ContextQMD
Back to Vllm

Kthena

docs/deployment/integrations/kthena.md

0.24.014.3 KB
Original Source

Kthena

Kthena is a Kubernetes-native LLM inference platform that transforms how organizations deploy and manage Large Language Models in production. Built with declarative model lifecycle management and intelligent request routing, it provides high performance and enterprise-grade scalability for LLM inference workloads.

This guide shows how to deploy a production-grade, multi-node vLLM service on Kubernetes.

We’ll:

  • Install the required components (Kthena + Volcano).
  • Deploy a multi-node vLLM model via Kthena’s ModelServing CR.
  • Validate the deployment.

1. Prerequisites

You need:

  • A Kubernetes cluster with GPU nodes.
  • kubectl access with cluster-admin or equivalent permissions.
  • Volcano installed for gang scheduling.
  • Kthena installed with the ModelServing CRD available.
  • A valid Hugging Face token if loading models from Hugging Face Hub.

1.1 Install Volcano

bash
helm repo add volcano-sh https://volcano-sh.github.io/helm-charts
helm repo update
helm install volcano volcano-sh/volcano -n volcano-system --create-namespace

This provides the gang-scheduling and network topology features used by Kthena.

1.2 Install Kthena

bash
helm install kthena oci://ghcr.io/volcano-sh/charts/kthena --version v0.1.0 --namespace kthena-system --create-namespace
  • The kthena-system namespace is created.
  • Kthena controllers and CRDs, including ModelServing, are installed and healthy.

Validate:

bash
kubectl get crd | grep modelserving

You should see:

text
modelservings.workload.serving.volcano.sh   ...

2. The Multi-Node vLLM ModelServing Example

Kthena provides an example manifest to deploy a multi-node vLLM cluster running Llama. Conceptually this is equivalent to the vLLM production stack Helm deployment, but expressed with ModelServing.

A simplified version of the example (llama-multinode) looks like:

  • spec.replicas: 1 – one ServingGroup (one logical model deployment).
  • roles:
    • entryTemplate – defines leader pods that run:
      • vLLM’s multi-node cluster bootstrap script.
      • vLLM OpenAI-compatible API server.
    • workerTemplate – defines worker pods to join the leader’s Ray cluster (Ray backend) or to join same distributed process group (multiprocessing backend).

Key points from the example YAML:

Image: vllm/vllm-openai:latest (matches upstream vLLM images). Commands:

??? code "Yaml" === "Multiprocessing (default)" Leader:

    ```yaml
    command:
      - sh
      - -c
      - >
        vllm serve meta-llama/Llama-3.1-405B-Instruct
          --tensor-parallel-size 8
          --pipeline-parallel-size 2
          --nnodes=2
          --node-rank=0
          --master-addr=$(ENTRY_ADDRESS)
          --port 8080
    ```

    Worker:

    ```yaml
    command:
      - sh
      - -c
      - >
        vllm serve meta-llama/Llama-3.1-405B-Instruct
          --tensor-parallel-size 8
          --pipeline-parallel-size 2
          --nnodes=2
          --node-rank=1
          --master-addr=$(ENTRY_ADDRESS)
          --headless
    ```

=== "Ray"
    Leader:

    ```yaml
    command:
      - sh
      - -c
      - >
        bash /vllm-workspace/examples/ray_serving/multi-node-serving.sh
        leader --ray_cluster_size=2; python3 -m
        vllm.entrypoints.openai.api_server --port 8080 --model
        meta-llama/Llama-3.1-405B-Instruct --tensor-parallel-size 8
        --pipeline-parallel-size 2
    ```

    Worker:

    ```yaml
    command:
      - sh
      - -c
      - >
        bash /vllm-workspace/examples/ray_serving/multi-node-serving.sh
        worker --ray_address=$(ENTRY_ADDRESS)
    ```

3. Deploying Multi-Node llama vLLM via Kthena

3.1 Prepare the Manifest

Recommended: use a Secret instead of a raw env var:

bash
kubectl create secret generic hf-token \
  -n default \
  --from-literal=HUGGING_FACE_HUB_TOKEN='<your-token>'

