使用 create_training_pipeline 方法为自定义作业创建训练流水线。
深入探索
如需查看包含此代码示例的详细文档，请参阅以下内容：
代码示例
Java
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
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 CreateTrainingPipelineCustomJobSample {
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 containerImageUri = "CONTAINER_IMAGE_URI";
String baseOutputDirectoryPrefix = "BASE_OUTPUT_DIRECTORY_PREFIX";
createTrainingPipelineCustomJobSample(
project, displayName, modelDisplayName, containerImageUri, baseOutputDirectoryPrefix);
}
static void createTrainingPipelineCustomJobSample(
String project,
String displayName,
String modelDisplayName,
String containerImageUri,
String baseOutputDirectoryPrefix)
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)) {
JsonObject jsonMachineSpec = new JsonObject();
jsonMachineSpec.addProperty("machineType", "n1-standard-4");
// A working docker image can be found at
// gs://cloud-samples-data/ai-platform/mnist_tfrecord/custom_job
// This sample image accepts a set of arguments including model_dir.
JsonObject jsonContainerSpec = new JsonObject();
jsonContainerSpec.addProperty("imageUri", containerImageUri);
JsonArray jsonArgs = new JsonArray();
jsonArgs.add("--model_dir=$(AIP_MODEL_DIR)");
jsonContainerSpec.add("args", jsonArgs);
JsonObject jsonJsonWorkerPoolSpec0 = new JsonObject();
jsonJsonWorkerPoolSpec0.addProperty("replicaCount", 1);
jsonJsonWorkerPoolSpec0.add("machineSpec", jsonMachineSpec);
jsonJsonWorkerPoolSpec0.add("containerSpec", jsonContainerSpec);
JsonArray jsonWorkerPoolSpecs = new JsonArray();
jsonWorkerPoolSpecs.add(jsonJsonWorkerPoolSpec0);
JsonObject jsonBaseOutputDirectory = new JsonObject();
// The GCS location for outputs must be accessible by the project's AI Platform
// service account.
jsonBaseOutputDirectory.addProperty("output_uri_prefix", baseOutputDirectoryPrefix);
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();
String trainingTaskDefinition =
"gs://google-cloud-aiplatform/schema/trainingjob/definition/custom_task_1.0.0.yaml";
String imageUri = "gcr.io/cloud-aiplatform/prediction/tf-cpu.1-15:latest";
ModelContainerSpec containerSpec =
ModelContainerSpec.newBuilder().setImageUri(imageUri).build();
Model modelToUpload =
Model.newBuilder()
.setDisplayName(modelDisplayName)
.setContainerSpec(containerSpec)
.build();
TrainingPipeline trainingPipeline =
TrainingPipeline.newBuilder()
.setDisplayName(displayName)
.setTrainingTaskDefinition(trainingTaskDefinition)
.setTrainingTaskInputs(trainingTaskInputs)
.setModelToUpload(modelToUpload)
.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
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Python 设置说明执行操作。如需了解详情，请参阅 Vertex AI Python API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
def create_training_pipeline_custom_job_sample(
project: str,
display_name: str,
model_display_name: str,
container_image_uri: str,
base_output_directory_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)
training_task_inputs_dict = {
"workerPoolSpecs": [
{
"replicaCount": 1,
"machineSpec": {"machineType": "n1-standard-4"},
"containerSpec": {
# A working docker image can be found at gs://cloud-samples-data/ai-platform/mnist_tfrecord/custom_job
"imageUri": container_image_uri,
"args": [
# AIP_MODEL_DIR is set by the service according to baseOutputDirectory.
"--model_dir=$(AIP_MODEL_DIR)",
],
},
}
],
"baseOutputDirectory": {
# The GCS location for outputs must be accessible by the project's AI Platform service account.
"output_uri_prefix": base_output_directory_prefix
},
}
training_task_inputs = json_format.ParseDict(training_task_inputs_dict, Value())
training_task_definition = "gs://google-cloud-aiplatform/schema/trainingjob/definition/custom_task_1.0.0.yaml"
image_uri = "gcr.io/cloud-aiplatform/prediction/tf-cpu.1-15:latest"
training_pipeline = {
"display_name": display_name,
"training_task_definition": training_task_definition,
"training_task_inputs": training_task_inputs,
"model_to_upload": {
"display_name": model_display_name,
"container_spec": {"image_uri": image_uri},
},
}
parent = f"projects/{project}/locations/{location}"
response = client.create_training_pipeline(
parent=parent, training_pipeline=training_pipeline
)
print("response:", response)
后续步骤
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