diff --git a/dependencies/develop.txt b/dependencies/develop.txt
index 6d9ad93f2..0e87993cb 100644
--- a/dependencies/develop.txt
+++ b/dependencies/develop.txt
@@ -15,6 +15,7 @@ sphinx >= 4.5
 sphinx-argparse-nni >= 0.4.0
 sphinx-copybutton
 sphinx-gallery
+sphinx-intl
 sphinx-tabs
 sphinxcontrib-bibtex
 git+https://github.com/bashtage/sphinx-material@6e0ef82#egg=sphinx_material
diff --git a/docs/.gitignore b/docs/.gitignore
index e9c3da3a7..522de2ff5 100644
--- a/docs/.gitignore
+++ b/docs/.gitignore
@@ -8,3 +8,6 @@ _build/
 
 # auto-generated reference table
 _modules/
+
+# Machine-style translation files
+*.mo
diff --git a/docs/Makefile b/docs/Makefile
index ba501f6f5..1d14ccfe7 100644
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -11,6 +11,11 @@ BUILDDIR      = build
 help:
 	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
 
+# Build message catelogs for translation
+i18n:
+	@$(SPHINXBUILD) -M getpartialtext "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+	sphinx-intl update -p "$(BUILDDIR)/getpartialtext" -d "$(SOURCEDIR)/locales" -l zh
+
 .PHONY: help Makefile
 
 # Catch-all target: route all unknown targets to Sphinx using the new
diff --git a/docs/extension/getpartialtext.py b/docs/extension/getpartialtext.py
new file mode 100644
index 000000000..5f9ee2adf
--- /dev/null
+++ b/docs/extension/getpartialtext.py
@@ -0,0 +1,30 @@
+"""
+Basically same as
+`sphinx gettext buidler <https://www.sphinx-doc.org/en/master/_modules/sphinx/builders/gettext.html>`_,
+but only get texts from files in a whitelist.
+"""
+
+import re
+
+from docutils import nodes
+from sphinx.application import Sphinx
+from sphinx.builders.gettext import MessageCatalogBuilder
+
+
+class PartialMessageCatalogBuilder(MessageCatalogBuilder):
+    name = 'getpartialtext'
+
+    def init(self):
+        super().init()
+
+        self.whitelist_docs = [re.compile(x) for x in self.config.gettext_documents]
+
+    def write_doc(self, docname: str, doctree: nodes.document) -> None:
+        for doc_re in self.whitelist_docs:
+            if doc_re.match(docname):
+                return super().write_doc(docname, doctree)
+
+
+def setup(app: Sphinx):
+    app.add_builder(PartialMessageCatalogBuilder)
+    app.add_config_value('gettext_documents', [], 'gettext')
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 168ad7597..845069b2c 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -61,6 +61,7 @@ extensions = [
 
     # Custom extensions in extension/ folder.
     'tutorial_links',  # this has to be after sphinx-gallery
+    'getpartialtext',
     'inplace_translation',
     'cardlinkitem',
     'codesnippetcard',
@@ -186,6 +187,18 @@ master_doc = 'index'
 # Usually you set "language" from the command line for these cases.
 language = None
 
