async def create_training_pipeline( self, request: pipeline_service.CreateTrainingPipelineRequest = None, *, parent: str = None, training_pipeline: gca_training_pipeline.TrainingPipeline = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> gca_training_pipeline.TrainingPipeline: r"""Creates a TrainingPipeline. A created TrainingPipeline right away will be attempted to be run. Args: request (:class:`google.cloud.aiplatform_v1.types.CreateTrainingPipelineRequest`): The request object. Request message for ``PipelineService.CreateTrainingPipeline``. parent (:class:`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 (:class:`google.cloud.aiplatform_v1.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: 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([parent, training_pipeline]) 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.CreateTrainingPipelineRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent if training_pipeline is not None: request.training_pipeline = training_pipeline # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.create_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((("parent", request.parent),)), ) # Send the request. response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,) # Done; return the response. return response
def create_training_pipeline( self, request: pipeline_service.CreateTrainingPipelineRequest = None, *, parent: str = None, training_pipeline: gca_training_pipeline.TrainingPipeline = None, retry: retries.Retry = gapic_v1.method.DEFAULT, timeout: float = None, metadata: Sequence[Tuple[str, str]] = (), ) -> gca_training_pipeline.TrainingPipeline: r"""Creates a TrainingPipeline. A created TrainingPipeline right away will be attempted to be run. Args: request (google.cloud.aiplatform_v1.types.CreateTrainingPipelineRequest): The request object. Request message for [PipelineService.CreateTrainingPipeline][google.cloud.aiplatform.v1.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_v1.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: 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([parent, training_pipeline]) 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.CreateTrainingPipelineRequest. # There's no risk of modifying the input as we've already verified # there are no flattened fields. if not isinstance(request, pipeline_service.CreateTrainingPipelineRequest): request = pipeline_service.CreateTrainingPipelineRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent if training_pipeline is not None: request.training_pipeline = training_pipeline # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[ self._transport.create_training_pipeline] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + (gapic_v1.routing_header.to_grpc_metadata( (("parent", request.parent), )), ) # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response