Following is a step by step guide for getting a new researcher up to speed with Run:AI and Kubernetes.
Change of Paradigms: from Docker to Kubernetes
As part of Run:AI, the organization is typically moving from Docker-based workflows to Kubernetes. This document is an attempt to help the researcher with this paradigm shift . It explains the basic concepts and provides links for further information about the Run:AI CLI.
Setup the Run:AI Command Line Interface
Run:AI CLI needs to be installed on the researcher machine. This document provides step by step instructions.
Provide the Researcher with a GPU Quota
To submit workloads with Run:AI, the researcher must be provided with a "project" which contains a GPU quota. Please see this document on how to create projects and set a quota.
Provide access to the Run:AI Administration UI
Some organizations would want to provide researchers with a more holistic view of what is happening in the cluster. You can do that by providing the appropriate access to the Run:AI Administration UI (app.run.ai). See this document for further information on how to provide access.
Schedule an Onboarding Session
It is highly recommended to schedule an onboarding session for researchers with a Run:AI customer success professional. Run:AI can help with the above transition, but adding to that, we at Run:AI have also acquired a large body of knowledge on data science best practices which can help streamline the researcher work as well as save money for the organization.
Researcher onboarding material also appears here.