[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-83308":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":17,"stars7d":18,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":19,"compositeScore":20,"rankGlobal":9,"rankLanguage":9,"license":9,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":21,"hasPages":21,"topics":23,"createdAt":9,"pushedAt":9,"updatedAt":34,"readmeContent":35,"aiSummary":36,"trendingCount":16,"starSnapshotCount":16,"syncStatus":37,"lastSyncTime":38,"discoverSource":39},83308,"volcano","volcano-sh\u002Fvolcano","volcano-sh","A Cloud Native Batch System (Project under CNCF)",null,"https:\u002F\u002Fgithub.com\u002Fvolcano-sh\u002Fvolcano","Go",5659,1401,84,427,0,24,25,72,40.44,false,"main",[24,25,26,27,28,29,30,31,32,33],"batch-systems","kubernetes","golang","hpc","bigdata","machine-learning","gene","ai","serving","training","2026-06-12 02:04:33","\u003Ca href=\"https:\u002F\u002Fvolcano.sh\u002F\">\n    \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fvolcano-sh\u002Fvolcano\u002Fmaster\u002Fdocs\u002Fimages\u002Fvolcano-horizontal-color.png\"\u002F>\n\u003C\u002Fa>\n\n-------\n\n[![Build Status](https:\u002F\u002Ftravis-ci.org\u002Fvolcano-sh\u002Fvolcano.svg?branch=master)](https:\u002F\u002Ftravis-ci.org\u002Fvolcano-sh\u002Fvolcano)\n[![Go Report Card](https:\u002F\u002Fgoreportcard.com\u002Fbadge\u002Fgithub.com\u002Fvolcano-sh\u002Fvolcano)](https:\u002F\u002Fgoreportcard.com\u002Freport\u002Fgithub.com\u002Fvolcano-sh\u002Fvolcano)\n[![RepoSize](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Frepo-size\u002Fvolcano-sh\u002Fvolcano.svg)](https:\u002F\u002Fgithub.com\u002Fvolcano-sh\u002Fvolcano)\n[![Release](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Frelease\u002Fvolcano-sh\u002Fvolcano.svg)](https:\u002F\u002Fgithub.com\u002Fvolcano-sh\u002Fvolcano\u002Freleases)\n[![LICENSE](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fvolcano-sh\u002Fvolcano.svg)](https:\u002F\u002Fgithub.com\u002Fvolcano-sh\u002Fvolcano\u002Fblob\u002Fmaster\u002FLICENSE)\n[![CII Best Practices](https:\u002F\u002Fbestpractices.coreinfrastructure.org\u002Fprojects\u002F3012\u002Fbadge)](https:\u002F\u002Fbestpractices.coreinfrastructure.org\u002Fprojects\u002F3012)\n[![OpenSSF Scorecard](https:\u002F\u002Fapi.scorecard.dev\u002Fprojects\u002Fgithub.com\u002Fvolcano-sh\u002Fvolcano\u002Fbadge)](https:\u002F\u002Fscorecard.dev\u002Fviewer\u002F?uri=github.com\u002Fvolcano-sh\u002Fvolcano)\n[![Gurubase](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGurubase-Ask%20Volcano%20Guru-006BFF)](https:\u002F\u002Fgurubase.io\u002Fg\u002Fvolcano)\n\n\n\n[Volcano](https:\u002F\u002Fvolcano.sh\u002F) is a Kubernetes-native batch scheduling system, extending and enhancing the capabilities of the standard kube-scheduler. It provides a comprehensive set of features specifically designed to manage and optimize various batch and elastic workloads, including Artificial Intelligence (AI) \u002F machine learning (ML) \u002F deep learning (DL), bioinformatics \u002F genomics, and other \"Big Data\" applications.\n\nThese workloads commonly leverage AI, Big Data, and HPC frameworks such as Spark, Flink, Ray, TensorFlow, PyTorch, Argo, MindSpore, PaddlePaddle, Kubeflow, MPI, Horovod, MXNet, KubeGene, and others, with which Volcano offers robust integration.\n\nVolcano incorporates over fifteen years of collective experience in operating diverse high-performance workloads at scale across multiple systems and platforms. It combines proven best practices and innovative concepts from the open-source community to deliver a powerful and flexible scheduling solution.