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Amazon has chosen MXNet as its deep learning framework of choice at AWS. MXNet is supported by public cloud providers including Amazon Web Services (AWS) and Microsoft Azure. 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). Supports an efficient deployment of a trained model to low-end devices for inference, such as mobile devices (using Amalgamation ), Internet of things devices (using AWS Greengrass), serverless computing (using AWS Lambda) or containers. MXNet supports Python, R, Scala, Clojure, Julia, Perl, MATLAB and JavaScript for front-end development, and C++ for back-end optimization. The framework allows developers to track, debug, save checkpoints, modify hyperparameters, and perform early stopping. MXNet supports both imperative and symbolic programming. with multiple GPUs or CPUs the framework approaches linear scale. MXNet can be distributed on dynamic cloud infrastructure using a distributed parameter server (based on research at Carnegie Mellon University, Baidu, and Google ). 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).