Merge pull request #4668 from microsoft/doc-refactor

docs/source/training_services.rst → docs/_removed/training_services.rst

@@ -3,13 +3,9 @@ Introduction to NNI Training Services
 
 ..  toctree::
     Overview <./TrainingService/Overview>
-    Local<./TrainingService/LocalMode>
     Remote<./TrainingService/RemoteMachineMode>
     OpenPAI<./TrainingService/PaiMode>
     Kubeflow<./TrainingService/KubeflowMode>
-    AdaptDL<./TrainingService/AdaptDLMode>
     FrameworkController<./TrainingService/FrameworkControllerMode>
-    DLTS<./TrainingService/DLTSMode>
     AML<./TrainingService/AMLMode>
-    PAI-DLC<./TrainingService/DLCMode>
     Hybrid<./TrainingService/HybridMode>

docs/extension/codesnippetcard.py

+"""
+Code snippet card, used in index page.
+"""
+
+from docutils.parsers.rst import Directive, directives
+from docutils.statemachine import StringList
+from docutils import nodes
+
+from sphinx.addnodes import pending_xref
+
+
+CARD_TEMPLATE_HEADER = """
+.. raw:: html
+
+    <div class="codesnippet-card admonition">
+
+    <div class="codesnippet-card-body">
+
+    <div class="codesnippet-card-title-container">
+
+    <div class="codesnippet-card-icon">
+
+.. image:: {icon}
+
+.. raw:: html
+
+    </div>
+
+    <h4>{title}</h4>
+    </div>
+
+"""
+
+CARD_TEMPLATE_FOOTER = """
+.. raw:: html
+
+    </div>
+"""
+
+CARD_TEMPLATE_LINK_CONTAINER_HEADER = """
+.. raw:: html
+
+    <div class="codesnippet-card-footer">
+"""
+
+CARD_TEMPLATE_LINK = """
+.. raw:: html
+
+    <div class="codesnippet-card-link">
+    {seemore}
+    <span class="material-icons right">arrow_forward</span>
+    </div>
+"""
+
+
+class CodeSnippetCardDirective(Directive):
+    option_spec = {
+        'icon': directives.unchanged,
+        'title': directives.unchanged,
+        'link': directives.unchanged,
+        'seemore': directives.unchanged,
+    }
+
+    has_content = True
+
+    def run(self):
+        anchor_node = nodes.paragraph()
+
+        try:
+            title = self.options['title']
+            link = directives.uri(self.options['link'])
+            icon = directives.uri(self.options['icon'])
+            seemore = self.options.get('seemore', 'For a full tutorial, please go here.')
+        except ValueError as e:
+            print(e)
+            raise
+
+        # header, title, icon...
+        card_rst = CARD_TEMPLATE_HEADER.format(title=title, icon=icon)
+        card_list = StringList(card_rst.split('\n'))
+        self.state.nested_parse(card_list, self.content_offset, anchor_node)
+
+        # code snippet
+        self.state.nested_parse(self.content, self.content_offset, anchor_node)
+
+        # close body
+        self.state.nested_parse(StringList(CARD_TEMPLATE_FOOTER.split('\n')), self.content_offset, anchor_node)
+
+        # start footer
+        self.state.nested_parse(StringList(CARD_TEMPLATE_LINK_CONTAINER_HEADER.split('\n')), self.content_offset, anchor_node)
+
+        # full tutorial link
+        link_node = pending_xref(CARD_TEMPLATE_LINK,
+                                 reftype='doc',
+                                 refdomain='std',
+                                 reftarget=link,
+                                 refexplicit=False,
+                                 refwarn=True,
+                                 refkeepformat=True)
+        # refkeepformat is handled in `patch_autodoc.py`
+        self.state.nested_parse(StringList(CARD_TEMPLATE_LINK.format(seemore=seemore).split('\n')), self.content_offset, link_node)
+        anchor_node += link_node
+
+        # close footer
+        self.state.nested_parse(StringList(CARD_TEMPLATE_FOOTER.split('\n')), self.content_offset, anchor_node)
+
+        # close whole
+        self.state.nested_parse(StringList(CARD_TEMPLATE_FOOTER.split('\n')), self.content_offset, anchor_node)
+
+        return [anchor_node]
+
+
+def setup(app):
+    app.add_directive('codesnippetcard', CodeSnippetCardDirective)

