For Cloud (Alveo U200/U250 Cards)

For Cloud (Alveo U200/U250 Cards) - 2.0 English

Vitis AI Library User Guide (UG1354)

Document ID

UG1354

Release Date

2022-01-20

Version

2.0 English

To run an example for the Alveo U200 and U250 Data Center Accelerator Cards, use these steps:

Load and run the Docker Container.

$./docker_run.sh -X xilinx/vitis-ai-cpu:<x.y.z>

Download and untar the model directory (vai_lib_u200_u250_models.tar.gz) package.
```
$cd /workspace/Vitis-AI-Library
$wget -O vai_lib_u200_u250_models.tar.gz https://www.xilinx.com/bin/public/openDownload?filename=vai_lib_u200_u250_models.tar.gz
$sudo tar -xvf vai_lib_u200_u250_models.tar.gz --absolute-names
```
Note: All models will download to the /usr/share/vitis_ai_library/models directory. Currently supported networks are classification, facedetect, facelandmark, reid, and yolov3.
To download a minimal validation set for Imagenet2012 using Collective Knowledge (CK), refer to the Alveo examples.

Set up the environment.

$source /workspace/alveo/overlaybins/setup.sh
$export LD_LIBRARY_PATH=$HOME/.local/${taget_info}/lib/:$LD_LIBRARY_PATH

Make sure to compile the entire Vitis AI Library according to the For Cloud (Alveo U50LV/U55C Cards, Versal VCK5000 Card) section. Run the classification image test example.

$HOME/build/build.${taget_info}/${project_name}/test_classification <model_dir> <img_path>

For example:

$~/build/build.Ubuntu.18.04.x86_64.Release/Vitis-AI-Library/classification/test_classification inception_v1 <img_path>

Run the classification accuracy test example.

$HOME/build/build..${taget_info}/${project_name}/test_classification_accuracy <model_dir> <img_dir_path> <output_file>

For example:

$~/build/build.Ubuntu.18.04.x86_64.Release/Vitis-AI-Library/classification/test_classification_accuracy inception_v1 <img_dir_path> <output_file>