async def get_training_pipeline(
self,
request: pipeline_service.GetTrainingPipelineRequest = None,
*,
name: str = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> training_pipeline.TrainingPipeline:
r"""Gets a TrainingPipeline.
Args:
request (:class:`google.cloud.aiplatform_v1.types.GetTrainingPipelineRequest`):
The request object. Request message for
``PipelineService.GetTrainingPipeline``.
name (:class:`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:
google.cloud.aiplatform_v1.types.TrainingPipeline:
The TrainingPipeline orchestrates tasks associated with training a Model. It
always executes the training task, and optionally may
also export data from AI Platform's Dataset which
becomes the training input,
``upload``
the Model to AI Platform, and evaluate the Model.
"""
# Create or coerce a protobuf request object.
# Sanity check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([name])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
request = pipeline_service.GetTrainingPipelineRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if name is not None:
request.name = name
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = gapic_v1.method_async.wrap_method(
self._client._transport.get_training_pipeline,
default_timeout=None,
client_info=DEFAULT_CLIENT_INFO,
)
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
)
# Send the request.
response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# Done; return the response.
return response
def get_training_pipeline(
self,
request: pipeline_service.GetTrainingPipelineRequest = None,
*,
name: str = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> training_pipeline.TrainingPipeline:
r"""Gets a TrainingPipeline.
Args:
request (google.cloud.aiplatform_v1.types.GetTrainingPipelineRequest):
The request object. Request message for
[PipelineService.GetTrainingPipeline][google.cloud.aiplatform.v1.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:
google.cloud.aiplatform_v1.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][google.cloud.aiplatform.v1.ModelService.UploadModel]
the Model to Vertex AI, and evaluate the Model.
"""
# Create or coerce a protobuf request object.
# Sanity check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([name])
if request is not None and has_flattened_params:
raise ValueError("If the `request` argument is set, then none of "
"the individual field arguments should be set.")
# Minor optimization to avoid making a copy if the user passes
# in a pipeline_service.GetTrainingPipelineRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
if not isinstance(request,
pipeline_service.GetTrainingPipelineRequest):
request = pipeline_service.GetTrainingPipelineRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if name is not None:
request.name = name
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[
self._transport.get_training_pipeline]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (gapic_v1.routing_header.to_grpc_metadata(
(("name", request.name), )), )
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
