Membuat pipeline pelatihan untuk set data pelatihan yang dikelola menggunakan metode create_training_pipeline.
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.v1.GcsDestination;
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.TrainingPipeline;
import com.google.gson.JsonArray;
import com.google.gson.JsonObject;
import com.google.protobuf.Value;
import com.google.protobuf.util.JsonFormat;
import java.io.IOException;
public class CreateTrainingPipelineCustomTrainingManagedDatasetSample {
public static void main(String[] args) throws IOException {
// TODO(developer): Replace these variables before running the sample.
String project = "PROJECT";
String displayName = "DISPLAY_NAME";
String modelDisplayName = "MODEL_DISPLAY_NAME";
String datasetId = "DATASET_ID";
String annotationSchemaUri = "ANNOTATION_SCHEMA_URI";
String trainingContainerSpecImageUri = "TRAINING_CONTAINER_SPEC_IMAGE_URI";
String modelContainerSpecImageUri = "MODEL_CONTAINER_SPEC_IMAGE_URI";
String baseOutputUriPrefix = "BASE_OUTPUT_URI_PREFIX";
createTrainingPipelineCustomTrainingManagedDatasetSample(
project,
displayName,
modelDisplayName,
datasetId,
annotationSchemaUri,
trainingContainerSpecImageUri,
modelContainerSpecImageUri,
baseOutputUriPrefix);
}
static void createTrainingPipelineCustomTrainingManagedDatasetSample(
String project,
String displayName,
String modelDisplayName,
String datasetId,
String annotationSchemaUri,
String trainingContainerSpecImageUri,
String modelContainerSpecImageUri,
String baseOutputUriPrefix)
throws IOException {
PipelineServiceSettings settings =
PipelineServiceSettings.newBuilder()
.setEndpoint("us-central1-aiplatform.googleapis.com:443")
.build();
String location = "us-central1";
// 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 client = PipelineServiceClient.create(settings)) {
JsonArray jsonArgs = new JsonArray();
jsonArgs.add("--model-dir=$(AIP_MODEL_DIR)");
// training_task_inputs
JsonObject jsonTrainingContainerSpec = new JsonObject();
jsonTrainingContainerSpec.addProperty("imageUri", trainingContainerSpecImageUri);
// AIP_MODEL_DIR is set by the service according to baseOutputDirectory.
jsonTrainingContainerSpec.add("args", jsonArgs);
JsonObject jsonMachineSpec = new JsonObject();
jsonMachineSpec.addProperty("machineType", "n1-standard-8");
JsonObject jsonTrainingWorkerPoolSpec = new JsonObject();
jsonTrainingWorkerPoolSpec.addProperty("replicaCount", 1);
jsonTrainingWorkerPoolSpec.add("machineSpec", jsonMachineSpec);
jsonTrainingWorkerPoolSpec.add("containerSpec", jsonTrainingContainerSpec);
JsonArray jsonWorkerPoolSpecs = new JsonArray();
jsonWorkerPoolSpecs.add(jsonTrainingWorkerPoolSpec);
JsonObject jsonBaseOutputDirectory = new JsonObject();
jsonBaseOutputDirectory.addProperty("outputUriPrefix", baseOutputUriPrefix);
JsonObject jsonTrainingTaskInputs = new JsonObject();
jsonTrainingTaskInputs.add("workerPoolSpecs", jsonWorkerPoolSpecs);
jsonTrainingTaskInputs.add("baseOutputDirectory", jsonBaseOutputDirectory);
Value.Builder trainingTaskInputsBuilder = Value.newBuilder();
JsonFormat.parser().merge(jsonTrainingTaskInputs.toString(), trainingTaskInputsBuilder);
Value trainingTaskInputs = trainingTaskInputsBuilder.build();
// model_to_upload
ModelContainerSpec modelContainerSpec =
ModelContainerSpec.newBuilder().setImageUri(modelContainerSpecImageUri).build();
Model model =
Model.newBuilder()
.setDisplayName(modelDisplayName)
.setContainerSpec(modelContainerSpec)
.build();
GcsDestination gcsDestination =
GcsDestination.newBuilder().setOutputUriPrefix(baseOutputUriPrefix).build();
// input_data_config
InputDataConfig inputDataConfig =
InputDataConfig.newBuilder()
.setDatasetId(datasetId)
.setAnnotationSchemaUri(annotationSchemaUri)
.setGcsDestination(gcsDestination)
.build();
// training_task_definition
String customTaskDefinition =
"gs://google-cloud-aiplatform/schema/trainingjob/definition/custom_task_1.0.0.yaml";
TrainingPipeline trainingPipeline =
TrainingPipeline.newBuilder()
.setDisplayName(displayName)
.setInputDataConfig(inputDataConfig)
.setTrainingTaskDefinition(customTaskDefinition)
.setTrainingTaskInputs(trainingTaskInputs)
.setModelToUpload(model)
.build();
LocationName parent = LocationName.of(project, location);
TrainingPipeline response = client.createTrainingPipeline(parent, trainingPipeline);
System.out.format("response: %s\n", response);
System.out.format("Name: %s\n", response.getName());
}
}
}
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_custom_training_managed_dataset_sample(
project: str,
display_name: str,
model_display_name: str,
dataset_id: str,
annotation_schema_uri: str,
training_container_spec_image_uri: str,
model_container_spec_image_uri: str,
base_output_uri_prefix: 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)
# input_data_config
input_data_config = {
"dataset_id": dataset_id,
"annotation_schema_uri": annotation_schema_uri,
"gcs_destination": {"output_uri_prefix": base_output_uri_prefix},
}
# training_task_definition
custom_task_definition = "gs://google-cloud-aiplatform/schema/trainingjob/definition/custom_task_1.0.0.yaml"
# training_task_inputs
training_container_spec = {
"imageUri": training_container_spec_image_uri,
# AIP_MODEL_DIR is set by the service according to baseOutputDirectory.
"args": ["--model-dir=$(AIP_MODEL_DIR)"],
}
training_worker_pool_spec = {
"replicaCount": 1,
"machineSpec": {"machineType": "n1-standard-8"},
"containerSpec": training_container_spec,
}
training_task_inputs_dict = {
"workerPoolSpecs": [training_worker_pool_spec],
"baseOutputDirectory": {"outputUriPrefix": base_output_uri_prefix},
}
training_task_inputs = json_format.ParseDict(training_task_inputs_dict, Value())
# model_to_upload
model_container_spec = {
"image_uri": model_container_spec_image_uri,
"command": [],
"args": [],
}
model = {"display_name": model_display_name, "container_spec": model_container_spec}
training_pipeline = {
"display_name": display_name,
"input_data_config": input_data_config,
"training_task_definition": custom_task_definition,
"training_task_inputs": training_task_inputs,
"model_to_upload": model,
}
parent = f"projects/{project}/locations/{location}"
response = client.create_training_pipeline(
parent=parent, training_pipeline=training_pipeline
)
print("response:", response)
Langkah selanjutnya
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