docs/extension/patch_autodoc.py

+"""Hack autodoc to get more fine-grained docstring rendering contol.
+
+autodoc and autosummary didn't expose many of their controls to sphinx users via config.
+To customize them, the "correct" approach seems to copy and paste all their code and rewrite some part.
+To avoid doing this, I monkey-patched some of the functions to keep the changes minimal.
+
+Note that some of them are related to sphinx internal APIs, which can be broken when sphinx got upgraded.
+Try to keep them updated, or pin to a particular sphinx version.
+"""
+
+import inspect
+import os
+from typing import List, Tuple, List
+
+import sphinx
+from docutils import nodes
+from docutils.nodes import Node
+
+
+class ClassNewBlacklistPatch:
+    """Force some classes to skip ``__new__`` when generating signature."""
+
+    original = None
+
+    def restore(self, *args, **kwargs):
+        assert self.original is not None
+        sphinx.ext.autodoc._CLASS_NEW_BLACKLIST = self.original
+
+    def patch(self, *args, **kwargs):
+        self.original = sphinx.ext.autodoc._CLASS_NEW_BLACKLIST
+
+        blacklist = []
+
+        import nni.retiarii.nn.pytorch
+        for name in dir(nni.retiarii.nn.pytorch):
+            obj = getattr(nni.retiarii.nn.pytorch, name)
+            if inspect.isclass(obj):
+                new_name = "{0.__module__}.{0.__qualname__}".format(obj.__new__)
+                if new_name not in blacklist:
+                    blacklist.append(new_name)
+
+        sphinx.ext.autodoc._CLASS_NEW_BLACKLIST = self.original + blacklist
+
+
+def disable_trace_patch(*args, **kwargs):
+    """Disable trace by setting an environment variable."""
+    os.environ['NNI_TRACE_FLAG'] = 'DISABLE'
+
+
+def trial_tool_import_patch(*args, **kwargs):
+    """Insert dummy trial tool variable to ensure trial_tool can be imported.
+    See nni/tools/trial_tool/constants.py
+    """
+    os.environ.update({
+        'NNI_OUTPUT_DIR': '/tmp',
+        'NNI_PLATFORM': 'unittest',
+        'NNI_SYS_DIR': '/tmp',
+        'NNI_TRIAL_JOB_ID': 'dummy',
+        'NNI_EXP_ID': 'dummy',
+        'MULTI_PHASE': 'dummy'
+    })
+
+
+class AutoSummaryPatch:
+    """Ignore certain files as they are completely un-importable. It patches:
+
+    - find_autosummary_in_files: Some modules cannot be imported at all due to dependency issues or some special design.
+      They need to skipped when running autosummary generate.
+    - Autosummary.get_table: The original autosummary creates an index for each module, and the module links in autosummary table
+      points to the corresponding generated module page (by using ``:py:module:xxx``). This doesn't work for us,
+      because we have used automodule else (other than autosummary) in our docs, and to avoid duplicate index,
+      we have to set ``:noindex:`` in autosummary template (see docs/templates/autosummary/module.rst).
+      This breaks most of the links, where they fail to link to generated module page by using index.
+      We here update the python domain role, to a general domain role (``:doc:``), and link to the page directly.
+    """
+
+    find_autosummary_original = None
+    get_table_original = None
+
+    def restore(self, *args, **kwargs):
+        assert self.find_autosummary_original is not None and self.get_table_original is not None
+        sphinx.ext.autosummary.generate.find_autosummary_in_files = self.find_autosummary_original
+        sphinx.ext.autosummary.Autosummary.get_table = self.get_table_original
+
+    def patch(self, app, config):
+        from sphinx.ext.autosummary import Autosummary
+        from sphinx.ext.autosummary.generate import AutosummaryEntry
+
+        self.find_autosummary_original = sphinx.ext.autosummary.generate.find_autosummary_in_files
+        self.get_table_original = Autosummary.get_table
+
+        def find_autosummary_in_files(filenames: List[str]) -> List[AutosummaryEntry]:
+            items: List[AutosummaryEntry] = self.find_autosummary_original(filenames)
