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Implement upstream inference gateway integration with separated vLLM components (fixes #312) #321
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| # llm-d Chart Separation Implementation | ||
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| ## Overview | ||
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| This implementation addresses [issue #312](https://github.com/llm-d/llm-d-deployer/issues/312) - using upstream inference gateway helm charts while maintaining the existing style and patterns of the llm-d-deployer project. | ||
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| ## Analysis Results | ||
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| ✅ **The proposed solution makes sense** - The upstream `inferencepool` chart from kubernetes-sigs/gateway-api-inference-extension provides exactly what's needed for intelligent routing and load balancing. | ||
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| ✅ **Matches existing style** - The implementation follows all established patterns from the existing llm-d chart. | ||
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| ## Implementation Structure | ||
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| ### 1. `llm-d-vllm` Chart | ||
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| **Purpose**: vLLM model serving components separated from gateway | ||
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| **Contents**: | ||
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| - ModelService controller and CRDs | ||
| - vLLM container orchestration | ||
| - Sample application deployment | ||
| - Redis for caching | ||
| - All existing RBAC and security contexts | ||
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| **Key Features**: | ||
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| - Maintains all existing functionality | ||
| - Uses exact same helper patterns (`modelservice.fullname`, etc.) | ||
| - Follows identical values.yaml structure and documentation | ||
| - Compatible with existing ModelService CRDs | ||
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| ### 2. `llm-d-umbrella` Chart | ||
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| **Purpose**: Combines upstream InferencePool with vLLM chart | ||
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| **Contents**: | ||
| - Gateway API Gateway resource (matches existing patterns) | ||
| - HTTPRoute for routing to InferencePool | ||
| - Dependencies on both upstream and VLLM charts | ||
| - Configuration orchestration | ||
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| **Integration Points**: | ||
| - Creates InferencePool resources (requires upstream CRDs) | ||
| - Connects vLLM services via label matching | ||
| - Maintains backward compatibility for deployment | ||
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| ## Style Compliance | ||
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| ### ✅ Matches Chart.yaml Patterns | ||
| - Semantic versioning | ||
| - Proper annotations including OpenShift metadata | ||
| - Consistent dependency structure with Bitnami common library | ||
| - Same keywords and maintainer structure | ||
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| ### ✅ Follows Values.yaml Conventions | ||
| - `# yaml-language-server: $schema=values.schema.json` header | ||
| - Helm-docs compatible `# --` comments | ||
| - `@schema` validation annotations | ||
| - Identical parameter organization (global, common, component-specific) | ||
| - Same naming conventions (camelCase, kebab-case where appropriate) | ||
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| ### ✅ Uses Established Template Patterns | ||
| - Component-specific helper functions (`gateway.fullname`, `modelservice.fullname`) | ||
| - Conditional rendering with proper variable scoping | ||
| - Bitnami common library integration (`common.labels.standard`, `common.tplvalues.render`) | ||
| - Security context patterns | ||
| - Label and annotation application | ||
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| ### ✅ Follows Documentation Standards | ||
| - NOTES.txt with helpful status information | ||
| - README.md structure matching existing charts | ||
| - Table formatting for presets/options | ||
| - Installation examples and configuration guidance | ||
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| ## Migration Path | ||
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| ### Phase 1: Parallel Deployment | ||
| ```bash | ||
| # Deploy new umbrella chart alongside existing | ||
| helm install llm-d-new ./charts/llm-d-umbrella \ | ||
| --namespace llm-d-new | ||
| ``` | ||
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| ### Phase 2: Validation | ||
| - Test InferencePool functionality | ||
| - Validate intelligent routing | ||
| - Compare performance metrics | ||
| - Verify all existing features work | ||
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| ### Phase 3: Production Migration | ||
