1. 平台為 Windows 11
安裝 WSL2 步驟如官網連結
https://ubuntu.com/tutorials/install-ubuntu-on-wsl2-on-windows-11-with-gui-support#2-install-wsl
選擇 Ubuntu 20.04.4 LTS
2. 安裝 GUI package
sudo apt install x11-apps
驗證用 xcalc， 若出現計算機表示成功。
3. 安裝 Docker
安裝 Docker (不使用 docker-desktop)
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
sudo service docker start
4. 安裝CUDA Toolkit
sudo apt-key del 7fa2af80
wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-wsl-ubuntu.pin
sudo mv cuda-wsl-ubuntu.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda-repo-wsl-ubuntu-11-7-local_11.7.0-1_amd64.deb
sudo dpkg -i cuda-repo-wsl-ubuntu-11-7-local_11.7.0-1_amd64.deb
sudo apt-get update
sudo apt-get -y install cuda
若出現:
Err:1 file:/var/cuda-repo-wsl-ubuntu-11-7-local InRelease
The following signatures couldn't be verified because the public key is not available: NO_PUBKEY BCD50790B81839D3
解法:
sudo cp /var/cuda-repo-wsl-ubuntu-11-7-local/*.gpg /usr/share/keyrings/
若出現:
The public CUDA GPG key does not appear to be installed. To install the key, run this command: sudo cp /var/cuda-repo-wsl-ubuntu-11-7-local/cuda-B81839D3-keyring.gpg /usr/share/keyrings/
解法:
sudo cp /var/cuda-repo-wsl-ubuntu-11-7-local/cuda-B81839D3-keyring.gpg /usr/share/keyrings/
sudo mv /usr/share/keyrings/cuda-archive-keyring.gpg /usr/share/keyrings/cuda-archive-keyring.gpg.bak
sudo cp /var/cuda-repo-wsl-ubuntu-11-7-local/cuda-B81839D3-keyring.gpg /usr/share/keyrings/cuda-archive-keyring.gpg
若出現:
W: GPG error: http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY A4B469963BF863CC
E: The repository 'http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 InRelease' is not signed.
解法:
sudo rm /etc/apt/sources.list.d/cuda.list
sudo rm /etc/apt/sources.list.d/nvidia-ml.list
sudo apt-key del 7fa2af80
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/3bf863cc.pub
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/7fa2af80.pub
安裝 nvidia-docker2
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt update
sudo apt install -y nvidia-docker2
sudo service docker stop
sudo service docker start
sudo docker run --rm --gpus all nvidia/cuda:11.0.3-base-ubuntu20.04 nvidia-smi
若出現 NVIDIA-SMI info 表示成功
5. 在 Ubuntu 下安裝 Autowave 環境
可參考 ted99tw 文章
a. 安裝build工具
sudo apt update
sudo apt upgrade
sudo apt install build-essential
b. 安裝ADE
cd ~
mkdir adehome
cd adehome
wget https://gitlab.com/ApexAI/ade-cli/uploads/f6c47dc34cffbe90ca197e00098bdd3f/ade+x86_64
mv ade+x86_64 ade
chmod +x ade
sudo mv ade /usr/local/bin
which ade
sudo ade update-cli
ade --version
cd ~/adehome
touch .adehome
c. 安裝Autoware.auto
git clone --recurse-submodules https://gitlab.com/autowarefoundation/autoware.auto/AutowareAuto.git
新增ADE啟動檔 .aderc-nvidia
cd AutowareAuto
vim .aderc-nvidia //並儲存
export ADE_DOCKER_RUN_ARGS="--cap-add=SYS_PTRACE --net=host --privileged --add-host ade:127.0.0.1 --expose=9090 -p 9090:9090 -v ${HOME}/.Xauthority:${HOME}/.Xauthority:ro -e XAUTHORITY=${HOME}/.Xauthority -e RMW_IMPLEMENTATION=rmw_cyclonedds_cpp --runtime=nvidia -v /dev/shm:/dev/shm -e DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix -e NVIDIA_VISIBLE_DEVICES=all -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,display"
export ADE_GITLAB=gitlab.com
export ADE_REGISTRY=registry.gitlab.com
export ADE_DISABLE_NVIDIA_DOCKER=false
export ADE_IMAGES="
registry.gitlab.com/autowarefoundation/autoware.auto/autowareauto/amd64/ade-foxy:master
registry.gitlab.com/autowarefoundation/autoware.auto/autowareauto/amd64/binary-foxy:master
registry.gitlab.com/autowarefoundation/autoware.auto/ade-lgsvl/foxy:2021.3
nvidia/cuda:11.0.3-base-ubuntu20.04
"
f. 啟動 ADE
$ source .aderc-nvidia
$ ade start
$ ade enter
若出現:
Got permission denied while trying to connect to the Docker daemon socket...
將 User 加入 docker group:
sudo usermod -aG docker $USER
g. ADE內編譯Autoware (in ADE)
cd AutowareAuto
vcs import < autoware.auto.$ROS_DISTRO.repos
colcon build
colcon test-result
6. 啟動AVP Demo
a. Run ROS 2 LGSVL Bridge
Check if you installed ROS 2 LGSVL Bridge.
If not, Install the ROS 2 LGSVL Bridge.
# In the ade container
source /opt/AutowareAuto/setup.bash
lgsvl_bridge
b. Run AVP Sim launch file
# In the ade container
source /opt/AutowareAuto/setup.bash
ros2 launch /opt/AutowareAuto/src/launch/autoware_demos/launch/avp_sim.launch.py
c. Run RViz2
# In the ade container
source /opt/AutowareAuto/setup.bash
rviz2 -d /opt/AutowareAuto/share/autoware_auto_launch/config/avp.rviz
