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Apache MXNet

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Apache MXNet
Developer(s)Apache Software Foundation
Stable release
1.9.1[1] / 10 May 2022; 2 years ago (10 May 2022)
Written inC++, Python, R, Java, Julia, JavaScript, Scala, Go, Perl
Operating systemWindows, macOS, Linux
TypeLibrary for machine learning and deep learning
LicenseApache License 2.0

Apache MXNet is an open-source deep learning software framework that trains and deploys deep neural networks. It aims to be 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]Apache MXNet was effectively abandoned due to a combination of factors including lack of significant contributions, outdated builds, and a shift in focus by its major backer, Amazon, towards other frameworks like PyTorch. The project saw no new releases for over a year, and there were very few pull requests or updates from contributors, leading to its move to the Apache Attic in 2023. Additionally, the community began migrating to other frameworks that offered more robust support and development activity.[5]


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).


MXNet can be distributed on dynamic cloud infrastructure using a distributed parameter server (based on research at Carnegie Mellon University, Baidu, and Google[6]). With multiple GPUs or CPUs, the framework can approach linear scale.


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.


The framework supports deployment of a trained model to low-end devices for inference, such as mobile devices by using Amalgamation.[7] Other deployment targets include 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)[8] and Microsoft Azure.[9] 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.[10]

See also[edit]


  1. ^ "Release 1.9.1". 10 May 2022. Retrieved 30 June 2022.
  2. ^ "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.
  3. ^ "Carlos Guestrin". guestrin.su.domains. Archived from the original on September 22, 2023.
  4. ^ "Apache MXNet - Apache Attic".
  5. ^ "Apache MXNet - Apache Attic". attic.apache.org. Retrieved 2024-06-05.
  6. ^ "Scaling Distributed Machine Learning with the Parameter Server" (PDF). Archived (PDF) from the original on August 13, 2023. Retrieved 2014-10-08.
  7. ^ "Amalgamation". Archived from the original on 2018-08-08. Retrieved 2018-05-08.
  8. ^ "Apache MXNet on AWS - Deep Learning on the Cloud". Amazon Web Services, Inc. Retrieved 13 May 2017.
  9. ^ "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.
  10. ^ "MXNet, Amazon's deep learning framework, gets accepted into Apache Incubator". Retrieved 2017-03-08.