maven packages defined in the following dependency to include MXNet in your Clojure project. To maximize leverage, the Clojure package has been built on the existing Scala package. Please refer to the MXNet-Scala setup guide for a detailed set of instructions to help you with the setup process that is required to use the Clojure dependency.
You can use the Maven packages defined in the following dependency to include MXNet in your Clojure project. To maximize leverage, the Clojure package has been built on the existing Scala package. Please refer to the MXNet-Scala setup guide for a detailed set of instructions to help you with the setup process that is required to use the Clojure dependency.
You can use the Maven packages defined in the following dependency to include MXNet in your Java project. The Java API is provided as a subset of the Scala API and is intended for inference only. Please refer to the MXNet-Java setup guide for a detailed set of instructions to help you with the setup process.
You can use the Maven packages defined in the following dependency to include MXNet in your Java project. The Java API is provided as a subset of the Scala API and is intended for inference only. Please refer to the MXNet-Java setup guide for a detailed set of instructions to help you with the setup process.
Refer to the Julia section of the MXNet Ubuntu installation guide.
Refer to the Perl section of the MXNet Ubuntu installation guide.
To enable the C++ package, build from source using `make USE_CPP_PACKAGE=1`.
Refer to the MXNet C++ setup guide for more info.
$ pip install mxnet
$ pip install mxnet==1.4.1
$ pip install mxnet==1.3.1
$ pip install mxnet==1.2.1
$ pip install mxnet==1.1.0
$ pip install mxnet==1.0.0
$ pip install mxnet=0.12.1
$ pip install mxnet==0.11.0
$ pip install mxnet --pre
MXNet offers MKL pip packages that will be much faster when running on Intel hardware.
Check the chart below for other options, refer to PyPI for other MXNet pip packages, or validate your MXNet installation.
NOTES:
mxnet-cu101mkl means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 10.1.
All MKL pip packages are experimental prior to version 1.3.0.
Docker images with MXNet are available at Docker Hub.
Step 1 Install Docker on your machine by following the docker installation instructions.
Note - You can install Community Edition (CE) to get started with MXNet.
Step 2 Pull the MXNet docker image.
$ docker pull mxnet/python
You can list docker images to see if mxnet/python docker image pull was successful.
$ docker images
REPOSITORY TAG IMAGE ID CREATED SIZE
mxnet/python latest 00d026968b3c 3 weeks ago 1.41 GB
Using the latest MXNet with Intel MKL-DNN is recommended for the fastest inference speeds with MXNet.
$ docker pull mxnet/python:1.3.0_cpu_mkl # Use sudo if you skip Step 2
$ docker images # Use sudo if you skip Step 2
REPOSITORY TAG IMAGE ID CREATED SIZE
mxnet/python 1.3.0_cpu_mkl deaf9bf61d29 4 days ago 678 MB
Step 4 Validate the installation.
To build from source, refer to the MXNet macOS installation guide.
MXNet developers should refer to the MXNet wiki’s Developer Setup on Mac.
This option is only available by building from source. Refer to the MXNet macOS installation guide.
Refer to the MXNet macOS installation guide.
MXNet developers should refer to the MXNet wiki’s Developer Setup on Mac.
To run MXNet you also should have OpenCV and OpenBLAS installed. You may install them with brew as follows:
brew install opencv
brew install openblas
To ensure MXNet R package runs with the version of OpenBLAS installed, create a symbolic link as follows:
ln -sf /usr/local/opt/openblas/lib/libopenblas.dylib /usr/local/opt/openblas/lib/libopenblasp-r0.3.1.dylib
Note: packages for 3.6.x are not yet available.
Install 3.5.x of R from CRAN. The latest is v3.5.3.
You can build MXNet-R from source, or you can use a pre-built binary:
cran <- getOption("repos")
cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/"
options(repos = cran)
install.packages("mxnet")
Will be available soon.
You can use the Maven packages defined in the following dependency to include MXNet in your Scala project. Please refer to the MXNet-Scala setup guide for a detailed set of instructions to help you with the setup process.
Not available at this time.
You can use the Maven packages defined in the following dependency to include MXNet in your Clojure project. To maximize leverage, the Clojure package has been built on the existing Scala package. Please refer to the MXNet-Scala setup guide for a detailed set of instructions to help you with the setup process that is required to use the Clojure dependency.
