зеркало из https://github.com/microsoft/nni.git
i18n toolchain based on sphinx-intl (#4759)
This commit is contained in:
Yuge Zhang
GitHub
Родитель
f5b89bb655
Коммит
fb0c273483
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@ -15,6 +15,7 @@ sphinx >= 4.5
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sphinx-argparse-nni >= 0.4.0
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sphinx-copybutton
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sphinx-gallery
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sphinx-intl
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sphinx-tabs
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sphinxcontrib-bibtex
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git+https://github.com/bashtage/sphinx-material@6e0ef82#egg=sphinx_material
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@ -8,3 +8,6 @@ _build/
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# auto-generated reference table
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_modules/
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# Machine-style translation files
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*.mo
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@ -11,6 +11,11 @@ BUILDDIR = build
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help:
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@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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# Build message catelogs for translation
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i18n:
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@$(SPHINXBUILD) -M getpartialtext "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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sphinx-intl update -p "$(BUILDDIR)/getpartialtext" -d "$(SOURCEDIR)/locales" -l zh
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.PHONY: help Makefile
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# Catch-all target: route all unknown targets to Sphinx using the new
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@ -0,0 +1,30 @@
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"""
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Basically same as
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`sphinx gettext buidler <https://www.sphinx-doc.org/en/master/_modules/sphinx/builders/gettext.html>`_,
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but only get texts from files in a whitelist.
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"""
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import re
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from docutils import nodes
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from sphinx.application import Sphinx
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from sphinx.builders.gettext import MessageCatalogBuilder
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class PartialMessageCatalogBuilder(MessageCatalogBuilder):
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name = 'getpartialtext'
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def init(self):
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super().init()
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self.whitelist_docs = [re.compile(x) for x in self.config.gettext_documents]
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def write_doc(self, docname: str, doctree: nodes.document) -> None:
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for doc_re in self.whitelist_docs:
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if doc_re.match(docname):
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return super().write_doc(docname, doctree)
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def setup(app: Sphinx):
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app.add_builder(PartialMessageCatalogBuilder)
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app.add_config_value('gettext_documents', [], 'gettext')
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@ -61,6 +61,7 @@ extensions = [
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# Custom extensions in extension/ folder.
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'tutorial_links', # this has to be after sphinx-gallery
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'getpartialtext',
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'inplace_translation',
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'cardlinkitem',
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'codesnippetcard',
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@ -186,6 +187,18 @@ master_doc = 'index'
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# Usually you set "language" from the command line for these cases.
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language = None
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# Translation related settings
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locale_dir = ['locales']
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# Documents that requires translation: https://github.com/microsoft/nni/issues/4298
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gettext_documents = [
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r'^index$',
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r'^quickstart$',
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r'^installation$',
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r'^(nas|hpo|compression)/overview$',
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r'^tutorials/(hello_nas|pruning_quick_start_mnist|hpo_quickstart_pytorch/main)$',
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]
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# List of patterns, relative to source directory, that match files and
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# directories to ignore when looking for source files.
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# This pattern also affects html_static_path and html_extra_path.
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@ -0,0 +1,144 @@
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# SOME DESCRIPTIVE TITLE.
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# Copyright (C) 2022, Microsoft
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# This file is distributed under the same license as the NNI package.
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# FIRST AUTHOR <EMAIL@ADDRESS>, 2022.
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#
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#, fuzzy
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msgid ""
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msgstr ""
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"Project-Id-Version: NNI \n"
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"Report-Msgid-Bugs-To: \n"
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"POT-Creation-Date: 2022-04-13 03:14+0000\n"
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"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
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"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
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"Language-Team: LANGUAGE <LL@li.org>\n"
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"MIME-Version: 1.0\n"
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"Content-Type: text/plain; charset=utf-8\n"
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"Content-Transfer-Encoding: 8bit\n"
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"Generated-By: Babel 2.9.1\n"
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#: ../../source/compression/overview.rst:2
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msgid "Overview of NNI Model Compression"
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msgstr ""
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#: ../../source/compression/overview.rst:4
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msgid ""
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"Deep neural networks (DNNs) have achieved great success in many tasks "
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"like computer vision, nature launguage processing, speech processing. "
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"However, typical neural networks are both computationally expensive and "
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"energy-intensive, which can be difficult to be deployed on devices with "
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"low computation resources or with strict latency requirements. Therefore,"
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" a natural thought is to perform model compression to reduce model size "
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"and accelerate model training/inference without losing performance "
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"significantly. Model compression techniques can be divided into two "
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"categories: pruning and quantization. The pruning methods explore the "
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"redundancy in the model weights and try to remove/prune the redundant and"
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" uncritical weights. Quantization refers to compress models by reducing "
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"the number of bits required to represent weights or activations. We "
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"further elaborate on the two methods, pruning and quantization, in the "
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"following chapters. Besides, the figure below visualizes the difference "
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"between these two methods."
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msgstr ""
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#: ../../source/compression/overview.rst:19
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msgid ""
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"NNI provides an easy-to-use toolkit to help users design and use model "
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"pruning and quantization algorithms. For users to compress their models, "
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"they only need to add several lines in their code. There are some popular"
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" model compression algorithms built-in in NNI. On the other hand, users "
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"could easily customize their new compression algorithms using NNI’s "
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"interface."
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msgstr ""
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#: ../../source/compression/overview.rst:24
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msgid "There are several core features supported by NNI model compression:"
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msgstr ""
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#: ../../source/compression/overview.rst:26
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msgid "Support many popular pruning and quantization algorithms."
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msgstr ""
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#: ../../source/compression/overview.rst:27
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msgid ""
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"Automate model pruning and quantization process with state-of-the-art "
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"strategies and NNI's auto tuning power."
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msgstr ""
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#: ../../source/compression/overview.rst:28
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msgid ""
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"Speedup a compressed model to make it have lower inference latency and "
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"also make it smaller."
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msgstr ""
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#: ../../source/compression/overview.rst:29
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msgid ""
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"Provide friendly and easy-to-use compression utilities for users to dive "
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"into the compression process and results."
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msgstr ""
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#: ../../source/compression/overview.rst:30
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msgid "Concise interface for users to customize their own compression algorithms."
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msgstr ""
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#: ../../source/compression/overview.rst:34
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msgid "Compression Pipeline"
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msgstr ""
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#: ../../source/compression/overview.rst:42
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msgid ""
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"The overall compression pipeline in NNI is shown above. For compressing a"
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" pretrained model, pruning and quantization can be used alone or in "
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"combination. If users want to apply both, a sequential mode is "
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"recommended as common practise."
