Class JobServiceAsyncClient (1.13.1) 

JobServiceAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.aiplatform_v1.services.job_service.transports.base.JobServiceTransport] = 'grpc_asyncio', client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)

A service for creating and managing Vertex AI's jobs.

Inheritance

builtins.object > JobServiceAsyncClient

Properties

transport

Returns the transport used by the client instance.

Returns
Type Description
JobServiceTransport The transport used by the client instance.

Methods

JobServiceAsyncClient

JobServiceAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.aiplatform_v1.services.job_service.transports.base.JobServiceTransport] = 'grpc_asyncio', client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)

Instantiates the job service client.

Parameters
Name Description
credentials Optional[google.auth.credentials.Credentials]

The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment.
transport Union[str, `.JobServiceTransport`]

The transport to use. If set to None, a transport is chosen automatically.
client_options ClientOptions

Custom options for the client. It won't take effect if a transport instance is provided. (1) The api_endpoint property can be used to override the default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT environment variable can also be used to override the endpoint: "always" (always use the default mTLS endpoint), "never" (always use the default regular endpoint) and "auto" (auto switch to the default mTLS endpoint if client certificate is present, this is the default value). However, the api_endpoint property takes precedence if provided. (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the client_cert_source property can be used to provide client certificate for mutual TLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not set, no client certificate will be used.
Exceptions
Type Description
google.auth.exceptions.MutualTlsChannelError If mutual TLS transport creation failed for any reason.

batch_prediction_job_path

batch_prediction_job_path(project: str, location: str, batch_prediction_job: str)

Returns a fully-qualified batch_prediction_job string.

cancel_batch_prediction_job

cancel_batch_prediction_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.CancelBatchPredictionJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Cancels a BatchPredictionJob.

Starts asynchronous cancellation on the BatchPredictionJob. The server makes the best effort to cancel the job, but success is not guaranteed. Clients can use xref_JobService.GetBatchPredictionJob or other methods to check whether the cancellation succeeded or whether the job completed despite cancellation. On a successful cancellation, the BatchPredictionJob is not deleted;instead its xref_BatchPredictionJob.state is set to CANCELLED. Any files already outputted by the job are not deleted.

from google.cloud import aiplatform_v1

async def sample_cancel_batch_prediction_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CancelBatchPredictionJobRequest(
        name="name_value",
    )

    # Make the request
    await client.cancel_batch_prediction_job(request=request)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.CancelBatchPredictionJobRequest, dict]

The request object. Request message for JobService.CancelBatchPredictionJob.
name `str`

Required. The name of the BatchPredictionJob to cancel. Format: projects/{project}/locations/{location}/batchPredictionJobs/{batch_prediction_job} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.

cancel_custom_job

cancel_custom_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.CancelCustomJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Cancels a CustomJob. Starts asynchronous cancellation on the CustomJob. The server makes a best effort to cancel the job, but success is not guaranteed. Clients can use xref_JobService.GetCustomJob or other methods to check whether the cancellation succeeded or whether the job completed despite cancellation. On successful cancellation, the CustomJob is not deleted; instead it becomes a job with a xref_CustomJob.error value with a google.rpc.Status.code][google.rpc.Status.code] of 1, corresponding to Code.CANCELLED, and xref_CustomJob.state is set to CANCELLED.

from google.cloud import aiplatform_v1

async def sample_cancel_custom_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CancelCustomJobRequest(
        name="name_value",
    )

    # Make the request
    await client.cancel_custom_job(request=request)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.CancelCustomJobRequest, dict]

The request object. Request message for JobService.CancelCustomJob.
name `str`

Required. The name of the CustomJob to cancel. Format: projects/{project}/locations/{location}/customJobs/{custom_job} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.

cancel_data_labeling_job

cancel_data_labeling_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.CancelDataLabelingJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Cancels a DataLabelingJob. Success of cancellation is not guaranteed.