Not available at this time.
You can use the Maven packages defined in the following dependency to include MXNet in your Java project. The Java API is provided as a subset of the Scala API and is intended for inference only. Please refer to the MXNet-Java setup guide for a detailed set of instructions to help you with the setup process.
Not available at this time.
Refer to the Julia section of the MXNet macOS installation guide.
Refer to the Perl section of the MXNet macOS installation guide.
To enable the C++ package, build from source using `make USE_CPP_PACKAGE=1`.
Refer to the MXNet C++ setup guide for more info.
For more installation options, refer to the MXNet macOS installation guide.
$ pip install mxnet
$ pip install mxnet==1.4.1
$ pip install mxnet==1.3.1
$ pip install mxnet==1.2.1
$ pip install mxnet==1.1.0
$ pip install mxnet==1.0.0
$ pip install mxnet==0.12.1
$ pip install mxnet==0.11.0
$ pip install mxnet --pre
MXNet offers MKL pip packages that will be much faster when running on Intel hardware.
Check the chart below for other options, refer to PyPI for other MXNet pip packages, or validate your MXNet installation.
NOTES:
mxnet-cu101mkl means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 10.1.
All MKL pip packages are experimental prior to version 1.3.0.
Docker images with MXNet are available at Docker Hub.
Step 1 Install Docker on your machine by following the docker installation instructions.
Note - You can install Community Edition (CE) to get started with MXNet.
Step 2 [Optional] Post installation steps to manage Docker as a non-root user.
Follow the four steps in this docker documentation to allow managing docker containers without sudo.
If you skip this step, you need to use sudo each time you invoke Docker.
Step 3 Pull the MXNet docker image.
$ docker pull mxnet/python # Use sudo if you skip Step 2
You can list docker images to see if mxnet/python docker image pull was successful.
$ docker images # Use sudo if you skip Step 2
REPOSITORY TAG IMAGE ID CREATED SIZE
mxnet/python latest 00d026968b3c 3 weeks ago 1.41 GB
Using the latest MXNet with Intel MKL-DNN is recommended for the fastest inference speeds with MXNet.
$ docker pull mxnet/python:1.3.0_cpu_mkl # Use sudo if you skip Step 2
$ docker images # Use sudo if you skip Step 2
REPOSITORY TAG IMAGE ID CREATED SIZE
mxnet/python 1.3.0_cpu_mkl deaf9bf61d29 4 days ago 678 MB
Step 4 Validate the installation.
Refer to the MXNet Windows installation guide
$ pip install mxnet-cu101
$ pip install mxnet-cu101==1.4.1
$ pip install mxnet-cu92==1.3.1
$ pip install mxnet-cu92==1.2.1
$ pip install mxnet-cu91==1.1.0
$ pip install mxnet-cu90==1.0.0
$ pip install mxnet-cu90==0.12.1
$ pip install mxnet-cu80==0.11.0
$ pip install mxnet-cu101 --pre
MXNet offers MKL pip packages that will be much faster when running on Intel hardware.
Check the chart below for other options, refer to PyPI for other MXNet pip packages, or validate your MXNet installation.
NOTES:
mxnet-cu101mkl means the package is built with CUDA/cuDNN and MKL-DNN enabled and the CUDA version is 10.1.
All MKL pip packages are experimental prior to version 1.3.0.
Anaconda is recommended.
CUDA should be installed first. Instructions can be found in the CUDA dependencies section of the MXNet Ubuntu installation guide.
Important: Make sure your installed CUDA version matches the CUDA version in the pip package. Check your CUDA version with the following command:
nvcc --version
Refer to #8671 for status on CUDA 9.1 support.
You can either upgrade your CUDA install or install the MXNet package that supports your CUDA version.
To build from source, refer to the MXNet Windows installation guide.
Note: packages for 3.6.x are not yet available.
Install 3.5.x of R from CRAN.
You can build MXNet-R from source, or you can use a pre-built binary:
cran <- getOption("repos")
cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/"
options(repos = cran)
install.packages("mxnet")
To run MXNet you also should have OpenCV and OpenBLAS installed.
You can build MXNet-R from source, or you can use a pre-built binary:
cran <- getOption("repos")
cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/GPU/cu92"
options(repos = cran)
install.packages("mxnet")
Change cu92 to cu90, cu91 or cuda100 based on your CUDA toolkit version. Currently, MXNet supports these versions of CUDA.
