[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-84331":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":9,"totalLinesOfCode":9,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":9,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":16,"stars30d":16,"stars90d":16,"forks30d":16,"starsTrendScore":16,"compositeScore":17,"rankGlobal":9,"rankLanguage":9,"license":9,"archived":18,"fork":18,"defaultBranch":19,"hasWiki":18,"hasPages":18,"topics":9,"createdAt":9,"pushedAt":9,"updatedAt":20,"readmeContent":21,"aiSummary":9,"trendingCount":16,"starSnapshotCount":16,"syncStatus":22,"lastSyncTime":23,"discoverSource":24},84331,"KAI-Scheduler","kai-scheduler\u002FKAI-Scheduler","kai-scheduler","KAI Scheduler is an open source Kubernetes Native scheduler for AI workloads at large scale",null,"https:\u002F\u002Fgithub.com\u002Fkai-scheduler\u002FKAI-Scheduler","Go",1307,204,19,63,0,47.94,false,"main","2026-06-12 00:04:13","[![License](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-Apache_2.0-blue.svg)](LICENSE) [![Coverage](https:\u002F\u002Fgithub.com\u002Fkai-scheduler\u002FKAI-scheduler\u002Fraw\u002Fcoverage-badge\u002Fbadges\u002Fcoverage.svg)](https:\u002F\u002Fgithub.com\u002Fkai-scheduler\u002FKAI-scheduler\u002Fblob\u002Fmain\u002F.github\u002Fworkflows\u002Fupdate-coverage-badge.yaml)\n[![Ask DeepWiki](https:\u002F\u002Fdeepwiki.com\u002Fbadge.svg)](https:\u002F\u002Fdeepwiki.com\u002Fkai-scheduler\u002FKAI-scheduler)\n[![OpenSSF Best Practices](https:\u002F\u002Fwww.bestpractices.dev\u002Fprojects\u002F12064\u002Fbadge)](https:\u002F\u002Fwww.bestpractices.dev\u002Fprojects\u002F12064)\n[![ACMM](https:\u002F\u002Fimg.shields.io\u002Fendpoint?url=https%3A%2F%2Fconsole.kubestellar.io%2Fapi%2Facmm%2Fbadge%3Frepo%3Dkai-scheduler%2FKAI-Scheduler)](https:\u002F\u002Fconsole.kubestellar.io\u002Facmm?repo=kai-scheduler%2FKAI-Scheduler&utm_source=github&utm_medium=badge&utm_campaign=acmm-outreach)\n\n\u003Cp align=\"center\">\n  \u003Cpicture>\n    \u003Csource media=\"(prefers-color-scheme: dark)\" srcset=\"docs\u002Fassets\u002Fkai-logo-dark.png\">\n    \u003Csource media=\"(prefers-color-scheme: light)\" srcset=\"docs\u002Fassets\u002Fkai-logo-light.png\">\n    \u003Cimg alt=\"KAI Scheduler\" src=\"docs\u002Fassets\u002Fkai-logo-light.png\" width=\"550\">\n  \u003C\u002Fpicture>\n\u003C\u002Fp>\n\nKAI Scheduler is a robust, efficient, and scalable [Kubernetes scheduler](https:\u002F\u002Fkubernetes.io\u002Fdocs\u002Fconcepts\u002Fscheduling-eviction\u002Fkube-scheduler\u002F) that optimizes GPU resource allocation for AI and machine learning workloads.\n\nDesigned 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.\nKAI Scheduler allows administrators of Kubernetes clusters to dynamically allocate GPU resources to workloads. \n\nKAI Scheduler supports the entire AI lifecycle, from small, interactive jobs that require minimal resources to large training and inference, all within the same cluster. \nIt ensures optimal resource allocation while maintaining resource fairness between the different consumers.\nIt can run alongside other schedulers installed on the cluster.\n\n## Latest News 🔥\n\n- [2026\u002F04] **KubeCon EU 2026 Talk:** Watch the recording of the presentation \"[GPU Reservations: Maximizing Utilization and Fairness Across Teams](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=O-OEqmvCkYg)\", to explore how KAI Scheduler manages GPU resource reservations to balance utilization and fairness across teams.