https://deploy-preview-499--volcano-sh.netlify.app/en/docs/ Recent content in Introduction on Volcano Hugo -- gohugo.io en-us ©2025 Volcano Mon, 28 Jan 2019 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/ This section contains the User Guide for Volcano. Ascend vNPU User Guide Capacity Plugin User Guide Cooldown Protection Plugin User Guide Extender User Guide GPU Number User Guide GPU Sharing User Guide How to Configure Scheduler Volcano Job Policy User Guide Volcano Job Time to Live User Guide Dynamic Resource Allocation (DRA) User Guide How to Tune Volcano Performance in Large-Scale Scenarios How to configure priorityclass for job MPI Plugin User Guide NUMA Aware User Guide Network Topology Aware Scheduling User Guide Nodegroup Plugin User Guide Pytorch Plugin User Guide Ray Plugin User Guide Resource Strategy Fit Plugin User Guide Task Topology Plugin User Guide HyperNode Auto Discovery User Guide Volcano Job Plugin – Env User Guide Volcano Job Plugin – SSH User Guide Volcano Job Plugin – SVC User Guide Volcano vGPU User Guide Scheduling Gates Queue Admission User Guide https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_scheduling_gates_queue_admission/ Mon, 04 May 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_scheduling_gates_queue_admission/ Overview This page describes how to enable and use the SchedulingGatesQueueAdmission feature to prevent cluster autoscalers (such as Cluster Autoscaler or Karpenter) from triggering unnecessary scale-ups when pods are waiting for Volcano queue capacity. Problem Volcano marks pods as Unschedulable for any allocation failure, whether it’s due to insufficient cluster resources (where autoscaling is appropriate) or queue capacity limits (where autoscaling is not needed). Cluster autoscalers cannot distinguish between these scenarios, causing unnecessary node scale-ups. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_vnpu/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_vnpu/ Introduction Volcano supports two vNPU modes for sharing Ascend devices: 1. MindCluster mode Description: The initial version of MindCluster—the official Ascend cluster scheduling add-on—required custom modifications and recompilation of Volcano. Furthermore, it was limited to Volcano release1.7 and release1.9, which complicated its use and restricted access to newer Volcano features. To address this, we have integrated its core scheduling logic for Ascend vNPU into Volcano’s native device-share plugin, which is designed specifically for scheduling and sharing heterogeneous resources like GPUs and NPUs. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_capacity_plugin/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_capacity_plugin/ Introduction Capacity plugin is a replacement of proportion plugin, but instead of dividing the queue’s deserved resources by weight, it realizes elastic queue capacity management i.e., queue’s resource borrowing and lending mechanism by specifying the amount of deserved resources for each dimension resource of the queue. A queue can use the idle resources of other queues, and when other queues submit jobs, they can reclaim the resources that have been lent, and the amount of reclaimed resources is the amount of queue’s deserved resources. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_cdp_plugin/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_cdp_plugin/ Background When we need to enable elastic training or serving, preemptible job’s pods can be preempted or back to running repeatedly, if no cooldown protection set, these pods can be preempted again after they just started for a short time, this may cause service stability dropped. So we add “cdp” plugin to ensure preemptible job’s pods can run for at least some time set by user. Environment setup Install volcano Refer to Install Guide to install volcano. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_extender/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_extender/ Install volcano 1. Install from source Refer to Install Guide to install volcano. 2. Deploy extender Deploy extender into kubernetes cluster. Extender needs to expose domain name or IP address and verbs that can be provided. 3. Update Volcano configuration kubectl edit cm -n volcano-system volcano-scheduler-configmap Users can view the meaning of the parameters through the documentation kind: ConfigMap apiVersion: v1 metadata: name: volcano-scheduler-configmap namespace: volcano-system data: volcano-scheduler.conf: | actions: "reclaim, allocate, backfill, preempt" tiers: - plugins: - name: priority - name: gang - name: conformance - plugins: - name: drf - name: predicates - name: extender arguments: extender. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_gpu_number/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_gpu_number/ Important Note Note GPU Number is deprecated in volcano v1.9, recommended to use the Volcano VGPU feature, which is provided by HAMI project, click here Environment setup Install volcano 1. Install from source Refer to Install Guide to install volcano. After installed, update the scheduler configuration: kubectl edit cm -n volcano-system volcano-scheduler-configmap For volcano v1.8.2+(v1.8.2 excluded), use the following configMap kind: ConfigMap apiVersion: v1 metadata: name: volcano-scheduler-configmap namespace: volcano-system data: volcano-scheduler. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_gpu_sharing/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_gpu_sharing/ Important Note Note GPU sharing is deprecated in volcano v1.9, recommended to use the Volcano VGPU feature, which is provided by HAMI project, click here Environment setup Install volcano 1. Install from source Refer to Install Guide to install volcano. After installed, update the scheduler configuration: kubectl edit cm -n volcano-system volcano-scheduler-configmap For volcano v1.8.2+(v1.8.2 excluded), use the following configMap kind: ConfigMap apiVersion: v1 metadata: name: volcano-scheduler-configmap namespace: volcano-system data: volcano-scheduler. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_configure_priorityclass_for_job/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_configure_priorityclass_for_job/ Background When a user creates a job, if there is no PriorityClassName specified in the template of the task, the task will use the PriorityClassName specified by the job. The user can specify PriorityClassName in each task’s template to override the configuration of job, so that priority can be configured separately for each task. Key Points If the task does not specify PriorityClassName but the job does, the task will use the job’s PriorityClass, and the PreemptionPolicy and priority value will also be the same as the job. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_configure_scheduler/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_configure_scheduler/ Requirements Before reading the guidance, please make sure you are aware of basic concepts such as action plugin session tier volcano job podgroup queue and so on. If they are still strange to you, please refer to Volcano Docs for more details. Before reading the guidance, please make sure you have general understanding of Volcano scheduling workflow. Background In order to adjust the scheduling process and algorithms to different scenarios, Volcano allows users to configure actions and plugins for volcano scheduler. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_enable_dra/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_enable_dra/ This document describes the steps required to enable Dynamic Resource Allocation (DRA) support in the Volcano scheduler. Prerequisites Before proceeding with the configuration steps, ensure your cluster meets the following prerequisites: Configure Cluster Nodes (Containerd) For nodes running containerd as the container runtime, you must enable the Container Device Interface (CDI) feature. This is crucial for containerd to properly interact with DRA drivers and inject dynamic resources into Pods. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_tune_volcano_performance/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_tune_volcano_performance/ This article originates from the “Volcano Large-Scale Performance Testing and Tuning” project of the Open Source Promotion Plan (OSPP), organized by the Chinese Academy of Sciences. All experimental data and analysis have been published in the author’s blog series (@Freshwlnd). 1. Introduction 1.1 Background and Objectives Volcano is a mainstream cloud-native batch processing system widely used in AI, big data, and HPC scenarios. This project aims to systematically reproduce and identify performance bottlenecks in Volcano under a load of tens of thousands of pods through large-scale performance testing, ultimately producing a practical tuning guide and proposing future architectural optimization directions. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_hypernode_auto_discovery/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_hypernode_auto_discovery/ Introduction This document describes how to use the HyperNode network topology auto-discovery feature in Volcano. This feature automatically discovers the network topology within the cluster and creates and maintains HyperNode custom resources (CRs) based on the discovered information. The Volcano scheduler leverages these HyperNode CRs for scheduling decisions, eliminating the need for users to manually maintain HyperNode information. Prerequisites Please Install Volcano with version >= v1.12.0 first. Configuration The HyperNode network topology discovery feature is configured via a ConfigMap. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_mpi_plugin/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_mpi_plugin/ Introduction MPI plugin is designed to optimize the user experience when running MPI jobs, it not only allows users to write less yaml, but also ensures the normal operation of MPI jobs. How the MPI Plugin Works The MPI plugin will do three things: Open ports used by MPI for all containers of the job Force open ssh and svc plugins add MPI_HOST environment variable for master pod, this environment variable includes the worker’s domain name, It is used by the --host parameter of mpiexec Parameters of the MPI Plugin Key Points If master or worker is configured, please ensure that the tasks corresponding to their values exist, and the roles of these tasks correspond to the meaning of the parameters If port is configured, make the port value of sshd the same as the value of the parameter. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_network_topology_aware_scheduling/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_network_topology_aware_scheduling/ 1 Background In the context of AI large model training, Model Parallelism divides the model across multiple nodes, requiring frequent and substantial data exchange between these nodes during training. At this point, the network transmission performance between nodes often becomes the bottleneck of training, significantly affecting training efficiency. Data centers have diverse network types (such as IB, RoCE, NVSwitch, etc.), and the network topology is complex, typically involving multiple layers of switches. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_nodegroup_plugin/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_nodegroup_plugin/ Introduction Nodegroup plugin is designed to isolate resources by assigning labels to nodes and set node label affinty on Queue. Usage assign label to node Assign label to node, label key is volcano.sh/nodegroup-name. kubectl label nodes <nodename> volcano.sh/nodegroup-name=<groupname> configure queue Create queue and bind nodegroup to it. apiVersion: scheduling.volcano.sh/v1beta1 kind: Queue metadata: name: default spec: reclaimable: true weight: 1 affinity: # added field nodeGroupAffinity: requiredDuringSchedulingIgnoredDuringExecution: - <groupname> preferredDuringSchedulingIgnoredDuringExecution: - <groupname> nodeGroupAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - <groupname> preferredDuringSchedulingIgnoredDuringExecution: - <groupname> submit a vcjob submit vcjob job-1 to default queue. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_numa_aware/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_numa_aware/ Environment setup Pre-Condition Enable cpu manager and set policy to “static” Enable topology manager and set the policy option you want Set the above conditions by editing the kubelet configuration file cat /var/lib/kubelet/config.yaml {...