Linux/WSL#

Depending on which distribution of Linux you use, the process of installing packages will look slightly different. Select the appropriate distribution below and follow along from there. These instructions also work for the Windows Subsystem for Linux (WSL).

Note

If you’re using Arch Linux or one of its derivatives, we assume you have the yay package manager installed to install dependencies from the AUR.

  1. Update your system’s package manager, then install gcc, git, and Python 3.10.

    sudo apt update

    sudo apt install build-essential git python3.10

    sudo pacman -Syu

    sudo pacman -S gcc git

    yay -S python310

  2. If you’re using an NVIDIA GPU, install CUDA Toolkit 11.8 and cuDNN.

    First, install and/or update your GPU drivers at this link. Select your GPU model and click “Search”, then click “Download”. After installing the drivers, reboot your system to ensure they take effect.

    Then, install CUDA Toolkit 11.8. Select your version of Linux, then follow the instructions to install CUDA for your operating system.

    To verify your installation of CUDA, use the following command.

    nvcc --version

    Finally, install cuDNN. You will need to register an NVIDIA Developer account, which you can do for free. You can choose cuDNN v8.9.7 that supports CUDA toolkit v11.8. Choose ‘Local Installer for Windows (Zip)’, download and extract it. And then copy the three folders ‘bin’, ‘lib’, and ‘include’ into where the CUDA toolkit is installed (typcially, ‘C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8’), and replace all the three folders with the same names. After that, you may need to add the ‘C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8’ to path via environmental variables.

  3. Upgrade pip, wheel, setuptools.

    python3 -m pip install --upgrade pip wheel setuptools

  4. Install wxPython

    apt-get install libgtk-3-dev

    sudo apt-get install git curl libsdl2-mixer-2.0-0 libsdl2-image-2.0-0 libsdl2-2.0-0

    python3 -m pip install -U -f https://extras.wxpython.org/wxPython4/extras/linux/gtk3/ubuntu-20.04 wxPython

  5. Install LabGym via pip.

    python3 -m pip install LabGym

  6. If you’re using an NVIDIA GPU, Install Pytorch v2.0.1 with CUDA 11.8.

    python3 -m pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118

 

Launch LabGym:

LabGym

The GUI will take a few minutes to start up during the first launch. If the LabGym GUI shows up, you have successfully installed LabGym!

If this doesn’t work, which typically is because the python3/script is not in your environment path. You can google ‘add python3 script to path linux’ to add it to path, or simply use the following commands to initiate LabGym:

python3

from LabGym import __main__

__main__.main()