docs: add Managed DRA guide for LLMInferenceService
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Assessment
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Newbie friendliness
- 70/100
- Issue type
- Documentation
- Clarity
- Mostly clear
- Activity status
- Quiet
- Tech stack
- kubernetes
- Domain
- documentation
Research direction
Start with the related PR #5352 and the existing auto-generated CRD API references for LLMInferenceService. Document DRA prerequisites, annotations, generated claims, single-node and LeaderWorkerSet examples, and the distinction from static GPU requests; the work is done when users can follow a complete example and understand when to use each approach.
Written by the indexing model from the issue text.
Description
Summary
KServe v0.20 added Managed Dynamic Resource Allocation (DRA) support for LLMInferenceService, allowing dynamic GPU provisioning via annotations. No documentation exists beyond auto-generated CRD API references.
What needs to be documented
- What DRA is and when to use it vs static GPU
requests/limits - Annotation-based configuration:
serving.kserve.io/dra-device-classandserving.kserve.io/dra-gpu-count - How the controller generates
ResourceClaimTemplatesand injectsResourceClaims - Support for both single-node and multi-node (LeaderWorkerSet) deployments
- Prerequisites: Kubernetes DRA feature gates enabled
- Example YAML
Related PR
Since
KServe v0.20
- Dominant language
- MDX
- Stars
- 113
- Forks
- 200
- Avg merge
- 5d 13h
- Merged PRs (30d)
- 9
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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