Installation

1. Clone the Vitis AI repository:

   cd ~
   git clone --recurse-submodules https://github.com/Xilinx/Vitis-AI

2. Install the cross-compilation system environment.

   cd Vitis-AI/setup/mpsoc/VART
   ./host_cross_compiler_setup.sh

   Note: The ~/petalinux_sdk_2021.1 path is recommended for the installation. Regardless of the path you choose for the installation, make sure the path has read-write permissions. In this section, it is installed in ~/petalinux_sdk_2021.1.
   Note:

   • For VAI 1.4, the KV260 vision starter kit is based on the 2020.2 release. Use host_cross_compiler_setup_2020.2.sh to install the cross-compiler.
   • For VAI 1.4, the VCK190 evaluation board is based on the 2020.2 release. Use the following commands to install the cross-compiler.

     cd Vitis-AI/setup/vck190
     ./host_cross_compiler_setup_2020.2.sh 

3. When the installation is complete, follow the prompts and execute the following command:

   source ~/petalinux_sdk_2021.1/environment-setup-cortexa72-cortexa53-xilinx-linux

   If you close the current terminal, you need to re-execute the above instructions in the new terminal interface.
4. To compile the library sample in the Vitis AI Library, take classification for example, execute the following command.

   cd ~/Vitis-AI/demo/Vitis-AI-Library/samples/classification
   bash -x build.sh

   The executable program is now produced.

5. To modify the library source code, view and modify them under ~/Vitis-AI/tools/Vitis-AI-Library.

   Before compiling the Vitis AI libraries, confirm the compiled output path. The default output path is $HOME/build.

   To change the default output path, modify the build_dir_default in cmake.sh. For example, you can change from build_dir_default=$HOME/build/build.${target_info}/${project_name} to build_dir_default=/workspace/build/build.${target_info}/${project_name}.

   Note: If you want to modify the build_dir_default, it is suggested to modify $HOME only.
6. Build the libraries all at once by executing the following command.

   cd ~/Vitis-AI/tools/Vitis-AI-Library
   ./cmake.sh --clean

   After compiling, you can find the generated AI libraries under build_dir_default. If you want to change the compilation rules, check and change the cmake.sh in the library directory.

   Note: All the source codes, samples, demos, and header files can be found in the ~/Vitis-AI/tools/Vitis-AI-Library location.

1. Clone the Vitis AI repository.

   git clone --recurse-submodules https://github.com/Xilinx/Vitis-AI
   cd Vitis-AI

2. Run Docker container according to the instructions in the docker installation guide.

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

   Note: A workspace folder is created by the docker runtime system and is mounted in /workspace of the docker runtime system.
3. Place the program, data and other files to be developed in the workspace folder. After the docker system starts, locate them in the /workspace of the docker system.

   Do not put the files in any other path of the docker system. They will be erased after you exit the docker system.

4. Select the model for your platform. You can find the download links for the latest models in the yaml files of the model in the Vitis-AI/models/AI-Model-Zoo location.
   • If the /usr/share/vitis_ai_library/model folder does not exist, create it first.

     sudo mkdir -p /usr/share/vitis_ai_library/models

   • For DPUCAHX8H of the Alveo U50 card, take resnet_v1_50_tf as an example.

     wget https://www.xilinx.com/bin/public/openDownload?filename=resnet_v1_50_tf-u50-u50lv-u280-DPUCAHX8H-r1.4.0.tar.gz -O resnet_v1_50_tf-u50-u50lv-u280-DPUCAHX8H-r1.4.0.tar.gz
     tar -xzvf resnet_v1_50_tf-u50-u50lv-u280-DPUCAHX8H-r1.4.0.tar.gz
     sudo cp resnet_v1_50_tf /usr/share/vitis_ai_library/models -r

   • For DPUCAHX8L of the Alveo U50LV card, take resnet_v1_50_tf as an example.

