Tutorials
Hands-on tutorials for learning HAMi by doing. Each lab is a step-by-step exercise with real, captured outputs: you build a cluster, install HAMi, and verify GPU partitioning behavior yourself.
Concepts
Background knowledge that the labs build on.
- GPU Software Stack Overview: the 5 layers from hardware to Kubernetes scheduling
- Understanding GPU Drivers: kernel modules, NVML, and how to troubleshoot from the bottom up
- HAMi Cluster Architecture: every component in a HAMi cluster and what breaks without it
Labs
Lab 1: Online Installation of HAMiBeginner
Build a GPU Kubernetes cluster from scratch on a cloud VM and install HAMi.
Lab 2: Local Fake GPU SetupBeginner
Learn the HAMi control plane on a laptop, no GPU required.
Lab 3: GPU Partitioning with HAMiIntermediate
Run multiple Pods on one GPU with enforced VRAM and compute limits.
Lab 4: GPU Slicing with Dynamic Resource AllocationAdvanced
The same outcome through Kubernetes-native Dynamic Resource Allocation (experimental).
Lab 5: Fake-GPU Scheduling with nvml-mockIntermediate
Simulate 8 A100 GPUs with HAMi scheduling features, no real GPU needed.
Lab 6: Run vLLM on HAMi GPU SharesIntermediate
Install HAMi on a GPU cluster and schedule vLLM inference services with GPU partitioning.
