The use and impact of machine learning is not only limited by technical capabilities, but also by the operational processes to develop, share, and deploy ML models. The industry needs simple and interchangeable building blocks that can be easily shared for experimentation then later composed into mature and robust workflows.

MLCube is a set of common conventions for creating ML software that can "plug-and-play" on many different systems. MLCube makes it easier for researchers to share innovative ML models, for a developer to experiment with many different models, and for software companies to create infrastructure for models. It creates opportunities by putting ML in the hands of more people.

MLCube isn’t a new framework or service; MLCube is a consistent interface to machine learning models in containers like Docker. Models published with the MLCube interface can be run on local machines, on a variety of major clouds, or in Kubernetes clusters - all using the same code. MLCommons™ provides simple open source “runners” for each of these environments that make training a model in an MLCube a single command, but MLCube is also designed to make it easy to build new infrastructure based on the interface.

Watch this video to get a better idea of how it works:

MLCube is currently a pre-alpha project with an active development team. We invite experimentation and feedback, code contributions, and partnerships with ML infra efforts.

Get Started

  1. Install MLCube Libraries
  2. Try out using MNist in an MLCube
  3. Try out building your own MLCube
  4. File an issue or ask a question on GitHub

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