Posted On: Sep 6, 2017

We’re excited about the availability of Apache MXNet version 0.11. With this release, MXNet hit major milestones, both in terms of community development and as an incubating Apache project. Contributors—including developers from Apple, Samsung and Microsoft—committed code to this release. There are over 400 contributors on the project so far. The project has now fully migrated its codebase to Apache and has made its first official release as an incubating project. This post covers some of the key features included with this release.

With the release of Core ML by Apple at WWDC 2017 developers can now easily integrate a machine learning model into their app, enabling them to bring intelligent new features to users with just a few lines of code. Apple has contributed code to the Apache MXNet project to facilitate the availability of cutting edge models for app developers. Apache MXNet now works with Core ML that enables developers to build and train machine learning models in the cloud with MXNet, and import them right into Xcode so you can easily build intelligent new features in your apps. You can choose from MXNet’s model zoo of pre-trained models for various applications or build your own model. This release provides you with a tool (in preview) to convert MXNet models to the Core ML format. For more details about the converter, see the MXNet tools GitHub repo.

This release also adds support for Keras v1.2, which is a popular high-level library for building neural networks. It provides users with an easy-to-use frontend interface that can drive various machine learning framework backends. MXNet is now available as a backend choice for users running Keras v1.2. MXNet gives you a simple way to set up multi-GPUs for training and superior performance that scales near-linearly. To read more about the benefits of using MXNet as a backend for Keras, refer to NVIDIA’s blog. Detailed instructions for setting up a training job with multiple GPU’s can be found at this GitHub repo.

MXNet’s 0.11 release adds other API enhancements, performance improvements, and bug fixes. You can find more details in the full release notes.