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msgstr ""
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#: ../../source/compression/overview.rst:46
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msgid ""
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"Note that NNI pruners or quantizers are not meant to physically compact "
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"the model but for simulating the compression effect. Whereas NNI speedup "
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"tool can truly compress model by changing the network architecture and "
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"therefore reduce latency. To obtain a truly compact model, users should "
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"conduct :doc:`pruning speedup <../tutorials/pruning_speedup>` or "
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":doc:`quantizaiton speedup <../tutorials/quantization_speedup>`. The "
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"interface and APIs are unified for both PyTorch and TensorFlow. Currently"
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" only PyTorch version has been supported, and TensorFlow version will be "
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"supported in future."
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msgstr ""
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#: ../../source/compression/overview.rst:52
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msgid "Model Speedup"
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msgstr ""
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#: ../../source/compression/overview.rst:54
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msgid ""
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"The final goal of model compression is to reduce inference latency and "
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"model size. However, existing model compression algorithms mainly use "
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"simulation to check the performance (e.g., accuracy) of compressed model."
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" For example, using masks for pruning algorithms, and storing quantized "
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"values still in float32 for quantization algorithms. Given the output "
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"masks and quantization bits produced by those algorithms, NNI can really "
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"speedup the model."
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msgstr ""
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#: ../../source/compression/overview.rst:59
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msgid "The following figure shows how NNI prunes and speeds up your models."
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msgstr ""
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#: ../../source/compression/overview.rst:67
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msgid ""
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"The detailed tutorial of Speedup Model with Mask can be found :doc:`here "
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"<../tutorials/pruning_speedup>`. The detailed tutorial of Speedup Model "
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"with Calibration Config can be found :doc:`here "
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"<../tutorials/quantization_speedup>`."
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msgstr ""
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#: ../../source/compression/overview.rst:72
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msgid ""
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"NNI's model pruning framework has been upgraded to a more powerful "
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"version (named pruning v2 before nni v2.6). The old version (`named "
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"pruning before nni v2.6 "
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"<https://nni.readthedocs.io/en/v2.6/Compression/pruning.html>`_) will be "
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"out of maintenance. If for some reason you have to use the old pruning, "
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"v2.6 is the last nni version to support old pruning version."
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msgstr ""
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@ -0,0 +1,207 @@
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# SOME DESCRIPTIVE TITLE.
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# Copyright (C) 2022, Microsoft
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# This file is distributed under the same license as the NNI package.
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# FIRST AUTHOR <EMAIL@ADDRESS>, 2022.
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#
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#, fuzzy
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msgid ""
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msgstr ""
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"Project-Id-Version: NNI \n"
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"Report-Msgid-Bugs-To: \n"
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"POT-Creation-Date: 2022-04-13 03:14+0000\n"
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"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
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"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
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"Language-Team: LANGUAGE <LL@li.org>\n"
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"MIME-Version: 1.0\n"
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"Content-Type: text/plain; charset=utf-8\n"
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"Content-Transfer-Encoding: 8bit\n"
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"Generated-By: Babel 2.9.1\n"
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#: ../../source/hpo/overview.rst:2
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msgid "Hyperparameter Optimization Overview"
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msgstr ""
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#: ../../source/hpo/overview.rst:4
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msgid ""
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"Auto hyperparameter optimization (HPO), or auto tuning, is one of the key"
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" features of NNI."
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msgstr ""
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#: ../../source/hpo/overview.rst:7
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msgid "Introduction to HPO"
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msgstr ""
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#: ../../source/hpo/overview.rst:9
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msgid ""
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"In machine learning, a hyperparameter is a parameter whose value is used "
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"to control learning process, and HPO is the problem of choosing a set of "
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"optimal hyperparameters for a learning algorithm. (`From "
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"<https://en.wikipedia.org/wiki/Hyperparameter_(machine_learning)>`__ "
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"`Wikipedia "
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"<https://en.wikipedia.org/wiki/Hyperparameter_optimization>`__)"
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msgstr ""
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#: ../../source/hpo/overview.rst:14
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msgid "Following code snippet demonstrates a naive HPO process:"
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msgstr ""
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#: ../../source/hpo/overview.rst:34
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msgid ""
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"You may have noticed, the example will train 4×10×3=120 models in total. "
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"Since it consumes so much computing resources, you may want to:"
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msgstr ""
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#: ../../source/hpo/overview.rst:37
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msgid ""
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":ref:`Find the best hyperparameter set with less iterations. <hpo-"
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"overview-tuners>`"
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msgstr ""
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#: ../../source/hpo/overview.rst:38
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msgid ":ref:`Train the models on distributed platforms. <hpo-overview-platforms>`"
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msgstr ""
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#: ../../source/hpo/overview.rst:39
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msgid ""
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":ref:`Have a portal to monitor and control the process. <hpo-overview-"
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"portal>`"
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msgstr ""
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#: ../../source/hpo/overview.rst:41
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msgid "NNI will do them for you."
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msgstr ""
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#: ../../source/hpo/overview.rst:44
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msgid "Key Features of NNI HPO"
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msgstr ""
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#: ../../source/hpo/overview.rst:49
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msgid "Tuning Algorithms"
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msgstr ""
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#: ../../source/hpo/overview.rst:51
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msgid ""
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"NNI provides *tuners* to speed up the process of finding best "
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"hyperparameter set."
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msgstr ""
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#: ../../source/hpo/overview.rst:53
|
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msgid ""
|
||||
"A tuner, or a tuning algorithm, decides the order in which hyperparameter"
|
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" sets are evaluated. Based on the results of historical hyperparameter "
|
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"sets, an efficient tuner can predict where the best hyperparameters "
|
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"locates around, and finds them in much fewer attempts."
|
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msgstr ""
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#: ../../source/hpo/overview.rst:57
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msgid ""
|
||||
"The naive example above evaluates all possible hyperparameter sets in "
|
||||
"constant order, ignoring the historical results. This is the brute-force "
|
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"tuning algorithm called *grid search*."
|
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msgstr ""
|
||||
|
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#: ../../source/hpo/overview.rst:60
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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."
