Deploy a Ray Serve application with a Stable Diffusion model on Google Kubernetes Engine (GKE)

Autopilot Standard

This guide provides an example of how to deploy and serve a Stable Diffusion model on Google Kubernetes Engine (GKE) using Ray Serve and the Ray Operator add-on as an example implementation.

About Ray and Ray Serve

Ray is an open-source scalable compute framework for AI/ML applications. Ray Serve is a model serving library for Ray used for scaling and serving models in a distributed environment. For more information, see Ray Serve in the Ray documentation.

You can use a RayCluster or RayService resource to deploy your Ray Serve applications. You should use a RayService resource in production for the following reasons:

In-place updates for RayService applications
Zero downtime upgrading for RayCluster resources
Highly available Ray Serve applications

Objectives

This guide is intended for Generative AI customers, new or existing users of GKE, ML Engineers, MLOps (DevOps) engineers, or platform administrators who are interested in using Kubernetes container orchestration capabilities for serving models using Ray.

Create a GKE cluster with a GPU node pool.
Create a Ray cluster using the RayCluster custom resource.
Run a Ray Serve application.
Deploy a RayService custom resource.

Costs

In this document, you use the following billable components of Google Cloud:

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

When you finish the tasks that are described in this document, you can avoid continued billing by deleting the resources that you created. For more information, see Clean up.

Before you begin

Cloud Shell is preinstalled with the software you need for this tutorial, including kubectl, and the gcloud CLI. If you don't use Cloud Shell, you must install the gcloud CLI.

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

Install the Google Cloud CLI.

To initialize the gcloud CLI, run the following command:

gcloud init

Note: If you installed the gcloud CLI previously, make sure you have the latest version by running

gcloud components
      update

Create or select a Google Cloud project.

Create a Google Cloud project:
```
gcloud projects create PROJECT_ID
```
Replace PROJECT_ID with a name for the Google Cloud project you are creating.
Select the Google Cloud project that you created:
```
gcloud config set project PROJECT_ID
```
Replace PROJECT_ID with your Google Cloud project name.

Make sure that billing is enabled for your Google Cloud project.

Enable the GKE API:

gcloud services enable container.googleapis.com

Install the Google Cloud CLI.

To initialize the gcloud CLI, run the following command:

gcloud init

Note: If you installed the gcloud CLI previously, make sure you have the latest version by running

gcloud components
      update

Create or select a Google Cloud project.

Create a Google Cloud project:
```
gcloud projects create PROJECT_ID
```
Replace PROJECT_ID with a name for the Google Cloud project you are creating.
Select the Google Cloud project that you created:
```
gcloud config set project PROJECT_ID
```
Replace PROJECT_ID with your Google Cloud project name.

Make sure that billing is enabled for your Google Cloud project.

Enable the GKE API:

gcloud services enable container.googleapis.com

Grant roles to your user account. Run the following command once for each of the following IAM roles: roles/container.clusterAdmin, roles/container.admin
```
gcloud projects add-iam-policy-binding PROJECT_ID --member="user:USER_IDENTIFIER" --role=ROLE
```
- Replace PROJECT_ID with your project ID.
- Replace USER_IDENTIFIER with the identifier for your user account. For example, user:myemail@example.com.
- Replace ROLE with each individual role.
Install RayServe.

Prepare your environment

To prepare up your environment, follow these steps:

Launch a Cloud Shell session from the Google Cloud console, by clicking Activate Cloud Shell in the Google Cloud console. This launches a session in the bottom pane of the Google Cloud console.

Set environment variables:

export PROJECT_ID=PROJECT_ID
export CLUSTER_NAME=rayserve-cluster
export COMPUTE_REGION=us-central1
export COMPUTE_ZONE=us-central1-c
export CLUSTER_VERSION=CLUSTER_VERSION
export TUTORIAL_HOME=`pwd`

Replace the following:

PROJECT_ID: your Google Cloud project ID.
CLUSTER_VERSION: the GKE version to use. Must be 1.30.1 or later.

