---
# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
title: "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](../backends/vllm/README.md) to demonstrate the workflow.

## Prerequisites

- A Kubernetes cluster with [Dynamo Kubernetes Platform](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 Operator

First, follow to [See Install Dynamo Kubernetes Platform](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:
  pvcs:
    - name: vllm-model-storage
      size: 100Gi
  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
      volumeMounts:
        - name: vllm-model-storage
          mountPoint: /models
```

Commit and push this file to your Git repository. FluxCD will detect the new CR and create the initial Dynamo deployment in your cluster.

## 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-operator>

# Check the DynamoGraphDeployment status
kubectl get dynamographdeployment llm-agg -n $NAMESPACE
```