from google.cloud import aiplatform_v1

async def sample_cancel_data_labeling_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CancelDataLabelingJobRequest(
        name="name_value",
    )

    # Make the request
    await client.cancel_data_labeling_job(request=request)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.CancelDataLabelingJobRequest, dict]

The request object. Request message for JobService.CancelDataLabelingJob.
name `str`

Required. The name of the DataLabelingJob. Format: projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.

cancel_hyperparameter_tuning_job

cancel_hyperparameter_tuning_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.CancelHyperparameterTuningJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Cancels a HyperparameterTuningJob. Starts asynchronous cancellation on the HyperparameterTuningJob. The server makes a best effort to cancel the job, but success is not guaranteed. Clients can use xref_JobService.GetHyperparameterTuningJob or other methods to check whether the cancellation succeeded or whether the job completed despite cancellation. On successful cancellation, the HyperparameterTuningJob is not deleted; instead it becomes a job with a xref_HyperparameterTuningJob.error value with a google.rpc.Status.code][google.rpc.Status.code] of 1, corresponding to Code.CANCELLED, and xref_HyperparameterTuningJob.state is set to CANCELLED.

from google.cloud import aiplatform_v1

async def sample_cancel_hyperparameter_tuning_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CancelHyperparameterTuningJobRequest(
        name="name_value",
    )

    # Make the request
    await client.cancel_hyperparameter_tuning_job(request=request)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.CancelHyperparameterTuningJobRequest, dict]

The request object. Request message for JobService.CancelHyperparameterTuningJob.
name `str`

Required. The name of the HyperparameterTuningJob to cancel. Format: projects/{project}/locations/{location}/hyperparameterTuningJobs/{hyperparameter_tuning_job} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.

common_billing_account_path

common_billing_account_path(billing_account: str)

Returns a fully-qualified billing_account string.

common_folder_path

common_folder_path(folder: str)

Returns a fully-qualified folder string.

common_location_path

common_location_path(project: str, location: str)

Returns a fully-qualified location string.

common_organization_path

common_organization_path(organization: str)

Returns a fully-qualified organization string.

common_project_path

common_project_path(project: str)

Returns a fully-qualified project string.

create_batch_prediction_job

create_batch_prediction_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.CreateBatchPredictionJobRequest, dict]] = None, *, parent: Optional[str] = None, batch_prediction_job: Optional[google.cloud.aiplatform_v1.types.batch_prediction_job.BatchPredictionJob] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates a BatchPredictionJob. A BatchPredictionJob once created will right away be attempted to start.

from google.cloud import aiplatform_v1

async def sample_create_batch_prediction_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    batch_prediction_job = aiplatform_v1.BatchPredictionJob()
    batch_prediction_job.display_name = "display_name_value"
    batch_prediction_job.input_config.gcs_source.uris = ['uris_value_1', 'uris_value_2']
    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_v1.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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.CreateBatchPredictionJobRequest, dict]

The request object. Request message for JobService.CreateBatchPredictionJob.
parent `str`

Required. The resource name of the Location to create the BatchPredictionJob in. Format: projects/{project}/locations/{location} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
batch_prediction_job BatchPredictionJob

Required. The BatchPredictionJob to create. This corresponds to the batch_prediction_job field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.BatchPredictionJob A job that uses a Model to produce predictions on multiple [input instances][google.cloud.aiplatform.v1.BatchPredictionJob.input_config]. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances.

create_custom_job

create_custom_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.CreateCustomJobRequest, dict]] = None, *, parent: Optional[str] = None, custom_job: Optional[google.cloud.aiplatform_v1.types.custom_job.CustomJob] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates a CustomJob. A created CustomJob right away will be attempted to be run.

from google.cloud import aiplatform_v1

async def sample_create_custom_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    custom_job = aiplatform_v1.CustomJob()
    custom_job.display_name = "display_name_value"
    custom_job.job_spec.worker_pool_specs.container_spec.image_uri = "image_uri_value"

    request = aiplatform_v1.CreateCustomJobRequest(
        parent="parent_value",
        custom_job=custom_job,
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.CreateCustomJobRequest, dict]

