Project-HAMi/HAMi
Heterogeneous GPU Sharing on Kubernetes
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
Ranks #122 of 2,845 repositories GStars tracks this week (top 4%).
1 release in the last 90 days, the latest 56 days ago, with 188 contributors on GitHub.
Peaked at #26 in the weekly ranking the week of Mar 29, its best in the past year.
Use Cases & Benefits
- Manages and virtualizes heterogeneous GPUs and AI devices on Kubernetes clusters for efficient resource sharing and scheduling.
- Provides unified device virtualization and sharing across multiple heterogeneous AI accelerators with zero application changes required.
- Use for enabling fine-grained GPU resource allocation and isolation in Kubernetes pods to optimize AI workload performance.
- Use for integrating multiple types of AI accelerators like NVIDIA GPUs, Cambricon MLU, and Hygon DCU under a single Kubernetes management layer.
- Use for deploying cloud-native AI infrastructure in diverse industries such as finance, telecommunications, and manufacturing with heterogeneous device support.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
HAMi, formerly known as 'k8s-vGPU-scheduler', is a Heterogeneous device management middleware for Kubernetes. It can manage different types of heterogeneous devices (like GPU, NPU, etc.), share heterogeneous devices among pods, make better scheduling decisions based on topology of devices and scheduling policies.
It aims to remove the gap between different Heterogeneous devices, and provide a unified interface for users to manage with no changes to their applications. As of December 2024, HAMi has been widely used not only in Internet, public cloud and private cloud, but also broadly adopted in various vertical industries including finance, securities, energy, telecommunications, education, and manufacturing. More than 50 companies or institutions are not only end users but also active contributors.
HAMi is a sandbox and landscape project of Cloud Native Computing Foundation(CNCF), CNAI Landscape project.
HAMi provides device virtualization for several heterogeneous devices including GPU, by supporting device sharing and device resource isolation. For the list of devices supporting device virtualization, see supported devices
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+732 stars over 82 days across 27 snapshot days; best day +39 on Aug 5.
Forks
868+221
Open issues
33-26
Compared with peers in Kubernetes
Fastest-growing repositories GStars tracks in this cohort for the week of Oct 11, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| Project-HAMi/HAMi | 4.8K | +199 | 75 | 56 days ago |
| seaweedfs/seaweedfs | 35.7K | +152 | 75 | 24 days ago |
| google/gvisor | 19.7K | +140 | 75 | 5 days ago |
| traefik/traefik | 65.2K | +133 | 75 | 5 days ago |
| ys-ll/uniterm | 881 | +106 | 75 | 5 days ago |
| llm-d/llm-d-router | 428 | +26 | 65 | 5 days ago |
| Category median | 19.7K | +133 | 75 | — |
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