3.2 Apply the ModelServing

Save one of the following manifests to modelserving.yaml:

??? code "modelserving.yaml" === "Multiprocessing (default)" yaml apiVersion: workload.serving.volcano.sh/v1alpha1 kind: ModelServing metadata: name: llama-multinode namespace: default spec: schedulerName: volcano replicas: 1 # group replicas template: restartGracePeriodSeconds: 60 gangPolicy: minRoleReplicas: 405b: 1 roles: - name: 405b replicas: 2 entryTemplate: spec: containers: - name: leader image: vllm/vllm-openai:latest env: - name: HUGGING_FACE_HUB_TOKEN valueFrom: secretKeyRef: name: hf-token key: HUGGING_FACE_HUB_TOKEN command: - sh - -c - "vllm serve meta-llama/Llama-3.1-405B-Instruct --tensor-parallel-size 8 --pipeline-parallel-size 2 --nnodes 2 --node-rank 0 --master-addr $(ENTRY_ADDRESS) --distributed-executor-backend mp --port 8080" resources: limits: nvidia.com/gpu: "8" memory: 1124Gi ephemeral-storage: 800Gi requests: ephemeral-storage: 800Gi cpu: 125 ports: - containerPort: 8080 readinessProbe: tcpSocket: port: 8080 initialDelaySeconds: 15 periodSeconds: 10 volumeMounts: - mountPath: /dev/shm name: dshm volumes: - name: dshm emptyDir: medium: Memory sizeLimit: 15Gi workerReplicas: 1 workerTemplate: spec: containers: - name: worker image: vllm/vllm-openai:latest command: - sh - -c - "vllm serve meta-llama/Llama-3.1-405B-Instruct --tensor-parallel-size 8 --pipeline-parallel-size 2 --nnodes 2 --node-rank 1 --master-addr $(ENTRY_ADDRESS) --distributed-executor-backend mp --headless" resources: limits: nvidia.com/gpu: "8" memory: 1124Gi ephemeral-storage: 800Gi requests: ephemeral-storage: 800Gi cpu: 125 env: - name: HUGGING_FACE_HUB_TOKEN valueFrom: secretKeyRef: name: hf-token key: HUGGING_FACE_HUB_TOKEN volumeMounts: - mountPath: /dev/shm name: dshm volumes: - name: dshm emptyDir: medium: Memory sizeLimit: 15Gi 

=== "Ray"
    ```yaml
    apiVersion: workload.serving.volcano.sh/v1alpha1
    kind: ModelServing
    metadata:
      name: llama-multinode
      namespace: default
    spec:
      schedulerName: volcano
      replicas: 1  # group replicas
      template:
        restartGracePeriodSeconds: 60
        gangPolicy:
          minRoleReplicas:
            405b: 1
        roles:
          - name: 405b
            replicas: 2
            entryTemplate:
              spec:
                containers:
                  - name: leader
                    image: vllm/vllm-openai:latest
                    env:
                      - name: HUGGING_FACE_HUB_TOKEN
                        valueFrom:
                          secretKeyRef:
                            name: hf-token
                            key: HUGGING_FACE_HUB_TOKEN
                    command:
                      - sh
                      - -c
                      - "bash /vllm-workspace/examples/ray_serving/multi-node-serving.sh leader --ray_cluster_size=2;
                        vllm serve meta-llama/Llama-3.1-405B-Instruct --port 8080 --tensor-parallel-size 8 --pipeline-parallel-size 2"
                    resources:
                      limits:
                        nvidia.com/gpu: "8"
                        memory: 1124Gi
                        ephemeral-storage: 800Gi
                      requests:
                        ephemeral-storage: 800Gi
                        cpu: 125
                    ports:
                      - containerPort: 8080
                    readinessProbe:
                      tcpSocket:
                        port: 8080
                      initialDelaySeconds: 15
                      periodSeconds: 10
                    volumeMounts:
                      - mountPath: /dev/shm
                        name: dshm
                volumes:
                - name: dshm
                  emptyDir:
                    medium: Memory
                    sizeLimit: 15Gi
            workerReplicas: 1
            workerTemplate:
              spec:
                containers:
                  - name: worker
                    image: vllm/vllm-openai:latest
                    command:
                      - sh
                      - -c
                      - "bash /vllm-workspace/examples/ray_serving/multi-node-serving.sh worker --ray_address=$(ENTRY_ADDRESS)"
                    resources:
                      limits:
                        nvidia.com/gpu: "8"
                        memory: 1124Gi
                        ephemeral-storage: 800Gi
                      requests:
                        ephemeral-storage: 800Gi
                        cpu: 125
                    env:
                      - name: HUGGING_FACE_HUB_TOKEN
                        valueFrom:
                          secretKeyRef:
                            name: hf-token
                            key: HUGGING_FACE_HUB_TOKEN
                    volumeMounts:
                      - mountPath: /dev/shm
                        name: dshm
                volumes:
                - name: dshm
                  emptyDir:
                    medium: Memory
                    sizeLimit: 15Gi
    ```
bash
kubectl apply -f modelserving.yaml

