Membuat teks dengan model Gemma2 dengan akselerator GPU

Contoh kode ini menunjukkan cara menjalankan interferensi pada model Gemma2 yang di-deploy ke endpoint Vertex AI dengan akselerator GPU.

Mempelajari lebih lanjut

Untuk dokumentasi mendetail yang menyertakan contoh kode ini, lihat artikel berikut:

Contoh kode

Go

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Go di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Go Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal. 

import (
	"context"
	"fmt"
	"io"

	"cloud.google.com/go/aiplatform/apiv1/aiplatformpb"

	"google.golang.org/protobuf/types/known/structpb"
)

// predictGPU demonstrates how to run interference on a Gemma2 model deployed to a Vertex AI endpoint with GPU accelerators.
func predictGPU(w io.Writer, client PredictionsClient, projectID, location, endpointID string) error {
	ctx := context.Background()

	// Note: client can be initialized in the following way:
	// apiEndpoint := fmt.Sprintf("%s-aiplatform.googleapis.com:443", location)
	// client, err := aiplatform.NewPredictionClient(ctx, option.WithEndpoint(apiEndpoint))
	// if err != nil {
	// 	return fmt.Errorf("unable to create prediction client: %v", err)
	// }
	// defer client.Close()

	gemma2Endpoint := fmt.Sprintf("projects/%s/locations/%s/endpoints/%s", projectID, location, endpointID)
	prompt := "Why is the sky blue?"
	parameters := map[string]interface{}{
		"temperature":     0.9,
		"maxOutputTokens": 1024,
		"topP":            1.0,
		"topK":            1,
	}

	// Encapsulate the prompt in a correct format for TPUs.
	// Pay attention that prompt should be set in "inputs" field.
	// Example format: [{'inputs': 'Why is the sky blue?', 'parameters': {'temperature': 0.9}}]
	promptValue, err := structpb.NewValue(map[string]interface{}{
		"inputs":     prompt,
		"parameters": parameters,
	})
	if err != nil {
		fmt.Fprintf(w, "unable to convert prompt to Value: %v", err)
		return err
	}

	req := &aiplatformpb.PredictRequest{
		Endpoint:  gemma2Endpoint,
		Instances: []*structpb.Value{promptValue},
	}

	resp, err := client.Predict(ctx, req)
	if err != nil {
		return err
	}

	prediction := resp.GetPredictions()
	value := prediction[0].GetStringValue()
	fmt.Fprintf(w, "%v", value)

	return nil
}

Java

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Java di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Java Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal. 


import com.google.cloud.aiplatform.v1.EndpointName;
import com.google.cloud.aiplatform.v1.PredictResponse;
import com.google.cloud.aiplatform.v1.PredictionServiceClient;
import com.google.cloud.aiplatform.v1.PredictionServiceSettings;
import com.google.gson.Gson;
import com.google.protobuf.InvalidProtocolBufferException;
import com.google.protobuf.Value;
import com.google.protobuf.util.JsonFormat;
import java.io.IOException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

public class Gemma2PredictGpu {

  private final PredictionServiceClient predictionServiceClient;

  // Constructor to inject the PredictionServiceClient
  public Gemma2PredictGpu(PredictionServiceClient predictionServiceClient) {
    this.predictionServiceClient = predictionServiceClient;
  }

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String endpointRegion = "us-east4";
    String endpointId = "YOUR_ENDPOINT_ID";

    PredictionServiceSettings predictionServiceSettings =
        PredictionServiceSettings.newBuilder()
            .setEndpoint(String.format("%s-aiplatform.googleapis.com:443", endpointRegion))
            .build();
    PredictionServiceClient predictionServiceClient =
        PredictionServiceClient.create(predictionServiceSettings);
    Gemma2PredictGpu creator = new Gemma2PredictGpu(predictionServiceClient);

    creator.gemma2PredictGpu(projectId, endpointRegion, endpointId);
  }

  // Demonstrates how to run inference on a Gemma2 model
  // deployed to a Vertex AI endpoint with GPU accelerators.
  public String gemma2PredictGpu(String projectId, String region,
               String endpointId) throws IOException {
    Map<String, Object> paramsMap = new HashMap<>();
    paramsMap.put("temperature", 0.9);
    paramsMap.put("maxOutputTokens", 1024);
    paramsMap.put("topP", 1.0);
    paramsMap.put("topK", 1);
    Value parameters = mapToValue(paramsMap);

