View xnni on Snyk Open Source Advisor

Package Health Score

51 / 100

security
Security review needed
popularity
Small
maintenance
Inactive
community
Active

Popularity

Small

GitHub Stars: 14.06K
Forks: 1.82K
Contributors: 180

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package xnni, we found that it has been starred 14,057 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Jun 27, 2019
Direct Vulnerabilities: 0
C
0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
2
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

MIT

Security Policy

Yes

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Maintenance

Inactive

archived

Commit Frequency

No Recent Commits

Open Issues: 399
Open PR: 18
Last Release: 5 years ago
Last Commit: 1 year ago

Further analysis of the maintenance status of xnni based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for xnni is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Active

Readme: Yes
Contributing.md: No
Code of Conduct: Yes
Contributors: 180
Funding: No

A good and healthy external contribution signal for xnni project, which invites more than one hundred open source maintainers to collaborate on the repository.

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Package

Python Versions Compatibility: >=3.5

Age: 5 years
Latest Release: 5 years ago
Dependencies: 14 Direct / 28 Total
Versions: 5
Maintainers: 1
Wheels: Linux

Readme

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 v0.7 has been released!

Supported Frameworks

Tuning Algorithms

Training Services

PyTorch
TensorFlow
Keras
MXNet
Caffe2
CNTK (Python language)
Chainer
Theano

Tuner

Assessor

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

Install & Verify

If you choose NNI Windows local mode and you use powershell to run script for the first time, you need to run powershell as administrator with this command first:

    Set-ExecutionPolicy -ExecutionPolicy Unrestricted

Install through pip

We support Linux, MacOS and Windows(local mode) in current stage, Ubuntu 16.04 or higher, MacOS 10.14.1 along with Windows 10.1809 are tested and supported. Simply run the following pip install in an environment that has python >= 3.5.

Linux and MacOS

    python3 -m pip install --upgrade nni

Windows

    python -m pip install --upgrade nni

Note:

--user can be added if you want to install NNI in your home directory, which does not require any special privileges.
Currently NNI on Windows only support local mode. Anaconda is highly recommanded to install NNI on Windows.
If there is any error like Segmentation fault, please refer to FAQ

Install through source code

We support Linux (Ubuntu 16.04 or higher), MacOS (10.14.1) and Windows local mode (10.1809) in our current stage.

Linux and MacOS

Run the following commands in an environment that has python >= 3.5, git and wget.

    git clone -b v0.7 https://github.com/Microsoft/nni.git
    cd nni	
    source install.sh

Windows

Run the following commands in an environment that has python >=3.5, git and powershell

  git clone -b v0.7 https://github.com/Microsoft/nni.git
  cd nni
  powershell ./install.ps1

For the system requirements of NNI, please refer to Install NNI
For NNI Windows local mode, please refer to NNI Windows local mode

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 v0.7 https://github.com/Microsoft/nni.git

Linux and MacOS

Run the mnist example.

    nnictl create --config nni/examples/trials/mnist/config.yml

Windows

Run the mnist example.

    nnictl create --config nni/examples/trials/mnist/config_windows.yml

Wait for the message INFO: Successfully started experiment! in the command line. This message indicates that your experiment has been successfully started. You can explore the experiment using the Web UI url.

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://223.255.255.1:8080   http://127.0.0.1:8080
-----------------------------------------------------------------------

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 Web UI url 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

xnni dependencies

astor colorama coverage gputil hyperopt json-tricks numpy pandas psutil pythonwebhdfs requests ruamel-yaml schema scipy

FAQs

What is xnni?

Neural Network Intelligence package for nitrogen. Visit Snyk Advisor to see a full health score report for xnni, including popularity, security, maintenance & community analysis.

Is xnni popular?

The python package xnni receives a total of 41 weekly downloads. As such, xnni popularity was classified as small. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is xnni well maintained?

We found indications that xnni is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is xnni safe to use?

While scanning the latest version of xnni, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 27 November-2024, at 13:43 (UTC).

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Neural Network Intelligence package for nitrogen For more information about how to use this package see README

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Readme

NNI v0.7 has been released!

Who should consider using NNI

Related Projects

Install & Verify

Documentation

How to

Tutorials

Contribute

License

xnni dependencies

FAQs

What is xnni?

Is xnni popular?

Is xnni well maintained?

Is xnni safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Fix identified vulnerabilities

Monitor for new issues

Package Health Score

Direct Usage Popularity

Security and license risk for latest version

Commit Frequency

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Check your requirements.txt

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Readme

NNI v0.7 has been released!

Who should consider using NNI

Related Projects

Install & Verify

Documentation

How to

Tutorials

Contribute

License

xnni dependencies

FAQs

What is xnni?

Is xnni popular?

Is xnni well maintained?

Is xnni safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Source Code

Open Source

Container

Config

Fix identified vulnerabilities

Monitor for new issues