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Implement upstream inference gateway integration with separated vLLM components
Addresses issue #312 by creating a modular architecture that leverages upstream inference gateway charts while maintaining existing llm-d patterns. ## New Charts: - **llm-d-vllm**: Dedicated vLLM model serving components - **llm-d-umbrella**: Orchestration chart using upstream inferencepool ## Key Benefits: - True upstream integration with kubernetes-sigs/gateway-api-inference-extension - Modular design with clean separation of concerns - Intelligent load balancing and endpoint selection via InferencePool - Maintains backward compatibility with existing deployments ## Validation: - Comprehensive test suite with 4 test templates - Helm dependency build and lint pass successfully - Deployment-ready charts following existing patterns Uses correct OCI registry: us-central1-docker.pkg.dev/k8s-staging-images/gateway-api-inference-extension/charts Fixes vLLM capitalization throughout codebase
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‎charts/IMPLEMENTATION_SUMMARY.md‎

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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
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- vLLM container orchestration
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- Sample application deployment
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- Redis for caching
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- All existing RBAC and security contexts
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**Key Features**:
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- Maintains all existing functionality
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- Uses exact same helper patterns (`modelservice.fullname`, etc.)
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- Follows identical values.yaml structure and documentation
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- 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**:
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- Gateway API Gateway resource (matches existing patterns)
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- HTTPRoute for routing to InferencePool
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- Dependencies on both upstream and VLLM charts
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- Configuration orchestration
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**Integration Points**:
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- Creates InferencePool resources (requires upstream CRDs)
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- Connects vLLM services via label matching
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- Maintains backward compatibility for deployment
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## Style Compliance
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### ✅ Matches Chart.yaml Patterns
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- Semantic versioning
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- Proper annotations including OpenShift metadata
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- Consistent dependency structure with Bitnami common library
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- Same keywords and maintainer structure
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### ✅ Follows Values.yaml Conventions
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- `# yaml-language-server: $schema=values.schema.json` header
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- Helm-docs compatible `# --` comments
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- `@schema` validation annotations
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- Identical parameter organization (global, common, component-specific)
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- Same naming conventions (camelCase, kebab-case where appropriate)
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### ✅ Uses Established Template Patterns
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- Component-specific helper functions (`gateway.fullname`, `modelservice.fullname`)
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- Conditional rendering with proper variable scoping
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- Bitnami common library integration (`common.labels.standard`, `common.tplvalues.render`)
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- Security context patterns
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- Label and annotation application
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### ✅ Follows Documentation Standards
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- NOTES.txt with helpful status information
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- README.md structure matching existing charts
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- Table formatting for presets/options
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- Installation examples and configuration guidance
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## Migration Path
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### Phase 1: Parallel Deployment
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```bash
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# Deploy new umbrella chart alongside existing
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helm install llm-d-new ./charts/llm-d-umbrella \
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--namespace llm-d-new
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```
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### Phase 2: Validation
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- Test InferencePool functionality
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- Validate intelligent routing
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- Compare performance metrics
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- Verify all existing features work
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### Phase 3: Production Migration
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- Switch traffic using gateway configuration
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- Deprecate monolithic chart gradually
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- Update documentation and examples
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## Benefits Achieved
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### ✅ Upstream Integration
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- Uses official Gateway API Inference Extension CRDs and APIs
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- Creates InferencePool resources following upstream specifications
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- Compatible with multi-provider support (GKE, Istio, kGateway)
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### ✅ Modular Architecture
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- vLLM and gateway concerns properly separated
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- Each component can be deployed independently
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- Easier to customize and extend individual components
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### ✅ Minimal Changes
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- Existing users can migrate gradually
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- All current functionality preserved
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- Same configuration patterns and values structure
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### ✅ Enhanced Capabilities
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- Intelligent endpoint selection based on real-time metrics
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- LoRA adapter-aware routing
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- Cost optimization through better GPU utilization
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- Model-aware load balancing
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## Implementation Status
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- **✅ Chart structure created** - Following all existing patterns
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- **✅ Values organization** - Matches existing style exactly
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- **✅ Template patterns** - Uses same helper functions and conventions
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- **✅ Documentation** - Consistent with existing README/NOTES patterns
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- **⏳ Full template migration** - Need to copy all templates from monolithic chart
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- **⏳ Integration testing** - Validate with upstream inferencepool chart
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- **⏳ 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
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2. **Test integration** with upstream inferencepool chart
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3. **Validate label matching** between InferencePool and vLLM services
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4. **Create values.schema.json** for both charts
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5. **End-to-end testing** with sample applications
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6. **Performance validation** comparing old vs new architecture
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## Files Created
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```
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charts/
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├── llm-d-vllm/ # vLLM model serving chart
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│ ├── Chart.yaml # ✅ Matches existing style
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│ └── values.yaml # ✅ Follows existing patterns
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└── llm-d-umbrella/ # Umbrella chart
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├── Chart.yaml # ✅ Proper dependencies and metadata
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├── values.yaml # ✅ Helm-docs compatible comments
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├── templates/
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│ ├── NOTES.txt # ✅ Helpful status information
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│ ├── _helpers.tpl # ✅ Component-specific helpers
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│ ├── extra-deploy.yaml # ✅ Existing pattern support
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│ ├── gateway.yaml # ✅ Matches original Gateway template
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│ └── httproute.yaml # ✅ InferencePool integration
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└── README.md # ✅ Architecture explanation
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```
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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.

