Flexible and Efficient Library for Deep Learning

Flexible

Supports both imperative and symbolic programmings.

Portable

Runs on CPUs or GPUs, on clusters, servers, destkops, or mobile phones

Multiple Lanuages

Supports over 7 programming languages, including C++, Python, R, Scala, Julia, Matlab, and Javascripts.

Auto-Differentiation

Calculates the gradient automatically for training a model.

Distributed on Cloud

Supports distributed training on multiple CPU/GPU machines, including AWS, GCE, Azure, and Yarn clusters.

Performance

The well-optimized C++ backend engine parallelize both I/O and computations

MXNet is developed by collaborators from multiple universities and companies. We sincerely thank the following organizations for supporing MXNet and sponsoring its major developers (alphabetical order).