---
# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
title: Detailed Installation Guide
---

Deploy and manage Dynamo inference graphs on Kubernetes with automated orchestration and scaling, using the Dynamo Kubernetes Platform.

## Before You Start

Determine your cluster environment:

**Shared/Multi-Tenant Cluster** (K8s cluster with existing Dynamo artifacts):
- CRDs already installed cluster-wide - skip CRD installation step
- A cluster-wide Dynamo operator is likely already running
- **Do NOT install another operator** - use the existing cluster-wide operator
- Only install a namespace-restricted operator if you specifically need to prevent the cluster-wide operator from managing your namespace (e.g., testing operator features you're developing)

**Dedicated Cluster** (full cluster admin access):
- You install CRDs yourself
- Can use cluster-wide operator (default)

**Local Development** (Minikube, testing):
- See [Minikube Setup](deployment/minikube.md) first, then follow installation steps below

To check if CRDs already exist:
```bash
kubectl get crd | grep dynamo
# If you see dynamographdeployments, dynamocomponentdeployments, etc., CRDs are already installed
```

To check if a cluster-wide operator already exists:
```bash
# Check for cluster-wide operator and show its namespace
kubectl get clusterrolebinding -o json | \
  jq -r '.items[] | select(.metadata.name | contains("dynamo-operator-manager")) |
  "Cluster-wide operator found in namespace: \(.subjects[0].namespace)"'

# If a cluster-wide operator exists: Do NOT install another operator
# Only install namespace-restricted mode if you specifically need namespace isolation
```

## Installation Paths

Platform is installed using Dynamo Kubernetes Platform [helm chart](https://github.com/ai-dynamo/dynamo/tree/main/deploy/helm/charts/platform/README.md).

**Path A: Pre-built Artifacts**
- Use case: Production deployment, shared or dedicated clusters
- Source: NGC published Helm charts
- Time: ~10 minutes
- Jump to: [Path A](#path-a-production-install)

**Path B: Custom Build from Source**
- Use case: Contributing to Dynamo, using latest features from main branch, customization
- Requirements: Docker build environment
- Time: ~30 minutes
- Jump to: [Path B](#path-b-custom-build-from-source)

All helm install commands could be overridden by either setting the values.yaml file or by passing in your own values.yaml:

```bash
helm install ...
  -f your-values.yaml
```

and/or setting values as flags to the helm install command, as follows:

```bash
helm install ...
  --set "your-value=your-value"
```

## Prerequisites

Before installing the Dynamo Kubernetes Platform, ensure you have the following tools and access:

### Required Tools

| Tool | Minimum Version | Description | Installation |
|------|-----------------|-------------|--------------|
| **kubectl** | v1.24+ | Kubernetes command-line tool | [Install kubectl](https://kubernetes.io/docs/tasks/tools/#kubectl) |
| **Helm** | v3.0+ | Kubernetes package manager | [Install Helm](https://helm.sh/docs/intro/install/) |
| **Docker** | Latest | Container runtime (Path B only) | [Install Docker](https://docs.docker.com/get-docker/) |

### Cluster and Access Requirements

- **Kubernetes cluster v1.24+** with admin or namespace-scoped access
- **Cluster type determined** (shared vs dedicated) — see [Before You Start](#before-you-start)
- **CRD status checked** if on a shared cluster
- **NGC credentials** (optional) — required only if pulling NVIDIA images from NGC

### Verify Installation

Run the following to confirm your tools are correctly installed:

```bash
# Verify tools and versions
kubectl version --client  # Should show v1.24+
helm version              # Should show v3.0+
docker version            # Required for Path B only

# Set your release version
export RELEASE_VERSION=0.x.x # any version of Dynamo 0.3.2+ listed at https://github.com/ai-dynamo/dynamo/releases
```

### Pre-Deployment Checks

Before proceeding, run the pre-deployment check script to verify your cluster meets all requirements:

```bash
./deploy/pre-deployment/pre-deployment-check.sh
```

This script validates kubectl connectivity, default StorageClass configuration, and GPU node availability. See [Pre-Deployment Checks](https://github.com/ai-dynamo/dynamo/tree/main/deploy/pre-deployment/README.md) for details.

