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fix: fix broken link in docs (#2870)
Signed-off-by: Julien Mancuso <jmancuso@nvidia.com> Co-authored-by: Dmitry Tokarev <dtokarev@nvidia.com>
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‎docs/guides/dynamo_deploy/README.md‎

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@@ -167,4 +167,5 @@ Key customization points include:
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- **[Examples](/examples/README.md)** - Complete working examples
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- **[Create Custom Deployments](/docs/guides/dynamo_deploy/create_deployment.md)** - Build your own CRDs
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- **[Operator Documentation](/docs/guides/dynamo_deploy/dynamo_operator.md)** - How the platform works
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- **[Helm Charts](/deploy/helm/README.md)** - For advanced users
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- **[Helm Charts](/deploy/helm/README.md)** - For advanced users
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- **[GitOps Deployment with FluxCD](/docs/guides/dynamo_deploy/fluxcd.md)** - For advanced users

‎docs/guides/dynamo_deploy/dynamo_operator.md‎

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3. Kubernetes resources (Deployments, Services, etc.) are created or updated to match the CR spec.
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4. Status fields are updated to reflect the current state.
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## Custom Resource Definitions (CRDs)
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For the complete technical API reference for Dynamo Custom Resource Definitions, see:
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**📖 [Dynamo CRD API Reference](../../../deploy/cloud/operator/docs/api_reference.md)**
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**📖 [Dynamo CRD API Reference](/docs/guides/dynamo_deploy/api_reference.md)**
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## Installation
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[See installation steps](installation_guide.md#overview)
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## GitOps Deployment with FluxCD
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This section describes how to use FluxCD for GitOps-based deployment of Dynamo inference graphs. GitOps enables you to manage your Dynamo deployments declaratively using Git as the source of truth. We'll use the [aggregated vLLM example](../../../components/backends/vllm/README.md) to demonstrate the workflow.
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### Prerequisites
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- A Kubernetes cluster with [Dynamo Cloud](installation_guide.md) installed
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- [FluxCD](https://fluxcd.io/flux/installation/) installed in your cluster
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- A Git repository to store your deployment configurations
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### Workflow Overview
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The GitOps workflow for Dynamo deployments consists of three main steps:
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1. Build and push the Dynamo Operator
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2. Create and commit a DynamoGraphDeployment custom resource for initial deployment
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3. Update the graph by building a new version and updating the CR for subsequent updates
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### Step 1: Build and Push Dynamo Cloud Operator
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First, follow to [See Install Dynamo Cloud](README.md).
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### Step 2: Create Initial Deployment
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Create a new file in your Git repository (e.g., `deployments/llm-agg.yaml`) with the following content:
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```yaml
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apiVersion: nvidia.com/v1alpha1
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kind: DynamoGraphDeployment
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metadata:
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name: llm-agg
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spec:
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services:
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Frontend:
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replicas: 1
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envs:
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- name: SPECIFIC_ENV_VAR
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value: some_specific_value
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Processor:
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replicas: 1
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envs:
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- name: SPECIFIC_ENV_VAR
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value: some_specific_value
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VllmWorker:
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replicas: 1
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envs:
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- name: SPECIFIC_ENV_VAR
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value: some_specific_value
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# Add PVC for model storage
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pvc:
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name: vllm-model-storage
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mountPath: /models
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size: 100Gi
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```
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Commit and push this file to your Git repository. FluxCD will detect the new CR and create the initial deployment in your cluster. The operator will:
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- Create the specified PVCs
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- Build container images for all components
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- Deploy the services with the configured resources
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### Step 3: Update Existing Deployment
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To update your pipeline, just update the associated DynamoGraphDeployment CRD
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The Dynamo operator will automatically reconcile it.
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### Monitoring the Deployment
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You can monitor the deployment status using:
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```bash
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export NAMESPACE=<namespace-with-the-dynamo-cloud-operator>
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# Check the DynamoGraphDeployment status
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kubectl get dynamographdeployment llm-agg -n $NAMESPACE
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```
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## Configuration
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- **Environment Variables:**
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| Name | Description | Default |
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|----------------------------------------------------|--------------------------------------|--------------------------------------------------------|
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| `LOG_LEVEL` | Logging verbosity level | `info` |
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| `DYNAMO_SYSTEM_NAMESPACE` | System namespace | `dynamo` |
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- **Flags:**
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| Flag | Description | Default |
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|-----------------------|--------------------------------------------|---------|
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| `--natsAddr` | Address of NATS server | "" |
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| `--etcdAddr` | Address of etcd server | "" |
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## Troubleshooting
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| Symptom | Possible Cause | Solution |
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|------------------------|-------------------------------|-----------------------------------|
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| Resource not created | RBAC missing | Ensure correct ClusterRole/Binding|
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| Status not updated | CRD schema mismatch | Regenerate CRDs with kubebuilder |
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| Image build hangs | Misconfigured DynamoComponent | Check image build logs |
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[See installation steps](/docs/guides/dynamo_deploy/installation_guide.md#overview)
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## Development

‎docs/guides/dynamo_deploy/fluxcd.md‎

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# GitOps Deployment with FluxCD
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This section describes how to use FluxCD for GitOps-based deployment of Dynamo inference graphs. GitOps enables you to manage your Dynamo deployments declaratively using Git as the source of truth. We'll use the [aggregated vLLM example](../../../components/backends/vllm/README.md) to demonstrate the workflow.
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## Prerequisites
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- A Kubernetes cluster with [Dynamo Cloud](/docs/guides/dynamo_deploy/installation_guide.md) installed
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- [FluxCD](https://fluxcd.io/flux/installation/) installed in your cluster
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- A Git repository to store your deployment configurations
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## Workflow Overview
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The GitOps workflow for Dynamo deployments consists of three main steps:
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1. Build and push the Dynamo Operator
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2. Create and commit a DynamoGraphDeployment custom resource for initial deployment
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3. Update the graph by building a new version and updating the CR for subsequent updates
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## Step 1: Build and Push Dynamo Cloud Operator
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First, follow to [See Install Dynamo Cloud](/docs/guides/dynamo_deploy/installation_guide.md).
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## Step 2: Create Initial Deployment
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Create a new file in your Git repository (e.g., `deployments/llm-agg.yaml`) with the following content:
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```yaml
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apiVersion: nvidia.com/v1alpha1
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kind: DynamoGraphDeployment
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metadata:
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name: llm-agg
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spec:
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services:
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Frontend:
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replicas: 1
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envs:
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- name: SPECIFIC_ENV_VAR
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value: some_specific_value
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Processor:
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replicas: 1
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envs:
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- name: SPECIFIC_ENV_VAR
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value: some_specific_value
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VllmWorker:
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replicas: 1
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envs:
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- name: SPECIFIC_ENV_VAR
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value: some_specific_value
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# Add PVC for model storage
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pvc:
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name: vllm-model-storage
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mountPath: /models
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size: 100Gi
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```
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Commit and push this file to your Git repository. FluxCD will detect the new CR and create the initial Dynamo deployment in your cluster.
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## Step 3: Update Existing Deployment
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To update your pipeline, just update the associated DynamoGraphDeployment CRD
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The Dynamo operator will automatically reconcile it.
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## Monitoring the Deployment
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You can monitor the deployment status using:
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```bash
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export NAMESPACE=<namespace-with-the-dynamo-cloud-operator>
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# Check the DynamoGraphDeployment status
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kubectl get dynamographdeployment llm-agg -n $NAMESPACE
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```

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