Deploy open models with a custom vLLM container

Although the various Vertex AI model serving options are sufficient for many use cases, you might need to use your own container images to serve models on Vertex AI. This document describes how to use a vLLM custom container image to serve models on Vertex AI on CPUs, GPUs, or TPUs. For more information about vLLM supported models, see the vLLM documentation.

The vLLM API server implements the OpenAI API protocol, but it does not support the Vertex AI request and response requirements. Therefore, you must use a Vertex AI Raw Inference Request to get inferences from models deployed to Vertex AI using a Prediction Endpoint. For more information about the Raw Prediction method in the Vertex AI Python SDK, see the Python SDK documentation.

You can source models from both Hugging Face and Cloud Storage. This approach offers flexibility, which lets you take advantage of the community-driven model hub (Hugging Face) and the optimized data transfer and security capabilities of Cloud Storage for internal model management or fine-tuned versions.

vLLM downloads the models from Hugging Face if a Hugging Face access token is provided. Otherwise, vLLM assumes the model is available on local disk. The custom container image lets Vertex AI download the model from Google Cloud in addition to Hugging Face.

Before you begin

In your Google Cloud project, enable the Vertex AI and Artifact Registry APIs.

gcloud services enable aiplatform.googleapis.com \  artifactregistry.googleapis.com

Configure Google Cloud CLI with your project ID and initialize Vertex AI SDK.

PROJECT_ID = "PROJECT_ID" LOCATION = "LOCATION" import vertexai vertexai.init(project=PROJECT_ID, location=LOCATION)

gcloud config set project {PROJECT_ID}

Create a Docker repository in Artifact Registry.

gcloud artifacts repositories create DOCKER_REPOSITORY \  --repository-format=docker \  --location=LOCATION \  --description="Vertex AI Docker repository"

Optional: If downloading models from Hugging Face, obtain a Hugging Face token.
1. Create a Hugging Face account if you don't have one.
2. For gated models like Llama 3.2, request and receive access on Hugging Face before proceeding.
3. Generate an access token: Go to Your Profile > Settings > Access Tokens.
4. Select New Token.
5. Specify a name and a role of at least Read.
6. Select Generate a token.
7. Save this token for the deployment steps.

Prepare container build files

The following Dockerfile builds the vLLM custom container image for GPUs, TPUs, and CPUs. This custom container downloads models from Hugging Face or Cloud Storage.

ARG BASE_IMAGE FROM ${BASE_IMAGE} ENV DEBIAN_FRONTEND=noninteractive # Install gcloud SDK RUN apt-get update && \  apt-get install -y apt-utils git apt-transport-https gnupg ca-certificates curl \  && echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] https://packages.cloud.google.com/apt cloud-sdk main" | tee -a /etc/apt/sources.list.d/google-cloud-sdk.list \  && curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | gpg --dearmor -o /usr/share/keyrings/cloud.google.gpg \  && apt-get update -y && apt-get install google-cloud-cli -y \  && rm -rf /var/lib/apt/lists/* WORKDIR /workspace/vllm # Copy entrypoint.sh to the container COPY ./entrypoint.sh /workspace/vllm/vertexai/entrypoint.sh RUN chmod +x /workspace/vllm/vertexai/entrypoint.sh ENTRYPOINT ["/workspace/vllm/vertexai/entrypoint.sh"]

Build the custom container image using Cloud Build. The following cloudbuild.yaml configuration file shows how to build the image for multiple platforms using the same Dockerfile.

steps: - name: 'gcr.io/cloud-builders/docker'  automapSubstitutions: true  script: |  #!/usr/bin/env bash  set -euo pipefail  device_type_param=${_DEVICE_TYPE}  device_type=${device_type_param,,}  base_image=${_BASE_IMAGE}  image_name="vllm-${_DEVICE_TYPE}"  if [[ $device_type == "cpu" ]]; then  echo "Quietly building open source vLLM CPU container image"  git clone https://github.com/vllm-project/vllm.git  cd vllm && DOCKER_BUILDKIT=1 docker build -t $base_image -f docker/Dockerfile.cpu . -q  cd ..  fi  echo "Quietly building container image for: $device_type"  docker build -t $LOCATION-docker.pkg.dev/$PROJECT_ID/${_REPOSITORY}/$image_name --build-arg BASE_IMAGE=$base_image . -q  docker push $LOCATION-docker.pkg.dev/$PROJECT_ID/${_REPOSITORY}/$image_name substitutions:  _DEVICE_TYPE: gpu  _BASE_IMAGE: vllm/vllm-openai  _REPOSITORY: my-docker-repo

The files are available in the googlecloudplatform/vertex-ai-samples GitHub repository. Clone the repository to use them:

git clone https://github.com/GoogleCloudPlatform/vertex-ai-samples.git

Build and push the container image

Build the custom container image using Cloud Build by submitting the cloudbuild.yaml file. Use substitutions to specify the target device type, which can be GPU, TPU, or CPU, and the corresponding base image.