+            items = [item for item in items if item.name not in config.autosummary_mock_imports]
+            return items
+
+        def get_table(autosummary, items: List[Tuple[str, str, str, str]]) -> List[Node]:
+            col_spec, autosummary_table = self.get_table_original(autosummary, items)
+            if 'toctree' in autosummary.options:
+                # probably within modules
+                table = autosummary_table[0]
+                tgroup = table[0]
+                tbody = tgroup[-1]
+                for row in tbody:
+                    entry = row[0]
+                    paragraph = entry[0]
+                    pending_xref = paragraph[0]
+
+                    # get the reference path and check whether it has been generated
+                    # if path to reference is changed, this should also be changed
+                    reftarget_path = 'reference/_modules/' + pending_xref['reftarget']
+
+                    if reftarget_path in autosummary.env.found_docs:
+                        # make :py:obj:`xxx` looks like a :doc:`xxx`
+                        pending_xref['refdomain'] = 'std'
+                        pending_xref['reftype'] = 'doc'
+                        pending_xref['refexplicit'] = False
+                        pending_xref['refwarn'] = True
+                        pending_xref['reftarget'] = '/' + reftarget_path
+                        # a special tag to enable `ResolveDocPatch`
+                        pending_xref['refkeepformat'] = True
+
+            return [col_spec, autosummary_table]
+
+        sphinx.ext.autosummary.generate.find_autosummary_in_files = find_autosummary_in_files
+        sphinx.ext.autosummary.Autosummary.get_table = get_table
+
+
+class ResolveDocPatch:
+    """Original :doc: role throws away all the format, and keep raw text only.
+    We wish to keep module names literal. This patch is to keep literal format in :doc: resolver."""
+
+    original = None
+
+    def restore(self, *args, **kwargs):
+        assert self.original is not None
+        sphinx.domains.std.StandardDomain._resolve_doc_xref = self.original
+
+    def patch(self, *args, **kwargs):
+        self.original = sphinx.domains.std.StandardDomain._resolve_doc_xref
+
+        def doc_xref_resolver(std_domain, env, fromdocname, builder, typ, target, node, contnode):
+            if not node.get('refkeepformat'):
+                # redirect to original implementation to make it safer
+                return self.original(std_domain, env, fromdocname, builder, typ, target, node, contnode)
+
+            # directly reference to document by source name; can be absolute or relative
+            from sphinx.domains.std import docname_join, make_refnode
+            refdoc = node.get('refdoc', fromdocname)
+            docname = docname_join(refdoc, node['reftarget'])
+            if docname not in env.all_docs:
+                return None
+            else:
+                innernode = node[0]  # no astext here, to keep literal intact
+                return make_refnode(builder, fromdocname, docname, None, innernode)
+
+        sphinx.domains.std.StandardDomain._resolve_doc_xref = doc_xref_resolver
+
+
+def setup(app):
+    # See life-cycle of sphinx app here:
+    # https://www.sphinx-doc.org/en/master/extdev/appapi.html#sphinx-core-events
+    patch = ClassNewBlacklistPatch()
+    app.connect('env-before-read-docs', patch.patch)
+    app.connect('env-merge-info', patch.restore)
+
+    patch = ResolveDocPatch()
+    app.connect('env-before-read-docs', patch.patch)
+    app.connect('env-merge-info', patch.restore)
+
+    app.connect('env-before-read-docs', disable_trace_patch)
+
+    # autosummary generate happens at builder-inited
+    app.connect('config-inited', trial_tool_import_patch)
+
+    autosummary_patch = AutoSummaryPatch()
+    app.connect('config-inited', autosummary_patch.patch)
+    app.connect('env-merge-info', autosummary_patch.restore)

