Add example of helm chart for vllm deployment on k8s

.github/workflows/lint-and-deploy.yaml

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+name: Lint and Deploy Charts
+
+on: pull_request
+
+jobs:
+  lint-and-deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+        with:
+          fetch-depth: 0
+
+      - name: Set up Helm
+        uses: azure/setup-helm@fe7b79cd5ee1e45176fcad797de68ecaf3ca4814 # v4.2.0
+        with:
+          version: v3.14.4
+
+       #Python is required because ct lint runs Yamale and yamllint which require Python.
+      - uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
+        with:
+          python-version: '3.13'
+
+      - name: Set up chart-testing
+        uses: helm/chart-testing-action@e6669bcd63d7cb57cb4380c33043eebe5d111992 # v2.6.1
+        with:
+          version: v3.10.1
+
+      - name: Run chart-testing (lint)
+        run: ct lint --target-branch ${{ github.event.repository.default_branch }} --chart-dirs examples/chart-helm --charts examples/chart-helm
+
+      - name: Setup minio
+        run: |
+          docker network create vllm-net
+          docker run -d -p 9000:9000 --name minio --net vllm-net \
+                     -e "MINIO_ACCESS_KEY=minioadmin" \
+                     -e "MINIO_SECRET_KEY=minioadmin" \
+                     -v /tmp/data:/data \
+                     -v /tmp/config:/root/.minio \
+                     minio/minio server /data
+          export AWS_ACCESS_KEY_ID=minioadmin
+          export AWS_SECRET_ACCESS_KEY=minioadmin
+          export AWS_EC2_METADATA_DISABLED=true
+          mkdir opt-125m
+          cd opt-125m && curl -O -Ls "https://huggingface.co/facebook/opt-125m/resolve/main/{pytorch_model.bin,config.json,generation_config.json,merges.txt,special_tokens_map.json,tokenizer_config.json,vocab.json}" && cd ..
+          aws --endpoint-url http://127.0.0.1:9000/ s3 mb s3://testbucket
+          aws --endpoint-url http://127.0.0.1:9000/ s3 cp opt-125m/ s3://testbucket/opt-125m --recursive
+
+      - name: Create kind cluster
+        uses: helm/kind-action@0025e74a8c7512023d06dc019c617aa3cf561fde # v1.10.0
+
+      - name: Build the Docker image vllm cpu
+        run: docker buildx build -f Dockerfile.cpu -t vllm-cpu-env .
+
+      - name: Configuration of docker images, network and namespace for the kind cluster
+        run: |
+          docker pull amazon/aws-cli:2.6.4
+          kind load docker-image  amazon/aws-cli:2.6.4 --name chart-testing
+          kind load docker-image vllm-cpu-env:latest --name chart-testing
+          docker network connect vllm-net "$(docker ps -aqf "name=chart-testing-control-plane")"
+          kubectl create ns ns-vllm
+
+      - name: Run chart-testing (install)
+        run: |
+          export AWS_ACCESS_KEY_ID=minioadmin
+          export AWS_SECRET_ACCESS_KEY=minioadmin
+          helm install --wait --wait-for-jobs --timeout 5m0s --debug --create-namespace --namespace=ns-vllm test-vllm examples/chart-helm -f examples/chart-helm/values.yaml --set secrets.s3endpoint=http://minio:9000 --set secrets.s3bucketname=testbucket --set secrets.s3accesskeyid=$AWS_ACCESS_KEY_ID --set secrets.s3accesskey=$AWS_SECRET_ACCESS_KEY --set resources.requests.cpu=1 --set resources.requests.memory=4Gi --set resources.limits.cpu=2 --set resources.limits.memory=5Gi --set image.env[0].name=VLLM_CPU_KVCACHE_SPACE --set image.env[1].name=VLLM_LOGGING_LEVEL --set-string image.env[0].value="1" --set-string image.env[1].value="DEBUG" --set-string extraInit.s3modelpath="opt-125m/" --set-string 'resources.limits.nvidia\.com/gpu=0' --set-string 'resources.requests.nvidia\.com/gpu=0' --set-string image.repository="vllm-cpu-env"
+    
+      - name: curl test
+        run: |
+          kubectl -n ns-vllm port-forward service/test-vllm-service 8001:80 &
+          sleep 10
+          CODE="$(curl -v -f --location http://localhost:8001/v1/completions \
+                  --header "Content-Type: application/json" \
+                  --data '{
+                          "model": "opt-125m",
+                          "prompt": "San Francisco is a",
+                          "max_tokens": 7,
+                          "temperature": 0
+                  }'):$CODE"
+          echo "$CODE"

