Build machine learning applications that train quickly and run anywhere

Apache MXNet on AWS is a fast and scalable training and inference framework with an easy-to-use, concise API for machine learning. MXNet includes the Gluon interface that allows developers of all skill levels to get started with deep learning on the cloud, on edge devices, and on mobile apps. In just a few lines of Gluon code, you can build linear regression, convolutional networks and recurrent LSTMs for object detection, speech recognition, recommendation, and personalization. You can get started with MxNet on AWS with a fully-managed experience using Amazon SageMaker, a platform to build, train, and deploy machine learning models at scale. Or, you can use the AWS Deep Learning AMIs to build custom environments and workflows with MxNet as well as other frameworks including TensorFlow, PyTorch, Chainer, Keras, Caffe, Caffe2, and Microsoft Cognitive Toolkit.

Benefits of deep learning using MXNet

Ease-of-Use with Gluon

MXNet’s Gluon library provides a high-level interface that makes it easy to prototype, train, and deploy deep learning models without sacrificing training speed. Gluon offers high-level abstractions for predefined layers, loss functions, and optimizers. It also provides a flexible structure that is intuitive to work with and easy to debug.

Greater Performance

Deep learning workloads can be distributed across multiple GPUs with near-linear scalability, which means that extremely large projects can be handled in less time. As well, scaling is automatic depending on the number of GPUs in a cluster. Developers also save time and increase productivity by running serverless and batch-based inferencing.

For IoT & the Edge

In addition to handling multi-GPU training and deployment of complex models in the cloud, MXNet produces lightweight neural network model representations that can run on lower-powered edge devices like a Raspberry Pi, smartphone, or laptop and process data remotely in real-time.

Flexibility & Choice

MXNet supports a broad set of programming languages—including C++, JavaScript, Python, R, Matlab, Julia, Scala, Clojure, and Perl—so you can get started with languages that you already know. On the backend, however, all code is compiled in C++ for the greatest performance regardless of what language is used to build the models.