Class PipelineServiceClient (1.12.1) 

PipelineServiceClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Optional[Union[str, google.cloud.aiplatform_v1beta1.services.pipeline_service.transports.base.PipelineServiceTransport]] = None, 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 pipelines. This includes both TrainingPipeline resources (used for AutoML and custom training) and PipelineJob resources (used for Vertex AI Pipelines).

Inheritance

builtins.object > PipelineServiceClient

Properties

transport

Returns the transport used by the client instance.

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

Methods

PipelineServiceClient

PipelineServiceClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Optional[Union[str, google.cloud.aiplatform_v1beta1.services.pipeline_service.transports.base.PipelineServiceTransport]] = None, 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 pipeline 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, PipelineServiceTransport]

The transport to use. If set to None, a transport is chosen automatically.
client_options google.api_core.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.
client_info google.api_core.gapic_v1.client_info.ClientInfo

The client info used to send a user-agent string along with API requests. If None, then default info will be used. Generally, you only need to set this if you're developing your own client library.
Exceptions
Type Description
google.auth.exceptions.MutualTLSChannelError If mutual TLS transport creation failed for any reason.

__exit__

__exit__(type, value, traceback)

Releases underlying transport's resources.

artifact_path

artifact_path(project: str, location: str, metadata_store: str, artifact: str)

Returns a fully-qualified artifact string.

cancel_pipeline_job

cancel_pipeline_job(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.pipeline_service.CancelPipelineJobRequest, 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 PipelineJob. Starts asynchronous cancellation on the PipelineJob. The server makes a best effort to cancel the pipeline, but success is not guaranteed. Clients can use xref_PipelineService.GetPipelineJob or other methods to check whether the cancellation succeeded or whether the pipeline completed despite cancellation. On successful cancellation, the PipelineJob is not deleted; instead it becomes a pipeline with a xref_PipelineJob.error value with a google.rpc.Status.code][google.rpc.Status.code] of 1, corresponding to Code.CANCELLED, and xref_PipelineJob.state is set to CANCELLED.

from google.cloud import aiplatform_v1beta1

def sample_cancel_pipeline_job():
    # Create a client
    client = aiplatform_v1beta1.PipelineServiceClient()

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

    # Make the request
    client.cancel_pipeline_job(request=request)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.CancelPipelineJobRequest, dict]

The request object. Request message for PipelineService.CancelPipelineJob.
name str

Required. The name of the PipelineJob to cancel. Format: projects/{project}/locations/{location}/pipelineJobs/{pipeline_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_training_pipeline

cancel_training_pipeline(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.pipeline_service.CancelTrainingPipelineRequest, 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 TrainingPipeline. Starts asynchronous cancellation on the TrainingPipeline. The server makes a best effort to cancel the pipeline, but success is not guaranteed. Clients can use xref_PipelineService.GetTrainingPipeline or other methods to check whether the cancellation succeeded or whether the pipeline completed despite cancellation. On successful cancellation, the TrainingPipeline is not deleted; instead it becomes a pipeline with a xref_TrainingPipeline.error value with a google.rpc.Status.code][google.rpc.Status.code] of 1, corresponding to Code.CANCELLED, and xref_TrainingPipeline.state is set to CANCELLED.

from google.cloud import aiplatform_v1beta1

def sample_cancel_training_pipeline():
    # Create a client
    client = aiplatform_v1beta1.PipelineServiceClient()

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

    # Make the request
    client.cancel_training_pipeline(request=request)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.CancelTrainingPipelineRequest, dict]

The request object. Request message for PipelineService.CancelTrainingPipeline.
name str

Required. The name of the TrainingPipeline to cancel. Format: projects/{project}/locations/{location}/trainingPipelines/{training_pipeline} 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.

context_path

context_path(project: str, location: str, metadata_store: str, context: str)

Returns a fully-qualified context string.

create_pipeline_job

create_pipeline_job(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.pipeline_service.CreatePipelineJobRequest, dict]] = None, *, parent: Optional[str] = None, pipeline_job: Optional[google.cloud.aiplatform_v1beta1.types.pipeline_job.PipelineJob] = None, pipeline_job_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]] = ())

