Update to 3.0 documentation in parallel with 3.5 release (#1135)

* Update ONNX Runtime docs, dpu/README.md * Fix model_zoo/README.md Github.IO link, update Zoo license link in HTML docs * Fix Docker image naming convention and run commands * Fix Docker image naming convention and run commands * Update CUDA container test to align with release CUDA version * Fix Docker image naming convention and run commands * Fix broken Model Zoo license agreement link * Minor text updates to workflow HTML * Further optimization of install instructions * Further optimization of install instructions * Correct Model Zoo links in board_setup * Consolidate install_docker into install * Correct spelling mistake * Minor text updates to workflow * Minor text updates to workflow-model-zoo * Further updates to install instructions * Further updates to install instructions * Cleanup VCK5000 setup instructions * Cleanup VCK5000 setup instructions * Cleanup VCK5000 setup instructions * Cleanup VCK5000 setup instructions * Cleanup VCK5000 setup instructions * Revise Alveo support, board_setup docs, Model Zoo readme * Revise Alveo support / unify board_setup docs / update Model Zoo readme * Revise Alveo support / unify board_setup docs / update Model Zoo readme * Revise Alveo support / unify board_setup docs / update Model Zoo readme * Revise Alveo support / unify board_setup docs / update Model Zoo readme * Revise Alveo support / unify board_setup docs / update Model Zoo readme * Fix Model Zoo downloader.py typo * Add Github.IO 404.html page * Add Github.IO 404.html page * Add Github.IO 404.html page * Add Github.IO 404.html page * Add Github.IO 404.html page * Add Github.IO 404.html page * Add Github.IO 404.html page * Fix VCK5000 board_setup doc error * Fix VCK5000 board_setup doc error * Fix VCK5000 board_setup doc error * Fix VCK5000 board_setup doc error * Fix VCK5000 board_setup doc error * Fix VCK5000 board_setup doc error * Fix VCK5000 board_setup doc error * Fix VCK5000 board_setup doc error * Fix VCK5000 board_setup doc error * Fix broken workflow link * Add V70 DPU description * Add V70 DPU description * Fix broken third-party link * Update EDD2020 dataset link * Updates for TOCTree structure * Major revisions to 3.0 docs * Rebrand quickstarts * Fix release notes links: * Fixed Command in Install.rst * Fixed quickstart optimization links * Fixed quickstart optimization links * Merging Mpsoc quick start and setting up the target * MPSOC Quick Start Upload Test * Update Sphinx theme * Updates to MPSoC quickstart * Updates to MPSoC quickstart * Updates to MPSoC quickstart * MPSOC Changes * New MPSOC + Quentons Changes * Remove Chapter 8 reference * Fixed MPSOC.rst * new pt tutorial * new pt tutorial * Deploy Section Completed for MPSOC * Minor updates to mpsoc.rst * Fixed integration images * Updated VCK190, Partial update of VCk5000 * Finalized Quickstarts * Updates to Quickstarts * Updates to Quickstarts * Add images and cleanup * Merge changes across all Quickstarts * Release editorial fixes * Add Linux CMA details * Update Linux CMA and DT Bindings * Fix CMA Titles * Update Online Library Install, Linux CMA * Remove .bak file, re-integrate Vitis/Vivado PNG * Update Online Install Available Note * Fix dumb numbering mistake * Editorial changes to Quickstarts * WSL links added + index.rst change * Update TOCTree structure Co-authored-by: Rhind <[email protected]> Co-authored-by: Aidan Rhind <[email protected]>
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diff --git a/README.md b/README.md
@@ -13,7 +13,7 @@
 
 
 <br />
-Xilinx&reg; Vitis&trade; AI is an Integrated Development Environment that can be leveraged to accelerate AI inference on Xilinx platforms. Vitis AI provides optimized IP, tools, libraries, models, as well as resources, such as example designs and tutorials that aid the user throughout the development process.  It is designed with high efficiency and ease-of-use in mind, unleashing the full potential of AI acceleration on Xilinx SoCs and Alveo Data Center accelerator cards.  
+AMD Vitis&trade; AI is an Integrated Development Environment that can be leveraged to accelerate AI inference on AMD adaptable platforms. Vitis AI provides optimized IP, tools, libraries, models, as well as resources, such as example designs and tutorials that aid the user throughout the development process.  It is designed with high efficiency and ease-of-use in mind, unleashing the full potential of AI acceleration on AMD adaptable SoCs and Alveo Data Center accelerator cards.  
 <br /> <br />
 
 
@@ -42,7 +42,7 @@ Optionally, configure git-lfs in order to reduce the local storage requirements.
 