The request object. Request message for JobService.CreateCustomJob.
parent `str`

Required. The resource name of the Location to create the CustomJob in. Format: projects/{project}/locations/{location} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
custom_job CustomJob

Required. The CustomJob to create. This corresponds to the custom_job field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.CustomJob Represents a job that runs custom workloads such as a Docker container or a Python package. A CustomJob can have multiple worker pools and each worker pool can have its own machine and input spec. A CustomJob will be cleaned up once the job enters terminal state (failed or succeeded).

create_data_labeling_job

create_data_labeling_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.CreateDataLabelingJobRequest, dict]] = None, *, parent: Optional[str] = None, data_labeling_job: Optional[google.cloud.aiplatform_v1.types.data_labeling_job.DataLabelingJob] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates a DataLabelingJob.

from google.cloud import aiplatform_v1

async def sample_create_data_labeling_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    data_labeling_job = aiplatform_v1.DataLabelingJob()
    data_labeling_job.display_name = "display_name_value"
    data_labeling_job.datasets = ['datasets_value_1', 'datasets_value_2']
    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_v1.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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.CreateDataLabelingJobRequest, dict]

The request object. Request message for JobService.CreateDataLabelingJob.
parent `str`

Required. The parent of the DataLabelingJob. Format: projects/{project}/locations/{location} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
data_labeling_job DataLabelingJob

Required. The DataLabelingJob to create. This corresponds to the data_labeling_job field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.DataLabelingJob DataLabelingJob is used to trigger a human labeling job on unlabeled data from the following Dataset:

create_hyperparameter_tuning_job

create_hyperparameter_tuning_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.CreateHyperparameterTuningJobRequest, dict]] = None, *, parent: Optional[str] = None, hyperparameter_tuning_job: Optional[google.cloud.aiplatform_v1.types.hyperparameter_tuning_job.HyperparameterTuningJob] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates a HyperparameterTuningJob

from google.cloud import aiplatform_v1

async def sample_create_hyperparameter_tuning_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    hyperparameter_tuning_job = aiplatform_v1.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_v1.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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.CreateHyperparameterTuningJobRequest, dict]

The request object. Request message for JobService.CreateHyperparameterTuningJob.
parent `str`

Required. The resource name of the Location to create the HyperparameterTuningJob in. Format: projects/{project}/locations/{location} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
hyperparameter_tuning_job HyperparameterTuningJob

Required. The HyperparameterTuningJob to create. This corresponds to the hyperparameter_tuning_job field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.HyperparameterTuningJob Represents a HyperparameterTuningJob. A HyperparameterTuningJob has a Study specification and multiple CustomJobs with identical CustomJob specification.

create_model_deployment_monitoring_job

create_model_deployment_monitoring_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.CreateModelDeploymentMonitoringJobRequest, dict]] = None, *, parent: Optional[str] = None, model_deployment_monitoring_job: Optional[google.cloud.aiplatform_v1.types.model_deployment_monitoring_job.ModelDeploymentMonitoringJob] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates a ModelDeploymentMonitoringJob. It will run periodically on a configured interval.

from google.cloud import aiplatform_v1

async def sample_create_model_deployment_monitoring_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    model_deployment_monitoring_job = aiplatform_v1.ModelDeploymentMonitoringJob()
    model_deployment_monitoring_job.display_name = "display_name_value"
    model_deployment_monitoring_job.endpoint = "endpoint_value"

    request = aiplatform_v1.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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.CreateModelDeploymentMonitoringJobRequest, dict]

The request object. Request message for JobService.CreateModelDeploymentMonitoringJob.
parent `str`

Required. The parent of the ModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
model_deployment_monitoring_job ModelDeploymentMonitoringJob

