open-edge-platform · eugene123tw · Aug 5, 2025 · Aug 6, 2025 · Aug 6, 2025 · Aug 6, 2025
@@ -1,19 +1,21 @@
-name: Build Docs for the latest
+# This workflow is purely for validating the docs build.
+name: Build Docs Validation
 
 on:
   workflow_dispatch: # run on request (no need for PR)
-  push:
-    branches:
-      - develop
+  pull_request:
+    paths:
+      - "docs/**"
+      - "src/**"
+      - "pyproject.toml"
+      - ".readthedocs.yaml"
 
 # Declare default permissions as read only.
 permissions: read-all
 
 jobs:
   Build-Docs:
     runs-on: ubuntu-24.04
-    permissions:
-      contents: write
     steps:
       - name: Checkout repository
         uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
@@ -26,54 +28,7 @@ jobs:
           pip install '.[ci_tox]'
       - name: Build-Docs
         run: tox -e build-doc
-      - name: Create gh-pages branch
+      - name: Check docs build
         run: |
-          if [[ ${{github.event_name}} == 'workflow_dispatch' ]]; then
-            echo RELEASE_VERSION="test_build" >> $GITHUB_ENV
-          else
-            echo RELEASE_VERSION=${GITHUB_REF#refs/*/} >> $GITHUB_ENV
-          fi
-          echo SOURCE_NAME=${GITHUB_REF#refs/*/} >> $GITHUB_OUTPUT
-          echo SOURCE_BRANCH=${GITHUB_REF#refs/heads/} >> $GITHUB_OUTPUT
-          echo SOURCE_TAG=${GITHUB_REF#refs/tags/} >> $GITHUB_OUTPUT
-
-          existed_in_remote=$(git ls-remote --heads origin gh-pages)
-
-          if [[ -z ${existed_in_remote} ]]; then
-            echo "Creating gh-pages branch"
-            git config --local user.email "[email protected]"
-            git config --local user.name "GitHub Action"
-            git checkout --orphan gh-pages
-            git reset --hard
-            touch .nojekyll
-            git add .nojekyll
-            git commit -m "Initializing gh-pages branch"
-            git push origin gh-pages
-            git checkout ${{steps.branch_name.outputs.SOURCE_NAME}}
-            echo "Created gh-pages branch"
-          else
-            echo "Branch gh-pages already exists"
-          fi
-      - name: Commit docs to gh-pages branch
-        run: |
-          git fetch
-          git checkout gh-pages
-          mkdir -p /tmp/docs_build
-          cp -r docs/build/html/* /tmp/docs_build/
-          rm -rf ${{ env.RELEASE_VERSION }}/*
-          echo '<html><head><meta http-equiv="refresh" content="0; url=stable/" /></head></html>' > index.html
-          mkdir -p ${{ env.RELEASE_VERSION }}
-          cp -r /tmp/docs_build/* ./${{ env.RELEASE_VERSION }}
-          rm -rf /tmp/docs_build
-          git config --local user.email "[email protected]"
-          git config --local user.name "GitHub Action"
-          if [[ ${{ env.RELEASE_VERSION }} != 'test_build' ]]; then
-            ln -sfn ${{ env.RELEASE_VERSION }} latest
-          fi
-          git add ./latest ${{ env.RELEASE_VERSION }}
-          git commit -m "Update documentation" -a || true
-      - name: Push changes
-        uses: ad-m/github-push-action@fcea09907c44d7a7a3331c9c04080d55d87c95fe # master
-        with:
-          github_token: ${{ secrets.GITHUB_TOKEN }}
-          branch: gh-pages
+          echo "Documentation built successfully!"
+          echo "Check docs/build/html/index.html for local preview"
@@ -0,0 +1,24 @@
+# ReadTheDocs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+version: 2
+
+# Set the OS, Python version and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.12"
+
+# Build documentation in the "docs/" directory with Sphinx
+sphinx:
+  configuration: docs/source/conf.py
+
+# Declare the Python requirements required to build your documentation
+python:
+  install:
+    - method: pip
+      path: .
+      extra_requirements:
+        - docs
+# Support for multiple versions (this is one of the main benefits of ReadTheDocs)
+# You can configure this later in the ReadTheDocs dashboard
@@ -1,15 +1,14 @@
-# Configuration file for the Sphinx documentation builder.
-#
-# This file only contains a selection of the most common options. For a full
-# list see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
+"""Configuration file for the Sphinx documentation builder.
 