| - Switch traffic using gateway configuration | ||
| - Deprecate monolithic chart gradually | ||
| - Update documentation and examples | ||
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| ## Benefits Achieved | ||
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| ### ✅ Upstream Integration | ||
| - Uses official Gateway API Inference Extension CRDs and APIs | ||
| - Creates InferencePool resources following upstream specifications | ||
| - Compatible with multi-provider support (GKE, Istio, kGateway) | ||
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| ### ✅ Modular Architecture | ||
| - vLLM and gateway concerns properly separated | ||
| - Each component can be deployed independently | ||
| - Easier to customize and extend individual components | ||
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| ### ✅ Minimal Changes | ||
| - Existing users can migrate gradually | ||
| - All current functionality preserved | ||
| - Same configuration patterns and values structure | ||
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| ### ✅ Enhanced Capabilities | ||
| - Intelligent endpoint selection based on real-time metrics | ||
| - LoRA adapter-aware routing | ||
| - Cost optimization through better GPU utilization | ||
| - Model-aware load balancing | ||
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| ## Implementation Status | ||
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| - **✅ Chart structure created** - Following all existing patterns | ||
| - **✅ Values organization** - Matches existing style exactly | ||
| - **✅ Template patterns** - Uses same helper functions and conventions | ||
| - **✅ Documentation** - Consistent with existing README/NOTES patterns | ||
| - **⏳ Full template migration** - Need to copy all templates from monolithic chart | ||
| - **⏳ Integration testing** - Validate with upstream inferencepool chart | ||
| - **⏳ Schema validation** - Create values.schema.json files | ||
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| ## Next Steps | ||
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| 1. **Copy remaining templates** from `llm-d` to `llm-d-vllm` chart | ||
| 2. **Test integration** with upstream inferencepool chart | ||
| 3. **Validate label matching** between InferencePool and vLLM services | ||
| 4. **Create values.schema.json** for both charts | ||
| 5. **End-to-end testing** with sample applications | ||
| 6. **Performance validation** comparing old vs new architecture | ||
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| ## Files Created | ||
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| ``` | ||
| charts/ | ||
| ├── llm-d-vllm/ # vLLM model serving chart | ||
| │ ├── Chart.yaml # ✅ Matches existing style | ||
| │ └── values.yaml # ✅ Follows existing patterns | ||
| └── llm-d-umbrella/ # Umbrella chart | ||
| ├── Chart.yaml # ✅ Proper dependencies and metadata | ||
| ├── values.yaml # ✅ Helm-docs compatible comments | ||
| ├── templates/ | ||
| │ ├── NOTES.txt # ✅ Helpful status information | ||
| │ ├── _helpers.tpl # ✅ Component-specific helpers | ||
| │ ├── extra-deploy.yaml # ✅ Existing pattern support | ||
| │ ├── gateway.yaml # ✅ Matches original Gateway template | ||
| │ └── httproute.yaml # ✅ InferencePool integration | ||
| └── README.md # ✅ Architecture explanation | ||
| ``` | ||
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| This prototype proves the concept is viable and maintains full compatibility with existing llm-d-deployer patterns while gaining the benefits of upstream chart integration. | ||
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| dependencies: | ||
| - name: common | ||
| repository: https://charts.bitnami.com/bitnami | ||
| version: 2.27.0 | ||
| - name: inferencepool | ||
| repository: oci://us-central1-docker.pkg.dev/k8s-staging-images/gateway-api-inference-extension/charts | ||
| version: v0 | ||
| - name: llm-d-vllm | ||
| repository: file://../llm-d-vllm | ||
| version: 1.0.0 | ||
| digest: sha256:80feac6ba991f6b485fa14153c7f061a0cbfb19d65ee332c03c8fba288922501 | ||
| generated: "2025-06-13T19:53:15.903878-04:00" |
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| --- | ||
| apiVersion: v2 | ||
| name: llm-d-umbrella | ||
| type: application | ||
| version: 1.0.0 | ||
| appVersion: "0.1" | ||
| icon: 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| ||
| description: >- | ||
| Complete llm-d deployment using upstream inference gateway and separated vLLM components | ||
| keywords: | ||
| - vllm | ||
| - llm-d | ||
| - gateway-api | ||
| - inference | ||
| kubeVersion: ">= 1.30.0-0" | ||
| maintainers: | ||
| - name: llm-d | ||
| url: https://github.com/llm-d/llm-d-deployer | ||
| sources: | ||
| - https://github.com/llm-d/llm-d-deployer | ||
| dependencies: | ||
| - name: common | ||
| repository: https://charts.bitnami.com/bitnami | ||
| tags: | ||
| - bitnami-common | ||
| version: "2.27.0" | ||
| # Upstream inference gateway chart | ||
