Class JobServiceAsyncClient (1.18.2)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_cancel_batch_prediction_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.CancelBatchPredictionJobRequest(
        name="name_value",
    )

    # Make the request
    await client.cancel_batch_prediction_job(request=request)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_cancel_custom_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.CancelCustomJobRequest(
        name="name_value",
    )

    # Make the request
    await client.cancel_custom_job(request=request)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_cancel_data_labeling_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.CancelDataLabelingJobRequest(
        name="name_value",
    )

    # Make the request
    await client.cancel_data_labeling_job(request=request)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_cancel_hyperparameter_tuning_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.CancelHyperparameterTuningJobRequest(
        name="name_value",
    )

    # Make the request
    await client.cancel_hyperparameter_tuning_job(request=request)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_create_batch_prediction_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    batch_prediction_job = aiplatform_v1beta1.BatchPredictionJob()
    batch_prediction_job.display_name = "display_name_value"
    batch_prediction_job.input_config.gcs_source.uris = ['uris_value1', 'uris_value2']
    batch_prediction_job.input_config.instances_format = "instances_format_value"
    batch_prediction_job.output_config.gcs_destination.output_uri_prefix = "output_uri_prefix_value"
    batch_prediction_job.output_config.predictions_format = "predictions_format_value"

    request = aiplatform_v1beta1.CreateBatchPredictionJobRequest(
        parent="parent_value",
        batch_prediction_job=batch_prediction_job,
    )

    # Make the request
    response = await client.create_batch_prediction_job(request=request)

    # Handle the response
    print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_create_custom_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    custom_job = aiplatform_v1beta1.CustomJob()
    custom_job.display_name = "display_name_value"
    custom_job.job_spec.worker_pool_specs.container_spec.image_uri = "image_uri_value"

    request = aiplatform_v1beta1.CreateCustomJobRequest(
        parent="parent_value",
        custom_job=custom_job,
    )

    # Make the request
    response = await client.create_custom_job(request=request)

    # Handle the response
    print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_create_data_labeling_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    data_labeling_job = aiplatform_v1beta1.DataLabelingJob()
    data_labeling_job.display_name = "display_name_value"
    data_labeling_job.datasets = ['datasets_value1', 'datasets_value2']
    data_labeling_job.labeler_count = 1375
    data_labeling_job.instruction_uri = "instruction_uri_value"
    data_labeling_job.inputs_schema_uri = "inputs_schema_uri_value"
    data_labeling_job.inputs.null_value = "NULL_VALUE"

    request = aiplatform_v1beta1.CreateDataLabelingJobRequest(
        parent="parent_value",
        data_labeling_job=data_labeling_job,
    )

    # Make the request
    response = await client.create_data_labeling_job(request=request)

    # Handle the response
    print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_create_hyperparameter_tuning_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    hyperparameter_tuning_job = aiplatform_v1beta1.HyperparameterTuningJob()
    hyperparameter_tuning_job.display_name = "display_name_value"
    hyperparameter_tuning_job.study_spec.metrics.metric_id = "metric_id_value"
    hyperparameter_tuning_job.study_spec.metrics.goal = "MINIMIZE"
    hyperparameter_tuning_job.study_spec.parameters.double_value_spec.min_value = 0.96
    hyperparameter_tuning_job.study_spec.parameters.double_value_spec.max_value = 0.962
    hyperparameter_tuning_job.study_spec.parameters.parameter_id = "parameter_id_value"
    hyperparameter_tuning_job.max_trial_count = 1609
    hyperparameter_tuning_job.parallel_trial_count = 2128
    hyperparameter_tuning_job.trial_job_spec.worker_pool_specs.container_spec.image_uri = "image_uri_value"

    request = aiplatform_v1beta1.CreateHyperparameterTuningJobRequest(
        parent="parent_value",
        hyperparameter_tuning_job=hyperparameter_tuning_job,
    )