\n- [2025\u002F11] **KubeCon NA 2025 Talk:** Watch the recording of the presentation \"[Lightning Talk: Mind the Topology: Smarter Scheduling for AI Workloads on Kubernetes](https:\u002F\u002Fyoutu.be\u002Fo5i7pTWZjfo?si=su5iTOAS4r4O1TPa)\" to learn how KAI's Topology-Aware Scheduling (TAS) optimizes placement for modern disaggregated serving architectures.\n- [2025\u002F11] **Integration with [Grove](https:\u002F\u002Fgithub.com\u002Fai-dynamo\u002Fgrove) & Dynamo:** KAI's Topology-Aware and Hierarchical Gang Scheduling capabilities are integrated with Grove to orchestrate complex, multi-component workloads like disaggregated serving and agentic pipelines at scale. Read the [blog post](https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fstreamline-complex-ai-inference-on-kubernetes-with-nvidia-grove\u002F) for more details.\n- [2025\u002F10] **[v0.10.0 Release:](https:\u002F\u002Fgithub.com\u002Fkai-scheduler\u002FKAI-scheduler\u002Freleases\u002Ftag\u002Fv0.10.0)** Major features released, including [Topology-Aware Scheduling (TAS)](https:\u002F\u002Fgithub.com\u002Fkai-scheduler\u002FKAI-scheduler\u002Ftree\u002Fmain\u002Fdocs\u002Ftopology), [Hierarchical PodGroups](https:\u002F\u002Fgithub.com\u002Fkai-scheduler\u002FKAI-scheduler\u002Ftree\u002Fmain\u002Fdocs\u002Fdeveloper\u002Fdesigns\u002Fhierarchical-podgroup), and [Time-based Fairshare](https:\u002F\u002Fgithub.com\u002Fkai-scheduler\u002FKAI-scheduler\u002Ftree\u002Fmain\u002Fdocs\u002Ftime-based-fairshare).\n- [2025\u002F10] **KubeRay Integration:** KAI Scheduler is now natively integrated for [Ray workloads on Kubernetes](https:\u002F\u002Fdocs.ray.io\u002Fen\u002Fmaster\u002Fcluster\u002Fkubernetes\u002Fk8s-ecosystem\u002Fkai-scheduler.html).\n- [2025\u002F08] **Time-Based Fairshare:** [Proposal for Time-based Fairshare](https:\u002F\u002Fgithub.com\u002Fkai-scheduler\u002FKAI-scheduler\u002Fblob\u002Fmain\u002Fdocs\u002Fdeveloper\u002Fdesigns\u002Ftime-based-fairshare\u002Ftime-based-fairshare.md) is discussed at batch-wg. [Watch the recording.](https:\u002F\u002Fzoom.us\u002Frec\u002Fplay\u002FuW5ex5dmQP8_7UqOv5UjOGq8IqZeIa8AhKILqvDUQ6CnBAIdJjPY-BLfUWnoYblvDP-ZIvAp48p7XJNv.Cx5t7x1DwGqJgIYB?eagerLoadZvaPages=&accessLevel=meeting&canPlayFromShare=true&from=share_recording_detail&startTime=1755010542000&componentName=rec-play&originRequestUrl=https%3A%2F%2Fzoom.us%2Frec%2Fshare%2Frd_j_7ZDpC8lXxGNdQwguK2ZunoM3R93HR1Eo4A9rxD7b5lWSbmojDKc8OZ00ZMK.QxgEeMOxMcuiDkIY%3FstartTime%3D1755010542000)\n- [2025\u002F04] **Project Introduction:** Recording of the [KAI Scheduler introduction presented at the batch-wg meeting](https:\u002F\u002Fzoom.us\u002Frec\u002Fplay\u002FE1weaHroJpuTdXx6s9pjMu6oS78BiA53wsnvV9MWe_rIdwmDLFOG8J4XEPNW8-hIp4-HSFNdsbbP7mcv.YstbxFdS7z7tOfKw?eagerLoadZvaPages=&accessLevel=meeting&canPlayFromShare=true&from=share_recording_detail&startTime=1744124229000&componentName=rec-play&originRequestUrl=https%3A%2F%2Fzoom.us%2Frec%2Fshare%2FwP2WH6bqd7Dj8dupZD3YQTMWgG4AP5361_0h5vicI69LNb25JdQB8wn6fkvtLw2f.rLrRcQTSO1OCyRNu%3FstartTime%3D1744124229000).\n\n## Key Features\n\n- [Batch Scheduling](docs\u002Fbatch\u002FREADME.md): Ensure all pods in a group are scheduled simultaneously or not at all.\n- Bin Packing & Spread Scheduling: Optimize node usage either by minimizing fragmentation (bin-packing) or increasing resiliency and load balancing (spread scheduling).