\n\nAs of 2025, Volcano has seen widespread adoption across numerous industries globally, including Internet\u002FCloud, Finance, Manufacturing, and Medical sectors. Many organizations and institutions are not only end-users but also active contributors to the project. Hundreds of contributors actively participate in code commits, pull request reviews, issue discussions, documentation updates, and design proposals. We encourage your participation in the ongoing development and growth of the Volcano project.\n\n\n> [!NOTE]\n> the scheduler is built based on [kube-batch](https:\u002F\u002Fgithub.com\u002Fkubernetes-sigs\u002Fkube-batch);\nrefer to [#241](https:\u002F\u002Fgithub.com\u002Fvolcano-sh\u002Fvolcano\u002Fissues\u002F241) and [#288](https:\u002F\u002Fgithub.com\u002Fvolcano-sh\u002Fvolcano\u002Fpull\u002F288) for more detail.\n\n![cncf_logo](docs\u002Fimages\u002Fcncf-logo.png)\n\nVolcano is an incubating project of the [Cloud Native Computing Foundation](https:\u002F\u002Fcncf.io\u002F) (CNCF). Please consider joining the CNCF if you are an organization that wants to take an active role in supporting the growth and evolution of the cloud native ecosystem. \n\n## Overall Architecture\n\n![volcano](docs\u002Fimages\u002Fvolcano-architecture.png)\n\n## Talks\n\n- [Intro: Kubernetes Batch Scheduling @ KubeCon 2019 EU](https:\u002F\u002Fsched.co\u002FMPi7)\n- [Volcano 在 Kubernetes 中运行高性能作业实践 @ ArchSummit 2019](https:\u002F\u002Farchsummit.infoq.cn\u002F2019\u002Fshenzhen\u002Fpresentation\u002F1817)\n- [Volcano：基于云原生的高密计算解决方案 @ Huawei Connection 2019](https:\u002F\u002Fe.huawei.com\u002Fcn\u002Fmaterial\u002Fevent\u002FHC\u002F09099dce0070415e9f26ada51b2216d7)\n- [Improving Performance of Deep Learning Workloads With Volcano @ KubeCon 2019 NA](https:\u002F\u002Fsched.co\u002FUaZi)\n- [Batch Capability of Kubernetes Intro @ KubeCon 2019 NA](https:\u002F\u002Fsched.co\u002FUajv)\n- [Optimizing Knowledge Distillation Training With Volcano @ KubeCon 2021 EU](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=cDPGmhVcj7Y&t=143s)\n- [Exploration About Mixing Technology of Online Services and Offline Jobs Based On Volcano @ KubeCon 2021 China](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=daqkUlT5ReY)\n- [Volcano - Cloud Native Batch System for AI, Big Data and HPC @ KubeCon 2022 EU](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=wjy35HfIP_k)\n- [How to Leverage Volcano to Improve the Resource Utilization of AI Pharmaceuticals, Autonomous Driving, and Smart Buildings @ KubeCon 2023 EU](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ujHDV5xteqU)\n- [Run Your AI Workloads and Microservices on Kubernetes More Easily and Efficiently @ KubeCon 2023 China](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=OO7zpyf7fgs)\n- [Optimize LLM Workflows with Smart Infrastructure Enhanced by Volcano @ KubeCon 2024 China](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=77Qn1-I-muQ)\n- [How Volcano Enable Next Wave of Intelligent Applications @ KubeCon 2024 China](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=IzR7zJQ8vMw)\n- [Leverage Topology Modeling and Topology-Aware Scheduling to Accelerate LLM Training @ KubeCon 2024 China](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=IB54LHQQ8lI)\n\n\n## Ecosystem\n\n- [Spark Operator](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fcomponents\u002Fspark-operator\u002Fuser-guide\u002Fvolcano-integration\u002F)\n- [Native