    # Make the request
    response = await client.create_hyperparameter_tuning_job(request=request)

    # Handle the response
    print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_create_model_deployment_monitoring_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    model_deployment_monitoring_job = aiplatform_v1beta1.ModelDeploymentMonitoringJob()
    model_deployment_monitoring_job.display_name = "display_name_value"
    model_deployment_monitoring_job.endpoint = "endpoint_value"

    request = aiplatform_v1beta1.CreateModelDeploymentMonitoringJobRequest(
        parent="parent_value",
        model_deployment_monitoring_job=model_deployment_monitoring_job,
    )

    # Make the request
    response = await client.create_model_deployment_monitoring_job(request=request)

    # Handle the response
    print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_delete_batch_prediction_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteBatchPredictionJobRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_batch_prediction_job(request=request)

    print("Waiting for operation to complete...")

    response = await operation.result()

    # Handle the response
    print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_delete_custom_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteCustomJobRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_custom_job(request=request)

    print("Waiting for operation to complete...")

    response = await operation.result()

    # Handle the response
    print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_delete_data_labeling_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteDataLabelingJobRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_data_labeling_job(request=request)

    print("Waiting for operation to complete...")

    response = await operation.result()

    # Handle the response
    print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_delete_hyperparameter_tuning_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteHyperparameterTuningJobRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_hyperparameter_tuning_job(request=request)

    print("Waiting for operation to complete...")

    response = await operation.result()

    # Handle the response
    print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_delete_model_deployment_monitoring_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteModelDeploymentMonitoringJobRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_model_deployment_monitoring_job(request=request)

    print("Waiting for operation to complete...")

    response = await operation.result()

    # Handle the response
    print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_get_batch_prediction_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetBatchPredictionJobRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_batch_prediction_job(request=request)

    # Handle the response
    print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_get_custom_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetCustomJobRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_custom_job(request=request)

    # Handle the response
    print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_get_data_labeling_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetDataLabelingJobRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_data_labeling_job(request=request)

    # Handle the response
    print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_get_hyperparameter_tuning_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetHyperparameterTuningJobRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_hyperparameter_tuning_job(request=request)

    # Handle the response
    print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_get_model_deployment_monitoring_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetModelDeploymentMonitoringJobRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_model_deployment_monitoring_job(request=request)

    # Handle the response
    print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_list_batch_prediction_jobs():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListBatchPredictionJobsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_batch_prediction_jobs(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_list_custom_jobs():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListCustomJobsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_custom_jobs(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_list_data_labeling_jobs():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListDataLabelingJobsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_data_labeling_jobs(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_list_hyperparameter_tuning_jobs():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListHyperparameterTuningJobsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_hyperparameter_tuning_jobs(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_list_model_deployment_monitoring_jobs():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListModelDeploymentMonitoringJobsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_model_deployment_monitoring_jobs(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_pause_model_deployment_monitoring_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.PauseModelDeploymentMonitoringJobRequest(
        name="name_value",
    )

    # Make the request
    await client.pause_model_deployment_monitoring_job(request=request)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_resume_model_deployment_monitoring_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ResumeModelDeploymentMonitoringJobRequest(
        name="name_value",
    )

    # Make the request
    await client.resume_model_deployment_monitoring_job(request=request)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_search_model_deployment_monitoring_stats_anomalies():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.SearchModelDeploymentMonitoringStatsAnomaliesRequest(
        model_deployment_monitoring_job="model_deployment_monitoring_job_value",
        deployed_model_id="deployed_model_id_value",
    )

    # Make the request
    page_result = client.search_model_deployment_monitoring_stats_anomalies(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1beta1

async def sample_update_model_deployment_monitoring_job():
    # Create a client
    client = aiplatform_v1beta1.JobServiceAsyncClient()

    # Initialize request argument(s)
    model_deployment_monitoring_job = aiplatform_v1beta1.ModelDeploymentMonitoringJob()
    model_deployment_monitoring_job.display_name = "display_name_value"
    model_deployment_monitoring_job.endpoint = "endpoint_value"

    request = aiplatform_v1beta1.UpdateModelDeploymentMonitoringJobRequest(
        model_deployment_monitoring_job=model_deployment_monitoring_job,
    )

    # Make the request
    operation = client.update_model_deployment_monitoring_job(request=request)

    print("Waiting for operation to complete...")

    response = await operation.result()

    # Handle the response
    print(response)