Note : You also need to have cuDNN installed on Windows. Check out this guide on the steps for installation.
MXNet-Scala for Windows is not yet available.
MXNet-Clojure for Windows is not yet available.
MXNet-Java for Windows is not yet available.
Refer to the Julia section of the MXNet Windows installation guide.
Refer to the Perl section of the MXNet Windows installation guide.
To enable the C++ package, build from source using `make USE_CPP_PACKAGE=1`.
Refer to the MXNet C++ setup guide for more info.
For more installation options, refer to the MXNet Windows installation guide.
MXNet is available on several cloud providers with GPU support. You can also find GPU/CPU-hybrid support for use cases like scalable inference, or even fractional GPU support with AWS Elastic Inference.
Alibaba
NVIDIA VM
Amazon Web Services
Amazon SageMaker - Managed training and deployment of MXNet models
AWS Deep Learning AMI - Preinstalled Conda environments for Python 2 or 3 with MXNet, CUDA, cuDNN, MKL-DNN, and AWS Elastic Inference
Dynamic Training on AWS - experimental manual EC2 setup or semi-automated CloudFormation setup
NVIDIA VM
Google Cloud Platform
NVIDIA VM
Microsoft Azure
NVIDIA VM
Oracle Cloud
NVIDIA VM
All NVIDIA VMs use the NVIDIA MXNet Docker container.
Follow the container usage instructions found in NVIDIA’s container repository.
MXNet should work on any cloud provider's CPU-only instances. Follow the Python pip install instructions, Docker instructions, or try the following preinstalled option.
Amazon Web Services
AWS Deep Learning AMI - Preinstalled Conda environments for Python 2 or 3 with MXNet and MKL-DNN.
MXNet supports the Debian based Raspbian ARM based operating system so you can run MXNet on Raspberry Pi 3B devices.
These instructions will walk through how to build MXNet for the Raspberry Pi and install the Python bindings for the library.
You can do a dockerized cross compilation build on your local machine or a native build on-device.
The complete MXNet library and its requirements can take almost 200MB of RAM, and loading large models with the library can take over 1GB of RAM. Because of this, we recommend running MXNet on the Raspberry Pi 3 or an equivalent device that has more than 1 GB of RAM and a Secure Digital (SD) card that has at least 4 GB of free memory.
Quick installation¶
You can use this pre-built Python wheel on a Raspberry Pi 3B with Stretch. You will likely need to install several dependencies to get MXNet to work. Refer to the following Build section for details.
Docker installation¶
Step 1 Install Docker on your machine by following the docker installation instructions.
Note - You can install Community Edition (CE)
Step 2 [Optional] Post installation steps to manage Docker as a non-root user.
Follow the four steps in this docker documentation to allow managing docker containers without sudo.
Build¶
This cross compilation build is experimental.
Please use a Native build with gcc 4 as explained below, higher compiler versions currently cause test failures on ARM.
The following command will build a container with dependencies and tools,
and then compile MXNet for ARMv7.
You will want to run this on a fast cloud instance or locally on a fast PC to save time.
The resulting artifact will be located in build/mxnet-x.x.x-py2.py3-none-any.whl.
Copy this file to your Raspberry Pi.
The previously mentioned pre-built wheel was created using this method.
ci/build.py -p armv7
Install using a pip wheel¶
Your Pi will need several dependencies.
Install MXNet dependencies with the following:
sudo apt-get update
sudo apt-get install -y \
apt-transport-https \
build-essential \
ca-certificates \
cmake \
curl \
git \
libatlas-base-dev \
libcurl4-openssl-dev \
libjemalloc-dev \
liblapack-dev \
libopenblas-dev \
libopencv-dev \
libzmq3-dev \
ninja-build \
python-dev \
python-pip \
software-properties-common \
sudo \
unzip \
virtualenv \
wget
Install virtualenv with:
sudo pip install virtualenv
Create a Python 2.7 environment for MXNet with:
virtualenv -p `which python` mxnet_py27
You may use Python 3, however the wine bottle detection example for the Pi with camera requires Python 2.7.
