Batch code prediction with a pre-trained model

Perform batch code prediction using a pre-trained code generation model.

Code sample

Go

Before trying this sample, follow the Go setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Go API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 

import (
	"context"
	"fmt"
	"io"

	aiplatform "cloud.google.com/go/aiplatform/apiv1"
	aiplatformpb "cloud.google.com/go/aiplatform/apiv1/aiplatformpb"
	"google.golang.org/api/option"
	"google.golang.org/protobuf/types/known/structpb"
)

// batchCodePredict perform batch code prediction using a pre-trained code generation model
func batchCodePredict(w io.Writer, projectID, location, name, outputURI string, inputURIs []string) error {
	// inputURI := []string{"gs://cloud-samples-data/batch/prompt_for_batch_code_predict.jsonl"}
	// outputURI: existing template path. Following formats are allowed:
	// 	- gs://BUCKET_NAME/DIRECTORY/
	// 	- bq://project_name.llm_dataset

	ctx := context.Background()
	apiEndpoint := fmt.Sprintf("%s-aiplatform.googleapis.com:443", location)
	// Pretrained code model
	model := "publishers/google/models/code-bison"
	parameters := map[string]interface{}{
		"temperature":     0.2,
		"maxOutputTokens": 200,
	}
	parametersValue, err := structpb.NewValue(parameters)
	if err != nil {
		fmt.Fprintf(w, "unable to convert parameters to Value: %v", err)
		return err
	}

	client, err := aiplatform.NewJobClient(ctx, option.WithEndpoint(apiEndpoint))
	if err != nil {
		return err
	}
	defer client.Close()

	req := &aiplatformpb.CreateBatchPredictionJobRequest{
		Parent: fmt.Sprintf("projects/%s/locations/%s", projectID, location),
		BatchPredictionJob: &aiplatformpb.BatchPredictionJob{
			DisplayName:     name,
			Model:           model,
			ModelParameters: parametersValue,
			InputConfig: &aiplatformpb.BatchPredictionJob_InputConfig{
				Source: &aiplatformpb.BatchPredictionJob_InputConfig_GcsSource{
					GcsSource: &aiplatformpb.GcsSource{
						Uris: inputURIs,
					},
				},
				// List of supported formarts: https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1#model
				InstancesFormat: "jsonl",
			},
			OutputConfig: &aiplatformpb.BatchPredictionJob_OutputConfig{
				Destination: &aiplatformpb.BatchPredictionJob_OutputConfig_GcsDestination{
					GcsDestination: &aiplatformpb.GcsDestination{
						OutputUriPrefix: outputURI,
					},
				},
				// List of supported formarts: https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1#model
				PredictionsFormat: "jsonl",
			},
		},
	}

	job, err := client.CreateBatchPredictionJob(ctx, req)
	if err != nil {
		return err
	}
	fmt.Fprint(w, job.GetDisplayName())

	return nil
}

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Java API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 


import com.google.cloud.aiplatform.v1.BatchPredictionJob;
import com.google.cloud.aiplatform.v1.GcsDestination;
import com.google.cloud.aiplatform.v1.GcsSource;
import com.google.cloud.aiplatform.v1.JobServiceClient;
import com.google.cloud.aiplatform.v1.JobServiceSettings;
import com.google.cloud.aiplatform.v1.LocationName;
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.HashMap;
import java.util.Map;

public class BatchCodePredictionSample {

  public static void main(String[] args) throws IOException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String location = "us-central1";
    // inputUri: URI of the input dataset.
    // Could be a BigQuery table or a Google Cloud Storage file.
    // E.g. "gs://[BUCKET]/[DATASET].jsonl" OR "bq://[PROJECT].[DATASET].[TABLE]"
    String inputUri = "gs://cloud-samples-data/batch/prompt_for_batch_code_predict.jsonl";
    // outputUri: URI where the output will be stored.
    // Could be a BigQuery table or a Google Cloud Storage file.
    // E.g. "gs://[BUCKET]/[OUTPUT].jsonl" OR "bq://[PROJECT].[DATASET].[TABLE]"
    String outputUri = "gs://YOUR_BUCKET/batch_code_predict_output";
    String codeModel = "code-bison";

    batchCodePredictionSample(project, location, inputUri, outputUri, codeModel);
  }

  // Perform batch code prediction using a pre-trained code generation model.
  // Example of using Google Cloud Storage bucket as the input and output data source
  public static BatchPredictionJob batchCodePredictionSample(
      String project, String location, String inputUri, String outputUri, String codeModel)
      throws IOException {
    BatchPredictionJob response;
    JobServiceSettings jobServiceSettings =  JobServiceSettings.newBuilder()
        .setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
    LocationName parent = LocationName.of(project, location);
    String modelName = String.format(
        "projects/%s/locations/%s/publishers/google/models/%s", project, location, codeModel);
    // Construct your modelParameters
    Map<String, String> modelParameters = new HashMap<>();
    modelParameters.put("maxOutputTokens", "200");
    modelParameters.put("temperature", "0.2");
    modelParameters.put("topP", "0.95");
    modelParameters.put("topK", "40");
    Value parameterValue = mapToValue(modelParameters);

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests.
    try (JobServiceClient client = JobServiceClient.create(jobServiceSettings)) {
      BatchPredictionJob batchPredictionJob =
          BatchPredictionJob.newBuilder()
              .setDisplayName("my batch code prediction job " + System.currentTimeMillis())
              .setModel(modelName)
              .setInputConfig(
                  BatchPredictionJob.InputConfig.newBuilder()
                      .setGcsSource(GcsSource.newBuilder().addUris(inputUri).build())
                      .setInstancesFormat("jsonl")
                      .build())
              .setOutputConfig(
                  BatchPredictionJob.OutputConfig.newBuilder()
                      .setGcsDestination(GcsDestination.newBuilder()
                          .setOutputUriPrefix(outputUri).build())
                      .setPredictionsFormat("jsonl")
                      .build())
              .setModelParameters(parameterValue)
              .build();

      response = client.createBatchPredictionJob(parent, batchPredictionJob);

      System.out.format("response: %s\n", response);
      System.out.format("\tName: %s\n", response.getName());
    }
    return response;
  }

  private static Value mapToValue(Map<String, String> 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();
  }
}

What's next

To search and filter code samples for other Google Cloud products, see the Google Cloud sample browser.