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's 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 that contains a GPU quota. Please see Working with Projects document on how to create Projects and set a quota.
Provide access to the Run:ai User Interface¶
See Setting up users for further information on how to provide access to users.
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 Researchers' work as well as save money for the organization.