Fine-tune Gemma open models using multiple GPUs on GKE

Autopilot Standard

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This tutorial shows you how to fine-tune Gemma large language model (LLM), family of open models, using graphical processing units (GPUs) on Google Kubernetes Engine (GKE) with the Transformers library from Hugging Face. Fine Tuning is a supervised learning process that improves a pre-trained model's ability to perform specific tasks by updating its parameters with a new dataset. In this tutorial, you download the 2B parameter pretrained Gemma family models from Hugging Face and fine-tune them on a GKE Autopilot or Standard cluster.

This guide is a good starting point if you need the granular control, scalability, resilience, portability, and cost-effectiveness of managed Kubernetes when fine tuning an LLM. If you need a unified managed AI platform to rapidly build and serve ML models cost effectively, we recommend that you try our Vertex AI solution.

Background

By serving Gemma using GPUs on GKE with the transformers library, you can implement a robust, production-ready inference serving solution with all the benefits of managed Kubernetes, including efficient scalability and higher availability. This section describes the key technologies used in this guide.

Gemma

Gemma is a set of openly available, lightweight generative artificial intelligence (AI) models released under an open license. These AI models are available to run in your applications, hardware, mobile devices, or hosted services.

In this guide we introduce Gemma for text generation. You can also tune these models to specialize in performing specific tasks.

The dataset you use in this document is b-mc2/sql-create-context.

To learn more, see the Gemma documentation.

GPUs

GPUs let you accelerate specific workloads running on your nodes such as machine learning and data processing. GKE provides a range of machine type options for node configuration, including machine types with NVIDIA H100, L4, and A100 GPUs.

Before you use GPUs in GKE, we recommend that you complete the following learning path:

1. Learn about current GPU version availability
2. Learn about GPUs in GKE

Hugging Face Transformers

With the Transformers library from Hugging Face, you can access cutting-edge pretrained models. The Transformers library lets you reduce time, resources, and computational costs associated with the complete model training.

In this tutorial, you use the Hugging Face APIs and tools to download and fine-tune these pretrained models.

Objectives

This guide is intended for new or existing users of GKE, ML Engineers, MLOps (DevOps) engineers, or platform administrators who are interested in using Kubernetes container orchestration capabilities for fine-tune LLMs on H100, A100, and L4 GPU hardware.

By the end of this guide, you should be able to perform the following steps:

1. Prepare your environment with a GKE cluster in Autopilot mode.
2. Create a fine-tune container.
3. Use GPU to fine-tune the Gemma 2B model and upload the model to Hugging Face.

Before you begin

• 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.

• In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

  Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

  Go to project selector

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

• Enable the required API.

  Enable the API

• In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

  Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

  Go to project selector

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

• Enable the required API.

  Enable the API

• Make sure that you have the following role or roles on the project: roles/container.admin, roles/iam.serviceAccountAdmin

  Check for the roles

  1. In the Google Cloud console, go to the IAM page.

     Go to IAM
  2. Select the project.

  3. In the Principal column, find all rows that identify you or a group that you're included in. To learn which groups you're included in, contact your administrator.

  4. For all rows that specify or include you, check the Role colunn to see whether the list of roles includes the required roles.

  Grant the roles

  1. In the Google Cloud console, go to the IAM page.

     Go to IAM
  2. Select the project.
  3. Click Grant access.

  4. In the New principals field, enter your user identifier. This is typically the email address for a Google Account.

  5. In the Select a role list, select a role.
  6. To grant additional roles, click Add another role and add each additional role.
  7. Click Save.

• Create a Hugging Face account, if you don't already have one.
• Ensure your project has sufficient quota for GPUs. To learn more, see About GPUs and Allocation quotas.

Get access to the model

To get access to the Gemma models for deployment to GKE, you must first sign the license consent agreement then generate a Hugging Face access token.

Sign the license consent agreement

You must sign the consent agreement to use Gemma. Follow these instructions:

1. Access the model consent page on Kaggle.com.
2. Verify consent using your Hugging Face account.
3. Accept the model terms.

Generate an access token

To access the model through Hugging Face, you'll need a Hugging Face token.

Follow these steps to generate a new token if you don't have one already:

1. Click Your Profile > Settings > Access Tokens.
2. Select New Token.
3. Specify a Name of your choice and a Role of at least Write.
4. Select Generate a token.
5. Copy the generated token to your clipboard.

Prepare your environment

In this tutorial, you use Cloud Shell to manage resources hosted on Google Cloud. Cloud Shell comes preinstalled with the software you'll need for this tutorial, including kubectl and gcloud CLI.

To set up your environment with Cloud Shell, follow these steps:

1. In the Google Cloud console, launch a Cloud Shell session by clicking Activate Cloud Shell in the Google Cloud console. This launches a session in the bottom pane of Google Cloud console.

