For Cloud (U200/U250) - 1.4.1 English

Vitis AI Library User Guide (UG1354)

Document ID
Release Date
1.4.1 English
  1. Load and run the Docker Container.
    $./ -X xilinx/vitis-ai-cpu:<x.y.z>
  2. Download and untar the model directory [vai_lib_u200_u250_models.tar.gz] package.
    $cd /workspace/Vitis-AI-Library
    $wget -O vai_lib_u200_u250_models.tar.gz
    $sudo tar -xvf vai_lib_u200_u250_models.tar.gz --absolute-names
    Note: All models will download to /usr/share/vitis_ai_library/models directory. Currently supported networks are classification, facedetect, facelandmark, reid, and yolov3.
  3. To download a minimal validation set for Imagenet2012 using Collective Knowledge (CK), refer to the Alveo examples.
  4. Setup the environment.
    $source /workspace/alveo/overlaybins/
    $export LD_LIBRARY_PATH=$HOME/.local/${taget_info}/lib/:$LD_LIBRARY_PATH
  5. Make sure to compile the entire Vitis AI Library according to the For Cloud (U50/U50LV/U280) section. Run the classification image test example.
    $HOME/build/build.${taget_info}/${project_name}/test_classification <model_dir> <img_path>
    For example:
    $~/build/build.Ubuntu.18.04.x86_64.Release/Vitis-AI-Library/classification/test_classification inception_v1 <img_path>
  6. Run the classification accuracy test example.
    $HOME/build/build..${taget_info}/${project_name}/test_classification_accuracy <model_dir> <img_dir_path> <output_file>
    For example:
    $~/build/build.Ubuntu.18.04.x86_64.Release/Vitis-AI-Library/classification/test_classification_accuracy inception_v1 <img_dir_path> <output_file>