|
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msgstr ""
|
||||
|
||||
#: ../../source/hpo/overview.rst:64
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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 ""
|
||||
|
||||
|
|
@ -0,0 +1,206 @@
|
|||
# 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: 2022-04-12 17:35+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/index.rst:4 ../../source/index.rst:52
|
||||
msgid "Get Started"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:12
|
||||
msgid "Hyperparameter Optimization"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:12
|
||||
msgid "Model Compression"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:12
|
||||
msgid "User Guide"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:23
|
||||
msgid "Python API"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:23
|
||||
msgid "References"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:32
|
||||
msgid "Misc"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:2
|
||||
msgid "NNI Documentation"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:44
|
||||
msgid ""
|
||||
"**NNI (Neural Network Intelligence)** is a lightweight but powerful "
|
||||
"toolkit to help users **automate**:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:46
|
||||
msgid ":doc:`Hyperparameter Optimization </hpo/overview>`"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:47
|
||||
msgid ":doc:`Neural Architecture Search </nas/overview>`"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:48
|
||||
msgid ":doc:`Model Compression </compression/overview>`"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:49
|
||||
msgid ":doc:`Feature Engineering </feature_engineering/overview>`"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:54
|
||||
msgid "To install the current release:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:60
|
||||
msgid ""
|
||||
"See the :doc:`installation guide </installation>` if you need additional "
|
||||
"help on installation."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:63
|
||||
msgid "Try your first NNI experiment"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:65
|
||||
msgid "To run your first NNI experiment:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:71
|
||||
msgid ""
|
||||
"you need to have `PyTorch <https://pytorch.org/>`_ (as well as "
|
||||
"`torchvision <https://pytorch.org/vision/stable/index.html>`_) installed "
|
||||
"to run this experiment."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:73
|
||||
msgid ""
|
||||
"To start your journey now, please follow the :doc:`absolute quickstart of"
|
||||
" NNI <quickstart>`!"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:76
|
||||
msgid "Why choose NNI?"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:79
|
||||
msgid "NNI makes AutoML techniques plug-and-play"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:223
|
||||
msgid "NNI eases the effort to scale and manage AutoML experiments"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:231
|
||||
msgid ""
|
||||
"An AutoML experiment requires many trials to explore feasible and "
|
||||
"potentially good-performing models. **Training service** aims to make the"
|
||||
" tuning process easily scalable in a distributed platforms. It provides a"
|
||||
" unified user experience for diverse computation resources (e.g., local "
|
||||
"machine, remote servers, AKS). Currently, NNI supports **more than 9** "
|
||||
"kinds of training services."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:242
|
||||
msgid ""
|
||||
"Web portal visualizes the tuning process, exposing the ability to "
|
||||
"inspect, monitor and control the experiment."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:253
|
||||
msgid ""
|
||||
"The DNN model tuning often requires more than one experiment. Users might"
|
||||
" try different tuning algorithms, fine-tune their search space, or switch"
|
||||
" to another training service. **Experiment management** provides the "
|
||||
"power to aggregate and compare tuning results from multiple experiments, "
|
||||
"so that the tuning workflow becomes clean and organized."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:259
|
||||
msgid "Get Support and Contribute Back"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:261
|
||||
msgid ""
|
||||
"NNI is maintained on the `NNI GitHub repository "
|
||||
"<https://github.com/microsoft/nni>`_. We collect feedbacks and new "
|
||||
"proposals/ideas on GitHub. You can:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:263
|
||||
msgid ""
|
||||
"Open a `GitHub issue <https://github.com/microsoft/nni/issues>`_ for bugs"
|
||||
" and feature requests."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:264
|
||||
msgid ""
|
||||
"Open a `pull request <https://github.com/microsoft/nni/pulls>`_ to "
|
||||
"contribute code (make sure to read the `contribution guide "
|
||||
"</contribution>` before doing this)."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:265
|
||||
msgid ""
|
||||
"Participate in `NNI Discussion "
|
||||
"<https://github.com/microsoft/nni/discussions>`_ for general questions "
|
||||
"and new ideas."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:266
|
||||
msgid "Join the following IM groups."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:272
|
||||
msgid "Gitter"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:273
|
||||
msgid "WeChat"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:280
|
||||
msgid "Citing NNI"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:282
|
||||
msgid ""
|
||||
"If you use NNI in a scientific publication, please consider citing NNI in"
|
||||
" your references."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:284
|
||||
msgid ""
|
||||
"Microsoft. Neural Network Intelligence (version |release|). "
|
||||
"https://github.com/microsoft/nni"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/index.rst:286
|
||||
msgid ""
|
||||
"Bibtex entry (please replace the version with the particular version you "
|
||||
"are using): ::"
|
||||
msgstr ""
|
||||
|
||||
|
|
@ -0,0 +1,130 @@
|
|||
# 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/installation.rst:2
|
||||
msgid "Install NNI"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:4
|
||||
msgid ""
|
||||
"NNI requires Python >= 3.7. It is tested and supported on Ubuntu >= "
|
||||
"18.04, Windows 10 >= 21H2, and macOS >= 11."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:8
|
||||
msgid "There are 3 ways to install NNI:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:10
|
||||
msgid ":ref:`Using pip <installation-pip>`"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:11
|
||||
msgid ":ref:`Build source code <installation-source>`"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:12
|
||||
msgid ":ref:`Using Docker <installation-docker>`"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:17
|
||||
msgid "Using pip"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:19
|
||||
msgid ""
|
||||
"NNI provides official packages for x86-64 CPUs. They can be installed "
|
||||
"with pip:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:25
|
||||
msgid "Or to upgrade to latest version:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:31
|
||||
msgid "You can check installation with:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:37
|
||||
msgid ""
|
||||
"On Linux systems without Conda, you may encounter ``bash: nnictl: command"
|
||||
" not found`` error. In this case you need to add pip script directory to "
|
||||
"``PATH``:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:48
|
||||
msgid "Installing from Source Code"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:50
|
||||
msgid "NNI hosts source code on `GitHub <https://github.com/microsoft/nni>`__."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:52
|
||||
msgid ""
|
||||
"NNI has experimental support for ARM64 CPUs, including Apple M1. It "
|
||||
"requires to install from source code."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:55
|
||||
msgid "See :doc:`/notes/build_from_source`."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:60
|
||||
msgid "Using Docker"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:62
|
||||
msgid ""
|
||||
"NNI provides official Docker image on `Docker Hub "
|
||||
"<https://hub.docker.com/r/msranni/nni>`__."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:69
|
||||
msgid "Installing Extra Dependencies"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:71
|
||||
msgid ""
|
||||
"Some built-in algorithms of NNI requires extra packages. Use ``nni"