GPU

DEVICE_TYPE="gpu" BASE_IMAGE="vllm/vllm-openai" cd vertex-ai-samples/notebooks/official/prediction/vertexai_serving_vllm/cloud-build && \ gcloud builds submit \  --config=cloudbuild.yaml \  --region=LOCATION \  --timeout="2h" \  --machine-type=e2-highcpu-32 \  --substitutions=_REPOSITORY=DOCKER_REPOSITORY,_DEVICE_TYPE=$DEVICE_TYPE,_BASE_IMAGE=$BASE_IMAGE

TPU

DEVICE_TYPE="tpu" BASE_IMAGE="vllm/vllm-tpu:nightly" cd vertex-ai-samples/notebooks/official/prediction/vertexai_serving_vllm/cloud-build && \ gcloud builds submit \  --config=cloudbuild.yaml \  --region=LOCATION \  --timeout="2h" \  --machine-type=e2-highcpu-32 \  --substitutions=_REPOSITORY=DOCKER_REPOSITORY,_DEVICE_TYPE=$DEVICE_TYPE,_BASE_IMAGE=$BASE_IMAGE

CPU

DEVICE_TYPE="cpu" BASE_IMAGE="vllm-cpu-base" cd vertex-ai-samples/notebooks/official/prediction/vertexai_serving_vllm/cloud-build && \ gcloud builds submit \  --config=cloudbuild.yaml \  --region=LOCATION \  --timeout="2h" \  --machine-type=e2-highcpu-32 \  --substitutions=_REPOSITORY=DOCKER_REPOSITORY,_DEVICE_TYPE=$DEVICE_TYPE,_BASE_IMAGE=$BASE_IMAGE

After the build finishes, configure Docker to authenticate with Artifact Registry:

gcloud auth configure-docker LOCATION-docker.pkg.dev --quiet

Upload model to Model Registry and deploy

Upload your model to Vertex AI Model Registry, create an endpoint, and deploy the model by completing these steps. This example uses Llama 3.2 3B, but you can adapt it for other models.

Define model and deployment variables. Set the DOCKER_URI variable to the image you built in the previous step (for example, for GPU):

DOCKER_URI = f"LOCATION-docker.pkg.dev/PROJECT_ID/DOCKER_REPOSITORY/vllm-gpu"

Define variables for the Hugging Face token and model properties. For example, for GPU deployment:

hf_token = "your-hugging-face-auth-token" model_name = "gpu-llama3_2_3B-serve-vllm" model_id = "meta-llama/Llama-3.2-3B" machine_type = "g2-standard-8" accelerator_type = "NVIDIA_L4" accelerator_count = 1

Upload model to Model Registry. The upload_model function varies slightly depending on the device type because of different vLLM arguments and environment variables.