 ## Repository Branching and Tagging Strategy
 
-To understand the branching and tagging strategy leveraged by this repository, please refer to [this page](https://xilinx.github.io/Vitis-AI/docs/install/branching_tagging_strategy.html)
+To understand the branching and tagging strategy leveraged by this repository, please refer to [this page](https://xilinx.github.io/Vitis-AI/3.0/html/docs/install/branching_tagging_strategy.html)
 
 ## Licenses
 

diff --git a/board_setup/README.md b/board_setup/README.md
@@ -9,7 +9,5 @@
 
 The Vitis-AI repository provides pre-built board images that can be leveraged by users who wish to test-drive the Vitis-AI workflow, run examples and evaluate models from the Model Zoo.  This directory provides the necessary scripts and files that will enable usage of these targets.
 
-As of the 3.0 release of Vitis AI, the target setup documentation has migrated to Github.IO.  **YOU MAY ACCESS THE TARGET SETUP DOCUMENTATION [ONLINE](https://xilinx.github.io/Vitis-AI/docs/board_setup/board_setup.html)** or **[OFFLINE](../docs/docs/board_setup/board_setup.html)**.
-
-For additional high-level details please refer to the [workflow documentation](https://xilinx.github.io/Vitis-AI/docs/workflow.html#test-drive-vitis-ai-on-a-supported-platform).
+As of the 3.0 release of Vitis AI, the target setup documentation has migrated to Github.IO.  **YOU MAY ACCESS THE QUICKSTART DOCUMENTATION [ONLINE](https://xilinx.github.io/Vitis-AI/3.0/html/docs/index.html)** or **[OFFLINE](../docs/docs/index.html)**.
 
diff --git a/docker/README.md b/docker/README.md
@@ -5,4 +5,4 @@
  </tr>
 </table>
 
-This folder contains the Docker container build files.  For information on building and using Vitis AI Docker containers, start [here](https://xilinx.github.io/Vitis-AI/docs/install/install.html)
+This folder contains the Docker container build files.  For information on building and using Vitis AI Docker containers, start [here](https://xilinx.github.io/Vitis-AI/3.0/html/docs/install/install.html)
diff --git a/docs/.buildinfo b/docs/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: c49266779c840928c2c6324bb8c0c308
+config: ba64e4748ef9ac0664b79876321fe1c8
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/docs/_downloads/08f25862a0dd85eb493ab84edb004a0e/ModelZoo_VAI3.0_Github.xlsx b/docs/_downloads/08f25862a0dd85eb493ab84edb004a0e/ModelZoo_VAI3.0_Github.xlsx
diff --git a/docs/_images/Etcher.png b/docs/_images/Etcher.png
diff --git a/docs/_images/Wallace.png b/docs/_images/Wallace.png
diff --git a/docs/_images/ZCU104_setup.png b/docs/_images/ZCU104_setup.png
diff --git a/docs/_images/ifconfig.png b/docs/_images/ifconfig.png
diff --git a/docs/_images/kria_setup.png b/docs/_images/kria_setup.png
diff --git a/docs/_images/quantize_p1.png b/docs/_images/quantize_p1.png
diff --git a/docs/_images/vck190-setup.png b/docs/_images/vck190-setup.png
diff --git a/docs/_images/vck5000.png b/docs/_images/vck5000.png
diff --git a/docs/_images/vitis_integration.PNG b/docs/_images/vitis_integration.PNG
diff --git a/docs/_images/vivado_integration.PNG b/docs/_images/vivado_integration.PNG
diff --git a/docs/_images/zcu102_setup.png b/docs/_images/zcu102_setup.png
diff --git a/docs/_sources/docs/board_setup/board_setup.rst.txt b/docs/_sources/docs/board_setup/board_setup.rst.txt
diff --git a/docs/_sources/docs/board_setup/board_setup_alveo.rst.txt b/docs/_sources/docs/board_setup/board_setup_alveo.rst.txt
diff --git a/docs/_sources/docs/board_setup/board_setup_mpsoc.rst.txt b/docs/_sources/docs/board_setup/board_setup_mpsoc.rst.txt
@@ -1,141 +1,5 @@
-Setting up a Zynq UltraScale+ MPSoC Target
-==========================================
+:orphan:
 
