Install CINN using docker

Step 1. Start a docker container

Start a docker container based on upstream image.

nvidia-docker run --name $CONTAINER_NAME -it --net=host registry.baidubce.com/paddlepaddle/paddle:2.2.0-gpu-cuda11.2-cudnn8 /bin/bash

If you are using the latest version of docker, try:

docker run --gpus all --name $CONTAINER_NAME -it --net=host registry.baidubce.com/paddlepaddle/paddle:2.2.0-gpu-cuda11.2-cudnn8 /bin/bash

And notice that if your cuda version is not 11.2, replace the docker image to the corresponding paddle image with identical cuda version here.

Step 2. Clone Source Code

After entering the container, clone the source code from github.

git clone https://github.com/PaddlePaddle/CINN.git

Step 3. Build CINN and do ci test

Build CINN and do ci test to verify correctness.

cd CINN

There are 5 kinds of ci test:

1. Test on CPU(X86) backends: bash ./build.sh ci

2. Test on CPU(X86) backends without mklcblas: bash ./build.sh mklcblas_off ci

3. Test on CPU(X86) backends without mkldnn: bash ./build.sh mkldnn_off ci

4. Test on NVGPU(cuda) backends with CUDNN library: bash ./build.sh gpu_on ci

5. Test on NVGPU(cuda) backends without CUDNN library: bash ./build.sh gpu_on cudnn_off ci