\n- [Workload Priority](docs\u002Fpriority\u002FREADME.md): Prioritize workloads effectively within queues.\n- [Separation of workload priority and preemptibility](https:\u002F\u002Fgithub.com\u002Fkai-scheduler\u002FKAI-scheduler\u002Ftree\u002Fmain\u002Fdocs\u002Fdeveloper\u002Fdesigns\u002Fpriority-preemptibility-separation): supports separation of workload priority and workloads preemptibility as two independent policies\n- [Hierarchical Queues](docs\u002Fqueues\u002FREADME.md): Manage workloads with two-level queue hierarchies for flexible organizational control.\n- [Resource distribution](docs\u002Ffairness\u002FREADME.md#resource-division-algorithm): Customize quotas, over-quota weights, limits, and priorities per queue.\n- [Fairness Policies](docs\u002Ffairness\u002FREADME.md#reclaim-strategies): Ensure equitable resource distribution using Dominant Resource Fairness (DRF) and resource reclamation across queues.\n- [Time-based Fairshare](https:\u002F\u002Fgithub.com\u002Fkai-scheduler\u002FKAI-scheduler\u002Ftree\u002Fmain\u002Fdocs\u002Ftime-based-fairshare): Over-time fair usage of resources, considering historical usage, time decay, and other parameters for fine-tuning.\n- [Min-guaranteed-runtime](https:\u002F\u002Fgithub.com\u002Fkai-scheduler\u002FKAI-scheduler\u002Ftree\u002Fmain\u002Fdocs\u002Fdeveloper\u002Fdesigns\u002Fmin-runtime): ensures a time period in which the scheduler must not preempt or reclaim a running workload, even if preemptible.\n- Workload Consolidation: Reallocate running workloads intelligently to reduce fragmentation and increase cluster utilization.\n- [Elastic Workloads](docs\u002Felastic\u002FREADME.md): Dynamically scale workloads within defined minimum and maximum pod or SubGroup thresholds.\n- Dynamic Resource Allocation (DRA): Support vendor-specific hardware resources through Kubernetes ResourceClaims (e.g., GPUs from NVIDIA or AMD).\n- [Topology-Aware Scheduling (TAS)](https:\u002F\u002Fgithub.com\u002Fkai-scheduler\u002FKAI-scheduler\u002Ftree\u002Fmain\u002Fdocs\u002Ftopology): supports optimized placement with [topology aware scheduling](https:\u002F\u002Fgithub.com\u002Fkai-scheduler\u002FKAI-scheduler\u002Ftree\u002Fmain\u002Fdocs\u002Fdeveloper\u002Fdesigns\u002Ftopology-awareness) and hierarchical topology aware scheduling for [Hierarchical PodGroups](https:\u002F\u002Fgithub.com\u002Fkai-scheduler\u002FKAI-scheduler\u002Ftree\u002Fmain\u002Fdocs\u002Fdeveloper\u002Fdesigns\u002Fhierarchical-podgroup).\n- [Hierarchical PodGroups](https:\u002F\u002Fgithub.com\u002Fkai-scheduler\u002FKAI-scheduler\u002Ftree\u002Fmain\u002Fdocs\u002Fdeveloper\u002Fdesigns\u002Fhierarchical-podgroup): supports gang scheduling with optimized topology aware scheduling of multi-level workloads, such as distributed and disaggregated workloads such as Dynamo\u002FGrove.\n- DRA support - supporting DRA for NVIDIA ComputeResources (GB200\u002FGB300)\n- Workload signatures: KAI Scheduler provides performance optimization for large  multi-pod submissions using workload signatures. \n- Scheduler explainability: based on K8S Events, every major step of the scheduling process is logged.\n\n- [GPU Sharing](docs\u002Fgpu-sharing\u002FREADME.md): Allow multiple workloads to efficiently share single or multiple GPUs, maximizing resource utilization.