NNI automates feature engineering, neural architecture search, hyperparameter tuning, and model compression for deep learning. Find the latest features, API, examples and tutorials in our official documentation (简体中文版点这里). Quick links:
- New release: v2.6 is available - released on Jan-19-2022
- New demo available: Youtube entry | Bilibili 入口 - last updated on May-26-2021
- New webinar: Introducing Retiarii: A deep learning exploratory-training framework on NNI - scheduled on June-24-2021
- New community channel: Discussions
- New emoticons release: nnSpider
(TBD: table)
See the NNI installation guide to install from pip, or build from source.
To install the current release:
$ pip install nni
To update NNI to the latest version, add --upgrade flag to the above commands.
[comment]: <> delete this before next release
NOTE: To run an experiment following instructions below, you need to build NNI from source. Installing from pip won't work until next release.
To run this experiment, you need to have PyTorch (as well as torchvision) installed.
$ nnictl helloIt will generate nni_hello_hpo folder in your current working directory, which contains a minimum hyper-parameter tuning example. It will also prompt you to run
python nni_hello_hpo/main.pyto launch your first NNI experiment. Use the web portal URL shown in the console to monitor the running status of your experiment.
For more usages, please see NNI tutorials.
If you want to contribute to NNI, be sure to review the contribution guidelines, which includes instructions of submitting feedbacks, best coding practices, and code of conduct.
We use GitHub issues to track tracking requests and bugs. Please use NNI Discussion for general questions and new ideas. For questions of specific use cases, please go to Stack Overflow.
Participating discussions via the following IM groups is also welcomed.
| Gitter | ||
|---|---|---|
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OR |  |
Over the past few years, NNI has received thousands of feedbacks on GitHub issues, and pull requests from hundreds of contributors. We appreciate all contributions from community to make NNI thrive.
| Type | Status |
|---|---|
| Fast test | |
| Full linux | |
| Full windows |
| Type | Status |
|---|---|
| Remote - linux to linux | |
| Remote - linux to windows | |
| Remote - windows to linux | |
| OpenPAI | |
| Frameworkcontroller | |
| Kubeflow | |
| Hybrid | |
| AzureML |
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.
- nn-Meter : An accurate inference latency predictor for DNN models on diverse edge devices.
We encourage researchers and students leverage these projects to accelerate the AI development and research.
The entire codebase is under MIT license.