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# Introduction

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

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AutoML experiment Training Services
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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, Cloud)
* 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

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## **Quick start: run an experiment at local**
Requirements:
* with NNI installed on your machine.
* NNI installed on your local machine

Run the following command to create an experiment for [mnist]
```bash
nnictl create --config ~/nni/examples/trials/mnist-annotation/config.yml
```
This command will start the experiment and WebUI. The WebUI endpoint will be shown in the output of this command (for example, `http://localhost:8080`). Open this URL using your browsers. You can analyze your experiment through WebUI, or open trials' tensorboard. Please refer to [here](docs/GetStarted.md) for the GetStarted tutorial.
This command will start an experiment and a WebUI. The WebUI endpoint will be shown in the output of this command (for example, `http://localhost:8080`). Open this URL in your browser. You can analyze your experiment through WebUI, or browse trials' tensorboard.

Please refer to [here](docs/GetStarted.md) for the GetStarted tutorial.

# Contribute
NNI is designed as an automatic searching framework with high extensibility. NNI has a very clear modular design. Contributing more tuner/assessor algorithms, training services, SDKs are really welcome. Please refer to [here](docs/ToContribute.md) for how to contribute.
# Contributing
This project welcomes contributions and suggestions, we are constructing the contribution guidelines, stay tuned =).

We use [GitHub issues](https://github.com/Microsoft/nni/issues) for tracking requests and bugs.

# Privacy Statement
The [Microsoft Enterprise and Developer Privacy Statement](https://privacy.microsoft.com/en-us/privacystatement) describes the privacy statement of this software.

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