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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 * merge * 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 * update * update nnictl * fix comment * revert dockerfile * update * update * update * 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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# Tutorial - Try different Tuners and Accessors | ||
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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**. | ||
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**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 | ||
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required fields: builtinTunerName and classArgs. | ||
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2. Customize your own tuner file | ||
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required fields: codeDirectory, classFileName, className and classArgs. | ||
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### **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) | ||
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**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 | ||
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required fields: builtinAssessorName and classArgs. | ||
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2. Customize your own tuner file | ||
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required fields: codeDirectory, classFileName, className and classArgs. | ||
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### **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) | ||
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## **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) |
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