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# 🚀 Dynamo Cloud Kubernetes Platform (Dynamo Deploy)

The Dynamo Cloud platform is a comprehensive solution for deploying and managing Dynamo inference graphs (also referred to as pipelines) in Kubernetes environments. It provides a streamlined experience for deploying, scaling, and monitoring your inference services. You can interface with Dynamo Cloud using the `deploy` subcommand available in the Dynamo CLI (e.g `dynamo deploy`)

## 📋 Overview

The Dynamo cloud platform consists of several key components:

- **Dynamo Operator**: A Kubernetes operator that manages the lifecycle of Dynamo inference graphs from build ➡️ deploy.
- **API Store**: Stores and manages service configurations and metadata related to Dynamo deployments. Needs to be exposed externally.
- **Custom Resources**: Kubernetes custom resources for defining and managing Dynamo services

These components work together to provide a seamless deployment experience, handling everything from containerization to scaling and monitoring.

![Dynamo Deploy](../../images/dynamo-deploy.png)

## 🎯 Prerequisites

Before getting started with the Dynamo cloud platform, ensure you have:

- A Kubernetes cluster (version 1.24 or later)
- [Earthly](https://earthly.dev/) installed for building components
- Docker installed and running
- Access to a container registry (e.g., Docker Hub, NVIDIA NGC, etc.)
- `kubectl` configured to access your cluster
- Helm installed (version 3.0 or later)

> [!TIP]
> Don't have a Kubernetes cluster? Check out our [Minikube setup guide](./minikube.md) to set up a local environment! 🏠

## 🏗️ Building Docker Images for Dynamo Cloud Components

The Dynamo cloud platform components need to be built and pushed to a container registry before deployment. You can build these components individually or all at once.

### ⚙️ Setting Up Environment Variables

First, set the required environment variables for building and pushing images:

```bash
# Set your container registry
export DOCKER_SERVER=<CONTAINER_REGISTRY>
# Set the image tag (e.g., latest, 0.0.1, etc.)
export IMAGE_TAG=<TAG>
```

Where:
- `<CONTAINER_REGISTRY>`: Your container registry (e.g., `nvcr.io`, `docker.io/<your-username>`, etc.)
- `<TAG>`: The version tag for your images (e.g., `latest`, `0.0.1`, `v1.0.0`)

> [!IMPORTANT]
> Make sure you're logged in to your container registry before pushing images:
> ```bash
> docker login <CONTAINER_REGISTRY>
> ```

### 🛠️ Building Components

You can build and push all platform components at once:

```bash
earthly --push +all-docker --DOCKER_SERVER=$DOCKER_SERVER --IMAGE_TAG=$IMAGE_TAG
```

## 🚀 Deploying the Dynamo Cloud Platform

Once you've built and pushed the components, you can deploy the platform to your Kubernetes cluster.

### 📋 Prerequisites

Before deploying Dynamo Cloud, ensure your Kubernetes cluster meets the following requirements:

#### 1. 🛡️ Istio Installation
Dynamo Cloud requires Istio for service mesh capabilities. Verify Istio is installed and running:

```bash
# Check if Istio is installed
kubectl get pods -n istio-system

# Expected output should show running Istio pods
# istiod-* pods should be in Running state
```

#### 2. 💾 PVC Support with Default Storage Class
Dynamo Cloud requires Persistent Volume Claim (PVC) support with a default storage class. Verify your cluster configuration:

```bash
# Check if default storage class exists
kubectl get storageclass

# Expected output should show at least one storage class marked as (default)
# Example:
# NAME                 PROVISIONER             RECLAIMPOLICY   VOLUMEBINDINGMODE      ALLOWVOLUMEEXPANSION   AGE
# standard (default)   kubernetes.io/gce-pd    Delete          Immediate              true                   1d
```

### 📥 Installation

1. Set the required environment variables:
```bash
export DOCKER_USERNAME=<your-docker-username>
export DOCKER_PASSWORD=<your-docker-password>
export DOCKER_SERVER=<your-docker-server>
export IMAGE_TAG=<TAG>  # Use the same tag you used when building the images
export NAMESPACE=dynamo-cloud    # change this to whatever you want!
```

> [!NOTE]
> DOCKER_USERNAME and DOCKER_PASSWORD are optional and only needed if you want to pull docker images from a private registry.
> A docker image pull secret will be created automatically if these variables are set. Its name will be `docker-imagepullsecret` unless overridden by the `DOCKER_SECRET_NAME` environment variable.

The Dynamo Cloud Platform auto-generates docker images for pipelines and pushes them to a container registry.
By default, the platform will use the same container registry as the platform components (specified by `DOCKER_SERVER`).
However, you can specify a different container registry for pipelines by additionally setting the following environment variables:

```bash
export PIPELINES_DOCKER_SERVER=<your-docker-server>
export PIPELINES_DOCKER_USERNAME=<your-docker-username>
export PIPELINES_DOCKER_PASSWORD=<your-docker-password>
```

2. [One-time Action] Create a new kubernetes namespace and set it as your default.

```bash
cd deploy/dynamo/helm
kubectl create namespace $NAMESPACE
kubectl config set-context --current --namespace=$NAMESPACE
```

3. Deploy the helm chart using the deploy script:

```bash
./deploy.sh
```

if you wish to be guided through the deployment process, you can run the deploy script with the `--interactive` flag:

```bash
./deploy.sh --interactive
```

4. 🌐 **Expose Dynamo Cloud Externally**

> [!NOTE]
> The script will automatically display information about the endpoint you can use to access Dynamo Cloud. In our docs, we refer to this externally available endpoint as `DYNAMO_CLOUD`.

The simplest way to expose the `dynamo-store` service within the namespace externally is to use a port-forward:

```bash
kubectl port-forward svc/dynamo-store <local-port>:80 -n $NAMESPACE
export DYNAMO_CLOUD=http://localhost:<local-port>
```

## 🎯 Next Steps

After deploying the Dynamo cloud platform, you can:

1. Deploy your first inference graph using the [Dynamo CLI](operator_deployment.md)
2. Deploy Dynamo LLM pipelines to Kubernetes using the [Dynamo CLI](../../../examples/llm/README.md)!
3. Manage your deployments using the Dynamo CLI

For more detailed information about deploying inference graphs, see the [Dynamo Deploy Guide](README.md).
