nni

An open source AutoML toolkit for neural architecture search and hyper-parameter tuning.

10 months after

Neural Network Intelligence

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NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning experiments. The tool dispatches and runs trial jobs that generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments (e.g. local machine, remote servers and cloud).

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Who should consider using NNI

  • You want to try different AutoML algorithms for your training code (model) at local
  • You want to run AutoML trial jobs in different environments to speed up search (e.g. remote servers and cloud)
  • As a researcher and data scientist, you want to implement your own AutoML algorithms and compare with other algorithms
  • As a ML platform owner, you want to support AutoML in your platform

Install & Verify

pip install

  • We only support Linux in current stage, Ubuntu 16.04 or higher are tested and supported. Simply run the following pip install in an environment that has python >= 3.5, git and wget.
    python3 -m pip install -v --user git+https://github.com/Microsoft/[email protected]
    source ~/.bashrc

verify install

  • The following example is an experiment built on TensorFlow, make sure you have TensorFlow installed before running it.

    nnictl create --config ~/nni/examples/trials/mnist/config.yml
  • In the command terminal, waiting for the message Info: Start experiment success! which indicates your experiment had been successfully started. You are able to explore the experiment using the Web UI url.

    
    Info: Checking experiment...
    ...
    Info: Starting experiment...
    Info: Checking web ui...
    Info: Starting web ui...
    Info: Starting web ui success!
  • Info: Web UI url: http://127.0.0.1:8080 http://10.172.141.6:8080

  • Info: Start experiment success! The experiment id is LrNK4hae, and the restful server post is 51188.

Documentation

Contribute

This project welcomes contributions and suggestions, we are constructing the contribution guidelines, stay tuned =).

We use GitHub issues for tracking requests and bugs.

License

The entire codebase is under MIT license

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