Initial Setup#

  1. Follow LabGym’s installation instructions for your operating system, up to but NOT including the installation of pipx. After following these instructions, you should have the following installed:

    • Git

    • A C/C++ Compiler

    • Python 3.10

    • CUDA 11.8 (if using GPU)

    • cuDNN (if using GPU)

  2. Install Python 3.9 using the OS-specific installation instructions described in Step 1. If you’re on Windows, install Python 3.9 from this link.

    Important

    It’s necessary to have both Python 3.9 and 3.10 installed in order to test LabGym on both versions of Python. This will ensure that, for example, you don’t accidentally use a feature of Python that is compatible with Python 3.10 but not Python 3.9, introducing an incompatibility for the users.

  3. Issue the following commands to properly configure Git. For more information on editor configuration, please see this source.

    $ git config --global user.name "First Last"
$ git config --global user.email "name@example.com"
$ git config --global core.editor editorname

  4. Create a GitHub account, then clone the repository.

    If you are a member of the Ye Lab, please send a Slack message to Henry Hu to add you to the “umyelab” organization on GitHub. This will give you access to make changes directly to the LabGym repository. Once you have access, navigate to the directory where you store your code on your local machine using cd, then use the following command to clone the repository.

    $ git clone https://github.com/umyelab/LabGym.git

    If you aren’t a member of the Ye Lab, create a fork by clicking the “Fork” button on LabGym’s GitHub page. Then, clone the repository using the following command.

    $ git clone https://github.com/<your-github-username>/LabGym.git

  5. Move into the LabGym directory, create a virtual environment, and activate it.

    $ cd LabGym
$ python3.10 -m venv .venv
$ source .venv/bin/activate

    > cd LabGym
> py -3.10 -m venv .venv
> .venv\bin\activate

  6. Install LabGym’s dependencies in the virtual environment.

    $ python -m pip install -e .

    Note

    Since you’re in a virtual environment, you should no longer need to use the OS-specific python commands.

    This command uses pip to install all the dependencies listed in LabGym’s pyproject.toml file, which is located in the current directory (hence the .). The -e flag installs LabGym in “editable mode”, which means the code is kept in place for you to make changes to.

  7. If you’re on Windows or Linux, install PyTorch. If you’re on macOS, PyTorch will already be installed.

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

    $ python -m pip install --index-url https://download.pytorch.org/whl/cpu torch==2.0.1 torchvision==0.15.2

  8. Install Detectron2.

    $ python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'

    On Windows, you will need to configure your Detectron2 installation for GPU use.

    First, download the Detectron2 code using the following command.

    > git clone https://github.com/facebookresearch/detectron2.git

    The code will now be present in a detectron2 subfolder within the LabGym folder.

    If you’re using a GPU, open the setup.py file inside the detectron2 folder using your text editor (e.g. VS Code) and make the following change.

    Old version:

    72if not is_rocm_pytorch:
73    define_macros += [("WITH_CUDA", None)]
74    extra_compile_args["nvcc"] = [
75        "-O3",
76        "-DCUDA_HAS_FP16=1",
77        "-D__CUDA_NO_HALF_OPERATORS__",
78        "-D__CUDA_NO_HALF_CONVERSIONS__",
79        "-D__CUDA_NO_HALF2_OPERATORS__",
80    ]
81else:
82    define_macros += [("WITH_HIP", None)]
83    extra_compile_args["nvcc"] = []

    New version:

    72if not is_rocm_pytorch:
73    define_macros += [("WITH_CUDA", None)]
74    extra_compile_args["nvcc"] = [
75        "-O3",
76        "-DCUDA_HAS_FP16=1",
77        "-D__CUDA_NO_HALF_OPERATORS__",
78        "-D__CUDA_NO_HALF_CONVERSIONS__",
79        "-D__CUDA_NO_HALF2_OPERATORS__",
80        "-DWITH_CUDA",
81    ]
82else:
83    define_macros += [("WITH_HIP", None)]
84    extra_compile_args["nvcc"] = []

    Save the setup.py file, then exit your text editor.

    Finally, reopen your terminal, cd to the main LabGym folder, then install Detectron2.

    > set CUDA_HOME=%CUDA_HOME_V11_8%
> python -m pip install -e detectron2

  9. Test your setup by launching LabGym. If the LabGym GUI shows up, your setup is successful!

    $ python -m LabGym

    Note

    Launching LabGym while developing is intentionally different than launching LabGym as a user. If you wanted to launch LabGym using the LabGym command, you would need to run python -m pip install -e . each time you made changes.

    Launching LabGym through python -m LabGym runs the __main__.py file, which allows you to immediately see the results of your changes.

  10. Install nox, the development workflow runner for LabGym.

    $ python -m pip install nox

  11. Install Ruff, a linter and formatter for Python. If you’re using VS Code, you can install the Ruff VS Code Extension.

    Then, configure your editor to format your code on save using Ruff. For example, on VS Code, you can add the following to your User settings.json:

    "[python]": {
   "editor.defaultFormatter": "charliermarsh.ruff"
   "editor.formatOnSave": true
}