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| Apache MXNet | |
|---|---|
| Developer | Apache Software Foundation |
| Stable release | |
| Written in | C++,Python,R,Java,Julia,JavaScript,Scala,Go,Perl |
| Operating system | Windows,macOS,Linux |
| Type | Library formachine learning anddeep learning |
| License | Apache License 2.0 |
| Website | mxnet |
| Repository | |
Apache MXNet is anopen-sourcedeep learningsoftwareframework that trains and deploysdeep neural networks. It aims to be scalable, allows fastmodel training, and supports a flexibleprogramming model and multipleprogramming languages (includingC++,Python,Java,Julia,MATLAB,JavaScript,Go,R,Scala,Perl, andWolfram Language). The MXNetlibrary isportable and canscale to multipleGPUs[2] and machines. It was co-developed byCarlos Guestrin at theUniversity 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Apache MXNet was effectively abandoned due to a combination of factors including lack of significant contributions, outdated builds, and a shift in focus by its major backer, Amazon, towards other frameworks like PyTorch.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 asconvolutional neural networks (CNNs) andlong short-term memory networks (LSTMs).
MXNet can be distributed on dynamiccloud infrastructure using adistributed parameter server (based on research atCarnegie Mellon University,Baidu, andGoogle[6]). With multiple GPUs orCPUs, the framework can approach linear scale.
MXNet supports both imperative and symbolic programming. The framework allows developers to track, debug, save checkpoints, modifyhyperparameters, and performearly stopping.
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 includeInternet of things devices (using AWS Greengrass),serverless computing (usingAWS Lambda), orcontainers. 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)
MXNet is supported bypublic cloud providers includingAmazon Web Services (AWS)[8] andMicrosoft Azure.[9] Currently, MXNet is supported byIntel,Baidu,Microsoft,Wolfram Research, and research institutions such asCarnegie Mellon,MIT, theUniversity of Washington, and theHong Kong University of Science and Technology.[10]
| Open source | |
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| Proprietary |
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