Serve Gemma open models using GPUs on GKE with Triton and TensorRT-LLM

This tutorial demonstrates how to deploy and serve a Gemma large language model (LLM) using GPUs on Google Kubernetes Engine (GKE) with the NVIDIA Triton and TensorRT-LLM serving stack. This provides a foundation for understanding and exploring practical LLM deployment for inference in a managed Kubernetes environment. You deploy a pre-built container with Triton and TensorRT-LLM to GKE. You also configure GKE to load the Gemma 2B and 7B weights.

This tutorial is intended for Machine learning (ML) engineers, Platform admins and operators, and for Data and AI specialists who are interested in using Kubernetes container orchestration capabilities for serving LLMs on H100, A100, and L4 GPU hardware. To learn more about common roles and example tasks that we reference in Google Cloud content, see Common GKE Enterprise user roles and tasks.

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 deployment solution.

Before reading this page, ensure that you're familiar with the following:

Background

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. You can use the Gemma models for text generation, however you can also tune these models for specialized tasks.

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.

TensorRT-LLM

NVIDIA TensorRT-LLM (TRT-LLM) is a toolkit with a Python API for assembling optimized solutions to define LLMs and build TensorRT engines that perform inference efficiently on NVIDIA GPUs. TensorRT-LLM includes features such as:

  • Optimized transformer implementation with layer fusions, activation caching, memory buffer reuse, and PagedAttention
  • In-flight or continuous batching to improve the overall serving throughput
  • Tensor parallelism and pipeline parallelism for distributed serving on multiple GPUs
  • Quantization (FP16, FP8, INT8)

To learn more, refer to the TensorRT-LLM documentation.

Triton

NVIDIA Triton Inference Server is a open source inference server for AI/ML applications. Triton supports high-performance inference on both NVIDIA GPUs and CPUs with optimized backends, including TensorRT and TensorRT-LLM. Triton includes features such as:

  • Multi-GPU, multi-node inference
  • Concurrent multiple model execution
  • Model ensembling or chaining
  • Static, dynamic, and continuous or in-flight batching of prediction requests

To learn more, refer to the Triton documentation.

Objectives

  1. Prepare your environment with a GKE cluster in Autopilot mode.
  2. Deploy a container with Triton and TritonRT-LLM to your cluster.
  3. Use Triton and TensorRT-LLM to serve the Gemma 2B or 7B model through curl.

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.

    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.

    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.

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 Cloud Shell activation icon 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=triton

    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.

Get access to the model

To get access to the Gemma models, you must sign in to the Kaggle platform, and get a Kaggle API token.

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

  1. Access the model consent page on Kaggle.com.
  2. Login to Kaggle if you haven't done so already.
  3. Click Request Access.
  4. In the Choose Account for Consent section, select Verify via Kaggle Account to use your Kaggle account for consent.
  5. Accept the model Terms and Conditions.

Generate an access token

To access the model through Kaggle, you need a Kaggle API token. Follow these steps to generate a new token if you don't have one already:

  1. In your browser, go to Kaggle settings.
  2. Under the API section, click Create New Token.

A file named kaggle.json file is downloaded.

Upload the access token to Cloud Shell

In Cloud Shell, upload the Kaggle API token to your Google Cloud project:

  1. In Cloud Shell, click More > Upload.
  2. Select File and click Choose Files.
  3. Open the kaggle.json file.
  4. Click Upload.

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=1.28

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} \
    --location=${REGION}-a \
    --workload-pool=${PROJECT_ID}.svc.id.goog \
    --release-channel=rapid \
    --machine-type=e2-standard-4 \
    --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=1,gpu-driver-version=latest \
    --project=${PROJECT_ID} \
    --location=${REGION}-a \
    --cluster=${CLUSTER_NAME} \
    --machine-type=g2-standard-12 \
    --num-nodes=1

    GKE creates a single node pool containing one L4 GPU node.