docs/source/index.rst

@@ -82,6 +82,7 @@ Documentation
    serving/openai_compatible_server
    serving/deploying_with_docker
    serving/deploying_with_k8s
+   serving/deploying_with_helm
    serving/deploying_with_nginx
    serving/distributed_serving
    serving/metrics

docs/source/serving/deploying_with_helm.rst

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+.. _deploying_with_helm:
+
+Deploying with Helm
+===================
+
+A Helm chart to deploy vLLM for Kubernetes
+
+Helm is a package manager for Kubernetes. It will help you to deploy vLLM on k8s and automate the deployment of vLLMm Kubernetes applications. With Helm, you can deploy the same framework architecture with different configurations to multiple namespaces by overriding variables values.
+
+This guide will walk you through the process of deploying vLLM with Helm, including the necessary prerequisites, steps for helm install and documentation on architecture and values file.
+
+Prerequisites
+-------------
+Before you begin, ensure that you have the following:
+
+- A running Kubernetes cluster
+- NVIDIA Kubernetes Device Plugin (`k8s-device-plugin`): This can be found at `https://github.com/NVIDIA/k8s-device-plugin/`
+- Available GPU resources in your cluster
+- S3 with the model which will be deployed
+
+Installing the chart
+--------------------
+
+To install the chart with the release name `test-vllm`
+
+.. code-block:: console
+
+    helm upgrade --install --create-namespace --namespace=ns-vllm test-vllm . -f values.yaml --set secrets.s3endpoint=$ACCESS_POINT --set secrets.s3buckername=$BUCKET --set secrets.s3accesskeyid=$ACCESS_KEY --set secrets.s3accesskey=$SECRET_KEY
+
+Uninstalling the Chart
+----------------------
+
+To uninstall the `test-vllm` deployment
+
+.. code-block:: console
+
+    helm uninstall test-vllm --namespace=ns-vllm
+
+The command removes all the Kubernetes components associated with the
+chart **including persistent volumes** and deletes the release.
+
+Architecture
+------------
+
+.. image:: architecture_helm_deployment.png
+
+Values
+------
+
+.. list-table:: Values
+   :widths: 25 25 25 25
+   :header-rows: 1
+
+   * - Key
+     - Type
+     - Default
+     - Description
+   * - autoscaling
+     - object
+     - {"enabled":false,"maxReplicas":100,"minReplicas":1,"targetCPUUtilizationPercentage":80}
+     - Autoscaling configuration
+   * - autoscaling.enabled
+     - bool
+     - false
+     - Enable autoscaling
+   * - autoscaling.maxReplicas
+     - int
+     - 100
+     - Maximum replicas
+   * - autoscaling.minReplicas
+     - int
+     - 1
+     - Minimum replicas
+   * - autoscaling.targetCPUUtilizationPercentage
+     - int
+     - 80
+     - Target CPU utilization for autoscaling
+   * - configs
+     - object
+     - {}
+     - Configmap
+   * - containerPort
+     - int
+     - 8000
+     - Container port
+   * - customObjects
+     - list
+     - []
+     - Custom Objects configuration
+   * - deploymentStrategy
+     - object
+     - {}
+     - Deployment strategy configuration
+   * - externalConfigs
+     - list
+     - []
+     - External configuration
+   * - extraContainers
+     - list
+     - []
+     - Additional containers configuration
+   * - extraInit
+     - object
+     - {"pvcStorage":"1Gi","s3modelpath":"relative_s3_model_path/opt-125m", "awsEc2MetadataDisabled": true}
+     - Additional configuration for the init container
+   * - extraInit.pvcStorage
+     - string
+     - "50Gi"
+     - Storage size of the s3
+   * - extraInit.s3modelpath
+     - string
+     - "relative_s3_model_path/opt-125m"
+     - Path of the model on the s3 which hosts model weights and config files
+   * - extraInit.awsEc2MetadataDisabled
+     - boolean
+     - true
+     - Disables the use of the Amazon EC2 instance metadata service
+   * - extraPorts
+     - list
+     - []
+     - Additional ports configuration
+   * - gpuModels
+     - list
+     - ["TYPE_GPU_USED"]
+     - Type of gpu used
+   * - image
+     - object
+     - {"command":["vllm","serve","/data/","--served-model-name","opt-125m","--host","0.0.0.0","--port","8000"],"repository":"vllm/vllm-openai","tag":"latest"}
+     - Image configuration
+   * - image.command
+     - list
+     - ["vllm","serve","/data/","--served-model-name","opt-125m","--host","0.0.0.0","--port","8000"]
+     - Container launch command
+   * - image.repository
+     - string
+     - "vllm/vllm-openai"
+     - Image repository
+   * - image.tag
+     - string
+     - "latest"
+     - Image tag
+   * - livenessProbe
+     - object
