diff --git a/README.md b/README.md index a6e6254fa8..52f50182a7 100644 --- a/README.md +++ b/README.md @@ -1,47 +1,41 @@ # Neural Network Intelligence +[![MIT licensed](https://img.shields.io/badge/license-MIT-yellow.svg)](https://github.com/Microsoft/nni/blob/master/LICENSE) [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/Microsoft.nni)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=6) [![Issues](https://img.shields.io/github/issues-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen) [![Bugs](https://img.shields.io/github/issues/Microsoft/nni/bug.svg)](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3Abug) [![Pull Requests](https://img.shields.io/github/issues-pr-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/pulls?q=is%3Apr+is%3Aopen) [![Version](https://img.shields.io/github/release/Microsoft/nni.svg)](https://github.com/Microsoft/nni/releases) -NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning experiments. -The tool dispatches and runs trial jobs that generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments (e.g. local machine, remote servers and cloud). +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.

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## **Who should consider using NNI** -* You want to try different AutoML algorithms for your training code (model) at local -* You want to run AutoML trial jobs in different environments to speed up search (e.g. remote servers and cloud) -* As a researcher and data scientist, you want to implement your own AutoML algorithms and compare with other algorithms -* As a ML platform owner, you want to support AutoML in your platform +* 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. ## **Install & Verify** -**Install through source code** +**pip install** * We only support Linux in current stage, Ubuntu 16.04 or higher are tested and supported. Simply run the following `pip install` in an environment that has `python >= 3.5`, `git` and `wget`. -```bash - git clone -b v0.3 https://github.com/Microsoft/nni.git - cd nni - source install.sh +``` +python3 -m pip install -v --user git+https://github.com/Microsoft/nni.git@v0.2 +source ~/.bashrc ``` -**Verify install** +**verify install** * The following example is an experiment built on TensorFlow, make sure you have `TensorFlow installed` before running it. -* And download the examples via clone the source code -```bash - cd ~ - git clone -b v0.3 https://github.com/Microsoft/nni.git -``` -* Then, run the mnist example ```bash nnictl create --config ~/nni/examples/trials/mnist/config.yml ``` -* In the command terminal, waiting for the message `Info: Start experiment success!` which indicates your experiment had been successfully started. You are able to explore the experiment using the `Web UI url`. +* Wait for the message `Info: Start experiment success!` in the command line. This message indicates that your experiment has been successfully started. You can explore the experiment using the `Web UI url`. ```diff Info: Checking experiment... ... @@ -49,7 +43,7 @@ The tool dispatches and runs trial jobs that generated by tuning algorithms to s Info: Checking web ui... Info: Starting web ui... Info: Starting web ui success! -+ Info: Web UI url: http://127.0.0.1:8080 http://10.172.141.6:8080 ++ Info: Web UI url: http://yourlocalhost:8080 http://youripaddress:8080 + Info: Start experiment success! The experiment id is LrNK4hae, and the restful server post is 51188. ``` @@ -80,9 +74,15 @@ The tool dispatches and runs trial jobs that generated by tuning algorithms to s * [Serve NNI as a capability of a ML Platform] - *coming soon* ## **Contribute** -This project welcomes contributions and suggestions, we are constructing the contribution guidelines, stay tuned =). +This project welcomes contributions and suggestions, we use [GitHub issues](https://github.com/Microsoft/nni/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](docs/SetupNNIDeveloperEnvironment.md) + +Before start coding, review and get familiar with the NNI Code Contribution Guideline: [Contributing](docs/CONTRIBUTING.md) -We use [GitHub issues](https://github.com/Microsoft/nni/issues) for tracking requests and bugs. +We are in construction of the instruction for [How to Debug](docs/HowToDebug.md), you are also welcome