Try Gemini 1.5 models, our newest multimodal models in Vertex AI, and see what you can build with a 1M token context window. Try Gemini 1.5 models, our newest multimodal models in Vertex AI, and see what you can build with a 1M token context window.

Get a training pipeline

Gets a training pipeline using the get_training_pipeline method.

Explore further

For detailed documentation that includes this code sample, see the following:

Train an AutoML Edge model using the Vertex AI API

Code sample

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.DeployedModelRef;
import com.google.cloud.aiplatform.v1.EnvVar;
import com.google.cloud.aiplatform.v1.FilterSplit;
import com.google.cloud.aiplatform.v1.FractionSplit;
import com.google.cloud.aiplatform.v1.InputDataConfig;
import com.google.cloud.aiplatform.v1.Model;
import com.google.cloud.aiplatform.v1.ModelContainerSpec;
import com.google.cloud.aiplatform.v1.PipelineServiceClient;
import com.google.cloud.aiplatform.v1.PipelineServiceSettings;
import com.google.cloud.aiplatform.v1.Port;
import com.google.cloud.aiplatform.v1.PredefinedSplit;
import com.google.cloud.aiplatform.v1.PredictSchemata;
import com.google.cloud.aiplatform.v1.TimestampSplit;
import com.google.cloud.aiplatform.v1.TrainingPipeline;
import com.google.cloud.aiplatform.v1.TrainingPipelineName;
import com.google.rpc.Status;
import java.io.IOException;

public class GetTrainingPipelineSample {
  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String trainingPipelineId = "YOUR_TRAINING_PIPELINE_ID";
    getTrainingPipeline(project, trainingPipelineId);
  }

  static void getTrainingPipeline(String project, String trainingPipelineId) throws IOException {
    PipelineServiceSettings pipelineServiceSettings =
        PipelineServiceSettings.newBuilder()
            .setEndpoint("us-central1-aiplatform.googleapis.com:443")
            .build();

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (PipelineServiceClient pipelineServiceClient =
        PipelineServiceClient.create(pipelineServiceSettings)) {
      String location = "us-central1";
      TrainingPipelineName trainingPipelineName =
          TrainingPipelineName.of(project, location, trainingPipelineId);

      TrainingPipeline trainingPipelineResponse =
          pipelineServiceClient.getTrainingPipeline(trainingPipelineName);

      System.out.println("Get Training Pipeline Response");
      System.out.format("\tName: %s\n", trainingPipelineResponse.getName());
      System.out.format("\tDisplay Name: %s\n", trainingPipelineResponse.getDisplayName());
      System.out.format(
          "\tTraining Task Definition: %s\n", trainingPipelineResponse.getTrainingTaskDefinition());
      System.out.format(
          "\tTraining Task Inputs: %s\n", trainingPipelineResponse.getTrainingTaskInputs());
      System.out.format(
          "\tTraining Task Metadata: %s\n", trainingPipelineResponse.getTrainingTaskMetadata());
      System.out.format("\tState: %s\n", trainingPipelineResponse.getState());
      System.out.format("\tCreate Time: %s\n", trainingPipelineResponse.getCreateTime());
      System.out.format("\tStart Time: %s\n", trainingPipelineResponse.getStartTime());
      System.out.format("\tEnd Time: %s\n", trainingPipelineResponse.getEndTime());
      System.out.format("\tUpdate Time: %s\n", trainingPipelineResponse.getUpdateTime());
      System.out.format("\tLabels: %s\n", trainingPipelineResponse.getLabelsMap());
      InputDataConfig inputDataConfig = trainingPipelineResponse.getInputDataConfig();

      System.out.println("\tInput Data Config");
      System.out.format("\t\tDataset Id: %s\n", inputDataConfig.getDatasetId());
      System.out.format("\t\tAnnotations Filter: %s\n", inputDataConfig.getAnnotationsFilter());
      FractionSplit fractionSplit = inputDataConfig.getFractionSplit();

      System.out.println("\t\tFraction Split");
      System.out.format("\t\t\tTraining Fraction: %s\n", fractionSplit.getTrainingFraction());
      System.out.format("\t\t\tValidation Fraction: %s\n", fractionSplit.getValidationFraction());
      System.out.format("\t\t\tTest Fraction: %s\n", fractionSplit.getTestFraction());
      FilterSplit filterSplit = inputDataConfig.getFilterSplit();

      System.out.println("\t\tFilter Split");
      System.out.format("\t\t\tTraining Filter: %s\n", filterSplit.getTrainingFilter());
      System.out.format("\t\t\tValidation Filter: %s\n", filterSplit.getValidationFilter());
      System.out.format("\t\t\tTest Filter: %s\n", filterSplit.getTestFilter());
      PredefinedSplit predefinedSplit = inputDataConfig.getPredefinedSplit();

      System.out.println("\t\tPredefined Split");
      System.out.format("\t\t\tKey: %s\n", predefinedSplit.getKey());
      TimestampSplit timestampSplit = inputDataConfig.getTimestampSplit();

      System.out.println("\t\tTimestamp Split");
      System.out.format("\t\t\tTraining Fraction: %s\n", timestampSplit.getTrainingFraction());
      System.out.format("\t\t\tTest Fraction: %s\n", timestampSplit.getTestFraction());
      System.out.format("\t\t\tValidation Fraction: %s\n", timestampSplit.getValidationFraction());
      System.out.format("\t\t\tKey: %s\n", timestampSplit.getKey());
      Model modelResponse = trainingPipelineResponse.getModelToUpload();

