Using NVIDIA GPUs for Cloudera Data Science Workbench Projects
A GPU is a specialized processor that can be used to accelerate highly parallelized, computationally-intensive workloads.
Minimum Required Roles: Cloudera Manager Cluster Administrator, CDSW Site Administrator
Because of their computational power, GPUs have been found to be particularly well-suited to deep learning workloads. Ideally, CPUs and GPUs should be used in tandem for data engineering and data science workloads. A typical machine learning workflow involves data preparation, model training, model scoring, and model fitting. You can use existing general-purpose CPUs for each stage of the workflow, and optionally accelerate the math-intensive steps with the selective application of special-purpose GPUs. For example, GPUs allow you to accelerate model fitting using frameworks such as Tensorflow, PyTorch, Keras, MXNet, and Microsoft Cognitive Toolkit (CNTK).
By enabling GPU support, data scientists can share GPU resources available on Cloudera Data Science Workbench hosts. Users can requests a specific number of GPU instances, up to the total number available on a host, which are then allocated to the running session or job for the duration of the run. Projects can use isolated versions of libraries, and even different CUDA and cuDNN versions via Cloudera Data Science Workbench's extensible engine feature.