+# Translation related settings
+locale_dir = ['locales']
+
+# Documents that requires translation: https://github.com/microsoft/nni/issues/4298
+gettext_documents = [
+    r'^index$',
+    r'^quickstart$',
+    r'^installation$',
+    r'^(nas|hpo|compression)/overview$',
+    r'^tutorials/(hello_nas|pruning_quick_start_mnist|hpo_quickstart_pytorch/main)$',
+]
+
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
diff --git a/docs/source/locales/zh/LC_MESSAGES/compression.po b/docs/source/locales/zh/LC_MESSAGES/compression.po
new file mode 100644
index 000000000..0ac8fc277
--- /dev/null
+++ b/docs/source/locales/zh/LC_MESSAGES/compression.po
@@ -0,0 +1,144 @@
+# 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/compression/overview.rst:2
+msgid "Overview of NNI Model Compression"
+msgstr ""
+
+#: ../../source/compression/overview.rst:4
+msgid ""
+"Deep neural networks (DNNs) have achieved great success in many tasks "
+"like computer vision, nature launguage processing, speech processing. "
+"However, typical neural networks are both computationally expensive and "
+"energy-intensive, which can be difficult to be deployed on devices with "
+"low computation resources or with strict latency requirements. Therefore,"
+" a natural thought is to perform model compression to reduce model size "
+"and accelerate model training/inference without losing performance "
+"significantly. Model compression techniques can be divided into two "
+"categories: pruning and quantization. The pruning methods explore the "
+"redundancy in the model weights and try to remove/prune the redundant and"
+" uncritical weights. Quantization refers to compress models by reducing "
+"the number of bits required to represent weights or activations. We "
+"further elaborate on the two methods, pruning and quantization, in the "
+"following chapters. Besides, the figure below visualizes the difference "
+"between these two methods."
+msgstr ""
+
+#: ../../source/compression/overview.rst:19
+msgid ""
+"NNI provides an easy-to-use toolkit to help users design and use model "
+"pruning and quantization algorithms. For users to compress their models, "
+"they only need to add several lines in their code. There are some popular"
+" model compression algorithms built-in in NNI. On the other hand, users "
+"could easily customize their new compression algorithms using NNI’s "
+"interface."
+msgstr ""
+
+#: ../../source/compression/overview.rst:24
+msgid "There are several core features supported by NNI model compression:"
+msgstr ""
+
+#: ../../source/compression/overview.rst:26
+msgid "Support many popular pruning and quantization algorithms."
+msgstr ""
+
+#: ../../source/compression/overview.rst:27
+msgid ""
+"Automate model pruning and quantization process with state-of-the-art "
+"strategies and NNI's auto tuning power."
+msgstr ""
+
+#: ../../source/compression/overview.rst:28
+msgid ""
+"Speedup a compressed model to make it have lower inference latency and "
+"also make it smaller."
+msgstr ""
+
+#: ../../source/compression/overview.rst:29
+msgid ""
+"Provide friendly and easy-to-use compression utilities for users to dive "
+"into the compression process and results."
+msgstr ""
+
+#: ../../source/compression/overview.rst:30
+msgid "Concise interface for users to customize their own compression algorithms."
+msgstr ""
+
+#: ../../source/compression/overview.rst:34
+msgid "Compression Pipeline"
+msgstr ""
+
+#: ../../source/compression/overview.rst:42
+msgid ""
+"The overall compression pipeline in NNI is shown above. For compressing a"
+" pretrained model, pruning and quantization can be used alone or in "
+"combination. If users want to apply both, a sequential mode is "
+"recommended as common practise."
+msgstr ""
+
+#: ../../source/compression/overview.rst:46
+msgid ""
+"Note that NNI pruners or quantizers are not meant to physically compact "
+"the model but for simulating the compression effect. Whereas NNI speedup "
+"tool can truly compress model by changing the network architecture and "
+"therefore reduce latency. To obtain a truly compact model, users should "
+"conduct :doc:`pruning speedup <../tutorials/pruning_speedup>` or "
+":doc:`quantizaiton speedup <../tutorials/quantization_speedup>`. The "
