Key Points to Note

Cloudera Data Science Workbench only supports CUDA-enabled NVIDIA GPU cards.

This topic assumes you have already installed or upgraded to the latest version of Cloudera Data Science Workbench.

  • Cloudera Data Science Workbench does not support heterogeneous GPU hardware in a single deployment.

  • Cloudera Data Science Workbench does not install or configure the NVIDIA drivers on the Cloudera Data Science Workbench gateway hosts. These depend on your GPU hardware and will have to be installed by your system administrator. The steps provided in this topic are generic guidelines that will help you evaluate your setup.

  • The instructions described in this topic require Internet access. If you have an airgapped deployment, you will be required to manually download and load the resources onto your hosts.

  • Cloudera Data Science Workbench 1.9.0 or later provides two options for supporting GPUs:
    • Support for Nvidia was introduced with ML Runtimes 2021.02. Airgapped environments will have access to ML Runtimes 2021.02 in the upcoming CDSW 1.10 release. See the documentation on the ML Runtimes Nvidia GPU Edition.
    • Cloudera Data Science Workbench still provides technical preview support for CUDA-enabled engines. However, CDSW does not include an engine image that supports Nvidia libraries. You must create your own custom CUDA-capable engine image using the instructions provided in Create a Custom CUDA-capable Engine Image.
  • For a list of known issues associated with this feature, refer Known Issues - GPU Support and ML Runtimes Release Notes