\n- Cloud & On-premise Support: Fully compatible with dynamic cloud infrastructures (including auto-scalers like Karpenter) as well as static on-premise deployments.\n\n> [!NOTE]\n> KAI Scheduler is built based on [kube-batch](https:\u002F\u002Fgithub.com\u002Fkubernetes-sigs\u002Fkube-batch).\n\n## Prerequisites\n\nBefore installing KAI Scheduler, ensure you have:\n\n- A running Kubernetes cluster\n- [Helm](https:\u002F\u002Fhelm.sh\u002Fdocs\u002Fintro\u002Finstall) CLI installed\n- [NVIDIA GPU-Operator](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fgpu-operator) installed in order to schedule workloads that request GPU resources\n\n## Installation\n\nKAI Scheduler will be installed in `kai-scheduler` namespace.\n\n> ⚠️ When submitting workloads, make sure to use a dedicated namespace. Do not use the `kai-scheduler` namespace for workload submission.\n\n### Installation Methods\n\nKAI Scheduler can be installed:\n\n- **From Production (Recommended)**\n- **From Source (Build it Yourself)**\n\n#### Install from Production\n\nLocate the latest release version in [releases](https:\u002F\u002Fgithub.com\u002Fkai-scheduler\u002FKAI-scheduler\u002Freleases) page.\nRun the following command after replacing `\u003CVERSION>` with the desired release version:\n\n```sh\nhelm upgrade -i kai-scheduler oci:\u002F\u002Fghcr.io\u002Fkai-scheduler\u002Fkai-scheduler\u002Fkai-scheduler -n kai-scheduler --create-namespace --version \u003CVERSION>\n```\n\n#### Build from Source\n\nFollow the instructions [here](docs\u002Fdeveloper\u002Fbuilding-from-source.md)\n\n## Flavor Specific Instructions\n\n### OpenShift\n\nWhen `gpu-operator` \u003Cv25.10.0 is installed, the following flag should be added to the installation command:\n\n```\n--set admission.gpuFractionRuntimeClassName=null\n```\nIf CDI is enabled, add `--set binder.cdiEnabled=true` to the installation command.\n\n## Support & Breaking changes\n\nFor details on our release lifecycle, LTS versions, and supported releases, see the [Support Policy](SUPPORT.md).\n\nRefer to the [Breaking Changes](https:\u002F\u002Fgithub.com\u002Fkai-scheduler\u002FKAI-scheduler\u002Fblob\u002Fmain\u002Fdocs\u002Fmigrationguides\u002FREADME.md) doc for more info\n\n## Quick Start\n\nTo start scheduling workloads with KAI Scheduler, please continue to [Quick Start example](docs\u002Fquickstart\u002FREADME.md)\n\n## Agent Skills\n\nRepo-local agent skills live under [`.agents\u002F`](.agents\u002FREADME.md). This directory is the shared source of truth for reusable agent workflows in this repository, including Codex and Claude Code integrations.\n\n## Roadmap\n\nYou can find the updated KAI Scheduler roadmap (historical, near year and future) [here](roadmap.md).\n\n## Community, Discussion, and Support\n\nWe’d love to hear from you! Here are the best ways to connect:\n\n### Contributing\n\nContributions are encouraged and appreciated! \nPlease have a look at KAI-scheduler's [contribution guide](https:\u002F\u002Fgithub.com\u002Fkai-scheduler\u002FKAI-scheduler\u002Fblob\u002Fmain\u002FCONTRIBUTING.md) before submitting PRs.\n\n### Slack\n\nJoin the [CNCF Slack](https:\u002F\u002Fcommunityinviter.com\u002Fapps\u002Fcloud-native\u002Fcncf) first and visit the [#kai-scheduler](https:\u002F\u002Fcloud-native.slack.com\u002Farchives\u002Fkai-scheduler) channel.