(kuberay-rayservice-incremental-upgrade)=

RayService Zero-Downtime Incremental Upgrades

This guide details how to configure and use the NewClusterWithIncrementalUpgrade strategy for a RayService with KubeRay. This feature was proposed in a Ray Enhancement Proposal (REP) and implemented with alpha support in KubeRay v1.5.1. If unfamiliar with RayServices and KubeRay, see the RayService Quickstart.

In previous versions of KubeRay, zero-downtime upgrades were supported only through the NewCluster strategy. This upgrade strategy involved scaling up a pending RayCluster with equal capacity as the active cluster, waiting until the updated Serve applications were healthy, and then switching traffic to the new RayCluster. While this upgrade strategy is reliable, it required users to scale 200% of their original cluster's compute resources which can be prohibitive when dealing with expensive accelerator resources.

The NewClusterWithIncrementalUpgrade strategy is designed for large-scale deployments, such as LLM serving, where duplicating resources for a standard blue/green deployment is not feasible due to resource constraints. This feature minimizes resource usage during RayService CR upgrades while maintaining service availability. Below we explain the design and usage.

Rather than creating a new RayCluster at 100% capacity, this strategy creates a new cluster and gradually scales its capacity up while simultaneously shifting user traffic from the old cluster to the new one. This gradual traffic migration enables users to safely scale their updated RayService while the old cluster auto-scales down, enabling users to save expensive compute resources and exert greater control over the pace of their upgrade. This process relies on the Kubernetes Gateway API for fine-grained traffic splitting.

Quickstart: Performing an Incremental Upgrade

1. Prerequisites

Before you can use this feature, you must have the following set up in your Kubernetes cluster:

1. Gateway API CRDs: The K8s Gateway API resources must be installed. You can typically install them with:

   bashCopy

   kubectl apply --server-side -f https://github.com/kubernetes-sigs/gateway-api/releases/download/v1.4.0/standard-install.yaml
   

   The RayService controller utilizes GA Gateway API resources such as a Gateway and HTTPRoute to safely split traffic during the upgrade.

2. A Gateway Controller: Users must install a Gateway controller that implements the Gateway API, such as Istio, Contour, or a cloud-native implementation like GKE's Gateway controller. This feature should support any controller that implements Gateway API with support for Gateway and HTTPRoute CRDs, but is an alpha feature that's primarily been tested utilizing Istio.

3. A GatewayClass Resource: Your cluster admin must create a GatewayClass resource that defines which controller to use. KubeRay will use this to create Gateway and HTTPRoute objects.

   Example: Istio GatewayClass

   yamlCopy

   apiVersion: gateway.networking.k8s.io/v1
   kind: GatewayClass
   metadata:
       name: istio
   spec:
       controllerName: istio.io/gateway-controller
   

   You will need to use the metadata.name (e.g. istio) in the gatewayClassName field of the RayService spec.

4. Ray Autoscaler: Incremental upgrades require the Ray Autoscaler to be enabled in your RayCluster spec, as KubeRay manages the upgrade by adjusting the target_capacity for Ray Serve which adjusts the number of Serve replicas for each deployment. These Serve replicas are translated into a resource load which the Ray autoscaler considers when determining the number of Pods to provision with KubeRay. For information on enabling and configuring Ray autoscaling on Kubernetes, see KubeRay Autoscaling.

Example: Setting up a RayService on kind:

The following instructions detail the minimal steps to configure a cluster with KubeRay and trigger a zero-downtime incremental upgrade for a RayService.

1. Create a kind cluster

kind create cluster --image=kindest/node:v1.29.0

We use v1.29.0 which is known to be compatible with recent Istio versions.

2. Install istio

istioctl install --set profile=demo -y

3. Install Gateway API CRDs

kubectl apply --server-side -f https://github.com/kubernetes-sigs/gateway-api/releases/download/v1.3.0/standard-install.yaml

4. Create a Gateway class with the following spec

echo "apiVersion: gateway.networking.k8s.io/v1
kind: GatewayClass
metadata:
  name: istio
spec:
  controllerName: istio.io/gateway-controller" | kubectl apply -f -

kubectl get gatewayclass
NAME           CONTROLLER                    ACCEPTED   AGE
istio          istio.io/gateway-controller   True       4s
istio-remote   istio.io/unmanaged-gateway    True       3s

