TrainingPipeline class final

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.

Inheritance
  • Object
  • ProtoMessage
  • TrainingPipeline

Constructors

TrainingPipeline({String name = '', required String displayName, InputDataConfig? inputDataConfig, required String trainingTaskDefinition, required Value? trainingTaskInputs, Value? trainingTaskMetadata, Model? modelToUpload, String modelId = '', String parentModel = '', PipelineState state = PipelineState.$default, Status? error, Timestamp? createTime, Timestamp? startTime, Timestamp? endTime, Timestamp? updateTime, Map<String, String> labels = const {}, EncryptionSpec? encryptionSpec})
TrainingPipeline.fromJson(Object? j)
factory

Properties

createTime → Timestamp?
Output only. Time when the TrainingPipeline was created.
final
displayName → String
Required. The user-defined name of this TrainingPipeline.
final
encryptionSpec → EncryptionSpec?
Customer-managed encryption key spec for a TrainingPipeline. If set, this TrainingPipeline will be secured by this key.
final
endTime → Timestamp?
Output only. Time when the TrainingPipeline entered any of the following states: PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, PIPELINE_STATE_CANCELLED.
final
error → Status?
Output only. Only populated when the pipeline's state is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.
final
hashCode → int
The hash code for this object.
no setterinherited
inputDataConfig → InputDataConfig?
Specifies Vertex AI owned input data that may be used for training the Model. The TrainingPipeline's training_task_definition should make clear whether this config is used and if there are any special requirements on how it should be filled. If nothing about this config is mentioned in the training_task_definition, then it should be assumed that the TrainingPipeline does not depend on this configuration.
final
labels → Map<String, String>
The labels with user-defined metadata to organize TrainingPipelines.
final
modelId → String
Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name.
final
modelToUpload → Model?
Describes the Model that may be uploaded (via ModelService.UploadModel) by this TrainingPipeline. The TrainingPipeline's training_task_definition should make clear whether this Model description should be populated, and if there are any special requirements regarding how it should be filled. If nothing is mentioned in the training_task_definition, then it should be assumed that this field should not be filled and the training task either uploads the Model without a need of this information, or that training task does not support uploading a Model as part of the pipeline. When the Pipeline's state becomes PIPELINE_STATE_SUCCEEDED and the trained Model had been uploaded into Vertex AI, then the model_to_upload's resource name is populated. The Model is always uploaded into the Project and Location in which this pipeline is.
final
name → String
Output only. Resource name of the TrainingPipeline.
final
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.
final
qualifiedName → String
The fully qualified name of this message, i.e., google.protobuf.Duration or google.rpc.ErrorInfo.
finalinherited
runtimeType → Type
A representation of the runtime type of the object.
no setterinherited
startTime → Timestamp?
Output only. Time when the TrainingPipeline for the first time entered the PIPELINE_STATE_RUNNING state.
final
state → PipelineState
Output only. The detailed state of the pipeline.
final
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.
final
trainingTaskInputs → Value?
Required. The training task's parameter(s), as specified in the training_task_definition's inputs.
final
trainingTaskMetadata → Value?
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.
final
updateTime → Timestamp?
Output only. Time when the TrainingPipeline was most recently updated.
final

Methods

noSuchMethod(Invocation invocation) → dynamic
Invoked when a nonexistent method or property is accessed.
inherited
toJson() → Object
override
toString() → String
A string representation of this object.
override

Operators

operator ==(Object other) → bool
The equality operator.
inherited

Constants

fullyQualifiedName → const String