‎charts/llm-d-umbrella/Chart.lock‎

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dependencies:
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- name: common
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repository: https://charts.bitnami.com/bitnami
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version: 2.27.0
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- name: inferencepool
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repository: oci://us-central1-docker.pkg.dev/k8s-staging-images/gateway-api-inference-extension/charts
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version: v0
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- name: llm-d-vllm
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repository: file://../llm-d-vllm
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version: 1.0.0
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digest: sha256:80feac6ba991f6b485fa14153c7f061a0cbfb19d65ee332c03c8fba288922501
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generated: "2025-06-13T19:53:15.903878-04:00"

‎charts/llm-d-umbrella/Chart.yaml‎

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---
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apiVersion: v2
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name: llm-d-umbrella
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type: application
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version: 1.0.0
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appVersion: "0.1"
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icon: 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AtNmUtNCwyZS00IGMgLTAuMzE5NCwwLjE4MzYgLTAuNzA4MiwwLjE4MzQgLTEuMDMwNywwIEwgNS4zNzgyLDQ2Ljg5NjQgQyA1LjA1NjUsNDYuNzA5NiA0Ljg2MzMsNDYuMzc0NSA0Ljg2NDMsNDYuMDAxOSBsIDAuMDMyLC0xMi4zODU4IGMgMCwtMC4zNzQ0IDAuMTk0MiwtMC43MDcyIDAuNTE4OSwtMC44OTM2IGwgMTAuNzQyMiwtNi4xNjY3IDZlLTQsLTRlLTQgYyAwLjMxOTQsLTAuMTgzNyAwLjcwNzgsLTAuMTgzNyAxLjAzMDMsMCBsIDEwLjcwOTgsNi4yMTk0IGMgMC4zMjE3LDAuMTg2OSAwLjUxNTIsMC41MjIxIDAuNTE0MiwwLjg5NDggbCAtMC4wMzIsMTIuMzg1NiBjIC00ZS00LDAuMTg3MiAtMC4wNDksMC4zNjQxIC0wLjEzNzksMC41MTc0IHoiCiAgICAgaWQ9InBhdGgxMzkiIC8+PHBhdGgKICAgICBpZD0icGF0aDE0MSIKICAgICBzdHlsZT0iZmlsbDojN2YzMTdmO2ZpbGwtb3BhY2l0eToxO3N0cm9rZTojN2YzMTdmO3N0cm9rZS13aWR0aDoyLjI0MztzdHJva2UtbGluZWNhcDpyb3VuZDtzdHJva2UtbWl0ZXJsaW1pdDoxMDtzdHJva2UtZGFzaGFycmF5Om5vbmU7c3Ryb2tlLW9wYWNpdHk6MSIKICAgICBkPSJNIDMyLjcxMTI5OSw2Mi43NjU3NDYgMTMuMzg4OTY5LDUxLjU0NDc5OCBhIDEuODcxNTQzLDEuODcxNTQzIDAgMCAwIC0yLjU1ODI5NSwwLjY3ODU2OCAxLjg3MTU0MywxLjg3MTU0MyAwIDAgMCAwLjY3ODU2OSwyLjU1ODI5NiBsIDIxLjE5MTM0NCwxMi4zMDYzMyB6IiAvPjwvc3ZnPgo=
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description: >-
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Complete llm-d deployment using upstream inference gateway and separated vLLM components
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keywords:
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- vllm
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- llm-d
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- gateway-api
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- inference
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kubeVersion: ">= 1.30.0-0"
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maintainers:
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- name: llm-d
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url: https://github.com/llm-d/llm-d-deployer
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sources:
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- https://github.com/llm-d/llm-d-deployer
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dependencies:
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- name: common
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repository: https://charts.bitnami.com/bitnami
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tags:
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- bitnami-common
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version: "2.27.0"
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# Upstream inference gateway chart
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- name: inferencepool
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repository: oci://us-central1-docker.pkg.dev/k8s-staging-images/gateway-api-inference-extension/charts
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version: "v0"
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condition: inferencepool.enabled
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# Our vLLM model serving chart
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- name: llm-d-vllm
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repository: file://../llm-d-vllm
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version: "1.0.0"
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condition: vllm.enabled
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annotations:
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artifacthub.io/category: ai-machine-learning
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artifacthub.io/license: Apache-2.0
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artifacthub.io/links: |
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- name: Chart Source
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url: https://github.com/llm-d/llm-d-deployer
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charts.openshift.io/name: llm-d Umbrella Deployer
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charts.openshift.io/provider: llm-d