Create Kubernetes Secret for Kaggle credentials

In this tutorial, you use a Kubernetes Secret for the Kaggle 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 Secret to store the Kaggle credentials:

    kubectl create secret generic kaggle-secret \
    --from-file=kaggle.json \
    --dry-run=client -o yaml | kubectl apply -f -

Create a PersistentVolume resource to store checkpoints

In this section, you create a PersistentVolume backed by a persistent disk to store the model checkpoints.

  1. Create the following trtllm_checkpoint_pv.yaml manifest:

    apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: model-data
spec:
  accessModes:
  - ReadWriteOnce
  resources:
    requests:
      storage: 100G

  2. Apply the manifest:

    kubectl apply -f trtllm_checkpoint_pv.yaml

Download the TensorRT-LLM engine files for Gemma

In this section, you run a Job to download the TensorRT-LLM engine files and store the files in the PersistentVolume you created earlier. The Job also prepares configuration files for deploying the model on the Triton server in the next step. This process can take a few minutes.

Gemma 2B-it

The TensorRT-LLM engine is built from the Gemma 2B-it (instruction tuned) PyTorch checkpoint of Gemma using bfloat16 activation, input sequence length=2048, and output sequence length=1024 targeted L4 GPUs. You can deploy the model on a single L4 GPU.

  1. Create the following job-download-gemma-2b.yaml manifest:

    apiVersion: v1
kind: ConfigMap
metadata:
  name: fetch-model-scripts
data:
  fetch_model.sh: |-
    #!/usr/bin/bash -x
    pip install kaggle --break-system-packages && \

    MODEL_NAME=$(echo ${MODEL_PATH} | awk -F'/' '{print $2}') && \
    VARIATION_NAME=$(echo ${MODEL_PATH} | awk -F'/' '{print $4}') && \
    ACTIVATION_DTYPE=bfloat16 && \

    TOKENIZER_DIR=/data/trt_engine/${MODEL_NAME}/${VARIATION_NAME}/${ACTIVATION_DTYPE}/${WORLD_SIZE}-gpu/tokenizer.model && \
    ENGINE_PATH=/data/trt_engine/${MODEL_NAME}/${VARIATION_NAME}/${ACTIVATION_DTYPE}/${WORLD_SIZE}-gpu/ && \
    TRITON_MODEL_REPO=/data/triton/model_repository && \

    mkdir -p /data/${MODEL_NAME}_${VARIATION_NAME} && \
    mkdir -p ${ENGINE_PATH} && \
    mkdir -p ${TRITON_MODEL_REPO} && \

    kaggle models instances versions download ${MODEL_PATH} --untar -p /data/${MODEL_NAME}_${VARIATION_NAME} && \
    rm -f /data/${MODEL_NAME}_${VARIATION_NAME}/*.tar.gz && \
    find /data/${MODEL_NAME}_${VARIATION_NAME} -type f && \
    find /data/${MODEL_NAME}_${VARIATION_NAME} -type f | xargs -I '{}' mv '{}' ${ENGINE_PATH} && \

    # copying configuration files
    echo -e "\nCreating configuration files" && \
    cp -r /tensorrtllm_backend/all_models/inflight_batcher_llm/* ${TRITON_MODEL_REPO} && \

