Apache MXNet
 |
 | This article contains content that is written like an advertisement. (April 2020) |
| Developer(s) | Apache Software Foundation |
|---|---|
| Stable release | 1.9.1[1]
/ 10 May 2022 |
| Repository | |
| Written in | C++, Python, R, Java, Julia, JavaScript, Scala, Go, Perl |
| Operating system | Windows, macOS, Linux |
| Type | Library for machine learning and deep learning |
| License | Apache License 2.0 |
| Website | mxnet |
Apache MXNet is an open-source deep learning software framework, used to train and deploy deep neural networks. It is scalable, allowing for 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] as well as multiple machines. It was co-developed by Carlos Guestrin at University of Washington (along with GraphLab).[3]
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).
Scalable[edit]
MXNet can be distributed on dynamic cloud infrastructure using a distributed parameter server (based on research at Carnegie Mellon University, Baidu, and Google[4]). With multiple GPUs or CPUs the framework approaches linear scale.
Flexible[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.
Portable[edit]
Supports an efficient deployment of a trained model to low-end devices for inference, such as mobile devices (using Amalgamation[5]), 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)[6] and Microsoft Azure.[7] Amazon has chosen MXNet as its deep learning framework of choice at AWS.[8][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]
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. Retrieved 13 May 2017.
- ^ https://homes.cs.washington.edu/~guestrin/open-source.html
- ^ "Scaling Distributed Machine Learning with the Parameter Server" (PDF). 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. 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.
