Deploy Gemma2 9B with TGI DLC on Cloud Run

Gemma 2 is an advanced, lightweight open model that enhances performance and efficiency while building on the research and technology of its predecessor and the Gemini models developed by Google DeepMind and other teams across Google. Text Generation Inference (TGI) is a toolkit developed by Hugging Face for deploying and serving LLMs, with high performance text generation. Google Cloud Run is a serverless container platform that allows developers to deploy and manage containerized applications without managing infrastructure, enabling automatic scaling and billing only for usage.

This example showcases how to deploy Gemma2 9B Instruct model quantized to INT4 using AWQ from the Hugging Face Hub with the Hugging Face DLC for TGI on Google Cloud Run with GPU support (in preview).

Setup / Configuration

First, you need to install gcloud in your local machine, which is the command-line tool for Google Cloud, following the instructions at Cloud SDK Documentation - Install the gcloud CLI.

Optionally, to ease the usage of the commands within this tutorial, you need to set the following environment variables for GCP:

export PROJECT_ID=your-project-id
export LOCATION=us-central1  # or any location where Cloud Run offers GPUs: https://cloud.google.com/run/docs/locations#gpu
export CONTAINER_URI=us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-generation-inference-cu124.2-3.ubuntu2204.py311
export SERVICE_NAME=gemma2-tgi

Then you need to login into your Google Cloud account and set the project ID you want to use to deploy Cloud Run.

gcloud auth login
gcloud auth application-default login  # For local development
gcloud config set project $PROJECT_ID

Once you are logged in, you need to enable the Cloud Run API, which is required for the Hugging Face DLC for TGI deployment on Cloud Run.

gcloud services enable run.googleapis.com

Deploy TGI on Cloud Run

Once you are all set, you can call the gcloud beta run deploy command (still on beta because GPU support is on preview, as mentioned above).

The gcloud beta run deploy command needs you to specify the following parameters:

• --image: The container image URI to deploy.
• --args: The arguments to pass to the container entrypoint, being text-generation-launcher for the Hugging Face DLC for TGI. Read more about the supported arguments in Text-generation-launcher arguments.
  • --model-id: The model ID to use, in this case, hugging-quants/gemma-2-9b-it-AWQ-INT4.
  • --quantize: The quantization method to use, in this case, awq. If not specified, it will be retrieved from the quantization_config->quant_method in the config.json file.
  • --max-concurrent-requests: The maximum amount of concurrent requests for this particular deployment. Having a low limit will refuse clients requests instead of having them wait for too long and is usually good to handle back pressure correctly. Set to 64, but default is 128.
• --port: The port the container listens to.
• --cpu and --memory: The number of CPUs and amount of memory to allocate to the container. Needs to be set to 4 and 16Gi (16 GiB), respectively; as that’s the minimum requirement for using the GPU.
• --no-cpu-throttling: Disables CPU throttling, which is required for using the GPU.
• --gpu and --gpu-type: The number of GPUs and the GPU type to use. Needs to be set to 1 and nvidia-l4, respectively; as at the time of writing this tutorial, those are the only available options as Cloud Run on GPUs is still under preview.
• --max-instances: The maximum number of instances to run, set to 3, but default maximum value is 7. Alternatively, one could set it to 1 too, but that could eventually lead to downtime during infrastructure migrations, so anything above 1 is recommended.
• --concurrency: the maximum number of concurrent requests per instance, set to 64. The value is not arbitrary, but determined after running and evaluating the results of text-generation-benchmark, as the most optimal balance between throughput and latency; where the current default for TGI being 128 is a bit too much. Note that this value is also aligned with the --max-concurrent-requests argument in TGI.
• --region: The region to deploy the Cloud Run service.
• --no-allow-unauthenticated: Disables unauthenticated access to the service, which is a good practice as adds an authentication layer managed by Google Cloud IAM.

gcloud compute routers create nat-router --network=default --region=$LOCATION
gcloud compute routers nats create vm-nat --router=nat-router --region=$LOCATION --auto-allocate-nat-external-ips --nat-all-subnet-ip-ranges

Finally, you can run the gcloud beta run deploy command to deploy TGI on Cloud Run as:

gcloud beta run deploy $SERVICE_NAME \
    --image=$CONTAINER_URI \
    --args="--model-id=hugging-quants/gemma-2-9b-it-AWQ-INT4,--max-concurrent-requests=64" \
    --set-env-vars=HF_HUB_ENABLE_HF_TRANSFER=1 \
    --port=8080 \
    --cpu=8 \
    --memory=32Gi \
    --no-cpu-throttling \
    --gpu=1 \
    --gpu-type=nvidia-l4 \
    --max-instances=3 \
    --concurrency=64 \
    --region=$LOCATION \
    --no-allow-unauthenticated

