Neural Network Intelligence

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).

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

• Overview
• Get started

How to

• Installation
• Use command line tool nnictl
• Use NNIBoard
• Define search space
• [Use NNI sdk] - coming soon
• Config an experiment
• [Use annotation]- coming soon
• Debug

Tutorials

• How to run an experiment on local (with multiple GPUs)?
• How to run an experiment on multiple machines?
• How to run an experiment on OpenPAI?
• [Try different tuners and assessors] - coming soon
• [How to run an experiment on K8S services?] - coming soon
• [Implement a customized tuner] - coming soon
• [Implement a customized assessor] - coming soon
• [Implement a custmoized weight sharing algorithm] - coming soon
• [How to integrate NNI with your own custmoized training service] - coming soon

Best practice

• [Compare different AutoML algorithms] - coming soon
• [Serve NNI as a capability of a ML Platform] - coming soon

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