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VART

README.md

Vitis AI Runtime v1.4

Introduction

Vitis AI Run time enables applications to use the unified high-level runtime API for both cloud and edge. Therefore, making cloud-to-edge deployments seamless and efficient. The Vitis AI Runtime API features are:

  • Asynchronous submission of jobs to the accelerator
  • Asynchronous collection of jobs from the accelerator
  • C++ and Python implementations
  • Support for multi-threading and multi-process execution

For edge users, click Quick Start For Edge to get started quickly.

For cloud users, click Quick Start For Cloud to get started quickly.

Vitis AI Runtime directory structure introduction

VART
├── README.md
├── adas_detection
│   ├── build.sh
│   └── src
├── common
│   ├── common.cpp
│   └── common.h
├── inception_v1_mt_py
│   ├── inception_v1.py
│   └── words.txt
├── pose_detection
│   ├── build.sh
│   └── src
├── resnet50
│   ├── build.sh
│   ├── src
│   └── words.txt
├── resnet50_mt_py
│   ├── resnet50.py
│   └── words.txt
├── segmentation
│   ├── build.sh
│   └── src
├── squeezenet_pytorch
│   ├── build.sh
│   ├── src
│   └── words.txt
└── video_analysis
	├── build.sh
	└── src

Quick Start For Edge

Setting Up the Host

For MPSOC, follow Setting Up the Host to set up the host for edge.
For VCK190, follow Setting Up the Host to set up the host for edge.

Setting Up the Target

For MPSOC, follow Setting Up the Target to set up the target.
For VCK190, follow Setting Up the Target to set up the target.

Running Vitis AI Examples

Follow Running Vitis AI Examples to run Vitis AI examples.

Note: When you update from VAI1.3 to VAI1.4, refer to the following to modify your compilation options.

  1. For Petalinux 2021.1, it uses OpenCV4, and for Petalinux 2020.2, it uses OpenCV3. So set the OPENCV_FLAGS as needed. You can refer to the following.
result=0 && pkg-config --list-all | grep opencv4 && result=1
if [ $result -eq 1 ]; then
	OPENCV_FLAGS=$(pkg-config --cflags --libs-only-L opencv4)
else
	OPENCV_FLAGS=$(pkg-config --cflags --libs-only-L opencv)
fi

Quick Start For Cloud

Setting Up the Host for Alveo

  1. Click Setup Alveo Accelerator Card to set up the Alveo Card.

  2. Take U50 DPUCAHX8H as an example, suppose you have followed the above steps to enter docker container and executed the following commands.

    conda activate vitis-ai-caffe
cd /workspace/setup/alveo
source setup.sh DPUCAHX8H

Setting Up the Host for VCK5000

  1. Click Setup VCK5000 Accelerator Card to set up the VCK5000 Card.

  2. Suppose you have followed the above steps and entered the docker container.

Running Vitis AI Examples

In the docker system, /workspace/demo/VART/ is the path for the following example. If you encounter any path errors in running examples, check to see if you follow the steps above to set the host. Then, follow the steps below to download the model and run the sample.

  1. Download the vitis_ai_runtime_r1.4.0_image_video.tar.gz package and unzip it.

    cd /workspace/demo
wget https://www.xilinx.com/bin/public/openDownload?filename=vitis_ai_runtime_r1.4.0_image_video.tar.gz -O vitis_ai_runtime_r1.4.0_image_video.tar.gz
tar -xzvf vitis_ai_runtime_r1.4.0_image_video.tar.gz -C VART

  2. 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 U50 as an example.
      cd /workspace
  wget https://www.xilinx.com/bin/public/openDownload?filename=resnet50-u50-u50lv-u280-DPUCAHX8H-r1.4.1.tar.gz -O resnet50-u50-u50lv-u280-DPUCAHX8H-r1.4.1.tar.gz
    • Install the model package.
      If the /usr/share/vitis_ai_library/models folder does not exist, create it first.
      sudo mkdir /usr/share/vitis_ai_library/models

    Then install the model package.

      tar -xzvf resnet50-u50-u50lv-u280-DPUCAHX8H-r1.4.1.tar.gz
  sudo cp resnet50 /usr/share/vitis_ai_library/models -r

  3. Compile the sample, take resnet50 as an example.

    cd /workspace/demo/VART/resnet50
bash -x build.sh

  4. Run the example, take U50 platform as an example.

    ./resnet50 /usr/share/vitis_ai_library/models/resnet50/resnet50.xmodel

    Note that different alveo cards correspond to different model files, which cannot be used alternately.

Launching Commands for VART Samples on U50/U50lv/U280/VCK5000
No. 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 resnet50.py 1 /usr/share/vitis_ai_library/models/resnet50/resnet50.xmodel
5 inception_v1_mt_py /usr/bin/python3 inception_v1.py 1 /usr/share/vitis_ai_library/models/inception_v1_tf/inception_v1_tf.xmodel
6 pose_detection ./pose_detection video/pose.webm /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.webm /usr/share/vitis_ai_library/models/ssd_traffic_pruned_0_9/ssd_traffic_pruned_0_9.xmodel
8 adas_detection ./adas_detection video/adas.webm /usr/share/vitis_ai_library/models/yolov3_adas_pruned_0_9/yolov3_adas_pruned_0_9.xmodel
9 segmentation ./segmentation video/traffic.webm /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