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. worker_pool_specs (Optional[Sequence[str]]): Serialized json spec of the worker pools

including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.

For more details about the WorkerPoolSpec, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/CustomJobSpec#WorkerPoolSpec

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.

enable_web_access (Optional[bool]): Whether you want Vertex AI to enable

[interactive shell access](https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell) to training containers. If set to true, you can access interactive shells at the URIs given by [CustomJob.web_access_uris][].

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.

reserved_ip_ranges (Optional[Sequence[str]]): A list of names for the reserved ip ranges

under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC 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.