Required. The ModelDeploymentMonitoringJob to create This corresponds to the model_deployment_monitoring_job field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringJob Represents a job that runs periodically to monitor the deployed models in an endpoint. It will analyze the logged training & prediction data to detect any abnormal behaviors.

custom_job_path

custom_job_path(project: str, location: str, custom_job: str)

Returns a fully-qualified custom_job string.

data_labeling_job_path

data_labeling_job_path(project: str, location: str, data_labeling_job: str)

Returns a fully-qualified data_labeling_job string.

dataset_path

dataset_path(project: str, location: str, dataset: str)

Returns a fully-qualified dataset string.

delete_batch_prediction_job

delete_batch_prediction_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.DeleteBatchPredictionJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes a BatchPredictionJob. Can only be called on jobs that already finished.

from google.cloud import aiplatform_v1

async def sample_delete_batch_prediction_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.DeleteBatchPredictionJobRequest, dict]

The request object. Request message for JobService.DeleteBatchPredictionJob.
name `str`

Required. The name of the BatchPredictionJob resource to be deleted. Format: projects/{project}/locations/{location}/batchPredictionJobs/{batch_prediction_job} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation_async.AsyncOperation An object representing a long-running operation. The result type for the operation will be `google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}.

delete_custom_job

delete_custom_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.DeleteCustomJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes a CustomJob.

from google.cloud import aiplatform_v1

async def sample_delete_custom_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.DeleteCustomJobRequest, dict]

The request object. Request message for JobService.DeleteCustomJob.
name `str`

Required. The name of the CustomJob resource to be deleted. Format: projects/{project}/locations/{location}/customJobs/{custom_job} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation_async.AsyncOperation An object representing a long-running operation. The result type for the operation will be `google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}.

delete_data_labeling_job

delete_data_labeling_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.DeleteDataLabelingJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes a DataLabelingJob.

from google.cloud import aiplatform_v1

async def sample_delete_data_labeling_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.DeleteDataLabelingJobRequest, dict]

The request object. Request message for JobService.DeleteDataLabelingJob.
name `str`

Required. The name of the DataLabelingJob to be deleted. Format: projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation_async.AsyncOperation An object representing a long-running operation. The result type for the operation will be `google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}.

delete_hyperparameter_tuning_job

delete_hyperparameter_tuning_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.DeleteHyperparameterTuningJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes a HyperparameterTuningJob.

from google.cloud import aiplatform_v1

async def sample_delete_hyperparameter_tuning_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.DeleteHyperparameterTuningJobRequest, dict]

The request object. Request message for JobService.DeleteHyperparameterTuningJob.
name `str`

Required. The name of the HyperparameterTuningJob resource to be deleted. Format: projects/{project}/locations/{location}/hyperparameterTuningJobs/{hyperparameter_tuning_job} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation_async.AsyncOperation An object representing a long-running operation. The result type for the operation will be `google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}.

delete_model_deployment_monitoring_job

delete_model_deployment_monitoring_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.DeleteModelDeploymentMonitoringJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes a ModelDeploymentMonitoringJob.

from google.cloud import aiplatform_v1

async def sample_delete_model_deployment_monitoring_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.DeleteModelDeploymentMonitoringJobRequest, dict]

The request object. Request message for JobService.DeleteModelDeploymentMonitoringJob.
name `str`

Required. The resource name of the model monitoring job to delete. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation_async.AsyncOperation An object representing a long-running operation. The result type for the operation will be `google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}.

endpoint_path

endpoint_path(project: str, location: str, endpoint: str)

Returns a fully-qualified endpoint string.

from_service_account_file

from_service_account_file(filename: str, *args, **kwargs)

Creates an instance of this client using the provided credentials file.

Parameter
Name Description
filename str

The path to the service account private key json file.
Returns
Type Description
JobServiceAsyncClient The constructed client.

from_service_account_info

from_service_account_info(info: dict, *args, **kwargs)

Creates an instance of this client using the provided credentials info.