|
||||
"[<algorithm-name>]`` to install their dependencies."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:74
|
||||
msgid ""
|
||||
"For example, to install dependencies of :class:`DNGO "
|
||||
"tuner<nni.algorithms.hpo.dngo_tuner.DNGOTuner>` :"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:80
|
||||
msgid ""
|
||||
"This command will not reinstall NNI itself, even if it was installed in "
|
||||
"development mode."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:82
|
||||
msgid "Alternatively, you may install all extra dependencies at once:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/installation.rst:88
|
||||
msgid ""
|
||||
"**NOTE**: SMAC tuner depends on swig3, which requires a manual downgrade "
|
||||
"on Ubuntu:"
|
||||
msgstr ""
|
||||
|
||||
|
|
@ -0,0 +1,270 @@
|
|||
# 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 03:17+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/nas/overview.rst:2
|
||||
msgid "Overview"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:4
|
||||
msgid ""
|
||||
"NNI's latest NAS supports are all based on Retiarii Framework, users who "
|
||||
"are still on `early version using NNI NAS v1.0 "
|
||||
"<https://nni.readthedocs.io/en/v2.2/nas.html>`__ shall migrate your work "
|
||||
"to Retiarii as soon as possible. We plan to remove the legacy NAS "
|
||||
"framework in the next few releases."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:6
|
||||
msgid ""
|
||||
"PyTorch is the **only supported framework on Retiarii**. Inquiries of NAS"
|
||||
" support on Tensorflow is in `this discussion "
|
||||
"<https://github.com/microsoft/nni/discussions/4605>`__. If you intend to "
|
||||
"run NAS with DL frameworks other than PyTorch and Tensorflow, please "
|
||||
"`open new issues <https://github.com/microsoft/nni/issues>`__ to let us "
|
||||
"know."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:9
|
||||
msgid "Basics"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:11
|
||||
msgid ""
|
||||
"Automatic neural architecture search is playing an increasingly important"
|
||||
" role in finding better models. Recent research has proven the "
|
||||
"feasibility of automatic NAS and has led to models that beat many "
|
||||
"manually designed and tuned models. Representative works include `NASNet "
|
||||
"<https://arxiv.org/abs/1707.07012>`__, `ENAS "
|
||||
"<https://arxiv.org/abs/1802.03268>`__, `DARTS "
|
||||
"<https://arxiv.org/abs/1806.09055>`__, `Network Morphism "
|
||||
"<https://arxiv.org/abs/1806.10282>`__, and `Evolution "
|
||||
"<https://arxiv.org/abs/1703.01041>`__. In addition, new innovations "
|
||||
"continue to emerge."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:13
|
||||
msgid ""
|
||||
"High-level speaking, aiming to solve any particular task with neural "
|
||||
"architecture search typically requires: search space design, search "
|
||||
"strategy selection, and performance evaluation. The three components work"
|
||||
" together with the following loop (from the famous `NAS survey "
|
||||
"<https://arxiv.org/abs/1808.05377>`__):"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:19
|
||||
msgid "In this figure:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:21
|
||||
msgid ""
|
||||
"*Model search space* means a set of models from which the best model is "
|
||||
"explored/searched. Sometimes we use *search space* or *model space* in "
|
||||
"short."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:22
|
||||
msgid ""
|
||||
"*Exploration strategy* is the algorithm that is used to explore a model "
|
||||
"search space. Sometimes we also call it *search strategy*."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:23
|
||||
msgid ""
|
||||
"*Model evaluator* is responsible for training a model and evaluating its "
|
||||
"performance."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:25
|
||||
msgid ""
|
||||
"The process is similar to :doc:`Hyperparameter Optimization "
|
||||
"</hpo/index>`, except that the target is the best architecture rather "
|
||||
"than hyperparameter. Concretely, an exploration strategy selects an "
|
||||
"architecture from a predefined search space. The architecture is passed "
|
||||
"to a performance evaluation to get a score, which represents how well "
|
||||
"this architecture performs on a particular task. This process is repeated"
|
||||
" until the search process is able to find the best architecture."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:28
|
||||
msgid "Key Features"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:30
|
||||
msgid ""
|
||||
"The current NAS framework in NNI is powered by the research of `Retiarii:"
|
||||
" A Deep Learning Exploratory-Training Framework "
|
||||
"<https://www.usenix.org/system/files/osdi20-zhang_quanlu.pdf>`__, where "
|
||||
"we highlight the following features:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:32
|
||||
msgid ":doc:`Simple APIs to construct search space easily <construct_space>`"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:33
|
||||
msgid ":doc:`SOTA NAS algorithms to explore search space <exploration_strategy>`"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:34
|
||||
msgid ""
|
||||
":doc:`Experiment backend support to scale up experiments on large-scale "
|
||||
"AI platforms </experiment/overview>`"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:37
|
||||
msgid "Why NAS with NNI"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:39
|
||||
msgid ""
|
||||
"We list out the three perspectives where NAS can be particularly "
|
||||
"challegning without NNI. NNI provides solutions to relieve users' "
|
||||
"engineering effort when they want to try NAS techniques in their own "
|
||||
"scenario."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:42
|
||||
msgid "Search Space Design"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:44
|
||||
msgid ""
|
||||
"The search space defines which architectures can be represented in "
|
||||
"principle. Incorporating prior knowledge about typical properties of "
|
||||
"architectures well-suited for a task can reduce the size of the search "
|
||||
"space and simplify the search. However, this also introduces a human "
|
||||
"bias, which may prevent finding novel architectural building blocks that "
|
||||
"go beyond the current human knowledge. Search space design can be very "
|
||||
"challenging for beginners, who might not possess the experience to "
|
||||
"balance the richness and simplicity."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:46
|
||||
msgid ""
|
||||
"In NNI, we provide a wide range of APIs to build the search space. There "
|
||||
"are :doc:`high-level APIs <construct_space>`, that enables incorporating "
|
||||
"human knowledge about what makes a good architecture or search space. "
|
||||
"There are also :doc:`low-level APIs <mutator>`, that is a list of "
|
||||
"primitives to construct a network from operator to operator."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:49
|
||||
msgid "Exploration strategy"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:51
|
||||
msgid ""
|
||||
"The exploration strategy details how to explore the search space (which "
|
||||
"is often exponentially large). It encompasses the classical exploration-"
|
||||
"exploitation trade-off since, on the one hand, it is desirable to find "
|
||||
"well-performing architectures quickly, while on the other hand, premature"
|
||||
" convergence to a region of suboptimal architectures should be avoided. "
|
||||
"The \"best\" exploration strategy for a particular scenario is usually "
|
||||
"found via trial-and-error. As many state-of-the-art strategies are "
|
||||
"implemented with their own code-base, it becomes very troublesome to "
|
||||
"switch from one to another."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:53
|
||||
msgid ""
|
||||
"In NNI, we have also provided :doc:`a list of strategies "
|
||||
"<exploration_strategy>`. Some of them are powerful yet time consuming, "
|
||||
"while others might be suboptimal but really efficient. Given that all "
|
||||
"strategies are implemented with a unified interface, users can always "
|
||||
"find one that matches their need."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:56
|
||||
msgid "Performance estimation"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:58
|
||||
msgid ""
|
||||
"The objective of NAS is typically to find architectures that achieve high"
|
||||
" predictive performance on unseen data. Performance estimation refers to "
|
||||
"the process of estimating this performance. The problem with performance "
