HAMi - example manifests

Get the code to run this lab

The commands on this page come from the repository, not the website. Clone it and enter this lesson's folder: git clone https://github.com/ld-singh/ai-factory-ops-lab && cd ai-factory-ops-lab/portfolio-lab/01-k8s-gpu-platform/hami/examples. Browse this lesson on GitHub

ILLUSTRATIVE, not copy-paste. HAMi's resource names (nvidia.com/gpumem, nvidia.com/gpucores, …), units, and defaults can change between releases. Confirm against the version you installed: https://project-hami.io/ and https://github.com/Project-HAMi/HAMi

These run in Lesson 1C: Sharing on a real GPU, on the real GPU from Lesson 6 - that's where the memory-cap enforcement is actually proven. Capture the in-pod nvidia-smi and any allocation-failure output into ../../../06-validation-reports/.

Files

File	Demonstrates
shared-pods.yaml	Two pods each requesting a ~2 GiB slice of ONE physical GPU, co-resident

Use

1. Install HAMi on the Lesson 6 machine (confirm chart/values in the docs).
2. Confirm the resource names in shared-pods.yaml match your HAMi version.
3. Apply, then verify co-residency and the per-pod memory cap:

kubectl get pods -o wide                 # both Running on the single GPU node
kubectl exec share-a -- nvidia-smi       # reports the ~2 GiB slice, not the full card