from google.cloud import aiplatform def upload_model_gpu(model_name, model_id, hf_token, accelerator_count, docker_uri): vllm_args = [ "python3", "-m", "vllm.entrypoints.openai.api_server", "--host=0.0.0.0", "--port=8080", f"--model={model_id}", "--max-model-len=2048", "--gpu-memory-utilization=0.9", "--enable-prefix-caching", f"--tensor-parallel-size={accelerator_count}", ] env_vars = { "HF_TOKEN": hf_token, "LD_LIBRARY_PATH": "$LD_LIBRARY_PATH:/usr/local/nvidia/lib64", } model = aiplatform.Model.upload( display_name=model_name, serving_container_image_uri=docker_uri, serving_container_args=vllm_args, serving_container_ports=[8080], serving_container_predict_route="/v1/completions", serving_container_health_route="/health", serving_container_environment_variables=env_vars, serving_container_shared_memory_size_mb=(16 * 1024), # 16 GB serving_container_deployment_timeout=1800, ) return model def upload_model_tpu(model_name, model_id, hf_token, tpu_count, docker_uri): vllm_args = [ "python3", "-m", "vllm.entrypoints.openai.api_server", "--host=0.0.0.0", "--port=8080", f"--model={model_id}", "--max-model-len=2048", "--enable-prefix-caching", f"--tensor-parallel-size={tpu_count}", ] env_vars = {"HF_TOKEN": hf_token} model = aiplatform.Model.upload( display_name=model_name, serving_container_image_uri=docker_uri, serving_container_args=vllm_args, serving_container_ports=[8080], serving_container_predict_route="/v1/completions", serving_container_health_route="/health", serving_container_environment_variables=env_vars, serving_container_shared_memory_size_mb=(16 * 1024), # 16 GB serving_container_deployment_timeout=1800, ) return model def upload_model_cpu(model_name, model_id, hf_token, docker_uri): vllm_args = [ "python3", "-m", "vllm.entrypoints.openai.api_server", "--host=0.0.0.0", "--port=8080", f"--model={model_id}", "--max-model-len=2048", ] env_vars = {"HF_TOKEN": hf_token} model = aiplatform.Model.upload( display_name=model_name, serving_container_image_uri=docker_uri, serving_container_args=vllm_args, serving_container_ports=[8080], serving_container_predict_route="/v1/completions", serving_container_health_route="/health", serving_container_environment_variables=env_vars, serving_container_shared_memory_size_mb=(16 * 1024), # 16 GB serving_container_deployment_timeout=1800, ) return model # Example for GPU: vertexai_model = upload_model_gpu(model_name, model_id, hf_token, accelerator_count, DOCKER_URI)

Create an endpoint.

endpoint = aiplatform.Endpoint.create(display_name=f"model_name-endpoint")

Deploy model to endpoint. Model deployment might take 20 to 30 minutes.

# Example for GPU: vertexai_model.deploy( endpoint=endpoint, deployed_model_display_name=model_name, machine_type=machine_type, accelerator_type=accelerator_type, accelerator_count=accelerator_count, traffic_percentage=100, deploy_request_timeout=1800, min_replica_count=1, max_replica_count=4, autoscaling_target_accelerator_duty_cycle=60, )

For TPUs, omit the accelerator_type and accelerator_count parameters, and use autoscaling_target_request_count_per_minute=60. For CPUs, omit the accelerator_type and accelerator_count parameters, and use autoscaling_target_cpu_utilization=60.

Load models from Cloud Storage

The custom container downloads the model from a Cloud Storage location instead of downloading it from Hugging Face. When you use Cloud Storage:

Set the model_id parameter in the upload_model function to a Cloud Storage URI, for example, gs://<var>my-bucket</var>/<var>my-models</var>/<var>llama_3_2_3B</var>.
Omit the HF_TOKEN variable from env_vars when you call upload_model.
When you call model.deploy, specify a service_account that has permissions to read from the Cloud Storage bucket.

Create an IAM Service Account for Cloud Storage access

If your model is in Cloud Storage, create a service account that Vertex Prediction endpoints can use to access the model artifacts.

SERVICE_ACCOUNT_NAME = "vertexai-endpoint-sa" SERVICE_ACCOUNT_EMAIL = f"SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com"

gcloud iam service-accounts create SERVICE_ACCOUNT_NAME \  --display-name="Vertex AI Endpoint Service Account" # Grant storage read permission gcloud projects add-iam-policy-binding PROJECT_ID \  --member="serviceAccount:SERVICE_ACCOUNT_EMAIL" \  --role="roles/storage.objectViewer"

When you deploy, pass the service account email to the deploy method: service_account=<var>SERVICE_ACCOUNT_EMAIL</var>.

Get predictions using endpoint

After you successfully deploy the model to the endpoint, verify the model response using raw_predict.

import json PROMPT = "Distance of moon from earth is " request_body = json.dumps( { "prompt": PROMPT, "temperature": 0.0, }, ) raw_response = endpoint.raw_predict( body=request_body, headers={"Content-Type": "application/json"} ) assert raw_response.status_code == 200 result = json.loads(raw_response.text) for choice in result["choices"]: print(choice)

Example output:

{  "index": 0,  "text": "384,400 km. The moon is 1/4 of the earth's",  "logprobs": null,  "finish_reason": "length",  "stop_reason": null,  "prompt_logprobs": null }

Deploy open models with a custom vLLM container Stay organized with collections Save and categorize content based on your preferences.