Skip to content
This repository has been archived by the owner on Sep 18, 2024. It is now read-only.

Commit

Permalink
Merge pull request #32 from microsoft/master
Browse files Browse the repository at this point in the history 
pull code
  • Loading branch information
@chicm-ms
chicm-ms authored Sep 9, 2019
2 parents 3a45961 + a0f7f67 commit 633db43
Show file tree
Hide file tree
Showing 126 changed files with 5,433 additions and 4,228 deletions.
2 changes: 1 addition & 1 deletion Makefile
View file
Original file line number Diff line number Diff line change
Expand Up @@ -54,7 +54,7 @@ NNI_NODE_FOLDER = $(NNI_DEPENDENCY_FOLDER)/nni-node-$(OS_SPEC)-x64
NNI_NODE ?= $(BIN_FOLDER)/node
NNI_YARN_TARBALL ?= $(NNI_DEPENDENCY_FOLDER)/nni-yarn.tar.gz
NNI_YARN_FOLDER ?= $(NNI_DEPENDENCY_FOLDER)/nni-yarn
NNI_YARN := PATH=$(BIN_FOLDER):$${PATH} $(NNI_YARN_FOLDER)/bin/yarn
NNI_YARN ?= PATH=$(BIN_FOLDER):$${PATH} $(NNI_YARN_FOLDER)/bin/yarn

## Version number
NNI_VERSION_VALUE = $(shell git describe --tags)
Expand Down
162 changes: 121 additions & 41 deletions README.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@ NNI (Neural Network Intelligence) is a toolkit to help users run automated machi
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 [v0.9](https://github.com/Microsoft/nni/releases) has been released! &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
### **NNI [v1.0](https://github.com/Microsoft/nni/blob/master/docs/en_US/Release_v1.0.md) has been released! &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**