    // Prompt used in the prediction
    String instance = "{ \"inputs\": \"Why is the sky blue?\"}";
    Value.Builder instanceValue = Value.newBuilder();
    JsonFormat.parser().merge(instance, instanceValue);
    // Encapsulate the prompt in a correct format for GPUs
    // Example format: [{'inputs': 'Why is the sky blue?', 'parameters': {'temperature': 0.8}}]
    List<Value> instances = new ArrayList<>();
    instances.add(instanceValue.build());

    EndpointName endpointName = EndpointName.of(projectId, region, endpointId);

    PredictResponse predictResponse = this.predictionServiceClient
        .predict(endpointName, instances, parameters);
    String textResponse = predictResponse.getPredictions(0).getStringValue();
    System.out.println(textResponse);
    return textResponse;
  }

  private static Value mapToValue(Map<String, Object> map) throws InvalidProtocolBufferException {
    Gson gson = new Gson();
    String json = gson.toJson(map);
    Value.Builder builder = Value.newBuilder();
    JsonFormat.parser().merge(json, builder);
    return builder.build();
  }
}

Node.js

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Node.js di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Node.js Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal. 

async function gemma2PredictGpu(predictionServiceClient) {
  // Imports the Google Cloud Prediction Service Client library
  const {
    // TODO(developer): Uncomment PredictionServiceClient before running the sample.
    // PredictionServiceClient,
    helpers,
  } = require('@google-cloud/aiplatform');
  /**
   * TODO(developer): Update these variables before running the sample.
   */
  const projectId = 'your-project-id';
  const endpointRegion = 'your-vertex-endpoint-region';
  const endpointId = 'your-vertex-endpoint-id';

  // Default configuration
  const config = {maxOutputTokens: 1024, temperature: 0.9, topP: 1.0, topK: 1};
  // Prompt used in the prediction
  const prompt = 'Why is the sky blue?';

  // Encapsulate the prompt in a correct format for GPUs
  // Example format: [{inputs: 'Why is the sky blue?', parameters: {temperature: 0.9}}]
  const input = {
    inputs: prompt,
    parameters: config,
  };

  // Convert input message to a list of GAPIC instances for model input
  const instances = [helpers.toValue(input)];

  // TODO(developer): Uncomment apiEndpoint and predictionServiceClient before running the sample.
  // const apiEndpoint = `${endpointRegion}-aiplatform.googleapis.com`;

  // Create a client
  // predictionServiceClient = new PredictionServiceClient({apiEndpoint});

  // Call the Gemma2 endpoint
  const gemma2Endpoint = `projects/${projectId}/locations/${endpointRegion}/endpoints/${endpointId}`;

  const [response] = await predictionServiceClient.predict({
    endpoint: gemma2Endpoint,
    instances,
  });

  const predictions = response.predictions;
  const text = predictions[0].stringValue;

  console.log('Predictions:', text);
  return text;
}

module.exports = gemma2PredictGpu;

// TODO(developer): Uncomment below lines before running the sample.
// gemma2PredictGpu(...process.argv.slice(2)).catch(err => {
//   console.error(err.message);
//   process.exitCode = 1;
// });

Python

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Python di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Python Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal. 

"""
Sample to run inference on a Gemma2 model deployed to a Vertex AI endpoint with GPU accellerators.
"""

from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value

# TODO(developer): Update & uncomment lines below
# PROJECT_ID = "your-project-id"
# ENDPOINT_REGION = "your-vertex-endpoint-region"
# ENDPOINT_ID = "your-vertex-endpoint-id"

# Default configuration
config = {"max_tokens": 1024, "temperature": 0.9, "top_p": 1.0, "top_k": 1}

# Prompt used in the prediction
prompt = "Why is the sky blue?"

# Encapsulate the prompt in a correct format for GPUs
# Example format: [{'inputs': 'Why is the sky blue?', 'parameters': {'temperature': 0.9}}]
input = {"inputs": prompt, "parameters": config}

# Convert input message to a list of GAPIC instances for model input
instances = [json_format.ParseDict(input, Value())]

# Create a client
api_endpoint = f"{ENDPOINT_REGION}-aiplatform.googleapis.com"
client = aiplatform.gapic.PredictionServiceClient(
    client_options={"api_endpoint": api_endpoint}
)

# Call the Gemma2 endpoint
gemma2_end_point = (
    f"projects/{PROJECT_ID}/locations/{ENDPOINT_REGION}/endpoints/{ENDPOINT_ID}"
)
response = client.predict(
    endpoint=gemma2_end_point,
    instances=instances,
)
text_responses = response.predictions
print(text_responses[0])

Langkah selanjutnya

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