Activate the environment, then install the wheel we created previously, or install this prebuilt wheel.
source mxnet_py27/bin/activate
pip install mxnet-x.x.x-py2.py3-none-any.whl
Test MXNet with the Python interpreter:
$ python
>>> import mxnet
If there are no errors then you’re ready to start using MXNet on your Pi!
Native Build¶
Installing MXNet from source is a two-step process:
Build the shared library from the MXNet C++ source code.
Install the supported language-specific packages for MXNet.
Step 1 Build the Shared Library
On Raspbian versions Wheezy and later, you need the following dependencies:
Git (to pull code from GitHub)
libblas (for linear algebraic operations)
libopencv (for computer vision operations. This is optional if you want to save RAM and Disk Space)
A C++ compiler that supports C++ 11. The C++ compiler compiles and builds MXNet source code. Supported compilers include the following:
G++ (4.8 or later). Make sure to use gcc 4 and not 5 or 6 as there are known bugs with these compilers.
Clang (3.9 - 6)
Install these dependencies using the following commands in any directory:
sudo apt-get update
sudo apt-get -y install git cmake ninja-build build-essential g++-4.9 c++-4.9 liblapack* libblas* libopencv* libopenblas* python3-dev python-dev virtualenv
Clone the MXNet source code repository using the following git command in your home directory:
git clone https://github.com/apache/incubator-mxnet.git --recursive
cd incubator-mxnet
Build:
mkdir -p build && cd build
cmake \
-DUSE_SSE=OFF \
-DUSE_CUDA=OFF \
-DUSE_OPENCV=ON \
-DUSE_OPENMP=ON \
-DUSE_MKL_IF_AVAILABLE=OFF \
-DUSE_SIGNAL_HANDLER=ON \
-DCMAKE_BUILD_TYPE=Release \
-GNinja ..
ninja -j$(nproc)
Some compilation units require memory close to 1GB, so it’s recommended that you enable swap as
explained below and be cautious about increasing the number of jobs when building (-j)
Executing these commands start the build process, which can take up to a couple hours, and creates a file called libmxnet.so in the build directory.
If you are getting build errors in which the compiler is being killed, it is likely that the
compiler is running out of memory (especially if you are on Raspberry Pi 1, 2 or Zero, which have
less than 1GB of RAM), this can often be rectified by increasing the swapfile size on the Pi by
editing the file /etc/dphys-swapfile and changing the line CONF_SWAPSIZE=100 to CONF_SWAPSIZE=1024,
then running:
sudo /etc/init.d/dphys-swapfile stop
sudo /etc/init.d/dphys-swapfile start
free -m # to verify the swapfile size has been increased
Step 2 Build cython modules (optional)
$ pip install Cython
$ make cython # You can set the python executable with `PYTHON` flag, e.g., make cython PYTHON=python3
MXNet tries to use the cython modules unless the environment variable MXNET_ENABLE_CYTHON is set to 0. If loading the cython modules fails, the default behavior is falling back to ctypes without any warning. To raise an exception at the failure, set the environment variable MXNET_ENFORCE_CYTHON to 1. See here for more details.
Step 3 Install MXNet Python Bindings
To install Python bindings run the following commands in the MXNet directory:
cd python
pip install --upgrade pip
pip install -e .
Note that the -e flag is optional. It is equivalent to --editable and means that if you edit the source files, these changes will be reflected in the package installed.
Alternatively you can create a whl package installable with pip with the following command:
ci/docker/runtime_functions.sh build_wheel python/ $(realpath build)
You are now ready to run MXNet on your Raspberry Pi device. You can get started by following the tutorial on Real-time Object Detection with MXNet On The Raspberry Pi.
Note - Because the complete MXNet library takes up a significant amount of the Raspberry Pi’s limited RAM, when loading training data or large models into memory, you might have to turn off the GUI and terminate running processes to free RAM.
Nvidia Jetson TX family¶
MXNet supports the Ubuntu Arch64 based operating system so you can run MXNet on NVIDIA Jetson Devices.
These instructions will walk through how to build MXNet for the Pascal based NVIDIA Jetson TX2 and install the corresponding python language bindings.
For the purposes of this install guide we will assume that CUDA is already installed on your Jetson device.
Install MXNet
Installing MXNet is a two-step process:
Build the shared library from the MXNet C++ source code.
Install the supported language-specific packages for MXNet.