5. Install and Configure MetalLB for LoadBalancer on kind

kubectl apply -f https://raw.githubusercontent.com/metallb/metallb/v0.14.7/config/manifests/metallb-native.yaml

Create an IPAddressPool with the following spec for MetalLB

echo "apiVersion: metallb.io/v1beta1
kind: IPAddressPool
metadata:
  name: kind-pool
  namespace: metallb-system
spec:
  addresses:
  - 192.168.8.200-192.168.8.250 # adjust based on your subnets range
---
apiVersion: metallb.io/v1beta1
kind: L2Advertisement
metadata:
  name: default
  namespace: metallb-system
spec:
  ipAddressPools:
  - kind-pool" | kubectl apply -f -

6. Install the KubeRay operator, following these instructions. The minimum version for this guide is v1.5.1. To use this feature, the RayServiceIncrementalUpgrade feature gate must be enabled. To enable the feature gate when installing the kuberay operator, run the following command:

helm install kuberay-operator kuberay/kuberay-operator --version v1.5.1 \
  --set featureGates\[0\].name=RayServiceIncrementalUpgrade \
  --set featureGates\[0\].enabled=true

7. Create a RayService with incremental upgrade enabled.

kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-service.incremental-upgrade.yaml

8. Update one of the fields under rayClusterConfig and re-apply the RayService to trigger a zero-downtime upgrade.

2. How it Works: The Upgrade Process

Understanding the lifecycle of an incremental upgrade helps in monitoring and configuration.

1. Trigger: You trigger an upgrade by updating the RayService spec, such as changing the container image or updating the resources used by a worker group in the rayClusterSpec.

2. Pending Cluster Creation: KubeRay detects the change and creates a new, pending RayCluster. It sets this cluster's initial target_capacity (the percentage of serve replicas it should run) to 0%.

3. Gateway and Route Creation: KubeRay creates a Gateway resource for your RayService and an HTTPRoute resource that initially routes 100% of traffic to the old, active cluster and 0% to the new, pending cluster.

4. The Upgrade Loop Begins: The KubeRay controller now enters a loop that repeats three phases until the upgrade is complete. This loop ensures that the total cluster capacity only exceeds 100% by at most maxSurgePercent, preventing resource starvation.

   Let's use an example: maxSurgePercent: 20 and stepSizePercent: 5.

   • Initial State:
     • Active Cluster target_capacity: 100%
     • Pending Cluster target_capacity: 0%
     • Total Capacity: 100%

   ---

   The Upgrade Cycle

   • Phase 1: Scale Up Pending Cluster (Capacity)

     • KubeRay checks the total capacity (100%) and sees it's $\le$ 100%. It increases the pending cluster's target_capacity by maxSurgePercent.
     • Active target_capacity: 100%
     • Pending target_capacity: 0% $\rightarrow$ 20%
     • Total Capacity: 120%
     • If the Ray Serve autoscaler is enabled, the Serve application will scale its num_replicas from min_replicas based on the new target_capacity. Without the Ray Serve autoscaler enabled, the new target_capacity value will directly adjust num_replicas for each Serve deployment. Depending on the updated value ofnum_replicas, the Ray Autoscaler will begin provisioning pods for the pending cluster to handle the updated resource load.

   • Phase 2: Shift Traffic (HTTPRoute)

     • KubeRay waits for the pending cluster's new pods to be ready. There may be a temporary drop in requests-per-second while worker Pods are being created for the updated Ray serve replicas.
     • Once ready, it begins to gradually shift traffic. Every intervalSeconds, it updates the HTTPRoute weights, moving stepSizePercent (5%) of traffic from the active to the pending cluster.
     • This continues until the actual traffic (trafficRoutedPercent) "catches up" to the pending cluster's target_capacity (20% in this example).

   • Phase 3: Scale Down Active Cluster (Capacity)

     • Once Phase 2 is complete (trafficRoutedPercent == 20%), the loop runs again.
     • KubeRay checks the total capacity (120%) and sees it's > 100%. It decreases the active cluster's target_capacity by maxSurgePercent.
     • Active target_capacity: 100% $\rightarrow$ 80%
     • Pending target_capacity: 20%
     • Total Capacity: 100%
     • The Ray Autoscaler terminates pods on the active cluster as they become idle.