|
||||
"estimation is mostly its scalability, i.e., how can I run and manage "
|
||||
"multiple trials simultaneously."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:60
|
||||
msgid ""
|
||||
"In NNI, we standardize this process is implemented with :doc:`evaluator "
|
||||
"<evaluator>`, which is responsible of estimating a model's performance. "
|
||||
"The choices of evaluators also range from the simplest option, e.g., to "
|
||||
"perform a standard training and validation of the architecture on data, "
|
||||
"to complex configurations and implementations. Evaluators are run in "
|
||||
"*trials*, where trials can be spawn onto distributed platforms with our "
|
||||
"powerful :doc:`training service </experiment/training_service/overview>`."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:63
|
||||
msgid "Tutorials"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:65
|
||||
msgid ""
|
||||
"To start using NNI NAS framework, we recommend at least going through the"
|
||||
" following tutorials:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:67
|
||||
msgid ":doc:`Quickstart </tutorials/hello_nas>`"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:68
|
||||
msgid ":doc:`construct_space`"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:69
|
||||
msgid ":doc:`exploration_strategy`"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:70
|
||||
msgid ":doc:`evaluator`"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:73
|
||||
msgid "Resources"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:75
|
||||
msgid ""
|
||||
"The following articles will help with a better understanding of the "
|
||||
"current arts of NAS:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:77
|
||||
msgid ""
|
||||
"`Neural Architecture Search: A Survey "
|
||||
"<https://arxiv.org/abs/1808.05377>`__"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/nas/overview.rst:78
|
||||
msgid ""
|
||||
"`A Comprehensive Survey of Neural Architecture Search: Challenges and "
|
||||
"Solutions <https://arxiv.org/abs/2006.02903>`__"
|
||||
msgstr ""
|
||||
|
||||
#~ msgid "Basics"
|
||||
#~ msgstr ""
|
||||
|
||||
#~ msgid "Basic Concepts"
|
||||
#~ msgstr ""
|
||||
|
||||
|
|
@ -0,0 +1,23 @@
|
|||
# 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/quickstart.rst:2
|
||||
msgid "Quickstart"
|
||||
msgstr ""
|
||||
|
||||
|
|
@ -0,0 +1,23 @@
|
|||
# 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: 2022-04-12 17:35+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"
|
||||
|
||||
#: ../../templates/globaltoc.html:6
|
||||
msgid "Overview"
|
||||
msgstr ""
|
||||
|
||||
|
|
@ -0,0 +1,749 @@
|
|||
# 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/tutorials/hello_nas.rst:13
|
||||
msgid ""
|
||||
"Click :ref:`here <sphx_glr_download_tutorials_hello_nas.py>` to download "
|
||||
"the full example code"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:22
|
||||
msgid "Hello, NAS!"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:24
|
||||
msgid ""
|
||||
"This is the 101 tutorial of Neural Architecture Search (NAS) on NNI. In "
|
||||
"this tutorial, we will search for a neural architecture on MNIST dataset "
|
||||
"with the help of NAS framework of NNI, i.e., *Retiarii*. We use multi-"
|
||||
"trial NAS as an example to show how to construct and explore a model "
|
||||
"space."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:28
|
||||
msgid ""
|
||||
"There are mainly three crucial components for a neural architecture "
|
||||
"search task, namely,"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:30
|
||||
msgid "Model search space that defines a set of models to explore."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:31
|
||||
msgid "A proper strategy as the method to explore this model space."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:32
|
||||
msgid ""
|
||||
"A model evaluator that reports the performance of every model in the "
|
||||
"space."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:34
|
||||
msgid ""
|
||||
"Currently, PyTorch is the only supported framework by Retiarii, and we "
|
||||
"have only tested **PyTorch 1.7 to 1.10**. This tutorial assumes PyTorch "
|
||||
"context but it should also apply to other frameworks, which is in our "
|
||||
"future plan."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:38
|
||||
msgid "Define your Model Space"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:40
|
||||
msgid ""
|
||||
"Model space is defined by users to express a set of models that users "
|
||||
"want to explore, which contains potentially good-performing models. In "
|
||||
"this framework, a model space is defined with two parts: a base model and"
|
||||
" possible mutations on the base model."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:46
|
||||
msgid "Define Base Model"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:48
|
||||
msgid ""
|
||||
"Defining a base model is almost the same as defining a PyTorch (or "
|
||||
"TensorFlow) model. Usually, you only need to replace the code ``import "
|
||||
"torch.nn as nn`` with ``import nni.retiarii.nn.pytorch as nn`` to use our"
|
||||
" wrapped PyTorch modules."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:52
|
||||
msgid "Below is a very simple example of defining a base model."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:93
|
||||
msgid ""
|
||||
"Always keep in mind that you should use ``import nni.retiarii.nn.pytorch "
|
||||
"as nn`` and :meth:`nni.retiarii.model_wrapper`. Many mistakes are a "
|
||||
"result of forgetting one of those. Also, please use ``torch.nn`` for "
|
||||
"submodules of ``nn.init``, e.g., ``torch.nn.init`` instead of "
|
||||
"``nn.init``."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:98
|
||||
msgid "Define Model Mutations"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:100
|
||||
msgid ""
|
||||
"A base model is only one concrete model not a model space. We provide "
|
||||
":doc:`API and Primitives </nas/construct_space>` for users to express how"
|
||||
" the base model can be mutated. That is, to build a model space which "
|
||||
"includes many models."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:103
|
||||
msgid "Based on the above base model, we can define a model space as below."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:134
|
||||
msgid "This results in the following code:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:189
|
||||
#: ../../source/tutorials/hello_nas.rst:259
|
||||
#: ../../source/tutorials/hello_nas.rst:551
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:247
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:284
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:65
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:107
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:172
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:218
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:255
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:283
|
||||
msgid "Out:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:210
|
||||
msgid ""
|
||||
"This example uses two mutation APIs, :class:`nn.LayerChoice "
|
||||
"<nni.retiarii.nn.pytorch.LayerChoice>` and :class:`nn.InputChoice "
|
||||
"<nni.retiarii.nn.pytorch.ValueChoice>`. :class:`nn.LayerChoice "
|
||||
"<nni.retiarii.nn.pytorch.LayerChoice>` takes a list of candidate modules "
|
||||
"(two in this example), one will be chosen for each sampled model. It can "
|
||||
"be used like normal PyTorch module. :class:`nn.InputChoice "
|
||||
"<nni.retiarii.nn.pytorch.ValueChoice>` takes a list of candidate values, "
|
||||
"one will be chosen to take effect for each sampled model."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:219
|
||||
msgid ""
|
||||
"More detailed API description and usage can be found :doc:`here "
|
||||
"</nas/construct_space>`."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:223
|
||||
msgid ""
|
||||
"We are actively enriching the mutation APIs, to facilitate easy "
|
||||
"construction of model space. If the currently supported mutation APIs "
|
||||
"cannot express your model space, please refer to :doc:`this doc "
|
||||
"</nas/mutator>` for customizing mutators."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:228
|
||||
msgid "Explore the Defined Model Space"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:230
|
||||
msgid ""
|
||||
"There are basically two exploration approaches: (1) search by evaluating "
|
||||
"each sampled model independently, which is the search approach in :ref"
|
||||
":`multi-trial NAS <multi-trial-nas>` and (2) one-shot weight-sharing "