    # updating configuration files
    python3 /tensorrtllm_backend/tools/fill_template.py -i ${TRITON_MODEL_REPO}/preprocessing/config.pbtxt tokenizer_dir:${TOKENIZER_DIR},tokenizer_type:sp,triton_max_batch_size:64,preprocessing_instance_count:1 && \
    python3 /tensorrtllm_backend/tools/fill_template.py -i ${TRITON_MODEL_REPO}/postprocessing/config.pbtxt tokenizer_dir:${TOKENIZER_DIR},tokenizer_type:sp,triton_max_batch_size:64,postprocessing_instance_count:1 && \
    python3 /tensorrtllm_backend/tools/fill_template.py -i ${TRITON_MODEL_REPO}/tensorrt_llm_bls/config.pbtxt triton_max_batch_size:64,decoupled_mode:False,bls_instance_count:1,accumulate_tokens:False && \
    python3 /tensorrtllm_backend/tools/fill_template.py -i ${TRITON_MODEL_REPO}/ensemble/config.pbtxt triton_max_batch_size:64 && \
    python3 /tensorrtllm_backend/tools/fill_template.py -i ${TRITON_MODEL_REPO}/tensorrt_llm/config.pbtxt triton_max_batch_size:64,decoupled_mode:False,max_beam_width:1,engine_dir:${ENGINE_PATH},max_tokens_in_paged_kv_cache:2560,max_attention_window_size:2560,kv_cache_free_gpu_mem_fraction:0.5,exclude_input_in_output:True,enable_kv_cache_reuse:False,batching_strategy:inflight_batching,max_queue_delay_microseconds:600,batch_scheduler_policy:guaranteed_no_evict,enable_trt_overlap:False && \

    echo -e "\nCompleted extraction to ${ENGINE_PATH}"
---
apiVersion: batch/v1
kind: Job
metadata:
  name: data-loader-gemma-2b
  labels:
    app: data-loader-gemma-2b
spec:
  ttlSecondsAfterFinished: 120
  template:
    metadata:
      labels:
        app: data-loader-gemma-2b
    spec:
      restartPolicy: OnFailure
      containers:
      - name: gcloud
        image: us-docker.pkg.dev/google-samples/containers/gke/tritonserver:2.42.0
        command:
        - /scripts/fetch_model.sh
        env:
        - name: KAGGLE_CONFIG_DIR
          value: /kaggle
        - name: MODEL_PATH
          value: "google/gemma/tensorrtllm/2b-it/2"
        - name: WORLD_SIZE
          value: "1"
        volumeMounts:
        - mountPath: "/kaggle/"
          name: kaggle-credentials
          readOnly: true
        - mountPath: "/scripts/"
          name: scripts-volume
          readOnly: true
        - mountPath: "/data"
          name: data
      volumes:
      - name: kaggle-credentials
        secret:
          defaultMode: 0400
          secretName: kaggle-secret
      - name: scripts-volume
        configMap:
          defaultMode: 0700
          name: fetch-model-scripts
      - name: data
        persistentVolumeClaim:
          claimName: model-data
      tolerations:
      - key: "key"
        operator: "Exists"
        effect: "NoSchedule"

  2. Apply the manifest:

    kubectl apply -f job-download-gemma-2b.yaml

  3. View the logs for the Job:

    kubectl logs -f job/data-loader-gemma-2b

    The output from the logs is similar to the following:

    ...
Creating configuration files
+ echo -e '\n02-16-2024 04:07:45 Completed building TensortRT-LLM engine at /data/trt_engine/gemma/2b/bfloat16/1-gpu/'
+ echo -e '\nCreating configuration files'
...

  4. Wait for the Job to complete:

    kubectl wait --for=condition=complete --timeout=900s job/data-loader-gemma-2b

    The output is similar to the following:

    job.batch/data-loader-gemma-2b condition met

  5. Verify the Job completed successfully (this may take a few minutes):

    kubectl get job/data-loader-gemma-2b

    The output is similar to the following:

    NAME             COMPLETIONS   DURATION   AGE
data-loader-gemma-2b   1/1           ##s        #m##s

Gemma 7B-it

The TensorRT-LLM engine is built from the Gemma 7B-it (instruction tuned) PyTorch checkpoint of Gemma using bfloat16 activation, input sequence length=1024, and output sequence length=512 targeted L4 GPUs. You can deploy the model on a single L4 GPU.