Step 1 Build the Shared Library
You need the following additional dependencies:
Git (to pull code from GitHub)
libatlas (for linear algebraic operations)
libopencv (for computer vision operations)
python pip (to load relevant python packages for our language bindings)
Install these dependencies using the following commands in any directory:
sudo apt-get update
sudo apt-get -y install git build-essential libatlas-base-dev libopencv-dev graphviz python-pip
sudo pip install pip --upgrade
sudo pip install setuptools numpy --upgrade
sudo pip install graphviz==0.8.4 \
jupyter
Clone the MXNet source code repository using the following git command in your home directory:
git clone https://github.com/apache/incubator-mxnet.git --recursive
cd incubator-mxnet
Edit the Makefile to install the MXNet with CUDA bindings to leverage the GPU on the Jetson:
cp make/crosscompile.jetson.mk config.mk
Edit the Mshadow Makefile to ensure MXNet builds with Pascal’s hardware level low precision acceleration by editing 3rdparty/mshadow/make/mshadow.mk and adding the following after line 122:
MSHADOW_CFLAGS += -DMSHADOW_USE_PASCAL=1
Now you can build the complete MXNet library with the following command:
make -j $(nproc)
Executing this command creates a file called libmxnet.so in the mxnet/lib directory.
Step 2 Install MXNet Python Bindings
To install Python bindings run the following commands in the MXNet directory:
cd python
pip install --upgrade pip
pip install -e .
Note that the -e flag is optional. It is equivalent to --editable and means that if you edit the source files, these changes will be reflected in the package installed.
Add the mxnet folder to the path:
cd ..
export MXNET_HOME=$(pwd)
echo "export PYTHONPATH=$MXNET_HOME/python:$PYTHONPATH" >> ~/.rc
source ~/.rc
You are now ready to run MXNet on your NVIDIA Jetson TX2 device.
Source Download¶
Download your required version of MXNet and build from source.
Table Of Contents
Installing MXNet
Quick installation
Docker installation
Build
Install using a pip wheel
Native Build
Nvidia Jetson TX family
Source Download
Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.
"Copyright © 2017-2018, The Apache Software Foundation
Apache MXNet, MXNet, Apache, the Apache feather, and the Apache MXNet project logo are either registered trademarks or trademarks of the Apache Software Foundation."
MXNet: A Growing Deep Learning Framework
MXNet: A Growing Deep Learning Framework
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MXNet: A Growing Deep Learning Framework
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Mellon, J., 2022: MXNet: A Growing Deep Learning Framework. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed March 12, 2024, https://insights.sei.cmu.edu/blog/mxnet-a-growing-deep-learning-framework/.
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Mellon, J. (2022, November 14). MXNet: A Growing Deep Learning Framework. Retrieved March 12, 2024, from https://insights.sei.cmu.edu/blog/mxnet-a-growing-deep-learning-framework/.
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Mellon, Jeffrey. "MXNet: A Growing Deep Learning Framework." Carnegie Mellon University, Software Engineering Institute's Insights (blog). Carnegie Mellon's Software Engineering Institute, November 14, 2022. https://insights.sei.cmu.edu/blog/mxnet-a-growing-deep-learning-framework/.
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IEEE Citation
J. Mellon, "MXNet: A Growing Deep Learning Framework," Carnegie Mellon University, Software Engineering Institute's Insights (blog). Carnegie Mellon's Software Engineering Institute, 14-Nov-2022 [Online]. Available: https://insights.sei.cmu.edu/blog/mxnet-a-growing-deep-learning-framework/. [Accessed: 12-Mar-2024].
