Skip to content

Latest commit

 

History

History
137 lines (114 loc) · 6.41 KB

README.md

File metadata and controls

137 lines (114 loc) · 6.41 KB

Vitis AI

Adaptable & Real-Time AI Inference Acceleration

Vitis AI Runtime Introduction

The Vitis AI Runtime (VART) enables applications to use the unified high-level runtime API for both data center and embedded. 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 embedded users, click Quick Start For Embedded to get started quickly.

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

Vitis AI Runtime directory structure introduction

vai_runtime
├── 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 Embedded

Setting Up the Host

For VEK280, follow Setting Up the Host) to set up the host for edge.

Setting Up the Target

For VEK280, 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, VAI2.5, VAI3.0 or VAI3.5, 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 Data Center

Setting Up the Host

For V70 Versal Card, follow Setup V70 Accelerator Card to set up the host.

Running Vitis AI Examples

In the docker system, /workspace/examples/vai_runtime/ 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, take resnet50 as an example.

  1. Download the vitis_ai_runtime_r3.5.x_image_video.tar.gz package and unzip it.

    cd /workspace/examples
wget https://www.xilinx.com/bin/public/openDownload?filename=vitis_ai_runtime_r3.5.0_image_video.tar.gz -O vitis_ai_runtime_r3.5.0_image_video.tar.gz
tar -xzvf vitis_ai_runtime_r3.5.0_image_video.tar.gz -C vai_runtime

  2. Download the model.

    • If the /usr/share/vitis_ai_library/models folder does not exist, create it first.
      sudo mkdir /usr/share/vitis_ai_library/models
    • For DPUCV2DX8G_v70 DPU IP, install the model package as follows.
      wget https://www.xilinx.com/bin/public/openDownload?filename=resnet50-v70-DPUCV2DX8G-r3.5.0.tar.gz -O resnet50-v70-DPUCV2DX8G-r3.5.0.tar.gz
  tar -xzvf resnet50-v70-DPUCV2DX8G-r3.5.0.tar.gz
  sudo cp resnet50 /usr/share/vitis_ai_library/models -r

  3. Compile the example.

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

  4. Run the example.

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

    Note that different Versal cards DPU IP correspond to different model files, which cannot be used alternately.

Launching Commands for VART Samples on V70
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