Before running the samples on the cloud, make sure that the U50 card is installed on the server and the docker system is loaded and running
- Download the
Vitis™ AI Development
Kit..
$git clone https://github.com/xilinx/vitis-aiThen you will find the Vitis AI Runtime samples under /workspace/vitis_ai/vart/samples in the docker system.
- Download the vitis_ai_runtime_r1.1_image_video.tar.gz package and unzip
it.
$tar -xzvf vitis_ai_runtime_r1.1_image_video.tar.gz -C vitis-ai/vart - Compile the sample, assuming resnet50 as an
example.
$cd /workspace/vitis_ai/vart/samples/resnet50 $bash –x build.shWhen the compilation is complete, the executable resnet50 is generated in the current directory.
- Run the
sample.
$./resnet50 model_dir_for_U50
The following table shows the run commands for all the Vitis AI samples in the cloud.
| ID | Example Name | Command |
|---|---|---|
| 1 | resnet50 | ./resnet50 model_dir_for_U50 |
| 2 | resnet50_mt_py | /usr/bin/python3 resnet50.py 1 model_dir_for_U50 |
| 3 | inception_v1_mt_py |
/usr/bin/python3 inception_v1.py 1 model_dir_for_U50 |
| 4 | pose_detection | ./pose_detection video/pose.mp4 model_dir_for_U50 |
| 5 | video_analysis | ./video_analysis video/structure.mp4 model_dir_for_U50 |
| 6 | adas_detection | ./adas_detection video/adas.avi model_dir_for_U50 |
| 7 | segmentation | ./segmentation video/traffic.mp4 model_dir_for_U50 |