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@misc{mellon_2022,author={Mellon, Jeffrey},title={MXNet: A Growing Deep Learning Framework},month={Nov},year={2022},howpublished={Carnegie Mellon University, Software Engineering Institute's Insights (blog)},url={https://insights.sei.cmu.edu/blog/mxnet-a-growing-deep-learning-framework/},note={Accessed: 2024-Mar-12}}
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MXNet: A Growing Deep Learning Framework
Jeffrey Mellon
November 14, 2022
PUBLISHED IN
Artificial Intelligence Engineering
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Deep learning refers to the component of machine learning (ML) that attempts to mimic the mechanisms deployed by the human brain. It consists of forming deep neural networks (DNNs), which have several (hidden) layers. Applications include virtual assistants (such as Alexa and Siri), detecting fraud, predicting election results, medical imaging for detecting and diagnosing diseases, driverless vehicles, and deepfake creation and detection. You may have heard of TensorFlow or PyTorch, which are widely used deep learning frameworks. As this post details, MXNet (pronounced mix-net) is Apache’s open-source spin on a deep-learning framework that supports building and training models in multiple languages, including Python, R, Scala, Julia, Java, Perl, and C++. An Overview of MXNetAlong with the aforementioned languages, trained MXNet models can be used for prediction in MATLAB and JavaScript. Regardless of the model-building language, MXNet calls optimized C++ as the back-end engine. Moreover, it is scalable and runs on systems ranging from mobile devices to distributed graphics processing unit (GPU) clusters. Not only does the MXNet framework enable fast model training, it scales automatically to the number of available GPUs across multiple hosts and multiple machines. MXNet also supports data synchronization over multiple devices with multiple users. MXNet research has been conducted at several universities, including Carnegie Mellon University, and Amazon uses it as its deep-learning framework due to its GPU capabilities and cloud computing integration.
Figure 1: MXNet architecture. Source:
https://mxnet.apache.org/versions/1.4.1/architecture/overview.html
Figure 1 describes MXNet’s capabilities. The MXNet engine allows for good resource utilization, parallelization, and reproducibility. Its KVStore is a distributed key-value store for data communication and synchronization over multiple devices. A user can push a key-value pair from a device to the store and pull the value on a key from the store.Imperative programming is a programming paradigm where the programmer explicitly instructs the machine on how to complete a task. It tends to follow a procedural rather than declarative style, which mimics the way the processor executes machine code. In other words, imperative programming does not focus on the logic of a computation or what the program will accomplish. Instead, it focuses on how to compute it as a series of tasks. Within MXNet, imperative programming specifies how to perform a computation (e.g., tensor operations). Examples of languages that incorporate imperative programming include C, C++, Python, and JavaScript. MXNet incorporates imperative programming using NDArray, which is useful for storing and transforming data, much like NumPy’s ndarray. Data is represented as multi-dimensional arrays that can run on GPUs to accelerate computing. Moreover MXNet contains data iterators that allow users to load images with their labels directly from directories. After retrieving the data, the data can be preprocessed and used to create batches of images and iterate through these batches before feeding them into a neural network.Lastly, MXNet provides the ability to blend symbolic and imperative programming. Symbolic programming specifies what computations to perform (e.g., declaration of a computation graph). Gluon, a hybrid programming interface, combines both imperative and symbolic interfaces, while keeping the capabilities and advantages of both. Importantly, Gluon is key to building and training neural networks, largely for image classification, deepfake detection, etc. There is a version of Gluon specifically designed for natural language processing (nlp), as well.Why MXNet Is Better for GPUsCPUs are composed of just a few cores with lots of cache memory that can handle a few software threads at a time. In contrast, a GPU is composed of hundreds of cores that can handle thousands of threads simultaneously. The parallel nature of neural networks (created from large numbers of identical neurons) maps naturally to GPUs, providing a significant computation speed-up over CPU-only training. Currently, GPUs are the platform of choice for training large, complex neural network-based systems. Overall, more GPUs on an MXNet training algorithm lead to significantly faster completion time due to the “embarrassingly parallel” nature of these computations.By default, MXNet relies on CPUs, but users can specify GPUs. For a CPU, MXNet will allocate data on the main memory and try to use as many CPU cores as possible, even if there is more than one CPU socket. If there are multiple GPUs, MXNet needs to specify which GPUs the NDArray will be allocated to. MXNet also requires users to move data between devices explicitly. The only requirement for performing an operation on a particular GPU is for users to guarantee that the inputs of the operation are already on that GPU. The output will be allocated on the same GPU, as well.Before delving into why MXNet is of interest to researchers, let’s compare available deep learning software. In Table 1 below, I outline some of the key similarities and differences between MXNet, TensorFlow, and PyTorch.
