Before running the samples on the cloud, make sure that the 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 is located in the path of /workspace/VART/ in the docker system.
- Download the vitis_ai_runtime_r1.2.0_image_video.tar.gz package and unzip
it.
$tar -xzvf vitis_ai_runtime_r1.2.0_image_video.tar.gz -C /workspace/VART - Compile the sample, take resnet50 as
an
example.
$cd /workspace/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/resnet50.xmodel
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/resnet50.xmodel |
| 2 | resnet50_mt_py | /usr/bin/python3 resnet50.py 1 model_dir_for_U50/resnet50.xmodel |
| 3 | inception_v1_mt_py |
/usr/bin/python3 inception_v1.py 1 model_dir_for_U50/inception_v1_tf.xmodel |
| 4 | pose_detection | ./pose_detection video/pose.mp4 model_dir_for_U50/sp_net.xmodel model_dir_for_U50/ssd_pedestrain_pruned_0_97.xmodel |
| 5 | video_analysis | ./video_analysis video/structure.mp4 model_dir_for_U50/ssd_traffic_pruned_0_9.xmodel |
| 6 | adas_detection | ./adas_detection video/adas.avi model_dir_for_U50/yolov3_adas_pruned_0_9.xmodel |
| 7 | segmentation | ./segmentation video/traffic.mp4 model_dir_for_U50/fpn.xmodel |