Using GPUs with Legacy Engines-Technical Preview To use GPUs with legacy engines, you must create a custom CUDA-capable engine image. Create a Custom CUDA-capable Engine ImageThe base engine image (docker.repository.cloudera.com/cdsw/engine:<version>) that ships with Cloudera Data Science Workbench will need to be extended with CUDA libraries to make it possible to use GPUs in jobs and sessions.Site Admins: Add the Custom CUDA Engine to your Cloudera Data Science Workbench DeploymentAfter you've created the custom CUDA engine, a site administrator must add this new engine to Cloudera Data Science Workbench. Project Admins: Enable the CUDA Engine for your ProjectProject administrators can use the following steps to make it the CUDA engine the default engine used for workloads within a particular project.Test the CUDA RuntimeYou can use the following simple examples to test whether the new CUDA ML Runtime is able to leverage GPUs as expected. Parent topic: Enabling Cloudera Data Science Workbench to use GPUs