Parameter
Name Description
info dict

The service account private key info.
Returns
Type Description
JobServiceAsyncClient The constructed client.

from_service_account_json

from_service_account_json(filename: str, *args, **kwargs)

Creates an instance of this client using the provided credentials file.

Parameter
Name Description
filename str

The path to the service account private key json file.
Returns
Type Description
JobServiceAsyncClient The constructed client.

get_batch_prediction_job

get_batch_prediction_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.GetBatchPredictionJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets a BatchPredictionJob

from google.cloud import aiplatform_v1

async def sample_get_batch_prediction_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetBatchPredictionJobRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.GetBatchPredictionJobRequest, dict]

The request object. Request message for JobService.GetBatchPredictionJob.
name `str`

Required. The name of the BatchPredictionJob resource. Format: projects/{project}/locations/{location}/batchPredictionJobs/{batch_prediction_job} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.BatchPredictionJob A job that uses a Model to produce predictions on multiple [input instances][google.cloud.aiplatform.v1.BatchPredictionJob.input_config]. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances.

get_custom_job

get_custom_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.GetCustomJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets a CustomJob.

from google.cloud import aiplatform_v1

async def sample_get_custom_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetCustomJobRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.GetCustomJobRequest, dict]

The request object. Request message for JobService.GetCustomJob.
name `str`

Required. The name of the CustomJob resource. Format: projects/{project}/locations/{location}/customJobs/{custom_job} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.CustomJob Represents a job that runs custom workloads such as a Docker container or a Python package. A CustomJob can have multiple worker pools and each worker pool can have its own machine and input spec. A CustomJob will be cleaned up once the job enters terminal state (failed or succeeded).

get_data_labeling_job

get_data_labeling_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.GetDataLabelingJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets a DataLabelingJob.

from google.cloud import aiplatform_v1

async def sample_get_data_labeling_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetDataLabelingJobRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.GetDataLabelingJobRequest, dict]

The request object. Request message for JobService.GetDataLabelingJob.
name `str`

Required. The name of the DataLabelingJob. Format: projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.DataLabelingJob DataLabelingJob is used to trigger a human labeling job on unlabeled data from the following Dataset:

get_hyperparameter_tuning_job

get_hyperparameter_tuning_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.GetHyperparameterTuningJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets a HyperparameterTuningJob

from google.cloud import aiplatform_v1

async def sample_get_hyperparameter_tuning_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetHyperparameterTuningJobRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.GetHyperparameterTuningJobRequest, dict]

The request object. Request message for JobService.GetHyperparameterTuningJob.
name `str`

Required. The name of the HyperparameterTuningJob resource. Format: projects/{project}/locations/{location}/hyperparameterTuningJobs/{hyperparameter_tuning_job} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.HyperparameterTuningJob Represents a HyperparameterTuningJob. A HyperparameterTuningJob has a Study specification and multiple CustomJobs with identical CustomJob specification.

get_model_deployment_monitoring_job

get_model_deployment_monitoring_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.GetModelDeploymentMonitoringJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets a ModelDeploymentMonitoringJob.

from google.cloud import aiplatform_v1

async def sample_get_model_deployment_monitoring_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetModelDeploymentMonitoringJobRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.GetModelDeploymentMonitoringJobRequest, dict]

The request object. Request message for JobService.GetModelDeploymentMonitoringJob.
name `str`

Required. The resource name of the ModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.ModelDeploymentMonitoringJob Represents a job that runs periodically to monitor the deployed models in an endpoint. It will analyze the logged training & prediction data to detect any abnormal behaviors.

get_mtls_endpoint_and_cert_source

get_mtls_endpoint_and_cert_source(
    client_options: Optional[google.api_core.client_options.ClientOptions] = None,
)

Return the API endpoint and client cert source for mutual TLS.

The client cert source is determined in the following order: (1) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is not "true", the client cert source is None. (2) if client_options.client_cert_source is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None.