  1. Create the following job-download-gemma-7b.yaml manifest:

    apiVersion: v1
kind: ConfigMap
metadata:
  name: fetch-model-scripts
data:
  fetch_model.sh: |-
    #!/usr/bin/bash -x
    pip install kaggle --break-system-packages && \

    MODEL_NAME=$(echo ${MODEL_PATH} | awk -F'/' '{print $2}') && \
    VARIATION_NAME=$(echo ${MODEL_PATH} | awk -F'/' '{print $4}') && \
    ACTIVATION_DTYPE=bfloat16 && \

    TOKENIZER_DIR=/data/trt_engine/${MODEL_NAME}/${VARIATION_NAME}/${ACTIVATION_DTYPE}/${WORLD_SIZE}-gpu/tokenizer.model && \
    ENGINE_PATH=/data/trt_engine/${MODEL_NAME}/${VARIATION_NAME}/${ACTIVATION_DTYPE}/${WORLD_SIZE}-gpu/ && \
    TRITON_MODEL_REPO=/data/triton/model_repository && \

    mkdir -p ${ENGINE_PATH} && \
    mkdir -p ${TRITON_MODEL_REPO} && \

    kaggle models instances versions download ${MODEL_PATH} --untar -p /data/${MODEL_NAME}_${VARIATION_NAME} && \
    rm -f /data/${MODEL_NAME}_${VARIATION_NAME}/*.tar.gz && \
    find /data/${MODEL_NAME}_${VARIATION_NAME} -type f && \
    find /data/${MODEL_NAME}_${VARIATION_NAME} -type f | xargs -I '{}' mv '{}' ${ENGINE_PATH} && \

    # copying configuration files
    echo -e "\nCreating configuration files" && \
    cp -r /tensorrtllm_backend/all_models/inflight_batcher_llm/* ${TRITON_MODEL_REPO} && \

    # updating configuration files
    python3 /tensorrtllm_backend/tools/fill_template.py -i ${TRITON_MODEL_REPO}/preprocessing/config.pbtxt tokenizer_dir:${TOKENIZER_DIR},tokenizer_type:sp,triton_max_batch_size:64,preprocessing_instance_count:1 && \
    python3 /tensorrtllm_backend/tools/fill_template.py -i ${TRITON_MODEL_REPO}/postprocessing/config.pbtxt tokenizer_dir:${TOKENIZER_DIR},tokenizer_type:sp,triton_max_batch_size:64,postprocessing_instance_count:1 && \
    python3 /tensorrtllm_backend/tools/fill_template.py -i ${TRITON_MODEL_REPO}/tensorrt_llm_bls/config.pbtxt triton_max_batch_size:64,decoupled_mode:False,bls_instance_count:1,accumulate_tokens:False && \
    python3 /tensorrtllm_backend/tools/fill_template.py -i ${TRITON_MODEL_REPO}/ensemble/config.pbtxt triton_max_batch_size:64 && \
    python3 /tensorrtllm_backend/tools/fill_template.py -i ${TRITON_MODEL_REPO}/tensorrt_llm/config.pbtxt triton_max_batch_size:64,decoupled_mode:False,max_beam_width:1,engine_dir:${ENGINE_PATH},max_tokens_in_paged_kv_cache:2560,max_attention_window_size:2560,kv_cache_free_gpu_mem_fraction:0.5,exclude_input_in_output:True,enable_kv_cache_reuse:False,batching_strategy:inflight_batching,max_queue_delay_microseconds:600,batch_scheduler_policy:guaranteed_no_evict,enable_trt_overlap:False && \

    echo -e "\nCompleted extraction to ${ENGINE_PATH}"
---
apiVersion: batch/v1
kind: Job
metadata:
  name: data-loader-gemma-7b
  labels:
    app: data-loader-gemma-7b
spec:
  ttlSecondsAfterFinished: 120
  template:
    metadata:
      labels:
        app: data-loader-gemma-7b
    spec:
      restartPolicy: OnFailure
      containers:
      - name: gcloud
        image: us-docker.pkg.dev/google-samples/containers/gke/tritonserver:2.42.0
        command:
        - /scripts/fetch_model.sh
        env:
        - name: KAGGLE_CONFIG_DIR
          value: /kaggle
        - name: MODEL_PATH
          value: "google/gemma/tensorrtllm/7b-it/2"
        - name: WORLD_SIZE
          value: "1"
        volumeMounts:
        - mountPath: "/kaggle/"
          name: kaggle-credentials
          readOnly: true
        - mountPath: "/scripts/"
          name: scripts-volume
          readOnly: true
        - mountPath: "/data"
          name: data
      volumes:
      - name: kaggle-credentials
        secret:
          defaultMode: 0400
          secretName: kaggle-secret
      - name: scripts-volume
        configMap:
          defaultMode: 0700
          name: fetch-model-scripts
      - name: data
        persistentVolumeClaim:
          claimName: model-data
      tolerations:
      - key: "key"
        operator: "Exists"
        effect: "NoSchedule"