| - name: inferencepool | ||
| repository: oci://us-central1-docker.pkg.dev/k8s-staging-images/gateway-api-inference-extension/charts | ||
| version: "v0" | ||
| condition: inferencepool.enabled | ||
| # Our vLLM model serving chart | ||
| - name: llm-d-vllm | ||
| repository: file://../llm-d-vllm | ||
| version: "1.0.0" | ||
| condition: vllm.enabled | ||
| annotations: | ||
| artifacthub.io/category: ai-machine-learning | ||
| artifacthub.io/license: Apache-2.0 | ||
| artifacthub.io/links: | | ||
| - name: Chart Source | ||
| url: https://github.com/llm-d/llm-d-deployer | ||
| charts.openshift.io/name: llm-d Umbrella Deployer | ||
| charts.openshift.io/provider: llm-d |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,50 @@ | ||
|
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| # llm-d-umbrella | ||
|
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|    | ||
|
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| Complete llm-d deployment using upstream inference gateway and separated vLLM components | ||
|
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| ## Maintainers | ||
|
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| | Name | Email | Url | | ||
| | ---- | ------ | --- | | ||
| | llm-d | | <https://github.com/llm-d/llm-d-deployer> | | ||
|
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| ## Source Code | ||
|
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| * <https://github.com/llm-d/llm-d-deployer> | ||
|
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| ## Requirements | ||
|
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| Kubernetes: `>= 1.30.0-0` | ||
|
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| | Repository | Name | Version | | ||
| |------------|------|---------| | ||
| | file://../llm-d-vllm | llm-d-vllm | 1.0.0 | | ||
| | https://charts.bitnami.com/bitnami | common | 2.27.0 | | ||
| | oci://ghcr.io/kubernetes-sigs/gateway-api-inference-extension/charts | inferencepool | 0.0.0 | | ||
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| ## Values | ||
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| | Key | Description | Type | Default | | ||
| |-----|-------------|------|---------| | ||
| | clusterDomain | Default Kubernetes cluster domain | string | `"cluster.local"` | | ||
| | commonAnnotations | Annotations to add to all deployed objects | object | `{}` | | ||
| | commonLabels | Labels to add to all deployed objects | object | `{}` | | ||
| | fullnameOverride | String to fully override common.names.fullname | string | `""` | | ||
| | gateway | Gateway API configuration (for external access) | object | `{"annotations":{},"enabled":true,"fullnameOverride":"","gatewayClassName":"istio","kGatewayParameters":{"proxyUID":""},"listeners":[{"name":"http","port":80,"protocol":"HTTP"}],"nameOverride":"","routes":[{"backendRefs":[{"group":"inference.networking.x-k8s.io","kind":"InferencePool","name":"vllm-inference-pool","port":8000}],"matches":[{"path":{"type":"PathPrefix","value":"/"}}],"name":"llm-inference"}]}` | | ||
| | inferencepool | Enable upstream inference gateway components | object | `{"enabled":true,"inferenceExtension":{"env":[],"externalProcessingPort":9002,"image":{"hub":"gcr.io/gke-ai-eco-dev","name":"epp","pullPolicy":"Always","tag":"0.3.0"},"replicas":1},"inferencePool":{"modelServerType":"vllm","modelServers":{"matchLabels":{"app.kubernetes.io/name":"llm-d-vllm","llm-d.ai/inferenceServing":"true"}},"targetPort":8000},"provider":{"name":"none"}}` | | ||
| | kubeVersion | Override Kubernetes version | string | `""` | | ||
| | llm-d-vllm.modelservice.enabled | | bool | `true` | | ||
| | llm-d-vllm.modelservice.vllm.podLabels."app.kubernetes.io/name" | | string | `"llm-d-vllm"` | | ||
| | llm-d-vllm.modelservice.vllm.podLabels."llm-d.ai/inferenceServing" | | string | `"true"` | | ||
| | llm-d-vllm.redis.enabled | | bool | `true` | | ||
| | llm-d-vllm.sampleApplication.enabled | | bool | `true` | | ||
| | llm-d-vllm.sampleApplication.model.modelArtifactURI | | string | `"hf://meta-llama/Llama-3.2-3B-Instruct"` | | ||
| | llm-d-vllm.sampleApplication.model.modelName | | string | `"meta-llama/Llama-3.2-3B-Instruct"` | | ||
| | nameOverride | String to partially override common.names.fullname | string | `""` | | ||
| | vllm | Enable vLLM model serving components | object | `{"enabled":true}` | | ||
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| ---------------------------------------------- | ||
| Autogenerated from chart metadata using [helm-docs v1.14.2](https://github.com/norwoodj/helm-docs/releases/v1.14.2) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,52 @@ | ||
| {{ template "chart.header" . }} | ||
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| {{ template "chart.description" . }} | ||
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| ## Prerequisites | ||
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| - Kubernetes 1.30+ | ||
| - Helm 3.10+ | ||
| - Gateway API CRDs installed | ||