2. Set the default environment variables:

   gcloud config set project PROJECT_ID
   export PROJECT_ID=$(gcloud config get project)
   export REGION=REGION
   export CLUSTER_NAME=finetuning
   export HF_TOKEN=HF_TOKEN
   export HF_PROFILE=HF_PROFILE
   

   Replace the following values:

   • PROJECT_ID: Your Google Cloud project ID.
   • REGION: A region that supports the accelerator type you want to use, for example, us-central1 for L4 GPU.
   • HF_TOKEN: The Hugging Face token you generated earlier.
   • HF_PROFILE: The Hugging Face Profile ID that you created earlier.

3. Clone the sample code repository from GitHub:

   git clone https://github.com/GoogleCloudPlatform/kubernetes-engine-samples
   cd kubernetes-engine-samples/ai-ml/llm-finetuning-gemma
   

Create and configure Google Cloud resources

Follow these instructions to create the required resources.

Create a GKE cluster and node pool

You can serve Gemma on GPUs in a GKE Autopilot or Standard cluster. We recommend that you use a Autopilot cluster for a fully managed Kubernetes experience. To choose the GKE mode of operation that's the best fit for your workloads, see Choose a GKE mode of operation.

Autopilot

In Cloud Shell, run the following command:

gcloud container clusters create-auto ${CLUSTER_NAME} \
  --project=${PROJECT_ID} \
  --region=${REGION} \
  --release-channel=rapid \
  --cluster-version=1.29

GKE creates an Autopilot cluster with CPU and GPU nodes as requested by the deployed workloads.

Standard

1. In Cloud Shell, run the following command to create a Standard cluster:

   gcloud container clusters create ${CLUSTER_NAME} \
     --project=${PROJECT_ID} \
     --region=${REGION} \
     --workload-pool=${PROJECT_ID}.svc.id.goog \
     --release-channel=rapid \
     --num-nodes=1
   

   The cluster creation might take several minutes.

2. Run the following command to create a node pool for your cluster:

   gcloud container node-pools create gpupool \
     --accelerator type=nvidia-l4,count=8,gpu-driver-version=latest \
     --project=${PROJECT_ID} \
     --location=${REGION} \
     --node-locations=${REGION}-a \
     --cluster=${CLUSTER_NAME} \
     --machine-type=g2-standard-96 \
     --num-nodes=1
   

   GKE creates a single node pool containing two L4 GPUs for each node.

Create a Kubernetes secret for Hugging Face credentials

In Cloud Shell, do the following:

1. Configure kubectl to communicate with your cluster:

   gcloud container clusters get-credentials ${CLUSTER_NAME} --location=${REGION}
   

2. Create a Kubernetes Secret that contains the Hugging Face token:

   kubectl create secret generic hf-secret \
     --from-literal=hf_api_token=${HF_TOKEN} \
     --dry-run=client -o yaml | kubectl apply -f -
   

Create a fine tuning container with Docker and Cloud Build

This container uses the PyTorch and Hugging Face Transformers code to fine tune the existing pre-trained Gemma model.

1. Create a Artifact Registry Docker Repository

   gcloud artifacts repositories create gemma \
       --project=${PROJECT_ID} \
       --repository-format=docker \
       --location=us \
       --description="Gemma Repo"
   

2. Build and push the image

   gcloud builds submit .
   

3. Export the IMAGE_URL for later use in this tutorial.

   export IMAGE_URL=us-docker.pkg.dev/$PROJECT_ID/gemma/finetune-gemma-gpu:1.0.0
   

Run a fine-tune Job on GKE

Deploy the Gemma fine tuning job

1. Open the finetune.yaml file.

   apiVersion: batch/v1
   kind: Job
   metadata:
     name: finetune-job
     namespace: default
   spec:
     backoffLimit: 2
     template:
       metadata:
         annotations:
           kubectl.kubernetes.io/default-container: finetuner
       spec:
         terminationGracePeriodSeconds: 600
         containers:
         - name: finetuner
           image: $IMAGE_URL
           resources:
             limits:
               nvidia.com/gpu: "8"
           env:
           - name: MODEL_NAME
             value: "google/gemma-2b"
           - name: NEW_MODEL
             value: "gemma-2b-sql-finetuned"
           - name: LORA_R
             value: "8"
           - name: LORA_ALPHA
             value: "16"
           - name: TRAIN_BATCH_SIZE
             value: "1"
           - name: EVAL_BATCH_SIZE
             value: "2"
           - name: GRADIENT_ACCUMULATION_STEPS
             value: "2"
           - name: DATASET_LIMIT
             value: "1000"
           - name: MAX_SEQ_LENGTH
             value: "512"
           - name: LOGGING_STEPS
             value: "5"
           - name: HF_TOKEN
             valueFrom:
               secretKeyRef:
                 name: hf-secret
                 key: hf_api_token
           volumeMounts:
           - mountPath: /dev/shm
             name: dshm
         volumes:
         - name: dshm
           emptyDir:
             medium: Memory
         nodeSelector:
           cloud.google.com/gke-accelerator: nvidia-l4
         restartPolicy: OnFailure

2. Apply the manifest to create the fine-tuning job:

   envsubst < finetune.yaml | kubectl apply -f -
   

   This instruction replaces the IMAGE_URL with the variable in the manifest.