-Introduction
-------------
-
-This directory contains instructions for running DPUCZDX8G on Zynq |reg| Ultrascale+ |trade| MPSoC targets. **DPUCZDX8G** is a configurable computation engine dedicated to convolutional neural networks. It supports a highly optimized instruction set, enabling the deployment of most convolutional neural networks.
-
-Step 1: Setup Cross-compiler
-----------------------------
-
-.. note:: Perform these steps this on your local host Linux operating system (not inside the docker container). By default, the cross compiler will be installed in ``~/petalinux_sdk_2022.2``.
-
-1. Run the following commands to install the cross-compilation environment:
-
-   .. code-block:: Bash
-
-      cd Vitis-AI/board_setup/mpsoc
-      ./host_cross_compiler_setup.sh
-
-
-2. When the installation is complete, follow the prompts and execute the following command:
-
-   .. code-block:: Bash
-
-      source ~/petalinux_sdk_2022.2/environment-setup-cortexa72-cortexa53-xilinx-linux
-
-   .. note:: If you close the current terminal, you must re-execute the above instructions in the new terminal interface.
-
-Step 2: Setup the Target
-------------------------
-
-The Vitis AI Runtime packages, VART samples, Vitis-AI-Library samples, and models are built into the board image, enhancing the user experience. Therefore, the user need not install Vitis AI Runtime packages and model packages on the board separately. However, following these steps, the users can still install the model or Vitis AI Runtime on their image or on the official image.
-
-1. Installing a Board Image.
-
-   a.  Download the SD card image from the appropriate link:
-
-      - `ZCU102 <https://www.xilinx.com/member/forms/download/design-license-xef.html?filename=xilinx-zcu102-dpu-v2022.2-v3.0.0.img.gz>`__
-      - `ZCU104 <https://www.xilinx.com/member/forms/download/design-license-xef.html?filename=xilinx-zcu104-dpu-v2022.2-v3.0.0.img.gz>`__
-      - `KV260 <https://www.xilinx.com/member/forms/download/design-license-xef.html?filename=xilinx-kv260-dpu-v2022.2-v3.0.0.img.gz>`__
-
-	.. note:: For the ZCU102/ZCU104/KV260, the version of the board image should be 2022.2 or above.
-
-   b.  Use Etcher software to burn the image file onto the SD card.
-
-   c.  Insert the imaged SD card into the target board.
-
-   d.  Plug in the power adapter and boot the board using the serial port to interact with the target.
-
-   e.  Configure the IP address and related settings for the board using the serial port.
-
-   For additional details, refer to `Setting Up the Evaluation Board <https://docs.xilinx.com/r/en-US/ug1414-vitis-ai/Setting-Up-the-Evaluation-Board>`__.
-
-2. (Optional) Run ``zynqmp_dpu_optimize.sh`` to optimize board settings.
-
-   The script runs automatically after the board boots up with the official image. But you can also find the ``dpu_sw_optimize.tar.gz`` in `DPUCZDX8G.tar.gz <https://www.xilinx.com/bin/public/openDownload?filename=DPUCZDX8G.tar.gz>`__.
-
-   .. code-block:: Bash
-
-       cd ~/dpu_sw_optimize/zynqmp/
-       ./zynqmp_dpu_optimize.sh
-
-3. (Optional) How to leverage Vitis AI with PetaLinux 2022.2
-
-   You can install the Vitis AI libraries on the target either at build-time or at run-time:
-
-   		- Build-Time: Rebuild the system by configuring PetaLinux. For ``VAI3.0 Recipes``, refer to `Vitis-AI-Recipes <../petalinux-recipes.html>`__
-   		- Run-Time: Install Vitis AI online via `dnf`. Execute ``dnf install packagegroup-petalinux-vitisai`` to complete the installation on the target. For more details, refer `VAI3.0 Online Install <../petalinux-recipes.html>`__
-
-4. (Optional) How to update Vitis AI Runtime on the target
-
-   If you have an updated version of the Vitis AI Runtime and wish to install the update to your target, follow these steps.
-
-   -  Copy the board_setup/mpsoc folder to the board using scp:
-
-      .. code-block:: Bash
-
-         scp -r board_setup/mpsoc root@IP_OF_BOARD:~/
-
-   -  Log in to the board using ssh. You can also use the serial port to login.
-   -  Now, install the Vitis AI Runtime. Execute the following commands:
-
-      ::
-		
-		cd ~/mpsoc
-		bash target_vart_setup.sh
-
-
-5. (Optional) Download the model.
-
-   You can now select a model from the Vitis AI Model Zoo `Vitis AI Model Zoo <../workflow-model-zoo.html>`__.  Navigate to the  `model-list subdirectory  <https://github.com/Xilinx/Vitis-AI/tree/master/model_zoo/model-list>`__  and select the model that you wish to test. For each model, a YAML file provides key details of the model. In the YAML file there are separate hyperlinks to download the model for each supported target.  Choose the correct link for your target platform and download the model.
-
-   a. Take the ZCU102 ``resnet50`` model as an example.
-
-      .. code-block:: Bash
-
-          cd /workspace
-          wget https://www.xilinx.com/bin/public/openDownload?filename=resnet50-zcu102_zcu104_kv260-r3.0.0.tar.gz -O resnet50-zcu102_zcu104_kv260-r3.0.0.tar.gz
-
-   b. Copy the downloaded file to the board using scp with the following command:
-
-      .. code-block:: Bash
-
-          scp resnet50-zcu102_zcu104_kv260-r3.0.0.tar.gz root@IP_OF_BOARD:~/
-
-   c. Log in to the board (via ssh or serial port) and install the model package:
-
-      .. code-block:: Bash
-
-          tar -xzvf resnet50-zcu102_zcu104_kv260-r3.0.0.tar.gz
-          cp resnet50 /usr/share/vitis_ai_library/models -r
-
-.. _mpsoc-run-vitis-ai-examples:
-
-Step 3: Run the Vitis AI Examples
----------------------------------
-
-1. Download the `vitis_ai_runtime_r3.0.0_image_video.tar.gz <https://www.xilinx.com/bin/public/openDownload?filename=vitis_ai_runtime_r3.0.0_image_video.tar.gz>`__ from host to the target using scp with the following command:
-
-   ``[Host]$scp vitis_ai_runtime_r3.0.*_image_video.tar.gz root@[IP_OF_BOARD]:~/``
-
-2. Unzip the ``vitis_ai_runtime_r3.0.0_image_video.tar.gz`` package on the target.
-
-      .. code-block:: Bash
-
-       cd ~
-       tar -xzvf vitis_ai_runtime_r*3.0._image_video.tar.gz -C Vitis-AI/examples/vai_runtime
-
-3. Navigate to the example directory on the target board. Take ``resnet50`` as an example.
-
-   ``cd ~/Vitis-AI/examples/vai_runtime/resnet50``
-
-4. Run the example.
-
-   ``./resnet50 /usr/share/vitis_ai_library/models/resnet50/resnet50.xmodel``
-
-   For examples with video input, only ``webm`` and ``raw`` format are supported by default with the official system image. If you want to support video data in other formats, you need to install the relevant packages on the system.
 