<p align="center">
<a href="#nni-has-been-released"><img src="docs/img/overview.svg" /></a>
Expand All @@ -27,8 +27,10 @@ The tool dispatches and runs trial jobs generated by tuning algorithms to search
<table>
<tbody>
<tr align="center" valign="bottom">
<td>
</td>
<td>
<b>Supported Frameworks</b>
<b>Frameworks & Libraries</b>
<img src="docs/img/bar.png"/>
</td>
<td>
Expand All @@ -42,26 +44,52 @@ The tool dispatches and runs trial jobs generated by tuning algorithms to search
</tr>
</tr>
<tr valign="top">
<td align="center" valign="middle">
<b>Built-in</b>
</td>
<td>
<ul><li><b>Supported Frameworks</b></li>
<ul>
<li>PyTorch</li>
<li>TensorFlow</li>
<li>Keras</li>
<li>TensorFlow</li>
<li>MXNet</li>
<li>Caffe2</li>
<li>CNTK (Python language)</li>
<li>Chainer</li>
<li>Theano</li>
<a href="docs/en_US/SupportedFramework_Library.md">More...</a><br/>
</ul>
</ul>
<ul>
<li><b>Supported Libraries</b></li>
<ul>
<li>Scikit-learn</li>
<li>XGBoost</li>
<li>LightGBM</li>
<a href="docs/en_US/SupportedFramework_Library.md">More...</a><br/>
</ul>
</ul>
<ul>
<li><b>Examples</b></li>
<ul>
<li><a href="examples/trials/mnist-pytorch">MNIST-pytorch</li></a>
<li><a href="examples/trials/mnist">MNIST-tensorflow</li></a>
<li><a href="examples/trials/mnist-keras">MNIST-keras</li></a>
<li><a href="docs/en_US/TrialExample/GbdtExample.md">Auto-gbdt</a></li>
<li><a href="docs/en_US/TrialExample/Cifar10Examples.md">Cifar10-pytorch</li></a>
<li><a href="docs/en_US/TrialExample/SklearnExamples.md">Scikit-learn</a></li>
<a href="docs/en_US/SupportedFramework_Library.md">More...</a><br/>
</ul>
</ul>
</td>
<td align="left">
<td align="left" >
<a href="docs/en_US/Tuner/BuiltinTuner.md">Tuner</a>
<br />
<ul>
<b style="margin-left:-20px">General Tuner</b>
<li><b>General Tuner</b></li>
<ul>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#Random">Random Search</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#Evolution">Naïve Evolution</a></li>
<b style="margin-left:-20px">Tuner for HPO</b>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#Evolution">Naïve Evolution</a></li>
</ul>
<li><b>Tuner for <a href="docs/en_US/CommunitySharings/HpoComparision.md">HPO</a></b></li>
<ul>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#TPE">TPE</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#Anneal">Anneal</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#SMAC">SMAC</a></li>
Expand All @@ -71,14 +99,19 @@ The tool dispatches and runs trial jobs generated by tuning algorithms to search
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#MetisTuner">Metis Tuner</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#BOHB">BOHB</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#GPTuner">GP Tuner</a></li>
<b style="margin-left:-20px">Tuner for NAS</b>
</ul>
<li><b>Tuner for <a href="docs/en_US/AdvancedFeature/GeneralNasInterfaces.md">NAS</a></b></li>
<ul>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#NetworkMorphism">Network Morphism</a></li>
<li><a href="examples/tuners/enas_nni/README.md">ENAS</a></li>
</ul>
</ul>
<a href="docs/en_US/Assessor/BuiltinAssessor.md">Assessor</a>
<ul>
<ul>
<li><a href="docs/en_US/Assessor/BuiltinAssessor.md#Medianstop">Median Stop</a></li>
<li><a href="docs/en_US/Assessor/BuiltinAssessor.md#Curvefitting">Curve Fitting</a></li>
</ul>
</ul>
</td>
<td>
Expand All @@ -93,11 +126,40 @@ The tool dispatches and runs trial jobs generated by tuning algorithms to search
</ul>
</td>
</tr>
<tr align="center" valign="bottom">
</td>
</tr>
<tr valign="top">
<td valign="middle">
<b>References</b>
</td>
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="docs/en_US/sdk_reference.rst">Python API</a></li>
<li><a href="docs/en_US/Tutorial/AnnotationSpec.md">NNI Annotation</a></li>
<li><a href="docs/en_US/Tutorial/Installation.md">Supported OS</a></li>
</ul>
</td>
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="docs/en_US/Tuner/CustomizeTuner.md">CustomizeTuner</a></li>
<li><a href="docs/en_US/Assessor/CustomizeAssessor.md">CustomizeAssessor</a></li>
</ul>
</td>
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="docs/en_US/TrainingService/SupportTrainingService.md">Support TrainingService</li>
<li><a href="docs/en_US/TrainingService/HowToImplementTrainingService.md">Implement TrainingService</a></li>
</ul>
</td>
</tr>
</tbody>
</table>





## **Who should consider using NNI**

* Those who want to try different AutoML algorithms in their training code (model) at their local machine.
Expand All @@ -120,7 +182,7 @@ We encourage researchers and students leverage these projects to accelerate the

**Install through pip**

* We support Linux, MacOS and Windows(local, remote and pai 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`.
* We support Linux, MacOS and Windows (local, remote and pai 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

Expand Down Expand Up @@ -149,7 +211,7 @@ Linux and MacOS
* Run the following commands in an environment that has `python >= 3.5`, `git` and `wget`.

```bash
git clone -b v0.9 https://github.com/Microsoft/nni.git
git clone -b v1.0 https://github.com/Microsoft/nni.git
cd nni
source install.sh
```
Expand All @@ -159,7 +221,7 @@ Windows
* Run the following commands in an environment that has `python >=3.5`, `git` and `PowerShell`

```bash
git clone -b v0.9 https://github.com/Microsoft/nni.git
git clone -b v1.0 https://github.com/Microsoft/nni.git
cd nni
powershell -ExecutionPolicy Bypass -file install.ps1
```
Expand All @@ -175,7 +237,7 @@ The following example is an experiment built on TensorFlow. Make sure you have *
* Download the examples via clone the source code.

```bash
git clone -b v0.9 https://github.com/Microsoft/nni.git
git clone -b v1.0 https://github.com/Microsoft/nni.git
```