|
||||
"based search, which is used in one-shot NAS. We demonstrate the first "
|
||||
"approach in this tutorial. Users can refer to :ref:`here <one-shot-nas>` "
|
||||
"for the second approach."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:235
|
||||
msgid ""
|
||||
"First, users need to pick a proper exploration strategy to explore the "
|
||||
"defined model space. Second, users need to pick or customize a model "
|
||||
"evaluator to evaluate the performance of each explored model."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:239
|
||||
msgid "Pick an exploration strategy"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:241
|
||||
msgid ""
|
||||
"Retiarii supports many :doc:`exploration strategies "
|
||||
"</nas/exploration_strategy>`."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:243
|
||||
msgid "Simply choosing (i.e., instantiate) an exploration strategy as below."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:273
|
||||
msgid "Pick or customize a model evaluator"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:275
|
||||
msgid ""
|
||||
"In the exploration process, the exploration strategy repeatedly generates"
|
||||
" new models. A model evaluator is for training and validating each "
|
||||
"generated model to obtain the model's performance. The performance is "
|
||||
"sent to the exploration strategy for the strategy to generate better "
|
||||
"models."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:279
|
||||
msgid ""
|
||||
"Retiarii has provided :doc:`built-in model evaluators </nas/evaluator>`, "
|
||||
"but to start with, it is recommended to use :class:`FunctionalEvaluator "
|
||||
"<nni.retiarii.evaluator.FunctionalEvaluator>`, that is, to wrap your own "
|
||||
"training and evaluation code with one single function. This function "
|
||||
"should receive one single model class and uses "
|
||||
":func:`nni.report_final_result` to report the final score of this model."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:284
|
||||
msgid ""
|
||||
"An example here creates a simple evaluator that runs on MNIST dataset, "
|
||||
"trains for 2 epochs, and reports its validation accuracy."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:367
|
||||
msgid "Create the evaluator"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:386
|
||||
msgid ""
|
||||
"The ``train_epoch`` and ``test_epoch`` here can be any customized "
|
||||
"function, where users can write their own training recipe."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:389
|
||||
msgid ""
|
||||
"It is recommended that the ``evaluate_model`` here accepts no additional "
|
||||
"arguments other than ``model_cls``. However, in the :doc:`advanced "
|
||||
"tutorial </nas/evaluator>`, we will show how to use additional arguments "
|
||||
"in case you actually need those. In future, we will support mutation on "
|
||||
"the arguments of evaluators, which is commonly called \"Hyper-parmeter "
|
||||
"tuning\"."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:394
|
||||
msgid "Launch an Experiment"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:396
|
||||
msgid ""
|
||||
"After all the above are prepared, it is time to start an experiment to do"
|
||||
" the model search. An example is shown below."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:417
|
||||
msgid ""
|
||||
"The following configurations are useful to control how many trials to run"
|
||||
" at most / at the same time."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:436
|
||||
msgid ""
|
||||
"Remember to set the following config if you want to GPU. "
|
||||
"``use_active_gpu`` should be set true if you wish to use an occupied GPU "
|
||||
"(possibly running a GUI)."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:456
|
||||
msgid ""
|
||||
"Launch the experiment. The experiment should take several minutes to "
|
||||
"finish on a workstation with 2 GPUs."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:474
|
||||
msgid ""
|
||||
"Users can also run Retiarii Experiment with :doc:`different training "
|
||||
"services </experiment/training_service/overview>` besides ``local`` "
|
||||
"training service."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:478
|
||||
msgid "Visualize the Experiment"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:480
|
||||
msgid ""
|
||||
"Users can visualize their experiment in the same way as visualizing a "
|
||||
"normal hyper-parameter tuning experiment. For example, open "
|
||||
"``localhost:8081`` in your browser, 8081 is the port that you set in "
|
||||
"``exp.run``. Please refer to :doc:`here "
|
||||
"</experiment/web_portal/web_portal>` for details."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:484
|
||||
msgid ""
|
||||
"We support visualizing models with 3rd-party visualization engines (like "
|
||||
"`Netron <https://netron.app/>`__). This can be used by clicking "
|
||||
"``Visualization`` in detail panel for each trial. Note that current "
|
||||
"visualization is based on `onnx <https://onnx.ai/>`__ , thus "
|
||||
"visualization is not feasible if the model cannot be exported into onnx."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:489
|
||||
msgid ""
|
||||
"Built-in evaluators (e.g., Classification) will automatically export the "
|
||||
"model into a file. For your own evaluator, you need to save your file "
|
||||
"into ``$NNI_OUTPUT_DIR/model.onnx`` to make this work. For instance,"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:520
|
||||
msgid "Relaunch the experiment, and a button is shown on Web portal."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:525
|
||||
msgid "Export Top Models"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:527
|
||||
msgid ""
|
||||
"Users can export top models after the exploration is done using "
|
||||
"``export_top_models``."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:563
|
||||
msgid "**Total running time of the script:** ( 2 minutes 15.810 seconds)"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:578
|
||||
msgid ":download:`Download Python source code: hello_nas.py <hello_nas.py>`"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:584
|
||||
msgid ":download:`Download Jupyter notebook: hello_nas.ipynb <hello_nas.ipynb>`"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hello_nas.rst:591
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:338
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:357
|
||||
msgid "`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:14
|
||||
msgid ""
|
||||
"Click :ref:`here "
|
||||
"<sphx_glr_download_tutorials_hpo_quickstart_pytorch_main.py>` to download"
|
||||
" the full example code"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:23
|
||||
msgid "NNI HPO Quickstart with PyTorch"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:24
|
||||
msgid ""
|
||||
"This tutorial optimizes the model in `official PyTorch quickstart`_ with "
|
||||
"auto-tuning."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:26
|
||||
msgid ""
|
||||
"There is also a :doc:`TensorFlow "
|
||||
"version<../hpo_quickstart_tensorflow/main>` if you prefer it."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:28
|
||||
msgid "The tutorial consists of 4 steps:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:30
|
||||
msgid "Modify the model for auto-tuning."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:31
|
||||
msgid "Define hyperparameters' search space."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:32
|
||||
msgid "Configure the experiment."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:33
|
||||
msgid "Run the experiment."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:40
|
||||
msgid "Step 1: Prepare the model"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:41
|
||||
msgid "In first step, we need to prepare the model to be tuned."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:43
|
||||
msgid ""
|
||||
"The model should be put in a separate script. It will be evaluated many "
|
||||
"times concurrently, and possibly will be trained on distributed "
|
||||
"platforms."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:47
|
||||
msgid "In this tutorial, the model is defined in :doc:`model.py <model>`."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:49
|
||||
msgid "In short, it is a PyTorch model with 3 additional API calls:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:51
|
||||
msgid ""
|
||||
"Use :func:`nni.get_next_parameter` to fetch the hyperparameters to be "
|
||||
"evalutated."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:52
|
||||
msgid ""
