Membuat pipeline pelatihan untuk klasifikasi tabel menggunakan metode create_training_pipeline.
Jelajahi lebih lanjut
Untuk dokumentasi mendetail yang menyertakan contoh kode ini, lihat artikel berikut:
Contoh kode
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.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.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.AutoMlTablesInputs;
import com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation;
import com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation.AutoTransformation;
import com.google.rpc.Status;
import java.io.IOException;
import java.util.ArrayList;
public class CreateTrainingPipelineTabularClassificationSample {
public static void main(String[] args) throws IOException {
// TODO(developer): Replace these variables before running the sample.
String project = "YOUR_PROJECT_ID";
String modelDisplayName = "YOUR_DATASET_DISPLAY_NAME";
String datasetId = "YOUR_DATASET_ID";
String targetColumn = "TARGET_COLUMN";
createTrainingPipelineTableClassification(project, modelDisplayName, datasetId, targetColumn);
}
static void createTrainingPipelineTableClassification(
String project, String modelDisplayName, String datasetId, String targetColumn)
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";
LocationName locationName = LocationName.of(project, location);
String trainingTaskDefinition =
"gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_tables_1.0.0.yaml";
// Set the columns used for training and their data types
Transformation transformation1 =
Transformation.newBuilder()
.setAuto(AutoTransformation.newBuilder().setColumnName("sepal_width").build())
.build();
Transformation transformation2 =
Transformation.newBuilder()
.setAuto(AutoTransformation.newBuilder().setColumnName("sepal_length").build())
.build();
Transformation transformation3 =
Transformation.newBuilder()
.setAuto(AutoTransformation.newBuilder().setColumnName("petal_length").build())
.build();
Transformation transformation4 =
Transformation.newBuilder()
.setAuto(AutoTransformation.newBuilder().setColumnName("petal_width").build())
.build();
ArrayList<Transformation> transformationArrayList = new ArrayList<>();
transformationArrayList.add(transformation1);
transformationArrayList.add(transformation2);
transformationArrayList.add(transformation3);
transformationArrayList.add(transformation4);
AutoMlTablesInputs autoMlTablesInputs =
AutoMlTablesInputs.newBuilder()
.setTargetColumn(targetColumn)
.setPredictionType("classification")
.addAllTransformations(transformationArrayList)
.setTrainBudgetMilliNodeHours(8000)
.build();
FractionSplit fractionSplit =
FractionSplit.newBuilder()
.setTrainingFraction(0.8)
.setValidationFraction(0.1)
.setTestFraction(0.1)
.build();
InputDataConfig inputDataConfig =
InputDataConfig.newBuilder()
.setDatasetId(datasetId)
.setFractionSplit(fractionSplit)
.build();
Model modelToUpload = Model.newBuilder().setDisplayName(modelDisplayName).build();
TrainingPipeline trainingPipeline =
TrainingPipeline.newBuilder()
.setDisplayName(modelDisplayName)
.setTrainingTaskDefinition(trainingTaskDefinition)
.setTrainingTaskInputs(ValueConverter.toValue(autoMlTablesInputs))
.setInputDataConfig(inputDataConfig)
.setModelToUpload(modelToUpload)
.build();
TrainingPipeline trainingPipelineResponse =
pipelineServiceClient.createTrainingPipeline(locationName, trainingPipeline);
System.out.println("Create Training Pipeline Tabular Classification 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 inputDataConfigResponse = trainingPipelineResponse.getInputDataConfig();
System.out.println("\tInput Data Config");
System.out.format("\t\tDataset Id: %s\n", inputDataConfigResponse.getDatasetId());
System.out.format(
"\t\tAnnotations Filter: %s\n", inputDataConfigResponse.getAnnotationsFilter());
FractionSplit fractionSplitResponse = inputDataConfigResponse.getFractionSplit();
System.out.println("\t\tFraction Split");
System.out.format(
"\t\t\tTraining Fraction: %s\n", fractionSplitResponse.getTrainingFraction());
System.out.format(
"\t\t\tValidation Fraction: %s\n", fractionSplitResponse.getValidationFraction());
System.out.format("\t\t\tTest Fraction: %s\n", fractionSplitResponse.getTestFraction());
FilterSplit filterSplit = inputDataConfigResponse.getFilterSplit();
System.out.println("\t\tFilter Split");
System.out.format("\t\t\tTraining Fraction: %s\n", filterSplit.getTrainingFilter());
System.out.format("\t\t\tValidation Fraction: %s\n", filterSplit.getValidationFilter());
System.out.format("\t\t\tTest Fraction: %s\n", filterSplit.getTestFilter());
PredefinedSplit predefinedSplit = inputDataConfigResponse.getPredefinedSplit();
System.out.println("\t\tPredefined Split");
System.out.format("\t\t\tKey: %s\n", predefinedSplit.getKey());
TimestampSplit timestampSplit = inputDataConfigResponse.getTimestampSplit();
System.out.println("\t\tTimestamp Split");
System.out.format("\t\t\tTraining Fraction: %s\n", timestampSplit.getTrainingFraction());
System.out.format("\t\t\tValidation Fraction: %s\n", timestampSplit.getValidationFraction());
System.out.format("\t\t\tTest Fraction: %s\n", timestampSplit.getTestFraction());
System.out.format("\t\t\tKey: %s\n", timestampSplit.getKey());
Model modelResponse = trainingPipelineResponse.getModelToUpload();
System.out.println("\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\tLables: %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
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.