+     - {"failureThreshold":3,"httpGet":{"path":"/health","port":8000},"initialDelaySeconds":15,"periodSeconds":10}
+     - Liveness probe configuration
+   * - livenessProbe.failureThreshold
+     - int
+     - 3
+     - Number of times after which if a probe fails in a row, Kubernetes considers that the overall check has failed: the container is not alive
+   * - livenessProbe.httpGet
+     - object
+     - {"path":"/health","port":8000}
+     - Configuration of the Kubelet http request on the server
+   * - livenessProbe.httpGet.path
+     - string
+     - "/health"
+     - Path to access on the HTTP server
+   * - livenessProbe.httpGet.port
+     - int
+     - 8000
+     - Name or number of the port to access on the container, on which the server is listening
+   * - livenessProbe.initialDelaySeconds
+     - int
+     - 15
+     - Number of seconds after the container has started before liveness probe is initiated
+   * - livenessProbe.periodSeconds
+     - int
+     - 10
+     - How often (in seconds) to perform the liveness probe
+   * - maxUnavailablePodDisruptionBudget
+     - string
+     - ""
+     - Disruption Budget Configuration
+   * - readinessProbe
+     - object
+     - {"failureThreshold":3,"httpGet":{"path":"/health","port":8000},"initialDelaySeconds":5,"periodSeconds":5}
+     - Readiness probe configuration
+   * - readinessProbe.failureThreshold
+     - int
+     - 3
+     - Number of times after which if a probe fails in a row, Kubernetes considers that the overall check has failed: the container is not ready
+   * - readinessProbe.httpGet
+     - object
+     - {"path":"/health","port":8000}
+     - Configuration of the Kubelet http request on the server
+   * - readinessProbe.httpGet.path
+     - string
+     - "/health"
+     - Path to access on the HTTP server
+   * - readinessProbe.httpGet.port
+     - int
+     - 8000
+     - Name or number of the port to access on the container, on which the server is listening
+   * - readinessProbe.initialDelaySeconds
+     - int
+     - 5
+     - Number of seconds after the container has started before readiness probe is initiated
+   * - readinessProbe.periodSeconds
+     - int
+     - 5
+     - How often (in seconds) to perform the readiness probe
+   * - replicaCount
+     - int
+     - 1
+     - Number of replicas
+   * - resources
+     - object
+     - {"limits":{"cpu":4,"memory":"16Gi","nvidia.com/gpu":1},"requests":{"cpu":4,"memory":"16Gi","nvidia.com/gpu":1}}
+     - Resource configuration
+   * - resources.limits."nvidia.com/gpu"
+     - int
+     - 1
+     - Number of gpus used
+   * - resources.limits.cpu
+     - int
+     - 4
+     - Number of CPUs
+   * - resources.limits.memory
+     - string
+     - "16Gi"
+     - CPU memory configuration
+   * - resources.requests."nvidia.com/gpu"
+     - int
+     - 1
+     - Number of gpus used
+   * - resources.requests.cpu
+     - int
+     - 4
+     - Number of CPUs
+   * - resources.requests.memory
+     - string
+     - "16Gi"
+     - CPU memory configuration
+   * - secrets
+     - object
+     - {}
+     - Secrets configuration
+   * - serviceName
+     - string
+     -
+     - Service name
+   * - servicePort
+     - int
+     - 80
+     - Service port
+   * - labels.environment
+     - string
+     - test
+     - Environment name
+   * - labels.release
+     - string
+     - test
+     - Release name

examples/chart-helm/.helmignore

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+*.png
+.git/
+ct.yaml
+lintconf.yaml
+values.schema.json
+/workflows

examples/chart-helm/Chart.yaml

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+apiVersion: v2
+name: chart-vllm
+description: Chart vllm
+
+# A chart can be either an 'application' or a 'library' chart.
+#
+# Application charts are a collection of templates that can be packaged into versioned archives
+# to be deployed.
+#
+# Library charts provide useful utilities or functions for the chart developer. They're included as
+# a dependency of application charts to inject those utilities and functions into the rendering
+# pipeline. Library charts do not define any templates and therefore cannot be deployed.
+type: application
+
+# This is the chart version. This version number should be incremented each time you make changes
+# to the chart and its templates, including the app version.
+# Versions are expected to follow Semantic Versioning (https://semver.org/)
+version: 0.0.1
+
+maintainers:
+  - name: mfournioux

examples/chart-helm/ct.yaml

@@ -0,0 +1,3 @@
+chart-dirs:
+  - charts
+validate-maintainers: false