MXNet
TensorFlow
PyTorch
Year Released
2015
2015
2016
Creator
Apache
Google Brain
Facebook members
Deep Learning Priority
Yes
Yes
Yes
Open-source
Yes
Yes
Yes
CUDA support
Yes
Yes
Yes
Simple Interactive Debugging
Yes
No
Yes
Multi-GPU training
Yes
Yes
Yes
Frequent community updates
Lesser
Higher
Lesser
Easy Learning
Yes
No (interface changes after every update)
Yes (Typically if Python User)
Interface for Monitoring
Yes
Yes
Yes
Table 1: A Comparison of MXNet, TensorFlow, PyTorchSome other features that are hard to put into the chart includeTensorFlow, which generally does better on CPU than MXNet, but MXNet generally does better (speed and performance wise) than PyTorch and TensorFlow on GPUs.MXNet, which has good ease of learning, resource utilization, and computation speed specifically on GPUs.Why MXNet Looks PromisingWith the rise of disinformation campaigns, such as deepfakes, coupled with new remote work environments brought about by the onslaught of COVID, deep learning is increasingly important in the realm of cybersecurity. Deep learning consists of forming algorithms made up of deep neural networks (DNNs) that have multiple layers, several of which are hidden. These deep learning algorithms are used to create and detect deepfakes. As DarkReading noted in a January 2022 article, malicious actors are deploying increasingly sophisticated impersonation attempts and organizations must prepare for the increasingly sophisticated threat of deepfakes. “What was a cleverly written phishing email from a C-level email account in 2021 could become a well-crafted video or voice recording attempting to solicit the same sensitive information and resources in 2022 and beyond.”The rise in deepfakes has also brought about a rise in the number of available deep learning frameworks. MXNet appears able to compete with two of the top industry frameworks, and could be a suitable piece for further research or to use in one’s research projects, including deepfake detection, self-driving cars, fraud detection, and even natural language processing applications. Deepfake detection is already being researched here at the SEI by my colleagues Catherine Bernaciak, Shannon Gallagher, Thomas Scanlon, Dominic Ross, and myself.MXNet has limitations, including having a relatively small community of members that update it, which limits their ability to fix bugs, improve content, and add new features. MXNet is not as popular as TensorFlow and PyTorch, even though it is actively used by businesses like Amazon. Despite these limitations, MXNet is a computationally efficient, scalable, portable, fast framework that provides a user-friendly experience to users who rely on several varying programming languages. Its GPU capabilities and high performance make it a deep-learning framework that should be more widely used and known.In a future post, I will provide details on an interactive notebook that CERT researchers have developed to give users hands-on experience with MXNet.
Additional Resources
Several employees of the CERT Division presented at the SEI Deepfakes Day 2022 on August 30, 2022. Deepfakes Day is a workshop hosted by the SEI that addresses the origins of deepfake media content, its technical underpinnings, and the available methods to detect and mitigate against deepfakes. Dr. William Corvey, Program Manager for DARPA’s SemaFor program, delivered the keynote.Official MXNET GitHub site:https://github.com/apache/incubator-mxnetView the SEI Blog Post How Easy Is it to Make and Detect a Deepfake?More info on GANs and other types of networks is available at the following link: https://d2l.ai/chapter_generative-adversarial-networks/gan.html This link offers example tutorials that use MXNet.Also check out the following articles and papers:MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed SystemshThe Top 5 Deep Learning Frameworks to Watch in 2021 and Why TensorFlowTop 10 Deep Learning Frameworks in 2022 You Can’t IgnoreThe Ultimate Guide to Machine Learning FrameworksPopular Deep Learning Frameworks: An OverviewTop 8 Deep Learning Frameworks
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What Is Apache MXNet? - Apache MXNet on AWS
What Is Apache MXNet? - Apache MXNet on AWSWhat Is Apache MXNet? - Apache MXNet on AWSAWSDocumentationApache MXNet on AWSDeveloper GuideWhat Is Apache MXNet?Apache MXNet (MXNet) is an open source deep learning framework that allows you to define,
train, and deploy deep neural networks on a wide array of platforms, from cloud
infrastructure to mobile devices. It is highly scalable, which allows for fast model
training, and it supports a flexible programming model and multiple languages. The MXNet library is portable and lightweight. It scales seamlessly on multiple GPUs on
multiple machines.MXNet supports programming in various languages including Python, R, Scala, Julia, and
Perl.This user guide has been deprecated and is no longer available. For more information on MXNet and related material, see the topics below.
MXNetMXNet is an Apache open source project. For more information about MXNet, see the
following open source documentation:
Getting
Started – Provides details for setting up MXNet on various
platforms, such as macOS, Windows, and Linux. It also explains how to set up
MXNet for use with different front-end languages, such as Python, Scala, R,
Julia, and Perl.