Clone the GitHub repository:

git clone https://github.com/GoogleCloudPlatform/kubernetes-engine-samples

Change to the working directory:

cd kubernetes-engine-samples/ai-ml/gke-ray/rayserve/stable-diffusion

Create a cluster with a GPU node pool

Create an Autopilot or Standard GKE cluster with a GPU node pool:

Autopilot

Create an Autopilot cluster:

gcloud container clusters create-auto ${CLUSTER_NAME}  \
    --enable-ray-operator \
    --cluster-version=${CLUSTER_VERSION} \
    --location=${COMPUTE_REGION}

Standard

Create a Standard cluster:

gcloud container clusters create ${CLUSTER_NAME} \
    --addons=RayOperator \
    --cluster-version=${CLUSTER_VERSION}  \
    --machine-type=g2-standard-8 \
    --location=${COMPUTE_ZONE} \
    --num-nodes=2 \
    --accelerator type=nvidia-l4,count=1,gpu-driver-version=latest

Deploy a RayCluster resource

To deploy a RayCluster resource:

Review the following manifest:

apiVersion: ray.io/v1
kind: RayCluster
metadata:
  name: stable-diffusion-cluster
spec:
  rayVersion: '2.9.0'
  headGroupSpec:
    rayStartParams:
      dashboard-host: '0.0.0.0'
    template:
      metadata:
      spec:
        containers:
        - name: ray-head
          image: rayproject/ray-ml:2.9.0
          ports:
          - containerPort: 6379
            name: gcs
          - containerPort: 8265
            name: dashboard
          - containerPort: 10001
            name: client
          - containerPort: 8000
            name: serve
          resources:
            limits:
              cpu: "2"
              memory: "8Gi"
            requests:
              cpu: "2"
              memory: "8Gi"
  workerGroupSpecs:
  - replicas: 1
    minReplicas: 1
    maxReplicas: 4
    groupName: gpu-group
    rayStartParams: {}
    template:
      spec:
        containers:
        - name: ray-worker
          image: rayproject/ray-ml:2.9.0
          resources:
            limits:
              cpu: 4
              memory: "16Gi"
              nvidia.com/gpu: 1
            requests:
              cpu: 3
              memory: "16Gi"
              nvidia.com/gpu: 1
        nodeSelector:
          cloud.google.com/gke-accelerator: nvidia-l4

This manifest describes a RayCluster resource.

Apply the manifest to your cluster:
```
kubectl apply -f ray-cluster.yaml
```

Verify the RayCluster resource is ready:

kubectl get raycluster

The output is similar to the following:

NAME                       DESIRED WORKERS   AVAILABLE WORKERS   CPUS   MEMORY   GPUS   STATUS   AGE
stable-diffusion-cluster   2                 2                   6      20Gi     0      ready    33s

In this output, ready in the STATUS column indicates the RayCluster resource is ready.

Connect to the RayCluster resource

To connect to the RayCluster resource:

Verify that GKE created the RayCluster service:

kubectl get svc stable-diffusion-cluster-head-svc

The output is similar to the following:

NAME                             TYPE        CLUSTER-IP       EXTERNAL-IP   PORT(S)                                AGE
pytorch-mnist-cluster-head-svc   ClusterIP   34.118.238.247   <none>        10001/TCP,8265/TCP,6379/TCP,8080/TCP   109s

Establish port-forwarding sessions to the Ray head:

kubectl port-forward svc/stable-diffusion-cluster-head-svc 8265:8265 2>&1 >/dev/null &
kubectl port-forward svc/stable-diffusion-cluster-head-svc 10001:10001 2>&1 >/dev/null &

Verify that the Ray client can connect to the Ray cluster using localhost:

ray list nodes --address http://localhost:8265

The output is similar to the following:

======== List: 2024-06-19 15:15:15.707336 ========
Stats:
------------------------------
Total: 3

Table:
------------------------------
    NODE_ID                                                   NODE_IP     IS_HEAD_NODE    STATE    NODE_NAME    RESOURCES_TOTAL                 LABELS
0  1d07447d7d124db641052a3443ed882f913510dbe866719ac36667d2  10.28.1.21  False           ALIVE    10.28.1.21   CPU: 2.0                        ray.io/node_id: 1d07447d7d124db641052a3443ed882f913510dbe866719ac36667d2
# Several lines of output omitted

Run a Ray Serve application

To run a Ray Serve application:

Run the Stable Diffusion Ray Serve application:

serve run stable_diffusion:entrypoint --working-dir=. --runtime-env-json='{"pip": ["torch", "torchvision", "diffusers==0.12.1"]}' --address ray://localhost:10001

The output is similar to the following:

