Kubernetes GPU virtualization and heterogeneous accelerator scheduling for AI infrastructure.
HAMi stands for Heterogeneous AI Computing Virtualization Middleware. Formerly known as k8s-vGPU-scheduler, HAMi helps platform teams share expensive GPUs and other AI accelerators across Kubernetes workloads, isolate device memory and compute, and schedule pods with device-aware policies without changing application code.
HAMi is a CNCF Incubating and CNCF Landscape project. It is also listed in the CNAI Landscape.
- Architecture
- GPU virtualization
- Supported devices and vendor guides
- Prerequisites
- Install and run a first workload
- Upgrade and migrate values
- Configuration and operations
- NVIDIA scheduling policies
- NVIDIA dynamic MIG
- Device allocation metrics
- Device usage metrics
- Grafana dashboards
- WebUI
- Volcano integration
- Kueue integration
- Benchmarks
- Troubleshooting
- Uninstall
HAMi is governed by maintainers and contributors. Governance is described in the HAMi community repository.
To contribute code, documentation, tests, or device backend improvements, read CONTRIBUTING.md.
For chat channels, mailing lists, meeting schedules, and meeting notes, visit the HAMi community page.
HAMi is licensed under the Apache License 2.0. See LICENSE for details.
Copyright Contributors to HAMi, established as HAMi a Series of LF Projects, LLC.