> **No cluster?** See [Minikube Setup](deployment/minikube.md) for local development.

**Estimated installation time:** 5-30 minutes depending on path

## Path A: Production Install

Install from [NGC published artifacts](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/collections/ai-dynamo/artifacts).

```bash
# 1. Set environment
export NAMESPACE=dynamo-system
export RELEASE_VERSION=0.x.x # any version of Dynamo 0.3.2+ listed at https://github.com/ai-dynamo/dynamo/releases

# 2. Install Platform (CRDs are automatically installed by the chart)
helm fetch https://helm.ngc.nvidia.com/nvidia/ai-dynamo/charts/dynamo-platform-${RELEASE_VERSION}.tgz
helm install dynamo-platform dynamo-platform-${RELEASE_VERSION}.tgz --namespace ${NAMESPACE} --create-namespace
```

> [!WARNING]
> **v0.9.0 Helm Chart Issue:** The initial v0.9.0 `dynamo-platform` Helm chart sets the operator image to v0.7.1 instead of v0.9.0. Use `RELEASE_VERSION=0.9.0-post1` or add `--set dynamo-operator.controllerManager.manager.image.tag=0.9.0` to your helm install command.

**For Shared/Multi-Tenant Clusters:**

If your cluster has namespace-restricted Dynamo operators, you MUST add namespace restriction to your installation:

```bash
# Add this flag to the helm install command above
--set dynamo-operator.namespaceRestriction.enabled=true
```

Note: Use the full path `dynamo-operator.namespaceRestriction.enabled=true` (not just `namespaceRestriction.enabled=true`).

If you see this validation error, you need namespace restriction:
```
VALIDATION ERROR: Cannot install cluster-wide Dynamo operator.
Found existing namespace-restricted Dynamo operators in namespaces: ...
```

> [!TIP]
> For multinode deployments, you need to install multinode orchestration components:
>
> **Option 1 (Recommended): Grove + KAI Scheduler**
>
> For production environments, Grove and KAI Scheduler should be installed **separately** from the dynamo-platform chart. This allows independent lifecycle management, version pinning, and upgrade control.
>
> **Compatibility Matrix:**
>
> | dynamo-platform | kai-scheduler | Grove |
> |-----------------|---------------|-------|
> | 1.0.x           | >= v0.13.0    | >= v0.1.0-alpha.6 |
>
> After installing them separately, enable Dynamo integration:
>
> ```bash
> --set "global.kai-scheduler.enabled=true"
> --set "global.grove.enabled=true"
> ```
>
> For **development/testing only**, you can install them as bundled subcharts:
>
> ```bash
> --set "global.grove.install=true"
> --set "global.kai-scheduler.install=true"
> ```
>
> Note: `global.kai-scheduler.install` / `global.grove.install` control whether the bundled subcharts are deployed. When set, integration is automatically enabled. `global.kai-scheduler.enabled` / `global.grove.enabled` can be set independently when using externally-managed installations.
>
> **Option 2: LeaderWorkerSet (LWS) + Volcano**
> - If using LWS for multinode deployments, you must also install Volcano (required dependency):
>   - [LWS Installation](https://github.com/kubernetes-sigs/lws#installation)
>   - [Volcano Installation](https://volcano.sh/en/docs/installation/) (required for gang scheduling with LWS)
> - These must be installed manually before deploying multinode workloads with LWS.
>
> See the [Multinode Deployment Guide](./deployment/multinode-deployment.md) for details on orchestrator selection.

> [!TIP]
> By default, Model Express Server is not used.
> If you wish to use an existing Model Express Server, you can set the modelExpressURL to the existing server's URL in the helm install command:

```bash
--set "dynamo-operator.modelExpressURL=http://model-express-server.model-express.svc.cluster.local:8080"
```

> [!TIP]
> By default, Dynamo Operator is installed cluster-wide and will monitor all namespaces.
> If you wish to restrict the operator to monitor only a specific namespace (the helm release namespace by default), you can set the namespaceRestriction.enabled to true.
> You can also change the restricted namespace by setting the targetNamespace property.

```bash
--set "dynamo-operator.namespaceRestriction.enabled=true"
--set "dynamo-operator.namespaceRestriction.targetNamespace=dynamo-namespace" # optional
```

### GPU Discovery for DynamoGraphDeploymentRequests with Namespace-Scoped Operators

GPU discovery is **enabled by default** for namespace-scoped operators. The Helm chart automatically provisions a ClusterRole/ClusterRoleBinding granting the operator read-only access to node GPU labels.

