Summary
Create and improve practical, reproducible learning materials that help users deploy, validate, and troubleshoot HAMi GPU sharing in realistic Kubernetes and AI workloads.
This issue is the upstream tracking issue for a proposed CNCF LFX Mentorship program. It builds on the tutorials/labs work tracked in #431 and should contribute to the maintained HAMi website and documentation. The former Project-HAMi/hami-workshop repository is archived, so new work should land in active HAMi repositories.
Scope
- Audit the current HAMi tutorials, demos, labs, and troubleshooting content and identify the highest-impact gaps.
- Add reproducible learning paths for installing HAMi and validating GPU sharing and isolation.
- Add practical examples for representative AI workloads such as vLLM, Ray, or SGLang, based on mentor-approved priorities.
- Document common failure modes, diagnostic commands, expected results, and recovery steps.
- Improve navigation and cross-links so users can move from concepts to hands-on labs.
- Validate examples against a documented environment and supported HAMi release.
Expected deliverables
- A short gap analysis and mentor-approved implementation plan.
- At least two substantial, reproducible labs or tutorials.
- A troubleshooting guide covering common installation, scheduling, and runtime issues.
- Tested manifests/scripts and clearly documented prerequisites.
- Contributions merged into the active HAMi website or another mentor-approved HAMi repository.
Acceptance criteria
- Instructions are reproducible from a clean environment with the documented prerequisites.
- Each lab includes learning goals, expected output, verification steps, and cleanup instructions.
- Commands and manifests pass the relevant repository checks.
- Content is reviewed for technical accuracy and discoverability by HAMi maintainers.
- Final documentation states the HAMi version and hardware/software assumptions used for validation.
Out of scope
- Reviving the archived
Project-HAMi/hami-workshop repository.
- Building a new documentation platform.
- Promising support for every accelerator, framework, or deployment model within one mentorship term.
Related work
Summary
Create and improve practical, reproducible learning materials that help users deploy, validate, and troubleshoot HAMi GPU sharing in realistic Kubernetes and AI workloads.
This issue is the upstream tracking issue for a proposed CNCF LFX Mentorship program. It builds on the tutorials/labs work tracked in #431 and should contribute to the maintained HAMi website and documentation. The former
Project-HAMi/hami-workshoprepository is archived, so new work should land in active HAMi repositories.Scope
Expected deliverables
Acceptance criteria
Out of scope
Project-HAMi/hami-workshoprepository.Related work