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:

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:

python -m pip install dask-cuda

Source

To install Dask-CUDA from source, the source code repository must be cloned from GitHub:

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