| - **InferencePool CRDs** (from Gateway API Inference Extension): | ||
| ```bash | ||
| kubectl apply -f https://github.com/kubernetes-sigs/gateway-api-inference-extension/raw/main/config/manifests/inferencepool-resources.yaml | ||
| ``` | ||
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| {{ template "chart.maintainersSection" . }} | ||
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| {{ template "chart.sourcesSection" . }} | ||
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| {{ template "chart.requirementsSection" . }} | ||
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| {{ template "chart.valuesSection" . }} | ||
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| ## Installation | ||
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| 1. Install prerequisites: | ||
| ```bash | ||
| # Install Gateway API CRDs (if not already installed) | ||
| kubectl apply -f https://github.com/kubernetes-sigs/gateway-api/releases/download/v1.0.0/standard-install.yaml | ||
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| # Install InferencePool CRDs | ||
| kubectl apply -f https://github.com/kubernetes-sigs/gateway-api-inference-extension/raw/main/config/manifests/inferencepool-resources.yaml | ||
| ``` | ||
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| 2. Install the chart: | ||
| ```bash | ||
| helm install my-llm-d-umbrella llm-d/llm-d-umbrella | ||
| ``` | ||
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| ## Architecture | ||
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| This umbrella chart combines: | ||
| - **Upstream InferencePool**: Intelligent routing and load balancing for inference workloads | ||
| - **llm-d-vLLM**: Dedicated vLLM model serving components | ||
| - **Gateway API**: External traffic routing and management | ||
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| The modular design enables: | ||
| - Clean separation between inference gateway and model serving | ||
| - Leveraging upstream Gateway API Inference Extension | ||
| - Intelligent endpoint selection and load balancing | ||
| - Backward compatibility with existing deployments | ||
|
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| {{ template "chart.homepage" . }} |
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| Thank you for installing {{ .Chart.Name }}. | ||
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| Your release is named `{{ .Release.Name }}`. | ||
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| To learn more about the release, try: | ||
|
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| ```bash | ||
| $ helm status {{ .Release.Name }} | ||
| $ helm get all {{ .Release.Name }} | ||
| ``` | ||
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| This umbrella chart combines: | ||
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| {{ if .Values.inferencepool.enabled }} | ||
| ✅ Upstream InferencePool - Intelligent routing and load balancing | ||
| {{- else }} | ||
| ❌ InferencePool - Disabled | ||
| {{- end }} | ||
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| {{ if .Values.vllm.enabled }} | ||
| ✅ vLLM Model Serving - ModelService controller and vLLM containers | ||
| {{- else }} | ||
| ❌ vLLM Model Serving - Disabled | ||
| {{- end }} | ||
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| {{ if .Values.gateway.enabled }} | ||
| ✅ Gateway API - External traffic routing to InferencePool | ||
| {{- else }} | ||
| ❌ Gateway API - Disabled | ||
| {{- end }} | ||
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| {{ if and .Values.inferencepool.enabled .Values.vllm.enabled .Values.gateway.enabled }} | ||
| 🎉 Complete llm-d deployment ready! | ||
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| Access your inference endpoint: | ||
| {{ if .Values.gateway.gatewayClassName }} | ||
| Gateway Class: {{ .Values.gateway.gatewayClassName }} | ||
| {{- end }} | ||
| {{ if .Values.gateway.listeners }} | ||
| Listeners: | ||
| {{- range .Values.gateway.listeners }} | ||
| {{ .name }}: {{ .protocol }}://{{ include "gateway.fullname" $ }}:{{ .port }} | ||
| {{- end }} | ||
| {{- end }} | ||
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| {{ if index .Values "llm-d-vllm" "sampleApplication" "enabled" }} | ||
| Sample application deployed with model: {{ index .Values "llm-d-vllm" "sampleApplication" "model" "modelName" }} | ||
| {{- end }} | ||
| {{- else }} | ||
| ⚠️ Incomplete deployment - enable all components for full functionality | ||
| {{- end }} |
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I am not totally against an llm-d umbrella chart, we could have that; but I believe it is key to have instructions to deploy the two core components of vllm-d independently:
[1] https://github.com/kubernetes-sigs/gateway-api-inference-extension/tree/main/config/charts/inferencepool
This allows composing with customers existing infra (most already have a gateway deployed for example) and composes with the IGW much better.