Spark](https:\u002F\u002Fspark.apache.org\u002Fdocs\u002F3.5.0\u002Frunning-on-kubernetes.html#using-volcano-as-customized-scheduler-for-spark-on-kubernetes)\n- [Flink](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fflink-on-k8s-operator\u002Fblob\u002Fmaster\u002Fdocs\u002Fvolcano_integration.md)\n- [KubeRay](https:\u002F\u002Fdocs.ray.io\u002Fen\u002Fmaster\u002Fcluster\u002Fkubernetes\u002Fk8s-ecosystem\u002Fvolcano.html)\n- [PyTorch](https:\u002F\u002Fgithub.com\u002Fvolcano-sh\u002Fvolcano\u002Fblob\u002Fmaster\u002Fdocs\u002Fuser-guide\u002Fhow_to_use_pytorch_plugin.md)\n- [TensorFlow](https:\u002F\u002Fgithub.com\u002Fvolcano-sh\u002Fvolcano\u002Ftree\u002Fmaster\u002Fexample\u002Fintegrations\u002Ftensorflow)\n- [kubeflow\u002Ftraining-operator](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fcomponents\u002Ftraining\u002Fuser-guides\u002Fjob-scheduling\u002F)\n- [kubeflow\u002Farena](https:\u002F\u002Fgithub.com\u002Fkubeflow\u002Farena\u002Fblob\u002Fmaster\u002Fdocs\u002Ftraining\u002Fvolcanojob\u002Fvolcanojob.md)\n- [MPI](https:\u002F\u002Fgithub.com\u002Fvolcano-sh\u002Fvolcano\u002Ftree\u002Fmaster\u002Fexample\u002Fintegrations\u002Fmpi)\n- [Horovod](https:\u002F\u002Fgithub.com\u002Fvolcano-sh\u002Fvolcano\u002Fblob\u002Fmaster\u002Fexample\u002Fkubecon-2019-china\u002Fhorovod-sample\u002Flm-horovod-tf-mnist-v0.5.yaml)\n- [PaddlePaddle](https:\u002F\u002Fgithub.com\u002Fvolcano-sh\u002Fvolcano\u002Ftree\u002Fmaster\u002Fexample\u002Fintegrations\u002Fpaddlepaddle)\n- [Cromwell](https:\u002F\u002Fgithub.com\u002Fbroadinstitute\u002Fcromwell\u002Fblob\u002Fdevelop\u002Fdocs\u002Fbackends\u002FVolcano.md)\n- [MindSpore](https:\u002F\u002Fgithub.com\u002Fvolcano-sh\u002Fvolcano\u002Ftree\u002Fmaster\u002Fexample\u002FMindSpore-example)\n- [MXNet](https:\u002F\u002Fgithub.com\u002Fvolcano-sh\u002Fvolcano\u002Ftree\u002Fmaster\u002Fexample\u002Fintegrations\u002Fmxnet\u002Ftrain)\n- [Argo](https:\u002F\u002Fgithub.com\u002Fvolcano-sh\u002Fvolcano\u002Ftree\u002Fmaster\u002Fexample\u002Fintegrations\u002Fargo)\n- [KubeGene](https:\u002F\u002Fgithub.com\u002Fvolcano-sh\u002Fkubegene)\n\n## Use Cases\n- [Why Spark chooses Volcano as built-in batch scheduler on Kubernetes?](https:\u002F\u002Fwww.cncf.io\u002Fblog\u002F2022\u002F06\u002F30\u002Fwhy-spark-chooses-volcano-as-built-in-batch-scheduler-on-kubernetes\u002F)\n- [ING Bank: How Volcano empowers its big data analytics platform](https:\u002F\u002Fwww.cncf.io\u002Fblog\u002F2023\u002F02\u002F21\u002Fing-bank-how-volcano-empowers-its-big-data-analytics-platform\u002F)\n- [Using Volcano as a custom scheduler for Apache Spark on Amazon EMR on EKS](https:\u002F\u002Fdocs.aws.amazon.com\u002Femr\u002Flatest\u002FEMR-on-EKS-DevelopmentGuide\u002Ftutorial-volcano.html)\n- [Deploy Azure Machine Learning extension on AKS or Arc Kubernetes cluster](https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fhow-to-deploy-kubernetes-extension?view=azureml-api-2&tabs=deploy-extension-with-cli)\n- [Practical Tips for Preventing GPU Fragmentation for Volcano Scheduler](https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Fpractical-tips-for-preventing-gpu-fragmentation-for-volcano-scheduler\u002F)\n- [Using Volcano in Large-Scale, Distributed Offline Computing](https:\u002F\u002Fvolcano.sh\u002Fen\u002Fblog\u002Fruitian2-en\u002F)\n- [OpenI-Octopus: How to Avoid Resource Preemption in Kubernetes Clusters](https:\u002F\u002Fvolcano.sh\u002Fen\u002Fblog\u002Fpengcheng-en\u002F)\n- [How Does Volcano Empower a Content Recommendation Engine in Xiaohongshu](https:\u002F\u002Fvolcano.sh\u002Fen\u002Fblog\u002Fxiaohongshu-en\u002F)\n- [How Ruitian Used Volcano to Run Large-Scale Offline HPC Jobs](https:\u002F\u002Fvolcano.sh\u002Fen\u002Fblog\u002Fruitian-en\u002F)\n- [Integrating Volcano into the Leinao Cloud