vai_q_pytorch Installation - 1.2 English

Docker container

Vitis AI provide docker container for quantization tools including vai_q_pytorch. After running a GPU container, activate conda environment vitis-ai-pytorch. All the requirements are ready there, vai_q_pytorch APIs can be called directly. vai_q_pytorch now only has GPU version, vitis-ai-pytorch environment only exists in GPU container.

conda activate vitis-ai-pytorch

Install from Source Code

vai_q_pytorch is designed to work as a Pytorch plugin and itself is a python package. It is open source in Vitis_AI_Quantizer.It is recommended to install vai_q_pytorch in conda environment, follow the steps here:

1. Set CUDA_HOME environment variable in .bashrc
   If CUDA library is installed in /usr/local/cuda, add the following line into .bashrc. If CUDA is in other directory, change the line accordingly.

   export CUDA_HOME=/usr/local/cuda 

2. Install Pytorch(1.1-1.4) and torchvision

   Here take pytorch 1.1 and torchvision 0.3.0 as an example, detailed instructions for other versions are in pytorch website.

   pip install torch==1.1.0 torchvision==0.3.0 
   

3. Install other dependencies

   pip install -r requirements.txt

4. Install vai_q_pytorch

   cd ./pytorch_binding 
   python setup.py install (for user) 
   python setup.py develop (for developer) 
   

5. Verify installation

   python -c "import pytorch_nndct"
   

To create deployed model for VAI compiler, XIR library needs to be installed. Right now XIR library is not public available, so use docker environment to generate deployed model if necessary.