|
||||
"Use :func:`nni.report_intermediate_result` to report per-epoch accuracy "
|
||||
"metrics."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:53
|
||||
msgid "Use :func:`nni.report_final_result` to report final accuracy."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:55
|
||||
msgid "Please understand the model code before continue to next step."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:60
|
||||
msgid "Step 2: Define search space"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:61
|
||||
msgid ""
|
||||
"In model code, we have prepared 3 hyperparameters to be tuned: "
|
||||
"*features*, *lr*, and *momentum*."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:64
|
||||
msgid ""
|
||||
"Here we need to define their *search space* so the tuning algorithm can "
|
||||
"sample them in desired range."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:66
|
||||
msgid "Assuming we have following prior knowledge for these hyperparameters:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:68
|
||||
msgid "*features* should be one of 128, 256, 512, 1024."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:69
|
||||
msgid ""
|
||||
"*lr* should be a float between 0.0001 and 0.1, and it follows exponential"
|
||||
" distribution."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:70
|
||||
msgid "*momentum* should be a float between 0 and 1."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:72
|
||||
msgid ""
|
||||
"In NNI, the space of *features* is called ``choice``; the space of *lr* "
|
||||
"is called ``loguniform``; and the space of *momentum* is called "
|
||||
"``uniform``. You may have noticed, these names are derived from "
|
||||
"``numpy.random``."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:77
|
||||
msgid ""
|
||||
"For full specification of search space, check :doc:`the reference "
|
||||
"</hpo/search_space>`."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:79
|
||||
msgid "Now we can define the search space as follow:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:102
|
||||
msgid "Step 3: Configure the experiment"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:103
|
||||
msgid ""
|
||||
"NNI uses an *experiment* to manage the HPO process. The *experiment "
|
||||
"config* defines how to train the models and how to explore the search "
|
||||
"space."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:106
|
||||
msgid ""
|
||||
"In this tutorial we use a *local* mode experiment, which means models "
|
||||
"will be trained on local machine, without using any special training "
|
||||
"platform."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:125
|
||||
msgid "Now we start to configure the experiment."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:128
|
||||
msgid "Configure trial code"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:129
|
||||
msgid ""
|
||||
"In NNI evaluation of each hyperparameter set is called a *trial*. So the "
|
||||
"model script is called *trial code*."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:147
|
||||
msgid ""
|
||||
"When ``trial_code_directory`` is a relative path, it relates to current "
|
||||
"working directory. To run ``main.py`` in a different path, you can set "
|
||||
"trial code directory to ``Path(__file__).parent``. (`__file__ "
|
||||
"<https://docs.python.org/3.10/reference/datamodel.html#index-43>`__ is "
|
||||
"only available in standard Python, not in Jupyter Notebook.)"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:154
|
||||
msgid ""
|
||||
"If you are using Linux system without Conda, you may need to change "
|
||||
"``\"python model.py\"`` to ``\"python3 model.py\"``."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:160
|
||||
msgid "Configure search space"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:178
|
||||
msgid "Configure tuning algorithm"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:179
|
||||
msgid "Here we use :doc:`TPE tuner </hpo/tuners>`."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:198
|
||||
msgid "Configure how many trials to run"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:199
|
||||
msgid ""
|
||||
"Here we evaluate 10 sets of hyperparameters in total, and concurrently "
|
||||
"evaluate 2 sets at a time."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:218
|
||||
msgid ""
|
||||
"``max_trial_number`` is set to 10 here for a fast example. In real world "
|
||||
"it should be set to a larger number. With default config TPE tuner "
|
||||
"requires 20 trials to warm up."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:222
|
||||
msgid "You may also set ``max_experiment_duration = '1h'`` to limit running time."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:224
|
||||
msgid ""
|
||||
"If neither ``max_trial_number`` nor ``max_experiment_duration`` are set, "
|
||||
"the experiment will run forever until you press Ctrl-C."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:230
|
||||
msgid "Step 4: Run the experiment"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:231
|
||||
msgid ""
|
||||
"Now the experiment is ready. Choose a port and launch it. (Here we use "
|
||||
"port 8080.)"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:233
|
||||
msgid ""
|
||||
"You can use the web portal to view experiment status: "
|
||||
"http://localhost:8080."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:263
|
||||
msgid "After the experiment is done"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:264
|
||||
msgid "Everything is done and it is safe to exit now. The following are optional."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:266
|
||||
msgid ""
|
||||
"If you are using standard Python instead of Jupyter Notebook, you can add"
|
||||
" ``input()`` or ``signal.pause()`` to prevent Python from exiting, "
|
||||
"allowing you to view the web portal after the experiment is done."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:296
|
||||
msgid ""
|
||||
":meth:`nni.experiment.Experiment.stop` is automatically invoked when "
|
||||
"Python exits, so it can be omitted in your code."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:299
|
||||
msgid ""
|
||||
"After the experiment is stopped, you can run "
|
||||
":meth:`nni.experiment.Experiment.view` to restart web portal."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:303
|
||||
msgid ""
|
||||
"This example uses :doc:`Python API </reference/experiment>` to create "
|
||||
"experiment."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:305
|
||||
msgid ""
|
||||
"You can also create and manage experiments with :doc:`command line tool "
|
||||
"</reference/nnictl>`."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:310
|
||||
msgid "**Total running time of the script:** ( 1 minutes 24.393 seconds)"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:325
|
||||
msgid ":download:`Download Python source code: main.py <main.py>`"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:331
|
||||
msgid ":download:`Download Jupyter notebook: main.ipynb <main.ipynb>`"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:13
|
||||
msgid ""
|
||||
"Click :ref:`here "
|
||||
"<sphx_glr_download_tutorials_pruning_quick_start_mnist.py>` to download "
|
||||
"the full example code"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:22
|
||||
msgid "Pruning Quickstart"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:24
|
||||
msgid ""
|
||||
"Model pruning is a technique to reduce the model size and computation by "
|
||||
"reducing model weight size or intermediate state size. There are three "
|
||||
"common practices for pruning a DNN model:"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:27
|
||||
msgid "Pre-training a model -> Pruning the model -> Fine-tuning the pruned model"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:28
|
||||
msgid ""
|
||||
"Pruning a model during training (i.e., pruning aware training) -> Fine-"
|
||||
"tuning the pruned model"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:29
|
||||
msgid "Pruning a model -> Training the pruned model from scratch"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:31
|
||||
msgid ""
|
||||
"NNI supports all of the above pruning practices by working on the key "
|
||||
"pruning stage. Following this tutorial for a quick look at how to use NNI"
|
||||
" to prune a model in a common practice."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:37
|
||||
msgid "Preparation"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:39
|
||||
msgid ""
|
||||
"In this tutorial, we use a simple model and pre-trained on MNIST dataset."
