feat: fluxcd guide to managing custom resources (#1220)

Signed-off-by: mohammedabdulwahhab <furkhan324@berkeley.edu>

docs/guides/dynamo_deploy/dynamo_operator.md

@@ -12,8 +12,7 @@ Dynamo operator is a Kubernetes operator that simplifies the deployment, configu
 - [Architecture](#architecture)
 - [Custom Resource Definitions (CRDs)](#custom-resource-definitions-crds)
 - [Installation](#installation)
-- [Usage](#usage)
-- [Examples](#examples)
+- [GitOps Deployment with FluxCD](#gitops-deployment-with-fluxcd)
 - [Reconciliation Logic](#reconciliation-logic)
 - [Configuration](#configuration)
 - [Troubleshooting](#troubleshooting)
@@ -190,6 +189,121 @@ spec:
 
 ---
 
+## 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](../../examples/llm/README.md) to demonstrate the workflow.
+
+### Prerequisites
+
+- A Kubernetes cluster with [Dynamo Cloud](dynamo_cloud.md) installed
+- [FluxCD](https://fluxcd.io/flux/installation/) installed in your cluster
+- A Git repository to store your deployment configurations
+- [Dynamo CLI](../../get_started.md#installation) installed locally
+
+### Workflow Overview
+
+The GitOps workflow for Dynamo deployments consists of three main steps:
+
+1. Build and push a pipeline to the Dynamo API store
+2. Create and commit a DynamoGraphDeployment custom resource for initial deployment
+3. Update the pipeline by building a new version and updating the CR for subsequent updates
+
+### Step 1: Build and Push Pipeline
+
+First, build and push your pipeline using the Dynamo CLI:
+
+```bash
+# Set your project root directory
+export PROJECT_ROOT=$(pwd)
+
+# Configure environment variables
+export KUBE_NS=dynamo-cloud
+export DYNAMO_CLOUD=http://localhost:8080  # If using port-forward
+# OR
+# export DYNAMO_CLOUD=https://dynamo-cloud.nvidia.com  # If using Ingress/VirtualService
+
+# Build and push the service
+cd $PROJECT_ROOT/examples/llm
+DYNAMO_TAG=$(dynamo build --push graphs.agg:Frontend | grep "Successfully built" |  awk '{ print $NF }' | sed 's/\.$//')
+```
+
+The `--push` flag ensures the pipeline is pushed to the remote API store, making it available for deployment.
+
+### 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:
+  dynamoComponent: frontend:jh2o6dqzpsgfued4  # Use the tag from Step 1
+  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:
+
+1. Build and push a new version of your pipeline:
+```bash
+DYNAMO_TAG=$(dynamo build --push graphs.agg:Frontend | grep "Successfully built" |  awk '{ print $NF }' | sed 's/\.$//')
+```
+
+2. Update the `dynamoComponent` field in your CR with the new tag:
+```yaml
+spec:
+  dynamoComponent: frontend:new_tag_here  # Update with new tag from Step 1
+```
+3. Commit and push the changes to your Git repository.
+
+The Dynamo operator will:
+- Detect the updated CR
+- Build new container images for the updated components
+- Perform a rolling update of the deployments when the new images are ready and the components are ready to serve traffic
+- Preserve existing PVCs and their data
+
+### Monitoring the Deployment
+
+You can monitor the deployment status using:
+
+```bash
+# Check the DynamoGraphDeployment status
+kubectl get dynamographdeployment llm-agg -n $KUBE_NS
+
+# Check the component deployments
+kubectl get dynamocomponentdeployment -n $KUBE_NS
+```
+
+---
+
 ## Deploying a Dynamo Pipeline using the Operator
 
 [See deployment steps](operator_deployment.md)