} cpuManagerPolicy: static topologyManagerPolicy: best-effort kubeReserved: cpu: 1000m Restart kubelet to take effect Run the following: 1. systemctl stop kubelet 2. rm -rf /var/lib/kubelet/cpu_manager_state 3. systemctl daemon-reload 4. systemctl start kubelet Install volcano 1. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_pytorch_plugin/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_pytorch_plugin/ Introduction Pytorch plugin is designed to optimize the user experience when running pytorch jobs, it not only allows users to write less yaml, but also ensures the normal operation of Pytorch jobs. How the Pytorch Plugin Works The Pytorch Plugin will do the following: Open ports used by Pytorch for all containers of the job Force open svc plugins Add some envs such like MASTER_ADDR, MASTER_PORT, WORLD_SIZE, RANK which pytorch distributed training needed to containers automatically Add an init container to worker pods to wait for the master node to be ready before starting (ensures master starts first) Parameters of the Pytorch Plugin Arguments ID Name Type Default Value Required Description Example 1 master string master No Name of Pytorch master –master=master 2 worker string worker No Name of Pytorch worker –worker=worker 3 port int 23456 No The port to open for the container –port=23456 4 wait-master-enabled bool false No Enable init container to wait for master –wait-master-enabled=true 5 wait-master-timeout int 300 No Timeout in seconds for waiting master (only effective when wait-master-enabled=true) –wait-master-timeout=600 6 wait-master-image string busybox:1. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_ray_plugin/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_ray_plugin/ Introduction Ray plugin is designed to optimize the user experience when deploying a ray cluster, it not only allows users to write less yaml, but also supports users to deploy a ray cluster. How the Ray Plugin Works The Ray Plugin will do three things: Configure the commands of head and worker nodes in a ray cluster. Open three ports used by ray head node. (GCS, Ray dashboard and Client server) Create a service mapped to the ray head node container ports. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_resource_strategy_fit_plugin/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_resource_strategy_fit_plugin/ Introduction The Resource Strategy Fit Plugin is a Volcano scheduler plugin that provides intelligent resource allocation strategies for pod scheduling. It supports both global configuration and pod-level annotations to optimize resource utilization across different workloads. Key Features Multiple Scoring Strategies: Supports LeastAllocated and MostAllocated strategies Resource-Specific Configuration: Configure different strategies for different resource types (CPU, Memory, GPU, etc.) Pod-Level Override: Allow individual pods to override global configuration via annotations Weighted Scoring: Fine-tune resource importance with configurable weights Wildcard Support: Use wildcard patterns for resource matching Installation 1. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_task_topology_plugin/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_task_topology_plugin/ Environment setup Install volcano Refer to Install Guide to install volcano. Update scheduler configmap After installed, update the scheduler configuration: kubectl edit configmap -n volcano-system volcano-scheduler-configmap Register task-topology plugin in configmap kind: ConfigMap apiVersion: v1 metadata: name: volcano-scheduler-configmap namespace: volcano-system data: volcano-scheduler.conf: | actions: "enqueue, allocate, backfill" tiers: - plugins: - name: priority - name: gang - name: conformance - plugins: - name: drf - name: predicates - name: task-topology arguments: task-topology. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_env_plugin/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_env_plugin/ Background Env Plugin is designed for business that a pod should be aware of its index in the task such as MPI and TensorFlow. The indices will be registered as environment variables automatically when the Volcano job is created. For example, a tensorflow job consists of 1 ps and 2 workers. And each worker maps to a slice of raw data. In order to make the workers be aware of its target slice, they get their index in the environment variables. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_ssh_plugin/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_ssh_plugin/ Background SSH Plugin is designed for the login without password for pods within a volcano job , which is necessary for workloads such as MPI. It often works with SVC plugin. Key Points If ssh-key-file-path is configured, please ensure the private and public keys exist under the target directory. Suggest keeping default value in most scenarios. If ssh-private-key or ssh-public-key is configured, please ensure the value is correct. Suggest keeping the default keys in most scenarios. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_svc_plugin/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_svc_plugin/ Background SVC Plugin is designed for the communication for pods within a volcano job, which is essential for workloads such as TensorFlow and MPI. For example, it is necessary for tensorflow job to contact with each other between ps and worker. Volcano job plugin svc enable pods within a job to visit each other by domain. Key Points Once svc plugin is configured, value of field hostname under spec will be filled out to be the pod name for all pods under the job automatically. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_job_policy/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_job_policy/ Background Policy provides an API of volcano job and task lifecycle management for users. For example, in some scenarios, especially in AI, big data and HPC field, it is required to restart a job if any master or worker fails. Users can easily achieve that by configuring policy for the volcano job under job.spec. Key Points Volcano allows users to configure a pair of Event(Events) and Action for a volcano job or a task. