Installation ============ Dask-CUDA can be installed using ``conda``, ``pip``, or from source. Conda ----- To use Dask-CUDA on your system, you will need: - NVIDIA drivers for your GPU; see `NVIDIA Driver Installation Quickstart Guide `_ for installation instructions - A version of NVIDIA CUDA Toolkit compatible with the installed driver version; see Table 1 of `CUDA Compatibility -- Binary Compatibility `_ for an overview of CUDA Toolkit driver requirements Once the proper CUDA Toolkit version has been determined, it can be installed using along with Dask-CUDA using ``conda``. To install the latest version of Dask-CUDA along with CUDA Toolkit 12.0: .. code-block:: bash conda install -c rapidsai -c conda-forge -c nvidia dask-cuda cuda-version=12.0 Pip --- When working outside of a Conda environment, CUDA Toolkit can be downloaded and installed from `NVIDIA's website `_; this package also contains the required NVIDIA drivers. To install the latest version of Dask-CUDA: .. code-block:: bash python -m pip install dask-cuda Source ------ To install Dask-CUDA from source, the source code repository must be cloned from GitHub: .. code-block:: bash git clone https://github.com/rapidsai/dask-cuda.git cd dask-cuda python -m pip install . Other RAPIDS libraries ---------------------- Dask-CUDA is a part of the `RAPIDS `_ suite of open-source software libraries for GPU-accelerated data science, and works well in conjunction with them. See `RAPIDS -- Getting Started `_ for instructions on how to install these libraries. Keep in mind that these libraries will require: - At least one CUDA-compliant GPU - A system installation of `CUDA `_