     wget https://www.xilinx.com/bin/public/openDownload?filename=resnet_v1_50_tf-u50-u50lv-u280-DPUCAHX8L-r1.4.0.tar.gz -O resnet_v1_50_tf-u50-u50lv-u280-DPUCAHX8L-r1.4.0.tar.gz
     tar -xzvf resnet_v1_50_tf-u50-u50lv-u280-DPUCAHX8L-r1.4.0.tar.gz
     sudo cp resnet_v1_50_tf /usr/share/vitis_ai_library/models -r

5. Download the cloud xclbin package from here. Untar it, select the Alveo card, and install it. For DPUCAHX8H, take U50 as an example.

   tar -xzvf xclbin-1.4.0.tar.gz
   sudo cp DPU_DPUCAHX8H/dpu_DPUCAHX8H_6E300_xilinx_u50_gen3x4_xdma_base_2.xclbin /opt/xilinx/overlaybins/dpu.xclbin
   export XLNX_VART_FIRMWARE=/opt/xilinx/overlaybins/dpu.xclbin

   For DPUCAHX8L, take U50lv as an example.

   tar -xzvf xclbin-1.4.0.tar.gz
   sudo cp DPU_DPUCAHX8L/dpu_DPUCAHX8L_1E250_xilinx_u50lv_gen3x4_xdma_base_2.xclbin /opt/xilinx/overlaybins/dpu.xclbin
   export XLNX_VART_FIRMWARE=/opt/xilinx/overlaybins/dpu.xclbin

6. If there is more than one card installed on the server and you want to specify some cards to run the program, you can set XLNX_ENABLE_DEVICES to achieve this function. The following is the usage of XLNX_ENABLE_DEVICES:
   • export XLNX_ENABLE_DEVICES=0 --only use device 0 for DPU
   • export XLNX_ENABLE_DEVICES=0,1,2 --select device 0, device 1 and device 2 to be used for DPU
   • If you do not set this environment variable, use all devices for DPU by default.
7. To compile the library sample in the Vitis AI Library, take classification for example, execute the following command:

   cd /workspace/demo/Vitis-AI-Library/samples/classification
   bash -x build.sh

   The executable program is now produced.

8. To modify the library source code, view and modify them under /workspace/tools/Vitis-AI-Library.

   Before compiling the AI libraries, confirm the compiled output path. The default output path is: $HOME/build.

   If you want to change the default output path, modify the build_dir_default in cmake.sh. Such as, change from build_dir_default=$HOME/build/build.${target_info}/${project_name} to build_dir_default=/workspace/build/build.${target_info}/${project_name}.

   Note: If you want to modify the build_dir_default, modify $HOME only.

   Execute the following command to build the libraries all at once:

   cd /workspace/tools/Vitis-AI-Library
   ./cmake.sh --clean

   After compiling, you can find the generated AI libraries under build_dir_default. If you want to change the compilation rules, check and change the cmake.sh in the library’s directory.

1. Clone the Vitis AI repository.

   $git clone --recurse-submodules https://github.com/Xilinx/Vitis-AI
   $cd Vitis-AI

2. Run Docker container according to the instructions in the docker installation guide.

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

   Note: A workspace folder is created by the docker runtime system, and is mounted in /workspace of the docker runtime system.
3. Activate the conda environment.

   $conda activate vitis-ai-caffe

4. To modify the library source code, view and modify them under /workspace/Vitis-AI-Library. Before compiling the AI libraries, confirm the compiled output path. The default output path is $HOME/build. If you want to change the default output path, modify the `build_dir_default` in cmake.sh.

   build_dir_default=$HOME/build/build.${target_info}/${project_name}
   to build_dir_default=/workspace/build/build.${target_info}/$
   {project_name}

   Note: If you want to modify the build_dir_default, it is suggested to modify $HOME.
5. Execute the following command to build all DPUCADX8G supported examples in the AI Library.

   $cd /workspace/Vitis-AI-Library
   $./cmake.sh --clean --type=release --cmake-options=-DCMAKE_PREFIX_PATH=$CONDA_PREFIX --cmake-options=-DENABLE_DPUCADX8G_RUNNER=ON

After successful building, you can find the generated AI libraries and executables under build_dir_default.