For Cloud - 1.4.1 English

Vitis AI User Guide (UG1414)

Document ID
Release Date
1.4.1 English

Before running the samples on the Cloud, ensure that either the Versal VCK5000 evaluation board or an Alveo card, such as U50, U50LV, or U280, is installed on the server, and the docker system is loaded and running.

If you have downloaded Vitis-AI, entered Vitis-AI directory, and then started Docker.

Thus, VART examples are located in the path of /workspace/demo/VART/ in the docker system.

  1. Download the vitis_ai_runtime_r2.0.0_image_video.tar.gz package and unzip it.
    tar -xzvf vitis_ai_runtime_r2.0.0_image_video.tar.gz -C /workspace/demo/VART
  2. Compile the sample. Take resnet50 as an example.
    cd /workspace/demo/VART/resnet50
    bash –x

    When the compilation is complete, the executable resnet50 is generated in the current directory.

  3. Download the model. The download link of the model is described in the YAML file of the model. You can find the YAML file in Vitis-AI/models/AI-Model-Zoo. Take resnet50 as an example:
    wget -O resnet50-u50-r2.0.0.tar.gz
  4. Untar the model on the target and install it.
    Note: If the /usr/share/vitis_ai_library/models folder does not exist, create it.
    sudo mkdir -p /usr/share/vitis_ai_library/models

    Then install the model package.

    tar -xzvf resnet50-u50-r2.0.0.tar.gz
    sudo cp resnet50 /usr/share/vitis_ai_library/models -r
  5. Run the sample.
    ./resnet50 /usr/share/vitis_ai_library/models/resnet50/resnet50.xmodel

The following table shows the run commands for all the Vitis AI samples in the cloud.

Table 1. Launching Commands for Vitis AI Samples for Cloud DPUs
ID Example Name Command
1 resnet50 ./resnet50 /usr/share/vitis_ai_library/models/resnet50/resnet50.xmodel
2 resnet50_pt ./resnet50_pt /usr/share/vitis_ai_library/models/resnet50_pt/resnet50_pt.xmodel ../images/001.jpg
3 resnet50_ext ./resnet50_ext /usr/share/vitis_ai_library/models/resnet50/resnet50.xmodel ../images/001.jpg
4 resnet50_mt_py /usr/bin/python3 1 /usr/share/vitis_ai_library/models/resnet50/resnet50.xmodel
5 inception_v1_mt_py /usr/bin/python3 1 /usr/share/vitis_ai_library/models/inception_v1_tf/inception_v1_tf.xmodel
6 pose_detection ./pose_detection video/pose.mp4 /usr/share/vitis_ai_library/models/sp_net/sp_net.xmodel /usr/share/vitis_ai_library/models/ssd_pedestrian_pruned_0_97/ssd_pedestrian_pruned_0_97.xmodel
7 video_analysis ./video_analysis video/structure.mp4 /usr/share/vitis_ai_library/models/ssd_traffic_pruned_0_9/ssd_traffic_pruned_0_9.xmodel
8 adas_detection ./adas_detection video/adas.avi /usr/share/vitis_ai_library/models/yolov3_adas_pruned_0_9/yolov3_adas_pruned_0_9.xmodel
9 segmentation ./segmentation video/traffic.mp4 /usr/share/vitis_ai_library/models/fpn/fpn.xmodel
10 squeezenet_pytorch ./squeezenet_pytorch /usr/share/vitis_ai_library/models/squeezenet_pt/squeezenet_pt.xmodel