Linux and MacOS
Expand Down Expand Up @@ -235,60 +297,78 @@ Maybe you want to read:

* [NNI overview](docs/en_US/Overview.md)
* [Quick start](docs/en_US/Tutorial/QuickStart.md)
* [Contributing](docs/en_US/Tutorial/Contributing.md)
* [Examples](docs/en_US/examples.rst)
* [References](docs/en_US/reference.rst)
* [WebUI tutorial](docs/en_US/Tutorial/WebUI.md)
* [Contributing](docs/en_US/Tutorial/Contributing.md)


## **How to**

* [Install NNI](docs/en_US/Tutorial/Installation.md)
* [Use command line tool nnictl](docs/en_US/Tutorial/Nnictl.md)
* [Use NNIBoard](docs/en_US/Tutorial/WebUI.md)
* [How to define search space](docs/en_US/Tutorial/SearchSpaceSpec.md)
* [How to define a trial](docs/en_US/TrialExample/Trials.md)
* [How to choose tuner/search-algorithm](docs/en_US/Tuner/BuiltinTuner.md)
* [Define a trial](docs/en_US/TrialExample/Trials.md)
* [Config an experiment](docs/en_US/Tutorial/ExperimentConfig.md)
* [How to use annotation](docs/en_US/TrialExample/Trials.md#nni-python-annotation)
* [Define search space](docs/en_US/Tutorial/SearchSpaceSpec.md)
* [choose tuner/search-algorithm](docs/en_US/Tuner/BuiltinTuner.md)
* [Use annotation](docs/en_US/TrialExample/Trials.md#nni-python-annotation)
* [Use NNIBoard](docs/en_US/Tutorial/WebUI.md)



## **Tutorials**

* [Run an experiment on local (with multiple GPUs)](docs/en_US/TrainingService/LocalMode.md)
* [Run an experiment on OpenPAI](docs/en_US/TrainingService/PaiMode.md)
* [Run an experiment on Kubeflow](docs/en_US/TrainingService/KubeflowMode.md)
* [Run an experiment on local (with multiple GPUs)](docs/en_US/TrainingService/LocalMode.md)
* [Run an experiment on multiple machines](docs/en_US/TrainingService/RemoteMachineMode.md)
* [Try different tuners](docs/en_US/Tuner/BuiltinTuner.md)
* [Try different assessors](docs/en_US/Assessor/BuiltinAssessor.md)
* [Implement a customized tuner](docs/en_US/Tuner/CustomizeTuner.md)
* [Implement a customized assessor](docs/en_US/Assessor/CustomizeAssessor.md)
* [Implement TrainingService in NNI](docs/en_US/TrainingService/HowToImplementTrainingService.md)
* [Use Genetic Algorithm to find good model architectures for Reading Comprehension task](docs/en_US/TrialExample/SquadEvolutionExamples.md)
* [Advanced Neural Architecture Search](docs/en_US/AdvancedFeature/AdvancedNas.md)


## **Contribute**
This project welcomes contributions and there are many ways in which you can participate in the project, for example:
* Review [source code changes](https://github.com/microsoft/nni/pulls)
* Review the [documentation](https://github.com/microsoft/nni/tree/master/docs) and make pull requests for anything from typos to new content
* Open [bug reports](https://github.com/microsoft/nni/issues/new/choose).
* Request a [new feature](https://github.com/microsoft/nni/issues/new/choose).
* Suggest or ask some questions on the [How to Debug](docs/en_US/Tutorial/HowToDebug.md) guidance document.
* Find the issues tagged with ['good first issue'](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) or ['help-wanted'](https://github.com/microsoft/nni/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22), these are simple and easy to start , we recommend new contributors to start with.