The API endpoint is determined in the following order: (1) if client_options.api_endpoint if provided, use the provided one. (2) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "always", use the default mTLS endpoint; if the environment variabel is "never", use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint.

More details can be found at https://google.aip.dev/auth/4114.

Parameter
Name Description
client_options google.api_core.client_options.ClientOptions

Custom options for the client. Only the api_endpoint and client_cert_source properties may be used in this method.
Exceptions
Type Description
google.auth.exceptions.MutualTLSChannelError If any errors happen.
Returns
Type Description
Tuple[str, Callable[[], Tuple[bytes, bytes]]] returns the API endpoint and the client cert source to use.

get_transport_class

get_transport_class()

Returns an appropriate transport class.

hyperparameter_tuning_job_path

hyperparameter_tuning_job_path(
    project: str, location: str, hyperparameter_tuning_job: str
)

Returns a fully-qualified hyperparameter_tuning_job string.

list_batch_prediction_jobs

list_batch_prediction_jobs(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.ListBatchPredictionJobsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists BatchPredictionJobs in a Location.

from google.cloud import aiplatform_v1

async def sample_list_batch_prediction_jobs():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ListBatchPredictionJobsRequest, dict]

The request object. Request message for JobService.ListBatchPredictionJobs.
parent `str`

Required. The resource name of the Location to list the BatchPredictionJobs from. Format: projects/{project}/locations/{location} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.services.job_service.pagers.ListBatchPredictionJobsAsyncPager Response message for JobService.ListBatchPredictionJobs Iterating over this object will yield results and resolve additional pages automatically.

list_custom_jobs

list_custom_jobs(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.ListCustomJobsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists CustomJobs in a Location.

from google.cloud import aiplatform_v1

async def sample_list_custom_jobs():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ListCustomJobsRequest, dict]

The request object. Request message for JobService.ListCustomJobs.
parent `str`

Required. The resource name of the Location to list the CustomJobs from. Format: projects/{project}/locations/{location} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.services.job_service.pagers.ListCustomJobsAsyncPager Response message for JobService.ListCustomJobs Iterating over this object will yield results and resolve additional pages automatically.

list_data_labeling_jobs

list_data_labeling_jobs(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.ListDataLabelingJobsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists DataLabelingJobs in a Location.

from google.cloud import aiplatform_v1

async def sample_list_data_labeling_jobs():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ListDataLabelingJobsRequest, dict]

The request object. Request message for JobService.ListDataLabelingJobs.
parent `str`

Required. The parent of the DataLabelingJob. Format: projects/{project}/locations/{location} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.services.job_service.pagers.ListDataLabelingJobsAsyncPager Response message for JobService.ListDataLabelingJobs. Iterating over this object will yield results and resolve additional pages automatically.

list_hyperparameter_tuning_jobs

list_hyperparameter_tuning_jobs(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.ListHyperparameterTuningJobsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists HyperparameterTuningJobs in a Location.

from google.cloud import aiplatform_v1

async def sample_list_hyperparameter_tuning_jobs():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ListHyperparameterTuningJobsRequest, dict]

The request object. Request message for JobService.ListHyperparameterTuningJobs.
parent `str`

Required. The resource name of the Location to list the HyperparameterTuningJobs from. Format: projects/{project}/locations/{location} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.services.job_service.pagers.ListHyperparameterTuningJobsAsyncPager Response message for JobService.ListHyperparameterTuningJobs Iterating over this object will yield results and resolve additional pages automatically.

list_model_deployment_monitoring_jobs

list_model_deployment_monitoring_jobs(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.ListModelDeploymentMonitoringJobsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists ModelDeploymentMonitoringJobs in a Location.

from google.cloud import aiplatform_v1

async def sample_list_model_deployment_monitoring_jobs():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ListModelDeploymentMonitoringJobsRequest, dict]

The request object. Request message for JobService.ListModelDeploymentMonitoringJobs.
parent `str`

