Class TrainingPipeline (1.79.0)

Version 1.79.0

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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
Name	Description
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.
model_id	str 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.
parent_model	str 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.
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	MutableMapping[str, str] 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.

Classes

LabelsEntry

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

The abstract base class for a message.

Parameters
Name	Description
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.

Methods

TrainingPipeline

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.