fix: fix broken link in docs

docs/guides/dynamo_deploy/README.md

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

docs/guides/dynamo_deploy/dynamo_operator.md

@@ -19,122 +19,15 @@ Dynamo operator is a Kubernetes operator that simplifies the deployment, configu
   3. Kubernetes resources (Deployments, Services, etc.) are created or updated to match the CR spec.
   4. Status fields are updated to reflect the current state.
 
-
-
 ## Custom Resource Definitions (CRDs)
 
 For the complete technical API reference for Dynamo Custom Resource Definitions, see:
 
-**📖 [Dynamo CRD API Reference](../../../deploy/cloud/operator/docs/api_reference.md)**
+**📖 [Dynamo CRD API Reference](/docs/guides/dynamo_deploy/api_reference.md)**
 
 ## Installation
 
-[See installation steps](installation_guide.md#overview)
-
-
-## GitOps Deployment with FluxCD
-
-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.
-
-### Prerequisites
-
-- A Kubernetes cluster with [Dynamo Cloud](installation_guide.md) installed
-- [FluxCD](https://fluxcd.io/flux/installation/) installed in your cluster
-- A Git repository to store your deployment configurations
-
-### Workflow Overview
-
-The GitOps workflow for Dynamo deployments consists of three main steps:
-
-1. Build and push the Dynamo Operator
-2. Create and commit a DynamoGraphDeployment custom resource for initial deployment
-3. Update the graph by building a new version and updating the CR for subsequent updates
-
-### Step 1: Build and Push Dynamo Cloud Operator
-
-First, follow to [See Install Dynamo Cloud](README.md).
-
-### Step 2: Create Initial Deployment
-
-Create a new file in your Git repository (e.g., `deployments/llm-agg.yaml`) with the following content:
-
-```yaml
-apiVersion: nvidia.com/v1alpha1
-kind: DynamoGraphDeployment
-metadata:
-  name: llm-agg
-spec:
-  services:
-    Frontend:
-      replicas: 1
-      envs:
-      - name: SPECIFIC_ENV_VAR
-        value: some_specific_value
-    Processor:
-      replicas: 1
-      envs:
-      - name: SPECIFIC_ENV_VAR
-        value: some_specific_value
-    VllmWorker:
-      replicas: 1
-      envs:
-      - name: SPECIFIC_ENV_VAR
-        value: some_specific_value
-      # Add PVC for model storage
-      pvc:
-        name: vllm-model-storage
-        mountPath: /models
-        size: 100Gi
-```
-
-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:
-- Create the specified PVCs
-- Build container images for all components
-- Deploy the services with the configured resources
-
-### Step 3: Update Existing Deployment
-
-To update your pipeline, just update the associated DynamoGraphDeployment CRD
-
-The Dynamo operator will automatically reconcile it.
-
-### Monitoring the Deployment
-
-You can monitor the deployment status using:
-
-```bash
-
-export NAMESPACE=<namespace-with-the-dynamo-cloud-operator>
-
-# Check the DynamoGraphDeployment status
-kubectl get dynamographdeployment llm-agg -n $NAMESPACE
-```
-
-## Configuration
-
-
-- **Environment Variables:**
-
-| Name                                               | Description                          | Default                                                |
-|----------------------------------------------------|--------------------------------------|--------------------------------------------------------|
-| `LOG_LEVEL`                                        | Logging verbosity level              | `info`                                                 |
-| `DYNAMO_SYSTEM_NAMESPACE`                          | System namespace                     | `dynamo`                                               |
-
-- **Flags:**
-  | Flag                  | Description                                | Default |
-  |-----------------------|--------------------------------------------|---------|
-  | `--natsAddr`          | Address of NATS server                     | ""      |
-  | `--etcdAddr`          | Address of etcd server                     | ""      |
-
-
-
-## Troubleshooting
-
-| Symptom                | Possible Cause                | Solution                          |
-|------------------------|-------------------------------|-----------------------------------|
-| Resource not created   | RBAC missing                  | Ensure correct ClusterRole/Binding|
-| Status not updated     | CRD schema mismatch           | Regenerate CRDs with kubebuilder  |
-| Image build hangs      | Misconfigured DynamoComponent | Check image build logs            |
+[See installation steps](/docs/guides/dynamo_deploy/installation_guide.md#overview)
 
 
 ## Development

docs/guides/dynamo_deploy/fluxcd.md

@@ -0,0 +1,77 @@
+# GitOps Deployment with FluxCD
+
+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.
+
+## Prerequisites
+
+- A Kubernetes cluster with [Dynamo Cloud](/docs/guides/dynamo_deploy/installation_guide.md) installed
+- [FluxCD](https://fluxcd.io/flux/installation/) installed in your cluster
+- A Git repository to store your deployment configurations
+
+## Workflow Overview
+
+The GitOps workflow for Dynamo deployments consists of three main steps:
+
+1. Build and push the Dynamo Operator
+2. Create and commit a DynamoGraphDeployment custom resource for initial deployment
+3. Update the graph by building a new version and updating the CR for subsequent updates
+
+## Step 1: Build and Push Dynamo Cloud Operator
+
+First, follow to [See Install Dynamo Cloud](/docs/guides/dynamo_deploy/installation_guide.md).
+
+## Step 2: Create Initial Deployment
+
+Create a new file in your Git repository (e.g., `deployments/llm-agg.yaml`) with the following content:
+
+```yaml
+apiVersion: nvidia.com/v1alpha1
+kind: DynamoGraphDeployment
+metadata:
+  name: llm-agg
+spec:
+  services:
+    Frontend:
+      replicas: 1
+      envs:
+      - name: SPECIFIC_ENV_VAR
+        value: some_specific_value
+    Processor:
+      replicas: 1
+      envs:
+      - name: SPECIFIC_ENV_VAR
+        value: some_specific_value
+    VllmWorker:
+      replicas: 1
+      envs:
+      - name: SPECIFIC_ENV_VAR
+        value: some_specific_value
+      # Add PVC for model storage
+      pvc:
+        name: vllm-model-storage
+        mountPath: /models
+        size: 100Gi
+```
+
+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:
+- Create the specified PVCs
+- Build container images for all components
+- Deploy the services with the configured resources
+
+## Step 3: Update Existing Deployment
+
+To update your pipeline, just update the associated DynamoGraphDeployment CRD
+
+The Dynamo operator will automatically reconcile it.
+
+## Monitoring the Deployment
+
+You can monitor the deployment status using:
+
+```bash
+
+export NAMESPACE=<namespace-with-the-dynamo-cloud-operator>
+
+# Check the DynamoGraphDeployment status
+kubectl get dynamographdeployment llm-agg -n $NAMESPACE
+```