|
||||
" If you are familiar with defining a model and training in pytorch, you "
|
||||
"can skip directly to `Pruning Model`_."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:121
|
||||
msgid "Pruning Model"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:123
|
||||
msgid ""
|
||||
"Using L1NormPruner to prune the model and generate the masks. Usually, a "
|
||||
"pruner requires original model and ``config_list`` as its inputs. "
|
||||
"Detailed about how to write ``config_list`` please refer "
|
||||
":doc:`compression config specification "
|
||||
"<../compression/compression_config_list>`."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:127
|
||||
msgid ""
|
||||
"The following `config_list` means all layers whose type is `Linear` or "
|
||||
"`Conv2d` will be pruned, except the layer named `fc3`, because `fc3` is "
|
||||
"`exclude`. The final sparsity ratio for each layer is 50%. The layer "
|
||||
"named `fc3` will not be pruned."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:153
|
||||
msgid "Pruners usually require `model` and `config_list` as input arguments."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:232
|
||||
msgid ""
|
||||
"Speedup the original model with masks, note that `ModelSpeedup` requires "
|
||||
"an unwrapped model. The model becomes smaller after speedup, and reaches "
|
||||
"a higher sparsity ratio because `ModelSpeedup` will propagate the masks "
|
||||
"across layers."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:269
|
||||
msgid "the model will become real smaller after speedup"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:307
|
||||
msgid "Fine-tuning Compacted Model"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:308
|
||||
msgid ""
|
||||
"Note that if the model has been sped up, you need to re-initialize a new "
|
||||
"optimizer for fine-tuning. Because speedup will replace the masked big "
|
||||
"layers with dense small ones."
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:329
|
||||
msgid "**Total running time of the script:** ( 0 minutes 58.337 seconds)"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:344
|
||||
msgid ""
|
||||
":download:`Download Python source code: pruning_quick_start_mnist.py "
|
||||
"<pruning_quick_start_mnist.py>`"
|
||||
msgstr ""
|
||||
|
||||
#: ../../source/tutorials/pruning_quick_start_mnist.rst:350
|
||||
msgid ""
|
||||
":download:`Download Jupyter notebook: pruning_quick_start_mnist.ipynb "
|
||||
"<pruning_quick_start_mnist.ipynb>`"
|
||||
msgstr ""
|
||||
|
||||
|
|
@ -305,16 +305,31 @@ To contribute a new tutorial, here are the steps to follow:
|
|||
* `How to add images to notebooks <https://sphinx-gallery.github.io/stable/configuration.html#adding-images-to-notebooks>`_.
|
||||
* `How to reference a tutorial in documentation <https://sphinx-gallery.github.io/stable/advanced.html#cross-referencing>`_.
|
||||
|
||||
Chinese translation
|
||||
^^^^^^^^^^^^^^^^^^^
|
||||
Translation (i18n)
|
||||
^^^^^^^^^^^^^^^^^^
|
||||
|
||||
We only maintain `a partial set of documents <https://github.com/microsoft/nni/issues/4298>`_ with Chinese translation. If you intend to contribute more, follow the steps:
|
||||
We only maintain `a partial set of documents <https://github.com/microsoft/nni/issues/4298>`_ with translation. Currently, translation is provided in Simplified Chinese only.
|
||||
|
||||
1. Add a ``xxx_zh.rst`` in the same folder where ``xxx.rst`` exists.
|
||||
2. Run ``python tools/chineselink.py`` under ``docs`` folder, to generate a hash string in your created ``xxx_zh.rst``.
|
||||
3. Don't delete the hash string, add your translation after it.
|
||||
* If you want to update the translation of an existing document, please update messages in ``docs/source/locales``.
|
||||
* If you have updated a translated English document, we require that the corresponding translated documents to be updated (at least the update should be triggered). Please follow these steps:
|
||||
|
||||
In case you modify an English document with Chinese translation already exists, you also need to run ``python tools/chineselink.py`` first to update the hash string, and update the Chinese translation contents accordingly.
|
||||
1. Run ``make i18n`` under ``docs`` folder.
|
||||
2. Verify that there are new messages in ``docs/source/locales``.
|
||||
3. Translate the messages.
|
||||
|
||||
* If you intend to translate a new document:
|
||||
|
||||
1. Update ``docs/source/conf.py`` to make ``gettext_documents`` include your document (probably adding a new regular expression).
|
||||
2. See the steps above.
|
||||
|
||||
|
||||
To build the translated documentation (for example Chinese documentation), please run:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
make -e SPHINXOPTS="-D language='zh'" html
|
||||
|
||||
If you ever encountered problems for translation builds, try to remove the previous build via ``rm -r docs/build/``.
|
||||
|
||||
.. _code-of-conduct:
|
||||
|
||||
|
|
|
|||
|
|
@ -38,8 +38,11 @@ stages:
|
|||
displayName: Sphinx sanity check (Chinese)
|
||||
|
||||
- script: |
|
||||
set -e
|
||||
cd docs
|
||||
python tools/chineselink.py check
|
||||
rm -rf build
|
||||
make i18n
|
||||
git diff --exit-code source/locales
|
||||
displayName: Translation up-to-date
|
||||
|
||||
- script: |
|
||||
|
|
|
|||
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