to contribute questions or suggestions on this area. ## **License** The entire codebase is under [MIT license](https://github.com/Microsoft/nni/blob/master/LICENSE) diff --git a/docs/GetStarted.md b/docs/GetStarted.md index f01ae914f6..e18fe4f4fc 100644 --- a/docs/GetStarted.md +++ b/docs/GetStarted.md @@ -36,7 +36,7 @@ An experiment is to run multiple trial jobs, each trial job tries a configuratio This command will be filled in the yaml configure file below. Please refer to [here]() for how to write your own trial. -**Prepare tuner**: NNI supports several popular automl algorithms, including Random Search, Tree of Parzen Estimators (TPE), Evolution algorithm etc. Users can write their own tuner (refer to [here](CustomizedTuner.md)), but for simplicity, here we choose a tuner provided by NNI as below: +**Prepare tuner**: NNI supports several popular automl algorithms, including Random Search, Tree of Parzen Estimators (TPE), Evolution algorithm etc. Users can write their own tuner (refer to [here](howto_2_CustomizedTuner.md), but for simplicity, here we choose a tuner provided by NNI as below: tuner: builtinTunerName: TPE diff --git a/docs/InstallNNI_Ubuntu.md b/docs/InstallNNI_Ubuntu.md index bc5de3089a..fc3f64f798 100644 --- a/docs/InstallNNI_Ubuntu.md +++ b/docs/InstallNNI_Ubuntu.md @@ -9,20 +9,19 @@ wget python pip should also be correctly installed. You could use "which pip" or "pip -V" to check in Linux. - - * Note: we don't support virtual environment in current releases. * __Install NNI through pip__ - python3 -m pip install --user nni-pkg + pip3 install -v --user git+https://github.com/Microsoft/nni.git@v0.2 + source ~/.bashrc * __Install NNI through source code__ - git clone -b v0.3 https://github.com/Microsoft/nni.git + git clone -b v0.2 https://github.com/Microsoft/nni.git cd nni + chmod +x install.sh source install.sh - ## Further reading * [Overview](Overview.md) * [Use command line tool nnictl](NNICTLDOC.md) diff --git a/docs/HowToContribute.md b/docs/SetupNNIDeveloperEnvironment.md similarity index 91% rename from docs/HowToContribute.md rename to docs/SetupNNIDeveloperEnvironment.md index 34384df4e0..a9a9cb9d01 100644 --- a/docs/HowToContribute.md +++ b/docs/SetupNNIDeveloperEnvironment.md @@ -1,4 +1,4 @@ -**How to contribute** +**Set up NNI developer environment** === ## Best practice for debug NNI source code @@ -51,4 +51,4 @@ After you change some code, just use **step 4** to rebuild your code, then the c --- At last, wish you have a wonderful day. -For more contribution guidelines on making PR's or issues to NNI source code, you can refer to our [CONTRIBUTING](./docs/CONTRIBUTING.md) document. +For more contribution guidelines on making PR's or issues to NNI source code, you can refer to our [CONTRIBUTING](./CONTRIBUTING.md) document. diff --git a/docs/howto_2_CustomizedTuner.md b/docs/howto_2_CustomizedTuner.md index f086f37eed..5b8c65a04b 100644 --- a/docs/howto_2_CustomizedTuner.md +++ b/docs/howto_2_CustomizedTuner.md @@ -1,4 +1,4 @@ -# Customized Tuner for Experts +# **How To** - Customize Your Own Tuner *Tuner receive result from Trial as a matric to evaluate the performance of a specific parameters/architecture configure. And tuner send next hyper-parameter or architecture configure to Trial.* diff --git a/examples/trials/ga_squad/README.md b/examples/trials/ga_squad/README.md index 08024d07be..35b830e08b 100644 --- a/examples/trials/ga_squad/README.md +++ b/examples/trials/ga_squad/README.md @@ -90,11 +90,11 @@ The evolution-algorithm based architecture for question answering has two differ The trial has a lot of different files, functions and classes. Here we will only give most of those files a brief introduction: -* `attention.py` contains an implementaion for attention mechanism in Tensorflow. +* `attention.py` contains an implementation for attention mechanism in Tensorflow. * `data.py` contains functions for data preprocessing. * `evaluate.py` contains the evaluation script. * `graph.py` contains the definition of the computation graph. -* `rnn.py` contains an implementaion for GRU in Tensorflow. +* `rnn.py` contains an implementation for GRU in Tensorflow. * `train_model.py` is a wrapper for the whole question answering model. Among those files, `trial.py` and `graph_to_tf.py` is special.