Required. The parent of the ModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.services.job_service.pagers.ListModelDeploymentMonitoringJobsAsyncPager Response message for JobService.ListModelDeploymentMonitoringJobs. Iterating over this object will yield results and resolve additional pages automatically.

model_deployment_monitoring_job_path

model_deployment_monitoring_job_path(
    project: str, location: str, model_deployment_monitoring_job: str
)

Returns a fully-qualified model_deployment_monitoring_job string.

model_path

model_path(project: str, location: str, model: str)

Returns a fully-qualified model string.

network_path

network_path(project: str, network: str)

Returns a fully-qualified network string.

parse_batch_prediction_job_path

parse_batch_prediction_job_path(path: str)

Parses a batch_prediction_job path into its component segments.

parse_common_billing_account_path

parse_common_billing_account_path(path: str)

Parse a billing_account path into its component segments.

parse_common_folder_path

parse_common_folder_path(path: str)

Parse a folder path into its component segments.

parse_common_location_path

parse_common_location_path(path: str)

Parse a location path into its component segments.

parse_common_organization_path

parse_common_organization_path(path: str)

Parse a organization path into its component segments.

parse_common_project_path

parse_common_project_path(path: str)

Parse a project path into its component segments.

parse_custom_job_path

parse_custom_job_path(path: str)

Parses a custom_job path into its component segments.

parse_data_labeling_job_path

parse_data_labeling_job_path(path: str)

Parses a data_labeling_job path into its component segments.

parse_dataset_path

parse_dataset_path(path: str)

Parses a dataset path into its component segments.

parse_endpoint_path

parse_endpoint_path(path: str)

Parses a endpoint path into its component segments.

parse_hyperparameter_tuning_job_path

parse_hyperparameter_tuning_job_path(path: str)

Parses a hyperparameter_tuning_job path into its component segments.

parse_model_deployment_monitoring_job_path

parse_model_deployment_monitoring_job_path(path: str)

Parses a model_deployment_monitoring_job path into its component segments.

parse_model_path

parse_model_path(path: str)

Parses a model path into its component segments.

parse_network_path

parse_network_path(path: str)

Parses a network path into its component segments.

parse_tensorboard_path

parse_tensorboard_path(path: str)

Parses a tensorboard path into its component segments.

parse_trial_path

parse_trial_path(path: str)

Parses a trial path into its component segments.

pause_model_deployment_monitoring_job

pause_model_deployment_monitoring_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.PauseModelDeploymentMonitoringJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Pauses a ModelDeploymentMonitoringJob. If the job is running, the server makes a best effort to cancel the job. Will mark xref_ModelDeploymentMonitoringJob.state to 'PAUSED'.

from google.cloud import aiplatform_v1

async def sample_pause_model_deployment_monitoring_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.PauseModelDeploymentMonitoringJobRequest(
        name="name_value",
    )

    # Make the request
    await client.pause_model_deployment_monitoring_job(request=request)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.PauseModelDeploymentMonitoringJobRequest, dict]

The request object. Request message for JobService.PauseModelDeploymentMonitoringJob.
name `str`

Required. The resource name of the ModelDeploymentMonitoringJob to pause. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.

resume_model_deployment_monitoring_job

resume_model_deployment_monitoring_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.ResumeModelDeploymentMonitoringJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Resumes a paused ModelDeploymentMonitoringJob. It will start to run from next scheduled time. A deleted ModelDeploymentMonitoringJob can't be resumed.

from google.cloud import aiplatform_v1

async def sample_resume_model_deployment_monitoring_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ResumeModelDeploymentMonitoringJobRequest(
        name="name_value",
    )

    # Make the request
    await client.resume_model_deployment_monitoring_job(request=request)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ResumeModelDeploymentMonitoringJobRequest, dict]

The request object. Request message for JobService.ResumeModelDeploymentMonitoringJob.
name `str`

Required. The resource name of the ModelDeploymentMonitoringJob to resume. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.

