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יצירת צינור אימון לסיווג תמונות

יצירת צינור עיבוד נתונים לאימון לצורך סיווג תמונות באמצעות השיטה create_training_pipeline.

המשך למידה

לקבלת הסבר מפורט שכולל את דוגמת הקוד הזו, קראו את המאמר:

אימון מודל לסיווג תמונות

דוגמת קוד

Java

לפני שמנסים את הדוגמה הזו, צריך לפעול לפי Javaהוראות ההגדרה במאמר Vertex AI quickstart using client libraries. מידע נוסף מופיע במאמרי העזרה של Vertex AI Java API.

כדי לבצע אימות ב-Vertex AI, צריך להגדיר את Application Default Credentials. מידע נוסף זמין במאמר הגדרת אימות לסביבת פיתוח מקומית.

import com.google.cloud.aiplatform.util.ValueConverter;
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.LocationName;
import com.google.cloud.aiplatform.v1.Model;
import com.google.cloud.aiplatform.v1.Model.ExportFormat;
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.schema.trainingjob.definition.AutoMlImageClassificationInputs;
import com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationInputs.ModelType;
import com.google.rpc.Status;
import java.io.IOException;

public class CreateTrainingPipelineImageClassificationSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String trainingPipelineDisplayName = "YOUR_TRAINING_PIPELINE_DISPLAY_NAME";
    String project = "YOUR_PROJECT_ID";
    String datasetId = "YOUR_DATASET_ID";
    String modelDisplayName = "YOUR_MODEL_DISPLAY_NAME";
    createTrainingPipelineImageClassificationSample(
        project, trainingPipelineDisplayName, datasetId, modelDisplayName);
  }

  static void createTrainingPipelineImageClassificationSample(
      String project, String trainingPipelineDisplayName, String datasetId, String modelDisplayName)
      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";
      String trainingTaskDefinition =
          "gs://google-cloud-aiplatform/schema/trainingjob/definition/"
              + "automl_image_classification_1.0.0.yaml";
      LocationName locationName = LocationName.of(project, location);

      AutoMlImageClassificationInputs autoMlImageClassificationInputs =
          AutoMlImageClassificationInputs.newBuilder()
              .setModelType(ModelType.CLOUD)
              .setMultiLabel(false)
              .setBudgetMilliNodeHours(8000)
              .setDisableEarlyStopping(false)
              .build();

      InputDataConfig trainingInputDataConfig =
          InputDataConfig.newBuilder().setDatasetId(datasetId).build();
      Model model = Model.newBuilder().setDisplayName(modelDisplayName).build();
      TrainingPipeline trainingPipeline =
          TrainingPipeline.newBuilder()
              .setDisplayName(trainingPipelineDisplayName)
              .setTrainingTaskDefinition(trainingTaskDefinition)
              .setTrainingTaskInputs(ValueConverter.toValue(autoMlImageClassificationInputs))
              .setInputDataConfig(trainingInputDataConfig)
              .setModelToUpload(model)
              .build();

      TrainingPipeline trainingPipelineResponse =
          pipelineServiceClient.createTrainingPipeline(locationName, trainingPipeline);

      System.out.println("Create Training Pipeline Image Classification Response");
      System.out.format("Name: %s\n", trainingPipelineResponse.getName());
      System.out.format("Display Name: %s\n", trainingPipelineResponse.getDisplayName());

      System.out.format(
          "Training Task Definition %s\n", trainingPipelineResponse.getTrainingTaskDefinition());
      System.out.format(
          "Training Task Inputs: %s\n", trainingPipelineResponse.getTrainingTaskInputs());
      System.out.format(
          "Training Task Metadata: %s\n", trainingPipelineResponse.getTrainingTaskMetadata());
      System.out.format("State: %s\n", trainingPipelineResponse.getState());

      System.out.format("Create Time: %s\n", trainingPipelineResponse.getCreateTime());
      System.out.format("StartTime %s\n", trainingPipelineResponse.getStartTime());
      System.out.format("End Time: %s\n", trainingPipelineResponse.getEndTime());
      System.out.format("Update Time: %s\n", trainingPipelineResponse.getUpdateTime());
      System.out.format("Labels: %s\n", trainingPipelineResponse.getLabelsMap());

      InputDataConfig inputDataConfig = trainingPipelineResponse.getInputDataConfig();
      System.out.println("Input Data Config");
      System.out.format("Dataset Id: %s", inputDataConfig.getDatasetId());
      System.out.format("Annotations Filter: %s\n", inputDataConfig.getAnnotationsFilter());