2024-06-19 18:20:58,444 INFO scripts.py:499 -- Running import path: 'stable_diffusion:entrypoint'.
2024-06-19 18:20:59,730 INFO packaging.py:530 -- Creating a file package for local directory '.'.
2024-06-19 18:21:04,833 INFO handle.py:126 -- Created DeploymentHandle 'hyil6u9f' for Deployment(name='StableDiffusionV2', app='default').
2024-06-19 18:21:04,834 INFO handle.py:126 -- Created DeploymentHandle 'xo25rl4k' for Deployment(name='StableDiffusionV2', app='default').
2024-06-19 18:21:04,836 INFO handle.py:126 -- Created DeploymentHandle '57x9u4fp' for Deployment(name='APIIngress', app='default').
2024-06-19 18:21:04,836 INFO handle.py:126 -- Created DeploymentHandle 'xr6kt85t' for Deployment(name='StableDiffusionV2', app='default').
2024-06-19 18:21:04,836 INFO handle.py:126 -- Created DeploymentHandle 'g54qagbz' for Deployment(name='APIIngress', app='default').
2024-06-19 18:21:19,139 INFO handle.py:126 -- Created DeploymentHandle 'iwuz00mv' for Deployment(name='APIIngress', app='default').
2024-06-19 18:21:19,139 INFO api.py:583 -- Deployed app 'default' successfully.

Establish a port-forwarding session to the Ray Serve port (8000):

kubectl port-forward svc/stable-diffusion-cluster-head-svc 8000:8000 2>&1 >/dev/null &

Run the Python script:
```
python generate_image.py
```
The script generates an image to a file named output.png. The image is similar to the following:

Deploy a RayService

The RayService custom resource manages the lifecycle of a RayCluster resource and Ray Serve application.

For more information about RayService, see Deploy Ray Serve Applications and Production Guide in the Ray documentation.

To deploy a RayService resource, follow these steps:

Review the following manifest:

apiVersion: ray.io/v1
kind: RayService
metadata:
  name: stable-diffusion
spec:
  serveConfigV2: |
    applications:
      - name: stable_diffusion
        import_path: ai-ml.gke-ray.rayserve.stable-diffusion.stable_diffusion:entrypoint
        runtime_env:
          working_dir: "https://github.com/GoogleCloudPlatform/kubernetes-engine-samples/archive/main.zip"
          pip: ["diffusers==0.12.1"]
  rayClusterConfig:
    rayVersion: '2.9.0'
    headGroupSpec:
      rayStartParams:
        dashboard-host: '0.0.0.0'
      template:
        spec:
          containers:
          - name: ray-head
            image: rayproject/ray-ml:2.9.0
            ports:
            - containerPort: 6379
              name: gcs
            - containerPort: 8265
              name: dashboard
            - containerPort: 10001
              name: client
            - containerPort: 8000
              name: serve
            resources:
              limits:
                cpu: "2"
                memory: "8Gi"
              requests:
                cpu: "2"
                memory: "8Gi"
    workerGroupSpecs:
    - replicas: 1
      minReplicas: 1
      maxReplicas: 4
      groupName: gpu-group
      rayStartParams: {}
      template:
        spec:
          containers:
          - name: ray-worker
            image: rayproject/ray-ml:2.9.0
            resources:
              limits:
                cpu: 4
                memory: "16Gi"
                nvidia.com/gpu: 1
              requests:
                cpu: 3
                memory: "16Gi"
                nvidia.com/gpu: 1
          nodeSelector:
            cloud.google.com/gke-accelerator: nvidia-l4

This manifest describes a RayService custom resource.

Apply the manifest to your cluster:
```
kubectl apply -f ray-service.yaml
```

Verify that the Service is ready:

kubectl get svc stable-diffusion-serve-svc

The output is similar to the following:

NAME                         TYPE        CLUSTER-IP     EXTERNAL-IP   PORT(S)    AGE 

stable-diffusion-serve-svc   ClusterIP   34.118.236.0   <none>        8000/TCP   31m

Configure port-forwarding to the Ray Serve Service:

kubectl port-forward stable-diffusion-serve-svc 8000:8000

Run the Python script from the previous section:
```
python generate_image.py
```
The script generates an image similar to the image generated in the previous section.

Clean up

Delete the project

Delete a Google Cloud project:

gcloud projects delete PROJECT_ID

Delete individual resources

To delete the cluster, type:

gcloud container clusters delete ${CLUSTER_NAME}

What's next

Explore reference architectures, diagrams, and best practices about Google Cloud. Take a look at our Cloud Architecture Center.