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 MXNet can be distributed on dynamic
cloud infrastructure using a
distributed parameter server (based on research at
Carnegie Mellon University,
Baidu, and
Google). With multiple GPUs or
CPUs, the framework can approach linear scale.
Flexibility MXNet supports both imperative and symbolic programming. The framework allows developers to track, debug, save checkpoints, modify
hyperparameters, and perform
early stopping.
Multiple languages MXNet supports Python, R, Scala, Clojure, Julia, Perl,
MATLAB, and JavaScript for front-end development and C++ for back-end optimization.
Portability The framework supports deployment of a trained model to low-end devices for inference, such as mobile devices by using Amalgamation. 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 MXNet is supported by
public cloud providers including
Amazon Web Services (AWS) and
Microsoft Azure. 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. ==See also==