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_job_ttl/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_job_ttl/ Background Similar to a standard Job resource, VolcanoJobs can be configured to be automatically garbage collected after they finish execution (either Complete or Failed). This is configured by setting spec.ttlSecondsAfterFinished which limits the lifetime of a Job. Key Points ttlSecondsAfterFinished is an optional parameter that can be configured on VolcanoJobs which defaults to nil. The value of ttlSecondsAfterFinished must be a positive integer and indicates the number of seconds after a job finishes executing (either Complete or Failed) before it becomes eligible for garbage collection. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_volcano_vgpu/ Tue, 03 Feb 2026 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_volcano_vgpu/ Background Knowledge of GPU Sharing Modes in Volcano Volcano supports two GPU sharing modes for virtual GPU (vGPU) scheduling: 1. HAMI-core (Software-based vGPU) Description: Leverages VCUDA, a CUDA API hijacking technique to enforce GPU core and memory usage limits, enabling software-level virtual GPU slicing. Use case: Ideal for environments requiring fine-grained GPU sharing. Compatible with all GPU types. 2. Dynamic MIG (Hardware-level GPU Slicing) Description: Utilizes NVIDIA’s MIG (Multi-Instance GPU) technology to partition a physical GPU into isolated instances with hardware-level performance guarantees. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_binpack_plugin/ Fri, 10 May 2024 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_binpack_plugin/ Introduction The Binpack plugin attempts to pack as many pods as possible onto a single node before moving on to the next. This minimizes the fragmentation of cluster resources and leaves larger, empty nodes available for future workloads that may require massive amounts of contiguous resources. By heavily utilizing a subset of nodes, it is also beneficial for cluster autoscalers to downscale completely empty nodes. Environment setup Update scheduler configmap Ensure the binpack plugin is enabled and configured with weights for different resources in the volcano-scheduler-configmap. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_drf_plugin/ Fri, 10 May 2024 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_drf_plugin/ Introduction The DRF (Dominant Resource Fairness) scheduling plugin allocates resources based on the dominant resource requested by a job. The dominant resource is the resource type (e.g., CPU or Memory) that takes up the largest percentage of the total cluster capacity. The DRF algorithm calculates the dominant resource share for each job and prioritizes jobs with the lowest share, ensuring fairness across multiple resource types. This is highly effective in mixed workloads where some jobs are CPU-intensive and others are memory-intensive. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_gang_plugin/ Fri, 10 May 2024 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_gang_plugin/ Introduction The Gang scheduling algorithm is one of the core scheduling plugins in Volcano. It meets the “All or nothing” scheduling requirements. This means that a group of pods (usually within a Job) will only be scheduled if the minimum required number of pods can be scheduled together. If the cluster does not have enough resources to satisfy the minimum number of running pods, none of them will be scheduled. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_nodeorder_plugin/ Fri, 10 May 2024 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_nodeorder_plugin/ Introduction The NodeOrder plugin is responsible for scoring nodes during the scheduling process to find the absolute best node for a pod. Rather than just finding any node that fits the pod’s requirements, NodeOrder ranks the nodes based on configurable scoring dimensions, such as affinity rules or resource availability. Environment setup Update scheduler configmap The nodeorder plugin must be enabled in the scheduler configuration. kind: ConfigMap apiVersion: v1 metadata: name: volcano-scheduler-configmap namespace: volcano-system data: volcano-scheduler. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_predicates_plugin/ Fri, 10 May 2024 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_predicates_plugin/ Introduction The Predicates plugin is the primary filtering engine for Volcano. It evaluates each node against the requirements of a pod to filter out nodes that are fundamentally incapable of running the pod. This includes checking if a node has enough CPU/Memory available, evaluating node selectors, matching node affinities, and checking if the node has the requested GPUs. Environment setup Update scheduler configmap The predicates plugin is a fundamental plugin and should almost always be enabled in your volcano-scheduler-configmap. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_priority_plugin/ Fri, 10 May 2024 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_priority_plugin/ Introduction The Priority plugin allows Volcano to schedule Pods and Jobs based on their assigned priority. It introduces preemptive capabilities: if a high-priority Job is submitted and the cluster lacks sufficient resources, Volcano will preempt (evict) lower-priority Jobs to free up resources for the high-priority Job. This is essential for ensuring that critical workloads always run. Environment setup Update scheduler configmap Ensure the priority plugin is enabled in your volcano-scheduler-configmap. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_proportion_plugin/ Fri, 10 May 2024 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_proportion_plugin/ Introduction The Proportion scheduling plugin ensures fair resource sharing across different queues based on their configured weights. It dynamically controls the maximum resources a queue can consume, preventing a single team or workload from starving others in a multi-tenant cluster. When the cluster is under heavy load, the plugin allocates resources proportionally to each queue’s weight. Environment setup Update scheduler configmap Ensure the proportion plugin is enabled in the volcano-scheduler-configmap. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_sla_plugin/ Fri, 10 May 2024 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_sla_plugin/ Introduction The SLA (Service Level Agreement) plugin is designed to prevent job starvation. Users can specify the maximum amount of time a job is allowed to wait in the Pending state. Once a job reaches its SLA timeout, the SLA plugin will boost its priority to ensure it gets scheduled as soon as possible, potentially preempting other jobs if necessary. Environment setup Update scheduler configmap Enable the sla plugin in the scheduler configuration. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_tdm_plugin/ Fri, 10 May 2024 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/user-guide/how_to_use_tdm_plugin/ Introduction TDM (Time Division Multiplexing) is a plugin designed for co-located environments where a single set of nodes is shared across different orchestration frameworks (like Kubernetes and Hadoop YARN). The TDM plugin enables time-sharing by marking certain nodes as “revocable”. Preemptable workloads can be scheduled on these nodes during specific time windows. When the time window ends, Volcano evicts these workloads to return the resources to the primary framework. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/ray_on_volcano/ Mon, 22 Dec 2025 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/ray_on_volcano/ Ray Introduction Ray is a unified distributed computing framework designed for AI/ML applications. Ray provides: Distributed Training: Scale machine learning workloads from a single machine to thousands of nodes Hyperparameter Tuning: Run parallel experiments with Ray Tune for efficient model optimization Distributed Data Processing: Process large datasets with Ray Data for batch inference and data preprocessing Reinforcement Learning: Train RL models at scale with Ray RLlib Serving: Deploy and scale ML models in production with Ray Serve General Purpose Distributed Computing: Build any distributed application with Ray Core APIs Running Ray on Volcano There are two approaches to deploy Ray clusters on Volcano: https://deploy-preview-499--volcano-sh.netlify.app/en/docs/cron_volcanojob/ Wed, 19 Nov 2025 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/cron_volcanojob/ Introduction Cron VolcanoJob, also known as cronvcjob or cronvj, is a custom resource type in Volcano. Users can now periodically create and run Volcano Jobs based on predefined schedules, similar to Kubernetes native CronJobs, enabling scheduled execution of batch computing tasks (such as AI and big data workloads). Example apiVersion: batch.volcano.sh/v1alpha1 kind: CronJob metadata: name: volcano-cronjob-example spec: schedule: "*/5 * * * *" concurrencyPolicy: Forbid startingDeadlineSeconds: 60 successfulJobsHistoryLimit: 5 failedJobsHistoryLimit: 3 jobTemplate: spec: schedulerName: volcano tasks: - replicas: 1 name: "task-1" template: spec: containers: - name: busybox-container image: busybox:latest command: ["/bin/sh", "-c", "date; echo Hello from Volcano CronJob"] restartPolicy: OnFailure policies: - event: PodEvicted action: RestartJob minAvailable: 1 View Cron VolcanoJob https://deploy-preview-499--volcano-sh.netlify.app/en/docs/gpu_virtualization/ Thu, 29 May 2025 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/gpu_virtualization/ Background Knowledge of GPU Sharing Modes in Volcano As AI applications proliferate, the demand for GPUs has surged due to their critical role in training complex models and executing high-performance inference. However, the high cost of GPUs necessitates maximizing their utilization in cloud-native environments. A common challenge is inefficient provisioning: a single GPU may be over-provisioned for smaller workloads or underutilized by larger ones that don’t fully saturate its capacity. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/multi_cluster_scheduling/ Tue, 21 Jan 2025 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/multi_cluster_scheduling/ Background With the rapid growth of enterprise business, a single Kubernetes cluster often cannot meet the demands of large-scale AI training and inference tasks. Users typically need to manage multiple Kubernetes clusters to achieve unified AI workload distribution, deployment, and management. Currently, many users are running Volcano across multiple clusters and using Karmada for management. To better support AI tasks in multi-cluster environments with features such as global queue management, task priority, and fair scheduling, the Volcano community has incubated the Volcano Global sub-project. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/network_topology_aware_scheduling/ Tue, 21 Jan 2025 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/network_topology_aware_scheduling/ Background In the context of AI large model training, Model Parallelism divides the model across multiple nodes, requiring frequent and substantial data exchange between these nodes during training. At this point, the network transmission performance between nodes often becomes the bottleneck of training, significantly affecting training efficiency. Data centers have diverse network types (such as IB, RoCE, NVSwitch, etc.), and the network topology is complex, typically involving multiple layers of switches. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/colocation/ Mon, 20 Jan 2025 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/colocation/ Background With the rapid development of cloud-native technologies, more and more workloads have gradually migrated to Kubernetes, adopting cloud-native approaches for development and maintenance. This has greatly simplified application deployment, orchestration, and operations. Kubernetes has gradually become the “operating system” of the cloud-native era. However, despite the adoption of cloud-native technologies, resource utilization in data centers remains relatively low. To improve resource utilization while ensuring the Service Level Objectives (SLOs) of high-priority workloads, Volcano has introduced a cloud-native colocation solution. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/descheduler/ Mon, 20 Jan 2025 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/descheduler/ Overview The scheduling within a cluster is the process of assigning pending Pods to nodes for execution, with Pod scheduling relying on the cluster’s scheduler. The scheduler calculates the optimal node for a Pod’s execution through a series of algorithms. However, the Kubernetes cluster environment is dynamic, with changes such as a node requiring maintenance, which would result in all Pods on that node being evicted to other nodes, once maintenance is complete, the previously evicted Pods do not automatically return to the node, as once a Pod is bound to a node, it does not trigger a descheduling. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/queue_resource_management/ Mon, 30 Dec 2024 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/queue_resource_management/ Overview Queue is one of the core concepts in Volcano, designed to support resource allocation and task scheduling in multi-tenant scenarios. Through queues, users can implement multi-tenant resource allocation, task priority control, resource preemption and reclamation, significantly improving cluster resource utilization and task scheduling efficiency. Core Features 1. Flexible Resource Configuration Supports multi-dimensional resource quota control (CPU, Memory, GPU, NPU, etc.) Provides three-level resource configuration mechanism: capability: Upper limit of queue resource usage deserverd: Deserved resource amount (when no other queues submit jobs, jobs in this queue can exceed the deserverd value; when multiple queues submit jobs and cluster resources are insufficient, resources exceeding the deserverd value can be reclaimed by other queues) guarantee: Reserved resource amount (reserved resources can only be used by this queue, other queues cannot use them) Recommendations and Notes: https://deploy-preview-499--volcano-sh.netlify.app/en/docs/unified_scheduling/ Mon, 30 Dec 2024 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/unified_scheduling/ 1. Overview As the industry’s leading cloud-native batch processing system scheduler, Volcano achieves support for all types of workloads through a unified scheduling system: Powerful batch scheduling capabilities: Perfect support for mainstream AI and big data frameworks like Ray, TensorFlow, PyTorch, MindSpore, Spark, Flink through VcJob Complete Kubernetes workload support: Direct scheduling of native workloads like Deployment, StatefulSet, Job, DaemonSet This unified scheduling capability allows users to manage all types of workloads using a single scheduler, greatly simplifying cluster management complexity. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/hierarchical_queue/ Sat, 28 Dec 2024 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/hierarchical_queue/ Background In multi-tenant scenarios, queues are a core mechanism for achieving fair scheduling, resource isolation, and task priority control. However, in the current version of Volcano, queues only support a flat structure and lack hierarchical concepts. In practical applications, different queues often belong to different departments, with hierarchical relationships between departments, leading to more refined requirements for resource allocation and preemption. To address this, Volcano latest version introduces the hierarchical queue feature, significantly enhancing queue capabilities. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/flink_on_volcano/ Tue, 29 Jun 2021 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/flink_on_volcano/ Flink introduction Apache Flink is an open-source streaming framework developed by the Apache Software Foundation. At its core, Apache Flink is a distributed streaming data streaming engine written in Java and Scala. Flink executes any stream data program in data parallelism and pipelining. Flink’s pipelined runtime system can execute both batch and stream programs. In addition, the Flink runtime itself supports the execution of iterative algorithms. The premise condition Make sure the deployed Kubernetes, Kubectl, Volcano are installed correctly. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/kubeflow_on_volcano/ Tue, 29 Jun 2021 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/kubeflow_on_volcano/ Kubeflow introduction Kubernetes has become the de facto standard for cloud native application choreography and management, and more and more applications are migrating to Kubernetes. The field of artificial intelligence and machine learning naturally contains a large number of computation-intensive tasks. Developers are very willing to build an AI platform based on Kubernetes and make full use of the resource management, application scheduling, operation and maintenance monitoring capabilities provided by Kubernetes. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/mindspore_on_volcano/ Tue, 29 Jun 2021 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/mindspore_on_volcano/ MindSpore introduction MindSpore is a new generation of deep learning framework launched by Huawei. It is derived from the best practices of the whole industry, which best matches the computing power of the rise processor, supports the flexible deployment of the whole scene of terminal, edge and cloud, and creates a brand new AI programming paradigm and lowers the threshold of AI development. MindSpore on Volcano Create a new mindSpore-cpu. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/pp_on_volcano/ Tue, 29 Jun 2021 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/pp_on_volcano/ paddlepaddle introduction PaddlePaddle is a deep learning framework open source by Baidu in September 2016, aiming to provide a safe, efficient, flexible, easy to use and extensible deep learning platform. In October 2018, the Paddle Fluid 1.0 version was released by the Paddle Fluid team, which comprehensively upgraded core capabilities such as neural network description, large-scale distributed training, and high-performance inference engine. For example, in the latest Paddle Fluid 1. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/spark_on_volcano/ Tue, 29 Jun 2021 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/spark_on_volcano/ Spark introduction Spark is a fast and versatile big data clustering computing system. It provides high-level APIs for Scala, Java, Python, and R, as well as an optimization engine that supports a generic computational graph for data analysis. It also supports a rich set of advanced tools, including Spark SQL for SQL and Dataframes, MLLib for machine learning, GraphX for graphics processing, and Spark Streaming for Streaming. Spark on Volcano Currently, there are two ways to support the integration of Spark on Kubernetes and volcano. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/plugins/ Thu, 13 May 2021 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/plugins/ Gang gang plugin Overview The Gang scheduling strategy is one of the core scheduling algorithms of the Volcano-Scheduler. It meets the scheduling requirements of “All or nothing” in the scheduling process and avoids the waste of cluster resources caused by arbitrary scheduling of Pod. The Gang scheduler algorithm is to observe whether the scheduled number of Pods under Job meets the minimum number of runs. When the minimum number of https://deploy-preview-499--volcano-sh.netlify.app/en/docs/actions/ Wed, 07 Apr 2021 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/actions/ Enqueue Overview The Enqueue action filters qualified jobs into the queue to be scheduled. When the minimum number of resource requests under a Job cannot be met, even if the scheduling action is performed for a pod under a Job, pod will not be able to schedule because the “Gang” constraint is not reached. A state refresh from “Pending” to “Inqueue” can only happen if the minimum resource size of the job is met. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/mpi_on_volcano/ Wed, 07 Apr 2021 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/mpi_on_volcano/ HPC introduction High Performance Computing (HPC) refers to the use of aggregated Computing power to handle data-intensive Computing tasks that cannot be performed by standard workstations. HPC = PBS + Maui + OpenMPI[1] PBS：Resource manager, which is responsible for managing resources for all nodes in the cluster Maui：Third-party task scheduler, support resource reservation, support various complex priority policies, support preemption, etc. OpenMPI：The upper communication environment, taking into account the functions of communication library, compilation and distributed start task. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/referrals/ Wed, 07 Apr 2021 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/referrals/ Background AI development process Deep learning generally involves four stages: data acquisition and processing, model training and evolution, model deployment, and model evaluation [1]. At the beginning of an enterprise’s involvement in deep learning, due to the lack of relevant experience and resources for deep learning, the team members usually choose to complete the above steps manually step by step in order to quickly meet the immediate needs of the project development period. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/tf_on_volcano/ Wed, 07 Apr 2021 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/tf_on_volcano/ TensorFlow introduction TensorFlow is a symbolic mathematical system based on data flow programming, which is widely used in programming and realization of various machine learning algorithms. Its predecessor is DistBelief, a neural network algorithm library of Google. TensorFlow on Volcano PS-worker model: Parameter Server performs model-related services, Work Server trains related services, inference calculation, gradient calculation, etc[1]. ps-worker TensorFlow on Kubernetes has many problems: Resource isolation. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/architecture/ Mon, 28 Jan 2019 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/architecture/ Overall Architecture Application scenarios of Volcano Volcano is designed for high-performance workloads running on Kubernetes. It follows the design and mechanisms of Kubernetes. Volcano architecture Volcano consists of scheduler / controllermanager / admission / vcctl: Scheduler Volcano Scheduler schedules jobs to the most suitable node based on actions and plug-ins. Volcano supplements Kubernetes to support multiple scheduling algorithms for jobs. ControllerManager (CM) Volcano CMs manage the lifecycle of Custom Resource Definitions (CRDs). https://deploy-preview-499--volcano-sh.netlify.app/en/docs/cli/ Mon, 28 Jan 2019 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/cli/ Introduction A Command Line Interface (CLI) is provided for you to manage resources. Configuration You can obtain the latest executable file by cloning the code from GitHub and running the following command in the root directory of the project: # make vcctl Copy the executable file to $PATH. You then can execute it anywhere. Command Line List Listing all jobs vcctl job list # vcctl job list Name Creation Phase JobType Replicas Min Pending Running Succeeded Failed Unknown RetryCount job-1 2020-09-01 Running Batch 1 1 0 1 0 0 0 0 Deleting a specific job vcctl job delete –name job-name [–namespace job-namespace] https://deploy-preview-499--volcano-sh.netlify.app/en/docs/membership/ Mon, 28 Jan 2019 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/membership/ Volcano Community Membership Note: We regularly update this document with the latest information and incorporate feedback from the Volcano community. This document gives a brief overview of the Volcano community roles and the requirements and responsibilities associated with them. Role Requirements Responsibilities Privileges Member Sponsored by 2 approvers, active in the community, has contributed to Volcano Gets new contributors on board Member of the Volcano GitHub organization Approver Sponsored by 2 maintainers, has extensive experience and knowledge of a particular domain, has actively contributed to code and review Reviews and approves contributions from community members Has write access to specific packages in relevant repositories Maintainer Sponsored by 2 owners, has shown good technical judgment in feature design/development and PR review Release planning and feature development/maintenance Has top-level write access to relevant repositories; name entry in the Maintainers file of the repositories Owner Sponsored by 3 owners, has proven ability to lead the entire Volcano project Drives the overall technical roadmap of the project and sets priorities of activities in release planning Has admin access to the Volcano GitHub organization Note: All Volcano community members must follow the Volcano Code of Conduct. