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hyperparameter_tune.rst 1.16 KB
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#############################
Auto (Hyper-parameter) Tuning
#############################

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Auto tuning is one of the key features provided by NNI; a main application scenario being
hyper-parameter tuning. Tuning specifically applies to trial code. We provide a lot of popular
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auto tuning algorithms (called Tuner), and some early stop algorithms (called Assessor).
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NNI supports running trials on various training platforms, for example, on a local machine,
on several servers in a distributed manner, or on platforms such as OpenPAI, Kubernetes, etc.
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Other key features of NNI, such as model compression, feature engineering, can also be further
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enhanced by auto tuning, which we'll described when introducing those features.
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NNI has high extensibility, advanced users can customize their own Tuner, Assessor, and Training Service
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according to their needs.
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..  toctree::
    :maxdepth: 2

    Write Trial <TrialExample/Trials>
    Tuners <builtin_tuner>
    Assessors <builtin_assessor>
    Training Platform <training_services>
    Examples <examples>
    WebUI <Tutorial/WebUI>
    How to Debug <Tutorial/HowToDebug>
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    Advanced <hpo_advanced>
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    HPO Benchmarks <hpo_benchmark>