Google Cloud Platform

Google Cloud Platform#

Introduction#

This script automatically configures a GKE LLM inference cluster. Make sure your GCP CLI is set up, logged in, and the region is properly configured. You must have the following dependencies installed:

  • gcloud (Google Cloud CLI)

  • kubectl (Kubernetes command-line tool)

  • helm (Kubernetes package manager)

Ensure that all the required tools are installed before proceeding. Additionally, ensure that the following GCP APIs are enabled:

  • Kubernetes Engine API

  • Cloud Resource Manager API

  • IAM API

  • Compute Engine API

To enable these APIs, run:

gcloud services enable container.googleapis.com cloudresourcemanager.googleapis.com iam.googleapis.com compute.googleapis.com

Steps to Follow#

1. Deploy GKE vLLM Stack#

1.1 Modify the Configuration#

Before running the deployment, ensure that the configuration file production_stack_specification.yaml is properly set up. You need to configure:

  • servingEngineSpec: Define the model repository, resource requests, and storage settings.

  • routerSpec: Set up routing resource limits and requests.

Modify these fields as needed to match your cluster requirements.

1.2 Execute the Deployment Script#

Run the deployment script by replacing YAML_FILE_PATH with the actual configuration file path:

sudo bash entry_point.sh YAML_FILE_PATH

After executing the script, Kubernetes will start deploying the vLLM inference stack. You can monitor the status of the deployment.

2. Validate Installation#

2.1 Monitor Deployment Status#

To check whether the pods for vLLM deployment are up and running, use:

kubectl get pods

Expected output:

NAME                                            READY   STATUS    RESTARTS   AGE
vllm-deployment-router-69b7f9748d-xrkvn         1/1     Running   0          75s
vllm-opt125m-deployment-vllm-696c998c6f-mvhg4   1/1     Running   0          75s

Note

It may take some time for the pods to reach the Running state, depending on cluster setup and image download speed.

2.2 Inspect Pod Logs#

If a pod is not transitioning to Running, use the following command to inspect logs:

kubectl logs -f <POD_NAME>

To get more detailed information about the pod, run:

kubectl describe pod <POD_NAME>

3. Uninstall#

To remove the deployed vLLM stack and clean up resources, run:

bash clean_up.sh production-stack

This command will remove all Kubernetes resources associated with the vLLM deployment.

4. Troubleshooting#

If you encounter issues, refer to the following solutions:

  • Pods stuck in Pending state: Check available resources and ensure that the cluster has enough nodes:

    kubectl describe nodes

  • Pods in CrashLoopBackOff state: Inspect logs to find the issue:

    kubectl logs <POD_NAME>

  • Cannot connect to GKE cluster: Ensure that your gcloud CLI is properly configured:

    gcloud container clusters get-credentials vllm-gke-cluster --region <REGION>

Following these steps should help ensure a successful deployment.