+"interface and APIs are unified for both PyTorch and TensorFlow. Currently"
+" only PyTorch version has been supported, and TensorFlow version will be "
+"supported in future."
+msgstr ""
+
+#: ../../source/compression/overview.rst:52
+msgid "Model Speedup"
+msgstr ""
+
+#: ../../source/compression/overview.rst:54
+msgid ""
+"The final goal of model compression is to reduce inference latency and "
+"model size. However, existing model compression algorithms mainly use "
+"simulation to check the performance (e.g., accuracy) of compressed model."
+" For example, using masks for pruning algorithms, and storing quantized "
+"values still in float32 for quantization algorithms. Given the output "
+"masks and quantization bits produced by those algorithms, NNI can really "
+"speedup the model."
+msgstr ""
+
+#: ../../source/compression/overview.rst:59
+msgid "The following figure shows how NNI prunes and speeds up your models."
+msgstr ""
+
+#: ../../source/compression/overview.rst:67
+msgid ""
+"The detailed tutorial of Speedup Model with Mask can be found :doc:`here "
+"<../tutorials/pruning_speedup>`. The detailed tutorial of Speedup Model "
+"with Calibration Config can be found :doc:`here "
+"<../tutorials/quantization_speedup>`."
+msgstr ""
+
+#: ../../source/compression/overview.rst:72
+msgid ""
+"NNI's model pruning framework has been upgraded to a more powerful "
+"version (named pruning v2 before nni v2.6). The old version (`named "
+"pruning before nni v2.6 "
+"<https://nni.readthedocs.io/en/v2.6/Compression/pruning.html>`_) will be "
+"out of maintenance. If for some reason you have to use the old pruning, "
+"v2.6 is the last nni version to support old pruning version."
+msgstr ""
+
diff --git a/docs/source/locales/zh/LC_MESSAGES/hpo.po b/docs/source/locales/zh/LC_MESSAGES/hpo.po
new file mode 100644
index 000000000..21df27dd2
--- /dev/null
+++ b/docs/source/locales/zh/LC_MESSAGES/hpo.po
@@ -0,0 +1,207 @@
+# 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 ""
+
diff --git a/docs/source/locales/zh/LC_MESSAGES/index.po b/docs/source/locales/zh/LC_MESSAGES/index.po
new file mode 100644
index 000000000..cf14e9b64
--- /dev/null
+++ b/docs/source/locales/zh/LC_MESSAGES/index.po
@@ -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 ""
+
diff --git a/docs/source/locales/zh/LC_MESSAGES/installation.po b/docs/source/locales/zh/LC_MESSAGES/installation.po
new file mode 100644
index 000000000..8960bf0f9
--- /dev/null
+++ b/docs/source/locales/zh/LC_MESSAGES/installation.po
@@ -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 ""
+
diff --git a/docs/source/locales/zh/LC_MESSAGES/nas.po b/docs/source/locales/zh/LC_MESSAGES/nas.po
new file mode 100644
index 000000000..44ecbb2e3
--- /dev/null
+++ b/docs/source/locales/zh/LC_MESSAGES/nas.po
@@ -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 ""
+
diff --git a/docs/source/locales/zh/LC_MESSAGES/quickstart.po b/docs/source/locales/zh/LC_MESSAGES/quickstart.po
new file mode 100644
index 000000000..38bf53498
--- /dev/null
+++ b/docs/source/locales/zh/LC_MESSAGES/quickstart.po
@@ -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 ""
+
diff --git a/docs/source/locales/zh/LC_MESSAGES/sphinx.po b/docs/source/locales/zh/LC_MESSAGES/sphinx.po
new file mode 100644
index 000000000..b059911f1
--- /dev/null
+++ b/docs/source/locales/zh/LC_MESSAGES/sphinx.po
@@ -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 ""
+
diff --git a/docs/source/locales/zh/LC_MESSAGES/tutorials.po b/docs/source/locales/zh/LC_MESSAGES/tutorials.po
new file mode 100644
index 000000000..e5ce19d0c
--- /dev/null
+++ b/docs/source/locales/zh/LC_MESSAGES/tutorials.po
@@ -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 ""
+
diff --git a/docs/source/notes/contributing.rst b/docs/source/notes/contributing.rst
index bc406fce0..73ecd54c6 100644
--- a/docs/source/notes/contributing.rst
+++ b/docs/source/notes/contributing.rst
@@ -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:
 
diff --git a/pipelines/fast-test.yml b/pipelines/fast-test.yml
index 681e79eaf..c8ad37529 100644
--- a/pipelines/fast-test.yml
+++ b/pipelines/fast-test.yml
@@ -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: |