-# -- Path setup -------------------------------------------------------------- #
+For the full list of built-in configuration values, see the documentation:
+https://www.sphinx-doc.org/en/master/usage/configuration.html
 
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
+-- Project information -----------------------------------------------------
+https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+"""
+
+# Copyright (C) 2022-2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
 
 import os
 import sys
@@ -44,7 +43,6 @@
     "sphinx_design",
     "myst_parser",  # Enhanced markdown support
     "sphinx.ext.todo",  # Support for TODO items
-    "sphinx.ext.githubpages",  # GitHub Pages support
     "sphinx.ext.coverage",  # Documentation coverage check
 ]
 
@@ -58,11 +56,6 @@
     "tasklist",
 ]
 
-source_suffix = {
-    ".rst": "restructuredtext",
-    ".md": "markdown",
-}
-
 suppress_warnings = [
     "ref.python",
     "autosectionlabel.*",
@@ -80,37 +73,10 @@
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_theme = "pydata_sphinx_theme"
+html_theme = "sphinx_book_theme"
 html_static_path = ["_static"]
-
-# Show source link & copyright
-html_show_sourcelink = True
-html_show_sphinx = False
-html_show_copyright = True
-html_copy_source = True
-
-
-html_theme_options = {
-    "navbar_center": [],
-    "navbar_end": ["search-field.html", "theme-switcher.html", "navbar-icon-links.html"],
-    "search_bar_text": "Search",
-    "logo": {
-        "image_light": "logos/otx-logo.png",
-        "image_dark": "logos/otx-logo.png",
-    },
-    "icon_links": [
-        {
-            "name": "GitHub",
-            "url": "https://github.com/open-edge-platform/training_extensions",
-            "icon": "fab fa-github",
-            "type": "fontawesome",
-        },
-    ],
-    "use_edit_page_button": True,
-    "show_nav_level": 3,
-    "navigation_depth": 6,
-    "show_toc_level": 3,
-}
+html_logo = "_static/logos/otx-logo.png"
+html_favicon = "_static/logos/geti-favicon-64.png"
 
 html_context = {
     "github_user": "open-edge-platform",
@@ -119,10 +85,6 @@
     "doc_path": "docs/source/",
 }
 
-html_css_files = [
-    "css/custom.css",
-]
-
 # -- Extension configuration -------------------------------------------------
 autodoc_docstring_signature = True
 autodoc_member_order = "bysource"

@@ -77,15 +77,15 @@ Models
 ******
 As mentioned above, the goal of visual anomaly detection is to learn a representation of normal behaviour in the data and then identify instances that deviate from this normal behaviour. OpenVINO Training Extensions supports several deep learning approaches to this task, including the following:
 
-+--------+-------------------------------------------------------------------------------------------------------------------+----------------------+-----------------+
-| Name   | Recipe                                                                                                            | Complexity (GFLOPs)  | Model size (MB) |
-+========+===================================================================================================================+======================+=================+
++--------+----------------------------------------------------------------------------------------------------------------------+----------------------+-----------------+
+| Name   | Recipe                                                                                                               | Complexity (GFLOPs)  | Model size (MB) |
++========+======================================================================================================================+======================+=================+
 | PADIM  | `padim <https://github.com/open-edge-platform/training_extensions/blob/develop/src/otx/recipe/anomaly_/padim.yaml>`_ | 3.9                  | 168.4           |
-+--------+-------------------------------------------------------------------------------------------------------------------+----------------------+-----------------+
++--------+----------------------------------------------------------------------------------------------------------------------+----------------------+-----------------+
 | STFPM  | `stfpm <https://github.com/open-edge-platform/training_extensions/blob/develop/src/otx/recipe/anomaly_/stfpm.yaml>`_ | 5.6                  | 21.1            |
-+--------+-------------------------------------------------------------------------------------------------------------------+----------------------+-----------------+
++--------+----------------------------------------------------------------------------------------------------------------------+----------------------+-----------------+
 | U-Flow | `uflow <https://github.com/open-edge-platform/training_extensions/blob/develop/src/otx/recipe/anomaly_/uflow.yaml>`_ | 59.6                 | 62.88           |
-+--------+-------------------------------------------------------------------------------------------------------------------+----------------------+-----------------+
++--------+----------------------------------------------------------------------------------------------------------------------+----------------------+-----------------+
 
 
 Clustering-based Models
@@ -161,12 +161,13 @@ Normalizing Flow Models
 Normalizing Flow models use invertible neural networks to transform image features into a simpler distribution, like a Gaussian. During inference, the Flow network is used to compute the likelihood of the input image under the learned distribution, assigning low probabilities to anomalous samples. OpenVINO Training Extensions currently supports `U-Flow: Unsupervised Anomaly Detection via Normalizing Flow <https://arxiv.org/pdf/2103.04257.pdf>`_.
 
 U-Flow
-^^^^^
+^^^^^^
 
-.. figure:: ../../../../../utils/images/uflow.png
-   :width: 600
-   :align: center
-   :alt: Anomaly Task Types
+.. # TODO: Add U-Flow figure when uflow.png image is available
+.. # figure:: ../../../../../utils/images/uflow.png
+.. #    :width: 600
+.. #    :align: center
+.. #    :alt: U-Flow Architecture
 
 U-Flow consists of four stages.
 