   ---

5. Completion & Cleanup: This cycle of (Scale Up Pending $\rightarrow$ Shift Traffic $\rightarrow$ Scale Down Active) continues until the pending cluster is at 100% target_capacity and 100% trafficRoutedPercent, and the active cluster is at 0%.

   KubeRay then promotes the pending cluster to active, updates the HTTPRoute to send 100% of traffic to it, and safely terminates the old RayCluster.

3. Example RayService Configuration

To use the feature, set the upgradeStrategy.type to NewClusterWithIncrementalUpgrade and provide the required options.

apiVersion: ray.io/v1
kind: RayService
metadata:
  name: rayservice-incremental-upgrade
spec:
  # This is the main configuration block for the upgrade
  upgradeStrategy:
    # 1. Set the type to NewClusterWithIncrementalUpgrade
    type: "NewClusterWithIncrementalUpgrade"
    clusterUpgradeOptions:
      # 2. The name of your K8s GatewayClass
      gatewayClassName: "istio"

      # 3. Capacity scaling: Increase new cluster's target_capacity
      #    by 20% in each scaling step.
      maxSurgePercent: 20

      # 4. Traffic shifting: Move 5% of traffic from old to new
      #    cluster every intervalSeconds.
      stepSizePercent: 5

      # 5. Interval seconds controls the pace of traffic migration during the upgrade.
      intervalSeconds: 10

  # This is your Serve config
  serveConfigV2: |
    applications:
      - name: my_app
        import_path: my_model:app
        route_prefix: /
        deployments:
          - name: MyModel
            num_replicas: 10
            ray_actor_options:
              resources: { "GPU": 1 }
            autoscaling_config:
              min_replicas: 0
              max_replicas: 20

  # This is your RayCluster config (autoscaling must be enabled)
  rayClusterSpec:
    enableInTreeAutoscaling: true
    headGroupSpec:
      # ... head spec ...
    workerGroupSpecs:
    - groupName: gpu-worker
      replicas: 0
      minReplicas: 0
      maxReplicas: 20
      template:
        # ... pod spec with GPU requests ...

4. Trigger the Upgrade

Incremental upgrades are triggered exactly like standard zero-downtime upgrades in KubeRay: by modifying the spec.rayClusterConfig in your RayService Custom Resource.

When KubeRay detects a change in the cluster specification (such as a new container image, modified resource limits, or updated environment variables), it calculates a new hash. If the hash differs from the active cluster and incremental upgrades are enabled, the NewClusterWithIncrementalUpgrade strategy is automatically initiated.

Updates to the cluster specifications can occur by running kubectl apply -f on the updated YAML configuration file, or by directly editing the CR using kubectl edit rayservice <your-rayservice-name>.

5. Monitoring the Upgrade

You can monitor the progress of the upgrade by inspecting the RayService status and the HTTPRoute object.

1. Check RayService Status:

   bashCopy

   kubectl describe rayservice rayservice-incremental-upgrade
   

   Look at the Status section. You will see both Active Service Status and Pending Service Status, which show the state of both clusters. Pay close attention to these two new fields:

   • Target Capacity: The percentage of replicas KubeRay is telling this cluster to scale to.
   • Traffic Routed Percent: The percentage of traffic KubeRay is currently sending to this cluster via the Gateway.

   During an upgrade, you will see Target Capacity on the pending cluster increase in steps (e.g., 20%, 40%) and Traffic Routed Percent gradually climb to meet it.

2. Check HTTPRoute Weights: You can also see the traffic weights directly on the HTTPRoute resource KubeRay manages.

   bashCopy

   kubectl get httproute rayservice-incremental-upgrade-httproute -o yaml
   

   Look at the spec.rules.backendRefs. You will see the weight for the old and new services change in real-time as the traffic shift (Phase 2) progresses.