OS](https:\u002F\u002Fvolcano.sh\u002Fen\u002Fblog\u002Fleinao-en\u002F)\n- [HPC on Volcano: How Containers Support HPC Applications in the Meteorological Industry](https:\u002F\u002Fvolcano.sh\u002Fen\u002Fblog\u002Fhpc-en\u002F)\n- [iQIYI:Volcano-based Cloud Native Migration Practices](https:\u002F\u002Fvolcano.sh\u002Fen\u002Fblog\u002Faiqiyi-en\u002F)\n- [PaddlePaddle Distributed Training on Volcano](https:\u002F\u002Fvolcano.sh\u002Fen\u002Fblog\u002Fpaddlepaddle-en\u002F)\n\n## Quick Start Guide\n\n### Prerequisites\n\n- Kubernetes 1.12+ with CRD support\n\n\nYou can try Volcano by one of the following two ways.\n\n> [!NOTE]\n> * For Kubernetes v1.17 and above, use CRDs under config\u002Fcrd\u002Fbases (recommended)\n> * For Kubernetes v1.16 and below, use CRDs under config\u002Fcrd\u002Fv1beta1 (deprecated)\n\n### Install with YAML files\n\nInstall Volcano on an existing Kubernetes cluster. This way is both available for x86_64 and arm64 architecture.\n\n```\nkubectl apply -f https:\u002F\u002Fraw.githubusercontent.com\u002Fvolcano-sh\u002Fvolcano\u002Fmaster\u002Finstaller\u002Fvolcano-development.yaml\n```\n\nEnjoy! Volcano will create the following resources in `volcano-system` namespace.\n\n\n```\nNAME                                       READY   STATUS      RESTARTS   AGE\npod\u002Fvolcano-admission-5bd5756f79-dnr4l     1\u002F1     Running     0          96s\npod\u002Fvolcano-controllers-687948d9c8-nw4b4   1\u002F1     Running     0          96s\npod\u002Fvolcano-scheduler-94998fc64-4z8kh      1\u002F1     Running     0          96s\n\nNAME                                TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)   AGE\nservice\u002Fvolcano-admission-service   ClusterIP   10.98.152.108   \u003Cnone>        443\u002FTCP   96s\n\nNAME                                  READY   UP-TO-DATE   AVAILABLE   AGE\ndeployment.apps\u002Fvolcano-admission     1\u002F1     1            1           96s\ndeployment.apps\u002Fvolcano-controllers   1\u002F1     1            1           96s\ndeployment.apps\u002Fvolcano-scheduler     1\u002F1     1            1           96s\n\nNAME                                             DESIRED   CURRENT   READY   AGE\nreplicaset.apps\u002Fvolcano-admission-5bd5756f79     1         1         1       96s\nreplicaset.apps\u002Fvolcano-controllers-687948d9c8   1         1         1       96s\nreplicaset.apps\u002Fvolcano-scheduler-94998fc64      1         1         1       96s\n\nNAME                               COMPLETIONS   DURATION   AGE\njob.batch\u002Fvolcano-admission-init   1\u002F1           48s        96s\n\n```\n\n### Install via helm\n\nTo install official release, please visit [helm-charts](https:\u002F\u002Fgithub.com\u002Fvolcano-sh\u002Fhelm-charts) for details.\n\n```bash\nhelm repo add volcano-sh https:\u002F\u002Fvolcano-sh.github.io\u002Fhelm-charts\nhelm install volcano volcano-sh\u002Fvolcano -n volcano-system --create-namespace\n```\n\nInstall from source code for developers:\n\n```bash\nhelm install volcano installer\u002Fhelm\u002Fchart\u002Fvolcano --namespace volcano-system --create-namespace\n\n# list helm release\nhelm list -n volcano-system\n```\n\n### Install from code\n\nIf you don't have a kubernetes cluster, try one-click install from code base:\n\n```bash\n.\u002Fhack\u002Flocal-up-volcano.sh\n```\n\nThis way is only available for x86_64 temporarily.\n\n### Install volcano agent\n\nPlease follow the guide [Volcano Agent](https:\u002F\u002Fvolcano.sh\u002Fen\u002Fdocs\u002Fcolocation) to install volcano agent.