training_pipelines

Overview

Name	training_pipelines
Type	Resource
Id	google.aiplatform.training_pipelines

Fields

Name	Datatype	Description
name	string	Output only. Resource name of the TrainingPipeline.
createTime	string	Output only. Time when the TrainingPipeline was created.
displayName	string	Required. The user-defined name of this TrainingPipeline.
encryptionSpec	object	Represents a customer-managed encryption key spec that can be applied to a top-level resource.
endTime	string	Output only. Time when the TrainingPipeline entered any of the following states: PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, PIPELINE_STATE_CANCELLED.
error	object	The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC. Each Status message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the API Design Guide.
inputDataConfig	object	Specifies Vertex AI owned input data to be used for training, and possibly evaluating, the Model.
labels	object	The labels with user-defined metadata to organize TrainingPipelines. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
modelId	string	Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.
modelToUpload	object	A trained machine learning Model.
parentModel	string	Optional. When specify this field, the model_to_upload will not be uploaded as a new model, instead, it will become a new version of this parent_model.
startTime	string	Output only. Time when the TrainingPipeline for the first time entered the PIPELINE_STATE_RUNNING state.
state	string	Output only. The detailed state of the pipeline.
trainingTaskDefinition	string	Required. A Google Cloud Storage path to the YAML file that defines the training task which is responsible for producing the model artifact, and may also include additional auxiliary work. The definition files that can be used here are found in gs://google-cloud-aiplatform/schema/trainingjob/definition/. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
trainingTaskInputs	any	Required. The training task's parameter(s), as specified in the training_task_definition's inputs.
trainingTaskMetadata	any	Output only. The metadata information as specified in the training_task_definition's metadata. This metadata is an auxiliary runtime and final information about the training task. While the pipeline is running this information is populated only at a best effort basis. Only present if the pipeline's training_task_definition contains metadata object.
updateTime	string	Output only. Time when the TrainingPipeline was most recently updated.

Methods

Name	Accessible by	Required Params	Description
get	SELECT	locationsId, projectsId, trainingPipelinesId	Gets a TrainingPipeline.
list	SELECT	locationsId, projectsId	Lists TrainingPipelines in a Location.
create	INSERT	locationsId, projectsId	Creates a TrainingPipeline. A created TrainingPipeline right away will be attempted to be run.
delete	DELETE	locationsId, projectsId, trainingPipelinesId	Deletes a TrainingPipeline.
_list	EXEC	locationsId, projectsId	Lists TrainingPipelines in a Location.
cancel	EXEC	locationsId, projectsId, trainingPipelinesId	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 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 TrainingPipeline.error value with a google.rpc.Status.code of 1, corresponding to Code.CANCELLED, and TrainingPipeline.state is set to CANCELLED.