installation_guide.md

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# Installation Guide for Dynamo Kubernetes Platform

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

## Quick Start Paths

Platform is installed using Dynamo Kubernetes Platform [helm chart](/deploy/cloud/helm/platform/README.md).

**Path A: Production Install**
Install from published artifacts on your existing cluster → [Jump to Path A](#path-a-production-install)

**Path B: Local Development**
Set up Minikube first → [Minikube Setup](minikube.md) → Then follow Path A

**Path C: Custom Development**
Build from source for customization → [Jump to Path C](#path-c-custom-development)

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

```bash
# Required tools
kubectl version --client  # v1.24+
helm version             # v3.0+
docker version           # Running daemon

# Set your inference runtime image
export RELEASE_VERSION=0.x.x # any version of Dynamo 0.3.2+ listed at https://github.com/ai-dynamo/dynamo/releases
export DYNAMO_IMAGE=nvcr.io/nvidia/ai-dynamo/vllm-runtime:${RELEASE_VERSION}
# Also available: sglang-runtime, tensorrtllm-runtime
```

> [!TIP]
> No cluster? See [Minikube Setup](minikube.md) for local development.

## Path A: Production Install

Install from [NGC published artifacts](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/collections/ai-dynamo/artifacts) in 3 steps.

```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 CRDs
helm fetch https://helm.ngc.nvidia.com/nvidia/ai-dynamo/charts/dynamo-crds-${RELEASE_VERSION}.tgz
helm install dynamo-crds dynamo-crds-${RELEASE_VERSION}.tgz --namespace default

# 3. Install Platform
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
```

> [!TIP]
> For multinode deployments, you need to enable Grove and Kai Scheduler.
> You might chose to install them manually or through the dynamo-platform helm install command.
> When using the dynamo-platform helm install command, Grove and Kai Scheduler are NOT installed by default. You can enable their installation by setting the following flags in the helm install command:

```bash
--set "grove.enabled=true"
--set "kai-scheduler.enabled=true"
```

> [!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
```

→ [Verify Installation](#verify-installation)

## Path C: Custom Development

Build and deploy from source for customization.

```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/cloud/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 $DOCKER_SERVER/dynamo-operator:$IMAGE_TAG --push .

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

# 4. Install CRDs
helm upgrade --install dynamo-crds ./crds/ --namespace default

# 5. Install Platform
helm dep build ./platform/
helm install dynamo-platform ./platform/ \
  --namespace ${NAMESPACE} \
  --set "dynamo-operator.controllerManager.manager.image.repository=${DOCKER_SERVER}/dynamo-operator" \
  --set "dynamo-operator.controllerManager.manager.image.tag=${IMAGE_TAG}" \
  --set "dynamo-operator.imagePullSecrets[0].name=docker-imagepullsecret"
```

→ [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-* pods Running
```

## Next Steps

1. **Deploy Model/Workflow**
   ```bash
   # Example: Deploy a vLLM workflow with Qwen3-0.6B using aggregated serving
   kubectl apply -f components/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](/components/backends/vllm/deploy/README.md)
   - [SGLang Deployments](/components/backends/sglang/deploy/README.md)
   - [TensorRT-LLM Deployments](/components/backends/trtllm/deploy/README.md)

3. **Optional:**
   - [Set up Prometheus & Grafana](metrics.md)
   - [SLA Planner Quickstart Guide](sla_planner_quickstart.md) (for SLA-aware scheduling and autoscaling)

## Troubleshooting

**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?**
```bash
./uninstall.sh  # Removes all CRDs and platform
```

## Advanced Options

- [Helm Chart Configuration](/deploy/cloud/helm/platform/README.md)
- [GKE-specific setup](gke_setup.md)
- [Create custom deployments](create_deployment.md)
- [Dynamo Operator details](dynamo_operator.md)
- [Model Express Server details](https://github.com/ai-dynamo/modelexpress)