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
Build a GPU Kubernetes cluster from scratch on a cloud VM and install HAMi.
Learn the HAMi control plane on a laptop, no GPU required.
Run multiple Pods on one GPU with enforced VRAM and compute limits.
The same outcome through Kubernetes-native Dynamic Resource Allocation (experimental).
Simulate 8 A100 GPUs with HAMi scheduling features, no real GPU needed.
Install HAMi on a GPU cluster and schedule vLLM inference services with GPU partitioning.
Share one non-MIG GPU between Pods on single-node k3s and prove HAMi-core enforces the memory cap.
Share one GPU with Volcano vGPU, then verify Gang scheduling and queue-level vGPU limits.
Enforce vGPU count, memory, and compute quotas for HAMi workloads before Pods reach the scheduler.
Simulate an asymmetric PCIe topology and verify HAMi's topology-aware scheduler avoids a poorly-connected GPU for multi-GPU requests and picks it for single-GPU requests, no real GPU required.
Package a model as a KitOps ModelKit, pull it from Jozu Hub with an initContainer, and serve it locally with SGLang (and optionally vLLM) on HAMi GPU shares.
