Class TrainingPipeline (1.6.2)

TrainingPipeline(mapping=None, *, ignore_unknown_fields=False, **kwargs)

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.

Attributes

NameDescription
name str
Output only. Resource name of the TrainingPipeline.
display_name str
Required. The user-defined name of this TrainingPipeline.
input_data_config google.cloud.aiplatform_v1.types.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.
training_task_definition str
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.
training_task_inputs google.protobuf.struct_pb2.Value
Required. The training task's parameter(s), as specified in the training_task_definition's ``inputs``.
training_task_metadata google.protobuf.struct_pb2.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.
model_to_upload google.cloud.aiplatform_v1.types.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.
state google.cloud.aiplatform_v1.types.PipelineState
Output only. The detailed state of the pipeline.
error google.rpc.status_pb2.Status
Output only. Only populated when the pipeline's state is ``PIPELINE_STATE_FAILED`` or ``PIPELINE_STATE_CANCELLED``.
create_time google.protobuf.timestamp_pb2.Timestamp
Output only. Time when the TrainingPipeline was created.
start_time google.protobuf.timestamp_pb2.Timestamp
Output only. Time when the TrainingPipeline for the first time entered the ``PIPELINE_STATE_RUNNING`` state.
end_time google.protobuf.timestamp_pb2.Timestamp
Output only. Time when the TrainingPipeline entered any of the following states: ``PIPELINE_STATE_SUCCEEDED``, ``PIPELINE_STATE_FAILED``, ``PIPELINE_STATE_CANCELLED``.
update_time google.protobuf.timestamp_pb2.Timestamp
Output only. Time when the TrainingPipeline was most recently updated.
labels Sequence[google.cloud.aiplatform_v1.types.TrainingPipeline.LabelsEntry]
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.
encryption_spec google.cloud.aiplatform_v1.types.EncryptionSpec
Customer-managed encryption key spec for a TrainingPipeline. If set, this TrainingPipeline will be secured by this key. Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately.

Inheritance

builtins.object > proto.message.Message > TrainingPipeline

Classes

LabelsEntry

LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

The abstract base class for a message.

Parameters
NameDescription
kwargs dict

Keys and values corresponding to the fields of the message.

mapping Union[dict, `.Message`]

A dictionary or message to be used to determine the values for this message.

ignore_unknown_fields Optional(bool)

If True, do not raise errors for unknown fields. Only applied if mapping is a mapping type or there are keyword parameters.