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简体中文

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

NNI v has been released!

Who should consider using NNI

• Those who want to try different AutoML algorithms in their training code (model) at their local machine.
• Those who want to run AutoML trial jobs in different environments to speed up search (e.g. remote servers and cloud).
• Researchers and data scientists who want to implement their own AutoML algorithms and compare it with other algorithms.
• ML Platform owners who want to support AutoML in their platform.

Related Projects

Targeting at openness and advancing state-of-art technology, Microsoft Research (MSR) had also released few other open source projects.

• OpenPAI : an open source platform that provides complete AI model training and resource management capabilities, it is easy to extend and supports on-premise, cloud and hybrid environments in various scale.
• FrameworkController : an open source general-purpose Kubernetes Pod Controller that orchestrate all kinds of applications on Kubernetes by a single controller.
• MMdnn : A comprehensive, cross-framework solution to convert, visualize and diagnose deep neural network models. The "MM" in MMdnn stands for model management and "dnn" is an acronym for deep neural network.
• SPTAG : Space Partition Tree And Graph (SPTAG) is an open source library for large scale vector approximate nearest neighbor search scenario.

We encourage researchers and students leverage these projects to accelerate the AI development and research.

Install & Verify

Install through pip

• We support Linux, MacOS and Windows(local, remote and pai mode) in current stage, Ubuntu or higher, MacOS along with Windows are tested and supported. Simply run the following in an environment that has .

Linux and MacOS

python3 -m pip install --upgrade nni

Windows

python -m pip install --upgrade nni

Note:

• can be added if you want to install NNI in your home directory, which does not require any special privileges.
• Currently NNI on Windows support local, remote and pai mode. Anaconda or Miniconda is highly recommended to install NNI on Windows.
• If there is any error like , please refer to FAQ

Install through source code

• We support Linux (Ubuntu or higher), MacOS () and Windows () in our current stage.

Linux and MacOS

• Run the following commands in an environment that has , and .

git clone -b v bharathealthcares.com cd nni source bharathealthcares.com

Windows

• Run the following commands in an environment that has , and

git clone -b v bharathealthcares.com cd nni powershell -ExecutionPolicy Bypass -file bharathealthcares.com1

For the system requirements of NNI, please refer to Install NNI

For NNI on Windows, please refer to NNI on Windows

Verify install

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

• Download the examples via clone the source code.

git clone -b v bharathealthcares.com

Linux and MacOS

nnictl create --config nni/examples/trials/mnist/bharathealthcares.com

Windows

nnictl create --config nni\examples\trials\mnist\config_bharathealthcares.com

• Wait for the message in the command line. This message indicates that your experiment has been successfully started. You can explore the experiment using the .

INFO: Starting restful server INFO: Successfully started Restful server! INFO: Setting local config INFO: Successfully set local config! INFO: Starting experiment INFO: Successfully started experiment! The experiment id is egchD4qy The Web UI urls are: http:// http:// You can use these commands to get more information about the experiment commands description 1. nnictl experiment show show the information of experiments 2. nnictl trial ls list all of trial jobs 3. nnictl top monitor the status of running experiments 4. nnictl log stderr show stderr log content 5. nnictl log stdout show stdout log content 6. nnictl stop stop an experiment 7. nnictl trial kill kill a trial job by id 8. nnictl --help get help information about nnictl

• Open the in your browser, you can view detail information of the experiment and all the submitted trial jobs as shown below. Here are more Web UI pages.

Documentation

How to

Tutorials

Contribute

This project welcomes contributions and suggestions, we use GitHub issues for tracking requests and bugs.

Issues with the good first issue label are simple and easy-to-start ones that we recommend new contributors to start with.

To set up environment for NNI development, refer to the instruction: Set up NNI developer environment

Before start coding, review and get familiar with the NNI Code Contribution Guideline: Contributing

We are in construction of the instruction for How to Debug, you are also welcome to contribute questions or suggestions on this area.

License

The entire codebase is under MIT license