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Vitis AI Runtime v2.5

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 VAI2.0 and VAI2.5, refer to the following to modify your compilation options.

  1. For Petalinux 2021.1 and above, 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 demonstration purposes, we provide the following pre-compiled DPU IP with VART Sample support. You can choose one of them according to your own Accelerator Card.

No. Accelerator Card DPU IP
1 U50LV DPUCAHX8H
2 U50LV DPUCAHX8H-DWC
3 U55C DPUCAHX8H-DWC
4 VCK5000-PROD DPUCVDX8H_4pe_miscdwc
5 VCK5000-PROD DPUCVDX8H_6pe_dwc
6 VCK5000-PROD DPUCVDX8H_6pe_misc
7 VCK5000-PROD DPUCVDX8H_8pe_normal

For U50LV and U55C Alveo Card, follow Setup Alveo Accelerator Card to set up the host.
For VCK5000-PROD Versal Card, follow Setup VCK5000 Accelerator Card to set up the host.

Running Vitis AI Examples

In the docker system, /workspace/examples/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_r2.5.x_image_video.tar.gz package and unzip it.

    cd /workspace/examples
wget https://www.xilinx.com/bin/public/openDownload?filename=vitis_ai_runtime_r2.5.0_image_video.tar.gz -O vitis_ai_runtime_r2.5.0_image_video.tar.gz
tar -xzvf vitis_ai_runtime_r2.5.0_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 yaml file as an example.

    • If the /usr/share/vitis_ai_library/models folder does not exist, create it first.
      sudo mkdir /usr/share/vitis_ai_library/models
    • For DPUCAHX8H DPU IP of U50LV card, install the model package as follows.
      wget https://www.xilinx.com/bin/public/openDownload?filename=resnet50-u50lv-DPUCAHX8H-r2.5.0.tar.gz -O resnet50-u50lv-DPUCAHX8H-r2.5.0.tar.gz
  tar -xzvf resnet50-u50lv-DPUCAHX8H-r2.5.0.tar.gz
  sudo cp resnet50 /usr/share/vitis_ai_library/models -r
    • For DPUCAHX8H-DWC DPU IP of U50LV and U55C card, install the model package as follows.
      wget https://www.xilinx.com/bin/public/openDownload?filename=resnet50-u55c-u50lv-DPUCAHX8H-DWC-r2.5.0.tar.gz -O resnet50-u55c-u50lv-DPUCAHX8H-DWC-r2.5.0.tar.gz
  tar -xzvf resnet50-u55c-u50lv-DPUCAHX8H-DWC-r2.5.0.tar.gz
  sudo cp resnet50 /usr/share/vitis_ai_library/models -r

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

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

  4. Run the example, take U50LV 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 U50lv/U55C/VCK5000-PROD
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