# Volcano <-> Kubeflow Trainer Integration Configuration Sample
# This file contains examples for integrating Volcano scheduler with Kubeflow TrainJob.

---
# 1. Volcano Queue for training workloads
apiVersion: scheduling.volcano.sh/v1beta1
kind: Queue
metadata:
  name: training-queue
spec:
  weight: 1
  reclaimable: true
  capability:
    # Adjust based on your cluster capacity
    cpu: "100"
    memory: "512Gi"
    nvidia.com/gpu: "16"

---
# 2. Example TrainJob with Volcano PodGroup scheduling
# TrainJob that uses Volcano as the scheduler via podGroupPolicy
apiVersion: trainer.kubeflow.org/v1alpha1
kind: TrainJob
metadata:
  name: example-distributed-training
  namespace: default
spec:
  runtimeRef:
    name: torch-distributed
  trainer:
    image: docker.io/your-org/your-training-image:latest
    numNodes: 2
    numProcPerNode: "auto"
    resourcesPerNode:
      requests:
        nvidia.com/gpu: "4"
        cpu: "16"
        memory: "64Gi"
      limits:
        nvidia.com/gpu: "4"
        cpu: "16"
        memory: "64Gi"

---
# 3. Volcano PodGroup for gang scheduling (auto-created by integration)
# This is a reference example - Volcano controller auto-creates PodGroups
# when scheduler annotation is set.
apiVersion: scheduling.volcano.sh/v1beta1
kind: PodGroup
metadata:
  name: example-distributed-training
  namespace: default
spec:
  # MinMember ensures all pods are scheduled together (gang scheduling)
  minMember: 2
  queue: training-queue
  priorityClassName: normal-priority
  minResources:
    cpu: "32"
    memory: "128Gi"
    nvidia.com/gpu: "8"

---
# 4. PriorityClass for training workloads
apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: training-priority
value: 100
globalDefault: false
description: "Priority class for distributed training workloads"

---
# 5. ClusterTrainingRuntime with Volcano scheduler annotation
# Use this runtime to automatically schedule TrainJobs with Volcano
apiVersion: trainer.kubeflow.org/v1alpha1
kind: ClusterTrainingRuntime
metadata:
  name: torch-distributed-volcano
  labels:
    trainer.kubeflow.org/framework: torch
spec:
  mlPolicy:
    torch:
      numProcPerNode: 1
    numNodes: 1
  template:
    spec:
      replicatedJobs:
      - name: node
        template:
          spec:
            template:
              metadata:
                annotations:
                  # Use Volcano as the scheduler
                  scheduling.volcano.sh/queue-name: training-queue
              spec:
                schedulerName: volcano
                containers:
                - name: node
                  image: ghcr.io/kubeflow/trainer/torch-runtime:latest
                  resources:
                    requests:
                      nvidia.com/gpu: "1"
                    limits:
                      nvidia.com/gpu: "1"