Before providing your hacks, there are a few simple guidelines that you need to follow:
* [How to debug](docs/en_US/Tutorial/HowToDebug.md)
* [Code Styles & Naming Conventions](docs/en_US/Tutorial/Contributing.md)
* How to Set up [NNI developer environment](docs/en_US/Tutorial/SetupNniDeveloperEnvironment.md)
* Review the [Contributing Instruction](docs/en_US/Tutorial/Contributing.md) and get familiar with the NNI Code Contribution Guideline

## **External Repositories**
Now we have some external usage examples run in NNI from our contributors. Thanks our lovely contributors. And welcome more and more people to join us!
* Run [ENAS](examples/tuners/enas_nni/README.md) in NNI
* Run [Neural Network Architecture Search](examples/trials/nas_cifar10/README.md) in NNI
Before providing your hacks, you can review the [Contributing Instruction](docs/en_US/Tutorial/Contributing.md) to get more information. In addition, we also provide you with the following documents:
* [NNI developer environment installation tutorial](docs/en_US/Tutorial/SetupNniDeveloperEnvironment.md)
* [How to debug](docs/en_US/Tutorial/HowToDebug.md)
* [Customize Your Own Advisor](docs/en_US/Tuner/CustomizeAdvisor.md)
* [Customize Your Own Tuner](docs/en_US/Tuner/CustomizeTuner.md)
* [Implement customized TrainingService](docs/en_US/TrainingService/HowToImplementTrainingService.md)


## **External Repositories and References**
With authors' permission, we listed a set of NNI usage examples and relevant articles.
* ### **External Repositories** ###
* Run [ENAS](examples/tuners/enas_nni/README.md) with NNI
* Run [Neural Network Architecture Search](examples/trials/nas_cifar10/README.md) with NNI
* [Automatic Feature Engineering](examples/trials/auto-feature-engineering/README.md) with NNI
* [Hyperparameter Tuning for Matrix Factorization](https://github.com/microsoft/recommenders/blob/master/notebooks/04_model_select_and_optimize/nni_surprise_svd.ipynb) with NNI

* ### **Relevant Articles** ###

* [Hyper Parameter Optimization Comparison](docs/en_US/CommunitySharings/HpoComparision.md)
* [Neural Architecture Search Comparison](docs/en_US/CommunitySharings/NasComparision.md)
* [Parallelizing a Sequential Algorithm TPE](docs/en_US/CommunitySharings/ParallelizingTpeSearch.md)
* [Automatically tuning SVD with NNI](docs/en_US/CommunitySharings/RecommendersSvd.md)
* [Automatically tuning SPTAG with NNI](docs/en_US/CommunitySharings/SptagAutoTune.md)
* **Blog (in Chinese)** - [AutoML tools (Advisor, NNI and Google Vizier) comparison](http://gaocegege.com/Blog/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/katib-new#%E6%80%BB%E7%BB%93%E4%B8%8E%E5%88%86%E6%9E%90) by [@gaocegege](https://github.com/gaocegege) - 总结与分析 section of design and implementation of kubeflow/katib

## **Feedback**
* Open [bug reports](https://github.com/microsoft/nni/issues/new/choose).<br/>
* Request a [new feature](https://github.com/microsoft/nni/issues/new/choose).
* Discuss on the NNI [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) in NNI
* Ask a question with NNI tags on [Stack Overflow](https://stackoverflow.com/questions/tagged/nni?sort=Newest&edited=true)or [file an issue](https://github.com/microsoft/nni/issues/new/choose)on GitHub.
* We are in construction of the instruction for [How to Debug](docs/en_US/Tutorial/HowToDebug.md), you are also welcome to contribute questions or suggestions on this area.
* Discuss on the NNI [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) in NNI.
* [File an issue](https://github.com/microsoft/nni/issues/new/choose) on GitHub.
* Ask a question with NNI tags on [Stack Overflow](https://stackoverflow.com/questions/tagged/nni?sort=Newest&edited=true).


## **License**

The entire codebase is under [MIT license](LICENSE)

Loading

0 comments on commit 633db43

Please sign in to comment.