For example:

apiVersion: gateway.networking.k8s.io/v1
kind: HTTPRoute
metadata:
  creationTimestamp: "2025-12-07T07:42:24Z"
  generation: 10
  name: stress-test-serve-httproute
  namespace: default
  ownerReferences:
  - apiVersion: ray.io/v1
    blockOwnerDeletion: true
    controller: true
    kind: RayService
    name: stress-test-serve
    uid: 83a785cc-8745-4ccd-9973-2fc9f27000cc
  resourceVersion: "3714"
  uid: 660b14b5-78df-4507-b818-05989b1ef806
spec:
  parentRefs:
  - group: gateway.networking.k8s.io
    kind: Gateway
    name: stress-test-serve-gateway
    namespace: default
  rules:
  - backendRefs:
    - group: ""
      kind: Service
      name: stress-test-serve-f6z4w-serve-svc
      namespace: default
      port: 8000
      weight: 90
    - group: ""
      kind: Service
      name: stress-test-serve-xclvf-serve-svc
      namespace: default
      port: 8000
      weight: 10
    matches:
    - path:
        type: PathPrefix
        value: /
status:
  parents:
  - conditions:
    - lastTransitionTime: "2025-12-07T07:42:24Z"
      message: Route was valid
      observedGeneration: 10
      reason: Accepted
      status: "True"
      type: Accepted
    - lastTransitionTime: "2025-12-07T07:42:24Z"
      message: All references resolved
      observedGeneration: 10
      reason: ResolvedRefs
      status: "True"
      type: ResolvedRefs
    controllerName: istio.io/gateway-controller
    parentRef:
      group: gateway.networking.k8s.io
      kind: Gateway
      name: stress-test-serve-gateway
      namespace: default

How to upgrade safely?

Since this feature is alpha and rollback is not yet supported, we recommend conservative parameter settings to minimize risk during upgrades.

Recommended Parameters

To upgrade safely, you should:

1. Scale up 1 worker pod in the new cluster and scale down 1 worker pod in the old cluster at a time
2. Make the upgrade process gradual to allow the Ray Serve autoscaler and Ray autoscaler to adapt

Based on these principles, we recommend:

• maxSurgePercent: Calculate based on the formula below
• stepSizePercent: Set to a value less than maxSurgePercent
• intervalSeconds: 60

Calculating maxSurgePercent

The maxSurgePercent determines the maximum percentage of additional resources that can be provisioned during the upgrade. To calculate the minimum safe value:

\begin{equation} \text{maxSurgePercent} = \frac{\text{resources per pod}}{\text{total cluster resources}} \times 100 \end{equation}

Example

Consider a RayCluster with the following configuration:

• excludeHeadService: true
• Head pod: No GPU
• 5 worker pods, each with 1 GPU (total: 5 GPUs)

For this cluster: \begin{equation} \text{maxSurgePercent} = \frac{1 \text{ GPU}}{5 \text{ GPUs}} \times 100 = 20% \end{equation}

With maxSurgePercent: 20, the upgrade process ensures:

• The new cluster scales up 1 worker pod at a time (20% of 5 = 1 pod)
• The old cluster scales down 1 worker pod at a time
• Your cluster temporarily uses 6 GPUs during the transition (5 original + 1 new)

This configuration guarantees you have sufficient resources to run at least one additional worker pod during the upgrade without resource contention.

Understanding intervalSeconds

Set intervalSeconds to 60 seconds to give the Ray Serve autoscaler and Ray autoscaler sufficient time to:

• Detect load changes
• Immediately scale replicas up or down to enforce new min_replicas and max_replicas limits (via target_capacity)
  • Scale down replicas immediately if they exceed the new max_replicas
  • Scale up replicas immediately if they fall below the new min_replicas
• Provision resources

A larger interval prevents the upgrade controller from making changes faster than the autoscaler can react, reducing the risk of service disruption.

Example Configuration

upgradeStrategy:
  maxSurgePercent: 20  # Calculated: (1 GPU / 5 GPUs) × 100
  stepSizePercent: 10  # Less than maxSurgePercent
  intervalSeconds: 60  # Wait 1 minute between steps

API Overview (Reference)

This section details the new and updated fields in the RayService CRD.

RayService.spec.upgradeStrategy

RayService.spec.upgradeStrategy.clusterUpgradeOptions

This block is required only if type is set to NewClusterWithIncrementalUpgrade.

RayService.status.activeServiceStatus & RayService.status.pendingServiceStatus

Three new fields are added to both the activeServiceStatus and pendingServiceStatus blocks to provide visibility into the upgrade process.

Next steps:

• See Deploy on Kubernetes for more information about deploying Ray Serve with KubeRay.
• See Ray Serve Autoscaling to configure your Serve deployments to scale based on traffic load.