google_cloud_pipeline_components.experimental.custom_job module

Module for supporting Google Vertex AI Custom Training Job Op.


custom_training_job Launch a Custom training job using Vertex CustomJob API.

project (str):

Required. Project to create the custom training job in.

location (Optional[str]):

Location for creating the custom training job. If not set, default to us-central1.

display_name (str): The name of the custom training job. timeout (Optional[str]): The maximum job running time. The default is 7

days. A duration in seconds with up to nine fractional digits, terminated by ‘s’, for example: “3.5s”.

restart_job_on_worker_restart (Optional[bool]): Restarts the entire

CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.

service_account (Optional[str]): Sets the default service account for
workload run-as account. The service account running the pipeline

submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service


for the CustomJob’s project.

tensorboard (Optional[str]): The name of a Vertex AI Tensorboard resource to

which this CustomJob will upload Tensorboard logs.

network (Optional[str]): The full name of the Compute Engine network to

which the job 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. If left unspecified, the job is not peered with any network.

base_output_directory (Optional[str]): The Cloud Storage location to store

the output of this CustomJob or HyperparameterTuningJob. see below for more details:

labels (Optional[Dict[str, str]]): The labels with user-defined metadata to organize CustomJobs.

See for more information.

encryption_spec_key_name (Optional[str]): Customer-managed encryption key

options for the CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.

gcp_resources (str):

Serialized gcp_resources proto tracking the custom training job. For more details, see