search_model_deployment_monitoring_stats_anomalies

search_model_deployment_monitoring_stats_anomalies(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.SearchModelDeploymentMonitoringStatsAnomaliesRequest, dict]] = None, *, model_deployment_monitoring_job: Optional[str] = None, deployed_model_id: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Searches Model Monitoring Statistics generated within a given time window.

from google.cloud import aiplatform_v1

async def sample_search_model_deployment_monitoring_stats_anomalies():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.SearchModelDeploymentMonitoringStatsAnomaliesRequest, dict]

The request object. Request message for JobService.SearchModelDeploymentMonitoringStatsAnomalies.
model_deployment_monitoring_job `str`

Required. ModelDeploymentMonitoring Job resource name. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job} This corresponds to the model_deployment_monitoring_job field on the request instance; if request is provided, this should not be set.
deployed_model_id `str`

Required. The DeployedModel ID of the [ModelDeploymentMonitoringObjectiveConfig.deployed_model_id]. This corresponds to the deployed_model_id field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.services.job_service.pagers.SearchModelDeploymentMonitoringStatsAnomaliesAsyncPager Response message for JobService.SearchModelDeploymentMonitoringStatsAnomalies. Iterating over this object will yield results and resolve additional pages automatically.

tensorboard_path

tensorboard_path(project: str, location: str, tensorboard: str)

Returns a fully-qualified tensorboard string.

trial_path

trial_path(project: str, location: str, study: str, trial: str)

Returns a fully-qualified trial string.

update_model_deployment_monitoring_job

update_model_deployment_monitoring_job(request: Optional[Union[google.cloud.aiplatform_v1.types.job_service.UpdateModelDeploymentMonitoringJobRequest, dict]] = None, *, model_deployment_monitoring_job: Optional[google.cloud.aiplatform_v1.types.model_deployment_monitoring_job.ModelDeploymentMonitoringJob] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Updates a ModelDeploymentMonitoringJob.

from google.cloud import aiplatform_v1

async def sample_update_model_deployment_monitoring_job():
    # Create a client
    client = aiplatform_v1.JobServiceAsyncClient()

    # Initialize request argument(s)
    model_deployment_monitoring_job = aiplatform_v1.ModelDeploymentMonitoringJob()
    model_deployment_monitoring_job.display_name = "display_name_value"
    model_deployment_monitoring_job.endpoint = "endpoint_value"

    request = aiplatform_v1.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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.UpdateModelDeploymentMonitoringJobRequest, dict]

The request object. Request message for JobService.UpdateModelDeploymentMonitoringJob.
model_deployment_monitoring_job ModelDeploymentMonitoringJob

Required. The model monitoring configuration which replaces the resource on the server. This corresponds to the model_deployment_monitoring_job field on the request instance; if request is provided, this should not be set.
update_mask `google.protobuf.field_mask_pb2.FieldMask`

Required. The update mask is used to specify the fields to be overwritten in the ModelDeploymentMonitoringJob resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to * to override all fields. For the objective config, the user can either provide the update mask for model_deployment_monitoring_objective_configs or any combination of its nested fields, such as: model_deployment_monitoring_objective_configs.objective_config.training_dataset. Updatable fields: - display_name - model_deployment_monitoring_schedule_config - model_monitoring_alert_config - logging_sampling_strategy - labels - log_ttl - enable_monitoring_pipeline_logs . and - model_deployment_monitoring_objective_configs . or - model_deployment_monitoring_objective_configs.objective_config.training_dataset - model_deployment_monitoring_objective_configs.objective_config.training_prediction_skew_detection_config - model_deployment_monitoring_objective_configs.objective_config.prediction_drift_detection_config This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation_async.AsyncOperation An object representing a long-running operation. The result type for the operation will be ModelDeploymentMonitoringJob Represents a job that runs periodically to monitor the deployed models in an endpoint. It will analyze the logged training & prediction data to detect any abnormal behaviors.