google_cloud_pipeline_components.experimental.custom_job module
Module for supporting Google Vertex AI Custom Training Job Op.
- google_cloud_pipeline_components.experimental.custom_job.CustomTrainingJobOp()
custom_training_job Launch a Custom training job using Vertex CustomJob API.
- Args:
- 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
- (https://cloud.google.com/vertex-ai/docs/pipelines/configure-project#service-account)
submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service
- Agent(https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents)
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: https://cloud.google.com/vertex-ai/docs/reference/rest/v1/GcsDestination
- labels (Optional[Dict[str, str]]): The labels with user-defined metadata to organize CustomJobs.
See https://goo.gl/xmQnxf 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.
- Returns:
- gcp_resources (str):
Serialized gcp_resources proto tracking the custom training job. For more details, see https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/proto/README.md.