We recommend using Google’s Deep Learning VM to quickly suitable a compatible VM instance.
In addition, we recommend attaching a GPU in order to render camera observations and train more quickly. We also recommend setting the vCPU count to be as high as possible.
In order to perform offscreen rendering, there are a number of additional dependencies to install.
Please run the following:
sudo apt update sudo apt upgrade sudo apt install -y xorg-dev libglu1-mesa libglu1-mesa-dev libgl1-mesa-dev freeglut3-dev mesa-common-dev xvfb libxinerama1 libxcursor1 mesa-utils sudo apt-get install xserver-xorg Now we need to identify which busid your GPU is using:
Now we need to identify which build your GPU is using and add it to your xorg config file:
# run this command to find your GPU bus id (for example PCI:0:30:0) nvidia-xconfig --query-gpu-info # replace the busid flag with your value # Note: with headless GPUs (e.g. Tesla T4), which don't have display outputs, remove the --use-display-device=none option sudo nvidia-xconfig --busid=PCI:0:30:0 --use-display-device=none --virtual=1280x1024
We can now start an X server:
sudo Xorg :0
Run the following to confirm that offscreen rendering is working.
DISPLAY=:0 glxinfo | grep version DISPLAY=:0 glxgears nvidia-smi # xorg should show up in the running programs
DISPLAY=:0 environment variable must be set before you launch Simulate.
Your VM is now set up for headless training. Follow the installation instructions from the README