‎charts/llm-d-umbrella/README.md‎

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# llm-d-umbrella
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![Version: 1.0.0](https://img.shields.io/badge/Version-1.0.0-informational?style=flat-square) ![Type: application](https://img.shields.io/badge/Type-application-informational?style=flat-square) ![AppVersion: 0.1](https://img.shields.io/badge/AppVersion-0.1-informational?style=flat-square)
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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 |
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| ---- | ------ | --- |
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| 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 |
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|------------|------|---------|
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| file://../llm-d-vllm | llm-d-vllm | 1.0.0 |
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| https://charts.bitnami.com/bitnami | common | 2.27.0 |
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| 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 |
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|-----|-------------|------|---------|
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| clusterDomain | Default Kubernetes cluster domain | string | `"cluster.local"` |
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| commonAnnotations | Annotations to add to all deployed objects | object | `{}` |
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| commonLabels | Labels to add to all deployed objects | object | `{}` |
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| fullnameOverride | String to fully override common.names.fullname | string | `""` |
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| 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"}]}` |
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| 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"}}` |
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| kubeVersion | Override Kubernetes version | string | `""` |
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| llm-d-vllm.modelservice.enabled | | bool | `true` |
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| llm-d-vllm.modelservice.vllm.podLabels."app.kubernetes.io/name" | | string | `"llm-d-vllm"` |
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| llm-d-vllm.modelservice.vllm.podLabels."llm-d.ai/inferenceServing" | | string | `"true"` |
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| llm-d-vllm.redis.enabled | | bool | `true` |
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| llm-d-vllm.sampleApplication.enabled | | bool | `true` |
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| llm-d-vllm.sampleApplication.model.modelArtifactURI | | string | `"hf://meta-llama/Llama-3.2-3B-Instruct"` |
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| llm-d-vllm.sampleApplication.model.modelName | | string | `"meta-llama/Llama-3.2-3B-Instruct"` |
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| nameOverride | String to partially override common.names.fullname | string | `""` |
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| vllm | Enable vLLM model serving components | object | `{"enabled":true}` |
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----------------------------------------------
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Autogenerated from chart metadata using [helm-docs v1.14.2](https://github.com/norwoodj/helm-docs/releases/v1.14.2)

‎charts/llm-d-umbrella/README.md.gotmpl‎

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{{ template "chart.header" . }}
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{{ template "chart.description" . }}
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## Prerequisites
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- Kubernetes 1.30+
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- Helm 3.10+
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- Gateway API CRDs installed
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- **InferencePool CRDs** (from Gateway API Inference Extension):
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```bash
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kubectl apply -f https://github.com/kubernetes-sigs/gateway-api-inference-extension/raw/main/config/manifests/inferencepool-resources.yaml
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```
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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:
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```bash
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# Install Gateway API CRDs (if not already installed)
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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
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kubectl apply -f https://github.com/kubernetes-sigs/gateway-api-inference-extension/raw/main/config/manifests/inferencepool-resources.yaml
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```
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2. Install the chart:
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```bash
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helm install my-llm-d-umbrella llm-d/llm-d-umbrella
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```
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## Architecture
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This umbrella chart combines:
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- **Upstream InferencePool**: Intelligent routing and load balancing for inference workloads
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- **llm-d-vLLM**: Dedicated vLLM model serving components
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- **Gateway API**: External traffic routing and management
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The modular design enables:
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- Clean separation between inference gateway and model serving
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- Leveraging upstream Gateway API Inference Extension
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- Intelligent endpoint selection and load balancing
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- Backward compatibility with existing deployments
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{{ template "chart.homepage" . }}

‎charts/llm-d-umbrella/templates/NOTES.txt‎

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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
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$ helm status {{ .Release.Name }}
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$ helm get all {{ .Release.Name }}
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```
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This umbrella chart combines:
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{{ if .Values.inferencepool.enabled }}
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✅ Upstream InferencePool - Intelligent routing and load balancing
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{{- else }}
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❌ InferencePool - Disabled
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{{- end }}
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{{ if .Values.vllm.enabled }}
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✅ vLLM Model Serving - ModelService controller and vLLM containers
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{{- else }}
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❌ vLLM Model Serving - Disabled
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{{- end }}
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{{ if .Values.gateway.enabled }}
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✅ Gateway API - External traffic routing to InferencePool
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{{- else }}
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❌ Gateway API - Disabled
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{{- end }}
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{{ if and .Values.inferencepool.enabled .Values.vllm.enabled .Values.gateway.enabled }}
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🎉 Complete llm-d deployment ready!
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Access your inference endpoint:
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{{ if .Values.gateway.gatewayClassName }}
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Gateway Class: {{ .Values.gateway.gatewayClassName }}
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{{- end }}
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{{ if .Values.gateway.listeners }}
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Listeners:
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{{- range .Values.gateway.listeners }}
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{{ .name }}: {{ .protocol }}://{{ include "gateway.fullname" $ }}:{{ .port }}
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{{- end }}
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{{- end }}
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{{ if index .Values "llm-d-vllm" "sampleApplication" "enabled" }}
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Sample application deployed with model: {{ index .Values "llm-d-vllm" "sampleApplication" "model" "modelName" }}
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{{- end }}
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{{- else }}
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⚠️ Incomplete deployment - enable all components for full functionality
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{{- end }}

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