3. Monitor the Job by running the following command:

   watch kubectl get pods
   

4. Check the logs of the job by running the following command:

   kubectl logs job.batch/finetune-job -f
   

   The Job resource downloads the model data then fine-tunes the model across all 8 GPU. This can take up to 20 minutes.

5. Once the Job is complete, go to your Hugging Face account. A new model named $HF_PROFILE/gemma-2b-sql-finetuned appears in your Hugging Face profile.

Serve the fine-tuned model on GKE

In this section, you deploy the vLLM container to serve the Gemma model.

1. Create the following serve-gemma.yaml manifest:

   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: vllm-gemma-deployment
   spec:
     replicas: 1
     selector:
       matchLabels:
         app: gemma-server
     template:
       metadata:
         labels:
           app: gemma-server
           ai.gke.io/model: gemma-2b
           ai.gke.io/inference-server: vllm
           examples.ai.gke.io/source: user-guide
       spec:
         containers:
         - name: inference-server
           image: us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:20240220_0936_RC01
           resources:
             requests:
               cpu: "2"
               memory: "7Gi"
               ephemeral-storage: "10Gi"
               nvidia.com/gpu: 1
             limits:
               cpu: "2"
               memory: "7Gi"
               ephemeral-storage: "10Gi"
               nvidia.com/gpu: 1
           command: ["python3", "-m", "vllm.entrypoints.api_server"]
           args:
           - --model=$(MODEL_ID)
           - --tensor-parallel-size=1
           env:
           - name: MODEL_ID
             value: google/gemma-2b
           - name: HUGGING_FACE_HUB_TOKEN
             valueFrom:
               secretKeyRef:
                 name: hf-secret
                 key: hf_api_token
           volumeMounts:
           - mountPath: /dev/shm
             name: dshm
         volumes:
         - name: dshm
           emptyDir:
               medium: Memory
         nodeSelector:
           cloud.google.com/gke-accelerator: nvidia-l4
   ---
   apiVersion: v1
   kind: Service
   metadata:
     name: llm-service
   spec:
     selector:
       app: gemma-server
     type: ClusterIP
     ports:
       - protocol: TCP
         port: 8000
         targetPort: 8000

2. Create environment variable for new MODEL_ID

   export MODEL_ID=$HF_PROFILE/gemma-2b-sql-finetuned
   

3. Replace MODEL_ID in the manifest:

   sed -i "s|google/gemma-2b|$MODEL_ID|g" serve-gemma.yaml
   

4. Apply the manifest:

   kubectl apply -f serve-gemma.yaml
   

   A Pod in the cluster downloads the model weights from Hugging Face and starts the serving engine.

5. Wait for the Deployment to be available:

   kubectl wait --for=condition=Available --timeout=700s deployment/vllm-gemma-deployment
   

6. View the logs from the running Deployment:

   kubectl logs -f -l app=gemma-finetune
   

The Deployment resource downloads the model data. This process can take a few minutes. The output is similar to the following:

INFO 01-26 19:02:54 model_runner.py:689] Graph capturing finished in 4 secs.
INFO:     Started server process [1]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)

Make sure the model is fully downloaded before proceeding to the next section.

Serve the model

In this section, you interact with the model.

Set up port forwarding

Once the model is deployed, run the following command to set up port forwarding to the model:

kubectl port-forward service/llm-service 8000:8000

The output is similar to the following:

Forwarding from 127.0.0.1:8000 -> 8000

Interact with the model using curl

In a new terminal session, use curl to chat with your model:

The following example command is for TGI

USER_PROMPT="Question: What is the total number of attendees with age over 30 at kubecon eu? Context: CREATE TABLE attendees (name VARCHAR, age INTEGER, kubecon VARCHAR)"

curl -X POST http://localhost:8000/generate \
  -H "Content-Type: application/json" \
  -d @- <<EOF
{
    "prompt": "${USER_PROMPT}",
    "temperature": 0.1,
    "top_p": 1.0,
    "max_tokens": 24
}
EOF

The following output shows an example of the model response:

{"generated_text":" Answer: SELECT COUNT(age) FROM attendees WHERE age > 30 AND kubecon = 'eu'\n"}

Depending on your query, you might have to change the max_token to get a better result. You can also use the instruction tunded model for better chat experience.

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

Delete the deployed resources

To avoid incurring charges to your Google Cloud account for the resources that you created in this guide, run the following command:

gcloud container clusters delete ${CLUSTER_NAME} \
  --region=${REGION}

What's next

• Learn more about GPUs in GKE.
• Learn how to use Gemma with TGI on other accelerators, including A100 and H100 GPUs, by viewing the sample code in GitHub.
• Learn how to deploy GPU workloads in Autopilot.
• Learn how to deploy GPU workloads in Standard.
• Explore the Vertex AI Model Garden.
• Discover how to run optimized AI/ML workloads with GKE platform orchestration capabilities.
• Learn how to use Assured Workloads to apply controls to a folder in Google Cloud to meet regulatory requirements.