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# Requirements
- Both Windows and Linux are supported.
- CUDA __v10.2 or higher__, a __C++14__ capable compiler, and CMake __v3.19 or higher__.
-[CUDA](https://developer.nvidia.com/cuda-toolkit)__v10.2 or higher__, a __C++14__ capable compiler, and [CMake](https://cmake.org/)__v3.19 or higher__.
- A high-end NVIDIA GPU that supports TensorCores and has a large amount of memory. The framework was tested primarily with an RTX 3090.
- __(optional)__ Python __3.7 or higher__ for interactive Python bindings. Run `pip install -r requirements.txt` to install the required dependencies.
- __(optional)__ [Python](https://www.python.org/)__3.7 or higher__ for interactive bindings. Also, run `pip install -r requirements.txt`.
- On some machines, `pyexr` refuses to install via `pip`. This can be resolved by installing OpenEXR from [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#openexr).
- __(optional)__ OptiX __7.3 or higher__ for faster mesh SDF training. Set the environment variable `OptiX_INSTALL_DIR` to the installation directory if it is not discovered automatically.
- __(optional)__ [OptiX](https://developer.nvidia.com/optix)__7.3 or higher__ for faster mesh SDF training. Set the environment variable `OptiX_INSTALL_DIR` to the installation directory if it is not discovered automatically.
If you are using Linux, install the following packages
We also recommend installing CUDA and OptiX in `/usr/local/` and adding the CUDA installation to your path.
We also recommend installing [CUDA](https://developer.nvidia.com/cuda-toolkit) and [OptiX](https://developer.nvidia.com/optix) in `/usr/local/` and adding the CUDA installation to your path.
For example, if you have CUDA 11.4, add the following to your `~/.bashrc`
