使用 create_training_pipeline 方法创建用于视频动作识别的训练流水线。
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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.util.ValueConverter;
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.PipelineServiceClient;
import com.google.cloud.aiplatform.v1.PipelineServiceSettings;
import com.google.cloud.aiplatform.v1.TrainingPipeline;
import com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlVideoActionRecognitionInputs;
import com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlVideoActionRecognitionInputs.ModelType;
import java.io.IOException;
public class CreateTrainingPipelineVideoActionRecognitionSample {
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 datasetId = "DATASET_ID";
String modelDisplayName = "MODEL_DISPLAY_NAME";
createTrainingPipelineVideoActionRecognitionSample(
project, displayName, datasetId, modelDisplayName);
}
static void createTrainingPipelineVideoActionRecognitionSample(
String project, String displayName, String datasetId, String modelDisplayName)
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)) {
AutoMlVideoActionRecognitionInputs trainingTaskInputs =
AutoMlVideoActionRecognitionInputs.newBuilder().setModelType(ModelType.CLOUD).build();
InputDataConfig inputDataConfig =
InputDataConfig.newBuilder().setDatasetId(datasetId).build();
Model modelToUpload = Model.newBuilder().setDisplayName(modelDisplayName).build();
TrainingPipeline trainingPipeline =
TrainingPipeline.newBuilder()
.setDisplayName(displayName)
.setTrainingTaskDefinition(
"gs://google-cloud-aiplatform/schema/trainingjob/definition/"
+ "automl_video_action_recognition_1.0.0.yaml")
.setTrainingTaskInputs(ValueConverter.toValue(trainingTaskInputs))
.setInputDataConfig(inputDataConfig)
.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());
}
}
}
Node.js
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 Node.js 设置说明执行操作。如需了解详情，请参阅 Vertex AI Node.js 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。
/**
* 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 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 createTrainingPipelineVideoActionRecognition() {
// Configure the parent resource
const parent = `projects/${project}/locations/${location}`;
// Values should match the input expected by your model.
const trainingTaskInputObj =
new definition.AutoMlVideoActionRecognitionInputs({
// modelType can be either 'CLOUD' or 'MOBILE_VERSATILE_1'
modelType: 'CLOUD',
});
const trainingTaskInputs = trainingTaskInputObj.toValue();
const modelToUpload = {displayName: modelDisplayName};
const inputDataConfig = {datasetId: datasetId};
const trainingPipeline = {
displayName: trainingPipelineDisplayName,
trainingTaskDefinition:
'gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_video_action_recognition_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 video action recognition response');
console.log(`Name : ${response.name}`);
console.log('Raw response:');
console.log(JSON.stringify(response, null, 2));
}
createTrainingPipelineVideoActionRecognition();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.cloud.aiplatform.gapic.schema import trainingjob
def create_training_pipeline_video_action_recognition_sample(
project: str,
display_name: str,
dataset_id: str,
model_display_name: str,
model_type: 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.AutoMlVideoActionRecognitionInputs(
# modelType can be either 'CLOUD' or 'MOBILE_VERSATILE_1'
model_type=model_type,
).to_value()
training_pipeline = {
"display_name": display_name,
"training_task_definition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_video_action_recognition_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)
后续步骤
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