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Document updates for v0.3 (#318)
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* refactor doc

* update with Mao's suggestions

* Set theme jekyll-theme-dinky

* update doc

* fix links

* fix links

* fix links

* merge

* fix links and doc errors

* merge

* merge

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* merge

* Quick fix nnictl config logic (#289)

* fix nnictl bug

* fix install.sh

* add desc for Dockerfile.build.base

* update document for Dockerfile

* update

* refactor port detect

* update

* refactor NNICTLDOC.md

* add document for pai and nnictl

* add default value for port

* add exception handling in trial_keeper.py

* fix port bug

* fix resume

* fix nnictl resume and fix nnictl stop

* fix document

* update

* refactor nnictl

* update

* update doc

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* update nnictl

* fix comment

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* fix nnictl error hit

* fix comments

* fix bash-completion

* fix paramiko install

* quick fix resume logic

* update

* quick fix nnictl

* merge

* updated the "Contribute" part (merged Gems' wiki in, updated ReadMe)

* fix link

* revise the installation cmd to v0.2

* revise to install v0.2

* Update nnictl_utils.py

* Update nnictl_utils.py

* Update nnictl_utils.py

* Update documentation for v0.3
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24 changes: 9 additions & 15 deletions README.md
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Expand Up @@ -82,24 +82,18 @@ You can use these commands to get more information about the experiment
* [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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# 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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