docs/extension/tutorial_links.py

+"""Creating hard links for tutorials in each individual topics."""
+
+import os
+import re
+
+HEADER = """.. THIS FILE IS A COPY OF {} WITH MODIFICATIONS.
+.. TO MAKE ONE TUTORIAL APPEAR IN MULTIPLE PLACES.
+
+"""
+
+def flatten_filename(filename):
+    return filename.replace('/', '_').replace('.', '_')
+
+def copy_tutorials(app):
+    # TODO: use sphinx logger
+    print('[tutorial links] copy tutorials...')
+    for src, tar in app.config.tutorials_copy_list:
+        target_path = os.path.join(app.srcdir, tar)
+        content = open(os.path.join(app.srcdir, src)).read()
+
+        # Add a header
+        content = HEADER.format(src) + content
+
+        # Add a prefix to labels to avoid duplicates.
+        label_map = {}
+
+        # find all anchors:   https://www.sphinx-doc.org/en/master/usage/restructuredtext/roles.html
+        # but not hyperlinks: https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html#external-links
+        for prefix, label_name in list(re.findall(r'(\.\.\s*_)(.*?)\:\s*\n', content)):
+            label_map[label_name] = flatten_filename(tar) + '_' + label_name
+            # anchor
+            content = content.replace(prefix + label_name + ':', prefix + label_map[label_name] + ':')
+            # :ref:`xxx`
+            content = content.replace(f':ref:`{label_name}`', f':ref:`{label_map[label_name]}')
+            # :ref:`yyy <xxx>`
+            content = re.sub(r"(\:ref\:`.*?\<)" + label_name + r"(\>`)", r'\1' + label_map[label_name] + r'\2', content)
+
+        open(target_path, 'w').write(content)
+
+
+def setup(app):
+    # See life-cycle of sphinx app here:
+    # https://www.sphinx-doc.org/en/master/extdev/appapi.html#sphinx-core-events
+    app.connect('builder-inited', copy_tutorials)
+    app.add_config_value('tutorials_copy_list', [], True, [list])

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docs/source/Assessor/BuiltinAssessor.rst

-.. role:: raw-html(raw)
-   :format: html
-
-
-Built-in Assessors
-==================
-
-NNI provides state-of-the-art tuning algorithms within our builtin-assessors and makes them easy to use. Below is a brief overview of NNI's current builtin Assessors.
-
-Note: Click the **Assessor's name** to get each Assessor's installation requirements, suggested usage scenario, and a config example. A link to a detailed description of each algorithm is provided at the end of the suggested scenario for each Assessor.
-
-Currently, we support the following Assessors:
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - Assessor
-     - Brief Introduction of Algorithm
-   * - `Medianstop <#MedianStop>`__
-     - Medianstop is a simple early stopping rule. It stops a pending trial X at step S if the trial’s best objective value by step S is strictly worse than the median value of the running averages of all completed trials’ objectives reported up to step S. `Reference Paper <https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/46180.pdf>`__
-   * - `Curvefitting <#Curvefitting>`__
-     - Curve Fitting Assessor is an LPA (learning, predicting, assessing) algorithm. It stops a pending trial X at step S if the prediction of the final epoch's performance worse than the best final performance in the trial history. In this algorithm, we use 12 curves to fit the accuracy curve. `Reference Paper <http://aad.informatik.uni-freiburg.de/papers/15-IJCAI-Extrapolation_of_Learning_Curves.pdf>`__
-
-
-Usage of Builtin Assessors
---------------------------
-
-Usage of builtin assessors provided by the NNI SDK requires one to declare the  **builtinAssessorName** and **classArgs** in the ``config.yml`` file. In this part, we will introduce the details of usage and the suggested scenarios, classArg requirements, and an example for each assessor.
-
-Note: Please follow the provided format when writing your ``config.yml`` file.
-
-:raw-html:`<a name="MedianStop"></a>`
-
-Median Stop Assessor
-^^^^^^^^^^^^^^^^^^^^
-
-..
-
-   Builtin Assessor Name: **Medianstop**
-
-
-**Suggested scenario**
-
-It's applicable in a wide range of performance curves, thus, it can be used in various scenarios to speed up the tuning progress. `Detailed Description <./MedianstopAssessor.rst>`__
-
-**classArgs requirements:**
-
-
-* **optimize_mode** (*maximize or minimize, optional, default = maximize*\ ) - If 'maximize', assessor will **stop** the trial with smaller expectation. If 'minimize', assessor will **stop** the trial with larger expectation.
-* **start_step** (*int, optional, default = 0*\ ) - A trial is determined to be stopped or not only after receiving start_step number of reported intermediate results.
-
-**Usage example:**
-
-.. code-block:: yaml
-
-   # config.yml
-   assessor:
-       builtinAssessorName: Medianstop
-       classArgs:
-         optimize_mode: maximize
-         start_step: 5
-
-:raw-html:`<br>`
-
-:raw-html:`<a name="Curvefitting"></a>`
-
-Curve Fitting Assessor
-^^^^^^^^^^^^^^^^^^^^^^
-
-..
-
-   Builtin Assessor Name: **Curvefitting**
-
-
-**Suggested scenario**
-
-It's applicable in a wide range of performance curves, thus, it can be used in various scenarios to speed up the tuning progress. Even better, it's able to handle and assess curves with similar performance. `Detailed Description <./CurvefittingAssessor.rst>`__
-
-**Note**\ , according to the original paper, only incremental functions are supported. Therefore this assessor can only be used to maximize optimization metrics. For example, it can be used for accuracy, but not for loss.
-
-**classArgs requirements:**
-
-
-* **epoch_num** (*int,** required***\ ) - The total number of epochs. We need to know the number of epochs to determine which points we need to predict.
-* **start_step** (*int, optional, default = 6*\ ) - A trial is determined to be stopped or not only after receiving start_step number of reported intermediate results.
-* **threshold** (*float, optional, default = 0.95*\ ) - The threshold that we use to decide to early stop the worst performance curve. For example: if threshold = 0.95, and the best performance in the history is 0.9, then we will stop the trial who's predicted value is lower than 0.95 * 0.9 = 0.855.
-* **gap** (*int, optional, default = 1*\ ) - The gap interval between Assessor judgements. For example: if gap = 2, start_step = 6, then we will assess the result when we get 6, 8, 10, 12...intermediate results.
-
-**Usage example:**
-
-.. code-block:: yaml
-
-   # config.yml
-   assessor:
-       builtinAssessorName: Curvefitting
-       classArgs:
-         epoch_num: 20
-         start_step: 6
-         threshold: 0.95
-         gap: 1

