Run:AI helps organization optimize resources of a data science operation. Below are the prerequisites of the Run:AI solution.
Important note: This document relates to the cloud version of Run:AI and discusses the prerequisites for the GPU Cluster.
- Kubernetes Master: Run:AI runs on top of Kubernetes. If your GPU cluster already has Kubernetes, then no further hardware is required for Kubernetes. If you are installing Kubernetes, it is best to have a separate machine that will act as the Kubernetes master. Such a machine is best with 4 CPUs and 8 GB RAM (with no special disk requirements)
- Shared data volume: Run:AI, via Kubernetes, abstracts away the machine on which a container is running. For containers to run anywhere, they need to be able to access data from any machine in a uniform way. Typically, this requires a NAS (Network attached storage) which allows any node to connect to storage outside the box.
Run:AI user interface runs from the cloud. All container nodes must be able to connect to the Run:AI cloud. Inbound connectivity (connecting from the cloud into nodes) is not required.
Usage of containers and images: The individual researcher work is based on container images. Containers allows IT to create standard software environments based on mix and match of various cutting-edge software