内容摘录
<a href="https://volcano.sh/">
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Gurubase
Volcano 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) / machine learning (ML) / deep learning (DL), bioinformatics / genomics, and other "Big Data" applications.
These 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.
Volcano 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.
As of 2025, Volcano has seen widespread adoption across numerous industries globally, including Internet/Cloud, 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.
[!NOTE]
the scheduler is built based on kube-batch;
refer to #241 and #288 for more detail.
!cncf_logo
Volcano is an incubating project of the Cloud Native Computing Foundation (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.
Overall Architecture
!volcano
Talks
Intro: Kubernetes Batch Scheduling @ KubeCon 2019 EU
Volcano 在 Kubernetes 中运行高性能作业实践 @ ArchSummit 2019
Volcano：基于云原生的高密计算解决方案 @ Huawei Connection 2019
Improving Performance of Deep Learning Workloads With Volcano @ KubeCon 2019 NA
Batch Capability of Kubernetes Intro @ KubeCon 2019 NA
Optimizing Knowledge Distillation Training With Volcano @ KubeCon 2021 EU
Exploration About Mixing Technology of Online Services and Offline Jobs Based On Volcano @ KubeCon 2021 China
Volcano - Cloud Native Batch System for AI, Big Data and HPC @ KubeCon 2022 EU
How to Leverage Volcano to Improve the Resource Utilization of AI Pharmaceuticals, Autonomous Driving, and Smart Buildings @ KubeCon 2023 EU
Run Your AI Workloads and Microservices on Kubernetes More Easily and Efficiently @ KubeCon 2023 China
Optimize LLM Workflows with Smart Infrastructure Enhanced by Volcano @ KubeCon 2024 China
How Volcano Enable Next Wave of Intelligent Applications @ KubeCon 2024 China
Leverage Topology Modeling and Topology-Aware Scheduling to Accelerate LLM Training @ KubeCon 2024 China
Ecosystem
Spark Operator
Native Spark
Flink
KubeRay
PyTorch
TensorFlow
kubeflow/training-operator
kubeflow/arena
MPI
Horovod
PaddlePaddle
Cromwell
MindSpore
MXNet
Argo
KubeGene
Use Cases
Why Spark chooses Volcano as built-in batch scheduler on Kubernetes?
ING Bank: How Volcano empowers its big data analytics platform
Using Volcano as a custom scheduler for Apache Spark on Amazon EMR on EKS
Deploy Azure Machine Learning extension on AKS or Arc Kubernetes cluster
Practical Tips for Preventing GPU Fragmentation for Volcano Scheduler
Using Volcano in Large-Scale, Distributed Offline Computing
OpenI-Octopus: How to Avoid Resource Preemption in Kubernetes Clusters
How Does Volcano Empower a Content Recommendation Engine in Xiaohongshu
How Ruitian Used Volcano to Run Large-Scale Offline HPC Jobs
Integrating Volcano into the Leinao Cloud OS
HPC on Volcano: How Containers Support HPC Applications in the Meteorological Industry
iQIYI:Volcano-based Cloud Native Migration Practices
PaddlePaddle Distributed Training on Volcano
Quick Start Guide
Prerequisites
Kubernetes 1.12+ with CRD support
You can try Volcano by one of the following two ways.
[!NOTE]
For Kubernetes v1.17 and above, use CRDs under config/crd/bases (recommended)
For Kubernetes v1.16 and below, use CRDs under config/crd/v1beta1 (deprecated)
Install with YAML files
Install Volcano on an existing Kubernetes cluster. This way is both available for x86_64 and arm64 architecture.
Enjoy! Volcano will create the following resources in volcano-system namespace.
Install via helm
To install official release, please visit helm-charts for details.
Install from source code for developers:
Install from code
If you don't have a kubernetes cluster, try one-click install from code base:
This way is only available for x86_64 temporarily.
Install volcano agent
Please follow the guide Volcano Agent to install volcano agent.
Install monitoring system
If you want to get prometheus and grafana volcano dashboard after volcano installed, try following commands:
Install dashboard
Please follow the guide Volcano Dashboard to install volcano dashboard.
Kubernetes compatibility
| | 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 |
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| Volcano HEAD (master) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | - | - |
| Volcano …