      FractionSplit fractionSplit = inputDataConfig.getFractionSplit();
      System.out.println("Fraction Split");
      System.out.format("Training Fraction: %s\n", fractionSplit.getTrainingFraction());
      System.out.format("Validation Fraction: %s\n", fractionSplit.getValidationFraction());
      System.out.format("Test Fraction: %s\n", fractionSplit.getTestFraction());

      FilterSplit filterSplit = inputDataConfig.getFilterSplit();
      System.out.println("Filter Split");
      System.out.format("Training Filter: %s\n", filterSplit.getTrainingFilter());
      System.out.format("Validation Filter: %s\n", filterSplit.getValidationFilter());
      System.out.format("Test Filter: %s\n", filterSplit.getTestFilter());

      PredefinedSplit predefinedSplit = inputDataConfig.getPredefinedSplit();
      System.out.println("Predefined Split");
      System.out.format("Key: %s\n", predefinedSplit.getKey());

      TimestampSplit timestampSplit = inputDataConfig.getTimestampSplit();
      System.out.println("Timestamp Split");
      System.out.format("Training Fraction: %s\n", timestampSplit.getTrainingFraction());
      System.out.format("Validation Fraction: %s\n", timestampSplit.getValidationFraction());
      System.out.format("Test Fraction: %s\n", timestampSplit.getTestFraction());
      System.out.format("Key: %s\n", timestampSplit.getKey());

      Model modelResponse = trainingPipelineResponse.getModelToUpload();
      System.out.println("Model To Upload");
      System.out.format("Name: %s\n", modelResponse.getName());
      System.out.format("Display Name: %s\n", modelResponse.getDisplayName());
      System.out.format("Description: %s\n", modelResponse.getDescription());

      System.out.format("Metadata Schema Uri: %s\n", modelResponse.getMetadataSchemaUri());
      System.out.format("Metadata: %s\n", modelResponse.getMetadata());
      System.out.format("Training Pipeline: %s\n", modelResponse.getTrainingPipeline());
      System.out.format("Artifact Uri: %s\n", modelResponse.getArtifactUri());

      System.out.format(
          "Supported Deployment Resources Types: %s\n",
          modelResponse.getSupportedDeploymentResourcesTypesList());
      System.out.format(
          "Supported Input Storage Formats: %s\n",
          modelResponse.getSupportedInputStorageFormatsList());
      System.out.format(
          "Supported Output Storage Formats: %s\n",
          modelResponse.getSupportedOutputStorageFormatsList());

      System.out.format("Create Time: %s\n", modelResponse.getCreateTime());
      System.out.format("Update Time: %s\n", modelResponse.getUpdateTime());
      System.out.format("Labels: %sn\n", modelResponse.getLabelsMap());

      PredictSchemata predictSchemata = modelResponse.getPredictSchemata();
      System.out.println("Predict Schemata");
      System.out.format("Instance Schema Uri: %s\n", predictSchemata.getInstanceSchemaUri());
      System.out.format("Parameters Schema Uri: %s\n", predictSchemata.getParametersSchemaUri());
      System.out.format("Prediction Schema Uri: %s\n", predictSchemata.getPredictionSchemaUri());

      for (ExportFormat exportFormat : modelResponse.getSupportedExportFormatsList()) {
        System.out.println("Supported Export Format");
        System.out.format("Id: %s\n", exportFormat.getId());
      }

      ModelContainerSpec modelContainerSpec = modelResponse.getContainerSpec();
      System.out.println("Container Spec");
      System.out.format("Image Uri: %s\n", modelContainerSpec.getImageUri());
      System.out.format("Command: %s\n", modelContainerSpec.getCommandList());
      System.out.format("Args: %s\n", modelContainerSpec.getArgsList());
      System.out.format("Predict Route: %s\n", modelContainerSpec.getPredictRoute());
      System.out.format("Health Route: %s\n", modelContainerSpec.getHealthRoute());

      for (EnvVar envVar : modelContainerSpec.getEnvList()) {
        System.out.println("Env");
        System.out.format("Name: %s\n", envVar.getName());
        System.out.format("Value: %s\n", envVar.getValue());
      }

      for (Port port : modelContainerSpec.getPortsList()) {
        System.out.println("Port");
        System.out.format("Container Port: %s\n", port.getContainerPort());
      }

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

      Status status = trainingPipelineResponse.getError();
      System.out.println("Error");
      System.out.format("Code: %s\n", status.getCode());
      System.out.format("Message: %s\n", status.getMessage());
    }
  }
}

Node.js

לפני שמנסים את הדוגמה הזו, צריך לפעול לפי Node.jsהוראות ההגדרה במאמר Vertex AI quickstart using client libraries. מידע נוסף מופיע במאמרי העזרה של Vertex AI Node.js API.

