Mastering Multi-Tenant GPU Kubernetes with Kamaji and HAMi

In today's cloud-native landscape, efficiently managing GPU resources across multiple tenants is crucial for maximizing hardware utilization and reducing costs. Kamaji and HAMi provide a robust solution for running multi-tenant GPU Kubernetes on simulated hardware, allowing you to create isolated environments for different teams or projects without the overhead of dedicated physical GPUs.

Kamaji operates by running each tenant's Kubernetes control plane as pods within a single management cluster. This architecture simplifies management while ensuring that each tenant remains isolated. The HAMi component allows pods to request specific slices of GPU resources, such as memory in MB and compute in percentage, effectively packing multiple pods onto a single GPU card. The integration of Konnectivity creates a secure tunnel that enables the API server in the management cluster to communicate with the tenant's worker nodes, facilitating seamless GPU pod creation. Key configuration parameters include serviceType, kubernetes.version, and podCidr, which help tailor the environment to your needs.

In production, be aware of a few critical gotchas. For instance, avoid labeling the worker with gpu=on, as this triggers HAMi's real NVIDIA device plugin, which can fail without the appropriate drivers. Additionally, the mock plugin does not behave like a real GPU, so ensure your testing reflects actual hardware constraints. This setup is lightweight, requiring only a single Linux host with minimal resources, making it ideal for development and testing scenarios.