kai-scheduler/KAI-Scheduler
KAI Scheduler is an open source Kubernetes Native scheduler for AI workloads at large scale
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
5 releases in the last 90 days, the latest 3 days ago, with 105 contributors on GitHub.
Use Cases & Benefits
- Provides a Kubernetes-native scheduler optimized for large-scale AI workloads requiring efficient GPU resource allocation.
- Enables dynamic, fair, and scalable GPU scheduling with advanced features like batch scheduling, workload priority, and GPU sharing.
- Use for managing GPU resources in large AI training clusters with thousands of nodes to maximize utilization and throughput.
- Use for running mixed AI workloads from small interactive jobs to large inference tasks within a single Kubernetes cluster.
- Use for integrating GPU scheduling in hybrid cloud and on-premise environments with support for vendor-specific hardware and autoscalers.
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
KAI Scheduler is a robust, efficient, and scalable Kubernetes scheduler that optimizes GPU resource allocation for AI and machine learning workloads.
Designed to manage large-scale GPU clusters, including thousands of nodes, and high-throughput of workloads, makes the KAI Scheduler ideal for extensive and demanding environments. KAI Scheduler allows administrators of Kubernetes clusters to dynamically allocate GPU resources to workloads.
KAI Scheduler supports the entire AI lifecycle, from small, interactive jobs that require minimal resources to large training and inference, all within the same cluster. It ensures optimal resource allocation while maintaining resource fairness between the different consumers. It can run alongside other schedulers installed on the cluster.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+185 stars over 86 days across 8 snapshot days.
Forks
323+76
Open issues
291+109
Compared with peers in Kubernetes
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| superplanehq/superplane | 8.8K | +435 | 60 | 7 days ago |
| aquasecurity/trivy | 39.0K | +204 | 45 | today |
| agentgateway/agentgateway | 6.0K | +183 | 60 | 9 days ago |
| argoproj/argo-cd | 24.6K | +58 | 60 | 1 day ago |
| cilium/cilium | 25.8K | +52 | 65 | 3 days ago |
| kai-scheduler/KAI-Scheduler | 1.6K | — | 65 | 3 days ago |
| Category median | 24.6K | +183 | 60 | — |
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