כדי לבצע אימות ב-Vertex AI, צריך להגדיר את Application Default Credentials. מידע נוסף זמין במאמר הגדרת אימות לסביבת פיתוח מקומית.

/**
 * TODO(developer): Uncomment these variables before running the sample.
 * (Not necessary if passing values as arguments)
 */
/*
const datasetId = 'YOUR DATASET';
const modelDisplayName = 'NEW MODEL NAME;
const trainingPipelineDisplayName = 'NAME FOR TRAINING PIPELINE';
const project = 'YOUR PROJECT ID';
const location = 'us-central1';
  */
// Imports the Google Cloud Pipeline Service Client library
const aiplatform = require('@google-cloud/aiplatform');

const {definition} =
  aiplatform.protos.google.cloud.aiplatform.v1.schema.trainingjob;
const ModelType = definition.AutoMlImageClassificationInputs.ModelType;

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

// Instantiates a client
const {PipelineServiceClient} = aiplatform.v1;
const pipelineServiceClient = new PipelineServiceClient(clientOptions);

async function createTrainingPipelineImageClassification() {
  // Configure the parent resource
  const parent = `projects/${project}/locations/${location}`;

  // Values should match the input expected by your model.
  const trainingTaskInputsMessage =
    new definition.AutoMlImageClassificationInputs({
      multiLabel: true,
      modelType: ModelType.CLOUD,
      budgetMilliNodeHours: 8000,
      disableEarlyStopping: false,
    });

  const trainingTaskInputs = trainingTaskInputsMessage.toValue();

  const trainingTaskDefinition =
    'gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_classification_1.0.0.yaml';

  const modelToUpload = {displayName: modelDisplayName};
  const inputDataConfig = {datasetId};
  const trainingPipeline = {
    displayName: trainingPipelineDisplayName,
    trainingTaskDefinition,
    trainingTaskInputs,
    inputDataConfig,
    modelToUpload,
  };
  const request = {parent, trainingPipeline};

  // Create training pipeline request
  const [response] =
    await pipelineServiceClient.createTrainingPipeline(request);

  console.log('Create training pipeline image classification response');
  console.log(`Name : ${response.name}`);
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}

createTrainingPipelineImageClassification();

Python

לפני שמנסים את הדוגמה הזו, צריך לפעול לפי Pythonהוראות ההגדרה במאמר Vertex AI quickstart using client libraries. מידע נוסף מופיע במאמרי העזרה של Vertex AI Python API.

כדי לבצע אימות ב-Vertex AI, צריך להגדיר את Application Default Credentials. מידע נוסף זמין במאמר הגדרת אימות לסביבת פיתוח מקומית.

from google.cloud import aiplatform
from google.cloud.aiplatform.gapic.schema import trainingjob


def create_training_pipeline_image_classification_sample(
    project: str,
    display_name: str,
    dataset_id: str,
    model_display_name: 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)
    training_task_inputs = trainingjob.definition.AutoMlImageClassificationInputs(
        multi_label=True,
        model_type="CLOUD",
        budget_milli_node_hours=8000,
        disable_early_stopping=False,
    ).to_value()

    training_pipeline = {
        "display_name": display_name,
        "training_task_definition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_classification_1.0.0.yaml",
        "training_task_inputs": training_task_inputs,
        "input_data_config": {"dataset_id": dataset_id},
        "model_to_upload": {"display_name": model_display_name},
    }
    parent = f"projects/{project}/locations/{location}"
    response = client.create_training_pipeline(
        parent=parent, training_pipeline=training_pipeline
    )
    print("response:", response)

המאמרים הבאים

כדי לחפש ולסנן דוגמאות קוד למוצרים אחרים של Google Cloud , אפשר להיעזר בדפדפן לדוגמאות שלGoogle Cloud .

יצירת צינור אימון לסיווג תמונות קל לארגן דפים בעזרת אוספים אפשר לשמור ולסווג תוכן על סמך ההעדפות שלך.

המשך למידה

דוגמת קוד

Java

Node.js

Python

המאמרים הבאים

יצירת צינור אימון לסיווג תמונות