Kthena will:

  • Create a ModelServing object.
  • Derive a PodGroup for Volcano gang scheduling.
  • Create the leader and worker pods for each ServingGroup and Role.

4. Verifying the Deployment

4.1 Check ModelServing Status

Use the snippet from the Kthena docs:

bash
kubectl get modelserving -oyaml | grep status -A 10

You should see something like:

yaml
status:
  availableReplicas: 1
  conditions:
    - type: Available
      status: "True"
      reason: AllGroupsReady
      message: All Serving groups are ready
    - type: Progressing
      status: "False"
      ...
  replicas: 1
  updatedReplicas: 1

4.2 Check Pods

List pods for your deployment:

bash
kubectl get pod -owide -l modelserving.volcano.sh/name=llama-multinode

Example output (from docs):

text
NAMESPACE   NAME                          READY   STATUS    RESTARTS   AGE   IP            NODE           ...
default     llama-multinode-0-405b-0-0    1/1     Running   0          15m   10.244.0.56   192.168.5.12   ...
default     llama-multinode-0-405b-0-1    1/1     Running   0          15m   10.244.0.58   192.168.5.43   ...
default     llama-multinode-0-405b-1-0    1/1     Running   0          15m   10.244.0.57   192.168.5.58   ...
default     llama-multinode-0-405b-1-1    1/1     Running   0          15m   10.244.0.53   192.168.5.36   ...

Pod name pattern:

  • llama-multinode-<group-idx>-<role-name>-<replica-idx>-<ordinal>.

The first number indicates ServingGroup. The second (405b) is the Role. The remaining indices identify the pod within the role.

6. Accessing the vLLM OpenAI-Compatible API

Expose the entry via a Service:

yaml
apiVersion: v1
kind: Service
metadata:
  name: llama-multinode-openai
  namespace: default
spec:
  selector:
    modelserving.volcano.sh/name: llama-multinode
    modelserving.volcano.sh/entry: "true"
    # optionally further narrow to leader role if you label it
  ports:
    - name: http
      port: 80
      targetPort: 8080
  type: ClusterIP

Port-forward from your local machine:

bash
kubectl port-forward svc/llama-multinode-openai 30080:80 -n default

Then:

  • List models:

    bash
    curl -s http://localhost:30080/v1/models

  • Send a completion request (mirroring vLLM production stack docs):

    bash
    curl -X POST http://localhost:30080/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/Llama-3.1-405B-Instruct",
    "prompt": "Once upon a time,",
    "max_tokens": 10
  }'

You should see an OpenAI-style response from vLLM.

7. Clean Up

To remove the deployment and its resources:

bash
kubectl delete modelserving llama-multinode -n default

If you’re done with the entire stack:

bash
helm uninstall kthena -n kthena-system   # or your Kthena release name
helm uninstall volcano -n volcano-system