使用 create_batch_prediction_job 方法创建批量预测作业。
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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.AcceleratorType;
import com.google.cloud.aiplatform.v1.BatchDedicatedResources;
import com.google.cloud.aiplatform.v1.BatchPredictionJob;
import com.google.cloud.aiplatform.v1.GcsDestination;
import com.google.cloud.aiplatform.v1.GcsSource;
import com.google.cloud.aiplatform.v1.JobServiceClient;
import com.google.cloud.aiplatform.v1.JobServiceSettings;
import com.google.cloud.aiplatform.v1.LocationName;
import com.google.cloud.aiplatform.v1.MachineSpec;
import com.google.cloud.aiplatform.v1.ModelName;
import com.google.protobuf.Value;
import java.io.IOException;
public class CreateBatchPredictionJobSample {
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 modelName = "MODEL_NAME";
String instancesFormat = "INSTANCES_FORMAT";
String gcsSourceUri = "GCS_SOURCE_URI";
String predictionsFormat = "PREDICTIONS_FORMAT";
String gcsDestinationOutputUriPrefix = "GCS_DESTINATION_OUTPUT_URI_PREFIX";
createBatchPredictionJobSample(
project,
displayName,
modelName,
instancesFormat,
gcsSourceUri,
predictionsFormat,
gcsDestinationOutputUriPrefix);
}
static void createBatchPredictionJobSample(
String project,
String displayName,
String model,
String instancesFormat,
String gcsSourceUri,
String predictionsFormat,
String gcsDestinationOutputUriPrefix)
throws IOException {
JobServiceSettings settings =
JobServiceSettings.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 (JobServiceClient client = JobServiceClient.create(settings)) {
// Passing in an empty Value object for model parameters
Value modelParameters = ValueConverter.EMPTY_VALUE;
GcsSource gcsSource = GcsSource.newBuilder().addUris(gcsSourceUri).build();
BatchPredictionJob.InputConfig inputConfig =
BatchPredictionJob.InputConfig.newBuilder()
.setInstancesFormat(instancesFormat)
.setGcsSource(gcsSource)
.build();
GcsDestination gcsDestination =
GcsDestination.newBuilder().setOutputUriPrefix(gcsDestinationOutputUriPrefix).build();
BatchPredictionJob.OutputConfig outputConfig =
BatchPredictionJob.OutputConfig.newBuilder()
.setPredictionsFormat(predictionsFormat)
.setGcsDestination(gcsDestination)
.build();
MachineSpec machineSpec =
MachineSpec.newBuilder()
.setMachineType("n1-standard-2")
.setAcceleratorType(AcceleratorType.NVIDIA_TESLA_K80)
.setAcceleratorCount(1)
.build();
BatchDedicatedResources dedicatedResources =
BatchDedicatedResources.newBuilder()
.setMachineSpec(machineSpec)
.setStartingReplicaCount(1)
.setMaxReplicaCount(1)
.build();
String modelName = ModelName.of(project, location, model).toString();
BatchPredictionJob batchPredictionJob =
BatchPredictionJob.newBuilder()
.setDisplayName(displayName)
.setModel(modelName)
.setModelParameters(modelParameters)
.setInputConfig(inputConfig)
.setOutputConfig(outputConfig)
.setDedicatedResources(dedicatedResources)
.build();
LocationName parent = LocationName.of(project, location);
BatchPredictionJob response = client.createBatchPredictionJob(parent, batchPredictionJob);
System.out.format("response: %s\n", response);
System.out.format("\tName: %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_batch_prediction_job_sample(
project: str,
display_name: str,
model_name: str,
instances_format: str,
gcs_source_uri: str,
predictions_format: str,
gcs_destination_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.JobServiceClient(client_options=client_options)
model_parameters_dict = {}
model_parameters = json_format.ParseDict(model_parameters_dict, Value())
batch_prediction_job = {
"display_name": display_name,
# Format: 'projects/{project}/locations/{location}/models/{model_id}'
"model": model_name,
"model_parameters": model_parameters,
"input_config": {
"instances_format": instances_format,
"gcs_source": {"uris": [gcs_source_uri]},
},
"output_config": {
"predictions_format": predictions_format,
"gcs_destination": {"output_uri_prefix": gcs_destination_output_uri_prefix},
},
"dedicated_resources": {
"machine_spec": {
"machine_type": "n1-standard-2",
"accelerator_type": aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80,
"accelerator_count": 1,
},
"starting_replica_count": 1,
"max_replica_count": 1,
},
}
parent = f"projects/{project}/locations/{location}"
response = client.create_batch_prediction_job(
parent=parent, batch_prediction_job=batch_prediction_job
)
print("response:", response)
后续步骤
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