The tool manages automated machine learning (AutoML) experiments, dispatches and runs experiments' trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different training environments like Local Machine, Remote Servers, OpenPAI, Kubeflow, FrameworkController on K8S (AKS etc.), DLWorkspace (aka. DLTS), AML (Azure Machine Learning), AdaptDL (aka. ADL), other cloud options and even Hybrid mode.
Who should consider using NNI
- Those who want to try different AutoML algorithms in their training code/model.
- Those who want to run AutoML trial jobs in different environments to speed up search.
- Researchers and data scientists who want to easily implement and experiement new AutoML algorithms , may it be: hyperparameter tuning algorithm, neural architect search algorithm or model compression algorithm.
- ML Platform owners who want to support AutoML in their platform
What's NEW!
- New release: v2.6.1 is available2 - released on Jan-18-2022
- 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
NNI capabilities in a glance
NNI provides CommandLine Tool as well as an user friendly WebUI to manage training experiements. With the extensible API, you can customize your own AutoML algorithms and training services. To make it easy for new users, NNI also provides a set of build-in stat-of-the-art AutoML algorithms and out of box support for popular training platforms.
Within the following table, we summarized the current NNI capabilities, we are gradually adding new capabilities and we'd love to have your contribution.
Frameworks & Libraries | Algorithms | Training Services | |
Built-in |
|
Hyperparameter Tuning
Exhaustive search
Heuristic search
Bayesian optimization
Pruning
Quantization
|
|
References |
Installation
Install
pip install
in an environment that has python 64-bit >= 3.6
.
Note:
- If there is any privilege issue, add --user to install NNI in the user directory.
- Currently NNI on Windows supports local, remote and pai mode. Anaconda or Miniconda is highly recommended to install NNI on Windows.
- If there is any error like Segmentation fault, please refer to FAQ. For FAQ on Windows, please refer to NNI on Windows.
Verify installation
-
Download the examples via clone the source code.git clone -b v2.6.1 https://github.com/Microsoft/nni.git
-
Run the MNIST example.Linux or macOSnnictl create --config nni/examples/trials/mnist-pytorch/config.ymlWindowsnnictl create --config nni\examples\trials\mnist-pytorch\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.
Releases and Contributing
Feedback
- File an issue on GitHub.
- Open or participate in a discussion.
- Discuss on the NNI Gitter in NNI.
Gitter | ||
---|---|---|
OR |
Test status
Essentials
Type | Status |
---|---|
Fast test | |
Full linux | |
Full windows |
Training services
Type | Status |
---|---|
Remote - linux to linux | |
Remote - linux to windows | |
Remote - windows to linux | |
OpenPAI | |
Frameworkcontroller | |
Kubeflow | |
Hybrid | |
AzureML |
Related Projects
Targeting at openness and advancing state-of-art technology, Microsoft Research (MSR) had also released few other open source projects.
We encourage researchers and students leverage these projects to accelerate the AI development and research.
License
The entire codebase is under MIT license