Apache MXNet 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 on AWS with a fully-managed MXNet experience with 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 TensorFlow and other popular frameworks such as TensorFlow, Caffe, Caffe2, Chainer, PyTorch, Keras, and Microsoft Cognitive Toolkit.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.