Vertex AI Training Pipeline

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Vertex AI Training Pipeline is a managed service in Google Cloud that automates the process of training machine learning models. It allows users to define and run end-to-end training workflows, including data preprocessing, model training, and evaluation. The service supports custom training code, pre-built algorithms, and integration with other Vertex AI components for scalable and reproducible ML development.

gcp.aiplatform_training_pipeline

Fields

ID	Type	Data Type	Description
_key	core	string
ancestors	core	array<string>
create_time	core	timestamp	Output only. Time when the TrainingPipeline was created.
datadog_display_name	core	string
encryption_spec	core	json	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.
end_time	core	timestamp	Output only. Time when the TrainingPipeline entered any of the following states: `PIPELINE_STATE_SUCCEEDED`, `PIPELINE_STATE_FAILED`, `PIPELINE_STATE_CANCELLED`.
error	core	json	Output only. Only populated when the pipeline's state is `PIPELINE_STATE_FAILED` or `PIPELINE_STATE_CANCELLED`.
gcp_display_name	core	string	Required. The user-defined name of this TrainingPipeline.
input_data_config	core	json	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.
labels	core	array<string>	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.
model_id	core	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.
model_to_upload	core	json	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.
name	core	string	Output only. Resource name of the TrainingPipeline.
organization_id	core	string
parent	core	string
parent_model	core	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`.
project_id	core	string
project_number	core	string
region_id	core	string
resource_name	core	string
start_time	core	timestamp	Output only. Time when the TrainingPipeline for the first time entered the `PIPELINE_STATE_RUNNING` state.
state	core	string	Output only. The detailed state of the pipeline.
tags	core	hstore_csv
training_task_definition	core	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.
update_time	core	timestamp	Output only. Time when the TrainingPipeline was most recently updated.
zone_id	core	string