# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2022, Microsoft
# This file is distributed under the same license as the NNI package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2022.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: NNI \n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2022-04-13 03:14+0000\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: LANGUAGE <LL@li.org>\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Generated-By: Babel 2.9.1\n"

#: ../../source/hpo/overview.rst:2
msgid "Hyperparameter Optimization Overview"
msgstr ""

#: ../../source/hpo/overview.rst:4
msgid ""
"Auto hyperparameter optimization (HPO), or auto tuning, is one of the key"
" features of NNI."
msgstr ""

#: ../../source/hpo/overview.rst:7
msgid "Introduction to HPO"
msgstr ""

#: ../../source/hpo/overview.rst:9
msgid ""
"In machine learning, a hyperparameter is a parameter whose value is used "
"to control learning process, and HPO is the problem of choosing a set of "
"optimal hyperparameters for a learning algorithm. (`From "
"<https://en.wikipedia.org/wiki/Hyperparameter_(machine_learning)>`__ "
"`Wikipedia "
"<https://en.wikipedia.org/wiki/Hyperparameter_optimization>`__)"
msgstr ""

#: ../../source/hpo/overview.rst:14
msgid "Following code snippet demonstrates a naive HPO process:"
msgstr ""

#: ../../source/hpo/overview.rst:34
msgid ""
"You may have noticed, the example will train 4×10×3=120 models in total. "
"Since it consumes so much computing resources, you may want to:"
msgstr ""

#: ../../source/hpo/overview.rst:37
msgid ""
":ref:`Find the best hyperparameter set with less iterations. <hpo-"
"overview-tuners>`"
msgstr ""

#: ../../source/hpo/overview.rst:38
msgid ":ref:`Train the models on distributed platforms. <hpo-overview-platforms>`"
msgstr ""

#: ../../source/hpo/overview.rst:39
msgid ""
":ref:`Have a portal to monitor and control the process. <hpo-overview-"
"portal>`"
msgstr ""

#: ../../source/hpo/overview.rst:41
msgid "NNI will do them for you."
msgstr ""

#: ../../source/hpo/overview.rst:44
msgid "Key Features of NNI HPO"
msgstr ""

#: ../../source/hpo/overview.rst:49
msgid "Tuning Algorithms"
msgstr ""

#: ../../source/hpo/overview.rst:51
msgid ""
"NNI provides *tuners* to speed up the process of finding best "
"hyperparameter set."
msgstr ""

#: ../../source/hpo/overview.rst:53
msgid ""
"A tuner, or a tuning algorithm, decides the order in which hyperparameter"
" sets are evaluated. Based on the results of historical hyperparameter "
"sets, an efficient tuner can predict where the best hyperparameters "
"locates around, and finds them in much fewer attempts."
msgstr ""

#: ../../source/hpo/overview.rst:57
msgid ""
"The naive example above evaluates all possible hyperparameter sets in "
"constant order, ignoring the historical results. This is the brute-force "
"tuning algorithm called *grid search*."
msgstr ""

#: ../../source/hpo/overview.rst:60
msgid ""
"NNI has out-of-the-box support for a variety of popular tuners. It "
"includes naive algorithms like random search and grid search, Bayesian-"
"based algorithms like TPE and SMAC, RL based algorithms like PPO, and "
"much more."
msgstr ""

#: ../../source/hpo/overview.rst:64
msgid "Main article: :doc:`tuners`"
msgstr ""

#: ../../source/hpo/overview.rst:69
msgid "Training Platforms"
msgstr ""

#: ../../source/hpo/overview.rst:71
msgid ""
"If you are not interested in distributed platforms, you can simply run "
"NNI HPO with current computer, just like any ordinary Python library."
msgstr ""

#: ../../source/hpo/overview.rst:74
msgid ""
"And when you want to leverage more computing resources, NNI provides "
"built-in integration for training platforms from simple on-premise "
"servers to scalable commercial clouds."
msgstr ""

#: ../../source/hpo/overview.rst:77
msgid ""
"With NNI you can write one piece of model code, and concurrently evaluate"
" hyperparameter sets on local machine, SSH servers, Kubernetes-based "
"clusters, AzureML service, and much more."
msgstr ""

#: ../../source/hpo/overview.rst:80
msgid "Main article: :doc:`/experiment/training_service/overview`"
msgstr ""

#: ../../source/hpo/overview.rst:85
msgid "Web Portal"
msgstr ""

#: ../../source/hpo/overview.rst:87
msgid ""
"NNI provides a web portal to monitor training progress, to visualize "
"hyperparameter performance, to manually customize hyperparameters, and to"
" manage multiple HPO experiments."
msgstr ""

#: ../../source/hpo/overview.rst:90
msgid "Main article: :doc:`/experiment/web_portal/web_portal`"
msgstr ""

#: ../../source/hpo/overview.rst:96
msgid "Tutorials"
msgstr ""

#: ../../source/hpo/overview.rst:98
msgid ""
"To start using NNI HPO, choose the quickstart tutorial of your favorite "
"framework:"
msgstr ""

#: ../../source/hpo/overview.rst:100
msgid ":doc:`PyTorch tutorial </tutorials/hpo_quickstart_pytorch/main>`"
msgstr ""

#: ../../source/hpo/overview.rst:101
msgid ":doc:`TensorFlow tutorial </tutorials/hpo_quickstart_tensorflow/main>`"
msgstr ""

#: ../../source/hpo/overview.rst:104
msgid "Extra Features"
msgstr ""

#: ../../source/hpo/overview.rst:106
msgid ""
"After you are familiar with basic usage, you can explore more HPO "
"features:"
msgstr ""

#: ../../source/hpo/overview.rst:108
msgid ""
":doc:`Use command line tool to create and manage experiments (nnictl) "
"</reference/nnictl>`"
msgstr ""

#: ../../source/hpo/overview.rst:109
msgid ":doc:`Early stop non-optimal models (assessor) <assessors>`"
msgstr ""

#: ../../source/hpo/overview.rst:110
msgid ":doc:`TensorBoard integration </experiment/web_portal/tensorboard>`"
msgstr ""

#: ../../source/hpo/overview.rst:111
msgid ":doc:`Implement your own algorithm <custom_algorithm>`"
msgstr ""

#: ../../source/hpo/overview.rst:112
msgid ":doc:`Benchmark tuners <hpo_benchmark>`"
msgstr ""