**To disable GPU discovery** (if your installer lacks ClusterRole creation permissions):

```bash
helm install dynamo-platform ... --set dynamo-operator.gpuDiscovery.enabled=false
```

When GPU discovery is disabled, you must provide hardware configuration manually in each DynamoGraphDeploymentRequest:

```yaml
spec:
  hardware:
    numGpusPerNode: 8
    gpuSku: "H100-SXM5-80GB"
    vramMb: 81920
```

> **Note**: If GPU discovery is disabled and no hardware config is provided, the DGDR will be rejected at admission time with a clear error message.

→ [Verify Installation](#verify-installation)

## Path B: Custom Build from Source

Build and deploy from source for customization, contributing to Dynamo, or using the latest features from the main branch.

Note: This gives you access to the latest unreleased features and fixes on the main branch.

```bash
# 1. Set environment
export NAMESPACE=dynamo-system
export DOCKER_SERVER=nvcr.io/nvidia/ai-dynamo/  # or your registry
export DOCKER_USERNAME='$oauthtoken'
export DOCKER_PASSWORD=<YOUR_NGC_CLI_API_KEY>
export IMAGE_TAG=${RELEASE_VERSION}

# 2. Build operator
cd deploy/operator

# 2.1 Alternative 1 : Build and push the operator image for multiple platforms
docker buildx create --name multiplatform --driver docker-container --bootstrap
docker buildx use multiplatform
docker buildx build --platform linux/amd64,linux/arm64 -t $DOCKER_SERVER/kubernetes-operator:$IMAGE_TAG --push .

# 2.2 Alternative 2 : Build and push the operator image for a single platform
docker build -t $DOCKER_SERVER/kubernetes-operator:$IMAGE_TAG . && docker push $DOCKER_SERVER/kubernetes-operator:$IMAGE_TAG

cd -

# 3. Create namespace and secrets to be able to pull the operator image (only needed if you pushed the operator image to a private registry)
kubectl create namespace ${NAMESPACE}
kubectl create secret docker-registry docker-imagepullsecret \
  --docker-server=${DOCKER_SERVER} \
  --docker-username=${DOCKER_USERNAME} \
  --docker-password=${DOCKER_PASSWORD} \
  --namespace=${NAMESPACE}

cd deploy/helm/charts

# 4. Install Platform (CRDs are automatically installed by the chart)
helm dep build ./platform/

# To install cluster-wide instead, set NS_RESTRICT_FLAGS="" (empty) or omit that line entirely.

NS_RESTRICT_FLAGS="--set dynamo-operator.namespaceRestriction.enabled=true"
helm install dynamo-platform ./platform/ \
  --namespace "${NAMESPACE}" \
  --set "dynamo-operator.controllerManager.manager.image.repository=${DOCKER_SERVER}/kubernetes-operator" \
  --set "dynamo-operator.controllerManager.manager.image.tag=${IMAGE_TAG}" \
  --set "dynamo-operator.imagePullSecrets[0].name=docker-imagepullsecret" \
  ${NS_RESTRICT_FLAGS}

```

→ [Verify Installation](#verify-installation)

## Verify Installation

```bash
# Check CRDs
kubectl get crd | grep dynamo

# Check operator and platform pods
kubectl get pods -n ${NAMESPACE}
# Expected: dynamo-operator-* and etcd-* and nats-* pods Running
```