 Launching Commands for VART Samples on Edge
 -------------------------------------------

diff --git a/docs/_sources/docs/board_setup/board_setup_vck190.rst.txt b/docs/_sources/docs/board_setup/board_setup_vck190.rst.txt
@@ -1,3 +1,5 @@
+:orphan:
+
 Setting up the Versal VCK190
 ============================
 

diff --git a/docs/_sources/docs/board_setup/board_setup_vck5000.rst.txt b/docs/_sources/docs/board_setup/board_setup_vck5000.rst.txt
@@ -1,7 +1,9 @@
+:orphan:
+
 Setting up a Versal Accelerator Card
 ====================================
 
-The Xilinx |reg| **DPUCVDX8H** for the Versal VCK5000 is a High Performance CNN processing engine.  The following instructions will help you to install the software and packages required to support the VCK5000.
+The AMD **DPUCVDX8H** for the Versal |trade| VCK5000 is a High Performance CNN processing engine.  The following instructions will help you to install the software and packages required to support the VCK5000.
 
 As a first step, it is recommended that you select the appropriate DPU configuration for your application:
 

diff --git a/docs/_sources/docs/board_setup/vai_install_to_target.rst.txt b/docs/_sources/docs/board_setup/vai_install_to_target.rst.txt
diff --git a/docs/_sources/docs/install/Alveo_X11.rst.txt b/docs/_sources/docs/install/Alveo_X11.rst.txt
@@ -1,7 +1,9 @@
+:orphan:
+
 X11 Support for Running Vitis AI Docker with Alveo
 ====================================================
 
-If you are running Vitis™ AI docker with Alveo™ card and want to use X11 support for graphics (for example, some demo applications in VART and Vitis AI Library for Alveo need to display images or video), add the following line into the *docker_run_params* variable definition in ``docker_run.sh`` script:
+If you are running Vitis |trade| AI docker with Alveo |trade| card and want to use X11 support for graphics (for example, some demo applications in VART and Vitis AI Library for Alveo need to display images or video), add the following line into the *docker_run_params* variable definition in ``docker_run.sh`` script:
 