@@ -55,25 +55,25 @@ Models
 
 We support the following ready-to-use model recipes:
 
-+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------+-----------------+
-| Model Name                                                                                                                                                                                                       | Complexity (GFLOPs) | Model params (M)|
-+==================================================================================================================================================================================================================+=====================+=================+
-| `MobileNet-V3-large <https://github.com/open-edge-platform/training_extensions/blob/develop/src/otx/recipe/classification/multi_class_cls/mobilenet_v3_large.yaml>`_                                                | 0.86                | 2.97            |
-+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------+-----------------+
-| `MobileNet-V3-small <https://github.com/open-edge-platform/training_extensions/blob/develop/src/otx/recipe/classification/multi_class_cls/tv_mobilenet_v3_small.yaml>`_                                             | 0.22                | 0.93            |
-+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------+-----------------+
-| `EfficinetNet-B0 <https://github.com/open-edge-platform/training_extensions/blob/develop/src/otx/recipe/classification/multi_class_cls/efficientnet_b0.yaml>`_                                                      | 1.52                | 4.09            |
-+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------+-----------------+
-| `EfficientNet-B3 <https://github.com/open-edge-platform/training_extensions/blob/develop/src/otx/recipe/classification/multi_class_cls/tv_efficientnet_b3.yaml>`_                                                   | 3.84                | 10.70           |
-+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------+-----------------+
-| `EfficientNet-V2-S <https://github.com/open-edge-platform/training_extensions/blob/develop/src/otx/recipe/classification/multi_class_cls/efficientnet_v2.yaml>`_                                                    | 5.76                | 20.23           |
-+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------+-----------------+
-| `EfficientNet-V2-l <https://github.com/open-edge-platform/training_extensions/blob/develop/src/otx/recipe/classification/multi_class_cls/tv_efficientnet_v2_l.yaml>`_                                               | 48.92               | 117.23          |
-+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------+-----------------+
-| `DeiT-Tiny <https://github.com/open-edge-platform/training_extensions/blob/develop/src/otx/recipe/classification/multi_class_cls/deit_tiny.yaml>`_                                                                  | 2.51                | 22.0            |
-+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------+-----------------+
-| `DINO-V2 <https://github.com/open-edge-platform/training_extensions/blob/develop/src/otx/recipe/classification/multi_class_cls/dino_v2.yaml>`_                                                                      | 12.46               | 88.0            |
-+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------+-----------------+
++-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+-------------------+
+| Model Name                                                                                                                                                                  | Complexity (GFLOPs) | Model params (M)   |
++=============================================================================================================================================================================+======================+===================+
+| `MobileNet-V3-large <https://github.com/open-edge-platform/training_extensions/blob/develop/src/otx/recipe/classification/multi_class_cls/mobilenet_v3_large.yaml>`_        | 0.86                | 2.97               |
++-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+-------------------+
+| `MobileNet-V3-small <https://github.com/open-edge-platform/training_extensions/blob/develop/src/otx/recipe/classification/multi_class_cls/tv_mobilenet_v3_small.yaml>`_     | 0.22                | 0.93               |
++-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+-------------------+
+| `EfficinetNet-B0 <https://github.com/open-edge-platform/training_extensions/blob/develop/src/otx/recipe/classification/multi_class_cls/efficientnet_b0.yaml>`_              | 1.52                | 4.09               |
++-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+-------------------+
+| `EfficientNet-B3 <https://github.com/open-edge-platform/training_extensions/blob/develop/src/otx/recipe/classification/multi_class_cls/tv_efficientnet_b3.yaml>`_           | 3.84                | 10.70              |
++-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+-------------------+
+| `EfficientNet-V2-S <https://github.com/open-edge-platform/training_extensions/blob/develop/src/otx/recipe/classification/multi_class_cls/efficientnet_v2.yaml>`_            | 5.76                | 20.23              |
++-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+-------------------+
+| `EfficientNet-V2-l <https://github.com/open-edge-platform/training_extensions/blob/develop/src/otx/recipe/classification/multi_class_cls/tv_efficientnet_v2_l.yaml>`_       | 48.92               | 117.23             |
++-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+-------------------+
+| `DeiT-Tiny <https://github.com/open-edge-platform/training_extensions/blob/develop/src/otx/recipe/classification/multi_class_cls/deit_tiny.yaml>`_                          | 2.51                | 22.0               |
++-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+-------------------+
+| `DINO-V2 <https://github.com/open-edge-platform/training_extensions/blob/develop/src/otx/recipe/classification/multi_class_cls/dino_v2.yaml>`_                              | 12.46               | 88.0               |
++-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+-------------------+
 
 `MobileNet-V3 <https://arxiv.org/abs/1905.02244>`_ is the best choice when training time and computational cost are in priority, nevertheless, this recipe provides competitive accuracy as well.
 `EfficientNet-B0/B3 <https://arxiv.org/abs/1905.11946>`_ consumes more Flops compared to MobileNet, providing better performance on large datasets.