  2. Apply the manifest:

    kubectl apply -f job-download-gemma-7b.yaml

  3. View the logs for the Job:

    kubectl logs -f job/data-loader-gemma-7b

    The output from the logs is similar to the following:

    ...
Creating configuration files
+ echo -e '\n02-16-2024 04:07:45 Completed building TensortRT-LLM engine at /data/trt_engine/gemma/7b/bfloat16/1-gpu/'
+ echo -e '\nCreating configuration files'
...

  4. Wait for the Job to complete:

    kubectl wait --for=condition=complete --timeout=900s job/data-loader-gemma-7b

    The output is similar to the following:

    job.batch/data-loader-gemma-7b condition met

  5. Verify the Job completed successfully (this may take a few minutes):

    kubectl get job/data-loader-gemma-7b

    The output is similar to the following:

    NAME             COMPLETIONS   DURATION   AGE
data-loader-gemma-7b   1/1           ##s        #m##s

Make sure the Job is completed successfully before proceeding to the next section.

Deploy Triton

In this section, you deploy a container that uses Triton with the TensorRT-LLM backend to serve the Gemma model you want to use.

  1. Create the following deploy-triton-server.yaml manifest:

    apiVersion: v1
kind: ConfigMap
metadata:
  name: launch-tritonserver
data:
  entrypoint.sh: |-
    #!/usr/bin/bash -x
    # Launch Triton Inference server

    WORLD_SIZE=1
    TRITON_MODEL_REPO=/data/triton/model_repository

    python3 /tensorrtllm_backend/scripts/launch_triton_server.py \
      --world_size ${WORLD_SIZE} \
      --model_repo ${TRITON_MODEL_REPO}

    tail -f /dev/null
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: triton-gemma-deployment
  labels:
    app: gemma-server
    version: v1 
spec:
  replicas: 1
  selector:
    matchLabels:
      app: gemma-server 
      version: v1
  template:
    metadata:
      labels:
        app: gemma-server
        ai.gke.io/model: gemma
        ai.gke.io/inference-server: triton
        examples.ai.gke.io/source: user-guide
        version: v1
    spec:
      containers:
      - name: inference-server 
        image: us-docker.pkg.dev/google-samples/containers/gke/tritonserver:2.42.0
        imagePullPolicy: IfNotPresent
        resources:
          requests:
            ephemeral-storage: "40Gi"
            memory: "40Gi"
            nvidia.com/gpu: 1
          limits:
            ephemeral-storage: "40Gi"
            memory: "40Gi"
            nvidia.com/gpu: 1
        command:
        - /scripts/entrypoint.sh
        volumeMounts:
        - mountPath: "/scripts/"
          name: scripts-volume
          readOnly: true
        - mountPath: "/data"
          name: data
        ports:
          - containerPort: 8000
            name: http
          - containerPort: 8001
            name: grpc
          - containerPort: 8002
            name: metrics
        livenessProbe:
          failureThreshold: 60
          initialDelaySeconds: 600
          periodSeconds: 5
          httpGet:
            path: /v2/health/live
            port: http
        readinessProbe:
          failureThreshold: 60
          initialDelaySeconds: 600
          periodSeconds: 5
          httpGet:
            path: /v2/health/ready
            port: http
      securityContext:
        runAsUser: 1000
        fsGroup: 1000
      volumes:
      - name: scripts-volume
        configMap:
          defaultMode: 0700
          name: launch-tritonserver
      - name: data
        persistentVolumeClaim:
          claimName: model-data
      nodeSelector:
        cloud.google.com/gke-accelerator: nvidia-l4
      tolerations:
      - key: "key"
        operator: "Exists"
        effect: "NoSchedule"
---
apiVersion: v1
kind: Service
metadata:
  name: triton-server
  labels:
    app: gemma-server 
spec:
  type: ClusterIP
  ports:
    - port: 8000
      targetPort: http
      name: http-inference-server
    - port: 8001
      targetPort: grpc
      name: grpc-inference-server
    - port: 8002
      targetPort: metrics
      name: http-metrics
  selector:
    app: gemma-server

  2. Apply the manifest:

    kubectl apply -f deploy-triton-server.yaml

  3. Wait for the deployment to be available:

    kubectl wait --for=condition=Available --timeout=900s deployment/triton-gemma-deployment

  4. View the logs from manifest:

    kubectl logs -f -l app=gemma-server

    The deployment resource launches the Triton server and loads the model data. This process can take a few minutes (up to 20 minutes or longer). The output is similar to the following:

    I0216 03:24:57.387420 29 server.cc:676]
+------------------+---------+--------+
| Model            | Version | Status |
+------------------+---------+--------+
| ensemble         | 1       | READY  |
| postprocessing   | 1       | READY  |
| preprocessing    | 1       | READY  |
| tensorrt_llm     | 1       | READY  |
| tensorrt_llm_bls | 1       | READY  |
+------------------+---------+--------+

....
....
....

I0216 03:24:57.425104 29 grpc_server.cc:2519] Started GRPCInferenceService at 0.0.0.0:8001
I0216 03:24:57.425418 29 http_server.cc:4623] Started HTTPService at 0.0.0.0:8000
I0216 03:24:57.466646 29 http_server.cc:315] Started Metrics Service at 0.0.0.0:8002

Serve the model

In this section, you interact with the model.

Set up port forwarding

Run the following command to set up port forwarding to the model:

kubectl port-forward service/triton-server 8000:8000

The output is similar to the following:

Forwarding from 127.0.0.1:8000 -> 8000
Forwarding from [::1]:8000 -> 8000
Handling connection for 8000

Interact with the model using curl

This section shows how you can perform a basic smoke test to verify your deployed instruction tuned model. For simplicity, this section describes the testing approach only using the 2B instruction tuned model.

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

USER_PROMPT="I'm new to coding. If you could only recommend one programming language to start with, what would it be and why?"

curl -X POST localhost:8000/v2/models/ensemble/generate \
  -H "Content-Type: application/json" \
  -d @- <<EOF
{
    "text_input": "<start_of_turn>user\n${USER_PROMPT}<end_of_turn>\n",
    "temperature": 0.9,
    "max_tokens": 128
}
EOF

The following output shows an example of the model response:

{
  "context_logits": 0,
  "cum_log_probs": 0,
  "generation_logits": 0,
  "model_name": "ensemble",
  "model_version": "1",
  "output_log_probs": [0.0,0.0,...],
  "sequence_end": false,
  "sequence_id": 0,
  "sequence_start": false,
  "text_output":"Python.\n\nPython is an excellent choice for beginners due to its simplicity, readability, and extensive documentation. Its syntax is close to natural language, making it easier for beginners to understand and write code. Python also has a vast collection of libraries and tools that make it versatile for various projects. Additionally, Python's dynamic nature allows for easier learning and experimentation, making it a perfect choice for newcomers to get started.Here are some specific reasons why Python is a good choice for beginners:\n\n- Simple and Easy to Read: Python's syntax is designed to be close to natural language, making it easier for"
}

Troubleshoot issues

  • If you get the message Empty reply from server, it's possible the container has not finished downloading the model data. Check the Pod's logs again for the Connected message which indicates that the model is ready to serve.
  • If you see Connection refused, verify that your port forwarding is active.

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