 .. code-block::
 

diff --git a/docs/_sources/docs/install/China_Ubuntu_servers.rst.txt b/docs/_sources/docs/install/China_Ubuntu_servers.rst.txt
@@ -1,7 +1,9 @@
+:orphan:
+
 Access to Ubuntu Mirrors from within China
 ==========================================
 
-Vitis™ AI Docker images leverage Ubuntu Bionic 18.04. In your Ubuntu installation, the file **/etc/apt/sources.list** specifies the default server location for Ubuntu packages. For example:
+Vitis |trade| AI Docker images leverage Ubuntu 20.04. In your Ubuntu installation, the file **/etc/apt/sources.list** specifies the default server location for Ubuntu packages. For example:
 
 .. code-block::
 
@@ -47,3 +49,8 @@ Next, modify your Ubuntu **/etc/apt/sources.list** to point to mirrors located i
      deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic-proposed main restricted universe multiverse
 
 In addition, multiple alternative mirrors are `here <https://momane.com/change-ubuntu-18-04-source-to-china-mirror>`__.
+
+.. |trade|  unicode:: U+02122 .. TRADEMARK SIGN
+   :ltrim:
+.. |reg|    unicode:: U+000AE .. REGISTERED TRADEMARK SIGN
+   :ltrim:
diff --git a/docs/_sources/docs/install/Vitis AI 2.0 Feb 2022 Patch.rst.txt b/docs/_sources/docs/install/Vitis AI 2.0 Feb 2022 Patch.rst.txt
@@ -29,7 +29,7 @@ New Packages
 -  `xnnc-2.0.1-py37hd51751d_27.tar.bz2 <https://www.xilinx.com/bin/public/openDownload?filename=xnnc-2.0.1-py37hd51751d_27.tar.bz2>`__
 
 Installation
-------------
+~~~~~~~~~~~~
 
 Download the packages from the link above. Apply the conda patch to the conda environment (Machine Learning
 framework) that you wish to update in this format

diff --git a/docs/_sources/docs/install/Vitis AI 2.5 Aug 2022 Patch.rst.txt b/docs/_sources/docs/install/Vitis AI 2.5 Aug 2022 Patch.rst.txt
@@ -21,7 +21,7 @@ New Packages
 -  `vart-2.5.0-py37h07a2524_202.tar.bz2 <https://www.xilinx.com/bin/public/openDownload?filename=vart-2.5.0-py37h07a2524_202.tar.bz2>`__
 
 Installation
-------------
+~~~~~~~~~~~~
 
 Download the packages from the link above. Apply the conda patch to the conda environment (Machine Learning
 framework) that you wish to update in this format.

diff --git a/docs/_sources/docs/install/branching_tagging_strategy.rst.txt b/docs/_sources/docs/install/branching_tagging_strategy.rst.txt
@@ -1,7 +1,7 @@
 Branching / Tagging Strategy
 ============================
 
-Each updated release of Vitis™ AI is pushed directly to `master <https://github.com/Xilinx/Vitis-AI/tree/master>`__ on the release day. In addition, at that time, a tag is created for the repository; for example, see the tag for `v3.0 <https://github.com/Xilinx/Vitis-AI/tree/v3.0>`__.
+Each updated release of Vitis |trade| AI is pushed directly to `master <https://github.com/Xilinx/Vitis-AI/tree/master>`__ on the release day. In addition, at that time, a tag is created for the repository; for example, see the tag for `v3.0 <https://github.com/Xilinx/Vitis-AI/tree/v3.0>`__.
 
 Following the release, the tagged version remains static, and additional inter-version updates are pushed to the master branch. Thus, the master branch is always the latest release and will have the latest fixes and documentation. The branch associated with a specific release (which will be “master” during the lifecycle of that release) will become a branch at the time of the next release.
 
@@ -10,3 +10,9 @@ Similarly, if you use a previous version of Vitis AI, the branch associated with
 The diagram below depicts the overall workflow:
 
 .. image:: ../reference/images/branching_strategy.PNG
+
+
+.. |trade|  unicode:: U+02122 .. TRADEMARK SIGN
+   :ltrim:
+.. |reg|    unicode:: U+000AE .. REGISTERED TRADEMARK SIGN
+   :ltrim: