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refactor doc
scarlett2018 Oct 18, 2018
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update with Mao's suggestions
scarlett2018 Oct 19, 2018
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Set theme jekyll-theme-dinky
scarlett2018 Oct 19, 2018
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update doc
scarlett2018 Oct 22, 2018
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scarlett2018 Oct 25, 2018
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fix links and doc errors
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scarlett2018 Oct 26, 2018
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Quick fix nnictl config logic (#289)
SparkSnail Oct 31, 2018
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updated the "Contribute" part (merged Gems' wiki in, updated ReadMe)
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scarlett2018 Nov 1, 2018
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scarlett2018 Nov 1, 2018
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revise the installation cmd to v0.2
scarlett2018 Nov 1, 2018
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revise to install v0.2
scarlett2018 Nov 1, 2018
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Merge branch 'v0.3' into master
chicm-ms Nov 1, 2018
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Update nnictl_utils.py
scarlett2018 Nov 1, 2018
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Update nnictl_utils.py
QuanluZhang Nov 1, 2018
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Update nnictl_utils.py
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Merge branch 'master' of https://github.com/scarlett2018/nni
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Update documentation for v0.3
scarlett2018 Nov 2, 2018
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24 changes: 9 additions & 15 deletions README.md
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Expand Up @@ -55,24 +55,18 @@ source ~/.bashrc
* [Installation](docs/InstallNNI_Ubuntu.md)
* [Use command line tool nnictl](docs/NNICTLDOC.md)
* [Use NNIBoard](docs/WebUI.md)
* [Define search space](docs/SearchSpaceSpec.md)
* [Use NNI sdk] - *coming soon*
* [How to define search space](docs/SearchSpaceSpec.md)
* [Config an experiment](docs/ExperimentConfig.md)
* [Use annotation]- *coming soon*
* [Use annotation](docs/AnnotationSpec.md)
* [Debug](docs/HowToDebug.md)
## **Tutorials**
* [How to run an experiment on local (with multiple GPUs)?](docs/tutorial_1_CR_exp_local_api.md)
* [How to run an experiment on multiple machines?](docs/tutorial_2_RemoteMachineMode.md)
* [How to run an experiment on OpenPAI?](docs/PAIMode.md)
* [Try different tuners and assessors] - *coming soon*
* [How to run an experiment on K8S services?] - *coming soon*
* [Implement a customized tuner] - *coming soon*
* [Implement a customized assessor] - *coming soon*
* [Implement a custmoized weight sharing algorithm] - *coming soon*
* [How to integrate NNI with your own custmoized training service] - *coming soon*
### **Best practice**
* [Compare different AutoML algorithms] - *coming soon*
* [Serve NNI as a capability of a ML Platform] - *coming soon*
* [Run an experiment on local (with multiple GPUs)?](docs/tutorial_1_CR_exp_local_api.md)
* [Run an experiment on multiple machines?](docs/tutorial_2_RemoteMachineMode.md)
* [Run an experiment on OpenPAI?](docs/PAIMode.md)
* [Try different tuners and assessors](docs/tutorial_3_tryTunersAndAccessors.md)
* [Implement a customized tuner](docs/howto_2_CustomizedTuner.md)
* [Implement a customized assessor](examples/assessors/README.md)
* [Use Genetic Algorithm to find good model architectures for Reading Comprehension task](examples/trials/ga_squad/README.md)

## **Contribute**
This project welcomes contributions and suggestions, we use [GitHub issues](https://github.com/Microsoft/nni/issues) for tracking requests and bugs.
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2 changes: 1 addition & 1 deletion docs/AnnotationSpec.md
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# Introduction
# NNI Annotation

For good user experience and reduce user effort, we need to design a good annotation grammar.

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2 changes: 1 addition & 1 deletion docs/ExperimentConfig.md
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Experiment config reference
# Experiment config reference
===

If you want to create a new nni experiment, you need to prepare a config file in your local machine, and provide the path of this file to nnictl.
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37 changes: 37 additions & 0 deletions docs/tutorial_3_tryTunersAndAccessors.md
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# Tutorial - Try different Tuners and Accessors

NNI provides an easy to adopt approach to set up parameter tuning algorithms as well as early stop policies, we call them **Tuners** and **Accessors**.

**Tuner** specifies the algorithm you use to generate hyperparameter sets for each trial. In NNI, we support two approaches to set the tuner.
1. Directly use tuner provided by nni sdk

required fields: builtinTunerName and classArgs.

2. Customize your own tuner file

required fields: codeDirectory, classFileName, className and classArgs.

### **Learn More about tuners**
* For detailed defintion and usage aobut the required field, please refer to [Config an experiment](ExperimentConfig.md)
* [Tuners in the latest NNI release](../src/sdk/pynni/nni/README.md)
* [How to implement your own tuner](howto_2_CustomizedTuner.md)


**Assessor** specifies the algorithm you use to apply early stop policy. In NNI, there are two approaches to set theassessor.
1. Directly use accessor provided by nni sdk

required fields: builtinAssessorName and classArgs.

2. Customize your own tuner file

required fields: codeDirectory, classFileName, className and classArgs.

### **Learn More about assessor**
* For detailed defintion and usage aobut the required field, please refer to [Config an experiment](ExperimentConfig.md)
* Find more about the detailed instruction about [enable accessor](EnableAssessor.md)
* [How to implement your own assessor](../examples/assessors/README.md)

## **Learn More**
* [How to run an experiment on local (with multiple GPUs)?](tutorial_1_CR_exp_local_api.md)
* [How to run an experiment on multiple machines?](tutorial_2_RemoteMachineMode.md)
* [How to run an experiment on OpenPAI?](PAIMode.md)
2 changes: 1 addition & 1 deletion examples/assessors/README.md
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@@ -1,4 +1,4 @@
# Customized Assessor for Experts
# Define your own Assessor

*Assessor receive intermediate result from Trial and decide whether the Trial should be killed. Once the Trial experiment meets the early stop conditions, the assessor will kill the Trial.*

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