Or as it follows if you created the Cloud NAT:

gcloud beta run deploy $SERVICE_NAME \
    --image=$CONTAINER_URI \
    --args="--model-id=hugging-quants/gemma-2-9b-it-AWQ-INT4,--max-concurrent-requests=64" \
    --set-env-vars=HF_HUB_ENABLE_HF_TRANSFER=1 \
    --port=8080 \
    --cpu=8 \
    --memory=32Gi \
    --no-cpu-throttling \
    --gpu=1 \
    --gpu-type=nvidia-l4 \
    --max-instances=3 \
    --concurrency=64 \
    --region=$LOCATION \
    --no-allow-unauthenticated \
    --vpc-egress=all-traffic \
    --subnet=default

Inference on Cloud Run

Once deployed, you can send requests to the service via any of the supported TGI endpoints, check TGI’s OpenAPI Specification to see all the available endpoints and their respective parameters.

All Cloud Run services are deployed privately by default, which means that they can’t be accessed without providing authentication credentials in the request headers. These services are secured by IAM and are only callable by Project Owners, Project Editors, and Cloud Run Admins and Cloud Run Invokers.

In this case, a couple of alternatives to enable developer access will be showcased; while the other use cases are out of the scope of this example, as those are either not secure due to the authentication being disabled (for public access scenarios), or require additional setup for production-ready scenarios (service-to-service authentication, end-user access).

Via Cloud Run Proxy

Cloud Run Proxy runs a server on localhost that proxies requests to the specified Cloud Run Service with credentials attached; which is useful for testing and experimentation.

gcloud run services proxy $SERVICE_NAME --region $LOCATION

Then you can send requests to the deployed service on Cloud Run, using the http://localhost:8080 URL, with no authentication, exposed by the proxy as shown in the examples below.

cURL

To send a POST request to the TGI service using cURL, you can run the following command:

curl http://localhost:8080/v1/chat/completions \
    -X POST \
    -H 'Content-Type: application/json' \
    -d '{
        "model": "tgi",
        "messages": [
            {
                "role": "user",
                "content": "What is Deep Learning?"
            }
        ],
        "max_tokens": 128
    }'

Python

To run the inference using Python, you can either use the huggingface_hub Python SDK (recommended) or the openai Python SDK.

huggingface_hub

You can install it via pip as pip install --upgrade --quiet huggingface_hub, and then run:

from huggingface_hub import InferenceClient

client = InferenceClient(base_url="http://localhost:8080", api_key="-")

chat_completion = client.chat.completions.create(
  model="hugging-quants/gemma-2-9b-it-AWQ-INT4",
  messages=[
    {"role": "user", "content": "What is Deep Learning?"},
  ],
  max_tokens=128,
)

openai

You can install it via pip as pip install --upgrade openai, and then run:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8080/v1/",
    api_key="-",
)

chat_completion = client.chat.completions.create(
    model="tgi",
    messages=[
        {"role": "user", "content": "What is Deep Learning?"},
    ],
    max_tokens=128,
)

(recommended) Via Cloud Run Service URL

Cloud Run Service has an unique URL assigned that can be used to send requests from anywhere, using the Google Cloud Credentials with Cloud Run Invoke access to the service; which is the recommended approach as it’s more secure and consistent than using the Cloud Run Proxy.

The URL of the Cloud Run service can be obtained via the following command (assigned to the SERVICE_URL variable for convenience):

SERVICE_URL=$(gcloud run services describe $SERVICE_NAME --region $LOCATION --format 'value(status.url)')

Then you can send requests to the deployed service on Cloud Run, using the SERVICE_URL and any Google Cloud Credentials with Cloud Run Invoke access. For setting up the credentials there are multiple approaches, some of those are listed below:

• Using the default identity token from the Google Cloud SDK:

  • Via gcloud as:
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  gcloud auth print-identity-token

  • Via Python as:
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  import google.auth
  from google.auth.transport.requests import Request as GoogleAuthRequest
  
  auth_req = GoogleAuthRequest()
  creds, _ = google.auth.default()
  creds.refresh(auth_req)
  
  id_token = creds.id_token

• Using a Service Account with Cloud Run Invoke access, which can either be done with any of the following approaches:

  • Create a Service Account before the Cloud Run Service was created, and then set the --service-account flag to the Service Account email when creating the Cloud Run Service. And use an Access Token for that Service Account only using gcloud auth print-access-token --impersonate-service-account=SERVICE_ACCOUNT_EMAIL.
  • Create a Service Account after the Cloud Run Service was created, and then update the Cloud Run Service to use the Service Account. And use an Access Token for that Service Account only using gcloud auth print-access-token --impersonate-service-account=SERVICE_ACCOUNT_EMAIL.

The recommended approach is to use a Service Account (SA), as the access can be controlled better and the permissions are more granular; as the Cloud Run Service was not created using a SA, which is another nice option, you need to now create the SA, gran it the necessary permissions, update the Cloud Run Service to use the SA, and then generate an access token to set as the authentication token within the requests, that can be revoked later once you are done using it.

• Set the SERVICE_ACCOUNT_NAME environment variable for convenience:

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  export SERVICE_ACCOUNT_NAME=tgi-invoker

• Create the Service Account:

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  gcloud iam service-accounts create $SERVICE_ACCOUNT_NAME

• Grant the Service Account the Cloud Run Invoker role:

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  gcloud run services add-iam-policy-binding $SERVICE_NAME \
      --member="serviceAccount:$SERVICE_ACCOUNT_NAME@$PROJECT_ID.iam.gserviceaccount.com" \
      --role="roles/run.invoker" \
      --region=$LOCATION

• Generate the Access Token for the Service Account:

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  export ACCESS_TOKEN=$(gcloud auth print-access-token --impersonate-service-account=$SERVICE_ACCOUNT_NAME@$PROJECT_ID.iam.gserviceaccount.com)

Now you can already dive into the different alternatives for sending the requests to the deployed Cloud Run Service using the SERVICE_URL AND ACCESS_TOKEN as described above.

cURL

To send a POST request to the TGI service using cURL, you can run the following command:

curl $SERVICE_URL/v1/chat/completions \
    -X POST \
    -H "Authorization: Bearer $ACCESS_TOKEN" \
    -H 'Content-Type: application/json' \
    -d '{
        "model": "tgi",
        "messages": [
            {
                "role": "user",
                "content": "What is Deep Learning?"
            }
        ],
        "max_tokens": 128
    }'

Python

To run the inference using Python, you can either use the huggingface_hub Python SDK (recommended) or the openai Python SDK.

huggingface_hub

You can install it via pip as pip install --upgrade --quiet huggingface_hub, and then run:

import os
from huggingface_hub import InferenceClient

client = InferenceClient(
    base_url=os.getenv("SERVICE_URL"),
    api_key=os.getenv("ACCESS_TOKEN"),
)

chat_completion = client.chat.completions.create(
  model="hugging-quants/gemma-2-9b-it-AWQ-INT4",
  messages=[
    {"role": "user", "content": "What is Deep Learning?"},
  ],
  max_tokens=128,
)

openai

You can install it via pip as pip install --upgrade openai, and then run:

import os
from openai import OpenAI

client = OpenAI(
    base_url=os.getenv("SERVICE_URL"),
    api_key=os.getenv("ACCESS_TOKEN"),
)

chat_completion = client.chat.completions.create(
    model="tgi",
    messages=[
        {"role": "user", "content": "What is Deep Learning?"},
    ],
    max_tokens=128,
)

Resource clean up

Finally, once you are done using TGI on the Cloud Run Service, you can safely delete it to avoid incurring in unnecessary costs e.g. if the Cloud Run services are inadvertently invoked more times than your monthly Cloud Run invoke allocation in the free tier.

To delete the Cloud Run Service you can either go to the Google Cloud Console at https://console.cloud.google.com/run and delete it manually; or use the Google Cloud SDK via gcloud as follows:

gcloud run services delete $SERVICE_NAME --region $LOCATION

Additionally, if you followed the steps in via Cloud Run Service URL and generated a Service Account and an access token, you can either remove the Service Account, or just revoke the access token if it is still valid.

• (recommended) Revoke the Access Token as:

gcloud auth revoke --impersonate-service-account=$SERVICE_ACCOUNT_NAME@$PROJECT_ID.iam.gserviceaccount.com

• (optional) Delete the Service Account as:

gcloud iam service-accounts delete $SERVICE_ACCOUNT_NAME@$PROJECT_ID.iam.gserviceaccount.com

Finally, if you decided to enable the VPC network via Cloud NAT, you can also remove the Cloud NAT (which is a paid product) as:

gcloud compute routers nats delete vm-nat --router=nat-router --region=$LOCATION
gcloud compute routers delete nat-router --region=$LOCATION

References

• Cloud Run Documentation - Overview
• Cloud Run Documentation - GPU services
• Google Cloud Blog - Run your AI inference applications on Cloud Run with NVIDIA GPUs