# GitLab Runner — RL training workloads (DQN, PPO)
# Runs on ci-training pool (L40S: 48GB VRAM)
# Mounts separate PVCs to avoid RWO conflicts with main runner

gitlabUrl: http://gitlab-webservice-default.foxhunt.svc.cluster.local:8181
# runnerToken set via --set at install time

replicas: 1

# Reuse the existing gitlab-runner SA (has pods/secrets/configmaps RBAC)
rbac:
  create: false
serviceAccount:
  create: false
  name: gitlab-runner

nodeSelector:
  k8s.scaleway.com/pool-name: infra

runners:
  cloneUrl: http://gitlab-webservice-default.foxhunt.svc.cluster.local:8181
  config: |
    [[runners]]
      clone_url = "http://gitlab-webservice-default.foxhunt.svc.cluster.local:8181"
      tag_list = ["kapsule", "l40s"]
      [runners.kubernetes]
        namespace = "foxhunt"
        service_account = "gitlab-runner"
        image = "rust:1.89-slim"
        privileged = false
        node_selector_overwrite_allowed = ".*"
        # Hyperopt RL jobs need 6000m CPU + 20Gi mem (PSO parallel trials)
        cpu_request_overwrite_max_allowed = "8000m"
        cpu_limit_overwrite_max_allowed = "8000m"
        memory_request_overwrite_max_allowed = "48Gi"
        memory_limit_overwrite_max_allowed = "48Gi"
        poll_timeout = 600
        # All RL runner jobs target GPU nodes → set nvidia runtime globally
        runtime_class_name = "nvidia"
        # Allow CI jobs to set pod annotations (training jobs expose Prometheus metrics)
        pod_annotations_overwrite_allowed = ".*"
        # Default resources for RL training on L40S
        cpu_request = "2000m"
        cpu_limit = "3800m"
        memory_request = "4Gi"
        memory_limit = "8Gi"
        helper_cpu_request = "100m"
        helper_cpu_limit = "500m"
        helper_memory_request = "128Mi"
        helper_memory_limit = "512Mi"
        image_pull_secrets = ["gitlab-registry"]
      [runners.kubernetes.node_selector]
        "k8s.scaleway.com/pool-name" = "ci-training"
      [runners.kubernetes.node_tolerations]
        "nvidia.com/gpu" = "NoSchedule"
        "node.cilium.io/agent-not-ready" = "NoSchedule"
      [runners.kubernetes.pod_labels]
        "app.kubernetes.io/part-of" = "foxhunt-ci"
      # Request GPU via K8s scheduler so only one training pod runs per GPU
      [[runners.kubernetes.pod_spec]]
        name = "build"
        patch_type = "strategic"
        patch = '{"containers":[{"name":"build","resources":{"requests":{"nvidia.com/gpu":"1"},"limits":{"nvidia.com/gpu":"1"}}}]}'
      # Training PVCs — separate from main runner to avoid RWO Multi-Attach errors
      [[runners.kubernetes.volumes.pvc]]
        name = "training-data-l4-pvc"
        mount_path = "/mnt/training-data"
        read_only = true
      [[runners.kubernetes.volumes.pvc]]
        name = "sccache-l4-pvc"
        mount_path = "/mnt/sccache"
        read_only = false

  tags: "kapsule,l40s"

concurrent: 2