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Installing gsplat on Windows

Install using a pre-compiled wheels

You can install gsplat from python wheels containing pre-compiled binaries for a specific pytorch and cuda version. These wheels are stored in the github releases and can be found using simple index pages under https://docs.gsplat.studio/whl. You obtain the wheel from this simple index page for a specific pytorch an and cuda version by appending these the version number after a + sign (part referred a local version). For example, to install gsplat for pytorch 2.0 and cuda 11.8 you can use

pip install gsplat==1.2.0+pt20cu118 --index-url https://docs.gsplat.studio/whl

Alternatively, you can specify the pytorch and cuda version in the index url using for example

pip install gsplat --index-url https://docs.gsplat.studio/whl/pt20cu118

This has the advantage that you do not have to pin a specific version of the package and as a result get automatically the latest package version.

Install from source

You can install gsplat by compiling the wheel. In this way it will build the CUDA code during installation. This can be done using either the source package from pypi.org the wheel from pypi.org or using a clone of the repository. In all case Visual Studio needs to be install and activated.

Visual studio setup

Setting up and activating Visual Studio can be done through these steps:

  1. Install Visual Studio Build Tools. If MSVC 143 does not work, you may also need to install MSVC 142 for Visual Studio 2019. And your CUDA environment should be set up properly.

  2. Activate your conda environment:

    conda activate <your_conda_environment>

    Replace <your_conda_environment> with the name of your conda environment. For example:

    conda activate gsplat

  3. Activate your Visual C++ environment: Navigate to the directory where vcvars64.bat is located. This path might vary depending on your installation. A common path is:

    C:\Program Files (x86)\Microsoft Visual Studio\2022\BuildTools\VC\Auxiliary\Build

  4. Run the following command:

    ./vcvars64.bat

    If the above command does not work, try activating an older version of VC:

    ./vcvarsall.bat x64 -vcvars_ver=<your_VC++_compiler_toolset_version>

    Replace <your_VC++_compiler_toolset_version> with the version of your VC++ compiler toolset. The version number should appear in the same folder.

    For example:

    ./vcvarsall.bat x64 -vcvars_ver=14.29

Install using the source package published on pypi.org

You can install gsplat from the published source package (and not the wheel) by activating Visual Studio (see above) and then using

pip install --no-binary=gsplat gsplat --no-cache-dir

The CUDA code will be compiled during the installation and the Visual Studio compiler cl.exe does not need to be added to the path, because the installation process as an automatic way to find it. We use --no-cache-dir to avoid the potential risk of getting pip using a wheel file from pypi.org that would have be downloaded previously and that does not have the binaries.

Install using the wheel published on pypi.org

Setting up and activating Visual Studio can be done through these steps: You can install the gsplat using the wheel published on pypi.org by activating Visual Studio (see above) and then using

pip install gsplat

The wheel that does not contain the compiled CUDA binaries. The CUDA code is not compiled during the installation when using wheels and will be compiled at the first import of gsplat which requires the Visual Studio executable cl.exe to be on the path (see pre-requisite section above).

Install using the clone of the Repository

This can be done through these steps:

  1. Clone the gsplat repository:

    git clone --recursive https://github.com/nerfstudio-project/gsplat.git

  2. Change into the gsplat directory:

    cd gsplat

  3. Activate visual Studio

  4. Install gsplat using pip:

    pip install .