Tutorials –
Provides step-by-step procedures for various deep learning tasks using
MXNet.
API Reference – For more information, see MXNet. At the top of this page,
choose a language from the API menu.
For all other information, see MXNet.Deep Learning AMIs (DLAMI)AWS provides Deep Learning Amazon Machine Images (DLAMIs) optimized
for both CPU and GPU EC2 instances. The DLAMI User's Guide explains how to
set up MXNet on AWS using these AMIs. Amazon SageMakerYou can use MXNet with Amazon SageMaker to train a models using your own custom Apache MXNet code.
Amazon SageMaker is a fully managed machine learning service. With Amazon SageMaker, data scientists
and developers can quickly and easily build and train machine learning models, and then directly deploy
them into a production-ready hosted environment.For more information, see the Amazon SageMaker documentation. Javascript is disabled or is unavailable in your browser.To use the Amazon Web Services Documentation, Javascript must be enabled. Please refer to your browser's Help pages for instructions.Document ConventionsDid this page help you? - YesThanks for letting us know we're doing a good job!If you've got a moment, please tell us what we did right so we can do more of it.Did this page help you? - NoThanks for letting us know this page needs work. We're sorry we let you down.If you've got a moment, please tell us how we can make the documentation bett
Machine Learning Inference Framework - Apache MXNet on AWS - AWS
Machine Learning Inference Framework - Apache MXNet on AWS - AWS
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Apache MXNet on AWS
Overview
Getting Started
Apache MXNet on AWS
Build machine learning applications that train quickly and run anywhere
Try Amazon SageMaker
Apache MXNet is a fast and scalable training and inference framework with an easy-to-use, concise API for machine learning.
MXNet includes the Gluon interface that allows developers of all skill levels to get started with deep learning on the cloud, on edge devices, and on mobile apps. In just a few lines of Gluon code, you can build linear regression, convolutional networks and recurrent LSTMs for object detection, speech recognition, recommendation, and personalization.
You can get started with MxNet on AWS with a fully-managed experience using Amazon SageMaker, a platform to build, train, and deploy machine learning models at scale. Or, you can use the AWS Deep Learning AMIs to build custom environments and workflows with MxNet as well as other frameworks including TensorFlow, PyTorch, Chainer, Keras, Caffe, Caffe2, and Microsoft Cognitive Toolkit.
Contribute to the Apache MXNet Project
Get involved at GitHub
Grab sample code, notebooks, and tutorial content at the GitHub project page.
Benefits of deep learning using MXNet
Ease-of-Use with Gluon
MXNet’s Gluon library provides a high-level interface that makes it easy to prototype, train, and deploy deep learning models without sacrificing training speed. Gluon offers high-level abstractions for predefined layers, loss functions, and optimizers. It also provides a flexible structure that is intuitive to work with and easy to debug.
Greater Performance
Deep learning workloads can be distributed across multiple GPUs with near-linear scalability, which means that extremely large projects can be handled in less time. As well, scaling is automatic depending on the number of GPUs in a cluster. Developers also save time and increase productivity by running serverless and batch-based inferencing.
For IoT & the Edge
In addition to handling multi-GPU training and deployment of complex models in the cloud, MXNet produces lightweight neural network model representations that can run on lower-powered edge devices like a Raspberry Pi, smartphone, or laptop and process data remotely in real-time.
Flexibility & Choice
MXNet supports a broad set of programming languages—including C++, JavaScript, Python, R, Matlab, Julia, Scala, Clojure, and Perl—so you can get started with languages that you already know. On the backend, however, all code is compiled in C++ for the greatest performance regardless of what language is used to build the models.
Customer momentum
Case studies
There are over 500 contributors to the MXNet project including developers from Amazon, NVIDIA, Intel, Samsung, and Microsoft. Learn about how customers are using MXNet for deep learning projects. For more case studies, see the AWS machine learning blog and the MXNet blog.
Uses deep learning to lower property damage losses from natural disasters
Celgene advances pharma research and discovery with the help of AI on MXNet
Curalate makes social sell with AI using Apache MXNet on AWS
Amazon SageMaker for machine learning
Learn more about Amazon SageMaker
Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.
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