      System.out.println("\t\tModel to upload");
      System.out.format("\t\tName: %s\n", modelResponse.getName());
      System.out.format("\t\tDisplay Name: %s\n", modelResponse.getDisplayName());
      System.out.format("\t\tDescription: %s\n", modelResponse.getDescription());
      System.out.format("\t\tMetadata Schema Uri: %s\n", modelResponse.getMetadataSchemaUri());
      System.out.format("\t\tMeta Data: %s\n", modelResponse.getMetadata());
      System.out.format("\t\tTraining Pipeline: %s\n", modelResponse.getTrainingPipeline());
      System.out.format("\t\tArtifact Uri: %s\n", modelResponse.getArtifactUri());
      System.out.format(
          "\t\tSupported Deployment Resources Types: %s\n",
          modelResponse.getSupportedDeploymentResourcesTypesList().toString());
      System.out.format(
          "\t\tSupported Input Storage Formats: %s\n",
          modelResponse.getSupportedInputStorageFormatsList().toString());
      System.out.format(
          "\t\tSupported Output Storage Formats: %s\n",
          modelResponse.getSupportedOutputStorageFormatsList().toString());
      System.out.format("\t\tCreate Time: %s\n", modelResponse.getCreateTime());
      System.out.format("\t\tUpdate Time: %s\n", modelResponse.getUpdateTime());
      System.out.format("\t\tLabels: %s\n", modelResponse.getLabelsMap());
      PredictSchemata predictSchemata = modelResponse.getPredictSchemata();

      System.out.println("\tPredict Schemata");
      System.out.format("\t\tInstance Schema Uri: %s\n", predictSchemata.getInstanceSchemaUri());
      System.out.format(
          "\t\tParameters Schema Uri: %s\n", predictSchemata.getParametersSchemaUri());
      System.out.format(
          "\t\tPrediction Schema Uri: %s\n", predictSchemata.getPredictionSchemaUri());

      for (Model.ExportFormat supportedExportFormat :
          modelResponse.getSupportedExportFormatsList()) {
        System.out.println("\tSupported Export Format");
        System.out.format("\t\tId: %s\n", supportedExportFormat.getId());
      }
      ModelContainerSpec containerSpec = modelResponse.getContainerSpec();

      System.out.println("\tContainer Spec");
      System.out.format("\t\tImage Uri: %s\n", containerSpec.getImageUri());
      System.out.format("\t\tCommand: %s\n", containerSpec.getCommandList());
      System.out.format("\t\tArgs: %s\n", containerSpec.getArgsList());
      System.out.format("\t\tPredict Route: %s\n", containerSpec.getPredictRoute());
      System.out.format("\t\tHealth Route: %s\n", containerSpec.getHealthRoute());

      for (EnvVar envVar : containerSpec.getEnvList()) {
        System.out.println("\t\tEnv");
        System.out.format("\t\t\tName: %s\n", envVar.getName());
        System.out.format("\t\t\tValue: %s\n", envVar.getValue());
      }

      for (Port port : containerSpec.getPortsList()) {
        System.out.println("\t\tPort");
        System.out.format("\t\t\tContainer Port: %s\n", port.getContainerPort());
      }

      for (DeployedModelRef deployedModelRef : modelResponse.getDeployedModelsList()) {
        System.out.println("\tDeployed Model");
        System.out.format("\t\tEndpoint: %s\n", deployedModelRef.getEndpoint());
        System.out.format("\t\tDeployed Model Id: %s\n", deployedModelRef.getDeployedModelId());
      }

      Status status = trainingPipelineResponse.getError();
      System.out.println("\tError");
      System.out.format("\t\tCode: %s\n", status.getCode());
      System.out.format("\t\tMessage: %s\n", status.getMessage());
    }
  }
}

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Node.js 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.

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const trainingPipelineId = 'YOUR_MODEL_ID';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';

// Imports the Google Cloud Model Service Client library
const {PipelineServiceClient} = require('@google-cloud/aiplatform');

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};

// Instantiates a client
const pipelineServiceClient = new PipelineServiceClient(clientOptions);

async function getTrainingPipeline() {
  // Configure the parent resource
  const name = pipelineServiceClient.trainingPipelinePath(
    project,
    location,
    trainingPipelineId
  );
  const request = {
    name,
  };
  // Get and print out a list of all the endpoints for this resource
  const [response] = await pipelineServiceClient.getTrainingPipeline(request);

  console.log('Get training pipeline response');
  console.log(`\tTraining pipeline name: ${response.displayName}`);
  console.log(`\tTraining pipeline state: ${response.state}`);
}
getTrainingPipeline();

Python

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

from google.cloud import aiplatform


def get_training_pipeline_sample(
    project: str,
    training_pipeline_id: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
    # The AI Platform services require regional API endpoints.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.PipelineServiceClient(client_options=client_options)
    name = client.training_pipeline_path(
        project=project, location=location, training_pipeline=training_pipeline_id
    )
    response = client.get_training_pipeline(name=name)
    print("response:", response)

What's next

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