## Next Steps

1. **Deploy Model/Workflow**
   ```bash
   # Example: Deploy a vLLM workflow with Qwen3-0.6B using aggregated serving
   kubectl apply -f examples/backends/vllm/deploy/agg.yaml -n ${NAMESPACE}

   # Port forward and test
   kubectl port-forward svc/agg-vllm-frontend 8000:8000 -n ${NAMESPACE}
   curl http://localhost:8000/v1/models
   ```

2. **Explore Backend Guides**
   - [vLLM Deployments](https://github.com/ai-dynamo/dynamo/tree/main/examples/backends/vllm/deploy/README.md)
   - [SGLang Deployments](https://github.com/ai-dynamo/dynamo/tree/main/examples/backends/sglang/deploy/README.md)
   - [TensorRT-LLM Deployments](https://github.com/ai-dynamo/dynamo/tree/main/examples/backends/trtllm/deploy/README.md)

3. **Optional:**
   - [Set up Prometheus & Grafana](./observability/metrics.md)
   - [SLA Planner Guide](../components/planner/planner-guide.md) (for SLA-aware scheduling and autoscaling)

## Troubleshooting

**"VALIDATION ERROR: Cannot install cluster-wide Dynamo operator"**

```
VALIDATION ERROR: Cannot install cluster-wide Dynamo operator.
Found existing namespace-restricted Dynamo operators in namespaces: ...
```

Cause: Attempting cluster-wide install on a shared cluster with existing namespace-restricted operators.

Solution: Add namespace restriction to your installation:
```bash
--set dynamo-operator.namespaceRestriction.enabled=true
```

Note: Use the full path `dynamo-operator.namespaceRestriction.enabled=true` (not just `namespaceRestriction.enabled=true`).

**CRDs already exist**

Cause: Installing CRDs on a cluster where they're already present (common on shared clusters).

Solution: Skip step 2 (CRD installation), proceed directly to platform installation.

To check if CRDs exist:
```bash
kubectl get crd | grep dynamo
```

**Pods not starting?**
```bash
kubectl describe pod <pod-name> -n ${NAMESPACE}
kubectl logs <pod-name> -n ${NAMESPACE}
```

**HuggingFace model access?**
```bash
kubectl create secret generic hf-token-secret \
  --from-literal=HF_TOKEN=${HF_TOKEN} \
  -n ${NAMESPACE}
```

**Bitnami etcd "unrecognized" image?**

```bash
ERROR: Original containers have been substituted for unrecognized ones. Deploying this chart with non-standard containers is likely to cause degraded security and performance, broken chart features, and missing environment variables.
```
This error that you might encounter during helm install is due to bitnami changing their docker repository to a [secure one](https://github.com/bitnami/charts/tree/main/bitnami/etcd#%EF%B8%8F-important-notice-upcoming-changes-to-the-bitnami-catalog).

just add the following to the helm install command:
```bash
--set "etcd.image.repository=bitnamilegacy/etcd" --set "etcd.global.security.allowInsecureImages=true"
```

**Clean uninstall?**

To uninstall the platform, you can run the following command:
```
helm uninstall dynamo-platform --namespace ${NAMESPACE}
```

To uninstall the CRDs, follow these steps:

Get all of the dynamo CRDs installed in your cluster:
```bash
kubectl get crd | grep "dynamo.*nvidia.com"
```

You should see something like this:
```
dynamocomponentdeployments.nvidia.com               2025-10-21T14:49:52Z
dynamocomponents.nvidia.com                         2025-10-25T05:16:10Z
dynamographdeploymentrequests.nvidia.com            2025-11-24T05:26:04Z
dynamographdeployments.nvidia.com                   2025-09-04T20:56:40Z
dynamographdeploymentscalingadapters.nvidia.com     2025-12-09T21:05:59Z
dynamomodels.nvidia.com                             2025-11-07T00:19:43Z
```

Delete each CRD one by one:
```bash
kubectl delete crd <crd-name>
```

## Advanced Options

- [Helm Chart Configuration](https://github.com/ai-dynamo/dynamo/tree/main/deploy/helm/charts/platform/README.md)
- [Create custom deployments](./deployment/create-deployment.md)
- [Dynamo Operator details](./dynamo-operator.md)
- [Model Express Server details](https://github.com/ai-dynamo/modelexpress)
