Using GPU acceleration for model training

In the previous section, you customized software components that your team needs to build models. In this section, you will see how RHODS makes it easy for you to use specific hardware for your workbench.

Imagine that you are working on a simple supervised learning model, and you do not need any specific hardware, such as a GPU, to complete your work. If you work on laptops, then the hardware is fixed and shipped with your laptop. You cannot change it dynamically and it would be expensive for organizations to give every data scientist specialized GPU hardware. It’s worse if there is a new model of the GPU and you already bought an older version for your team. RHODS enables you to provision hardware on-demand for your team, so if one member needs a GPU, they can just select it from the UI and start using it. Then, when their work is done, the GPU is released back to the hardware pool. This dynamic nature not only reduces costs but...