Skip to content

Latest commit

 

History

History
 
 

vck190

README.md

Vitis AI v1.4

Introduction

This directory contains instructions for running DPUCVDX8G on Versal AI Core platforms. DPUCVDX8G is a configurable computation engine dedicated for convolutional neural networks. It includes a set of highly optimized instructions, and supports most convolutional neural networks, such as VGG, ResNet, GoogleNet, YOLO, SSD, MobileNet, FPN, and others. With Vitis-AI, Xilinx has integrated all the edge and cloud solutions under a unified API and toolset.

Step1: Setup cross-compiler

  1. Run the following command to install cross-compilation system environment.

Please install it on your local host linux system, not in the docker system.

./host_cross_compiler_setup.sh

Note that the Cross Compiler will be installed in ~/petalinux_sdk_2021.1 by default.
For VCK190 ES1 board, use host_cross_compiler_setup_2020.2.sh to install the cross-compiler. For VCK190 Production board, use host_cross_compiler_setup.sh to install the cross-compiler.

  1. When the installation is complete, follow the prompts and execute the following command.
source ~/petalinux_sdk_2021.1/environment-setup-cortexa72-cortexa53-xilinx-linux

Note that if you close the current terminal, you need to re-execute the above instructions in the new terminal interface.

Step2: Setup the Target

To improve the user experience, the Vitis AI Runtime packages, VART samples, Vitis-AI-Library samples and models have been built into the board image. Therefore, user does not need to install Vitis AI Runtime packages and model package on the board separately. However, users can still install the model or Vitis AI Runtime on their own image or on the official image by following these steps.

  1. Installing a Board Image.

    • Download the SD card system image files from the following links:

      VCK190 Production board

      VCK190 ES1 board

      Note: The version of the VCK190 ES1 board image is 2020.2 and the VCK190 production board image is 2021.1.
      If you use 2020.2 system, use the corresponding 2020.2 cross-compiler.

    • Use Etcher software to burn the image file onto the SD card.

    • Insert the SD card with the image into the destination board.

    • Plug in the power and boot the board using the serial port to operate on the system.

    • Set up the IP information of the board using the serial port.

    For the details, please refer to Setting Up the Evaluation Board

  2. (Optional) How to install the Vitis AI for PetaLinux 2021.1
    There are two ways to install the dependent libraries of Vitis-AI. One is to rebuild the system by configuring PetaLinux and the other is to install the Vitis-AI online via dnf.

    • Build-Time
      To obtain the yocto recipes of VAI1.4 via petalinux-upgrade command, then rebuild the petalinux project . More details please refer to the PetaLinux Tools Documentation:Reference Guide(UG1144) Chapter 6 Upgrading the Workspace.
      For 2021.1 update1 release, run the following command and source the tool's setting script. 
      rm <path to petalinux tool>/components/yocto/source/aarch64
petalinux-upgrade -u 'http://petalinux.xilinx.com/sswreleases/rel-v2021/sdkupdate/2021.1_update1/' -p 'aarch64'
source settings.sh
      Note that if you do not remove the old aarch64 file first, the upgrade may not be correct on some host system.
    • Run-Time
      Execute dnf install packagegroup-petalinux-vitisai to complete the installation on the target.
      Note: If you use this method, ensure that the board is connected to the Internet.

  3. (Optional) How to update Vitis AI Runtime and install them separately.

    If you want to update the Vitis AI Runtime or install them to your custom board image, follow these steps.

    • Copy the following folder to the board using scp.
    scp -r vck190 root@IP_OF_BOARD:~/
    • Log in to the board using ssh. You can also use the serial port to login.
    • Install the Vitis AI Runtime. Execute the following command.
    cd ~/vck190
bash target_vart_setup.sh

  4. (Optional) Download the model. For each model, there will be a yaml file which is used for describe all the details about the model. In the yaml, you will find the model's download links for different platforms. Please choose the corresponding model and download it. Click Xilinx AI Model Zoo to view all the models.

    • Take resnet50 of VCK190 as an example.
      cd /workspace
  wget https://www.xilinx.com/bin/public/openDownload?filename=resnet50-vck190-r1.4.1.tar.gz -O resnet50-vck190-r1.4.1.tar.gz
    • Copy the downloaded file to the board using scp with the following command.
      scp resnet50-vck190-r1.4.1.tar.gz root@IP_OF_BOARD:~/
    • Log in to the board (using ssh or serial port) and install the model package.
      tar -xzvf resnet50-vck190-r1.4.1.tar.gz
  cp resnet50 /usr/share/vitis_ai_library/models -r

Step3: Run the Vitis AI Examples

Follow Running Vitis AI Examples to run Vitis AI examples.

References