/**
* TODO(developer): Uncomment these variables before running the sample.\
* (Not necessary if passing values as arguments)
*/
// const datasetId = 'YOUR_DATASET_ID';
// const modelDisplayName = 'YOUR_MODEL_DISPLAY_NAME';
// const trainingPipelineDisplayName = 'YOUR_TRAINING_PIPELINE_DISPLAY_NAME';
// const targetColumn = 'YOUR_TARGET_COLUMN';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
const aiplatform = require('@google-cloud/aiplatform');
const {definition} =
aiplatform.protos.google.cloud.aiplatform.v1.schema.trainingjob;
// Imports the Google Cloud Pipeline Service Client library
const {PipelineServiceClient} = aiplatform.v1;
// 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 createTrainingPipelineTablesClassification() {
// Configure the parent resource
const parent = `projects/${project}/locations/${location}`;
const transformations = [
{auto: {column_name: 'sepal_width'}},
{auto: {column_name: 'sepal_length'}},
{auto: {column_name: 'petal_length'}},
{auto: {column_name: 'petal_width'}},
];
const trainingTaskInputsObj = new definition.AutoMlTablesInputs({
targetColumn: targetColumn,
predictionType: 'classification',
transformations: transformations,
trainBudgetMilliNodeHours: 8000,
disableEarlyStopping: false,
optimizationObjective: 'minimize-log-loss',
});
const trainingTaskInputs = trainingTaskInputsObj.toValue();
const modelToUpload = {displayName: modelDisplayName};
const inputDataConfig = {
datasetId: datasetId,
fractionSplit: {
trainingFraction: 0.8,
validationFraction: 0.1,
testFraction: 0.1,
},
};
const trainingPipeline = {
displayName: trainingPipelineDisplayName,
trainingTaskDefinition:
'gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_tables_1.0.0.yaml',
trainingTaskInputs,
inputDataConfig,
modelToUpload,
};
const request = {
parent,
trainingPipeline,
};
// Create training pipeline request
const [response] =
await pipelineServiceClient.createTrainingPipeline(request);
console.log('Create training pipeline tabular classification response');
console.log(`Name : ${response.name}`);
console.log('Raw response:');
console.log(JSON.stringify(response, null, 2));
}
createTrainingPipelineTablesClassification();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.
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
def create_training_pipeline_tabular_classification_sample(
project: str,
display_name: str,
dataset_id: str,
model_display_name: str,
target_column: 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)
# set the columns used for training and their data types
transformations = [
{"auto": {"column_name": "sepal_width"}},
{"auto": {"column_name": "sepal_length"}},
{"auto": {"column_name": "petal_length"}},
{"auto": {"column_name": "petal_width"}},
]
training_task_inputs_dict = {
# required inputs
"targetColumn": target_column,
"predictionType": "classification",
"transformations": transformations,
"trainBudgetMilliNodeHours": 8000,
# optional inputs
"disableEarlyStopping": False,
# supported binary classification optimisation objectives:
# maximize-au-roc, minimize-log-loss, maximize-au-prc,
# maximize-precision-at-recall, maximize-recall-at-precision
# supported multi-class classification optimisation objective:
# minimize-log-loss
"optimizationObjective": "minimize-log-loss",
# possibly required inputs
# required when using maximize-precision-at-recall
# "optimizationObjectiveRecallValue": 0.5, # 0.0 - 1.0
# required when using maximize-recall-at-precision
# "optimizationObjectivePrecisionValue": 0.5, # 0.0 - 1.0
}
training_task_inputs = json_format.ParseDict(training_task_inputs_dict, Value())
training_pipeline = {
"display_name": display_name,
"training_task_definition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_tabular_1.0.0.yaml",
"training_task_inputs": training_task_inputs,
"input_data_config": {
"dataset_id": dataset_id,
"fraction_split": {
"training_fraction": 0.8,
"validation_fraction": 0.1,
"test_fraction": 0.1,
},
},
"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)
Langkah selanjutnya
Untuk menelusuri dan memfilter contoh kode untuk produk Google Cloud lainnya, lihat browser contoh Google Cloud.