Class PipelineJob (1.29.0)

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

An instance of a machine learning PipelineJob.

Attributes

Name

Description

name

str
Output only. The resource name of the PipelineJob.

display_name

str
The display name of the Pipeline. The name can be up to 128 characters long and can consist of any UTF-8 characters.

create_time

google.protobuf.timestamp_pb2.Timestamp
Output only. Pipeline creation time.

start_time

google.protobuf.timestamp_pb2.Timestamp
Output only. Pipeline start time.

end_time

google.protobuf.timestamp_pb2.Timestamp
Output only. Pipeline end time.

update_time

google.protobuf.timestamp_pb2.Timestamp
Output only. Timestamp when this PipelineJob was most recently updated.

pipeline_spec

google.protobuf.struct_pb2.Struct
The spec of the pipeline.

state

google.cloud.aiplatform_v1.types.PipelineState
Output only. The detailed state of the job.

job_detail

 google.cloud.aiplatform_v1.types.PipelineJobDetail

Output only. The details of pipeline run. Not available in the list view.

error

google.rpc.status_pb2.Status
Output only. The error that occurred during pipeline execution. Only populated when the pipeline's state is FAILED or CANCELLED.

labels

MutableMapping[str, str]
The labels with user-defined metadata to organize PipelineJob. 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. Note there is some reserved label key for Vertex AI Pipelines. - vertex-ai-pipelines-run-billing-id , user set value will get overrided.

runtime_config

 google.cloud.aiplatform_v1.types.PipelineJob.RuntimeConfig

Runtime config of the pipeline.

encryption_spec

 google.cloud.aiplatform_v1.types.EncryptionSpec

Customer-managed encryption key spec for a pipelineJob. If set, this PipelineJob and all of its sub-resources will be secured by this key.

service_account

str
The service account that the pipeline workload runs as. If not specified, the Compute Engine default service account in the project will be used. See https://cloud.google.com/compute/docs/access/service-accounts#default_service_account Users starting the pipeline must have the iam.serviceAccounts.actAs permission on this service account.

network

str
The full name of the Compute Engine network __ to which the Pipeline Job's workload should be peered. For example, projects/12345/global/networks/myVPC . Format __ is of the form projects/{project}/global/networks/{network} . Where {project} is a project number, as in 12345 , and {network} is a network name. Private services access must already be configured for the network. Pipeline job will apply the network configuration to the Google Cloud resources being launched, if applied, such as Vertex AI Training or Dataflow job. If left unspecified, the workload is not peered with any network.

reserved_ip_ranges

MutableSequence[str]
A list of names for the reserved ip ranges under the VPC network that can be used for this Pipeline Job's workload. If set, we will deploy the Pipeline Job's workload within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].

template_uri

str
A template uri from where the PipelineJob.pipeline_spec , if empty, will be downloaded.

template_metadata

 google.cloud.aiplatform_v1.types.PipelineTemplateMetadata

Output only. Pipeline template metadata. Will fill up fields if PipelineJob.template_uri is from supported template registry.

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.

RuntimeConfig

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

The runtime config of a PipelineJob.