Creates a PipelineJob. A PipelineJob will run immediately when created.

from google.cloud import aiplatform_v1beta1

def sample_create_pipeline_job():
    # Create a client
    client = aiplatform_v1beta1.PipelineServiceClient()

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

    # Make the request
    response = client.create_pipeline_job(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.CreatePipelineJobRequest, dict]

The request object. Request message for PipelineService.CreatePipelineJob.
parent str

Required. The resource name of the Location to create the PipelineJob 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.
pipeline_job google.cloud.aiplatform_v1beta1.types.PipelineJob

Required. The PipelineJob to create. This corresponds to the pipeline_job field on the request instance; if request is provided, this should not be set.
pipeline_job_id str

The ID to use for the PipelineJob, which will become the final component of the PipelineJob name. If not provided, an ID will be automatically generated. This value should be less than 128 characters, and valid characters are /a-z][0-9]-/. This corresponds to the pipeline_job_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_v1beta1.types.PipelineJob An instance of a machine learning PipelineJob.

create_training_pipeline

create_training_pipeline(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.pipeline_service.CreateTrainingPipelineRequest, dict]] = None, *, parent: Optional[str] = None, training_pipeline: Optional[google.cloud.aiplatform_v1beta1.types.training_pipeline.TrainingPipeline] = 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 TrainingPipeline. A created TrainingPipeline right away will be attempted to be run.

from google.cloud import aiplatform_v1beta1

def sample_create_training_pipeline():
    # Create a client
    client = aiplatform_v1beta1.PipelineServiceClient()

    # Initialize request argument(s)
    training_pipeline = aiplatform_v1beta1.TrainingPipeline()
    training_pipeline.display_name = "display_name_value"
    training_pipeline.training_task_definition = "training_task_definition_value"
    training_pipeline.training_task_inputs.null_value = "NULL_VALUE"

    request = aiplatform_v1beta1.CreateTrainingPipelineRequest(
        parent="parent_value",
        training_pipeline=training_pipeline,
    )

    # Make the request
    response = client.create_training_pipeline(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.CreateTrainingPipelineRequest, dict]

The request object. Request message for PipelineService.CreateTrainingPipeline.
parent str

Required. The resource name of the Location to create the TrainingPipeline 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.
training_pipeline google.cloud.aiplatform_v1beta1.types.TrainingPipeline

Required. The TrainingPipeline to create. This corresponds to the training_pipeline 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_v1beta1.types.TrainingPipeline The TrainingPipeline orchestrates tasks associated with training a Model. It always executes the training task, and optionally may also export data from Vertex AI's Dataset which becomes the training input, upload the Model to Vertex AI, and evaluate the Model.

custom_job_path

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

Returns a fully-qualified custom_job string.

delete_pipeline_job

delete_pipeline_job(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.pipeline_service.DeletePipelineJobRequest, 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 PipelineJob.

from google.cloud import aiplatform_v1beta1

def sample_delete_pipeline_job():
    # Create a client
    client = aiplatform_v1beta1.PipelineServiceClient()

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

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.DeletePipelineJobRequest, dict]

The request object. Request message for PipelineService.DeletePipelineJob.
name str

Required. The name of the PipelineJob resource to be deleted. Format: projects/{project}/locations/{location}/pipelineJobs/{pipeline_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.Operation 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_training_pipeline

delete_training_pipeline(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.pipeline_service.DeleteTrainingPipelineRequest, 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 TrainingPipeline.

from google.cloud import aiplatform_v1beta1

def sample_delete_training_pipeline():
    # Create a client
    client = aiplatform_v1beta1.PipelineServiceClient()

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

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.DeleteTrainingPipelineRequest, dict]

The request object. Request message for PipelineService.DeleteTrainingPipeline.
name str

Required. The name of the TrainingPipeline resource to be deleted. Format: projects/{project}/locations/{location}/trainingPipelines/{training_pipeline} 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.Operation 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.

execution_path

execution_path(project: str, location: str, metadata_store: str, execution: str)

Returns a fully-qualified execution 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
PipelineServiceClient 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
PipelineServiceClient 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
PipelineServiceClient The constructed client.

