[CNRWB][Tracking Issue] Cloud Native Research Workloads Book

[mrbobbytables]

One of the key areas identified by the CNCF Research User Group (RUG) for improvement¹ has been in the realm of documentation and best practices for running Research Workloads in a "Cloud Native" way; or - on top of Kubernetes.

Taking inspiration from the Scientific OpenStack Community², RUG has decided to do something similar and put together a book of user stories and best practices.

The complete break down of chapters and topics is still open for additions or modifications, but the current break down includes:

• Cluster Operations:
  • Broad configuration management + cluster-lifecycle
  • Management of clusters on top of OpenStack
  • Integration with Parallel / HPC file systems and access management
  • Managing / Exposing GPUs
  • Large Scale Clusters (1000+ nodes)
• Making Research Cloud Native:
  • Scalable JupyterHub
  • KubeFlow / ML lifecycle
  • Batch / MPI
  • Workflows / Workflow Engines / GitOps
  • DataOps (Data lifecycle management)

(Will link to break out issues as they're made)

Until a better acronym can be decided, issues related to this effort will be prefixed with CNRWB ("Cloud Native Research Workloads Book").

[1] CNCF Research User Group KubeCon NA 2019 Survey [2] Crossroads of Cloud and HPC: OpenStack for Scientific Research

[rochaporto]

We're closing these tickets for now, as we work in recipes for topics coming out of the regular meetings. Can check later if it makes sense to pick it up again.