docs/source/Assessor/CustomizeAssessor.rst

-Customize Assessor
-==================
-
-NNI supports to build an assessor by yourself for tuning demand.
-
-If you want to implement a customized Assessor, there are three things to do:
-
-
-#. Inherit the base Assessor class
-#. Implement assess_trial function
-#. Configure your customized Assessor in experiment YAML config file
-
-**1. Inherit the base Assessor class**
-
-.. code-block:: python
-
-   from nni.assessor import Assessor
-
-   class CustomizedAssessor(Assessor):
-       def __init__(self, ...):
-           ...
-
-**2. Implement assess trial function**
-
-.. code-block:: python
-
-   from nni.assessor import Assessor, AssessResult
-
-   class CustomizedAssessor(Assessor):
-       def __init__(self, ...):
-           ...
-
-       def assess_trial(self, trial_history):
-           """
-           Determines whether a trial should be killed. Must override.
-           trial_history: a list of intermediate result objects.
-           Returns AssessResult.Good or AssessResult.Bad.
-           """
-           # you code implement here.
-           ...
-
-**3. Configure your customized Assessor in experiment YAML config file**
-
-NNI needs to locate your customized Assessor class and instantiate the class, so you need to specify the location of the customized Assessor class and pass literal values as parameters to the __init__ constructor.
-
-.. code-block:: yaml
-
-   assessor:
-     codeDir: /home/abc/myassessor
-     classFileName: my_customized_assessor.py
-     className: CustomizedAssessor
-     # Any parameter need to pass to your Assessor class __init__ constructor
-     # can be specified in this optional classArgs field, for example
-     classArgs:
-       arg1: value1
-
-Please noted in **2**. The object ``trial_history`` are exact the object that Trial send to Assessor by using SDK ``report_intermediate_result`` function.
-
-The working directory of your assessor is ``<home>/nni-experiments/<experiment_id>/log``\ , which can be retrieved with environment variable ``NNI_LOG_DIRECTORY``\ ,
-
-More detail example you could see:
-
-* :githublink:`medianstop-assessor <nni/algorithms/hpo/medianstop_assessor.py>`
-* :githublink:`curvefitting-assessor <nni/algorithms/hpo/curvefitting_assessor/>`
-

docs/source/Assessor/MedianstopAssessor.rst

-Medianstop Assessor on NNI
-==========================
-
-Median Stop
------------
-
-Medianstop is a simple early stopping rule mentioned in this `paper <https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/46180.pdf>`__. It stops a pending trial X after step S if the trial’s best objective value by step S is strictly worse than the median value of the running averages of all completed trials’ objectives reported up to step S.