\n\n### Install monitoring system\n\nIf you want to get prometheus and grafana volcano dashboard after volcano installed, try following commands:\n\n```bash\nkubectl create -f installer\u002Fvolcano-monitoring.yaml\n```\n\n### Install dashboard\n\nPlease follow the guide [Volcano Dashboard](https:\u002F\u002Fgithub.com\u002Fvolcano-sh\u002Fdashboard#volcano-dashboard) to install volcano dashboard.\n\n## Kubernetes compatibility\n|                       | Kubernetes 1.35 | Kubernetes 1.34 | Kubernetes 1.33 | Kubernetes 1.32 | Kubernetes 1.31 | Kubernetes 1.30 | Kubernetes 1.29 | Kubernetes 1.28 | Kubernetes 1.27 | Kubernetes 1.26 | Kubernetes 1.25 | Kubernetes 1.24 | Kubernetes 1.23 | Kubernetes 1.22 | Kubernetes 1.21 |\n|-----------------------|-----------------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|\n| Volcano HEAD (master) | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | -               | -               |\n| Volcano v1.14         | -               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | -               | -               |\n| Volcano v1.13         | -               | -               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | -               | -               |\n| Volcano v1.12         | -               | -               | -               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               |\n| Volcano v1.11         | -               | -               | -               | -               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               |\n| Volcano v1.10         | -               | -               | -               | -               | -               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               | ✓               |\n\nKey:\n* `✓` Volcano and the Kubernetes version are exactly compatible.\n* `+` Volcano has features or API objects that may not be present in the Kubernetes version.\n* `-` The Kubernetes version has features or API objects that Volcano can't use.\n\n\n## Citing Volcano\n\nIf Volcano helps your research, we appreciate your citations. Here is the BibTeX entry:\n\n```bibtex\n@misc{volcano2025,\n  title={Volcano: A Cloud Native Batch System},\n  author={Klaus Ma and Kevin Wang and others},\n  year={2025},\n  howpublished={\\url{https:\u002F\u002Fgithub.com\u002Fvolcano-sh\u002Fvolcano}},\n}\n```\n\n## Meeting\n\nWe hold community meetings for different timezones. See [Volcano Community Meeting Info](https:\u002F\u002Fgithub.com\u002Fvolcano-sh\u002Fcommunity#community-meeting).\n\nResources:\n- [Meeting notes and agenda](https:\u002F\u002Fdocs.google.com\u002Fdocument\u002Fd\u002F1YLbF8zjZBiR9PbXQPB22iuc_L0Oui5A1lddVfRnZrqs\u002Fedit)\n- [Meeting link](https:\u002F\u002Fzoom.us\u002Fj\u002F91804791393)\n- [Meeting Calendar](https:\u002F\u002Fcalendar.google.com\u002Fcalendar\u002Fb\u002F1\u002Fembed?src=volcano.sh.bot@gmail.com) | [Subscribe](https:\u002F\u002Fcalendar.google.com\u002Fcalendar\u002Fb\u002F1?cid=dm9sY2Fuby5zaC5ib3RAZ21haWwuY29t)\n\n## Contact\n\nIf you have any question, feel free to reach out to us in the following ways:\n\n[Volcano Slack Channel](https:\u002F\u002Fcloud-native.slack.com\u002Farchives\u002FC011GJDQS0N) | [Join](https:\u002F\u002Fslack.cncf.io\u002F)\n\n[Mailing List](https:\u002F\u002Fgroups.google.com\u002Fforum\u002F#!forum\u002Fvolcano-sh)\n\nWeChat: Please add WeChat account `k8s2222` and request an invitation to the group chat.\n","Volcano 是一个基于 Kubernetes 的云原生批处理系统，旨在扩展和增强标准 kube-scheduler 的能力。它提供了丰富的功能来管理和优化各种批处理和弹性工作负载，特别适用于人工智能（AI）、机器学习（ML）、大数据分析以及高性能计算（HPC）等场景。Volcano 支持与 Spark、Flink、TensorFlow、PyTorch 等主流框架的集成，能够有效提升资源利用率和任务调度效率。凭借超过十五年的高负载运行经验，Volcano 结合了业界最佳实践与创新理念，为企业级用户提供强大且灵活的调度解决方案。该系统广泛应用于互联网\u002F云计算、金融、制造及医疗等行业，并拥有活跃的开源社区支持。",2,"2026-06-11 04:10:51","trending"]