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/installation/ Mon, 28 Jan 2019 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/installation/ The easiest way to get started with Volcano is to download the release package from GitHub and follow the following steps: Prerequisites Kubernetes 1.12+ with CRD support. Installation Modes Install with YAML files Install from code Install with Helm Install with YAML files Install Volcano on an existing Kubernetes cluster. This way is both available for x86_64 and arm64 architecture. kubectl apply -f https://raw.githubusercontent.com/volcano-sh/volcano/master/installer/volcano-development.yaml You can also replace master of above url with specific tag/branch (such as release-1. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/schduler_introduction/ Mon, 28 Jan 2019 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/schduler_introduction/ Introduction Volcano scheduler is the component responsible for pod scheduling. It consists of a series of actions and plugins. Actions define the action that should be executed in every step. Plugins provide the action algorithm details in different scenarios. Volcano scheduler is highly scalable. You can specify and implement actions and plugins based on your requirements. Workflow Volcano scheduler workflow Volcano scheduler works as follows: Watches for and caches the jobs submitted by the client. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/podgroup/ Mon, 28 Jan 2019 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/podgroup/ Introduction PodGroup is a group of pods with strong association and is mainly used in batch scheduling, for example, ps and worker tasks in TensorFlow. PodGroup is of a Custom Resource Definition (CRD) type. Example apiVersion: scheduling.volcano.sh/v1beta1 kind: PodGroup metadata: creationTimestamp: "2020-08-11T12:28:55Z" generation: 5 name: test namespace: default ownerReferences: - apiVersion: batch.volcano.sh/v1alpha1 blockOwnerDeletion: true controller: true kind: Job name: test uid: 028ecfe8-0ff9-477d-836c-ac5676491a38 resourceVersion: "109074" selfLink: /apis/scheduling.volcano.sh/v1beta1/namespaces/default/podgroups/job-1 uid: eb2508f5-3349-439c-b94d-4ac23afd71ff spec: minMember: 1 minResources: cpu: "3" memory: "2048Mi" priorityClassName: high-priority queue: default status: conditions: - lastTransitionTime: "2020-08-11T12:28:57Z" message: '1/0 tasks in gang unschedulable: pod group is not ready, 1 minAvailable. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/queue/ Mon, 28 Jan 2019 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/queue/ Introduction Queue is a collection of PodGroups, which adopts FIFO. It is also used as the basis for resource division. Example apiVersion: scheduling.volcano.sh/v1beta1 kind: Queue metadata: creationTimestamp: "2024-12-30T09:31:12Z" generation: 1 name: test resourceVersion: "987630" uid: 88babd01-c83f-4010-9701-c2471c1dd040 spec: capability: cpu: "8" memory: 16Gi # deserved field is only used by capacity plugin deserved: cpu: "4" memory: 8Gi guarantee: resource: cpu: "2" memory: 4Gi priority: 100 reclaimable: true # weight field is only used by proportion plugin weight: 1 status: allocated: cpu: "0" memory: "0" state: Open Key Fields guarantee, optional guarantee indicates the resources reserved for all PodGroups in this queue. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/tutorials/ Mon, 28 Jan 2019 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/tutorials/ This section provides guidance to help you quickly get started with Volcano, from deploying a basic Volcano Job/Deployment, to integrating with Volcano Queues Prerequisites A Kubernetes cluster with Volcano components need to be installed successfully. If you haven’t installed Volcano yet, please refer to Installation. Quick Start: Deploy a Volcano Job This quick start guide will walk you through deploying a simple Volcano Job. By default, Volcano Jobs use the default queue if no specific queue is provided. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/contribution/ Mon, 28 Jan 2019 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/contribution/ Welcome Welcome to Volcano! Before You Start Code of Conduct Community discussions Community Expectations Getting Started Your First Contribution Find Something to Work On Find a Good Topic Work on an Issue File an Issue Contribution Workflow Open a Pull Request Code Review Commit Message Format Testing Before You Start Code of Conduct All Volcano contributors must read and observe the Code of Conduct. https://deploy-preview-499--volcano-sh.netlify.app/en/docs/vcjob/ Mon, 28 Jan 2019 00:00:00 +0000 https://deploy-preview-499--volcano-sh.netlify.app/en/docs/vcjob/ Introduction VolcanoJob, referred to as vcjob, is a CRD object for Volcano. Different from a Kubernetes job, it provides more advanced features such as specified scheduler, minimum number of members, task definition, lifecycle management, specific queue, and specific priority. VolcanoJob is ideal for high performance computing scenarios such as machine learning, big data applications, and scientific computing. Example apiVersion: batch.volcano.sh/v1alpha1 kind: Job metadata: name: test-job spec: minAvailable: 3 schedulerName: volcano priorityClassName: high-priority policies: - event: PodEvicted action: RestartJob plugins: ssh: [] env: [] svc: [] maxRetry: 5 queue: default volumes: - mountPath: "/myinput" - mountPath: "/myoutput" volumeClaimName: "testvolumeclaimname" volumeClaim: accessModes: [ "ReadWriteOnce" ] storageClassName: "my-storage-class" resources: requests: storage: 1Gi tasks: - replicas: 6 name: "default-nginx" template: metadata: name: web spec: containers: - image: nginx imagePullPolicy: IfNotPresent name: nginx resources: requests: cpu: "1" restartPolicy: OnFailure Key Fields schedulerName schedulerName indicates the scheduler that will schedule the job.