\n\n### Bi-weekly Community Call\n\n**When:** Every other Monday at 17:00 CEST  \n[Convert to your time zone](https:\u002F\u002Fdateful.com\u002Ftime-zone-converter?t=17&tz2=Germany) | [Add to your calendar](https:\u002F\u002Fcalendar.google.com\u002Fcalendar\u002Fevent?action=TEMPLATE&tmeid=N2Q2bjhoNXAzMGc0cWpnZTQ4OGtpdXFhanFfMjAyNTA2MDlUMTUwMDAwWiAxZjQ2OTZiOWVlM2JiMWE1ZWIzMTAwODBkNDZiZmMwMDZjNTUxYWFiZmU1YTM3ZGM2YTc0NTFhYmNhMmE1ODk0QGc&tmsrc=1f4696b9ee3bb1a5eb310080d46bfc006c551aabfe5a37dc6a7451abca2a5894%40group.calendar.google.com&scp=ALL)  | [Meeting notes & agenda](https:\u002F\u002Fdocs.google.com\u002Fdocument\u002Fd\u002F13K7NGdPebOstlrsif1YLjGz1x-aJafMXeIgqbO7WghI\u002Fedit?usp=sharing)\n\n### Mailing List\n\nJoin the [kai-scheduler mailing list](https:\u002F\u002Fgroups.google.com\u002Fg\u002Fkai-scheduler) to receive updates on biweekly meetings.\n\n### Technical Issues & Feature Requests  \nPlease open a [GitHub issue](https:\u002F\u002Fgithub.com\u002Fkai-scheduler\u002FKAI-scheduler\u002Fissues\u002Fnew\u002Fchoose) for bugs, feature suggestions, or technical help. This helps us keep track of requests and respond effectively.\n\n---\n\n## Performance Dashboards\n\nKAI Scheduler provides public dashboards for monitoring performance and scale testing:\n\n- **[Scale Tests Dashboard](https:\u002F\u002Fkai-scheduler.github.io\u002FKAI-Scheduler\u002Fscale-tests\u002F)**: View historical results from scale tests that validate scheduler performance at large cluster sizes (hundreds to thousands of nodes). Tests run every 24 hours on dedicated infrastructure and measure scheduling performance, topology-aware scheduling, resource allocation, and system stability under load. The dashboard displays execution times, pass\u002Ffail status, detailed failure logs, and 30-day historical trends. See [scale tests documentation](docs\u002Fdeveloper\u002Fscale-tests.md) for technical details.\n\n- **[Benchmarks Dashboard](https:\u002F\u002Fkai-scheduler.github.io\u002FKAI-Scheduler\u002Fdev\u002Fbench\u002F)**: Track scheduler performance benchmarks across commits to the main branch. The dashboard shows per-commit benchmark history for core scheduler operations, with automatic alerts when performance regresses beyond thresholds.\n\n---\n\n\u003Cdiv align=\"center\">\n    \u003Cpicture>\n      \u003Csource media=\"(prefers-color-scheme: dark)\" srcset=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fcncf\u002Fartwork\u002Frefs\u002Fheads\u002Fmain\u002Fother\u002Fcncf\u002Fhorizontal\u002Fcolor-whitetext\u002Fcncf-color-whitetext.svg\">\n      \u003Csource media=\"(prefers-color-scheme: light)\" srcset=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fcncf\u002Fartwork\u002Frefs\u002Fheads\u002Fmain\u002Fother\u002Fcncf\u002Fhorizontal\u002Fcolor\u002Fcncf-color.svg\">\n      \u003Cimg width=\"300\" alt=\"Cloud Native Computing Foundation logo\" src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fcncf\u002Fartwork\u002Frefs\u002Fheads\u002Fmain\u002Fother\u002Fcncf\u002Fhorizontal\u002Fcolor-whitetext\u002Fcncf-color-whitetext.svg\">\n    \u003C\u002Fpicture>\n    \u003Cp>KAI Scheduler is \u003Ca href=\"https:\u002F\u002Fcncf.io\">Cloud Native Computing Foundation\u003C\u002Fa> sandbox project.\u003C\u002Fp>\n\u003C\u002Fdiv>\n\nCopyright Contributors to KAI Scheduler, established as KAI Scheduler a Series of LF Projects, LLC.\nFor website terms of use, trademark policy and other project policies please see [lfprojects.org\u002Fpolicies](https:\u002F\u002Flfprojects.org\u002Fpolicies\u002F).\n",2,"2026-06-11 04:12:51","trending"]