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_pipeline_job

get_pipeline_job(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.pipeline_service.GetPipelineJobRequest, 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 PipelineJob.

from google.cloud import aiplatform_v1beta1

def sample_get_pipeline_job():
    # Create a client
    client = aiplatform_v1beta1.PipelineServiceClient()

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

    # Make the request
    response = client.get_pipeline_job(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.GetPipelineJobRequest, dict]

The request object. Request message for PipelineService.GetPipelineJob.
name str

Required. The name of the PipelineJob resource. Format: projects/{project}/locations/{location}/pipelineJobs/{pipeline_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_v1beta1.types.PipelineJob An instance of a machine learning PipelineJob.

get_training_pipeline

get_training_pipeline(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.pipeline_service.GetTrainingPipelineRequest, 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 TrainingPipeline.

from google.cloud import aiplatform_v1beta1

def sample_get_training_pipeline():
    # Create a client
    client = aiplatform_v1beta1.PipelineServiceClient()

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

    # Make the request
    response = client.get_training_pipeline(request=request)

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.GetTrainingPipelineRequest, dict]

The request object. Request message for PipelineService.GetTrainingPipeline.
name str

Required. The name of the TrainingPipeline resource. Format: projects/{project}/locations/{location}/trainingPipelines/{training_pipeline} 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_v1beta1.types.TrainingPipeline The TrainingPipeline orchestrates tasks associated with training a Model. It always executes the training task, and optionally may also export data from Vertex AI's Dataset which becomes the training input, upload the Model to Vertex AI, and evaluate the Model.

list_pipeline_jobs

list_pipeline_jobs(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.pipeline_service.ListPipelineJobsRequest, 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 PipelineJobs in a Location.

from google.cloud import aiplatform_v1beta1

def sample_list_pipeline_jobs():
    # Create a client
    client = aiplatform_v1beta1.PipelineServiceClient()

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

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

    # Handle the response
    for response in page_result:
        print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.ListPipelineJobsRequest, dict]

The request object. Request message for PipelineService.ListPipelineJobs.
parent str

Required. The resource name of the Location to list the PipelineJobs 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_v1beta1.services.pipeline_service.pagers.ListPipelineJobsPager Response message for PipelineService.ListPipelineJobs Iterating over this object will yield results and resolve additional pages automatically.

list_training_pipelines

list_training_pipelines(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.pipeline_service.ListTrainingPipelinesRequest, 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 TrainingPipelines in a Location.

from google.cloud import aiplatform_v1beta1

def sample_list_training_pipelines():
    # Create a client
    client = aiplatform_v1beta1.PipelineServiceClient()

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

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

    # Handle the response
    for response in page_result:
        print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.ListTrainingPipelinesRequest, dict]

The request object. Request message for PipelineService.ListTrainingPipelines.
parent str

Required. The resource name of the Location to list the TrainingPipelines 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_v1beta1.services.pipeline_service.pagers.ListTrainingPipelinesPager Response message for PipelineService.ListTrainingPipelines Iterating over this object will yield results and resolve additional pages automatically.

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_artifact_path

parse_artifact_path(path: str)

Parses a artifact 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_context_path

parse_context_path(path: str)

Parses a context 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_endpoint_path

parse_endpoint_path(path: str)

Parses a endpoint path into its component segments.

parse_execution_path

parse_execution_path(path: str)

Parses a execution 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_pipeline_job_path

parse_pipeline_job_path(path: str)

Parses a pipeline_job path into its component segments.

parse_training_pipeline_path

parse_training_pipeline_path(path: str)

Parses a training_pipeline path into its component segments.

pipeline_job_path

pipeline_job_path(project: str, location: str, pipeline_job: str)

Returns a fully-qualified pipeline_job string.

training_pipeline_path

training_pipeline_path(project: str, location: str, training_pipeline: str)

Returns a fully-qualified training_pipeline string.